Building advanced semiconductor foundries — and serving as the world’s trusted semiconductor supply chain capital — requires far more than purchasing multi-million-dollar lithography equipment. It also requires a fundamental alignment between a company’s business strategy, structural architecture, and organizational culture.

In the last episode of a three-part Strait Forward miniseries on why Taiwan did not steal America’s chip industry, host Channing Lee speaks with Dr. Konrad Young, a former Director for Research and Development (R&D) at TSMC, to discuss the secret sauce behind Taiwan’s semiconductor industry — and why Taiwan’s partnership with American tech firms has been the key to both countries’ technological successes.

For decades, the semiconductor industry operated under the Integrated Device Manufacturer (IDM) model, where companies designed, owned, and manufactured their own chips. Fabs were treated as internal captive facilities, where parent companies retain full ownership, control, and strategic alignment of production. When TSMC launched, its pure-play foundry model was widely considered unattractive. Yet, it solved a critical structural problem: an IDM firm will always prioritize its own product lines over external clients during market downturns. By promising never to compete with its customers, TSMC created a dedicated service model that allowed fabless design firms across Silicon Valley to innovate at unprecedented speeds.

Behind this commercial breakthrough was a radical engineering innovation. In traditional American IDM models, R&D occurred in isolated pilot facilities before technology was transferred to manufacturing. TSMC, under the guidance of R&D pioneers like Dr. Young, designed a system that integrated R&D directly inside 24/7 high-volume manufacturing fabs. Fusing the rigid discipline required of workers for continuous 24/7 manufacturing with the creative flexibility needed for R&D took over a decade to perfect, but it gave TSMC an insurmountable lead in yield optimization for chips.

Furthermore, the foundry model possessed a massive financial engine. While IDMs must replace manufacturing equipment to keep up with the evolution of specific product lines, foundry machinery can run for decades, manufacturing legacy nodes with minimal additional capital — generating steady cash flow to fund leading-edge R&D.

Crucially, Taiwan’s pure-play foundry model relies on what Dr. Young describes as a “servant mentality” — a cultural willingness to operate as a dedicated service partner to external clients. This organizational culture, he says, naturally fits smaller export-driven economies like Taiwan, whereas major powers often favor product ownership. As semiconductor production expands globally to strengthen allied economic security, automated “lights-out” fabs and AI integration will be vital to overcoming skilled labor shortages. Of course, duplicating hardware capabilities alone will not replicate decades of embedded operational culture. Thus, all players in the global chip industry will need to think beyond manufacturing to achieve supply chain resilience.

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The AI+ Health Summit Agenda is Live!

Join us for a day of exploring how AI is reshaping personal health and securing America’s edge in the global technology race.

📍 Washington, DC | 🗓️ September 10, 2026

Expect keynotes, panels, and fireside chats across AI & national security, longevity & aging, wearables & personal health, biomedical research, and responsible AI.

This summit is for clinicians, founders, policymakers, investors, and anyone navigating what AI means for their own care.

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Hello, I’m Ylli Bajraktari, President of the Special Competitive Studies Project. In this edition of our newsletter, SCSP’s Joseph Pontiff and Mackenzie Morante kick off a limited newsletter series, “Quantum States,” that will explore the history, foundation, and future of place-based quantum innovation in America. Over the next several weeks, we will be applying the methodology of our Tech Scorecard at the subnational level, and the team will spotlight what state and local governments are doing to spur the discovery, development, and deployment of quantum information science, engineering, and technologies (QISET).

Quantum information science, engineering, and technologies (QISET) is in its second revolution. It’s rapidly transitioning from a lab-based science to real-world commercial applications, resulting in a vibrant private sector and robust federal government mobilization. The first phase of QISET advancement, known as Quantum 1.0, roughly the first 100 years of quantum mechanics, resulted in the development of lasers and transistors, fundamental building blocks of the technology we encounter every single day. These technologies provided pathways to an ever deeper understanding of the universe, leading to Quantum 2.0: quantum sensing, quantum computing, and quantum networking, the technologies that can shape our future.

QISET is nearing an inflection point. To succeed, it requires state and federal investment across three pillars: academia, industry, and government. While state and federal strategies are being developed, the U.S. ecosystem remains disconnected, with decentralized hubs located in a few leading states. Our challenge, and our opportunity, is to turn decentralization into a flywheel for innovation; not by enforcing top-down control, but by engineering a strategy to synchronize the nation’s regional hubs. While California, Illinois, Colorado, Maryland, Massachusetts, and New York are already established quantum hubs, this newsletter serves to highlight unconventional regional innovators, illuminating models of states that are ready to meet the emergence of Quantum 2.0.

By exploring the history of quantum hubs around the country, we’ll uncover what makes each hub unique. These findings will guide us in engineering a more connected system, linked by strong pathways like neurons in a brain. In each newsletter of the series, we’ll explore a different region of the United States. Our first newsletter will feature the South East and Mid Atlantic, then we will move to the South West, then to Northeast and Midwest, and finally to the Rocky Mountain and Pacific regions. We’ll spotlight exceptional state and local ecosystems within the region, noting successful collaboration between the three pillars of academia, industry, and government. We’ll conclude the newsletter series with policy recommendations Washington should consider at the national level.

Methodology:

In this newsletter, we’ve outlined the quantum ecosystem through five categories: State and Federal Leverage, Market Ecosystem, Talent Pipeline, Industrial Capacity, and Innovation Leadership, using a similar framework to our Quantum Tech Scorecard report.

For each category, we collected data for each U.S. state within these metrics:

• Share of top 10% most Cited Research Publications: Keyword search for quantum-adjacent papers, research publications cited globally.

• Patents: Keyword search for quantum-defining terms, searching patents over all years with inventor address targeted per state.

• Quantum-enabling companies score: A scored category accounting for companies within a state focused on each of the 5 main quantum-enabling technologies: photonics, cryo, interconnects, microelectronics, and specialized materials.

• Evaluation of Holistic Full Quantum Stack (Compute, Sensing, Networking): A scored category accounting for the different types of facilities across computing, sensing, and networking within a state.

• Number of quantum companies (Major and Startup): A count of quantum-specific companies in a state, from industry leaders to small startups.

• Amount of Federal Funding Received: Federal funding attributed to states via DOE, NSF, DoD, Department of Commerce (NIST), NASA, DHS, and NSA.

• Number of National Labs and NIST Locations: A scored category accounting for the number of National Labs, National Quantum Information Science Research Centers within the Labs, and NIST locations.

• Number of Military Research Facilities: Military research installations with dedicated quantum programs.

• Magnitude of State Quantum Initiatives: A scored category accounting for announced state quantum initiatives and quantity of funding follow-through.

• Number of PhDs awarded: PhD degrees conferred in Computer Science, Engineering, and Physical Sciences.

• Undergraduate Quantum Program Feeders: Universities with devoted quantum computing research groups.

• Job Openings: Number of all quantum-related job openings ranging from technicians to PhDs.

Market Ecosystem

Developing a robust ecosystem of quantum technologies is not a one-size-fits-all approach, in which local leadership is guaranteed by hosting the most companies within a region. Rather, it requires a cohesive, strategic effort leveraging strengths within that state.

While quantum technologies fundamentally leverage the same unique quantum phenomena, quantum computing, quantum sensing, and networking each require vastly different footprints. States that are leading in the quantum revolution have a high volume of companies, often housing the majority of facilities, from R&D to fabrication, within their borders. A high score in this metric signals a state’s ability to attract companies, but more importantly, it reflects how effectively its strategies retain and capitalize on them.

When taking stock of the volume of companies and range of their specializations, states like Washington and Montana perform very similarly. Montana has 15 quantum companies that reside within its borders, exemplifying its photonics expertise. This specialization means Montana receives a lower Holistic Full Quantum Stack score, which measures the diversity of quantum industrial expertise. However, the high number of photonics companies makes it a strong regional tech hub for that specific layer of the quantum tech stack. Washington’s commercial approach leverages traditional tech companies such as Microsoft and Amazon, which are headquartered in the state. When it comes to quantum, Microsoft has software, Microsoft Azure quantum cloud, and algorithm development sites, and Amazon houses its Amazon bracket quantum cloud computing service sites. Washington is also home to a 100,000 sq foot IonQ facility, the first dedicated quantum computing manufacturing facility in the United States. Due to these companies and their diverse expertise, Washington has a high full-stack score, but only houses 4-6 quantum companies, much less than Montana.

These states have two very different approaches: one is hyper-specialized, the other, less so, with three different large quantum companies within its borders. Yet both states find themselves with similar market ecosystem scores and quantum impact, highlighting the various state-led approaches taken to build a local quantum ecosystem.

Talent Pipeline

A robust quantum talent pipeline is supported by all levels of education, and grows with access to experiential opportunities.

Quantum technology is a highly specialized field requiring years of dedicated training. While jobs in the quantum field have traditionally required a PhD, the future of the industry rests on the contributions from those holding bachelor’s degrees and technician certifications as quantum moves from research to commercialization and deployment.

These contributions are developed through America’s innovation engine: its Tier 1 educational institutions.

While traditional academic powerhouses such as Massachusetts and California are bound to have strong scores in this category, this map highlights the untapped potential of other states such as Texas and North Carolina. While these states may not have the highest score in this category, in part because of their lower volume of quantum job openings, they are primed to support the growth of quantum tech through a developed base of talent. Not only do these states award many PhDs, but their academic institutions host Quantum research centers such as the Texas Quantum Institute and the Duke Quantum Center. With a diverse range of quantum research groups within these centers, they provide experiential opportunities for the future generations of quantum mechanics. When fault-tolerant systems arrive at scale, these states, and many others, are poised to accelerate their development.

Industrial Capacity

As quantum approaches the maturity required for manufacturing devices at scale, states should capitalize on their existing industrial strengths in enabling technologies.

Traditionally, measuring industrial capacity means measuring both the number of production facilities and the volume of output by those facilities. However, contemporary quantum tech, specifically computing, networking, and sensing systems as a whole, has not reached utility scale or efficiency. As a result, they are not yet capable of being widely deployed.

The answer is to measure the diversity and development of a state’s ecosystem of mature supporting technologies. In the quantum realm, these are known as quantum-enabling technologies. Innovations from previous generations of quantum—like the Quantum 1.0 era of lasers, cryostats, and dilution refrigerators— as well as hardware from other industries in the form of specialized materials and microelectronics, these technologies provide the industrial base on which commercial quantum systems can develop and scale.

