I think I can put the finger on how it differs and why the AI version seems more "generic." I think the difference is in the "post production" that became more common in pop music in the 2010's. I'm not at all a musician so I don't know the right terms for it, but the AI version has a bunch more of little "flairs" and auto-tuning and audio tweaks which I assume is put in during post-production that makes it sound "slick."
However, it seems to me that all pop music uses the same post-production tricks these days, and so it all sounds somewhat formulaic even if the individual songs themselves are very different. As such the original version may sound more interesting simply by being different.
So the 2001 version sounds more like an "MTV Unplugged" performance whereas the AI version sounds more like the professional and polished version that gets released commercially.
Each has their allure, however. I suspect the AI version will do better with younger crowds.
Alternate viewpoint: the ideas and insights in the content are more important than the voice. The voice absolutely is critical in getting your ideas across and making a point effectively, but there is value in having fresh ideas being broadcast into the world.
To the extent that LLMs help this where otherwise people would simply have not spoken up, I think it's OK. Of course, the slopisms are an instant turn-off and limit the reach of those ideas, but at least they're out there now.
Personally I've developed a knack of quickly skimming through the bland language to get a sense of whether there is anything interesting enough to re-read more closely. It's become so ingrained that I don't mind wading through all the noise from all the other lazy, content-free slop to get to that little bit of signal.
If I'm working on something open source I actually would not mind as much. (I can understand why many folks don't like the models being trained with impunity on ALL the open-source code out there, but personally I think it's fine for my open-source code.)
However, I am working on some projects where I think I've stumbled on genuinely interesting and valuable technical approaches, and I'm still figuring out how to capitalize on them. As such I am a bit paranoid about my ideas leaking via some training dataset.
Heck, it could end up as nothing more than a blog post that nobody reads, but at least I get to publish it.
> Training included trillions of tokens of Cursor data which capture a wide-range of user interactions with codebases and software tools.
This -- training on work done on hard, real-world tasks -- seems to be how most frontier models are making capability gains these days. In fact people make decent money doing that for data companies like Mercor. However it's also striking that Cursor managed to gather so much of such data.
Turns out Cursor will train on everything you do unless you opt-out, even if you're already paying for it with cash! Are that many people really not opting out?
This is why it seems like a significant concern to me: It's very clear that typical, run-of-the-mill coding has been completely commoditized, so the primary value remaining is either in novel use-cases and applications, or novel technical solutions to hard problems.
Presumably the value for novel use-cases could be captured by building a business around it via the usual moats (distribution, relationships, network effects, first mover advantage, etc.) so the code and techniques do not matter as much.
However novel technical solutions, which are already hard to monetize without building a whole damn business around it, could at least be capitalized on by simply being able to claim credit for it. I'd at least like the option of being "paid in exposure" if I'm not getting paid in cash. But having them "leaked" unwittingly via the training corpus to whosoever happens to prompt the model with the same problem removes even that option.
I know people have been calling out this risk forever, and I don't use any tool that I can't opt-out of training completely, but the scale at which this is happening -- on an ongoing basis, mind you, after training on the data of the whole world, and that too after paying for the product -- is surprising. I'm bullish on the technology but we really should be way more careful handing these AI companies even more of our intellectual crown jewels.
I used to think Claude Code was released much earlier too, and my initial theory was that OpenAI as a follower had the benefit of more powerful models... but when I looked it up, Codex was first released in April 2025 [1], whereas CC Beta was released in Feb 2025 [2] -- only a couple of months apart!
I'm sure each lab is keenly aware of what the other is doing (how else could they time so many of their releases so close to each other?) so it's highly probable they started developing each app about the same time, and likely even knew the technical details involved. Which is why it's additionally interesting that OpenAI started off with Rust while CC used Electron.
That analogy only works partially, because when IE6 was released, it was the best browser by far. IE only became terrible once MSFT actively stopped developing it, and other browsers kept getting better.
On the other hand, Claude Code was the best coding agent when it was released, but there's no way Anthropic is going to let its cash cow stagnate. Like, I think pretty much all of Anthropic's revenue spike in the last few months was driven by the tokenmaxxing mania.
