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<div style="display: none; max-height: 0px; overflow: hidden;">Anthropic plans to raise $10 billion at a valuation of $350 billion. The funding round is expected to close in the coming weeks </div>
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<h1><strong>TLDR AI <span id="date">2026-01-08</span></strong></h1>
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<h1><strong>Headlines & Launches</strong></h1>
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Ffidjisimo.substack.com%2Fp%2Fchatgpt-health%3Futm_source=tldrai/1/0100019b9df577b4-9e325909-0b3e-4609-84fb-39434ff9f034-000000/u9cAwvZA-iqV9eJ7cdY_6pw62ClJHcyoZSaLkuPd8sQ=439">
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<strong>ChatGPT Health (6 minute read)</strong>
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OpenAI has introduced ChatGPT Health, a new feature designed to assist users with health and wellness by securely integrating their medical information into a personalized ChatGPT experience.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Ffinance.yahoo.com%2Fnews%2Fopenai-reserves-50-billion-stock-001357025.html%3Futm_source=tldrai/1/0100019b9df577b4-9e325909-0b3e-4609-84fb-39434ff9f034-000000/OyGUoethrw_B-lqGqc93Yy10cuv-LofsDEzdKds-l2U=439">
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<strong>OpenAI earmarks $50 billion for employee stock grant pool (1 minute read)</strong>
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OpenAI set aside an employee stock grant pool equivalent to 10% of the company last fall, when it was valued at $500 billion. The startup has already given $80 billion in vested equity. It is in preliminary talks with some investors about raising funds at a valuation of around $750 billion.
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<strong>Anthropic Raising $10 Billion at $350 Billion Value (3 minute read)</strong>
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Anthropic plans to raise $10 billion at a valuation of $350 billion. The funding round is expected to close in the coming weeks, and the total amount of the deal could change. AI companies collected a record $222 billion in funding last year. This year is slated to be a big year for public offerings.
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<h1><strong>Deep Dives & Analysis</strong></h1>
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.interconnects.ai%2Fp%2F8-plots-that-explain-the-state-of%3Futm_source=tldrai/1/0100019b9df577b4-9e325909-0b3e-4609-84fb-39434ff9f034-000000/uwl_pQsjQnB0ULV835In82UncIYJOkV1NzwW8Rxer6I=439">
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<strong>8 plots that explain the state of open models (6 minute read)</strong>
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Chinese companies are making strong, open AI models that are applying increasing pressure on the US AI economy. Dethroning Qwen in adoption in 2026 looks impossible, but there are areas of opportunity. The US could very well have the smartest open models again in 2026, even if they're used far less across the ecosystem. This post contains 8 plots that show the impact of Qwen, DeepSeek, Llama, GPT-OSS, Nemotron, and other new entrants to the ecosystem.
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<strong>Context is the next data platform—and why context graphs are key to understanding processes (9 minute read)</strong>
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Context graphs shed light on how work really gets done in the enterprise. AI agents are a major unlock with automation, but only if their reasoning is grounded in the right enterprise context. It's not enough to just understand the enterprise data, systems also need relationship knowledge. Context graphs enable agents to learn and automate distributed work by capturing processes and learning intent over time.
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<strong>Recursive Language Models and Context Folding (28 minute read)</strong>
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Recursive Language Model (RLM) is a flexible solution to long-context limitations in LLM agents. RLMs avoid summarization by delegating memory management to scripts and sub-models.
