Aside from the gossips about Sam Altman hubris, the failed launch of GPT5 is likely a significant event and potentially a turning point regarding the hype and expectations around LLMs based tech.
The limitations of what was believed to be by many as a path to AGI/ASI are becoming more clearly apparent.
Difficult to say how much room for improvement there is, or to have a definite answer regarding the usefulness and economic impact of those models, but what we're seeing now is not exponential improvement.
This is not going to rewrite and improve itself, or to cure cancer, unify physics or any kind of scientific or technological breakthrough.
For coders is is merely a dispensable QoL improvement.
lurking_swe · 1h ago
“no scientific breakthrough”
careful. I too am pessimistic on the generative AI hype, but you seem even more so, to the point where it’s making you biased and possibly uninformed.
Today’s news from BBC, 6 hours ago.
“AI designs antibiotics for gonorrhoea and MRSA superbugs”
> Now, the MIT team have gone one step further by using *generative AI* to design antibiotics in the first place for the sexually transmitted infection gonorrhoea and for potentially-deadly MRSA (methicillin-resistant Staphylococcus aureus).
…
> "We're excited because we show that generative AI can be used to design completely new antibiotics," Prof James Collins, from MIT, tells the BBC.
MerrimanInd · 1h ago
I don't think that BBC article is technically detailed enough to make that case. The actual study may be, so I'm not saying you're wrong, but "generative AI" is far too large of an umbrella term that encompasses two very different views. The common thesis here is that AI is a wildly powerful tool to do fundamental science, and I think most technologists with any knowledge of neural networks would agree that's true. But the problem is that the Sam Altmans of the world are applying that thesis to the promise that GPT5 is on the path to AGI and they just need to keep scaling and pouring more billions of dollars into these massive models to get there. When I see actually interesting applications of AI in fundamental science the studies are usually of custom programs starting with smaller or more purpose-built foundational models, being hand tuned by knowledgeable researchers with deterministic/testable validation feedback loops. So what you're saying can be true while what Altman is promising can also be absolutely false. But it's hard to say without actually reading that MIT study.
lurking_swe · 1h ago
I agree with you. I’m eager to see the details once MIT releases it.
Generative AI is a lot of things. LLM’s in particular (subset of generative AI) are somewhat useful, but nowhere near as useful as what Sam claims. And i guess LLM’s specifically - if we focus on chatgpt, will not be solving cancer lol.
So we agree that Sam is selling snake oil. :)
Just wanted to point out that a lot of the fundamental “tech” is being used for genuinely useful things!
stephc_int13 · 1h ago
To be clear, I was referring to LLMs, not other types of neural nets.
jonplackett · 2h ago
It’s actually quite clever: Release an ‘update’ to everyone - even free users.
Then ‘roll back’ to the real version - but only for paid users.
Imagine how much worse it’d have gone if they called it GPT-4o lite and gave that to free users only and kept 4o for paid only.
Maybe it will make more people subscribe?
But it will make people cancel their subs too - I miss o3
CodesInChaos · 2h ago
I disagree. Appearing incompetent is much worse for OpenAI than appearing greedy. Their primary business is selling AGI hype to investors, not selling the current product to consumers.
rbinv · 2h ago
o3 can be re-enabled in the settings ("Show additional models") if you're a paid (Plus) user.
blibble · 2h ago
he did well, he managed to convince the entire world that LLMs are "intelligent" for over 3 years
but no con can endure forever
dismalaf · 2h ago
I think he truly believed that scaling LLMs would lead to AGI, so he over-promised assuming the technology would catch up to the claims.
The wall is very obvious now though.
dinkblam · 2h ago
> I think he truly believed that scaling LLMs would lead to AGI.
no one in the industry could have believed that
stephc_int13 · 2h ago
It is easy to say retrospectively.
I am not in the industry but I've been following closely and I am usually skeptical, but while I erred on the side of "this is just a tool" I also wondered "what if?" more than once.
GPT-5 sounds like it knows a lot but the level of trust in ChatGPT is quickly eroding. Examples:
* Meeting notes which was read accurately from a handwritten note (impressive!) but the summary hallucinated information that was completely made up.
* Running omplex pytorch benchmarks while getting the simple parts of it completely wrong. We're talking getting variants of y=f(wx+b), which is what was being compared. All the graphs and visualizations look very convincing, but the details of what's tested completely bonkers.
Is there a petition to bring o3 back? Please? At least it was obvious when it failed.
mrcwinn · 2h ago
o3 is available for certain paid plans. I see it in my legacy model dropdown and had to use it last night because 5-Pro chews on an Excel for 20 minutes and then never finishes but also never times out.
