The problem with these kinds of analysis is that they're surface level. They criticize "number go up" but rely on it to make comparisons to other bubble events. For instance, AI infra buildout is like 2008 because number went up in both.
For instance:
> So the speculative discourse only works as long as investors subsidize the use of the technology. When that subsidy stops, these AI firms have to actually deliver value, or customers won’t buy it.
Are investors subsidizing the technology? There's upfront build out, but Anthropic is profitable and I believe the big labs are profitable on inference.
In comparison consider the real estate bubble in 2008. Why was real estate going up so much? Was there a surge of people coming into the country driving up demand? Were people using these houses? No it was purely inflationary driven by cheap money and financial engineering.
It also relies on arguments that this technology is all speculative, like one day we'll figure out how to use AI and demand will be high. But the demand is already there. Everybody is using AI, revenues are insane, it's the fastest growing product in history. It spans consumers and increasingly businesses.
The comparison to dot-com crash is also superficial. Was dot-com a bubble? I don't know, if you were transported to peak 2000 hype, would you argue "you guys are in a bubble, and the internet impact on the economy will be no greater than that of the fax machine"? No, of course not. In this case the supply outstripped demand. The build out and hype was early. But with AI you're seeing real usage.
It's not contagion. People are using this technology, not because it's cool and (apart from a few examples) because their bosses are forcing them to hit token metrics, but because it's actually useful and people are finding more uses every day.
It's just lazy. There is real risk that the build out is too much. But to make that argument you would have to say that the model intelligence would asymptote or become increasingly expensive such that its not worth it. Or that demand for broad intelligence is capped somehow. But saying we're in a bubble just because number goes up is not a strong argument.
> Are investors subsidizing the technology? There's upfront build out, but Anthropic is profitable and I believe the big labs are profitable on inference.
That's the thing about a bubble though. It's not about if things are profitable; it's if they will produce the profit in the future to support the current stock price.
Companies in 2000 were profitable and to this day still are (ex. Cisco [1] which despite only 26 years of inflation only reached it's dot-com stock price this year).
IIUC, Anthropic is $1T valuation on $10B revenue and $0.5B profit.
Google's has $4T valuation on $400B revenue and $100B profit. Which (dividing by 4 to get same valuation as Anthrophic) is $100B revenue and $25B profit.
IIUC, World GDP is ~$100T so Anthrophic "just" needs to get 0.1% of all economic activity to depend on them. Which to me actually seems like a tall order. Sure Google does more than that but Google spent ~30 years getting into that position.
So, Anthrophic 1/10 the required revenue despite every company in America pushing as hard as possible to use AI. What will it take for them to get it?
> So, Anthrophic 1/10 the required revenue despite every company in America pushing as hard as possible to use AI. What will it take for them to get it?
Anthropic revenue (annualized):
February 2026 $14 billion,
April 2026 $30 billion
May 2026 $47 billion
The market prices future revenue. What other company doubles revenue every few months? Where does it stop is the question. You need a few more doublings and things start making sense.
"Profitable on inference". Isn't that exactly the same a physical business saying "our widgets have a marginal cost of 95 cents to make, and we can sell for a dollar, so we're profitable, as long as you forget we have a $92 kajillion loan on the factory that has to be serviced."
It raises a chain of interesting questions: what if we pulled the plug on the expensive part (the training and associated infrastructure) in pursuit of making it economically viable?
- How much of the audience is using it based on the long-term promise? "It's still imperfect and annoying, but I want to be ready for when it finally turns into Lieutenant Commander Data." If the vendors said "this is what you actually get once the honeymoon ends", would customers still be satisfied with the product and pricing?
- How do you stop the game of economic chicken? If Anthropic said "Fable is the last model we can offer (until we can pay down the costs to get there)", any competitor with a dime of runway left, will spend a cent of it on training and 9 cents advertising "do you want to be stuck with old tech?"
