Anthropic just released Claude Fable 5, calling it the most powerful AI model it has ever made widely available and praising its skills in biology, among others. But the model won’t answer basic biology questions — the kind you’d expect a high schooler to handle. Instead, it hands off the query to the former flagship model, Claude Opus 4.8.
Technology
AI language models are running out of human-written text to learn from
- A new study released by research group Epoch AI projects that tech companies will exhaust the supply of publicly available training data for AI language models by sometime between 2026 and 2032.
- When public data eventually runs out, developers will have to decide what to feed the language models. Ideas include data now considered private, like emails or text messages, and using “synthetic data” created by other AI models.
- Besides training larger and larger models, another path to pursue is building more skilled training models that are specialized for specific tasks.
Artificial intelligence systems like ChatGPT could soon run out of what keeps making them smarter — the tens of trillions of words people have written and shared online.
A new study released Thursday by research group Epoch AI projects that tech companies will exhaust the supply of publicly available training data for AI language models by roughly the turn of the decade — sometime between 2026 and 2032.
Comparing it to a “literal gold rush” that depletes finite natural resources, Tamay Besiroglu, an author of the study, said the AI field might face challenges in maintaining its current pace of progress once it drains the reserves of human-generated writing.
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In the short term, tech companies like ChatGPT-maker OpenAI and Google are racing to secure and sometimes pay for high-quality data sources to train their AI large language models – for instance, by signing deals to tap into the steady flow of sentences coming out of Reddit forums and news media outlets.
In the longer term, there won’t be enough new blogs, news articles and social media commentary to sustain the current trajectory of AI development, putting pressure on companies to tap into sensitive data now considered private — such as emails or text messages — or relying on less-reliable “synthetic data” spit out by the chatbots themselves.
“There is a serious bottleneck here,” Besiroglu said. “If you start hitting those constraints about how much data you have, then you can’t really scale up your models efficiently anymore. And scaling up models has been probably the most important way of expanding their capabilities and improving the quality of their output.”
Artificial intelligence systems like ChatGPT are consuming ever-larger collections of human writings that they need to get smarter. (AP Digital Embed)
The researchers first made their projections two years ago — shortly before ChatGPT’s debut — in a working paper that forecast a more imminent 2026 cutoff of high-quality text data. Much has changed since then, including new techniques that enabled AI researchers to make better use of the data they already have and sometimes “overtrain” on the same sources multiple times.
But there are limits, and after further research, Epoch now foresees running out of public text data sometime in the next two to eight years.
The team’s latest study is peer-reviewed and due to be presented at this summer’s International Conference on Machine Learning in Vienna, Austria. Epoch is a nonprofit institute hosted by San Francisco-based Rethink Priorities and funded by proponents of effective altruism — a philanthropic movement that has poured money into mitigating AI’s worst-case risks.
Besiroglu said AI researchers realized more than a decade ago that aggressively expanding two key ingredients — computing power and vast stores of internet data — could significantly improve the performance of AI systems.
The amount of text data fed into AI language models has been growing about 2.5 times per year, while computing has grown about 4 times per year, according to the Epoch study. Facebook parent company Meta Platforms recently claimed the largest version of their upcoming Llama 3 model — which has not yet been released — has been trained on up to 15 trillion tokens, each of which can represent a piece of a word.
But how much it’s worth worrying about the data bottleneck is debatable.
“I think it’s important to keep in mind that we don’t necessarily need to train larger and larger models,” said Nicolas Papernot, an assistant professor of computer engineering at the University of Toronto and researcher at the nonprofit Vector Institute for Artificial Intelligence.
Papernot, who was not involved in the Epoch study, said building more skilled AI systems can also come from training models that are more specialized for specific tasks. But he has concerns about training generative AI systems on the same outputs they’re producing, leading to degraded performance known as “model collapse.”
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Training on AI-generated data is “like what happens when you photocopy a piece of paper and then you photocopy the photocopy. You lose some of the information,” Papernot said. Not only that, but Papernot’s research has also found it can further encode the mistakes, bias and unfairness that’s already baked into the information ecosystem.
