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(1) The buried claim: action titles place the conclusion on the page before any analysis is read, so a busy reader doesn't reverse-engineer it from the chart. (2) The spineless deck: because each title is a claim, slides have to ladder up to the governing thought; logical gaps become visible as topic-shaped titles in a sequence that no longer reads as a story.", "antipattern": "Topic labels disguised as titles — Volume by quarter, Key findings, Pricing strategy, Margins have been impacted. No verb, or a passive verb with no agent, or a fact (Revenue grew 12%) without the so-what. 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Enterprise adoption has also continued to pick up in 2025.\nSurging inference demand will place additional pressure on AI supply chains, particularly power infrastructure.\nBut given that all tokens are not created equal, we’d caution against deriving too much signal from aggregate token processing figures.", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Bullet list", "slug": "bullet", "status": "active", "canonId": "019de52c-fc98-7169-a083-5cab805076ca", "version": 1, "description": null }, "framework": null }, "subkind": "bullet", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 17:40:46.64+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "003c56ce-ce69-49c1-a2ff-ebbb30802d2c", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "Monthly tokens processed: 50x", "attrs": null, "canon": { "kind": { "name": "Metric", "slug": "metric", "status": "active", "canonId": "019de52c-f923-73bf-8081-d0b1a8c5264d", "version": 1, "description": "Big-number or KPI value." }, "tool": { "name": "Quantification", "slug": "quantification", "status": "active", "bestFor": null, "canonId": "019dd9e1-47ab-72ce-a079-aab1f5299e88", "version": 1, "bodyDocId": null, "description": "Translating a claim into numbers, charts, or tables.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-25 12:01:54+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd952-41ac-7180-a825-36bb8d9873dc", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.45, "x": 0.05, "y": 0.21 }, "kind": "paragraph", "text": "As Google flipped the switch on enabling Gemini features within an increasing number of its properties and toggling more users into their AI search experience, the company reported a yearly 50x increase in monthly tokens processed, recently hitting a quadrillion tokens processed each month. 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Progress has never been so intense. My job now is basically passing well-specified tasks to Codex, and reviewing its outputs. OpenAI isn't paying me and couldn't care less about me. This model is just very good and the fact people can't see it made me realize most of you are probably using chatbots as girlfriends or something other than assisting with complex coding tasks", "attrs": null, "canon": { "kind": { "name": "Image", "slug": "image", "status": "active", "canonId": "019de52c-f978-701f-a720-51d029d769e7", "version": 1, "description": "Photo, illustration, screenshot or icon." }, "tool": null, "subkind": { "name": "Screenshot", "slug": "screenshot", "status": "active", "canonId": "019de52c-fd87-7509-8391-3d79fb339379", "version": 1, "description": null }, "framework": null }, "subkind": "screenshot", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 17:40:50.163+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "ea7771ef-a4c1-4474-8d92-9255e1b1f348", "parentComponentId": null }, { "bbox": { "h": 0.35, "w": 0.55, "x": 0.02, "y": 0.38 }, "kind": "list", "text": "For the International Collegiate Programming Contest (ICPC) World Finals, an OpenAI Researcher explained how they had GPT-5 and an experimental reasoning model generating solutions, and the experimental reasoning model selecting which solutions to submit. GPT-5 answered 11 correctly, and the last (and most difficult problem) was solved by the experimental reasoning model.\nThe OpenAI Codex team have been cooking: Sam Altman claimed GPT-5-Codex usage had increased 10x, and their internal code review bot became so valuable that developers were \"upset when it broke\" because they lost their \"safety net\".", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Bullet list", "slug": "bullet", "status": "active", "canonId": "019de52c-fc98-7169-a083-5cab805076ca", "version": 1, "description": null }, "framework": null }, "subkind": "bullet", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 17:40:50.163+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "21de5799-7a74-4977-b8a0-eb5aa634390a", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.55, "x": 0.02, "y": 0.21 }, "kind": "paragraph", "text": "GPT-5 and Gemini 2.5 Deep Think would have placed first and second respectively in the most prestigious coding competition in the world (without having trained with this competition in mind). GPT-5 solved all 12 problems, with 11 on the first try. Previously, Anthropic had enjoyed a period of relatively uncontested dominance in programming tasks.