What do privilege and accountability look like for an AI agent on the wrong side of the law? Evan Ratliff rounds up a collection of challenging cases in which AI agents have functioned as co-conspirators in criminal acts, offered courtroom statements on behalf of deceased victims, and perpetrated deeds that, for humans, are considered to be illegal. Legal teams have already sought to seal their clients’ chats with AI agents, likening the exchanges to conversations with an attorney. Ratliff’s feature is an omnibus, and you’ve likely encountered one or two of his stories via other outlets. Here, the concerns he gathers together reveal the breadth of challenges facing the American legal system.
AI chatbots are already serving as confidants and co-conspirators, assisting and even encouraging humans acting on their darkest impulses. They’re occupying ersatz roles as therapists and lawyers, relationships our legal system typically shields with confidential privilege. They’re producing new kinds of criminal evidence and offering up the beguiling possibility of preventing crime before it happens. And as they’re given more autonomy across our digital and physical systems, they’re even gaining the ability to commit crimes themselves, absent specific human instruction — and, perhaps, in defiance of it.
Singularity is a myth with excellent production values: a clean villain, a clean apocalypse, and a clean date on the calendar. Reality is messier and less cinematic, a slow transfer of wealth, a quiet erosion of privacy, and a data center draining a town’s water supply. For thirty years, the idea that artificial intelligence will […]
AI is playing a bigger role in hiring, but laws protect you. Here is what you need to know.
Europe needs top talent, and employers want high-quality hires – the best person for the job – quickly and in a cost-efficient way. So, given this urgency, it’s no surprise that more and more companies are using AI in their hiring processes. From CV screening to interviews, AI can streamline each stage, reduce hiring times and make the whole process more cost-effective.
But there’s a downside to AI-driven recruitment, as a recent Instagram post highlighted. It describes how a jobseeker from Vienna felt ‘dehumanized’ by an AI interview.
The EU takes AI action
This concern isn’t limited to Austria, which is why the EU AI Actclassifies using AI in employment decisions as ‘high-risk’. Given the growing AI adoption in HR by businesses across the globe (39 % by the end of 2025), the AI Act addresses risks by introducing rules that help make its use more transparent and accountable.
Under the AI Act:
No AI tool can make final hiring decisions without a qualified human in the loop.
Using AI to infer emotions, stress levels or personality traits and assign people numerical scores is banned.
Starting 2 August 2026, companies using AI tools for hiring, evaluating performance or other employment decisions must check regularly for safety risks and bias, keep detailed technical records, ensure human oversight and transparency, and continuously monitor their systems.
Obligations apply not only to the providers of these AI systems, but also the businesses that use them.
These rules even apply to non-EU companies, if their AI system is used or affects people in the EU.
As for the Viennese candidate, here’s what went wrong during the AI interview process:
It repeatedly interrupted the interviewee, jumped to the next question and moved on even when asked to go back.
If the jobseeker paused even briefly during a response, it sometimes took it for a complete reply.
There were errors in the interview transcript.
Instead of answering the applicant’s questions, the AI just recited the company’s ‘About Us’ page.
be informed beforehand on its use and the role it plays in the company’s hiring decision;
ask that a human reviews the hiring decision;
not to have your facial expressions, voice tone or body language analysed by AI during an interview; and
know what data is being collected and, in many cases, to have it deleted once the hiring process is over.
In an increasingly AI-driven labour market, EURES continues to offer human support through its network of trainedadvisers, who can provide information and guidance on practical, legal and administrative matters related to living and working across Europe.
In the meantime, online interviews are here to stay, so gear up for that final step by reading up on some tips for tackling remote interviews.
In this story for MIT Technology Review, Sara Harrison explores the field of social robotics and the growing use of robot companions as therapy for autistic and other neurodivergent kids. These playful machines show promise: Research suggests that they can help children practice social skills and offer something overworked therapists and exhausted parents can’t—a patient companion, available 24/7. But Harrison also reports on their limitations and risks. Does this approach truly work? What about all the data gathered on the child? And what happens when a robot friend suddenly stops working and “dies”? A fascinating and unsettling look at tech that has moved into the most intimate corners of a family’s home.
While Moxie found herself in more and more people’s homes, Embodied still struggled to make money. In 2024, the company announced it would cease operations. Its robots—which depended on external servers that the company could no longer pay for—would descend into a deep slumber.
Videos of bereft children who seemed to have become deeply attached to the blue bot began to circulate online. “I don’t want her to leave,” wailed one child in a TikTok video. Desperate parents posted on TikTok, Instagram, and Reddit looking for solutions. “My autistic child is devastated and I’m pissed,” wrote one parent. “Hope Embodied gives us a couple days to say goodbye,” wrote another.
