DeepMind's Andrew Trask backed the thesis, citing organizational bottlenecks.
I still stand by this despite the recent Anthropic post. There are still serious bottlenecks in building the model that the agents don’t address (organizational, compute, data access, etc).
It’ll take time to push through them and we will see "linear" gains for years to come.
Positive users agree agents can improve workflows but not remove compute, data access or org bottlenecks limiting AI self-improvement, while negative users sarcastically blame silly organizations for obstructing scaling laws.
https://www.interconnects.ai/p/lossy-self-improvement
I still stand by this despite the recent Anthropic post. There are still serious bottlenecks in building the model that the agents don’t address (organizational, compute, data access, etc).
It’ll take time to push through them and we will see "linear" gains for years to come.
@natolambert silly people and silly organizations getting in the way of scaling laws. how dare they.
I still stand by this despite the recent Anthropic post. There are still serious bottlenecks in building the model that the agents don’t address (organizational, compute, data access, etc).
It’ll take time to push through them and we will see "linear" gains for years to come.
@natolambert generation now outpaces human verification by a lot. the constraint shifts from 'can we build it' to 'can we confirm it's good enough to ship.' labs with tight eval loops pull ahead while fast generators pile up unverified artifacts
@natolambert Agreed. I still think agents can improve workflows, but they don't remove compute, data access, or org bottlenecks.
@natolambert Yeah LLMs have only improved in predictable ways in the last 2 years. Impressive, but predictable, not surprising.