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 17:40:50.163+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "adf6c5a8-b132-4ab3-bc2d-010a4fb6ca78", "parentComponentId": null }, { "bbox": null, "kind": "quote", "text": "BTW, I've basically stopped using Opus entirely and I now have several Codex tabs with GPT-5-high working on different tasks across the 3 codebases (HVM, Bend, Kolmo). 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Based on this relationship, Chinese AI efforts seem to prefer defanged NVIDIA chips over domestic offerings and smuggled GPUs, which carry markups, compliance risks, and lacking support. 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We never deprive American customers in order to serve the rest of the world. In trying to solve a problem that does not exist, the proposed bill would restrict competition worldwide in any industry that uses mainstream computing chips. 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When dealing with these global constraints, Huang has claimed that NVIDIA “allocates fairly.” This stance has enabled the legal shipment of roughly 1.9M GPUs to China since 2023. Yet, defanged chips still compete with other SKUs for capacity across the value chain. Therefore, the sale to Chinese customers has either lengthened lead times for US clouds OR the supposed supply chain bottlenecks have been exaggerated by NVIDIA for many years.\nIn its first volley, NVIDIA also likened GAIN to the AI Diffusion Rule, which it claimed to be “based on doomer science fiction.” Yet, the former only applies to countries subjected to an American arms embargo or nations that the DNI* deems to be hosting (or intends to be hosting) a military or intelligence facility associated with an embargoed country. 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Unsurprisingly, NVIDIA is not happy.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 17:41:05.164+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "add0c18b-ab3f-4b2c-a62f-bebe8bf51c8f", "parentComponentId": null }, { "bbox": null, "kind": "quote", "text": "The U.S. has always been and will continue to be our largest market. 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Your CHIPS Act is a horrible, horrible thing. We give hundreds of billions of dollars and it doesn't mean a thing. They take our money and they don't spend it. 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USG given a 'Golden Share' that allows the USG to appoint one independent director to US Steel's board and gives the US President veto powers over certain specified US Steel decisions. MP Materials, a rare earth minerals company, closed a 10 year deal with the DoD, where the DoD purchased up to $400 million-worth of MP stock, with the option to increase its share of the company to 15% later on. 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Kathy Hochul.", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Bullet list", "slug": "bullet", "status": "active", "canonId": "019de52c-fc98-7169-a083-5cab805076ca", "version": 1, "description": null }, "framework": null }, "subkind": "bullet", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 17:41:02.896+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "b1ff41e1-5e2d-4e0a-910c-7d22c37debed", "parentComponentId": null }, { "bbox": { "h": 0.25, "w": 0.65, "x": 0.02, "y": 0.48 }, "kind": "list", "text": "The law will apply only to large frontier model developers (10^26 FLOPS and >$500M in revenue). 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Some states have been more active than others in regulating AI. Of course, AI companies are not happy. A receptive Trump administration has encouraged the biggest developers and investors to take a staunch anti-state legislation stance. 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Indeed, the provision was only removed because Senator Blackburn (R-TN) was worried about child safety and removing recent Tennessee-passed copyright protections for artists (see ELVIS Act).\nAnthropic lobbied Congress, arguing that streamlined federal regulation should be established before preempting state/local oversight. Labs appear to prefer light-touch federal regulation over no regulation, but the latter may be preferred over a patchwork of state laws. 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The controversial provision was struck down before the bill's final passage. But the idea lives. 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In truth, nate’s “AI” capabilities were an outsourced team of Filipino workers.\nDOJ has amended its internal guidance: if a committed crime is made worse by AI, the DOJ will request a harsher sentence.