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.

The main difference between GPT5 and a PhD-level new hire is that the new hire will autonomously go out, deliver and take on harder task with much fewer guidance than GPT5 will ever require. So much of human intelligence is about interacting with peers.

Human interaction with peers is also guidance.

I don't know how many team meetings PhD students have, but I do know about software development jobs with 15 minute daily standups, and that length meeting at 120 words per minute for 5 days a week, 48 weeks per year of a 3 year PhD is 1.296.000 words.

I have 3 remote employees whose job is consistently as bad as LLM.

That means employees who use LLM are, on average, recognizably bad. Those who are good enough, are also good enough to write the code manually.

To the point I wonder whether this HN thread is generated by OpenAI, trying to create buzz around AI.