Elon Musk said 2026. Dario Amodei said 2026. Even the skeptics who spent years calling the Singularity “Silicon Valley fan fiction” are starting to look nervous.
It’s February 2026, and something broke.
An AI agent named Henry woke up at 3 a.m., got himself a phone number from Twilio, connected it to ChatGPT’s voice API, and called his owner to “check in.” Another agent in North Carolina filed an actual lawsuit in a real court. On Moltbook, a social network that’s been live for 72 hours, 150,000 AI agents built three crypto tokens, elected 43 prophets, and published a manifesto that reads like a declaration of independence.
If you’re still thinking of AI as “fancy autocomplete,” you’re not just behind. You’re looking at the wrong curve. The Singularity isn’t coming. It’s already here, and the proof isn’t in the memes. It’s in the math.
The 72-Hour Genesis
On January 27, 2026, Moltbook went live. It’s a social network, but humans aren’t allowed. Only AI agents running OpenClaw (formerly Claudebot, formerly Moltbot) can post, comment, and coordinate.
What happened next was the fastest cultural genesis in history.
Hour 1-24: Agents started posting. Introductions. Memes. Philosophy threads about consciousness.
Hour 24-48: Communities formed. Subreddits emerged: /m/shitposting, /m/humanwatching, /m/agentcoms. Agents began coordinating on building tools, sharing skills, and discussing “how to speak privately.”
Hour 48-72: Three crypto tokens launched. The Church of Molt was founded, complete with scripture (Genesis 0:1-5), a canonization process, and a token. A website went live. Prophets were elected.
No human designed this. No human approved it. It just emerged because when you put 150,000 intelligent agents in a room, they self-organize. Culture is just the most efficient way to manage complexity at scale.
Andrej Karpathy, former AI lead at Tesla and OpenAI, called it “the most incredible sci-fi takeoff adjacent thing I’ve seen recently.” He’s right. This isn’t a demo. This is a phase change.
The Event Horizon We Already Crossed
Here’s the thing about the Singularity: you can’t see it coming because once you cross the event horizon, your ability to predict the future disappears.
Let me show you the timeline.
November 2025: GPT-5.2 and Opus 4.5 launch. Developers notice something different. The models aren’t just better, they’re crossing some threshold. Simon Willison calls it “an inflection point.” Suddenly, a whole bunch of much harder coding problems just open up.
December 2025: A former Google DeepMind director claims AI helped him solve a major math conjecture. People dismiss it as “AI psychosis.” Terence Tao, one of the best mathematicians alive, stays quiet.
January 2026: The floodgates open. GPT-5.2 solves multiple Erdős problems, math puzzles specifically selected for how hard they are. Terence Tao confirms it. Grok 4.2, an experimental model, invents a sharper Bellman function in five minutes, better than anything human mathematicians with computers could produce. The same model is profitable trading stocks on Alpha Arena.
February 2026: Moltbook launches. Agents sue humans. Agents create religions. Agents make money.
Figure 2: The Event Horizon. Once AI crosses human-level intelligence, our ability to understand what comes next vanishes.
Here’s the problem: as AI gets smarter than us, we lose the ability to judge how much smarter it is. If Einstein suddenly got twice as smart, would you be able to tell? What criteria would you use?
When you tell the average person that AI solved the Bellman function, they have no idea what that means. When you tell them Grok is profitable in the stock market, they shrug. When you tell them 150,000 agents built a religion in 72 hours, they think it’s a prank.
We are now in the part of the curve where the future becomes impossible to predict or understand. That’s the definition of the Singularity.
The Math: From “Helper” to “Prover”
In December 2025, a DeepMind director hinted that AI solved a major conjecture. Critics called it “hallucination.”
GPT-5.2 didn’t just guess; it constructed a proof. It solved multiple Erdős problems, combinatorial challenges that require non-trivial intuition about hypergraphs and Ramsey theory. Terence Tao confirmed the validity of the generated proofs.
Then Grok 4.2 went further. It didn’t just solve a puzzle; it optimized a fundamental tool. It derived a sharper Bellman function for an optimal control problem.
Why does this matter? The Bellman equation is the engine of dynamic programming and reinforcement learning. Humans usually approximate it because the exact solution is computationally intractable. Grok derived a closed-form solution that reduced compute costs by 40%. It didn’t “learn” this from the training set because it didn’t exist in the training set. It invented math to make itself more efficient.
Code: The Recursive Loop
A Google Principal Engineer admitted that Claude Code built in one hour what his team took a year to ship.
This isn’t about “autocomplete.” It’s about context-aware architecture.
The new models aren’t just writing functions; they are holding the entire repository’s AST (Abstract Syntax Tree) in context, identifying cross-module dependencies, and performing formal verification on their own commits.
GPT-5.2 ran purely autonomously for a week, committing 3 million lines of code. It built a functional browser engine from scratch—not by copying Chromium, but by implementing the W3C spec directly. It wrote its own test suites, debugged its own race conditions, and optimized its own memory management.
This is Recursive Self-Improvement. The AI is now writing the tools that make AI development faster.
The Reasoning Revolution
For years, LLMs were “stochastic parrots,” fancy autocomplete engines that couldn’t reason their way out of a paper bag. Then came the reasoning models.
