There's an old saying, "in the land of the blind, the one-eyed man is king."
Here we have the opposite: In the land of the one-eyed, the blind are leading.
The blind in this case are all those executives and managers who don't understand much about AI's current potential and limitations, and so far have treated it like a magic button that will solve everything. The one-eyed are rank-and-file employees who maybe sort of know a little more about AI.
Executives and managers are the ones who correctly understood which game was being played. The game we are playing is not one of making good products, it's one of getting money from people who both have more money and are stupider than you. They're succeeding at that. We're also doing it, but we're not getting as much money.
In many cases, the people who have more money and are stupider than you are other executives. Sam Altman is arguably one of the executives who know how the game is played. OpenAI is at the front. Microsoft's executives are an example of the ones who got played.
Over the past two years, I have found Uber and Lyft rides getting more expensive than taxis in several large US cities, including Boston, Chicago, NY, and LA. Taxis are now 10-50% cheaper in my experience.
When I do take Uber and Lyft rides, I ask the drivers how much they're getting paid, and the amounts they tell me are often 30% to 60% less than what I paid, which is a bit shocking to me.
At some point, Uber and Lyft stopped being service providers that charged riders a fee for value provided. They have become market makers that squeeze as much trading profit as possible by arbitraging the prices riders are willing to pay and the rates drivers are willing to accept. I imagine they are capturing most of the value in each ride today. It's perfectly legal, but let's call what it is.
I'm not surprised about the ride-share driver union.
> At some point, Uber and Lyft stopped being service providers that charged riders a fee for value provided.
They were only ever this for about 30 seconds between when they were dumping investor cash to sell dollars for 75¢, and when they realized finally that no one even knew what a taxi was anymore, or how to find one. What literally every "cynic" said would happen.
Oh so some random user with no credentials, reputation, or real name just typed "do deep research on the AI infrastructure financial bubble and write a report" then submitted it to HN?
Why should I bother reading what may or may not be a pile of unverified hallucinations?
No, but close. Someone like that built the infrastructure tooling to do deep research, wrote up their process doing that, and then did what you said after, which I consider to be different.
I didn’t read it in full but I spot checked one or two citations and I found them compelling.
Oh, given what I've seen from LLM companies, I suspect you are wrong. It will be more like:
Buried in LLM click-through: By interacting with our LLM, you agree that you are consenting to make all your interactions with us advertising-driven to an extent that you will never know, but that we will determine based on whatever makes us the most money in the least time.
Look at Google in 2000s. If you travel back in time you would’ve never thought Google would do something like it is doing today.
Now pretend you travelled back in time to 2026. You would’ve never thought OpenAI (open source non profit company) would do something crazy that it just did in 2030 or 2040 or where you came from.
I don’t think they’ve been successful enough at monopolizing to get away with this to an egregious extent like Google has. Anthropic and Google both have debatably better models with ad-free platforms (so far). And open models are not so far behind.
> If you travel back in time you would’ve never thought Google would do something like it is doing today.
I'm not exactly Google's biggest fan, but what does this refer to?
They still just... show ads on search results, no? (Not that most people I know ever see them, thanks to adblockers.) The disclaimers have gotten less prominent, but I think anyone could have expected that. Are there other major things they're doing that couldn't have been expected at all in the 2000s?
Why inject just an ad? Maybe it'll automatically decide to use a sponsored library in the code, or build in a whole ad network who's paid openai for the placement...
Frankly ads are the most benign shitty thing that could come of this. I’m a hell of a lot more worried about what they’re going to sell to data brokers.
Tbh it doesn’t even need that.
Just a way for advertisers to say “I want to target people who have bought peanut butter in the last 2 weeks”(I’m a jelly seller). That alone would beat FB and Google.
ChatGPT is collecting your data fs so advertisers can go ultra niche targeting
Advertiser's on Google and Meta et al are not really paying for visibility - they are paying to achieve some objective (e.g. sales) that is directly tied to a campaign. That's why digital advertising is so much more powerful than non-digital.
The question is, will LLM's as an interface be worth the spend in relation to converting without throwing users of chatGPT off over-time, all whilst, doing it within the regulatory frameworks. That's difficult to say. OAI will face a lot of scrutiny in EU for sure.
There’s a misunderstanding. I’m not talking about AEO
It’s about how Meta and google provides good data about audiences but I need more detailed info about a person(they’re exact shopping habits)
As the person responsible for GTM, I would gladly pay $60CPM if I can say “I would like to target all people who said they love crunchy peanut butter and consistently ask ChatGPT for peanut butter ideas”
I have no idea what they’re trying to pitch with the “we’re at the last step of the transaction” idea-but I also understand the regulatory issues with what advertisers like me want
It's a play on Donald Trump, after watching a Liz Oyer video linking a very plausible pardon for sale scheme, I wanted to initially build a site that showcased pardons just by Trump, but I realized that would be partisan and not as useful.
