Comment by metalspot
3 hours ago
You don't need bigger or better models. Current models are more than adequate for any task.
The key thing is tool use.
The model is fixed but tool use introduces new information into the system, so once you have a model that is good enough to effectively use the full suite of tools available (debug, test, profile, etc) and write code to effectively combine them and synthesize their results for a particular task, there is nothing that it can't solve.
New models are continually ingesting new information from usage, and embedding the results from people running this process back into the base model, which makes it more efficient, gets more things right the first time, etc, but there is no step gap in functional outcomes left.
Computing and programming are a limited domain with a small fixed set of constructs that can be applied in an infinite number of combinations. Nothing ever closes the gap between the ability of the model to automate the assembly of these constructs and the necessity of expert human direction to pick what system to build out of the infinite range of possible systems.
Frontier models can keep producing very expensive one-shot demos to pitch to investors but they are never going to replace engineers.
> You don't need bigger or better models. Current models are more than adequate for any task.
At the very least we need _current_ models, though. The weights have all sorts of information baked into them, like "how do Python unit test drivers work" or "what does idiomatic C# look like," that will just naturally go stale over time as the frameworks improve. In five years, Qwen 3.8 will still be targeting the Python of 2026.
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