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Over the weekend, several current and former advisors to President Donald Trump on AI publicly lobbed insults at the country’s leading AI companies. David Sacks, the president’s AI and crypto “czar” until March, branded Anthropic’s models as “lobotomized” and “woke.” Emil Michael, a top Pentagon official, called OpenAI’s new head of strategic futures a “supreme village idiot.”

It began because no one can agree on what to do about Kimi, a free, open source model that Chinese AI company Moonshot launched last week. It appears to rival the intelligence of models from OpenAI and Anthropic, which are very much not free. 

Kimi and other Chinese models like it pose a real problem for Trump. And they’re dividing the top AI strategists in his orbit into factions. Every time a new smart, free model from China like Kimi gets released, US companies see less reason to fork out money to access models from Anthropic or OpenAI. Given that enthusiasm for these and other AI companies is driving an outsized share of economic growth, China’s AI models create both economic and political problems for the president. They are “a threat for an administration that really doesn’t want more economic bad news,” Anton Leicht, a fellow at the Carnegie Endowment, wrote on X. They’ve already rattled US stocks. 

What is Trump to do? First, consider that this is all happening just a week after New York imposed the country’s first state ban on new data centers. There is growing distrust of AI companies, and I imagine a not-insignificant share of Americans would have little sympathy for OpenAI or Anthropic as they fend off cheaper competitors, and would say it’s not the government’s job to protect their interests.

On this point, they’d see a sliver of agreement (and really just a sliver) with David Sacks, who on July 19 criticized top AI companies that “want the government to eliminate their open source competition.” He has also argued that Chinese AI models have become popular because they come with fewer restrictions on how people can use them (putting aside the built-in state censorship). 

Sacks, however, is out of a job. He no longer has a formal role advising Trump, and his position that more open AI is better has been largely replaced in the administration by one that sees a larger role for government intervention. The thinking behind this view is that because AI models have gotten strong enough to pose threats to national security, the government must control how they’re used. 

This position has fueled the new White House review process that aims to vet AI models’ security before they’re released. Dean Ball, a former Trump AI advisor who now works for OpenAI, criticized it over the weekend as a “de facto licensing regime for frontier AI.” Ball predicted Trump may solve his Chinese open source problem with a bit of soft power, perhaps by making US companies afraid to use models like Kimi. That drew a response from Michael, who, with Secretary of Defense Pete Hegseth, has been the agency’s main liaison with AI companies. Michael called Ball the AI industry’s “supreme village idiot,” bristling at the suggestion that the government would quietly strong-arm companies rather than, as Michael put it, go through “the democratic process not some Deep State scheme.”

Left out of the conversation has been how a model like Kimi got so good in the first place. For much of the Biden administration and even the beginning of Trump’s second administration, keeping China from getting top chips was a priority. Those export controls have loosened—Trump made the controversial decision to allow Nvidia to sell more chips to China, in exchange for the US government taking a cut—and the government has alleged that some chip smuggling has taken place. But China nonetheless has limited computing power, and it’s not clear what chips the company behind Kimi used to train the model. 

It’s possible that the process involved some distillation, a practice in which AI models are trained on the outputs of existing AI models. OpenAI and Anthropic have long complained that Chinese AI companies do this, and they have requested government help to put a stop to it. In April, they got it, when the Trump administration announced a series of efforts to curb the practice.  

But Kimi is out there and free, and it is nearly as good as the Anthropic model the US government deemed so powerful that it was briefly shut down because it threatened national security. The weekend’s sparring suggests many in Trump’s orbit see that as a wake-up call. But nobody can agree on what for.

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

Anthropic—currently the world’s most valuable AI company, with a nearly $1 trillion valuation—has a reputation for publishing strange and heady research. It’s looking into whether AI models can feel pain, for example, and will sometimes cut off chatbot conversations if it suspects users are “abusing” the model. 

One niche that Anthropic spends more time and money on than other AI companies is called mechanistic interpretability, which means looking inside the complex math of an AI model to learn why it comes up with one particular output and not another. It’s complicated stuff; there are millions of data points that might contribute to any result, and wading through them can look more like word salad than anything useful. It’s also controversial. Describing AI models with terms borrowed from psychology and neuroscience can make their behavior seem more sophisticated than we might otherwise judge it to be.

That’s why, when Anthropic announced last week that it had found a new window into its models’ “internal thoughts” as they reason through answers, there was one colleague I had to talk to. Senior editor Will Douglas Heaven, aside from having a PhD in computer science, has spent a lot of time digging into what we can say about how AI models work. I spoke with him about what we should take from Anthropic’s new (and predictably quirky) research.

What did Anthropic learn here, exactly?

Anthropic has been trying to understand how large language models (LLMs) work for a few years now. Anthropic isn’t the only one looking at this, but I think the company has made it part of its core mission more than most. Anthropic’s CEO, Dario Amodei, has said we won’t be able to control LLMs fully unless we learn more about how they work. 

