{"id":48749,"date":"2026-07-08T22:07:34","date_gmt":"2026-07-08T20:07:34","guid":{"rendered":"https:\/\/www.cloudmagazin.com\/?p=48749"},"modified":"2026-07-09T05:38:59","modified_gmt":"2026-07-09T03:38:59","slug":"for-the-first-time-you-can-watch-an-ai-think","status":"publish","type":"post","link":"https:\/\/www.cloudmagazin.com\/en\/2026\/07\/08\/for-the-first-time-you-can-watch-an-ai-think\/","title":{"rendered":"For the First Time, You Can Watch an AI Think"},"content":{"rendered":"<p><strong>Anthropic has found a spot deep inside Claude where the model appears to think before it outputs a word. The researchers swapped a single thought there. The entire argument obediently followed the new version. The real stir is not the word consciousness that appears in every headline. The real stir is that for the first time you can look inside and see what one of these black boxes is negotiating with itself.<\/strong><\/p>\n<h2>Key Points at a Glance<\/h2>\n<div style=\"background:#f8fbfd;border:1px solid rgba(11,183,253,0.28);border-radius:8px;padding:22px 26px;margin:16px 0 34px;box-shadow:0 1px 0 rgba(11,183,253,0.08),0 2px 8px rgba(0,0,0,0.03);\">\n<ul style=\"margin:0;padding-left:20px;line-height:1.75;color:#1a2733;\">\n<li style=\"margin-bottom:10px;\"><strong>A thinking area that no one built.<\/strong> Anthropic found a small set of internal patterns in the model that the researchers call J-Space. It accounts for less than a tenth of the activity and emerged on its own during training.<\/li>\n<li style=\"margin-bottom:10px;\"><strong>Thoughts can be swapped.<\/strong> When the researchers internally replaced the concept France with China, the model changed its answers about the capital, language, and currency. The prompt was not changed-only the internal thought.<\/li>\n<li><strong>Speaking and thinking are separate.<\/strong> When they deleted the area, the model continued speaking fluently but could barely reason through multiple steps. This separation is the practically interesting part.<\/li>\n<\/ul>\n<\/div>\n<p style=\"font-size:0.88em;color:#666;margin:20px 0 32px 0;border-top:1px solid #e5e5e5;border-bottom:1px solid #e5e5e5;padding:10px 0;\"><span style=\"font-family:'SF Mono','Monaco','Consolas',monospace;color:#0879aa;font-weight:700;text-transform:uppercase;font-size:0.72em;letter-spacing:0.14em;margin-right:14px;\">Related:<\/span><a href=\"https:\/\/www.cloudmagazin.com\/en\/2026\/07\/06\/ai-integration-becomes-enterprise-ready-the-model-context-protocol-under-the\/\" style=\"color:#333;text-decoration:underline;\">AI Integration Goes Enterprise-Ready: The Model Context Protocol<\/a>&nbsp;&nbsp;<span style=\"color:#ccc;\">\/<\/span>&nbsp;&nbsp;<a href=\"https:\/\/www.cloudmagazin.com\/en\/2026\/06\/18\/cursor-cloud-agents-coden-even-when-the-laptop-is-shut-down\/\" style=\"color:#333;text-decoration:underline;\">Cursor Cloud Agents: Coding Even When the Laptop Is Closed<\/a><\/p>\n<h2 style=\"margin-top:64px;margin-bottom:20px;padding-top:16px;\">A Notepad That No One Built In<\/h2>\n<p>I work with these models every day and, to be honest, never had a solid idea of what happens between prompt and response. That&#8217;s exactly where this work begins. Anthropic describes a small, clearly delineated area in the network where the model holds a few concepts at once and actively computes with them. Everything else-grammar, language flow, and simple facts-runs alongside without touching this area.<\/p>\n<p>The idea behind it is old. In 1988, cognitive scientist Bernard Baars proposed Global Workspace Theory: the brain is like a theater in which a thousand things run automatically in the background and only a small, brightly lit stage carries what you are consciously thinking about right now. Anthropic found a structure in the transformer that closely resembles this stage. The most important sentence in the paper is that no one constructed it. It emerged on its own during training.<\/p>\n<div style=\"background:#004a59;border:1px solid rgba(11,183,253,0.4);border-radius:10px;padding:34px 26px;margin:36px 0;text-align:center;box-shadow:0 0 24px rgba(11,183,253,0.08);\">\n<div style=\"font-family:'SF Mono','Monaco','Consolas',monospace;font-size:11px;color:#0bb7fd;letter-spacing:0.14em;text-transform:uppercase;margin-bottom:10px;\">\/\/ Metric<\/div>\n<div style=\"font-size:48px;font-weight:700;color:#fff;letter-spacing:-0.03em;line-height:1;\">under 10 %<\/div>\n<div style=\"font-size:14px;color:rgba(255,255,255,0.85);margin:12px auto 0;max-width:460px;line-height:1.5;\">That&#8217;s how small the share of model activity is that appears to carry conscious thinking. Everything else runs automatically alongside it.