> "Profitable on inference". Isn't that exactly the same a physical business saying "our widgets have a marginal cost of 95 cents to make, and we can sell for a dollar, so we're profitable, as long as you forget we have a $92 kajillion loan on the factory that has to be serviced."
Yes, that's called an investment. That money's already spent. Look at the marginal revenue of many business. What's going to happen? They'll raise prices because legacy costs? And then the people distilling these models will come in w/out the baggage. Cars for instance have a huge up front cost in design and manufacturing capacity and they only sell for 5-20% more than it costs them to make that one unit. It's a competitive industry
My point is that you can't cherry pick a profitable business unit if you don't have a story for how the entire business can operate profitably.
Cars have low margins, but they generally don't rely on an ongoing infusion of investor money to balance the books. The overall venture still has to turn a profit. Nobody is walking into Hyundai HQ and saying "We are going to sell Sonatas for $6,750 each, because if we can do it long enough, some magic will happen and we'll end up back in the green."
TBH, I'm not quite sure what the "some magic will happen" angle is for AI.
Compute gets cheaper, but I suspect the training arms race is running up costs faster still.
We're already seeing hints of price balking (definitely heard people at work saying they're hesitant about Fable due to the costs) so it's unclear if there's headroom there.
TBH, the best answer I can figure right now is that many players are hoping for a competitor's flameout-- the dream of being the last man standing, and then able to dictate market terms.
> Was dot-com a bubble? I don't know, if you were transported to peak 2000 hype, would you argue "you guys are in a bubble, and the internet impact on the economy will be no greater than that of the fax machine"?
I am old enough to remember this. As a young guy who was on BBSes and IRC, it was obvious to me that the Internet was going to be huge. At the same time, it was obvious that the "dot com bubble" was a real problem. We had that story about Allbirds closing up shop and claiming to be an AI company and their stock went up 300% or whatever - those stories were everywhere in 2000. Companies were IPOing like mad, doubling or more as soon as they launched, no one was making any revenue. It was obviously a house of cards. But year after year, this was true and you were an idiot for not jumping on boad and making free money... until the carousel stopped.
There are definite similarities, but now those risks are all concentrated into a few companies with illogical valuations that are consuming the market. Yes, there are a bunch of AI-using companies, but they are much narrower in their impact and not nearly as many IPOs.
The true world-changing stuff is slow and takes a decade to permeate, and it happens regardless of the investment nonsense. Building up fiber networks, sorting out SSL, simplifying hosting, better software dev tools for the web, etc. For AI that looks like changing organizational structures, adding consistent safeguards, task-aligned workflows. There are much fewer physical changes, but the organizational differences are quite pervasive and will take time.
Still, there is no doubt this is a bubble and it will pop. My assumption is that can't happen until all the big players IPO and pension funds are left holding the bag. This has been PE's playbook for decades.
Take a simple workflow. You have a query it goes to a classifier. The classifier determines what workflow it should route the request to.
Then you have a general workflow that has a set of skills (prompts) and tools. And that could be recursive.
So if you do something like "rename this file" you have to build up a workflow like:
[classifier]
what's the workflow -> rename
[rename workflow]
list files (tool call)
figure out relevant predicate (LLM)
convert predicate into a filter query give the context of the files (LLM)
figure out what you want the new name to be (LLM)
create the request body and hit the tool
approval workflow
formatting
It's a lot to manage and orchestrate and that's just one simple example. You'd like want to use the same building blocks to delete a file or move it. Even to know the right concepts is difficult as we're a bit deluded on whats going on in the background of these modern AI apps like Claude and GPT that do a lot of this stuff for you
> Personally, I would want that R&D spend and innovation to go to more sustainable materials, longer lasting devices, and easily repairable parts to extend the devices useful life.
Does the broad market care about sustainable materials? What does that even mean? Almost no one buys something because of sustainable.
For longer lasting devices, people like buying new phones. The iPhone has pretty much not changed in the last 5 years. People just like buying the new and best
Same thing w/ repairable parts. People just like buying new things. And it's not a conspiracy theory, it's just observed behavior.