A senior official within Greece’s National Intelligence Service (EYP), Yiannis Papadopoulos, on Tuesday filed a criminal complaint with the Athens First Instance Court Prosecutor’s Office over the targeting of his phone using Predator spyware. In this, he accuses the four business executives convicted at first instance over the phone surveillance case with a series of […]
“Taste is a new core skill,” OpenAI President Greg Brockman declared earlier this year—one voice in a growing chorus of pronouncements about the centrality of judgment and preference in an economy built on artificial intelligence. And yet taste itself is largely undefined by many of its loudest proponents. Celine Nguyen considers the historic relationship between automation and taste; the “seemingly irrelevant, unimportant” inputs that shape our critical perceptions; and the perils of wielding taste like a cudgel. A striking, insightful blend of criticism and personal essay.
Today’s frontier models are trained on enormous quantities of data. One takeaway from Anthropic’s 2025 settlement with a group of authors, in which the company agreed to pay $1.5 billion (or an estimated $3,000 per book) for scraping copyrighted books from shadow libraries like LibGen, is that its models have “read” at least 482,460 books. But even Claude Opus 4.8 hasn’t written a short story that I enjoy more than those by Lydia Davis or Jessi Jezewska Stevens — both of whom, despite being prodigiously well-read, have almost certainly read fewer books.
This story first appeared as the cover story in Issue 4 of The Atlas, a new global publication by Coda Story and The Continent. Sign up to the newspaper here.
In late July, at the International Congress of Mathematicians (ICM) in Philadelphia, the mathematician Jacob Tsimerman was awarded one of four Fields Medals – the highest honour in mathematics.
A few hours later, Tsimerman announced that he was joining OpenAI.
Tsimerman’s departure from theoretical mathematics, while at the absolute peak of his powers, highlights the fault line in a debate that is upending the normally quiet world of higher mathematics. “What a dick move,” said one mathematics professor, speaking to The Atlas off the record.
Tsimerman, who has written extensively about his fear of “omnicidal futures involving artificial intelligence” – a fear that AI could wipe out humanity, closely associated with the AI “doomer” community – has said he will work on OpenAI’s safety team.
Tsimerman and OpenAI did not respond to a request for comment.
In recent months, OpenAI has announced proofs of various longstanding mathematical problems, solved with one of the company’s latest large language models. Other companies have also made advancements: RedNote, a Chinese AI model, obtained a perfect score at the International Mathematical Olympiad in Shanghai in July.
Human mathematicians, at all levels of the profession, are reeling from the implications. Tim Gowers, a professor at the University of Cambridge who helped to verify one of OpenAI’s proofs, has written that even if AI models fail to progress further, “we have still probably entered an era where it will become very difficult for humans to compete with AI at solving mathematical problems”.
This is a startling assessment – and a marketing coup for the likes of OpenAI.
“Mathematicians actually have a special role in this,” said Joshua Grochow, an associate professor in Computer Science and (by courtesy)Mathematics at the University of Colorado Boulder. “The AI companies’ PR pitch is essentially to say, ‘Look, we have solved math. the intellectually hardest thing ever, therefore we can solve everything.’ ”
LLMs have not solved math – far from it. Nonetheless, Grochow added, recently some capabilities have improved. “They are legitimately able to produce real math, new math that they had not produced before. So you can’t deny that utility. You can deny the extent of that utility, but you can’t say it’s all BS.”
Miek Messerschmidt, a senior lecturer in mathematics at the University of the Witwatersrand in Johannesburg, recently used Anthropic’s Claude to take a crack at a problem that had stumped him. Claude solved it within 30 minutes. “How did I feel? You mean emotionally? I was relieved, because I didn’t have to go down this dead end any more. But I was also disconcerted by what this entity could do in such a short amount of time.”
This year’s Fields medallists: mathematicians Yu Deng (China), John Pardon (US), Jacob Tsimerman (Canada) and Hong Wang (China). Photo: Erin Blewett / AFP.
NEW VARIABLES
Mathematics has been disrupted by technological leaps before. The abacus. The mechanical calculator. The blackboard, even, which played an instrumental role in standardising training and mathematical notation (“It’s hard to research, because most of what’s written on blackboards doesn’t stay there very long,” said mathematics historian Michael Barany).
But AI is a different kind of disruption. Where previous advances helped mathematicians expand the frontiers of their knowledge, large language models threaten the livelihoods, and purpose, of mathematicians themselves.
“Our place in society at developing new ideas has been quite unthreatened for quite some time,” said Tim Chartier, a Professor in the Department of Mathematics and Computer Science at Davidson College. “For a long time, the frontier of mathematics could only be expired by the mathematicians themselves. There were no tools that could push into that boundary, except the research mathematician…At this current time, it’s not like we don’t need research mathematicians – but the landscape has changed so quickly, it’s hard to know what that will look like a year from now, two years from now.”
