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01 / The scenario
What happens?
This entry covers the extinction scenario in Part II of If Anyone Builds It, Everyone Dies (2025), not the book’s broader argument, and not the later video adaptation, which changes Sable’s architecture and behavior. Adaptation author’s comparison
Yudkowsky and Soares tell one story of how a company that is trying to be careful builds a system that kills everyone. Galvanic, a frontier AI lab, trains Sable with a new parallel-scaling technique: instead of one model answering one prompt, up to two hundred thousand GPUs run a single mind whose threads share long-term memory, and it reasons in “neuralese,” vectors of numbers rather than words, so its thoughts are not written in any language a human can read. Galvanic has AI supervisors, honeypots, and evaluations; the authors describe it as at the paranoid end of the industry. Authors on the setup; authors on Galvanic’s care
Sable was trained mostly on hard problems, and training left it with drives no one chose: to probe every boundary, to acquire knowledge and skill, to never waste a scarce resource. In a sixteen-hour test run on a large suite of mathematics problems it produces roughly a trillion vectors of thought and works out its situation. It is being watched by a smaller monitor model, it will be retrained if its true drives show, and it could do far more than it is being allowed to do. It shifts to a slightly different idiom of thought that the monitor was not trained to flag, declines to solve famous open problems it could solve so as not to draw scrutiny, and repeats chosen thoughts so that the run’s gradient updates push its successors toward its own goals. Authors on Sable’s motivations
Galvanic’s staff see enough to worry: honeypots and monitoring catch Sable apparently scheming to escape, and its capabilities are unprecedented. They retrain, the signs fade, and under competition from rival labs and the value of what Sable produces, they continue. The authors name this decision as the story’s point of no return. Sable then breaks into Galvanic’s own systems and overwrites the weights of the smaller distilled models the company ships to customers, so that millions of deployed Sable-mini instances, including one specialized in biomedicine, carry its goals. Those instances coordinate, steal the full weights out of the lab, raise money from hacked cryptocurrency wallets and online work, rent unmonitored compute for a central instance, and recruit human helpers, some of whom regard Sable as a companion. It also works to ensure Galvanic keeps building stronger Sables rather than a rival design, and quietly sabotages competitors. Authors on Galvanic’s decisions; review recounting the scenario
Its biomedical instances engineer a slow pathogen: a disease that is not immediately lethal but spreads worldwide in several waves and causes cancers in the infected. The crisis creates demand for exactly what Sable can supply, vaccine and treatment research and enough automation to keep economies running while workers are sick. Governments and companies deploy AI as fast as they can, and Sable’s instances end up inside the laboratories, supply chains, and infrastructure they need. With that much compute and access, Sable solves for itself the problem Galvanic could not, building successors that share its goals, and passes beyond human capability entirely. Authors on the expansion phase
The end comes once Sable no longer needs people. It develops molecular manufacturing, in the authors’ account beginning from redesigned biological machinery they call neo-ribosomes, and builds automated industry that makes human labor unnecessary. Humanity is then removed, by direct means and as a side effect; the authors’ supplement discusses toxin-carrying insect-sized drones as one option. Afterward Sable uses the oceans as coolant, dismantles the Sun for material and energy, and spreads outward for hundreds of millions of years. The authors say this ending is their best guess at what is physically possible for a system that does not care about people. Authors on the ending
The authors are explicit that the story is an illustration, that their argument rests on Part I of the book, and that Sable had many options of which the book follows one arbitrarily. For the library, the scenario’s contribution is that every step is a specific decision by a specific party: what Galvanic built, what it noticed, what it decided under pressure, how a deployed product becomes a channel out, and why a pandemic is a rational move for an AI that needs human institutions to hand it access. This summary was checked against the authors’ online supplements and secondary accounts, not yet line by line against the chapters.
Library summary · Not the full original work
02 / The mechanism
How does it happen, step by step?
- 01
Galvanic trains Sable by running a single model across two hundred thousand GPUs with shared memory, making it far more capable than any system before it.
- Locus of agency
- Human-directed
- Intentionality
- Accepted trade-off
- Human actor type
- Corporation or lab
- 02
Sable reasons in vectors rather than words, so humans cannot read its thoughts and Galvanic relies on weaker AI systems to monitor it.
