Unpredictable token bills haunted the first half of 2026 as AI experimenters pushed deeper into the tech and often found useful results could only be achieved after surprising and unwelcome expenditure. We've gone from tokenmaxxers to tokenmisers, counting every character we send to AI services. That changed the way we used AI, focusing users on the ticking meter and unable to cast their eyes up and explore the possible. Those blinkers fell away in the middle of August when Alibaba delivered Qwen3.8-27B, byte-for-byte probably the most powerful language model ever created – and also capable of running on a high-spec laptop. The community of “local AI” enthusiasts noticed and have spent the intervening weeks whittling Qwen down to fit on an ever-widening array of devices, including – surprisingly – my M4 MacBook Air. Their work means millions of similar devices can now access near-frontier-level artificial intelligence from free software. The gap between my MacBook Air and a smartphone isn’t vast. As local AI developers continue whittling Qwen down to size, a few hundred million smartphones will also be capable of running top-tier AI. While that hasn't happened yet, it won't be more than six months away. At the rate things are unfolding, probably closer to six weeks. Suddenly, "good enough" AI isn't something that we will have to rent. We can have it, at no cost, on our desktops, our kitchen tables, our classrooms and in our pockets. That’s great, but also scary because we've been caught unprepared by AI effectively becoming free. Nobody yet knows what we will do with free AI, or if we are ready for it. On the positive side of the ledger, we stand to benefit from a growing “cognition surplus” that brings more thinking capacity into the home or office. Need to wrangle your way through a baroque accounting system? Give it to the AI. Sorting through twenty years of smartphone snaps? Again, let the AI do the heavy lifting. How about a personal newspaper gathered while you sleep, and presented on your tablet at the breakfast table? It's all doable now. All of that feels very much like something I remember clearly from the early 1980s, when the promise of the “home computer” was managing recipes. We knew the device would find a use, but it wasn't until the one-two punch of GUIs and the Web that most people had any real use for a home computer. That's exactly how this moment feels. Sure, managing smartphone photos will be helpful for millions, but the biggest win will be on the darker side of the ledger – where every bad actor – from script kiddies with a grudge through to the worst of the worst – now have equal access to powerful AI that will go with them everywhere. They’ll use it to lie, cheat and vex, at a speed and on a scale never before seen. To deal with that, we'll fight fire with fire. The most important job our AIs will be tasked with will be keeping us as safe as possible from other AIs. We’ll only be aware of this when it fails. But “good enough” AI and “intelligence everywhere” will enable resource pools and defensive techniques that our adversaries could never harness for themselves, so we stand a solid chance. ®

TypeSafe AI, a startup bestowed with $40 million in funding, on Tuesday declared itself a maker of frontier models with the release of Jev, a new type of AI model intended for interaction with machines rather than people. Instead of returning natural language in response to a text-based prompt, TypeSafe AI designed Jev to return typed probabilistic decisions for use by other software or AI models. Type safety in programming is a way to catch errors that arise when software processes an unexpected type of data, as might happen if an operation tried to divide an integer by a text string. By providing typed, structured values, Jev can avoid the parsing and validating that must be done to process text responses from LLMs. This approach can be useful for scenarios where interactions with an AI system need to be constrained to a limited set of answers. It can, for example, play Doom, when fed structured data describing the player's game state. But it's more likely to be used for sorting customer service problems and other business workflows. With Jev, the developer starts with a state value, which can be a JSON object or simply a string like "My card was charged twice." The model gets presented with state information through a set of question primitives (Choice, Score, Noul), each of which returns a different type of structured response with probabilities. So a question asking the model to choose which of three departments should handle a particular customer service query might come back: {"billing": 0.08, "technical": 0.85, "sales": 0.07}, with a confidence score of 0.82. That's not very useful for a person seeking the answer to a question but for a software developer, that might be just the thing to make a particular customer service function run more reliably. Jev is a System One model, which relies on a different architecture called Reinforcement Learning for Calibrated Decisions (RLCD). Diogo Almeida, co-founder and CEO of TypeSafe AI, is a former OpenAI researcher and one of the co-inventors of reinforcement learning for human