Benedict Evans may be right after all; frontier models look more and more like telecom companies in the 90s. Billions and billions of investment in infrastructure while others further up the stack captured all the value.
There will be frontier models that are non-commoditized, but they'll be kept guarded and hidden away, and you'll only get the final result, so that they can't be distilled and their harness can't be reverse engineered. They'll be billed like employees, rather than like a tool.
If all you have is the starting point and the finishing point, the lack of the path taken from one point to another limits your ability to train models that can efficiently recreate the work, and increases its cost enough that it's possible the US labs can progress capabilities faster than Chinese labs can distill that behavior.
This just looks like a capex problem. There is no evidence that Anthropic has secret sauce above and beyond access to capital. If there is secret sauce, it's unclear that it changes the required amount of capital by all that much.
China will spend all of the money required to catch up, Google and OpenAI will both spend money to catch up as well. NVidia and others will not allow a frontier lab to become the AI bottleneck.
Isn't that what they are doing already? The model is already guarded and hidden and i only get to send it what i want. Talk with it to clarify my requirements. And i can switch to a different provider for cheaper/better results.
In spite of their deeper pockets, massive datacenters, colosal amounts of user data, and hundreds of thousands of top developers, even Amazon, Meta, Microsoft, and Google are well behind.
I think Evans is completely wrong. There are only 2 truly frontier models. (at least for now). And Anthropic seems to be leaving OpenAI behind so there might be only 1 in the near future. (which is scary/dangerous)
I wish there was a case where I find Evans is wrong. As far as my memory served me, I failed to record a single one.
I disagree that Amazon, Meta, Microsoft, and Google are "well" behind. If anything the frontier model advantage seems to be at best 6 - 9 months. And that the Chinese model are all doing well.
One of Steve Jobs's line, "It is a feature, not a product." Even if Apple were a generation behind or 1 year behind frontier model. The advantage of default is enough to hold a lot of its user.
To put it simply, even if OpenAI or Anthropic were better, there is zero chances they would topple Apple in hardware sales, user or ecosystem. On the other hand, even if Apple's AI were 6 - 9 months or a generation behind, most user would settle for it and damage OpenAI / Anthropic.
Just top of my head (and I don't even follow his takes that closely), just check his takes on Magic Leap which he consistently promoted using quite dramatic langauge (along with the entire AR space) and check how it panned out.
> On the other hand, even if Apple's AI were 6 - 9 months or a generation behind,
Do you mean Google's AI with Apple wrappers? Apple's in-house AI is further behind Google, amd very far from the frontier according to your ranking. IMO, Google is on the frontier - I recall Altman calling for an OpenAI all-hands-on deck when Gemini was released because of how good it was compared to ChatGPT. I also suspect Google has the lowest operating expenses due to scale, experience and luck/planning (TPUs), there will come a time when AI investments will slow down, and the cost of revenue will become more important.
Even their own employees get frustrated if they can't use Claude or Codex. 6-9 months is a big difference and I think it's closer to 9 than 6. And never mind the harness etc are also many months behind.
This is just wishful thinking. I am sure someone from gossip media will also find Apple employees who are ready to leave job if Apple disallows Claude usage.
If anything Apple should notice it is Anthropic has got a really good marketing team and it would be no shame if they pick a trick or two from them.
Remember the implicit “pareto” in “frontier models”.
Anthropic and OpenAI are far behind state of the art for the entire curve except the “extremely expensive for barely measurable improvements” part.
GLM is probably the third most expensive frontier model (benchmarks and reviews will say for sure), and is apparently ~Opus 4.6 for 10% the inference cost.
The last I checked, qwen was still owning the 24-32GiB RAM range (it runs reasonably without a GPU!) and somewhere around 3.5-4 generation models.
Also, even anthropic says Mythos ~= ChatGPT 5.5, so it’s unlikely either one is leaving the other behind. The big problem they both have is they asked for the government to gate keep model releases and use cases, and their wish was granted.
That’s knocked them back 6 months already. Anthropic’s only frontier offering has been taken down.
