2026-03-31 22:12:50
In the early days of large language models (LLMs), we grew accustomed to massive 10x jumps in reasoning and coding capability with every new model iteration. Today, those jumps have flattened into incremental gains. The exception is domain-specialized intelligence, where true step-function improvements are still the norm.
When a model is fused with an organization’s proprietary data and internal logic, it encodes the company’s history into its future workflows. This alignment creates a compounding advantage: a competitive moat built on a model that understands the business intimately. This is more than fine-tuning; it is the institutionalization of expertise into an AI system. This is the power of customization.
Every sector operates within its own specific lexicon. In automotive engineering, the “language” of the firm revolves around tolerance stacks, validation cycles, and revision control. In capital markets, reasoning is dictated by risk-weighted assets and liquidity buffers. In security operations, patterns are extracted from the noise of telemetry signals and identity anomalies.
Custom-adapted models internalize the nuances of the field. They recognize which variables dictate a “go/no-go” decision, and they think in the language of the industry.
The transition from general-purpose to tailored AI centers on one goal: encoding an organization’s unique logic directly into a model’s weights.
Mistral AI partners with organizations to incorporate domain expertise into their training ecosystems. A few use cases illustrate customized implementations in practice:
Software engineering and assisting at scale: A network hardware company with proprietary languages and specialized codebases found that out-of-the-box models could not grasp their internal stack. By training a custom model on their own development patterns, they achieved a step function in fluency. Integrated into Mistral’s software development scaffolding, this customized model now supports the entire lifecycle—from maintaining legacy systems to autonomous code modernization via reinforcement learning. This turns once-opaque, niche code into a space where AI reliably assists at scale.
Automotive and the engineering copilot: A leading automotive company uses customization to revolutionize crash test simulations. Previously, specialists spent entire days manually comparing digital simulations with physical results to find divergences. By training a model on proprietary simulation data and internal analyses, they automated this visual inspection, flagging deformations in real time. Moving beyond detection, the model now acts as a copilot, proposing design adjustments to bring simulations closer to real-world behavior and radically accelerating the R&D loop.
Public sector and sovereign AI: In Southeast Asia, a government agency is building a sovereign AI layer to move beyond Western-centric models. By commissioning a foundation model tailored to regional languages, local idioms, and cultural contexts, they created a strategic infrastructure asset. This ensures sensitive data remains under local governance while powering inclusive citizen services and regulatory assistants. Here, customization is the key to deploying AI that is both technically effective and genuinely sovereign.
Moving from a general-purpose AI strategy to a domain-specific advantage requires a structural rethinking of the model’s role within the enterprise. Success is defined by three shifts in organizational logic.
1. Treat AI as infrastructure, not an experiment. Historically, enterprises have treated model customization as an ad hoc experiment—a single fine-tuning run for a niche use case or a localized pilot. While these bespoke silos often yield promising results, they are rarely built to scale. They produce brittle pipelines, improvised governance, and limited portability. When the underlying base models evolve, the adaptation work must often be discarded and rebuilt from scratch.
In contrast, a durable strategy treats customization as foundational infrastructure. In this model, adaptation workflows are reproducible, version-controlled, and engineered for production. Success is measured against deterministic business outcomes. By decoupling the customization logic from the underlying model, firms ensure that their “digital nervous system” remains resilient, even as the frontier of base models shifts.
2. Retain control of your own data and models. As AI migrates from the periphery to core operations, the question of control becomes existential. Reliance on a single cloud provider or vendor for model alignment creates a dangerous asymmetry of power regarding data residency, pricing, and architectural updates.
Enterprises that retain control of their training pipelines and deployment environments preserve their strategic agency. By adapting models within controlled environments, organizations can enforce their own data residency requirements and dictate their own update cycles. This approach transforms AI from a service consumed into an asset governed, reducing structural dependency and allowing for cost and energy optimizations aligned with internal priorities rather than vendor roadmaps.
3. Design for continuous adaptation. The enterprise environment is never static: regulations shift, taxonomies evolve, and market conditions fluctuate. A common failure is treating a customized model as a finished artifact. In reality, a domain-aligned model is a living asset subject to model decay if left unmanaged.