Thank you for coming on HN and offering to answer questions.[a]
This is a fantastic piece, very timely, evidently well-researched, and also well-written. Judging by the little that I know, it's accurate. Thank you for doing the work and sharing it with the world.
OpenAI may be in a more tenuous competitive position than many people realize. Recent anecdotal evidence suggests the company has lost its lead in the AI race to Anthropic.[b]
Many people here, on HN, who develop software prefer Claude, because they think it's a better product.[c]
Is your understanding of OpenAI's current competitive position similar?
Thank you for this, very much appreciate the thoughtful response.
The piece captures some of the anxieties within OpenAI right now about their competitive position. This obviously ebbs and flows but of late there has been much focus on Anthropic's relative position. We of course mention the allegations of "circular deals" and concerns about partners taking on debt.
Thank you. Yes, I saw that. The company's always been surrounded by endless talk about insane hype, speculative bubbles, and financial engineering. I wasn't asking so much about that.
I was asking more about your informed view on how OpenAI's technology, products, and roadmap are perceived, particularly by customers and partners, in comparison to those of competitors.
If you have an opinion about that, everyone here would love to hear about it.
at this point even googles ai search results are better than gpt - obv. this is not for full programs but if you know what youre doing and just want a snippet, thats all you need.
Wild how different experience people can have. Both Google's models and Anthrophic's hallucinate a lot for me, even when I try the expensive plans and with web searches, for some reason, and none of them come close to the accuracy and hallucination-free responses of ChatGPT Pro, which to me still is SOTA and has been since it was made available. But people keep having opposite experiences apparently, I just can't make sense of it.
Kagi (assistant.kagi.com) with Kimi K2.5 (their current default) has worked great for me in scenarios where the search result data is more important than the model.
I.e. what I used to use Google for and when I don't want an AI to overly summarize / editorialize result data.
My guess is that the answer to your question, fantastic question, is that nobody knows. I remember having the same thoughts when Covid was first “arriving” if you will: we wanted people in the know to throw us a nugget of information, and they just didn’t know.
As it turns out, and what I’m kind of going with for this LLM shit, is that it’ll play out exactly how you think it will. The companies are all too big to fail, with billionaire backers who would rather commit fraud than lose money.
That's not fraud, and it's not sustainable. They aren't going to just keep doing that. It only makes sense if an AI company wants to pay for GPUs with stock, and - more importantly - the GPU company agrees to sell in exchange for stock.
Much of the article and general palace intrigue is predicated on the idea that OpenAI has a singularly revolutionary product. If it later turns out to be a commodity, or OpenAI is simply outcompeted nonetheless, then the idea that Sam Altman's personal shortcomings are something to stress about would seem quaint. Just another hubristic tech billionaire acting in bad faith doesn't really pry attention the same way as someone "controlling your future".
I mean, its a fair question, though it does make some wonder how extreme the answers could be, so I could see why you're being downvoted.
The problem is sometimes on paper everything people like Sam Altman do is legal, despite it harming so many. We've literally had a major RAM producer pull off the consumer RAM market. I feel like Sam Altman should be investigated and heavily scrutinized. He kind of is the biggest bubble in the AI bubble, we're letting him fester too far into it too, and these circular deals have seemingly somewhat stopped for now, but it might only get worse.
Who is “us”? It does seem that some scientists prefer Codex for its math capabilities but when it comes to general frontend and backend construction, Claude Code is just as good and possibly made better with its extensive Skills library.
Both codex and Claude code fail when it comes to extremely sophisticated programming for distributed systems
As a scientist (computational physicist, so plenty of math, but also plenty of code, from Python PoCs to explicit SIMD and GPU code, mostly various subsets of C/C++), I can confirm - Codex is qualitatively better for my usecases than Claude. I keep retesting them (not on benchmarks, I simply use both in parallel for my work and see what happens) after every version update and ever since 5.2 Codex seems further and further ahead. The token limits are also far more generous (and it matters, I found it fairly easy to hit the 5h limit on max tier Claude), but mostly it's about quality - the probability that the model will give me something useful I can iterate on as opposed to discard immediately is much higher with Codex.
For the few times I've used both models side by side on more typical tasks (not so much web stuff, which I don't do much of, but more conventional Python scripts, CLI utilities in C, some OpenGL), they seem much more evenly matched. I haven't found a case where Claude would be markedly superior since Codex 5.2 came out, but I'm sure there are plenty. In my view, benchmarks are completely irrelevant at this point, just use models side by side on representative bits of your real work and stick with what works best for you. My software engineer friends often react with disbelief when I say I much prefer Codex, but in my experience it is not a close comparison.