My take is most of Claude Code's problems originate from insufficient compute capacity and all kinds of workarounds they're doing to mitigate that fundamental limitation.
Interestingly I just skimmed through a video linked in TFA: https://www.youtube.com/watch?v=SlGRN8jh2RI and Boris Cherny explains that they chose TypeScript and React for Claude Code primarily because it was "on distribution" and the models back then just weren't good enough at other languages.
So it's interesting that Codex is written in Rust. Amongst other things it could mean OpenAI had more powerful models that could handle Rust, or their engineers had to handhold the agents a lot more up front, or Rust has structural advantages that could overcome being less represented in the training data.
>... Dario bizarrely copying Altman’s 2023 fire-and-brimstone playbook that had already massively backfired.
I've said this before, they always knew it was terrible marketing, but they just can't help themselves because they actually believe it.
From multiple accounts, the people working at these labs, who are most exposed to the latest models' capabilities and how they're being used out in the world, are simultaneously excited and terrified about what they're building.
In a way that's even scarier than the "Capitalist sociopaths marketing AI to other Capitalist sociopaths" rationale everybody assumes.
I last looked into this a decade+ ago so my memory is fuzzy, and there are a lot of economists who looked at a lot of different things, but the authors I recall from the time were Kenneth Sokoloff, Petra Moser, Adam Mossoff, Zorina Khan, Bhaven Sampat, and Bronwyn Hall. Undoubtedly there are dozens more, but I just happen to remember these offhand.
This book gets a lot of airtime in discussions of IP but the authors have a narrative they are trying to push and they don't let inconvenient things like facts or history get in the way.
The book cherry-picks its sources, and even then contains several mischaracterizations and exaggerations of those works. There are many other economists who have shown significant beneficial aspects of patents with empirical data but they conveniently don’t get mentioned at all.
Yep, the very first chapter of the book starts with patents and steam power, and they got called out on it by actual experts on the topic. Note the “still” in the title – this is after the book was already “revised” once. The book was not revised after this last note, so the exaggerations still stand.
The rest of the book had many similar issues. Once I started digging into their sources, I could not get past the first few chapters, but anecdotally others on HN have also pointed out glaring inaccuracies. I remember thinking wryly that it should be called “Against Intellectual Honesty.”
The original study itself had at least one developer who later revealed that he had filtered out tasks he prefered not to do without AI: https://xcancel.com/ruben_bloom/status/1943536052037390531 -- given the N was 16, and he seems to have been one of the more AI-experienced devs, and we don't know if the other devs did this, the results of the first study itself could be questioned.
Agreed, there's nothing sticky about the models right now, but I see the big AI players making moves that hint at long-term success, and even dominance, in the enterprise space, which is where the real money is. As Microsoft has shown you can create stickiness for things that are already heavily commoditized. For instance, the FDE play itself could be a huge business.
Plus the big factor in my mind for why these frontier labs will succeed -- and this is very fuzzy and hand-wavy -- is that they are very business- and government-savvy and execute extremely fast. They have the most powerful AI models at their fingertips with sharp people who know how to use them, along with insane levels of funding, and are showing the world how a truly AI-empowered organization can operate. I suspect they will thrive despite all the forces arrayed against them.
There's nothing sticky today but you can bet they're working maniacally to fix that. These companies will make most of their money in the enterprise space and there are probably unlimited ways to engineer stickiness in an enterprise setting. Like, MSFT still rakes in those billions despite pretty much every one of their products having commodity competitors.
The AI labs are also making moves to secure long-term enterprise presence, such as their Forward Deployed Engineer strategy. I think that is a trojan horse play that could make enterprises dependent on them forever, much like so many companies are still dependent on IBM's mainframes. As an extreme example, you could imagine a company's core business logic encoded in the weights of a proprietary model custom-trained and hosted by one of these model providers, something even more inscrutable and sticky than ancient COBOL codebases.
Right, that number is more of an estimate of the value proposition of the entire AI industry rather than projections of revenue or valuations... it's essentially an estimate on how much the market could theoretically bear. Whether the companies can capture that value is, to your point, rightly a different question.