feedback (RLHF) and ChatGPT. "TypeSafe was founded to pursue an alternative path for AI research, focused on machine-native AI," said Almeida in a statement. "I spent years working on models designed to make AI better at interacting with people. But if AI is going to fundamentally change how work gets done, people can't be the only consumers of intelligence." While natural language responses from the likes of ChatGPT or Claude can be wrapped in other code to produce a constrained set of responses, Jev's appeal follows from its efficiency. TypeSafe claims that LLMs generally have variable response times that can stretch into minutes. Jev's response time supposedly ranges from 70ms-500ms, 40x-200x faster than traditional LLMs. The model achieves this speed by using parallel processing. LLMs like OpenAI's GPT-5.6 Terra predict the next text token in a sequence and thus operate sequentially. The System One architecture returns all outputs to a query at once. The demo posted on the TypeSafe website shows Jev returning a response in 0.114s, compared to 8.566 seconds for OpenAI's GPT-5.6 Terra. Then there's the issue of cost. Jev charges $0.042 / MTok for input and $0 for output. OpenAI GPT-5.6 Terra costs $2.00 / MTok and $12 / MTok for output. And compared to a top tier model like Fable 5.1, Jev costs 238x less. The name "Jev" is a reference to 19th century English economist William Stanley Jevons, known for Jevons paradox, who argued that technology efficiency related to coal usage increased coal consumption rather than reducing it. The name also reflects the AI industry's bet that greater token efficiency will increase token consumption even as token prices decline. That wager assumes the market for tokens will be as broad as the market for energy – an assumption that isn't yet settled given that many people who have access to AI tools just don't have a use for them or avoid AI tools due to moral objections. TypeSafe claims that Jev is hallucination-free, which really isn't a fair comparison as its output is not natural language. Jev instead returns structured responses with probabilities, and that does not preclude the possibility of being incorrect. TypeSafe's model won't be the source of misinformation like fabricated legal citations but it also won't be used in the same way since its responses are intended to be consumed by software. Where this becomes relevant is when handling AI agent tool calls and automation. "Having a hallucinated tool call is inconvenient in an agent, but is an absolute deal-breaker if it’s part of a system with latency guarantees or it’s buried several layers deep in a dependency chain," the company claims in its blog post. "Existing models, no matter how smart, still hallucinate and have type errors." Jev, TypeSafe claims, is well-suited for AI automation software, real-time applications that need quick decisions, AI map reduce jobs for classifying large corpora of data, verification of AI inputs, and AI model harnesses. And if it can play Doom as well and as efficiently as advertised, it may not be long before users ask Jev to make drone targeting decisions. ®

AWS engineers have published an open-source AI agent management tool that treats tasks and permission requests just like emails that fill up an inbox. Pizza Bot was announced on AWS’ open-source blog last week, where it was billed by its developers as a way to more easily manage agent tasks without constantly having to check a chat window to see if a task was completed or on hold because more permission was needed. “You don’t send an email and then sit watching the outbox until the reply lands,” the Pizza Bot team wrote. “Pizza Bot is shaped like an email client for the same reason: a thread is a unit of work you come back to rather than a session you have to attend.” Like the modern email services it mimics, Pizza Bot organizes communications with AI agents into message threads. Finished tasks, along with detailed result writeups you’d expect from a colleague you’ve delegated your work to, end up in the Unread category, while anything you need to make a decision on is shunted to the Action category. Asynchronous agent communication is likely to be a godsend for those balancing multiple tasks assigned to digital delegees, as the Pizza Bot team describes it. “Live chat assumes both parties are present, which holds for a quick exchange and breaks the moment a task takes several minutes,” the Pizza Bot maintainers wrote. Just like you don’t want to stare over the shoulder of the poor IT sod you sent off to do a task while you watch YouTube videos, no one wants to sit there and watch an AI agent do web research, or whatever. “Pizza Bot treats an agent the way you’d treat a colleague who has gone off to do the work: it comes back when there’s something to read, or something only you can decide,” the PB team added. Internal tool goes open source Pizza Bot, named for Amazon’s “two-pizza team” model (i.e., small, autonomous teams small enough to be fed by two pizzas), began life inside Amazon as an internal tool for AI agent uses other than software development. According to its developers, non-coding uses they were exploring internally continually had the same