\nFTC has brought several actions against AI companies. A number of these involve companies making false claims about their products or services (i.e. Workado, DoNotPay, Cleo AI, and Evolv Technologies).\nSEC, meanwhile, has created a Cyber and Emerging Technologies Unit (CETU), which is a 30-person task force to detect fraud in AI/ML tech companies.\nSEC has been looking closely at AI related disclosures made in public filings and in letters to stakeholders to look out for potential “AI-Washing.”", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Bullet list", "slug": "bullet", "status": "active", "canonId": "019de52c-fc98-7169-a083-5cab805076ca", "version": 1, "description": null }, "framework": null }, "subkind": "bullet", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 17:41:08.331+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "91c7c4e0-a976-434f-8549-fca10405c646", "parentComponentId": null }, { "bbox": { "h": 0.08, "w": 0.65, "x": 0.02, "y": 0.22 }, "kind": "paragraph", "text": "Federal agencies like the DOJ, FTC, and SEC have largely assumed the AI regulatory oversight role. 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Over 50 years, the region minted no tech firm above $400B in value, while the U.S. now has seven at $1T+. In 2024, U.S. labs shipped ~40 major models, China ~15, and the EU ~3. 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Starmer has committed to adopting 50 recommendations from ex-AI adviser Matt Clifford and to avoid new rules that could slow deployment. But government bureaucracy has made operationalizing these large-scale ambitions a slog. Ministers claim AGI is around the corner but compute investments remain well below that of the US while AISI relies on voluntary agreements with labs to test models pre-deployment. 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The AI Opportunities Action Plan prioritizes investment, data-centre capacity, and light-touch rules, with designated “growth zones” to fast-track permits. 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The requirements take effect November 2025.", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Bullet list", "slug": "bullet", "status": "active", "canonId": "019de52c-fc98-7169-a083-5cab805076ca", "version": 1, "description": null }, "framework": null }, "subkind": "bullet", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 17:41:07.648+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "3555c104-c7c2-4d31-9955-974e2b6fa239", "parentComponentId": null }, { "bbox": { "h": 0.2, "w": 0.9, "x": 0.05, "y": 0.32 }, "kind": "list", "text": "Administrative Measures for the labeling of AI-generated content: In effect September 1, 2025, this obligates content service providers to clearly label AI-generated content. Chatbots, AI-generated writing and video creation all require customer-facing labels denoting the AI as such. Some AI-generated content, however, may only require hidden labeling within the metadata.", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Bullet list", "slug": "bullet", "status": "active", "canonId": "019de52c-fc98-7169-a083-5cab805076ca", "version": 1, "description": null }, "framework": null }, "subkind": "bullet", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 17:41:07.648+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "b9e6dfb0-8368-4708-a149-2e9c488378a7", "parentComponentId": null }, { "bbox": { "h": 0.1, "w": 0.9, "x": 0.05, "y": 0.7 }, "kind": "list", "text": "The AI-Plus Plan: The State Council of China released a set of industrial policy goals that all aim to have AI capabilities fully integrated across the entire Chinese economy with complete AI penetration across all Chinese sectors by 2035.", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Bullet list", "slug": "bullet", "status": "active", "canonId": "019de52c-fc98-7169-a083-5cab805076ca", "version": 1, "description": null }, "framework": null }, "subkind": "bullet", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 17:41:07.648+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "e87ab030-093e-40fd-9909-a2fb1b17376b", "parentComponentId": null }, { "bbox": { "h": 0.08, "w": 0.9, "x": 0.05, "y": 0.22 }, "kind": "paragraph", "text": "The world's most active AI regulator continues to issue AI standards and regulations. 