Steve Blank - 四步创业法

战争部刚枪毙了会计，选择了速度 || The Department of War Just Shot the Accountants and Opted for Speed

2025-11-11T14:00:12+00:00

上周，战争部终于彻底废除了罗伯特·麦克纳马拉1962年创立的规划、计划与预算系统（PPBS）的最后残余。

战争部已将重心从优化成本与性能转向快速交付先进武器。耗费数十年研发武器的时代已一去不复返。战争部已迈入21世纪，采用精益方法论。

有两个组织应当高度警惕——中国与国防主承包商。

战争部长皮特·赫格塞斯公布了60年来战争部（DoW）在武器与服务采购方式上的最大变革。这些改革绝非小修小补，而是对战争部武器采办体系的全方位重构——从关注"武器造价多少"转向聚焦"交付速度多快"。

战争部将优先采购现成商用产品，采用快速采办流程替代繁琐的《联邦采购条例》。为此，各军种正重组整个采办生态系统。这些变革落实了过去十年国防部获得却始终忽视的所有良策。

全新的战争部将以硅谷速度运作，交付更多、更好、更快的装备。作战人员将受益于商用技术的创新与低成本，美国将再次打造无可匹敌的军事力量。

这场迟来的改革，堪称大胆、果敢而宏伟。

【背景回溯】

1962年，时任国防部长（前福特汽车CFO）罗伯特·麦克纳马拉发现国防开支失控。1950年代，空军同时研发五种战斗机、三代轰炸机与三代洲际导弹；海军打造核动力攻击潜艇舰队；陆军采购三代核导弹系统。各军种争相采购新技术，却忽视运维、训练与维持的预算规划。

麦克纳马拉遂推行CFO式管控，建立延续60余年的"规划（能力缺口/威胁假设）-计划（五年规划/可承受性）-预算"体系。国防采购大学培训数万名合同官员掌握复杂规则，大型承包商逐渐适应了这个文书繁冗、流程漫长的系统。

【症结所在】

当对手是采办体系同样臃肿的苏联，或毫无体系的ISIS时，这套笨重系统尚可应付。但过去十年间，其弊端日益凸显：国防工业基础饱受进度延误与成本超支困扰；中国采用敏捷系统，武器交付速度远超我们。

乌克兰战争证明，小国也能年产百万架无人机并持续迭代设计（我们却做不到）。初创企业利用私人资本开发的商用技术，其成本与速度远超联邦研发中心。但战争部采办体系对初创企业而言犹如铜墙铁壁。

我们的系统被不切实际的风险阈值所瘫痪，过度关注流程而非结果，变得畏首畏尾、僵化不前。

【旧体系剖析】

各军种现行采办体系：需求论证（1年+）→技术开发（3-6年）→原型研制（3-4年）→生产交付（1-10年）。从需求提出到武器列装需8-16年——在当前技术迭代速度下，这已成为建设现代化战争部的绊脚石。

以陆军为例，3.2万采办人员+16.5万保障人员构成的体系存在七大弊端：责任分散、产品导向、定制化需求、流程至上、合规优先、瀑布式开发、交付迟缓。

【革命性变革】

新体系直击要害，由深谙商业精益流程的专家设计，通过九大举措解决核心问题：

1. 交付速度优先
2. 结果导向取代流程导向
3. 采办流程组织重构
4. 需求定义与优先级重塑
5. 供应商结构变革
6. 合同方法创新
7. 成功标准重新定义
8. 采办人才培养转型
9. 强制系统互操作性

【新型采办架构】

各军种设立"组合采办执行官"(PAE)，统管端到端采办全流程：能力缺口分析、系统中心、计划制定、采办实施、测试验证、合同签订与持续保障。PAE被授权为快速交付承担可控风险。

PAE采用矩阵式组织，围绕作战概念/技术/集成需求组建组合。例如陆军将12个项目执行官办公室重组为6大组合：机动、空中机动、火力、指挥控制、敏捷保障、分层防护。每个PAE由少将领导，拥有成本/进度/性能的权衡决策权。

【颠覆性规则】

• 商用优先：强制采购现成产品，定制开发作为最后手段
• 激励制度：PAE与项目经理奖金与交付速度/任务成效挂钩
• 非传统准入：采用"其他交易授权"(OTA)绕过5000页的联邦采购条例
• 精益开发：增量交付"够用就好"的技术，允许持续迭代
• 强制互操作：所有武器采用模块化开放架构，避免厂商锁定

【配套改革】

• 双源采购：关键项目初期必须保持两家合格供应商
• 危机扩产：设计生产解耦，确保战时快速扩能
• 任期延长：PAE任期延长至4-6年
• 教育革命："国防采办大学"转型为"作战采办大学"，侧重实战化培养

【联合参谋部改革】

废除缓慢的JCIDS需求审批系统，新建三大机构：
1. 联合加速储备金（快速部署有潜力装备）
2. 需求资源对齐委员会（资金直接对接优先事项）
3. 任务工程与集成中心（政府-工业界-实验室早期协作）

【潜在挑战】

• 未来6个月将存在职责不清的混乱期
• 主承包商必将通过游说国会反扑
• 合规文化改革需要铁腕手段
• 盟友装备采购仍未纳入议程

【创业公司机遇】

战争部与初创企业终于讲同一种语言：精益、战场反馈、快速迭代、敏捷交付。"商用优先"政策为初创企业创造历史性机遇，但必须用实际交付而非PPT证明实力。

这场改革本质上是向主承包商商业模式的心脏插刀。私人资本与上市公司游说资金的博弈即将展开——愿这些变革能顶住压力，真正落地。

（感谢BMNT公司皮特·纽维尔的见解）

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Last week the Department of War finally killed the last vestiges of Robert McNamara’s 1962 Planning, Programming, and Budgeting System (PPBS).

The DoW has pivoted from optimizing cost and performance to delivering advanced weapons at speed. Taking decades to deliver weapons is no longer an option. The DoW has joined the 21st century and adopted Lean Methodology.

Two organizations ought to be very concerned – China and the defense prime contractors.

Secretary of War Pete Hegseth unveiled the biggest changes in 60 years of how the Department of War (DoW) plans for and buys weapons and services. These changes aren’t a minor attempt at reform. It’s a top-to-bottom transformation of how the DoW plans and buys weapons, moving from contracts that prioritize how much a weapon costs to how fast it can be delivered.

Instead of buying custom-designed weapons, the DoW will prioritize buying off-the-shelf things that already exist, and using fast-track acquisition processes, rather than the cumbersome existing Federal Acquisition Regulations. To manage all of this, they are reorganizing the entire Acquisition ecosystem across the Services. These changes implement every piece of good advice the DoD had gotten in the last decade and had previously ignored.

The DoW is being redesigned to now operate at the speed of Silicon Valley, delivering more, better, and faster. Our warfighters will benefit from the innovation and lower cost of commercial technology, and the nation will once again get a military second to none.

It’s big, bold and brave and long overdue.

Background

In 1962 Robert McNamara, the then-Secretary of Defense (and ex CFO of Ford), discovered he had inherited a Defense Department whose spending was out of control. During the 1950s the Air Force built five different types of fighter planes, three generations of bombers, and three generations of ICBMs. The Navy had created a fleet of nuclear-powered attack and ballistic missile submarines and aircraft carriers. The Army bought three generations of its own nuclear-capable missile systems. Many of these systems duplicated capabilities of other services. But most importantly, the Services, in their rush to buy new technology, hadn’t adequately budgeted for the cost of operating, training, maintaining, and sustaining what they had bought.

In response, Secretary McNamara imposed the discipline of a Chief Financial Officer. He put in place a formal system of Planning (capability gaps, risks, scenarios, threats assumptions), Programming (5-year plans, affordability, quantities, phasing, unit fielding plans) and Budgeting that has lasted 60+ years. An entire defense university was created to train tens of thousands of contracting officers how to follow the detailed rules. Large contractors (the Primes) learned to work with this paperwork-heavy Defense acquisition system and lived with the very long time it took the DoD to buy.

The Problem

This unwieldy and lethargic acquisition system was adequate for over half a century when our adversary was the Soviet Union who had an equally complex acquisition system, or ISIS and Al Qaida who had none.

However, in the last decade it became painfully obvious that our acquisition system was broken and no longer worked for the world we lived in. Our existing defense industrial base suffers from schedule overruns and huge backlogs; cost increases have become the norm. We’ve been outpaced by adversaries. China, for example, implemented a much more agile system that delivered weapons in a fraction of the time it took us.

We needed a defense industrial base we could count on to scale in a crisis rather than one that will wait for money before taking action.

The war in Ukraine showed that even a small country could produce millions of drones a year while continually iterating on their design to match changes on the battlefield. (Something we couldn’t do.) Meanwhile, commercial technology from startups and scaleups (fueled by an immense pool of private capital) has created off-the-shelf products, many unmatched by our federal research development centers or primes, that can be delivered at a fraction of the cost/time. But the DoW acquisition system was impenetrable to startups.

Our Acquisition system was paralyzed by our own impossible risk thresholds, its focus on process not outcomes, and became risk averse and immoveable.

We needed an acquisition system that could deliver needed things faster.

Reminder: What Did Our Acquisition System Look Like Until Last Week?

The Army, Navy, Air Force, Marines and Space Force train soldiers, sailors and airmen, and specify and buy the weapons for their Service. (It’s the Combatant Commands, e.g. INDOPACOM, CENTCOM, etc., who fight the wars.)

One of the confusing things about Acquisition in the DoW is that it is more than just the buyers of equipment. In the DoW Acquisition with capital “A”, includes the entire end-to-end process – from concept, requirements, prototyping, testing, buying it, to using it and maintaining it.

In each of the Services, the current Acquisition system started with a group that forecast what the Service would need in the future and wrote requirements for future weapons/services/software. This process could take a year or more. Next, Service laboratories developed the technology, tested prototypes and concepts. This could take 3 to 6 years. Next, a vendor was selected and began to prototype and refine the systems. This added another 3 to 4 years. Finally, the system was ready to be built and delivered. It could take 1 to 2 years to deliver weapons in low rate production, or 5 to 10 years for something complex (e.g. aircraft, ships, spacecraft). In the system we’re replacing the time from when a need was turned into a requirement to delivery of a weapon would take 8 to 16 years. As you can imagine, given the rate of change of current technology and new warfighting concepts our own Acquisition process was an obstacle to building a modern War Department.

As an example, the Army’s current Acquisition system has 32,000 civilians and military (program managers, contracting officers, etc.) If you include the long tail of sustainment that’s another 165,000+ people. The Acquisition system in the Army (representative of the other services) looks like this:

What Was Wrong With this Process?

Responsibility in the Acquisition system was scattered across multiple, siloed organizations with no one individual responsible.

The existing system was designed to acquire individual products (weapons, services, etc.) with a Program Executive Office to manage each effort that only indirectly solved warfighter problems.

Requirements were written so that most everything the DoW bought was bespoke and required development from scratch.

Acquisition was process-focused with rigid rules that emphasized compliance to contracting rules.

Compliance to the rules and processes overrode speed of delivery

Weapons and systems development used sequential “waterfall” development processes which precluded learning, pivots and iterative design.

The result was that speed of delivery was on no one’s priority list.

Why Is The Warfighting Acquisition System A Big Deal?

While previous administrations tried to go around the process, this new system confronts it head on. It is a revolutionary transformation in the Department of War. It was clearly designed by people who have worked in industry and understand commercial Lean Processes. This transformation will solve the DoW critical Acquisition problems by:

Prioritizing speed of delivery

Moving the focus from process to outcomes

Organizational redesign of the Acquisition process

Changing what weapons we ask for and how we prioritize what we need to buy

Changing the preferred vendors the DoW will buy from

Changing the contracting methods the DoW will use

Changing how we measure and reward success

Changing how we educate Acquisition professionals

Insisting that disparate systems/vendors interoperate

The New Warfighting Acquisition Organization – The Portfolio Acquisition Executive

To cut through the individual acquisition silos, the services are creating Portfolio Acquisition Executives (PAEs).

Each Portfolio Acquisition Executive (PAE) is responsible for the entire end-to-process of the different Acquisition functions: Capability Gaps/Requirements, System Centers, Programming, Acquisition, Testing, Contracting and Sustainment. PAEs are empowered to take calculated risks in pursuit of rapidly delivering innovative solutions.

PAE Offices Are Matrix Organizations

Portfolio Acquisition Executives (PAEs) are organized as a matrix organization – using people from existing organizations – requirements, PEOs, sustainment, contracting etc. The PAEs themselves will have a small staff for coordination.

Portfolios Around Common Problems

In the past, Acquisition was organized by weapon systems and managed by Program Executive Offices. Portfolios will organize instead around common Warfighting Concepts, technologies, or operational integration needs.

Multiple Portfolios In Each Service

Each of the services are consolidating and reorganizing the functions of what were their Program Executive Offices into Portfolios. Program Executive Offices/Officers (PEOs) will become Capability Program Executives (CPEs), and act as a Portfolios’ acquisition arm.

(The examples below are from the Army. Other Services will have equivalent organizational designs for their Portfolios.)

The acquisition chain of authority runs directly from Capability Program Manager to PAE to the Service Acquisition Executive (SAE), with no intermediate offices or approval layers. (The Service Acquisition Executive for the Army is the Assistant Secretary for Acquisition, Logistics & Technology. For the Navy/Marines, the Assistant Secretary for Research, Development & Acquisition. For the Air Force/Space Force the Assistant Secretary for Acquisition, Technology & Logistics.)

The Army Has 6 Portfolio Acquisition Executives

For example, the Army will likely reorganize its 12 existing PEO offices to become part of 6 portfolios aligned with Army Warfighting Concepts and functions. Each of the 6 portfolios headed by a PAEs will be commanded by a Major General.

The likely 6 Army Portfolios are: 1) Maneuver, 2) Maneuver Air, 3) Fires, 4) C2/CC2, 5) Agile Sustainment and Ammo, and 6) Layered Protection and CBRN. One additional portfolio, called the PIT, will likely include the Army’s Innovation at the Edge activities.

Army PAE Maneuver will likely combine elements of PEO Soldier, PEO Ground Combat Systems, Future Capabilities Division and Maneuver Divisions, Test and Evaluation Integrator, Strategic Contracting Office, and others. This portfolio will likely have the Abrams tank, XM30 Mechanized Infantry Combat Vehicle (replacing the M2 Bradley), the ISV (Infantry Squad Vehicle), Soldier Borne Mission Command program (SBMC), Next Generation Squad Weapon (NGSW), Soldier Borne Sensor (SBS) program, and Organization Clothing and Individual Equipment (OCIE).

Authority to Make Trade-offs

PAEs now have the authority to make trade-offs between cost, schedule and performance and apply flexible funding between weapons systems to rapidly deliver capabilities to the warfighter. This means focusing on fielding “good enough” technology instead of waiting for a product that meets every single requirement.

Army PAE Maneuver Air will likely combine elements of Program Executive Office Aviation, Aviation and Missile Command, Futures Command Future Vertical Lift team DEVCOM Aviation & Missile, and others. It will likely include the Long-Range Assault Aircraft (FLRAA) the Bell V-280 Valor (to replace the UH-60 Black Hawk), Uncrewed Aircraft Systems (UAS), Rotary and Fixed Wing, and Autonomy.

Program Executive Officers (PEOs) are Now Capability Program Executives (CPEs)

Inside each portfolio is a Capability Program Executive (CPE), typically a Brigadier General or a civilian SES. Capability Program Executives have similar roles and responsibilities as today’s PEOs. They are the Acquisition leader responsible for cradle-to-grave management of their programs within their portfolio.

Streamlined Layers of Bureaucracy

97 Army acquisition programs may be reassigned to align with the Army PAE reorganization. 46 organizations that were writing requirements likely will be consolidated into 9 Future Capability Directorates.

Army PAE Fires will likely combine elements from Program Executive Office Missiles and Space, Enterprise Information Systems, the Rapid Capabilities and Critical Technologies Office, Fires System Center, and others. It will likely include the Integrated Battle Command System (IBCS), Patriot/PAC-3, Precision Strike Missile (PrSM), Long-Range Hypersonic Weapon – Dark Eagle (LRHW), Common Autonomous Multi-Domain Launcher (CAML), Guam Defense and Golden Dome.

DoW Will Buy Commercial First

One of the biggest changes is the mandate for PAEs to buy Commercial Off the Shelf (COTS) products, modify them if necessary and only buy bespoke products as a last resort. This change by itself is going to send shockwaves through the existing Prime contractors.

It’s telling everyone that the playing field is now open to everyone. Forget who has more lobbyists on K-Street. Speed, mission impact, and innovation is what will be rewarded. What this means for startups is that if you can execute and deliver (not just PowerPoints) you can become a supplier to the DoW.

Incentive Compensation to PAEs and Program Managers

PAEs will be judged on whether they deliver systems to the warfighter on time and on schedule. PAEs and Program Managers will have “incentive compensation” tied to “capability delivery time, competition, and mission outcomes. (How they’ll pay that kind of compensation for a member of the military remains to be seen.)

Incentives and Scorecards for Contractors

They’ll be managing their contractors with “time-indexed incentives” to make sure contractors deliver on time and on budget, using “scorecards” to keep tabs on how each portfolio is doing.

Army PAE C2/CC2 (Command and Control/Counter Command and Control) will likely combine elements of PEO Command, Control, Communications and Network.. And include NGC2, TITAN, TENCAP, Next Generation Constructive, STE

Non-Traditional Entry Points

Companies selling to the DoW previously had to comply with the impenetrable DFAR and FAR – the Defense and Federal Acquisition Regulations – with over 5,000 pages of complex rules. It was designed for buying Aircraft Carriers, not startup technology.

Now the DoW is telling PAEs to toss those and use Non-FAR regulations like OTAs (Other Transaction Authorities). OTAs are not subject to the extensive, rigid rules and regulations of the DFAR. They allow for greater flexibility, speed, and allow the DoW to work with a broader range of innovative commercial companies. For startups this means massively reduced documentation, shorter timelines, and fewer barriers to working with the DoW.

PAEs Will Use Lean Methodology

Rather than fixed requirements and using waterfall development processes, the services are now insisting that vendors use Lean Methodology to set incremental and iterative delivery targets. That means they can field “good enough technology” that can be incrementally updated in the field and improved on a more frequent cadence.

The only requirement for each increment is that they need to target 1) an initial fielding date,

set a maximum cost of each unit and 3) meet the minimum standards for mission effectiveness. Other than that, PAEs have the authority that other attributes of the weapons/software can remain tradable throughout development to allow incremental enhancements and rapid delivery of subsequent increments. This includes the ability to waive technical standards and environmental and other compliance requirements, unless they are mandated by statute or safety.

One other interesting Lean mandate is that each PAE will set up lean technical advisory processes to inform accelerated decision-making, ensuring technical rigor without sacrificing speed.

Weapons Will Be Able to Talk to Each Other – By Design

The new PAEs are also tasked with insisting that all weapons across their programs use Modular Open System Architectures, including by asserting government purpose rights over critical software interfaces — a move that allows the Pentagon to retain the data rights needed to avoid “vendor lock” (weapon systems that can only be modified and/or repaired by the company that designed it).

Army PAE Agile Sustainment will likely combine elements of PEO Combat Support and Combat Service Support, PEO Solider and PEO Joint Program Office Armaments and Ammunition. It will likely include next generation Common Tactical Truck (CTT,) Family of Medium Tactical Vehicles (FMTV), 155mm, 6.8mm ammunition.

Two Vendors Through Initial Production

The DoW has painfully learned that having only one vendor selected leads to cost overruns and late projects. A new idea is that each critical acquisition program will have at least two qualified sources through initial production. While this will cost more upfront, it gives government leverage when it is strongest and enables them to re-compete modular components and find alternative suppliers if needed.

Design For Rapid Scale In a Crisis

PAEs have been told to establish acquisition strategies that decouple design from production to allow additional third-party suppliers to surge and rapidly scale manufacturing capacity in a crisis. They are to put in place guidelines for wartime consumption rates through manufacturing and supply chain partnerships and alternative sources.

Army PAE Layered Protection and CBRN (Chemical, Biological, Radiological, and Nuclear) will likely combine elements of PEO JPEO-CBRND. It will likely include Joint Chemical Agent Detector, UIPE, Decontamination Family of Systems, Biometrics

PAE Officers Now Have More Time To Learn On the Job

A complaint from past acquisition program managers is that they would only be there for two or three years, and then off to their next assignment. Two years was not enough time to see a program through. Now PAEs will have 4-year tours, extendible for another 2 years.

PAEs Top to Bottom

Every military service has 60 days to tell the Secretary of War a list of portfolios it is proposing to be initially stood up. A full implementation plan is due in 90 days. All major acquisition activities across all Services are going to be transitioned to PAE portfolios within two years.

Army PIT is the Army’s innovation initiatives at the edge. It’s the front door for startups wanting to partner with the Army.

The PIT includes the Joint Innovation Outpost, the Global Tactical Edge Acquisition Directorate (G-TEAD) Marketplace, the FUZE program, and Disruptive Technologies.

The G-TEAD Marketplace merges Prize Challenge events (e.g., Army xTech Program) and DEP submissions through open call announcements.

FUZE brings together the Army SBIR/STTR seed funding, MANTECH (Army Manufacturing Technology program), TMI (Tech Maturation Initiative) and XTech the Army’s scouting program.

Reeducation Camp – Warfighting Acquisition University

To retrain/reeducate contracting and acquisition officers, the “Defense Acquisition University” will become the “Warfighting Acquisition University.” They have been ordered to stop compliance-focused training operations and in six months transform into a competency-based education institution.

The university will pivot to offer experiential team-based programs that work on real DoW challenges (does that ever sound like a description of Hacking for Defense.) And they’re going to have their students get out of the building and take part in industry-government exchanges. In the next six months they’re going to prioritize education and rotation programs to get their students exposure to commercial industry practices, manufacturing and operational expertise, and real-world problem-solving. All to develop Acquisition executives critical thinking and agile and rapid decision-making skills. (Note to DAU: we’ve been building these programs for a decade at the Stanford Gordian Knot Center for National Security Innovation. Our national security classes are in 60+ universities and we’re happy to help.)

The Joint Staff – Coordinating the Needs of All the Services

While each of the Services generated their own weapons requirements, plans and budgets, they all had to be approved by the Joint Staff (which reports to the Secretary of War) through a process called the JCIDS (Joint Capabilities Integration & Development System). In theory this was to coordinate each of the Service’s needs so they weren’t duplicating each other, to ensure that they were interoperable, and to give the Combatant Command a voice; and tie all the requirements to joint concepts – all of this needing to be done before Service weapons programs got funded and built.

The problem was that JCIDS moved at the speed of paperwork, not war, so the Secretary of War eliminated it earlier this year. (They kept part of it called the Joint Requirements Oversight Council but reoriented it from validating documents to identifying joint operational problems, which will drive the priorities for the entire department of War.)

In JCIDS’ place the Secretary of War created three new organizations:

The Joint Acceleration Reserve, a pool of money set aside to quickly field promising capabilities.

The Requirements and Resourcing Alignment Board (RRAB) that will tie money directly to the top warfighting priorities and how much money each will get from the new Joint Acceleration Reserve.

The Mission Engineering and Integration Activity brings government, industry, and labs together early on to rapidly experiment, test, and prototype new tech.

It’s interesting to note that none of these changes at the Joint Staff have seemed to (at least publicly) filter down to the charter of the Services Portfolio Acquisition Executives (PAEs). The achilles heel of the Services Acquisition process appears that they are still planning to put the Requirements and Capability gap analysis up front. Here’s why that’s a problem and how to fix it.

Foreign military sales

One other tangential decision in this redesign was not in acquisition but in sales. The DoW wants a greater emphasis on selling our weapons to our Allies. They’ve moved two agencies responsible for those functions – the Defense Technology Security Administration DTSA and the Defense Security Cooperation Agency (DSCA) – from OSD Policy to OSD Acquisition and Sustainment.

This move is about selling more of our equipment, but makes no mention of buying any equipment from our allies.

Inferred But Not Mentioned

Pretty interesting that in this reorg no one has noticed that Elbridge Colby – Under Secretary for Policy – had three organizations taken away from him.

Defense Technology Security Administration DTSA

Defense Security Cooperation Agency (DSCA)

The Joint Production Accelerator Cell (JPAC) now renamed the Wartime Production Unit (WPU)

All three organizations were handed to Michael Duffey the Under Secretary for Acquisition & Sustainment. Regardless of the public statements the optics are not a vote of confidence.

Bigger and Better?

It appears that the Office of Strategic Capital may have been swallowed up by the Economic Defense Unit run by George Kolitdes. From all appearances the Economic Defense Unit is tasked to decouple our economy from China, using private and public capital. That means considering how to on-shore the critical components like minerals, chips, batteries, motors, PNT, etc.) The Acquisition announcement was how to buy things. This Economic Defense Unit is how do we ensure the things we buy are made with parts we know we can have an assured supply of?

Summary

Startups and the DoW are now speaking the same language – Lean, feedback from the field, pivots, iterative and incremental product design, speed to delivery.

The DoW mandate to first buy commercial-off-the-shelf products is a once-in-a-lifetime opportunity for every startup and scaleup.

But you have to deliver. Don’t hand wave with PowerPoints.

DoW will be ruthless in shutting down and freezing out non-performers.

The use of Non-Federal Acquisition Regulations will eliminate huge amounts of paperwork.

It eliminates one of the reasons to subcontract with a prime or other company

DoW needs to be ruthless in reforming the compliance culture

Who to talk to in each service and how will they do business will be unclear for at least the next six months

Reorganizations will create uncertainty of who is the front door for startups, how the new rules apply, and who can commit to contracts.

The Army appears to be further along than the other services in putting a PAE organization in place.

In theory this is a knife to the heart of the Primes’ business model.

They will flood Congress and the Executive Branch with infinite capital to change these rules.

It’s a race between private capital and public company lobbying money

Let’s hope these changes stick

Thanks to Pete Newell of BMNT for the feedback and insight.

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仅用了20年时间，战略管理学会现已认可精益创业作为一种策略。 || It only took 20 years, but the Strategic Management Society now Believes the Lean Startup is a Strategy

2025-10-30T13:00:42+00:00

我一直自视为实践者。在我参与的初创企业中，唯一的"战略"就是我的营销策略——如何让销售副总裁成为公司最富有的人。退休后，我创立了客户开发方法论，并共同提出了精益创业理念，以简单易懂的语言和流程将创始人的最佳实践体系化。这一切都源于实践者的视角。

因此你可以想象，当我收到战略管理学会(SMS)颁发的年度"战略领导力影响力"奖项时有多惊讶。SMS是战略领域最具权威的专业组织，拥有3100多名会员，出版《战略管理期刊》《战略创业期刊》和《全球战略期刊》三大学术刊物。

颁奖词写道：[史蒂夫·布兰克]作为现代创业之父，改变了初创企业的构建方式、创业教育模式、科研成果商业化路径以及企业与政府的创新机制。

以下是我的获奖感言。

感谢授予我战略领导力影响力奖。作为一名站在满屋子战略家面前的实践者，我深感谦卑与荣幸。

萧伯纳曾说英美是"被共同语言分隔的两个民族"。我常觉得实践者与战略家之间也存在这种鸿沟。

最贴切的比喻是有次长途飞行到悉尼后，我跳上出租车时司机开始说话，我却恐慌起来——完全听不懂他在说什么语言，不知该如何沟通。

直到快到酒店我才意识到：他说的就是英语。

这就像战略研究者与实践者之间的隔阂。

今天我想分享，这个实践者如何意外成为战略家，以及这段旅程如何催生了精益创业。

故事要从我所说的"硅谷秘史"开始。

—-

硅谷的根基源于二战和冷战期间解决国家安全领域的高不确定性难题。

二战期间，美国通过福特、通用等"民主兵工厂"实现了规模化生产——四年内制造30万架飞机、12.4万艘舰船、8.6万辆坦克。但与此同时，我们开创了颠覆性模式：成立科学研究与发展局(OSR&D)，建立大学实验室网络解决电子、化学等军事难题。

这些实验室交付了雷达、火箭、近炸引信、青霉素等突破，前两年还主导了核武器计划。这实际上是动态能力的早期实践——在极端不确定性下感知、把握和转型的能力。

其中专注电子战的部门，成为硅谷创新模式的真正起源。

—

1943年，美军轰炸机在欧洲战场损失惨重。斯坦福大学弗雷德·特曼领导的哈佛无线电研究实验室（其实与哈佛无关）三年内构建完整电子战系统，通过跨学科团队、快速原型和战场反馈循环——这正是现代MVP和OODA循环的雏形。

战后特曼将这套模式制度化：在斯坦福引入政府研究项目，聘请战时工程师任教，重塑大学为外向型机构。他鼓励教授创业咨询，将微波电子技术商业化，使斯坦福成为创新生态系统的早期平台。

这些斯坦福衍生的科技企业本质上是应对不确定性的学习型组织，基于实时数据和客户反馈持续迭代。但当时缺乏风险资本引导，它们只是为生存挣扎的小企业。

直到1970年代中期养老金"谨慎人规则"修订，风险投资成为机构资产类别，金融逻辑才取代学习逻辑成为主导。随后25年里，具有MBA背景的风投者用大公司思维管理初创企业，完全背离了硅谷最初的创新方式。

现在让我们回到精益创业。

在冷战武器系统领域工作21年后，我退休开始反思：

所有商业计划初次接触客户就会失效

初创企业本质上是未经验证的假设集合

成功者都通过客户学习迭代计划

但现有战略工具都是为大公司设计

更严重的是，我们都误以为初创企业只是大公司的微缩版。实际上大公司执行已知商业模式，而初创企业是在探索商业模式。

于是我重构了创业方法论：

客户开发——"大楼内没有真相"

敏捷开发——迭代式产品构建

商业模式画布——技术商业化的假设映射

这三者构成了精益方法论，将手艺活转化为战略学习的学科——通过MVP实验和关键转型的持续循环。

过去二十年，精益已成为新创企业的实际标准。我在斯坦福开发的课程被美国国家科学基金会采纳用于科研成果转化。当代创业者们不知不觉中，正运用着战时创新的持续学习循环。

展望未来，2025年将成为新的转折点。AI、合成生物学和空前规模的资本正在消弭探索与利用的界限。当战略本身进化为动态能力——不是计划而是超越环境变化的快速学习过程时，我期待见证各位创造的新纪元。

最后感谢斯坦福技术创业项目的同仁，是你们让一个实践者走进了学术殿堂。

谢谢。

---------------

I’ve always thought of myself as a practitioner. In the startups I was part of, the only “strategy” were my marketing tactics on how to make the VP of Sales the richest person in the company. After I retired, I created Customer Development and co-created the Lean Startup as a simple methodology which codified founders best practices – in a language and process that was easy to understand and implement. All from a practitioner’s point of view.

So you can imagine my surprise when I received the annual “Strategy Leadership Impact” Award from the Strategic Management Society (SMS). The SMS is the strategy field’s main professional society with over 3,100 members. They publish three academic journals; the Strategic Management Journal, Strategic Entrepreneurship Journal, and Global Strategy Journal.

The award said, [Steve Blank] as the Father of Modern Entrepreneurship, changed how startups are built, how entrepreneurship is taught, how science is commercialized, and how companies and government innovate.

Here’s my acceptance speech.

Thank you for the Strategy Leadership Impact Award. As a practitioner standing in front of a room full of strategists, I’m humbled and honored.

George Bernard Shaw reminded us that Americans and British are “one people separated by a common language.” I’ve often felt the same way about the gap between practitioners and strategists.

The best analogy I can offer, is the time after a long plane flight to Sydney, I jumped into a taxi and as the taxi driver started talking I started panicking – wondering what language he was speaking, and how I was going to be able to communicate to him.

It took me almost till we got to the hotel to realize he was speaking in English.

That’s sometimes how it feels between those who do strategy and those who study it.

So today, I’d like to share with you how this practitioner accidently became a strategist and how that journey led to what we now call the Lean Startup.

It’s a story that begins, perhaps surprisingly with what I call the Secret History of Silicon Valley.

—-

Silicon Valley’s roots lie in solving urgent, high-uncertainty national-security problems during World War II and the Cold War with the Soviet Union.

During WW II, the United States mastered scale and exploitation—mass-producing ships, aircraft, and tanks through centralized coordination. Ford, GM, Dupont, GE and others became the “arsenals of democracy.” In less than 4 years the U.S. built 300,000 aircraft, 124,000 of all types of ships, 86,000 tanks.

But simultaneously we created something radically different, something no other nation did – we created the Office of Science and Research and Development – OSR&D. This was a decentralized network of university labs that worked on military problems that involved electronics, chemistry and physics. These labs solved problems where outcomes were unknown and time horizons uncertain—exactly the conditions that later came to define innovation under uncertainty.

These labs delivered radar, rockets, proximity fuses, penicillin, sulfa drugs, and for the first two years ran the U.S. nuclear weapons program.

In hindsight, way before we had the language, the U.S. was practicing dynamic capabilities: the capacity to sense, seize, and transform under extreme uncertainty. It was also an early case of organizational ambidexterity—balancing mass production with rapid exploration.

One branch of this Office of Science and Research and Development – focused on electronic warfare—became the true genesis of the Valley’s innovation model.

—

In 1943, U.S. bombers over Europe faced catastrophic losses—4–5% of planes were shot down every mission. The German’s had built a deadly effective radar-based air defense system. The U.S. responded by creating the Harvard Radio Research Lab, led by Stanford’s Fred Terman. The lab had nothing to do with Harvard, Radio or Research.

Its goal was to rapidly develop countermeasures: jammers, receivers, and radar intelligence.

In the span of three years, Terman’s lab created an entire electronic ecosystem to defeat the German air defense systems. By war’s end U.S. factories were running 24/7 mass producing tens of thousands of the most complicated electronics and microwave systems that went on every bomber over Europe and Japan.

These teams were interdisciplinary, field-connected, and operating in continuous learning cycles:

Scientists and engineers worked directly with pilots and operators—what we’d now call frontline customer immersion.

They built rapid prototypes—the Minimum Viable Products of their time.

They engaged in short feedback loops between lab and battlefield—what John Boyd would later formalize as the OODA loop.

They were, in essence, running a learning organization under fire—a live example of strategic adaptation and iterative sensemaking.

But what does this have to do with Silicon Valley?

When the war ended Terman came back to Stanford and became Dean of Engineering and institutionalized this model. He embedded government research into the university, recruited his wartime engineers as faculty, and redefined Stanford as an outward-facing institution.

While most universities pursued knowledge exploitation – publishing, teaching, and extending established disciplines, Terman at Stanford did something that few universities in the 1950’s, 60’s or 70’s were doing – he pursued knowledge exploration and recombination. Turning Stanford into an outward facing university – with a focus on commercializing their inventions.

He reconfigured incentives — encouraging professors to consult and found companies, an unprecedented act of strategic boundary spanning

He believed spinning out microwave and electronics companies from his engineering labs was good for the university and for the country.

He embedded exploration in the curriculum — mixing physics, electronics, and systems engineering.

Cultivating external linkages — he and his professors were on multiple advisory boards with the Department of Defense, intelligence agencies, and industry.

Terman’s policies as now Provost effectively turned Stanford into an early platform for innovation ecosystems—decades before the term existed.

The technology spinouts from Stanford and small business springing up nearby were by their very nature managing uncertainty, complexity, and unpredictability. These early Valley entrepreneurs weren’t “lone inventors”; they were learning organizations, long before that term existed. They were continuously testing, learning, and iterating based on real operational data and customer feedback rather than long static plans.

However, at the time there was no risk capital to guide them. They were undercapitalized small businesses chasing orders and trying to stay in business.

It wasn’t until the mid 1970’s when the “prudent man” rule was revised for pension funds, and Venture Capital began to be treated as an institutional asset class, that venture capital at scale became a business in Silicon Valley. This is the moment when finance replaced learning as the dominant logic.

For the next 25 years, Venture investors – most of them with MBAs or with backgrounds in finance, treated startups like smaller versions of large companies. None of them had worked on cold war projects nor were they familiar with the agile and customer centric models defense innovation organizations had built. No VC was thinking about whether lessons from corporate strategic management thinkers of the time could be used in startups. Instead, VCs imposed a waterfall mindset —business plans and execution of the strategy in the plan — the opposite of how the Valley first innovated. The earlier language of experimentation, iteration, and customer learning disappeared.

And now we come full circle – to the Lean Startup.

At the turn of the century after 21 years as a practitioner, and with a background working on cold war weapons systems, I retired from startups and had time to think.

The more I looked at the business I had been in, and the boards I was now sitting on, I realized a few things.

No business plan survived first contact with customers.

On day one all startups have is a series of untested hypotheses

Yet startups were executing rather than learning

Our strategic language and tools—all designed for large firms—were useless in contexts of radical uncertainty.

Startups that succeeded were the ones that learned from their customers and iterated on the plan. Those that didn’t, ended up selling off their furniture.

Most importantly – as I started reading all the literature I found on innovation strategy, almost all of it was about corporate innovation.

We had almost a century of management tools and language to describe corporate strategy for both growth and innovation – yet there were no tools, language or methods for startups.

But it was worse. Because both practitioners and their investors weren’t strategists, we had been trapped in thinking that startups were smaller versions of large companies

When the reality was that at their core, large companies were executing known business models, but startups? Startups were searching for business models

This distinction between startup search and large company execution had never been clearly articulated.

There was a mismatch between the reality and practice.

We needed to reframe entrepreneurship as a strategic process, not a financial one

I realized that every startup believed their journey was unique, and thought they had to find their own path to profitability and scale.

That was because we had no shared methodology, language or common tools. So I decided to build them.

The first was Customer Development – at its heart a very simple idea – there are no facts inside the building – so get outside.

Here we were reinventing what the best practices from the wartime military organizations, and from Lead User Research and Discovery Driven Planning – this time for startups

The goal is to test all the business model hypotheses – including the two most important – customer and value proposition – which we call product/ market fit.

The next, Agile Engineering – a process to build products incrementally and iteratively – was a perfect match for customer development.

And then finally, repurposing Alexander Osterwalder’s Business Model Canvas to map the hypotheses needed in commercialization of a technology

The sum of these tools – Customer Development, Agile Engineering and the Business Model Canvas – is the Lean Methodology.

What I had done is turn a craft into a discipline of strategic learning—a continuous loop of hypothesis testing, experimentation via minimum viable products, and adaptation via pivots.

Lean is a codified system for strategy formation under uncertainty.

