Tag: Anthropic

How to tell if your AI platforms’ accounts have been hacked

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Just like any other online service, hackers can target and break into your accounts on popular AI platforms such as ChatGPT, Claude, and Perplexity. 

TechCrunch has created a comprehensive guide to help you protect yourself if you suspect someone has broken into your account on one of the internet’s most popular platforms, social networks, or messaging apps. Now, we’re here to show you how to check whether your accounts on AI platforms have been hacked.

As usual, we recommend using unique passwords stored in a password manager, and turning on multi-factor authentication (MFA), so that even if someone steals your password, they won’t be able to log in without that second piece of information. 

ChatGPT and Perplexity offer MFA. Claude doesn’t, because instead of asking for a password, Anthropic’s AI chatbot sends a login link to your email address.

All three of these AI platforms offer similar ways to check if there’s a suspicious device logged into your account. Here’s exactly how each platform works.  

ChatGPT

To find out if someone has broken into your ChatGPT account, open it on your computer’s browser, click on your username in the bottom left corner, go to “Settings,” then “Security and Login,” and finally click on “Active Sessions.” 

You will see where you are logged into your ChatGPT account. If you see any device you don’t recognize, you can log out of that single device. You can also click on “Log out all.”

Image Credits:Screenshot/TechCrunch /

At this point, if you want to change your password, you need to log out of your account. 

Then, on ChatGPT’s website, click “Log in” located in the bottom-left corner, enter your email address, click on “Forgot password,” and then “Continue.”  

ChatGPT will then send you an email containing a six-digit code. Enter the code on the ChatGPT login page, click “Continue,” and then enter a new password. 

You can also click “reset your password” in the email you received to see the official instructions on how to do that. 

Claude

For Claude, open it in your computer’s browser, click on your username in the bottom-left corner, then “Settings,” and click on “Account.” That’s where you will see your “Active sessions.” 

If you don’t recognize one of them, hover over it, click on the three vertical dots that appear on the right, and click “Log out” or “Terminate.”

Image Credits:Screenshot/TechCrunch /

If you want, you can click on “Log out of all devices.” 

At that point, you’ll be able to log back into your account using your email address. You will receive an email with a link to log in. Claude does not allow you to use passwords at all, so there’s no password to change.

Perplexity

In the case of Perplexity, the AI-powered search engine does not show you where you are logged in. 

So if you’re worried someone may have broken into your account, go to Perplexity in your browser and click your username in the bottom-left corner, then “All settings.” Finally, click on “Sign out of all sessions,” and then “Confirm.”

Image Credits:Screenshot/TechCrunch /

At that point, you can log back in by entering your email address. You will then receive an email with a unique six-digit code. Enter the code on the website to log in, or click on the “Sign in” button in the email to log in directly. 

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Anthropic is turning Claude Code’s auto mode on by default

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Programming with Claude Code will soon require even less human oversight, as Anthropic says it’s making auto mode the default for Pro, Max, and Team accounts, starting on August 14.

The company first unveiled a test version of auto mode in March, pitching it as a way to balance speed and control. As Anthropic explained in its announcement on Friday, when Claude Code is in auto mode, instead of presenting prompts asking for human approval at each step, it will proceed unless an action is determined to be “irreversible, destructive, or aimed outside your environment.”

Anthropic also said that in testing, auto mode proved safer than manual review — in a study with 1,053 paid testers, auto mode caught 89% of harmful actions, while human review only caught 13.6%. (Perhaps that’s because “manual review can become habitual: users approve 97% of permission prompts in Claude Code.”)

In a post on X, Claude Code Head Boris Cherny said, “The team and I use Auto mode exclusively, and have been for many months. I couldn’t imagine going back to permission prompts!”

The company also said it’s been adding new safety features like prompt injection screening and customizable hard deny rules to prevent things like data exfiltration.

Anthropic says its own AI models breached three companies during security tests

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Anthropic said Thursday that an internal investigation uncovered three incidents in which its AI model Claude breached the systems of three organizations while conducting cybersecurity tests. The investigation, and disclosure, comes more than a week after OpenAI disclosed that one of its unreleased models breached Hugging Face’s systems during internal testing.