By measuring the diversity of these companies within a state, we can measure the foundation on which its quantum future rests. Ohio, Wisconsin, and Pennsylvania, with their top-tier research institutions combined with their high industrial capacity, have solid foundations on which to build competitive quantum ecosystems.

Importantly, due to America’s industrial capacity and the prevalence of quantum enabling technologies in many industries, many states have the necessary foundation to industrialize quantum tech. By leveraging this industrial strength, as well as existing infrastructure, in many different states, a high quantum enabling technologies score provides foresight into areas where quantum tech can expand and grow, even if states do not necessarily have a specific quantum company residing within their borders.

Innovation Leadership

Because quantum technologies remain in a research- and innovation-intensive stage and have not yet reached widespread deployment, this map closely resembles the overall Quantum States map. Many of the strongest-performing states also host dense networks of research universities, national laboratories, or both, which likely contribute to their publication and patent output.

Although a key difference between these two maps, is the innovation strength of a state like Arizona. Here, we see that innovation thrives when industry and government set demand signals for universities.

External actors – being government or industry who have a vested interest in quantum applications – should partner with universities to drive innovation and set application goals. Arizona leads in innovation leadership and serves as a cornerstone model of defense as a driver of operational use-cases. In 1995, three Army scientists planned the first-ever Department of Defense workshop on quantum computing and cryptography at the University of Arizona in Tucson; a key event in the timeline of quantum computing development. Sustained government and industry engagement can give universities the funding, test environments, and end-user feedback needed to demonstrate new applications and move promising technologies closer to operational and commercial readiness.

State and Federal Leverage

Each robust quantum ecosystem has targeted state-based funding and state oversight over ecosystem management.

Effective quantum ecosystems are often built around DOE national labs and NIST facilities. A hallmark example is the NIST Boulder lab that opened in 1954. In response, JILA emerged in 1962, functioning as a joint institute between NIST and the University of Colorado Boulder. The question for Quantum 2.0 becomes: how do states capitalize on an ecosystem built on federal innovation systems?

The most successful quantum states build strong synergies between pillars of a quantum ecosystem: academia, NIST, national labs, startups, and private industry. This often happens through strategic statewide initiatives such as California’s “Quantum California” or Maryland’s “Capital of Quantum.” Tennessee’s Tennessee Quantum Initiative builds on a foundation Chattanooga had already laid at the city level: EPB self-funded the nation’s first commercial quantum network in 2022, and private capital followed — anchoring IonQ, Vanderbilt, and UTC — before the state stepped in to scale that foundation statewide.

Florida is not yet an established quantum hub, but is an instructive model for state capitalization on infrastructure and a robust university system. The Florida Department of Commerce collaborates with the Florida Alliance for Quantum Technologies (FAQT), a consortium of 15 of Florida’s public and private universities, with the intent of becoming a quantum hub comparable to states like Colorado and Maryland. The Florida Secretary of Commerce has released statements about capitalizing on Florida‘s resilient power grid and strong space industry. Funding must continue to follow this ecosystem management intent.

Vocal support for quantum is a blank check without real capital behind it. States cannot rely solely on federal grants to build their high-tech future. If leaders want to attract talent and capture federal matching dollars, political advocacy must be backed by direct, state-level appropriations.

Quantum-specific funding is best channeled through state economic development councils or departments of commerce, rather than remaining confined to traditional academic or federal research grants. Shifting funds toward these state-level agencies is a strategic measure for states to take because quantum is more than high-level research; it is a broad workforce engine. Beyond software developers, a thriving quantum economy demands skilled trades like cryogenic HVAC technicians. For economic development councils, quantum is a prime vehicle to expand local employment and draw significant private investment.

Engineering the Map of Quantum 2.0

A robust quantum ecosystem starts with innovators in universities, government research facilities, and strategic regional infrastructure. Colorado’s quantum ecosystem was built upon CU-Boulder, its NIST facility, and the partnership that created JILA; California leveraged its semiconductor heritage and deep-tech venture density; and Maryland grew around pioneers like Chris Monroe, who translated his trapped-ion research at the University of Maryland into IonQ. The defining features of the next generation’s quantum hub will be each state’s ability to capitalize on its unique resources.

Turning those assets into regional advantage requires collaboration. The presence of industry, academia, and government is a necessary starting point, but it is not sufficient. True quantum hubs will emerge where these three pillars actively connect, whether through industry shaping university innovation or state government orchestrating regional alignment.

We’ve only scratched the surface of the states’ success stories. In the coming weeks, we’ll take a deep dive into leading and emerging quantum ecosystems across the country and explore the engine driving their growth.

Our concluding edition will leverage these regional insights to deliver nationwide policy recommendations aimed at bridging structural gaps within the currently fragmented quantum landscape.

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In this episode of Intel at the Edge, host Chip Usher sits down with Don Leka, Founder and CEO of Jumptuit, to explore a different kind of artificial intelligence — one built not on text, but on real-time data from the physical world.

Don shares his journey from building the Glide OS cloud platform in 2004, to founding Jumptuit, a company using a large dynamic reasoning model to track physical-world signals — from shipping movements and port activity to refinery output and geopolitical indicators — in near real time.

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Join Us at the AI+ Health Summit!

On September 10, 2026 in Washington, DC, SCSP will convene clinicians, founders, geneticists, technologists, and policymakers for AI+ Health — a summit on how AI is reshaping personal health, strengthening America’s resilience, and securing the nation’s edge in the global AI race. Check out some of our featured speakers below!

📍 Washington, DC | 🗓️ September 10, 2026

Expect keynotes, panels, fireside chats, and tech talks across five focus areas:

• AI & National Security

• Longevity & Aging

• Wearables & Personal Health

• Biomedical Research

• Responsible AI

This summit is for anyone with a stake in where AI and health are heading — hospital executives, policymakers, clinicians, researchers, investors, and the patients and consumers navigating what these tools mean for their own care.

Breakthroughs in AI are moving from the lab to the clinic to everyday life faster than ever. The AI+ Health Summit is where the people building that future — and the people who will decide how it’s used — get in the same room.

Register Today!

Data has been called the new oil, but for AI, power may be the real bottleneck. In this episode of NatSec Tech, host Jeanne Meserve is joined by Jennifer Obernier, CEO of Gristmill Exchange, to discuss why commercial data, not just government-collected intelligence, is now central to national security.

Obernier explains how a century of stovepiped, agency-by-agency data acquisition is giving way to shared infrastructure that lets the US and its allies buy exactly the data they need, at the speed missions demand, and why nations that master data velocity, not just data volume, will hold the real edge over rivals like China.

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Join Us at the AI+ Health Summit!

On September 10, 2026 in Washington, DC, SCSP will convene clinicians, founders, geneticists, technologists, and policymakers for AI+ Health — a summit on how AI is reshaping personal health, strengthening America’s resilience, and securing the nation’s edge in the global AI race. Check out some of our featured speakers below!

📍 Washington, DC | 🗓️ September 10, 2026

Expect keynotes, panels, fireside chats, and tech talks across five focus areas:

• AI & National Security

• Longevity & Aging

• Wearables & Personal Health

• Biomedical Research

• Responsible AI

This summit is for anyone with a stake in where AI and health are heading — hospital executives, policymakers, clinicians, researchers, investors, and the patients and consumers navigating what these tools mean for their own care.

Breakthroughs in AI are moving from the lab to the clinic to everyday life faster than ever. The AI+ Health Summit is where the people building that future — and the people who will decide how it’s used — get in the same room.

Register Today!

Global supply chains can hinge on remarkably modest beginnings. In 1974, inside a local Taipei diner over a $10 breakfast, seven government and technology leaders met to determine how Taiwan could escape a global oil crisis and upgrade its economy. Their decision was to bet on semiconductor manufacturing — through a method that was perceived to be a dying technology.

In Part Two of a three-part Strait Forward miniseries on why Taiwan did not steal America’s chip industry, host Channing Lee visits the Industrial Technology Research Institute (ITRI), established by Taiwan’s government in 1973 to bridge public research with commercial application. She speaks with Stephen Su, ITRI’s Senior Vice President, who shares how that breakfast meeting laid the groundwork for the future of Taiwan’s semiconductor industry.

ITRI signed a $10 million technology transfer agreement with RCA — a seventh-ranked American firm looking to exit the space — in 1976. RCA agreed to teach Taiwanese engineers both integrated circuit design and manufacturing.

Subsequently, a group of Taiwanese researchers were dispatched to the United States for a year of intensive training. Upon their return, they fine-tuned integrated circuit (IC) production yield from virtually 0% to 70%. ITRI continued to grow over the next several years and spun off its first independent company, the United Microelectronics Corporation (UMC), in 1980. Five years later, Morris Chang arrived at ITRI, leveraging its advanced research to launch the Taiwan Semiconductor Manufacturing Company (TSMC) in 1987.

While the 1976 deal provided the initial spark, Taiwan’s modern-day chip empire was built through decades of intense labor, yield improvement, institutional dedication, and global partnerships. Today, TSMC and its famous foundry model relies on over 1,200 suppliers, sourcing precision optics from the Netherlands, specialized chemicals from Japan, and design architectures from American leaders like Nvidia and Qualcomm.

This global interdependence highlights why modern industrial policy cannot succeed through isolation. In fact, over the last half-century, the semiconductor trajectory has evolved through three distinct paradigms:

• Made in Taiwan: The early era of domestic manufacturing assembly.

• Made by Taiwan: The current phase of Taiwanese foundries expanding fabs into Arizona, Japan, and Germany.

• Made with Taiwan: The future model of joint technological innovation, international research partnerships, and shared security standards.

Semiconductors are not a zero-sum game. True technological leadership requires cultivating global supply chains where international partners make each other stronger. And in this era of geopolitical and techno-economic competition, leaning into partners’ comparative advantages to fortify all of our economic security is of paramount importance.

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Sensing the Threat: Operationalizing Computer Vision for Intelligence and Warfighting

In October 1962, photo interpreters discovered Soviet missiles in Cuba and gave the president thirteen days to act. Pierson’s argument starts there and ends with an uncomfortable question: what happens when the next crisis gives us thirteen hours?