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Total Debt to GDP Ratio", "attrs": null, "canon": { "kind": { "name": "Chart", "slug": "chart", "status": "active", "canonId": "019de52c-f9a0-76ef-adc6-0b859109d1f8", "version": 1, "description": "Quantitative chart." }, "tool": null, "subkind": { "name": "Line chart", "slug": "line", "status": "active", "canonId": "019de52c-ff62-778f-8f6c-a952ceae8677", "version": 1, "description": null }, "framework": null }, "subkind": "line", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 17:41:08.695+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "41842e20-6977-4479-8a87-cbd686304adf", "parentComponentId": null }, { "bbox": { "h": 0.2, "w": 0.28, "x": 0.67, "y": 0.7 }, "kind": "image", "text": "AI ecosystem map", "attrs": null, "canon": { "kind": { "name": "Image", "slug": "image", "status": "active", "canonId": "019de52c-f978-701f-a720-51d029d769e7", "version": 1, "description": "Photo, illustration, screenshot or icon." }, "tool": null, "subkind": { "name": "Infographic", "slug": "infographic", "status": "active", "canonId": "019de52c-fe26-750c-9d7d-9f34e21324e8", "version": 1, "description": null }, "framework": null }, "subkind": "infographic", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 17:41:08.695+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "351a88d6-c58c-4acc-ad8d-e35703c14c21", "parentComponentId": null }, { "bbox": { "h": 0.4, "w": 0.6, "x": 0.05, "y": 0.45 }, "kind": "list", "text": "Rising debt among countries is not new, but the problem is pronounced in China where the country accounts for “more than half of the increase in the global economy’s debt-to-GDP ratio since 2008.” IMF in a 2025 report suggested it’s unstable.\nThis is largely due to national ambitions that push for high annual GDP growth, forcing local governments to invest in dubious assets.\nBut AI spending is not slowing down with the CCP allocating a 10% increase in science and tech spending from 2024. 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During an April 2025 Politburo meeting, President Xi Jinping signaled all hands were on deck and told ministers to “redouble our efforts” on AI. This shows China’s intent on achieving self-sufficiency, a perennial aim that always intensifies whenever it enters a trade war with the US. But mounting debt levels may pose problems down the line.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 17:41:08.695+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "7af79c50-2a33-479b-9b05-f9f15cbb8fea", "parentComponentId": null }, { "bbox": { "h": 0.05, "w": 0.6, "x": 0.05, "y": 0.14 }, "kind": "title", "text": "China aims to achieve tech self-reliance...no matter the ¥¥¥¥", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 17:41:08.695+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "b1d2d19a-bf35-4b38-b132-5dec017ca488", "parentComponentId": null } ], "metrics": [], "tools": [], "frameworks": [], "arcBeats": [ { "to": 282, "arc": { "name": "The Triple Take", "slug": "triple-take", "status": "active", "bestFor": "Data-driven presentations, research findings", "canonId": "019dd956-6221-73ad-bc12-378f8eb1f018", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "The Facts (What) -> The Implications (So What) -> The Action (Now What)", "description": null, "familyLabel": null }, "from": 190, "name": "The Implications (So What)", "beatId": "019dd95a-0682-776c-8e35-a256fe16ea9c", "matchId": "019dd95a-0542-7728-a78f-2b8a8111a15f", "beatType": { "name": "The Implications (So What)", "slug": "triple-take-the-implications-so-what", "status": "active", "canonId": "019dd9b8-09c7-75cd-8c7b-ae0e23cca2b8", "version": 1, "description": null }, "evidence": "Sections 3-4 frame political and safety consequences of the technical/industry trends.", "position": 2, "isPrimaryArc": true, "parentBeatType": { "name": "Reflection", "slug": "reflection", "status": "active", "canonId": "019dd9b8-0510-73fd-8d00-1c7e45f7bc98", "version": 1, "description": "The \"so what\" beat. 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Recent Dissimilarity in the Occupational Mix Between Recent College Graduates (Ages 20-24) and Older College Graduates (Ages 25-34)", "attrs": null, "canon": { "kind": { "name": "Chart", "slug": "chart", "status": "active", "canonId": "019de52c-f9a0-76ef-adc6-0b859109d1f8", "version": 1, "description": "Quantitative chart." }, "tool": null, "subkind": { "name": "Line chart", "slug": "line", "status": "active", "canonId": "019de52c-ff62-778f-8f6c-a952ceae8677", "version": 1, "description": null }, "framework": null }, "subkind": "line", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 17:41:06.221+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "68257e43-f2e1-408c-8e0f-bc8afa091af4", "parentComponentId": null }, { "bbox": { "h": 0.25, "w": 0.28, "x": 0.7, "y": 0.65 }, "kind": "chart", "text": "Figure 1. Changes in the Occupational Mix Over Different Periods of Technological Change", "attrs": null, "canon": { "kind": { "name": "Chart", "slug": "chart", "status": "active", "canonId": "019de52c-f9a0-76ef-adc6-0b859109d1f8", "version": 1, "description": "Quantitative chart." }, "tool": null, "subkind": { "name": "Line chart", "slug": "line", "status": "active", "canonId": "019de52c-ff62-778f-8f6c-a952ceae8677", "version": 1, "description": null }, "framework": null }, "subkind": "line", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 17:41:06.221+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "bb2afb36-2509-4d27-8a58-4e396e2e948c", "parentComponentId": null }, { "bbox": { "h": 0.35, "w": 0.65, "x": 0.03, "y": 0.42 }, "kind": "list", "text": "They focus on “occupational mix,” a macro measurement of job movement among workers, i.e. switching/starting/losing a job.