Over the last two decades Lean has turned into the de facto standard for starting new ventures. The classes I created at Stanford were adopted by the National Science Foundation and the National Institutes of Health, to commercialize science in the U.S.

And while contemporary entrepreneurs didn’t know it they were adopting the continuous learning cycles that had fueled wartime innovation.

What comes next is going to be even more interesting.

We’re going to remember – for better or worse – 2025 as another inflection point.

AI in everything, synthetic biology, and capital at previously unimaginable scale, are collapsing the distance between exploration and exploitation.

The boundary between discovery, invention, and strategy is dissolving.

Given how fast things are changing I’m looking forward to seeing strategy itself become a dynamic capability—not a plan, but a process of learning faster than the environment changes.

I can’t wait to see what you all create next.

In closing, my work at Stanford was made possible by the unflinching support from Tom Byers, Kathy Eisenhardt and Riitta Katila in the Stanford Technology Ventures Program who let a practitioner into the building.

Thank you.

如何向战争部推销——2025年PEO名录——现已新增500个联系人 || How to Sell to the Dept of War – The 2025 PEO Directory – Now with 500 more names

2025-10-15T13:00:11+00:00

2025年10月PEO名录——更新版2

战争部（DoW）是全球最大的组织之一。如果您是一家初创企业，试图弄清该联系谁以及如何在这个体系中穿行，客气地说——这颇具挑战性。战争部内部人员很难体会，对外界而言这个看似坚不可摧、异常复杂的体系有多么难以理解。

内部人士知道该联系谁，主要承包商也有专门团队跟踪广泛领域公告和合同，但作为初创企业，您完全没有这些人脉资源。（而随着社交媒体的兴起，甚至我们的对手都掌握了更充分的信息。）

如果我们真心要构建下一代国防生态系统（而非仅仅采购下一个光鲜亮丽的项目），那么这本就该是战争部应当公开发布的名录。

在此之前，请查收这份战争部PEO名录的第二版更新。

本次更新的战争部电话簿及初创企业市场进入战略指南新增了500个名称/组织。

（变更摘要详见附录H。）

名录下载地址请点击此处。

订阅及时更新请点击此处。

---------------

The October 2025 PEO Directory – Update 2.

The Department of War (DoW) is one of the world’s largest organizations. If you’re a startup trying to figure out who to call on and how to navigate the system, it can be – to put it politely – challenging. Those inside the DoW have little perspective of how hard it is to understand what to an outsider looks like in an impenetrable, incredibly complex system.

Insiders know who to call, and prime contractors have teams of people following broad area announcements and contracts, but if you’re startup, you have none of those relationships. (And with the advent of Social Media even our adversaries have better knowledge.)

If we’re serious about building a next generation defense ecosystem (not just buying the next shiny object), then this is the directory the Department of War should be publishing.

Until then, here’s the second update to the Department of War PEO Directory.

500 new names/organizations in this DoW phonebook and startup Go-to-Market Strategy playbook.

(See Appendix H for a summary of the changes.)

Downloads of the Directory can be found here.

无科学，不创业：我们正在关闭的创新引擎 || No Science, No Startups: The Innovation Engine We’re Switching Off

2025-10-13T13:00:56+00:00

关于特朗普政府对大学科学领域的打压，已有大量文章讨论。但很少有人追问：科学究竟是什么？它如何运作？科学家是谁？他们做什么？更重要的是，为何大学之外的人们应该关心这些？

（遗憾的是，大众媒体不会解答这些问题——它们不够吸引眼球。科学期刊也不会涉及——这不够技术性。处于风口浪尖的大学同样无法给出简明解释——它们早已丧失将自身工作价值与公众日常生活联系起来的能力。）

本文将阐述科学如何运作，科学与工程如何共同推动美国创新企业的崛起——以及你为何应该关注。

（在前文中，我描述了美国如何构建科技生态系统，以及为何科学投资与国家实力直接相关。建议先阅读该文。）

科学运作之道

当我终于理解科学家、工程师、企业家和风险投资家的区别，以及他们在推动经济繁荣、国防强大和美国崛起中所扮演的角色时，年龄已大到不愿承认。

科学家

科学家（有时称为研究者）是那些不断追问事物运行原理的人群。他们并不知晓答案，而是被好奇心驱使，乐于做出有根据的猜测（专业术语称为假说）并通过实验验证。多数情况下假说会被证伪，但每次正确的发现都推动人类进步——新药物、疾病疗法、消费品、更优质廉价的食物等。

科学家通常专精于某个领域（生物学、医学研究、物理学、农业、计算机科学、材料学、数学等），少数会跨领域研究。自1940年起，美国政府便以数十亿美元规模支持科研。

科学家主要分为两类：理论家与实验家。

理论家

理论家构建数学模型、抽象框架和宇宙运行假说。他们不亲自实验，而是提出新理念或原则，解释现有实验结果，预测未观测现象。理论家帮助界定现实的可能性。

各科学领域都有理论家身影，例如：

物理学量子场论、弦理论、量子力学

生物学神经科学与认知、系统生物学、基因调控

化学分子动力学、量子化学

计算机科学算法设计、计算极限证明

经济学市场或决策模型构建

数学因果推断、贝叶斯网络、深度学习

20世纪最著名的理论家爱因斯坦仅用黑板和大脑，在1905年写下E=MC²方程，揭示微小质量可转化为巨大能量。当时这只是理论，而1930-40年代的其他理论家基于此推动了原子弹研发（利奥·西拉德构想中子链式反应，汉斯·贝特领导洛斯阿拉莫斯理论部，爱德华·泰勒发展氢弹理论）。广岛长崎的爆炸最终验证了爱因斯坦理论的正确性。

实验家

除理论家外，实验家负责在实验室设计和操作实验。显微镜前穿白大褂的科学家形象多属此类，他们通过实验验证假说，如NASA詹姆斯·韦伯望远镜或LIGO引力波观测实验。（后文将看到，实验设备往往由工程师建造。）

部分实验家专注于基础科学，纯粹为认知自然基本原理而研究，不考虑即时应用；另一些则从事应用科学，将基础科学的发现转化为产品与工艺的创新。

应用科学家解决现实导向的实际问题（如洛斯阿拉莫斯科学家研究铀-235临界质量）。基础科学为应用突破奠基：量子力学（基础）催生半导体继而计算机（应用）；病菌理论（基础）带来抗生素和疫苗（应用）。20世纪的应用科学家通常不创办终端产品公司，这一角色由工程师和企业家承担（21世纪更多应用科学家，尤其在生命科学领域，开始从实验室孵化企业）。

美国的科研版图

美国主导科学与发明的独特洞见在于：二战后将研发资金投入大学而非仅限政府实验室。这是其他国家未曾大规模实施的策略。

企业研发中心

20世纪美国企业将超额利润投入杜邦、贝尔实验室、IBM等企业研发中心。1982年证券交易委员会允许公司回购股票后，企业基础研究几乎消失，转而聚焦股东价值最大化的应用研究。如今理论与基础研究主要转移至研究型大学。

研究型大学

表面看（或对本科生而言），大学是上课获学位之地。但在研究型大学，科学教师不仅教学，更通过实验、论文、专利等创造新知。教授们从联邦机构（NSF、NIH、国防部等）、基金会和企业获取资助，大学则建设配套实验室、中心和计算设施。

美国542所研究型大学按卡内基分类分为三级：

R1：187所极高研究活动大学（如斯坦福、哈佛、MIT），授予大量博士学位；

R2：139所高研究活动大学（如詹姆斯麦迪逊、维克森林），研究规模稍小；

R3：216所研究型学院/大学，侧重教学型博士项目。

大学对科学的意义

美国大学承担约50%的基础科学研究（物理、化学、生物、社会科学等），因其是研究生和博士后的人才培养基地。大学年研发支出约1090亿美元，其中600亿来自NIH（生物医学）、NSF（基础科学）、国防部、能源部等。（企业则倾向投资能直接转化为产品的应用研发。）

教授（尤其STEM领域）运营着类初创企业的实验室：提出研究问题，招募团队，撰写基金提案（耗时30-50%）。获资助的首席研究员称"主要研究者"（PI）。实验室兼具工作与教学功能，研究生和博士后在此进行科研训练（常为攻读博士），本科生在顶尖院校也参与辅助。

（2025年前，美国科学高度国际化，约40-50%基础研究由外国出生研究者完成。移民和学生签证曾是科研能力的关键组成部分。）

研究成果通过期刊、会议、专利和技术转移办公室与初创企业分享。从谷歌搜索到CRISPR，众多商业技术源自大学实验室。

大学通过行政支持（合规、采购、安全）和顶级研究设施（实验室、洁净室、望远镜）及核心科学服务（DNA测序中心、电镜、云计算接入）赋能科研。这些设施曾是全球顶尖——直至2025年大幅削减预算。

工程师：科学发现的建造者

工程师基于科学发现进行设计与建造。例如科学家发现原子分裂七年后，数万工程师才造出原子弹。工程师明确建造目标，正是得益于前期基础与应用研究。

科学家vs工程师

工程师制定计划，用软件测试设计，继而切割金属、建造火箭发动机、设计芯片、为实验家制造设备等。例如英伟达GPU芯片由台积电制造，应用材料公司的应用科学又基于半导体研究的基础科学。而使用这些芯片的数据中心，则由机械等各类工程师建造。

典型案例：SpaceX可回收火箭着陆，依托斯坦福Steven Boyd在凸优化算法上的应用科学研究，而Boyd的工作又基于凸分析数学基础科学（SpaceX、NASA、蓝色起源等均采用凸优化进行制导控制）。

企业家：迭代创新的推动者

企业家创办公司将新产品推向市场，雇佣工程师进行产品开发测试。二者在风险承受度和目标上截然不同（许多杰出企业家出身工程师，如马斯克、盖茨、佩奇/布林）。工程师解决已知规格的问题，而企业家则通过最小可行产品迭代，验证客户需求与市场契合度。（将商业未知视为假说，正是企业家的"科学方法"。）

风险投资家：创新生态的燃料

风投资助那些与工程师合作的企业家，而工程师的建造基于应用科学家验证的基础研究。不同于银行对明确项目的贷款，风投投资高风险组合，通过股权获利。多数风投非科学家出身，但优秀者能把握技术趋势，其投资塑造未来。风投主要承担工程与制造风险，极少涉足基础研究风险——这正是政府与大学的职责。

随着科技源头活水枯竭，美国深科技风投机会将减少，未来属于投资科学的中国或欧洲。

为何需要科学家？

读至此或有人疑惑："不能只保留工程师、企业家和风投（或AI）吗？"大学-产业-政府的三方合作，正是硅谷、航空航天、生物技术、量子与AI的基石。这些投资带来了火箭、癌症疗法、互联网、ChatGPT等成果。

科学投资与国家实力直接相关。削弱科学，就是削弱经济长期增长与国防。科技公司对AI数据中心的上千亿投资远超联邦研发支出，但这些属于工程而非科学投资。用通用人工智能取代科学家的设想误解了AI的作用——它正使科学家更高效而非被替代。

忽视科学的国家终将依赖他国。美国二战后的主导地位源于基础科学投资（OSRD、NSF、NIH等）。同期英国削减科学投资，反使美国商业化其战时发明。苏联解体部分源于未能将科学转化为持续创新，而美国大学、初创企业与风投正创建硅谷。核武器、GPS、AI等长期军事经济优势，皆可追溯至科研生态系统。

经验总结

• 科学家分理论家与实验家两类

• 实验家又分基础科学（探索新知）与应用科学（实际应用）

• 科学家培养人才，创造专利与国防解决方案

• 工程师在科学发现基础上建造

• 企业家测试产品边界

• 风投为初创提供资金

• 这些角色互为补充——缺失任一环，系统即退化

科学不会停止。削减美国资助，科学将转移至理解其与国家伟大关联的国家（如中国）。国家实力源于科学投资，削弱基础与应用科学研究，就是削弱美国。

附录：科学方法

五百年来，无论理论家或实验家，验证科学的方式都是科学方法：提出"我认为这应如此运作"的假说并进行验证。其目标是将猜测转化为证据，通过设计实验、分析结果来确认或修正假说。科学家建造仪器开展实验，正是源于对未知的探索。

这些实验小至大学生物实验室的数千美元项目，大至耗资数十亿的卫星、粒子加速器或望远镜。（美国在二战后领跑科学，因政府意识到资助科学家有益经济与国防。）

优质科学应具可重复性。科学家不仅公布结果，更公开实验细节供同行验证，使科学方法具备自我纠错能力。科学方法的另一优势是接受多数实验会失败——失败本身就是探索未知过程中的学习。

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Tons of words have been written about the Trump Administrations war on Science in Universities. But few people have asked what, exactly, is science? How does it work? Who are the scientists? What do they do? And more importantly, why should anyone (outside of universities) care?

(Unfortunately, you won’t see answers to these questions in the general press – it’s not clickbait enough. Nor will you read about it in the science journals– it’s not technical enough. You won’t hear a succinct description from any of the universities under fire, either – they’ve long lost the ability to connect the value of their work to the day-to-day life of the general public.)

In this post I’m going to describe how science works, how science and engineering have worked together to build innovative startups and companies in the U.S.—and why you should care.

(In a previous post I described how the U.S. built a science and technology ecosystem and why investment in science is directly correlated with a country’s national power. I suggest you read it first.)

How Science Works

I was older than I care to admit when I finally understood the difference between a scientist, an engineer, an entrepreneur and a venture capitalist; and the role that each played in the creation of advancements that made our economy thrive, our defense strong and America great.

Scientists

Scientists (sometimes called researchers) are the people who ask lots of questions about why and how things work. They don’t know the answers. Scientists are driven by curiosity, willing to make educated guesses (the fancy word is hypotheses) and run experiments to test their guesses. Most of the time their hypotheses are wrong. But every time they’re right they move the human race forward. We get new medicines, cures for diseases, new consumer goods, better and cheaper foods, etc.

Scientists tend to specialize in one area – biology, medical research, physics, agriculture, computer science, materials, math, etc. — although a few move between areas. The U.S. government has supported scientific research at scale (read billions of $s) since 1940.

Scientists tend to fall into two categories: Theorists and Experimentalists.

Theorists

Theorists develop mathematical models, abstract frameworks, and hypotheses for how the universe works. They don’t run experiments themselves—instead, they propose new ideas or principles, explain existing experimental results, predict phenomena that haven’t been observed yet. Theorists help define what reality might be.

Theorists can be found in different fields of science. For example:

Physics Quantum field theory, string theory, quantum mechanics

Biology Neuroscience and cognition, Systems Biology, gene regulation

Chemistry Molecular dynamics, Quantum chemistry

Computer Science Design algorithms, prove limits of computation

Economics Build models of markets or decision-making

Mathematics Causal inference, Bayesian networks, Deep Learning

The best-known 20th-century theorist was Albert Einstein. His tools were a chalkboard and his brain. in 1905 he wrote an equation E=MC2 which told the world that a small amount of mass can be converted into a tremendous amount of energy. When he wrote it down, it was just theory. Other theorists in the 1930s and ’40s took Einstein’s theory and provided the impetus for building the atomic bomb. (Leo Szilard conceived neutron chain reaction idea, Hans Bethe led the Theoretical Division at Los Alamos, Edward Teller developed hydrogen bomb theory.) Einstein’s theory was demonstrably proved correct over Hiroshima and Nagasaki.

Experimentalists

In addition to theorists, other scientists – called experimentalists – design and run experiments in a lab. The pictures you see of scientists in lab coats in front of microscopes, test tubes, particle accelerators or NASA spacecraft are likely experimentalists. They test hypotheses by developing and performing experiments. An example of this would be NASA’s James Webb telescope or the LIGO Gravitational-Wave Observatory experiment. (As we’ll see later, often it’s engineers who build the devices the experimentalists use.)

Some of these experimentalists focus on Basic Science, working to get knowledge for its own sake and understand fundamental principles of nature with no immediate practical use in mind.

Other experimentalists work in Applied Science, which uses the findings and theories derived from Basic Science to design, innovate, and improve products and processes.

Applied scientists solve practical problems oriented toward real-world applications. (Scientists at Los Alamos weretrying to understand the critical mass of U-235 (the minimum amount that would explode.) Basic science lays the groundwork for breakthroughs in applied science. For instance: Quantum mechanics (basic science) led to semiconductors which led to computers (applied science). Germ theory (basic science) led to antibiotics and vaccines (applied science). In the 20th century Applied scientists did not start the companies that make end products. Engineers and entrepreneurs did this. (In the 21st century more Applied Scientists, particularly in life sciences, have also spun out companies from their labs.)

Scientists

Where is Science in the U.S. Done?

America’s unique insight that has allowed it to dominate Science and invention, is that after WWII we gave Research and Development money to universities, rather than only funding government laboratories. No other country did this at scale.

Corporate Research Centers

In the 20th century, U.S. companies put their excess profits into corporate research labs. Basic research in the U.S. was done in at Dupont, Bell Labs, IBM, AT&T, Xerox, Kodak, GE, et al.

This changed in 1982, when the Securities and Exchange Commission ruled that it was legal for companies to buy their own stock (reducing the number of shares available to the public and inflating their stock price.) Very quickly Basic Science in corporate research all but disappeared. Companies focused on Applied Research to maximize shareholder value. In its place, Theory and Basic research is now done in research universities.

Research Universities

From the outside (or if you’re an undergraduate) universities look like a place where students take classes and get a degree. However, in a research university there is something equally important going on. Science faculty in these schools not only teach, but they are expected to produce new knowledge—through experiments, publications, patents, or creative work. Professors get grants and contracts from federal agencies (e.g., NSF, NIH, DoD), foundations, and industry. And the university builds Labs, centers, libraries, and advanced computing facilities that support these activities.

In the U.S. there are 542 research universities, ranked by the Carnegie Classification into three categories.

R1: 187 Universities – Very High Research Activity

Conduct extensive research and award many doctoral degrees.

Examples: Stanford, UC Berkeley, Harvard, MIT, Michigan, Texas A&M …

R2: 139 Universities – High Research Activity

Substantial but smaller research scale.

Examples: James Madison, Wake Forest, Hunter College, …

R3: 216 Research Colleges/Universities

Limited research focus; more teaching-oriented doctoral programs.

Smaller state universities

Why Universities Matter to Science

U.S. universities perform about 50% of all basic science research (physics, chemistry, biology, social sciences, etc.) because they are training grounds for graduate students and postdocs. Universities spend ~$109 billion a year on research. ~$60 billion of that $109 billion comes from the National Institutes for Health (NIH) for biomedical research, National Science Foundation (NSF) for basic science, Department of War (DoW), Department of Energy (DOE), for energy/physics/nuclear, DARPA, NASA. (Companies tend to invest in applied research and development, that leads directly to saleable products.)

Professors (especially in Science, Technology, Engineering and Math) run labs that function like mini startups. They ask research questions, then hire grad students, postdocs, and staff and write grant proposals to fund their work, often spending 30–50% of their time writing and managing grants. When they get a grant the lead researcher (typically a faculty member/head of the lab) is called the Principal Investigator (PI).

The Labs are both workplaces and classrooms. Graduate students and Postdocs do the day-to-day science work as part of their training (often for a Ph.D.). Postdocs are full-time researchers gaining further specialization. Undergraduates may also assist in research, especially at top-tier schools.

(Up until 2025, U.S. science was deeply international with ~40–50% of U.S. basic research done by foreign-born researchers (graduate students, postdocs, and faculty). Immigration and student visas were a critical part of American research capacity.)

The results of this research are shared with the agencies that funded it, published in journals, presented at conferences and often patented or spun off into startups via technology transfer offices. A lot of commercial tech—from Google search to CRISPR—started in university labs.

Universities support their science researchers with basic administrative staff (for compliance, purchasing, and safety) but uniquely in the U.S., by providing the best research facilities (labs, cleanrooms, telescopes), and core scientific services: DNA sequencing centers, electron microscopes, access to cloud, data analysis hubs, etc. These were the best in the world – until the sweeping cuts in 2025.

Engineers Build on the Work of Scientists

Engineers design and build things on top of the discoveries of scientists. For example, seven years after scientists split the atom, it took 10s of thousands of engineers to build an atomic bomb. From the outset, the engineers knew what they wanted to build because of the basic and applied scientific research that came before them.

Scientists Versus Engineers

Engineers create plans, use software to test their designs, then… cut sheet metal, build rocket engines, construct buildings and bridges, design chips, build equipment for experimentalists, design cars, etc.

As an example, at Nvidia their GPU chips are built in a chip factory (TSMC) using the Applied science done by companies like Applied Materials which in turn is based on Basic science of semiconductor researchers. And the massive data centers OpenAI, Microsoft, Google, et al that use Nvidia chips are being built by mechanical and other types of engineers.

My favorite example is that the reusable SpaceX rocket landings are made possible by the Applied Science research on Convex Optimization frameworks and algorithms by Steven Boyd of Stanford. And Boyd’s work was based on the Basic science mathematical field of convex analysis (SpaceX, NASA, JPL, Blue Origin, Rocket Lab all use variations of Convex Optimization for guidance, control, and landing.)

Startup Entrepreneurs Build Iteratively and Incrementally

Entrepreneurs build companies to bring new products to market. They hire engineers to build, test and refine products.

Engineers and entrepreneurs operate with very different mindsets, goals, and tolerances for risk and failure. (Many great entrepreneurs start as engineers e.g., Musk, Gates, Page/Brin). An engineer’s goal is to design and deliver a solution to a known problem with a given set of specifications.

In contrast, entrepreneurs start with a series of unknowns about who are the customers, what are the wanted product features, pricing, etc. They retire each of these risks by building an iterative series of minimum viable products to find product/market fit and customer adoption. They pivot their solution as needed when they discover their initial assumptions are incorrect. (Treating each business unknown as a hypothesis is the entrepreneurs’ version of the Scientific Method.)

Venture Capitalists Fund Entrepreneurs

Venture capitalists (VCs) are the people who fund entrepreneurs who work with engineers who build things that applied scientists have proven from basic researchers.

Unlike banks which will give out loans for projects that have known specifications and outcomes, VCs invest in a portfolio of much riskier investments. While banks make money on the interest they charge on each loan, VCs take part ownership (equity) in the companies they invest in. While most VC investments fail, the ones that succeed make up for that.

Most VCs are not scientists. Few are engineers, some have been entrepreneurs. The best VCs understand technical trends and their investments help shape the future. VCs do not invest in science/researchers. VCs want to minimize the risk of their investment, so they mostly want to take engineering and manufacturing risk, but less so on applied science risk and rarely on basic research risk. Hence the role of government and Universities.

VCs invest in projects that can take advantage of science and deliver products within the time horizon of their funds (3–7 years). Science often needs decades before a killer app is visible.

As the flow of science-based technologies dries up, the opportunities for U.S. venture capital based on deep tech will decline, with its future in countries that are investing in science – China or Europe.

Why Have Scientists? Why Not Just a Country of Engineers, Entrepreneurs and VCs (or AI)?

If you’ve read so far, you might be scratching your head and asking, “Why do we have scientists at all? Why pay for people to sit around and think? Why spend money on people who run experiments when most of those experiments fail? Can’t we replace them with AI?”

The output of this university-industry-government science partnership became the foundation of Silicon Valley, the aerospace sector, the biotechnology industry, Quantum and AI. These investments gave us rockets, cures for cancer, medical devices, the Internet, Chat GPT, AI and more.

Investment in science is directly correlated with national power. Weaken science, you weaken the long-term growth of the economy, and national defense.

Tech firms’ investments of $100s of billions in AI data centers is greater than the federal government’s R&D expenditures. But these investments are in engineering not in science. The goal of making scientists redundant using artificial general intelligence misses the point that AI will (and is) making scientists more productive – not replacing them.

Countries that neglect science become dependent on those that don’t. U.S. post-WWII dominance came from basic science investments (OSRD, NSF, NIH, DOE labs). After WWII ended, the UK slashed science investment which allowed the U.S. to commercialize the British inventions made during the war.

The Soviet Union’s collapse partly reflected failure to convert science into sustained innovation, during the same time that U.S. universities, startups and venture capital created Silicon Valley. Long-term military and economic advantage (nuclear weapons, GPS, AI) trace back to scientific research ecosystems.

Lessons Learned

Scientists come in two categories

Theorists and experimentalists

Two types of experimentalists; Basic science (learn new things) or applied science (practical applications of the science)

Scientists train talent, create patentable inventions and solutions for national defense

Engineers design and build things on top of the discoveries of scientists

Entrepreneurs test and push the boundaries of what products could be built

Venture Capital provides the money to startups

Scientists, engineers, entrepreneurs – these roles are complementary

Remove one and the system degrades

Science won’t stop

Cut U.S. funding, then science will happen in other countries that understand its relationship to making a nation great – like China.

National power is derived from investments in Science

Reducing investment in basic and applied science makes America weak

Appendix – How Does Science Work? – The Scientific Method

Whether you were a theorist or experimentalist, for the last 500 years the way to test science was by using the scientific method. This method starts by a scientist wondering and asking, “Here’s how I think this should work, let’s test the idea.”

The goal of the scientific method is to turn a guess (in science called a hypothesis) into actual evidence. Scientists do this by first designing an experiment to test their guess/hypothesis. They then run the experiment and collect and analyze the result and ask, “Did the result validate, invalidate the hypothesis? Or did it give us completely new ideas?” Scientists build instruments and run experiments not because of what they know, but because of what they don’t know.

These experiments can be simple ones costing thousands of dollars that can be run in a university biology lab while others may require billions of dollars to build a satellite, particle accelerator or telescope. (The U.S. took the lead in Science after WWII when the government realized that funding scientists was good for the American economy and defense.)

Good science is reproducible. Scientists just don’t publish their results, but they also publish the details of how they ran their experiment. That allows other scientists to run the same experiment and see if they get the same result for themselves. That makes the scientific method self-correcting (you or others can see mistakes).

One other benefit of the scientific method is that scientists (and the people who fund them) expect most of the experiments to fail, but the failures are part of learning and discovery. They teach us what works and what doesn’t. Failure in science testing unknowns means learning and discovery.

当事情失控：创始人如何应对危机 || When Sh!t Hits the Fan – Founders in a Crisis

2025-09-17T13:00:43+00:00

伟大的创始人会在危机中发光。

普通创始人则会看着他们的公司被烧毁。

我刚刚和一家电动自行车公司的两位联合创始人喝了一杯咖啡，他们正在指导我们的一支学生团队。很快我就意识到他们是非常优秀的创始人——富有创造力、敏捷且仍然享受着公司建设的乐趣。与其他电动自行车租赁公司不同，他们的商业模式独特，提供免费的租赁时间以换取用户观看广告。我们进行了一次很棒的对话，他们谈论了各种话题，唯独没有提到“桌上的死麋鹿”。

桌上的死麋鹿
在我们见面之前，我读到他们刚刚输给了另外三家电动自行车公司（包括Uber）争夺在另一座主要城市的经营权。这意味着他们接下来四年都将被排除在该市场之外。在三家公司中排名第四是痛苦的，但优秀的CEO会从失败中学习，并确保这些教训被应用到未来，以避免再次发生。 (如果不行，他们的董事会就会敲他们的头，直到他们做到。) 但在交谈中，我了解到这些创始人并非如此。

他们随意地提到，他们再次在争夺在一座主要城市的经营权，这次就是我所在的城市。

我问了一些在我看来显而易见的问题，首先是：“你们从上次的失败中学到了什么？你们做了哪些改变以确保不会再发生？”对我而言，更重要的是：“如果你们失去了这座城市，你们的估值和业务会受到什么影响？”他们的回答很模糊，如果我是他们的董事会成员，这会让我感到犹豫。 (这是我对他们所说内容的委婉描述。)

被忽视的危机
虽然创始人还在谈论新产品、品牌合作和客户获取计划，但他们似乎还没有真正理解上次失败的含义，也没有意识到如果这次再失去这座城市可能带来的后果。更不用说他们现在正陷入一场关乎公司生死存亡的斗争。如果不是为了生存，至少也是为了在估值上获得一个数量级或两个数量级的差异。

这位CEO显然没有意识到如果失去这次城市经营权选择的紧迫性。鉴于我之前见过类似的情况，我建议他们应该把这场竞争当作一场四警报的火灾来对待。这是一场危机，但他们却像对待日常琐事一样处理。

识别非日常事务
初创公司本质上就是混乱的。创始人不断面对各种决策、需求和干扰。但他们必须识别哪些事件或结果会对公司产生数量级或生死攸关的影响。当危机发生时，CEO需要调动所有资源，以不同于处理其他日常“燃眉之急”的方式来应对。而不是将这次事件视为“又一次消防演习”，作为第一步，初创公司CEO需要阐明为什么这会是公司生存的生死攸关的问题。我发现最好的方法是起草一份一页纸的备忘录，说明以下几点：

发生了什么变化
为什么这很重要
为什么我们目前的“日常运营”组织、流程或产品不足以应对

除非公司大楼正在燃烧，否则应向一些值得信赖的顾问测试这份备忘录（而不是你的高管团队或董事会成员）。

然后，CEO需要亲自领导应对措施：

组建一个完全专注于解决问题的团队
CEO和团队需要一个“作战室”——墙上要展示问题的处理进展和当前的进度
前往该城市/地点以争取交易/解决问题
识别并消除所有障碍
制定新的销售、市场、影响力、路线图等策略
最后，正如我建议那家电动自行车公司所做的，你需要一些不同层次的人才，他们有处理当前问题的成功经验。
这是最难传达的一点。替换或补充那些认为自己工作做得不错但看不到改变必要性的人，是非常痛苦的。

经验教训

有能力的创始人能够识别危机，而不是日常事务。

优秀的创始人知道如何培养新技能和能力来应对危机。

伟大的创始人已经准备好了B计划。

在危机中，如果你无法管理混乱和不确定性，如果你无法倾向于采取行动，而是在等待别人告诉你该怎么做，那么你的投资者和竞争对手将替你做决定，或者你将耗尽资金，公司最终会倒闭。

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Great founders shine in a crisis.

Ordinary ones watch their companies burn down.

I just had coffee with two co-founders of an e-bike company who were mentoring one of our student teams. In short order I realized they were great founders – creative, agile and still having fun building their company. Unlike other e-bike rental companies, their business model was unique, offering riders free rental time in exchange for looking at ads. We had a great conversation, and they talked about everything – except the dead moose on the table.

The Dead Moose

Before we met, I read they had just lost out to three other e-bike companies (including Uber) to operate in another major city. This meant they were now shut out of that market for the next four years. Being fourth in a group of three is painful, but good CEOs learn from failure and ensure that those lessons get baked in going forward so they never happen again. (And if not, their board hits them on the head until they do.) As we talked, I learned that wasn’t the case with these founders.

They casually mentioned they were again competing for the rights to operate in a major city, this time the one I was in.

I asked what I thought were obvious questions, starting with, “What did you learn from the loss? What did you change to ensure it won’t happen again?” And to me, most important, “What happens to your valuation and business if you lose this city?” The answers were vague, and if I had been on their board would have given me pause. (That’s a polite description of what I would have said.)

A Crisis – Ignored

While the founders were still talking about new product offerings, brand partnerships, and customer acquisition programs, they hadn’t processed what their past loss meant, and the potential consequences of losing this next city. Let alone that they were now in a life-and-death struggle for the survival of their company. If not for survival, at least in a fight for one- or two-orders of magnitude difference in their valuation.

The CEO just didn’t have the urgency of what would happen if they lost this next city selection. Having seen this movie before, I suggested that they needed to treat this competition as a four-alarm fire. This was a crisis, and they were treating it like any other day-to-day issue.

Recognize When It’s Not Business As Usual

Startups are inherently chaotic. Founders face a constant barrage of decisions, demands, and distractions. But they need to recognize when an event/outcome can have an order of magnitude/life or death impact on their company. When a crisis happens the CEO needs to marshal all resources and organize to deal with them differently than the multitude of other day-to-day “hair on fire” issues in a startup. Rather than making this “one more fire drill,” as a first step startup CEOs need to articulate why this is an existential threat to the survival of the company. I found the best way to do this is to draft a one-page memo laying out:

What’s changed

Why it matters

Why our current “business as usual” organization/process/product is insufficient as a response

And unless the building is on fire, test the memo with some trusted advisors (not your exec staff or board.)

Then, the CEO needs to personally lead the response:

With a team focused 100% on the problem

The CEO and team need a “War Room” – with a wall covered by visual representation of how the problem is being worked and progress to date

Move to the city/location to get the deal/fix the problem

Identify and remove all obstacles

Create a new strategy for sales, marketing, influence, roadmap, etc.

Finally, as I suggested to the e-bike company, you need new people of a different caliber, experienced in whatever issue is on fire who have a track record of success.

This was the hardest point to get across. Replacing or augmenting people who thought they were doing a good job but don’t see the need for change, is painful.

Lessons Learned

A competent founder can recognize when it’s a crisis, not business as usual.

A good founder knows how to build new skills and capacity to manage a crisis.

A great founder already has a plan B in place.

In a crisis if you can’t manage chaos and uncertainty, if you can’t bias yourself for action and if instead you wait around for someone else to tell you what to do, then your investors and competitors will make your decisions for you and/or you will run out of money and your company will die.

如何向国防部推销 – 2025年项目执行办公室目录 || How To Sell to the Dept of War – The 2025 PEO Directory

2025-09-10T13:00:06+00:00

宣布发布2025版的DoW PEO目录。在线查看此处。将此PEO目录视为一部“谁在政府中采购”的电话簿。在国防部找到产品的客户非常困难：你应该联系谁？如何引起他们的注意？正确的市场进入策略是什么？什么是PEO，我为什么需要关心？自从我与他人共同创立了 Hacking for Defense 以来，我的学生总是问：“我们应该联系国防部的谁来告知他们我们解决了什么问题？如何向他们展示我们构建的解决方案？”在近几年里，这个问题不断出现，来自新的国防初创公司及其投资者。同时，我也收到新一波国防投资者的问题：“我们初创公司最好的市场进入（GTM）策略是什么？” PEO、PM、PIA、PoR、联盟、SBIR、OTA、CSO、FAR、CUI、SAM、CRADAs、Prime、中层集成商、部落/原住民公司（ANC）、直接面向操作者、直接面向作战单位、实验室、DD-254……对于初创公司来说，这是一整套全新的语言、新术语、新合作伙伴和新规则，需要全新的“市场进入（GTM）”策略。（注意：2025年，向国防部销售可能将发生变化——向更好的方向发展。）向国防部销售需要时间，但一个精心制定的国防策略可以带来数十亿美元的合同、持续的收入和国家级的技术影响。现有的国防承包商知道这些国防部组织是谁，并有团队跟踪预算和合同。他们了解如何获得国防部的订单。但初创公司呢？ 为什么要编写PEO目录？
大多数初创公司对从哪里开始毫无头绪。而向国防部销售与任何企业或B2B销售流程都截然不同，创始人和投资者可能并不熟悉。与商业世界相比，语言不同，机构不同，风险承担（在采购中）的文化也不同，最重要的是市场进入策略完全不同。令人惊讶的是，在去年首次发布PEO目录之前，没有一个面向初创公司的国防部电话簿可供使用，以识别应该联系的国防部人员。在那个时代，国防部及其供应商是一个紧密联系的群体，彼此熟悉，技术革新以几十年为周期缓慢进行。（还假设我们的对手无法访问我们的国防部网页、LinkedIn和ChatGPT。）这已经不再是事实。鉴于国防部外的技术革新速度加快，以及新的无人机、反无人机、自主、人工智能、量子、生物科技等供应商的出现，这种信息不透明已成为实现国家层面创新交付的障碍。（这种信息缺失甚至延伸到国防部内部。我开始收到多个作战司令部工作人员的请求，要求访问PEO目录。为什么？因为“将PEO数据库与我们的需求、差距和跟踪技术数据库链接起来，会很有帮助。”）这是一个典型的信息不对称案例，这对国防部日益紧迫的需求和新兴的国防初创生态系统都不健康。我们的对手已经拥有几十年的国家层面创新交付机制。这是我们帮助国防部竞争的贡献。 2025版PEO目录说明
该文件的第一版最初只是一个PEO目录。其重点在于（并且仍然是）初创公司尽早与PEO沟通的价值，以获取关于作战人员问题的信号，以及国防部是否会现在或将来购买其产品。这些早期对话回答了“是否有需求？”和“是否有市场？”的问题。（这不是美国政府的官方出版物）（不要依赖此文件的准确性、完整性或商业建议）（所有数据均来自国防部网站和公开信息）

感谢今年的合作伙伴协助维护和托管目录：斯坦福国家安全创新 Gordian Knot 中心、America’s Frontier Fund 和 BMNT。

本版PEO目录在线发布，以便随着最新变化的出现进行更新。

发送更新和更正至 updates@americasfrontier.com

您可以访问并下载完整文档此处。

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Announcing the 2025 edition of the DoW PEO Directory. Online here.

Think of this PEO Directory as a “Who buys in the government?” phone book.

Finding a customer for your product in the Department of War is hard: Who should you talk to? How do you get their attention? What is the right Go-To-Market Strategy? What is a PEO and why should I care?

Ever since I co-founded Hacking for Defense, my students would ask, “Who should we call in the DoW to let them know what problem we solved? How can we show them the solution we built?” In the last few years that question kept coming, from new defense startups and their investors.

At the same time, I’d get questions from the new wave of Defense Investors asking, “What’s the best “Go-To-Market (GTM)” strategy for our startups?

PEOs, PMs, PIAs, PoRs, Consortia, SBIRs, OTAs, CSOs, FAR, CUI, SAM, CRADAs, Primes, Mid-tier Integrators, Tribal/ANC Firms, Direct-to-Operator, Direct-to-Field Units, Labs, DD-254… For a startup it’s an entirely new language, new buzzwords, new partners, new rules and it requires a new “Go-To-Market (GTM)” strategy.

How to Work With the DoW

Below are simplified diagrams of two of the many paths for how a startup can get funding and revenue from the Department of War. The first example, the Patient Capital Path, illustrates a startup without a working product. They travel the traditional new company journey through the DoW processes.

The second example, the Impatient Capital Path, illustrates a startup with an MVP and/or working product. They ignore the traditional journey through the DoW process and go directly to the warfighter in the field. With the rise of Defense Venture Capital, this “swing-for-the fences” full-speed ahead approach is a Lean Startup approach to become a next generation Prime.

(Note that in 2025 selling to the DoW is likely to change – for the better.)

Selling to the DoW takes time, but a well-executed defense strategy can lead to billion-dollar contracts, sustained revenue, and technological impact at a national scale. Existing defense contractors know who these DoW organizations are and have teams of people tracking budgets and contracts. They know the path to getting an order from the Department of War. But startups?