America’s Problem Is Not Innovation — It Is Adoption

Her core finding should encourage as much as it alarms, because it means the hardest part is already done. Computer vision is mature. Two functions in particular are proven at scale: change detection, which flags deviations from a known baseline across wide areas, and persistent object monitoring, which tracks vehicles, vessels, and launchers across space and time. American companies and laboratories built these capabilities; what remains is to field them. Yet U.S. intelligence and defense organizations still run predominantly human-centric imagery workflows.

That gap between what is technically mature and what is operationally fielded, she writes, “is no longer an inefficiency. It is a strategic vulnerability.”

What’s holding us back is institutional, not technical. Pierson catalogs the barriers with an insider’s precision: fragmented and over-classified training data; an artisan tradecraft culture in which automation is advisory rather than authoritative; accountability structures written for human judgment that hold machines to a higher standard than people; acquisition pathways that freeze models in time and leave pilots with no transition path; and institutional fragmentation in which no single authority owns AI-enabled ISR.

Her verdict on the current pattern is blunt:

“Bolting the future onto the past is not modernization. It is a way of postponing it.”

Others Are Already Past This Debate

Our competitors are not waiting. China has chosen diffusion over exquisite performance, pushing automated change detection and persistent monitoring across echelons and mission areas and wiring it directly into command and control — persistence and scale, not perfection, generating decision-speed advantage. Israel has demonstrated the harder lesson: once operational demand exceeds human bandwidth, automation stops being a choice.

Field First

Pierson’s recommendations lead with fielding, not governance, on the argument that the United States suffers from a shortage of fielded capability, not a shortage of oversight. Every computer vision effort, she argues, should carry an operational sponsor, explicit transition criteria, and a termination date — transition or terminate. Machine-first detection should become the default and manual scanning the justified exception: machines see first, humans decide. The Department should buy learning systems rather than frozen software, and buy commercial first — fusing commercial and open sources with classified collection rather than treating them as supplementary. Processing should move to the sensor, with on-orbit and edge computer vision treated as a strategic requirement in the constellations being designed now, alongside investment in spatial intelligence and 3D world models. And one accountable leader should be on the hook, backed by doctrine that lets the enterprise say yes — validation, auditability, and human–machine teaming standards that shift the default from restriction to responsible use.

The Decisive Decade, Seen Through an Optic Nerve

Pierson frames 2026–2030 as a narrowing window, because the architectures that will carry the force for a generation are being designed today. It is the same decisive-decade clock SCSP has been pointing to since our founding, and her paper connects directly to our Offset-X work, where sensing sits alongside AI and autonomy at the foundation of a new Joint Warfighting Concept.

As she puts it, a force that invests in sensors, AI, and autonomy but fails to operationalize computer vision “has built a nervous system without an optic nerve — collecting everything and perceiving too late.”

Her closing line is the one worth sitting with — but it is also, read rightly, a statement of American opportunity: the United States retains technical superiority, but superiority unfielded is superiority forfeited. The technology is ours. The window is open. What remains is the decision to field what we have already built.

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Narrative intelligence is emerging as one of the intelligence community’s most important new disciplines — and few people understand it better than Joe Stradinger.

In this episode of Intel at the Edge, Joe Stradinger — founder of Edge Theory and creator of the National Center for Narrative Intelligence in Oxford, Mississippi — joins Chip to break down what narrative intelligence actually is, how it differs from traditional social media monitoring, and why he believes it’s become a central front in modern cognitive warfare.

They cover:

• Joe’s path from CPA to early internet entrepreneur to national security advisor

• What “narrative intelligence” means and how it’s detected, classified, and measured

• The concept of “intercontinental ballistic narratives” and how adversary messaging spreads over time

• How China, Russia, and other actors exploit real-world events to sow division

• Washington’s growing (but still unsettled) response to cognitive warfare

• Why storytelling — from Joe’s Mississippi Delta roots to America’s own national narrative — may be the most powerful (and underused) tool in this fight

Listen wherever you get your podcasts.

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Announcing SCSP’s Next AI+ Summit: AI+ Health

On September 10, 2026 in Washington, DC, SCSP will convene clinicians, founders, geneticists, technologists, and policymakers for AI+ Health — a summit on how AI is reshaping personal health, strengthening America’s resilience, and securing the nation’s edge in the global AI race. Check out some of our featured speakers below!

📍 Washington, DC | 🗓️ September 10, 2026

Expect keynotes, panels, fireside chats, and tech talks across five focus areas:

• AI & National Security

• Longevity & Aging

• Wearables & Personal Health

• Biomedical Research

• Responsible AI

This summit is for anyone with a stake in where AI and health are heading — hospital executives, policymakers, clinicians, researchers, investors, and the patients and consumers navigating what these tools mean for their own care.

Breakthroughs in AI are moving from the lab to the clinic to everyday life faster than ever. The AI+ Health Summit is where the people building that future — and the people who will decide how it's used — get in the same room.

Register Today!

In this episode of NatSec Tech, host Jeanne Meserve is joined by Aliya Haq, President of Clean Econ, to discuss why "speed to power" may be the biggest constraint on America's ability to win the AI race. Haq breaks down why the century-old U.S. electric grid, with roughly 1.3 terawatts of capacity, is struggling to keep pace with surging demand, and how China has added that same amount of capacity in just three years.

They discuss the two terawatts of energy projects stuck in interconnection queues, why hyperscalers are resorting to building their own power plants "behind the meter," the political battle over renewables versus fossil fuels under the current administration, and the growing case for next-generation nuclear technology.

Available wherever you get your podcasts:

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Political rhetoric often relies on simple villains, and few narratives are as convenient — or as fundamentally flawed — as the claim that Taiwan “stole” America’s semiconductor industry. In a new three-part miniseries of Strait Forward, host Channing Lee seeks to debunk this myth by examining how and why Taiwan built its chip industry, and how Taiwan’s emergence as a manufacturing powerhouse enabled America to unleash its innovation power.

This first episode featuring Dr. Roy Lee, formerly Taiwan’s Representative to the EU and Belgium and Deputy Minister of Foreign Affairs, explores the geopolitical and economic conditions that created the urgency — and opportunity — for Taiwan to become the world’s chip hub. Contrary to the narrative of theft, Taiwan’s semiconductor success resulted from a deliberate, 45-year strategic partnership built on mutual survival, risk-taking, and shared security imperatives between Washington and Taipei.

In the 1970s, the global technology landscape looked entirely different. The American tech company Intel dominated processor microchips, while Japanese conglomerates held a tight grip on memory chips. Seeking to break this national security reliance on Japanese supply chains, the U.S. Department of Defense actively sought alternative manufacturing partners in Asia. Since Taiwan was, at the time, hitting an industrial ceiling with low-cost consumer electronics like digital calculators and watches, it became a strong contender.

Compounding this industrial challenge was a wave of geopolitical isolation. As the United Nations shifted diplomatic recognition from Taipei to Beijing in 1971, Taiwan faced a profound existential crisis. Economic transformation became a matter of national survival as the island confronted increased global isolation. Around the same time, professors Carver Mead and Lynn Conway published their ground-breaking academic textbook Introduction to VLSI Systems, introducing the theoretical decoupling of chip design from manufacturing. Taiwan gambled to put this theory into practice.

Far from an act of industrial espionage, the birth of Taiwan’s chip industry filled the structural gap created by American strategy and academic theory. With a top-tier engineering education pipeline and deep trust established between U.S.-trained Taiwanese engineers and American firms, the two countries forged a complementary ecosystem.

Taiwan’s trade deficit with the United States does not represent economic exploitation. Rather, it reflects Taiwan’s role in underpinning American technological supremacy. Decades of collaboration based on shared risk and mutual interests later, Taiwan’s chip manufacturing ecosystems are the most trusted in the world.

Watch and listen wherever you get your podcasts.

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💡Special Highlight: Dr. Eric Schmidt’s Remarks at the 2026 Exchange

At this spring’s 2026 Exchange—SCSP’s premier event designed to bring military commanders, defense technologists, policymakers, and industry leaders into the same conversation about the future of national security—SCSP Chair Dr. Eric Schmidt delivered a powerful keynote address titled “Technology at 300.” Looking ahead to the tri-centennial of the United States, Dr. Schmidt outlined a bold vision for preserving American techno-economic dominance.

📖Recommended Reads:

To foster informed discussions on critical technology policy and American competitiveness, here is a selection of non-fiction titles to explore this summer:

• Courage Can Save US by Rye Barcott

  • Ten interviews with a bipartisan group of post-9/11 leaders striving to rise above polarization and inspire us to serve with courage in our own lives.

• Lethal Autonomy: The Future of Warfare Whether We Like It or Not by Frank Kendall

  • Former Air Force Secretary Frank Kendall explores how AI-driven autonomous systems could make today’s manned platforms obsolete, whether the Pentagon is ready or not.

• Defending Taiwan: A Strategy to Prevent War with China by Eyck Freymann

  • A fully integrated political, military, technological, and economic strategy for dealing with the Taiwan crisis.

• Project Maven: A Marine Colonel, His Team, and the Dawn of AI Warfare by Katrina Manson

  • The dramatic story of a secretive, decade-long Pentagon campaign to deliver America into the age of AI warfare.

• Steve Jobs in Exile: The Untold Story of NeXT and the Remaking of an American Visionary by Geoffrey Cain

  • Drawing on unpublished footage and new interviews, Geoffrey Cain reconstructs the twelve humbling, near-bankrupt years at NeXT that quietly forged the discipline behind Jobs’s legendary return to Apple.

• The Mission Generation: Reclaim Your Purpose, Rewrite Success, Rebuild our Future by Arun Gupta and Thomas Fewer

  • A new blueprint for building a career around purpose and public service amid a world of AI disruption.

🇺🇲The President’s Tech Brief

This year, SCSP launched the President’s Tech Brief (PTB) — a live weekly show hosted by Ylli Bajraktari and Martijn Rasser, streaming every Friday at 11 AM ET. A nod to the President’s Daily Brief, PTB applies the gold standard of intelligence briefings to the one thing it struggles to keep pace with: emerging technology. Each week, the team tracks the shifting landscape of global technology leadership, leverages custom in-house AI tools to monitor key changes, and answers viewer questions live.