I use both Claude and Codex and don’t see any meaningful difference between the two. My use case is modeling semi complex physical processes (energy and manufacturing) in code for simulations. I also have to do a good fair of automation via scripting in Python or PowerShell for manipulating data as well as legacy code analysis (C, Fortran, COBOL). Given I provide the models with the information and documentation they need, both perform very similarly. I recently did a full codebase review (for design patterns and vulnerabilities) and both Codex and Fable agreed 100% about the most critical findings. I do very little front end development, although some of my automation scripts have TUIs and again no problem with either Claude or Codex generating them for me. At this point I go with the less expensive, which seems to be Codex. With the $100 plan I rarely hit the limits. With Claude I max out my plan in about 4-6 hours of work.
Yes. Fable is much more organized and consistent at taking small bites of the (sorry) apple when solving a problem. Specifically I'm talking about a machine learning problem I'd been working on for awhile with Opus and it was (and is, again) constantly stating that all the signal is exploited, everything is now overfit, etc, etc, etc. The first day I pointed Fable at the situation I got a 10% improvement by paying attention to the little details that Opus instead took slightly negative results and extrapolated to "fully exploited". I've had to drop back, again, to forcing Opus to explain what it's looked at and the detail it has quietly assumed away.
It's like the difference to talking to two smartest kids in a class, but one really belongs a grade higher - and the other hasn't learned yet to ask the questions that encourage it to dig in that little bit more for the additional multi-order effects.
Had a very similar experience. Opus went "look, t-sne shows your features are neatly clustered" (it didn't) and left it at that. Fable didn't fully explore the problem/data, but it did go much further, implementing models to check for correlations and adjust feature clusters. Opus was able to finish the job after Fable was cut, but required much prodding (doing exactly what you described: pointing it towards things that look off and asking it, are you sure that's all there is to this?).
I have used Fable only once to do an in depth codebase review of a complex system. I asked it to flag deviations from a particular design and also compile a list of vulnerabilities. It took about 15-20 minutes. The result was very similar to Codex for the most critical findings, different suggestions on how to address them but it found exactly the same critical issues as Codex. This is still not a good test to evaluate Fable. But my feeling is that the latest models are all pretty good and now it comes down to your personal setup and workflow, that’s where you can get the productivity gains IMO. It’s like picking between MacOS or Windows as development environment. For some Windows sucks and for a some is the opposite, but both groups of people can be equally productive if they know their environments well and know how to go around their respective limitations.
I constantly hit safety blocks in Fable (I’m trying to write secure software, which is equivalent to finding security holes, so banned).
I didn’t use it on big enough tasks to notice any improvement.
I had been hitting plan limits pretty regularly, but fixed it by changing my workflow. That also increased the success rate of claude by an order of magnitude.
> I think Evans is completely wrong. There are only 2 truly frontier models. (at least for now). And Anthropic seems to be leaving OpenAI behind so there might be only 1 in the near future. (which is scary/dangerous)
Truly fascinating ecosystem and community in general, as experiences differ so wildly. Anthropic's models seems far behind OpenAI to me, especially when you get into "Pro" territory, and there doesn't seem to be any worthy competition to Pro Mode available at all.
And this is said with someone who use both platforms, and spend a lot of my day interacting with agents and LLMs in various ways. The interesting part is that probably so do you too, and probably your experience and what you share lines up with what you experience! Yet we come away with basically opposite takeaways :) I don't think either of us are wrong either, somehow.
I agree with what you're saying.
I have a Claude plan for work and I prefer using Claude more than any other LLM I've tried.
Having recently tried the Codex 100€ plan with GPT-5.5 in high/xhigh, I don't think it's worse that the Opus models, just different.
I've noticed that depending on how you talk to it, you get wildly different outputs. This seems to happen less with Opus: it mostly understand what I want. GPT is often a bit too literal.
> I've noticed that depending on how you talk to it, you get wildly different outputs. This seems to happen less with Opus: it mostly understand what I want. GPT is often a bit too literal.
Yeah, exact prompting matters a lot, seemingly more than people think. There is definitely tradeoffs between how literal the models takes the prompts, on one hand it's useful for the model to ignore their own instinct when you know better, so they don't go chasing geese randomly, but on the other hand it's useful sometimes when they self-direct, when you misworded something and it's obvious you meant something different because of the context, and similar things. They're basically good at different things.
Really agree every model isn't equal and they aren't as interchangeable without adjusting how you prompt them as people seem to think.
