{"id":103942,"date":"2026-07-13T19:14:23","date_gmt":"2026-07-13T19:14:23","guid":{"rendered":"https:\/\/youzum.net\/what-anthropics-latest-ai-discovery-does-and-doesnt-show\/"},"modified":"2026-07-13T19:14:23","modified_gmt":"2026-07-13T19:14:23","slug":"what-anthropics-latest-ai-discovery-does-and-doesnt-show","status":"publish","type":"post","link":"https:\/\/youzum.net\/de\/what-anthropics-latest-ai-discovery-does-and-doesnt-show\/","title":{"rendered":"What Anthropic\u2019s latest AI discovery does\u2014and doesn\u2019t\u2014show"},"content":{"rendered":"

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first,\u00a0<\/em>sign up here<\/em><\/a>.<\/em><\/p>\n

Anthropic\u2014currently the world\u2019s most valuable AI company, with a nearly $1 trillion valuation\u2014has a reputation for publishing strange and heady research. It\u2019s looking into whether AI models can feel pain<\/a>, for example, and will sometimes cut off<\/a> chatbot conversations if it suspects users are \u201cabusing\u201d the model.\u00a0<\/p>\n

One niche that Anthropic spends more time and money on than other AI companies is called mechanistic interpretability, which means looking inside the complex math of an AI model to learn why it comes up with one particular output and not another. It\u2019s complicated stuff; there are millions of data points that might contribute to any result, and wading through them can look more like word salad than anything useful. It\u2019s also controversial. Describing AI models with terms borrowed from psychology and neuroscience can make their behavior seem more sophisticated than we might otherwise judge it to be.<\/p>\n

That\u2019s why, when Anthropic announced last week<\/a> that it had found a new window into its models\u2019 \u201cinternal thoughts\u201d as they reason through answers, there was one colleague I had to talk to. Senior editor Will Douglas Heaven, aside from having a PhD in computer science, has spent a lot of time<\/a> digging into what we can say about how AI models work. I spoke with him about what we should take from Anthropic\u2019s new (and predictably quirky) research.<\/p>\n

What did Anthropic learn here, exactly?<\/strong><\/p>\n

Anthropic has been trying to understand how large language models (LLMs) work for a few years now. Anthropic isn\u2019t the only one looking at this, but I think the company has made it part of its core mission more than most. Anthropic\u2019s CEO, Dario Amodei, has said we won\u2019t be able to control LLMs fully unless we learn more about how they work.\u00a0<\/p>\n

So this new research is very much in that context. It goes deeper into the weird mechanisms inside LLMs than ever before. What Anthropic learned was that LLMs have a space inside them\u2014which Anthropic calls the J-space\u2014filled with words that don\u2019t appear in their output but that seem to influence the way they puzzle through problems. All this was hidden until Anthropic developed a new technique to probe its model Claude, so it\u2019s a genuine discovery.\u00a0<\/p>\n

Sometimes these words keep track of where the LLM has got to in a particular task, sometimes they look more like flashes of recognition (for example, \u201cprotein\u201d might pop up when you give an LLM only the letters of a protein sequence), and sometimes they represent a kind of internal commentary on the model\u2019s decision-making. In my favorite example, Claude decided to cheat on a coding test when the word \u201cpanic\u201d appeared.<\/p>\n

Anthropic also found that LLMs are able to describe and manipulate the words in this space. So somehow they seem to be making use of it.\u00a0<\/p>\n

Let\u2019s step back for a second. I don\u2019t think of large language models as <\/strong>simple<\/em><\/strong>, but they\u2019re also not magic. There\u2019s a bunch of math that learns relationships between words, right? So why is it so hard to \u201cpeer\u201d into an LLM to know what\u2019s going on?<\/strong><\/p>\n

Yeah, they\u2019re not magic! I think the fact we don\u2019t fully understand them plays into the mythmaking. And it\u2019s worth noting that the whole narrative that Anthropic is leaning into here\u2014that they\u2019ve built this really mysterious technology, but don\u2019t worry, because they\u2019re also the ones to figure it out\u2014very much fits with the company\u2019s vibe. [See how Anthropic warned that its new models were so good at coding they posed a global cybersecurity risk, only for the US government to shut them down<\/a> shortly thereafter.]<\/p>\n

So yes: LLMs are just math. And yet it\u2019s vastly complex math. Not only are today\u2019s LLMs made out of hundreds of billions of numbers, but running them triggers a cascade of millions and millions of calculations. I wrote last year that if you printed out even a medium-size LLM on pieces of paper, it would cover a city the size of San Francisco<\/a>.\u00a0<\/p>\n