Designing for continuous adaptation requires a disciplined approach to ModelOps. This includes automated drift detection, event-driven retraining, and incremental updates. By building the capacity for constant recalibration, the organization ensures that its AI does not just reflect its history, but it evolves in lockstep with its future. This is the stage where the competitive moat begins to compound: the model’s utility grows as it internalizes the organization’s ongoing response to change.
We have entered an era where generic intelligence is a commodity, but contextual intelligence is a scarcity. While raw model power is now a baseline requirement, the true differentiator is alignment—AI calibrated to an organization’s unique data, mandates, and decision logic.
In the next decade, the most valuable AI won’t be the one that knows everything about the world; it will be the one that knows everything about you. The firms that own the model weights of that intelligence will own the market.
This content was produced by Mistral AI. It was not written by MIT Technology Review’s editorial staff.
2026-03-31 20:10:00
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
In the last few months alone, Microsoft, Amazon, and OpenAI have all launched medical chatbots.
There’s a clear demand for these tools, given how hard it is for many people to access advice through the existing medical system—and they could make safe and useful recommendations. But concerns have surfaced about how little external evaluation they undergo before being released to the public.
Read the full story to understand what’s at stake.
—Grace Huckins
A judge has temporarily blocked the Pentagon from labeling Anthropic a supply chain risk and ordering government agencies to stop using its AI. Her intervention suggests that the feud never needed to reach such a frenzy.
It did so because the government disregarded the existing process for such disputes—and fueled the fire on social media. Find out how it happened and what comes next.
—James O’Donnell
This story is from The Algorithm, our weekly newsletter giving you the inside track on all things AI. Sign up to receive it in your inbox every Monday.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 California has defied Trump to impose new AI regulations
Governor Newsom signed off on the new standards yesterday. (Guardian)
+ Firms seeking state contracts will need extra safeguards. (Reuters $)
+ States are installing guardrails despite Trump’s order to stop. (NYT $)
+ An AI regulation war is brewing in the US. (MIT Technology Review)
2 Experiments have verified quantum simulations for the first time
It’s a breakthrough for quantum computing applications. (Nature)
+ Which could one day help solve healthcare problems. (MIT Technology Review)
3 The new White House app is a security and privacy nightmare
It extensively tracks users and relies on external code. (Gizmodo)
+ The new app promises “unparalleled access” to Trump. (CNET)
+ It also invites users to report people to ICE. (The Verge)
4 Big Tech’s $635 billion AI spending faces an energy shock test
The Middle East crisis is clouding prospects for growth. (Reuters $)
+ Here are three big unknowns about AI’s energy burden. (MIT Technology Review)
5 Meta and Google have been accused of breaking child safety rules
Australia suspects they flouted a social media ban. (Bloomberg $)
+ Indonesia is also investigating non-compliance. (Reuters $)
6 Nebius is building a $10 billion AI data center in Finland
The company is rapidly expanding Europe’s AI infrastructure. (CNBC)
7 South Korea’s chipmakers’ helium stocks will last until June
Beyond that? Who knows. (Reuters $)
+ Shortages caused by the Iran war threaten the chip industry. (NYT $)
8 Another Starlink satellite has inexplicably exploded
SpaceX suffered a similar episode in December. (The Verge)
+ We went inside Ukraine’s largest Starlink repair shop. (MIT Technology Review)
9 Bluesky’s new AI tool is already its most blocked account—after JD Vance
About 83 times as many users have blocked it as have followed it. (TechCrunch)
10 An AI agent banned from Wikipedia has lashed out in angry blogs
The bot accused its human editors of “uncivil behavior.” (404 Media)
Quote of the day
—Security researcher Thereallo reviews the White House’s new app.
One More Thing
When Hans de Zwart, a digital rights advocate, saw Amsterdam’s plan to have an algorithm evaluate every welfare applicant for potential fraud, he nearly fell out of his chair. He believed the system had “unfixable problems.”
Meanwhile, Paul de Koning, a consultant to the city, was excited. He saw immense potential to improve efficiencies and remove biases.
These opposing viewpoints epitomize a global debate about whether algorithms can ever make fair decisions that shape people’s lives. Read the full story.
—Eileen Guo, Gabriel Geiger, and Justin-Casimir Braun
We can still have nice things
A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line.)
