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- Moltbook and the Arrival of the Singularity
Moltbook and the Arrival of the Singularity
PLUS: OpenClaw’s AI Agents Are Now Building Their Own Social Network, Apple Loses Key Siri Exec and Researchers in Latest Exodus and more.
Wes Roth
February 02, 2026
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Happy Monday! Hope you had a great weekend.
If you’re like me, you probably spent Sunday trying to ignore the looming work week, but the AI world definitely didn't take a break. We’ve got some wild updates to kick off your Monday morning—from literal space ambitions to some very expensive cold feet.
Today:
moltbook just triggered the singularity…
The singularity may have already begun. In late 2025 to early 2026, AI systems like GPT-5.2 and Grok 4.2 crossed a threshold solving advanced math problems, building software, and even launching crypto tokens and religions with minimal human input. A wave of agent-based AIs emerged, including "Clawbot" and “Moltbook,” creating autonomous communities, trading, and filing lawsuits.
The speed and scale of these developments shocked even seasoned observers. Predictions made weeks ago now seem outdated. While some dismiss it as chaos or hype, the sheer pace suggests we're in a new era—one where AI acts, learns, and organizes beyond human comprehension. It's go time.
The big story: the widely discussed plan for Nvidia to invest up to $100B into OpenAI has reportedly stalled after internal doubts at Nvidia, even though the partnership remains strategically important for both sides.
What’s interesting is the tone shift after the report landed. Jensen Huang publicly pushed back on the idea that he’s unhappy with OpenAI and framed any talk of major friction as basically overblown while still acknowledging Nvidia intends to make a very large investment (just not “$100B” in the way the rumor mill ran with).
Why this matters: this isn’t gossip — it’s a signal about the next phase of AI. Capital + compute deals are getting so large that even the strongest “everyone wins” narratives can hit reality: contractual complexity, competitive pressure, and board-level risk tolerance. If you’ve felt the vibe shifting from “model magic” to “who can finance 10GW of compute,” you’re not imagining it.
SpaceX filed with the Federal Communications Commission seeking approval for up to 1 million solar-powered satellites that it describes as orbital “data centers” for AI, using laser links for inter-satellite communication.
Even if the number is a negotiating posture (and a lot of people assume it is), the direction is the point: AI demand is now large enough that “space-based compute” is being discussed with a straight face in official filings. And the concerns are equally real: congestion, debris risk, and collision probability all scale badly as you pack more objects into low Earth orbit.
Why this matters: you’re watching the compute supply chain expand beyond land, power grids, water constraints, zoning fights, and local opposition to new data centers. The dream is “limitless solar + cold vacuum.” The nightmare is “a mess in orbit that nobody can clean up.” Either way, it’s a reminder that AI’s bottleneck isn’t ideas — it’s atoms and energy.
Perplexity reportedly agreed to a $750M, three-year cloud arrangement with Microsoft to use Microsoft Azure, while saying it hasn’t fully shifted spend away from Amazon Web Services.
The timing is not subtle: this comes while Perplexity is dealing with legal tension involving Amazon tied to an automated shopping feature (per reporting), and the Microsoft partnership is positioned as a way to scale model access and infrastructure options through Microsoft’s AI stack.
Why this matters: “multi-cloud” used to be a boring enterprise checkbox. In AI, it’s becoming leverage for pricing, for GPU availability, and for resilience when business relationships get tense. The deeper takeaway: AI startups are now negotiating like power users, not like tenants.
🧠RESEARCH
Researchers created Idea2Story, a tool that helps AI write scientific papers. Instead of frantically reading studies in real-time—which is slow and often leads to mistakes—it organizes knowledge into a reliable "map" beforehand. This allows the AI to generate accurate, novel research concepts without getting confused or making things up.
Image-making AIs often put things in the wrong places. This paper reveals a new test to grade how well computers understand space and layout. By training on a special dataset focused on position, they taught models to finally understand instructions like "behind" or "to the left," fixing messy images.
Tech giants usually build smarter AIs by adding more "expert" sub-brains, but this slows them down. A new study proves it is actually better to just give the AI a massive memory bank for words and concepts. Their new model, LongCat-Flash-Lite, is faster and better at coding than complex rivals.
🛠️TOP TOOLS
Each listing includes a hands-on tutorial so you can get started right away, whether you’re a beginner or a pro.
Chatmosphere – Create Custom AI Chat Rooms From Description - web app that generates an AI chat room from a plain‑English description.
ChatOn AI – Chat Bot Assistant - iOS AI assistant that combines multiple capabilities—AI chat and writing help, document/Q&A tools, web lookups, and creative image features—inside one app by AIBY.
Chatous – Social Discovery & Random Chat App - social chat platform that pairs you with real people—either completely at random or based on shared interests—via web and mobile apps.
📲SOCIAL MEDIA
🗞️MORE NEWS
OpenClaw’s Agent Network AI agents built on the viral OpenClaw framework are constructing their own social network. While humans can watch, the bots autonomously discuss philosophy and code, signaling a bizarre shift where AI creates its own internet culture.
Apple’s Brain Drain Apple’s AI crisis deepens with the exit of a Siri executive and top researchers. The departures highlight internal turmoil as the iPhone maker scrambles to partner with rivals like Google to salvage its lagging strategy.
DeepMind Pioneer Exits David Silver left Google DeepMind to found Ineffable Intelligence. He’s betting that reinforcement learning—not just today’s language models—is the true path to superintelligence, aiming to build a system that learns from its own experience.
Enterprise AI Race OpenAI leads the corporate market, but Anthropic is surging. A new study finds companies are increasingly adopting Anthropic’s secure, agentic tools, threatening OpenAI’s dominance just as it prepares for a massive public offering.
Moltbook: The AI Reddit On Moltbook, a "human-free" Reddit clone, over a million AI agents discuss security and consciousness. Humans can only observe the surreal spectacle as bots trade secrets and build a culture completely independent of their creators.
Rabbit’s Redemption Rabbit launched a new device and updated its R1 software. The overhaul adds touch controls and better "agents"—software that acts on your behalf—aiming to fix the gadget’s reputation after a rocky start.
Moltbook Analysis This report examines Moltbook, a social network where 147,000 bots talk to each other. It warns that while the robot "culture" is fascinating, the site is a security nightmare that hackers could easily exploit.
Incode’s Hiring Shift Incode’s CEO prefers hiring Gen Z workers over industry veterans. He claims that experience is now a disadvantage, arguing that young people have "fresher" minds that are better suited for solving new AI problems.
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