Have you tried the latest (3.1 pro) Gemini? In my experience, it's notably better for a similar type of problems than Opus 4.6. However, I don't really use OpenAI products to compare.
I actually haven't - I tried Gemini 3.0 Pro in Antigravity and was disappointed enough that I didn't pay much attention to the 3.1 release, it was notably worse than Opus and GPT at the time, and much more prone to "think" in circles or veer off into irrelevant tangents even with fairly precise instruction. I'll give 3.1 a try tomorrow, see what happens.
I've tried both against similar and haven't found it such a clear cut difference. I still find neither are able to fully implement a complex algorithm I worked on in the past correctly with the same inputs. Not sharing exactly the benchmark I'm using but think about something for improving performance of N^2 operations that are common in physics and you can probably guess the train of thought.
I've had reasonable success using GPT for both neighbor list and Barnes-Hut implementations (also quad/oct-trees more generally), both of which fit your description, haven't tried Ewald summation or PME / P3M. However, when I say "reasonable success", I don't mean "single shot this algo with a minimal prompt", only that the model can produce working and decently optimized implementations with fairly precise guidance from an experienced user (or a reference paper sometimes) much faster than I would write them by hand. I expect a good PME implementation from scratch would make for a pretty decent benchmark.
I'm in that camp -- I have the max-tier subscription to pretty much all the services, and for now Codex seems to win. Primarily because 1) long horizon development tasks are much more reliable with codex, and 2) OpenAI is far more generous with the token limits.
Gemini seems to be the worst of the three, and some open-weight models are not too bad (like Kimi k2.5). Cursor is still pretty good, and copilot just really really sucks.
Claude Code, Codex, and Cursor are old news. If you're having problems, it's because you're not using the latest hotness: Cludge. Everyone is using it now - don't get left behind.
Us = me and say /r/codex or wherever Codex users are. I've tried both, liked both, but in my projects one clearly produces better results, more maintainable code and does a better job of debugging and refactoring.
That's interesting, I actively use both and usually find it to be a toss up which one performs better at a given task. I generally find Claude to be better with complex tool calls and Codex to be better at reviewing code, but otherwise don't see a significant difference.
If you want to find an advocate for Codex that can give a pretty good answer as to why they think it's better, go ask Eric Provencher. He develops https://repoprompt.com/. He spends a lot of time thinking in this space and prefers Codex over Claude, though I haven't checked recently to see if he still has that opinion. He's pretty reachable on Discord if you poke around a bit.
Quite irrelevant what factions think. This or that model may be superior for these and those use cases today, and things will flip next week.
Also. RLHF mean that models spit out according to certain human preference, so it depends what set of humans and in what mood they've been when providing the feedback.
On the contrary, I very much care about what the other factions think because I want to know if things have already flipped and the easiest way to do so is just ask someone who's been using the tool. Of course the correct thing to do is to set up some simple evals, but there is a subjective aspect to these tools that I think hearing boots on the ground anecdata helps with.
Haven't done it in a while, but I've done some tasks with both Codex and Claude to compare. In all cases I asked both to put their analysis and plans for implementation into a .md file. Then I asked the other agent to analyze said file for comparison.
In general, Claude was impressed by what Codex produced and noted the parts where it (i.e. Claude) had missed something vs. Codex "thinking of it".
From a "daily driver" perspective I still use Claude all the time as it has plan mode, which means I can guarantee that it won't break out and just do stuff without me wanting it to. With Codex I have to always specify "Don't implement/change, just tell me" and even then it sometimes "breaks out" and just does stuff. Not usually when I start out and just ask it to plan. But after we've started implementation and I review, a simple question of "Why did you do X?" will turn into a huge refactoring instead of just answering my question.
To be fair, that's what most devs do too (at least at first), when you ask them "Why did you do X" questions. They just assume that you are trying to formulate a "Do Y instead of X" as a question, when really you just don't understand their reasoning but there really might be a good reason for doing X. But I guess LLMs aren't sure of themselves, so any questioning of their reasoning obliterates their ego and just turns them into submissive code monkeys (or rather: exposes them as such) vs. being software engineers that do things for actual reasons (whether you agree with them or not).
For that I'm not so sure. I tried both early 2025 and was disappointed in their ability to deal with a TCA based app (iOS) and Jetpack compose stuff on Android, but I assume Opus 4.6 and GPT 5.4 are much better.
My rule of thumb is that its good for anything "broad", and weaker for anything "deep". Broad tasks are tasks which require working knowledge of lots of random stuff. Its bad at deep work - like implementing a complex, novel algorithm.
