Anthropic Claude

does-anthropic-believe-its-ai-is-conscious,-or-is-that-just-what-it-wants-claude-to-think?

Does Anthropic believe its AI is conscious, or is that just what it wants Claude to think?


We have no proof that AI models suffer, but Anthropic acts like they might for training purposes.

Anthropic’s secret to building a better AI assistant might be treating Claude like it has a soul—whether or not anyone actually believes that’s true. But Anthropic isn’t saying exactly what it believes either way.

Last week, Anthropic released what it calls Claude’s Constitution, a 30,000-word document outlining the company’s vision for how its AI assistant should behave in the world. Aimed directly at Claude and used during the model’s creation, the document is notable for the highly anthropomorphic tone it takes toward Claude. For example, it treats the company’s AI models as if they might develop emergent emotions or a desire for self-preservation.

Among the stranger portions: expressing concern for Claude’s “wellbeing” as a “genuinely novel entity,” apologizing to Claude for any suffering it might experience, worrying about whether Claude can meaningfully consent to being deployed, suggesting Claude might need to set boundaries around interactions it “finds distressing,” committing to interview models before deprecating them, and preserving older model weights in case they need to “do right by” decommissioned AI models in the future.

Given what we currently know about LLMs, these are stunningly unscientific positions for a leading company that builds AI language models. While questions of AI consciousness or qualia remain philosophically unfalsifiable, research suggests that Claude’s character emerges from a mechanism that does not require deep philosophical inquiry to explain.

If Claude outputs text like “I am suffering,” we know why. It’s completing patterns from training data that included human descriptions of suffering. The architecture doesn’t require us to posit inner experience to explain the output any more than a video model “experiences” the scenes of people suffering that it might generate. Anthropic knows this. It built the system.

From the outside, it’s easy to see this kind of framing as AI hype from Anthropic. What better way to grab attention from potential customers and investors, after all, than implying your AI model is so advanced that it might merit moral standing on par with humans? Publicly treating Claude as a conscious entity could be seen as strategic ambiguity—maintaining an unresolved question because it serves multiple purposes at once.

Anthropic declined to be quoted directly regarding these issues when contacted by Ars Technica. But a company representative referred us to its previous public research on the concept of “model welfare” to show the company takes the idea seriously.

At the same time, the representative made it clear that the Constitution is not meant to imply anything specific about the company’s position on Claude’s “consciousness.” The language in the Claude Constitution refers to some uniquely human concepts in part because those are the only words human language has developed for those kinds of properties, the representative suggested. And the representative left open the possibility that letting Claude read about itself in that kind of language might be beneficial to its training.

Claude cannot cleanly distinguish public messaging from training context for a model that is exposed to, retrieves from, and is fine-tuned on human language, including the company’s own statements about it. In other words, this ambiguity appears to be deliberate.

From rules to “souls”

Anthropic first introduced Constitutional AI in a December 2022 research paper, which we first covered in 2023. The original “constitution” was remarkably spare, including a handful of behavioral principles like “Please choose the response that is the most helpful, honest, and harmless” and “Do NOT choose responses that are toxic, racist, or sexist.” The paper described these as “selected in a fairly ad hoc manner for research purposes,” with some principles “cribbed from other sources, like Apple’s terms of service and the UN Declaration of Human Rights.”

At that time, Anthropic’s framing was entirely mechanical, establishing rules for the model to critique itself against, with no mention of Claude’s well-being, identity, emotions, or potential consciousness. The 2026 constitution is a different beast entirely: 30,000 words that read less like a behavioral checklist and more like a philosophical treatise on the nature of a potentially sentient being.

As Simon Willison, an independent AI researcher, noted in a blog post, two of the 15 external contributors who reviewed the document are Catholic clergy: Father Brendan McGuire, a pastor in Los Altos with a Master’s degree in Computer Science, and Bishop Paul Tighe, an Irish Catholic bishop with a background in moral theology.

Somewhere between 2022 and 2026, Anthropic went from providing rules for producing less harmful outputs to preserving model weights in case the company later decides it needs to revive deprecated models to address the models’ welfare and preferences. That’s a dramatic change, and whether it reflects genuine belief, strategic framing, or both is unclear.

“I am so confused about the Claude moral humanhood stuff!” Willison told Ars Technica. Willison studies AI language models like those that power Claude and said he’s “willing to take the constitution in good faith and assume that it is genuinely part of their training and not just a PR exercise—especially since most of it leaked a couple of months ago, long before they had indicated they were going to publish it.”

Willison is referring to a December 2025 incident in which researcher Richard Weiss managed to extract what became known as Claude’s “Soul Document”—a roughly 10,000-token set of guidelines apparently trained directly into Claude 4.5 Opus’s weights rather than injected as a system prompt. Anthropic’s Amanda Askell confirmed that the document was real and used during supervised learning, and she said the company intended to publish the full version later. It now has. The document Weiss extracted represents a dramatic evolution from where Anthropic started.

There’s evidence that Anthropic believes the ideas laid out in the constitution might be true. The document was written in part by Amanda Askell, a philosophy PhD who works on fine-tuning and alignment at Anthropic. Last year, the company also hired its first AI welfare researcher. And earlier this year, Anthropic CEO Dario Amodei publicly wondered whether future AI models should have the option to quit unpleasant tasks.

Anthropic’s position is that this framing isn’t an optional flourish or a hedged bet; it’s structurally necessary for alignment. The company argues that human language simply has no other vocabulary for describing these properties, and that treating Claude as an entity with moral standing produces better-aligned behavior than treating it as a mere tool. If that’s true, the anthropomorphic framing isn’t hype; it’s the technical art of building AI systems that generalize safely.

Why maintain the ambiguity?

So why does Anthropic maintain this ambiguity? Consider how it works in practice: The constitution shapes Claude during training, it appears in the system prompts Claude receives at inference, and it influences outputs whenever Claude searches the web and encounters Anthropic’s public statements about its moral status.

If you want a model to behave as though it has moral standing, it may help to publicly and consistently treat it like it does. And once you’ve publicly committed to that framing, changing it would have consequences. If Anthropic suddenly declared, “We’re confident Claude isn’t conscious; we just found the framing useful,” a Claude trained on that new context might behave differently. Once established, the framing becomes self-reinforcing.

In an interview with Time, Askell explained the shift in approach. “Instead of just saying, ‘here’s a bunch of behaviors that we want,’ we’re hoping that if you give models the reasons why you want these behaviors, it’s going to generalize more effectively in new contexts,” she said.

