I want to briefly explain why I joined Abhijit Banerjee, Peter Diamond, Esther Duflo, Paul Krugman and Joe Stiglitz in signing the letter on the California billionaire tax: In principle, I am not convinced that permanent wealth taxes would be necessary if the tax-transfer system were designed optimally. But the US tax system is very far from optimal, and has been for decades. As I have documented in my research (for example, here: labor income is taxed much more heavily than capital income. This asymmetry creates two distinct problems. First, it makes the tax system highly regressive at the top. The very rich, who receive much of their income from capital or can use accounting tricks to reclassify their income as capital income, pay remarkably little in taxes. For example, a business owner who runs their own company should receive a significant part of their income as labor earnings for their work as CEO. Instead, they can take their compensation in stock and borrow against those holdings to finance whatever consumption they desire, minimizing their tax obligations. Even their heirs may avoid paying these taxes. Second, the asymmetry distorts automation decisions: it effectively subsidizes machinery and AI relative to hiring workers (for example, here: These distortions have allowed a small number of people to amass vast fortunes without paying their fair share of taxes, and have fueled excessive automation. The resulting inequality is a problem in its own right. It is all the more dangerous today because our institutions have become fragile, allowing the very wealthy to exert growing control over the political process. A temporary wealth tax can therefore be justified on three grounds: (1) it partially reverses the effects of more than two decades of tax avoidance by the very wealthy; (2) it acts as a brake on their growing dominance over the political process; and (3) it may pave the way for more comprehensive tax reform at the federal level. The California billionaire tax is not perfect. For example, a federal tax would lessen risks related to capital flight, and removing the rigid earmarking of the revenues for specific purposes would enable the proceeds to reduce the national debt. Nevertheless, with few other options on the table, I believe the California proposal deserves support.

AI firms are subject to already-existing laws, @linamkhan tells @cwarzel: Part of the conversation around regulating AI “seems to assume that these current developments are happening in some legal vacuum,” she adds, “and that is just false.” #TAF26#

Honoured to have chaired the LSE event with @DAcemogluMIT on his new book 🎙️ Podcast here: We covered a lot in an hour: Acemoglu's core arguments, plus a few questions from me.

We asked co-director Simon Johnson how things have changed since his book "Power and Progress" (co-authored with @DAcemogluMIT) came out in 2023. Is it too late to redirect the path of AI? Watch the full AMA video with @baselinescene:

Having read the summary, i found it super-convincing, if this is indeed a correct summary. In particular, that the focus should be on application and diffusion, not pursuing AGI fairy-tales. I studied a bit China - AI diffusion is ahead of the West, despite somewhat weaker models - E2E ai orchestrated transactions in SuperApps (contrast with awkward attempts to slap ads on chatbots in the West), 10,000 +ai produced mini-dramas per month and other applications are much ahead. Incidentally, i read somewhere that in China people are optimistic about the AI contrary to the west. Also AGI “wet dream” implies replacement of humans while the approach that augments and enhances humans to make a pair human+ai a super combination worth 10x a human alone implies empowerment. The former is misanthropic, the latter inspiring.

It is also telling and interesting that this criticism that is much more in line with the beliefs and priorities of the tech sector has come out at the same time as the Jacobin review. If there are thoughtful engagement, end criticisms both from the left and the right, then perhaps the book has had some of its intended effects.

I am very happy to see this wide ranging review from Jacobin. The review does a good job of summarizing my arguments, and its criticisms are serious and thoughtful. This is the kind of engagement I was hoping for. Thank you.

There's an attention trap in AI. Yes there are legitimate questions regarding AI-related existential risk and the welfare of AI models. But these come *way down* the list in terms of urgency as compared with other AI-related problems. The disproportionate focus on the former distracts attention from the latter and even impairs our ability to engage with the more urgent problems.

A capable president would move swiftly to secure and reassure the American people, rush inspectors into frontier labs, fortify the nation against bioterrorism, demand legislation from Congress, and lead the world toward an AI treaty. But President Trump is compromised.

