| So the team I lead does a lot of research around all the “plumbing” around LLMs. Both technical and from a product-market perspectives. What I’ve learned is that for the most part that AI revolution is not going to be because of PHD-level LLMs. It will be because people are better equipped to use the high-schooler level LLMs to do their work more efficiently. We have some knowledge graph experiments where LLMs continuously monitor user actions on Slack, GitHub etc and build up an expertise store. It learns about your work, your workflows and then you can RAG them. In user testing, people most closely associated this experience to having someone just being able to read their minds and essentially auto-suggest their work outputs. Basically it’s like another team member. Since these are just nodes in a knowledge graph, you can mix and match expertise bases that span several skills too. Eg: A Pm who understands the nuances of technical feasibility. And it didn’t require user training or prompting LLMs. So while GPT-5 may be delayed, I don’t think that’s stopping or slowing down a revolution in knowledge-worker productivity. |
Progress in the applied domain (the sort of progress that makes a different in the economy) will come predominantly from integrating and orchestrating LLMs, with improvements to models adding a little bit of extra fuel on top.
If we never get any model better than what we have now (several GPT-4-quality models and some stronger models like o1/o3) we will still have at least a decade of improvements and growth across the entire economy and society.
We haven't even scratched the surface in the quest to understand how to best integrate and orchestrate LLMs effectively. These are very early days. There's still tons of work to do in memory, RAG, tool calling, agentic workflows, UI/UX, QA, security, ...
At this time, not more than 0.01% of the applications and services that can be built using currently available AI and that can meaningfully increase productivity and quality have been built or even planned.
We may or may not get to AGI/ASI soon with the current stack (I'm actually cautiously optimistic), but the obsessive jump from the latest research progress at the frontier labs to applied AI effectiveness is misguided.