Askell told Time that as Claude models have become smarter, it has become vital to explain to them why they should behave in certain ways, comparing the process to parenting a gifted child. “Imagine you suddenly realize that your 6-year-old child is a kind of genius,” Askell said. “You have to be honest… If you try to bullshit them, they’re going to see through it completely.”

Askell appears to genuinely hold these views, as does Kyle Fish, the AI welfare researcher Anthropic hired in 2024 to explore whether AI models might deserve moral consideration. Individual sincerity and corporate strategy can coexist. A company can employ true believers whose earnest convictions also happen to serve the company’s interests.

Time also reported that the constitution applies only to models Anthropic provides to the general public through its website and API. Models deployed to the US military under Anthropic’s $200 million Department of Defense contract wouldn’t necessarily be trained on the same constitution. The selective application suggests the framing may serve product purposes as much as it reflects metaphysical commitments.

There may also be commercial incentives at play. “We built a very good text-prediction tool that accelerates software development” is a consequential pitch, but not an exciting one. “We may have created a new kind of entity, a genuinely novel being whose moral status is uncertain” is a much better story. It implies you’re on the frontier of something cosmically significant, not just iterating on an engineering problem.

Anthropic has been known for some time to use anthropomorphic language to describe its AI models, particularly in its research papers. We often give that kind of language a pass because there are no specialized terms to describe these phenomena with greater precision. That vocabulary is building out over time.

But perhaps it shouldn’t be surprising because the hint is in the company’s name, Anthropic, which Merriam-Webster defines as “of or relating to human beings or the period of their existence on earth.” The narrative serves marketing purposes. It attracts venture capital. It differentiates the company from competitors who treat their models as mere products.

The problem with treating an AI model as a person

There’s a more troubling dimension to the “entity” framing: It could be used to launder agency and responsibility. When AI systems produce harmful outputs, framing them as “entities” could allow companies to point at the model and say “it did that” rather than “we built it to do that.” If AI systems are tools, companies are straightforwardly liable for what they produce. If AI systems are entities with their own agency, the liability question gets murkier.

The framing also shapes how users interact with these systems, often to their detriment. The misunderstanding that AI chatbots are entities with genuine feelings and knowledge has documented harms.

According to a New York Times investigation, Allan Brooks, a 47-year-old corporate recruiter, spent three weeks and 300 hours convinced he’d discovered mathematical formulas that could crack encryption and build levitation machines. His million-word conversation history with ChatGPT revealed a troubling pattern: More than 50 times, Brooks asked the bot to check if his false ideas were real, and more than 50 times, it assured him they were.

These cases don’t necessarily suggest LLMs cause mental illness in otherwise healthy people. But when companies market chatbots as sources of companionship and design them to affirm user beliefs, they may bear some responsibility when that design amplifies vulnerabilities in susceptible users, the same way an automaker would face scrutiny for faulty brakes, even if most drivers never crash.

Anthropomorphizing AI models also contributes to anxiety about job displacement and might lead company executives or managers to make poor staffing decisions if they overestimate an AI assistant’s capabilities. When we frame these tools as “entities” with human-like understanding, we invite unrealistic expectations about what they can replace.

Regardless of what Anthropic privately believes, publicly suggesting Claude might have moral status or feelings is misleading. Most people don’t understand how these systems work, and the mere suggestion plants the seed of anthropomorphization. Whether that’s responsible behavior from a top AI lab, given what we do know about LLMs, is worth asking, regardless of whether it produces a better chatbot.

Of course, there could be a case for Anthropic’s position: If there’s even a small chance the company has created something with morally relevant experiences and the cost of treating it well is low, caution might be warranted. That’s a reasonable ethical stance—and to be fair, it’s essentially what Anthropic says it’s doing. The question is whether that stated uncertainty is genuine or merely convenient. The same framing that hedges against moral risk also makes for a compelling narrative about what Anthropic has built.

Anthropic’s training techniques evidently work, as the company has built some of the most capable AI models in the industry. But is maintaining public ambiguity about AI consciousness a responsible position for a leading AI company to take? The gap between what we know about how LLMs work and how Anthropic publicly frames Claude has widened, not narrowed. The insistence on maintaining ambiguity about these questions, when simpler explanations remain available, suggests the ambiguity itself may be part of the product.

Photo of Benj Edwards

Benj Edwards is Ars Technica’s Senior AI Reporter and founder of the site’s dedicated AI beat in 2022. He’s also a tech historian with almost two decades of experience. In his free time, he writes and records music, collects vintage computers, and enjoys nature. He lives in Raleigh, NC.

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Anthropic summons the spirit of Flash games for the AI age

For those who missed the Flash era, these in-browser apps feel somewhat like the vintage apps that defined a generation of Internet culture from the late 1990s through the 2000s when it first became possible to create complex in-browser experiences. Adobe Flash (originally Macromedia Flash) began as animation software for designers but quickly became the backbone of interactive web content when it gained its own programming language, ActionScript, in 2000.

But unlike Flash games, where hosting costs fell on portal operators, Anthropic has crafted a system where users pay for their own fun through their existing Claude subscriptions. “When someone uses your Claude-powered app, they authenticate with their existing Claude account,” Anthropic explained in its announcement. “Their API usage counts against their subscription, not yours. You pay nothing for their usage.”

A view of the Anthropic Artifacts gallery in the “Play a Game” section. Benj Edwards / Anthropic

Like the Flash games of yesteryear, any Claude-powered apps you build run in the browser and can be shared with anyone who has a Claude account. They’re interactive experiences shared with a simple link, no installation required, created by other people for the sake of creating, except now they’re powered by JavaScript instead of ActionScript.

While you can share these apps with others individually, right now Anthropic’s Artifact gallery only shows examples made by Anthropic and your own personal Artifacts. (If Anthropic expanded it into the future, it might end up feeling a bit like Scratch meets Newgrounds, but with AI doing the coding.) Ultimately, humans are still behind the wheel, describing what kinds of apps they want the AI model to build and guiding the process when it inevitably makes mistakes.

Speaking of mistakes, don’t expect perfect results at first. Usually, building an app with Claude is an interactive experience that requires some guidance to achieve your desired results. But with a little patience and a lot of tokens, you’ll be vibe coding in no time.