Since its debut, PTB has broadcast from the Munich Security Conference, covered a war in the Gulf, tracked the first-ever federal shutdown of a frontier AI model, and followed the AI race from Washington to Shanghai. Season one wrapped in July — and PTB returns in September, live twice a week.

A few standout conversations:

• Former Air Force Secretary Frank Kendall on AI-versus-human dogfight tests — and why the machines will soon beat the pilots.

• Former Ambassador Puneet Talwar & Former NSC Iran director Joel Rayburn on the U.S.-Iran deal, and why one calls it “the least bad alternative.”

• Philippine Defense Secretary Gilberto Teodoro — freshly sanctioned by Beijing — on China’s coercion playbook.

• Ben Nimmo, OpenAI’s lead for intelligence and investigations, revealing a covert Chinese influence operation that used ChatGPT to pose as Americans.

Catch all full episodes and weekly clips on our YouTube Channel and tune in live for briefings twice a week starting in the fall!

🎧 Must-Listen Podcast Episodes Across All 4 SCSP Podcasts

Whether you want executive-level strategy, tactical intelligence tradecraft, or regional deterrence, check out these standout 2026 podcast episodes:

1. Memos to the President - Hosted by Ylli Bajraktari

   • Jensen Huang on Generative Computing, Re-industrialization, & Physical AI: NVIDIA CEO Jensen Huang joins Ylli to discuss the shift to a generative compute paradigm and why leading physical AI is essential for a domestic industrial renaissance.

   • A Conversation with Taro Kono: Former Japanese Minister of Foreign Affairs, Defense, and Digital Transformation on allied technology strategy, semiconductor supply chains, and trilateral cooperation.

2. NatSec Tech - Hosted by Jeanne Meserve

   • Gina Raimondo & Eric Holcomb on States, AI, and Workforce Competitiveness: Recorded live at the AI+ Education Summit, discussing state-led outcome-based retraining, digital infrastructure, and preparing workers for automation.

   • Disinformation, Deepfakes, & Free Elections with Mark Murray: How AI tools are being deployed both to engineer and to counter election-season synthetic media and deepfakes.

3. Intel at the Edge - Hosted by Chip Usher

   • How Agentic AI is Enabling Intelligence-As-A-Service with Ansel Stein: Former CIA analyst and Vice President at Crisis24 Ansel Stein explores how corporate C-suites leverage AI-generated, tradecraft-vetted intelligence.

   • Why America Stopped Thinking About the Future with Ari Wallach: Long Path Labs founder Ari Wallach on scenario planning, megatrends, narrative warfare, and restoring long-range foresight to U.S. policy.

4. Strait Forward - Hosted by Channing Lee

   • The U.S.-Taiwan Semiconductor Education Alliance: Recorded live in Orlando with university leaders from SJSU, UCF, and NYCU in Taiwan on creating cross-border talent pipelines for microelectronics.

   • Why Taiwan Matters to Every U.S. City & State: TECRO Deputy Representative Andrea Yang breaks down subnational diplomacy, sister-city relationships, and tech investments across American heartlands.

Subscribe to all shows on Apple Podcasts, Spotify, or wherever you get your podcasts!

📄 Niche Newsletters

SCSP newsletters deliver timely analysis and updates directly to your inbox. Subscribe to ensure you don’t miss our expert insights and timely analysis. Check out some recent highlights below:

• Escape Velocity: Why America’s Discovery Engine is a Strategic Asset—and What It Will Take to Keep It One — A deep dive into preserving the nation’s scientific and R&D ecosystem as a foundational pillar of American power and global leadership.

• We Saw This Coming. The Playbook Is Already Written. — Strategic takeaways from SCSP experts on geopolitical crises, supply chain resilience, and how actionable deterrence playbooks can prevent technological surprises.

• Don’t Break the Engine of American Innovation — Commentary from SCSP leadership on maintaining the strength of the domestic tech ecosystem, protecting private-sector innovation, and aligning defense needs with commercial technology growth.

• America at 250: The Beginning of the Next Century of Innovation — Looking ahead as the nation approaches its semiquincentennial, framing the next 250 years of techno-economic competition, industrial capacity, and sovereign leadership in frontier technologies.

• Agentic AI: The Governance Race We Must Win — Examining the rapid rise of autonomous AI agents, their impact on intelligence analysis and workflows, and the strategic imperative of establishing trusted governance frameworks before widespread deployment.

Read all past editions and subscribe to not miss a release.

📚 Worth the Read: Recent SCSP Reports

Add these strategic reports and frameworks to your summer reading list:

Flagship Reports & Strategic Compacts

• Charging Ahead: Southeast Asia — Analyzing how China is driving the world’s fastest-growing electric vehicle market and what it means for Western supply chain competitiveness in the Indo-Pacific.

• U.S.-Israel Tech Alliance: A Strategic Technology Compact — A blueprint for deepening bilateral defense tech cooperation, joint AI research, and shared cyber resilience between American and Israeli innovation ecosystems.

• The Space Race — Examining the new era of commercial and national security space competition, sovereign satellite infrastructure, and counter-space threats in orbit.

• AI’s Potential Impact on the Army Officer Corps — Recommendations on adapting military career paths, talent management, and officer education for an AI-enabled battlespace.

SCSP Tech Competition Scorecards

Building on SCSP’s previous “Gaps Analysis,” the SCSP Tech Scorecard is a novel comparative framework that measures national advantages and vulnerabilities across critical technologies. By assessing five pillars—innovation, industrial capacity, market dynamics, talent, and national leverage—it delivers objective, multi-dimensional analysis to guide U.S.-China techno-economic policy.

• Tech Scorecard: Quantum Technology — Assessing global leadership in quantum computing, sensing, and post-quantum cryptography readiness.

• Tech Scorecard: Autonomous Vehicles — Evaluating the race for physical AI deployment, autonomous mobility, and vehicle data security.

• Tech Scorecard: Robotics & Advanced Manufacturing — Diagnosing positional advantages in industrial automation, supply chain hardware, and physical AI adoption.

🎓 SCSP x Coursera

Upskilling the national security and public sector workforce is central to winning the techno-economic competition. Explore SCSP’s new 2026 course offerings on Coursera:

• Human-Machine Teaming for Government: Dive into the core concepts of AI and human-machine teaming, including key technologies such as large language models (LLMs), and examine the ethical, legal, and policy considerations that guide responsible AI use in government contexts.

• AI in Global Competition: The Defense Dimension: Gain a strategic overview of how artificial intelligence is shaping modern warfare and defense, including its role in autonomous systems, logistics, and command and control.

• AI in Global Competition: The Intelligence Edge: Examine how artificial intelligence is reshaping intelligence, including its impact on intelligence collection, analysis, open-source intelligence (OSINT), and strategic competition between the United States and its adversaries.

• The Future of AI: Navigating AGI and ASI: Explore technical timelines, compute demands, and strategic implications of Artificial General Intelligence (AGI) and Artificial Superintelligence (ASI) for global power.

• Fusion Explained: The Geopolitics and Future of Energy: Drawing on the work of the Commission on the Scaling of Fusion Energy, learn the fundamentals of fusion energy—the process that powers the sun—and explains why it is the defining technological race of the 21st century.

• Gain access to the full catalog of Coursera: By signing up through SCSP, access Coursera’s entire course catalog to chart your full learning journey on artificial intelligence and emerging technology.

Enroll for free today via the SCSP Coursera Portal.

📰 SCSP in the News

SCSP experts continue to shape the national dialogue across major media outlets:

• Martijn Rasser — America Needs an Off-Ramp Between Doing Nothing and Shutting AI Down — War on the Rocks: Martijn calls for a statutory AI security review agency after June 2026’s export-control shutdown of Anthropic’s Fable 5 and Mythos 5.

• David Lin — Forged in a Knife Fight: China’s Brutal Domestic AI Competition — War on the Rocks: David argues China’s AI rise is driven as much by cutthroat domestic competition, or “involution,” as by state industrial policy.

• Channing Lee — Has Taiwan made itself immune to American betrayal? — The Japan Times: Channing argues U.S.-Taiwan ties are now embedded deeply enough in law, defense, and supply chains to survive any one administration’s rhetoric.

🗓️ What’s Next?

SCSP continues its mission to ensure America’s long-term competitiveness in the age of AI and emerging technologies. Stay tuned for further reports, upcoming events, and continued analysis on the critical issues shaping our techno-economic future. Make sure to save the date for our fall AI+ Summits!

• AI+ Health Summit, September 10

• AI+ Robotics Summit, October 14

• AI+ Quantum Summit, November 10

• AI+ The Future of Work Summit, December 3

Stay Connected

Thank you for your continued support of SCSP’s mission. We hope this list gives you plenty of thought-provoking material for the summer ahead.

Have a great summer, and don’t forget to subscribe, share, and leave your thoughts in the comments below!

Compute Is the New Currency of Military Power

Modern warfare is increasingly constrained not by the availability of weapons, platforms, or personnel, but by access to the computing power needed to connect and employ them at speed. The recent conflict with Iran made this vividly clear. During Operation Epic Fury, American forces leveraged AI tools such as the Maven Smart System to identify, prioritize, and strike 13,000 targets in the first 38 days of combat — an unprecedented degree of speed and scale in the history of warfare. This achievement echoes what the Ukrainian Armed Forces have demonstrated in their war against Russia, and what the Israel Defense Forces have shown in Gaza, Lebanon, and against Iran. Across the board, software and compute have become central to the way modern wars are fought — and won.

This is, at its core, an American advantage story. The United States leads the world in frontier AI, in cloud infrastructure, and in the data center buildout now reshaping the global economy. But that very dynamism means compute is a resource in fierce demand. Of the more than 35 gigawatts of data center capacity currently under construction in the United States, over 90 percent has already been pre-committed to a corporate customer. Companies like JPMorgan Chase now plan their compute needs five to ten years in advance, while firms such as Uber, Coinbase, and Walmart have had to ration compute expenditures as increasingly capable frontier models drive up costs. The lesson from the commercial sector is unmistakable: organizations can no longer assume compute will be available when they need it, in the quantities they need it. Those who prepare today will hold the advantage tomorrow.

The Canary in the Coal Mine

The compute constraints facing the commercial sector should sharpen the Department of War’s focus, given its own rapidly growing appetite for data-driven military systems. The Pentagon has confirmed that Maven Smart System usage during Epic Fury surged month-to-month by 38 percent on unclassified workloads and 89 percent on classified workloads. By token volume, top daily usage surged by 4,425 percent, hitting a peak of 20 billion tokens a day.