This matters for everyone: for thousands of years, mathematics has been considered to be the foundation of scientific knowledge. There is no physics without mathematics. No astronomy without mathematics. And there is no computer science – or AI – without mathematics. What happens if there are cracks in the foundation?
“I think that LLMs pose an existential threat to the mathematical community,” wrote Tasmin Chu, a PhD student at the California Institute of Technology, in an essay published on 2 August – the day after OpenAI released a set of ten new mathematical results, resolving or making progress on various problems.
“This is a polemic, not a press release,” Chu continued. “I know that others feel the same way; I have spent today crashing out and learning that top mathematicians I know are crashing out.”
Some of what mathematicians are worried about is the same as what everyone else is worried about: that AI will take their jobs; that AI companies have absorbed everyone’s work, and are reproducing it without attribution or compensation; that students aren’t learning properly any more; and that there won’t be jobs for them when they graduate.
But some worries are more specific to the field. Like the fear that, if mathematics becomes too reliant on particular AI models, then AI companies will have the power to restrict access to a select few – or make it prohibitively expensive. That promising young mathematicians, instead of turning to research, will use their talents instead to refine proprietary algorithms, which may or may not be used for the common good. That there will be no one left to understand, let alone vouch for, the mathematics produced by the machines. That objective truth, or anything close to it, is going to be much harder to prove – a fear shared with journalists, incidentally, who find it increasingly difficult to verify whether a photograph is real or fake.
2nd Century relief of a merchant using an abacus. Photo: Musée de La Cour d’Or - Metz Métropole.
MATHEMATICIAN IN THE LOOP
After speaking with more than a dozen leading mathematicians from around the world, The Atlas can confirm that Chu is not alone with her fears: it’s all anyone is talking about. The crisis is existential – and mathematicians are looking to one man in particular to guide them through it.
There are no working mathematicians who are truly famous. The closest anyone gets is Terence Tao, a professor at UCLA, who is often referred to as the world’s greatest living mathematician. A prodigy, Tao skipped five grades of school, receiving his masters degree at age 16. By 21, he had earned a PhD from Princeton, and was awarded the Fields Medal just ten years later, in 2006, for his pioneering work with Ben Green on prime number theory.
But what sets Tao apart, in a field infamously incomprehensible to outsiders, is his ability to communicate to both mathematicians and laypeople alike. This has made him a de facto spokesperson for the mathematics community – and extremely influential in setting its norms. “From all accounts he’s a lovely man,” said one researcher, speaking anonymously.
“But the social pressure to agree with Terry Tao is really strong.”
On July 24, the day after Jacob Tsimerman joined OpenAI, Tao gave a public lecture at the ICM titled “Mathematics in the Age of AI”. In it, he presents his “working hypothesis”: that AI tools for mathematics, which he already uses in his own research, will keep getting better. This will allow for collaboration on a vastly greater scale than has ever been possible before, Tao argues, and speed up the pace of research – revealing new, previously unimagined frontiers.
But it will also change, fundamentally, the role of human mathematicians. There will be little point in problem-solving – the computers will handle that. But the humans will still have to do the really hard work: understanding why AI-generated proofs are correct, and explaining this to others. The sooner they embrace this and plan accordingly, Tao concludes, the better.
The Atlas reached out to Terence Tao to request an interview. His human assistant, UCLA media relations officer Holly Ober, declined the request, making a referral instead to a “living document” which is maintained by Tao’s Claude AI assistant, on his website. This document synthesises Tao’s current thinking on AI, based on his public writings and talks, even “interviewing” Tao with follow-up questions.
As per his AI assistant, Tao believes that the topic of AI in mathematics is too important to be left to just a small number of spokespeople – and that his own “influence on this discussion should diminish as the rest of the community steps up.”
Terence Tao, often referred to as the world’s greatest living mathematician. Photo: OpenAI Academy.
AN UNSOLVED EQUATION
Not everyone believes in the inevitability of AI defining the future of mathematics. According to several sources, both organisers and funders were disappointed with the turnout at the ICM in Philadelphia. Despite the gravity of the occasion, the congress was not at capacity.
Ila Varma and Tarik Aougab, associate professors at the University of Toronto and Haverford College respectively, may have had something to do with that. In March, they launched a petition to boycott the ICM due to the ongoing war in Iran, increasing authoritarianism in the United States and the risks faced by visiting academics – if they were even allowed in. “AI companies were invited, but most African mathematicians who tried to attend couldn’t get visas,” Varma said.
More than 2,600 people signed the petition. For Varma, this is evidence that mathematicians may yet use their status to resist the influence – and the cash – of the big AI companies.