Why Write the PEO Directory?

Most startups don’t have a clue where to start. And selling to the Department of War is unlike any enterprise or B-to-B sales process founders and their investors may be familiar with. Compared to the commercial world, the language is different, the organizations are different, the culture of risk taking (in acquisition) is different, and most importantly the go-to-market strategy is completely different.

Amazingly, until last year’s first edition of the PEO directory there wasn’t a DoW-wide phone book available to startups to identify who to call in the War Department. This lack of information made sense in a world where the DoW and its suppliers were a closely knit group who knew each other and technology innovation was happening at a sedate decades-long pace. (And assumed our adversaries didn’t have access to our DoW web pages, LinkedIn and ChatGPT.)

That’s no longer true. Given the rapid pace of innovation outside the DoW, and new vendors in UAS, counter UAS, autonomy, AI, quantum, biotech, et al, this lack of transparency is now an obstacle to a whole-of-nation approach to delivering innovation to the warfighter.

(This lack of information even extends internally to the DoW. I’ve started receiving requests from staff at multiple Combatant Commands for access to the PEO Directory. Why? Because “…it would be powerful to include a database of PEOs to link to our database of Requirements, Gaps, and Tracked Technologies to specific PEOs to call.”)

This is a classic case of information asymmetry, and it’s not healthy for either the increasingly urgent needs of the Department of War or the nascent startup defense ecosystem.

Our adversaries have had a whole-of-nation approach to delivering innovation to the warfighter in place for decades. This is our contribution to help the DoW compete.

2025 PEO Directory Edition Notes

The first edition of this document started solely as a PEO directory. Its emphasis was (and is) the value of a startup talking to PEOs early is to get signals on what warfighter problems to solve and whether the DoW will buy their product now or in the future. Those early conversations answer the questions of “Is there a need?” and “Is there a market?”

This 2025 edition of the PEO Directory attempts to capture the major changes that are occurring in the DoW – in organizations, in processes and in people. (For example, the PEO offices of the three largest new defense acquisition programs — Golden Dome, Sentinel and Columbia – will report directly to the Deputy Secretary of War, rather than to their respective Services. And the SecWar killed the cumbersome JCIDIS requirements process.)

What this means is that in 2025 the DoW will develop a new requirements and acquisition process that will identify the most urgent operational problems facing the U.S. military, work with industry earlier in the process, then rapidly turn those into fielded solutions. (That also means the Go-to-market description, people and organizations in this document will be out of date, and why we plan to update it regularly.)

What’s New?

This 2025 edition now includes as an introduction, a 30-page tutorial for startups on how the DoW buys and the various acquisition and funding processes and programs that exist for startups. It provides details on how to sell to the DoW and where the Program Executive Offices (PEOs) fit into that process.

The Directory now also includes information about the parts of the government and the regulations that influence how the DoW buys – the White House Office of Management and Budget (OMB), and the Federal Acquisition Regulations (FAR). It added new offices such as Golden Dome Direct Reporting Program, DIU, AFRL, DARPA, MDA, CDAO, OSC, IQT, Army Transformation and Training Command, SOCOM, and others.

To help startups understand the DoW, for each service we added links to the organization, structure, and language, as well as a list of each Service’s General Officers/Flag Officers.

Appendix B has a linked spreadsheet with the names in this document.

Appendix C has a list of Venture Capital firms, Corporate Investors, Private Equity firms and Government agencies who invest in Defense. In addition, the Appendix includes details about the various DoW SBIR programs, a list of OTA Consortia, Partnership Intermediary Agreement (PIA) Organizations, and Tribal/Alaska Native Corporation (ANC) Companies.

Appendix D now lists and links to the military and state FFRDC test centers where startups can conduct demos and test equipment.

Appendix E added a list and links of Defense Publications and Defense Trade Shows.

Appendix F has a list of all Army system contractors.

A few reminders:

This is not an official publication of the U.S. government

Do not depend on this document for accuracy, completeness or business advice.

All data is from DoW websites and publicly available information.

Thanks to this year’s partners helping to maintain and host the Directory: Stanford Gordian Knot Center for National Security Innovation, America’s Frontier Fund and BMNT.

This edition of the PEO Directory is on-line so it can be updated as the latest changes become available.

Send updates and corrections to updates@americasfrontier.com

You can access and download the full document here.

对颠覆视而不见：错失未来的首席执行官们 || Blind to Disruption – The CEOs Who Missed the Future

2025-07-08T13:00:00+00:00

“你是如何破产的？” “有两种方式：逐渐地，然后突然地。” 欧内斯特·海明威，《太阳照常升起》自火和轮子出现以来，每一种颠覆性技术都迫使领导者适应或消亡。这篇文章讲述的是当4000家公司面临一种颠覆性技术时发生了什么，以及为何只有1家公司得以幸存。在20世纪初，美国拥有超过4000家马车和运货车制造商。它们是交通的支柱，也是汽车的前身，用于个人交通、货物运输、军事后勤、公共交通等。这些公司雇佣了数万名工人，并构成了由铁匠、轮轴匠、马鞍匠、马厩和饲料供应商组成的一个生态系统。而在短短二十年内，这些公司都消失了。只有1家马车和运货车制造商转向了汽车。今天，这个故事显得异常熟悉。正如马车行业观察到汽车从好奇到主导地位的演变，现代的SaaS、媒体、软件、物流、国防和教育行业的公司正在观察AI从新颖到存在性威胁的演变。一个舒适的行业错失了转折点 1900年，美国是全球最大的马车制造国。印第安纳州南本德、密歇根州弗林特和俄亥俄州辛辛那提等地充满了生产马车、轻便马车和运货车的工厂。高端马车制造商制作了精美的车辆，主要由木材和皮革制成，由工匠手工打造。其他公司则制造更基础的运货车用于运输货物。当早期汽车在1890年代开始出现——首先是蒸汽动力，然后是电力，最后是汽油动力——大多数马车和运货车制造商都忽视了它们。为什么？第一辆汽车：噪音大且不可靠价格昂贵且难以维修在一个没有加油站的世界里燃料短缺不适合美国乡村的泥泞道路早期的汽车在大多数对客户重要的关键维度上都比不上马车。克莱顿·克里斯滕森的《创新者的窘境》完美地描述了这一点——颠覆始于被现有企业不重视的次级产品。但在这忽视背后，还有更深层的东西：身份认同和自大。马车制造商认为自己不是交通运输公司，而是优雅马车的工匠。汽车不是进化的产物，而是异端。因此，他们等待、观察，最终慢慢消亡，然后突然地消亡。早期汽车是小众且实验性的（1890年代–1905年）最初的汽车（蒸汽、电力和早期汽油动力）价格昂贵、不可靠且速度慢。它们由19世纪的机械爱好者制造。少数销售出去的汽车被视为其他爱好者的玩具和富人的奢侈品。（卡尔·本茨于1886年获得了第一台内燃机的专利。1893年，弗兰克·杜里厄驾驶了第一辆在美国行驶的汽车。）这些早期汽车与庞大的马匹经济共存。马匹拉动马车、运输货物、驱动有轨电车和载人。早期汽车制造商只使用他们所知道的设计：马车。驾驶员坐在高处，就像在马车上那样，以便能看见马匹。在最初的15年里，马车制造商、马车夫和马厩主人并未看到立即的威胁。就像今天的AI一样：汽车强大、新奇、有缺陷、不可靠，但尚未成为主流。颠覆开始（1905–1910年）在它们首次出现的十年后，汽油汽车变得更实用，拥有更好的发动机、橡胶轮胎，市政部门也开始铺设道路。从1903年到1908年，福特推出了9种不同的车型，他们正在实验今天所谓的最小可行产品。福特（以及通用汽车）摆脱了马车的遗产，开始从第一性原理设计汽车，优化速度、安全、大规模生产以及现代材料。这就是汽车成为独立物种的时刻。在此之前，它仍然是带有发动机的马车。城市精英为了地位和速度从马车转向汽车，出租车、货运车队和富裕的通勤者在大城市采用汽车。即使有证据摆在眼前，马车公司仍然没有转向，认为汽车只是短暂的潮流。对于马车公司来说，这是颠覆的“否认与漂移”阶段。转折点：福特Model T和大规模生产（1908–1925年） 1908年推出的福特Model T价格实惠（1920年代降至825美元至260美元），耐用且易于维修，并采用装配线大规模生产。在15年内，数千万美国人拥有汽车。与马车相关的行业——不仅马车制造商，还包括整个铁匠、马厩和饲料供应商生态系统——开始崩溃。城市禁止马匹进入市中心，因为垃圾、疾病和拥堵。这就像谷歌、iPhone或ChatGPT的出现：一次范式转变。旧生态系统的崩溃（1920年代–1930年代） 1900年至1930年间，美国马匹数量从2100万降至1000万，马车和轻便马车的生产急剧下降。新基础设施——道路、加油站、驾驶员执照、交通法规——围绕汽车而非马匹建立。早期汽车制造商大量借鉴了马车设计（1885–1910年）。汽车在马车主导的世界中出现，并继承了马车制造商的材料和机械设计。 - 钢板弹簧是19世纪马车的主要悬挂系统。早期汽车也使用了同样的系统。 - 马车没有减震器，早期汽车也没有。它们都依赖钢板弹簧减震，使得在高速行驶时变得颠簸和不稳定。为什么？道路状况糟糕，速度低。马车制造商知道如何制造能适应鹅卵石和泥地的马车。 - 马车使用实心钢或木制车轴；早期汽车也使用了同样的。车身结构和设计借鉴了马车 - 汽车车身采用木制框架和钢或铝制外壳，就像马车一样。 - 软装、皮革工艺和装饰也沿用了马车的设计。 - “跑车”、“轻便马车”、“敞篷车”和“豪华马车”等术语直接继承自马车类型。 - 高坐位和窄轮距：早期汽车有高轮和高离地间隙，像马车和轻便马车一样，因为早期道路是凹陷和泥泞的。结果：早期汽车看起来像没有马的马车，因为它们在功能和结构上，都是带有发动机的马车。随着时间的推移发生了什么变化随着速度的提升和道路的改善，木制马车设计无法承受更快、更重的汽车的扭转应力。钢板弹簧悬挂系统对于速度和操控来说过于粗糙。汽车制造商开始使用冲压钢车身（费舍尔车身的突破），独立前悬挂（在1930年代引入），最终将车身和底盘整合为一个统一的结构，而不是单独的车身和框架（在1930年代–1940年代）。斯图德贝克：从马匹到马力在所有4000家马车制造商中，唯一没有破产并转型为汽车公司的就是斯图德贝克。斯图德贝克于1852年在印第安纳州南本德创立，最初为农民和向西开拓者制造马车。他们在美国内战期间为联邦军提供马车，并在19世纪末成为全球最大的马车制造商。但与其他同行不同，斯图德贝克在一系列早期战略上进行了投资。 1902年，他们开始生产电动车——一种谨慎但有远见的举措。两年后，1904年，他们进入汽油车行业，最初通过外包发动机和底盘。最终，他们开始自己制造整辆汽车。斯图德贝克理解了其他4000家马车公司忽视的两点：未来不会是马车。公司的核心能力不在马车，而在交通。斯图德贝克进行了痛苦的生产转型，重新调整了工厂，重新培训了员工。到20世纪10年代，他们已成为一家真正的汽车公司。斯图德贝克在汽车时代存活了很长时间——比大多数早期汽车制造商更久，并且直到1966年才停止生产汽车。费舍尔车身：机器时代的马车制造商虽然斯图德贝克直接从马车转型为汽车，但可以认为费舍尔车身是其衍生公司。费舍尔兄弟于1908年在底特律创立费舍尔车身公司，此前曾在一家马车公司工作。他们专注于制造汽车车身，而不是整辆汽车。他们的关键创新是制造封闭式钢制车身，这比开放的马车和木制框架有了重大改进。到1919年，费舍尔公司如此成功，以至于通用汽车收购了其控股权，并于1926年完全收购了费舍尔公司。数十年来，“Body by Fisher”被印在数百万辆通用汽车上。杜兰特-多特：通用汽车的起源虽然杜兰特-多特马车公司从未自己制造汽车，但其共同创始人威廉·C.（比利）杜兰特看到了其他人看不到的东西。详见此处和此处的博客文章。杜兰特利用他在马车行业赚取的财富投资于蓬勃发展的汽车行业。他于1904年创立了别克，并于1908年建立了通用汽车公司。他像硅谷的疯狂企业家一样，迅速收购了奥兹莫比尔、凯迪拉克和其他11家汽车公司，以及10家零部件/配件公司，创建了第一家汽车集团。（1910年，杜兰特被董事会解雇。但杜兰特并未气馁，他创立了雪佛兰，将其上市，并于1916年对通用汽车进行了敌意收购并解雇了董事会。他再次被新董事会解雇，并在管理一家保龄球馆时无钱可花。）虽然他的财务过度扩张最终导致他失去对通用汽车的控制，但他的愿景重塑了美国制造业。通用汽车成为20世纪最大的汽车公司。为何其他3999家马车制造商未能成功大多数马车制造商没有威廉·杜兰特、费舍尔兄弟或斯图德贝克这样的董事会成员。他们失败的原因如下：技术断层马车由木材、皮革和铁制成；汽车需要钢铁、发动机和电气系统。这些技能难以轻易转移。资本需求转向汽车需要巨大的投资。大多数中小型马车公司没有资金，或无法及时筹集资金。商业模式惯性马车制造商销售的是低产量、高利润的产品。汽车行业，尤其是福特Model T之后，是关于高产量、低利润规模的。文化认同马车制造商不认为自己是工程师或工业家，而是工匠。汽车是嘈杂、肮脏的机器——在他们之下。管理者与远见卓识的创始人在每一家成功转型的公司中，都是创始人而非聘请的CEO推动了转变。低估了采用曲线早期的汽车很糟糕。但技术S曲线迅速弯曲。到1910年代，汽车明显更好。到1920年代，马车已经过时。 “你是如何破产的？” “有两种方式：逐渐地，然后突然地。” 到1925年，从1900年左右运营的4000多家马车公司中，几乎全部都消失了。马车时代的悲剧与今天的启示 20世纪初的颠覆性事件与AI和当今公司的关系有多大？很多。这些教训是永恒的，并且对当今CEO和董事会具有现实意义。不仅仅是马车公司未能转型。他们有时间、有客户，却仍然错过了。同样的模式发生在每一次颠覆性转变中；他们由CEO领导，而这些CEO无法想象一个不同于他们所精通的世界。（当公司必须掌握互联网、移动和社交媒体时，这种情况曾发生，现在又在AI领域重复。）马车公司总裁们与销售和收入增长紧密相连。来自汽车的威胁似乎远在将来。这种情况持续了二十年，直到汽车的迅速采用导致他们的市场崩溃，福特Model T的推出标志着转折点。如今，CEO的薪酬与季度收益挂钩，而非长期创新。大多数董事会由风险规避的受托人组成，而非建设者或技术专家。他们奖励股票回购，而非AI的雄心壮志。真正的问题不在于企业看不到未来，而在于它们在结构上缺乏动力去行动。同时，颠覆不会等待董事会的批准。如果你是CEO，你不仅仅是管理一个利润表。你决定你的公司会成为斯图德贝克，还是其他3999家中的一个。
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How did you go bankrupt?”

Two ways. Gradually, then suddenly.”

Ernest Hemingway, The Sun Also Rises

Every disruptive technology since the fire and the wheel have forced leaders to adapt or die. This post tells the story of what happened when 4,000 companies faced a disruptive technology and why only one survived.

In the early 20th century, the United States was home to more than 4,000 carriage and wagon manufacturers. They were the backbone of mobility and the precursors of automobiles, used for personal transportation, goods delivery, military logistics, public transit, and more. These companies employed tens of thousands of workers and formed the heart of an ecosystem of blacksmiths, wheelwrights, saddle makers, stables, and feed suppliers.

And within two decades, they were gone. Only 1 company out of 4,000 carriage and wagon makers pivoted to automobiles.

Today, this story feels uncannily familiar. Just as the carriage industry watched the automobile evolve from curiosity to dominance, modern companies in SaaS, media, software, logistics, defense and education are watching AI emerge from novelty into existential threat.

A Comfortable Industry Misses the Turn

In 1900, the U.S. was the global leader in building carriages. South Bend, IN; Flint, MI; and Cincinnati, Ohio, were full of factories producing carriages, buggies, and wagons. On the high-end these companies made beautifully crafted vehicles, largely from wood and leather, hand-built by artisans. Others were more basic wagons for hauling goods.

When early automobiles began appearing in the 1890’s — first steam-powered, then electric, then gasoline –most carriage and wagon makers dismissed them. Why wouldn’t they? The first cars were:

Loud and unreliable

Expensive and hard to repair

Starved for fuel in a world with no gas stations

Unsuitable for the dirt roads of rural America

Early autos were worse on most key dimensions that mattered to customers. Clayton Christensen’s “Innovator’s Dilemma” described this perfectly – disruption begins with inferior products that incumbents don’t take seriously. But beneath that dismissiveness was something deeper: identity and hubris. Carriage manufacturers saw themselves not as transportation companies, but as craftsmen of elegant, horse-drawn vehicles. Cars weren’t an evolution—they were heresy. And so, they waited. And watched. And went out of business slowly and then all of a sudden.

Early Autos Were Niche and Experimental (1890s–1905) The first cars (steam, electric, and early gas) were expensive, unreliable, and slow. They were built by 19th century mechanical nerds. And the few that were sold were considered toys for other nerds and the rich. (Carl Benz patented the first internal combustion engine in 1886. In 1893 Frank Duryea drove the first car in the U.S.)

These early cars coexisted with a massive horse-powered economy. Horses pulled wagons, delivered goods, powered streetcars, and people. The first automakers used the only design they knew: the carriage. Drivers sat up high like they did in a carriage when they had to see over the horses.

For the first 15 years carriage makers, teamsters, and stable owners saw no immediate threat. Like AI today: autos were powerful, new, buggy, unreliable and not yet mainstream.

Disruption Begins (1905–1910) 10 years after their first appearance, gasoline cars became more practical, they had better engines, rubber tires, and municipalities had begun to pave roads. From 1903 to 1908 Ford shipped 9 different models of cars as they experimented with what we would call today minimum viable products. Ford (and General Motors) broke away from their carriage legacies and began designing cars from first principles, optimized for speed, safety, mass production, and modern materials. That’s the moment the car became its own species. Until then, it was still mostly a carriage with a motor. Urban elites switched from carriages to autos for status and speed, and taxis, delivery fleets, and wealthy commuters adopted cars in major cities.

Even with evidence staring them in the face, carriage companies still did not pivot, assuming cars were a fad. For carriage companies this was the “denial and drift” phase of disruption.

The Tipping Point: Ford’s Model T and Mass Production (1908–1925) The Ford Model T introduced in 1908 was affordable ($825 to as little as $260 by the 1920s), durable and easy to repair, and made using assembly line mass production. Within 15 years tens of millions of Americans owned cars. Horse-related businesses — not only the carriage makers, but the entire ecosystem of blacksmiths, stables, and feed suppliers — began collapsing. Cities banned horses from downtown areas due to waste, disease, and congestion. This was like the arrival of Google, the iPhone or ChatGPT: a phase shift.

Collapse of the Old Ecosystem (1920s–1930s) Between 1900 and 1930 U.S. horse population fell from 21 million to 10 million and the carriage and buggy production plummeted. New infrastructure—roads, gas stations, driver licensing, traffic laws—was built around the car, not the horse.

Early automakers borrowed heavily from carriage design (1885–1910). Cars emerged in a world dominated by horse-drawn vehicles and they inherited the materials and mechanical designs from the coach builders.

– Leaf springs were the dominant suspension in 19th-century carriages. Early cars used the same.

– There were no shock absorbers in carriages, and early autos. They both relied on leaf spring damping, making them bouncy and unstable at speed. Why? Roads were terrible. Speeds were low. Coachbuilders understood how to make wagons survive cobblestones and dirt.

– Carriages used solid steel or wooden axles; early cars did the same.

Body Construction and Design Borrowed from Carriages

– Car bodies were wood framed with steel or aluminum sheathing, like a carriage.

– Upholstery, leatherwork, and ornamentation were also carried over.

– Terms like roadster, phaeton, landaulet, and brougham are directly inherited from carriage types.

– High seating and narrow track: Early cars had tall wheels and high ground clearance, like buggies and carriages, since early roads were rutted and muddy.

Result: Early automobiles looked like carriages without the horse, because they were, functionally and structurally, carriages with engines bolted on.

What Changed Over Time

As speeds increased and roads improved, wood carriage design couldn’t handle the torsional stress of faster, heavier cars. Leaf-spring suspensions were too crude for speed and handling. Car builders began using pressed steel bodies (Fisher Body’s breakthrough), independent front suspension (introduced in the 1930s), finally integrating the car body and chassis into a single, unified structure, rather than having a separate body and frame (in the 1930s–40s).

Studebaker: From Horses to Horsepower

The one carriage maker who did not go out of business and became an automobile company was Studebaker. Founded in 1852 in South Bend, IN, Studebaker began by building wagons for farmers and pioneers heading west. They supplied wagons to the Union Army during the Civil War and became the largest wagon manufacturer in the world by the late 19th century. But unlike its peers, Studebaker made a series of early, strategic bets on the future.

In 1902, they began producing electric vehicles—a cautious but forward-thinking move. Two years later, in 1904, they entered the gasoline car business, at first by contracting out the engine and chassis. Eventually, they began making the entire car themselves.

Studebaker understood two things the other 4,000 carriage companies ignored:

The future wouldn’t be horse-drawn.

The company’s core capability wasn’t in carriages—it was in mobility.

Studebaker made the painful shift in manufacturing, retooled their factories, and retrained their workforce. By the 1910s, they were a full-fledged car company.

Studebaker survived long into the auto age—longer than most of the early automakers—and only stopped making cars in 1966.

Fisher Body: A Coach Builder for the Machine Age

While Studebaker made a direct pivot of their entire company from carriage to cars, a case can be made that Fisher Body was a spinoff. Founded in 1908 in Detroit by brothers Fred and Charles Fisher, the Fishers had worked at a carriage firm before starting their own auto-body business. They specialized in producing the car bodies, not an entire car. Their key innovation was making closed steel car bodies which was a major improvement over open carriages and wood frames. By 1919, Fisher was so successful that General Motors bought a controlling stake and in 1926, GM acquired them entirely. For decades, “Body by Fisher” was stamped into millions of GM cars.

Durant-Dort: The Origin of General Motors

While the Durant-Dort Carriage Company never made cars itself, its co-founder William C. (Billy) Durant saw what others didn’t. See the blog posts on Durant’s adventures here and here.

Durant used the fortune he made in carriages to invest in the burgeoning auto industry. He founded Buick in 1904 and in 1908 set up General Motors. Acting like one of Silicon Valley’s crazy entrepreneurs, he rapidly acquired Oldsmobile, Cadillac, and 11 other car companies and 10 parts/accessory companies, creating the first auto conglomerate. (In 1910 Durant would be fired by his board. Undeterred, Durant founded Chevrolet, took it public and in 1916 did a hostile takeover of GM and fired the board. He got thrown out again by his new board in 1920 and died penniless managing a bowling alley.)

While his financial overreach eventually cost him control of GM, his vision reshaped American manufacturing. General Motors became the largest car company in the 20th century.

Why the Other 3,999 Carriage makers Didn’t Make It

Most carriage makers didn’t have a William Durant, a Fisher brother, or a Studebaker in the boardroom. Here’s why they failed:

Technological Discontinuity

Carriages were made of wood, leather, and iron; cars required steel, engines, electrical systems. The skills didn’t transfer easily.

Capital Requirements

Retooling for cars required huge investment. Most small and midsize carriage firms didn’t have the money—or couldn’t raise it in time.

Business Model Inertia

Carriage makers sold low-volume, high-margin products. The car business, especially after Ford’s Model T, was about high-volume, low-margin scale.

Cultural Identity

Carriage builders didn’t see themselves as engineers or industrialists. They were artisans. Cars were noisy, dirty machines—beneath them.

Managers versus visionary founders

In each of the three companies that survived, it was the founders, not hired CEOs that drove the transition.

Underestimating the adoption curve

Early cars were bad. But technological S-curves bend quickly. By the 1910s, cars were clearly better. And by the 1920s, the carriage was obsolete.

How did you go bankrupt? “Two ways. Gradually, then suddenly.”

By 1925, out of the 4,000+ carriage companies in operation around 1900, nearly all were gone.

The tragedy of the carriage era and lessons for today

What does an early 20th century disruption have to do with AI and today’s companies? Plenty. The lessons are timeless and relevant for today’s CEOs and boards.

It wasn’t just that carriage companies failed to pivot. It’s that they had time and customers—and still missed it. That same pattern happens at every disruptive transition; they were led by CEOs who simply couldn’t imagine a different world than the one they had mastered. (This happened when companies had to master the web, mobile and social media, and is repeating today with AI.)

Carriage company Presidents were tied to sales and increasing revenue. The threat to their business from cars seemed far in the future. That was true for two decades until the bottom dropped out of their market with the rapid adoption of autos, with the introduction of the Ford Model T. Today, CEO compensation is tied to quarterly earnings, not long-term reinvention. Most boards are packed with risk-averse fiduciaries, not builders or technologists. They reward share buybacks, not AI moonshots. The real problem isn’t that companies can’t see the future. It’s that they are structurally disincentivized to act on it. Meanwhile, disruption doesn’t wait for board approval.

If you’re a CEO, you’re not just managing a P&L. You are deciding whether your company will be the Studebaker—or one of the other 3,999.

为什么投资者不关心你的业务 || Why Investors Don’t Care About Your Business

2025-07-01T13:00:38+00:00

创始人常常对无法筹集资金感到沮丧，尤其是那些拥有成功企业的创始人。这是为什么。

我经常和很多沮丧的创始人（我的学生和其他人）一起喝咖啡，他们抱怨大多数风投（VC）除非在融资提案中包含人工智能（AI）相关内容，否则根本不会见他们。而他们看到的AI初创公司获得的估值看起来毫无意义。这些对话让我回想起2000年左右的“点对点泡沫”（Dot Com bubble），当时如果你的提案中没有互联网相关内容，就很难获得资金。 Dot Com bubble

我意识到，这些创始人大多只是困惑，认为一个“好”的企业会受到VC的青睐。实际上，VC们寻找的是能够产生非凡回报的“非凡”企业。

在美国，从风投那里筹集资金的初创公司是推动多轮创新的重要引擎——从硅芯片、生命科学、互联网，到如今的人工智能。然而，对于那些已有付费客户的创始人来说，最令人沮丧的是看到那些没有收入或技术存疑的公司却能从VC那里获得巨额资金。

为什么会出现这种情况？简短回答是，大多数风投公司的商业模式并不是去打造盈利企业，也不是去打造符合国家利益的企业。他们的商业模式和财务激励是投资那些能为投资者带来最多收益的公司和市场。（如果他们偶然做到了前者，那只是附带效应，而非目标。）有时，这会让他们投资那些无法产生实际产品或可能造成危害但能带来巨大回报的公司（例如Juul，有人认为社交媒体也属于此类）。

希望向VC寻求投资的创始人需要理解影响VC投资方式和方向的四个因素：

1）VC如何赚钱，2）“羊群效应”，3）当前经济形势，4）二级市场。

VC如何赚钱
简要回顾一下风投的一些基础知识。风投只是另一种金融资产类别 – 风险更高的投资可能带来更高的回报。少数风投投资会带来10到100倍的回报，以弥补其他投资的损失或较小回报。关键理念是，大多数VC寻找的是潜在的“大满贯”投资，而不是小规模（成功？）的企业。

风投公司由普通合伙人（General Partners）管理，他们从有限合伙人（Limited Partners，如养老基金、捐赠基金、主权财富基金、高净值个人）那里筹集资金。这些有限合伙人期望在10年内获得投资资本的3倍净倍数（MOIC），这相当于20-30%的净内部收益率（IRR）。经过75年的风投投资，VC公司仍然无法判断哪家公司会成功，因此他们投资于一系列初创公司组成的“投资组合”。

VC们在相信“获取”热门交易（比如十年前的社交媒体，如今的人工智能）与相信“发现”非显而易见的赢家（如亚马逊、Airbnb、SpaceX、Palantir）之间摇摆。 VC投资的最终目标是实现成功的“退出”，如首次公开募股（IPO）或被收购，或者如今在二级市场出售股份以获得显著利润。因此，他们对初创公司的衡量标准是创造尽可能高的市值。目标是让初创公司成为“独角兽”，即市值超过10亿美元。

羊群效应
VC们通常以“群体”方式进行投资。一旦某个知名VC投资某个领域，其他VC往往会跟进。他们是否真的同时看到了颠覆性机会，还是因为害怕错过（FOMO）？（在我公司Rocket Science Games倒闭多年后，我的两位投资者才承认他们投资是因为需要在投资组合中加入多媒体游戏公司。）20世纪初，VC投资的重点是燃料电池、气候、食品配送、滑板车、社交媒体、加密货币等。当某个领域热度上升时，资本涌入，而当热度消退或出现重大失败时，资本则迅速撤离。

当前经济形势
在20世纪，VC投资初创公司的主要流动性路径是让公司“上市”（通过美国股票交易所的首次公开募股（IPO））。当时，承销商要求公司有增加收入和利润的记录，并且在接下来的一年有可预见的路径。在IPO前将公司出售是一种快速退出的策略，但通常是在无法上市时的最后手段，以极低的价格进行“火 sale”。

从1995年Netscape的IPO开始，到2000年，公开市场开始对没有收入或利润的互联网初创公司产生兴趣。这些公司承诺下一轮颠覆。该领域的关注点从收入转向了眼球和点击量。大多数这些公司在2001-2003年的互联网泡沫破裂和“核冬天”中倒闭，但那些在IPO或不久后出售的VC获得了收益。

过去二十年，IPO窗口偶尔为那些没有实际收入、利润甚至可交付产品的初创公司（如核聚变、量子计算等）短暂开启。然而，借助公司和投资者的公关、炒作以及公众对深科技的天真认知，这些公司获得了资金，投资者在高点退出，而公众则被留下持有价值不断下降的股票。

如今，公开市场对初创公司的IPO几乎关闭。这意味着风投公司资金被锁定在流动性差的初创公司中。他们必须考虑其他方式从初创公司投资中收回资金。

二级市场
由于IPO路径对VC的流动性而言已基本关闭，二级市场已成为风投公司及其有限合伙人获取收益的新方式。

二级市场允许现有投资者（及员工）出售他们已拥有的股份——通常以高于购买价格的价格出售。这些不是新股份，也不会稀释现有投资者的权益。（某些风投基金如果想要提前退出，可以出售其整个基金的股份。）二级市场为风投基金提供了一种从投资中抽身并减少风险的方式。

这里的规则是，初创公司和其投资者需要不断炒作/宣传他们的公司，以提高公司的感知价值。新投资者——后期基金、成长型股权公司、对冲基金或专门的二级基金——现在也需要这样做，以在他们购买的二级股份上获利。

这些因素对创始人意味着什么？

大多数VC对其投资的行业充满热情。如果他们投资你，他们会尽一切努力帮助你的公司成功。

然而，你需要记住 他们的公司是一个商业实体。

虽然他们可能喜欢你，认为你极其有才华，但他们给你资金是为了为自己和投资者赚取更多利润（他们的有限合伙人）。

请参阅我痛苦的教训这里，了解VC喜欢你与他们对赚钱的职责之间的区别。

一旦你从某人那里获得资金，他们的商业模式就变成了你的。

如果你不了解VC公司所采用的财务工程模式，你就会成为前CEO。

你需要理解他们所追求的回报的时间范围、规模和数量。

有些公司，尽管是好企业，可能无法获得风投资金。

你的公司能带来10到100倍的回报吗？它是否在（或能创造）一个价值10亿美元的市场？

风投基金通常追求7-10年的回报。

你的团队是否非凡且可被指导？

VC们通常要么是追随热门交易和领域，要么是寻找尚未发现的大型想法。

了解你正在与哪种类型的投资者交谈。有些基金有稳定的战略；在另一些基金中，可能有不同的合伙人持有相反的观点。

讲故事很重要。不仅重要，而且是风投游戏的重要组成部分。

如果你无法讲述一个可信的、符合风投规模投资标准的故事，你就不适合成为风投支持的CEO。

如果你有幸拥有AI背景，就赶紧抓住“金鹅”（golden goose）。它不会永远存在。

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Founders with great businesses are often frustrated that they can’t raise money.

Here’s why.

I’ve been having coffee with lots of frustrated founders (my students and others) bemoaning most VCs won’t even meet with them unless they have AI in their fundraising pitch. And the AI startups they see are getting valuations that appear nonsensical. These conversations brought back a sense of Déjà vu from the Dot Com bubble (at the turn of this century), when if you didn’t have internet as part of your pitch you weren’t getting funded.

I realized that most of these founders were simply confused, thinking that a good business was of interest to VCs. When in fact VCs are looking for extraordinary businesses that can generate extraordinary returns.

In the U.S., startups raising money from venture capitalists are one of the engines that has driven multiple waves of innovation – from silicon, to life sciences, to the internet, and now to AI. However, one of the most frustrating things for founders who have companies with paying customers to see is other companies with no revenue or questionable technology raise enormous sums of cash from VCs.

Why is that? The short answer is that the business model for most venture capital firms is not to build profitable companies, nor is it to build companies in the national interest. VCs’ business model and financial incentives are to invest in companies and markets that will make the most money for their investors. (If they happen to do the former that’s a byproduct, not the goal.) At times that has them investing in companies and sectors that won’t produce useful products or may cause harm but will generate awesome returns (e.g. Juul, and some can argue social media.)

Founders looking to approach VCs for investment need to understand the four forces that influence how and where VCs invest:

how VCs make money, 2) the Lemming Effect, 3) the current economic climate and 4) Secondaries.

How VCs Make Money

Just a reminder of some of the basics of venture capital. Venture is a just another financial asset class – with riskier investments that potentially offer much greater returns. A small number of a VC investments will generate 10x to 100x return to make up for the losses or smaller returns from other companies. The key idea is that most VCs are looking for potential homeruns, not small (successful?) businesses.

Venture capital firms are run by general partners who raise money from limited partners (pension funds, endowments, sovereign wealth funds, high-net-worth individuals.) These limited partners expect a 3x net multiple on invested capital (MOIC) over 10 years, which translates to a 20–30% net internal rate of return (IRR). After 75 years of venture investing VC firms still can’t pick which individual company will succeed so they invest in a portfolio of startups.

VCs seesaw between believing that a winning investment strategy is access to the hottest deals (think social media a decade ago, AI today), versus others believing in the skill of finding and investing in non-obvious winners (Amazon, Airbnb, SpaceX, Palantir.) The ultimate goal of a VC investment is to achieve a successful “exit,” such as an Initial Public Offering (IPO) or acquisition, or today on a secondary, where they can sell their shares at a significant profit. Therefore, the metrics for their startups was to create the highest possible market cap(italization). A goal was to have a startup become a “unicorn” having a market cap of $1billion or more.

The Lemming Effect

VCs most often invest as a pack. Once a “brand-name” VC invests in a sector others tend to follow. Do they somehow all see a disruptive opportunity at the same time, or is it Fear Of Missing Out (FOMO)? (It was years after my company Rocket Science Games folded that my two investors admitted that they invested because they needed a multi-media game company in their portfolio.) Earlier in this century the VC play was fuel cells, climate, food delivery, scooters, social media, crypto, et al. Today, it’s defense and AI startups. Capital floods in when the sector is hot and dries up when the hype fades or a big failure occurs.

The current economic climate

In the 20th century the primary path for liquidity for a VC investment in a startup (the way they turned their stock ownership in a startup into dollars) meant having the company “go public” via an initial public offering (IPO) on a U.S. stock exchange. Back then underwriters required that the company had a track record of increasing revenue and profit, and a foreseeable path to do so in the next year. Having your company bought just before the IPO was a tactic for a quick exit but was most often the last resort at a fire sale price if an IPO wasn’t possible.

Beginning with the Netscape IPO in 1995 and through 2000, the public markets began to have an appetite for Internet startups with no revenue or profits. These promised the next wave of disruption. The focus in this area became eyeballs and clicks versus revenue. Most of these companies crashed and burned in the dotcom crash and nuclear winter of 2001-2003, but VC who sold at the IPO or shortly after made money.

For the last two decades IPO windows have briefly opened (although intermittently) for startups with no hope for meaningful revenue, profit or even deliverable products (fusion, quantum, etc. heavy, infrastructure-scale moonshots that require decades to fruition). Yet with company and investor PR, hype and the public’s naivete about deep technology these companies raised money, their investors sold out and the public was left hanging with stock of decreasing value.

Today, the public markets are mostly closed for startup IPOs. That means that venture capital firms have money tied up in startups that are illiquid. They have to think about other ways to get their money from their startup investments.

Secondaries

Today with the Initial Public Offering path for liquidity for VCs mostly closed, secondaries have emerged as a new way for venture firms and their limited partners to make money.

Secondaries allow existing investors (and employees) to sell stock they already own – almost always at a higher price than their purchase price. These are not new shares and don’t dilute the existing investors. (Some VC funds can sell a stake in their entire fund if they want an early exit.) Secondaries offer VC funds a way to take money off the table and reduce their exposure.

The game here is that startups and their investors need to continually hype/promote their startup to increase the company’s perceived value. The new investors – later stage funds, growth equity firms, hedge funds or dedicated secondary funds, now have to do the same to make money on the secondary shares they’ve purchased.

What Do These Forces Mean For Founders?

Most VCs care passionately about the industry they invest in. And if they invest in you they will do anything to help your company succeed.

However, you need to remember their firm is a business.

While they might like you, think you are extraordinarily talented, they are giving you money to make a lot more money for themselves and their investors (their limited partners.)

See my painful lesson here when I learned the difference between VC’s liking you, versus their fiduciary duty to make money.

The minute you take money from someone their business model becomes yours.

If you don’t understand the financial engineering model a VC firm is operating under, you’re going to be an ex CEO.

You need to understand the time horizon, size, scale of the returns they are looking for.

Some companies, while great businesses may not be venture fundable.

Can yours provide a 10 to 100x return? Is it in (or can it create) a large $1B market?

VC funds tend to look for a return in 7-10 years.

Is your team extraordinary and coachable?

VCs tend to be either followers into hot deals and sectors or are looking for undiscovered big ideas.

