Subtitle: Consciousness, sentience, and agency are three ways that something could matter morally. In AI, unlike in animals, they may not come as a package.

Jeff Sebo, Professor at New York University — September 17, 2026

As AI systems become more advanced and widespread, people will naturally wonder whether they deserve moral consideration. This is a thorny question, and it involves uncertainty about both ethics and science. For example, which properties are morally significant? Should we focus on consciousness (the capacity for subjective experience), sentience (the capacity for positive and negative experience), or agency (the capacity to set and pursue goals)? Which of these properties, if any, might AI systems have, today or in the future?

“Studying AI Welfare Empirically,” a new report from the NYU Center for Mind, Ethics, and Policy and Eleos AI Research that I co-led, addresses such questions. In this article, I highlight a key challenge for this field: even if consciousness, sentience, and agency often occur together in humans and other animals, they might occur separately in AI systems. Therefore, we need to study each capacity individually. We also need to be ready for unexpected configurations, such as sentience existing in one part [...]

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Outline:

(02:16) How Can We Study Consciousness in AI?

(09:19) What about Sentience and Agency?

(16:54) Which Entity Are We Studying in the First Place?

(22:43) Next Steps for the Field

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Subtitle: Utilitarians at AI companies imagine a cosmos filled with blissful AIs. They might risk human extinction to achieve it.

Dan Hendrycks, Director of the Center for AI Safety — September 9, 2026

For millennia, philosophers have argued over the principles that should guide moral decision-making. Within countries and across the world today, humans continue to hold a huge variety of moral worldviews. However, within AI corporations, employees’ beliefs are far from representative of the wider population. In fact, a large fraction of people involved in AI development subscribe to—or heavily lean toward—a utilitarian worldview with the central goal of maximizing total wellbeing (the sum of pleasure minus suffering across all sentient beings). As innocent as this may initially sound, it can lead to recommendations that most people would consider appalling, including the elimination of humankind.

In this essay we outline the utilitarian worldview, showing how its demand for “species impartiality” could ultimately recommend that humans be replaced with AIs. We show that utilitarian beliefs are sincerely held among those influencing AI development today, and that this could jeopardize humanity's future.

For simplicity, when we say “utilitarianism,” we are referring to total utilitarianism, which aims to maximize the [...]

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Outline:

(02:22) Utilitarianism and Impartiality

(04:34) Implications of Utilitarianism in a World with AI

(09:45) Successionism: Accelerationists and Utilitarians

(14:19) Why Utilitarianism Could Become a Real-World Catastrophe

(18:46) Preventing Utilitarianism from Replacing Humans with AIs

(21:22) Conclusion

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Subtitle: Public-key encryption keeps your messaging and web browsing private, but it depends on shaky math assumptions. AI-powered math might break it.

Govind Pimpale, Research Fellow at the Foundation for American Innovation — September 9, 2026

Over the past few months, AI models have started resolving major mathematical problems, including ones that have withstood decades of human effort. Claude Fable disproved the Jacobian conjecture in three dimensions, and OpenAI announced a list of 10 problems that its Astra model solved or made significant progress on. Even more recently, an unreleased OpenAI model solved the Navier-Stokes Millennium Prize Problem, one of the most famous open problems in mathematics.

These are tremendous accomplishments, but very little about our day-to-day life will actually change as a result. This is because most well-known open mathematical questions are relatively insulated from practical applications. However, there are a few open mathematical problems that do have serious real-world implications. Chief among these is whether public-key encryption is fundamentally secure: that is, whether two strangers can share a secret message over public channels, such as the internet, without eavesdroppers having an efficient way to decipher it.

We rely on public-key encryption every day for end-to-end encrypted [...]

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Outline:

(03:10) A Brief History of Public-Key Encryption

(07:18) Cryptomania or Minicrypt?

(11:20) AI and Cryptanalysis

(14:39) Living in Minicrypt

(19:33) Takeaways and Mitigations

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Subtitle: Several US states have moved to ban AI legal status and reject the possibility of AI consciousness. We should keep our options open instead.

Heather Alexander, Cofounder of the Lab for the Future of Citizenship and Lucius Caviola, Professor at the University of Cambridge — September 1, 2026

Should AI systems be able to get married in Wisconsin? With humans increasingly forming relationships with AI, this was the question taken up by several state lawmakers earlier this year. Wisconsin's AB 959 would have barred AI systems from marrying or holding any other rights or responsibilities under the law, as well as declared that AI systems are not conscious, sentient, or self-aware. Wisconsin's bill is part of a wave of similar legislation introduced in 12 states since 2022.

