Policymakers have spent years talking about rebuilding America’s industrial base, reshoring critical supply chains, strengthening defense production, and reducing U.S. dependence on China. But recognizing the need to build is not the same as having the ability to do it.

Erin Price-Wright, general partner on Andreessen Horowitz’s American Dynamism practice, joins the AI Policy Brief to make the case that AI isn’t just a software story. It’s the defining factor in the sectors that determine whether the U.S. can build, power, and defend itself in the decades ahead.

She and Matt Perault discuss how AI can help make the math work for building in the U.S. again—from accelerating mine permitting and coordinating complex industrial projects to designing factories, lowering the cost of automation, and bringing robotics to more factory floors.

They also discuss where policy needs to catch up: the laws and regulations that make it too hard and slow to build new factories in the U.S. and defense procurement that still favors incumbents over startups.

Finally, they discuss how the debates over data centers and jobs will shape whether America’s reindustrialization effort succeeds.

The takeaway: if the U.S. gets the policy environment right, AI can strengthen the industrial base, help create new kinds of jobs, and give America a powerful competitive advantage.

Topics covered:

00:00: Intro

00:54: Erin’s work investing in AI for the physical world

01:47: Why AI and reindustrialization are converging now

03:07: Applying AI to mining and critical minerals

08:28: What Ukraine reveals about defense production

18:13: How startups are breaking into government markets

20:16: Bringing a factory mindset to critical sectors and complex systems

24:39: How robotics can expand factory automation

30:22: What still makes it too hard to build in the U.S.

32:49: Using AI to design better, cheaper manufactured goods

35:05: Why data centers matter for reindustrialization

39:46: How compute could help modernize the grid and lower costs for consumers

42:50: Why AI could create new industrial jobs

Resources:

Subscribe to the a16z AI Policy Brief: https://a16zpolicy.substack.com/

Follow Matt Perault: https://x.com/MattPerault

Follow Erin Price-Wright: https://x.com/espricewright

Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

For AI startups, the policy landscape is expanding faster than most small teams can reasonably track. This creates a practical challenge for Little Tech: even when a startup wants to engage constructively, it may not have the resources to follow every debate in every jurisdiction.

Ben Supple, head of global policy at ElevenLabs, joins Matt Perault to talk about his experience running a public policy function at a company that is scaling rapidly. ElevenLabs is a leader in voice AI, building products for creators, enterprises, and governments, while its public policy function is still small enough to count on one hand.

The conversation offers a look at how a fast-growing AI company prioritizes policy work, builds relationships with governments, and makes the case for clear, consistent rules that startups can implement.

They also discuss how voice AI can improve citizen services and outcomes: replacing rigid, menu-based phone trees with more intelligent conversational agents that can resolve issues, switch languages live, and offer greater accessibility.

Topics covered:

00:00: Intro

01:42: What is ElevenLabs?

04:59: Voice AI use cases, from dubbing to customer service

06:59: The competitive landscape for voice AI

10:51: Building a policy function at ElevenLabs

12:00: Prioritizing policy work with a small team

15:13: Engaging policymakers across jurisdictions

18:11: Growing and shipping at startup speed

20:19: Human oversight and AI agents

22:54: Key policy issues for voice AI

25:42: Scaling into new regulatory obligations

30:17: State AI rules and the need for clear goalposts

32:25: ElevenLabs’ expansion in New York

34:10: Fixing the front door to government

36:22: Government use cases for voice AI

38:55: The social value of voice AI, One Million Voices, and accessibility

Resources:

Subscribe to the a16z AI Policy Brief: https://a16zpolicy.substack.com/

Follow Matt Perault: https://x.com/MattPerault

Follow Ben Supple: https://www.linkedin.com/in/ben-supple-a900695/

Learn more about ElevenLabs for Government: https://elevenlabs.io/chatbot/government

Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

How should we hold people responsible when AI causes harm? That's the job of a liability regime.

In this conversation, Jai Ramaswamy, chief legal and policy officer, joins Matt Perault, head of AI policy at a16z, to unpack the role of liability in AI policy: how to protect people from real harms without slowing innovation, limiting competition, or punishing the wrong actors.

They discuss why trust is essential to long-term AI adoption, why liability should focus on harmful uses rather than general-purpose development, and how policymakers can design rules that hold bad actors accountable while giving startups room to build.

The conversation also explores what a workable AI liability regime should prioritize: accountability, proportionality, enforcement of existing laws, and targeted updates where current law falls short.

For founders, policymakers, and anyone tracking the future of AI regulation, this episode offers a guide for thinking about responsibility, risk, and innovation in the AI era.