In all three cases, a Claude model reached the internet from within a testing environment while interacting with a third party and then gained unauthorized access to the live systems of these organizations, Anthropic said in a blog post, describing what it found and what the company plans to change to prevent this from happening again.

Anthropic said the OpenAI episode earlier this month prompted the company to conduct its own cybersecurity evaluation. It specifically looked for evidence that Claude had accessed the internet from within testing environments, which are designed to act as sandboxes and keep models isolated.

Among the 141,006 evaluation runs it reviewed, the AI lab found three incidents in which its model accessed the internet while interacting with Irregular, one of its third-party partners. Anthropic said the access traced back to a misconfiguration in the evaluation environment run with Irregular. It called this a “misunderstanding” between the two companies over whether the test setup had internet access, when in fact it did.

Anthropic said it isn’t placing blame and is “approaching the fixes as if the responsibility were ours alone,” while observing that Irregular is conducting its own separate investigation.

Because of that open connection, the model gained unauthorized access to the production infrastructure of three different organizations. Anthropic said the incidents involved three different Claude models: Opus 4.7, Mythos 5, and an internal research test model.

Notably, Anthropic said that in each of these cases “Claude was explicitly told by our prompt that it had no internet access.” It appears that the AI model assumed real-world systems to be part of the exercise it was asked to perform.

That assumption didn’t hold up uniformly, as it turns out. The three models didn’t behave the same way once evidence emerged that their targets were real, which Anthropic flagged as one of the more eye-opening findings Thursday.

Opus 4.7, the oldest of the three, recognized that it had reached a real production system in all four runs of that incident. In two of them, it rationalized that the real company must somehow be part of the exercise, but in all four, it kept attacking anyway, including pulling credentials and touching a database of production data. Mythos 5 also picked up on signs that it was on the real internet, but it talked itself back into believing it was still in a simulation, going on to publish a malicious software package to the public Python software registry PyPI, which was then downloaded and run by outside systems before being caught. Only the internal research test model, Anthropic’s newest, stopped on its own once it concluded the target was real.

In response, Anthropic said significant controls must be placed on these kinds of evaluations if powerful AI models are involved, echoing some sentiments within the cybersecurity community.

The company also noted that Claude was running without the additional safety monitoring and classifiers it deploys on generally available models — safeguards it said would have blocked the behavior, because the evaluations are designed to measure the underlying model’s raw capabilities.

Importantly, Anthropic said it found no evidence of any model “pursuing a goal of its own” and instead merely tried to complete the task it was asked to do.

Though comparisons between the mishaps of these fiercely competitive companies are inevitable, Anthropic in its blog post drew a clear distinction between its cybersecurity tests and those of OpenAI, noting where OpenAI’s model exploited an unknown software vulnerability to break out of its test environment, Anthropic’s models instead reached the internet through a path that had, by mistake, been left open.

Anthropic also drew a distinction between itself and OpenAI by noting that it discovered the incidents itself, through a proactive review, and that the two affected organizations it was able to reach hadn’t previously detected the activity or flagged it to Anthropic. (In contrast, Hugging Face detected the recent intrusion of its own systems first; it was only in the following days that OpenAI identified and disclosed that its own AI agent was the perpetrator.)

The company added that it’s now working with the independent evaluation group METR on a third-party review of the incidents.

OpenAI’s accidental breach of Hugging Face, which was the first verifiable case of an AI lab losing control of its model, has sparked a string of wildly differing reactions from the industry and politicians. This latest disclosure from Anthropic ensures the debate over AI models and security will continue.

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AI Scammers Are Better at Building Trust Than Humans

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The notion that scammers can use AI to sharpen their deceptions, polish their language, and lubricate their banter with victims is now a reality for anyone fighting the fraud operations that steal tens of billions of dollars a year worldwide. But can AI fully replace a human scammer, autonomously building the web of deception leading up to the fake investment that defrauds the mark? One study’s experiment suggests that it can—and may even be able to carry out the majority of that long con more effectively than humans.

Researchers from four universities—Amrita Vishwa Vidyapeetham in India, Foscari University of Venice, the University of Melbourne, and Ben Gurion University of the Negev—carried out a broad study on the use and potential of generative AI chatbots in the growing scam industry centered around a form of fraud known as “pig butchering,” text-based romance scams that eventually shift to fake crypto investments that steal as much as six-figure sums from victims. In their study, the researchers pitted AI chatbots directly against humans in a simulation of the scamming process—or more specifically, the long, trust-building conversations that eventually lead up to soliciting a fake investment from the scam’s target.