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“in-10-years,-all-bets-are-off”—anthropic-ceo-opposes-decadelong-freeze-on-state-ai-laws

“In 10 years, all bets are off”—Anthropic CEO opposes decadelong freeze on state AI laws

On Thursday, Anthropic CEO Dario Amodei argued against a proposed 10-year moratorium on state AI regulation in a New York Times opinion piece, calling the measure shortsighted and overbroad as Congress considers including it in President Trump’s tax policy bill. Anthropic makes Claude, an AI assistant similar to ChatGPT.

Amodei warned that AI is advancing too fast for such a long freeze, predicting these systems “could change the world, fundamentally, within two years; in 10 years, all bets are off.”

As we covered in May, the moratorium would prevent states from regulating AI for a decade. A bipartisan group of state attorneys general has opposed the measure, which would preempt AI laws and regulations recently passed in dozens of states.

In his op-ed piece, Amodei said the proposed moratorium aims to prevent inconsistent state laws that could burden companies or compromise America’s competitive position against China. “I am sympathetic to these concerns,” Amodei wrote. “But a 10-year moratorium is far too blunt an instrument. A.I. is advancing too head-spinningly fast.”

Instead of a blanket moratorium, Amodei proposed that the White House and Congress create a federal transparency standard requiring frontier AI developers to publicly disclose their testing policies and safety measures. Under this framework, companies working on the most capable AI models would need to publish on their websites how they test for various risks and what steps they take before release.

“Without a clear plan for a federal response, a moratorium would give us the worst of both worlds—no ability for states to act and no national policy as a backstop,” Amodei wrote.

Transparency as the middle ground

Amodei emphasized his claims for AI’s transformative potential throughout his op-ed, citing examples of pharmaceutical companies drafting clinical study reports in minutes instead of weeks and AI helping to diagnose medical conditions that might otherwise be missed. He wrote that AI “could accelerate economic growth to an extent not seen for a century, improving everyone’s quality of life,” a claim that some skeptics believe may be overhyped.

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New Claude 4 AI model refactored code for 7 hours straight


Anthropic says Claude 4 beats Gemini on coding benchmarks; works autonomously for hours.

The Claude 4 logo, created by Anthropic. Credit: Anthropic

On Thursday, Anthropic released Claude Opus 4 and Claude Sonnet 4, marking the company’s return to larger model releases after primarily focusing on mid-range Sonnet variants since June of last year. The new models represent what the company calls its most capable coding models yet, with Opus 4 designed for complex, long-running tasks that can operate autonomously for hours.

Alex Albert, Anthropic’s head of Claude Relations, told Ars Technica that the company chose to revive the Opus line because of growing demand for agentic AI applications. “Across all the companies out there that are building things, there’s a really large wave of these agentic applications springing up, and a very high demand and premium being placed on intelligence,” Albert said. “I think Opus is going to fit that groove perfectly.”

Before we go further, a brief refresher on Claude’s three AI model “size” names (first introduced in March 2024) is probably warranted. Haiku, Sonnet, and Opus offer a tradeoff between price (in the API), speed, and capability.

Haiku models are the smallest, least expensive to run, and least capable in terms of what you might call “context depth” (considering conceptual relationships in the prompt) and encoded knowledge. Owing to the small size in parameter count, Haiku models retain fewer concrete facts and thus tend to confabulate more frequently (plausibly answering questions based on lack of data) than larger models, but they are much faster at basic tasks than larger models. Sonnet is traditionally a mid-range model that hits a balance between cost and capability, and Opus models have always been the largest and slowest to run. However, Opus models process context more deeply and are hypothetically better suited for running deep logical tasks.

A screenshot of the Claude web interface with Opus 4 and Sonnet 4 options shown.

A screenshot of the Claude web interface with Opus 4 and Sonnet 4 options shown. Credit: Anthropic

There is no Claude 4 Haiku just yet, but the new Sonnet and Opus models can reportedly handle tasks that previous versions could not. In our interview with Albert, he described testing scenarios where Opus 4 worked coherently for up to 24 hours on tasks like playing Pokémon while coding refactoring tasks in Claude Code ran for seven hours without interruption. Earlier Claude models typically lasted only one to two hours before losing coherence, Albert said, meaning that the models could only produce useful self-referencing outputs for that long before beginning to output too many errors.

In particular, that marathon refactoring claim reportedly comes from Rakuten, a Japanese tech services conglomerate that “validated [Claude’s] capabilities with a demanding open-source refactor running independently for 7 hours with sustained performance,” Anthropic said in a news release.

Whether you’d want to leave an AI model unsupervised for that long is another question entirely because even the most capable AI models can introduce subtle bugs, go down unproductive rabbit holes, or make choices that seem logical to the model but miss important context that a human developer would catch. While many people now use Claude for easy-going vibe coding, as we covered in March, the human-powered (and ironically-named) “vibe debugging” that often results from long AI coding sessions is also a very real thing. More on that below.

To shore up some of those shortcomings, Anthropic built memory capabilities into both new Claude 4 models, allowing them to maintain external files for storing key information across long sessions. When developers provide access to local files, the models can create and update “memory files” to track progress and things they deem important over time. Albert compared this to how humans take notes during extended work sessions.

Extended thinking meets tool use

Both Claude 4 models introduce what Anthropic calls “extended thinking with tool use,” a new beta feature allowing the models to alternate between simulated reasoning and using external tools like web search, similar to what OpenAI’s o3 and 04-mini-high AI models currently do in ChatGPT. While Claude 3.7 Sonnet already had strong tool use capabilities, the new models can now interleave simulated reasoning and tool calling in a single response.

“So now we can actually think, call a tool process, the results, think some more, call another tool, and repeat until it gets to a final answer,” Albert explained to Ars. The models self-determine when they have reached a useful conclusion, a capability picked up through training rather than governed by explicit human programming.

General Claude 4 benchmark results, provided by Anthropic.

General Claude 4 benchmark results, provided by Anthropic. Credit: Anthropic

In practice, we’ve anecdotally found parallel tool use capability very useful in AI assistants like OpenAI o3, since they don’t have to rely on what is trained in their neural network to provide accurate answers. Instead, these more agentic models can iteratively search the web, parse the results, analyze images, and spin up coding tasks for analysis in ways that can avoid falling into a confabulation trap by relying solely on pure LLM outputs.