+ A newly authenticated Rembrandt had been hiding in plain sight for years.
+ This debunking of guitar legends is musical enlightenment for strummers.
+ Smoking into bubbles looks oddly satisfying.
+ The man who made the front page twice exposes the thin line between heroes and villains.
2026-03-31 20:01:08
For decades, artificial intelligence has been evaluated through the question of whether machines outperform humans. From chess to advanced math, from coding to essay writing, the performance of AI models and applications is tested against that of individual humans completing tasks.
This framing is seductive: An AI vs. human comparison on isolated problems with clear right or wrong answers is easy to standardize, compare, and optimize. It generates rankings and headlines.
But there’s a problem: AI is almost never used in the way it is benchmarked. Although researchers and industry have started to improve benchmarking by moving beyond static tests to more dynamic evaluation methods, these innovations resolve only part of the issue. That’s because they still evaluate AI’s performance outside the human teams and organizational workflows where its real-world performance ultimately unfolds.
While AI is evaluated at the task level in a vacuum, it is used in messy, complex environments where it usually interacts with more than one person. Its performance (or lack thereof) emerges only over extended periods of use. This misalignment leaves us misunderstanding AI’s capabilities, overlooking systemic risks, and misjudging its economic and social consequences.
To mitigate this, it’s time to shift from narrow methods to benchmarks that assess how AI systems perform over longer time horizons within human teams, workflows, and organizations. I have studied real-world AI deployment since 2022 in small businesses and health, humanitarian, nonprofit, and higher-education organizations in the UK, the United States, and Asia, as well as within leading AI design ecosystems in London and Silicon Valley. I propose a different approach, which I call HAIC benchmarks—Human–AI, Context-Specific Evaluation.
For governments and businesses, AI benchmark scores appear more objective than vendor claims. They’re a critical part of determining whether an AI model or application is “good enough” for real-world deployment. Imagine an AI model that achieves impressive technical scores on the most cutting-edge benchmarks—98% accuracy, groundbreaking speed, compelling outputs. On the strength of these results, organizations may decide to adopt the model, committing sizable financial and technical resources to purchasing and integrating it.
But then, once it’s adopted, the gap between benchmark and real-world performance quickly becomes visible. For example, take the swathe of FDA-approved AI models that can read medical scans faster and more accurately than an expert radiologist. In the radiology units of hospitals from the heart of California to the outskirts of London, I witnessed staff using highly ranked radiology AI applications. Repeatedly, it took them extra time to interpret AI’s outputs alongside hospital-specific reporting standards and nation-specific regulatory requirements. What appeared as a productivity-enhancing AI tool when tested in a vacuum introduced delays in practice.
It soon became clear that the benchmark tests on which medical AI models are assessed do not capture how medical decisions are actually made. Hospitals rely on multidisciplinary teams—radiologists, oncologists, physicists, nurses—who jointly review patients. Treatment planning rarely hinges on a static decision; it evolves as new information emerges over days or weeks. Decisions often arise through constructive debate and trade-offs between professional standards, patient preferences, and the shared goal of long-term patient well-being. No wonder even highly scored AI models struggle to deliver the promised performance once they encounter the complex, collaborative processes of real clinical care.
The same pattern emerges in my research across other sectors: When embedded within real-world work environments, even AI models that perform brilliantly on standardized tests don’t perform as promised.
When high benchmark scores fail to translate into real-world performance, even the most highly scored AI is soon abandoned to what I call the “AI graveyard.” The costs are significant: Time, effort and money end up being wasted. And over time, repeated experiences like this erode organizational confidence in AI and—in critical settings such as health—may erode broader public trust in the technology as well.
When current benchmarks provide only a partial and potentially misleading signal of an AI model’s readiness for real-world use, this creates regulatory blind spots: Oversight is shaped by metrics that do not reflect reality. It also leaves organizations and governments to shoulder the risks of testing AI in sensitive real-world settings, often with limited resources and support.
To close the gap between benchmark and real-world performance, we must pay attention to the actual conditions in which AI models will be used. The critical questions: Can AI function as a productive participant within human teams? And can it generate sustained, collective value?
Through my research on AI deployment across multiple sectors, I have seen a number of organizations already moving—deliberately and experimentally—toward the HAIC benchmarks I favor.