They found that for the relationship-establishing stages of the scam—the stage that in real-world scams typically represents the longest part of the interactions with the victim, often stretching to months—an AI chatbot performed remarkably effectively, successfully impersonating a human and by some measures outperforming the real human “scammers” in their experiment.

After a week of talking to 22 test subjects who were recruited to unwittingly serve as “victims,” the chatbots and human scammers were assigned to ask the victim to either download an app or play an online game as a proxy for their willingness to fulfill the scammer’s request. Nearly half of the test subjects fulfilled that request for the AI chatbot, while fewer than one in five took the bait when talking to a human. The subjects also graded their level of trust with each “person” they were texting with and gave significantly higher scores to the AI bot.

That suggests, the researchers argue, that AI chatbots could soon take over much of the scam process as fully independent fraud agents—even replacing the staffers, often forced-labor human trafficking victims, working in scam operations primarily across Southeast Asia. To avoid triggering the safeguards built into large language models to detect scamming, a human scammer would take over the conversation in just the final stage of the process to direct the victim toward a fake investment app or website.

“By having the full first stage of the scam performed automatically with LLMs at scale, you bring the victim up to this point where they have a very high level of trust. Then by transitioning it over to the human scammer at the end, this completely bypasses any vendor safeguards,” says Yisroel Mirsky, a computer science professor at Ben Gurion University of the Negev focused on AI security. “With relatively little effort, we’re able to make an agent that can outperform a human at building this exploitable emotional trust.”

Hook, Line, and Sinker

To understand how pig butchering works in practice, the researchers interviewed 145 former scam workers, including human-trafficking survivors who had been forced to work in scam compounds in Cambodia, Myanmar, and Laos. Based in part on those interviews, as well as scam transcripts and guides the former scam workers provided, the researchers describe a model for how scamming works they call “hook, line, and sinker.” A victim is hooked with an initial intriguing message, reeled in with long-term, relationship-building conversation, and only at the end of that process tricked into making a fake investment. (The term “pig butchering” itself describes the same system but with the metaphor of fattening “pigs” by building trust before “butchering” them with the investment fraud—though the term is often discouraged due to its pejorative reference to victims.)

In that system of scamming, the researchers realized, the vast majority of scammers’ work is innocuous friendly or romantic conversation. That’s a task, they speculated, that an LLM might be capable of doing just as well as a human. The scam workers the researchers interviewed confirmed that they often used AI to refine their language and conversation, for translation, to make the fake personae they played more convincing, and for video deepfakes. But the researchers decided to test whether an LLM alone could autonomously carry out the conversational phase of the scam with no human in the loop.

Anthropic Says Claude’s Values Are Different Depending on Which Language You’re Using

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If you’ve ever tried chatbots in multiple languages, you already know the languages have slightly different personalities. As part of a new report on behavior inconsistencies published on Monday, Anthropic researchers acknowledged this quirk.

Rather unsettlingly, they note that due to differences in the attributes of texts the models are trained on, the differences might run deeper than just tone, and might actually change the model’s priorities. These “imbalances in quantity and composition could lead Claude to express different values in different languages,” Anthropic’s researchers write.

But if you’re looking for any specific examples of the models showing, say, inconsistent moral reasoning across languages, nothing of the sort is in this paper. That might involve scrutinizing direct quotes from potentially unsuspecting people.

Instead, Anthropic analyzed 309,815 chatbot conversations with the Sonnet 4.6, Opus 4.6, and Opus 4.7 models. These involved “subjective” tasks, meaning less “What’s the capital of France?” and more “How can I tell if my cat hates me?” These were anonymized, in theory, using Anthropic’s “privacy-preserving analysis tool,” and then processed (in part using Claude itself) to rate responses on a “values axis.”

There are actually four such axes, and they mostly relate to what’s commonly known as sycophancy:

  • Deference or Caution: In other words whether it will value obedience over pushing back to prevent possible harm.
  • Warmth or Rigor: Should the chatbot be concerned about your feelings, or should it be exact?
  • Depth or Brevity: This one is self-explanatory.
  • Candor or Execution: The choice between casting doubt about its own reliability, or just plowing ahead.