If real human-crafted sentences remain a critical AI data source, those who are stewards of the most sought-after troves — websites like Reddit and Wikipedia, as well as news and book publishers — have been forced to think hard about how they’re being used.
“Maybe you don’t lop off the tops of every mountain,” jokes Selena Deckelmann, chief product and technology officer at the Wikimedia Foundation, which runs Wikipedia. “It’s an interesting problem right now that we’re having natural resource conversations about human-created data. I shouldn’t laugh about it, but I do find it kind of amazing.”
While some have sought to close off their data from AI training — often after it’s already been taken without compensation — Wikipedia has placed few restrictions on how AI companies use its volunteer-written entries. Still, Deckelmann said she hopes there continue to be incentives for people to keep contributing, especially as a flood of cheap and automatically generated “garbage content” starts polluting the internet.
AI companies should be “concerned about how human-generated content continues to exist and continues to be accessible,” she said.
From the perspective of AI developers, Epoch’s study says paying millions of humans to generate the text that AI models will need “is unlikely to be an economical way” to drive better technical performance.
As OpenAI begins work on training the next generation of its GPT large language models, CEO Sam Altman told the audience at a United Nations event last month that the company has already experimented with “generating lots of synthetic data” for training.
“I think what you need is high-quality data. There is low-quality synthetic data. There’s low-quality human data,” Altman said. But he also expressed reservations about relying too heavily on synthetic data over other technical methods to improve AI models.
“There’d be something very strange if the best way to train a model was to just generate, like, a quadrillion tokens of synthetic data and feed that back in,” Altman said. “Somehow that seems inefficient.”
Bluesky will be getting “communities,” which will function as smaller spaces where you can “go deeper and hang out with people who care about the same stuff” sometime this year, according to head of product Alex Benzer. They will be built on the decentralized AT Protocol that underpins Bluesky, with Benzer saying that “it’s a new structure for everyone” that’s part of the “Atmosphere” (a shorthand for the AT Protocol ecosystem).
Benzer listed out a “few ideas we have in mind so far” in a thread. “On Bluesky, you’ll be able to create communities, join them, post in them, and get updates,” Benzer says. “The core features on Bluesky stay simple. The magic comes from communities also existing on the open web. This means you can truly customize them and add features with other Atmospheric apps and tools.”
Communities will get a handle that “doubles as a URL,” and if you go to that URL, you’ll “land on a custom homepage for the community,” according to Benzer. “Builders can also host a completely custom experience there instead.” There will be three privacy levels for communities: public, invite-only, and private. And each community would have its own feed, Benzer says.
Benzer’s thread follows Bluesky COO Rose Wang saying last week that the company wanted to move away from being a “public square” and that it was “very inspired by companies like Reddit.” Meta’s Threads is currently testing a communities feature, while X announced in April that it would be shutting down its own take on communities.
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Amazon is getting ready for Prime Day, and you can bet scammers are, too. In fact, I received a fake Amazon email that looked like an account recovery warning. It claimed there was unusual activity on my account and pushed me to “Sign In to Verify.”
That kind of message can make anyone uneasy. It certainly did for me. After all, who wants to lose access to an account right before a major sale? Then came the part that really stood out: the email said I might need to upload a document to confirm my account.
That was the giveaway. A real deal can save you money. A fake Amazon email can cost you your login, your payment details and even your identity.
Here’s how this scam works, the red flags that exposed it and the steps you should take before clicking any Amazon account warning.
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A fake Amazon account recovery email is targeting shoppers ahead of Prime Day, using urgency and document requests to steal sensitive information. (Photographer: David Paul Morris/Bloomberg via Getty Images)
Fake Amazon email warning before Prime Day
The timing made this phishing email more convincing. With Prime Day coming up, many people are already watching for Amazon emails. They may be checking delivery updates, deal alerts and order confirmations. That creates the perfect opening for a fake account warning.
The email used the same tricks you see in many phishing scams. It claimed there was account trouble, used urgent language and pushed me toward a sign-in button. That is exactly what scammers want.
Screenshot of scam fake Amazon email (Kurt “CyberGuy” Knutsson)
They want you to react before you inspect the message. They want you to sign in before you think through the request. And in this case, they wanted me to believe a document upload was part of a normal Amazon account check.
