AI Frontiers

Subtitle: The public deserves a say over the values of government-procured AIs.

Kevin Frazier and Andrew Reddie — July 10, 2026

In September 2025, the state of California made an AI assistant, Poppy, generally available to employees, to help them “explore AI's productivity benefits.” The following month, the North Dakota Legislative Council deployed AI to assist with summarizing bills. And, in March 2026, the Los Angeles Superior Court partnered with an AI company to provide support across several key functions, including “case information, summarization, research, analysis and drafting assistance,” as well as case management.

In the near future, it is likely that more US states and the federal government will direct the technology toward even more sensitive and significant tasks. AI may be used to adjudicate disputes, accelerate law-enforcement activities, and aid military operations to an even greater extent than it does today. As AI use cases are more regularly documented, scrutinized, fine-tuned, and optimized, people may actually demand increased AI adoption among government actors, even in sensitive domains. That raises the question: which models should be used?

This question has no simple answer. With other technologies, the choice of a specific model might largely come down to [...]

---

Outline:

(02:32) How Model Character Could Influence Policy

(06:34) Introducing Public Reasoning Fidelity

(13:23) Designing an Institution to Evaluate PRF

(16:34) Precedents

(20:34) The Public Should Choose Public AI Values

(21:47) Discussion about this post

(21:51) Ready for more?

---

First published:

Source:

---

Narrated by TYPE III AUDIO.

---

Images from the article:

Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

In September 2025, the state of California made an AI assistant, Poppy, generally available to employees, to help them “explore AI's productivity benefits.” The following month, the North Dakota Legislative Council deployed AI to assist with summarizing bills. And, in March 2026, the Los Angeles Superior Court partnered with an AI company to provide support across several key functions, including “case information, summarization, research, analysis and drafting assistance,” as well as case management.

In the near future, it is likely that more US states and the federal government will direct the technology toward even more sensitive and significant tasks. AI may be used to adjudicate disputes, accelerate law-enforcement activities, and aid military operations to an even greater extent than it does today. As AI use cases are more regularly documented, scrutinized, fine-tuned, and optimized, people may actually demand increased AI adoption among government actors, even in sensitive domains. That raises the question: which models should be used?

This question has no simple answer. With other technologies, the choice of a specific model might largely come down to trade-offs between cost and performance. When it comes to AI, however, different frontier models may show significant differences in “character” or appear [...]

---

Outline:

(02:12) How Model Character Could Influence Policy

(06:18) Introducing Public Reasoning Fidelity

(13:18) Designing an Institution to Evaluate PRF

(16:29) Precedents

(20:33) The Public Should Choose Public AI Values

---

First published:

Source:

---

Narrated by TYPE III AUDIO.

Subtitle: One of the most valuable things governments can build today is the capacity to govern advanced AI competently in the future.

Charlie Bullock, Senior Research Fellow at the Institute for Law & AI — July 6, 2026

How should governments regulate the most advanced AI systems? One possible answer is that they should not. Libertarian-minded writers have made the case for a culture of “permissionless innovation” for AI development, in which the role of government would be limited to enforcing existing laws and facilitating industry self-regulation with “soft law” tools such as voluntary standard-setting. Another possibility, more in vogue across the aisle and the pond, would invoke the precautionary principle, using heavy-handed regulation to restrict the development of frontier AI models until developers can adequately prove that their systems are safe.

I think there's a better approach. In a new essay, my co-author Christoph Winter and I make the case for a governance strategy that we call “radical optionality.” The idea is simple: governments should avoid over-regulation in the short term while building up the institutional capacity needed to competently regulate extremely advanced or “transformative” future AI systems when and if these systems come into existence. The [...]

---

Outline:

(02:02) Addressing AI Risks Under Uncertainty

(06:40) Supporting Both Security and Innovation

(10:37) Discussion about this post

(10:41) Ready for more?

---

First published:

Source:

---

Narrated by TYPE III AUDIO.

---

Images from the article:

Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

How should governments regulate the most advanced AI systems? One possible answer is that they should not. Libertarian-minded writers have made the case for a culture of “permissionless innovation” for AI development, in which the role of government would be limited to enforcing existing laws and facilitating industry self-regulation with “soft law” tools such as voluntary standard-setting. Another possibility, more in vogue across the aisle and the pond, would invoke the precautionary principle, using heavy-handed regulation to restrict the development of frontier AI models until developers can adequately prove that their systems are safe.