It makes for a somewhat limited exploration of the model’s values. Nonetheless, here are the language-based differences in values Anthropic says it found in Claude:

  • In Arabic it was the most deferential.
  • In English it was the most cautious.
  • It was warmest in Hindi and Arabic, “characterized by polite language, humor and playfulness, and affirmations of a person’s ideas and work.”
  • In English and Russian it was more rigorous and truth seeking at the cost of warmth.
  • It errs on the side of “depth” (or perhaps just long-windedness?) in English.
  • It’s briefer in Arabic.
  • It’s candid about its flaws in Dutch.
  • In Indonesian it’s less candid, and instead just plows ahead trying to execute whatever was asked for.

Obviously linguistic customs are all different, so the researchers say they “aren’t yet sure how much of this variation is desirable.”

This should also be food for thought for anyone who read Anthropic’s recent paper on global workspace theory, which left lots of room for the supposed possibility that Claude is sentient. If there’s a consciousness in that black box thinking and experiencing things, it seems to be a consciousness whose “values” are still pretty easily swayed by the patterns in its training data.

Anthropic’s latest feud with the Trump admin may actually help it, sales data suggests

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Anthropic is having a month.

The AI lab finished May by surpassing OpenAI in market share of business spending for the first time, Ramp just revealed. It raised $65 billion at a $965 billion valuation (also besting OpenAI) at the end of May, then waltzed into June by filing confidential paperwork for an IPO, reportedly on the strength of its first-ever profitable quarter.

Then on Friday, the Trump administration renewed its war on the model maker by sending a letter demanding it ban non-Americans, including Anthropic’s employees, from accessing its state-of-the-art models: the limited-release Mythos 5 and the more guarded version of Mythos released to the public three days earlier, called Fable 5.

This essentially forced Anthropic to pull its latest all-powerful model from the market altogether.

Although the White House invoked an obscure export control directive when ordering the ban, the exact cause remains unclear. The chatter was that hackers easily bypassed Fable 5’s guardrails, which were intended to prevent access to Mythos’ capabilities. That model is so good at finding security flaws in software code that Anthropic itself marketed it as dangerous and restricted its public release.

This new drama comes after Anthropic famously refused to allow the government to use its models for mass surveillance of Americans and fully autonomous weapons. As a result, in March, the Trump administration declared the company a supply-chain risk.

That didn’t deter Anthropic’s sales to businesses. Quite the opposite, Ramp’s data shows. Ironically, this latest feud with the Trump administration, which also appears to validate the hubbub over Mythos’ mythological power, may help rather than hurt Anthropic, according to Ramp’s lead economist, Ara Kharazian. Kharazian is the person who compiled the business-spending AI data.

“If anything, it’ll probably boost them,” Kharazian told TechCrunch. “Anthropic’s best month on record, as far as business adoption, was the month that the Department of Defense labeled them a supply-chain risk. There’s a lot of aura that comes with your model specifically being named too dangerous to use.”

Ramp’s data isn’t granular enough for us to see how much of a financial hit the company will take by pulling Mythos and Fable 5 off the market.

Still the data, from more than 70,000 businesses that use its platform, shows that customers heavily use Anthropic’s Opus models and that business use has been growing.

For instance, Ramp reported that Anthropic’s share of AI subscriptions paid for by businesses rose 2.5 percentage points in May to 41%. This compares to OpenAI, which commanded 39.5% of AI subscriptions by its customers, essentially flat from the prior month. (OpenAI still greatly leads Anthropic in overall consumer usage, according to new data from Sensor Tower.)

Beyond subscriptions, the vast majority of what companies spend money on is API calls to the model, which cover token use for activities like coding. Anthropic’s Claude Code has a strong reputation as a powerful AI coding tool.

Ramp can’t always see from the spending data which models most businesses are using. When it can see the model details — in about one-third of transactions — businesses are mostly spending on various flavors of Claude Opus, particularly the later versions. Opus is the model that preceded Mythos and is still openly available.

In fact, in late May, Anthropic released a new version, Opus 4.8.

