> I think the reality is that… people are willing to take on MUCH greater risk today for reward than they were prior to the pandemic.
I’ll quibble that people have no idea of the risks they’re taking. I read somewhere that amateur stock traders spent something like 4 minutes researching their purchase. Balanced portfolios are just too boring and tedious.
> Anthropic relies heavily on a combination of chips designed by Amazon Web Services known as Trainium, as well as Google’s in-house designed TPU processors, to train its AI models. Google largely uses its TPUs to train Gemini. Both chips represent major competitive threats to Nvidia’s best-selling products, known as graphics processing units, or GPUs.
So which leading AI company is going to build on Nvidia, if not OpenAI?
"Largely" is doing a lot of heavy lifting here. Yes Google and Amazon are making their own GPU chips, but they are also buying as many Nvidia chips as they can get their hands on. As are Microsoft, Meta, xAI, Tesla, Oracle and everyone else.
google buys nvidia GPUs for cloud, I don't think they use them much or at all internally. The TPUs are both used internally, and in cloud, and now it looks like they are delivering them to customers in their own data centers.
The various AI accelerator chips, such as TPUs and NVidia GPUs, are only compatible to extent that some of the high level tools like PyTorch and Triton (kernel compiler) may support both, which is like saying that x86 and ARM chips are compatible since gcc supports them both as targets, but note this does not mean that you can take a binary compiled for ARM and run it on an x86 processor.
For these massive, and expensive to train, AI models the differences hit harder since at the kernel level, where the pedal hits the metal, they are going to be wringing every last dollar of performance out of the chips by writing hand optimized kernels for them, highly customized to the chip's architecture and performance characteristics. It may go deeper than that too, with the detailed architecture of the models themselves tweaked to best perform on a specific chip.
So, bottom line is that you can't just take a model "compiled to run on TPUs", and train it on NVidia chips just because you have spare capacity there.
1. Sit out and buy the tech you need from competitors.
2. Spend to the tune of ~$100B+ in infra and talent, with no guarantee that the effort will be successful.
Meta picked option 2, but Apple has always had great success with 1 (search partnership with Google, hardware partnerships with Samsung etc.) so they are applying the same philosophy to AI as well. Their core competency is building consumer devices, and they are happy to outsource everything else.
This whole thread is about whether the most valuable startup of all time will be able to raise enough money to see the next calendar year.
It's definitely rational to decide to pay wholesale for LLMs given:
- consumer adoption is unclear. The "killer app" for OS integration has yet to ship by any vendor.
- owning SOTA foundation models can put you into a situation where you need to spend $100B with no clear return. This money gets spent up front regardless of how much value consumers derive from the product, or if they even use it at all. This is a lot of money!
- as apple has "missed" the last couple of years of the AI craze, there has been no meaningful ill effects to their business. Beyond the tech press, nobody cares yet.
Well they tried and they failed. In that case maybe the smartest move is not to play. Looks like the technology is largely turning into a commodity in the long run anyways. So sitting this out and letting others make the mistakes first might not be the worst of all ideas.
I mean, they tried. They just tried and failed. It may work out for them, though — two years ago it looked like lift-off was likely, or at least possible, so having a frontier model was existential. Today it looks like you might be able to save many billions by being a fast follower. I wouldn’t be surprised if the lift-off narrative comes back around though; we still have maybe a decade until we really understand the best business model for LLMs and their siblings.
They tried to do something that probably would have looked like Copilot integration into Windows, and they chose not to do that, because they discovered that it sucked.
So, they failed in an internal sense, which is better than the externalized kind of failure that Microsoft experienced.
I think that the nut that hasn't been cracked is: how do you get LLMs to replace the OS shell and core set of apps that folks use. I think Microsoft is trying by shipping stuff that sucks and pissing off customers, while Apple tried internally declined to ship it. OpenClaw might be the most interesting stab in that direction, but even that doesn't feel like the last word on the subject.
I think you are right. Their generative AI was clearly underwhelming. They have been losing many staff from their AI team.
I’m not sure it matters though. They just had a stonking quarter. iPhone sales are surging ahead. Their customers clearly don’t care about AI or Siri’s lacklustre performance.
> Their customers clearly don’t care about AI or Siri’s lacklustre performance.