It\u2019s impossible to make sense of any of that math without specialist tools that highlight specific parts of an LLM at specific times. You need to know where to look and how to look. And building those tools requires understanding something of that complex math in the first place.\u00a0<\/p>\n

You\u2019ve written elsewhere about this concept of studying LLMs the way one might study an organism\u2019s brain. Is it fair to use \u201cbrain-like\u201d terms when talking about how an LLM works?<\/strong><\/p>\n

I don\u2019t love using those kinds of terms. LLMs are not brains. Talking like this is misleading because it can suggest that LLMs are capable of more human-like things than they are or that we can make assumptions about how they might behave that we shouldn\u2019t. The whole anthropomorphization thing is also tied up with a bunch of strong ideological positions about what this technology is and what it\u2019s going to be<\/a>.\u00a0<\/p>\n

But at the same time, we lack a good alternative vocabulary for talking about what these models are doing. I can understand why people reach for words like \u201cthink\u201d and \u201cunderstand\u201d and \u201cbrain-like\u201d\u2014they\u2019re convenient shorthand.\u00a0<\/p>\n

Anthropic compares this new space it found inside LLMs to the space that some neuroscientists think our brains use to keep track of conscious thoughts. I asked the company how seriously we should take that comparison and it said in a statement: \u201cDrawing these analogies was helpful to us in designing our experiments, as they allowed us to make many non-obvious experimental predictions about the J-space that turned out to be true. At the same time, it\u2019s important to note that there are some important differences between the J-space (and language models in general) and the human brain, so we don\u2019t mean to claim there\u2019s a perfect correspondence.\u201d\u00a0<\/p>\n

What\u2019s a problem in AI that this new concept of the J-space might be used to solve?<\/strong><\/p>\n

Anthropic has said that monitoring the J-space could be a way to catch models doing something they shouldn\u2019t. Because words pop up in this space that don\u2019t appear in a model\u2019s output, they can tell you things about its behavior that you might not have noticed otherwise\u2014such as when it is giving biased responses or when it is weighing the pros and cons of cheating.\u00a0<\/p>\n

That\u2019s the theory, at least. I think it\u2019s better to think of this result as one more step on the path to understanding this technology overall than as something that will be useful by itself.\u00a0<\/p>\n

Read more in Will\u2019s full story about the new research<\/em><\/a>.\u00a0<\/em><\/p>","protected":false},"excerpt":{"rendered":"

