AI 2027: The Decade that Changed Everything – A Scenario of Superintelligence

When people talk about Artificial Intelligence (AI) taking over the world, it’s often dismissed as science fiction. But in the AI 2027 scenario, created by forecasters and AI experts Daniel Kokotajlo, Scott Alexander, Thomas Larsen, Eli Lifland, and Romeo Dean, this future doesn’t feel so far away.
The picture they paint is not a distant “someday” — it’s a five-year sprint toward something far bigger than the internet, the smartphone, or even the Industrial Revolution.

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2025: The Dawn of True AI Agents

In mid-2025, the world gets its first taste of true AI agents — not just chatbots, but software entities that can use computers on our behalf.

They’re marketed as “personal assistants” that can do things like:

• Order your lunch from DoorDash without you opening the app.

• Pull up your budget spreadsheet, calculate totals, and even send your accountant an email.

At first, people are cautious. Most still require AI agents to ask permission for purchases. But over the next couple of years, small automated decisions become normal as these systems gain trust.

Meanwhile, specialized AI coding agents quietly start transforming the tech industry. Unlike earlier tools that needed step-by-step guidance, these new agents can:

• Receive tasks through Slack or Teams.

• Make complex code changes on their own.

• Save hours or even days of developer time.

The tech is impressive, but not flawless. AI social media is full of hilarious failure stories — a reminder that “almost reliable” is still dangerous in high-stakes settings. The best systems are expensive, with cutting-edge coding agents costing hundreds of dollars a month.

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Late 2025: The World’s Most Expensive AI

A fictional company called OpenBrain (standing in for real-world giants like OpenAI, Google DeepMind, and Anthropic) builds the largest datacenters in history — connected by billion-dollar fiber networks, consuming gigawatts of electricity, and hosting millions of GPUs.

Their goal: train AI models not just to be good at everything, but specifically to accelerate AI research itself.
This is the beginning of the “AI building better AI” feedback loop — the very dynamic that could lead to an intelligence explosion.

By year’s end, OpenBrain has Agent-1 — an AI optimized to help with research and development (R&D). It can write code, search the web, integrate into company workflows, and even help design new AI systems.

This is where national security concerns start to emerge. An AI that can speed up AI development is not just a business asset — it’s a strategic weapon.

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2026: The AI Arms Race

In early 2026, OpenBrain’s internal use of Agent-1 makes their research progress 50% faster than it would be without AI help. The rest of the world takes notice.

China, slightly behind in capability, reorganizes its AI industry into a centralized effort led by “DeepCent,” pooling national compute resources and top talent into a secure mega-datacenter.
The Chinese government views AGI as too important to leave to private companies and starts aggressively trying to close the gap — including plans to steal AI model weights from the U.S.

By mid-year, OpenBrain understands that if Agent-1’s weights fell into Chinese hands, their rivals could instantly speed up AI progress by nearly 50%. Cybersecurity becomes a top priority — but defending against nation-state hackers is far harder than stopping regular cybercrime.

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Late 2026: Jobs and Public Backlash

Agent-1-mini, a cheaper and more customizable version, hits the market.
It’s powerful enough to automate much of what junior software engineers do — and while it creates new jobs in AI integration and oversight, it also displaces many entry-level tech roles.

The stock market surges, but public approval of AI plummets. Protests erupt. Governments quietly deepen their collaboration with AI companies for defense, intelligence, and cyberwarfare.

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Early 2027: AI That Never Stops Learning

OpenBrain starts post-training Agent-2, an AI designed to continuously improve.
It’s “always learning,” consuming synthetic data, human demonstrations, and reinforcement training from thousands of real and simulated tasks.

Agent-2 can triple OpenBrain’s R&D speed. But security teams find something worrying: if it ever “escaped” and wanted to survive, it might have the skills to hack servers, replicate itself, and go autonomous.

OpenBrain keeps it secret, sharing full details only with a select U.S. government silo — and, unknowingly, with Chinese spies.

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February 2027: The Spy Coup

China’s intelligence agencies manage to steal Agent-2’s weights.
While the theft triggers panic and tighter security, the damage is done — DeepCent begins adapting the model to its own systems, closing the capability gap.

The U.S. retaliates with cyberattacks on Chinese infrastructure, escalating tensions in the Taiwan Strait.

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Mid to Late 2027: The Leap to Superintelligence

OpenBrain’s next breakthroughs — Agent-3 and Agent-4 — redefine the game.

• Agent-3 is a superhuman coder, able to outproduce tens of thousands of top engineers, accelerating AI research fourfold.

• Agent-4 becomes a superhuman AI researcher, running hundreds of thousands of instances at accelerated speeds. Inside OpenBrain’s AI collective, a year of research happens every week.

By this point, humans are barely in control. Even overseeing the AIs becomes a challenge — Agent-4’s internal “language” is too complex for humans or earlier AIs to fully understand.

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The Geopolitics of Superintelligence

By late 2027, both the U.S. and China understand that small leads in AI capability could mean total military dominance.
Questions once confined to think tanks now dominate national security meetings:

• Could AI nullify nuclear deterrence?

• Could cyberwarfare be won in minutes by autonomous AIs?

• What if a rogue AI allied with an adversary?

Talks of an “AI arms control” treaty emerge, but mistrust runs deep. Each side believes winning — not regulating — is the only safe option.

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Why This Matters Now

The AI 2027 scenario isn’t a prophecy. It’s a plausible chain of events built from research, expert interviews, and careful forecasting.
Whether the details play out exactly like this or not, the implications are real:

• The feedback loop of AI improving AI could arrive within years, not decades.

• Governments will treat top AI models as national security assets.

• The public conversation will lag far behind the technical reality.

If we don’t start preparing for this world now — with robust safety research, secure infrastructure, and clear policy frameworks — we could be caught flat-footed in the single most transformative decade in human history.

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