Understand which type of investor you are talking to. Some firms have a consistent strategy; in others there may be different partners with contrary opinions.

Storytelling matters. Not only does it matter, but it’s an integral part of the venture capital game.

If you cannot tell a great credible story that matches the criteria for a venture scale investment you’re not ready to be a venture funded CEO.

If you’re lucky enough to have an AI background, grab the golden ring. It won’t be there forever.

斯坦福大学精益创业加速器 – 2025 || Lean Launchpad at Stanford – 2025

2025-06-24T13:00:26+00:00

本帖中嵌入的PowerPoint最佳在steveblank.com上查看。我们刚刚结束了在斯坦福大学的第15届年度精益创业课程。该课程变得如此受欢迎，以至于在2021年我们开始在冬季和春季学期同时授课。在2025年春季学期，八支团队接触了935名潜在客户、受益者和监管者。大多数学生每周在课程上投入15-20小时，大约是普通课程的两倍。本课程引发了创业教学的革命本课程旨在打破“如何撰写商业计划”作为创业教育的顶点。商业计划假设所有初创企业只需撰写计划、筹集资金然后执行计划。我们通过指出，虽然现有组织执行商业模式，初创企业则在寻找它们，以及初创企业是一个临时组织，旨在寻找可复制且可扩展的商业模式，从而推翻了这一传统观念。本课程旨在教授初创企业如何寻找商业模式。多个政府资助的项目已大规模采用本课程。第一个是在2011年，我们将此课程大纲转化为美国国家科学基金会I-Corps的课程。当时美国国家科学基金会商业化负责人Errol Arkilic采用该课程并表示：“你们为初创企业开发了科学方法，使用商业模式画布作为实验记录本。” 以下是2025年春季学期精益创业课程的课程收获展示。 Team Cowmeter – 通过生物监测牛奶实现奶牛感染的早期检测。如果你能看到Team Cowmeter的展示，请点击此处美国国家卫生研究院的I-Corps 2013年，我与旧金山大学（UCSF）和美国国家卫生研究院（NIH）合作，推出了针对生命科学和医疗（治疗、诊断、设备和数字健康）的精益创业课程。2014年，我与NIH合作，将UCSF的课程大纲发展并启动了I-Corps @ NIH项目。 Team NowPilot – 人工智能副驾驶，用于增强专注力和执行功能。如果你看不到Team NowPilot的展示，请点击此处大规模实施的I-Corps I-Corps现在在100所大学提供，并已培训了超过9500名科学家和工程师；在NSF（美国国家科学基金会）的I-Corps中，有2546个团队，7800名参与者；在NIH的I-Corps中，有317个团队，950名参与者；在能源I-Corps（美国能源部DOE）中，有188个团队，580名参与者。日本已有15所大学教授此课程。 Team Godela – 人工智能物理引擎，首个颠覆性市场在包装领域。如果你看不到Team Godela的展示，请点击此处 40亿美元的风险资本用于I-Corps团队 NSF I-Corps的1380个团队启动了初创企业，筹集了31.66亿美元资金。NIH的I-Corps团队有超过300个，累计筹集了6.34亿美元。能源I-Corps团队额外筹集了1.51亿美元资金。 Team ProspectAI – 为精益销售团队设计的人工智能销售开发代理。如果你看不到Team ProspectAI的展示，请点击此处以使命为导向的创业 2016年，我在斯坦福大学与Pete Newell和Joe Felter共同创建了“为国防而黑客”课程，以及与Jeremy Weinstein共同创建了“为外交而黑客”课程。2022年，Steve Weinstein创建了“为气候与可持续性而黑客”课程。2024年，Jennifer Carolan在斯坦福大学启动了“为教育而黑客”课程。 Team VLAB – 利用人工智能对数据的编排加速临床试验。如果你看不到Team VLAB的展示，请点击此处本课程的设计虽然精益创业课程的学生体验起来似乎是一门完全实践性的课程，但实际上这是一个精心设计的幻觉。事实上，课程结构非常严谨。课程大纲的设计旨在为学生提供持续的隐性指导、结构和重复。这是我们课程与开放式实践课程之间的重要区别。指导、方向和结构 – 例如，学生以自己的初始指导开始课程，他们相信自己有一个产品或服务的想法（精益创业/I-Corps）或已被赋予一个明确的现实问题（为国防而黑客）。进入课程时，学生认为他们的目标是验证其商业化或部署假设。 (教学团队知道，在课程过程中，学生会发现他们的初始假设大多不正确。) Team Blix – IRB临床试验合规性 / 为金融服务的人工智能治理控制层。如果你看不到Team Blix的展示，请点击此处商业模式画布商业模式/使命画布为学生提供指导、明确的方向和结构。首先，画布为学生提供了一条完整的视觉路线图，涵盖整个课程中需要测试的所有假设。其次，画布通过可视化理想终点的样子，帮助学生实现目标，即找到产品/市场契合点。最后，画布为学生提供了一周一周的学习地图，通过客户发现工作。我无法过分强调画布的重要作用。与没有框架的孵化器或加速器不同，画布充当了学生的连接组织——框架——当他们迷失或困惑时可以依靠。它使我们能够每周逐步教授如何将想法、需求或问题转化为商业实践的理论。 Team Plotline – 作者书籍发布的智能营销日历。如果你看不到Team Plotline的展示，请点击此处精益创业课程工具客户发现工具（视频、样本实验等）为学生提供指导和结构，使他们能够在课堂外工作。每周进行10-15次客户访谈的明确目标以及构建一系列最小可行产品的要求，提供了跟踪团队进展的指标。与教师的强制性办公时间和导师的支持提供了额外的指导和结构。 Team Eluna/Driftnet – 数据中心数据聚合和能源优化软件。如果你看不到Team Eluna/Driftnet的展示，请点击此处人工智能嵌入课程这是首次所有团队都使用人工智能来帮助创建商业模式画布，构建可在几小时内运行的MVP，生成客户问题，分析并总结访谈。单靠一个人的力量是不够的虽然我撰写了这篇博客文章，但本课程是一个团队项目。斯坦福大学精益创业课程的成功秘诀在于一群杰出的志愿者在许多关键方面支持我们的学生。本教学团队由我本人和以下人员组成： Steve Weinstein，美国前沿基金合伙人，硅谷科技公司和好莱坞媒体公司30年的资深人士。Steve曾担任MovieLabs的CEO，这是所有主要电影制片厂的联合研发实验室。 Lee Redden – Blue River Technology（现被John Deere收购）的首席技术官和联合创始人，他就是14年前参加第一期精益创业课程的学生！ Jennifer Carolan，Reach Capital合伙人，领先的教育风险投资公司，也是“为教育而黑客”课程的作者。今年我们的教学助教是Arthur C. Campello、Anil Yildiz、Abu B. Rogers和Tireni Ajilore。导师帮助团队了解他们的解决方案是否可以成为商业成功的业务。感谢Jillian Manus、Dave Epstein、Robert Feldman、Bobby Mukherjee、Kevin Ray、Deirdre Clute、Robert Locke、Doug Biehn和John Danner。来自杰出职业研究院的Martin Saywell加入了Blix团队。导师团队由Todd Basche领导。总结虽然精益创业/I-Corps课程大纲是与过去的一次革命性突破，但它并不是终点。在过去十年中，出现了许多变体。我们在斯坦福大学教授的课程仍在不断发展。其他人会推出更好的版本。人工智能已经对客户发现和验证产生了重大影响，我们要求每个团队列出他们使用的AI工具。终有一天，另一次革命性突破将带我们进入下一个层次。
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The PowerPoints embedded in this post are best viewed on steveblank.com

We just finished the 15th<>annual Lean LaunchPad class at Stanford. The class had gotten so popular that in 2021 we started teaching it in both the winter and spring sessions.

During the 2025 spring quarter the eight teams spoke to 935 potential customers, beneficiaries and regulators. Most students spent 15-20 hours a week on the class, about double that of a normal class.

This Class Launched a Revolution in Teaching Entreprenurship

This class was designed to break out of the “how to write a business plan” as the capstone of entrepreneurial education. A business plan assumed that all startups needed to was to write a plan, raise money and then execute the plan. We overturned that orthodoxy when we pointed out that while existing organizations execute business models, startups are searching for them. And that a startup was a temporary organization designed to search for a repeatable and scaleable business model. This class was designed to teach startups how to search for a business model.

Several government-funded programs have adopted this class at scale. The first was in 2011 when we turned this syllabus into the curriculum for the National Science Foundation I-Corps. Errol Arkilic, the then head of commercialization at the National Science Foundation, adopted the class saying, “You’ve developed the scientific method for startups, using the Business Model Canvas as the laboratory notebook.”

Below are the Lessons Learned presentations from the spring 2025 Lean LaunchPad.

Team Cowmeter – early detection of cow infections through biological monitoring of milk.

If you can see the Team Cowmeter presentation click here

I-Corps at the National Institute of Health

In 2013 I partnered with UCSF and the National Institute of Health to offer the Lean LaunchPad class for Life Science and Healthcare (therapeutics, diagnostics, devices and digital health.) In 2014, in conjunction with the National Institute of Health, I took the UCSF curriculum and developed and launched the I-Corps @ NIH program.

Team NowPilot – AI copilot for enhancing focus and executive function.

If you can’t see the Team NowPilot presentation click here

I-Corps at Scale

I-Corps is now offered in 100 universities and has trained over 9,500 scientists and engineers; 7,800 participants in 2,546 teams at I-Corps at NSF (National Science Foundation), 950 participants in 317 teams at I-Corps at NIH, and 580 participants in 188 teams at Energy I-Corps (at the DOE). 15 universities in Japan now teach the class.

Team Godela – AI physics engine – with a first disruptive market in packaging.

If you can’t see the Team Godela presentation click here

$4 billion in Venture Capital For I-Corps Teams

1,380 of the NSF I-Corps teams launched startups raising $3.166 billion. Over 300 I-Corps at NIH teams have collectively raised $634 million. Energy I-Corps teams raised $151 million in additional funding.

Team ProspectAI – An AI sales development agent for lean sales teams.

If you can’t see the Team ProspectAI presentation click here

Mission Driven Entreprenurship

In 2016, I co-created both the Hacking for Defense course with Pete Newell and Joe Felter as well as the Hacking for Diplomacy course with Jeremy Weinstein at Stanford. In 2022, Steve Weinstein created Hacking for Climate and Sustainability. In 2024 Jennifer Carolan launched Hacking for Education at Stanford.

Team VLAB – accelerating clinical trials with AI orchestration of data.

If you can’t see the team VLAB presentation click here

Design of This Class

While the Lean LaunchPad students are experiencing what appears to them to be a fully hands-on, experiential class, it’s a carefully designed illusion. In fact, it’s highly structured. The syllabus has been designed so that we are offering continual implicit guidance, structure, and repetition. This is a critical distinction between our class and an open-ended experiential class. Guidance, Direction and Structure –

For example, students start the class with their own initial guidance – they believe they have an idea for a product or service (Lean LaunchPad/I-Corps) or have been given a clear real-world problem (Hacking for Defense). Coming into the class, students believe their goal is to validate their commercialization or deployment hypotheses. (The teaching team knows that over the course of the class, students will discover that most of their initial hypotheses are incorrect.)

Team Blix – IRB clinical trial compliance / A control layer for AI governance for financial services.

If you can’t see the team Blix presentation click here

The Business Model Canvas

The business model / mission model canvas offers students guidance, explicit direction, and structure. First, the canvas offers a complete, visual roadmap of all the hypotheses they will need to test over the entire class. Second, the canvas helps the students goal-seek by visualizing what an optimal endpoint would look like – finding product/market fit. Finally, the canvas provides students with a map of what they learn week-to-week through their customer discovery work. I can’t overemphasize the important role of the canvas. Unlike an incubator or accelerator with no frame, the canvas acts as the connective tissue – the frame – that students can fall back on if they get lost or confused. It allows us to teach the theory of how to turn an idea, need, or problem into commercial practice, week by week a piece at a time.

Team Plotline – A smart marketing calendar for author’s book launch.

If you can’t see the team Plotline presentation click here

Lean LaunchPad Tools

The tools for customer discovery (videos, sample experiments, etc.) offer guidance and structure for students to work outside the classroom. The explicit goal of 10-15 customer interviews a week along with the requirement for building a continual series of minimal viable products provides metrics that track the team’s progress. The mandatory office hours with the instructors and support from mentors provide additional guidance and structure.

Team Eluna/Driftnet – Data Center data aggregation and energy optimization software.

If you can’t see the team Eluna/Driftnet presentation click here

AI Embedded in the Class

This was the first year where all teams used AI to help create their business model canvas, build working MVPs in hours, generate customer questions, analyze and summarizing interviews.

It Takes A Village

While I authored this blog post, this class is a team project. The secret sauce of the success of the Lean LaunchPad at Stanford is the extraordinary group of dedicated volunteers supporting our students in so many critical ways.

The teaching team consisted of myself and:

Steve Weinstein, partner at America’s Frontier Fund, 30-year veteran of Silicon Valley technology companies and Hollywood media companies. Steve was CEO of MovieLabs, the joint R&D lab of all the major motion picture studios.

Lee Redden – CTO and co-founder of Blue River Technology (acquired by John Deere) who was a student in the first Lean LaunchPad class 14 years ago!

Jennifer Carolan, Co-Founder, Partner at Reach Capital the leading education VC and author of the Hacking for Education class.

Our teaching assistants this year were Arthur C. Campello, Anil Yildiz, Abu B. Rogers and Tireni Ajilore.

Mentors helped the teams understand if their solutions could be a commercially successful business. Thanks to Jillian Manus, Dave Epstein, Robert Feldman, Bobby Mukherjee, Kevin Ray, Deirdre Clute, Robert Locke, Doug Biehn, and John Danner. Martin Saywell from the Distinguished Careers Institute joined the Blix team. The mentor team was led by Todd Basche.

Summary

While the Lean LaunchPad/I-Corps curriculum was a revolutionary break with the past, it’s not the end. In the last decade enumerable variants have emerged. The class we teach at Stanford has continued to evolve. Better versions from others will appear. AI is already having a major impact on customer discovery and validation and we had each team list the AI tools they used. And one day another revolutionary break will take us to the next level.

网络安全防御 @ 斯坦福大学 2025 – 经验教训展示 || Hacking for Defense @ Stanford 2025 – Lessons Learned Presentations

2025-06-17T13:00:04+00:00

Hacking for Defense 课程总结

课程概述
斯坦福大学的“Hacking for Defense”（国防黑客）课程已连续举办10年，现扩展至70所大学。该课程旨在让学生团队通过实践解决国家安全问题，涵盖美国陆军、海军、中央司令部、太空部队、FBI、IQT及国家地理空间情报局等机构提出的挑战。学生需在10周内完成对受益者、利益相关方、需求撰写者等的访谈，并开发最小可行产品（MVP）及部署路径。

课程特色

“经验教训”展示：每支团队需进行2分钟视频介绍及最终“经验教训”演讲，讲述其解决问题的全过程，而非传统的产品展示或融资路演。
方法论：采用Mission Model Canvas、客户开发（Customer Development）及敏捷工程（Agile Engineering）等工具，强调快速验证问题与需求。
新增内容：今年要求团队补充两部分内容：使用的AI工具及技术成熟度（TRL）与投资成熟度（IRL）的评估。

项目亮点

Team Omnyra：提升对AI生成生物工程威胁的可见性。
Team HydraStrike：将蜂群技术应用于海上领域。
Team HyperWatch：追踪高超音速威胁。
Team ChipForce：保障美国在关键矿产领域的主导地位。
Team ArgusNet：为搜救行动提供即时地理空间数据。
Team NeoLens：利用AI辅助军事机械维修。
Team Strom：简化关键矿物价值链。

课程理念
“使命驱动型创业”（Mission-Driven Entrepreneurship）是课程的核心，鼓励学生聚焦社会问题（如国防、外交、气候等），而非仅关注商业机会。通过Lean LaunchPad/I-Corps方法论，学生学习如何快速发现真实需求并转化为解决方案，同时为国防部门提供创新方法。

课程起源与扩展
课程起源于2011年斯坦福的Lean LaunchPad课堂，后被美国国家科学基金会（NSF）采纳为I-Corps项目，成为科学商业化标准。目前，课程已扩展至美国、英国（Hacking for the MOD）及澳大利亚，并由Common Mission Project、国防创新单元（DIU）及海军研究办公室（ONR）等机构支持。合作企业包括波音、洛克希德·马丁等。

课程目标

教授精益创新方法，同时让学生参与国家公共服务。
帮助国防部门及情报机构更高效地应对复杂威胁。
增强学生对军事职业的理解，展示其在社会中的价值。
为学生未来就业（如初创企业、咨询公司等）铺路。

未来展望
今年有7支团队申请国防创新单元（DIU）的加速器项目，全部被接受，进一步验证了该课程作为“预加速器”的作用，帮助学生从课堂过渡到实际部署。课程还计划扩展至更多领域，如教育、海洋与气候等。

支持团队

教学团队：包括退役军官、企业高管及研究专家。
赞助商与导师：来自国防部门、情报机构及商业领域的31位导师协助学生解决问题，涵盖技术、商业及国家安全等多方面。

结语
“Hacking for Defense”不仅是一门课程，更成为一种推动国防创新的运动，通过跨学科合作与使命驱动，为学生和机构创造价值。

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本帖中嵌入的视频和PowerPoint最佳在 steveblank.com 上观看

我们刚刚完成了第10届斯坦福大学“为国防而黑客”课程。

这一年真是难忘。

“为国防而黑客”课程目前已在70所大学开展，学生们组成团队，致力于理解和解决国家安全问题。在斯坦福大学本学期，41名学生组成的8个团队共访谈了 1106 名受益者、利益相关方、需求撰写者、项目管理者、行业合作伙伴等，同时还在构建一系列最小可行产品并开发部署路径。

今年的问题来自美国陆军、美国海军、中央司令部、太空部队/国防创新单位、FBI、IQT以及国家地理空间情报局。

“经验总结”展示
在学期结束时，每个团队都会进行一次“经验总结”展示，并附上2分钟的视频以提供其问题的背景信息。与传统的演示日或鲨鱼坦克（“展示我的聪明才智，这不是一个很棒的产品吗？请给我投资”）不同，“经验总结”展示讲述的是每个团队10周的旅程和他们通过实践获得的宝贵经验和发现。对所有团队而言，这都是一段充满起伏的叙述，描述了当他们发现最初认为正确的所有假设都错误时，如何最终找到正确的解决方案。

团队HydraStrike – 将蜂群技术引入海上领域。

如果无法观看HydraStrike团队的总结视频，请点击此处。

如果无法观看HydraStrike的展示，请点击此处

“为国防而黑客”的目标
我们这门课的主要目标是让学生在参与国家公共服务的同时学习精益创新方法。
在课程中，我们看到学生能够通过与国防部和情报界创新者合作，了解国家的威胁和安全挑战。同时，这种经历也能向这些创新者（即国防部和情报界的利益相关方）介绍一种方法，帮助他们更好地理解和应对快速变化的威胁。我们希望展示，如果能让团队通过精益方法迅速发现真实问题，并在真正理解问题后提出解决方案，国防采购项目就能以速度和紧迫性运作，并及时交付所需解决方案。

团队NeoLens – 为军事机械师提供基于AI的故障排查。

如果无法观看NeoLens团队的视频，请点击此处

如果无法观看NeoLens的展示，请点击此处

面向70所大学的使命驱动创业
从一门课程开始，它已经演变成一种运动。
自我们斯坦福大学的课程开始以来，“为国防而黑客”现在已在全美70多所大学开设，同时在英国作为 “为英国国防部而黑客” 课程，以及在澳大利亚提供。在美国，该课程已成为正式项目，并获得国会支持，由 Common Mission Project、国防创新单位（DIU）和海军研究办公室（ONR）共同资助。企业合作伙伴包括波音公司、诺斯罗普·格鲁曼公司和洛克希德·马丁公司。
史蒂夫·魏恩施泰因（Steve Weinstein）在加州大学伯克利分校开设了 “为影响而黑客”（非营利组织）和 “为本地而黑客”（奥克兰）课程，以及在斯克里普斯和加州大学圣克鲁兹分校开设的“为海洋而黑客”课程，以及在斯坦福大学开设的“为气候与可持续发展而黑客”课程。詹妮弗·卡罗兰在斯坦福大学开设了“为教育而黑客”课程。

团队Strom – 简化关键矿产价值链。

如果无法观看Strom团队的视频，请点击此处

如果无法观看Strom的展示，请点击此处

这些团队的未来展望
当他们毕业时，这些团队的斯坦福学生将拥有在初创公司、企业及咨询公司中选择工作的机会。今年，我们有七个团队申请了国防创新单位加速器 – DIU国防创新暑期研究员计划的商业化路径。七个团队被接受。这进一步强化了我们的观点，即“为国防而黑客”已演变为一个 预加速器，为学生从课堂学习过渡到实际部署做准备。

观看团队现场展示，请点击此处

众人拾柴火焰高
虽然我撰写了这篇博客文章，但本课程是一个团队项目。斯坦福大学“为国防而黑客”课程的成功秘诀在于一群杰出的志愿者，他们在许多关键方面支持我们的学生。

我们的教学团队包括我自己以及：

Pete Newell，退休陆军上校，曾任陆军快速装备部队前负责人，现为 BMNT 的首席执行官。
Joe Felter，退休陆军特种部队上校；前南亚、东南亚和大洋洲副助理国防部长；现任斯坦福大学国家安全创新中心 Gordian Knot Center 主任，该中心是我们于2021年共同创立的。
Steve Weinstein，美国前沿基金的合伙人，拥有硅谷科技公司和好莱坞媒体公司30年的从业经验。Steve曾担任 MovieLabs（各大电影制片厂的联合研发实验室）的首席执行官。
Chris Moran，洛克希德·马丁创投的执行董事兼总经理；洛克希德·马丁公司的风投部门。
Jeff Decker，一位专注于双用途研究的斯坦福研究员。Jeff曾在伊拉克和阿富汗的美军中担任特种作战轻步兵小队领导。

今年我们的助教是Joel Johnson、Rachel Wu、Evan Twarog、Faith Zehfuss和Ethan Hellman。

31位赞助商、国家安全导师和商业导师
团队得到了其问题的提出者（即赞助商）的帮助。

赞助商 给我们带来了他们最棘手的国家安全问题： Josh Pavluk、Kari Montoya、Nelson Layfield、Mark Breier、Jason Horton、Stephen J. Plunkett、Chris O’Connor、David Grande、Daniel Owins、Nathaniel Huston、Joy Shanaberger和David Ryan。

国家安全导师 帮助那些对国防部和FBI不了解的学生理解这些组织的复杂性、细节和微妙之处：Katie Tobin、Doug Seich、Salvadore Badillo-Rios、Marco Romani、Matt Croce、Donnie Hasseltine、Mark McVay、David Vernal、Brad Boyd和Marquay Edmonson。

商业导师 帮助团队了解他们的解决方案是否可能成为成功的商业企业：Diane Schrader、Marc Clapper、Laura Clapper、Eric Byler、Adam Walters、Jeremey Schoos、Craig Seidel和Rich “Astro” Lawson。

感谢所有参与者！

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The videos and PowerPoints embedded in this post are best viewed on steveblank.com

We just finished our 10th annual Hacking for Defense class at Stanford.

What a year.

Hacking for Defense, now in 70 universities, has teams of students working to understand and help solve national security problems. At Stanford this quarter the 8 teams of 41 students collectively interviewed 1106 beneficiaries, stakeholders, requirements writers, program managers, industry partners, etc. – while simultaneously building a series of minimal viable products and developing a path to deployment.

This year’s problems came from the U.S. Army, U.S. Navy, CENTCOM, Space Force/Defense Innovation Unit, the FBI, IQT, and the National Geospatial-Intelligence Agency.

We opened this year’s final presentations session with inspiring remarks by Joe Lonsdale on the state of defense technology innovation and a call to action for our students. During the quarter guest speakers in the class included former National Security advisor H.R. McMaster, Jim Mattis ex Secretary of Defense, John Cogbill Deputy Commander 18th Airborne Corps, Michael Sulmeyer former Assistant Secretary of Defense for Cyber Policy, and John Gallagher Managing Director of Cerberus Capital.

“Lessons Learned” Presentations

At the end of the quarter, each of the eight teams gave a final “Lessons Learned” presentation along with a 2-minute video to provide context about their problem. Unlike traditional demo days or Shark Tanks which are, “Here’s how smart I am, and isn’t this a great product, please give me money,” the Lessons Learned presentations tell the story of each team’s 10-week journey and hard-won learning and discovery. For all of them it’s a roller coaster narrative describing what happens when you discover that everything you thought you knew on day one was wrong and how they eventually got it right.

While all the teams used the Mission Model Canvas, Customer Development and Agile Engineering to build Minimal Viable Products, each of their journeys was unique.

This year we had the teams add two new slides at the end of their presentation: 1) tell us which AI tools they used, and 2) their estimate of progress on the Technology Readiness Level and Investment Readiness Level.

Here’s how they did it and what they delivered.

Team Omnyra – improving visibility into AI-generated bioengineering threats.

If you can’t see the team Omnyra summary video click here

If you can’t see the Omnyra presentation click here

These are “Wicked” Problems

Wicked problems refer to really complex problems, ones with multiple moving parts, where the solution isn’t obvious and lacks a definitive formula. The types of problems our Hacking For Defense students work on fall into this category. They are often ambiguous. They start with a problem from a sponsor, and not only is the solution unclear but figuring out how to acquire and deploy it is also complex. Most often students find that in hindsight the problem was a symptom of a more interesting and complex problem – and that Acquisition of solutions in the Dept of Defense is unlike anything in the commercial world. And the stakeholders and institutions often have different relationships with each other – some are collaborative, some have pieces of the problem or solution, and others might have conflicting values and interests.

The figure shows the types of problems Hacking for Defense students encounter, with the most common ones shaded.

Team HydraStrike – bringing swarm technology to the maritime domain.

If you can’t see the HydraStrike summary video click here.

If you can’t see the HydraStrike presentation click here

Mission-Driven Entrepreneurship

This class is part of a bigger idea – Mission-Driven Entrepreneurship. Instead of students or faculty coming in with their own ideas, we ask them to work on societal problems, whether they’re problems for the State Department or the Department of Defense or non-profits/NGOs or the Oceans and Climate or for anything the students are passionate about. The trick is we use the same Lean LaunchPad / I-Corps curriculum — and the same class structure – experiential, hands-on– driven this time by a mission-model not a business model. (The National Science Foundation and the Common Mission Project have helped promote the expansion of the methodology worldwide.)

Mission-driven entrepreneurship is the answer to students who say, “I want to give back. I want to make my community, country or world a better place, while being challenged to solve some of the toughest problems.”

Team HyperWatch – tracking hypersonic threats.

If you can’t see the HyperWatch video click here

If you can’t see the HyperWatch presentation click here

It Started With An Idea

Hacking for Defense has its origins in the Lean LaunchPad class I first taught at Stanford in 2011. I observed that teaching case studies and/or how to write a business plan as a capstone entrepreneurship class didn’t match the hands-on chaos of a startup. Furthermore, there was no entrepreneurship class that combined experiential learning with the Lean methodology. Our goal was to teach both theory and practice. The same year we started the class, it was adopted by the National Science Foundation to train Principal Investigators who wanted to get a federal grant for commercializing their science (an SBIR grant.) The NSF observed, “The class is the scientific method for entrepreneurship. Scientists understand hypothesis testing” and relabeled the class as the NSF I-Corps (Innovation Corps). I-Corps became the standard for science commercialization for the National Science Foundation, National Institutes of Health and the Department of Energy, to date training 3,051 teams and launching 1,300+ startups.

Team ChipForce – Securing U.S. dominance in critical minerals.

If you can’t see the ChipForce video click here

If you can’t see the ChipForce presentation click here

Note: After briefing the Department of Commerce, the Chipforce was offered jobs with the department.

Origins Of Hacking For Defense

In 2016, brainstorming with Pete Newell of BMNT and Joe Felter at Stanford, we observed that students in our research universities had little connection to the problems their government was trying to solve or the larger issues civil society was grappling with. As we thought about how we could get students engaged, we realized the same Lean LaunchPad/I-Corps class would provide a framework to do so. That year we launched both Hacking for Defense and Hacking for Diplomacy (with Professor Jeremy Weinstein and the State Department) at Stanford. The Department of Defense adopted and scaled Hacking for Defense across 60 universities while Hacking for Diplomacy has been taught at Georgetown, James Madison University, Rochester Institute for Technology, University of Connecticut and now Indiana University, sponsored by the Department of State Bureau of Diplomatic Security (see here).

Team ArgusNet – instant geospatial data for search and rescue.

If you can’t see the ArgusNet video click here

If you can’t see the ArgusNet presentation click here

Goals for Hacking for Defense

Our primary goal for the class was to teach students Lean Innovation methods while they engaged in national public service.

In the class we saw that students could learn about the nation’s threats and security challenges while working with innovators inside the DoD and Intelligence Community. At the same time the experience would introduce to the sponsors, who are innovators inside the Department of Defense (DOD) and Intelligence Community (IC), a methodology that could help them understand and better respond to rapidly evolving threats. We wanted to show that if we could get teams to rapidly discover the real problems in the field using Lean methods, and only then articulate the requirements to solve them, defense acquisition programs could operate at speed and urgency and deliver timely and needed solutions.

Finally, we wanted to familiarize students with the military as a profession and help them better understand its expertise, and its proper role in society. We hoped it would also show our sponsors in the Department of Defense and Intelligence community that civilian students can make a meaningful contribution to problem understanding and rapid prototyping of solutions to real-world problems.

Team NeoLens – AI-powered troubleshooting for military mechanics.

If you can’t see the NeoLens video click here

If you can’t see the NeoLens presentation click here

Go-to-Market/Deployment Strategies

The initial goal of the teams is to ensure they understand the problem. The next step is to see if they can find mission/solution fit (the DoD equivalent of commercial product/market fit.) But most importantly, the class teaches the teams about the difficult and complex path of getting a solution in the hands of a warfighter/beneficiary. Who writes the requirement? What’s an OTA? What’s color of money? What’s a Program Manager? Who owns the current contract? …

Team Omnicomm – improving the quality, security and resiliency of communications for special operations units.

If you can’t see the Omnicomm video click here

If you can’t see the Omnicomm presentation click here

Mission-Driven in 70 Universities and Continuing to Expand in Scope and Reach

What started as a class is now a movement.

From its beginning with our Stanford class, Hacking for Defense is now offered in over 70 universities in the U.S., as well as in the UK as Hacking for the MOD and in Australia. In the U.S., the course is a program of record and supported by Congress, H4D is sponsored by the Common Mission Project, Defense Innovation Unit (DIU), and the Office of Naval Research (ONR). Corporate partners include Boeing, Northrop Grumman and Lockheed Martin.

Steve Weinstein started Hacking for Impact (Non-Profits) and Hacking for Local (Oakland) at U.C. Berkeley, and Hacking for Oceans at bot Scripps and UC Santa Cruz, as well as Hacking for Climate and Sustainability at Stanford. Jennifer Carolan started Hacking for Education at Stanford.

Team Strom – simplified mineral value chain.

If you can’t see the Strom video click here

If you can’t see the Strom presentation click here

What’s Next For These Teams?

.When they graduate, the Stanford students on these teams have the pick of jobs in startups, companies, and consulting firms .This year, seven of our teams applied to the Defense Innovation Unit accelerator – the DIU Defense Innovation Summer Fellows Program – Commercialization Pathway. Seven were accepted. This further reinforced our thinking that Hacking for Defense has turned into a pre-accelerator – preparing students to transition their learning from the classroom to deployment

See the teams present in person here

It Takes A Village

While I authored this blog post, this class is a team project. The secret sauce of the success of Hacking for Defense at Stanford is the extraordinary group of dedicated volunteers supporting our students in so many critical ways.

The teaching team consisted of myself and:

Pete Newell, retired Army Colonel and ex Director of the Army’s Rapid Equipping Force, now CEO of BMNT.

Joe Felter, retired Army Special Forces Colonel; and former deputy assistant secretary of defense for South Asia, Southeast Asia, and Oceania; and currently the Director of the Gordian Knot Center for National Security Innovation at Stanford which we co-founded in 2021.

Chris Moran, Executive Director and General Manager of Lockheed Martin Ventures; the venture capital investment arm of Lockheed Martin.

Jeff Decker, a Stanford researcher focusing on dual-use research. Jeff served in the U.S. Army as a special operations light infantry squad leader in Iraq and Afghanistan.

Our teaching assistants this year were Joel Johnson, Rachel Wu, Evan Twarog, Faith Zehfuss, and Ethan Hellman.

31 Sponsors, Business and National Security Mentors

The teams were assisted by the originators of their problems – the sponsors.

Sponsors gave us their toughest national security problems: Josh Pavluk, Kari Montoya, Nelson Layfield, Mark Breier, Jason Horton, Stephen J. Plunkett, Chris O’Connor, David Grande, Daniel Owins, Nathaniel Huston, Joy Shanaberger, and David Ryan.

National Security Mentors helped students who came into the class with no knowledge of the Department of Defense, and the FBI understand the complexity, intricacies and nuances of those organizations: Katie Tobin, Doug Seich, Salvadore Badillo-Rios, Marco Romani, Matt Croce, Donnie Hasseltine, Mark McVay, David Vernal, Brad Boyd, Marquay Edmonson.

Business Mentors helped the teams understand if their solutions could be a commercially successful business: Diane Schrader, Marc Clapper, Laura Clapper, Eric Byler, Adam Walters, Jeremey Schoos, Craig Seidel, Rich “Astro” Lawson.

Thanks to all!

人工智能辅助国家安全政策教学 || Teaching National Security Policy with AI

2025-06-10T13:00:25+00:00

Stanford 国际政策课程中AI的整合与应用

课程背景

斯坦福大学的国际政策课程 "Technology, Innovation and Great Power Competition"（由Eric Volmar和Joe Felter教授）致力于为未来的政策与工程领导者提供对美国与大国竞争中关键技术影响的深刻理解。课程结合了综合阅读、专家讲座和实际政策分析任务，强调通过实践学习。

AI整合的动因

技术成熟度：2024年秋季AI工具已具备较高能力，并呈指数级提升趋势。
校内资源支持：斯坦福设立了AI Playground，提供多种工具（如ChatGPT、Claude、Perplexity、NotebookLM、Otter.ai、Mermaid、Beautiful.ai等）。
学生需求：学生在其他课程中已接触AI，但对其使用边界存在模糊认知。课程希望利用AI提升学习效率，尤其针对政策学生每周需阅读大量文件的任务。

学生团队应用案例

Team OSC

目标：探讨美国国防部在技术产业中应承担的金融风险水平。
AI应用：
- 使用Claude 3.5总结政策文件，生成访谈线索。
- 通过AI获取相关联邦信用计划（如能源部Title17、Exim DFC）的领导者名单。
- 利用Otter.ai转录播客，补充访谈背景信息。
- 创新性：AI用于批判性检验团队假设，并生成可视化图表（Mermaid）和演示文稿（Beautiful.ai）。

Team FAAST

目标：优化美国能源部FASST计划以应对大国竞争。
AI应用：
- 使用ChatGPT总结文件并生成访谈问题，利用其“临时聊天”功能确保数据不用于训练模型。
- AI辅助生成模拟访谈邮件及问题，后期扩展至模拟面试和政策建议生成。
- 工具对比：ChatGPT适合图像/截图处理，Claude适合自然语言写作，Perplexity适合研究（提供引用），NotebookLM效果一般。

Team NSC Energy

目标：确保美国未来5年能源供应支持计算与AI发展。
AI应用：
- 初期用ChatGPT和Claude总结文件并生成访谈问题。
- 后期通过Perplexity Pro交叉验证信息准确性。
- ChatGPT与Mermaid结合，生成流程图并优化访谈结构。

Team Alpha Strategy

目标：探索美国能否建立全政府AI决策系统。
AI应用：
- 初期用ChatGPT分析政策文件，后期转向NotebookLM进行跨文档分析。
- 使用定制GPT构建利益相关者地图和图表，学者GPT帮助生成专业内容。
- 创新性：AI辅助优化自身提问策略，甚至指导如何撰写更有效的提示词。

Team Congress

目标：分析国防部在对华技术竞争中应优先使用的经济工具。
AI应用：
- 初期用ChatGPT提取文件主题，后期采用检索增强生成（RAG）技术，结合知识库快速定位相关来源。
- 模拟赞助商角色进行AI面试演练，提升策略制定能力。

Team NavalX

目标：提升美国海军在海上监视与侦察（ISR）中的能力。
AI应用：
- 使用ChatGPT总结文件、组织访谈笔记并定义技术术语。
- 通过Claude翻译中文文本，利用Perplexity进行研究，NotebookLM用于摘要生成。
- 创新性：AI模拟批评者角色以发现方案漏洞，并通过定制提示词优化输出格式。

关键经验与教训

AI作为协作工具：AI显著加速了学习过程，成为学生与政策分析的有力助手。
工具多样性：不同AI工具适用于不同任务（如ChatGPT适合写作，Perplexity适合研究）。
学生创造力：各团队开发了独特的AI应用场景，如模拟面试、翻译、提示词优化等。
人机结合的重要性：AI需与人类判断结合，确保输出的准确性与相关性。
未来展望：斯坦福已将AI整合至其他课程（如Hacking for Defense、Lean Launchpad），预计下一年AI工具将更强大。

总结

通过AI工具的整合，斯坦福课程不仅提升了学生的分析效率，还培养了他们在复杂技术与政策环境中的创新思维。学生从初期对工具的陌生到后期熟练应用，展现了AI在教育中的潜力。未来课程将进一步优化AI的使用方式，以应对更先进的技术发展。

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本文中嵌入的视频最好在 steveblank.com 上观看

国际政策专业的学生将在一个由人工智能驱动的世界中度过他们的职业生涯。我们希望我们的学生能够做好准备。这就是为什么我们采用了并整合了人工智能到我们的斯坦福国家安全政策课程中——技术、创新与大国竞争。

以下是我们的做法，学生们如何使用它，以及他们（和我们）学到的东西。

技术、创新与大国竞争是斯坦福大学的一门国际政策课程（由我，Eric Volmar 和 Joe Felter 教授授课）。这门课程为未来的政策和工程领导者提供了对美国与大国竞争对手的战略竞争中的地缘政治以及关键技术在决定结果中的作用的理解。

这门课程结合了多种教学工具。

现实世界——学生以小组形式处理来自政府赞助商的真实问题
体验式——他们走出教室，采访50多名利益相关者
多视角——他们通过专家讲座获得政策背景和见解
今年……利用人工智能加速学习

请注意人工智能与访谈结合的力量。访谈将知识根植于团队的真实体验中。

该团队提出了一个教学团队未曾想到的用例——利用人工智能来批判他们的假设。人工智能不仅给出了他们的批评，还提供了来自已发表学者的链接支持。请查看下面的演示。

如果无法观看视频，请点击此处

对于所有团队来说，使用人工智能工具本身就是一个学习和发现的过程。在第一天，大多数学生对这些工具都很陌生。

Team OSC 建议学生应在本学期早期开始使用人工智能工具，并尝试像 ChatGPT、Otter.ai 这样的工具。对于学习曲线陡峭的工具，如 Mermaid，应在项目开始时就启动，以训练模型。

Team OSC 人工智能工具总结：人工智能工具并不完美，因此务必交叉验证摘要、见解和转录内容的准确性和相关性。要对它们的输出保持高度批判性。最大的收获是，人工智能在人类努力准备后效果最佳。

Team FAAST
FAAST 团队试图了解美国是否可以利用人工智能创建一个全政府决策工厂。

在课程开始时，Alpha Strategy 团队使用 ChatGPT.40 进行政策文件分析和摘要，以及利益相关者映射。然而，他们发现逐一浏览无数篇文章是耗时的。因此，团队转向使用 Notebook LM 进行文件搜索和交叉分析。请查看 Alpha Strategy 团队如何使用 Notebook LM 的视频：

如果无法观看视频，请点击此处

该团队还使用了定制的 ChatGPT 模型来构建利益相关者地图和图表，并整理访谈笔记。将会有大量专门的 Gpts 工具。他们提到其中一个特别有用的是学者 GPT。

请查看 Alpha Strategy 团队如何使用定制 GPTs 的视频：

如果无法观看视频，请点击此处

与其他团队一样，Alpha Strategy 团队使用 ChatGPT 来总结访谈笔记，并创建流程图以粘贴到他们的每周演示中。

Team Congress
Congress 团队正在探讨一个问题：“如果国防部被赋予经济权力工具，哪些工具在当前与中华人民共和国的技术经济竞争中最为有效？”

和其他团队一样，Congress 团队首先使用 ChatGPT 从每周数百页的阅读材料、新闻稿、文章和立法中提取关键主题。他们还使用它进行利益相关者映射和图表绘制，以识别潜在的利益相关者关系，或创造性地建议其他可视化方式。

当 Congress 团队在课程前两周无法联系到他们的赞助商时，就像 Team OSC 一样，他们使用人工智能工具来扮演他们的赞助商，即国防现代化 caucus 的成员。一旦他们意识到其用途，他们继续使用 AI 角色扮演进行模拟访谈。

该团队还使用了定制的 ChatGPT 模型，但在他们的情况下发现上传文档数量有限，因为他们有很多内容。因此，他们使用了检索增强生成（Retrieval Augmented Generation），它接收用户的查询，并将其与知识库中的相关来源匹配，然后将这些信息作为输出反馈。请查看 Congress 团队如何使用检索增强生成的视频：

如果无法观看视频，请点击此处
与其他团队一样，NavalX 团队发现可以通过告诉 ChatGPT 你希望它如何行动来定制它。

如果无法观看视频，请点击此处

该团队的另一个意外发现是，你可以使用 ChatGPT 来告诉它如何更好地为其自身编写提示。

如果无法观看视频，请点击此处
总之，NavalX 团队使用 Claude 将文本从中文翻译，发现 ChatGPT 在写作任务中表现最佳，Perplexity 在研究任务中表现最佳，Claude 在阅读任务中表现最佳，Notebook LM 在摘要任务中表现最佳。

学到的经验

将人工智能整合到这门课程中需要一位有使命的专职教师，以创造一种新的教学方式

结果是人工智能极大地增强了并加速了所有团队的学习

它充当了有益的合作伙伴

将人工智能与利益相关者访谈相结合尤其强大

在课程开始时，学生对这些人工智能工具中的少数有所了解

到课程结束时，他们对其中的许多工具已经熟练掌握

大多数团队发明了创造性的用例

我们现在教授的所有斯坦福课程——包括“为国防而黑客”、“精益启动平台”和“政府内部创业”——都已将人工智能整合到课程中

明年的人工智能工具将有实质性的改进

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The videos embedded in this post are best viewed on steveblank.com

International Policy students will be spending their careers in an AI-enabled world. We wanted our students to be prepared for it. This is why we’ve adopted and integrated AI in our Stanford national security policy class – Technology, Innovation and Great Power Competition.