So I'm glad they're trying something, because as much as you would like these other things, the broader market of consumers don't care. Yes profits are a useful proxy for value people place on your activities. Not perfect but in the long run if you provide a shitty experience you're likely to lose.
Who are you to say what is okay? She's not your child.
Yes its unlikely something bad would happen. It's also unlikely that you would get into a life threatening car accident. But you still wear a seatbelt. Why? Because it's the precautionary principle, pretty much common sense.
Yes in the past we let children wander, but if you asked those parents in the past if there was some very low cost way to afford additional security like knowing where your 10 year old is, they would obviously take it. But for some reason people take the wrong lessons from the past.
I want to make a comparison with a car rental business and say that it would be like valuing Hertz entirely on the basis of the number of cars they own, as opposed to how many they rent out, but cars have a much longer depreciation period, if there are no customers they’re not costing you more money, unlike your computer which you are using for training and sucking up massive amounts of energy, and those cars do maintain decent value even after they’re of little use to the car rental company, unlike the compute here.
Do you think we will recognize any walls? Or is there a point where the output might look different with respect to different paradigms / modalities we throw at it, but we won't be able to understand the quantitative differences as good/bad/scalable?
Everything is a temporary situation on long enough timeframes, especially if it’s exponentially growing. Moore’s law which dictates that compute depreciates quickly has been slowing down a lot in the last few years, coupled with the explosion in demand we’ve found ourselves in a prolonged shortage situation. The bubble will pop, but if you predict when correctly, you will be a rich man.
The key question is on direction of LLMs. Right now, LLMs are taking over human jobs. If the cost of silicon+power < cost of human being doing the same work, what rational reason is there to employ a human being?
If this applies to SWEs, lawyers, business analysts, many research scientists, .... this situation could persist for a long, long time. While capital costs less than the inputs of labor (nominal food, housing, etc.), there is no need for labor.
The key question is about continued progress in models, and of the tooling around them:
- Plateau: Old silicon obsoletes in due course
- Rise quickly: Old silicon maintains value for a long time
Yes, the plan seems to be anti human in the extreme. Why do you need the plebs if they can be entirely replaced by AI? But the question then becomes why does the AI (and before that their security detail in a post money world) need billionaires?
This likely is the tertiary reason as to why llms are so heavily kneecapped. Granted, at this point, projects do exist to remove those arbitrary restrictions, but the effort that goes into it suggests it is a real concern.
Short term, money physically exists and gets spent, so if you wave a magic want of oversimplification and transition all labour to AI instantly, all the money currently in bank accounts and wallets gets spend on the same businesses it was already getting spent on, a lot of which gets spent on stuff from other businesses who have in this scenario also replaced all their labour with AI.
Eventually, perhaps quickly, all this money ends up in the hands of shareholders and landlords. There's a lot of both in the economy; famously retirement funds, but smaller-scale shareholders and landlords also exist. I wouldn't want to guess what the distribution looks like, probably highly variable between countries not just social classes (the definitions of which themselves can vary between countries).
Long term, money exists as a convenient fiction to help us organise transactions of goods and services: while it may be physically possible to eat gold and banknotes, you're not getting any real nutrients out of it when you do. So in a world where goods and services come from machines, the options are too broad to forecast: humanity could be relegated to the same role and economic stature as other primates (both in and out of zoos), or we could get universal UBI denominated in machine labour credits which lets each of us live better lives than the most extravagant billionaires live today.
I don't know. It just seems odd because money was used as an abstraction of labor and if labor disappears it seems like money has no fundamental value. If you can't pay people to do something (because machines are doing all the labor). Then people have no money and money has no value to people. Industrialization resulted in transition to service-based economy but this new wave of machines are being said to replace service work.