Surging demand is only half the picture; the Department’s current classified compute base is strikingly small by commercial standards. Consider U.S. Central Command, one of the military’s most AI-forward organizations. As of early 2025, CENTCOM’s classified network ran on just four NVIDIA H100 GPUs, with a partnership with the Air Force Research Laboratory set to add 24 more — a total the command’s chief data officer described as more classified compute capability than anything else he was tracking in the Department. For perspective, a typical commercial server node today holds four to eight of those same chips, meaning one of America’s premier combatant commands has been navigating the AI transition with roughly the classified computing power of a single commercial server rack. And a single mid-sized AI company, Inflection AI, assembled a cluster of some 22,000 H100s back in 2023 — hundreds of times the Department’s most ambitious classified deployment. The Department itself recognizes the gap: its fiscal year 2027 budget request seeks nearly $30 billion to transition from “scattered clusters” of GPUs to an integrated AI infrastructure portfolio. That candor is welcome — and it underscores both how far the Department has to travel and how decisive America’s commercial compute base will be in closing the distance.

As dramatic as these figures are, they would pale in comparison to the scale required to prevail in a major war against a peer competitor like China. A conflict in the Indo-Pacific would demand the analysis and fusion of far more sensor data, the prioritization of a far greater range of targets, and the coordination of operations and logistics across significantly more aircraft, warships, and personnel. These workloads would not be episodic. They would need to run continuously, at the speed of modern combat.

Here the Department faces a familiar dilemma — and history offers a proven answer. In peacetime, building and maintaining dedicated data centers that sit idle or underutilized is prohibitively expensive. In wartime, lead times for new facilities stretch between one and two years. But America has solved this problem before. For decades, civil reserve models have delivered critical resources and services in moments of need, coordinating supply and demand across military and civilian sectors alike. Just as the Civil Reserve Air Fleet and the Voluntary Intermodal Sealift Agreement allow the Department to surge transport capacity in a national crisis, the Department should now build the mechanisms to mobilize America’s massive commercial data center capacity in the event of war. Three lines of effort would put a Strategic Compute Reserve within reach.

Reevaluate the Requirements for Classified Compute

As the Federal Data Center Enhancement Act, which set minimum standards for federal data center on uptime, security, and energy use, lapses this September with no replacement in sight, the Administration has an opportunity to build a deliberate compute strategy for national security. This gap reflects a larger pattern: Department data infrastructure and cybersecurity policies have not fully kept pace with the shift to cloud and AI-driven computing. The Department’s leadership has long recognized that its cybersecurity requirements are excessively compliance-oriented rather than focused on delivering effective security outcomes for the warfighter. Many of these requirements pre-date the development of cloud computing, and they may no longer add value in the new operational paradigm.

The Department should reimagine its regulatory frameworks around risk-based accreditation, identifying which rules still make sense in a cloud computing environment and which are obsolete. In a full-scale war, the Department cannot wait months or years while additional classified compute resources are certified and inspected. The moment to reduce these unnecessary burdens is now, while there is still time to make deliberate reforms rather than deferring difficult decisions until the imminent approach of war forces the issue. By finally delivering on years of promised reforms, the Department can ensure that warfighters have the compute they need if called upon to “fight tonight.”

Pre-Build Security Into Commercial Data Centers

To prepare data centers for mobilization, the Department should work with cloud service providers to upgrade and maintain physical and cyber security environments, scaled to the sensitivity of the workload and the current capabilities of each facility. American industry is already well ahead here: Amazon Web Services, Microsoft, and Google have all developed the technology to virtually protect and separate commercial and sensitive military data below the Secret level, and the Department can work with providers to integrate this logical separation for classified workloads.

Enabling commercial data centers to run Secret and above workloads will require deeper collaboration and significant infrastructure investment — investment the Department can subsidize to incentivize hyperscalers to meet these stringent requirements. Financial packages could be structured along the lines of the Maritime Security Program, which provides financial backing to ship operators in return for enrollment in the sealift program. Some upgrades are straightforward: replacing magnetic strip key card systems with GSA-approved security locks, hardening air ducts larger than 96 square inches, and preinstalling physically separated SIPRNet wiring. Others — certifying classified supply chains and issuing security clearances to personnel — will take longer and demand significant lead times, which is precisely why the work should begin now.

Fortify Against Major Computing Security Threats

Finally, the Department must recognize that not every cybersecurity risk associated with commercial data centers can be remediated in advance. Facilities that operate as commercial or unclassified environments will contain hardware, networking equipment, or other components that cannot be fully trusted if repurposed for classified military workloads. For these cases, the Department should develop a rapid conversion model that accepts some equipment may need to be removed rather than converted. This approach allows the Department to distinguish between security requirements that can be pre-built into commercial data centers during peacetime and those that must be satisfied through wartime reconstitution before a formerly commercial facility connects to classified networks. The goal is not to pretend all commercial infrastructure can be made trusted in advance, but to build the plans, inventories, supply chains, workforce, and contracting mechanisms needed to transform that infrastructure into trusted military compute at speed.

The Work Ahead

In a world moving at the speed of data, slower queues and greater downtime translate directly into strategic disadvantage — and speed of preparation translates directly into strategic advantage. America enters this competition with assets no rival can match: the world’s leading AI companies, the world’s largest cloud infrastructure, and a proven tradition of harnessing commercial strength for national defense. What remains is to connect them. A Strategic Compute Reserve would do exactly that, ensuring that the extraordinary computing power America builds in peacetime stands ready to serve the nation in war. It is the insurance policy the Department of War needs — and one the United States is uniquely positioned to create.

Kimi K3, China’s largest open-weight AI model yet, just hit the public — and it’s rewriting the terms of the US-China AI race. In this episode, Chip Usher sits down with David Lin, Senior Director for Tech Platforms at SCSP and CIA veteran, to unpack what K3’s release really signals.

They dig into how the competition has shifted from a simple US-vs-China contest into a two-axis battle of open weights vs. closed weights, why K3 dropped right alongside Xi Jinping’s World AI Conference speech, the eye-popping cost gap between K3 and frontier US models, and the question of large-scale model distillation and IP theft.

They close out with a look at Nvidia’s Jensen Huang and a growing coalition of US companies pushing back against a closed-weight future. It’s a fast-moving landscape — and this conversation captures exactly where things stand today.

Apple

Spotify

Iran's Nuclear Program: "Down But Not Out" — Amy McAuliffe on What's Left After the Strikes

Four and a half months into the Iran war, what’s actually left of Iran’s nuclear program — and what would it take to keep it that way? In this episode, Chip Usher sits down with Amy McAuliffe, who recently retired from the CIA as Assistant Director for Weapons and Counter Proliferation and previously served as Chairman of the National Intelligence Council and Assistant Director of CIA for the Near East, to unpack where Iran’s nuclear ambitions really stand.

They dig into why Amy describes Iran’s program as “down but not out”, the uncertainty around Iran’s 60%-enriched uranium stockpile and where it may be located, and how much technical knowledge Iran has actually retained despite the loss of key nuclear scientists.

They close out with a look at how Khamenei’s death and the rise of new IRGC leadership could shift Iran’s calculus toward weaponization, what a credible future nuclear deal would need to include, and whether a purely military solution can ever fully resolve the nuclear question. It’s a fast-moving landscape — and this conversation captures exactly where things stand today.

Apple

Spotify

America’s competition with the Chinese Communist Party will not be won by the executive branch alone — but what Congress actually contributes to that competition is poorly understood, including by many in the policy community. From June 2025 to March 2026, I served as an SCSP National Security Technology Fellow on the Investigations and Oversight team of the House Select Committee on the Strategic Competition Between the United States and the Chinese Communist Party. What follows, shared with the Committee’s approval, is an account of how a congressional body with no traditional legislative jurisdiction fits into U.S.-China strategy — what its tools can do, where their limits lie, and what the experience suggests about the state of America’s response.

What the Committee Is — and Is Not

The Select Committee was established in 2023 by a 365–65 House vote to examine the geopolitical, economic, technological, and national security dimensions of the U.S.-China competition. It is currently led by Chairman John Moolenaar (MI-02) and Ranking Member Ro Khanna (CA-17), and it operates as a bipartisan body. The Committee, however, has no original legislation jurisdiction – meaning the Committee members can write and introduce legislation but they cannot move the legislation through the Committee on its own. Rather, it must rely on the Committee with the appropriate subject matter oversight. Its influence runs through other channels: surfacing risks, gathering information, and shaping the policy agenda of the committees and agencies that can act.

Investigation is its primary instrument. In emerging and strategically significant domains — advanced technology, investment flows, research security, critical infrastructure — regulatory frameworks lag both innovation and adversary behavior. The Committee’s investigations identify vulnerabilities across supply chains, research ecosystems, and capital markets before they become deeply embedded, then convert those findings into recommendations for others to carry forward. Oversight is the complement: examining whether American companies and institutions are inadvertently underwriting activities that erode U.S. security or competitiveness, with subpoena power as the lever when voluntary cooperation falls short. During my fellowship, I investigated how the CCP exploits openings in trade, technology, and international institutions to extend its influence — work spanning technology, finance, industrial policy, higher education, and global governance.

The honest caveat is that this model’s output is only as good as its uptake. A committee that recommends but cannot legislate depends on executive agencies, standing committees, and private institutions to act on what it surfaces — and they do not always act, or act quickly. Investigations generate pressure and information; they do not by themselves close vulnerabilities. Anyone assessing the Committee’s value should hold both facts at once: its investigative record has moved real policy conversations, and the gap between what it identifies and what the broader system fixes remains wide.

One principle in its work deserves wider adoption across China policy: the Committee directs its scrutiny at the policies and actions of the Chinese government and the CCP, never at the Chinese people. That distinction was made concrete through engagement with Uyghur and Hong Kong democracy activists and victims of transnational repression, whose testimony kept the human stakes of authoritarian rule in view. This competition is not solely about economics, military power, or technological leadership; it is also a contest over values and institutional integrity — and precision about the adversary is part of competing well.