I think there's a better approach. In a new essay, my co-author Christoph Winter and I make the case for a governance strategy that we call “radical optionality.” The idea is simple: governments should avoid over-regulation in the short term while building up the institutional capacity needed to competently regulate extremely advanced or “transformative” future AI systems when and if these systems come into existence. The point of this approach is to maximize optionality by providing our institutions with tools that can be used to respond to a wide range of foreseen or unforeseen future developments.

In this piece, we explain the rationale behind maximizing [...]

---

Outline:

(01:40) Addressing AI Risks Under Uncertainty

(06:26) Supporting Both Security and Innovation

---

First published:

Source:

---

Narrated by TYPE III AUDIO.

Subtitle: American leaders agree that the AI race will shape the balance of power with China. But they can’t agree on how to ensure the technology advances American values.

Bill Drexel, Senior Fellow at the Hudson Institute — June 30, 2026

When officials in Washington warn about losing the AI race to China, the conversation turns quickly to military and economic advantage—and rightly so. Advanced AI will reshape everything from weapons systems to medicine, with massive implications for our geopolitical competitiveness. But beneath the great-power competition on AI lies a moral one. The ethical character of the most transformative technology in generations—one that will mediate an ever-larger share of human experience—will be a byproduct of superpower rivalry. At stake is the future of the relationship between individuals and the state, of privacy and control, of human agency and algorithmic authority.

The fear is not merely that China might build better systems, deploy them more widely, or out-sell American competitors. It is that those systems could carry a tide of new norms shaped by a government that surveils its citizens, suppresses dissent, harbors eugenic ambitions, and treats individual autonomy as a problem. As AI comes to dominate our lives [...]

---

Outline:

(03:04) Breakaway Tech Dominance

(07:03) Encoded Values

(12:59) Emergent Control Regimes

(18:02) A Rebalanced Approach

(24:57) Discussion about this post

(25:01) Ready for more?

---

First published:

Source:

---

Narrated by TYPE III AUDIO.

---

Images from the article:

Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

When officials in Washington warn about losing the AI race to China, the conversation turns quickly to military and economic advantage—and rightly so. Advanced AI will reshape everything from weapons systems to medicine, with massive implications for our geopolitical competitiveness. But beneath the great-power competition on AI lies a moral one. The ethical character of the most transformative technology in generations—one that will mediate an ever-larger share of human experience—will be a byproduct of superpower rivalry. At stake is the future of the relationship between individuals and the state, of privacy and control, of human agency and algorithmic authority.

The fear is not merely that China might build better systems, deploy them more widely, or out-sell American competitors. It is that those systems could carry a tide of new norms shaped by a government that surveils its citizens, suppresses dissent, harbors eugenic ambitions, and treats individual autonomy as a problem. As AI comes to dominate our lives as thoroughly as digital media already has—shaping our health and finances, how our children learn, how we are tracked, even our species’ genetic makeup—this battle for AI's soul will affect us all intimately.

The contours of that battle are almost always left [...]

---

Outline:

(02:37) Breakaway Tech Dominance

(06:45) Encoded Values

(12:51) Emergent Control Regimes

(18:05) A Rebalanced Approach

---

First published:

Source:

---

Narrated by TYPE III AUDIO.

Subtitle: For decades, no nuclear power could disarm its rivals without provoking devastating retaliation. A large AI lead could change that.

Govind Pimpale — June 25, 2026

Since the late 1950s, Mutual Assured Destruction (MAD) has served as a limiting factor on great-power conflict. The doctrine holds that, if two opposing nations have nuclear weapons that can survive one another's initial strike, then the near-certainty of devastating retaliation will deter each side from launching a large-scale nuclear attack. Despite this logic, military planners have long considered the possibility of a counterforce nuclear attack, where a superpower uses nuclear weapons to cripple the nuclear capabilities of its enemy. If such an attack were executed preemptively, as a so-called “first strike,” it could both start and end a great-power conflict in a matter of hours: without retaliatory capacity, the defender would be at the mercy of the aggressor's remaining nuclear weapons and forced to surrender.

The reason this does not happen is that a truly successful counterforce strike is nearly impossible to pull off: a would-be attacker doesn’t know the locations of all opposing missile launchers and submarines, nor does it have missiles with sufficient precision and speed to destroy [...]

---

Outline:

(02:47) Why MAD Has Worked So Far

(06:02) Why AI Could Dramatically Cut Military Infrastructure Costs

(11:31) What Could We Build?

(15:22) The Defender's Options

(17:28) AI May Disrupt Nuclear Deterrence

(19:04) Discussion about this post

(19:08) Ready for more?