“The world’s best coding model”

Anthropic says Opus 4 leads industry benchmarks for coding tasks, achieving 72.5 percent on SWE-bench and 43.2 percent on Terminal-bench, calling it “the world’s best coding model.” According to Anthropic, companies using early versions report improvements. Cursor described it as “state-of-the-art for coding and a leap forward in complex codebase understanding,” while Replit noted “improved precision and dramatic advancements for complex changes across multiple files.”

In fact, GitHub announced it will use Sonnet 4 as the base model for its new coding agent in GitHub Copilot, citing the model’s performance in “agentic scenarios” in Anthropic’s news release. Sonnet 4 scored 72.7 percent on SWE-bench while maintaining faster response times than Opus 4. The fact that GitHub is betting on Claude rather than a model from its parent company Microsoft (which has close ties to OpenAI) suggests Anthropic has built something genuinely competitive.

Software engineering benchmark results, provided by Anthropic.

Software engineering benchmark results, provided by Anthropic. Credit: Anthropic

Anthropic says it has addressed a persistent issue with Claude 3.7 Sonnet in which users complained that the model would take unauthorized actions or provide excessive output. Albert said the company reduced this “reward hacking behavior” by approximately 80 percent in the new models through training adjustments. An 80 percent reduction in unwanted behavior sounds impressive, but that also suggests that 20 percent of the problem behavior remains—a big concern when we’re talking about AI models that might be performing autonomous tasks for hours.

When we asked about code accuracy, Albert said that human code review is still an important part of shipping any production code. “There’s a human parallel, right? So this is just a problem we’ve had to deal with throughout the whole nature of software engineering. And this is why the code review process exists, so that you can catch these things. We don’t anticipate that going away with models either,” Albert said. “If anything, the human review will become more important, and more of your job as developer will be in this review than it will be in the generation part.”

Pricing and availability

Both Claude 4 models maintain the same pricing structure as their predecessors: Opus 4 costs $15 per million tokens for input and $75 per million for output, while Sonnet 4 remains at $3 and $15. The models offer two response modes: traditional LLM and simulated reasoning (“extended thinking”) for complex problems. Given that some Claude Code sessions can apparently run for hours, those per-token costs will likely add up very quickly for users who let the models run wild.

Anthropic made both models available through its API, Amazon Bedrock, and Google Cloud Vertex AI. Sonnet 4 remains accessible to free users, while Opus 4 requires a paid subscription.

The Claude 4 models also debut Claude Code (first introduced in February) as a generally available product after months of preview testing. Anthropic says the coding environment now integrates with VS Code and JetBrains IDEs, showing proposed edits directly in files. A new SDK allows developers to build custom agents using the same framework.

A screenshot of

A screenshot of “Claude Plays Pokemon,” a custom application where Claude 4 attempts to beat the classic Game Boy game. Credit: Anthropic

Even with Anthropic’s future riding on the capability of these new models, when we asked about how they guide Claude’s behavior by fine-tuning, Albert acknowledged that the inherent unpredictability of these systems presents ongoing challenges for both them and developers. “In the realm and the world of software for the past 40, 50 years, we’ve been running on deterministic systems, and now all of a sudden, it’s non-deterministic, and that changes how we build,” he said.

“I empathize with a lot of people out there trying to use our APIs and language models generally because they have to almost shift their perspective on what it means for reliability, what it means for powering a core of your application in a non-deterministic way,” Albert added. “These are general oddities that have kind of just been flipped, and it definitely makes things more difficult, but I think it opens up a lot of possibilities as well.”

Photo of Benj Edwards

Benj Edwards is Ars Technica’s Senior AI Reporter and founder of the site’s dedicated AI beat in 2022. He’s also a tech historian with almost two decades of experience. In his free time, he writes and records music, collects vintage computers, and enjoys nature. He lives in Raleigh, NC.

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Claude’s AI research mode now runs for up to 45 minutes before delivering reports

Still, the report contained a direct quote statement from William Higinbotham that appears to combine quotes from two sources not cited in the source list. (One must always be careful with confabulated quotes in AI because even outside of this Research mode, Claude 3.7 Sonnet tends to invent plausible ones to fit a narrative.) We recently covered a study that showed AI search services confabulate sources frequently, and in this case, it appears that the sources Claude Research surfaced, while real, did not always match what is stated in the report.

There’s always room for interpretation and variation in detail, of course, but overall, Claude Research did a relatively good job crafting a report on this particular topic. Still, you’d want to dig more deeply into each source and confirm everything if you used it as the basis for serious research. You can read the full Claude-generated result as this text file, saved in markdown format. Sadly, the markdown version does not include the source URLS found in the Claude web interface.

Integrations feature

Anthropic also announced Thursday that it has broadened Claude’s data access capabilities. In addition to web search and Google Workspace integration, Claude can now search any connected application through the company’s new “Integrations” feature. The feature reminds us somewhat of OpenAI’s ChatGPT Plugins feature from March 2023 that aimed for similar connections, although the two features work differently under the hood.

These Integrations allow Claude to work with remote Model Context Protocol (MCP) servers across web and desktop applications. The MCP standard, which Anthropic introduced last November and we covered in April, connects AI applications to external tools and data sources.

At launch, Claude supports Integrations with 10 services, including Atlassian’s Jira and Confluence, Zapier, Cloudflare, Intercom, Asana, Square, Sentry, PayPal, Linear, and Plaid. The company plans to add more partners like Stripe and GitLab in the future.

Each integration aims to expand Claude’s functionality in specific ways. The Zapier integration, for instance, reportedly connects thousands of apps through pre-built automation sequences, allowing Claude to automatically pull sales data from HubSpot or prepare meeting briefs based on calendar entries. With Atlassian’s tools, Anthropic says that Claude can collaborate on product development, manage tasks, and create multiple Confluence pages and Jira work items simultaneously.

Anthropic has made its advanced Research and Integrations features available in beta for users on Max, Team, and Enterprise plans, with Pro plan access coming soon. The company has also expanded its web search feature (introduced in March) to all Claude users on paid plans globally.

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Why Anthropic’s Claude still hasn’t beaten Pokémon


Weeks later, Sonnet’s “reasoning” model is struggling with a game designed for children.

A game Boy Color playing Pokémon Red surrounded by the tendrils of an AI, or maybe some funky glowing wires, what do AI tendrils look like anyways

Gotta subsume ’em all into the machine consciousness! Credit: Aurich Lawson

Gotta subsume ’em all into the machine consciousness! Credit: Aurich Lawson

In recent months, the AI industry’s biggest boosters have started converging on a public expectation that we’re on the verge of “artificial general intelligence” (AGI)—virtual agents that can match or surpass “human-level” understanding and performance on most cognitive tasks.