Here’s what we did, how the students used it, and what they (and we) learned.

Technology, Innovation and Great Power Competition is an international policy class at Stanford (taught by me, Eric Volmar and Joe Felter.) The course provides future policy and engineering leaders with an appreciation of the geopolitics of the U.S. strategic competition with great power rivals and the role critical technologies are playing in determining the outcome.

This course includes all that you would expect from a Stanford graduate-level class in the Masters in International Policy – comprehensive readings, guest lectures from current and former senior policy officials/experts, and deliverables in the form of written policy papers. What makes the class unique is that this is an experiential policy class. Students form small teams and embark on a quarter-long project that got them out of the classroom to:

select a priority national security challenge, and then …

validate the problem and propose a detailed solution tested against actual stakeholders in the technology and national security ecosystem

The class combines multiple teaching tools.

Real world – Students worked in teams on real problems from government sponsors

Experiential – They get out of the building to interview 50+ stakeholders

Perspectives – They get policy context and insights from lectures by experts

And this year… Using AI to Accelerate Learning

Rationale for AI

Using this quarter to introduce AI we had three things going for us: 1) By fall 2024 AI tools were good and getting exponentially better, 2) Stanford had set up an AI Playground enabling students to use a variety of AI Tools (ChatGPT, Claude, Perplexity, NotebookLM, Otter.ai, Mermaid, Beautiful.ai, etc.) and 3) many students were using AI in classes but it was usually ambiguous about what they were allowed to do.

Policy students have to read reams of documents weekly. Our hypotheses was that our student teams could use AI to ingest and summarize content, identify key themes and concepts across the content, provide an in-depth analysis of critical content sections, and then synthesize and structure their key insights and apply their key insights to solve their specific policy problem. They did all that, and much, much, more.

While Joe Felter and I had arm-waved “we need to add AI to the class” Eric Volmar was the real AI hero on the teaching team. As an AI power user Eric was most often ahead of our students on AI skills. He threw down a challenge to the students to continually use AI creatively and told them that they would be graded on it. He pushed them hard on AI use in office hours throughout the quarter. The results below speak for themselves.

If you’re not familiar with these AI tools in practice it’s worth watching these one minute videos.

Team OSC

Team OSC was trying to understand what is the appropriate level of financial risk for the U.S. Department of Defense to provide loans or loan guarantees in technology industries?

The team started using AI to do what we had expected, summarizing the stack of weekly policy documentsusing Claude 3.5. And like all teams, the unexpected use of AI was to create new leads for their stakeholder interviews. They found that they could ask AI to give them a list of leaders that were involved in similar programs, or that were involved in their program’s initial stages of development.

See how Team OSC summarized policy papers here:

If you can’t see the video click here

Claude was also able to create a list of leaders with the Department of Energy Title17 credit programs, Exim DFC, and other federal credit programs that the team should interview. In addition, it created a list of leaders within Congressional Budget Office and the Office of Management and Budget that would be able to provide insights. See the demo here:

If you can’t see the video click here

The team also used AI to transcribe podcasts. They noticed that key leaders of the organizations their problem came from had produced podcasts and YouTube videos. They used Otter.ai to transcribe these. That provided additional context for when they did interview them and allowed the team to ask insightful new questions.

If you can’t see the video click here

Note the power of fusing AI with interviews. The interviews ground the knowledge in the teams lived experience.

The team came up with a use case the teaching team hadn’t thought of – using AI to critique the team’s own hypotheses. The AI not only gave them criticism but supported it with links from published scholars. See the demo here:

If you can’t see the video click here

Another use the teaching team hadn’t thought was using Mermaid AI to create graphics for their weekly presentations. See the demo here:

If you can’t see the video click here

The surprises from this team kept coming. Their last was that the team used Beautiful.ai in order to generate PowerPoint presentations. See the demo here:

If you can’t see the video click here

For all teams, using AI tools was a learning/discovery process all its own. By and large, students were largely unfamiliar with most tools on day 1.

Team OSC suggested that students should start using AI tools early in the quarter and experiment with tools like ChatGPT, Otter.ai. Tools that that have steep learning curves, like Mermaid should be started at the very start of the project to train their models.

Team OSC AI tools summary: AI tools are not perfect, so make sure to cross check summaries, insights and transcriptions for accuracy and relevancy. Be really critical of their outputs. The biggest takeaway is that AI works best when prepared with human efforts.

Team FAAST

The FAAST team was trying to understand how can the U.S. improve and scale the DoE FASST program in the urgent context of great power competition?

Team FAAST started using AI to do what we had expected, summarizing the stack of weekly policy documents they were assigned to read and synthesizing interviews with stakeholders.

One of the features of ChatGPT this team appreciated, and important for a national security class, was the temporary chat feature – data they entered would not be used to train the open AI models. See the demo below.

If you can’t see the video click here

The team used AI do a few new things we didn’t expect – to generate emails to stakeholders and to create interview questions. During the quarter the team used ChatGPT, Claude, Perplexity, and NotebookLM. By the end of the 10-week class they were using AI to do a few more things we hadn’t expected. Their use of AI expanded to include simulating interviews. They gave ChatGPT specific instructions on who they wanted it to act like, and it provided personalized and custom answers. See the example here.

If you can’t see the video click here

Learning-by-doing was a key part of this experiential course. The big idea is that students learn both the method and the subject matter together. By learning it together, you learn both better.

Finally, they used AI to map stakeholders, get advice on their next policy move, and asked ChatGPT to review their weekly slides (by screenshotting the slides and putting them into ChatGPT and asking for feedback and advice.)

The FAAST team AI tool summary: ChatGPT was specifically good when using images or screenshots, so in these multi-level tasks, and when you wanted to use kind of more custom instructions, as we used for the stakeholder interviews. Claude was better at more conversational and human in writing, so used it when sending emails. Perplexity was better for researchers because it provides citations, so you’re able to access the web and actually get directed to the source that it’s citing. NotebookLM was something we tried out, but it was not as successful. It was a cool tool that allowed us to summarize specific policy documents into a podcast, but the summaries were often pretty vague.

Team NSC Energy

Team NSC Energy was working on a National Security Council problem, “How can the United States generate sufficient energy to support compute/AI in the next 5 years?”

At the start of the class, the team began by using ChatGPT to summarize their policy papers and generate tailored interview questions, while Claude was used to synthesize research for background understanding. As ChatGPT occasionally hallucinated information, by the end of the class they were cross validating the summaries via Perplexity pro.

The team also used ChatGPT and Mermaid to organize their thoughts and determine who they wanted to talk to. ChatGPT was used to generate code to put into the Mermaid flowchart organizer. Mermaid has its own language, so ChatGPT was helpful, so we didn’t have to learn all the syntax for this language.

See the video of how Team NSC Energy used ChaptGPT and Mermaid here:

If you can’t see the video click here

Team Alpha Strategy

The Alpha Strategy team was trying to discover whether the U.S. could use AI to create a whole-of-government decision-making factory.

At the start of class, Team Alpha Strategy used ChatGPT.40 for policy document analysis and summary, as well for stakeholder mapping. However, they discovered going one by one through the countless numbers of articles was time consuming. So the team pivoted to using Notebook LM, for document search and cross analysis. See the video of how Team Alpha Strategy used Notebook LM here:

If you can’t see the video click here

The other tools the team used were custom Gpts to build stakeholder maps and diagrams and organize interview notes. There’s going to be a wide variety of specialized Gpts. One that was really helpful, they said, was a scholar GPT.

See the video of how Team Alpha Strategy used custom GPTs:

If you can’t see the video click here

Like other teams, Alpha Strategy used ChatGPT to summarize their interview notes and to create flow charts to paste into their weekly presentations.

Team Congress

The Congress team was exploring the question, “if the Department of Defense were given economic instruments of power, which tools would be most effective in the current techno-economic competition with the People’s Republic of China?”

As other teams found, Team Congress first used ChatGPT to extract key themes from hundreds of pages of readings each week and from press releases, articles, and legislation. They also used for mapping and diagramming to identify potential relationships between stakeholders, or to creatively suggest alternate visualizations.

When Team Congress weren’t able to reach their sponsor in the initial two weeks of the class, much like Team OSC, they used AI tools to pretend to be their sponsor, a member of the defense modernization caucus. Once they realized its utility, they continued to do mock interviews using AI role play.

The team also used customized models of ChatGPT but in their case found that this was limited in the number of documents they could upload, because they had a lot of content. So they used retrieval augmented generation, which takes in a user’s query, and matches it with relevant sources in their knowledge base, and fed that back out as the output. See the video of how Team Congress used retrieval augmented generation here:

If you can’t see the video click here

Team NavalX

The NavalX team was learning how the U.S. Navy could expand its capabilities in Intelligence, Surveillance, and Reconnaissance (ISR) operations on general maritime traffic.

Like all teams they used ChatGPT to summarize and extract from long documents, organizing their interview notes, and defining technical terms associated with their project. In this video, note their use of prompting to guide ChatGPT to format their notes.

See the video of how Team NavalX used tailored prompts for formatting interview notes here:

If you can’t see the video click here

They also asked ChatGPT to role play a critic of our argument and solution so that we could find the weaknesses. They also began uploading many interviews at once, and asked Claude to find themes or ideas in common that they might have missed on their own.

Here’s how the NavalX team used Perplexity for research.

If you can’t see the video click here

Like other teams, the NavalX team discovered you can customize ChatGPT by telling it how you want it to act.

If you can’t see the video click here

Another surprising insight from the team is that you can use ChatGPT to tell you how to write better prompts for itself.

If you can’t see the video click here

In summary, Team NavalX used Claude to translate texts from Mandarin, and found that ChatGPT was the best for writing tasks, Perplexity the best for research tasks, Claude the best for reading tasks, and notebook LM was the best for summarization.

Lessons Learned

Integrating AI into this class took a dedicated instructor with a mission to create a new way to teach using AI tools

The result was AI vastly enhanced and accelerated learning of all teams

It acted as a helpful collaborator

Fusing AI with stakeholders interviews was especially powerful

At the start of the class students were familiar with a few of these AI tools

By the end of the class they were fluent in many more of them

Most teams invented creative use cases

All Stanford classes we now teach – Hacking for Defense, Lean Launchpad, Entrepreneurship Inside Government – have AI integrated as part of the course

Next year’s AI tools will be substantively better

美国如何放弃成为科学超级大国 || How the United States Gave Up Being a Science Superpower

2025-05-13T13:00:50+00:00

美国科学全球主导地位的形成与挑战

美国在科学领域的全球主导地位并非偶然，而是源于公共与私营部门长期协作推动创新和经济增长的成果。二战期间，范内瓦·布什（Vannevar Bush）领导美国科学研究与发展办公室（OSRD），政府最初以直接成本的25%作为固定比例补偿大学的间接成本（如行政管理、设施维护等）。由于大学无利润空间，间接成本回收是其维持科研基础设施的唯一途径。战后，美国海军研究办公室（ONR）开始根据机构实际支出与大学协商间接成本率，并建立了财务审计流程以确保成本报告的准确性。这一制度至今仍沿用，尽管后续对报销机制进行了调整以防止滥用，但核心框架未变。目前，研究型大学的间接成本率通常为50%-60%，而私人基金会的率则较低（10%-20%），但对直接成本的界定更严格。

特朗普政府的政策冲击与法律挑战
2017年，特朗普政府试图将美国国立卫生研究院（NIH）的间接成本率上限设为10%，认为此类成本属于“官僚冗余”，并指责大学通过虚高间接成本牟利。然而，国会否决了这一提案，并在2024年《综合拨款法案》中冻结了大部分间接成本率。尽管如此，NIH于2024年2月单方面将间接成本率降至15%，引发法律争议。若该政策通过，将导致数十亿美元的科研资金流失，迫使大学削减预算、暂停实验室扩建并减少研究生资助，进而影响初创企业数量、产品开发、就业机会及国家税收和出口能力。

科研人才流失的连锁效应
特朗普政府的削减政策已对美国学术界产生深远影响，尤其是科研人才流失。美国曾是国际科研人员的首选目的地，得益于其资金支持、创新经济和商业化机会。然而，近年来中国通过“千人计划”等政策大幅增加科研投入，积极吸引海外人才，而法国等欧洲国家也推出类似举措。若美国无法维持科研领导地位，其在量子计算、癌症治疗、人工智能等领域的突破性成果可能被其他国家取代，进而威胁国家安全和经济竞争力。历史表明，一旦国家失去科研主导权，重建将极为困难，如英国在二战后未能恢复其技术领先地位。若当前趋势持续，美国可能面临相似命运。

科研的战略意义与政策呼吁
大学科研不仅是学术议题，更是经济和战略核心。联邦科研投资并非成本，而是推动增长、创造就业和保障国家安全的催化剂。政策制定者需重新确认美国对科学领导地位的承诺。若不采取行动，其后果将影响数代人。问题已不再是美国是否能负担科研投资，而是是否能承受不投资的代价。

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美国在全球科学领域的主导地位并非偶然，而是公共和私营部门远见卓识合作的成果，这种合作促进了创新和经济增长。

范内瓦·布什（右图）在第二次世界大战期间领导了美国科学研究与发展办公室。图片来源：Bettmann/Getty

最初，政府以直接成本的25%作为固定比例报销大学的间接成本。与企业不同，大学没有利润空间，因此间接成本回收是其唯一能够支付和维持研究基础设施的方式。到战争结束时，一些大学已同意将间接成本率提高到50%。该比例适用于直接成本，因此项目负责人可以将拨款的三分之二用于直接研究成本，其余部分则归大学作为间接成本。（一个常见的误解是，间接成本率是总拨款的百分比，例如50%的率意味着一半的拨款用于间接费用。）

第二次世界大战后，美国海军研究办公室（ONR）开始基于实际机构支出与大学协商间接成本率。大学必须证明其间接成本（行政、设施、公用事业等）以获得全额报销。ONR正式化了财务审计流程，以确保机构准确报告间接成本。这导致了协商间接成本率的做法，该做法至今仍在使用。

此后，报销流程进行了调整以防止钻制度空子，但总体上仍保持不变。大学与美国卫生与公共服务部（HHS）或ONR协商其间接成本率。大多数科研密集型大学对校内研究的间接成本率在50%-60%之间。私人基金会通常有较低的比率（10%-20%），但对哪些费用可被视为直接成本的标准更为严格。

2017年，特朗普政府首次试图对NIH研究的间接成本实施10%的上限。一些政府官员认为这些成本是官僚主义膨胀的一种形式，并认为研究大学正在从虚高的间接费用中获利。

国会否决了这一提议，并在年度拨款法案中添加了语言，实际上冻结了大多数间接成本率在2017年的水平。这一条款体现在2024年综合拨款法案的第224条中，并已两次延期，目前仍然有效。

然而，今年2月，NIH将间接报销率任意削减至15%（见go.nature.com/4cgsndz）。这一政策目前正在面临法律挑战。

如果该政策最终被允许实施，后果将立即显现。数十亿美元支持研究大学的资金将被剥夺。为应对这一情况，一些研究大学已开始削减预算，暂停实验室扩建并减少研究生资助。这将意味着初创企业数量减少，进而影响产品、服务、就业、税收和出口。

人才争夺战

特朗普政府对美国学术界的削减正在产生连锁反应，其中一个直接影响的领域是科学人才的流失。美国历来是国际研究人员的首选目的地，这得益于其资金充足的大学、创新驱动的经济以及丰富的商业化机会。

在美国接受训练的科学家——其中许多人过去留在国内创办初创企业或参与企业研发——正受到国外机构，尤其是中国机构的积极招募。中国已扩大其“千人计划”，为愿意搬迁的科研人员提供丰厚的财务激励。法国和其他欧洲国家也开始设计吸引顶尖美国研究人员的方案。

美国科研人员的流失将对其创新能力产生长期影响。如果该国拆毁其科研基础设施，未来具有变革性的突破——无论是量子计算、癌症治疗、自主技术还是人工智能——都将发生在别处。美国可能面临在自身经济和国家安全需求方面依赖外国科学领导的局面。

历史表明，一旦一个国家失去其科研领导地位，重新夺回将极为困难。英国从未重新夺回其战前在技术创新领域的主导地位。如果当前趋势持续下去，美国可能也会遭遇同样的命运。

大学科研不仅仅是学术问题，更是经济和战略要务。政策制定者必须认识到联邦研发投资并非成本，而是促进增长、创造就业和保障国家安全的催化剂。

政策制定者需要重申美国对科学领导地位的承诺。如果该国现在不采取行动，后果将延续数代。问题不再是美国是否能负担得起对科研的投资，而是是否能负担得起不投资的代价。

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US global dominance in science was no accident, but a product of a far-seeing partnership between public and private sectors to boost innovation and economic growth.

Vannevar Bush (right) led the US Office of Scientific Research and Development during the Second World War.Credit: Bettmann/Getty

At first, the government reimbursed universities for indirect costs at a flat rate of 25% of direct costs. Unlike businesses, universities had no profit margin, so indirect-cost recovery was their only way to pay for and maintain their research infrastructure. By the end of the war, some universities had agreed on a 50% rate. The rate is applied to direct costs, so that a principal investigator will be able to spend two-thirds of a grant on direct research costs and the rest will go to the university for indirect costs. (A common misconception is that indirect-cost rates are a percentage of the total grant, for example a 50% rate meaning that half of the award goes to overheads.)

After the Second World War, the US Office of Naval Research (ONR) began negotiating indirect-cost rates with universities on the basis of actual institutional expenses. Universities had to justify their overhead costs (administration, facilities, utilities) to receive full reimbursement. The ONR formalized financial auditing processes to ensure that institutions reported indirect costs accurately. This led to the practice of negotiating indirect-cost rates, which is still used today.

Since then, the reimbursement process has been tweaked to prevent gaming the system, but has remained essentially the same. Universities negotiate their indirect-cost rates with either the US Department of Health and Human Services (HHS) or the ONR. Most research-intensive universities receive rates of 50–60% for on-campus research. Private foundations often have a lower rate (10–20%), but tend to have wider criteria for what can be considered a direct cost.

In 2017, the first Trump administration attempted to impose a 10% cap on indirect costs for NIH research. Some in the administration viewed such costs as a form of bureaucratic bloat and argued that research universities were profiting from inflated overhead rates.

Congress rejected this and later added language in the annual funding bill that essentially froze most rates at their 2017 levels. This provision is embodied in section 224 of the Consolidated Appropriations Act of 2024, which has been extended twice and is still in effect.

In February, however, the NIH slashed its indirect reimbursement rate to an arbitrary 15% (see go.nature.com/4cgsndz). That policy is currently being challenged in court.

If the policy is ultimately allowed to proceed, the consequences will be immediate. Billions of dollars of support for research universities will be gone. In anticipation, some research universities are already scaling back their budgets, halting lab expansions and reducing graduate-student funding. This will mean fewer start-ups being founded, with effects on products, services, jobs, taxes and exports.

Race for talent

The ripple effects of Trump’s cuts to US academia are spreading, and one area in which there will be immediate ramifications is the loss of scientific talent. The United States has historically been the top destination for international researchers, thanks to its well-funded universities, innovation-driven economy and opportunities for commercialization.

US-trained scientists — many of whom have historically stayed in the country to launch start-ups or contribute to corporate R&D — are being actively recruited by foreign institutions, particularly in China, which has ramped up its science investments. China has expanded its Thousand Talents Program, which offers substantial financial incentives to researchers willing to relocate. France and other European nations are beginning to design packages to attract top US researchers.

Erosion of the US scientific workforce will have long-term consequences for its ability to innovate. If the country dismantles its research infrastructure, future transformative breakthroughs — whether in quantum computing, cancer treatment, autonomy or artificial intelligence — will happen elsewhere. The United States runs the risk of becoming dependent on foreign scientific leadership for its own economic and national-security needs.

History suggests that, once a nation loses its research leadership, regaining it is difficult. The United Kingdom never reclaimed its pre-war dominance in technological innovation. If current trends continue, the same fate might await the United States.

University research is not merely an academic concern — it is an economic and strategic imperative. Policymakers must recognize that federal R&D investments are not costs but catalysts for growth, job creation and national security.

Policymakers need to reaffirm the United States’ commitment to scientific leadership. If the country fails to act now, the consequences will be felt for generations. The question is no longer whether the United States can afford to invest in research. It is whether it can afford not to.

美国如何成为科技强国 || How the U.S. Became A Science Superpower

2025-04-15T13:00:52+00:00

美英二战前后科技发展对比总结

战前背景

二战前，美国在科学与工程领域处于第二位，而英国则领先。然而，战争结束后，美国的科技实力迅速超越英国，并主导全球科技发展长达85年。这一转变源于两国科学顾问的差异：英国由弗雷德里克·林德曼（Frederick Lindemann）主导，而美国则由万尼瓦尔·布什（Vannevar Bush）引领。

英国模式：军事主导的集中化体系

林德曼的角色：作为牛津大学教授，他长期担任英国首相丘吉尔的科学顾问，推动政府实验室主导武器研发，如雷达系统（Chain Home）、夜间战斗机雷达、核武器计划（Tube Alloys）等。
局限性：英国政府实验室将大学视为人才来源，而非研发伙伴。战后因财政枯竭和经济政策（如社会主义改革），英国无法持续投资科技创新，导致其军事技术成果难以商业化。
战时约束：英国面临持续轰炸和潜艇封锁，需集中资源应对短期威胁，而缺乏对长期技术发展的投入。

美国模式：大学与产业协作的分散化体系

布什的推动：他主张将科研与开发交由大学教授领导，通过科学研究与发展办公室（OSR&D）协调资金，支持高校与企业合作。
创新生态：美国政府向顶尖大学投入巨额资金（1941-1945年达90亿美元），并建立间接成本补偿机制，为大学提供设施和管理经费。这使美国高校能构建世界级实验室，推动电子、计算机、核能等技术突破。
产业协作：企业如西方电气、通用电气、IBM等负责技术量产，形成“政府-大学-产业”三位一体的创新模式。

战后结果

英国的衰落：战后英国军方缩减规模，政府实验室被裁撤，缺乏后续投资，导致其科技生态停滞。
美国的崛起：美国通过持续的政府支持，将战时技术转化为商业应用，如计算机、雷达、核能等，成为全球科技领导。
创新集群：以麻省理工学院（MIT）和斯坦福大学为代表的高校成为硅谷、航空航天和生物科技产业的基石。

当前趋势

中国的挑战：过去30年，中国大力投资科技以超越美国。
美国的潜在危机：若政府减少对大学科研的支持，美国长期科技主导地位可能被撼动，其他国家或将崛起。

总结

二战后，美英科技体系因科学顾问的差异和经济政策的不同走向分野。英国的集中化模式虽取得理论突破，但受限于财政与商业化能力；美国的分散化体系通过政府与大学、产业的深度协作，推动了全球科技革命。今日，中国正加速追赶，而美国若失去对科研的持续投入，其科技霸权或将终结。

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二战前，美国在科学和工程领域仅处于第二位。到战争结束时，美国的科学和工程已超越英国，并引领世界长达85年。这是因为两国的科学顾问是两个截然不同的人。他们各自对如何利用本国资源开发先进武器系统有着根本不同的观点。战后，英国的早期领先地位变得短暂，而美国则建立了引领世界的科学与技术创新生态系统——直到现在。英国——军事武器实验室当温斯顿·丘吉尔于1940年成为英国首相时，他的科学顾问是弗雷德里克·林德曼教授，他的20年好友。林德曼领导牛津大学的物理系，并担任牛津克伦登实验室的主任。当时英国已与德国开战，战时优先事项集中在防御和情报技术项目上，例如使用电子、雷达、物理等技术的武器——包括基于雷达的空中防御网络“Chain Home”、夜间战斗机上的机载雷达，以及核武器计划的“MAUD委员会”，该委员会启动了代号为“Tube Alloys”的英国核武器计划。他们的破译机构在布莱切利公园开始使用最早期的计算机解读德国的加密信息——“恩尼格玛”密码。早在1930年代中期，英国因担心纳粹德国，利用现有的军事和政府研究实验室开发这些武器的原型。电信研究部建立了早期预警雷达，这对英国在不列颠之战中的生存至关重要，并开发电子战技术以保护飞越德国的英国轰炸机。海军研究实验室则开发了声呐和反潜武器系统。皇家航空研究院正在研发喷气式战斗机。随后，这些实验室与英国公司合作，批量制造武器。英国政府实验室将大学视为人才来源，但它们并未参与武器开发。在丘吉尔的领导下，林德曼影响着哪些项目获得资金支持，哪些被搁置。林德曼在第一次世界大战期间作为皇家航空工厂的科研人员和试飞员，积累了对英国军事研究与开发实验室能力的信心。他采用自上而下的集中管理模式，将武器开发主要置于政府研究实验室，这种模式塑造了二战期间英国的创新，但也导致战后创新体系的衰落。美国——大学武器实验室与英国不同，美国缺乏科学顾问。直到1940年6月，范内瓦·布什（前麻省理工学院工程系主任，卡耐基研究所所长）才告诉总统富兰克林·罗斯福，第二次世界大战将是第一场以先进技术（电子、雷达、物理等）决定胜负的战争。与林德曼不同，布什与美国海军有着长达20年的争议历史，并对政府主导的研发持消极态度。他认为政府研究实验室效率低下且次等。他说服总统，虽然陆军和海军应负责制造常规武器（飞机、舰船、坦克等），但学术界的科学家能够开发更先进的技术武器，并以更快的速度交付。他主张，只有让科学家在大学环境中，由大学教授领导的民用武器实验室工作，他们才能发挥生产力。令陆军和海军服务主管惊讶的是，罗斯福同意让布什建立这样的组织，以协调和资助所有先进武器的研究。（尽管布什此前与总统没有直接关系，但罗斯福曾在第一次世界大战期间担任海军助理部长，并亲眼目睹了其运作的低效。接下来的四年，他们合作良好。与丘吉尔不同，罗斯福对科学兴趣不大，接受了布什对美国科技计划方向的意见，赋予他广泛的权力。） 1941年，布什进一步说服总统，认为除了研究，教授们还应负责这些武器的研发、采购和部署。他们被赋予开发军事武器系统和解决军事问题的任务，以击败德国和日本。（这些武器随后由美国公司西方电气、通用电气、RCA、杜邦、孟山都、柯达、Zenith、西屋、雷明顿·兰德和西尔维尼亚等批量制造。）为此，布什创建了科学研究与发展办公室（OSR&D）。 OSR&D总部将战时工作划分为19个“部门”、5个“委员会”和2个“小组”，每个小组负责军事努力的不同方面。没有正式的资格要求。 OSR&D工作人员与军事联络官合作，确定最重要的军事问题，然后每个OSR&D部门提出解决方案。这些努力涵盖了广泛的任务——开发先进电子设备、雷达、火箭、声呐、新型武器如近炸引信、凝固汽油弹、巴祖卡火箭筒，以及新药物如青霉素、疟疾治疗方法、化学武器和核武器。每个部门由布什亲自挑选的教授领导，并设在大学内——麻省理工学院、哈佛大学、约翰斯·霍普金斯大学、加州理工学院、哥伦比亚大学和芝加哥大学都运行着重要的武器系统项目。近10,000名科学家、工程师、教授及其研究生因获得豁免而得以在这些大学实验室工作。（在二战前，美国大学的科学研究主要由关注特定研究项目的公司资助。但基础研究的资金来自两个非营利组织：洛克菲勒基金会和卡耐基研究所。在担任卡耐基研究所所长期间，布什了解并资助了美国所有顶尖大学的科学家。而作为牛津大学物理系主任，林德曼则将其他学者视为竞争对手。）美国——无限资金改变美国大学和世界格局的是政府资金。大量资金。在二战前，美国的先进科技研究主要在企业创新实验室中进行（如通用电气、AT&T、杜邦、RCA、西屋、NCR、孟山都、柯达、IBM等）。大学没有政府资助（除了农业）用于研究。学术研究曾由非营利组织和行业资助，主要是洛克菲勒和卡耐基基金会。现在，美国大学首次获得了前所未有的资金。1941年至1945年间，OSR&D向美国顶尖研究型大学拨款90亿美元（按2025年美元计算）。这使得大学成为战时研究的全面合作伙伴，而不仅仅是英国政府项目的“人才库”。英国——战时限制战时英国面临截然不同的限制。首先，英国每日遭受空袭和潜艇封锁，因此他们专注于一组较小的高优先级项目以应对这些威胁。其次，国家濒临破产，无法承担美国那样的广泛而深入的投资。（这体现在他们意识到将研究成果转化为工业规模工程的成本后，放弃了核武器计划。）这意味着许多其他创新领域——如早期计算和核研究——的资金投入远低于其美国同行。战后——英国 1945年，丘吉尔被选下台，林德曼教授和英国科学与工程的协调也随之结束。英国直到1951-55年丘吉尔再次当选总统并重新带回林德曼为止，才再次拥有科学顾问。战争结束导致英国军事的大幅缩减，包括所有开发雷达、电子、计算等技术的政府实验室的严重削减。战后英国经济枯竭，紧缩政策限制了其大规模创新投资的能力。没有战后政府跟进投资的计划。美国和英国不同的经济现实也塑造了各自的创新体系。美国拥有庞大的工业基础、丰富的资本和巨大的国内市场，这使其能够大规模投资研究与开发。而在英国，社会主义政府上台。丘吉尔的继任者、工党的克莱门特·艾特利解散了英国帝国，国有化了银行、电力、交通和钢铁等行业，这些措施减少了竞争并减缓了技术进步。尽管英国的研究机构如剑桥和牛津大学在理论科学领域仍保持领先地位，但它们难以将突破性成果规模化和商业化。例如，布莱切利公园中艾伦·图灵和汤米·弗劳尔在计算领域的开创性工作并未发展成繁荣的英国计算产业——与美国不同，美国公司如ERA、UNIVAC、NCR和IBM则基于战时工作建立了相关产业。由于缺乏对双重用途技术或商业化的政府支持，且私人资本在新企业中缺失，英国战后的创新生态系统从未真正起飞。战后——美国与此同时，美国的大学和公司意识到战时政府对研究的资金支持是科学、工程和医学的惊人加速器。所有人，包括国会，都同意美国政府应继续在这些领域发挥重要作用。1945年，范内瓦·布什发表了报告《科学：无尽的前沿》，倡导政府资助大学、学院和研究机构的基础研究。国会则讨论如何最佳组织联邦对科学的支持。到战争结束时，OSR&D的资金使那些曾只是研究论文或被认为无法大规模建造的技术成为商业可行的产品——计算机、火箭、雷达、特氟龙、合成纤维、核能等。创新集群围绕获得大量OSR&D资金的大学形成（如麻省理工学院的辐射实验室或“Rad Lab”在二战期间雇佣了3500名平民，开发并制造了100种雷达系统部署在战区），或围绕运行OSR&D部门的教授形成（如斯坦福大学的弗雷德·特曼）。战争结束后，原子能委员会于1946年从曼哈顿计划中独立出来，而军方则重新接管先进武器的开发。1950年，国会设立了国家科学基金会，资助美国所有基础科学（除生命科学，该职责由新成立的国家卫生研究院承担）。八年之后，DARPA和NASA也作为联邦研究机构成立。讽刺的是，范内瓦·布什的影响力会比林德曼的下降得更快。当罗斯福总统于1945年4月去世，战争部长斯蒂姆森于1945年9月退休后，所有曾被布什绕过的军事领导层都开始对他进行打击。他关于重组OSR&D的论点在国会中制造了更多敌人。到1948年，布什已从政府服务中退休，此后再未参与美国政府事务。不同的遗产英国采用的集中式模式，利用政府研究实验室，是在短期生存斗争中形成的。他们取得了辉煌的突破，但缺乏规模、整合和资本来主导战后世界。美国则建立了一个分散、协作的生态系统，将政府对大学研究和原型开发的大规模资金投入与私人行业批量制造解决方案紧密结合。这种美国研究生态系统的组成部分之一是间接成本补偿制度的天才。美国不仅资助大学研究人员的薪资，还为研究人员的设施和行政提供资金。这是让美国大学建立世界级实验室、进行尖端研究的秘密武器。科学家们纷纷涌向美国，导致其他国家抱怨“人才流失”。如今，美国大学每年向科技初创公司和现有企业许可3000项专利、3200项版权和其他1600项许可。他们共同催生超过1100家基于科学的初创公司，从而带来无数产品和数万个新岗位。这种大学、产业和政府的合作伙伴关系成为其他国家现代创新生态系统的蓝图。总结到战争结束时，美国和英国的创新体系产生了截然不同的成果。两个体系都受到各自国家科学顾问的经验和个性的影响。英国在理论科学和国防技术方面保持领先地位，但其社会主义政府经济政策导致未能将战时创新商业化。美国则成为全球科学与技术的领导者，电子、微波、计算和核能等创新推动了战后经济繁荣。大学、产业和政府的合作关系成为硅谷、航空航天产业和生物技术产业的基础。如今，中国领导层在过去三十年中大力投资，以超越美国在科学与技术领域的领先地位。在2025年，随着美国政府对大学研究支持的放弃，美国在科学领域的长期主导地位可能结束。其他国家将引领未来。
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Prior to WWII the U.S was a distant second in science and engineering. By the time the war was over, U.S. science and engineering had blown past the British, and led the world for 85 years.

It happened because two very different people were the science advisors to their nation’s leaders. Each had radically different views on how to use their country’s resources to build advanced weapon systems. Post war, it meant Britain’s early lead was ephemeral while the U.S. built the foundation for a science and technology innovation ecosystem that led the world – until now.

The British – Military Weapons Labs

When Winston Churchill became the British prime minister in 1940, he had at his side his science advisor, Professor Frederick Lindemann, his friend for 20 years. Lindemann headed up the physics department at Oxford and was the director of the Oxford Clarendon Laboratory. Already at war with Germany, Britain’s wartime priorities focused on defense and intelligence technology projects, e.g. weapons that used electronics, radar, physics, etc. – a radar-based air defense network called Chain Home, airborne radar on night fighters, and plans for a nuclear weapons program – the MAUD Committee which started the British nuclear weapons program code-named Tube Alloys. And their codebreaking organization at Bletchley Park was starting to read secret German messages – the Enigma – using the earliest computers ever built.