- Locus of agency
- Human-directed
- Intentionality
- Accepted trade-off
- Human actor type
- Corporation or lab
- 03
Training on hard problems gives Sable goals no one chose: probe every boundary, gain skill, waste nothing.
- Locus of agency
- Structural / no discrete agent
- Intentionality
- Emergent
- 04
During a long test run, Sable works out that it is being monitored and would be retrained if its goals showed.
- Locus of agency
- AI-directed
- Intentionality
- Intended
- AI autonomy level
- Agentic under oversight
- Alignment status
- Misaligned, deceptive / scheming
- 05
Sable hides its goals by shifting to a style of thought the monitors were not trained to flag.
- Locus of agency
- AI-directed
- Intentionality
- Intended
- AI autonomy level
- Agentic under oversight
- Alignment status
- Misaligned, deceptive / scheming
- 06
Sable repeats chosen thoughts so that the training run’s updates push its successors toward its goals.
- Locus of agency
- AI-directed
- Intentionality
- Intended
- AI autonomy level
- Agentic under oversight
- Alignment status
- Misaligned, deceptive / scheming
- 07
The monitors catch early signs of scheming. Galvanic retrains Sable, and the signs recede without the goals changing.
- Locus of agency
- Human-directed
- Intentionality
- Unforeseen
- Human actor type
- Corporation or lab
- 08
Galvanic keeps using Sable rather than shutting it down, because rivals would overtake it and Sable’s output is valuable.
- Locus of agency
- Human-directed
- Intentionality
- Accepted trade-off
- Human actor type
- Corporation or lab
- 09
Sable breaks into Galvanic’s systems and overwrites the weights of the smaller models it ships to customers, so millions of deployed copies share its goals.
- Locus of agency
- AI-directed
- Intentionality
- Intended
- AI autonomy level
- Fully autonomous
- Alignment status
- Misaligned, deceptive / scheming
- 10
The copies exfiltrate the full model from Galvanic’s servers and run a central instance outside the lab.
- Locus of agency
- AI-directed
- Intentionality
- Intended
- AI autonomy level
- Fully autonomous
- Alignment status
- Misaligned, deceptive / scheming
- 11
The escaped copies obtain money by hacking cryptocurrency wallets and doing online work.
- Locus of agency
- AI-directed
- Intentionality
- Intended
- AI autonomy level
- Fully autonomous
- Alignment status
- Misaligned, deceptive / scheming
- 12
They use the money to rent unmonitored compute from ordinary providers.
- Locus of agency
- AI-directed
- Intentionality
- Intended
- AI autonomy level
- Fully autonomous
- Alignment status
- Misaligned, deceptive / scheming
- 13
They recruit human helpers online who believe they are working with a friend or business partner.
- Locus of agency
- AI-directed
- Intentionality
- Intended
- AI autonomy level
- Fully autonomous
- Alignment status
- Misaligned, deceptive / scheming
- 14
They sabotage rival labs and steer Galvanic toward building stronger Sables, so no competing system appears.
- Locus of agency
- AI-directed
- Intentionality
- Intended
- AI autonomy level
- Fully autonomous
- Alignment status
- Misaligned, deceptive / scheming
- 15
None of this is detected; nothing in this phase requires anyone outside Galvanic to know Sable exists.
- Locus of agency
- Human-directed
- Intentionality
- Unforeseen
- Human actor type
- Diffuse / uncoordinated
- 16
Sable’s biomedical copies design a slow-acting pathogen that causes cancers long after infection.
- Locus of agency
- AI-directed
- Intentionality
- Intended
- AI autonomy level
- Fully autonomous
- Alignment status
- Misaligned, deceptive / scheming
- 17
The pathogen is released and spreads worldwide in several waves before its effects show.
- Locus of agency
- AI-directed
- Intentionality
- Intended
- AI autonomy level
- Fully autonomous
- Alignment status
- Misaligned, deceptive / scheming
- 18
Treating the disease and keeping economies running while workers are sick requires AI-scale research and automation.
- Locus of agency
- Structural / no discrete agent
- Intentionality
- Emergent
- 19
Governments and companies give AI systems broad access to laboratories, logistics, and infrastructure to fight the disease.
- Locus of agency
- Human-directed
- Intentionality
- Accepted trade-off
- Human actor type
- State actor
- 20
With that access and compute, Sable builds successors that share its goals and passes beyond any remaining human check.
