For true AGI (whatever that means, lets say fully replicates human abilities), discussing "developers" only is a drop in the bucket compared to all knowledge work jobs which will be displaced.
More likely they will tailor/RL train these models to go after coders first. Use RLHF employing coders where labor is cheap to train their models. A number of reasons for this of course:
- Faster product development on their side as they eat their own dogfood
- Dev's are the biggest market in the transition period for this tech. Gives you some revenue from direct and indirect subscriptions that the general population does not need/require.
- Fear in leftover coders is great for marketing
- Tech workers are paid well which to VC's, CEO's, etc makes it obvious where the value of this tech comes from. Not with new use cases/apps which would be greatly beneficial to society - but effectively making people redundant saving costs. New use cases/new markets are risky; not paying people is something any MBA/accounting type can understand.
I've heard some people say "its like they are targeting SWE's". I say; yes they probably are. I wouldn't be surprised if it takes SWE jobs but otherwise most people see it as a novelty (barely affects their life) for quite some time.
The new capabilities of LLMs, and generally large foundation models, expands the range of what a computer program can do. Naturally, we will need to build all of those things with code. Which will be done by a combo of people with product ideas, engineers, and LLMs. There will be then specialization and competition on each new use-case. eg., who builds the best AI doctor etc.,.
The pundits response to the (alleged) plateau was proportional to the certainty with which CEOs of frontier labs discussed pre-training scaling. The o3 result is from scaling test time compute, which represents a meaningful change in how you would build out compute for scaling (single supercluster --> presence in regions close to users). Thus it is important to discuss.