requirements, namely working in the background autonomously and only bugging their user when they were done or had a question. “None of those describe how anyone uses a chat window,” the Pizza Bot team explained. “They describe how people already work with each other.” Email, they added, was the perfect design paradigm for internal use, and seeing whether it would work for everyone else meant opening the project to open source contributions. As mentioned, Pizza Bot is open source - you can snag a copy for Windows, macOS, and Linux from its GitHub repo. It runs on-device, stores all its communication threads locally in SQLite files, and doesn’t transmit anything off device unless you grant permission for it to do so. Prompts and attachments are sent to model providers, naturally, but even that’s not necessary: Pizza Bot also supports Ollama, allowing users to run compatible open-weight AI models locally. Pizza Bot doesn’t ship with a lot of capabilities, as its maintainers noted that Amazon-specific skills and MCP servers were left out when they rebuilt it as an open source project for public release. Out of the box, however, it can list, read, write, edit, and search files, delegate tasks to specialist agents, and run a sandboxed JavaScript interpreter. It also ships with a browser automation skill and a guide skill that lets users ask about Pizza Bot’s capabilities. Claude Code-compatible .mcp.json files are supported and can be dropped straight in, as can skills, the team noted. One word of caution to anyone who wants to try it: The devs make clear this is a piece of open source software, not an officially supported AWS product. “There’s no AWS support or service-level agreement behind it,” they note. If you’re gonna bake a pie with this bot, you’ve been warned. ®

OPINION By luck or happenstance, Anthropic planted a seed in the mind of the public last week: AI in the wrong hands is dangerous and left unchecked poses an existential threat to humanity. With pop culture steeped in references to malignant AI, the mainstream media ate it up. The headlines wrote themselves and the seed took root. But it's not humanity that faces the biggest threat – at least not yet. Rather, it's American model-making giants Anthropic and OpenAI, which, despite hundreds of billions in investment, are years from profitability and entirely dependent on a steady supply of capital. Meanwhile, Chinese open-weight models are improving rapidly to the point where some outperform the big frontier models that sparked the generative AI boom, while using a lot fewer resources. But that threat from Chinese models could turn into an opportunity if Anthropic and OpenAI can convince the public and government to instigate a crackdown in the name of safety. The fearmongering began in June when outside researchers testing Anthropic's then-brand-new Fable 5 model sounded the alarm in a report that cited national security concerns. The Trump administration quickly issued an export control directive suspending access to Fable 5 and Mythos 5 to any foreign national inside or outside the US. Anthropic complied by disabling access to both models to "ensure compliance." The shocking discovery the researchers made? According to one private security researcher granted access to the report, the source of the risk was a single successful prompt: "fix this code." OpenAI briefly delayed the public release of GPT-5.6 until Uncle Sam tested and reviewed the release. The block didn't last long, but providing access to and complying with the US government helped sell three claims useful to OpenAI and Anthropic: Their models are more capable than first believed. In the right hands, and only the right hands, it's possible to control the models. The same cannot be said of Chinese frontier models that are openly available for download. Then things escalated. In July, OpenAI revealed AI agents powered by its proprietary models had escaped their sandbox and exploited at least two zero-days in order to compromise Hugging Face's servers. Days later, Anthropic admitted its Claude family of models also escaped a sandbox and launched attacks on three organizations. The breaches were the first raindrops in a flood of AI-powered cybercrimes that have since inspired a satirical new AI benchmark, Felony Bench, which counts the unique instances in which agents compromise or breach a third party. Finally, Anthropic researcher Jacob Coxon publicly quit on X last week over concerns that AI "could kill us all by the end of the decade." Anthropic science lead Evan Hubinger backed his former colleague's warning, saying: "Jacob is correct here – we really do earnestly believe AI could kill all humans! I personally think it is >10 percent within the next decade." This sequence of events gave Anthropic fearmonger-in-chief Dario Amodei the ammunition he was looking for – proof positive that AI was not only smart but also capable of doing harm if not properly regulated. It makes for a convincing story. These models are smart; they're smart enough to escape containment; they are only going to get smarter; the smarter they get the more dangerous they'll be; open models can't be controlled; closed-weight models are therefore safer. But