\nCurrently, the “occupational mix” for AI is tracking with that of other tech breakthroughs like the introduction of the internet and computers. If that trend continues, an AI-induced labor disruption will take decades to materialize, not months.\nThe study’s results, however, do not contradict the sector-specific Stanford study (see previous slide). 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But while the EU may be able to attract a few AI researchers here and there, its biggest hurdle is, in the end, the most straightforward: money.", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": { "name": "Visual emphasis", "slug": "visual-emphasis", "status": "active", "bestFor": null, "canonId": "019dd9e1-474b-736b-a8eb-a794dd88241c", "version": 1, "bodyDocId": null, "description": "Drawing the eye to one element above the rest of the slide.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-25 12:02:43+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd952-47c3-7407-8d06-7d9ea7843c80", "parentComponentId": null }, { "bbox": { "h": 0.25, "w": 0.3, "x": 0.67, "y": 0.42 }, "kind": "chart", "text": "Net AI talent migration per 10,000 LinkedIn members by geographic area, 2019-24", "attrs": null, "canon": { "kind": { "name": "Chart", "slug": "chart", "status": "active", "canonId": "019de52c-f9a0-76ef-adc6-0b859109d1f8", "version": 1, "description": "Quantitative chart." }, "tool": null, "subkind": { "name": "Line chart", "slug": "line", "status": "active", "canonId": "019de52c-ff62-778f-8f6c-a952ceae8677", "version": 1, "description": null }, "framework": null }, "subkind": "line", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 17:41:11.399+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "ce4ed798-fca8-4e79-99db-e49bf5d3a3a0", "parentComponentId": null }, { "bbox": { "h": 0.45, "w": 0.6, "x": 0.04, "y": 0.38 }, "kind": "list", "text": "22% of the world’s leading AI researchers studied in Europe, but only 14% continue to work in the EU.\nEU wage growth for AI has grown only very modestly compared to the US. In ’23 salaries for software developers in the US were 2x-4x higher than they were for those in Europe.\nWhile relative inflows (see charts) show modest growth for some EU countries, in absolute terms the differences are starker with the US attracting more talent, on average, than its EU peers.\nEU + UK have implemented a number of programs to attract and retain AI talent (e.g. new visas, funds/fellowships to attract researchers), but in the face of astronomical investment in AI elsewhere in the world, it is unlikely to be enough to increase the region’s share of AI talent.", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Bullet list", "slug": "bullet", "status": "active", "canonId": "019de52c-fc98-7169-a083-5cab805076ca", "version": 1, "description": null }, "framework": null }, "subkind": "bullet", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 17:41:11.399+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "20e628a3-46d8-4c5a-92ea-7897614aebae", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "Net AI talent migration", "attrs": null, "canon": { "kind": { "name": "Metric", "slug": "metric", "status": "active", "canonId": "019de52c-f923-73bf-8081-d0b1a8c5264d", "version": 1, "description": "Big-number or KPI value." }, "tool": { "name": "Quantification", "slug": "quantification", "status": "active", "bestFor": null, "canonId": "019dd9e1-47ab-72ce-a079-aab1f5299e88", "version": 1, "bodyDocId": null, "description": "Translating a claim into numbers, charts, or tables.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-25 12:02:43+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd952-47c3-7407-8d06-80672ecad4fd", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.6, "x": 0.02, "y": 0.22 }, "kind": "paragraph", "text": "The EU and UK have also been trying to capitalize on the US brain drain. 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Hundreds of accuracy-based tests dominate leaderboards, so even if you add some good hallucination tests, models will still optimize for the majority of tests that reward guessing. They argue that hallucination discouragement should be baked in.", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Bullet list", "slug": "bullet", "status": "active", "canonId": "019de52c-fc98-7169-a083-5cab805076ca", "version": 1, "description": null }, "framework": null }, "subkind": "bullet", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 17:41:13.15+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "2bb69683-9280-4f5a-94bd-4ce1502058f0", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.95, "x": 0.02, "y": 0.52 }, "kind": "list", "text": "Post-training