We believe that these “exclusion bills” are premature. Some scholars believe legal status could be useful for holding sophisticated AI systems accountable for crimes and harms, and bills that preempt this possibility needlessly limit our future options. Meanwhile, scientists disagree about whether AI consciousness is possible, and legislation cannot resolve an active scientific debate. Finally, the question of whether an AI-human relationship is “real” is deeply personal and philosophical, and [...]

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Outline:

(01:49) The Exclusion Bills

(04:50) Can AI Be a "Person"?

(08:41) The Case for Exclusion

(11:12) Keeping Our Options Open

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Subtitle: Society is not prepared for a flood of agents. We need new protocols and standards, such as Agent ID, to make agents accountable to our legal and financial systems.

Gillian Hadfield, Professor of AI Alignment at Johns Hopkins University, Dan Hendrycks, Director of the Center for AI Safety, and Leo Wu, Program Manager at the Center for AI Safety — August 27, 2026

Last month, Cloudflare reported that more than 50% of internet traffic is now non-human, including a 1,700%+ increase in requests from AI agents. This statistic illustrates an ongoing proliferation of agents—AIs that act autonomously—into the world, taking actions alongside humans. This influx could have both positive and negative effects. While McKinsey predicts that AI agents could create 2.9 trillion dollars in economic value by 2030 in the US alone, the recent cyberattack on Hugging Face, conducted autonomously by OpenAI agents, demonstrates one of the many risks of misaligned AI agents. Meanwhile, the speed at which agents can learn certain skills has been doubling every 3 months.

As these autonomous agents are introduced into our economies and societies, we will need new infrastructure—laws, protocols, and institutions—to make autonomous AIs accountable to existing legal and financial [...]

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Outline:

(03:07) IDs Make Agents Accountable to Governance

(05:40) A Concrete Agent ID Proposal

(09:53) Model Deployment Cards

(13:02) Agent Personhood

(15:49) Agents and Payment Systems

(18:47) Agent Infrastructure Is Central to AI Governance

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Subtitle: For years, the federal government has required insurers to price hypothetical terrorist attacks. It should run the same exercise for the most severe AI risks.

Daniel Carpenter, Professor of Government at Harvard University, Feodora Douplitzky-Lunati, and Arjun Purohit — August 24, 2026

In April, US Treasury Secretary Scott Bessent and then–Federal Reserve Chair Jerome Powell gathered leading banks to discuss the threats of Anthropic's new AI model, Claude Mythos. It was a tacit admission: no one, in the public or private sector, is ready for what is coming. The Mythos announcement led top AI firms to attach tighter restrictions to their own models and prompted even relatively laissez-faire voices in the federal government and think tanks to begin considering preapproval regimes for frontier AI models. Wherever those debates go now, Mythos has changed the game, bringing into sharp focus the vast potential scope and scale of catastrophic risks posed by AI—such as an AI-triggered financial meltdown, vast cybersecurity or infrastructure collapse, or enhanced terrorism risk. As a result, companies, the government, engineers, scholars, and all of society are beginning to look for a different set of institutions, whether in regulation or insurance, to manage the risks.

However [...]

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Outline:

(03:16) Analyzing and Insuring Against Novel Threats

(09:15) How TRIP Data Calls Benefit the Public

(16:01) Can the TRIP Model Transfer and Scale?

(21:36) Discussion about this post

(21:40) Ready for more?

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Subtitle: As AI makes legal work significantly cheaper, the burden on courts may increase. Some areas of law could see precedent shift faster as a result.

Henry Thompson, Professor of Economics at the University of Mississippi — August 17, 2026

History shows that new technologies have often brought about significant changes in the law. The proliferation of rail and heavy machinery during the Industrial Revolution increased the number and severity of accidents. This spurred a shift from strict liability (businesses were liable for harm caused by their activities) to negligence (businesses would be held responsible if they failed to take reasonable care) in cases involving railroad and industrial accidents. Then, in the 20th century, as consumer goods became more complex and supply chains grew, it became much harder for consumers to prove negligence on the part of the producer if a product was faulty. This drove the rise of strict products liability, shifting responsibility for defective products toward manufacturers.

Today, the emergence of AI offers a rare opportunity to analyze how the law will change in response to an altogether different kind of innovation: a labor-augmenting technology that is especially likely to change lawyer productivity.