People use a model as their daily driver, get very familiar with it and it's behavior, and then go and use another model and have a hard time. It's very difficult to separate "the model is bad" from "the model works differently".
> Also missing from these discussions are e.g. Qwen, which is at least as good as one back from OpenAI or Anthropic’s frontiers.
They're missing in the discussion because the ones you can run locally, aren't actually "one step away from other closed-source labs" in practice when you use them. They might benchmark as such, but they're sadly far away from measuring up to those scores except for very specific use cases, even when you have say 96GB of VRAM available to run the bigger models even most (at home) consumers won't be able to run.
As someone who just spent the last three days (tried using both, ended up using mostly Codex) implementing DiffusionGemma in Rust, I think they're more or less equal when it comes to machine learning and AI. They get stuck at different points, but wouldn't say one is a clear winner over the other. HPC I have no idea so I'll take your word for it :)
You mean the model that was available for a whole of three days? No, I had played around with it a tiny bit, but not much than that. I guess time will tell if it gets close.
That's true now, but long-term (maybe just a few years) it doesn't seem feasible for the status quo to continue from a financial point of view.
Spend for compute seems like it needs to increase to get the next iterations of models, and even if they IPO the money might run out before they can solidify their revenue streams.
All while Google just needs to survive long enough with their good-enough models and do it without really putting themselves in any existential financial risk.
And ideally the chinese models are also still there keeping everyone honest.
The true dystopic worst case is a Google monopoly on cutting edge AI.
I think it's highly likely that there will remain one or two companies on the very bleeding edge of AI development for the foreseeable future.
But what I think a lot of people miss is that the market for the truly bleeding edge (developing bio-tech, building the most sophisticated software stacks (probably with a tilt towards simulation, GPU kernel optimization, etc)) is not the whole market.
There's a plethora of use-cases for models that are not on the bleeding edge. If I can solve my relatively simple problems with an off-the-shelf model for a minuscule fraction of the cost of the frontier, I'm going to.
There was a time when Opus was the only model really worth using, I think that was maybe 4.4 or 4.5, but I agree Sonnet is pretty good now and can be used quite often.
Maybe I’m alone in thinking this but I think the long term victor will be the one that works out pricing best.
Fable might well be a better model but it’s too expensive for everyday AI use. Definitely if we’re talking about the kind of stuff you’re going to want to do on your phone. Even for coding, I’m not going to reach for Fable (well, when I can…) for 95% of the work I do.
I don’t believe a mature AI industry is going to have a one size fits all, single winner.
Yes, and pricing is one of the features of a commodity, because users can jump back and forth between services, it becomes a pricing race to the bottom. Agree also that you don’t need the best model all the time. You could have the most powerful model draft the design, requirements, guidelines, policies or whatnot then get the lower tier models execute it. Then again you can have the most powerful model do the testing and review, and give back feedback, rinse and repeat. Just like in the real world you don’t need an entire staff of lead engineers.
I'm perfectly happy at claude opus 4.6. All improvements since then have not meaningfully improved my day to day. If i can get 4.6 on my laptop for 5-10k, i'd gladly start shifting my ~1k/month Anthropic spend over.
Some of the harness even let you run a local model for most things, and only pay for the latest frontier models when needed, which cuts down cost drastically.
> And Anthropic seems to be leaving OpenAI behind so there might be only 1 in the near future.
Well, in domains like SWE where Anthropic's putting in the effort. I don't they'll make the claims that OpenAI makes about how their models are pushing the life sciences forward, for example.
It is much better. Imagine if the whole Manhattan project could have been outsourced and costs you nothing. I expect in a short time that open source models will be almost or almost parity by 2030 and running on consumer devices.
Market phenomena like this are a bit like the Manhattan project in that you pay for it, and make use of it, whether you want to or not. It's functionally very similar to the government doing something.
Last I checked the telcos made plenty of money in the 90s. Should Verizon be getting a cut of my Claude Pro subscription, since I use FIOS to access it?
This is what everybody is TRYING to do. They built something and will do everything they can to charge outsized rent on it far past the value it provides to take revenue from anyone downstream.
The fact that telcos couldn't charge rent was a primary reason the Internet was so successful.