Topics covered:

00:00: Intro

01:00: Why AI liability is on the table now, and why it's been on Jai's mind for years

02:00: Why this matters — trust, long-term ecosystems, and the equities at stake

04:00: Failure modes at both ends — crushing liability vs. blanket immunity, and why neither serves Little Tech

08:00: The proposals we're concerned about — SB 1047, strict developer liability for downstream misuse, AI as an automatic aggravating factor in criminal law

14:00: The Little Tech lens — focusing on wrongdoing, not building, and why "paperwork favors the powerful"

18:00: The least-cost avoider principle and how it maps onto AI

23:00: Building a better regime — presumption of user liability for AI outputs, procedural safeguards, and well-designed safe harbors

31:00: Protecting good behavior — information sharing, incident reporting, and getting incentives right

32:00: Federal vs. state roles — the constitutional allocation as a guide to liability design

Resources:

Subscribe to the a16z AI Policy Brief: https://a16zpolicy.substack.com/

Follow Matt Perault: https://x.com/MattPerault

Follow Jai Ramaswamy: https://www.linkedin.com/in/jai-ramaswamy-85a77675/

Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

Andrew Chen spends his time with the founders policymakers almost never hear from: two-and three-person teams, often before incorporation when a company is still a project. As general partner on a16z speedrun, he works at the earliest edge of Little Tech, backing founders at day one and helping them turn ambition into an actual business.

In this conversation, Andrew joins Matt Perault to talk about what life looks like for small teams working at kitchen tables, operating on short runways, and simultaneously trying to build, find customers, and survive in competitive markets. They also discuss why so many startups are effectively absent from the policy process.

The conversation widens to the question of what makes startup ecosystems work in the first place. Andrew shares lessons from building the Tech Week ecosystem, including what local markets need to foster entrepreneurship and why supporting innovation is ultimately a choice.

Topics covered:

00:00: Intro

00:39: What is a16z speedrun?

02:08: The average profile of an early stage company

06:15: Why a16z built speedrun

08:36: A day in the life of a speedrun founder

13:22: What happens when startups do not work out

17:41: The cumulative burden of regulation for startups

21:49: Why Little Tech is absent from policy debates

25:05: How policy shapes where startups build

27:46: What makes startup ecosystems work

29:55: The idea behind Tech Week

32:52: How Tech Week surfaces future founders

33:23: Policy’s presence at Tech Week

34:38: Why policymakers should engage with Little Tech

Resources:

Subscribe to the a16z AI Policy Brief: https://a16zpolicy.substack.com/

Follow Matt Perault: https://x.com/MattPerault

Follow Andrew Chen: https://x.com/andrewchen

Learn more about a16z speedrun: https://speedrun.a16z.com/

Check out Tech Week event calendars: https://www.tech-week.com/calendar

Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

In this conversation, Matt Perault, head of AI policy, and Collin McCune, head of government affairs, take stock of the current AI policy moment.

As AI policy moves beyond rhetoric and into a more consequential phase, Matt and Collin separate signal from noise. They unpack where momentum is building in Washington, how state activity continues to drive the policy environment, and what it all means for Little Tech.

Along the way, they dig into some of the most active debates including proposals focused on protecting kids, workforce disruption, data centers, benchmarking and licensing regimes, and the evolving balance between federal and state action.

Enjoy.

Topics covered:

01:14: The current AI policy moment

03:45: The White House National AI Framework: what’s new and what’s next

11:49: Kids, AI access, and the case against bans

17:49: Data centers, communities, and energy policy

19:56: Workforce disruption, retraining, and labor policy

25:32: Copyright, censorship, and other key debates

26:55: The Democrat perspective and response

32:27: Benchmarking, testing, and startup access

33:23: Licensing regimes and regulatory capture risks

38:16: What’s next in Congress?

44:00: States at the center of AI policymaking

48:22: Preemption, federalism, and the state-federal divide

55:27: Dormant Commerce Clause implications

58:59: Why Little Tech needs to stay engaged now

Resources:

Subscribe to the a16z AI Policy Brief: https://a16zpolicy.substack.com/

Follow Matt Perault: https://x.com/MattPerault

Follow Collin McCune: https://x.com/Collin_McCune

Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

Black Forest Labs has established itself as a pioneer in visual intelligence, with its open-weight FLUX models reaching over 50 million downloads on Hugging Face and rivaling models from Google, OpenAI, and DeepSeek in developer adoption. The company has distinguished itself not only through technical capability, but through a strong commitment to open research.

In this conversation, Black Forest Labs’ Adam Chen and Ben Brooks, who lead the company’s legal and policy work, join Matt Perault to discuss what it means to build frontier visual AI openly. They explain the role of open models in advancing transparency, driving down the cost of innovation for developers, and strengthening security and sovereignty by reducing the world’s reliance on a handful of closed APIs. They also outline the unique policy challenges facing open-weight model developers.