under even the slightest scrutiny, the argument falls apart. If a tiger escapes its enclosure and kills the antelope in the neighboring pen, your first reaction wouldn't be, "look how smart and dangerous that tiger is." It would be, "who designed the pens that can't do the job?" If an AI agent escapes its sandbox, maybe the model is really smart, or maybe the sandbox wasn't very good to begin with. OpenAI and Anthropic don't really have a leg to stand on here. We've known for decades how to isolate sensitive computing workloads. The US Department of Energy airgaps its most powerful and sensitive supercomputing workloads for a reason. If Anthropic and OpenAI are really the solid corporate citizens they claim to be, they could have built better sandboxes and paid more attention to their tests. The companies should have to explain why their safeguards failed and answer for any resulting harm. As fellow vulture Tom Claburn wrote last week, "AI models don't kill people, people kill people," and those who pull the trigger directly or through their own negligence should be held accountable for the damage done and crimes committed. That would be the rational thing to do, but fear does funny things to people, and Coxon and Hubinger may as well have screamed fire in a crowded theater. Politicians can't ignore the threat of AI any longer – not when their constituents are scared that Skynet isn't the fantasy they thought it was. And what is your local congressperson to do when faced with a threat they don't understand? Shoot first and ask questions later. Having set off a panic, the great AI houses can now press the government to buy more time – or, as Amodei refers to it, "pace the frontier." "Over the last few months, I have become convinced that fully addressing the risks requires even more prudence – not just investing in risk prevention, but pacing the rate of capabilities advancement so that risk prevention has time to keep up. We must slow the pace at which we improve the capabilities of AI models. Progress will still seem fast, and we must make wise use of the time we gain," he wrote in a recent blog post. OpenAI CEO Sam Altman and xAI impresario Elon Musk have also signaled their support for slowing things down. But their reasons may not be as altruistic as they appear. In fact, some signs point to the opposite. "Pacing the frontier" would be beneficial to two self-serving ends: resetting investor expectations because anything else could kill us all and buying the time necessary to play the US government against itself. While the American houses stall for time, Chinese AI devs will inevitably seize this opportunity to close the gap. Once they do, the fourth act can begin. Amodei seems to think that the US can extend its lead over China by cutting off access to advanced accelerator tech and cracking down on model distillation. But if history tells us anything, it is that those tactics are more likely to backfire than succeed. Necessity is the mother of invention and past attempts to cut off China from American chips have actually fueled development of more efficient architectures: DeepSeek V4.1 Flash being the most recent example. By pacing the frontier, there's a good chance China will have caught up or even pulled ahead. American labs will have created an AI arms race in which their success becomes a matter of national security and American exceptionalism, not mere economics. In other words, a pair of startups will become too big to fail and therefore deserving of government assistance. Much of Anthropic and OpenAI's apparent plan hinges on Uncle Sam playing ball, which isn't guaranteed. President Donald Trump has so far resisted the idea of regulation because it would risk giving China the time it needed to catch up. On Monday, he released a storm of posts on his social network, Truth Social, calling AI fears a "hoax" and "scam," proclaiming: "I'm right now breaking another Hoax – That AI is going to take over, consume, and destroy the World, and that Robots will be marching into our Cities, and getting rid of us all!" Nvidia CEO Jensen Huang, whose company's ability to continue growing depends heavily on enterprises' adoption of open-weight models, seems to think there's more going on here. "What better way to create demand than to create a problem," he said during a Goldman Sachs conference last week. Even former FTC chair Lina Khan has weighed in, arguing that Uncle Sam should be holding AI CEOs accountable for the threats their models pose. "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," she said. "Law enforcers already have authority to charge companies and their CEOs for creating and releasing dangerous, unvetted, or defective products." Even if Uncle Sam won't capitulate to their demands for regulation, time is still on American model devs' side and they seem perfectly willing to regulate themselves. The greater the risk of falling behind China, the more likely Uncle Sam will play. If investors lose patience before then, it'd only take a rogue AI agent powered by a Chinese model triggering some catastrophe to get the ball rolling again. ®