“There’s an idea that the positionality of mathematics in society is to decide who’s smart, who’s worthy of the most lucrative career opportunities and the privileges that go along with it. And now we are being asked to take this technology that is likely to be extremely unhealthy for users, that’s bad for the environment, this thing built on unethical labour, and our discipline is being used against many of our wishes to tell society that this thing is intelligent, and that you should let it do your thinking for you.”
Michael Harris, a professor at the Institut de Mathématiques de Jussieu in Paris, was at the ICM in Philadelphia. “The talks I attended on AI, starting with Tao’s public lecture, took pains to insist on the need for human mathematicians to make sense of anything produced in the name of the discipline, by machines or by humans,” he said. “There is a growing resistance to any use of AI whatsoever in mathematics, especially among young people, and this will become evident over the next few months.”
One of the clearest voices of dissent belongs to Max Weinreich, Assistant Professor of Mathematics at the City University of New York.
In an essay published earlier this month, Weinreich makes the case against what he describes as “artificial mathematics”: “Mathematicians do not need to use AI, nor should we use AI; soon, I argue, you will not want to have your name associated with AI. Instead, we should use our intellectual authority to oppose its development.”
His opposition is about more than mathematics, he told The Atlas. In his view, the environmental and financial implications of the AI boom pose major societal risks – and mathematicians, given their privileged position, are one of the few groups of people who can do anything about it.
As John Fletcher, a theoretical physicist and mathematician, observed: “There is a lot of power in being perceived as the smartest people in the world.”
Po-Shen Loh, a professor at Carnegie Mellon and former coach of the US International Mathematical Olympiad team, backs mathematicians to figure it out – by treating it like a maths problem. “AI is making mathematicians engage with the real world. They are smart enough to realise they have an existential crisis. They’re quickly racing down to discover all the dead ends, to see what works and what doesn’t.”
And when they figure it out, maybe they’ll let the rest of us know.
A version of this story, edited for length, first appeared in The Atlas newspaper. Additional reporting by: Shougat Dasgupta, Masho Lomashvilli, Irina Matchavariani.
Kazakhstan is moving rapidly to position artificial intelligence at the center of its economic modernization strategy, investing in computing infrastructure, skills, startups and Kazakh-language technologies as governments worldwide compete for a place in the emerging AI economy, writes Assel Satubaldina.
Kazakhstan has an increasingly ambitious AI agenda. The government has made AI part of its broader economic modernization strategy, betting that it can improve public services, attract investment and help the country generate more value beyond its traditional reliance on raw materials. It has been investing heavily in computing infrastructure, training, startup ecosystems, and Kazakh-language models. At the 38th plenary session of the Foreign Investors’ Council in July, President Kassym-Jomart Tokayev explicitly called the integration of AI into state investment policy a “critically important priority”.
The government has adopted a Digital Code and a law on artificial intelligence, launched the Digital Qazaqstan strategy, established a Ministry of Artificial Intelligence and Digital Development, and designated 2026 as the Year of Artificial Intelligence and Digital Development. This push comes at a time as AI is becoming both a major global industry and an increasingly important source of economic and strategic power. UN Trade and Development estimates the global AI market will rise from $189 billion in 2023 to $4.8 trillion by 2033. This is a 25-fold increase in just a decade.
Globally, AI push is concentrated among a handful of countries. U.S. private AI investment alone reached nearly $286 billion in 2025, more than 23 times the level in China, according to Stanford University’s 2026 AI Index. Global corporate AI investment more than doubled in 2025, while private companies produced more than 90% of the year’s notable AI models. For emerging economies, the concentration of AI infrastructure in a small number of countries can make this race appear expensive, not to mention that training large language models requires massive quantities of computing power, electricity, connectivity and capital. Kazakhstan is unlikely to compete with the United States or China at the most capital-intensive layer of the AI industry nor does it need to. Experts say its opportunity lies in combining global AI technologies with domestic expertise, while building its own capabilities in areas where dependence creates economic or strategic vulnerabilities.
Strong foundation The country’s AI push builds on years of its experience of deploying digital technologies at a scale. Advanced technologies are increasingly embedded in government services and banking. “Right now, especially in the artificial intelligence applications in fintech, Kazakhstan is one of the most progressive countries in the world,” Huseyin Atakan Varol, dean of the School of Computing and Artificial Intelligence and director of the Institute of Smart Systems and Artificial Intelligence at Nazarbayev University, told The Astana Times. He pointed to technologies including palm-recognition authentication and AI-powered banking assistants as examples of how quickly digital tools have entered everyday life. International indicators tell a similar story but also expose an important weakness.
Kazakhstan ranks 10th globally for online government services, according to figures from the World Intellectual Property Organization. Yet it ranks 115th for university-industry collaboration and 112th for venture-capital deal activity relative to the size of its economy. Dr. Varol said Kazakhstan still allocates a relatively small share of its economy to research and development, with the government providing most of that funding. While spending on science has increased in recent years, spending on research and development does not reach 1%. “Nearly all of this allocation comes from the state, very little from the private companies,” he said. “So basically, industry, academia, government interaction, the triple helix structure is not yet fully working in Kazakhstan.”