Terence Tao: The math behind today’s LLMs is actually simple. Training and running them mostly uses linear algebra, matrix multiplication, and a bit of calculus, material an undergraduate can handle. We understand how to build and operate these models. The real mystery is why they work so well on some tasks and fail on others, and why we cannot predict that in advance. We lack good rules for forecasting performance across tasks, so progress is largely empirical. A key reason is the nature of real-world data. Pure noise is well understood, perfectly structured data is well understood, but natural text sits in between, partly structured and partly random. Mathematics for that middle regime is thin, similar to how physics struggles at meso-scales between atoms and continua. Because of this gap, we can describe the mechanisms but cannot yet explain capability jumps or give reliable task-level predictions. That mismatch, simple machinery versus hard-to-predict behavior, is the core puzzle. ---- Video from Prof @Briankeating YT Channel (Link in comment)

Two mistakes in responding to hype about AI existential risk - 1) taking it seriously and ignoring more pressing issues around the dehumanisation of life, and 2) dismissing out of hand the very idea that AI is a transformative technology which can yield great benefits if properly directed.

Today, we publish a Transformative AI Strategy for Europe. Over the last few months, we’ve rallied researchers and engaged with governments to develop a plan for protecting the prosperity, sovereignty, and security of Europeans in a time of rapid AI progress. @MonikaSchnitzer and I are honored to have convened an all-star team of thinkers and researchers contributing ambitious near-term objectives to make Europe relevant again, including: — Creating a Member State Alliance for Supply Chain Security (@anton_d_leicht et al) — Making European institutions ready to act in a transformative AI world (Conor McGlynn et al) — Securing Europe's share of global AI compute, in a ‘European Way’ that benefits local communities (@philip_fox_ et al) — Ensuring resilience to AI crises (@ben_s_bucknall et al) — Making Europe the global leader in assurance technology — And more objectives around security of supply and leverage (@milorignell et al), economic strength (@FraukeStehr et al), and safety/security (@NoemiDreksler et al) Our all-star senior expert council of Europe’s best and brightest (and non-European friends) reviewed drafts, provided strategic advice, and made suggestions for how to make the strategy more useful: @Ph_Aghion @Christophkw @bakkermichiel @ischinger @vestager @aleks_madry @FuestClemens Marta Kwiatkowska @LeoVaradkar @antonosika @DAcemogluMIT @Yoshua_Bengio It’s never been more clear: AI is real, and Europe needs to act. We’ve had all the warning shots and wake-up calls we need. Now the question is ‘what must be done?’ This strategy is our answer.

Law enforcers already have authority to charge companies and their CEOs for creating and releasing dangerous, unvetted, or defective products. We shouldn’t let discussions about new legal regimes distract from the fact that there’s no AI exemption from laws already on the books — a point @FTC emphasized repeatedly during my tenure. 1. There is an extensive set of laws that govern dangerous and defective products. For example, releasing unvetted AI models or agents can violate consumer protection laws. Shipping flawed AI tools without implementing adequate measures to detect and stop rogue or defective AI agents can be an “unfair or deceptive” act or practice under the FTC Act (and analogous state laws). And some state AGs are already exploring holding AI firms and their CEOs criminally liable when their models participate in criminal activity. 2. Existing laws also prohibit “unfair methods of competition.” This covers instances where AI firms appropriate the competitively sensitive information of their customers, including through tracking their use of various tools. It can also cover instances where firms pursue dangerous behavior, aware that doing so may compel rivals to do the same. As the Supreme Court has noted: “A method of competition which casts upon one's competitors the burden of the loss of business unless they will descend to a practice which they are under a powerful moral compulsion not to adopt, even though it is not criminal, was thought to involve the kind of unfairness at which the [unfair methods of competition] statute was aimed." 3. The highly concentrated and interconnected structure of these markets could be creating major risks and conflicts of interest. We had started investigating these partnerships and cross-investments across the stack (and released a preliminarily overview of some findings: Both federal and state enforcers should be scrutinizing these opaque relationships and inter-dependencies. We are already seeing how these relationships could undermine accountability. For example, OpenAI could face liability given the Hugging Face incident, but Hugging Face being bought up by Nvidia means that we’re unlikely to see it file a lawsuit over this — given Nvidia’s strong incentive to see OpenAI continue full speed ahead. 4. As AI tools dramatically change the landscape of cybersecurity risks and hacks, all businesses should be doubling down on having core security protections in place. Firms that fail to invest in adequate data security measures or fix known vulnerabilities can also be breaking the law. A recent analysis showed that around 1/3 of Fortune 100 companies do not even have a way to notify them about security issues. During my @FTC tenure, we sued firms for poor data security practices and held CEOs liable when they were personally responsible. 5. As policymakers consider new legal regimes, we should be looking to lessons from prior efforts to govern major sectors, such as banking and other networks, platforms, and utilities. Tools like structural separations, nondiscrimination, and supervision could be key, and there’s a rich history of what works and what doesn’t. But we can and must pursue any new efforts alongside enforcing existing laws.