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<h1><strong>Engineering & Research</strong></h1>
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fmiro.com%2Fevents%2Fwebinars%2Fbridging-the-ai-implementation-gap%2F%3Futm_campaign=tldr-second%26utm_source=TLDR%26utm_medium=newsletter%26utm_content=Sponsorship%26src=-newsletter_glb/1/0100019b9df577b4-9e325909-0b3e-4609-84fb-39434ff9f034-000000/x152xWOiBitqsmWpOC86PvgMIOfuGjQrlNAJZ6KR344=439">
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<strong>Are AI tools focusing too much on individual productivity? (Sponsor)</strong>
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Most companies aren't getting the AI results they want. <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fmiro.com%2Fevents%2Fsecret-to-ai-success-forrester-study%2F%3Futm_campaign=tldr-second%26utm_source=TLDR%26utm_medium=newsletter%26utm_content=Sponsorship%26src=-newsletter_glb/1/0100019b9df577b4-9e325909-0b3e-4609-84fb-39434ff9f034-000000/OOCa5aOVnkcvkN3RQyX-5xkIEWpM-1TbHpxFTxg7Z_E=439" rel="noopener noreferrer nofollow" target="_blank"><span>This Forrester study</span></a> shows how an overemphasis on individual productivity, instead of collaboration, could be the culprit. For a look at how collaboration-first AI lives up to the hype, join the Miro webinar on January 22nd. <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fmiro.com%2Fevents%2Fwebinars%2Fbridging-the-ai-implementation-gap%2F%3Futm_campaign=tldr-second%26utm_source=TLDR%26utm_medium=newsletter%26utm_content=Sponsorship%26src=-newsletter_glb/2/0100019b9df577b4-9e325909-0b3e-4609-84fb-39434ff9f034-000000/x6r1yMdkXybo_PvnBDdKOhDxtxzYZJ-o7ug1uQvK66k=439" rel="noopener noreferrer nofollow" target="_blank"><span>Register now.</span></a>
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fhuggingface.co%2Fspaces%2FHuggingFaceFW%2FFinePDFsBlog%3Futm_source=tldrai/1/0100019b9df577b4-9e325909-0b3e-4609-84fb-39434ff9f034-000000/yrs9L4R3J3eph0b6cW-grVVm5gSqS6tdCw47ERcSlhU=439">
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<strong>FinePDFs: Liberating 3T of the finest tokens from PDFs (52 minute read)</strong>
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PDFs have been largely ignored by the open source AI community, despite being a source of high-quality data for pretraining. Hugging Face has created a new pretraining dataset of more than 3T tokens spanning over 1,000 languages extracted from PDFs. This post details how the dataset was created.
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<strong>Open Gaming Foundation Model (GitHub Repo)</strong>
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NitroGen is a generalist gaming agent trained on internet gameplay videos capable of predicting controller actions from pixels and adapting to unseen games through post-training.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Farxiv.org%2Fabs%2F2601.02439%3Futm_source=tldrai/1/0100019b9df577b4-9e325909-0b3e-4609-84fb-39434ff9f034-000000/OO_kjPgkBCIQhyHiH39p5nA_2XNN_0VhctU_RESBUHo=439">
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<strong>Visual Web Agents (29 minute read)</strong>
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WebGym is a large-scale environment with nearly 300,000 real-world web tasks for training visual agents. It has a high-throughput RL system that boosts training speed and a fine-tuned Qwen-3-VL that outperforms GPT-4o and GPT-5-Thinking on unseen web navigation tasks.
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<div style="text-align: center;"><span style="font-size: 36px;">🎁</span></div></div>
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<div style="text-align: center;"><strong><h1>Miscellaneous</h1></strong></div>
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<strong>Consumer AI Predictions (4 minute read)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
Screenless and always-on AI devices will fail, while mini-apps enable UGC personal software and a US consumer super-app. By 2030, consumer AI will split into a reliable task assistant and an AI companion, performance marketing will collapse, and AI-generated virtual creators will explode. The biggest win will go to whoever solves AI discovery beyond chat and search.
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<strong>Brendan Foody on Teaching AI and the Future of Knowledge Work (56 minute read)</strong>
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Brendan Foody, the youngest unicorn founder and CEO of Mercor, is transforming AI training by hiring experts like poets and economists to create evaluation frameworks for frontier models. Mercor pays top talent to teach AI models that are rapidly improving in economically valuable tasks.
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<strong>Google Supports Gemini-Powered Audio Lessons (1 minute read)</strong>
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Google now lets educators create podcast-style audio lessons using Gemini in Google Classroom.
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<strong>AI will compromise your cybersecurity posture (22 minute read)</strong>
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The breakneck speed at which AI tools are being integrated into existing systems and workflows without proper threat modeling, testing, and security analysis is dangerous.
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<strong>Building Threat Models with MCP and AI Agents (12 minute read)</strong>
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AI agents and MCP servers enhance threat modeling by automating the synthesis of organizational context, enabling security teams to map threats, identify detection gaps, and target high-priority threats efficiently.
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