The argument from the recurrence trend and capability pressure
The argument
Frontier developers have already begun moving computation out of the visible chain: OpenAI's Astra (2026) uses looped recurrent depth, and Anthropic and Google DeepMind were reported to be discussing it. Recurrent depth increases capacity for unverbalized reasoning, so it is a capability gain. Under competitive pressure, developers that gain capability or efficiency from latent computation will scale it until reasoning is mostly latent, and this can happen within the period before AI research is automated.
Does it work?
- Does it follow?
- slight even if the premises holdEven if the premises are true, they make the conclusion only a little more likely.constitution →
- Do the premises hold?
- 1 supported
Suppose its premises hold. Labs already use some recurrence, and recurrent depth adds latent reasoning capacity. That shows labs have an incentive to adopt latent computation. It does not show that they will scale it until it replaces the chain of thought, or that they will do so before automation. Both steps also have to overcome training difficulty and labs' public monitorability commitments. The premise that recurrence adds latent capacity is supported. The weak link is the unstated step from "some recurrence" to "mostly latent reasoning within the window".
Premises
- SupportedThe evidence favors the claim, but the examination is incomplete or the evidence is indirect.constitution →Recurrent depth in language models increases capacity for reasoning without verbalized chain of thought, reducing monitorability.
Where it is made
Passages where sources make, criticize or mention this argument. They record where it appears, not how strong it is, and none has precedence over another.
No source is recorded as making it.