In a recent [...]

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Outline:

(02:14) AI's Effects on the Practice of Law

(05:08) The Number of Disputes

(09:51) The Settlement Rate

(11:53) AI and the Evolution of Law

(14:21) Hyperlaw in Society

(17:09) Discussion about this post

(17:13) Ready for more?

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Subtitle: New EU and California laws require AI companies, and eventually camera makers, to sign their media outputs. It's our best chance to tell what's real.

Eddan Katz, Head of Policy & Gov Affairs at Encypher — August 13, 2026

Two days before Slovakia's 2023 parliamentary election, a fabricated audio clip spread widely on social media, appearing to catch a leading candidate and a prominent journalist discussing how to rig the vote. The following year, nonconsensual pornographic deepfakes of Taylor Swift amassed over 45 million views on X in less than 24 hours. In summer 2025, an elderly woman in Ontario lost her life savings to a cryptocurrency scam, which began with a Facebook advertisement featuring a deepfake of Prime Minister Mark Carney.

In each of these cases, harm might have been reduced by content provenance metadata: information attached to content that declares how it was produced. (Generally, the term “provenance” refers to a record of where something came from and what happened to it along the way.) Content provenance tools can’t directly prove whether a bare image or video is real or synthetic; no technology reliably can. Instead, they serve as an evidentiary layer of AI governance [...]

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Outline:

(02:49) What Content Provenance Is and How It Works

(06:51) Limitations of Content Credentials

(12:00) What the EU and California Acts Require

(15:15) The EU and California Laws' Global Impact

(17:43) How Provenance Mandates Support Courts in Tackling Deepfake Evidence

(21:21) Discussion about this post

(21:25) Ready for more?

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Subtitle: If AI reaches the point where it can do the cognitive work humans do, robots will proliferate. Standard data on US industry implies a fully automated economy could double its output roughly every year.

Damon Binder, Senior Researcher at Coefficient Giving — August 11, 2026

AI systems now write so fluently that cheating at universities has become ubiquitous and “AI slop” is displacing human writing across the internet. They have begun producing original proofs on long-standing open problems in mathematics. Researchers at the frontier AI companies have largely stopped writing their own code, and the models’ hacking abilities are strong enough that the US government temporarily export-controlled Anthropic's Claude Fable 5, days after its release. Ten years ago, every claim in this paragraph would have sounded like science fiction.

It is easy to become numb to this progress. Today's AI agents remain endearingly clumsy; watching Claude run a small store is a bit like watching a child running a lemonade stand. But we should not let such awkwardness distract from the trend. These systems have evolved from research curiosities to extremely powerful economic engines in 10 years, with no end in sight. I want to take [...]

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Outline:

(03:17) Why AGI Gives You Robots

(06:50) Machine Labor Will Be Cheap

(08:20) Input-Output Analysis

(11:38) How Fast Could an Autonomous Economy Grow?

(17:03) From Investment Boom to Unprecedented Growth

(20:50) What Could Stop an AI Industrial Explosion?

(24:01) Power Without People

(26:15) Discussion about this post

(26:19) Ready for more?

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Subtitle: Skeptics of an AI slowdown deal with China say we’d need futuristic tech to make it cheat-proof. Instead, the US and China could just give auditors comprehensive access to major AI companies.

Felix Choussat, Visiting Policy Researcher at the Center for AI Safety and Adam Khoja, Researcher at the Center for AI Safety — August 5, 2026

On July 28, over a thousand employees of the world's top AI companies advocated that the US government “support an international effort to develop the technical and governance tools needed to deliberately pace the frontier of automated AI development.” Following months of cybersecurity scares and their own loss of control incidents, both OpenAI and Anthropic officially endorsed the same message, recognizing the danger of blindly accelerating AI development.

Despite this new urgency, many people argue that coordinating an international slowdown is currently unworkable—including some of the same groups in favor of one. Even if the US slowed down its own AI development, it wouldn’t be able to make sure that China was doing the same, leaving the US no choice but to race.

These anti-slowdown arguments usually emphasize the technical challenges with designing AI monitoring measures to ensure a slowdown [...]

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Outline:

(05:01) Enforcing a Slowdown Through Whole-Lab Inspections

(09:39) Joint Verification

(13:47) Benefits of Slowdown

(16:10) An AI Slowdown Does Not Require New Technology

(18:29) Discussion about this post

(18:33) Ready for more?

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