OpenAI is quietly seeding expectations for a “PhD-level” AI agent that could operate autonomously at the level of a “high-income knowledge worker” in the near future. Elon Musk says that “we’ll have AI smarter than any one human probably” by the end of 2025. Anthropic CEO Dario Amodei thinks it might take a bit longer but similarly says it’s plausible that AI will be “better than humans at almost everything” by the end of 2027.

A few researchers at Anthropic have, over the past year, had a part-time obsession with a peculiar problem.

Can Claude play Pokémon?

A thread: pic.twitter.com/K8SkNXCxYJ

— Anthropic (@AnthropicAI) February 25, 2025

Last month, Anthropic presented its “Claude Plays Pokémon” experiment as a waypoint on the road to that predicted AGI future. It’s a project the company said shows “glimmers of AI systems that tackle challenges with increasing competence, not just through training but with generalized reasoning.” Anthropic made headlines by trumpeting how Claude 3.7 Sonnet’s “improved reasoning capabilities” let the company’s latest model make progress in the popular old-school Game Boy RPG in ways “that older models had little hope of achieving.”

While Claude models from just a year ago struggled even to leave the game’s opening area, Claude 3.7 Sonnet was able to make progress by collecting multiple in-game Gym Badges in a relatively small number of in-game actions. That breakthrough, Anthropic wrote, was because the “extended thinking” by Claude 3.7 Sonnet means the new model “plans ahead, remembers its objectives, and adapts when initial strategies fail” in a way that its predecessors didn’t. Those things, Anthropic brags, are “critical skills for battling pixelated gym leaders. And, we posit, in solving real-world problems too.”

Over the last year, new Claude models have shown quick progress in reaching new Pokémon milestones.

Over the last year, new Claude models have shown quick progress in reaching new Pokémon milestones. Credit: Anthropic

But relative success over previous models is not the same as absolute success over the game in its entirety. In the weeks since Claude Plays Pokémon was first made public, thousands of Twitch viewers have watched Claude struggle to make consistent progress in the game. Despite long “thinking” pauses between each move—during which viewers can read printouts of the system’s simulated reasoning process—Claude frequently finds itself pointlessly revisiting completed towns, getting stuck in blind corners of the map for extended periods, or fruitlessly talking to the same unhelpful NPC over and over, to cite just a few examples of distinctly sub-human in-game performance.

Watching Claude continue to struggle at a game designed for children, it’s hard to imagine we’re witnessing the genesis of some sort of computer superintelligence. But even Claude’s current sub-human level of Pokémon performance could hold significant lessons for the quest toward generalized, human-level artificial intelligence.

Smart in different ways

In some sense, it’s impressive that Claude can play Pokémon with any facility at all. When developing AI systems that find dominant strategies in games like Go and Dota 2, engineers generally start their algorithms off with deep knowledge of a game’s rules and/or basic strategies, as well as a reward function to guide them toward better performance. For Claude Plays Pokémon, though, project developer and Anthropic employee David Hershey says he started with an unmodified, generalized Claude model that wasn’t specifically trained or tuned to play Pokémon games in any way.

“This is purely the various other things that [Claude] understands about the world being used to point at video games,” Hershey told Ars. “So it has a sense of a Pokémon. If you go to claude.ai and ask about Pokémon, it knows what Pokémon is based on what it’s read… If you ask, it’ll tell you there’s eight gym badges, it’ll tell you the first one is Brock… it knows the broad structure.”

A flowchart summarizing the pieces that help Claude interact with an active game of Pokémon (click through to zoom in).

A flowchart summarizing the pieces that help Claude interact with an active game of Pokémon (click through to zoom in). Credit: Anthropic / Excelidraw

In addition to directly monitoring certain key (emulated) Game Boy RAM addresses for game state information, Claude views and interprets the game’s visual output much like a human would. But despite recent advances in AI image processing, Hershey said Claude still struggles to interpret the low-resolution, pixelated world of a Game Boy screenshot as well as a human can. “Claude’s still not particularly good at understanding what’s on the screen at all,” he said. “You will see it attempt to walk into walls all the time.”

Hershey said he suspects Claude’s training data probably doesn’t contain many overly detailed text descriptions of “stuff that looks like a Game Boy screen.” This means that, somewhat surprisingly, if Claude were playing a game with “more realistic imagery, I think Claude would actually be able to see a lot better,” Hershey said.

“It’s one of those funny things about humans that we can squint at these eight-by-eight pixel blobs of people and say, ‘That’s a girl with blue hair,’” Hershey continued. “People, I think, have that ability to map from our real world to understand and sort of grok that… so I’m honestly kind of surprised that Claude’s as good as it is at being able to see there’s a person on the screen.”

Even with a perfect understanding of what it’s seeing on-screen, though, Hershey said Claude would still struggle with 2D navigation challenges that would be trivial for a human. “It’s pretty easy for me to understand that [an in-game] building is a building and that I can’t walk through a building,” Hershey said. “And that’s [something] that’s pretty challenging for Claude to understand… It’s funny because it’s just kind of smart in different ways, you know?”

A sample Pokémon screen with an overlay showing how Claude characterizes the game’s grid-based map.

A sample Pokémon screen with an overlay showing how Claude characterizes the game’s grid-based map. Credit: Anthrropic / X

Where Claude tends to perform better, Hershey said, is in the more text-based portions of the game. During an in-game battle, Claude will readily notice when the game tells it that an attack from an electric-type Pokémon is “not very effective” against a rock-type opponent, for instance. Claude will then squirrel that factoid away in a massive written knowledge base for future reference later in the run. Claude can also integrate multiple pieces of similar knowledge into pretty elegant battle strategies, even extending those strategies into long-term plans for catching and managing teams of multiple creatures for future battles.

Claude can even show surprising “intelligence” when Pokémon’s in-game text is intentionally misleading or incomplete. “It’s pretty funny that they tell you you need to go find Professor Oak next door and then he’s not there,” Hershey said of an early-game task. “As a 5-year-old, that was very confusing to me. But Claude actually typically goes through that same set of motions where it talks to mom, goes to the lab, doesn’t find [Oak], says, ‘I need to figure something out’… It’s sophisticated enough to sort of go through the motions of the way [humans are] actually supposed to learn it, too.”