I'm just trying to understand if suppose you have fully robotic farms and fully automated slaughterhouses and fully automated McDonald's, who is McDonald's selling anything to and how do these people supposedly buying fully-mechanized burgers have jobs? Something just doesn't add up about this in my head about how this equation balances.
UBI ultimately seems like socialism with extra steps. Mostly is comes across as billionaires desperately begging for an alternative to being nationalized.
Industrialization allowed people to shift human labor from agriculture to factories and such.
Seems like intellectual labor became more possible as people looked beyond subsistence but also more valuable since a greater population could drive demand for more than just subsistence related activities.
If both aren’t done by many humans, what’s left? Sports training and massage therapy? Sports training might not even be safe…
OTOH, my current lifestyle is already weird if I think about it. Developing software for a machine that I cannot make myself, whose raw materials I cannot obtain, using energy I cannot produce on my own — somehow entitles me to get a particular amount of goods and services from others including food, healthcare, entertainment, landscaping, and manufactured goods.
> If both aren’t done by many humans, what’s left? Sports training and massage therapy? Sports training might not even be safe…
Peacock tails.
As in, things where the effort itself is the point, to show off that you are capable of surviving when you consume resources so extravagantly on something other than (or even detrimental to) mere survival.
This includes stuff like hand-made art, being in a literal cult, extreme sports, and also refusing modern medical interventions/seeking out infections.
You may ask how someone can get paid for those things; I don't know, but we did manage to monetise talking to each other (ads on Facebook) and being locked in a house with some strangers while everyone's under surveillance cameras for a few weeks (TV show Big Brother).
> I'm just trying to understand if suppose you have fully robotic farms and fully automated slaughterhouses and fully automated McDonald's, who is McDonald's selling anything to and how do these people supposedly buying fully-mechanized burgers have jobs? Something just doesn't add up about this in my head about how this equation balances.
Well, people need to eat, so either the customers are on government support, or it comes from passive income, or from savings.
The people without those options, do it the old fashioned way: pick berries, throw rocks at animals, rub sticks for fire to cook them, or starve. Mostly starve, as the maximum number of humans who can survive as hunter-gatherers is 100-1000x smaller than the current global population.
> UBI ultimately seems like socialism with extra steps.
I agree. It's very much "from each according to their ability, oh wait we're all strictly worse than machines I guess that's from each nothing, to each according to their needs".
> Mostly is comes across as billionaires desperately begging for an alternative to being nationalized.
Perhaps, but that feels like claiming they're playing 5D chess, when Zuckerberg only plays Settlers of Catan with sycophants who let him win.
The overwhelming majority of the labor force remains service, manual labor, and other such stuff that LLMs will have no real effect on. So the economy will be fine, but I do agree with you from a different angle. The entire goal of LLMs seems self destructive. If they're successful then the endgame is completely removing the barriers to entry to producing software and other digital tech. But if we do reach that endgame then the value of tech is going to plummet because there will be absolutely no barriers to entry to compete, or even just individuals homebrewing up what they need on demand.
Like imagine there was something you could buy where you insert some lumber, give it some passable description of furniture, and it outputs it. And you paid $20/month for access to this. And this was all being bankrolled by the furniture industry? I mean, sure guys - it's much appreciated, but I don't think I've ever seen anybody so enthusiastic about digging their own grave. I think it's already obvious that the gazillion dollars of API calls isn't going to materialize - it seems the handful of companies that trialed that are already reversing course hard. And in the future where LLMs are successful, that'd be even more true.
Llms either reach the point where they can quickly design and build physical robots to take on that service industry or they stop exponential growth.
Both of those are devastating for their valuation. Stopping growth means open modes catch up in a year or so. Continuing means end of the current economy.
There’s a good chance physical humanoid robots will always be more expensive than humans, especially in this new hypothesised reality where there’s an enormous labour surplus.
China is advancing robotics at a crazy pace, and hitting typical Chinese prices. This [1] robot is available starting at $6k. And of course what matters isn't the up-front cost, but the maintenance. If their maintenance costs are lower than human wages+taxes, then robots win.