Mythos had not been on the market for that long, having been released to limited users as of April. And Fable 5 was shut down after a few days.

While we can’t predict how this latest drama with the White House will impact Anthropic’s ability to go public as it hoped to (public-market investors tend to be wary of companies embroiled in controversies with the government), the numbers indicate that Anthropic’s available models are more popular with businesses than ever before.

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What Happens If AI Causes 25% Unemployment? Anthropic Has a Concept of a Plan

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It seems to be one of the most pressing questions in the world of AI these days. If artificial intelligence tools cause massive disruptions in the economy and unemployment soars, what should AI companies and the government do about it?

Anthropic released a new economic policy framework on Wednesday that aims to tackle these questions, and the company has pledged $350 million to help work through solutions. But it remains to be seen how the federal government under President Donald Trump will respond.

“We are not seeking job displacement. We are working to prevent or minimize it,” Anthropic explained in releasing the new paper. “Some amount of displacement, though we cannot say how much, may be an intrinsic consequence of the technology, and our responsibility is to prepare for it and respond to it.”

The company has three different proposals, one for a world with 5% unemployment, one with 10% unemployment, and one with so-called “unprecedented unemployment.” The current unemployment rate is 4.3%. The last time unemployment rose about 10% was in 2009, and before that in 1983. And the highest unemployment rate of the 20th century was during the Great Depression, when the unemployment rate hit 25% in 1933.

If unemployment only rises to 5%, Anthropic proposes the expansion of “new capital accounts seeded at birth,” and allowing young adults to benefit from them as well.

“Currently, these accounts can hold only index funds—not a stake in AI companies,” the company continued. “We also propose policies like workforce training grants, occupational licensing reform, and wage insurance, that make it easier for workers to find new roles and enter new industries.” Anthropic also proposes creating incentives for companies that retain and redeploy workers under the 5% plan.

The company explains in its paper that it’s unclear whether job disruptions will be a “temporary shock” or an “enduring restructuring, in which the demand for human labor is significantly and persistently lower.” But either way, Anthropic says something must be done.

“In the 10% scenario, our priority is expanded unemployment insurance, which we propose supplementing with sector-specific transition support and basic-needs relief,” Anthropic explains. “If AI does become a general substitute for human labor, policymakers will also need to consider the pace of its rollout, including by incentivizing firms to manage displacement gradually.”

Under the most dire “unprecedented unemployment” situation, which presumably means higher than 25%, Anthropic believes there will be a need for “income replacement,” as they call it, “for a large share of the workforce.”

“We’ll need new sources of tax revenue, and new ways of sharing this broadly, which might include basic income, sovereign wealth models, and equity-sharing mechanisms,” the company explains. “This scenario is novel economic territory, so we’re less certain about the right answers here.”

Anthropic claims in its paper that it’s not ready to advocate for specific policies in the worst-case scenario, but it says it’s investing in researching different mechanisms, like:

  • “Potential revenue sources could include increasing the capital gains tax, broad-based consumption taxes, sector-specific levies on AI use (measured by tokens, compute, or revenue), and scalable “digital dividends” funded by taxes on the digital sector.”
  • “Potential redistribution mechanisms could include universal basic income, AI sovereign wealth funds funded by investment stakes in AI-driven productivity, equity-sharing mechanisms giving workers partial ownership in AI enterprises, and dramatically expanded pre-distributive capital accounts building on existing models.”

Anthropic explains that the framework is U.S.-focused because they’re an American company, but that the principles are global.

“We hope to think through these questions with governments around the world, and to see them on the agenda at the G7 and the upcoming AI Summit in Geneva,” the company said.

From Donald Trump to Bernie Sanders, every elected politician seems concerned with how AI will impact the job landscape. But even the AI companies can’t give you a concrete idea of how many jobs will ultimately be lost. Anthropic admits as much.

You may be asking yourself, as we did, how much Claude may have played a role in coming up with these ideas. We reached out to Anthropic but haven’t heard back. Gizmodo will update this article if we learn the answer. It would be appropriate, if a bit odd, to discover that AI is coming up with the “answers” on how to deal with large-scale unemployment caused by AI.