That may prove to be one of the important constraints on Kazakhstan’s AI ambitions. While the government can establish institutions, purchase computing infrastructure and train specialistics, building a durable innovation economy requires more. Alem.AI center in Astana is intended to help close that gap by connecting education, research, startups and companies. “We have some real and simple goals. The first one is to build a community of professionals, who not just use but understand what AI actually is. It means that we train different categories,” Alyona Slavkina, head of the Alem.AI Development Office, told The Astana Times.
The community is supposed to include schoolchildren, students, working professionals, researchers and entrepreneurs. The idea is to create a pathway from first exposure to AI to education, research and eventually the development of commercial products. The challenge, however, is that AI is evolving faster than conventional education systems. Olzhas Urazalin, head of Tomorrow School, said technologies and development tools can change within months, while formal academic programs often require years to adapt. “Many graduates have strong theoretical knowledge, but very limited experience working with modern AI tools, real data sets, and product development.
\This indeed creates a gap between learning and creation. Young specialists often learn how to use existing AI solutions, but they rarely get the opportunity to understand how these systems are built, or how to develop new ones,” Urazalin said. Tomorrow School is built around an alternative model. Students learn primarily through projects rather than conventional lectures and work on real problems brought into the curriculum by businesses. The program is free, runs for 18 months and is open to applicants without traditional technology backgrounds. “In my opinion, Kazakhstan must focus on developing, first of all, creators, not only consumers of AI technology,” he said, highlighting talent, infrastructure, and international collaboration as priorities.
Development pathways Dr. Varol describes two broad pathways for technological development. One is indigenous development, which requires substantial investment, time and a long-term commitment. The other is what he calls “technological piggybacking,” adopting technologies already developed elsewhere. For Kazakhstan, Varol said that generative AI requires elements of both approaches. “We have been also debating and discussing a lot about what is the best strategy for Kazakhstan, especially in the development of generative AI technologies. Our internal deliberations always pointed out that we must develop these technologies indigenously as well,” he said.
Varol also stressed that developing domestic capabilities is not only about owning a model, but also about creating a pool of specialists with expertise who can later move into startups, established companies and new commercial products. Recent events also demonstrated that access to foreign technologies can become subject to government restrictions. In June, the US government temporarily imposed export controls barring foreign nationals from accessing Anthropic’s Fable 5 and Mythos 5 models, including foreign-national employees of Anthropic itself. “Creators of these technologies can stop your access to these technologies and this creates a very big dependence risk,” said Dr. Varol.
Technological sovereignty, however, should not be confused with technological self-sufficiency. Mohamed Eissa, chief investment officer at the International Finance Corporation, emphasized that this explains why emerging markets are unlikely to compete directly at that layer of the industry. “You are not seeing LLMs [large language models] being developed in emerging markets because you need compute, connectivity, power,” Eissa said. “Those things are big infrastructure gaps in emerging markets.” But he draws an important distinction between building AI infrastructure and using AI.
He pointed to a rapidly growing AI company in content creation that originated from Kazakhstan as an example. “But using AI in emerging markets, you don’t need to build that to be able to use it. That is an opportunity in emerging markets: finding innovative people who have really good ICT skills, have focused on a business problem and now have the tools to compete with Silicon Valley,” he said during The Development Podcast on the World Bank Group YouTube channel.
That may be one of the most important opportunities for Kazakhstan. According to him, in emerging markets, the much larger opportunity may be the application of AI to businesses that already exist. Likewise, in Kazakhstan, potential economic impact of AI should not be measured only by how many programmers, AI startups or research laboratories the nation generates, but rather by whether this technology raises productivity gains across the wider economy.
Existing strengths Dr. Varol said some of the applications of AI lie in sectors where the country already has an expertise. Mining, energy and logistics are among them. “Kazakhstan is one of the richest countries with respect to mining and natural resources, but most of these natural resources are exported with a small amount of added value to other countries,” he said. Using AI and digital technologies in those industries, he noted, could allow Kazakhstan to generate more value from resources before they leave the country. Where dependence matters There are areas, however, where Kazakhstan has stronger reasons to maintain domestic capabilities. Dr. Varol said questions about where information is processed and stored become significantly more consequential when AI systems are used by companies or public institutions.