I would rather say their products didn’t just loose in value for not getting an improvement there. Everyone agrees that Siri sucks, but I’m pretty sure they tried to replace it with a natural language version built from the ground up, and realised it just didn’t work out yet: yes, they have a bad, but at least kinda-working voice assistant with lots of integrations into other apps. But replacing that with something that promises to do stuff and then does nothing, takes long to respond, and has less integrations due to the lack of keywords would have been a bad idea if the technology wasn’t there yet.
We do know that they made a number of promises on AI[1] and then had to roll them back because the results were so poor[2]. They then went on to fire the person responsible for this division[3].
That doesn't sound like a financial decision to me.
Sure, Siri is, but do people really buy their phone based off of a voice assistant? We're nowhere near having an AI-first UX a la "Her" and it's unclear we'll even go in that direction in the next 10 years.
I think Apple is waiting for the bubble to deflate, then do something different. And they have the ready to use user base to provide what they can make money from.
If they were taking that approach, they would have absolutely first-class integration between AI tools and user data, complete with proper isolation for security and privacy and convenient ways for users to give agents access to the right things. And they would bide their time for the right models to show up at the right price with the right privacy guarantees.
They apparently are working on and are going to release 2(!) different versions of siri. Idk, that just screams "leadership doesn't know what to do and can't make a tough decision" to me. but who knows? maybe two versions of siri is what people will want.
It sounds like the first one, based on Gemini, will be more a more limited version of the second ("competitive with Gemini 3"). IDK if the second is also based on Gemini, but I'd be surprised if that weren't the case.
Seems like it's more a ramp-up than two completely separate Siri replacements.
Nvidia had the chance to build its own AI software and chose not to. It was a good choice so far, better to sell shovels than go to the mines - but they still could go mining if the other miners start making their own shovels.
If I were Nvidia I would be hedging my bets a little. OpenAI looks like it's on shaky ground, it might not be around in a few years.
They do build their own software, though. They have a large body of stuff they make. My guess is that it’s done to stay current, inform design and performance, and to have something to sell enterprises along with the hardware; they have purposely not gone after large consumer markets with their model offerings as far as I can tell.
That’s interesting, I didn’t know that about Anthropic. I guess it wouldn’t really make sense to compete with OpenAI and everyone else for Nvidia chips if they can avoid it.
It's almost as if everyone here was assuming that Nvidia would have no competition for a long time, but it has been known for a long time, there are many competitors coming after their data center revenues. [0]
> So which leading AI company is going to build on Nvidia, if not OpenAI?
It's xAI.
But what matters is that there is more competition for Nvidia and they bought Groq to reduce that. OpenAI is building their own chips as well as Meta.
The real question is this: What happens when the competition catches up with Nvidia and takes a significant slice out of their data center revenues?
> [Seth is using AI to try] to take over the job of the farmer. Planting, harvesting, etc. is the job of a farmhand (or custom operator).
Ok then Seth is missing the point of the challenge: Take over the role of the farmhand.
> Everyone is working to try to automate the farmhand out of a job, but the novelty here is the thinking that it is actually the farmer who is easiest to automate away.
Everyone knows this. There is nothing novel here. Desk jockeys who just drive computers all day (the Farmer in this example) are _far_ easier to automate away than the hands-on workers (the farmhand). That’s why it would be truly revolutionary to replace the farmhand.
Or, said another way: Anything about growing corn that is “hands on” is hard to automate, all the easy to automate stuff has already been done. And no, driving a mouse or a web browser doesn’t count as “hands on”.
> all the easy to automate stuff has already been done.
To be fair, all the stuff that hasn't been automated away is the same in all cases, farmer and farmhand alike: Monitoring to make sure the computer systems don't screw up.
The bet here is that LLMs are past the "needs monitoring" stage and can buy a multi-million dollar farm, along with everything else, without oversight and Seth won't be upset about its choices in the end. Which, in fairness, is a more practical (at least less risky form a liability point of view) bet than betting that a multi-million dollar X9 without an operator won't end up running over a person and later upside-down in the ditch.
He may have many millions to spend on an experiment, but to truly put things to the test would require way more than that. Everyone has a limit. An MVP is a reasonable start. v2 can try to take the concept further.
More specifically, when employees are granted options contracts the strike price of those contracts is based on the last valuation of the company prior to the grant. If all is going well and the valuation is increasing those options are also increasing in value. Here we have a sale which values the company lower than the prior valuation. Recent option grants will likely be underwater, earlier grants would still be profitable.
> The valuations you see, like $12bn, are for preferred stock.
