Thoughts on The Curve

I spent the weekend debating AI timelines, among other things, at The Curve conference. This translates as spending the weekend thinking about the trajectory of AI progress with a mix of DC and SF types. This is a worthwhile event that served as a great, high-bandwidth way to check in on timelines and expectations of the AI industry.
Updating timelines
My most striking takeaway is that the AI 2027 sequence of events, from AI models automating research engineers to later automating AI research, and potentially a singularity if your reasoning is so inclined, is becoming a standard by which many debates on AI progress operate under and tinker with. It’s good that many people are taking the long term seriously, but there’s a risk in so many people assuming a certain sequence of events is a sure thing and only debating the timeframe by which they arrive.
I’ve documented my views on the near term of AI progress and not much has changed, but through repetition I’m developing a more refined version of the arguments. I add this depth to my takes in this post.
I think automating the “AI Research Engineer (RE)” is doable in the 3-7 year range — meaning the person that takes a research idea, implements it, and compares it against existing baselines is entirely an AI that the “scientists” will interface with.
In some areas the RE is arguably already automated. Within 2 years a lot of academic AI research engineering will be automated with the top end of tools — I’m not sure academics will have access to these top end of tools but that is a separate question. An example I would give is coming up with a new optimizer and testing it on a series of ML baselines from 100M to 10B parameters. At this time I don’t expect the models to be able to implement the newest problems the frontier labs are facing alone. I also expect academics to be fully priced out from these tools.
Within 1-3 years we’ll have tools that make existing REs unbelievably productive (80-90% automated), but there are still meaningful technical bottlenecks that are solvable but expensive. The compute increase per available user has a ceiling too. Labs will be spending $200k+ per year per employee on AI tools easily (ie the inference cost), but most consumers will be at tiers of $20k or less due to compute scarcity.
Within 3-4 years the augmented research engineers will be able to test any idea that the scientists come up with at the frontier labs, but many complex system problems will need some (maybe minimal) amount of human oversight. Examples would include modifying RL implementations for extremely long horizon tasks or wacky new ideas on continual learning. This is so far out that the type of research idea almost isn’t worth speculating on.
These long timelines are strongly based on the fact that the category of research engineering is too broad. Some parts of the RE job will be fully automated next year, and more the next. To check the box of automation the entire role needs to be replaced. What is more likely over the next few years, each engineer is doing way more work and the job description evolves substantially. I make this callout on full automation because it is required for the distribution of outcomes that look like a singularity due to the need to remove the human bottleneck for an ever accelerating pace of progress. This is a point to reinforce that I am currently confident in a singularity not happening.
Up-skilling employees as their roles become irrelevant creates a very different dynamic. The sustained progress on code performance over the next few years will create a constant feeling of change across the technology industry. The range of performance in software is very high and it is possible to perceive relatively small incremental improvements.
These are very complex positions to hold, so they’re not that useful as rhetorical devices. Code is on track to being solved, but the compute limits and ever increasing complexity of codebases and projects (ie. L...