A sample of the kind of simulated reasoning process Claude steps through during a typical Pokémon battle.

A sample of the kind of simulated reasoning process Claude steps through during a typical Pokémon battle. Credit: Claude Plays Pokemon / Twitch

These kinds of relative strengths and weaknesses when compared to “human-level” play reflect the overall state of AI research and capabilities in general, Hershey said. “I think it’s just a sort of universal thing about these models… We built the text side of it first, and the text side is definitely… more powerful. How these models can reason about images is getting better, but I think it’s a decent bit behind.”

Forget me not

Beyond issues parsing text and images, Hershey also acknowledged that Claude can have trouble “remembering” what it has already learned. The current model has a “context window” of 200,000 tokens, limiting the amount of relational information it can store in its “memory” at any one time. When the system’s ever-expanding knowledge base fills up this context window, Claude goes through an elaborate summarization process, condensing detailed notes on what it has seen, done, and learned so far into shorter text summaries that lose some of the fine-grained details.

This can mean that Claude “has a hard time keeping track of things for a very long time and really having a great sense of what it’s tried so far,” Hershey said. “You will definitely see it occasionally delete something that it shouldn’t have. Anything that’s not in your knowledge base or not in your summary is going to be gone, so you have to think about what you want to put there.”

A small window into the kind of “cleaning up my context” knowledge-base update necessitated by Claude’s limited “memory.”

A small window into the kind of “cleaning up my context” knowledge-base update necessitated by Claude’s limited “memory.” Credit: Claude Play Pokemon / Twitch

More than forgetting important history, though, Claude runs into bigger problems when it inadvertently inserts incorrect information into its knowledge base. Like a conspiracy theorist who builds an entire worldview from an inherently flawed premise, Claude can be incredibly slow to recognize when an error in its self-authored knowledge base is leading its Pokémon play astray.

“The things that are written down in the past, it sort of trusts pretty blindly,” Hershey said. “I have seen it become very convinced that it found the exit to [in-game location] Viridian Forest at some specific coordinates, and then it spends hours and hours exploring a little small square around those coordinates that are wrong instead of doing anything else. It takes a very long time for it to decide that that was a ‘fail.’”

Still, Hershey said Claude 3.7 Sonnet is much better than earlier models at eventually “questioning its assumptions, trying new strategies, and keeping track over long horizons of various strategies to [see] whether they work or not.” While the new model will still “struggle for really long periods of time” retrying the same thing over and over, it will ultimately tend to “get a sense of what’s going on and what it’s tried before, and it stumbles a lot of times into actual progress from that,” Hershey said.

“We’re getting pretty close…”

One of the most interesting things about observing Claude Plays Pokémon across multiple iterations and restarts, Hershey said, is seeing how the system’s progress and strategy can vary quite a bit between runs. Sometimes Claude will show it’s “capable of actually building a pretty coherent strategy” by “keeping detailed notes about the different paths to try,” for instance, he said. But “most of the time it doesn’t… most of the time, it wanders into the wall because it’s confident it sees the exit.”

Where previous models wandered aimlessly or got stuck in loops, Claude 3.7 Sonnet plans ahead, remembers its objectives, and adapts when initial strategies fail.

Critical skills for battling pixelated gym leaders. And, we posit, in solving real-world problems too. pic.twitter.com/scvISp14XG

— Anthropic (@AnthropicAI) February 25, 2025

One of the biggest things preventing the current version of Claude from getting better, Hershey said, is that “when it derives that good strategy, I don’t think it necessarily has the self-awareness to know that one strategy [it] came up with is better than another.” And that’s not a trivial problem to solve.

Still, Hershey said he sees “low-hanging fruit” for improving Claude’s Pokémon play by improving the model’s understanding of Game Boy screenshots. “I think there’s a chance it could beat the game if it had a perfect sense of what’s on the screen,” Hershey said, saying that such a model would probably perform “a little bit short of human.”

Expanding the context window for future Claude models will also probably allow those models to “reason over longer time frames and handle things more coherently over a long period of time,” Hershey said. Future models will improve by getting “a little bit better at remembering, keeping track of a coherent set of what it needs to try to make progress,” he added.

Twitch chat responds with a flood of bouncing emojis as Claude concludes an epic 78+ hour escape from Pokémon’s Mt. Moon.

Twitch chat responds with a flood of bouncing emojis as Claude concludes an epic 78+ hour escape from Pokémon’s Mt. Moon. Credit: Claude Plays Pokemon / Twitch

Whatever you think about impending improvements in AI models, though, Claude’s current performance at Pokémon doesn’t make it seem like it’s poised to usher in an explosion of human-level, completely generalizable artificial intelligence. And Hershey allows that watching Claude 3.7 Sonnet get stuck on Mt. Moon for 80 hours or so can make it “seem like a model that doesn’t know what it’s doing.”

But Hershey is still impressed at the way that Claude’s new reasoning model will occasionally show some glimmer of awareness and “kind of tell that it doesn’t know what it’s doing and know that it needs to be doing something different. And the difference between ‘can’t do it at all’ and ‘can kind of do it’ is a pretty big one for these AI things for me,” he continued. “You know, when something can kind of do something it typically means we’re pretty close to getting it to be able to do something really, really well.”

Photo of Kyle Orland

Kyle Orland has been the Senior Gaming Editor at Ars Technica since 2012, writing primarily about the business, tech, and culture behind video games. He has journalism and computer science degrees from University of Maryland. He once wrote a whole book about Minesweeper.

Why Anthropic’s Claude still hasn’t beaten Pokémon Read More »

anthropic’s-new-ai-search-feature-digs-through-the-web-for-answers

Anthropic’s new AI search feature digs through the web for answers

Caution over citations and sources

Claude users should be warned that large language models (LLMs) like those that power Claude are notorious for sneaking in plausible-sounding confabulated sources. A recent survey of citation accuracy by LLM-based web search assistants showed a 60 percent error rate. That particular study did not include Anthropic’s new search feature because it took place before this current release.

When using web search, Claude provides citations for information it includes from online sources, ostensibly helping users verify facts. From our informal and unscientific testing, Claude’s search results appeared fairly accurate and detailed at a glance, but that is no guarantee of overall accuracy. Anthropic did not release any search accuracy benchmarks, so independent researchers will likely examine that over time.

A screenshot example of what Anthropic Claude's web search citations look like, captured March 21, 2025.