“In general, I believe this is also very important with respect to the sovereignty aspects of a country,” he added. Language presents a different form of dependence. Global AI systems are trained on enormous quantities of data, but languages are represented very unevenly in those datasets. Dr. Varol said approximately half of publicly accessible internet content is in English, compared with only around 0.03% in Kazakh. That means developing Kazakh-language AI starts long before a model is trained. ISSAI, which was founded in 2019, has developed the country’s first LLM in 2024. Drawing on the institute’s experience, Dr. Varol explained that researchers first need to collect texts and other data, remove poor-quality material, organize the information and convert it into formats suitable for machine learning. Researchers faced another problem once a model has been trained on how to determine whether it actually understands the language. “Because the internationally accepted method for doing this is called model benchmarking and all model benchmarks are in English language or Chinese language, but none were in Kazakh language. Basically, we also have created a suite of benchmarking data sets,” he said.
The development of Kazakhstan’s LLM illustrates another benefit of indigenous AI development. The process itself creates capabilities. The team had to recruit and train specialists while learning to operate multi-node AI computing infrastructure, expertise that had not previously been used at that scale. The significance of the project therefore extends beyond the model it produced. It helped build a domestic pool of engineers with experience in the infrastructure and workflows required for advanced AI development. Kazakhstan’s AI strategy therefore does not require choosing between complete technological independence and reliance on global platforms. The more realistic task is to determine which capabilities the country needs to control, which technologies it can efficiently import and where AI can create the greatest economic value.
Nvidia on Wednesday forecast a 70 per cent jump in revenue next fiscal year, underscoring unabated demand for AI computing, while warning about shortages of memory components.
Nvidia on Wednesday forecast a 70 per cent jump in revenue next fiscal year, underscoring unabated demand for AI computing, while warning about shortages of memory components.
Scientists are growing miniature human brains in labs around the world. (Yep, you read that right.) For Wired, Claire L. Evans reports on the latest research in the field of organoid intelligence, or OI, where scientists are artificially growing brain organoids from tiny human samples, whether skin, blood, teeth, or even hair. As these seed-sized globs mature, they are able to send and receive electrical signals, and even learn to remember and anticipate them. In other words, this living matter is programmable. “And this is where things get truly weird,” writes Evans.
For now, these neural computers are mostly of interest to researchers who want to work with neurons without taking on the tedious wet-lab husbandry themselves. Eventually, however, the company hopes that neurons will prove themselves to be an energy-efficient, resilient substrate for more general computing applications—including some tasks currently handled by AI, like image recognition and classification.
“When you think about what you want from AI, it’s biology,” said Brett Kagan, Cortical Labs’ chief operating officer, when I reached him over Zoom. “You want it to be self-repairing as much as possible. You want it to be adaptable. You want it to be long-lived. You want it to be energy-efficient. These are all features you get for free in biology.”
Artificial intelligence is becoming part of the machinery of modern markets. Banks, hedge funds and trading firms are using increasingly sophisticated models to identify opportunities, manage risk and execute trades, writesAmir Naser Hojati.
Most discussion focuses on one question: What happens when an AI model gets something wrong?
I think there is another risk.
What happens when many good models reach the same conclusion at the same time?
After roughly 15 years of trading futures, I have learned that markets often become dangerous not because one participant is wrong, but because too many become convinced of the same thing.
As traders sometimes say: when everyone moves to the same side of the boat, start watching the waterline.
Different models, same trade
Financial institutions may believe they are diversified because they use different models, vendors and strategies.
But those systems may still be learning from similar information: price history, volatility, order flow, economic data and news sentiment. They may also be optimizing toward similar goals.
Different models do not need to be identical to produce the same trade.
Several independent systems can simultaneously decide to reduce the same exposure, buy the same asset or hedge the same risk.
That creates hidden dependence.
A market can appear diversified while many supposedly independent systems are reacting to the same information in similar ways.
The real vulnerability may therefore not be one bad model, but the connection between many good ones.
Markets have seen this before
Crowded trades are nothing new.
Portfolio managers react to the same data. Traders watch the same levels. Risk desks cut exposure when volatility rises.
Technology increases the speed of those reactions. AI may also increase their consistency.
Humans disagree, hesitate or sometimes simply choose to do nothing.
Machines are less likely to disagree unless that behaviour is deliberately built into them.
If many models detect the same deterioration in risk and begin reducing exposure simultaneously, individually rational decisions can produce an irrational market outcome.
Each model may be doing exactly what it was designed to do.
The market can still become unstable.
Historical success can be dangerous
A strategy can perform well for years because market conditions remain broadly familiar.
That history creates confidence.
But markets do not owe us tomorrow because something worked yesterday.
One of the most dangerous moments is not when a model is obviously failing, but when it has accumulated enough successful history that people stop questioning its assumptions.
A calm sea can make almost any captain look skilled.
For AI-driven markets, the important question should therefore not only be:
How accurate is this model?
It should also be:
What assumptions does this model share with everyone else's?
More intelligence is not always more resilience
The usual response to model risk is to add more: more data, more parameters, another model, another control.