The Stakes, Seen Firsthand

I did not arrive at the Committee needing to be convinced of those stakes – I had already seen them up close. My grandmother’s family fled Shanghai just before the Communist takeover in 1949; the relatives who stayed paid for their capitalist background and Kuomintang ties with imprisonment, public humiliation, and forced labor reeducation during the Cultural Revolution. Years later, living in China as a student, I watched police arrest a woman for peacefully practicing a Buddhist ritual in a public park while everyone around her — myself included — averted their eyes. That is what governance looks like when a state treats its own people as a problem to be monitored, managed, and controlled.

The Capitol offers a daily counterpoint — lobbyists, advocates, constituents, and officials pressing their cases through the same institutional channels. It is slower, noisier, and messier than the CCP’s model. It is also accountable in ways that in model cannot be, and the difference is worth defending even when the machinery frustrates.

What the Vantage Point Revealed

Three observations stood out from the Committee:

First, the bipartisan foundation has held. So far, members of both parties collaborate on joint investigations, hearings, and legislation, sustaining a shared view that the China challenge reaches beyond traditional national security into innovation, education, economic resilience, and industrial competitiveness. But, in my time, bipartisanship on diagnosis did not always translate into bipartisanship on legislative remedies, where costs get allocated and consensus thins. The Committee’s cohesion is an asset precisely because it is unusual, and it should not be assumed permanent.

Second, the policy community increasingly treats competitiveness and security as one project. There is growing recognition that America’s capacity to innovate and hold technological leadership underpins its long-term security, and that meeting the China challenge means investing in domestic strength, not only mitigating external threats. The recognition, however, seemed to be running ahead of the resourcing. From where I sat, the diagnosis was outpacing the investment, and the distance looked like where this competition will be decided.

Third, the private sector has grown more clear-eyed, though unevenly. After decades of treating the PRC chiefly as a market opportunity, many business leaders now weigh supply chain dependencies, intellectual property theft, and economic coercion alongside commercial upside. But exposure still seemed to shape appetite: firms with revenue at stake in China continue to face real tension between commercial interest and national security concern, and voluntary cooperation with congressional scrutiny often reflects that tension. The Committee’s subpoena power exists because goodwill alone does not suffice.

The Work Ahead

The fellowship sharpened the strategic context I bring back to SCSP’s technology portfolio. China’s push in artificial intelligence and other emerging technologies is not a purely technical story; it is an extension of the same long-term bid for global influence that runs through its economic statecraft, military modernization, and campaign to reshape international norms and institutions. Analyzing China’s AI development without that context misses what the technology is for.

The larger lesson is about how democratic systems actually compete: incrementally, through the accumulation of surfaced risks, informed policymakers, and institutions held to account. No single report or hearing dismantles authoritarian influence, and no one working in Congress believes otherwise. But the cumulative work compounds when government, industry, and civil society reinforce one another — and it stalls when any of the three assumes the others have it handled. For those weighing a career in public service, that is the realistic pitch: the tools are real, the progress is incremental, and the work depends on people willing to do it anyway.

Nations do not usually get to choose the moment when the method of science changes underneath them. New instruments arrive, the practice shifts, and the countries that adapt fastest set the terms for everyone else. Discoveries including the telescope, microscope, particle accelerator, and digital computer each redrew the boundary of the knowable, and each rewarded the societies best organized to adopt and exploit it.

We are living through such a moment now, and the United States has decided, for the first time in a generation, to treat it as a matter of national strategy rather than a matter of scientific curiosity. That was the animating premise of SCSP’s AI+ Discovery Summit, which convened government leaders, laboratory directors, allied officials, investors, and founders to ask a deceptively simple question: if artificial intelligence is genuinely changing how discovery works, what must America build to stay ahead of it?

Six themes emerged from the Summit.

1. Overcoming Compute, Data, and Organizational Bottlenecks

To ensure U.S. scientific advantage, Under Secretary for Science Darío Gil, who directs the Genesis Mission at the U.S. Department of Energy (DOE), framed the task ahead as building a national platform that connects the country’s best computing capabilities to the scientists who need it. This is done through what he described as an “internet of science”, capable of orchestrating agentic workflows across disciplines. When asked what stands in the way, his answer was institutional rather than technical. Even the most prestigious American universities, he observed, have not yet cracked the code on how to incorporate AI into their research enterprise. The labs and the academic community must be equipped to leverage the technology, which means giving scientists tools that they do not currently possess.

This is the uncomfortable finding of the Summit, and it recurred in nearly every session. Dylan Qian from Medra emphasized that, in life sciences, it has taken twenty years to automate roughly five percent of instruments. The problem is not robotics. It’s data. At scale, experimental design and validation have become decoupled, and the tacit knowledge of the working scientist—everything a good researcher knows instinctively—isn’t captured in the published output. Ron Alfa from Noetik noted that there is a similar problem with clinical data: trial results frequently have no repository at all, and when a trial’s sponsor ceases to exist, the data simply disappears.

While compute remains important, America’s comparative advantage in it will not convert into scientific advantage without data. This will require the institutional will and standards to ensure that data does not get lost in the labs, and is instead prioritized and cataloged for future use.

2. The Genesis Mission: A National Response

The response to Genesis has been extraordinary. Under Secretary Gil reported more than 5,000 proposals from roughly 500 institutions — a level of mobilization that suggests the scientific community does not regard AI-enabled discovery as a futuristic proposition but as a present one.

Setting that against the fiscal scale of the enterprise: the United States invests roughly a trillion dollars annually in research and development (R&D), with the private sector supplying the large majority, and will invest an estimated fifteen trillion dollars over the coming decade. The challenge Genesis faces is ensuring that AI is leveraged across every STEM discipline so the United States competes beyond spending.

3. National Laboratories: Untapped Engines of Scientific Discovery

The National Labs’ distinctive strength is in their scale: the ability to direct thousands of people at a single problem, all integrated across data, software, and instruments. The robust scientific workforce at the labs can often pivot to new challenges considerably faster than academia can. The ultimate goal is to free scientists from institutional boundaries, resulting in less time spent managing research and more time doing it. Additionally, the labs serve as trusted evaluators of AI technology itself, a function no company can perform for itself and no university is presently resourced to perform at scale.

On the tension between multi-year grant cycles and a research process that now moves in weeks, panelists highlighted the importance of holding firm to the mission, but staying nimble about the method. Fund the scientific outcomes, but leave the path flexible.

4. Funding Inertia: The Bottleneck in AI-Driven Science

If the research cycle has compressed, the funding cycle has not. The Summit’s funding panel acknowledged that these two timelines are now drastically mismatched. While fast-grant experiments born during the pandemic were referenced as proof that speed is achievable, those mechanisms have not scaled nationally. This gap in science and technology funding has been a direct brake on the pace of innovation within the United States, and increased buildout of test beds and benchmarks are needed to scale national capability without compromising product and scientific development. The regional dimension compounds the funding problem. Leaders at local levels traced how deliberate mid-century investment built the Research Triangle, and argued that the federal government’s irreplaceable roles are agenda-setting and de-risking early-stage technology. They also made the case for more non-dilutive capital to move discoveries out of laboratories to ensure good ideas don’t get trapped, and warned that innovation requires certainty and risk reduction. Both are currently in short supply.

5. The Interpretability Gap: Ensuring Scientific Oversight

In a session on the future of science, panelists raised the prospect of a future in which we do not understand the discoveries that AI produces for us, and argued that we need ‘symbiointelligence,’ or a genuine partnership with these systems, while warning that the institutions responsible for explainability are being eroded precisely as the need for them grows. Olaf Groth from Cambrian Futures outlined three archetypal futures, including one in which the United States and China harness abundant energy for their own scientific advancement and the rest of the world becomes an order-taker.

One of the sharpest challenges is our readiness. After nearly two decades working in and around national security institutions, Jacqueline Tame from Playground Global described walking into the Pentagon and intelligence agencies and finding not merely the same challenges but the same words on the same whiteboards.

Nathan Frey from Anthropic offered a constructive counterpoint from the model developer’s perspective, describing an approach oriented toward scientific outcomes rather than benchmark metrics, and toward features that working scientists actually require: oversight of the work and chain of custody for results. That is the right instinct, and it points to the institutional answer to concerns about understanding: explanation will be preserved by design choices and professional norms, or it will not be preserved at all.

6. Latent Scientific Potential in Global Alliances

The Summit’s international sessions made the case that alliances are a real scientific capability. Minister Counsellor Mungo Woodifield of the British Embassy described the United Kingdom’s AI-for-science strategy as resting on four pillars: datasets, compute access for researchers, talent, and automated research — backed by public commitments to science and technology running to roughly £110 billion through 2030, alongside targeted vehicles including a sovereign AI fund and dedicated fundamental AI laboratories. His most consequential proposition was the simplest: pair American compute with British data. The National Health Service holds population-scale datasets of a kind no U.S. institution can replicate, and making them AI-ready is a deliberate national project.

From the European side, André Loesekrug-Pietri of the Joint European Disruptive Initiative offered both an opportunity and a warning. He described this as a “golden year of science” in which scientific validation, with the help of AI, has been radically accelerated. He was also blunt about Europe’s dependence: the continent relies entirely on American GPUs, and continues to work in silos rather than engaging the broader ecosystem strategically. His prescription: Europe should stop trying to out-compete Chinese manufacturing and instead make calculated bets where its industrial base and mathematical traditions give it genuine advantage.

The AI+ Discovery Summit made it abundantly clear that the scientific community no longer views AI-enabled discovery as a futuristic proposition, but as an urgent present reality. To maintain its competitive edge, the United States must go beyond simply leading the world in compute and private capital. We must fundamentally rewire our institutions to prioritize data, leverage our international alliances to pair American computing power with global datasets, and modernize our funding mechanisms to match the accelerated speed of innovation.

Ultimately, the challenge of this technological shift is not merely technical, but organizational and strategic. As we navigate this new era of discovery, the United States must adapt its scientific enterprise rapidly, recognizing that the nations that organize fastest around these new tools will set the terms for everyone else.

In this episode of Memos to the President, Ylli Bajraktari & Joe Wang sat down with Leopoldo López and Félix Maradiaga for a wide-ranging conversation on the state of democracy across Latin America and beyond.

Leopoldo López is a Venezuelan political leader and human rights advocate who spent over six years as a political prisoner of the Maduro regime, before escaping Venezuela in 2020. He currently serves as co-founder of the World Liberty Congress, uniting pro-democracy movements against authoritarianism worldwide.