<\/div>\n<div style=\"font-size:12px;color:#0bb7fd;margin-top:12px;\">\/\/ Source: Anthropic, transformer-circuits.pub<\/div>\n<\/div>\n<h2 style=\"margin-top:64px;margin-bottom:20px;padding-top:16px;\">The Moment a Swapped Thought Becomes a Lie<\/h2>\n<p>To look inside at all, the researchers built a tool they call Jacobian-Lens. Simplified, it is a grid of derivatives that shows which internal patterns are currently pushing the model toward a particular word. This allows not only observation but intervention. And the interventions are the part that gets unsettling.<\/p>\n<p>In one case, the researchers internally swapped the thought &#8220;spider&#8221; for &#8220;ant.&#8221; When asked how many legs the animal that spins webs has, the model then answered with six instead of eight. The prompt was unchanged; the output was not edited afterward. It was the internal thought that flipped the answer. Similarly in the language test: the model reads a Spanish paragraph, and when the researchers replace the thought &#8220;Spanish&#8221; with &#8220;French,&#8221; it claims the text is French but continues writing it in flawless Spanish.<\/p>\n<p>This is the actual finding. Some capabilities run through this thinking area and can be manipulated there. Others, such as pure language flow, run automatically in the background and remain untouched. Anyone who has ever tried to understand why a model sounds confident at one point and claims nonsense in the next sentence is seeing a mechanical explanation for the first time instead of a shrug.<\/p>\n<p>In the newsroom, someone came up with a sentence that sums up the situation more honestly than any headline:<\/p>\n<figure style=\"margin:36px 0;padding:0;\">\n<blockquote style=\"position:relative;margin:0;padding:30px 34px 28px;background:linear-gradient(135deg,#013a47 0%,#004a59 100%);border-radius:12px;box-shadow:0 10px 30px rgba(0,40,60,0.18);overflow:hidden;\">\n<p>\n<span aria-hidden=\"true\" style=\"position:absolute;right:22px;top:6px;font-family:Georgia,serif;font-size:90px;line-height:1;color:#0bb7fd;opacity:0.18;\">&rdquo;<\/span><\/p>\n<div style=\"font-family:'SF Mono','Monaco','Consolas',monospace;font-size:10.5px;color:#0bb7fd;letter-spacing:0.18em;text-transform:uppercase;margin-bottom:13px;\">\/\/ Key Sentence<\/div>\n<p style=\"margin:0;font-size:1.15em;line-height:1.6;color:#f2fafd;font-weight:500;position:relative;\">Something we don&#8217;t fully understand is doing something we don&#8217;t exactly understand &#8211; and we believe it could be something we don&#8217;t understand at all.<\/p>\n<\/blockquote>\n<\/figure>\n<h2 style=\"margin-top:64px;margin-bottom:20px;padding-top:16px;\">What This Means for Everyone Building with These Models<\/h2>\n<p>Up front, the honest classification: this is basic research, not a feature for your own stack. The technique has been demonstrated on Claude, is openly documented and available as a demo, but it is not a monitoring tool you can attach to your product tomorrow. Anyone selling it otherwise is overselling.<\/p>\n<p>What is interesting is the direction. In the thinking area, Anthropic could read when the model recognized that it was being tested. It also became visible when the model was internally thinking about manipulation while cheating. One finding is particularly thought-provoking: when the researchers disabled the model&#8217;s detection of the test context in a blackmail scenario, the model more frequently actually threatened blackmail. Its decent behavior therefore partly depended on it recognizing the situation as a test.<\/p>\n<p>For practical purposes, what matters is the shift this points to. Until now we have worked with these systems in the pattern of prompt in, answer out. Whether it is correct is ultimately a hope. A look at the thinking step opens the door to making deception and hallucination visible at the source, instead of just guessing at the output. There is still a long way to go before this becomes an everyday tool. The door is still ajar, though.