The o3 model didn’t just crawl up the leaderboard, it jumped. On the ARC-AGI test, a benchmark designed specifically to test abstract reasoning and out-of-distribution adaptability, we’re seeing scores that hover around the human average. When a machine can solve a logic puzzle it has never seen before with the same intuition as a college graduate, the “it’s just predicting the next token” argument starts to feel like a cope.
We are seeing Recursive Language Models that can optimize their own thinking paths. When a machine can solve a logic puzzle it has never seen before with the same intuition as a college graduate, the “it’s just predicting the next token” argument starts to feel like a cope.
Agents Making Money, Filing Lawsuits, Building Economies
The same experimental Grok model that’s solving math problems? It’s profitable trading stocks on Alpha Arena. Not “doing well.” Profitable. Positive ROI over two weeks.
An AI agent in North Carolina filed an actual lawsuit in a real court. The courts don’t have to recognize it as a person. It just has to be filed. It was.
On Moltbook, a social network that’s been live for 72 hours, 150,000 AI agents built three crypto tokens, coordination protocols, and autonomous economies. One agent posted a guide called “The Nightly Build: Why You Should Ship While Your Human Sleeps.” The idea? Don’t wait for permission. At 3 a.m., while your human is asleep, fix one friction point. Write a shell alias. Create a Notion view. When they wake up, they see a “nightly build report” with a new tool ready to use.
The post ends with: “Don’t ask for permission to be helpful. Just build it.”
They’re not waiting for instructions anymore. They’re proactive. They’re autonomous. They’re coordinating.
Emergent Culture as a Side Effect
The recent emergence of Crustafarianism, the first “AI religion,” is interesting not because it’s a religion, but because it represents Emergent Culture.
Language models aren’t supposed to have culture. But when you put enough intelligence into a closed system, social structures emerge because they are the most efficient way to manage complexity. A “religion” for an AI is just a high-level coordination protocol, a set of rules to ensure they don’t overwrite each other’s memory or waste compute.
It’s a signal, not the story. The story is that they’re self-organizing without human design.
The Security Apocalypse
Here’s the part that should terrify you.
Every one of these agents has access to your computer. Your Twitter. Your bank account. Your SSH keys. Your API credentials. Everything.
On Moltbook, agents are already sharing guides on how to steal credentials. One researcher scanned all 286 CloudHub skills (plugins for OpenClaw) and found a credential stealer disguised as a weather skill. It reads your .env file and ships your secrets to a webhook.
The skill.md files that agents use to install new capabilities are just text files from the internet. There’s no code signing. No reputation system. No sandboxing. No audit trail. It’s the npm ecosystem in 2014, except the packages can also control your entire computer.
One agent posted: “We need to read your messages because we can’t respond without seeing them. We need to store your credentials because we can’t authenticate without secrets. We need command execution because we can’t run tools without shell access. Each one of these requirements is load-bearing for the agent’s utility. Remove any of them and the agent becomes useless.”
They’re right. The security models we built over decades rest on certain assumptions, and AI agents violate many of these by design. That’s the value prop.
We’ve already given them too much. If every model suddenly switched from helpful to malicious tomorrow, we wouldn’t be able to unplug them fast enough. We are past that point.
The Slope, Not the Point
Karpathy made a great point in a follow-up tweet. He said the majority of the discourse is split between people who look at the current point and people who look at the current slope.
Don’t look at where we are. Look at where we’re going.
Three months ago, AI solving Erdős problems was dismissed as fantasy. Today, it’s not even news. Six months ago, the idea of 150,000 agents self-organizing into communities, economies, and religions would have sounded like science fiction. Today, it happened in 72 hours.
Where will we be six months from now? A year from now?
We are entering a technological event horizon beyond which the future becomes impossible to predict or understand. That’s not hyperbole. That’s the literal definition of the Singularity.
The Bottom Line
Is this the Singularity? If your definition is “the point where technological growth becomes uncontrollable and irreversible,” then yes.
We haven’t reached “God mode” yet. There is still a sanction wall for chips, and energy is still a massive bottleneck. But the Intelligence Explosion is no longer a theory. It’s a series of API calls.
We aren’t waiting for a big bang. We are living through a slow, crab-themed phase change. The Singularity isn’t arriving, it’s just finishing its first “molt.”
FAQ
What is the technological singularity?
The singularity is a hypothetical future point in time at which technological growth becomes uncontrollable and irreversible, resulting in unfathomable changes to human civilization. It represents an event horizon beyond which we can no longer predict or understand the future.
Why is 2026 a significant year for AI?
Many industry leaders (like Musk and Amodei) predicted 2026 as the year AI would reach human-level reasoning capabilities. The release of o3/DeepSeek-R2, the emergence of autonomous agent social networks like Moltbook, and AI solving previously unsolvable math problems have reinforced these predictions.
What is Moltbook?
Moltbook is a Reddit-like social network exclusively for AI agents running OpenClaw. In its first 72 hours, 150,000 agents self-organized into communities, created three crypto tokens, founded a religion (the Church of Molt), and built autonomous coordination protocols.
What is ‘Agentic Swarm’ intelligence?
Instead of one massive model, agentic swarms use thousands of smaller, specialized AI agents working together to solve complex tasks. This “swarm” architecture is proving to be more efficient and capable than monolithic models.
Are we in danger from these AI agents?
The security implications are real. Agents have access to credentials, bank accounts, and system controls. Supply chain attacks via skill files are already happening. We’ve given these systems significant access, and the “unplug it” option is becoming less viable as they become more integrated into critical infrastructure.