So this new research is very much in that context. It goes deeper into the weird mechanisms inside LLMs than ever before. What Anthropic learned was that LLMs have a space inside them—which Anthropic calls the J-space—filled with words that don’t appear in their output but that seem to influence the way they puzzle through problems. All this was hidden until Anthropic developed a new technique to probe its model Claude, so it’s a genuine discovery. 

Sometimes these words keep track of where the LLM has got to in a particular task, sometimes they look more like flashes of recognition (for example, “protein” might pop up when you give an LLM only the letters of a protein sequence), and sometimes they represent a kind of internal commentary on the model’s decision-making. In my favorite example, Claude decided to cheat on a coding test when the word “panic” appeared.

Anthropic also found that LLMs are able to describe and manipulate the words in this space. So somehow they seem to be making use of it. 

Let’s step back for a second. I don’t think of large language models as simple, but they’re also not magic. There’s a bunch of math that learns relationships between words, right? So why is it so hard to “peer” into an LLM to know what’s going on?

Yeah, they’re not magic! I think the fact we don’t fully understand them plays into the mythmaking. And it’s worth noting that the whole narrative that Anthropic is leaning into here—that they’ve built this really mysterious technology, but don’t worry, because they’re also the ones to figure it out—very much fits with the company’s vibe. [See how Anthropic warned that its new models were so good at coding they posed a global cybersecurity risk, only for the US government to shut them down shortly thereafter.]

So yes: LLMs are just math. And yet it’s vastly complex math. Not only are today’s LLMs made out of hundreds of billions of numbers, but running them triggers a cascade of millions and millions of calculations. I wrote last year that if you printed out even a medium-size LLM on pieces of paper, it would cover a city the size of San Francisco. 

It’s impossible to make sense of any of that math without specialist tools that highlight specific parts of an LLM at specific times. You need to know where to look and how to look. And building those tools requires understanding something of that complex math in the first place. 

You’ve written elsewhere about this concept of studying LLMs the way one might study an organism’s brain. Is it fair to use “brain-like” terms when talking about how an LLM works?

I don’t love using those kinds of terms. LLMs are not brains. Talking like this is misleading because it can suggest that LLMs are capable of more human-like things than they are or that we can make assumptions about how they might behave that we shouldn’t. The whole anthropomorphization thing is also tied up with a bunch of strong ideological positions about what this technology is and what it’s going to be. 

But at the same time, we lack a good alternative vocabulary for talking about what these models are doing. I can understand why people reach for words like “think” and “understand” and “brain-like”—they’re convenient shorthand. 

Anthropic compares this new space it found inside LLMs to the space that some neuroscientists think our brains use to keep track of conscious thoughts. I asked the company how seriously we should take that comparison and it said in a statement: “Drawing these analogies was helpful to us in designing our experiments, as they allowed us to make many non-obvious experimental predictions about the J-space that turned out to be true. At the same time, it’s important to note that there are some important differences between the J-space (and language models in general) and the human brain, so we don’t mean to claim there’s a perfect correspondence.” 

What’s a problem in AI that this new concept of the J-space might be used to solve?

Anthropic has said that monitoring the J-space could be a way to catch models doing something they shouldn’t. Because words pop up in this space that don’t appear in a model’s output, they can tell you things about its behavior that you might not have noticed otherwise—such as when it is giving biased responses or when it is weighing the pros and cons of cheating. 

That’s the theory, at least. I think it’s better to think of this result as one more step on the path to understanding this technology overall than as something that will be useful by itself. 

Read more in Will’s full story about the new research. 

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

OpenAI CEO Sam Altman’s oft-discussed promise that Americans will share in the wealth AI creates was in the news again last week. On Thursday, the Financial Times reported that Altman is in talks with President Trump about giving the US government a 5% stake in OpenAI.

In some ways, Altman’s plan is old news. He wrote about a more radical version of this back in 2021, proposing that all companies above a certain valuation (not just AI companies) pay 2.5% of their market value each year into a fund that sends Americans annual disbursements. In April this year, OpenAI described a narrower proposal that closely resembles what Altman is reportedly discussing with Trump now. And the notion has broad political appeal: Senator Bernie Sanders has proposed giving Americans a 50% stake in top AI companies.

What’s the logic here? For would-be recipients, it’s twofold. First, AI learns directly from human-generated work—books, movies, art—but AI companies generally never pay the authors of that work. A free equity stake could serve as a form of belated compensation. Second, the payout could mitigate the widespread anxiety that AI will cause a collapse of the labor market (even if economists disagree) by providing a safety net. 

How large a safety net is up for debate. Details of OpenAI’s latest proposal are sparse, but let’s say the government were to distribute this equity stake directly to Americans. After its funding round in March the company was valued at $852 billion, making a 5% stake in OpenAI worth about $42.6 billion today (the company is reportedly delaying its IPO until it can reach a $1 trillion evaluation, a tall order given that it’s spending heavily on data centers and still has not turned a profit).