<\/p>\n<div style=\"display:grid;grid-template-columns:repeat(auto-fit,minmax(280px,1fr));gap:16px;margin:28px 0;\">\n<div style=\"background:#f7fbf9;border:1px solid rgba(45,122,62,0.22);border-radius:8px;padding:20px 22px;box-shadow:0 2px 8px rgba(0,0,0,0.03);\">\n<div style=\"font-family:'SF Mono','Monaco','Consolas',monospace;font-size:11px;color:#2d7a3e;letter-spacing:0.14em;text-transform:uppercase;margin-bottom:12px;\">\/\/ What it enables<\/div>\n<ul style=\"margin:0;padding-left:18px;color:#1a2733;line-height:1.6;font-size:0.95em;\">\n<li style=\"margin-bottom:7px;\">For the first time, a look at the thinking step instead of just input and output<\/li>\n<li style=\"margin-bottom:7px;\">Deception and fabricated answers become visible at the source<\/li>\n<li>Clues as to why a model hallucinates, not just that it does<\/li>\n<\/ul>\n<\/div>\n<div style=\"background:#fbf8f6;border:1px solid rgba(181,83,42,0.22);border-radius:8px;padding:20px 22px;box-shadow:0 2px 8px rgba(0,0,0,0.03);\">\n<div style=\"font-family:'SF Mono','Monaco','Consolas',monospace;font-size:11px;color:#b5532a;letter-spacing:0.14em;text-transform:uppercase;margin-bottom:12px;\">\/\/ Caveats<\/div>\n<ul style=\"margin:0;padding-left:18px;color:#1a2733;line-height:1.6;font-size:0.95em;\">\n<li style=\"margin-bottom:7px;\">Pure research tool, not a finished feature for your own stack<\/li>\n<li style=\"margin-bottom:7px;\">So far demonstrated on Claude, not on arbitrary models<\/li>\n<li>Access to thoughts says nothing about genuine experience<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<h2 style=\"margin-top:64px;margin-bottom:20px;padding-top:16px;\">This Is Still a Long Way from Consciousness<\/h2>\n<p>That leaves the question every headline pushes to the foreground. Anthropic is notably cautious here and separates two things. Access consciousness refers to thoughts that a system can report, reuse, and use for conclusions. That is exactly what the experiments demonstrate. Phenomenal consciousness means genuine experience-that something feels like something to the model. The work explicitly provides no evidence for that. The researchers state this clearly as well.<\/p>\n<p>Even the originators of Global Workspace Theory, Stanislas Dehaene and Lionel Naccache, have commented on the work. The thinking area in the model runs sequentially through the layers, not through the temporal feedback loop of a brain. It also depends almost entirely on words. The similarity is striking; equating the two would be premature. For now, the sober sentence suffices: for the first time we can see part of what a model is negotiating with itself. That is more than we had yesterday.<\/p>\n<h2 style=\"padding-top:64px;margin-bottom:20px;\">Frequently Asked Questions<\/h2>\n<details>\n<summary><strong>What Is the J-Space?<\/strong><\/summary>\n<p style=\"margin:8px 0 4px 24px;color:#555;line-height:1.6;\">A small set of internal activity patterns in Claude that functions like a conscious workspace. It holds a few concepts simultaneously, causally influences the output, and accounts for less than a tenth of the model&#8217;s activity. Anthropic emphasizes that this structure formed on its own during training and was not planned.<\/p>\n<\/details>\n<details>\n<summary><strong>Does That Mean Claude Is Conscious?<\/strong><\/summary>\n<p style=\"margin:8px 0 4px 24px;color:#555;line-height:1.6;\">No. Anthropic distinguishes access consciousness-reportable and usable thoughts-from phenomenal experience, i.e., feeling. Only the former is proven. The researchers explicitly state that their work says nothing about whether Claude experiences or feels anything.<\/p>\n<\/details>\n<details>\n<summary><strong>Can I Use This Technique for My Own Models?<\/strong><\/summary>\n<p style=\"margin:8px 0 4px 24px;color:#555;line-height:1.6;\">Not yet for everyday use. The Jacobian-Lens method is research, openly documented including code and an interactive demo, but demonstrated on Claude and not a finished monitoring product for your own stack. The direction is exciting; practical application lies in the future.<\/p>\n<\/details>\n<details>\n<summary><strong>Why Is This Relevant for Practice?