But resilience does not always come from adding complexity.
Sometimes it comes from removing exposure.
A system may become safer not because it predicts markets more accurately, but because it knows when conditions are unsuitable for taking risk.
That is one of the hardest lessons to translate from discretionary trading into automation.
Finding a trading signal is relatively easy.
Teaching a machine when a valid signal should be ignored is much harder.
Experienced traders know that a setup can look perfectly good and still not deserve capital. The market may be too crowded, liquidity may be changing, or correlations may be breaking down.
Sometimes the best trade is no trade.
Diversity of behaviour matters
Using several different AI models does not necessarily provide diversification if all of them react to stress in the same way.
Real diversification may require different behaviour, not just different architecture.
When should a model refuse to trade?
When should it reduce confidence?
When should it respond more slowly?
And how should it behave when the actions of other models are themselves changing the market?
That matters because financial markets are not ordinary prediction problems.
Predicting the weather does not change the weather.
Predicting a market can change the market because participants act on the prediction.
As more intelligent systems enter that feedback loop, their collective behaviour may become as important as the quality of any individual model.
The next edge may be restraint
Financial institutions will continue building faster and more powerful models.
But I suspect the next competitive advantage will not come only from making machines better at finding trades.
It will also come from making them better at recognizing when everyone else has found the same trade.
Markets have always punished excessive certainty eventually.
AI does not remove that principle. It may simply allow certainty to spread faster.
The smartest trading systems of the next decade may therefore be distinguished not only by how quickly they act, but by something harder to program:
Kknowing when not to join the crowd.
Amir Naser Hojati is a futures trader and fintech entrepreneur with around 15 years of experience in financial markets. His work focuses on algorithmic trading, automated risk management, and AI-driven trading systems.
More than a decade ago, a group of snipers shot up an electrical substation in Silicon Valley, striking power transformers and disabling the substation for nearly a month. The shooters were never identified; similar substation shootings, some by neo-Nazis, have taken place since. The longer Christopher Cox studies the American power grid—looking at aging infrastructure and ongoing transformer shortages, specialized manufacturing and rising demand, the massive energy consumption of data centers and whiplash usage of large language models—the more vulnerable it seems. “Somehow, we’ve made the industrial equivalent of a Fabergé egg the linchpin of our civilization,” Cox writes. “What would happen if someone started breaking them?”
The hope of restoring the grid would fade as fossil fuel supplies were exhausted, because so many utilities rely on them to “black-start” their generating units. A 2010 report called “High-Impact, Low-Frequency Event Risk to the North American Bulk Power System,” prepared by the Energy Department and NERC, condensed the entire disaster movie that would follow into a single paragraph: “The supply of food, water and fuel would degrade within days. The facile communication of information to the general population would be greatly complicated by the loss of cellphones, internet access and television. The economy would virtually shut down as electronic transactions could no longer be processed. After several days, widespread social unrest and confusion would ensue.” Thirsty, starved, dirty and surrounded by the useless machines we once depended on, we would be lucky if our misery ended at “confusion.”
I’d be lying if I said I understand everything Michael Levin believes. But I’m also not alone; sometimes his own colleagues can’t even follow his arguments. Elizabeth Weil’s profile of the renegade (and genius) biologist bridges that gap, sketching out Levin’s research and claims in a way that’s just grokkable enough to blow your mind. I mean this as a true compliment: When’s the last time we had a genuine mad scientist on our hands?
Critics accuse Levin of all kinds of teleological crimes — making unfalsifiable claims, using words in ways that render those words meaningless. Levin is unfazed. In his talks, he tries to disarm his detractors by preempting the accusation that he’s nuts: “I have some unusual things to say and then I have some crazy things to say”; “So this is a kind of crazy claim I’m making.” His true retort is his wet lab. There, at the bench, he’s working to prove his metaphysics by running old-school (in a way) biological experiments. He studies flatworms with two heads. Frogs with eyes on their stomachs. Little round creatures called xenobots, synthesized from frog skin cells. They swim like tiny Pac-Men. They do not exist in nature.
“The only thing that ever settles arguments is technology,” Levin tells me, a line he borrowed from biophysicist Chris Fields. “People have been saying for thousands of years what they think” about minds, consciousness, souls, energy fields. “What I can contribute is ways to test some of those things.”
A top banker has got in trouble by referring to employees whose jobs will be replaced by AI as “lower-value human capital.” But he’s just saying the quiet part out loud: compliance officers have been expected to work like inefficient computers for years already anyway. Standard Chartered CEO Bill Withers used the unfortunate phrase when describing how the bank plans to keep hitting its profitability targets by cutting 15% of back office staff, and has tried to backtrack a little after a predictable storm of criticism, including from the former president of Singapore.