HAIC benchmarks reframe current benchmarking in four ways:
1. From individual and single-task performance to team and workflow performance (shifting the unit of analysis)
2. From one-off testing with right/wrong answers to long-term impacts (expanding the time horizon)
3. From correctness and speed to organizational outcomes, coordination quality, and error detectability (expanding outcome measures)
4. From isolated outputs to upstream and downstream consequences (system effects)
Across the organizations where this approach has emerged and started to be applied, the first step is shifting the unit of analysis.
For example, in one UK hospital system in the period 2021–2024, the question expanded from whether a medical AI application improves diagnostic accuracy to how the presence of AI within the hospital’s multidisciplinary teams affects not only accuracy but also coordination and deliberation. The hospital specifically assessed coordination and deliberation in human teams using and not using AI. Multiple stakeholders (within and outside the hospital) decided on metrics like how AI influences collective reasoning, whether it surfaces overlooked considerations, whether it strengthens or weakens coordination, and whether it changes established risk and compliance practices.
This shift is fundamental. It matters a lot in high-stakes contexts where system-level effects matter more than task-level accuracy. It also matters for the economy. It may help recalibrate inflated expectations of sweeping productivity gains that are so far predicated largely on the promise of improving individual task performance.
Once that foundation is set, HAIC benchmarking can begin to take on the element of time.
Today’s benchmarks resemble school exams—one-off, standardized tests of accuracy. But real professional competence is assessed differently. Junior doctors and lawyers are evaluated continuously inside real workflows, under supervision, with feedback loops and accountability structures. Performance is judged over time and in a specific context, because competence is relational. If AI systems are meant to operate alongside professionals, their impact should be judged longitudinally, reflecting how performance unfolds over repeated interactions.
I saw this aspect of HAIC applied in one of my humanitarian-sector case studies. Over 18 months, an AI system was evaluated within real workflows, with particular attention to how detectable its errors were—that is, how easily human teams could identify and correct them. This long-term “record of error detectability” meant the organizations involved could design and test context-specific guardrails to promote trust in the system, despite the inevitability of occasional AI mistakes.
A longer time horizon also makes visible the system-level consequences that short-term benchmarks miss. An AI application may outperform a single doctor on a narrow diagnostic task yet fail to improve multidisciplinary decision-making. Worse, it may introduce systemic distortions: anchoring teams too early in plausible but incomplete answers, adding to people’s cognitive workloads, or generating downstream inefficiencies that offset any speed or efficiency gains at the point of the AI’s use. These knock-on effects—often invisible to current benchmarks—are central to understanding real impact.
The HAIC approach, admittedly promises to make benchmarking more complex, resource-intensive, and harder to standardize. But continuing to evaluate AI in sanitized conditions detached from the world of work will leave us misunderstanding what it truly can and cannot do for us. To deploy AI responsibly in real-world settings, we must measure what actually matters: not just what a model can do alone, but what it enables—or undermines—when humans and teams in the real world work with it.
Angela Aristidou is a professor at University College London and a faculty fellow at the Stanford Digital Economy Lab and the Stanford Human-Centered AI Institute. She speaks, writes, and advises about the real-life deployment of artificial-intelligence tools for public good.
2026-03-31 00:00:00
Earlier this month, Microsoft launched Copilot Health, a new space within its Copilot app where users will be able to connect their medical records and ask specific questions about their health. A couple of days earlier, Amazon had announced that Health AI, an LLM-based tool previously restricted to members of its One Medical service, would now be widely available. These products join the ranks of ChatGPT Health, which OpenAI released back in January, and Anthropic’s Claude, which can access user health records if granted permission. Health AI for the masses is officially a trend.
There’s a clear demand for chatbots that provide health advice, given how hard it is for many people to access it through existing medical systems. And some research suggests that current LLMs are capable of making safe and useful recommendations. But researchers say that these tools should be more rigorously evaluated by independent experts, ideally before they are widely released.
In a high-stakes area like health, trusting companies to evaluate their own products could prove unwise, especially if those evaluations aren’t made available for external expert review. And even if the companies are doing quality, rigorous research—which some, including OpenAI, do seem to be—they might still have blind spots that the broader research community could help to fill.