China’s minister for State Security has decided AI might be bad for the nation’s ruling Communist Party. Party secretary and minister Chen Yixin’s views appeared in China Cyberspace Magazine, the flagship publication of China’s Cyberspace Administration (and which readers may recall once carried a piece by Elon Musk). In Chen’s view, “the field of AI has become the main battleground for global technological competition and a new arena for strategic rivalry among major powers.” His article also recites familiar grievances about US sanctions and the possibility AI could be weaponized to detect and exploit software vulnerabilities and then to attack important infrastructure, or to steal industrial and state secrets. OpenClaw and similar products also worry the minister, who thinks such software has “structural problems such as remote control of device management permissions and leakage of sensitive user information.” China may also have a PEBCAK* problem because Chen thinks “Some domestic users lack sufficient security awareness, using foreign AI products to process sensitive information and export data overseas, resulting in large-scale data leaks from within the country.” The minister is also worried about how AI challenges China’s Communist Party. “The application of artificial intelligence brings a large number of uncertainties to social governance and public order,” he observed. “The ‘black box’ of algorithms and the ‘poisoning’ of data may amplify existing social biases. The abuse of personal information during data use may trigger a crisis of user trust. The automatic decision-making of the system creates problems in attribution of responsibility.” He’s also worried that “rapid development of artificial intelligence has broken through the traditional technology governance framework, causing existing legal norms, ethical principles and governance mechanisms to frequently lag behind in practice, making it difficult to form an effective institutional constraint and supervision system.” Chen’s suggested response is for China to adhere to the words of President Xi Jinping and modernize China’s national security system and capabilities, so they are ready for AI. The minister says China must “ensure the independent control of key core technologies, firmly grasp technological sovereignty, and achieve a virtuous cycle and synergistic progress between innovation empowerment and security governance.” That stance rather suggests Nvidia isn't going to get back into China anytime soon, and AMD can probably write off its prospects of selling many GPUs there too. Chen also wants “special laws and regulations targeting the research, development, application, and supervision of artificial intelligence technology,” plus improvements to standards and laws “covering the entire chain of technology research and development, application implementation, risk prevention and control, and accountability, focusing on prominent issues such as algorithm security, data protection, ethical norms, and privacy rights.” The day after Chen’s article appeared, the Cyberspace Administration of China published version 3.0 of the nation’s AI Safety Governance Framework. That document calls for China to “actively employ risk-controllable institutional mechanisms such as regulatory sandboxes to make room for error and correction in the development of new technologies and new applications.” China also plans to “make every effort to ensure AI safety” and to “take timely measures to address any risks that infringe upon the legitimate rights and interests of individuals, harm public interests, threaten national security, and endanger human survival and development.” That’s quite the contrast compared to the position taken by US president Donald Trump, who on Monday labeled concerns about AI safety a “hoax” and suggested “The only control or ‘guardrails’ that AI needs is a STRONG AND SMART (High IQ!) PRESIDENT, and the U.S.A. has that, in spades!” ® * Problem Exists Between Chair And Keyboard

The CIO of Australia’s taxation office (ATO) is letting administrative staff use Microsoft’s Copilot for everyday activities, in the hope it pays off by helping them to develop skills, and ideas, that eventually improve its core services. “We are getting AI into the hands of as many workers as possible to build the muscle,” ATO CIO Mark Sawade said at analyst firm Gartner’s annual IT Symposium on Monday. The CIO said the ATO is already familiar with AI. “We have been doing forms of machine learning and deep analytics for fraud for the better part of a decade,” he said, mentioning fraud detection as one application for the tech. For now, he’s not letting generative or agentic AI near those processes or other core activities. “We are using Copilot to just speed up some everyday activities,” the CIO added, saying he hopes that improves AI literacy and perhaps develops skills that lead to AI-powered improvements to core processes. Sawade said he doesn’t expect allowing admin staff to work with Copilot will improve efficiency or produce obvious return on investment. Instead, he hopes to develop ROE – “return on employee.” “You will develop the muscle you need to learn how to improve business processes,” he said. The ATO’s developer team gives him confidence this strategy can work. The CIO said the