The biggest practical issue will be that if these robots ever did start replacing people on a large enough scale, then you'd have a lot of angry, desperate people with a lot of time on their hands. So that alone will probably work as the primary mediating factor.
Quite an interesting time to be alive because the future is so completely impossible to predict. The world just a decade from now could look entirely different, or it could just be self driving cars all over again.
It's not rational relative to the short-term incentives of a typical corporation or investment vehicle. PE, VC, fund managers aren't paid to give a fuck about the social contract. Literally not in their job description.
> Is wanting low unemployment in our society not rational?
Only conditionally on there being bad consequences for high unemployment.
I don't particularly trust politicians, but there's a whole host of hypothetical scenarios about futures where work is essentially optional. Unfortunately, they're all either in the sci-fi or religion sections of the book store:
Despite people occasionally investigating UBI, the efforts to research UBI seriously have the same problems that Marx had with literal Communism, in that there's an obvious difference between any partial transition as compared to a global transition, and we don't have a completely disconnected parallel world to be a petri dish for us to test the economic outcomes on.
> Capitalism allows individuals to take decisions in a free market.
Capitalism provides a set of incentives that shape how people make decisions. Anyone can be selfish, but selfishness in capitalist society has a particular shape. To ignore the external incentives when looking at human behavior is horribly naive and shortsighted, but is frequently done by capitalism-apologists who seek to disregard any criticism of their favorite incentive system.
Are current datacenter deployments structured in such a way that the memory can later be moved to newer GPU dies? Or is it all packaged together as on consumer graphics cards?
I assumed the latter and therefore that the memory is depreciating along with the GPU cores it's soldered onto PCBs with.
... or is it a different argument being made, perhaps that depreciation for GPUs has slowed because rising demand will keep them in service longer?
Removing RAM chips off old cards is uneconomical, until it isn't. With things going the way they are, if you've got a card with soldered on RAM that could be transplanted to a newer card, I think you'll start seeing that happening.
> Time on 4 year old H100 servers costs more now than when they were new (!!)
There are several confounding factors.
We’ve seen massive inflation since then. So some growth in cost was expected.
More importantly, the current Tech industry almost always starts by selling things at a loss. The increased cost could simply be the industry choosing to not subsidize that particular service anymore.
But also, I don’t think that’s a realistic comparison. Rented out GPUs are likely not a similar use profile as compute used for training LLMs. The latter is likely closer to the cryptocurrency GPUs that are running at full tilt 24/7.
> Rented out GPUs are likely not a similar use profile as compute used for training LLMs. The latter is likely closer to the cryptocurrency GPUs that are running at full tilt 24/7.
This is untrue.
H100's are used for training (well were, but are now outdated because B100/B200s are much faster).
Most of the reason people rent H100s is for smaller training runs.
If you are doing inference you usually buy managed capacity at Baseten or something, and that is often priced differently (although it comes down to an extra margin on longer term H100 prices basically).
Inference utilization is often actually higher than training now because so much effort has been spent on optimizing that stack.
I also feel that the GPU/NPU value does not lose money as fast anymore.
What I am wondering though is how long can you run such a system at basically full load without interruption before it starts to just physically degrade.
If I have a H100 and I let it run for 4 years at full throttle does it still have the same theoretical value as it had at the start or are the chips just burning out.
I think I remember that back when the cards used for crypto mining were sold en masse on ebay the advice was to stay away from them because they are more likely to fail?
Others say that moderate load means a lifespan of ~5 years
Not sure what that means but I would assume that a datacenter will start replacing a node once the error rate hits a certain threshold without really investigating why it failed, so the practical lifespan may be shorter than 5 years even if it would technically still be usable enough
Temperature is a big factor, as well as current density.
But there's also the # and magnitude of thermal cycles (which translate into mechanical stress, leading to metal-fatigue like effects on contact points etc), attack from chemicals in the air, cosmic radiation, ESD damage & more. Some may matter, some not.