No, the valuation is for the whole company, all of its shares, preferred and common. How this value is distributed among shareholders depends on the deal, but generally there is a “seniority”, roughly: creditors (debt holders) are paid first, preferred shares next, then common shareholders last. This order can be negotiated as part of the sale.
> So no employees got stock priced at $12bn, but all of them get paid at a $5.15bn valuation.
It’s just not possible to know what each individual employee’s outcome is. We don’t know how much of that 5.5 billion will be left over for common shareholders including the employees. Note that employees have received salaries so their overall outcome is greater than zero dollars, but perhaps their total compensation outcome is lower than they hoped for the time they put in.
> Not saying they did well, but depending on the 409a valuations, they still might have made money.
Yes, some might have and some might have not. We just don’t know without more details.
Edit: singron’s answer (sibling comment) attempts to model the employee outcome in a rough but reasonable way.
The way they get to $12.5bn is multiplying the preferred share price by the total outstanding shares. But the common shares, while still included in that calculation, are not worth the same amount of money.
They have a different strike price for options that is set via a 409a.
It’s possible that employees got, at the peak, grants with strike prices at a $2bn 409a valuation. We don’t know. What we do know is that no employee ever got grants with a strike price of a $12.5bn valuation. That’s just not how this works.
The tariffs haven’t really started hitting consumers yet, believe it or not. Major retailers rushed to get their Christmas goods into the U.S. before the deadlines. In 2026 we’ll start to see more of the impacts.
Probably because the bidder is an aggregator sending the referral to many law firms, not just one. Individual law firms thus get outbid for an exclusive referral click, but can still pay the aggregator for a non-exclusive referral.
How does that work? The user is expecting to click a link and go to a page, not multiple pages. Or does the aggregator have an interstitial page? It sounds like a generally good idea but I don't understand how it works from the user's perspective.
Roughly: An individual lawyer creates a "national brand" law firm and advertises it widely to find new clients. Of course, an individual lawyer can only take so many clients a year, so advertising nationally doesn't make sense since they'd quickly have more clients than they could ever manage. Additionally, a lawyer advertising nationally would need to be licensed in many jurisdictions and possibly also be able to handle many different legal specialties. This is essentially impossible for one person to do themselves.
Instead, the lawyer sends clients to their "associates" in the client's jurisdiction with the specialty that the client needs. For this referral, the lawyer gets a fee, agreed to beforehand.
That is not what Eric S. Raymond (esr) was describing.
GNUnix was developed using the Cathedral-style, Linux was developed using the bazaar-style. How Linux development was coordinated was thought to be impossible for something that had to be as solid as an operating system. The essay is a deep dive, exploring the conditions that the Linux project needed to ship an OS.
> In large companies, speaking up about what you see as a “bad project” is a good thing. But only in moderation. Sometimes the mark of seniority is realizing that arguing with people who won’t listen isn’t worth it; it’s better to save your counsel.
> Teams don't just work together magically and "organically".
This is the “employees are resources” mentality, and is common in mature companies. In a startup, however, you need to hire for individuals that will “make it rain”, and not wait around for some process or manager to tell them to communicate or do work. If your employees are not coming together as a team and figuring things out without your handholding you’ve hired the wrong people.
I've never worked in web development, where it seems to me the majority of LLM coding assistants are deployed.
I work on safety critical and life sustaining software and hardware. That's the perspective I have on the world. One question that comes up is "why does it take so long to design and build these systems?" For me, the answer is: that's how long it takes humans to reach a sufficient level of understanding of what they're doing. That's when we ship: when we can provide objective evidence that the systems we've built are safe and effective. These systems we build, which are complex, have to interact with the real world, which is messy and far more complicated.
Writing more code means that's more complexity for humans (note the plurality) to understand. Hiring more people means that's more people who need to understand how the systems work. Want to pull in the schedule? That means humans have to understand in less time. Want to use Agile or this coding tool or that editor or this framework? Fine, these tools might make certain tasks a little easier, but none of that is going to remove the requirement that humans need to understand complex systems before they will work in the real world.
So then we come to LLMs. It's another episode of "finally, we can get these pesky engineers and their time wasting out of the loop". Maybe one day. But we are far from that today. What matters today is still how well do human engineers understand what they're doing. Are you using LLMs to help engineers better understand what they are building? Good. If that's the case you'll probably build more robust systems, and you _might_ even ship faster.
Are you trying to use LLMs to fool yourself into thinking this still isn't the game of humans needing to understand what's going on? "Let's offload some of the understanding of how these systems work onto the AI so we can save time and money". Then I think we're in trouble.