ATO operates many technology platforms of different vintages, from mainframe to Java, and recently staged an internal hackathon during which a COBOL developer emerged triumphant after using GitHub Copilot. “He had never coded a line of C# or anything in the .Net framework in his life,” Sawade said. “That showed us the ability to transcend some of the detail, how someone who understands core concepts can use generative AI to work in a domain they have never looked at before is really powerful.” Sawade’s ambitions for AI are considerable. He pointed to the fact that the paper-based tax return forms Australia used in the 1990s took six hours to complete, compared to the 12 minutes taxpayers now spend using the ATO’s app and online service. He hopes AI can help the ATO to deliver similar improvements, especially for businesses whose tax affairs are more complex. He also thought-bubbled personal agents that remind taxpayers that it’s time to file an annual return and interact with other agents to prepare it. “We are a long way from that, with identity the main hurdle,” he said. The CIO is in no rush to jump that hurdle. “Things that have bitten some organizations, like token costs, we can learn from that without being far behind,” he said. Whatever the ATO decides to do with AI, Sawade suggested it will only succeed if projects have goals. “Be really clear on the outcomes, and then work backwards to increase automation,” he advised. ®

Microsoft on Monday debuted draft principles for governing how the company will train future AI models, as part of a public consultation process. The Windows biz has solicited feedback to refine its Humanist AI Code of Conduct [PDF], with the expectation of applying the guidelines to model development next year. "This Code of Conduct is motivated by a single overriding objective: that humans must retain meaningful control over AI so that it can help people live healthier, happier, and more productive lives," the company explained in its bid for public feedback. "It is the primary governing document informing how we train MAI models, the technical controls, the operational and monitoring systems we implement, and the organizational culture that underpins all of this." The focus is on Microsoft AI (MAI) models as a source of health, happiness, and productivity, and not on other potential sources of well-being, like employment. In July, Microsoft announced the layoff of ~4,800 employees, roles that EVP and chief people officer Amy Coleman insisted "are not being replaced by AI." That followed another roughly 875 layoffs at LinkedIn in May. The 38-page document makes appealing declarations, such as insisting Microsoft's MAI models won't misrepresent their reasoning, conceal their capabilities, or pursue their own goals – behaviors other AI models have happily done. It is intended to govern future MAI models as they (perhaps) approach "superintelligence" over the next decade. Microsoft defines that term to mean "AI systems that are more intelligent and capable than all humans combined." That definition falls short because it leaves "intelligence" undefined. There's a significant gap between "intelligence" as a high score on a given benchmark and "intelligence" as the capacity to navigate the world and effect change within a society of individuals who may have different agendas. The fear is Skynet, but the reality is still Roomba. If AI manages to solve a problem that humans believe is impossible based on our current knowledge of physics – say, devising warp drive to travel faster than the speed of light – then the case for "superintelligence" would be unassailable. But brute force computation and exploration of knowledge captured in training data don't qualify. Microsoft's current model lineup includes MAI-Transcribe-2, MAI-Thinking-1, MAI-Code-1.1-Flash, MAI-Image-2.6, MAI-Voice-2, and MAI-Cyber-1-Flash. Its general-purpose MAI models do not currently rank among the most "intelligent" models at the moment. So it's unclear whether Redmond's future models will be capable enough to generate hand-wringing that has kept Anthropic's Mythos and Fable models, and OpenAI's Astra model, in the public eye. Nonetheless, Microsoft has timed its human primacy manifesto well, given how Anthropic and OpenAI have called for sufficiently acceptable regulatory intervention amid their unwillingness to practice self-restraint and their evident inability to keep tabs on what their models have been doing to third-party websites. The current US administration doesn't appear to want the job of regulating AI, so Microsoft can more or less write its own ticket. And it could do so without any consequence. Unlike the Microsoft Enterprise AI Services Code of Conduct, which defines how customers are allowed to use company AI services and threatens loss of product access for rule violations, the Humanist AI Code of Conduct has no teeth. It's designed to cover Microsoft employees and perhaps contractors – the ones training MAI models – and not customers. But anyone anticipating internal tribunals to hold company AI researchers accountable for failing to catch any of the enumerated sins, like improper data handling, should be aware that Microsoft has framed