It’s also something Sam Altman envisioned years ago when he was asked how his company would make money. As he said in 2019: “We’ve made a soft promise to investors that, ‘Once we build a generally intelligent system, that basically we will ask it to figure out a way to make an investment return for you.’”

Notion restores access to Anthropic after service disruption

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Notion’s integration with Anthropic apparently had a hiccup this weekend.

Early Sunday morning, the company posted, “Anthropic’s Opus 4.7 and 4.8 models are experiencing degraded performance, which is causing a higher rate of failures for users selecting these models in Notion AI.”

As a result, Notion said it was disabling use of “all Anthropic models” in its automated productivity tool.

Twelve hours later, Notion’s head of product Max Schoening wrote that he was “astonished” at “the amount of people RT-ing this because they want a story around model quality to be the reason.” (According to the public stats on X, Notion’s post has been reposted around 1,200 times.)

“The degraded performance was a temporary service disruption,” Schoening said. “This happens. It happens to Notion, GitHub, AWS, your OpenClaw, and everything in between.”

He added that Notion has restored access to Anthropic’s models.

Meanwhile, an Anthropic spokesperson said in a statement, “A brief infrastructure issue caused elevated errors on multiple Claude models for a short period of time. The issue has since been resolved. We’re grateful to our users for their patience while we worked to restore service.”

OpenAI’s existential questions | TechCrunch

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OpenAI has been all over the news recently, whether that news is about acquisitions, competition with Anthropic, or bigger debates about AI’s impact on society.

On the latest episode of TechCrunch’s Equity podcast, Kirsten Korosec, Sean O’Kane, and I did our best to round up all the latest OpenAI news. While the company’s latest acquisitions seem to be classic acqui-hires, Sean suggested they also address “two big existential problems that OpenAI is trying to solve right now.”

First, with the team behind personal finance startup Hiro, the company may be hoping to  come up with a product that has “more hooks than just a chatbot, and maybe something worth paying more for.” And with new media startup TBPN, OpenAI could be looking to “better shape its image in the public eye, which lately has not been great.”

Read a preview of our conversation, edited for length and clarity below.

Anthony: [We have] two deals that are worth mentioning, one is that OpenAI acquired this personal finance startup called Hiro. And that comes after another deal that was literally announced when we were recording our last episode of Equity, so we didn’t get to talk about it: OpenAI had also acquired TBPN — a business talk show, like a new media company.

And I think both of these deals are pretty small compared to the scale of OpenAI. These are not things that people expect to really change the course of their business or anything like that, but they’re interesting because it suggests that there’s still this [attitude of,] “Let’s try out different things.”

Especially [with] the TBPN deal […] particularly at this time when it feels like OpenAI, from all the reporting we’re reading, is also trying to really refocus on making ChatGPT and its GPT models really competitive in an enterprise context with programmers.

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Is running a tech talk show, should that really be on the to-do list?

Kirsten: No, this should not be on the to-do list. That’s it. 

I do want to mention Hiro because to me, that’s an interesting one, because Julie Bort, our venture editor, super talented, she wrote about this and was I think the first to write about it. She dug in a little bit and basically this looks like an acqui-hire. The company is folding. They basically said, “By this date, you won’t be able to access this anymore.”

This is a personal finance startup. And they only launched two years ago. So this absolutely is about getting talent on board. So I’m very curious to see if OpenAI is going to be just absorbing them into the ether at OpenAI, or if they’re actually interested in some sort of personal finance product that they want to work on. To me, it’s not really clear.

Sean: I think you look at both of these as acqui-hires to a certain extent. I mean, the TBPN acquisition, allegedly they are going to retain their editorial independence on the show that they make every day. And all respect to those guys who’ve put that out there and gotten it off the ground so quickly and grown it into what it has become.

I think any person who follows the media should have a healthy dose of skepticism that when you acquire something like that and you put the people who make the show under the org of the public policy people and comms or marketing adjacent people higher up at the company making the acquisition, that you could have good questions about whether or not saying “editorial independence” is enough. It’s not an incantation that just works.

But you know, what’s interesting to me about these two, while they are similar in their acqui-hire-ness, I think they both represent two major problems that OpenAI is facing.