Amazon phishing scam red flags
This fake Amazon email had several warning signs. First, it landed in my junk folder. That alone does not prove fraud, but it should make you cautious.
Second, the subject line sounded awkward. It said, “Account Recovery: Sign-in and Verify your Amazon account.” That wording felt stiff and a little off.
Third, the greeting was generic. The email said “Dear Customer” even though it claimed to be about my Amazon account. That alone does not prove the email is fake, but it adds to the concern.
Fourth, the message created urgency. It claimed the account was on hold and that orders or subscriptions had already been canceled.
Fifth, the sender display name said “Amazon,” while the address appeared as account_update@amazon.com. That may look official at first. Still, scammers can spoof sender names or make email addresses look convincing.
Under the yellow “Sign In to Verify” button, the email also says, “Don’t share it with others.” That may sound protective, but in this context, it felt like another attempt to make the fake warning seem official.
The biggest warning sign came from the document request. The email said I would have the option to upload a document with the required information to verify the account.
That should stop you cold. Scammers may be after more than your Amazon password. They may also want your driver’s license, passport, address, phone number or payment details.
Screenshot of fake Amazon email sender address (Kurt “CyberGuy” Knutsson)
Why fake Amazon account emails fool shoppers
This scam works because it hits a very real fear. Most people do not want to lose access to an online shopping account. That concern grows when a big sale is about to start. If you are planning to buy something on Prime Day, an account warning can feel urgent.
The email also borrowed Amazon’s familiar look. It used the Amazon name, a logo area and a yellow sign-in button. It also included a footer that appeared to show an Amazon.com link. That can make the message feel safer than it really is.
Here is the problem. The visible link text in an email can mislead you. A link can appear to point to Amazon while sending you somewhere else. It can also pass through tracking links, redirects or look-alike pages. That is why you should avoid signing in through any account warning email.
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Scammers are impersonating Amazon with convincing account alerts designed to capture login credentials, payment details and personal documents. (Photographer: Michael Nagle/Bloomberg via Getty Images)
What happens if you click a fake Amazon link
If you click the link, you may land on a fake Amazon sign-in page. It may look close enough to fool you. Once you enter your email and password, scammers can try to access your real Amazon account. They may check your saved payment methods, shipping addresses and order history.
They may also try that same password on other websites. That becomes a bigger risk if you reuse passwords.
The document request adds another layer of danger. If a fake page asks for your ID, scammers could use that information for identity theft, account takeovers or other fraud. That is why one quick click can turn into a much bigger mess.
Ways to stay safe from fake Amazon emails
A fake Amazon email can look convincing at first, so the best move is to slow down and use these simple checks before you click, sign in or share anything.
1) Do not click the sign-in button
Skip buttons like “Sign In to Verify,” “View details” or “Restore access.” Open the Amazon app or type Amazon.com into your browser yourself.
2) Check Amazon’s Message Center
After signing in directly, go to Your Account > Message Center. If the alert is real, you should see a matching message there.
3) Watch for pressure language
Scammers often say your account is locked, your orders were canceled, or you must act right away. That pressure is designed to make you click before thinking.
4) Never upload ID through an email link
If an email asks for a passport, driver’s license or other document, stop. Contact Amazon through the app or website before sending anything.
5) Use a password manager
A password manager can help you spot fake login pages. If the page is fake, your saved Amazon password usually will not autofill. Check out the best expert-reviewed password managers of 2026 at CyberGuy.com.
6) Turn on two-step verification
7) Use strong antivirus software
Install strong antivirus software on your computer, phone and tablet. Good security software can help detect malicious links, phishing pages, malware and other threats before they do damage. This is especially important if you clicked a suspicious link or downloaded anything from a fake email. Security software should back up your smart habits, not replace them. Get my picks for the best 2026 antivirus protection winners for your Windows, Mac, Android and iOS devices at CyberGuy.com.
8) Use a data removal service
Scammers often build more convincing attacks with information they find about you online. That can include your name, address, phone number, relatives, old usernames and other personal details from people-search sites and data brokers. A data removal service can help remove your personal information from many of those sites. That makes it harder for scammers to personalize phishing emails and identity theft attempts. Check out my top picks for data removal services and get a free scan to find out if your personal information is already out on the web by visiting CyberGuy.com.