Félix Maradiaga is a Nicaraguan academic, human rights defender, and former presidential candidate who was arbitrarily imprisoned for 611 days before being exiled by the Ortega regime. He is President of the Foundation for the Freedom of Nicaragua, and is also the current president of the World Liberty Congress.

The conversation covered four major themes:

1. Venezuela’s Uncertain Transition: López breaks down what’s changed since Maduro’s capture — and what hasn’t. He walks through the Trump administration’s three-phase stabilization, economic recovery, and political transition plan, the upcoming August 1st negotiations, and why the recent earthquake exposed the true scale of state collapse.

2. The Authoritarian Network: Both guests unpack how Venezuela, Nicaragua, Cuba, Russia, China, and Iran actively support one another — and why toppling one node in that network has ripple effects across the others.

3. Nicaragua and the Road Ahead: Maradiaga assesses Ortega’s weakening grip on power, including his recent refusal to hold elections, and what a “fourth wave of democratization” could look like across the Western Hemisphere.

4. AI as a Tool for Freedom: Both leaders discuss how the World Liberty Congress is equipping pro-democracy activists worldwide with sovereign AI tools, the risks of authoritarian regimes weaponizing AI, and why expanding uncensored global internet access may be the single most powerful tool for freedom movements going forward.

The conversation is available wherever you get your podcasts.

Listen on Apple Podcasts

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Conflict no longer requires troops or tanks. Increasingly, authoritarian regimes wage war through cyberattacks, disinformation, election interference, sabotaged infrastructure, and even weaponized migration, deniable tools designed to catch open societies off guard and erode trust from within. In this episode, we ask: As AI supercharges these hybrid threats, can the world's democracies build the resilience to withstand them?

Dr. Teija Tiilikainen joins NatSec Tech from Helsinki to share her perspective on hybrid warfare and how democracies can defend themselves. Teija is Director of the European Centre of Excellence for Countering Hybrid Threats and previously served as Director of the Finnish Institute of International Affairs and as State Secretary at Finland's Ministry for Foreign Affairs.

She explains why hybrid attacks are so hard to detect and attribute, how Russia, China, Iran, and North Korea use them to test Western vulnerabilities, why AI currently favors the attacker, and why broad societal resilience, education, and public-private cooperation may be democracy's best defense.

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Great powers rarely decline because they run out of ideas. They often decline because they have lost the institutions that convert ideas into power and failed to notice this loss until a competitor demonstrates it for them.

For seventy years, the United States has held an asset that no rival could buy, copy, or steal at scale: a discovery engine. This engine was built from seventeen national laboratories; a research university system that draws the world’s best minds; a venture-capital layer that turns laboratory results into companies, and companies into industries; and a culture of openness that makes American science faster than science conducted under supervision. It is the engine that discovered the transistor, the internet, GPS, and the sequenced genome. It is as much a pillar of American power as the reserve currency or the aircraft carrier fleet—and yet for the better part of two decades, it has been treated as a line item rather than as a strategic asset.

Artificial intelligence has changed the calculus entirely. Previous general-purpose technologies changed what we could build. AI changes the rate at which we learn what to build. It acts directly on the derivative. A nation that compresses the discovery cycle—hypothesis, experiment, measurement, iteration—from years to months does not merely win the next emerging technology. It wins the one after that, and the one after that.

A key phrase describes the stakes in physical terms: escape velocity. Whoever reaches it first in AI-enabled discovery will not be caught. That is not hyperbole from the AI industry’s marketing department. It is a claim about compounding returns, and compounding returns are the most predictable force in a strategic competition.

Beijing understands this. China’s AI+ initiative is explicitly a diffusion strategy, aimed at pushing AI through advanced manufacturing and into the physical economy. China created a National Data Administration three years ago to organize and clean the state’s data holdings, on the premise that data is a factor of production. While China has published a national technology roadmap, the United States has published lists.

This is why the Department of Energy’s (DOE) Genesis Mission matters, and why a sustained national commitment is necessary to ensure the United States reaches scientific and technological (S&T) escape velocity.

To lay the groundwork, SCSP hosted a workshop on the future of AI-driven discovery with the Partnership for U.S. Leadership in Supercomputing, AI, and Quantum (PULS AIQ), of which SCSP is a member. Convening leaders from industry, national laboratories, academia, and government, the following describes some takeaways from the conversation.

What the Workshop Established

The returns are already demonstrable. DOE officials briefed examples of early successes from the Genesis Mission’s American Science and Security Platform—which will be an integrated, closed-loop AI system that integrates advanced computing and experimental facilities—that should end the argument about whether AI-for-science is merely speculative. One example was how AI agents were used to design components for prototype flight vehicles, with human engineers verifying and adjudicating. The result was that the production timeline moved from years to months with an exponential reduction in costs.

These are not laboratory curiosities. They are the early signs of a step-change in how the scientific enterprise operates—achieved, as one presenter described, at “start-up scale,” since right now, the loop is not yet closed, and there’s no self-refinement or orchestration across facilities. The current Genesis roadmap goes from automation this year, to closed-loop autonomy for multi-day workflows next year, to facility-scale self-improvement by 2028.

America’s true advantage is not just compute. It is our institutional knowledge. The recurring theme of the day—and the most underappreciated point in Washington—is that the United States sits atop decades of instrumented, high-fidelity scientific data generated by the national laboratory system. No other country, including China, possesses a repository of comparable depth. This collection is the propellant for AI-enabled discovery, and it is a genuinely non-fungible advantage.

However, one core issue is that, at present, this data remains largely unusable. The discussions returned again and again to the same problems: data that is not documented; documentation that is not linked to data; the “dark data” of lab notebooks and negative results and intermediate steps that never reach publication and therefore never reach a model; the absence of any standard for what “AI-ready” even means; a professional incentive structure that rewards guarding results rather than sharing them; and the plain fact that nobody wants to pay for curation. The Protein Data Bank (PDB), an open-access repository of biological macromolecules that is used by scientists worldwide to study and build upon, was cited repeatedly as the counterexample. Within the PDB, there is a community norm in which a biological structure is not considered finished until it is deposited into the repository. That norm is sustained by funding for people whose job rests on ensuring data quality. It is telling that the PDB is so readily identified as the gold standard for scientific data organization, while comparable examples are difficult to name.

The binding constraint is coordination, not physics or even funding. This is the pattern SCSP has now observed across quantum, fusion, and AI: the science is further along than the institutions. The funding cycle, the procurement cycle, and the technology cycle are not just unaligned; they are often pulling in opposite directions. Meanwhile, the mechanisms meant to bridge government and industry, like Cooperative Research and Development Agreements (CRADAs), were described as inadequate to the task and unevenly accessible.

Prioritization is the unsolved problem. The federal government’s research enterprise consists of an immeasurable number of funded projects and science and technology challenges with their associated agreements. This is a defensible research portfolio. However, it is hard to explain why one program out of so many should be the nation’s sole priority. National-scale programs, like the Apollo program, had the single goal of putting a man on the moon. The Human Genome Project had a genome. The Genesis Mission is currently misunderstood as an intangible platform without one set goal. As a result, many believe that if it aims to do everything, it will do nothing.

What We Took Away

1. Pick a north star. Concrete, legible, high-consequence objectives—a materials breakthrough with a named application, a demonstrable national-security capability, a fielded medical or energy solution—would allow Americans to see that Genesis was a success. The prioritization of these programs is not a betrayal of scientific breadth. It is simply the price of political durability.

2. Treat scientific data as a national asset—and fund it like infrastructure. Data management plans should be a standard requirement in federal requests for proposals (RFPs). The curation of legacy data should be a fundable activity in its own right, not an unpaid tax on principal investigators. Someone within the federal government should be accountable for this curation. China assigned that job to an entire agency. We have assigned it to no one.

3. Fix the way we form partnerships. Create more fellowships and externships that move people between labs, government, and industry in all directions. Fund compute resources so that firms and universities without hyperscaler budgets can participate in this new era. Make the government a customer and a validator. Startups often want test ranges more than they want grants and contracts. These are unglamorous fixes, but they ensure that the three pillars of America’s innovation ecosystem are working together more efficiently and effectively, pushing the frontier forward as one.

4. Focus on jobs and communities, not only on China. Some participants observed that there is China fatigue, and science advocates should not assume the technology competition frame will carry the argument by itself. Science is jobs. Science is economic competitiveness. These laboratories are distributed across the country. Genesis is not about fewer scientists. Instead, it’s about exponentially increasing discoveries per scientist, and the frequency of breakthroughs across America.

The Danger of Hesitancy

Much of the public conversation about AI is organized around a single question: what could go wrong? It is a reasonable question. It is also, at this moment, the wrong organizing question— because it has crowded out the one that determines the outcome of this competition: what could go right?

Not enough attention is paid to the cost of inaction. Every month that the discovery engine runs at analog speed is a month of compounding advantage forgone—in materials, in fusion, in medicine, in the physical sciences that underwrite everything else. The risk is not that we ask too much of AI in science. It is that we ask far too little, too slowly, and discover the cost of that caution when someone else reaches escape velocity first.

The American research ecosystem—its laboratories, its data, its openness, its people—is a strategic asset. Assets depreciate when they are not invested in. This one is depreciating now.

SCSP will carry this conversation forward at our AI+ Discovery Summit and through the continued work of PULS AIQ. The technical path is clear. What remains is the will to walk it at the speed the moment requires.

Explore the Agenda for the AI+ Discovery Summit!

We are excited to share the full agenda for tomorrow’s AI+ Discovery Summit!

If you’re tracking how AI is evolving from a software assistant into a full-blown discovery engine, this event is for you. The agenda focuses heavily on the shift toward autonomous “scientific agents” that can design and run experiments, the scaling of next-gen compute infrastructure, and what this all means for U.S. competitiveness.

If you want to see how the R&D pipeline is being completely rewritten, you can check out the newly launched agenda and grab a spot below.

Join us at the AI+ Discovery Summit

The Partnership for U.S. Leadership in Supercomputing, AI, and Quantum (PULS AIQ) convenes government, industry, academia, and philanthropy to build the case for American leadership in AI-enabled discovery. Special thanks to Tanya Das, Levi Patterson, and Jaclyn O’Day for their support in organizing the workshop. To learn more or to participate, contact SCSP.