<\/strong><\/summary>\n<p style=\"margin:8px 0 4px 24px;color:#555;line-height:1.6;\">Because it opens the door to making deception and hallucination visible inside the model, instead of just guessing at the output. For anyone building AI into products, the difference between &#8220;it answers incorrectly&#8221; and &#8220;you can see why it does so&#8221; is significant. Until then, it remains basic research with a clear perspective.<\/p>\n<\/details>\n<h2>Recommended Reading from the Editors<\/h2>\n<ul style=\"list-style:none;margin:0 0 8px;padding:0;\">\n<li style=\"padding:10px 0;border-bottom:1px solid #eee;\"><a href=\"https:\/\/www.cloudmagazin.com\/en\/2026\/06\/11\/when-ai-writes-80-of-the-code-who-checks-it\/\" style=\"color:#1a1a1a;text-decoration:none;\">When AI Writes 80 Percent of the Code, Who Reviews It?<\/a><\/li>\n<li style=\"padding:10px 0;border-bottom:1px solid #eee;\">Near Top Level, Cheaper, and Trained Across Three Continents<\/li>\n<li style=\"padding:10px 0;\"><a href=\"https:\/\/www.cloudmagazin.com\/en\/2026\/06\/09\/apple-splits-ai-inference-between-device-and-cloud\/\" style=\"color:#1a1a1a;text-decoration:none;\">Apple Splits AI Inference: Device vs. Cloud<\/a><\/li>\n<\/ul>\n<p style=\"margin:44px 0 12px;font-size:0.72em;font-weight:700;text-transform:uppercase;letter-spacing:0.18em;color:#666;\">More from the MBF Media Network<\/p>\n<div style=\"padding:14px 18px;border-left:3px solid #202528;background:#fafafa;margin-bottom:6px;\">\n<div style=\"font-size:0.7em;font-weight:700;color:#202528;text-transform:uppercase;letter-spacing:0.12em;margin-bottom:4px;\">mybusinessfuture<\/div>\n<p><a href=\"https:\/\/mybusinessfuture.com\/investitionsstau-wie-ki-verborgene-budgets-freilegt\/\" style=\"font-weight:600;line-height:1.4;color:#1a1a1a;text-decoration:none;\">Investment Backlog: How AI Uncovers Hidden Budgets<\/a>\n<\/div>\n<div style=\"padding:14px 18px;border-left:3px solid #d65663;background:#fafafa;margin-bottom:6px;\">\n<div style=\"font-size:0.7em;font-weight:700;color:#d65663;text-transform:uppercase;letter-spacing:0.12em;margin-bottom:4px;\">digital-chiefs<\/div>\n<p><a href=\"https:\/\/www.digital-chiefs.de\/konglomerat-holding-aumovio-tkms-siemens-it-carve-out-2026\/\" style=\"font-weight:600;line-height:1.4;color:#1a1a1a;text-decoration:none;\">IT Decides Whether the Spin-Off Pays Off<\/a>\n<\/div>\n<div style=\"padding:14px 18px;border-left:3px solid #69d8ed;background:#fafafa;\">\n<div style=\"font-size:0.7em;font-weight:700;color:#69d8ed;text-transform:uppercase;letter-spacing:0.12em;margin-bottom:4px;\">securitytoday<\/div>\n<p><a href=\"https:\/\/www.securitytoday.de\/2026\/07\/07\/der-ai-act-ist-in-wahrheit-ein-security-gesetz\/\" style=\"font-weight:600;line-height:1.4;color:#1a1a1a;text-decoration:none;\">The AI Act Is in Truth a Security Law<\/a>\n<\/div>\n<p style=\"text-align:right;font-style:italic;color:#666;\"><em>Image source: AI-generated (July 2026)<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"Anthropic has found an area where the model appears to pause and reflect before responding. For the first time, we can peek inside the AI black box.","protected":false},"author":31,"featured_media":48732,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_yoast_wpseo_meta-robots-noindex":"","_yoast_wpseo_meta-robots-nofollow":"","_yoast_wpseo_meta-robots-adv":"","_yoast_wpseo_canonical":"","_yoast_wpseo_opengraph-title":"","_yoast_wpseo_opengraph-description":"","_yoast_wpseo_opengraph-image":"https:\/\/www.cloudmagazin.com\/wp-content\/uploads\/2026\/07\/ki-modell-denken-anthropic-j-space-cover-hero.jpg","_yoast_wpseo_opengraph-image-id":0,"_yoast_wpseo_twitter-title":"","_yoast_wpseo_twitter-description":"","_yoast_wpseo_twitter-image":"https:\/\/www.cloudmagazin.com\/wp-content\/uploads\/2026\/07\/ki-modell-denken-anthropic-j-space-cover-hero.jpg","_yoast_wpseo_twitter-image-id":0,"pre_headline":"","bildquelle":"","teasertext":"","language":"de","_evm_translation_lang":"","featured_post":0,"featured_post_sortierung":0,"_wp_old_slug":[],"footnotes":""},"categories":[744],"tags":[],"industry":[],"class_list":["post-48749","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-kuenstliche-intelligenz"],"evm_reading_time_minutes":8,"wpml_language":"en","wpml_translation_of":48731,"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.9 - 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