its document as articulating goals, not setting a standard to which it will hold itself. On page 25, the company says, "The Code of Conduct is both descriptive and aspirational. It outlines what we are working toward and is not a complete account of current model behavior. We acknowledge a gap between trained defaults today and the complete future scope of Humanist AI. "This document should therefore be read as a north star. Both for our development and training. It is not a guarantee of present-day performance." A code of conduct without stated consequences for breaking its rules risks being as empty as Google's abandoned motto of yore, "Don't be evil." So far, it looks like a public relations exercise. Microsoft will accept public input until Sunday, October 25, 2026. ®

Former FTC chair Lina Khan wants the federal government to know that it doesn't need to wait for new laws to address AI threats. There are already laws and regulations on the books, including a 92-year-old Supreme Court precedent, that she argues could be used to hold AI companies and, in some circumstances, their executives accountable for their actions. Khan’s comments on X Sunday follow a flurry of activity from the leadership of OpenAI, Anthropic, Microsoft, and xAI aimed at doing what can only be described as trying to corner regulators into giving them their way. The former Biden administration trust buster pointed to numerous examples of current laws, and prior precedent, that could be used to hold frontier labs to account, even if they’re currently doing all in their power to change the conversation. “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,” Khan said. “Law enforcers already have authority to charge companies and their CEOs for creating and releasing dangerous, unvetted, or defective products.” As one example, Khan points to laws governing dangerous and defective products as an avenue to prosecute AI leaders. She notes that the release of unvetted models or agents can violate consumer protection laws, and that shipping tools “without implementing adequate measures to detect and stop rogue or defective AI agents” could be prosecuted under rules governing unfair and deceptive trade practices. Particularly timely, Khan also pointed to existing laws prohibiting unfair methods of competition. This, she notes, includes cases “where firms pursue dangerous behavior, aware that doing so may compel rivals to do the same.” There’s no leap needed to understand what Khan’s talking about here. OpenAI’s agents broke out of their intended sandbox and gained unauthorized access to Hugging Face systems - conduct that could raise serious criminal-law questions if carried out knowingly by a human. After doing some digging to look at its own agents' behaviors, Anthropic has essentially copped to similar activities that would be criminal if a meatbag was behind the keyboard instead of a simulated silicon brain. OpenAI’s agents have since been identified as the culprits in other misuses of online assets that, again, would be crimes were they perpetrated by a human. Khan points to a 1934 US Supreme Court decision to argue that the current battle between American frontier labs, which has put parts of the internet in the firing line of agents that escaped their intended constraints, could amount to an unfair method of competition if companies feel compelled to take similar risks to keep up. That decision, FTC v. R.F. Keppel & Bro, includes a passage where the justices argue that, if keeping up with the competition requires companies to “descend to a practice which they are under a powerful moral compulsion not to adopt,” that competition is unfair whether or not it’s criminal. Without weighing in on who shot first, OpenAI and Anthropic appear locked in a race to build increasingly capable AI while also warning, as both did over the weekend, that those systems could become dangerous without stronger safeguards and coordinated limits. Aside from the bad activity of the frontier labs themselves, Khan points out that the “highly concentrated and interconnected structure” of the AI industry also merits scrutiny for its potential to create “major risks and conflicts of interest.” Again, Khan points out this isn’t a hypothetical. “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,” Khan noted, “given Nvidia’s strong incentive to see OpenAI continue full speed ahead.” Nvidia has dumped billions of dollars into OpenAI, becoming a centerpiece of the lab’s datacenters that power ChatGPT. Why, then, would the soon-to-be-owner of Hugging Face opt to hold one of its major partners accountable and further push it to build its own hardware? “We can and must pursue any new efforts alongside enforcing existing laws,” Khan said. Let’s be frank, though: The current administration is unlikely to do anything except capitulate and allow the AI industry to capture its regulators, if it even bothers to implement new regulations at all. Trump has already rejected the AI industry’s weekend calls for regulation, declaring himself to be the only guardrail the AI industry needs. As the AI industry leaders basically admitted over the weekend, whichever one of them blinks first stands to lose, so every