I do think open weight and other competitor models, especially with better harnesses, will play a significant role in the equation and will result in less concentration in the market. However, I do also think the big AI companies will capture a lot of that value. Partially for the same reasons that the cloud industry has been growing like gangbusters, even pre-AI, despite on-prem being much cheaper: companies will outsource anything that is not deemed a "core competency" for their business.
A lot of the problems you mentioned will be relegated to the consumer market and won't apply to enterprise contracts -- which is where the real money is.
> That seems really large, but it's ~2-3x Walmart's yearly revenue, and OpenAI and Anthropic both have estimated valuations that compare to Walmart's market cap.
...
It's also before cutthroat pricing really kicks in.
Right, that's more of an estimate on the value proposition of the overall AI industry, rather than valuations of the industry or specific players. While I don't think OpenAI and Anthropic will capture all of the potential upside, I do suspect they will do much better than other players despite the competition (https://news.ycombinator.com/item?id=48740472)
> And this is before we consider that they need to do it for cheaper or why would anyone bother.
Typically yes, but there are reasons companies may be willing to pay the same amount or even more, such as "AI doesn't need sleep, holidays, insurance, or benefits" and "AI is easier to procure and replace than humans."
> The studies I've seen recently (at least in the software space) put it at something like a 10% increase in coding speed...
Curious to see which studies you're looking at, the studies I'm thinking of (some here: https://news.ycombinator.com/item?id=45379452) are from 2024 - 2025, so already old and before agents really took off.
However, your point about meetings and agreements and documenting is much more germane. My theory is that the largest productivity gains -- and subsequent labor displacement -- will come from reducing coordination overhead: https://news.ycombinator.com/item?id=48040999
FWIW I do think that availability of competitive open weight and other non-frontier models, along with improvements in harnesses that can get good results out of these models, will result in less concentration and a healthier marketplace.
However, these frontier labs are also making moves that could let them capture a disproportionate share of the upside. One possibility is a situation analogous to the smartphone manufacturing space, where there are dozens of players but just a handful (e.g. Apple, Samsung in smartphones) capture the lion's share of the revenue.
If LLMs can boost their productivity even by an average of 5% (studies from ~2024 put it in the ~30% range depending on task) that is ~1.5 - 2.5T in value annually. Even if the AI industry can capture a fraction of that, that is a huuuge monetization opportunity.
Note, at 5% productivity boost, humans are not just in the loop, they are the loop. AGI or large-scale replacement of humans is not even needed, but the financial opportunity is already immense, and it scales with how much human productivity can be improved (i.e. how much work can be offloaded to LLMs.)
Now, I don't think AGI will happen soon (or has already happened, depending on how you define it) but I do think humans will be a much smaller part of the loop and large-scale job displacement will happen once companies figure out how to properly use AI.
At this point, the financial upside for the AI industry is extremely high but will be limited by the social turmoil that will inevitably ensue (which we're already seeing brewing in the data center backlash.)
Wait, where are we assigning human-like agency in this case? Agency to me means the ability to do something by itself. Here the LLM is not doing anything, it is just responding with information to queries from people, that those people may then act on. (Which you can say about Google searches too, yet we don't ascribe agency to Google.)
Also, on further thought, I think the "Predator vs Dinosaurs" movie does exist, except it's not obvious at first glance. It's this Adam Driver movie called "65", and while the protagonist is obviously played by a human, the timeline (65 million years ago on Earth) and a couple of key scenes opens the interpretation that these are actually an alien species just portrayed by humans to make the narrative more relatable. In that interpretation, it's possible for this alien species to be an ancestor of the Predator species.
However, it seems to me that all pop music uses the same post-production tricks these days, and so it all sounds somewhat formulaic even if the individual songs themselves are very different. As such the original version may sound more interesting simply by being different.
So the 2001 version sounds more like an "MTV Unplugged" performance whereas the AI version sounds more like the professional and polished version that gets released commercially.
Each has their allure, however. I suspect the AI version will do better with younger crowds.