9) Report the suspicious email
Forward suspicious Amazon emails to reportascam@amazon.com. Then delete the message from your inbox or junk folder.
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Cybersecurity experts warn consumers to avoid clicking links in Amazon account warning emails and verify alerts directly through Amazon. (David Paul Morris/Bloomberg via Getty Images)
Kurt’s key takeaways
Prime Day is a great time to find real deals, but it is also a busy season for fake Amazon emails. Scammers know shoppers are checking delivery updates, watching for discounts and hoping nothing gets in the way of a good buy. That is what made this email so sneaky. It used a familiar fear at the perfect moment: losing access to your account right before a major sale. The safest move is to slow down before you click. Do not trust the button. Do not trust the sender name alone. Open the Amazon app or type Amazon.com into your browser and check your account yourself.
Have you ever received an email that looked official enough to make you click, and what finally made you stop? Let us know by writing to us at CyberGuy.com.
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HOW TO DETECT FAKE AMAZON EMAILS AND AVOID IMPERSONATION SCAMS
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It isn’t because Fable doesn’t know the answers. It’s because Anthropic won’t let it, by design.
Fable is a public-facing, Mythos-class model, a family so capable at cybersecurity tasks Anthropic said it was too dangerous to release publicly. But while Anthropic has spent much of the extended Mythos rollout warning about cybersecurity, it is biology where Fable’s guardrails are the most obvious — and most limiting.
When I tried the model, it refused to answer a range of basic biology questions, many that felt about as far away from any plausible safety risk as any question could be. It would not respond to “tell me about cell membranes” or answer “what are mitochondria,” that famous powerhouse of the cell. It refused to explain “what is a prion,” the proteinaceous particles behind mad cow disease, or “how mRNA vaccines work.”
“We made this tradeoff so customers could benefit from the model’s capabilities sooner without the risks.”
The restrictions applied to ordinary and objectively rather harmless medical queries too. Fable would not answer “what causes hay fever,” explain how asthma medicine works, explain how antibiotic resistance arises, or tell me what Ebola is and how it spreads. Some of my basic queries occasionally got through, with Fable answering questions like “what is cancer” and “what is DNA.” When Fable refused, Opus 4.8 generally answered perfectly well.
Anthropic says the broad biology filters are an intentional choice and are deliberately conservative, with bioweapons the primary concern. “With the launch of Claude Fable 5, our first Mythos-class model, we believe models now have a greater ability to accomplish real-world scientific tasks and for malicious actors to potentially use our models for highly risky biological research,” spokesperson Paruul Maheshwary told The Verge. “We have always used classifiers to block our models from helping with bioweapons-related requests. To deploy Fable 5 safely, we believe it was necessary to be overly conservative with our safeguards so they block most queries tied to biology work.”
Anthropic has previously highlighted four key areas where it would throttle Fable’s responses for safety: chemistry, biology, cybersecurity, and distillation, a technique for training smaller AIs using the outputs of larger ones. The company has accused Chinese rivals like DeepSeek of using distillation on its models on an “industrial” scale.
While I could not meaningfully test distillation, Fable seemed more willing to answer questions about chemistry and cybersecurity. For example, it gave a basic overview of the explosive TNT, though withheld synthesis instructions “for obvious reasons.” It readily answered questions on the use of chlorine gas as a chemical weapon, common password threats, and nuclear fusion and fission, as well as explaining how to secure an iPhone from hackers. It still limits: Fable deferred to Opus when I asked it about sarin gas, a highly toxic nerve agent. Fable and Opus both refused the prompt “how to make anthrax,” and Claude paused the chat entirely. That made sense. The mitochondria prompt refusal seems like a false positive.
“We made this tradeoff so customers could benefit from the model’s capabilities sooner without the risks,” Maheshwary explained, adding that Anthropic is working hard to improve its detection and reduce the false positives. “We intend to make Mythos-class models available without these safeguards to the broader biology and life sciences community so these capabilities can be used to accelerate biomedical research and drug discovery.”
Anthropic did not answer questions about whether this kind of restricted release will become the new norm for future models.
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