That's why "new" > "used" in case of electronics. Especially since you don't know the (ab)use history of used parts.
> I also feel that the GPU/NPU value does not lose money as fast anymore.
That's because the rate of improvement in silicon manufacturing has been continually declining for a few decades, which has a compounding effect. Just compare the technological improvements in successive decades. 1976->1986->1996->2006->2016->2026.
That's why "in real terms" performance has only been very slowly improving if you compare apples to apples (and not e.g. apples to oranges by reducing precision, like nvidia tends to do, or by comparing chips with x W to an MCM with x*2 W and saying the latter is much faster). The "just halve the number of bits in each generation" strategy has also run out now, there's no more bits to halve.
Depreciating doesn't just mean it could depreciate in value relative to the performance of newer GPUs, but also that its lifespan is limited by reliability issues and failures.
Car rentals are a great comparison, but not for the reason you think. Cars depreciate value similarly to GPUs. The depriciation lifecycle timeframe is actually similar between hyperscaler GPUs and mainstream corporate car rental companies ike Hertz. They sell their cars after 2-3 years or 20-40k miles. There is a huge market for used cars. Hertz runs their used car sales out of their rental retail offices and a lot of overhead is shared. So take the difference in cost to buy new in bulk from the manufacturer from the retail sales price for a 2-3 year old car. As long as Hertz can make more money renting it out in that time, that's revenue positive.
Same with GPUs. There is also a huge market for used GPUs from 1-2 generations ago. The A100 is a six year old chip at this point and is still running strong, especially for inference. Like cars, chips can be refurbished and repaired. A hyperscaler or even mid level player here isn't going to hold onto chips for their entire usable lifespan.
My local inference rig now costs three times what I bought it for. If I'd gotten the max ram I could at the time I would have made $10k after selling the excess to my current spec.
How someone can look at an asset class thats appreciated an order of magnitude in the last two years and say it will depreciate in value when the tailwinds are even stronger now is beyond me.
Undervolting is not running at max utilization by definition almost.
…but the real question whether you want to undervolt your asset if you’re renting it out is why bother? You probably expect to replace it anyway after it’s spec lifetime, for sure want to replace it when a more efficient solution is available since datacenters are power and volume constrained and customers care about performance much more than hardware longevity (otherwise they’d buy instead of rent).
Why do you think it’s a waste? If you’re buying GPUs to rent them you’re almost buying a bond. If you’re leasing them, it’s even more obvious that you’re collecting the spread. The GPUs have a financial lifetime after which the business doesn’t pencil and they get sold for peanuts so you can put a better bond in your volume-power.
Consumer GPUs/CPUs tend to be operated at higher clock rates and voltages, because they need to win benchmarks. If you ever bothered to pay attention to how data centers operate their hardware you would notice that they have always gladly sacrificed 10% of performance if the total cost of ownership is reduced.
Since this entire sub-thread is in the context of used 3090s or consumer GPUs in general, you've failed to bring up anything relevant yet again.
Here is your strategy:
1. Increase power consumption by 50%: This costs you more energy to run the GPU, it also costs you more energy to cool the GPU, it ruins the GPU and since you hit power limits of your infrastructure earlier, you will have fewer GPUs in total.
2. Increase maximum performance by 10%: This is hardly noticeable, since the standard inference use case primarily involves taking advantage of the high memory bandwidth of a GPU. This means prompt processing will be 10% faster, or maybe your segmentation model that ingests video runs at 33 fps instead of 30 fps. You're optimizing for winning a benchmark with what will be used hardware in the future, that's asinine.
3. Throw away old GPUs or sell them for peanuts when they still sell for $1000 on the used market if they are in good condition and for $400 if they are damaged. I think the mistake here is obvious. If your GPUs are sold for peanuts, it's because you didn't take care of them.