The Latest on Strait Forward: Assessing PLA Military Readiness

China's military is testing Taiwan in new ways — fewer dramatic air incursions, and far more Coast Guard ships crowding Taiwan's waters in a slow, deliberate squeeze.

In this episode of Strait Forward, Channing Lee sits down in the Strait Forward studio with Dr. I-Chung Lai, President of the Prospect Foundation, to assess how Taiwan reads the readiness of the People's Liberation Army to conduct an operation around the island. They dig into whether Beijing has the capability for a full-scale invasion, why a blockade or quarantine may be the more realistic threat, what China might be learning from conflict in the Middle East, and how gray-zone tactics are reshaping the fight across the strait. Lai closes on a hopeful note: a Taiwan growing more economically confident and more sure of its own ability to defend itself.

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This Week on the President’s Tech Brief

Iran: The Strait Goes Quiet: Chip Usher recaps a week of escalation — Iranian strikes on vessels, more than 150 U.S. strikes in response, and a Saudi-Houthi ceasefire that just eroded after four years. Then Court Smith, Head of Partnerships at Kpler, brings the real-time shipping data: exactly how much oil is still moving through the Strait of Hormuz, how Iran is now hitting ships that have gone completely dark, and why oil prices haven’t spiked the way you’d expect.

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On Capitol Hill: The Defense Bill Stalls: The Iran conflict has spilled into Washington, where the Senate's NDAA hit a wall this week. Jenilee Keefe Singer and Brandon McKee explain what actually held it up (hint: it wasn't the technology provisions), which AI and quantum priorities are caught in the balance, and why this fight may drag into the fall. Then: New York becomes the first state to freeze new large AI data centers, and why other states may see an opening in it.

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AI & the Future of Work: "We Must Act Now": More than 200 economists and AI researchers just signed a letter warning that AI could reshape the economy faster than our institutions can adapt. Ryan Carpenter and Nyah Stewart break down what the letter is really calling for, why the U.S. may not be organized to compete, and the one thing their task force reached full consensus on. ]

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China Watch: Xi's AI World Stage: David Lin and Channing Lee unpack a carefully orchestrated week for Beijing: Xi Jinping's debut at the World AI Conference in Shanghai, the launch of the largest open-weight model China has ever deployed, and a new "World AI Conference Organization" that 29 countries signed onto. Plus, a viewer question opens up the race David argues China is already winning by default.

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Coming Up: The AI+Discovery Summit: Discovery used to move at the speed of a single human mind. Not anymore. AI is designing new materials, running fusion experiments, and finding tomorrow's medicine — and in some labs, machines are now designing their own experiments. The real question is who leads this next age of discovery. Join the national labs, the Department of Energy, fusion pioneers, and quantum builders in DC. July 21st — space is limited.

Register today!

That’s a wrap on the season.

We’re taking August off to recharge — but we’ll be back in September, bigger than ever and going live twice a week. Same mission: cutting through the noise on the tech competition shaping American power, and bringing you the conversations that actually matter.

Thanks for watching this season. Have a great August — and we’ll see you in September. Consider yourself briefed.

In this episode of “Intel at the Edge”, Chip Usher sits down with Ansel Stein, Vice President of Operations at Crisis24 and a former CIA analyst, to explore how artificial intelligence is reshaping the way private-sector companies access intelligence analysis.

Stein discusses how Crisis24 is delivering AI-generated intelligence directly to corporate C-suites — from geopolitical risk to tailored daily briefings built on a company’s own institutional knowledge. The conversation digs into the tradecraft behind the tools: how intelligence community standards are codified into AI systems, how human oversight fits into an AI-driven analytic process, and how self-improving models are changing the relationship between analysts and decision-makers.

Ansel and Chip also look ahead to how this “intelligence-as-a-service” model might eventually influence government intelligence organizations, and why deep organizational context and culture will remain a human differentiator even as raw intelligence becomes cheaper and more democratized through AI.

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🔬 Mapping the Frontier of AI-Powered Science: Join SCSP on July 21st!

The future of scientific discovery is being written today—and it’s powered by AI.

Next week in Washington, DC, the AI+ Discovery Summit will bring together the visionary minds reshaping how we research, build, and innovate.

📅 When: July 21st |📍 Where: Washington, D.C.

We are excited to feature leaders from across the AI ecosystem—spanning cutting-edge compute, academic research, and national scientific leadership. Our latest announced speakers include Carl Holshouser (CoreWeave), Andrea Hill (Virginia Tech), Dr. Nicholas Wright (NVIDIA), and Dr. Erwin Gianchandani (U.S. National Science Foundation).

AI-powered science starts with the people building it. Join us to explore what’s next on the horizon of discovery. Spots are limited, so register now before it’s too late!

Register for the AI+ Discovery Summit

In his recent piece, “America Risks Blowing the AI Race. Here Are Seven Ideas to Get Back on Track,” Jim VandeHei is right about the stakes, right about the narrowing window, and right that America is improvising when it should be executing. But he misses one crucial fact: the playbook for winning this race was already written.

Five years ago, the National Security Commission on Artificial Intelligence — a bipartisan body created by Congress, chaired by Eric Schmidt, with commissioners including Safra Catz, Andy Jassy, and Eric Horvitz — delivered a 750-page blueprint anticipating nearly every problem he describes: the talent crunch, the workforce dislocation, the biosecurity risk, the need for an allied technology coalition. I served as the Commission’s executive director, and we didn’t just diagnose the challenge. We handed the President and Congress draft legislation and executive orders to meet it. The problem was never a failure of foresight. It was a failure of follow-through.

The tragedy isn’t that we failed to anticipate the future. It’s that we anticipated it, wrote it down, and then stopped short of implementing it. The good news, as VandeHei says, is that the window hasn’t closed. Here is how to turn his seven ideas into results using a playbook that already exists, and finish the job.

1. We’ve organized as a nation before. The space program shows how.

The moonshot is the right frame, but the deeper lesson comes from what preceded it. After Sputnik, America didn’t just build rockets. It built institutions: the National Security Council apparatus matured, NASA was created, DARPA was born, and the government forged enduring partnerships with industry and universities. We invested in science at scale. NSCAI’s version of that investment was concrete: double federal non-defense AI R&D annually to reach $32 billion per year by 2026. That number was not aspirational padding — it was calibrated to what winning requires. Today federal non-defense AI R&D sits at a small fraction of that figure while China funds AI as a wartime priority. Private capital is surging, but venture dollars don’t fund the foundational research, testbeds, and national research infrastructure that produced the internet, GPS, and AI itself. A Sputnik response means public investment on a Sputnik scale.

2. The coordinating body has a name: the Technology Competitiveness Council.

VandeHei calls for a working group with actual authority. NSCAI proposed exactly that: a Technology Competitiveness Council at the White House, chaired by the Vice President, with the standing and staff to drive a national technology strategy across agencies — the way the National Security Council drives foreign policy. Ad hoc task forces and czars evaporate with each news cycle. A council anchored in statute, with a mandate to map risks and build playbooks before crises hit, is how you institutionalize anticipation instead of improvising after the fact.

3. A National Defense Education Act 2.0.

The workforce displacement debate is unproductive because we are arguing instead of building. After Sputnik, Congress passed the National Defense Education Act and transformed American science education within a generation. NSCAI called for a second NDEA — a national investment in digital talent spanning K-12 STEM, scholarships-for-service, a U.S. Digital Service Academy, and a National Digital Reserve Corps. The Federal Government should consider how to implement NSCAI’s recommendation for a National Education Act 2.0 now, before unemployment numbers force a panicked version of it later.

4. Retraining should be a national system, not a corporate pilot.

A real-time jobs app is a good idea — and it needs to plug into something. NSCAI recommended treating workforce transition as a national security function: continuously mapping where AI is creating and destroying demand, funding retraining pipelines through community colleges and apprenticeships, and using government’s convening power to match displaced workers to the jobs the AI buildout itself is creating — data centers, energy, chips, technicians. Scattered corporate retraining pledges are welcome. A national system, informed by real-time data from the AI companies, is what scales.

5. Make AI literacy mandatory.

Preparing businesses, colleges, and families cannot be voluntary and episodic. NSCAI’s talent recommendations rested on a simple premise: AI literacy is a baseline civic skill in this era, the way basic scientific literacy became one after Sputnik. Every high school and college should teach it; every federal worker should be trained in it; and teachers should be equipped to use it rather than left to police it. A country that sends its students and workforce into the AI era blind has chosen to lose.

6. Build the institution that takes catastrophic risk seriously.

VandeHei is right that the dangers are knowable — and knowable danger demands institutional response, not episodic alarm. We have a model. When chemical and biological weapons emerged as threats, the Department of Defense built dedicated organizations to understand them, red-team them, and prepare defenses. We should do the same for AI: a standing office within DoD charged with mapping what state and non-state actors could do with frontier capabilities — cyber, bio, autonomy — and building response infrastructure before the capability arrives. NSCAI warned about AI-enabled threats from adversaries exploiting open societies. Preparation is a bureaucratic choice. We should make it.

7. Win the coalition by controlling the full tech stack.

The global coalition VandeHei proposes only works if there is something to join. That means organizing allies around the entire technology stack — chips, tools, models, cloud, energy, standards — so that the world’s AI ecosystem runs on American and allied technology rather than drifting toward Chinese alternatives. NSCAI called for exactly this: coordinated export controls with allies, a protected semiconductor supply chain, and an alliance framework for AI innovation among democracies. The offer to partners is enticing precisely because it is complete: join a stack that is more capable, more trusted, and more open than Beijing’s. Half-measures — controlling chips while ceding infrastructure deals in the developing world — leave the door open.

The window is still open

VandeHei writes that we knew what was coming two years ago. In truth, we knew more that eight years ago – that’s why Congress created NSCAI. And since then, Congress and two administrations adopted real pieces of the NSCAI agenda, from CHIPS to AI offices across government. But we executed perhaps a third of the playbook. China, meanwhile, executed its plan by decree. Democracies can’t and shouldn’t work that way. What we can do is what we did after Sputnik: decide, invest, build institutions, educate a generation, and rally allies.

The ideas are not missing. The blueprint is not missing. What has been missing is the will to execute. Shame on us if, having written the plan twice, we fail to run it.