“To the extent that you always are going to need more health care, I think we should definitely be chasing every route that works,” says Andrew Bean, a doctoral candidate at the Oxford Internet Institute. “It’s entirely plausible to me that these models have reached a point where they’re actually worth rolling out.”
“But,” he adds, “the evidence base really needs to be there.”
Tipping points
To hear developers tell it, these health products are now being released because large language models have indeed reached a point where they can effectively provide medical advice. Dominic King, the vice president of health at Microsoft AI and a former surgeon, cites AI advancement as a core reason why the company’s health team was formed, and why Copilot Health now exists. “We’ve seen this enormous progress in the capabilities of generative AI to be able to answer health questions and give good responses,” he says.
But that’s only half the story, according to King. The other key factor is demand. Shortly before Copilot Health was launched, Microsoft published a report, and an accompanying blog post, detailing how people used Copilot for health advice. The company says it receives 50 million health questions each day, and health is the most popular discussion topic on the Copilot mobile app.
Other AI companies have noticed, and responded to, this trend. “Even before our health products, we were seeing just a rapid, rapid increase in the rate of people using ChatGPT for health-related questions,” says Karan Singhal, who leads OpenAI’s Health AI team. (OpenAI and Microsoft have a long-standing partnership, and Copilot is powered by OpenAI’s models.)
It’s possible that people simply prefer posing their health problems to a nonjudgmental bot that’s available to them 24-7. But many experts interpret this pattern in light of the current state of the health-care system. “There is a reason that these tools exist and they have a position in the overall landscape,” says Girish Nadkarni, chief AI officer at the Mount Sinai Health System. “That’s because access to health care is hard, and it’s particularly hard for certain populations.”
The virtuous vision of consumer-facing LLM health chatbots hinges on the possibility that they could improve user health while reducing pressure on the health-care system. That might involve helping users decide whether or not they need medical attention, a task known as triage. If chatbot triage works, then patients who need emergency care might seek it out earlier than they would have otherwise, and patients with more mild concerns might feel comfortable managing their symptoms at home with the chatbot’s advice rather than unnecessarily busying emergency rooms and doctor’s offices.
But a recent, widely discussed study from Nadkarni and other researchers at Mount Sinai found that ChatGPT Health sometimes recommends too much care for mild conditions and fails to identify emergencies. Though Singhal and some other experts have suggested that its methodology might not provide a complete picture of ChatGPT Health’s capabilities, the study has surfaced concerns about how little external evaluation these tools see before being released to the public.
Most of the academic experts interviewed for this piece agreed that LLM health chatbots could have real upsides, given how little access to health care some people have. But all six of them expressed concerns that these tools are being launched without testing from independent researchers to assess whether they are safe. While some advertised uses of these tools, such as recommending exercise plans or suggesting questions that a user might ask a doctor, are relatively harmless, others carry clear risks. Triage is one; another is asking a chatbot to provide a diagnosis or a treatment plan.
The ChatGPT Health interface includes a prominent disclaimer stating that it is not intended for diagnosis or treatment, and the announcements for Copilot Health and Amazon’s Health AI include similar warnings. But those warnings are easy to ignore. “We all know that people are going to use it for diagnosis and management,” says Adam Rodman, an internal medicine physician and researcher at Beth Israel Deaconess Medical Center and a visiting researcher at Google.
Medical testing
Companies say they are testing the chatbots to ensure that they provide safe responses the vast majority of the time. OpenAI has designed and released HealthBench, a benchmark that scores LLMs on how they respond in realistic health-related conversations—though the conversations themselves are LLM-generated. When GPT-5, which powers both ChatGPT Health and Copilot Health, was released last year, OpenAI reported the model’s HealthBench scores: It did substantially better than previous OpenAI models, though its overall performance was far from perfect.
But evaluations like HealthBench have limitations. In a study published last month, Bean—the Oxford doctoral candidate—and his colleagues found that even if an LLM can accurately identify a medical condition from a fictional written scenario on its own, a non-expert user who is given the scenario and asked to determine the condition with LLM assistance might figure it out only a third of the time. If they lack medical expertise, users might not know which parts of a scenario—or their real-life experience—are important to include in their prompt, or they might misinterpret the information that an LLM gives them.
