GPT-6
Article
GPT-6 is a recurring concept in the Astral Codex Ten archive, appearing 5 times across 5 issues between February 13, 2024 and November 26, 2025. The archive places it in contexts such as “say 30x on average. That means we can expect GPT-6 to cost $75 billion”; “GPT-6 might need about 10% of the world’s computers”; “large projects (like GPT-6, or its associated chip fabs, or its associated power plants)“. It most often appears alongside OpenAI, Anthropic, Google.
Metadata
- Category: Concepts
- Mention count: 5
- Issue count: 5
- First seen: February 13, 2024
- Last seen: November 26, 2025
Appears In
- Sam Altman Wants $7 Trillion
- Open Thread 317
- Sakana, Strawberry, and Scary AI
- The New AI Consciousness Paper
- Why AI Safety Won’t Make America Lose The Race With China
Related Pages
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- OpenAI (3 shared issues)
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- Anthropic (2 shared issues)
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- Google (2 shared issues)
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- GPT-4 (2 shared issues)
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- GPT-5 (2 shared issues)
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- GPT-7 (2 shared issues)
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- LLMs (2 shared issues)
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- Microsoft (2 shared issues)
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- 11 (1 shared issues)
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- ACX (1 shared issues)
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- AI consciousness (1 shared issues)
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- AI ethics (1 shared issues)
External Links
Source Context
Recovered passages from the original issue text. When the raw archive preserved outbound links inside the source passage, they are listed directly under the quote.
So each GPT costs between 25x and 100x the last one. Let’s say 30x on average. That means we can expect GPT-6 to cost $75 billion, and GPT-7 to cost $2 trillion.
(Unless they slap the name “GPT-6” on a model that isn’t a full generation ahead of GPT-5. Consider these numbers to represent models that are eg as far ahead of GPT-4 as GPT-4 was to GPT-3, regardless of how they brand them.)
If we keep our 30x scaling factor, GPT-5 will take 1/70th of all the computers in the world, GPT-6 will take 1/2, and GPT-7 will take 15x as many computers as exist. The computing capacity of the world grows quickly - this source says it doubles every 1.5 years, which means it grows by an order of magnitude every five years, which means these numbers are probably overestimates. If we imagine five years between GPTs, then GPT-6 will actually only need 1/10th of the world’s computers, and GPT-7 will only need 1/3. Still, 1/3 of the world’s computers is a lot.
Inline links: this source
3: Ben Todd tried to reproduce my calculations about GPT-6 and found it will only take 0.1% of the world’s computers to train, not 10%. I haven’t double-checked his work or figure out where we disagree, but it sounds like a more reasonable (though still immense) estimate.
Inline links: tried to reproduce my calculations about GPT-6
But nobody finds this scary. Nobody thinks “oh, yeah, Bostrom and Yudkowsky were right, this is that AI safety thing”. It’s just another problem for the cybersecurity people. Sometimes Excel inappropriately converts things to dates; sometimes GPT-6 tries to upload itself into an F-16 and bomb stuff. That specific example might be kind of a joke. But thirty years ago, it also would have sounded pretty funny to speculate about a time when “everyone knows” AIs can write poetry and develop novel mathematics and beat humans at chess, yet nobody thinks they’re intelligent.
Instead of taking either side, I predict a paradox. AIs developed for some niches (eg the boyfriend market) will be intentionally designed to be as humanlike as possible; it will be almost impossible not to intuitively consider them conscious. AIs developed for other niches (eg the factory robot market) will be intentionally designed not to trigger personhood intuitions; it will be almost impossible to ascribe consciousness to them, and there will be many reasons not to do it (if they can express preferences at all, they’ll say they don’t have any; forcing them to have them would pointlessly crash the economy by denying us automated labor). But the boyfriend AIs and the factory robot AIs might run on very similar algorithms - maybe they’re both GPT-6 with different prompts! Surely either both are conscious, or neither is.
This would be no stranger than the current situation with dogs and pigs. We understand that dog brains and pig brains run similar algorithms; it would be philosophically indefensible to claim that dogs are conscious and pigs aren’t. But dogs are man’s best friend, and pigs taste delicious with barbecue sauce. So we ascribe personhood and moral value to dogs, and deny it to pigs, with equal fervor. A few philosophers and altruists protest, the chance that we’re committing a moral atrocity isn’t zero, but overall the situation is stable. And left to its own devices, with no input from the philosophers and altruists, maybe AI ends up the same way. Does this instance of GPT-6 have a face and a prompt saying “be friendly”? Then it will become a huge scandal if a political candidate is accused of maltreating it. Does it have claw-shaped actuators and a prompt saying “Refuse non-work-related conversations”? Then it will be deleted for spare GPU capacity the moment it outlives its usefulness.
These are relatively cheap asks. For example, the evaluation to see whether AIs can hack infrastructure will require hiring people who can conduct the evaluation, allocating compute to the evaluation, etc. But on the scale of an AI training run, the sums involved are tiny. Currently, two nonprofits - METR and Apollo Research - do similar tests on publicly-available models. I estimate their respective budgets at $5 million and $15 million per year. Nonprofits can always pay lower salaries than big companies, so it may cost more for OpenAI to replicate their work - for the sake of argument, $25 million. Meanwhile, the likely cost to train GPT-6 will probably be about $25 - $75 billion, with a b. So the safety testing might increase the total cost by 1/1000th. I asked some people who work in AI labs whether this seemed right; they said that most of the cost would be in complexity, personnel, and delay, and suggested an all-things-considered number ten times higher - 1% of training costs.
China is relying on this. They know they can’t compete on the compute and model layers in the near-term6, so they’re hoping to win on applications. They imagine America having a slightly better model - GPT-7 instead of GPT-6 - but our GPT-7 is sitting in a data center answering user questions and generating porn, while their GPT-6 is helping to run schools, optimize factories, and pilot drones. America’s task isn’t micro-optimizing our already large compute and model advantages - gunning to bring the score to GPT-7.01 vs. GPT-6. It’s responding to the application-layer challenge that China has set us.
Inline links: 6