> Are you trying to use LLMs to fool yourself into thinking this still isn't the game of humans needing to understand what's going on?
This is a key question. If you look at all the anti-AI stuff around software engineering, the pervading sentiment is “this will never be a senior engineer”. Setting aside the possibility of future models actually bridging this gap (this would be AGI), let’s accept this as true.
You don’t need an LLM to be a senior engineer to be an effective tool, though. If an LLM can turn your design into concrete code more quickly than you could, that gives you more time to reason over the design, the potential side effects, etc. If you use the LLM well, it allows you to give more time to the things the LLM can’t do well.
Fully agree. In my own usage of AI (which I came to a bit late but have tried to fully embrace so I know what it can and can't do) I've noticed a very unusual side effect: I spend way more of my time documenting and reviewing designs than I used to, and that has been a big positive. I've always been very (maybe too) thoughtful about design and architecture, but I usually focused on high-level design and then would get to some coding as a way of evaluating/testing my designs. I could then throw away v0 using lessons learned and start a v1 on a solid track. Now however, I find myself able to get a lot further in nailing down the design to the point I don't have to build and throw away v0. The prototype is often highly salvageable with the help of the LLM doing the refactoring/iterating that used to make "starting over" a more optimal path. That in turn allows me to maintain the context and velocity of the design much better since there aren't days, or weeks, or even months between the "lessons learned" that then have to go back and revise the design.
The caveat here though, is if I didn't have the decades of experience writing/designing software by hand, I don't think I'd have the skills needed to reap the above benefit.
" They make it easier to explore ideas, to set things up, to translate intent into code across many specialized languages. But the real capability—our ability to respond to change—comes not from how fast we can produce code, but from how deeply we understand the system we are shaping. Tools keep getting smarter. The nature of learning loop stays the same."
Learning happens when your ideas break, when code fails, unexpected things happen. And in order to have that in a coding agent you need to provide a sensitive skin, which is made of tests, they provide pain feedback to the agent. Inside a good test harness the agent can't break things, it moves in a safe space with greater efficiency than before. So it was the environment providing us with understanding all alone, and we should make an environment where AI can understand what are the effects of its actions.
maybe. I think we're really just starting this, and I suspect that trying to fuse neural networks with symbolic logic is a really interesting direction to try to explore.
that's kind of not what we're talking about. a pretty large fraction of the community thinks programming is stone cold over because we can talk to an LLM
and have it spit out some code that eventually compiles.
personally I think there will be a huge shift in the way things are done. it just won't look like Claude.
I don't know why you're being downvoted, I think you're right.
I think LLMs need different coding languages, ones that emphasise correctness and formal methods. I think we'll develop specific languages for using LLMs with that work better for this task.
Of course, training an LLM to use it then becomes a chicken/egg problem, but I don't think that's insurmountable.
I don't think "understanding" should be the criteria, you can't commit your eyes in the PR. What you can commit is a test that enforces that understanding programatically. And we can do many many more tests now than before. You just need to ensure testing is deep and well designed.
I suspect that we are going to have a wave of gurus who show up soon to teach us how to code with LLMs. There’s so much doom and gloom in these sorts of threads about the death of quality code that someone is going to make money telling people how to avoid that problem.
The scenario you describe is a legitimate concern if you’re checking in AI generated code with minimal oversight. In fact I’d say it’s inevitable if you don’t maintain strict quality control. But that’s always the case, which is why code review is a thing. Likewise you can use LLMs without just checking in garbage.
The way I’ve used LLMs for coding so far is to give instructions and then iterate on the result (manually or with further instructions) until it meets my quality standards. It’s definitely slower than just checking in the first working thing the LLM churns out, but it’s sill been faster than doing it myself, I understand it exactly as well because I have to in order to give instructions (design) and iterate.
My favorite definition of “legacy code” is “code that is not tested” because no matter who writes code, it turns into a minefield quickly if it doesn’t have tests.
How do you know that it's actually faster than if you'd just written it yourself? I think the review and iteration part _is_ the work, and the fact that you started from something generated by an LLM doesn't actually speed things up. The research that I've seen also generally backs this idea up -- LLMs _feel_ very fast because code is being generated quickly, but they haven't actually done any of the work.
Because I’ve been a software engineer for over 20 years. If I look at a feature and feel like it will take me a day and an LLM churns it out in a hour including the iterating, I’m confident that using the LLM was meaningfully faster. Especially since engineers (including me) are notoriously bad at accurate estimation and things usually take at least twice as long as they estimate.