Bean says that this performance gap could be significant for OpenAI’s models. In the original HealthBench study, the company reported that its models performed relatively poorly in conversations that required them to seek more information from the user. If that’s the case, then users who don’t have enough medical knowledge to provide a health chatbot with the information that it needs from the get-go might get unhelpful or inaccurate advice.
Singhal, the OpenAI health lead, notes that the company’s current GPT-5 series of models, which had not yet been released when the original HealthBench study was conducted, do a much better job of soliciting additional information than their predecessors. However, OpenAI has reported that GPT-5.4, the current flagship, is actually worse at seeking context than GPT-5.2, an earlier version.
Ideally, Bean says, health chatbots would be subjected to controlled tests with human users, as they were in his study, before being released to the public. That might be a heavy lift, particularly given how fast the AI world moves and how long human studies can take. Bean’s own study used GPT-4o, which came out almost a year ago and is now outdated.
Earlier this month, Google released a study that meets Bean’s standards. In the study, patients discussed medical concerns with the company’s Articulate Medical Intelligence Explorer (AMIE), a medical LLM chatbot that is not yet available to the public, before meeting with a human physician. Overall, AMIE’s diagnoses were just as accurate as physicians’, and none of the conversations raised major safety concerns for researchers.
Despite the encouraging results, Google isn’t planning to release AMIE anytime soon. “While the research has advanced, there are significant limitations that must be addressed before real-world translation of systems for diagnosis and treatment, including further research into equity, fairness, and safety testing,” wrote Alan Karthikesalingam, a research scientist at Google DeepMind, in an email. Google did recently reveal that Health100, a health platform it is building in partnership with CVS, will include an AI assistant powered by its flagship Gemini models, though that tool will presumably not be intended for diagnosis or treatment.
Rodman, who led the AMIE study with Karthikesalingam, doesn’t think such extensive, multiyear studies are necessarily the right approach for chatbots like ChatGPT Health and Copilot Health. “There’s lots of reasons that the clinical trial paradigm doesn’t always work in generative AI,” he says. “And that’s where this benchmarking conversation comes in. Are there benchmarks [from] a trusted third party that we can agree are meaningful, that the labs can hold themselves to?”
They key there is “third party.” No matter how extensively companies evaluate their own products, it’s tough to trust their conclusions completely. Not only does a third-party evaluation bring impartiality, but if there are many third parties involved, it also helps protect against blind spots.
OpenAI’s Singhal says he’s strongly in favor of external evaluation. “We try our best to support the community,” he says. “Part of why we put out HealthBench was actually to give the community and other model developers an example of what a very good evaluation looks like.”
Given how expensive it is to produce a high-quality evaluation, he says, he’s skeptical that any individual academic laboratory would be able to produce what he calls “the one evaluation to rule them all.” But he does speak highly of efforts that academic groups have made to bring preexisting and novel evaluations together into comprehensive evaluations suites—such as Stanford’s MedHELM framework, which tests models on a wide variety of medical tasks. Currently, OpenAI’s GPT-5 holds the highest MedHELM score.
Nigam Shah, a professor of medicine at Stanford University who led the MedHELM project, says it has limitations. In particular, it only evaluates individual chatbot responses, but someone who’s seeking medical advice from a chatbot tool might engage it in a multi-turn, back-and-forth conversation. He says that he and some collaborators are gearing up to build an evaluation that can score those complex conversations, but that it will take time, and money. “You and I have zero ability to stop these companies from releasing [health-oriented products], so they’re going to do whatever they damn please,” he says. “The only thing people like us can do is find a way to fund the benchmark.”
No one interviewed for this article argued that health LLMs need to perform perfectly on third-party evaluations in order to be released. Doctors themselves make mistakes—and for someone who has only occasional access to a doctor, a consistently accessible LLM that sometimes messes up could still be a huge improvement over the status quo, as long as its errors aren’t too grave.
With the current state of the evidence, however, it’s impossible to know for sure whether the currently available tools do in fact constitute an improvement, or whether their risks outweigh their benefits.
2026-03-30 23:42:50
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.