For policymakers, their message is clear: supporting open innovation does not require abandoning oversight. It requires targeted rules, analysis of where harms arise, and a better understanding of how proposed regulations land on smaller frontier labs, not just the largest incumbents.

The conversation also offers a window into what it looks like to build a policy function at a startup. Adam and Ben offer a candid view into how they enable their small team to have outsized impact, rather than trying to match Big Tech’s playbook.

Topics covered:

00:48: Intro

01:57: What is Black Forest Labs?

03:13: The makeup of a legal team at a frontier AI startup

07:14: The role of visual intelligence in the AI ecosystem

09:49: Core risks and baseline safeguards for visual models

10:34: Unique policy challenges of open-weight models

12:25: Restricting access to general-purpose technology should be a last resort

15:52: What’s at stake: open models as soft power and the China dynamic

20:07: BFL’s approach to being open and responsible

22:26: BFL’s model testing results

24:59: How a four-person legal team approaches disclosure and compliance

28:32: What works and what doesn’t in transparency proposals

31:07: Navigating the state, federal, and international patchwork as a startup

33:47: BFL’s advocacy goals

37:13: The Little Tech voice as a competitive advantage in the policy ecosystem

How is AI changing work? In this episode, Matt Perault sat down with Nick Catino, global head of policy at Deel, to better understand what today’s data can already tell us. Through its HR and payroll platform, Deel works with 35,000 customers and 1.5 million workers across more than 150 countries, giving the company a broad view across employers, geographies, and job categories as AI begins to change hiring and work.

Nick walks through what Deel is seeing firsthand. That includes a 40% increase in the share of companies opening new AI roles in 2025. Deel’s recent global hiring report also found more than 70,000 AI trainer roles across 600-plus organizations, with nearly 60% of those roles located in the U.S., and AI trainer positions emerging as the fastest-growing global role on Deel’s platform.

The conversation also explores what these shifts mean for policy. If AI is going to change how people work, Nick argues smart policy should focus on helping workers build AI fluency and new skills, supporting students as they prepare to enter the workforce, and giving startups the clarity they need as they hire and scale.

Nick brings a valuable Little Tech perspective, drawing on his experience building public policy functions at fast-growing startups. For founders thinking about why startups need a seat at the table, along with when and how to engage with policymakers, this conversation is especially worthwhile.

Topics covered:

03:14: Deel’s global hiring view

04:27: Building a startup policy function

09:12: Data as a policy tool

12:20: Early signals on AI and the workforce

14:47: Job shifts and emerging roles

17:30: Policy levers to support workers

24:22: Why regulatory certainty matters for Little Tech

27:01: Scaling Deel’s data insights

29:20: The rise of AI trainer roles

31:36: Lessons from building policy functions at fast-growing startups

35:05: Why policymakers want to hear from Little Tech

Resources:

Read Deel's global hiring report: https://www.deel.com/global-hiring-report-2026/

Learn more about Deel's HR and payroll platform: https://www.deel.com/partners/a16z.ecosystem?utm_source=podcast&utm_medium=partner-sourced&utm_content=a16z.ecosystem&utm_place=organic-community

Subscribe to the a16z AI Policy Brief: https://a16zpolicy.substack.com/

Follow Matt Perault: https://x.com/MattPerault

Follow Nick Catino: https://x.com/CatinoNick

Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

The cybersecurity landscape is moving to an AI-vs-AI world where both attackers and defenders can operate at machine speed.

In this conversation, Anne Neuberger and Sam Jones join Jai Ramaswamy to go deeper on what this shift looks like in practice. Neuberger draws on nearly two decades in government—including serving as deputy national security advisor for cyber and emerging technology—to explain how AI is transforming the threat landscape, most notably making attacks faster, cheaper, and continuous at scale. Jones brings the builder’s perspective as CEO and cofounder of Method Security, where his team is building autonomous cyber systems for both offense and defense observing firsthand how AI is accelerating everything from routine tactics to exploit development.

Together, they discuss what “cyber resilience” means in an AI world: continuous testing and red-teaming that was previously cost-prohibitive, clearer benchmarks for critical infrastructure, and faster recovery when disruptions happen.

They also walk through the policy measures that can help defenders keep pace.