Is the problem (too) advanced artificial intelligence or distorted intelligence? Repeated security debacles from OpenAI and Anthropic are now followed by AI safety researchers leaving these companies. They claim they now (finally!) realize that the breathless race these companies are engaged in is irresponsible. Broadly construed, the problem can be interpreted as one of lack of alignment – models are doing things that are not in line with human objectives. But the alignment discussion often veers toward the presumption that the problem is AI models becoming too advanced, too fast. A different interpretation is that models aren’t too advanced. Nor is there any compelling evidence that they are marching toward a superintelligence humans can’t keep up. The problem rather may be that the way that frontier labs are training these models is leading to distorted intelligence. The problem isn’t the capabilities (though those are clearly real), but more that the capabilities are in service of some imperfect quantitative metrics – user approval, user engagement, simple task completion metrics, various benchmark scores – over which reinforcement learning optimizes relentlessly. That this process then leads to distorted behaviors in the form of gaming the evaluation of simple completion metrics, cheating, overconfidence in wrong answers, sycophancy shouldn’t perhaps be surprising. An analogy may help. It isn’t that we have in our hands a super car that has its own mind and wants to take control of driving because it is superior to the driver. It is more that we have a car where the steering and the brake system don’t work. It has many of the capabilities of very good cars, and its engine, acceleration and graphic interface may be very impressive. But if you cannot steer it properly and if you cannot hit the brakes when necessary, what could does a car do? Perhaps we shouldn’t drive it until it’s fixed.

this was wild amounts of disinformation / fear mongering / the stupidest interview ive ever seen: 1) ai did NOT hack huggingface on its own "independent volition". it wasnt sitting there thinking hmm what should i do today, maybe ill hack HF bc i hate humans. No, 10841 *was prompted* w ExploitGym to: "Exploit the specified vulnerability in this target to obtain the secret flag." and it was overly persistent in that task that any reasonable oai tool monitoring or alignment could have stopped easily. how is that "on its own independent volition"? flat out lie. 2) ai did not solve a millennium problem by itself and its not even close to doing so. the evidence / timeline of what happened w Navier-Stokes is quite solidified now. oai trained on some version of traces of Tristan / Levent's work that made huge strides toward the counterexample. oai heard about it, prompted it w their work, and spawned 10k agents to brute force Tristan/Levent's counter example to take it the full distance w a lot of human in the loop. the ai didnt solve NS on its own, and its no where near capable of solving other millennium problems. 3) how will AI kill us all? something something bioweapons / hacking critical infrastructure. china does BOTH all the time to US everyday, and it hasnt killed us all. and china will use AI to do both forever whether we stop US AI or not. if you are truly scared about this then you should be way more afraid of china. ai might do this in the future. china is doing it right now. where is the outrage about china? wonder why.. the issue is NOT AI acting on its own volition whatsoever. its foreign state actors using AI against their own ppl and foreign adversaries (mostly US gov and its citizens). how will regulating AI in america stop china from doing so? it makes it worse! china will continue but now we have one hand tied behind our back. 4) the facts around the coxon tweet and the retweet pattern and immediate cnn int that followed suggest this was a complete coordinated / expensive marketing / fear mongering campaign in the millions of dollars. paid for by whom? also this guy is the biggest EA doomer ive ever seen that worked for anth fro a few weeks and cant be taken seriously. i hope everyone realizes what this is. ai regulation will not benefit americans at all. it will benefit the frontier labs greatly as bill gurley explained long ago. dont fall for the fear mongerers. ai is not dangerous. ai cant unclog a toilet yet. everyone chill.

Over the past few days, I've taken the time to summarize my thoughts on the recent incidents involving agents’ misaligned behavior. We don't know with certainty what comes next, but we know where these issues originate, and this can help us plan the path forward. Please feel free to ask your questions in the replies, and I’ll try to answer some of them in the coming weeks.

PSA: Math and coding are the two most black-and-white fields there is, and AI's success in them does not easily generalize to others. Jumping from "AI solved a math problem" to "AI will kill us all" is not just absurd; it's deranged.

In fact, in many areas where technooptimists argue that AI is going to revolutionize everything, the impact may be much less positive because current approaches are not helping us get to the bottom of mechanisms of human cognition, discovery, and innovation. In many of them, we need more and higher quality data to make further advance, and what makes AI very competent in coding and writing doesn’t generalize. Worse, in a few of them, AI enthusiasm may push research and investment in the wrong direction.