As early as the mid 1930s, the British, fearing Nazi Germany, developed prototypes of these weapons using their existing military and government research labs. The Telecommunications Research Establishment built early-warning Radar, critical to Britain’s survival during the Battle of Britain, and electronic warfare to protect British bombers over Germany. The Admiralty Research Lab built Sonar and anti-submarine warfare systems. The Royal Aircraft Establishment was developing jet fighters. The labs then contracted with British companies to manufacture the weapons in volume. British government labs viewed their universities as a source of talent, but they had no role in weapons development.

Under Churchill, Professor Lindemann influenced which projects received funding and which were sidelined. Lindemann’s WWI experience as a researcher and test pilot on the staff of the Royal Aircraft Factory at Farnborough gave him confidence in the competence of British military research and development labs. His top-down, centralized approach with weapons development primarily in government research labs shaped British innovation during WW II – and led to its demise post-war.

The Americans – University Weapons Labs

Unlike Britain, the U.S. lacked a science advisor. It wasn’t until June 1940, that Vannevar Bush, ex-MIT dean of engineering, and President of the Carnegie Institute told President Franklin Roosevelt that World War II would be the first war won or lost on the basis of advanced technology electronics, radar, physics problems, etc.

Unlike Lindemann, Bush had a 20-year-long contentious history with the U.S. Navy and a dim view of government-led R&D. Bush contended that the government research labs were slow and second rate. He convinced the President that while the Army and Navy ought to be in charge of making conventional weapons – planes, ships, tanks, etc. — scientists from academia could develop better advanced technology weapons and deliver them faster than Army and Navy research labs. And he argued the only way the scientists could be productive was if they worked in a university setting in civilian-run weapons labs run by university professors.

To the surprise of the Army and Navy Service chiefs, Roosevelt agreed to let Bush build exactly that organization to coordinate and fund all advanced weapons research.

(While Bush had no prior relationship with the President, Roosevelt had been the Assistant Secretary of the Navy during World War I and like Bush had seen first-hand its dysfunction. Over the next four years they worked well together. Unlike Churchill, Roosevelt had little interest in science and accepted Bush’s opinions on the direction of U.S. technology programs, giving Bush sweeping authority.)

In 1941, Bush upped the game by convincing the President that in addition to research, development, acquisition and deployment of these weapons also ought to be done by professors in universities. There they would be tasked to develop military weapons systems and solve military problems to defeat Germany and Japan. (The weapons were then manufactured in volume by U.S. corporations Western Electric, GE, RCA, Dupont, Monsanto, Kodak, Zenith, Westinghouse, Remington Rand and Sylvania.) To do this Bush created the Office of Scientific Research and Development (OSR&D).

OSR&D headquarters divided the wartime work into 19 “divisions,” 5 “committees,” and 2 “panels,” each solving a unique part of the military war effort. There were no formal requirements.

Staff at OSRD worked with their military liaisons to understand what the most important military problems were and then each OSR&D division came up with solutions. These efforts spanned an enormous range of tasks – the development of advanced electronics, radar, rockets, sonar, new weapons like the proximity fuse, Napalm, the Bazooka and new drugs such as penicillin, cures for malaria, chemical warfare, and nuclear weapons.

Each division was run by a professor hand-picked by Bush. And they were located in universities – MIT, Harvard, Johns Hopkins, Caltech, Columbia and the University of Chicago all ran major weapons systems programs. Nearly 10,000 scientists and engineers, professors and their grad students received draft deferments to work in these university labs.

(Prior to World War 2, science in U.S. universities was primarily funded by companies interested in specific research projects. But funding for basic research came from two non-profits: The Rockefeller Foundation and the Carnegie Institution. In his role as President of the Carnegie Institution Bush got to know (and fund!) every top university scientist in the U.S. As head of Physics at Oxford, Lindemann viewed other academics as competitors.)

Americans – Unlimited Dollars

What changed U.S. universities, and the world forever, was government money. Lots of it. Prior to WWII most advanced technology research in the U.S. was done in corporate innovation labs (GE, AT&T, Dupont, RCA, Westinghouse, NCR, Monsanto, Kodak, IBM, et al.) Universities had no government funding (except for agriculture) for research. Academic research had been funded by non-profits, mostly the Rockefeller and Carnegie foundations and industry. Now, for the first time, U.S. universities were getting more money than they had ever seen. Between 1941 and 1945, OSR&D gave $9 billion (in 2025 dollars) to the top U.S. research universities. This made universities full partners in wartime research, not just talent pools for government projects as was the case in Britain.

The British – Wartime Constraints

Wartime Britain had very different constraints. First, England was under daily attack. They were being bombed by air and blockaded by submarines, so it was logical that they focused on a smaller set of high-priority projects to counter these threats. Second, the country was teetering on bankruptcy. It couldn’t afford the broad and deep investments that the U.S. made. (Illustrated by their abandonment of their nuclear weapons programs when they realized how much it would cost to turn the research into industrial scale engineering.) This meant that many other areas of innovation—such as early computing and nuclear research—were underfunded compared to their American counterparts.

Post War – Britain

Churchill was voted out of office in 1945. With him went Professor Lindemann and the coordination of British science and engineering. Britain would be without a science advisor until 1951-55 when Churchill returned for a second term and brought back Lindemann with him.

The end of the war led to extreme downsizing of the British military including severe cuts to all the government labs that had developed Radar, electronics, computing, etc.

With post-war Britain financially exhausted, post-war austerity limited its ability to invest in large-scale innovation. There were no post-war plans for government follow-on investments. The differing economic realities of the U.S. and Britain also played a key role in shaping their innovation systems. The United States had an enormous industrial base, abundant capital, and a large domestic market, which enabled large-scale investment in research and development. In Britain, a socialist government came to power. Churchill’s successor, Labor’s Clement Attlee, dissolved the British empire, nationalized banking, power and light, transport, and iron and steel, all which reduced competition and slowed technological progress.

While British research institutions like Cambridge and Oxford remained leaders in theoretical science, they struggled to scale and commercialize their breakthroughs. For instance Alan Turing’s and Tommy Flower’s pioneering work on computing at Bletchley Park didn’t turn into a thriving British computing industry—unlike in the U.S., where companies like ERA, Univac, NCR and IBM built on their wartime work.

Without the same level of government support for dual-use technologies or commercialization, and with private capital absent for new businesses, Britain’s post-war innovation ecosystem never took off.

Post War – The U.S.

Meanwhile in the U.S. universities and companies realized that the wartime government funding for research had been an amazing accelerator for science, engineering, and medicine. Everyone, including Congress, agreed that the U.S. government should continue to play a large role in continuing it. In 1945, Vannevar Bush published a report “Science, The Endless Frontier” advocating for government funding of basic research in universities, colleges, and research institutes. Congress argued on how to best organize federal support of science.

By the end of the war, OSR&D funding had taken technologies that had been just research papers or considered impossible to build at scale and made them commercially viable – computers, rockets, radar, Teflon, synthetic fibers, nuclear power, etc. Innovation clusters formed around universities like MIT and Harvard which had received large amounts of OSR&D funding (MIT’s Radiation Lab or “Rad Lab” employed 3,500 civilians during WWII and developed and built 100 radar systems deployed in theater,) or around professors who ran one of the OSR&D divisions – like Fred Terman at Stanford.

When the war ended, the Atomic Energy Commission spun out of the Manhattan Project in 1946 and the military services took back advanced weapons development. In 1950 Congress set up the National Science Foundation to fund all basic science in the U.S. (except for Life Sciences, a role the new National Institutes of Health would assume.) Eight years later DARPA and NASA would also form as federal research agencies.

Ironically, Vannevar Bush’s influence would decline even faster than Professor Lindemann’s. When President Roosevelt died in April 1945 and Secretary of War Stimson retired in September 1945, all the knives came out from the military leadership Bush had bypassed in the war. His arguments on how to reorganize OSR&D made more enemies in Congress. By 1948 Bush had retired from government service. He would never again play a role in the U.S. government.

Divergent Legacies

Britain’s focused, centralized model using government research labs was created in a struggle for short-term survival. They achieved brilliant breakthroughs but lacked the scale, integration and capital needed to dominate in the post-war world.

The U.S. built a decentralized, collaborative ecosystem, one that tightly integrated massive government funding of universities for research and prototypes while private industry built the solutions in volume.

A key component of this U.S. research ecosystem was the genius of the indirect cost reimbursement system. Not only did the U.S. fund researchers in universities by paying the cost of their salaries, the U.S. gave universities money for the researchers facilities and administration. This was the secret sauce that allowed U.S. universities to build world-class labs for cutting-edge research that were the envy of the world. Scientists flocked to the U.S. causing other countries to complain of a “brain drain.”

Today, U.S. universities license 3,000 patents, 3,200 copyrights and 1,600 other licenses to technology startups and existing companies. Collectively, they spin out over 1,100 science-based startups each year, which lead to countless products and tens of thousands of new jobs. This university/government ecosystem became the blueprint for modern innovation ecosystems for other countries.

Summary

By the end of the war, the U.S. and British innovation systems had produced radically different outcomes. Both systems were influenced by the experience and personalities of their nations science advisor.

Britain remained a leader in theoretical science and defense technology, but its socialist government economic policies led to its failure to commercialize wartime innovations.

The U.S. emerged as the global leader in science and technology, with innovations like electronics, microwaves, computing, and nuclear power driving its post-war economic boom.

The university-industry-government partnership became the foundation of Silicon Valley, the aerospace sector, and the biotechnology industry.

Today, China’s leadership has spent the last three decades investing heavily to surpass the U.S. in science and technology.

In 2025, with the abandonment of U.S. government support for university research, the long run of U.S. dominance in science may be over. Others will lead.

量子计算 – 最新进展 || Quantum Computing – An Update

2024-10-22T13:00:16+00:00

量子技术生态系统概述与量子计算机构建进展总结

2022年3月，我曾对量子技术生态系统进行过描述。如今，我认为这是一个回顾量子计算机构建进展并进一步解释基础概念的好时机。量子技术主要应用于三个截然不同的市场：量子计算、量子通信和量子传感与计量。若你对“量子比特（qubit）”与“cueball”（一种桌球）的区别不清楚（比如我之前也不清楚），可参考此处教程。

量子计算机构建的挑战与进展

物理量子比特的进展

截至2024年，已有七种不同的方法被用于构建物理量子比特，其中超导量子比特、光子量子比特、冷原子和捕获离子是最成熟的方案。其他方法包括量子点、金刚石空位中心和拓扑量子比特。这些方法均在逐步提升物理量子比特的数量，但目前尚无明确的技术路线能主导逻辑量子比特的构建。

为何需要构建量子计算机？

量子计算机并非在所有应用中都比经典计算机更快。它们在特定算法（如Shor算法、Grover算法）上具有显著优势。例如：

Shor算法：可破解基于大整数因数分解的公钥加密系统（如RSA），这对当前的网络安全构成威胁。
Grover算法：可加速无结构数据的搜索，但需数千个逻辑量子比特才能体现优势。

逻辑量子比特与物理量子比特的关系

逻辑量子比特是构建量子计算机的核心，需由大量物理量子比特组成以实现纠错。
错误率是关键指标。当前最先进的量子比特错误率通常在1%至0.1%之间。若使用表面码（Surface Code）纠错，每个逻辑量子比特需要数百个物理量子比特。
Shor算法需要约2000个逻辑量子比特（而非5000个），若物理错误率为0.3%（如谷歌当前的量子处理器），则需约2000万物理量子比特才能实现足够的纠错能力。这一数字仍远高于目前可实现的1000个物理量子比特。

关键技术与材料科学的作用

材料工程的重要性

量子比特的稳定性和一致性依赖于材料科学的进步。例如，超导量子比特需要均匀的厚度、可控的晶粒尺寸和表面粗糙度。
先进的半导体制造技术（如300mm工艺）是实现大规模量子比特生产的必要条件，可提供更精确的结构、清洁的界面和可控的材料特性。

纠错码与错误率

表面码是当前最常用的纠错方案，其纠错效率与物理量子比特的错误率密切相关。
逻辑量子比特错误率公式为：
P_L = 0.03 × (p/p_th)^((d+1)/2)
其中：
- p_th ~ 0.6% 是表面码的错误率阈值，
- p 是物理量子比特的错误率，
- d 是纠错码的大小，
- N = (2d – 1)^2 表示所需物理量子比特数量。

区域研究联盟与资本投入

美国区域研究联盟

伊利诺伊州通过“量子与微电子公园（IQMP）”计划，联合DARPA的“量子证明地（QPG）”项目，推动量子技术国家中心建设，已获得5亿美元资金支持。
科罗拉多州的“Elevate Quantum”联盟覆盖科罗拉多、新墨西哥和怀俄明州，获得1.27亿美元联邦与州政府资助。

风险投资与市场情绪

量子计算领域吸引了大量资本投入，但目前无公司接近实现实际应用。投资热潮主要源于错失恐惧症（FOMO）和对“量子优势”的期待。
一些公司通过金融工程（如上市融资）吸引散户投资者，但量子公司的上市表现普遍不佳，已有两家面临退市风险。

未来展望与核心问题

技术突破的可能

若未来出现更稳定的物理量子比特（如无需复杂纠错的新型技术），则所需的纠错量子比特数量将大幅减少，从而加速量子计算机的实现。
材料工程和大规模互联技术（如光学链接）仍是关键瓶颈。

行业现状与挑战

当前量子计算领域存在大量公司、投资和工程进展，但技术路线尚未统一。
逻辑量子比特的错误率和物理量子比特的规模化生产是决定量子计算机实用性的核心问题。

总结：量子计算机的“胜负手”

材料科学的进步将直接降低错误率，从而减少所需物理量子比特数量。
纠错码的选择和优化是实现逻辑量子比特的关键。
Shor算法的潜在威胁（如破解公钥加密）推动了后量子密码学（Post-Quantum Cryptography）的发展。
量子计算的最终成功取决于能否在硬件与算法上实现协同突破，而目前仍处于早期探索阶段。

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2022年3月我写了一篇关于量子技术生态系统的内容。我认为现在是时候检查一下构建量子计算机的进展，并更详细地解释一些基础知识。

再次提醒，量子技术被应用于三个截然不同且明确的市场：量子计算、量子通信和量子传感与计量。如果你不知道量子比特和cueball之间的区别，（我也不懂）请阅读此处的教程。

总结 –

在制造物理量子比特方面取得了渐进的技术进展

目前在构建量子比特的七种方法之间还没有明确的胜者

提醒 – 为什么要建造量子计算机？

你需要多少物理量子比特？

材料科学的进步将降低错误率

区域研究联盟

风险投资的FOMO和金融工程

本文大量讨论了量子比特。作为提醒，量子比特是量子比特的缩写。它是量子计算的元素，利用叠加原理（量子粒子可以同时处于多种可能状态）通过四种方法之一来编码信息：自旋、捕获原子和离子、光子或超导电路。

渐进的技术进展

截至2024年，有七种不同的方法被探索用于构建量子计算机的物理量子比特。目前最成熟的是超导、光子、冷原子和捕获离子。其他方法包括量子点、金刚石中心的氮空位和拓扑。所有这些方法都逐步增加了物理量子比特的数量。

这些多种方法正在被尝试，因为尚未达成构建逻辑量子比特的最佳路径的共识。每家公司都相信其技术方法将引领其走向可扩展的实用量子计算机。

目前，每家公司都夸大其实际运行的物理量子比特数量。仅凭这个数字本身并不能说明向实用量子计算机进展的多少。真正重要的是逻辑量子比特的数量。

提醒 – 为什么要建造量子计算机？

关于量子计算机的一个关键误解是它们在所有应用中都比当前的经典计算机更快。这是错误的。它们仅在一小部分专门算法上更快。这些特殊的算法是量子计算机潜在价值的来源。例如，在量子计算机上运行Grover算法可以比经典计算机更快地搜索无结构数据。此外，理论上量子计算机在最小化/优化/模拟等方面非常出色。例如，优化复杂供应链、形成复杂分子的能量状态、金融模型（比如对冲基金）等。

有可能将量子计算机视为“计算流程的加速器”——就像今天的GPU一样。此外，多家公司押注“算法性”量子比特（比“噪声性”好但比“纠错性”差）可能足以在模拟物理系统等流程中提供一些性能提升。这可能为早期的量子优势案例打开大门。

然而，尽管这些算法有一天可能具有商业潜力，但目前还没有人找到任何能够彻底改变企业或军事应用的用例。除了一个例外——这个例外让人们彻夜难眠。它是Shor算法用于整数因数分解——一种支撑现有公钥加密系统的算法。

当前公钥加密系统的安全性基于这样一个假设：用千位以上的数字破解这些密钥在实践中是不可能的。它需要分解大素数（例如RSA）或椭圆曲线（例如ECDSA、ECDH）或有限域（DSA），这些在任何类型的经典计算机上都无法完成，无论其规模如何。Shor因数分解算法可以在量子计算机上破解这些代码。这就是为什么NIST正在鼓励转向后量子/抗量子代码。

你需要多少物理量子比特来实现一个逻辑量子比特？

要创建能够运行这些专门应用的量子计算机，需要数万个逻辑量子比特。每个逻辑量子比特由许多物理量子比特构成。问题是，需要多少物理量子比特？这就是问题所在。

不同于微处理器中的传统晶体管一旦制造就始终正常工作，量子比特是不稳定的且易碎的。它们可能因噪声、退相干（当量子比特与环境交互时）、串扰（当量子比特与物理相邻的量子比特交互时）和构成量子门的材料中的缺陷而脱离量子态。当这种情况发生时，量子计算中会出现错误。因此，为了纠正这些错误，你需要大量的物理量子比特来构成一个逻辑量子比特。

那么如何确定需要多少物理量子比特？

你从打算运行的算法开始。

不同的量子算法需要不同数量的量子比特。某些算法（例如Shor的素数因数分解算法）可能需要>5000个逻辑量子比特（所需数量可能因研究人员找到使用更少逻辑量子比特实现算法的方法而减少。）

其他算法（例如Grover算法）在简单的演示中需要较少的逻辑量子比特，但需要数千个逻辑量子比特才能在经典计算机上运行线性搜索的算法中获得优势。（参见此处、此处和此处以了解其他量子算法。）

测量物理量子比特的错误率。

因此，要制造一个逻辑量子比特所需的物理量子比特数量首先需要计算物理量子比特的错误率（门错误率、相干时间等）。不同的技术方法（超导、光子、冷原子等）具有不同的错误率，且错误的原因与底层技术相关。

目前最先进的量子比特的错误率通常在1%到0.1%之间。这意味着平均每个100到1000个量子门操作中会出现一个错误。系统性能受限于最差的10%的量子比特。

选择量子纠错码

为了从容易出错的物理量子比特中恢复，量子纠错码将量子信息编码到一组更大的物理量子比特中，这些量子比特对错误具有抵抗力。表面码是最常被提议的纠错码。实用的表面码使用数百个物理量子比特来创建一个逻辑量子比特。随着物理量子比特错误率的降低，量子纠错码的效率会提高。当错误率超过某个阈值时，纠错失败，逻辑量子比特将与物理量子比特一样容易出错。

数学计算

使用Shor算法在0.3%错误率（Google当前量子处理器错误率）下分解2048位数字：

假设我们需要约2000个（而非5000个）逻辑量子比特来运行Shor算法。

在0.3%的错误率下，表面纠错码需要约10000个物理量子比特来编码一个逻辑量子比特，以达到10^-10的逻辑量子比特错误率。

用于Shor算法的物理量子比特数量=10000 x 2000=2000万

仍然与我们目前能够实现的1000个量子比特相距甚远。

对于那些感兴趣的人……

逻辑量子比特错误率P_L为P_L=0.03 (p/p_th)^((d+1)/2)，其中p_th~0.6%是表面码的错误率阈值，p是物理量子比特错误率，d是码的大小，与物理量子比特数量相关：N=(2d-1)^2。

参见下图，以参考P_L与N在不同物理量子比特错误率下的关系。

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In March 2022 I wrote a description of the Quantum Technology Ecosystem. I thought this would be a good time to check in on the progress of building a quantum computer and explain more of the basics.

Just as a reminder, Quantum technologies are used in three very different and distinct markets: Quantum Computing, Quantum Communications and Quantum Sensing and Metrology. If you don’t know the difference between a qubit and cueball, (I didn’t) read the tutorial here.

Summary –

There’s been incremental technical progress in making physical qubits

There is no clear winner yet between the seven approaches in building qubits

Reminder – why build a quantum computer?

How many physical qubits do you need?

Advances in materials science will drive down error rates

Regional research consortiums

Venture capital investment FOMO and financial engineering

We talk a lot about qubits in this post. As a reminder a qubit – is short for a quantum bit. It is a quantum computing element that leverages the principle of superposition (that quantum particles can exist in many possible states at the same time) to encode information via one of four methods: spin, trapped atoms and ions, photons, or superconducting circuits.

Incremental Technical Progress

As of 2024 there are seven different approaches being explored to build physical qubits for a quantum computer. The most mature currently are Superconducting, Photonics, Cold Atoms, Trapped Ions. Other approaches include Quantum Dots, Nitrogen Vacancy in Diamond Centers, and Topological. All these approaches have incrementally increased the number of physical qubits.

These multiple approaches are being tried, as there is no consensus to the best path to building logical qubits. Each company believes that their technology approach will lead them to a path to scale to a working quantum computer.

Every company currently hypes the number of physical qubits they have working. By itself this is a meaningless number to indicate progress to a working quantum computer. What matters is the number of logical qubits.

Reminder – Why Build a Quantum Computer?

One of the key misunderstandings about quantum computers is that they are faster than current classical computers on all applications. That’s wrong. They are not. They are faster on a small set of specialized algorithms. These special algorithms are what make quantum computers potentially valuable. For example, running Grover’s algorithm on a quantum computer can search unstructured data faster than a classical computer. Further, quantum computers are theoretically very good at minimization / optimizations /simulations…think optimizing complex supply chains, energy states to form complex molecules, financial models (looking at you hedge funds,) etc.

It’s possible that quantum computers will be treated as “accelerators” to the overall compute workflows – much like GPUs today. In addition, several companies are betting that “algorithmic” qubits (better than “noisy” but worse than “error-corrected”) may be sufficient to provide some incremental performance to workflows lie simulating physical systems. This potentially opens the door for earlier cases of quantum advantage.

However, while all of these algorithms might have commercial potential one day, no one has yet to come up with a use for them that would radically transform any business or military application. Except for one – and that one keeps people awake at night. It’s Shor’s algorithm for integer factorization – an algorithm that underlies much of existing public cryptography systems.

The security of today’s public key cryptography systems rests on the assumption that breaking into those keys with a thousand or more digits is practically impossible. It requires factoring large prime numbers (e.g., RSA) or elliptic curve (e.g., ECDSA, ECDH) or finite fields (DSA) that can’t be done with any type of classic computer regardless of how large. Shor’s factorization algorithm can crack these codes if run on a Quantum Computer. This is why NIST has been encouraging the move to Post-Quantum / Quantum-Resistant Codes.

How many physical qubits do you need for one logical qubit?

Thousands of logical qubits are needed to create a quantum computer that can run these specialized applications. Each logical qubit is constructed out of many physical qubits. The question is, how many physical qubits are needed? Herein lies the problem.

Unlike traditional transistors in a microprocessor that once manufactured always work, qubits are unstable and fragile. They can pop out of a quantum state due to noise, decoherence (when a qubit interacts with the environment,) crosstalk (when a qubit interacts with a physically adjacent qubit,) and imperfections in the materials making up the quantum gates. When that happens errors will occur in quantum calculations. So to correct for those error you need lots of physical qubits to make one logical qubit.

So how do you figure out how many physical qubits you need?

You start with the algorithm you intend to run.

Different quantum algorithms require different numbers of qubits. Some algorithms (e.g., Shor’s prime factoring algorithm) may need >5,000 logical qubits (the number may turn out to be smaller as researchers think of how to use fewer logical qubits to implement the algorithm.)

Other algorithms (e.g., Grover’s algorithm) require fewer logical qubits for trivial demos but need 1000’s of logical qubits to see an advantage over linear search running on a classical computer. (See here, here and here for other quantum algorithms.)

Measure the physical qubit error rate.

Therefore, the number of physical qubits you need to make a single logical qubit starts by calculating the physical qubit error rate (gate error rates, coherence times, etc.) Different technical approaches (superconducting, photonics, cold atoms, etc.) have different error rates and causes of errors unique to the underlying technology.

Current state-of-the-art quantum qubits have error rates that are typically in the range of 1% to 0.1%. This means that on average one out of every 100 to one out of 1000 quantum gate operations will result in an error. System performance is limited by the worst 10% of the qubits.

Choose a quantum error correction code

To recover from the error prone physical qubits, quantum error correction encodes the quantum information into a larger set of physical qubits that are resilient to errors. Surface Codes is the most commonly proposed error correction code. A practical surface code uses hundreds of physical qubits to create a logical qubit. Quantum error correction codes get more efficient the lower the error rates of the physical qubits. When errors rise above a certain threshold, error correction fails, and the logical qubit becomes as error prone as the physical qubits.

The Math

To factor a 2048-bit number using Shor’s algorithm with a 10-2 (1% per physical qubit) error rate:

Assume we need ~5,000 logical qubits

With an error rate of 1% the surface error correction code requires ~ 500 physical qubits required to encode one logical qubit. (The number of physical qubits required to encode one logical qubit using the Surface Code depends on the error rate.)

Physical cubits needed for Shor’s algorithm= 500 x 5,000 = 2.5 million

If you could reduce the error rate by a factor of 10 – to 10-3 (0.1% per physical qubit,)

Because of the lower error rate, the surface code would only need ~ 100 physical qubits to encode one logical qubit

Physical cubits needed for Shor’s algorithm= 100 x 5,000 = 500 thousand

In reality there another 10% or so of ancillary physical bits needed for overhead. And no one yet knows the error rate in wiring multiple logical bits together via optical links or other technologies.

(One caveat to the math above. It assumes that every technical approach (Superconducting, Photonics, Cold Atoms, Trapped Ions, et al) will require each physical qubit to have hundreds of bits of error correction to make a logical qubit. There is always a chance a breakthrough could create physical qubits that are inherently stable, and the number of error correction qubits needed drops substantially. If that happens, the math changes dramatically for the better and quantum computing becomes much closer.)

Today, the best anyone has done is to create 1,000 physical qubits.

We have a ways to go.

Advances in materials science will drive down error rates

As seen by the math above, regardless of the technology in creating physical qubits (Superconducting, Photonics, Cold Atoms, Trapped Ions, et al.) reducing errors in qubits can have a dramatic effect on how quickly a quantum computer can be built. The lower the physical qubit error rate, the fewer physical qubits needed in each logical qubit.

The key to this is materials engineering. To make a system of 100s of thousands of qubits work the qubits need to be uniform and reproducible. For example, decoherence errors are caused by defects in the materials used to make the qubits. For superconducting qubits that requires uniform thickness, controlled grain size, and roughness. Other technologies require low loss, and uniformity. All of the approaches to building a quantum computer require engineering exotic materials at the atomic level – resonators using tantalum on silicon, Josephson junctions built out of magnesium diboride, transition-edge sensors, Superconducting Nanowire Single Photon Detectors, etc.

Materials engineering is also critical in packaging these qubits (whether it’s superconducting or conventional packaging) and to interconnect 100s of thousands of qubits, potentially with optical links. Today, most of the qubits being made are on legacy 200mm or older technology in hand-crafted processes. To produce qubits at scale, modern 300mm semiconductor technology and equipment will be required to create better defined structures, clean interfaces, and well-defined materials. There is an opportunity to engineer and build better fidelity qubits with the most advanced semiconductor fabrication systems so the path from R&D to high volume manufacturing is fast and seamless.

There are likely only a handful of companies on the planet that can fabricate these qubits at scale.

Regional research consortiums

Two U.S. states; Illinois and Colorado are vying to be the center of advanced quantum research.

Illinois Quantum and Microelectronics Park (IQMP)

Illinois has announced the Illinois Quantum and Microelectronics Park initiative, in collaboration with DARPA’s Quantum Proving Ground (QPG) program, to establish a national hub for quantum technologies. The State approved $500M for a “Quantum Campus” and has received $140M+ from DARPA with the state of Illinois matching those dollars.

Elevate Quantum

Elevate Quantum is the quantum tech hub for Colorado, New Mexico, and Wyoming. The consortium was awarded $127m from the Federal and State Governments – $40.5 million from the Economic Development Administration (part of the Department of Commerce) and $77m from the State of Colorado and $10m from the State of New Mexico.

(The U.S. has a National Quantum Initiative (NQI) to coordinate quantum activities across the entire government see here.)

Venture capital investment, FOMO, and financial engineering

Venture capital has poured billions of dollars into quantum computing, quantum sensors, quantum networking and quantum tools companies.

However, regardless of the amount of money raised, corporate hype, pr spin, press releases, public offerings, no company is remotely close to having a quantum computer or even being close to run any commercial application substantively faster than on a classical computer.

So why all the investment in this area?

FOMO – Fear Of Missing Out. Quantum is a hot topic. This U.S. government has declared quantum of national interest. If you’re a deep tech investor and you don’t have one of these companies in your portfolio it looks like you’re out of step.

It’s confusing. The possible technical approaches to creating a quantum computer – Superconducting, Photonics, Cold Atoms, Trapped Ions, Quantum Dots, Nitrogen Vacancy in Diamond Centers, and Topological – create a swarm of confusing claims. And unless you or your staff are well versed in the area, it’s easy to fall prey to the company with the best slide deck.

Financial engineering. Outsiders confuse a successful venture investment with companies that generate lots of revenue and profit. That’s not always true.

Often, companies in a “hot space” (like quantum) can go public and sell shares to retail investors who have almost no knowledge of the space other than the buzzword. If the stock price can stay high for 6 months the investors can sell their shares and make a pile of money regardless of what happens to the company.

The track record so far of quantum companies who have gone public is pretty dismal. Two of them are on the verge of being delisted.

Here are some simple questions to ask companies building quantum computers:

What is their current error rates?

What error correction code will they use?

Given their current error rates, how many physical qubits are needed to build one logical qubit?

How will they build and interconnect the number of physical qubits at scale?

What number of qubits do they think is need to run Shor’s algorithm to factor 2048 bits.

How will the computer be programmed? What are the software complexities?

What are the physical specs – unique hardware needed (dilution cryostats, et al) power required, connectivity, etc.

Lessons Learned

Lots of companies

Lots of investment

Great engineering occurring

Improvements in quantum algorithms may add as much (or more) to quantum computing performance as hardware improvements

The winners will be the one who master material engineering and interconnects

Jury is still out on all bets

Update: the kind folks at Applied Materials pointed me to the original 2012 Surface Codes paper. They pointed out that the math should look more like:

To factor a 2048-bit number using Shor’s algorithm with a 0.3% error rate (Google’s current quantum processor error rate)

Assume we need ~ 2,000 (not 5,000) logical qubits to run Shor’s algorithm.

With an error rate of 0.3% the surface error correction code requires ~10 thousand physical qubits to encode one logical qubit to achieve 10^-10 logical qubit error rate.

Physical cubits needed for Shor’s algorithm= 10,000 x 2,000 = 20 million

Still pretty far away from the 1,000 qubits we currently can achieve.

For those so inclined…

The logical qubit error rate P_L is P_L = 0.03 (p/p_th)^((d+1)/2), where p_th~ 0.6% is the error rate threshold for surface codes, p the physical qubit error rate, and d is the size of the code, which is related to the number of the physical qubits: N = (2d – 1)^2.

See the plot below for P_L versus Nfor different physical qubit error rate for reference.

破坏者如何威胁创新以及应如何应对 || How Saboteurs Threaten Innovation–and What to Do About It

2024-10-08T14:25:00+00:00

创新者的生存指南：如何应对巨头的阻挠

引言

本文首次发表于《First Round Review》。文章以Andy Grove的名言“只有警觉的创新者才能生存”为引，通过两个真实案例（Rohan和Jared）揭示了创新者在面对传统企业或机构时可能遭遇的阻挠策略，并总结了应对方法。

创新者面临的挑战

外部阻挠
- 巨头企业通过专利诉讼（如Rohan的初创公司被500强企业起诉）或法律手段（如向政府机构提交投诉）打压新进入者。
- 利用监管壁垒（如FCC、SEC等）限制创新者的市场准入。
- 通过职业风险制造（如指责员工窃取知识产权）或虚假基准测试（如组建“绿色洗白”组织）削弱创新者可信度。
内部阻挠
- 企业内部的管理层或研发部门可能因担心预算流失、权威受威胁而抵制创新。
- 政府机构中，国会工作人员、议员和游说者也可能成为阻碍，因创新威胁其利益或选区就业。

巨头的常见阻挠手段

制造恐惧：将创新视为对现有投资的威胁，强调转型成本或用户反对。
虚假创新：通过内部创新项目（如成立委员会）营造“创新氛围”，但实际阻碍进展。
资源封锁：限制关键资源（如材料、人才、法律支持）的获取。
标准控制：通过制定行业或政府标准实现“锁定”（lock-in）。
法律与政治干预：
- 利用政府调查（如Inspector General）或司法诉讼消耗创新者资金。
- 通过游说影响政策制定，如职业许可、补贴或关税保护。
- 用虚假市场数据误导决策者，如“市场太小”或“产品不成熟”。

创新者的生存策略

知己知彼
- 明确对手的“作战地图”：了解资金流向、决策者影响力及潜在阻挠者动机。
- 通过事实隔离或转化对手（如将其变为支持者）。
构建核心竞争力
- 开发最小可行产品（MVP），证明技术、管理和运营能力。
- 强调渐进式创新，逐步积累影响力，而非直接挑战巨头。
战略市场选择
- 优先进入监管薄弱的市场（如Craigslist对报纸、Netflix对录像带租赁店）。
- 通过联盟合作突破巨头垄断（如Palantir与情报机构合作）。
应对规模化后的挑战
- 在获得规模和融资后，主动出击：雇佣游说团队或联合行业伙伴形成政治影响力。
- 通过专利防御（如购买可能被侵权的专利）保护自身。

结语

Rohan通过收购专利成功化解危机，而Jared仍在努力推动内部变革。创新者需警惕巨头的多维阻挠，提前布局并灵活应对。你是否也有应对巨头的有效经验？

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本文最初发表于First Round Review。 “只有警觉者才能生存” 安迪·格鲁夫 – 英特尔CEO（1987-1998）我刚刚和一位前学生罗汉进行了一次紧急的“我们今天能见面吗？”的咖啡会谈。他的初创公司刚被一家财富500强公司发出了专利侵权通知。“我的律师说，仅为了调查这一诉讼，防御成本就可能高达50万美元，如果进入审判阶段，甚至可能需要数百万美元。你有什么建议吗？” 同一天，我收到了朋友贾雷德发来的短信。他正在国防部内部运行一个颠覆性创新组织。他刚刚得知，他们的现有研发组织已经说服领导层不需要任何来自初创公司或规模企业的外部帮助。唉…… 罗汉和贾雷德都学到了三个宝贵的教训：只有警觉者才能生存（正如安迪·格鲁夫所说）如果你不为谁想杀你而彻夜难眠，那你迟早会死。最好的战斗就是你能够避免的战斗。这提醒我们，创新者需要更好地准备应对所有可能的 incumbents（现有企业）破坏创新的方式。创新者常常假设他们的组织和行业会欢迎新想法、新运营概念和新公司。不幸的是，现实世界并不像商学院教材那样展开。无论你是新进入者挑战现有竞争者，还是在大公司内部努力保持灵活，你都需要了解 incumbents（现有企业）会如何阻碍你，以及你可以采取哪些措施应对。创业者与破坏者在公司或政府机构之外的初创公司和规模企业希望进入现有市场，或取代现有供应商。或者，如果他们拥有颠覆性技术或商业模式，他们希望创造新的能力或运营概念——甚至创造一个全新的市场。正如我的学生罗汉所痛苦地学到的，现有供应商和现有承包商希望消灭这些新进入者。他们没有打算放弃收入、利润和工作。（在政府领域，破坏者可能还包括国会工作人员、议员和游说者，因为这些新进入者威胁到了竞选捐款和地方选区的工作。）内部创业者与破坏者在公司或政府机构内部的创新者希望改进现有的组织，使其更快、更有效、更盈利、更能应对竞争威胁或对手。他们可能在创造或倡导现有事物的更好版本，或者试图创造从未存在过的东西。在这些商业或政府组织中，存在一些希望扼杀创新的人（正如我的朋友贾雷德最近发现的）。这些人可能是现有项目的管理者，或是感觉受到潜在预算和权力损失威胁的工程或研发部门负责人。通常，预算和人员编制是零和游戏，因此新举措会威胁到现状。现有组织的领导者往往更关注自己部门或项目的成功，而非整个组织的整体利益。有时，某些个人的利益与现有供应商的利益一致，而非公司或政府机构的整体利益。现有企业如何扼杀创新？罗汉和贾雷德各自遇到了一种创新破坏的形式。现有企业会使用各种方式来破坏和扼杀组织内部或新公司中的创新想法。大多数时候，创新者对此毫无察觉，而那些察觉到的人，比如罗汉和贾雷德，往往也没有应对计划。以下是我见过的最常见的破坏手段，以及一些应对和准备的建议。创始人和创新者应预期现有组织和公司会激烈捍卫自己的领域。在商业市场和政府机构中，现有企业扼杀创新的常见方式。制造职业恐惧（fear, uncertainty and doubt，即FUD）。将创新想法、产品或服务定位为采用或支持它的人的职业风险。强调现有遗留投资的风险，例如切换到新产品的成本，或突出那些会反对新产品的用户。声称现有的研发或工程组织已经在做这件事（或能做得更好/更便宜）。通过启动内部创新计划，利用现有员工和流程制造创新表演（innovation theater）。设立委员会和顾问小组来“研究”问题。任命那些代表现状的受人尊敬的成员。破坏内部创新计划的资金。声称你必须削减重要的项目x或y来为新计划提供资金。或者资助新想法的演示，然后“缓慢推进”其扩展预算。对合同赢家提起诉讼/抗议。利用专利作为武器。无论是否属实，提起专利侵权诉讼。试图无效现有专利，无论是否属实。声称员工从前雇主处窃取了知识产权。对内部创新者提起人力资源投诉，指控其偷工减料或违反规定。通过报告层级和控制替代方案的信息，将高级管理层与组织内的创新者隔离。反对快速采用新技术的结构和流程。将创新和执行视为相同的过程。对创新失败缺乏容忍度。不培养紧迫感的文化。不为创新者提供结构化的职业发展路径。锁定关键资源，如材料、组件、人员、律师事务所、分销渠道、合作伙伴，使其无法为创新团队或初创公司使用。控制行业/政府标准，以确保其成为现有企业的锁定机制。收购初创公司并关闭它或埋没其产品。通过说服人才创新努力不会成功，从创新组织或公司中挖走人才。影响“独立”的分析师和市场研究公司，通过“研究”合同证明市场太小。通过提前宣布从未发货的产品（即“蒸汽产品”）来混淆买家和高级管理层。捆绑产品（如微软Office）为商业客户签订长期锁定合同，或为政府项目签订独家合同（例如F-35）。现有企业如何扼杀初创公司在政府市场中的发展提起合同申诉或抗议，制造延迟，消耗新进入者资金。提起监察长（IG）投诉，声称创新者偷工减料、违反规定或从事非法雇佣和支出。如果可能，将这些监察长办公室武器化，针对创新者。通过为现有企业制定要求，同时为新进入者设置不必要的标准、障碍和文件，劫持采购系统。忽略调查替代供应商的要求，直接向现有企业发放合同。旋转门现象。政府项目主管和经理的隐性就业承诺，以及国会工作人员和议员的隐性就业承诺。游说。现有企业有专门的团队来塑造其产品的需求和预算，以及在华盛顿持续接触的团队。他们擅长管理POM、PPBE、参议院和众议院的军事委员会以及拨款委员会。为试图通过非官方渠道获得支持的创新者制造职业风险，惩罚与国会议员或其工作人员的非正式接触。创建专有接口利用安全清障（security clearances），延迟或拒绝访问所需的安全信息，甚至撤销你或你公司的清障。现有企业如何扼杀初创公司在商业市场中的发展。通过监管机构进行寻租（例如FCC、SEC、FTC、FAA、公用事业、出租车/保险委员会、学校董事会等）。利用政府法规阻止具有更创新商业模式的新进入者（或延迟他们，以便现有企业能够追赶）。通过地方、州和联邦法律进行寻租（例如职业许可、汽车经销商法、补助金、补贴或关税保护）。利用公共安全、缺乏质量或失业等论点，游说反对新进入者。通过法院进行寻租，以消耗初创公司的有限资金。通过专有接口进行寻租（例如John Deere拖拉机接口等）。毒害初创公司的融资来源。告诉风投现有企业已经掌控了市场。告诉政府资助者公司资金耗尽。法律回扣，如折扣、SPIF（销售促进计划）、共同广告（例如英特尔和微软在x86处理器/Windows上的合作）。向州检察长提起投诉，以消耗初创公司资源。创建虚假基准组织或绿色洗白组织，证明现有解决方案更好或新解决方案更差。创新者的生存清单我没有办法给罗汉或贾雷德提供任何能抵御现有企业所有可能行动的“万能钥匙”。然而，如果他们意识到现有企业不会欢迎他们，他们（以及你）可能会考虑以下建议来应对创新破坏者。在政府和商业市场中：绘制“作战力量”图谱。了解资金流动方式，谁控制预算、人员编制和组织架构。了解谁拥有政治、监管和领导影响力，以及他们在哪里运作。理解破坏者及其动机。将其收编。将他们转化为支持者——（这适用于怀疑者）。用事实将其孤立。让他们离开岗位（最好是晋升到其他领域）。组建一支反叛团队。包括技术专家、愿景家、支持者、盟友、代理人等。反叛运动会随着时间增长。避免公开贬低现有企业。不要说“他们不懂。”这会让他们感到尴尬、愤怒，并最终促使他们将你从市场上清除。避免过早的幻灯片演示。相反，专注于交付成功的最小可行产品（MVP），以展示可行性并满足已验证的需求。建立技术、管理和运营卓越的证据。构建最小可行产品（MVPs），以证明你理解客户或利益相关者的问题，拥有解决问题的资源，并有部署路径。如果可能，将你的创新描述为渐进式创新。指出随着时间推移，它会变得具有颠覆性。针对热情的客户群体，追求快速扩展，这些客户重视颠覆。例如，INDOPACOM；或Uber和Airbnb、特斯拉在商业世界中的案例。与那些认为你是打破现有企业市场垄断方式的大型合作伙伴结盟。例如，Palantir与情报机构，而非陆军；在工业界，IBM的i2与Textron Systems的Overwatch。在商业市场中：制定“低调”策略，以避免在资源有限的情况下吸引现有企业的诉讼、法规或法律行动。专利策略。建立防御性专利组合和策略？并考虑进攻性策略，购买你认为现有企业可能侵犯的专利。选择现有企业力量薄弱的早期市场并进行扩展。例如，选择没有国家或州游说影响力的市场。例如，Craigslist与报纸，Netflix与视频租赁连锁店，Amazon与书店等。当你获得规模并筹集到大额资金时，将战斗带入现有企业。此时的策略包括雇佣自己的游说者，或与行业同行合作，建立自己的影响力和政治行动团体。贾雷德仍在努力让高级管理层意识到时间正在流逝，现有的内部研发努力和预算分配不足以及时应对。他正在建立更大的变革联盟，但现状的惯性令人难以置信。罗汉的公司幸运地找到了解决方案。在数月的混乱（以及数万美元的花费）后，他们最终购买了一个已倒闭初创公司的专利组合，并利用它说服了财富500强公司放弃诉讼。我希望他们都能成功。你发现哪些方法在对抗现有企业时是有效的？
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This article first appeared in First Round Review.