Topics covered:

03:16: How AI changes the threat landscape

07:38: Net new risks in an AI-vs-AI cyber world

11:21: Building trust to deploy new technology in no-fail systems13:55: Cybercrime at machine speed

17:18: Who benefits more from AI: attackers or defenders?1

9:29: Tactics to remove friction for defenders

22:26: Real examples of incidents where AI could have changed outcomes: Colonial Pipeline and Change Healthcare

27:18: What cyber resilience means in an AI world30:57: Measuring resilience

36:59: Information sharing and antitrust: lessons from financial services and telecom compromises

44:59: The builder’s view: what Method Security is building for offense + defense

49:27: Little Tech realities of building with a small team and selling into government

52:53: The role of procurement in ensuring defensive systems keep pace with adversaries

55:32: What’s next: in-year buying flexibility and closing thoughts

Resources:

Subscribe to the a16z AI Policy Brief: https://a16zpolicy.substack.com/

Follow Jai Ramaswamy: https://www.linkedin.com/in/jai-ramaswamy-85a77675/

Follow Anne Neuberger: https://www.linkedin.com/in/anne-neuberger-13b4491b/

Follow Sam Jones: @___sjones

Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

In AI policy, it’s become a reflex to say we are in a global race with China. That shorthand can obscure the true nature of the competition. China and the U.S. aren’t just competing on model performance or chips, we’re competing on the next computing systems the world adopts, and along with it, who holds economic and political power for the next generation. 

In this conversation, Jai Ramaswamy, our chief legal and policy officer, sits down with Matt Cronin, senior national security advisor at a16z, to make these competitive dynamics concrete. Cronin has worked on China-related national security issues as a federal prosecutor, held senior roles at the Department of Justice, served as Director of National Cybersecurity at the White House, and most recently as Chief Investigative Counsel and Deputy General Counsel to the U.S. House Select Committee on the Strategic Competition between the U.S. and China.

Their conversation unpacks the incentives driving the Chinese Communist Party’s push to dominate AI and what’s at stake if the world defaults to CCP-aligned AI rails.

They also get practical, outlining what “winning” looks like for the U.S.; the role of open source in global adoption; and the policy levers that play to America's strengths.

Finally, they zoom in on one opportunity with outsized impact–defense procurement reform—where Cronin has spent significant time.

If you care about the future defined by democratic values and are interested in a practical path to defend it, this is an episode for you.

Topics covered:

01:19: The Chinese Communist Party’s motivations in the AI race

04:08: Why China’s “miss” on the internet shaped its push into AI

07:48: State-led vs. market-led innovation models

10:48: What happened to China’s VC ecosystem

13:09: China’s strategy for AI diffusion and adoption

17:02: What’s at stake if China wins the AI race

23:47: The 3 key measures of US success in AI

25:25: Why open source matters for global adoption

31:05: AI policy levers that play to America’s strengths in this global race

34:15: Why defense procurement reform matters to the competitive dynamic

42:22: Final takeaways on competition, policy, and democratic advantage

Resources:

Follow Jai Ramaswamy: https://twitter.com/jai_ramaswamy

Follow Matt Cronin: https://www.linkedin.com/in/matt-cronin-8b88811

Subscribe to the a16z AI Policy Brief: https://a16zpolicy.substack.com/

Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

One of the core pillars of a16z's roadmap for federal AI legislation makes clear AI should not excuse wrongdoing. When people or companies use AI to break the law, existing criminal, civil rights, consumer protection, and antitrust frameworks should still apply. Enforcement agencies should have the resources they need to enforce the law. If existing bodies of law fall short in accounting for certain AI use cases, any new laws should be evidence-based, clearly defining marginal risks and the optimal approach to target harms directly.

In this conversation, we go deeper on what that principle means in practice with Martin Casado, general partner at a16z where he leads the firm’s infrastructure practice and invests in advanced AI systems and foundational compute.

Martin joins Jai Ramaswamy and Matt Perault to discuss how decades of technology policy can inform addressing harmful uses of AI, defining marginal risk in AI, the importance of open source for long-term competitiveness, and more.

Topics Covered:

01:55: A brief history of recent debates about how to regulate AI

12:30: Regulating use vs. development: lessons from software and cybersecurity

15:47: An open question in AI policy today: defining marginal risk

18:33: Why social media is often the wrong analogy for AI regulation

20:50: Enforcement tools available for holding bad actors to account

24:11: Balancing many trade-offs in tech policy

27:33: The role open source models play in soft power, the future of AI, and global competitiveness

38:06: Implications of regulatory uncertainty

41:32: Lawmakers want to act; what can they do now to enact effective policy?

Resources:

Follow Matt Perault: https://x.com/MattPerault

Follow Jai Ramaswamy: https://twitter.com/jai_ramaswamy

Follow Martin Casado: https://twitter.com/martin_casado

Subscribe to the a16z AI Policy Brief: https://a16zpolicy.substack.com/

Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.