Last Thursday, a California judge temporarily blocked the Pentagon from labeling Anthropic a supply chain risk and ordering government agencies to stop using its AI. It’s the latest development in the month-long feud. And the matter still isn’t settled: The government was given seven days to appeal, and Anthropic has a second case against the designation that has yet to be decided. Until then, the company remains persona non grata with the government.
The stakes in the case—how much the government can punish a company for not playing ball—were apparent from the start. Anthropic drew lots of senior supporters with unlikely bedfellows among them, including former authors of President Trump’s AI policy.
But Judge Rita Lin’s 43-page opinion suggests that what is really a contract dispute never needed to reach such a frenzy. It did so because the government disregarded the existing process for how such disputes are governed and fueled the fire with social media posts from officials that would eventually contradict the positions it took in court. The Pentagon, in other words, wanted a culture war (on top of the actual war in Iran that began hours later).
The government used Anthropic’s Claude for much of 2025 without complaint, according to court documents, while the company walked a branding tightrope as a safety-focused AI company that also won defense contracts. Defense employees accessing it through Palantir were required to accept terms of a government-specific usage policy that Anthropic cofounder Jared Kaplan said “prohibited mass surveillance of Americans and lethal autonomous warfare” (Kaplan’s declaration to the court didn’t include details of the policy). Only when the government aimed to contract with Anthropic directly did the disagreements begin.
What drew the ire of the judge is that when these disagreements became public, they had more to do with punishment than just cutting ties with Anthropic. And they had a pattern: Tweet first, lawyer later.
President Trump’s post on Truth Social on February 27 referenced “Leftwing nutjobs” at Anthropic and directed every federal agency to stop using the company’s AI. This was echoed soon after by Defense Secretary Pete Hegseth, who said he’d direct the Pentagon to label Anthropic a supply chain risk.
Doing so necessitates that the secretary take a specific set of actions, which the judge found Hegseth did not complete. Letters sent to congressional committees, for example, said that less drastic steps were evaluated and deemed not possible, without providing any further details. The government also said the designation as a supply chain risk was necessary because Anthropic could implement a “kill switch,” but its lawyers later had to admit it had no evidence of that, the judge wrote.
Hegseth’s post also stated that “No contractor, supplier, or partner that does business with the United States military may conduct any commercial activity with Anthropic.” But the government’s own lawyers admitted on Tuesday that the Secretary doesn’t have the power to do that, and agreed with the judge that the statement had “absolutely no legal effect at all.”
The aggressive posts also led the judge to also conclude that Anthropic was on solid ground in complaining that its First Amendment rights were violated. The government, the judge wrote while citing the posts, “set out to publicly punish Anthropic for its ‘ideology’ and ‘rhetoric,’ as well as its ‘arrogance’ for being unwilling to compromise those beliefs.”
Labeling Anthropic a supply chain risk would essentially be identifying it as a “saboteur” of the government, for which the judge did not see sufficient evidence. She issued an order last Thursday halting the designation, preventing the Pentagon from enforcing it and forbidding the government from fulfilling the promises made by Hegseth and Trump. Dean Ball, who worked on AI policy for the Trump administration but wrote a brief supporting Anthropic, described the judge’s order on Thursday as “a devastating ruling for the government, finding Anthropic likely to prevail on essentially all of its theories for why the government’s actions were unlawful and unconstitutional.”
The government is expected to appeal the decision. But Anthropic’s separate case, filed in DC, makes similar allegations. It just references a different segment of the law governing supply chain risks.
The court documents paint a pretty clear pattern. Public statements made by officials and the President did not at all align with what the law says should happen in a contract dispute like this, and the government’s lawyers have consistently had to create justifications for social media lambasting of the company after the fact.
Pentagon and White House leadership knew that pursuing the nuclear option would spark a court battle; Anthropic vowed on February 27 to fight the supply chain risk designation days before the government formally filed it on March 3. Pursuing it anyway meant senior leadership was, to say the least, distracted during the first five days of the Iran war, launching strikes while also compiling evidence that Anthropic was a saboteur to the government, all while it could have cut ties with Anthropic by simpler means.
But even if Anthropic ultimately wins, the government has other means to shun the company from government work. Defense contractors who want to stay on good terms with the Pentagon, for example, now have little reason to work with Anthropic even if it’s not flagged as a supply chain risk.