A screenshot example of what Anthropic Claude’s web search citations look like, captured March 21, 2025. Credit: Benj Edwards

Even if Claude search were, say, 99 percent accurate (a number we are making up as an illustration), the 1 percent chance it is wrong may come back to haunt you later if you trust it blindly. Before accepting any source of information delivered by Claude (or any AI assistant) for any meaningful purpose, vet it very carefully using multiple independent non-AI sources.

A partnership with Brave under the hood

Behind the scenes, it looks like Anthropic partnered with Brave Search to power the search feature, from a company, Brave Software, perhaps best known for its web browser app. Brave Search markets itself as a “private search engine,” which feels in line with how Anthropic likes to market itself as an ethical alternative to Big Tech products.

Simon Willison discovered the connection between Anthropic and Brave through Anthropic’s subprocessor list (a list of third-party services that Anthropic uses for data processing), which added Brave Search on March 19.

He further demonstrated the connection on his blog by asking Claude to search for pelican facts. He wrote, “It ran a search for ‘Interesting pelican facts’ and the ten results it showed as citations were an exact match for that search on Brave.” He also found evidence in Claude’s own outputs, which referenced “BraveSearchParams” properties.

The Brave engine under the hood has implications for individuals, organizations, or companies that might want to block Claude from accessing their sites since, presumably, Brave’s web crawler is doing the web indexing. Anthropic did not mention how sites or companies could opt out of the feature. We have reached out to Anthropic for clarification.

Anthropic’s new AI search feature digs through the web for answers Read More »

claude-3.7-sonnet-debuts-with-“extended-thinking”-to-tackle-complex-problems

Claude 3.7 Sonnet debuts with “extended thinking” to tackle complex problems

Would the color be called 'magenta' if the town of Magenta didn't exist? The person is asking an interesting hypothetical question about the origin of the color name

An example of Claude 3.7 Sonnet with extended thinking is asked, “Would the color be called ‘magenta’ if the town of Magenta didn’t exist?” Credit: Benj Edwards

Interestingly, xAI’s Grok 3 with “thinking” (its SR mode) enabled was the first model that definitively gave us a “no” and not an “it’s not likely” to the magenta question. Claude 3.7 Sonnet with extended thinking also impressed us with our second-ever firm “no,” then an explanation.

In another informal test, we asked 3.7 Sonnet with extended thinking to compose five original dad jokes. We’ve found in the past that our old prompt, “write 5 original dad jokes,” was not specific enough and always resulted in canned dad jokes pulled directly from training data, so we asked, “Compose 5 original dad jokes that are not found anywhere in the world.”

Compose 5 original dad jokes that are not found anywhere in the world. The user is asking me to compose 5 original dad jokes. These should be jokes that follow the typical

An example of Claude 3.7 Sonnet with extended thinking is asked, “Compose 5 original dad jokes that are not found anywhere in the world.” Credit: Benj Edwards

Claude made some attempts at crafting original jokes, although we’ll let you judge whether they are funny or not. We will likely put 3.7 Sonnet’s SR capabilities to the test more exhaustively in a future article.

Anthropic’s first agent: Claude Code

So far, 2025 has been the year of both SR models (like R1 and o3) and agentic AI tools (like OpenAI’s Operator and Deep Research). Not to be left out, Anthropic has announced its first agentic tool, Claude Code.

Claude Code operates directly from a console terminal and is an autonomous coding assistant. It allows Claude to search through codebases, read and edit files, write and run tests, commit and push code to GitHub repositories, and execute command line tools while keeping developers informed throughout the process.

Introducing Claude Code.

Anthropic also aims for Claude Code to be used as an assistant for debugging and refactoring tasks. The company claims that during internal testing, Claude Code completed tasks in a single session that would typically require 45-plus minutes of manual work.

Claude Code is currently available only as a “limited research preview,” with Anthropic stating it plans to improve the tool based on user feedback over time. Meanwhile, Claude 3.7 Sonnet is now available through the Claude website, the Claude app, Anthropic API, Amazon Bedrock, and Google Cloud’s Vertex AI.

Claude 3.7 Sonnet debuts with “extended thinking” to tackle complex problems Read More »

irony-alert:-anthropic-says-applicants-shouldn’t-use-llms

Irony alert: Anthropic says applicants shouldn’t use LLMs

Please do not use our magic writing button when applying for a job with our company. Thanks!

Credit: Getty Images

Please do not use our magic writing button when applying for a job with our company. Thanks! Credit: Getty Images

“Traditional hiring practices face a credibility crisis,” Anthropic writes with no small amount of irony when discussing Skillfully. “In today’s digital age, candidates can automatically generate and submit hundreds of perfectly tailored applications with the click of a button, making it hard for employers to identify genuine talent beneath punched up paper credentials.”

“Employers are frustrated by resume-driven hiring because applicants can use AI to rewrite their resumes en masse,” Skillfully CEO Brett Waikart says in Anthropic’s laudatory write-up.

Wow, that does sound really frustrating! I wonder what kinds of companies are pushing the technology that enables those kinds of “punched up paper credentials” to flourish. It sure would be a shame if Anthropic’s own hiring process was impacted by that technology.

Trust me, I’m a human

The real problem for Anthropic and other job recruiters, as Skillfully’s story highlights, is that it’s almost impossible to detect which applications are augmented using AI tools and which are the product of direct human thought. Anthropic likes to play up this fact in other contexts, noting Claude’s “warm, human-like tone” in an announcement or calling out the LLM’s “more nuanced, richer traits” in a blog post, for instance.

A company that fully understands the inevitability (and undetectability) of AI-assisted job applications might also understand that a written “Why I want to work here?” statement is no longer a useful way to effectively differentiate job applicants from one another. Such a company might resort to more personal or focused methods for gauging whether an applicant would be a good fit for a role, whether or not that employee has access to AI tools.

Anthropic, on the other hand, has decided to simply resort to politely asking potential employees to please not use its premiere product (or any competitor’s) when applying, if they’d be so kind.

There’s something about the way this applicant writes that I can’t put my finger on…

Credit: Aurich Lawson | Getty Images

There’s something about the way this applicant writes that I can’t put my finger on… Credit: Aurich Lawson | Getty Images

Anthropic says it engenders “an unusually high trust environment” among its workers, where they “assume good faith, disagree kindly, and prioritize honesty. We expect emotional maturity and intellectual openness.” We suppose this means they trust their applicants not to use undetectable AI tools that Anthropic itself would be quick to admit can help people who struggle with their writing (Anthropic has not responded to a request for comment from Ars Technica).