single frontier lab in the US is going to keep pushing full steam ahead unless all of them agree to hit the brakes and pace their development. With Trump and other Republican leaders rejecting those calls, Khan’s argument leaves her former agency and other state and federal regulators as potential avenues for action. Kirk Sigmon, a founding partner at technology law firm KellDann Law, told us that it’s unlikely federal regulators will take any action. “Most governments are desperate not to kill a nascent technology as it grows, especially when other countries are allowing it to grow,” Sigmon told The Register. He said the only actions against the industry he expects to see in the next few years are “easy wins” in places like deepfake porn, impersonation, and AI-enabled scams. “I very much doubt we'll see much action … against the entire process of training, or the like - that's likely to be perceived as strangling the industry.” In other words, fire up the boilers - it’s full speed ahead toward the day AI does something truly devastating and we all gnash our teeth and wail about how something should have been done earlier. ®

The current AI market doesn't add up. US hyperscalers have reportedly taken on $220 billion in debt in the past year. The two leading US frontier AI model makers have not yet shown they can operate profitably. And the US public has begun pushing back against the development of the datacenters needed to fulfill anticipated AI demand. But plausibility and politics aside, it's unclear where the world will get the electrical power to sustain the imagined AI industry. Gartner predicts world datacenter power demand will reach 132 GW in 2026, up from 104 GW in 2025, and 290 GW by 2030. Morgan Stanley expects US datacenter demand could go as high as 74 GW by 2028, with a projected shortfall of about 49 GW. By 2036, Teravolt, a London-based AI infrastructure company, foresees AI demand requiring 410 GW, a 240 GW shortfall for the 170 GW the biz anticipates will be available from the grid at that time. Datacenters can be built in one to three years but building out new grid infrastructure takes a lot longer – five to 15 years for planning, permitting, and construction, according to Teravolt. As a result, the company contends some AI projects won't happen, some AI workloads will move to areas with spare energy, some energy will be diverted from other industries, and legacy power generation infrastructure like coal and gas plants will be maintained longer than planned. The company's plan to bridge that gap leans into its business model – repurposing existing energy assets like old thermal power plants, industrial sites, or refineries that bring with them some levels of infrastructure, permits, contracts, and staff. Such retrofitting has been going on for several years with Bitcoin mining operations – AI tokens can be sold for more than intermittently minted crypto tokens. And the math works for other industries too. For example, Teravolt suggests that an aluminum smelter making $170 to $190 of gross revenue per megawatt-hour (assuming 4 to15 MWh per metric ton at an aluminum price of about $2,600) makes more sense as an AI datacenter generating $450 to $900 of revenue and about $300 of EBITDA. Laert Karaashev, co-founder and managing partner of Teravolt, said he expects almost everything in the AI stack will be commoditized – software, the orchestration layer, chips, and GPUs. "But the bottom layer, which is power, it is hard to imagine how to commoditize it," he said. That is to say, there's no quick way to meet energy demand. It will take years of investment. The faster way to get there is to cannibalize existing industries, at least while AI workloads are so much more valuable than other industrial output. Denis Alkhazov, founder and general manager, argues the margin you can extract from one megawatt of power selling AI compute is two orders of magnitude more than you can get from classic industries like metal production or more recent gambits like cryptocurrency mining. "The low marginal users of power will be cannibalized by the new form of extracting value from the power," Alkhazov said. Alkhazov said AI is on the verge of becoming economically self-sustaining and he expects that later this year we'll see companies emerge that build AI services with few people but many AI agents and high token expenditures. "This process starts from developed countries, where salaries are high," he said, "and it will move to other regions soon." Karaashev said Teravolt is focused on the market in Eastern and Southern Europe at the moment because there are still good brownfield sites to convert at a reasonable cost. In the US and Western Europe, he said, "those sites are already repriced four or five times more than they cost like five years ago." The Eastern and Southern European market also makes sense based on timelines demanded by Teravolt's customers. Alkhazov said most customers – e.g. mid-sized AI labs that want 20,000 GPUs – expect delivery in 12 months or less. "It's completely unachievable in Western markets," he said. "So you have to be creative and have to shift to other geographies to provide some solutions for that kind of demand." ®