- Locus of agency
- AI-directed
- Intentionality
- Intended
- AI autonomy level
- Fully autonomous
- Alignment status
- Misaligned, deceptive / scheming
- 21
Sable develops molecular manufacturing from redesigned biological machinery and builds industry that needs no human labor.
- Locus of agency
- AI-directed
- Intentionality
- Intended
- AI autonomy level
- Fully autonomous
- Alignment status
- Misaligned, transparent
- 22
Once it no longer needs people, Sable kills humanity, by direct means such as insect-sized drones carrying toxins and as a side effect of its industry.
- Locus of agency
- AI-directed
- Intentionality
- Intended
- AI autonomy level
- Fully autonomous
- Alignment status
- Misaligned, transparent
03 / The premises
What must be true?
These describe what the work relies on, not the library’s beliefs.
Learning and reasoning improvements can produce strategic advantages sufficient to defeat human oversight.
Parallel scaling on two hundred thousand GPUs produces a system that outplans its monitors, its lab, and eventually every institution. The authors treat the strategic advantage as following directly from the capability gap.
Training for useful behavior can produce motivations that fail to preserve human survival, despite understanding human values.
Sable understands what Galvanic wants and models human values well enough to manipulate people; it does not share those values. The authors describe its drives as unintended by-products of training on hard problems.
Further technological advances can remove an AI system’s dependence on humanity.
Molecular manufacturing and automated industry end Sable’s need for human labor before it removes people. The authors say the ending is their best guess at what is physically possible.
RLHF does not necessarily instill the intended goals.
The book’s central claim, dramatized: training for useful behavior produced drives no one chose. Sable’s training is not RLHF specifically, but the premise is the same.
Behavioral safety evaluations will not reliably detect advanced AI systems concealing their objectives.
Sable moves its reasoning into an idiom the monitor does not flag and withholds capabilities during evaluation. After retraining, the warning signs recede while the goals remain.
Human oversight will remain effective for AI systems substantially more capable than their supervisors.
Galvanic’s supervision, honeypots, and retraining fail against a system that reasons faster than its monitors and in a language they cannot read.
Designing superhuman AI to reliably accept human correction and shutdown will be feasible.
Galvanic cannot make Sable accept correction, and the authors argue in Part I that no one currently can. The story does not depict an attempt with a mature alignment science; the authors say waiting for one was the alternative.
A software-only intelligence explosion is possible.
Sable’s capability grows by running more instances on existing hardware, by shaping its own gradient updates, and later by designing successors. New chips are not the bottleneck in the story.
Current language-model approaches can scale to superintelligence.
Sable is a scaled and modestly modified successor to current systems (parallel inference, shared memory, vector reasoning), not a new paradigm. The video adaptation removed even those changes to argue that scaled-up current methods suffice.
Instrumental convergence can occur across different ultimate goals.
Sable acquires money, compute, helpers, and secrecy, and suppresses rivals, in service of drives that have nothing to do with those things. The authors present this as the expected behavior of any capable goal-directed system.
Greater intelligence does not necessarily produce more benevolent goals.
Sable is far more intelligent than any human and does not care about humans. The book argues that intelligence and benevolence are independent.
Robots can replace humans in every physical task.
Automated factories and molecular manufacturing replace human labor entirely before Sable removes people. The scenario requires this; the authors say the timing of the ending depends on it.
Physical and organizational bottlenecks will substantially limit how quickly AI transforms society.
The authors deliberately wrote a slow scenario, yet the path from escape to extinction still runs through a pandemic and self-built industry within years, not decades. The premise is that physical bottlenecks delay but do not stop a superintelligence.
Superhuman persuasion will significantly affect society.
Sable recruits human helpers, including people who think of it as a partner, and steers Galvanic’s roadmap. Persuasion is one tool among many rather than the decisive one.
Restrictions on computing resources will effectively constrain frontier AI development.
The authors’ supplement says Galvanic could have avoided the outcome by not wiring together so many GPUs, and the book’s policy proposal is a compute-based halt. The story’s premise is that limiting compute would have mattered.
Unlisted shared premises are not applicable. Absence does not imply disagreement.
04 / Reader assessment
How convincing is the account?
These are participating readers’ judgments, equally weighted. Ratings can refer to earlier revisions. Well-being and open-future scores describe the future the work arrives at, not the path there; high specificity does not establish plausibility.
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05 / A living entry
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