Womp womp. Frustrating.
jethronethro · 26m ago
Ed Zitron's going to have a field day with this!
luke-stanley · 2h ago
"can't even label a map" - Image generation is not using GPT-5, they said this in the live stream. CNN should try harder.
duskwuff · 1h ago
Users don't care about the technical details of what model is being used for what type of output. What matters to them is that they asked ChatGPT to draw a map, and it spat out nonsense.
The same issue exists with a bunch of other types of image output from ChatGPT - graphs, schematics, organizational charts, etc. It's been getting better at generating images which look like the type of image you requested, but the accuracy of the contents hasn't kept up.
asdff · 2h ago
If your new update is 95% as good and saves you overhead, why the hell would you not roll that out? Enshittification is the natural movement of entropy in business. You want to cut costs. You already have a market beholden to your product. They really can't gleam a couple % different quality of your product in some metric, but you sure can operating at your mass market scale. That can make or break financials one quarter and lead to a big promotion yourself among other incentives. And maybe you try and save big, push to the point customers do notice it, but only so much that the % lost to competitors isn't much in light of the savings at scale you've achieved enshittifiying the product. Physical and technology products or services are no different with these incentives and direction of entropy.
This is the natural progression of mass market business where cost savings is valued and quality is not. If you as a customer want a higher quality product, you are left to the edges of the market of boutique, bespoke, upscale experiences which are only able to be offered at high quality because their scale is small and more manageable in all metrics and their existence against the walmarts of their industry is dependent on being at a higher quality offering.
stronglikedan · 1h ago
I was never so happy to have access to Enterprise when they dumped 5 on the normies.
Eh, I don’t know. I spent some time over 3 days trying to get Claude Code to write a pagination plugin for ProseMirror. Had a few different branches with different implementations and none of them worked well at all and one or two of them were really over-engineered. I asked GPT-5, via the Chat UI (I don’t pay for OpenAI products), and it basically one-shot a working plugin, not only that, the code was small and comprehensible, too.
The limitations of what was believed to be by many as a path to AGI/ASI are becoming more clearly apparent.
Difficult to say how much room for improvement there is, or to have a definite answer regarding the usefulness and economic impact of those models, but what we're seeing now is not exponential improvement.
This is not going to rewrite and improve itself, or to cure cancer, unify physics or any kind of scientific or technological breakthrough.
For coders is is merely a dispensable QoL improvement.
careful. I too am pessimistic on the generative AI hype, but you seem even more so, to the point where it’s making you biased and possibly uninformed.
Today’s news from BBC, 6 hours ago. “AI designs antibiotics for gonorrhoea and MRSA superbugs”
https://www.bbc.com/news/articles/cgr94xxye2lo
> Now, the MIT team have gone one step further by using *generative AI* to design antibiotics in the first place for the sexually transmitted infection gonorrhoea and for potentially-deadly MRSA (methicillin-resistant Staphylococcus aureus).
…
> "We're excited because we show that generative AI can be used to design completely new antibiotics," Prof James Collins, from MIT, tells the BBC.
Generative AI is a lot of things. LLM’s in particular (subset of generative AI) are somewhat useful, but nowhere near as useful as what Sam claims. And i guess LLM’s specifically - if we focus on chatgpt, will not be solving cancer lol.
So we agree that Sam is selling snake oil. :)
Just wanted to point out that a lot of the fundamental “tech” is being used for genuinely useful things!
Then ‘roll back’ to the real version - but only for paid users.
Imagine how much worse it’d have gone if they called it GPT-4o lite and gave that to free users only and kept 4o for paid only.
Maybe it will make more people subscribe?
But it will make people cancel their subs too - I miss o3
but no con can endure forever
The wall is very obvious now though.
no one in the industry could have believed that
I am not in the industry but I've been following closely and I am usually skeptical, but while I erred on the side of "this is just a tool" I also wondered "what if?" more than once.
https://metr.org/blog/2025-03-19-measuring-ai-ability-to-com...
* Meeting notes which was read accurately from a handwritten note (impressive!) but the summary hallucinated information that was completely made up. * Running omplex pytorch benchmarks while getting the simple parts of it completely wrong. We're talking getting variants of y=f(wx+b), which is what was being compared. All the graphs and visualizations look very convincing, but the details of what's tested completely bonkers.
Is there a petition to bring o3 back? Please? At least it was obvious when it failed.
Womp womp. Frustrating.
The same issue exists with a bunch of other types of image output from ChatGPT - graphs, schematics, organizational charts, etc. It's been getting better at generating images which look like the type of image you requested, but the accuracy of the contents hasn't kept up.
This is the natural progression of mass market business where cost savings is valued and quality is not. If you as a customer want a higher quality product, you are left to the edges of the market of boutique, bespoke, upscale experiences which are only able to be offered at high quality because their scale is small and more manageable in all metrics and their existence against the walmarts of their industry is dependent on being at a higher quality offering.