OpenAI, Google and Anthropic are struggling to build more advanced AI

Question for the group here: do we honestly feel like we've exhausted the options for delivering value on top of the current generation of LLMs?

I lead a team exploring cutting edge LLM applications and end-user features. It's my intuition from experience that we have a LONG way to go.

GPT-4o / Claude 3.5 are the go-to models for my team. Every combination of technical investment + LLMs yields a new list of potential applications.

For example, combining a human-moderated knowledge graph with an LLM with RAG allows you to build "expert bots" that understand your business context / your codebase / your specific processes and act almost human-like similar to a coworker in your team.

If you now give it some predictive / simulation capability - eg: simulate the execution of a task or project like creating a github PR code change, and test against an expert bot above for code review, you can have LLMs create reasonable code changes, with automatic review / iteration etc.

Similarly there are many more capabilities that you can ladder on and expose into LLMs to give you increasingly productive outputs from them.

Chasing after model improvements and "GPT-5 will be PHD-level" is moot imo. When did you hire a PHD coworker and they were productive on day-0 ? You need to onboard them with human expertise, and then give them execution space / long-term memories etc to be productive.

Model vendors might struggle to build something more intelligent. But my point is that we already have so much intelligence and we don't know what to do with that. There is a LOT you can do with high-schooler level intelligence at super-human scale.

Take a naive example. 200k context windows are now available. Most people, through ChatGPT, type out maybe 1500 tokens. That's a huge amount of untapped capacity. No human is going to type out 200k of context. Hence why we need RAG, and additional forms of input (eg: simulation outcomes) to fully leverage that.

> combining a human-moderated knowledge graph with an LLM with RAG allows you to build "expert bots" that understand your business context / your codebase / your specific processes and act almost human-like similar to a coworker in your team

It's been a while though, we've had great models now for a 18 months plus. Why are we still yet to see these type of applications rolling out on a wide scale?

My anecdotal experience is that almost universally, 90-95% type accuracy you get from them is just not good enough. Which is to say, having something be wrong 10% or even 5% of the time is worse than not having at all. At best, you need to implement applications like that in an entirely new paradigm that is designed to extract value without bearing the costs of the risks.

It doesn't mean LLMs can't be useful, but they are kind of stuck with applications that inherently mesh with human oversight (like programming etc). And the thing about those is that they don't really scale, because the human oversight has to scale up with whatever the LLM is doing.