Some of us would say GPT 5 has failed to deliver. With all those posts trying to count characters or ask GPT 5 to solve logical problems where it keeps failing more often than not, it’s not that difficult to see why that would be happening.
I don’t necessarily agree, even though one thing is certain – there is no AGI yet.
In that sense, in terms of what was promised by some of the AI companies vs what we ultimately got, well, with all due respect for the efforts, I won’t be too surprised if investors start asking questions. That’s not why I’m writing this, though.
It was a twisted reality anyways where we would be expecting an AI model that’s basically using texts as training data to figure out everything else about this world just by looking at the relationships between text tokens, even if the models can see a lot more relationships than us, humans, can.
Asking such a model to solve logical problems or to count something might not even be reasonable to begin with. Except that, of course, Open AI could have equipped Chat GPT with a simple tool to count those characters after all this time. But that sSo till would not be GPT 5, that would be a tool that would work with any other model that can work with tools.
With all this in mind, perhaps it’s good GPT 5 has confirmed the limitations. From the markets perspective, maybe some stocks will finally stop rising as if there were no limits. Not that I care too much (as long as they don’t fall too much). However, from the technical perspective, maybe it’s time to stop waiting for another magical LLM breakthrough and to start recognizing those LLM models for what they are.
No, not just “stochastic parrots” – that’s not, really, fair, and that reducing the models too much. I think a better option would be to recognize them as very powerful text understanding tools which can uncover relationships between different pieces of text faster and better than most of us can.
You want to make an email sound more formal? You want to organize your resume? You want to get a summary of a long blog post? LLM-s can do it in no time.
You want LLM-s to solve some problems for you along the way? That’s where they do require tools, since they can be used to formulate the task in the way those tools would understand, but they can’t solve most of those problems themselves.
In that sense, the fact that GPT 5 did not seem to get that much close to the AGI does not, really, change anything for “agentic” AI except that now it’s becoming obvious that in the agentic AI world tools mean as much or even more than the LLM-s. Which probably means the show will go on, even if the focus and expectation will shift – instead of expecting the LLM-s to become perfect on their own, agentic AI world will double down on the agents/tools development.