I have tested throwing several features at an LLM lately and I have no doubt that I’m significantly faster when using an LLM. My experience matches what Antirez describes. This doesn’t make me 10x faster, mostly because so much of my job is not coding. But in term of raw coding, I can believe it’s close to 10x.
> I know exactly what the result should be, the LLM is just typing it for me.
This is the mental model people should be working with. The LLM is there to tighten the loop from thought to code. You doing need to test it like an engineer. You just need to use it to make you more efficient.
It so happens that you *can^ give an LLM half-baked thoughts and it will sometimes still do a good job because the right thing is so straightforward. But in general the more vague and unclear your own thoughts, the lower quality the results, necessitating more iterations to refine.
> My favorite definition of “legacy code” is “code that is not tested” because no matter who writes code, it turns into a minefield quickly if it doesn’t have tests.
Unfortunately, "tests" don't do it, they have to be "good tests". I know, because I work on a codebase that has a lot of tests and some modules have good tests and some might as well not have tests because the tests just tell you that you changed something.
> My favorite definition of “legacy code” is “code that is not tested” because no matter who writes code, it turns into a minefield quickly if it doesn’t have tests.
On the contrary, legacy code has, by definition, been battle tested in production. I would amend the definition slightly to:
“Legacy code is code that is difficult to change.”
Lacking tests is one common reason why this could be, but not the only possible reason.
It’s from Working Effectively with Legacy Code. I don’t recall the exact definition but it’s something to that effect. Legacy = lack of automated tests.
The biggest barrier to changing code is usually insufficient automated testing. People are terrified of changing code when they can’t verify the results before breaking production.
More glibly legacy code is “any code I don’t want to deal with”. I’ve seen code written 1 year prior officially declared “legacy” because new coding standards were being put in place and no one wanted to update the old code to match.
I think it was Cory Doctorow who compared AI-generated code to asbestos.
Back in its day,
asbestos was in everything,
because of how useful it seemed.
Fast forward decades and now asbestos abatement is a hugely expensive and time-consuming requirement for any remodeling or teardown project.
Lead paint has some of the same history.
I see where you're coming from, and I agree with the implication that this is more of an issue for inexperienced devs. Having said that, I'd push back a bit on the "legacy" characterization.
For me, if I check in LLM-generated code, it means I've signed off on the final revision and feel comfortable maintaining it to a similar degree as though it were fully hand-written. I may not know every character as intimately as that of code I'd finished writing by hand a day ago, but it shouldn't be any more "legacy" to me than code I wrote by hand a year ago.
It's a bit of a meme that AI code is somehow an incomprehensible black box, but if that is ever the case, it's a failure of the user, not the tool. At the end of the day, a human needs to take responsibility for any code that ends up in a product. You can't just ship something that people will depend on not to harm them without any human ever having had the slightest idea of what it does under the hood.
Some of those words appear in my comment, but not in the way you're implying I used them.
My argument was that 1) LLM output isn't inherently "legacy" unless vibe coded, and 2) one should not vibe code software that others depend on to remain stable and secure. Your response about "abandonware" is a non sequitur.
I presume that through some process one can exorcise the legacy/vibe-codiness away. Perhaps code review of every line? (This would imply that the bottleneck to LLM output is human code review.) Or would having the LLM demonstrate correctness via generated tests be sufficient?
Just to clarify, you're inferring several things that I didn't say:
* I was agreeing with you that all vibe code is effectively legacy, but obviously not all legacy code is vibe code. Part of my point is also that not all LLM code is vibe code.
* I didn't comment on the dependability of legacy code, but I don't believe that strict vibe code should ever be depended on in principle.
As far as non-vibe coding with LLMs, I'd definitely suggest some level of human review and participation in the overall structure/organization. Even if the developer hasn't pored through it line by line, they should have signed off on the tech stack/dependencies/architecture and have some idea of what the file layout and internal modules/interfaces look like. If a major bug is ever discovered, the developer should know enough to confidently code review the fix or implement it by hand if necessary.
Take responsibility by leaving a good documentation of your code and a beefy set of tests, future agents and humans will have a point to bootstrap from, not just plain code.
I’ll quibble that people have no idea of the risks they’re taking. I read somewhere that amateur stock traders spent something like 4 minutes researching their purchase. Balanced portfolios are just too boring and tedious.
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