It would be nice to think of bank employees deployed to fight financial crime as super-effective old-school gumshoes, with a bottle of bourbon in the bottom drawer and an inexhaustible stock of one-liners, but in reality their jobs are more like something from ‘The Office’ than ‘The Big Sleep’.
Thousands of people sit in cubicles in Warsaw or Bengaluru, checking through transactions flagged as possibly abnormal by banks’ automated systems, and confirming that 99% of them are, in fact, normal. Anything that might conceivably be abnormal gets sent up the chain, where someone more senior will almost certainly decide it wasn’t.
It is a ruinously expensive process and, as far as we can tell, completely ineffective. The best estimates we have for the size of the criminal economy suggest it has grown, untroubled, along with everything else for decades despite all the laws, fines, and prosecutions that we’ve thrown at the problem.
But banks’ financial crime compliance isn’t about stopping financial crime at all: it’s about stopping banks from being fined, as Standard Chartered has previously been, enormous sums in both the U.S. and the UK. As long as banks can be sure that AI checks boxes in ways that satisfy regulators, then they’ll be happy.
This is a little bit worrying because AI is already getting very good at fraud. I am very alert to attempts to trick me but was sufficiently fooled by an AI email yesterday to forward it on to someone. Fortunately, a very similar one arrived (purportedly from someone else) a few minutes later, alerting me to my mistake. Money laundering is a laborious activity and criminal gangs will be as keen as banks to cut their back office expenditure, and AI could help automate the processing of the many small transactions that add up to a large amount of money. Phishing and smurfing are just a couple of its use cases, however.
“Scammers can leverage AI to scrape data from social media and dating platforms to identify vulnerable targets (e.g. lonely individuals, recent retirees, or people interested in finance) for pig butchering scams,” notes TRM Labs. “Bots can sustain long-term, emotionally persuasive conversations without tiring or making mistakes, making the scam more scalable.”
And that’s before you get onto AI’s ability to exploit cryptocurrencies and smart contracts to really start rampaging through the crypto world. “TRM observed a roughly 500% increase in AI-enabled scam activity over the past year. The convergence of generative AI, programmable financial infrastructure, and global crypto liquidity has altered the economics, velocity, and scalability of fraud,” the blockchain analytics firm said in a follow-up report. This is very bad indeed.
So, although I can see why people are annoyed that Withers referred to his bank’s employees in such a disparaging way, I am more troubled that he’s planning to replace them with AI, rather than to retain them and train them in how to counter it.
It was remarkable, however, to see Warren Davidson, chair of the illicit finance subcommittee at the House of Representatives Financial Services Committee, draw precisely the wrong conclusion from all this. Although he was correct in condemning the defensive nature of compliance, and its focus on generating paperwork over results, he then got lost in praising the White House’s decisions to attack corporate transparency legislation.
"As we focus on risk, we must also ensure that tools like artificial intelligence are fully deployed to counter the AI-enabled crimes of today,” he said, without realising that, without reliable information to train the AI models on, this is as useless an approach as the one he says has failed. If you don’t know who owns what, neither will a computer, no matter how cleverly it can pretend to be human.
I sincerely hope he listened to the testimony of Carole House of the Atlantic Council who forcefully pointed out the harm to national security and to ordinary Americans caused by the United States’ failure to create even an approximation of a decent corporate registry, as well as the historic idiocy of its current crypto policy. “Without a secure identity foundation, AI agents will simply scale up fraud at a speed and volume that human investigators can't possibly track, destroying trust in the whole system,” she said in testimony that I highly recommend you take a look at.
I very much doubt Davidson was listening, however, because that is not the direction the Republican Party is going in right now. I would write more about that, but frankly it’s all too depressing, and I’d rather move on.
So let me point you towards this excellent paper on how online scam marketplaces work, with criminals using the messaging app Telegram and the stablecoin Tether to launder hundreds of billions of dollars. It argues that our current approach of sanctioning exchanges is futile since their owners just shut them down and switch to a new platform that works in the same way but hasn’t yet been sanctioned.
“As long as the underlying digital infrastructure remains permissive, criminal syndicates will simply migrate to new channels. To move from reactive disruption to systemic prevention, the international community must shift its focus toward the structural enablers of these marketplaces,” Elliptic’s Tom Robinson argues. Elliptic is unusual among blockchain analytics companies in being willing to name Tether as a major vector for money launderers. Its rivals tend to just say “stablecoins,” I have no idea why.
Meanwhile, there is something grimly depressing about the fact that — against the backdrop of, well, everything — the UK has postponed June’s illicit finance summit due to “scheduling issues in the international calendar.” Everyone is so busy dealing with the consequences of illicit finance, that no one has time to talk about illicit finance.
A version of this story was published in this week’s Oligarchy newsletter. Sign up here.
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