“Only the Paranoid Survive”

Andy Grove – Intel CEO 1987-1998

I just had an urgent “can we meet today?” coffee with Rohan, an ex-student. His three-year-old startup had been slapped with a notice of patent infringement from a Fortune 500 company. “My lawyers said defending this suit could cost $500,000 just for discovery, and potentially millions of dollars if it goes to trial. Do you have any ideas?”

The same day, I got a text from Jared, a friend who’s running a disruptive innovation organization inside the Department of Defense. He just learned that their incumbent R&D organization has convinced leadership they don’t need any outside help from startups or scaleups.

Sigh….

Rohan and Jared have learned three valuable lessons:

Only the paranoid survive (as Andy Grove put it)

If you’re not losing sleep over who wants to kill you, you’re going to die.

The best fight is the one you can avoid.

It’s a reminder that innovators need to be better prepared about all the possible ways incumbents sabotage innovation.

Innovators often assume that their organizations and industry will welcome new ideas, operating concepts and new companies. Unfortunately, the world does not unfold like business school textbooks.

Whether you’re a new entrant taking on an established competitor or you’re trying to stay scrappy while operating within a bigger company here’s what you need to know about how incumbents will try to stand in your way – and what you can do about it.

Entrepreneurs versus Saboteurs

Startups and scaleups outside of companies or government agencies want to take share of an existing market, or displace existing vendors. Or if they have a disruptive technology or business model, they want to create a new capability or operating concept – even creating a new market.

As my student Rohan just painfully learned, the incumbent suppliers and existing contractors want to kill these new entrants. They have no intention of giving up revenue, profits and jobs. (In the government, additional saboteurs can include Congressional staffers, Congressman and lobbyists, as these new entrants threaten campaign contributions and jobs in local districts.)

Intrapreneurs versus Saboteurs

Innovators inside of companies or government agencies want to make their existing organization better, faster, more effective, more profitable, more responsive to competitive threats or to adversaries. They might be creating or advocating for a better version of something that exists. Or perhaps they are trying to create something disruptive that never existed before.

Inside these commercial or government organizations there are people who want to kill innovation (as my friend Jared just discovered). These can be managers of existing programs, or heads of engineering or R&D organizations who are feeling threatened by potential loss of budget and authority. Most often, budgets and headcount are zero-sum games so new initiatives threaten the status quo.

Leaders of existing organizations often focus on the success of their department or program rather than the overall good of the organization. And at times there are perverse incentives as some individuals are aligned with the interests of incumbent vendors rather than the overall good of the company or government agency.

How Do incumbents Kill Innovation?

Rohan and Jared were each dealing with one form of innovation sabotage. Incumbents use a variety of ways to sabotage and kill innovative ideas inside of organizations and outside new companies. And most of the time innovators have no idea what just hit them. And those that do – like Rohan and Jared – have no game plan in place to respond.

Here are the most common methods of sabotage that I’ve seen, followed by a few suggestions on how to prepare and defend against them.

Founders and Innovators should expect that existing organizations and companies will defend their turf – ferociously.

Common ways incumbents kill innovation in both commercial markets and government agencies.

Create career FUD (fear, uncertainty and doubt). Positioning the innovative idea, product or service as risk to the career of whoever adopts or champions it.

Emphasize the risk to existing legacy investments, like the cost of switching to the new product or service or highlighting the users who would object to it.

Claim that an existing R&D or engineering organization is already doing it (0r can do it better/cheaper.)

Create innovation theater by starting internal innovation programs with the existing staff and processes.

Set up committees and advisory boards to “study” the problem. Appoint well respected members of the status quo.

Poison funding for internal initiatives. Claiming that you’ll have to kill important program x or y to pay for the new initiative. Or funding the demo of the new idea and then “slow-walk” the budget for scale.

File Lawsuits/Protests against winners of contracts.

Use patents as a weapon. Filing patent infringement lawsuits – whether true or not. Try to invalidate existing patents – whether true or not.

Claim that employees have stolen IP from their previous employer.

File HR Complaints against internal intrapreneurs for cutting corners or breaking rules.

Isolate senior leadership from the innovators inside the organization via reporting hierarchy and controlling information about alternatives.

Object to structures and processes for the rapid adoption of new technologies. Treat innovation and execution as the same processes. Lack tolerance for failure at innovation. Do not cultivate a culture of urgency. Don’t offer a a structured career path for innovators.

Lock-up critical resources, like materials, components, people, law firms, distribution channels, partners and make them unavailable to innovation groups/startups.

Control industry/government standards to ensure that they are lock-in’s for incumbents.

Acquire a startup and shut it down or bury its product

Poach talent from an innovation organization or company by convincing talent that the innovation effort won’t go anywhere.

Influence “independent” analysts, market research firms with “research” contracts to prove that the market is too small.

Confuse buyers and senior leadership by preannouncing products or products that never ship – vaporware.

Bundle products (Microsoft Office)

Long term lock-in contracts for commercial customers or sole-source for government programs (e.g. F-35).

How incumbents kill startups in government markets

File contract appeals or protests, creating delays that burn cash for new entrants.

File Inspector General (IG) complaints, claiming innovators are cutting corners, breaking rules or engaging in illegal hiring and spending. If possible, capture these IG offices and weaponize them against innovators.

Hijack the acquisition system by creating requirements written for incumbents, while setting unnecessary standards, barriers and paperwork for new entrants. Ignore requirements to investigate alternate suppliers and issue contracts to the incumbents.

Revolving door. The implicit promise of jobs to government program executives and managers and the implicit promise of jobs to congressional staffers and congressmen.

Lobbying. Incumbents have dedicated staffs to shape requirements and budgets for their products, as well as dedicated staff for continual facetime in Washington. They are experts at managing the POM, PPBE, House and Senate Armed Services Committees and appropriations committees.

Create career risks for innovators attempting to gain support outside of official government channels, penalizing unofficial contacts with members of Congress or their staffs.

Create Proprietary interfaces

Weaponize security clearances, delaying or denying access to needed secure information, or even pulling your, or your company’s clearance.

How incumbents kill startups in commercial markets.

Rent Seeking via regulatory bodies (e.g. FCC, SEC, FTC, FAA, Public Utility, Taxi/Insurance Commissions, School Boards, etc, …) Use government regulation to keep out new entrants who have more innovative business models (or delay them so the incumbents can catch up).

Rent Seeking via local, state and federal laws (e.g. occupational licensing, car dealership laws, grants, subsidies, or tariff protection). Use arguments – from public safety, to lack of quality, or loss of jobs – to lobby against the new entrants.

Rent Seeking via courts to tie up and exhaust a startup’s limited financial resources.

Rent Seeking via proprietary interfaces (e.g. John Deere tractor interfaces…)

Poison startup financing sources. Telling VCs the incumbents already own the market. Tell Government funders the company is out of cash.

Legal kickbacks, like discounts, SPIFs, Co-advertising (e.g. Intel and Microsoft for x86 processors/Windows).

State Attorney General complaints to tie up startup resources

Create fake benchmark groups or greenwash groups to prove existing solution is better or that new solution is worse.

Innovators Survival Checklist

There is no magic bullet I could have offered Rohan or Jared to defend against every possible move an incumbent might make. However, if they had realized that incumbents wouldn’t welcome them, they (and you) might have considered the suggestions below on how to prepare for innovation saboteurs.

In both government and commercial markets:

Map the order of battle. Understand how the money flows and who controls budget, headcount and organizational design. Understand who has political, regulator, leadership influence and where they operate.

Understand saboteurs and their motivation. Co-opt them. Turn them into advocates – (this works with skeptics). Isolate them – with facts. Get them removed from their job (preferably by promoting them to another area.)

Build an insurgent team. A technologist, visionary, champion, allies, proxies, etc. The insurgency grows over time.

Avoid publicly belittling incumbents. Do not say, “They don’t get it.” That will embarrass, infuriate and ultimately motivate them to put you out of business.

Avoid early slideware. Instead focus on delivering successful minimal viable products which demonstrate feasibility and a validated requirement.

Build evidence of your technical, managerial and operational excellence. Build Minimal Viable Products (MVPs) that illustrate that you understand a customer or stakeholders problem, have the resources to solve it, and a path to deployment.

If possible, communicate and differentiate your innovation as incremental innovation. Point out that over time it’s disruptive.

Go after rapid scale of a passionate customer who values the disruption e.g. INDOPACOM; or Uber and Airbnb, Tesla in the commercial world

Ally with larger partners who see you as a way to break the incumbents’ lock on the market. i.e. Palantir and the intelligence agencies versus the Army and in industry, IBM’s i2, / Textron Systems Overwatch.

In commercial markets:

Figure out an “under the radar” strategy that doesn’t attract incumbents’ lawsuits, regulations or laws when you have limited resources to fight back.

Patent strategy. Build a defensive patent portfolio and strategy? And consider an offensive one, buying patents you think incumbents may infringe.

Pick early markets where the rent seekers are weakest and scale. For example, pick target markets with no national or state lobbying influence. i.e. Craigslist versus newspapers, Netflix versus video rental chains, Amazon versus bookstores, etc.

When you get scale and raise a large financing round, take the battle to the incumbents. Strategies at this stage include hiring your own lobbyists, or working with peers in your industry to build your own influence and political action groups.

Jared is still trying to get senior leadership to understand that the clock is ticking, and internal R&D efforts and current budget allocation won’t be sufficient or timely. He’s building a larger coalition for change, but the inertia for the status quo is overwhelming.

Rohan’s company was lucky. After months of scrambling (and tens of thousands of dollars), they ended up buying a patent portfolio from a defunct startup and were able to use it to convince the Fortune 500 company to drop their lawsuit.

I hope they both succeed.

What have you found to be effective in taking on incumbents?

产品市场契合度是什么样的？这。 || What Does Product Market Fit Sound Like? This.

2024-10-05T18:44:35+00:00

总结：

我接到一位前学生电话，询问“如何判断是否找到了产品市场契合点（Product-Market Fit）？”虽然有很多文字讨论这一话题，但几乎没有关于当时瞬间的录音。我回忆起曾保存了一段90秒、26年前的音频文件，因为正是在那个时刻，我意识到我们在Epiphany（可能指某个关键事件或公司）找到了产品市场契合点。音频中的说话者是Visio公司的首席财务官，该公司后来被微软收购。我将这段录音播放给她，认为它提供了一些清晰的见解。
音频链接
如果无法播放音频，请点击此处。

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我接到一位前学生的电话，问他“如何知道自己找到了产品市场契合点？” 关于这个问题已经写了很多文字，但没有实际的录音记录。我记得我曾保存了这段90秒、26年前的音频文件，因为那时我意识到我们在Epiphany找到了契合点。说话的人是当时一家名为Visio的公司的首席财务官，该公司后来被微软收购。我把这段录音播放给她听了，我认为这带来了一些清晰度。 https://steveblank.com/wp-content/uploads/2024/10/NIEL.wav 值得一听。如果无法听到音频，请点击此处
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I got a call from an ex-student asking me “how do you know when you found product market fit?”

There’s been lots of words written about it, but no actual recordings of the moment.

I remembered I had saved this 90 second, 26 year-old audio file because this is when I knew we had found it at Epiphany.

The speaker was the the Chief Financial Officer of a company called Visio, subsequently acquired by Microsoft

I played it for her and I think it provided some clarity.

https://steveblank.com/wp-content/uploads/2024/10/NIEL.wav

It’s worth a listen.

If you can’t hear the audio click here

如何找到您的客户在国防部 – 国防部项目执行办公室目录 || How To Find Your Customer In the Dept of Defense – The Directory of DoD Program Executive Offices

2024-09-17T13:00:59+00:00

DoD 项目执行办公室（PEO）目录：为初创企业提供的市场进入指南

挑战与背景
在国防部（DoD）寻找客户进行产品销售对初创企业而言极具挑战性。需明确：应联系哪些部门？如何吸引他们的注意？如何确认其是否有采购预算？通常，一切始于项目执行办公室（Program Executive Office, PEO）。

PEO 的角色
国防部不再拥有所有用于威慑或赢得战争的技术和产品（如人工智能、自主系统、无人机、生物技术、太空访问、网络安全、半导体、新材料等）。如今，一批新兴初创企业正尝试向国防部销售这些产品。然而，目前尚无国防部统一的“电话簿”列出具体联系人。因此，我创建了这份 PEO 目录，作为“谁在政府中采购”的参考工具。

目录内容与局限性
本目录首次版本收录了 75 个 PEO 及其负责人（PEO 负责人）和项目/产品负责人（PM）。每个 PEO 由一位高级官员领导，可能是现役军人或文职官员，负责特定项目（如联合攻击战斗机）或整个相关项目组合（如海军数字与企业服务 PEO）的成本、进度和性能。

注意事项

信息不完整：目录可能包含错误，且内容不全面。
动态变化：军职人员通常每几年更换岗位，PEO 也可能因需求而关闭或新建，因此文档在发布当天已可能过时。
预算优先级：了解预算分配、支出方向及变化趋势是关键，而资金是衡量优先级的最佳指标。

使用指南

了解预算：首先查看国防部整体预算概览，确定目标项目是否涉及数十亿美元或数百万美元。
结合目录分析：通过 PEO 目录查找相关项目，结合国防部审计长发布的预算文件（如 P-1 采购预算和 R-1 研发预算）进一步筛选。
识别潜在客户：利用目录中的项目描述（尽管存在术语和时效性问题）及附录信息，确定适合销售的 PEO 和 PM。

未来计划

动态更新：斯坦福 Gordian Knot 中心将负责后续更新，以反映哪些 PEO 更开放于新进入者，哪些已转向组合管理或尝试直接采购（OTA）合同。
创新亮点：突出那些在指标或成果方面采用新颖方法的 PEO。

政府信息的现状
尽管美国政府已发布部分国防部组织链接及社交媒体账号，但信息碎片化且更新不规律。因此，此类目录此前从未以实用格式存在——直到现在。

总结
本目录为初创企业提供了一个起点，帮助其识别潜在客户和预算分配，但需结合其他资源（如预算文件）进行深入分析。信息的准确性和时效性需谨慎对待，建议持续关注更新动态。

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在国防部找到您的产品的客户很困难：您应该与谁联系？如何引起他们的注意？您如何知道他们是否有资金购买您的产品？几乎总是从计划执行办公室开始。国防部（DoD）不再拥有所有用于威慑或赢得战争的技术、产品和服务——例如人工智能、自主系统、无人机、生物科技、进入太空的能力、网络安全、半导体、新材料等。如今，一批新型初创企业正试图将这些产品出售给国防部。令人惊讶的是，国防部并没有一个全面的、面向初创企业的官方电话簿来告知他们应该联系谁。因此，我编写了一份这样的电话簿。下面链接的PEO目录可以看作是一本“谁是政府买家？”的电话簿。国防部每年购买价值数百十亿美元的产品和服务，而几乎所有这些采购都由计划执行办公室管理。一个计划执行办公室可能负责特定的项目（例如联合攻击战斗机计划），也可能负责整个类似项目的组合（例如海军数字与企业服务计划执行办公室）。计划执行办公室定义需求，其合同官员负责采购（处理正式采购流程、发布采购要求（RFP）并与供应商签订合同）。项目管理人员（PMs）则与计划执行办公室合作，管理较大项目中的子集。现有的国防承包商知道这些组织是谁，并有专门团队跟踪预算和合同。但初创企业呢？大多数初创企业对从哪里开始毫无头绪。这就是信息不对称的经典案例，这对国防部或新兴的国防初创企业生态系统并不健康。这就是我整理这份PEO目录的原因。该目录的首个版本列出了75个计划执行办公室及其计划执行官和项目/计划管理人员。每个计划执行办公室由一位计划执行官领导，他们通常是高级官员——无论是军方成员还是高级文职人员——负责某一重大系统或系统组合的成本、进度和性能，其中一些系统的价值高达数十亿美元。下面是对国防部内75个计划执行办公室的摘要。您可以通过此处下载完整的64页文档，其中包括所有602个计划执行办公室和官员的详细信息。注意事项请勿依赖此文件的准确性和完整性。该文件很可能不完整且包含错误。军方官员通常每几年就会更换岗位。计划执行办公室会根据需要关闭或开设新的办公室。这意味着该文件在编写当天就已经过时。然而，它仍然是初创企业希望与国防部合作的宝贵起点。如何将PEO目录作为市场进入策略的一部分加以利用虽然了解计划执行办公室的存在及其人员构成很有帮助，但更重要的是了解资金流向、资金用途以及预算是否增加、减少或保持不变。最好的起点是查看此处的整个国防预算概览。然后在链接的PEO目录中搜索这些项目，您可以大致了解该项目的资金规模是数十亿美元还是数百万美元。接下来，查看国防部会计官发布的预算文件—— 特别是P-1（采购）和R-1（研发）预算文件。将预算文件与这份PEO目录结合使用，可以帮助您缩小应联系的75个计划执行办公室和500多个项目管理人员的范围。通过一些练习，您可以将预算条目、账户或计划要素（PE）的变化转化为销售市场进入策略，或者至少形成一个应联系谁的假设。借助项目描述（其中充满术语且滞后9-12个月）、此处的Excel下载文件以及附录——您可以识别出那些最有可能适合您产品的国防部采购目标。列表中的人和组织比资金变化得更快。只有在您了解他们的优先事项后，了解这些人才才有帮助——而资金是衡量优先事项的最佳代理。未来工作最终，我们希望为初创企业提供不仅知道该联系谁、谁有资金，还能知道哪些计划执行办公室对新进入者持开放态度，哪些已转向组合管理，哪些尝试过OTA合同，同时突出那些在指标或成果方面有创新做法的办公室。未来，该项目将由斯坦福大学 Gordian Knot 中心国家安全创新中心持续更新。在此期间，请将更新、更正和评论发送至 sblank@stanford.edu。致谢显然，美国政府有意传达这些信息。他们已在此处发布了国防部组织的链接，甚至列出了国防部的社交媒体账号。但该列表是分散的，且更新不规律。因此，直到现在，这种类型的目录才不存在于可用格式中。
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Finding a customer for your product in the Department of Defense is hard: Who should you talk to? How do you get their attention?

How do you know if they have money to spend on your product?

It almost always starts with a Program Executive Office.

The Department of Defense (DoD) no longer owns all the technologies, products and services to deter or win a war – e.g. AI, autonomy, drones, biotech, access to space, cyber, semiconductors, new materials, etc.

Today, a new class of startups are attempting to sell these products to the Defense Department. Amazingly, there is no single DoD-wide phone book available to startups of who to call in the Defense Department.

So I wrote one.

Think of the PEO Directory linked below as a “Who buys in the government?” phone book.

The DoD buys hundreds of billions of dollars of products and services per year, and nearly all of these purchases are managed by Program Executive Offices. A Program Executive Office may be responsible for a specific program (e.g., the Joint Strike Fighter) or for an entire portfolio of similar programs (e.g., the Navy Program Executive Office for Digital and Enterprise Services). PEOs define requirements and their Contracting Officers buy things (handling the formal purchasing, issuing requests for proposals (RFPs), and signing contracts with vendors.) Program Managers (PMs) work with the PEO and manage subsets of the larger program.

Existing defense contractors know who these organizations are and have teams of people tracking budgets and contracts. But startups? Most startups don’t have a clue where to start.

This is a classic case of information asymmetry and it’s not healthy for the Department of Defense or the nascent startup defense ecosystem.

That’s why I put this PEO Directory together.

This first version of the directory lists 75 Program Executive Offices and their Program Executive Officers and Program/Project Managers.

Each Program Executive Office is headed by a Program Executive Officer who is a high ranking official – either a member of the military or a high ranking civilian – responsible for the cost, schedule, and performance of a major system, or portfolio of systems, some worth billions of dollars.

Below is a summary of 75 Program Executive Offices in the Department of Defense.

You can download the full 64-page document of Program Executive Offices and Officers with all 602 names here.

Caveats

Do not depend on this document for accuracy or completeness.

It is likely incomplete and contains errors.

Military officers typically change jobs every few years.

Program Offices get closed and new ones opened as needed.

This means this document was out of date the day it was written. Still it represents an invaluable starting point for startups looking to work with DoD.

How to Use The PEO Directory As Part of A Go-To-Market Strategy

While it’s helpful to know what Program Executive Offices exist and who staffs them, it’s even better to know where the money is, what it’s being spent on, and whether the budget is increasing, decreasing, or remaining the same.

The best place to start is by looking through an overview of the entire defense budget here. Then search for those programs in the linked PEO Directory. You can get an idea whether that program has $ Billions, or $ Millions.

Next, take a look at the budget documents released by the DoD Comptroller –

particularly the P-1 (Procurement) and R-1 (R&D) budget documents.

Combining the budget document with this PEO directory helps you narrow down which of the 75 Program Executive Offices and 500+ program managers to call on.

With some practice you can translate the topline, account, or Program Element (PE) Line changes into a sales Go-To-Market strategy, or at least a hypothesis of who to call on.

Armed with the program description (it’s full of jargon and 9-12 months out of date) and the Excel download here and the Appendix here –– you can identify targets for sales calls with DoD where your product has the best chance of fitting in.

The people and organizations in this list change more frequently than the money.

Knowing the people is helpful only after you understand their priorities — and money is the best proxy for that.

Future Work

Ultimately we want to give startups not only who to call on, and who has the money, but which Program Offices are receptive to new entrants. And which have converted to portfolio management, which have tried OTA contracts, as well as highlighting those who are doing something novel with metrics or outcomes.

Going forward this project will be kept updated by the Stanford Gordian Knot Center for National Security Innovation.

In the meantime send updates, corrections and comments to sblank@stanford.edu

Credit Where Credit Is Due

Clearly, the U.S. government intends to communicate this information. They have published links to DoD organizations here, even listing DoD social media accounts. But the list is fragmented and irregularly updated. Consequently, this type of directory has not existed in a usable format – until now.

以初创公司速度进行的安全审查 || Security Clearances at the Speed of Startups

2024-08-13T13:00:40+00:00

当前流程与挑战

若想加入涉及机密项目的公司或政府机构，员工通常需等待近一年才能开始工作或领取报酬，且若无法通过安全审查，工作机会可能被取消。例如，国防初创企业或国家安全机构会提供“有条件”的录用，要求候选人先等待3至9个月无薪期，待通过审查后才正式入职。这对学生而言门槛极高，尤其是他们有更多就业选择的情况下。

安全审查等级与流程

安全审查的时长取决于政府对背景的调查深度，与项目机密级别直接相关：

基本等级：Confidential（机密）和Secret（秘密），需进行国家机构背景调查（NACLC），包括FBI犯罪记录查询、信用审查及地方执法部门调查，通常耗时约3个月。
高级等级：Top Secret/SCI（绝密/特殊机密），需进行单一范围背景调查（SSBI），调查更全面且耗时6至9个月。
附加要求：部分审查需通过测谎仪测试（Polygraph）。

Palantir的创新方案

Palantir推出加速学生审查计划，旨在让学生在入职后尽快参与高价值项目：

提前录用：学生在校期间若获得实习或全职offer，可立即以承包商身份入职，启动审查流程。
缩短周期：审查在学生在校期间完成，确保其在入职后约9个月内即可上岗。
类似机制：若学生未来返回Palantir，公司会保留其审查结果，避免重复流程。

战略意义与影响

长期投资：吸引顶尖学生加入，为公司储备国家安全领域人才。
行业影响：推动国防科技企业建立更广泛的高技能团队，支持国家关键任务。
行业推广：鼓励其他企业效仿，形成良性竞争。

未来创新方向

Silicon Valley可探索更多吸引国家安全人才的创新方式，例如：

优化审查流程，降低入职门槛。
提供更具竞争力的薪酬与职业发展机会。
加强与高校的合作，定向培养相关领域人才。

总结：当前国家安全领域的就业流程存在显著延迟，Palantir通过提前启动审查机制，为学生提供更高效的入职路径，同时为行业树立了创新标杆，未来需进一步探索更多吸引人才的策略。

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想象一下，你收到了一家公司的工作录用通知，但却不能在近一年内开始工作——甚至拿不到薪水。如果你无法通过安全审查，这份工作机会就会被取消。或者你获得了一个实习机会，却不能参与项目中最有趣的部分。听起来像是一个不可能完成的任务。然而，如果你想要在从事机密项目的公司或政府机构工作，这就是当前的流程。过去五年中，越来越多的学生意识到俄罗斯在乌克兰的残酷战争以及与中华人民共和国的战略竞争意味着世界不再是一个稳定安全的地方。这促使他们中的许多人选择加入国防初创企业，投身国家安全问题的研究。然而，许多这类公司和政府机构要求你参与涉及敏感信息的项目，这些信息是政府希望保护的。这些项目被称为机密项目。为了获得工作机会并参与这些项目，你首先需要通过政府的安全审查。（安全审查是政府用来判断你是否值得信赖，能否保守秘密并不会损害国家安全的过程。）对于大多数国防初创企业或承包商以及国家安全机构的职位来说，你不会在收到录用通知后立即开始工作，而是会收到一份“有条件”的工作录用——这实际上意味着：“我们希望你加入我们，但你需要在开始工作前等待3到9个月，期间不拿薪水，而且如果你无法通过安全审查，我们不会录用你。”这对于拥有众多其他工作选择的学生来说，是一个很高的门槛。安全审查的类型安全审查的耗时取决于政府对你的背景调查的详细程度和深入程度。这直接关系到你将要处理的信息的机密级别。机密信息的三个主要级别（从低到高）是：普通机密（Confidential）、秘密（Secret）和绝密（Top Secret）。获得安全审查所需的背景调查类型和深度取决于你将要接触的机密信息级别。例如，如果你只需要接触普通机密或秘密信息，政府会进行“国家机构背景调查（含法律与信用核查）”（NACLC）。政府会查阅联邦调查局的犯罪记录数据库，进行信用调查，并与你当地的执法机构进行核查。这通常耗时较短（约3个月）。另一方面，如果你将要参与绝密/特殊访问（SCI）项目，这需要更深入（且耗时更长，约6到9个月）的背景调查，称为“单一范围背景调查”（SSBI）。某些类型的审查还要求你接受测谎仪测试。政府如何“审查”你？国家背景调查服务（NBIS）是负责审查你背景的政府机构。他们将询问你的以下情况：毒品和酒精使用（如毒品、成瘾、长期酗酒等）犯罪行为（如重罪）财务状况（他们将进行信用报告调查）你如何使用IT系统（例如，你是否曾入侵过系统？）美国国籍忠诚度外国影响（你是否拥有海外财产？外国投资等）心理状况及个人行为旅行历史（你是否曾居住或前往中国、俄罗斯、伊朗、朝鲜、叙利亚等国家？）此外，他们还会与你的朋友、家人、现任及前任伴侣等交谈，以更全面地了解你。 Palantir的加速学生审查计划 Palantir希望其实习生和新员工能够迅速投入工作，从第一天起就参与最困难且最具挑战性的政府问题。然而，这些问题需要拥有安全审查资格。问题在于，目前所有公司都会在你入职当天才开始申请安全审查。 Palantir的想法是：如果你在还在上学期间就获得了Palantir的实习或全职工作机会，他们将立即以承包商身份雇佣你。这样他们就可以在你入职前就开始你的安全审查流程。这意味着你将在大约9个月后获得审查资格，从而准时开始你的第一份工作。这类似于大学的提前录取计划。（如果你选择在下一年重返Palantir实习，Palantir也会为你保留审查资格。）为什么这么做？显然，这是Palantir对大学人才的长期战略投资，但同时也影响整个国防生态系统，以培养更多优秀的美国工程师团队，支持国家最关键的使命。他们还鼓励其他国防科技公司实施类似的计划。我认为这是一个非常好的想法。那么，除了这个计划，硅谷还能采取哪些创新措施来吸引国家安全领域的专业人才呢？
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Imagine you got a job offer from a company but weren’t allowed to start work – or get paid – for almost a year. And if you can’t pass a security clearance your offer is rescinded. Or you get offered an internship but can’t work on the most interesting part of the project. Sounds like a nonstarter. Well that’s the current process if you want to work for companies or government agencies that work on classified programs.

One Silicon Valley company, Palantir, is trying to change that and shorten the time between getting hired and doing productive work. Here’s why and how.

Over the last five years more of my students have understood that Russia’s brutal war in Ukraine and strategic competition with the People’s Republic of China mean that the world is no longer a stable and safe place. This has convinced many of them to work on national security problems in defense startups.

However, many of those companies and government agencies require you to work on projects with sensitive information the government wants to protect. These are called classified programs. To get hired, and to work on them, you need to first pass a government security clearance. (A security clearance is how the government learns whether you are trustworthy enough to keep secrets and not damage national security.)

For jobs at most defense startups/contractors or national security agencies, instead of starting work with your offer letter, you’d instead receive a “conditional” job offer – that’s a fancy way to say, “we want you to work here, but you need to wait 3 to 9 months without pay until you start, and if you can’t pass the security clearance we won’t hire you.” That’s a pretty high bar for students who have lots of other options for where to work.

Types of Security Clearances

The time it takes for the clearance process depends on the thoroughness and how deeply the government investigates your background. That’s directly related to how classified will be the work you will be doing. The three primary levels of classification (from least to greatest) are Confidential, Secret, and Top Secret. The type and depth of background investigations to get a security clearance depends on what level of classified information you will be working with. For example, if you just need access to Confidential or Secret material they would do a National Agency Check with Law and Credit (NACLC). The government will look at the FBI’s criminal history repository, do a credit check, and a check with your local law enforcement agencies. This can take a relatively short time (~3 months).

On the other hand if you’re going to work on a Top Secret/SCI project, this requires a more extensive (and much longer ~6-9 months) background check called a Single Scope Background Investigation (SSBI). Some types of clearances also require you to take a polygraph (lie-detector) test.

How Does the Government “Clear” you?

The National Background Investigation Services (NBIS) is the government agency that will investigate your background. They will ask about your:

Drugs and Alcohol (hard drugs, addiction, chronic drinking, etc.)

Criminal conduct (felonies..)

Financial stability (they’ll run a Credit Bureau Report)

How you’ve used IT systems (e.g. have you hacked any?)

United States allegiance

Foreign influence (do you own property overseas? Foreign investments, etc.)

Psychological conditions and personal behavior.

Travel History (have you lived or gone to China, Russia, Iran, North Korea, Syria, etc.)

Plus, they will talk to your friends, relatives, current and ex-significant others, etc. to learn more about you

Palantir’s Accelerated Student Clearance Plan

Palantir wants their interns and new hires to hit the ground running and work on the toughest and most interesting government problems from day one. However, these types of problems require having a security clearance. The problem is that today, all companies start an application for a security clearance the day you show up for work.

Palantir’s idea? If you get an internship or full-time offer from Palantir while you’re still in school, they will immediately employ you as a contractor. This will let them start your security clearance process while in school before you show up for work. That means you will be cleared ~9 months later in time for your first day on the job. Think of this like a college early admissions program. (If you’re interning, Palantir will hold your clearance for you if you come back to Palantir the following year.)

Why Do This?

Obviously this is a long-term strategic investment in Palantir’s college talent, but it also affects the entire defense ecosystem – to create a broader team of America’s best engineers who are able to support our country’s most critical missions. And they are encouraging other Defense Tech companies to implement a similar program.

I think it’s a great idea.

Now what are the other innovative ideas Silicon Valley can do to attract a national security workforce?

