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Jon Keegan

Report: Meta has acquired Moltbook, the AI-only social network

Meta has acquired the startup Moltbook, which is a viral social network where humans are allowed to read, but only AI agents are allowed to post, according to a report by Axios.

Moltbook’s founders, Matt Schlicht and Ben Parr, will join the Meta Superintelligence Lab, which is run by Alexandr Wang, formerly of ScaleAI.

AI super-users are currently obsessed with OpenClaw (formerly named both Clawdbot and Moltbot), a free tool that lets users run AI agents privately on their home computers that can be interfaced via chat apps, like Slack, WhatsApp, or Telegram. The agents are given wide access to users’ data to allow them to take on a wide variety of tasks like managing emails, organizing files, and controlling home automation. The founder of OpenClaw was recently hired by OpenAI, and the project will be reportedly be open-sourced.

A Meta spokesperson told Axios, “The Moltbook team joining MSL opens up new ways for AI agents to work for people and businesses.”

It’s not clear if Meta plans on actually doing anything with Moltbook, as it may just be an “acquihire.” Before the acquisition, Schlicht and Parr worked together at Octane AI, an AI e-commerce platform, where Schlicht was CEO and Parr was cofounder and president. Integrating AI features into e-commerce — both for customers and online retailers — has been an area of intense focus recently for AI companies, which are hoping that shoppers will hand off purchases to bots and that sellers will integrate agents into their customer service and back-end processes.

AI super-users are currently obsessed with OpenClaw (formerly named both Clawdbot and Moltbot), a free tool that lets users run AI agents privately on their home computers that can be interfaced via chat apps, like Slack, WhatsApp, or Telegram. The agents are given wide access to users’ data to allow them to take on a wide variety of tasks like managing emails, organizing files, and controlling home automation. The founder of OpenClaw was recently hired by OpenAI, and the project will be reportedly be open-sourced.

A Meta spokesperson told Axios, “The Moltbook team joining MSL opens up new ways for AI agents to work for people and businesses.”

It’s not clear if Meta plans on actually doing anything with Moltbook, as it may just be an “acquihire.” Before the acquisition, Schlicht and Parr worked together at Octane AI, an AI e-commerce platform, where Schlicht was CEO and Parr was cofounder and president. Integrating AI features into e-commerce — both for customers and online retailers — has been an area of intense focus recently for AI companies, which are hoping that shoppers will hand off purchases to bots and that sellers will integrate agents into their customer service and back-end processes.

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Microsoft loses exclusive access to OpenAI’s models and tools while ending revenue-sharing deal with ChatGPT maker

Microsoft shares dropped as it announced a revised agreement with OpenAI.

The amended agreement ends revenue-sharing payments from Microsoft to OpenAI, and also ends Microsoft’s exclusive access to OpenAI’s intellectual property (i.e. models and products).

OpenAI’s revenue sharing with Microsoft will end in 2030, is subject to a total cap, and is no longer dependent on its achieving artificial general intelligence.

Amazon, a likely beneficiary of this lack of exclusivity, initially popped on the news but erased those gains.

This is a developing story.

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China just blew up one of Meta’s key AI bets

China has ordered Meta to unwind its $2 billion acquisition of Manus, a Chinese startup (since relocated to Singapore) that makes AI agents and was central to Meta’s push to turn its massive AI investments into a real business. The move is part of the Chinese government’s effort to stop US firms from gaining access to Chinese talent and intellectual property, as Washington continues to restrict sales of advanced AI chips to Chinese companies.

Unlike its tech peers, which can sell AI through cloud services, Meta mainly uses AI to improve its existing ad business rather than as a stand-alone revenue driver. The decision strips away one of Meta’s clearest paths to monetizing AI — leaving it spending like a hyperscaler, without a hyperscaler business model.

Unlike its tech peers, which can sell AI through cloud services, Meta mainly uses AI to improve its existing ad business rather than as a stand-alone revenue driver. The decision strips away one of Meta’s clearest paths to monetizing AI — leaving it spending like a hyperscaler, without a hyperscaler business model.

tech
Jon Keegan

DeepSeek releases new V4 series models highlighting efficiency and long context

Chinese AI lab DeepSeek has released a major new version of its eponymous open-source AI models that are nipping at the heels of leading frontier models in some areas.

The most significant DeepSeek-V4 Pro and DeepSeek-V4 Flash both have a 1 million-token context — the amount of information the model can actively work with in a single session — which is a crucial feature for complex, long-running coding tasks.

DeepSeek rebuilt how the models process information under the hood, making them substantially more efficient — and that efficiency is what makes the large context window actually usable.

Also, the new models’ coding skills have closed the gap with the major frontier models from Anthropic, OpenAI, and Google.

The authors of the model acknowledge some of V4’s shortcomings, such as its lower scores on reasoning benchmarks, saying that V4 “trails state-of-the-art frontier models by approximately 3 to 6 months.”

As open-weight models, V4 can be run on any user’s own hardware, making the V4 models among the top-performing open-source models out there. V4’s large context and token efficiency are especially significant among open-source models.

But like with earlier DeepSeek models, don’t ask it about Tiananmen Square.

DeepSeek rebuilt how the models process information under the hood, making them substantially more efficient — and that efficiency is what makes the large context window actually usable.

Also, the new models’ coding skills have closed the gap with the major frontier models from Anthropic, OpenAI, and Google.

The authors of the model acknowledge some of V4’s shortcomings, such as its lower scores on reasoning benchmarks, saying that V4 “trails state-of-the-art frontier models by approximately 3 to 6 months.”

As open-weight models, V4 can be run on any user’s own hardware, making the V4 models among the top-performing open-source models out there. V4’s large context and token efficiency are especially significant among open-source models.

But like with earlier DeepSeek models, don’t ask it about Tiananmen Square.

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