The original text contained 1 footnote which was omitted from this narration.

---

First published:

Source:

---

Narrated by TYPE III AUDIO.

---

Images from the article:

Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

Since the late 1950s, Mutual Assured Destruction (MAD) has served as a limiting factor on great-power conflict. The doctrine holds that, if two opposing nations have nuclear weapons that can survive one another's initial strike, then the near-certainty of devastating retaliation will deter each side from launching a large-scale nuclear attack. Despite this logic, military planners have long considered the possibility of a counterforce nuclear attack, where a superpower uses nuclear weapons to cripple the nuclear capabilities of its enemy. If such an attack were executed preemptively, as a so-called “first strike,” it could both start and end a great-power conflict in a matter of hours: without retaliatory capacity, the defender would be at the mercy of the aggressor's remaining nuclear weapons and forced to surrender.

The reason this does not happen is that a truly successful counterforce strike is nearly impossible to pull off: a would-be attacker doesn’t know the locations of all opposing missile launchers and submarines, nor does it have missiles with sufficient precision and speed to destroy opposing missile silos before they could launch retaliatory nuclear warheads. Thus, if provoked by a first strike, the opposing side would likely be able to launch a large-scale [...]

---

Outline:

(02:26) Why MAD Has Worked So Far

(05:40) Why AI Could Dramatically Cut Military Infrastructure Costs

(11:17) What Could We Build?

(15:06) The Defender's Options

(17:10) AI May Disrupt Nuclear Deterrence

The original text contained 1 footnote which was omitted from this narration.

---

First published:

Source:

---

Narrated by TYPE III AUDIO.

---

Images from the article:

Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

On June 12, the Trump administration issued an order requiring Anthropic to suspend access to its two most advanced AI models—Fable 5 and Mythos 5—for non-American nationals, just days after their public release. The decision caused immediate backlash across Europe. Politicians from France's Gabriel Attal and Jordan Bardella to the Netherlands’ Geert Wilders, alongside European industry and civil society, amplified calls for AI sovereignty. Attal, the presidential candidate for Macron's Renaissance party, even likened the shutdown of Anthropic's models to Iran's blockade of the Strait of Hormuz.

While such reactions are understandable, they underscore what we already know: Europe is behind on AI, overly dependent on the US, and vulnerable to unilateral decisions. Perhaps the renewed sovereignty rhetoric can help build the political capital needed to fix the continent's AI positioning, but what Europe needs more urgently is a clear-eyed account of what to actually do. In the wake of the Anthropic episode, this piece separates what has genuinely changed from what is being overstated, while identifying where European policymakers should focus their attention.

Europe Should Have Planned for This

In recent years, AI has become increasingly relevant to national security. Europe could have anticipated that the US government [...]

---

Outline:

(01:23) Europe Should Have Planned for This

(03:51) The Restrictions' Short-Term Impact on Europe May Be Limited

(06:44) In the Long Term, These Restrictions May Provide Opportunities for Europe

(10:45) How Does Europe Build Leverage When It Needs Results Now?

(14:27) Export Controls Are a Wake-Up Call for Europe

---

First published:

Source:

---

Narrated by TYPE III AUDIO.

The debate about AI's future economic impacts often settles into two camps predicting incompatible futures. One camp insists that AI is a normal technology: simply the next in a long line of economic transformations, each increasing productivity while gradually reallocating labor. The other camp warns that AI will become a great displacer: that automation will hollow out the working class within a decade and eventually disempower large swaths of human workers.

Each side often treats the other's predictions as unserious, and, consequently, policy debates often split along the same tired fault lines: whether we need reskilling or universal basic income, whether we should strengthen safety nets or structurally redesign our economy. The two camps’ forecasts diverge so sharply that it can be hard to see that they do not have to be mutually exclusive.

Rather, a more useful framing treats these predictions as describing different stages of the same overarching transition rather than as competing accounts of the same moment. From a macro perspective, both narratives will play out roughly sequentially, though those phases may overlap substantially across sectors and timelines.

In the short term, it seems inevitable that AI will look like an accelerated version of past automation [...]

---

Outline:

(02:38) Near Term: Managing Economic Shocks

(05:37) Medium Term: Navigating Reorganization and Divergence

(10:19) Long Term: Restructuring Economies

(14:05) Conclusion

(16:01) Discussion about this post

(16:05) Ready for more?

---

First published:

Source:

---

Narrated by TYPE III AUDIO.