Remember $0.10 per text message? You bet in some alternate timeline AT&T charges $0.10 per webpage visit and we're stuck on 100kbps connections because the monopoly doesn't want to innovate.
I haven’t fact checked, but according to Evans big telecom builders didn’t make a lot of money after all the capacity investment. Some actually went bankrupt or got acquired as distressed assets. Big tech was very profitable monetizing that same infrastructure.
Some went bankrupt, with Worldcom being the most famous example...though that was fraud. But even those that remained had large amounts of debt that never ends as there's always CAPEX for upgrades to networks to fund (both fixed and wireless). Now a lot of the debt is also from some of them going on media ownership adventures, but even those that didn't eventually got folded into larger companies (eg Sprint).
Most of the ones that survived did so due to being able to pick up distressed assets and at values that could then be profitably monetized - a move that it would not surprise me to see repeat itself in the LLM space (we'll see).
He denies comparing them to telecom companies and even says at various points in his writing. Instead he compares their usage to the usage of mobile data.
Extremely tangential, but this is my favourite upshot of AI. For decades, companies have been walling off their services and forcing us into their fuckass UIs. Now over the course of the last twelve months, suddenly everything has an MCP and I can use it through my command line chat interface.
Any company that doesn't adapt gets so hammered by people's AI-DIY web scrapers that they have no choice but to cave.
The play here seems pretty evidence, if I may assume. Apple creates an interface that is generalized enough so you can easily swap models, and while Claude is preferred by Apple today, it may be any provider or even local models in the future, and the APIs the developers use remain the same, so "migration" becomes easier.
for the on-device model, yes it runs on the Neural Engine (at the moment) so a newer chip means faster, cheaper local inference. For the server side path this Claude package is about your machine is irrelevant since it's a network call. The same API covers both, so "best machine for AI" only bites when the session is actually local.
But we can imagine that the balance of what's on-device vs what's remote will move continuously towards the former as time, improved HW and improved local models keep progressing
Apple's been trying to make the marketing appeal that "Private Compute Cloud" is also a hardware project. Given it seems to rely on low level details of device Hardware Security Modules, it's maybe even at least a little bit more than just "marketing spin".
looks like it is not "Private iCloud Compute" at all.
Anthropic literally says "Requests go directly from your app to the Claude API; Apple is not in the request path and does not see prompts or responses." — Apple straight up lied
No, that post is about Claude for Foundation Models. That is not the same as Apple Intelligence.
the Swift package for Claude for Foundation Models is about sending calls to Claude. That had nothing to do with Apples models which do use local models and models on Private Cloud Compute.
Your accusation that "Apple straight up lied" is based on misunderstanding TFA.
It's been clear for years now that eventually ai will be embedded at the os level. Apple even recognized it way back when they first introduced Apple Intelligence. Yes they're commoditizing llms or whatever. But this has been a user facing feature they've been iterating on for years now
They’re typically a bit better on high TDP stuff, and a bit worse on low TDP. They mostly match in the middle. I have a $500 AMD NUC and a slightly older $2000 MBP. Inference throughput is within 2x.
The comparison is a little messy: AMD currently maxes out at 128GB of RAM vs Apple’s discontinued 512. Apple has nothing to rival the Steam Deck.
This is what originally made Microsoft the most lucrative tech company of its day.
Android succeeded at this to an extent with phones, but Apple has been able to keep its products differentiated enough in the minds of consumers to maintain their premium pricing. So far.
The betas of the next OS's include a Siri AI chatbot, and the AI features are built into various parts of the OS. A user has no idea what model is powering any of it - Apple controls the UX.
The article is about (from the eyes of a user) white-labeled usage of Claude models on Apple devices, this subthread is about white-labeled usage of LLMs on Apple devices, how is it not relevant?
> The users won't know if you used Foundation Models API or integrated with OpenAI/Anthropic/Gemini SDK directly.
That's the point! That's the whole "white-labeling" part, and what the commentator earlier is talking about. You're very close in understanding the context here!
I think there is an opportunity for a new hardware company to enter the market. I know this is just hypothetical but I believe that AI is revolutionary enough where a new approach to hardware and UI/UX will enable far more value to be derived from AI. I think the incumbents like Apple will stick to their familiar platforms and could get beaten out by a new competitor that is AI native to the core. Maybe? ¯\_(ツ)_/¯