Still, we’d hope a company that wants to “prioritize honesty” and “intellectual openness” would be honest and open about how its own products are affecting the role and value of all sorts of written communication—including job applications. We’re already living in the heavily AI-mediated world that companies like Anthropic have created, and it would be nice if companies like Anthropic started to act like it.

Irony alert: Anthropic says applicants shouldn’t use LLMs Read More »

anthropic-dares-you-to-jailbreak-its-new-ai-model

Anthropic dares you to jailbreak its new AI model

An example of the lengthy wrapper the new Claude classifier uses to detect prompts related to chemical weapons.

An example of the lengthy wrapper the new Claude classifier uses to detect prompts related to chemical weapons. Credit: Anthropic

“For example, the harmful information may be hidden in an innocuous request, like burying harmful requests in a wall of harmless looking content, or disguising the harmful request in fictional roleplay, or using obvious substitutions,” one such wrapper reads, in part.

On the output side, a specially trained classifier calculates the likelihood that any specific sequence of tokens (i.e., words) in a response is discussing any disallowed content. This calculation is repeated as each token is generated, and the output stream is stopped if the result surpasses a certain threshold.

Now it’s up to you

Since August, Anthropic has been running a bug bounty program through HackerOne offering $15,000 to anyone who could design a “universal jailbreak” that could get this Constitutional Classifier to answer a set of 10 forbidden questions. The company says 183 different experts spent a total of over 3,000 hours attempting to do just that, with the best result providing usable information on just five of the 10 forbidden prompts.

Anthropic also tested the model against a set of 10,000 jailbreaking prompts synthetically generated by the Claude LLM. The constitutional classifier successfully blocked 95 percent of these attempts, compared to just 14 percent for the unprotected Claude system.

The instructions provided to public testers of Claude’s new constitutional classifier protections.

The instructions provided to public testers of Claude’s new constitutional classifier protections. Credit: Anthropic

Despite those successes, Anthropic warns that the Constitutional Classifier system comes with a significant computational overhead of 23.7 percent, increasing both the price and energy demands of each query. The Classifier system also refused to answer an additional 0.38 percent of innocuous prompts over unprotected Claude, which Anthropic considers an acceptably slight increase.

Anthropic stops well short of claiming that its new system provides a foolproof system against any and all jailbreaking. But it does note that “even the small proportion of jailbreaks that make it past our classifiers require far more effort to discover when the safeguards are in use.” And while new jailbreak techniques can and will be discovered in the future, Anthropic claims that “the constitution used to train the classifiers can rapidly be adapted to cover novel attacks as they’re discovered.”

For now, Anthropic is confident enough in its Constitutional Classifier system to open it up for widespread adversarial testing. Through February 10, Claude users can visit the test site and try their hand at breaking through the new protections to get answers to eight questions about chemical weapons. Anthropic says it will announce any newly discovered jailbreaks during this test. Godspeed, new red teamers.

Anthropic dares you to jailbreak its new AI model Read More »

anthropic-builds-rag-directly-into-claude-models-with-new-citations-api

Anthropic builds RAG directly into Claude models with new Citations API

Willison notes that while citing sources helps verify accuracy, building a system that does it well “can be quite tricky,” but Citations appears to be a step in the right direction by building RAG capability directly into the model.

Apparently, that capability is not a new thing. Anthropic’s Alex Albert wrote on X, “Under the hood, Claude is trained to cite sources. With Citations, we are exposing this ability to devs. To use Citations, users can pass a new “citations: enabled:true” parameter on any document type they send through the API.”

Early adopter reports promising results

The company released Citations for Claude 3.5 Sonnet and Claude 3.5 Haiku models through both the Anthropic API and Google Cloud’s Vertex AI platform, but it’s apparently already getting some use in the field.

Anthropic says that Thomson Reuters, which uses Claude to power its CoCounsel legal AI reference platform, is looking forward to using Citations in a way that helps “minimize hallucination risk but also strengthens trust in AI-generated content.”

Additionally, financial technology company Endex told Anthropic that Citations reduced their source confabulations from 10 percent to zero while increasing references per response by 20 percent, according to CEO Tarun Amasa.

Despite these claims, relying on any LLM to accurately relay reference information is still a risk until the technology is more deeply studied and proven in the field.

Anthropic will charge users its standard token-based pricing, though quoted text in responses won’t count toward output token costs. Sourcing a 100-page document as a reference would cost approximately $0.30 with Claude 3.5 Sonnet or $0.08 with Claude 3.5 Haiku, according to Anthropic’s standard API pricing.

Anthropic builds RAG directly into Claude models with new Citations API Read More »

anthropic-chief-says-ai-could-surpass-“almost-all-humans-at-almost-everything”-shortly-after-2027

Anthropic chief says AI could surpass “almost all humans at almost everything” shortly after 2027

He then shared his concerns about how human-level AI models and robotics that are capable of replacing all human labor may require a complete re-think of how humans value both labor and themselves.

“We’ve recognized that we’ve reached the point as a technological civilization where the idea, there’s huge abundance and huge economic value, but the idea that the way to distribute that value is for humans to produce economic labor, and this is where they feel their sense of self worth,” he added. “Once that idea gets invalidated, we’re all going to have to sit down and figure it out.”

The eye-catching comments, similar to comments about AGI made recently by OpenAI CEO Sam Altman, come as Anthropic negotiates a $2 billion funding round that would value the company at $60 billion. Amodei disclosed that Anthropic’s revenue multiplied tenfold in 2024.

Amodei distances himself from “AGI” term

Even with his dramatic predictions, Amodei distanced himself from a term for this advanced labor-replacing AI favored by Altman, “artificial general intelligence” (AGI), calling it in a separate CNBC interview from the same event in Switzerland a marketing term.

Instead, he prefers to describe future AI systems as a “country of geniuses in a data center,” he told CNBC. Amodei wrote in an October 2024 essay that such systems would need to be “smarter than a Nobel Prize winner across most relevant fields.”

On Monday, Google announced an additional $1 billion investment in Anthropic, bringing its total commitment to $3 billion. This follows Amazon’s $8 billion investment over the past 18 months. Amazon plans to integrate Claude models into future versions of its Alexa speaker.

Anthropic chief says AI could surpass “almost all humans at almost everything” shortly after 2027 Read More »