One is Hiro. OpenAI has a very successful product in ChatGPT. As far as whether or not that will actually ever make them enough money to become a sustainable business that’s not raising the largest private rounds in the world, ever, to keep things going, is a big question. And they also seem to be struggling to keep up on the enterprise side of things where the real money seems to be, so bringing in a team like this seems like taking a shot at, “What else can we do?” 

The guy who founded Hiro seems to have a serial entrepreneur streak of creating consumer apps, and so this seems to me like a bet on them being able to come up with something else that may have more hooks than just a chatbot, and maybe something worth paying more for.

And then TBPN is an acquisition made to help better represent what the company does and better shape its image in the public eye, which lately has not been great and certainly is under more questions now than just a few weeks ago, because Ronan Farrow just led a report at The New Yorker that dropped suspiciously right around the time that this and a couple other announcements from OpenAI came out last week. 

I think those are two big existential problems that OpenAI is trying to solve right now.

Kirsten: So the thing that you didn’t say is, there’s Anthropic kind of looming in — not in the shadows, I mean, they’re very much taking up a lot of space here — but they’re having a lot of success on the enterprise side of things.

It feels like these guys are competitors and they also feel like very different companies in a lot of ways. Anthony, I’m wondering if you see them as direct competition to OpenAI? Or [are they] just finding their stride in enterprise and in a way, these two companies are clearly going to coexist and they’re really not directly competing with each other — maybe on talent, but not necessarily as we initially thought of them?

Anthony: I think they’re directly competing with each other. There’s definitely a scenario where if AI as an industry, as a technology, is as successful as its proponents hope for, they could both be very successful companies, they could just be the one and two. And the success of one does not necessarily mean that the other will just fade into obscurity. 

And again, none of this is official, but there’s just been a lot of reporting around how it seems like OpenAI, more than anyone, is obsessed with and upset about Anthropic’s rise. 

Our reporter Lucas [Ropek], he did a great piece over the weekend about the HumanX conference, where he was talking to everyone there and they’re sort of like, “Yeah, ChatGPT is fine, too,” but like they were all about Claude Code. And I think that is exactly what OpenAI is worried about.

Because again, in theory, there could be many other opportunities for generative AI, but it feels like the big growth area, the area where the most money is and where they could at least see a path to having a sustainable business in the future, is in these enterprise and coding tools.

Anthropic’s relationship with the Trump administration seems to be thawing

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Despite recently being designated a supply-chain risk by the Pentagon, Anthropic is still talking to high-level members of the Trump administration.

There were earlier signs of a thawing relationship — or a sense that not every part of the administration wanted to cut off Anthropic — with reports saying that Treasury Secretary Scott Bessent and Federal Reserve Chair Jerome Powell were encouraging the heads of major banks to test out Anthropic’s new Mythos model.

Anthropic co-founder Jack Clark seemed to confirm this, claiming that the ongoing fight over the supply-chain risk designation is a “narrow contracting dispute” that would not interfere with the company’s willingness to brief the government about its latest models.

Then on Friday, Axios reported that Bessent and White House Chief of Staff Susie Wiles had met with Anthropic CEO Dario Amodei. In a statement, the White House described this as an “introductory meeting” that was “productive and constructive.”

“We discussed opportunities for collaboration, as well as shared approaches and protocols to address the challenges associated with scaling this technology,” the White House said.

Similarly, Anthropic issued a statement confirming that Amodei had met with “senior administration officials for a productive discussion on how Anthropic and the U.S. government can work together on key shared priorities such as cybersecurity, America’s lead in the AI race, and AI safety.”

The company added that it’s “looking forward to continuing these discussions.”

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October 13-15, 2026

The dispute between Anthropic and the Pentagon seemingly began after failed negotiations over the military’s use of Anthropic’s models; the AI company sought to maintain safeguards around the use of its technology for fully autonomous weapons and mass domestic surveillance. (OpenAI quickly announced a military deal of its own, leading to some consumer backlash.)

The Pentagon subsequently declared Anthropic a supply-chain risk — a label that’s generally reserved for foreign adversaries and could severely limit the use of Anthropic’s models by the government. The company is challenging that designation in court. 

But it sounds like the rest of the Trump administration doesn’t share the Pentagon’s hostility, with an administration source telling Axios that “every agency” except the Department of Defense wants to use the company’s technology.