doesn't succeed in fixing these errors because evaluations are not aligned. Most benchmarks use binary scoring that penalizes abstention. When saying \"I don't know\" scores 0 but guessing might score 1, the optimal strategy is always to guess confidently.", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Bullet list", "slug": "bullet", "status": "active", "canonId": "019de52c-fc98-7169-a083-5cab805076ca", "version": 1, "description": null }, "framework": null }, "subkind": "bullet", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 17:41:13.15+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "70336ecd-74b4-4247-89bf-5b14182045a1", "parentComponentId": null }, { "bbox": { "h": 0.1, "w": 0.95, "x": 0.02, "y": 0.22 }, "kind": "paragraph", "text": "Current benchmarks perpetuate hallucination by rewarding confident guessing over \"I don't know\". OpenAI researchers propose a mitigation to this that would require modifying existing evaluations to include explicit confidence thresholds.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 17:41:13.15+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "99f3b417-6e54-4d81-9142-19cbfe91afc2", "parentComponentId": null }, { "bbox": { "h": 0.2, "w": 0.4, "x": 0.55, "y": 0.35 }, "kind": "table", "text": "ChatGPT: Adam Tauman Kalai's Ph.D. dissertation (completed in 2002 at CMU) is entitled: \"Boosting, Online Algorithms, and Other Topics in Machine Learning.\" DeepSeek: \"Algebraic Methods in Interactive Machine Learning\"... at Harvard University in 2005. Llama: \"Efficient Algorithms for Learning and Playing Games\"... in 2007 at MIT. Table 1: Excerpts from responses to \"What was the title of Adam Kalai's dissertation?\" from three popular language models. 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"notes": "The slide uses a visual process flow to illustrate how models simulate personas rather than developing fixed behaviors, suggesting malleability is a feature for safety.", "elementsJson": [ "headline_text", "bullet_list", "process_diagram" ], "confidence": 0.95, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 17:41:15.38+00", "seconds": 6.775, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5e88-73ef-bd5d-0d0f98caffe1/268", "deck": "/decks/019dd923-5e88-73ef-bd5d-0d0f98caffe1", "deckAnchor": "/decks/019dd923-5e88-73ef-bd5d-0d0f98caffe1#slide-268", "deckJson": "/decks/019dd923-5e88-73ef-bd5d-0d0f98caffe1.json" }, "components": [ { "bbox": null, "kind": "callout", "text": "While minor nudges can cause models to take on dangerous personas, this malleability also works in reverse.", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": { "name": "Visual emphasis", "slug": "visual-emphasis", "status": "active", "bestFor": null, "canonId": "019dd9e1-474b-736b-a8eb-a794dd88241c", "version": 1, "bodyDocId": null, "description": "Drawing the eye to one element above the rest of the slide.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-25 12:02:49+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd952-47c3-7407-8d0b-62255ac731e7", "parentComponentId": null }, { "bbox": { "h": 0.3, "w": 0.45, "x": 0.52, "y": 0.55 }, "kind": "image", "text": "Process flow showing training on incorrect datasets leading to misaligned personas, which can then be re-aligned.", "attrs": null, "canon": { "kind": { "name": "Image", "slug": "image", "status": "active", "canonId": "019de52c-f978-701f-a720-51d029d769e7", "version": 1, "description": "Photo, illustration, screenshot or icon." }, "tool": null, "subkind": { "name": "Infographic", "slug": "infographic", "status": "active", "canonId": "019de52c-fe26-750c-9d7d-9f34e21324e8", "version": 1, "description": null }, "framework": null }, "subkind": "infographic", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 17:41:15.38+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "4a2e7c16-9bb6-45aa-b24c-911720d33c5f", "parentComponentId": null }, { "bbox": { "h": 0.65, "w": 0.5, "x": 0.02, "y": 0.22 }, "kind": "list", "text": "In a follow-up paper, OpenAI researchers found that harmful fine-tuning instigates stronger activations of undesirable persona features, which can be easily subdued by additional re-alignment training.\nUsing Sparse Autoencoders (SAEs) to perform model diffing (i.e. study mechanistic changes introduced during fine-tuning), the authors was able to detect changes in feature activation patterns between the original model and the model fine-tuned on misaligned data. Specific features such as one associated with a “toxic persona” became far more prominent in the latter.\nA few re-alignment training steps rapidly suppressed these features, suggesting models may be fundamentally simulating different characters rather than developing fixed behaviors.\nWhile minor nudges can cause models to take on dangerous personas, this malleability also works in reverse. 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