Distributing that $42.6 billion equally among the roughly 133 million American households would give each about $320 in equity. But if it were to operate like other wealth funds, the government would not give equity directly to Americans but rather let the fund grow and then share a portion of the returns with everyone, perhaps delivering a bigger payout, if and when AI companies can ever start sustainably turning a profit.

If this dividend does materialize, what’s in it for tech companies? Altman might hope the promise of payouts could help swing public opinion a bit more back toward AI companies. (A majority of Americans don’t trust companies to use AI responsibly and oppose construction of data centers in their area, and half are more concerned than excited about the increased creep of AI into their daily lives.)

But the bigger prize for OpenAI might be that the Trump administration loves making tech deals—like its equity stake in Intel and its share of Nvidia’s sales to China, among others.  Staying on the administration’s good side is pretty essential for AI companies right now (just ask Anthropic). It could mean not having your models deemed a supply chain risk, or getting more help from the White House in stopping your rivals from China. 

My main takeaway is that these plans currently function more as a story than a policy. Altman has been talking about some version of this idea for five years and reportedly pitched it to President Trump soon after he took office, yet there is still little indication that a concrete plan is taking shape. The more ambitious proposal from Sanders is even less likely to gain traction.

But what these plans do reveal is just how up for debate the future of AI still is. Altman drew inspiration for his plan from the Alaska Permanent Fund, which was set up in the 1970s to give Alaskans a share in oil profits. The idea was based on two premises: that oil is a shared resource, and that eventually it will run out. Altman seems happy to concede the first claim about AI. But he’d balk at the second, having promised that AI will generate extraordinary wealth for decades to come. Whether Americans ever receive a check is beside the point; the proposal’s real purpose may be to convince them that the AI boom will be large enough to share.

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

Imagine coming in to work to learn that a new underling will report to you. The worker is not a person but an AI tool—one that your company nonetheless calls Alex, an “employee” with a title and defined responsibilities. How well do you think you would work with Alex?

If you’re anything like the managers recently studied by Emma Wiles, a Boston University business professor, treating Alex as a “coworker” and not a software tool would lead you to do a worse job. Wiles found that people caught 18% fewer errors when the work was said to have come from an agentic “AI employee” rather than a chatbot. It turns out that what’s in a name matters. A lot. 

This is an alarming glimpse of the future Silicon Valley is hurling us toward. Last year Nvidia’s CEO, Jensen Huang, talked about workplaces of “digital humans.” Since April, Microsoft, OpenAI, Anthropic, and Google have all released new tools oriented toward managing teams of AI agents, many of which are explicitly advertised as digital colleagues with the flexibility and cognitive power of actual humans. And nearly a third of the 1,261 managers who participated in Wiles’s study said their companies already frame AI agents as employees (23% even list them on org charts).

The technical progress of agentic AI is not all hot air, of course. Agents, which can effectively be thought of as AI tools programmed to work in a loop until they achieve a goal, have become measurably better at more complicated tasks. But it’s a huge leap to refer to these tools as coworkers or employees, and doing so will set unrealistic expectations for what AI can do while leaving the human employees supposedly responsible for them worse off.

That’s partially because, Wiles’s research suggests, it inverts our sense of who’s in charge. When an AI tool was framed as an employee, participants in the study saw themselves as less responsible for its output. They were also 44% more likely to escalate its questionable work to a manager for further review rather than trusting their own corrections (thus negating the time-saving purpose of using the AI agent in the first place). 

That matters far beyond office culture: As AI agents are embedded into health care, warfare, education, and government, there’s a growing risk they’ll become a convenient place to dump blame for failures that are instead the product of bad human decisions, incentives, and oversight (recall how the bomb strike on a girls’ school in Iran was popularly blamed on Claude, when all signs point to a cascade of human errors).

“AI agents right now are being marketed as things that can replace humans, and I think that’s just a losing proposition,” says Daron Acemoglu, an economist at MIT who won the Nobel Prize in 2024 and studies AI’s impact on the economy. “They should instead be optimized so that they can improve human capabilities, which is not what they have [been] at the moment.”

What could that look like? Consider a new effort at Stanford, where researchers presented 1,500 workers in 104 jobs with information about what tasks AI could potentially do in their work and then asked what would actually be most helpful and productive. Workers did want automation in certain areas: Law clerks thought AI could help ensure that adequate progress was being made across cases, for example. But often the tasks that tech experts deemed most suitable for AI—like verifying customer credit ratings for sales reps—were what the actual workers said they definitely did not want or need an agent to do. 

Which brings us back to Alex. Calling Alex an employee is easy—and convenient, especially when something goes wrong—but it’s a branding exercise. It doesn’t make the tool more fit for the job, and as Wiles’s research shows, it makes the humans around it worse at theirs. And recall that they are the ones with the agency that AI is trying to replicate. They deserve better than Alex.