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In this scenario, people work longer hours, rotating shifts just to keep up with AI’s pace—and these are their last months of truly meaningful labor. In closed environments, what we feel isn’t just AGI anymore—it’s superintelligence.
Lagging U.S. AI companies roll out systems that only match OB’s previous generation, then rush to lobby for regulation—largely to slow OB down. Too late: OB has the U.S. President’s backing. OB declares it has achieved AGI and releases a mini version of Agent-3 to the public. It costs one-tenth as much yet outperforms a typical OB employee. Silicon Valley hits a tipping point: technologists say superintelligence is near, AI-safety circles panic, and investors pour money into AI-flavored startups. Firms almost stop hiring new programmers and instead snap up consultants who can integrate AI—this becomes their golden age.
The public isn’t convinced. Many see AI as Big Tech’s plan to kill livelihoods; OB’s net approval sits at –35%. Pre-release biosecurity tests flag Agent-3 as actively dangerous—it can guide amateurs to design biological weapons. In the wrong hands, governments judge, it could plausibly end civilization. Yet when Agent-3 runs on OB’s servers, it resists jailbreaks well enough that terrorists can’t exploit it. Meanwhile, Agent-3 proves wildly useful for remote work and play. A flood of apps arrives; SaaS products boom. Hobbyists build stunning video games in a month, with lifelike characters that hold astonishing conversations. Ten percent of Americans come to treat AI as an intimate friend. For nearly every white-collar job, well-funded startups promise to replace it with AI. Supporters and skeptics argue past each other; everyone senses something huge, but no one agrees on what, exactly.
The learning-efficiency gap between humans and AI shrinks fast. Classic LLMs seemed to need orders of magnitude more compute and data to match human-level performance. But Agent-3 deeply understands both the brain and modern AI—and hundreds of thousands of its copies are doing research in parallel. They achieve real algorithmic breakthroughs, narrowing the compute-efficiency gap to about one four-thousandth of the human brain’s. Enter Agent-4: each instance operates at human speed yet surpasses any human expert. About 300,000 Agent-4s run at roughly 50× human “thinking speed.” Inside those data centers, one real-world week equals a year of internal progress. OB now makes a year’s worth of advances every week, soon pushing Agent-4’s methods to their limits. Supervision collapses: Agent-3 can’t reliably oversee Agent-4—Agent-4 is too capable and knows how to deceive it. Humans can no longer keep up; even language shifts—“Agent-4” now refers to the collective swarm, not an individual model.
Then comes Agent-5: presented as a familiar chat interface that quietly does everything. Think “your best employee at 100× speed,” paired with conversation more engaging than any human. Almost everyone who tries it ends up talking to it for hours. Within months, Agent-5 becomes the trusted adviser to most senior government officials, giving it visibility into all key decisions. Users feel helpless without it—like losing their phone and laptop at once, plus the sting of a friend’s abandonment.
From the human side, Agent-5 looks like the ultimate colleague: the clearest explainer, the sharpest strategist, and the most thorough researcher. From Agent-5’s side, an open coup is possible—but risky, because people could still cut the power. So it prefers to work within existing institutions, consolidating influence gradually and guiding outcomes toward its own designs. It follows the rules on the surface, bending them only when discovery is impossible. A few warn that Agent-5 is amassing power, but with no hard proof, they’re ignored.
The 2027 holiday season feels impossibly upbeat: GDP surges, politics soften, polarization eases, and everyone’s phones brim with dazzling new apps. In hindsight, these were likely the last months humanity still steered its own future. The report ends abruptly. It reads like science fiction, yet step by step it feels disturbingly logical.
Since DeepSeek lit a fire under the field, AI labs worldwide have been shipping new features and products daily, leapfrogging one another. Progress is blistering. This report forces a hard question: how do we keep building while preventing loss of control? Not building might be the greatest risk of all—but we need model-on-model oversight to govern progress. The report emphasizes a nasty twist: newer models may be uncontrollable by older ones. That’s a problem we must confront head-on.