GPT-5 demonstrates exponential growth in task completion times:
https://metr.org/blog/2025-03-19-measuring-ai-ability-to-com...
Exponential would be at 3.6 hours
For all of them, getting access to full-blown GPT-5 will probably be mind-blowing, even if it's severely rate-limited. OpenAI's previous/current generation of models haven't really been ergonomic enough (with the clunky model pickers) to be fully appreciated by less tech-savvy users, and its full capabilities have been behind a paywall.
I think that's why they're making this launch is a big deal. It's just an incremental upgrade for the power users and the people that are paying money, but it'll be a step-change in capability to everyone else.
replacing huge swathes of the white collar workforce
"incremental upgrade for power users" is not at all what this house of cards is built on
It's like no one looked at the charts, ever, and they just came straight from.. gpt2? I don't think even gpt3 would have fucked that up.
I don't know any of those people, but everyone that has been with OAI for longer than 2 years 1.5m bonuses, and somehow they can't deliver a bar chart with sensible at axes?
it has to be released because it's not much better and OpenAI needs the team to stop working on it. They have serious competition now and can't afford to burn time / money on something that isn't shifting the dial.
So you might get that initial MvP out the door quickly, but when the complexity grows even just a little bit, you will be forced to stop and look at the plan and try to get it to develop it saying things like: "use Design agent to ultrathink about the dependencies of the current code change on other APIs and use TDD agent to make sure tests are correct in accordance with the requirements I stated" and then one finds that even the all the thinking there are bugs that you will have to fix.
Source: I just tried max pro on two client python projects and it was horrible after week 2.
The actual benchmark improvements are marginal at best - we're talking single-digit percentage gains over o3 on most metrics, which hardly justifies a major version bump. What we're seeing looks more like the plateau of an S-curve than a breakthrough. The pricing is competitive ($1.25/1M input tokens vs Claude's $15), but that's about optimization and economics, not the fundamental leap forward that "GPT-5" implies. Even their "unified system" turns out to be multiple models with a router, essentially admitting that the end-to-end training approach has hit diminishing returns.
The irony is that while OpenAI maintains their secretive culture (remember when they claimed o1 used tree search instead of RL?), their competitors are catching up or surpassing them. Claude has been consistently better for coding tasks, Gemini 2.5 Pro has more recent training data, and everyone seems to be converging on similar performance levels. This launch feels less like a victory lap and more like OpenAI trying to maintain relevance while the rest of the field has caught up. Looking forward to seeing what Gemini 3.0 brings to the table.