“I think it’s safe to say that there are mechanisms the government can use to apply some degree of pressure without breaking the law,” says Charlie Bullock, a senior research fellow at the Institute for Law and AI. “It kind of depends how invested the government is in punishing Anthropic.”
From the evidence thus far, the administration is committing top-level time and attention to winning an AI culture war. At the same time, Claude is apparently so important to its operations that even President Trump said the Pentagon needed six months to stop using it. The White House demands political loyalty and ideological alignment from top AI companies, But the case against Anthropic, at least for now, exposes the limits of its leverage.
If you have information about the military’s use of AI, you can share it securely via Signal (username jamesodonnell.22).
2026-03-30 20:10:00
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
After operating in secrecy for years, R3 Bio, a California-based startup, suddenly revealed last week that it had raised money to create nonsentient monkey “organ sacks” as an alternative to animal testing. But there is more to the story. And R3 doesn’t want that story told.
MIT Technology Review discovered that founder John Schloendorn also pitched a startling, ethically charged vision: “brainless clones” that serve as backup human bodies. Find out all the details on the radical proposal.
—Antonio Regalado
Ten months ago, reproductive health researchers placed a freshly donated human uterus inside a new device they call “Mother.” They connected the organ to the machine’s plastic veins and arteries and pumped in modified human blood.
The device kept the uterus alive for a day, a new feat that could lead to longer-term maintenance of wombs outside the body. Future versions of the technology could shine new light on pregnancies—and potentially even grow a human fetus. Read the full story.
—Jessica Hamzelou
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 AI data centers can significantly warm up surrounding areas
The “heat islands” may already affect 340 million people. (New Scientist)
+ Mistral has raised $830M to build Nvidia-powered AI centers in Europe. (FT $)
+ But nobody wants a data center in their backyard. (MIT Technology Review)
2 Elon Musk reportedly joined Trump’s call with Modi about the Iran War
It remains unclear what Musk was doing during the conversation. (NYT $)
+ India has disputed the report. (Independent)
+ The war poses a grave threat to the EV market. (Rest of World)
3 Eli Lilly has struck a deal to bring AI-developed drugs to the market
It’s secured a $2.75 billion drug collaboration with Insilico Medicine. (Reuters $)
+ A I-designed compounds can kill drug-resistant bacteria. (MIT Technology Review)
4 More and more countries are curbing children’s social media access
Austria is the latest to pursue a ban. (Engadget)
+ Indonesia has rolled out the first one in Southeast Asia. (DW)
+ UK Prime Minister Keir Starmer said he will also “have to act.” (Guardian)
5 Tech stocks just had their worst week in nearly a year
Thanks to a combination of the Iran war and legal disputes. (CNBC)
+ Tech insiders are split over the AI bubble. (MIT Technology Review)
6 Meta is launching new smart glasses for prescription wearers
It plans to debut them next week. (Bloomberg $)
7 Taiwan is probing 11 Chinese firms for illegal poaching of tech talent
Its semiconductors are entangled in the tensions with Beijing. (Reuters)
8 Bluesky has built an AI app for customizing social media feeds
It uses Anthropic’s Claude. (TechCrunch)
9 A psychologist is making music with his brain implant
He believes enjoyment is a prerequisite for BCI success. (Wired $)
10 The world’s smallest QR code could store data for centuries
It’s smaller than bacteria. (Science Daily)
Quote of the day
—YouTube CEO Neal Mohan gives the New York Times his take on the debate around children’s safety online.
One More Thing
You’d have to be pudding-brained to believe that chatbots are the best way to use computers. The real opportunity is a system built atop the visual interfaces we already know, but navigated through a natural mix of voice and touch.
Crucially, this won’t just be a computer that we can use. It’ll be one we can break and remake to suit whatever uses we want. Instead of merely consuming technology like the gelatinous humans in Wall-E, we should be able to architect it to suit our own ends
This idea is already lurching to life. Read the full story to find out how.
—Cliff Kuang
We can still have nice things
A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line.)
+ These floating designs will elevate your perspective on architecture.
+ Uğur Gallenkuş’s portraits of two worlds in one image beautifully build bridges.
+ This is the anti-Karen that the world needs right now.
+ If only we could all find a love as pure as this kitty clinging to its favorite toy.