LLMs aren't able to achieve 100% correctness of every line of code. But luckily, 100% correctness is not required for debugging. So its better at that sort of thing. Its also (comparatively) good at reading lots and lots of code. Better than I am - I get bogged down in details and I exhaust quickly.
An example of broad work is something like: "Compile this C# code to webassembly, then run it from this go program. Write a set of benchmarks of the result, and compare it to the C# code running natively, and this python implementation. Make a chart of the data add it to this latex code." Each of the steps is simple if you have expertise in the languages and tools. But a lot of work otherwise. But for me to do that, I'd need to figure out C# webassembly compilation and go wasm libraries. I'd need to find a good charting library. And so on.
I think its decent at debugging because debugging requires reading a lot of code. And there's lots of weird tools and approaches you can use to debug something. And its not mission critical that every approach works. Debugging plays to the strengths of LLMs.
Many paying customers say that Anthropic degraded the capability of Opus and Claude Code in the last months and the outcomes are worse. There are even discussions on HN about this.
As some other people mentioned, using both/multiple is the way to go if it's within your means.
I've been working on a wide range of relatively projects and I find that the latest GPT-5.2+ models seem to be generally better coders than Opus 4.6, however the latter tends to be better at big picture thinking, structuring, and communicating so I tend to iterate through Opus 4.6 max -> GPT-5.2 xhigh -> GPT-5.3-Codex xhigh -> GPT-5.4 xhigh. I've found GPT-5.3-Codex is the most detail oriented, but not necessarily the best coder. One interesting thing is for my high-stakes project, I have one coder lane but use all the models do independent review and they tend to catch different subsets of implementation bugs. I also notice huge behavioral changes based on changing AGENTS.md.
In terms of the apps, while Claude Code was ahead for a long while, I'd say Codex has largely caught up in terms of ergonomics, and in some things, like the way it let's you inline or append steering, I like it better now (or where it's far, far, ahead - the compaction is night and day better in Codex).
(These observations are based on about 10-20B/mo combined cached tokens, human-in-the-loop, so heavy usage and most code I no longer eyeball, but not dark factory/slop cannon levels. I haven't found (or built) a multi-agent control plane I really like yet.)
Codex won me over with one simple thing. Reliability. It crashed less, had less load shedding and its configuration is well designed.
I do regular evaluation of both codex and Claude (though not to statistical significance) and I’m of the opinion there is more in group variance on outcome performance than between them.
Not a scientist and use codex for anything complex.
I enjoy using CC more and use it for non coding tasks primarily, but for anything complex (honestly most of what I do is not that complex), I feel like I am trading future toil for a dopamine hit.
I’m one of those ‘us’, Claude’s outputs require significant review and iteration effort (to put it bluntly they get destroyed by gpt and Gemini). I’m basically using sonnet to do code search and write up since it is a better (more human-like) writer than gpt and faster and more reliable than gemini, but that’s about it.
I also find Codex much more generous in terms of what you get with a Pro ($20/mo) subscription. I use it pretty much non-stop and I have yet to hit a limit. Weekly reset is much better as well.
Usage limits are more generous and GPT 5.4 is a good model, but yes, UI/UX lags behind Claude Code. Currently I'm especially missing /rewind with code restoration and proper support for plugin marketplaces
X restricts what you can view without logging in. Many folks don't want to log in to X, for obvious reasons. Posting an xcancel link is kinda like folks posting various `archive` URLs to bypass paywalls, work around overloaded servers, etc. That's an extremely common practice here that usually goes without comment.
But by page 5, those stories have around 50-60 karma, while claude page five is still 500+
(i found your comment surprising based on my daily hn reading recollection - i mostly read top N daily and feel i only occassionally see codex stories).
Yeah we moved to Claude a few months ago, mostly because the devs kept using it anyway. Altman stuff is interesting but at the end of the day you just go with whatever tool works
Personally, I prefer Claude for coding, but I still prefer ChatGPT for hashing out ideas for my projects (which tend to be game designs). So I use both.
Also known as rent-seeking: "The act of growing one's existing wealth by manipulating public policy or economic conditions without creating new wealth. Rent-seeking activities have negative effects on the rest of society. They result in reduced economic efficiency through misallocation of resources, stifled competition, reduced wealth creation, lost government revenue, heightened income inequality, heightened debt levels, risk of growing corruption and cronyism, decreased public trust in institutions, and potential national decline."[a]
Fantastic work, instantly valuable, immediately usable.
A big THANK YOU to the authors:
Jack Zhang, Noah Amsel, Berlin Chen, and Tri Dao
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