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first,\u00a0sign up here. Anthropic\u2014currently the world\u2019s most valuable AI company, with a nearly $1 trillion valuation\u2014has a reputation for publishing strange and heady research. It\u2019s looking into whether AI models can feel pain, for example, and will sometimes cut off chatbot conversations if it suspects users are \u201cabusing\u201d the model.\u00a0 One niche that Anthropic spends more time and money on than other AI companies is called mechanistic interpretability, which means looking inside the complex math of an AI model to learn why it comes up with one particular output and not another. It\u2019s complicated stuff; there are millions of data points that might contribute to any result, and wading through them can look more like word salad than anything useful. It\u2019s also controversial. Describing AI models with terms borrowed from psychology and neuroscience can make their behavior seem more sophisticated than we might otherwise judge it to be. That\u2019s why, when Anthropic announced last week that it had found a new window into its models\u2019 \u201cinternal thoughts\u201d as they reason through answers, there was one colleague I had to talk to. Senior editor Will Douglas Heaven, aside from having a PhD in computer science, has spent a lot of time digging into what we can say about how AI models work. I spoke with him about what we should take from Anthropic\u2019s new (and predictably quirky) research. What did Anthropic learn here, exactly? Anthropic has been trying to understand how large language models (LLMs) work for a few years now. Anthropic isn\u2019t the only one looking at this, but I think the company has made it part of its core mission more than most. Anthropic\u2019s CEO, Dario Amodei, has said we won\u2019t be able to control LLMs fully unless we learn more about how they work.\u00a0 So this new research is very much in that context. It goes deeper into the weird mechanisms inside LLMs than ever before. What Anthropic learned was that LLMs have a space inside them\u2014which Anthropic calls the J-space\u2014filled with words that don\u2019t appear in their output but that seem to influence the way they puzzle through problems. All this was hidden until Anthropic developed a new technique to probe its model Claude, so it\u2019s a genuine discovery.\u00a0 Sometimes these words keep track of where the LLM has got to in a particular task, sometimes they look more like flashes of recognition (for example, \u201cprotein\u201d might pop up when you give an LLM only the letters of a protein sequence), and sometimes they represent a kind of internal commentary on the model\u2019s decision-making. In my favorite example, Claude decided to cheat on a coding test when the word \u201cpanic\u201d appeared. Anthropic also found that LLMs are able to describe and manipulate the words in this space. So somehow they seem to be making use of it.\u00a0 Let\u2019s step back for a second. I don\u2019t think of large language models as simple, but they\u2019re also not magic. There\u2019s a bunch of math that learns relationships between words, right? So why is it so hard to \u201cpeer\u201d into an LLM to know what\u2019s going on? Yeah, they\u2019re not magic! I think the fact we don\u2019t fully understand them plays into the mythmaking. And it\u2019s worth noting that the whole narrative that Anthropic is leaning into here\u2014that they\u2019ve built this really mysterious technology, but don\u2019t worry, because they\u2019re also the ones to figure it out\u2014very much fits with the company\u2019s vibe. [See how Anthropic warned that its new models were so good at coding they posed a global cybersecurity risk, only for the US government to shut them down shortly thereafter.] So yes: LLMs are just math. And yet it\u2019s vastly complex math. Not only are today\u2019s LLMs made out of hundreds of billions of numbers, but running them triggers a cascade of millions and millions of calculations. I wrote last year that if you printed out even a medium-size LLM on pieces of paper, it would cover a city the size of San Francisco.\u00a0 It\u2019s impossible to make sense of any of that math without specialist tools that highlight specific parts of an LLM at specific times. You need to know where to look and how to look. And building those tools requires understanding something of that complex math in the first place.\u00a0 You\u2019ve written elsewhere about this concept of studying LLMs the way one might study an organism\u2019s brain. Is it fair to use \u201cbrain-like\u201d terms when talking about how an LLM works? I don\u2019t love using those kinds of terms. LLMs are not brains. Talking like this is misleading because it can suggest that LLMs are capable of more human-like things than they are or that we can make assumptions about how they might behave that we shouldn\u2019t. The whole anthropomorphization thing is also tied up with a bunch of strong ideological positions about what this technology is and what it\u2019s going to be.\u00a0 But at the same time, we lack a good alternative vocabulary for talking about what these models are doing. I can understand why people reach for words like \u201cthink\u201d and \u201cunderstand\u201d and \u201cbrain-like\u201d\u2014they\u2019re convenient shorthand.\u00a0 Anthropic compares this new space it found inside LLMs to the space that some neuroscientists think our brains use to keep track of conscious thoughts. I asked the company how seriously we should take that comparison and it said in a statement: \u201cDrawing these analogies was helpful to us in designing our experiments, as they allowed us to make many non-obvious experimental predictions about the J-space that turned out to be true. At the same time, it\u2019s important to note that there are some important differences between the J-space (and language models in general) and the human brain, so we don\u2019t mean to claim there\u2019s a perfect correspondence.\u201d\u00a0 What\u2019s a problem in AI that this new concept of the J-space might be used to solve? Anthropic<\/p>","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"pmpro_default_level":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"_pvb_checkbox_block_on_post":false,"footnotes":""},"categories":[52,5,7,1],"tags":[],"class_list":["post-103942","post","type-post","status-publish","format-standard","hentry","category-ai-club","category-committee","category-news","category-uncategorized","pmpro-has-access"],"acf":[],"yoast_head":"\nWhat Anthropic\u2019s latest AI discovery does\u2014and doesn\u2019t\u2014show - 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To get stories like this in your inbox first,\u00a0sign up here. Anthropic\u2014currently the world\u2019s most valuable AI company, with a nearly $1 trillion valuation\u2014has a reputation for publishing strange and heady research. It\u2019s looking into whether AI models can feel pain, for example,…","_links":{"self":[{"href":"https:\/\/youzum.net\/de\/wp-json\/wp\/v2\/posts\/103942","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/youzum.net\/de\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/youzum.net\/de\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/youzum.net\/de\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/youzum.net\/de\/wp-json\/wp\/v2\/comments?post=103942"}],"version-history":[{"count":0,"href":"https:\/\/youzum.net\/de\/wp-json\/wp\/v2\/posts\/103942\/revisions"}],"wp:attachment":[{"href":"https:\/\/youzum.net\/de\/wp-json\/wp\/v2\/media?parent=103942"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/youzum.net\/de\/wp-json\/wp\/v2\/categories?post=103942"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/youzum.net\/de\/wp-json\/wp\/v2\/tags?post=103942"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}