Iñigo Laucirica retweetledi
I think that over the next five years we are likely to see both substantial progress toward something like 'weak AGI', i.e. systems that can do most cognitive tasks humans can do, and growing diminishing returns to raw frontier model improvement in the economic sense.
The point isn't that scaling stops working but rather that (a) achieving each additional increment of capability at the model level will require disproportionately greater expenditure of compute, data, engineering effort, and capital; and (b) 'weak AGI' will probably come from the combination of strong models with scaffolding, tools, memory, retrieval, planning, decomposition, verification, and other system-level affordances around them.
As a result, deployment design and scaffolding becomes more important over time, not less. The old view that wrappers are disposable because the next model jump will wash them away seems naive. If frontier gains become more input-intensive, then the question increasingly becomes how much capability you can extract, route, verify, and compose from a model within a given budget.
Recent developments point in this direction too. What seems to matter is whether a given pipeline is structured to exploit capabilities well: assigning subproblems appropriately, using division of labour intelligently, and compensating for weaknesses with tools and process. It seems quite plausible that there's more alpha on the harness side at the moment, than in merely betting on scaling alone.
English