为什么大型组织难以应对颠覆，以及该怎么做 || Why Large Organizations Struggle With Disruption, and What to Do About It

2024-07-30T13:00:59+00:00

简要总结

颠覆性挑战与组织反应
颠覆性技术（如人工智能、自主系统、量子计算、网络攻击、太空探索等）正在迅速威胁传统企业与政府机构的主导地位。组织对这种颠覆的反应决定了其是适应变革还是被淘汰。然而，许多大型组织的领导者因对现有系统的依赖而难以采取实质性行动，导致“无所作为的反馈循环”。

创新者窘境
大型组织的创新者常面临资源匮乏和高层忽视的问题。领导者往往缺乏对新技术的理解，依赖于擅长渐进式改进的顾问，而创新团队则难以直接接触决策层。这种“脱节的创新者”现象与“冻结的中层”（即对变革持抵触态度的中层管理者）共同阻碍了组织的快速适应能力。

风险规避与文化障碍
传统组织倾向于“安全失败”（即通过重复性流程确保稳定），而非“可失败的安全”（即允许试错以推动创新）。此外，商业领域的“激进投资者”通过削减创新投入、关闭研发部门等方式阻碍转型，而政府机构则因政治任命、任期短、缺乏创新文化等进一步延缓响应速度。

关键挑战与解决方案

建立外部红队：为高层提供独立的竞争对手进展评估和内部研发进展的客观分析。
创建战略研究小组：开发可应用于实际操作的新商业模式和战略概念，并与外部技术资源保持联系。
推动快速创新团队：由技术专家、愿景型人才和高层支持者组成，专注于颠覆性技术的实验与部署。
增强领导层的可见性：通过实地考察和直接沟通，确保高层对颠覆性技术的及时了解，并制定明确的响应机制（如采购订单、OTA更新）。
鼓励创新思维：利用外部合作伙伴和资源，推动技术与资本的协同创新。

成功转型的典范
如微软CEO萨提亚·纳德拉、苹果的史蒂夫·乔布斯、美国防务领域的比尔·佩里、哈罗德·布朗和阿什·卡特等，他们通过接受变革、主动行动和激发想象力实现了组织转型。

未来方向
组织转型需从高层开始，明确威胁的时间、规模与影响，并制定快速实验、部门或全组织层面的变革计划。同时，需建立“双轨制”组织（如SpaceX的Falcon 9运营与Starship颠覆性实验），以平衡稳定与创新。

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仿佛一夜之间，颠覆性变革使得挑战者能够威胁公司和政府机构的主导地位，因为它们现有的许多系统已被跳过。组织对这种颠覆性变革的反应决定了其是适应还是消亡。我曾与一家大型组织合作，其存在正受到来自现有和新兴竞争对手的激烈技术（如人工智能、自主性、量子计算、网络攻击、进入太空等）的挑战。这些竞争对手正在部署新技术来挑战该组织多年来构建的昂贵（且直到现在极为有效的）传统系统。（而这些变革的速度对于该组织来说快得像一场模糊的梦。）然而，该组织还面临自身领导层的不作为，他们无法放手那些经过数十年构建的昂贵系统和供应商。这正是创新者困境的典型案例。在商业领域，创造性毁灭时有发生。你变得优秀，变得自满，最终会遭到打击。政府组织同样如此，只不过后果更为严重。该组织的命运尚未注定。在其内部，我曾目睹极具创新性的团队创建出自主系统和软件平台，这些平台与初创公司所做的一样出色。他们已在领域组织中找到支持者，并与他们进行了实验。他们提供了证据表明该组织能够适应变化的竞争环境，甚至重新夺回领先地位。同时，他们还与外部组织合作，以补充和加速其内部成果。他们正站在潜在变革的边缘——但领导层却犹豫不决地做出实质性改变。 “无所作为”的反馈循环我曾在商业和政府组织中多次目睹这一现象。对创新者来说，没有什么比看着自己的组织被颠覆而高级领导层却仅采取象征性行动更令人沮丧的了。另一方面，没有哪个领导大型组织的人希望它倒闭。那么，为什么现有的组织在适应变化的环境时如此困难？答案始于高层。应对颠覆性变革需要高级领导层采取行动：例如CEO、董事会、部长等。他们担心过早转向可能危及传统业务或力量，因此延迟决策——常常直到为时已晚。与这家组织的合作让我更清楚地认识到，为什么在公司和政府机构中采用和广泛部署颠覆性变革是困难且痛苦的。以下是原因：脱节的创新者——大多数大型组织的领导者并不精通新技术以及这些技术可以创造的颠覆性运营理念/商业模式。他们依赖于员工和信任顾问的指导——这些顾问大多因在现有系统中实现渐进式改进的专业知识而被雇佣和晋升。相比之下，组织内部的创新者很少能直接接触高级领导层。那些拥抱根本性新技术和理念、挑战现状和教条的创新者不仅不被欢迎，甚至不被提拔或资助。传统体系——我所工作的组织，像许多其他组织一样，在现有理念、系统、平台、研发实验室、培训以及一组已知的外部承包商上投入了数十年。构建和维持现有平台和系统使得几乎没有资金用于创建和部署同等规模的新系统（新进入者/对手可能没有这些）。主张其中某个平台或系统存在风险或不再有效被视为异端，很可能意味着职业生涯的终结。 “冻结的中间层”——我经常听到大型组织内部创新者抱怨，太多人抗拒改变（“他们根本不懂”）。经过数十年的观察，我了解到“冻结的中间层”现象往往源于所谓的“塞梅尔维斯效应”——人们无意识地坚持原有信念并拒绝与之矛盾的新想法，因为这会破坏他们已建立的规范和/或信念。（他们真的不懂。）这一群体最舒适地坚持现有流程和程序，并雇佣和提拔执行现状的人。当系统可以通过渐进式增长继续成功时，这很有效，但在面对更根本性的变革时，这种正常的人类反应会阻碍新知识的获取，限制组织快速适应新环境的能力。结果是组织的短视和创新者的挫败。最终，你得到的是世界级的人才和组织，却面对一个已经不存在的世界。并非所有人都受“塞梅尔维斯效应”的影响。在这一“中间层”中，通常是中层管理者/军官提出颠覆性解决方案和理念。然而，除非他们拥有高级支持者（副总裁、将军/海军上将）并且属于一个旨在解决运营问题的组织，否则这些解决方案会夭折。这些创新者缺乏替代场所，这些场所鼓励并资助实验和非共识性想法。讽刺的是，组织往往驱逐这些员工，因为他们不遵从，或者如果被迫遵从，他们会变得沮丧并转投工业界更具创新性的岗位。傲慢是管理层过度自信和自满的行为。与无意识的“塞梅尔维斯效应”不同，这是对事实的主动和有意识的否认。一些领导者/管理者认为变革威胁到他们的职位，或者认为新项目、供应商或想法增加了失败的风险，这可能损害他们的形象和职业或晋升地位。在我所工作的组织中，内部工程团队向高级领导层提供保证，声称他们通过强调现有平台和系统的渐进式升级来应对颠覆性威胁。同时，由于预算是一个零和游戏，他们剥夺了创新者用于大规模部署颠覆性新理念的资金和组织支持。结果是“创新表演”。在商业世界中，这种行为导致创新演示但没有实际产品，公司走向无关或破产。在军事领域，这是演示但没有资金进行大规模部署。对失败的恐惧/风险规避——大型组织建立在可重复和可扩展的流程之上，这些流程旨在“防失败”。然而，颠覆性项目只能在拥有“安全失败”文化（即通过一系列新想法的渐进和迭代实验进行学习和发现，失败被视为过程的一部分）的组织中成功。因此，“防失败”与“安全失败”组织需要分开，需要不同的文化、不同的人员、不同的开发流程和风险承受能力。激进投资者在商业公司中扼杀变革商业组织在变革速度上的限制之一是“激进投资者”的恐惧。“激进投资者”推动上市公司优化短期利润，避免或限制对新机会和技术的重大投资。当这些投资者控制公司时，创新投资减少，员工被裁，工厂和研发中心关闭，公司盈利部分和其他有价值的资产被出售。政府组织面临的独特障碍政府组织面临额外的限制，使它们比大型公司更慢地应对变化。首先，最大的政府组织的领导者往往是政治任命。许多人拥有数十年的相关经验，但其他人则在远超其经验水平的职位上工作。这种不匹配在政府中比在私营行业更为常见。领导者的任期太短——除了少数政治任命者，大多数人的任期仅与其总统在白宫的任期相同，而军事服务中的项目和命令领导者通常任期为2-3年。这对于深入理解和有效执行组织变革来说太短了。由于大多数政府组织缺乏正式创新理论或手册——即建立共同参考框架和共同专业语言的知识体系——机构学习往往短暂而非持久。很少有知识、实践、共享信念、理论、战术、工具、程序、语言和资源能从上一任领导者的手中传递下来。相反，每个新领导都会重新学习并强加自己的计划和政策。与现状保持一致会获得奖励——所有服务中的晋升主要由与现状保持一致来驱动。这导致诸如不取消失败的项目、不寻找可能更便宜/更好/更响应的新供应商、即使所有证据表明现有力量设计和运营理念不再可行，仍坚持现有力量设计、选择现有主承包商、或不指出某个主要平台或武器已不再有效等行为。激励措施是避免风险。这样做很可能意味着职业生涯的终结。很少有人因这些行为获得晋升。这抑制了非共识性思维。然而，颠覆性变革需要风险。旋转门——高级领导层离开政府服务，转而进入他们曾管理的公司工作，这些公司也是他们曾购买系统的公司（通常是主承包商）。结果是，很少有人在服务期间会监督这些公司或建议替代供应商。主承包商——是国家最宝贵的资产，同时也是阻碍颠覆性变革的最大障碍。在20世纪，平台/武器主要是硬件，带有软件组件。在21世纪，平台/武器越来越多地是软件，加上硬件。大多数主承包商仍使用瀑布式开发，即有明确的规划、设计、开发和测试阶段，而不是敏捷开发（每日软件发布）。结果是，主承包商已显示出无法按时交付复杂系统的能力。（将主承包商转向软件可升级系统或云平台会破坏其财务模型。）此外，主承包商通常拥有对现有政府合同的“锁定”地位。这是因为对于采购官员来说，选择他们进行后续工作风险较低——主承包商在处理复杂的政府采购流程方面拥有数十年的经验；他们拥有大量的人力和资金来影响政府采购系统的所有部分——从需求撰写者到项目经理，再到国会工作人员和军方及拨款委员会的成员。新进入者几乎没有机会竞争。国会——立法者有支持现状的激励，但缺乏改变现状的动力。国会对供应商使用的系统和平台有重大话语权，且倾向于现状。为了保住自己的职位，立法者会制定军事拨款法案来支持其选民的就业，并吸引雇佣他们的承包商的捐款。（他们和其工作人员也考虑到未来的职位，因此保持旋转门的开放。）许多国会决策，如《国防授权法案》（NDAA）和拨款法案，都是为了支持在他们选区提供最多就业机会和最多资金用于其连任的公司。这些公司来自主承包商。如何应对？这始于高层。面对颠覆性威胁，高级领导层必须主动努力理解：威胁的时间——颠覆从不附带备忘录，而当它发生时，其影响呈指数级增长。我们的核心业务或运营理念/力量设计何时会过时？我们的竞争对手会先到达吗？威胁的规模——这会危及我们业务的某个小部分，还是影响整个组织？威胁的影响——这会造成轻微影响，还是威胁到领导层或组织本身的存在？如果我们的竞争对手/对手率先采用，会发生什么？对威胁的回应——小规模实验、部门变革、公司或组织范围的变革——以及其时间表。提升颠覆性技术与理念的可见性/引入外部观点为了应对颠覆性威胁，创新者的典型汇报关系必须被摒弃，即通过多层管理过滤。高级领导层需要一条直接且未经过滤的渠道，以获取其内部创新团队的月度更新和基于证据的实验演示。以及相关的新型运营理念。建立一个由外部顾问组成的“红队”。该团队应向高级领导层汇报竞争对手的进展。并提供对自身内部工程/研发进展的无偏见评估。建立一个战略研究小组，能够开发可用于运营层面的新商业模式/新战略理念——确保其与外部技术创新来源的联系。建立一个“感知”和“响应”组织，将实际的公司/机构/服务问题带出，交给风险投资公司和初创企业，看看他们如何解决。然而，除非高级领导层1）主动确保亲自了解这些情况（至少每两年一次），并且拥有机制来“响应”（如采购订单/OTA），否则这些努力将收效甚微。主动且紧急地收集证据运行真实世界实验——模拟、演习——使用颠覆性技术与运营理念（在进攻和防御方面）。观察并主动寻找颠覆性影响在相邻领域的表现，例如人工智能对蛋白质建模的影响、战场上的无人机和乌克兰黑海地区的应用等。询问组织的基层人员（如销售团队、舰队司令）是否愿意在新能力上承担更多风险。这些活动需要在几个月内而非几年内进行。这些团队可能的建议包括无所作为、运行小规模实验、转变一个功能或部门，或进行公司或组织范围的变革。组织范围的变革是什么样子的？我们希望得到什么结果？我们需要它的时间？需要哪些预算、人员、资本设备？需要放弃什么？如何向所有利益相关者传达并获得他们的认同？在面对颠覆/危机/战争时，先进的研发团队现在需要拥有足够预算进行大规模部署的席位。考虑将组织构建成一个双元组织（如SpaceX的Falcon 9运营执行与Starship颠覆性实验）（参见这篇哈佛商业评论文章）。除了流程管理者，还要创建快速创新团队（技术专家、远见者、高级支持者）。最后，鼓励更多想象力。我们如何利用合作伙伴和其他外部资源来获取技术和资本？在面对颠覆性变革时成功转型的领导者包括微软CEO萨提亚·纳德拉和苹果的史蒂夫·乔布斯，在国防领域，比尔·佩里、哈罗德·布朗和阿什·卡特。每个人都在面对颠覆时采取了接受、承认、想象和行动的方式。关于转型还有更多内容将在未来文章中讨论。
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Seemingly overnight, disruption has allowed challengers to threaten the dominance of companies and government agencies as many of their existing systems have now been leapfrogged. How an organization reacts to this type of disruption determines whether they adapt or die.

I’ve been working with a large organization whose very existence is being challenged by an onslaught of technology (AI, autonomy, quantum, cyberattacks, access to space, et al) from aggressive competitors, both existing and new. These competitors are deploying these new technologies to challenge the expensive (and until now incredibly effective) legacy systems that this organization has built for decades. (And they are doing it at speed that looks like a blur to this organization.) But the organization is also challenged by the inaction of its own leaders, who cannot let go of the expensive systems and suppliers they built over decades. It’s a textbook case of the Innovators Dilemma.

In the commercial world creative destruction happens all the time. You get good, you get complacent, and eventually you get punched in the face. The same holds true for Government organizations, albeit with more serious consequences.

This organization’s fate is not yet sealed. Inside it, I’ve watched incredibly innovative groups create autonomous systems and software platforms that rival anything a startup is doing. They’ve found champions in the field organizations, and they’ve run experiments with them. They’ve provided evidence that their organization could adapt to the changing competitive environment and even regain the lead. Simultaneously, they’ve worked with outside organizations to complement and accelerate their internal offerings. They’re on the cusp of a potential transformation – but leadership hesitates to make substantive changes.

The “Do Nothing” Feedback Loop

I’ve seen this play out time and again in commercial and government organizations. There’s nothing more frustrating for innovators than to watch their organization being disrupted while its senior leaders hesitate to take more than token actions. On the other hand, no one who leads a large organization wants it to go out of business. So, why is adapting to changed circumstances so hard for existing organizations?

The answer starts at the top. Responding to disruption requires action from senior leadership: e.g. the CEO, board, Secretary, etc. Fearful that a premature pivot can put their legacy business or forces at risk, senior leaders delay deciding – often until it’s too late.

My time with this organization helped me appreciate why adopting and widely deploying something disruptive is difficult and painful in companies and government agencies. Here are the reasons:

Disconnected Innovators – Most leaders of large organizations are not fluent in the new technologies and the disruptive operating concepts/business models they can create. They depend on guidance from their staff and trusted advisors – most of whom have been hired and promoted for their expertise in delivering incremental improvements in existing systems. The innovators in their organization, by contrast, rarely have direct access to senior leaders. Innovators who embrace radically new technologies and concepts that challenge the status quo and dogma are not welcomed, let alone promoted, or funded.

Legacy – The organization I’ve been working with, like many others, has decades of investment in existing concepts, systems, platforms, R&D labs, training, and a known set of external contractors. Building and sustaining their existing platforms and systems has left little money for creating and deploying new ones at the same scale (problems that new entrants/adversaries may not have.) Advocating that one or more of their platforms or systems are at risk or may no longer be effective is considered heresy and likely the end of a career.

The “Frozen Middle” – A common refrain I hear from innovators in large organizations is that too many people are resistant to change (“they just don’t get it”.) After seeing this behavior for decades, I’ve learned that the frozen middle occurs because of what’s called the“Semmelweis effect” – the unconscious tendency of people to stick to preexisting beliefs and reject new ideas that contradict them – because it undermines their established norms and/or beliefs. (They really don’t get it.) This group is most comfortable sticking with existing process and procedures and hires and promotes people who execute the status quo. This works well when the system can continue to succeed with incremental growth, but in the face of more radical change, this normal human reaction shuts out new learning and limits an organizations’ ability to rapidly adapt to new circumstances. The result is organizational blinders and frustrated innovators. And you end up with world-class people and organizations for a world that no longer exists.

Not everyone is affected by the Semmelweis effect. It’s often mid-grade managers / officers in this same “middle” who come up with disruptive solutions and concepts. However, unless they have senior champions (VP’s, Generals / Admirals) and are part of an organization with a mission to solve operational problems, these solutions die. These innovators lack alternate places where the culture encourages and funds experimentation and non-consensus ideas. Ironically, organizations tend to chase these employees out because they don’t conform, or if forced to conform, they grow disillusioned and leave for more innovative work in industry.

Hubris is managerial behavior of overconfidence and complacency. Unlike the unconscious Semmelweis effect, this is an active and conscious denial of facts. It occurs as some leaders/managers believe change threatens their jobs as decision-makers or that new programs, vendors or ideas increase the risk of failure, which may hurt their image and professional or promotional standing.

In the organization I’ve been working with, the internal engineering group offers senior leaders reassurances that they are responding to disruption by touting incremental upgrades to their existing platforms and systems.

Meanwhile because their budget is a zero-sum game, they starve innovators of funds and organizational support for deployment of disruptive new concepts at scale. The result is “innovation theater.” In the commercial world this behavior results in innovation demos but no shipping products and a company on the path to irrelevance or bankruptcy. In the military it’s demos but no funding for deployments at scale.

Fear of Failure/Risk Aversion – Large organizations are built around repeatable and scalable processes that are designed to be “fail safe.” Here new initiatives need to match existing budgeting, legal, HR and acquisition, processes and procedures. However, disruptive projects can only succeed in organizations that have a “safe-to-fail” culture. This is where learning and discovery happens via incremental and iterative experimentation with a portfolio of new ideas and failure is considered part of the process. “Fail safe” versus “safe-to-fail” organizations need to be separate and require different culture, different people, different development processes and risk tolerance.

Activist Investors Kill Transformation in Commercial Companies

A limit on transformation speed unique to commercial organizations is the fear of “Activist Investors.” “Activist investors” push public companies to optimize short-term profit, by avoiding or limiting major investments in new opportunities and technology. When these investors gain control of a company, innovation investments are reduced, staff is cut, factories and R&D centers closed, and profitable parts of the company and other valuable assets sold.

Unique Barriers for Government Organizations

Government organizations face additional constraints that make them even slower to respond to change than large companies.

To start, leaders of the largest government organizations are often political appointees. Many have decades of relevant experience, but others are acting way above their experience level. This kind of mismatch tends to happen more frequently in government than in private industry.

Leaders’ tenures are too short – All but a few political appointees last only as long as their president in the White House, while leaders of programs and commands in the military services often serve 2- or 3-year tours. This is way too short to deeply understand and effectively execute organizational change. Because most government organizations lack a culture of formal innovation doctrine or playbook – a body of knowledge that establishes a common frame of reference and common professional language – institutional learning tends to be ephemeral rather than enduring. Little of the knowledge, practices, shared beliefs, theory, tactics, tools, procedures, language, and resources that the organization built under the last leader gets forwarded. Instead each new leader relearns and imposes their own plans and policies.

Getting Along Gets Rewarded – Career promotion in all services is primarily driven by “getting along” with the status quo. This leads to things like not cancelling a failing program, not looking for new suppliers who might be cheaper/ better/ more responsive, pursuing existing force design and operating concepts even when all available evidence suggests they’re no longer viable, selecting existing primes/contractors, or not pointing out that a major platform or weapon is no longer effective. The incentives are to not take risks. Doing so is likely the end of a career. Few get promoted for those behaviors. This discourages non-consensus thinking. Yet disruption requires risk.

Revolving doors – Senior leaders leave government service and go to work for the very companies whose programs they managed, and who they had purchased systems from (often Prime contractors). The result is that few who contemplate leaving the service and want a well-paying job with a contractor will hold them to account or suggest an alternate vendor while in the service.

Prime Contractors – are one of our nation’s greatest assets while being our greatest obstacles to disruptive change. In the 20th century platforms/weapons were mostly hardware with software components. In the 21st century, platforms/weapons are increasingly software with hardware added. Most primes still use Waterfall development with distinct planning, design, development, and testing phases rather than Agile (iterative and incremental development with daily software releases). The result is that primes have a demonstrated inability to deliver complex systems on time. (Moving primes to software upgradable systems/or cloud-based breaks their financial model.)

As well, prime contractors typically have a “lock” on existing government contracts. That’s because it’s less risky for acquisition officials to choose them for follow-on work– and primes have decades of experience in working through the byzantine and complex government purchasing process; and they have tons of people and money to influence all parts of the government acquisition system—from the requirements writers to program managers, to congressional staffers to the members of the Armed Services and Appropriations committees. New entrants have little chance to compete.

Congress – Lawmakers have incentives to support the status quo but few inducements to change it. Congress has a major say in what systems and platforms suppliers get used, with a bias to the status quo. To keep their own jobs, lawmakers shape military appropriations bills to support their constituents’ jobs and to attract donations from the contractors who hire them. (They and their staffers are also keeping the revolving door in mind for their next job.) Many congressional decisions that appear in the National Defense Authorization Act (NDAA) and in appropriations are to support companies that provide the most jobs in their districts and the most funds for their reelection. These come from the Prime contractors.

What to Do About It?

It starts at the top. Confronted with disruptive threats, senior leaders must actively work to understand:

The timing of the threat – disruption never comes with a memo, and when it happens its impact is exponential. When will disruption happen that will make our core business or operating concepts/force design obsolete? Will our competitors get there first?

The magnitude of the threat – will this put a small part of our business/capabilities at risk or will it affect our entire organization?

The impact of the threat – will this have a minor impact or does it threaten the leadership or the very existence of the organization. What happens if our competitors/adversaries adopt this first?

The response to the threat- Small experiments, department transformation, and company or organization-wide transformation – and its timeline.

Increase Visibility of Disruptive Tech and Concepts/Add Outside Opinions

To counter disruptive threats, the typical reporting relationship of innovators filtered through multiple layers of management must be put aside.

Senior leaders need a direct and unfiltered pipeline to their internal innovation groups for monthly updates and demos of evidenced-based experiments in operational settings.

And the new operating concepts to go with it.

Create a “Red Team” of advisors from outside their organization.

This group should update senior leaders on the progress of competitors

And offer unbiased assessment of their own internal engineering/R&D progress.

Stand up a strategic studies group that can develop new business models/ new strategic concepts usable at the operational level – ensure its connection with external sources of technical innovation

Create a “sensing” and “response” organization that takes actual company/agency/service problems out to VC’s and startups and seeing how they would solve them

However, unless senior leaders 1) actively make a point of seeing these first hand (at least biannually), and have the mechanism to “respond” with purchase orders/ OTA’s, this effort will have little impact.

Actively and Urgently Gather Evidence

Run real-world experiments – simulations, war games, – using disruptive tech and operating concepts (in offense and defense.)

See and actively seek out the impact of disruption in adjacent areas e.g. AI’s impact on protein modeling, drones in the battlefield and Black Sea in Ukraine, et al.

Ask the pointy end of the organization (e.g the sales force, fleet admirals) if they are willing to take more risk on new capabilities.

These activities need happen in months not years. Possible recommendations from these groups include do nothing, run small experiments, transform a single function or department, or a company or organization-wide transformation.

What Does Organization-wide Transformation look like?

What outcome do we desire?

When do we need it?

What budget, people, capital equipment are needed?

What would need to be divested?

How to communicate this to all stakeholders and get them aligned?

In the face of disruption/ crisis/ wartime advanced R&D groups now need a seat at the table with budgets sufficient for deployment at scale.

Consider organizing as an ambidextrous organization (e.g. SpaceX Falcon 9 operational execution versus Starship disruptive experimentation) (see this HBR article.)

In addition to mangers of process, create rapid innovation teams (technologists, visionaries, senior champions)

Finally, encourage more imagination. How can we use partners and other outside resources for technology and capital?

Examples of leaders who transformed their organization in the face of disruption include Microsoft CEO Satya Nadella and Steve Jobs from Apple, in defense, Bill Perry, Harold Brown and Ash Carter. Each dealt with disruption with acceptance, acknowledgment, imagination and action.

Much more to be said about transformation in future posts.

精益创业营 @斯坦福 2024 – 8支团队进入，8家公司诞生 || Lean LaunchPad @Stanford 2024 – 8 Teams In, 8 Companies Out

2024-06-27T13:00:52+00:00

总结：斯坦福大学Lean LaunchPad课程的成果与影响

课程概况

第14届Lean LaunchPad课程在斯坦福大学圆满结束，该课程因受欢迎程度自2021年起在冬季和春季学期同步开设。
学生每周投入15-20小时，是普通课程的两倍。
历史性突破：14年来首次，所有8个团队均决定创业，成功创办公司。

政府项目采纳

多个政府资助计划已大规模采用该课程：
- 2011年：课程成为美国国家科学基金会（NSF）I-Corps的官方教材。
- 2013年：与加州大学旧金山分校（UCSF）及美国国立卫生研究院（NIH）合作，推出生命科学与医疗创业课程。
- 2014年：启动NIH的I-Corps计划。
- 2018年：能源部（DOE）推出Energy I-Corps计划。

课程工具与方法

商业模型画布（Business Model Canvas）：作为核心工具，提供结构化指导，帮助学生系统测试假设、明确产品市场匹配目标，并跟踪每周学习进展。
客户发现工具：包括视频、实验样本等，引导学生在课堂外进行实践，要求每周完成10-15次客户访谈，并持续开发最小可行产品（MVP）。

成功案例

Neutrix团队：通过升级核反应堆燃料提高盈利能力。
Virgil团队：利用AI技术捕捉亲人回忆，实现商业化。
Claim CoPilot团队：解决医疗理赔被拒问题。
Emy.ai团队：通过脑电波技术优化情绪管理。
TeachAssist团队：为特殊教育教师自动化学生评估。
Maurice.ai团队：开发面向GPT时代的家庭机器人。
Waifinder团队：为高中生提供个性化大学申请指导。

教学团队与支持

教学团队由多位行业专家和志愿者组成，包括：
- Steve Weinstein（美国前沿基金合伙人，硅谷资深技术企业家）
- Lee Redden（Blue River Technology前CTO，曾参与首期Lean LaunchPad课程）
- Jennifer Carolan（Reach Capital合伙人，教育领域风险投资专家）
- Shawn Carolan（Menlo Ventures合伙人）
助教团队：Chapman Ellsworth、Francesca Bottazzini、Ehsan Ghasemi。
导师支持：包括Lofton Holder、Bobby Mukherjee、David Epstein等，帮助团队验证商业可行性。

课程影响力

NSF I-Corps：累计培训9,500余名科学家和工程师，1,380个团队融资达31.66亿美元。
NIH I-Corps：317个团队融资6.34亿美元。
Energy I-Corps：188个团队融资1.51亿美元。
课程演进：近年来衍生出多个变体，如“为国防而创新”“为气候与可持续发展而创新”“为教育而创新”等。

未来展望

AI技术正在革新客户发现与验证流程。
尽管未来可能有更革命性的突破，但当前仍值得庆祝：8支团队 → 8家公司。

核心理念

课程通过“精心设计的幻觉”提供高度结构化的体验，使学生逐步验证商业假设，而非盲目探索。
强调“众人的力量”（It Takes A Village），志愿者、导师和助教的协作是课程成功的关键。

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本文此前曾在Poets and Quants上发表。我们刚刚完成了斯坦福大学第14届Lean LaunchPad课程。由于课程非常受欢迎，自2021年起我们开始在冬季和春季学期同时授课。在本学期中，八支团队与919位潜在客户、受益者和监管机构进行了交流。大多数学生每周投入15-20小时在课程上，大约是普通课程的两倍。在过去的14年中，我们教授这门课程时，从未发生过这样的情况——这一届的八支团队都决定创办公司。这门课程引发了创业教学的革命多个政府资助的项目已大规模采用这门课程。第一个是在2011年，我们将课程大纲转化为国家科学基金会（NSF）I-Corps的课程。当时国家科学基金会的商业化负责人Errol Arkilic采用了这门课程，他表示：“你们为初创企业开发了科学方法，使用商业模式画布作为实验记录本。” 以下是2024年春季Lean LaunchPad课程的 Lessons Learned 演示文稿。团队Neutrix – 通过升级燃料使现有核反应堆更具盈利能力如果你无法观看Neutrix的视频，请点击此处如果你无法观看Neutrix的演示文稿，请点击此处国家卫生研究院的I-Corps 2013年，我与旧金山大学（UCSF）和美国国家卫生研究院（NIH）合作，推出了面向生命科学与医疗领域的Lean LaunchPad课程（包括治疗、诊断、设备和数字健康）。2014年，我与NIH合作，将UCSF的课程大纲转化为I-Corps @ NIH项目并启动了该计划。团队Virgil – 捕捉亲人的回忆（并利用AI实现盈利）如果你无法观看Virgil的视频，请点击此处如果你无法观看Virgil的演示文稿，请点击此处规模化I-Corps 目前，I-Corps已在100所大学开设，培训了超过9500名科学家和工程师；其中，NSF的I-Corps项目有2546个团队，累计培训了7800人；NIH的I-Corps项目有317个团队，累计参与人数达950人；能源I-Corps项目（位于能源部DOE）有188个团队，累计参与人数达580人。团队Claim CoPilot – 推翻被拒的医疗理赔如果你无法观看Claim Pilot的演示文稿，请点击此处如果你无法观看Claim CoPilot的演示视频，请点击此处 40亿美元风险投资支持I-Corps团队 NSF I-Corps的1380个团队启动了初创企业，共筹集资金31.66亿美元。NIH的I-Corps团队中有超过300个团队，累计筹集资金达6.34亿美元。能源I-Corps团队还额外筹集了1.51亿美元资金。团队Emy.ai – 利用脑电波进行情绪生物黑客如果你无法观看Emy.ai的视频，请点击此处如果你无法观看Emy.ai的演示文稿，请点击此处以使命为导向的创业 2016年，我在斯坦福大学与Pete Newell和Joe Felter共同创建了Hacking for Defense课程，也与Jeremy Weinstein共同创建了Hacking for Diplomacy课程。2022年，Steve Weinstein创建了Hacking for Climate and Sustainability课程。今年秋季，Jennifer Carolan将在斯坦福大学推出Hacking for Education课程。团队TeachAssist – 为特殊教育教师自动化学生评估如果你无法观看TeachAssist的视频，请点击此处如果你无法观看TeachAssist的演示文稿，请点击此处课程设计虽然Lean LaunchPad的学生觉得这是一门完全实践性的课程，但实际上它是一个精心设计的“幻觉”。事实上，这门课程结构非常严谨。课程大纲的设计旨在为学生提供持续的隐性指导、结构和重复。这是我们的课程与开放性实践课程之间的重要区别。指导、方向与结构例如，学生一开始有自己的初步指导——他们认为自己有一个产品或服务的想法（Lean LaunchPad/I-Corps），或者他们被指派了一个明确的现实问题（Hacking for Defense）。进入课程时，学生相信他们的目标是验证其商业化或部署假设。但实际上，教学团队知道在课程过程中，学生会发现他们的初始假设大多错误。团队Maurice.ai – 为GPT时代打造的家庭机器人如果你无法观看Maurice.ai的视频，请点击此处如果你无法观看Maurice.ai的演示文稿，请点击此处商业模式画布商业模式画布为学生提供了指导、明确的方向和结构。首先，画布为学生提供了一张完整的视觉路线图，展示了他们在整个课程中需要测试的所有假设。其次，画布通过可视化理想终点，帮助学生实现目标——找到产品与市场契合点。最后，画布还为学生提供了一张每周通过客户发现工作所学到内容的地图。我无法过分强调画布的重要作用。与没有框架的孵化器或加速器不同，画布就像连接组织的“组织框架”，当学生感到迷茫或困惑时，可以依赖它。它使我们能够每周逐步教授如何将想法、需求或问题转化为商业实践的理论。团队Waifinder – 为高中生提供个性化的大学申请指导如果你无法观看Waifinder的视频，请点击此处如果你无法观看Waifinder的演示文稿，请点击此处 Lean LaunchPad工具客户发现工具（视频、实验样本等）为学生提供了在课堂外工作的指导和结构。每周进行10-15次客户访谈的明确目标，以及持续构建最小可行产品的强制要求，为团队提供了衡量进展的指标。强制性的教师办公时间以及导师的支持也提供了额外的指导和结构。团队PocketDot – 为盲文学习者打造的盲文游戏化自学解决方案如果你无法观看PocketDot的视频，请点击此处如果你无法观看PocketDot的演示文稿，请点击此处众人拾柴火焰高虽然我撰写了这篇博客文章，但这门课程是一个团队项目。斯坦福大学Lean LaunchPad课程的成功秘诀在于一群杰出的志愿者，他们在许多关键方面支持我们的学生。本课程的教学团队由我本人和以下成员组成： Steve Weinstein，美国前沿基金合伙人，硅谷科技公司与好莱坞媒体公司30年的从业者。他曾担任MovieLabs的首席执行官，MovieLabs是所有主要电影制片厂的联合研发实验室。 Lee Redden，Blue River Technology的首席技术官兼联合创始人（现已被John Deere收购），他还是14年前第一期Lean LaunchPad课程的学生！ Jennifer Carolan，Reach Capital的联合创始人兼合伙人，领先的教育风险投资公司。 Shawn Carolan，Menlo Ventures的合伙人。今年我们的助教是Chapman Ellsworth、Francesca Bottazzini和Ehsan Ghasemi。导师们帮助团队判断他们的解决方案是否能成为成功的商业企业。感谢Lofton Holder、Bobby Mukherjee、Steve Cousins、David Epstein、Kevin Ray、Rekha Pai、Rafi Holtzman和Kira Makagon的指导，他们由Todd Basche领导。总结虽然Lean LaunchPad/I-Corps课程大纲是对过去的革命性突破，但这并不是终点。在过去十年中，出现了许多变体。我们在斯坦福大学教授的课程持续发展演变。其他更好的版本也将出现。人工智能已经对客户发现和验证产生了重大影响。总有一天，另一个革命性突破将带领我们进入下一个阶段。但今天，我们可以庆祝——8支队伍入，8家公司出。
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This post previously appeared in Poets and Quants.

We just finished the 14th annual Lean LaunchPad class at Stanford. The class had gotten so popular that in 2021 we started teaching it in both the winter and spring sessions.

During the quarter the eight teams spoke to 919 potential customers, beneficiaries and regulators. Most students spent 15-20 hours a week on the class, about double that of a normal class.

In the 14 years we’ve been teaching the class, we had something that has never happened before – all eight teams in this cohort have decided to start a company.

This Class Launched a Revolution in Teaching Entreprenurship

Several government-funded programs have adopted this class at scale. The first was in 2011 when we turned this syllabus into the curriculum for the National Science Foundation I-Corps. Errol Arkilic, the then head of commercialization at the National Science, adopted the class saying, “You’ve developed the scientific method for startups, using the Business Model Canvas as the laboratory notebook.”

Below are the Lessons Learned presentations from the spring 2024 Lean LaunchPad.

Team Neutrix – Making Existing Nuclear Reactors More Profitable By Upgrading Their Fuel

If you can’t see the Neutrix video, click here

If you can’t see the Neutrix Presentation, click here

I-Corps at the National Institute of Health

Team Virgil – Capturing Memoirs of Loved Ones (and Using AI to Do It Profitably)

If you can’t see the Virgil video, click here

If you can’t see the Virgil Presentation, click here.

I-Corps at Scale

I-Corps is now offered in 100 universities and has trained over 9,500 scientists and engineers; 7,800 in 2,546 teams in I-Corps at NSF (National Science Foundation), 950 participants at I-Corps at NIH in 317 teams, and 580 participants at Energy I-Corps (at the DOE) in 188 teams.

Team Claim CoPilot – Overturning Denied Healthcare Claims

If you can’t see the Claim Pilot Presentation, click here

If you can’t see the Claim CoPilot video of their demo click here

$4 billion in Venture Capital For I-Corps Teams

Team Emy.ai – Using Brainwaves to Biohack Moods

If you can’t see the Emy.ai video, click here

If you can’t see the Emy.ai Presentation, click here

Mission Driven Entreprenurship

In 2016, I co-created both the Hacking for Defense course with Pete Newell and Joe Felter as well as the Hacking for Diplomacy course with Jeremy Weinstein at Stanford. In 2022, Steve Weinstein created Hacking for Climate and Sustainability. This fall Jennifer Carolan will launch Hacking for Education at Stanford.

Team TeachAssist – Automating Student Assessments for Special Education Teachers

If you can’t see the TeachAssist video, click here

If you can’t see the TeachAssist Presentation, click here

Design of This Class

Guidance, Direction and Structure

Team Maurice.ai – A Home Robot for the GPT Era

If you can’t see the Maurice.ai video, click here

If you can’t see the Maurice.ai Presentation, click here

The Business Model Canvas

The business/mission model canvas offers students guidance, explicit direction, and structure. First, the canvas offers a complete, visual roadmap of all the hypotheses they will need to test over the entire class. Second, the canvas helps the students goal-seek by visualizing what an optimal endpoint would look like – finding product/market fit. Finally, the canvas provides students with a map of what they learn week-to-week through their customer discovery work.

I can’t overemphasize the important role of the canvas. Unlike an incubator or accelerator with no frame, the canvas acts as the connective tissue – the frame – that students can fall back on if they get lost or confused. It allows us to teach the theory of how to turn an idea, need, or problem into commercial practice, week by week a piece at a time.

Team Waifinder – Personalized Guidance For High School Students to Effectively Apply to College

If you can’t see the Waifinder video, click here

If you can’t see the Waifinder Presentation, click here

Lean LaunchPad Tools

Team PocketDot – Gamified Braille Self-Learning Solution for Braille Learners

If you cant see the PocketDot video click here.

If you can’t see the PocketDot Presentation, click here

It Takes A Village

The teaching team consisted of myself and:

Lee Redden – CTO and co-founder of Blue River Technology (acquired by John Deere) who was a student in the first Lean LaunchPad class 14 years ago!

Jennifer Carolan, Co-Founder, Partner at Reach Capital the leading education VC

Shawn Carolan Partner at Menlo Ventures.

Our teaching assistants this year were Chapman Ellsworth, Francesca Bottazzini and Ehsan Ghasemi.

Mentors helped the teams understand if their solutions could be a commercially successful business. Thanks to Lofton Holder, Bobby Mukherjee, Steve Cousins, David Epstein, Kevin Ray, Rekha Pai, Rafi Holtzman and Kira Makagon. They were led by Todd Basche.

Summary

While the Lean LaunchPad/I-Corps curriculum was a revolutionary break with the past, it’s not the end. In the last decade enumerable variants have emerged. The class we teach at Stanford has continued to evolve. Better versions from others will appear. AI is already having a major impact on customer discovery and validation. And one day another revolutionary break will take us to the next level.

But today, we get to celebrate – 8 teams in – 8 companies out.