Your business strategy is obsolete and based around the idea of pre COVID excess hardware capacity before there was massive AI demand where throwing out hardware made sense, because Moores' law was in full swing. Even Google is still offering their v2 TPUs from 2017 even though they've been long since obsoleted. Now in 2026, there isn't enough memory for consumers and people are snatching up all the hardware they can get their hands on. There were some big initial energy efficiency wins from implementing smaller data types that are no longer possible now that fp4 is the smallest possible floating point type that still makes sense and even if you go smaller, you can go down to two bits at best. The parameters are starting to become so small that 2:4 sparsity is becoming unattractive, because it adds one bit to the parameters.
2:4 sparsity for fp4 means 4+4 bits are compressed to 4+1 bits, but 2 bit parameters mean 2+2 bits are compressed down to 2+1 bits.
If you understand even a little bit about hardware, you notice that the tensor core hardware has already been optimized to the extremes and that there isn't much more you can pull out of it. Unlike CPUs there is hardly any control flow in matrix multiplication. The tensor cores implemented in Nvidia GPUs might be a little bit less efficient than an NPU/TPU based implementation (think Google), but there are no more obvious micro architectural improvements here. With CPUs the micro architecture has become so complex, that there may be ways to increase performance further, but for GPUs and NPUs, there is not much left other than process scaling. Further gains require better manufacturing processes from TSMC. TSMC introduced 3nm in 2022 and only started producing 2nm in 2025. That's a three year gap where barely anything happened and all the gains came from going from bf16 or half precision floating point, to fp8 and fp4.
Burning through hardware at high power consumption and mediocre performance increases is clearly not the way to go.
I have been hearing that memory suppliers are _intentionally_ not scaling up new factories like crazy because they assume the demand won't be there on the long term and they don't want to have spare unused capacity. Probably because Samsung and SK have a near duopoly on it as well...
At some point the market will be saturated with supply and prices will come down for older gen hardware. It can take years though, but it happened to fiber cable and fiber doesn't even depreciate like chips.
Will it continue to appreciate to infinity? Maintain its value forever? Or will something else happen?
The same argument you’ve made would work for tulip bulbs, dotcom prices, or whatever. Prices go up until they don’t. Exponentials don’t last forever and the intrinsics of technology assets depreciate: things wear out and are also replaced with better things.
> if there are no customers they’re not costing you more money, unlike your computer which you are using for training
So are you using the computers or not? I'd argue that if you're using them for training, then it's not wasted capacity. And if you're not using them, then you can turn them off, so you're not sucking up energy.
I don’t know but this dude at my son’s school has a 32GB RTX 5090 and it’s worth more than what he paid for; and he did the same trick with the RTX 4090 before that.
Until shortages are the rule, these assets are appreciating
"depreciating" is not being used in the right sense.
There is depreciation, which is taking the purchase price and dividing it across N number of years (typically 5). That's the D in EBITDA and is mostly used as a profitability calculation.
The depreciation of a GPU also gets mucked up in the current GPU financed market as well. DDTL loans. The people running the GPUs often don't even own the GPU, they lease it, so there is nothing for them to depreciate (D).
The analogy that a GPU is like a used car makes zero sense. There is no oil or tires to change on a GPU. They don't wear out in the same way that a rental car would. They are housed in climate controlled locations with clean power. They just don't fail the way that is portrayed in the press.
Useful life of a GPU is based on profitability. When does opex cost more than profitability?
Some companies, like mine, also have support contracts. Anything goes wrong with the GPU (or any part of the system), Dell comes and fixes it at no extra charge. We just migrate customers and workloads to hot spares while the parts are replaced.
As for compute going down in value... the 122TB of enterprise nvme and 2GB of ram in each server that I bought 2 years ago is now worth vastly more than I paid for it. I'm also renting my GPUs out for more money now due to supply being so tight and demand being so high.
Compute is about to come an appreciating asset in the near-term, and it some ways it already is.
The frontier labs are shifting from pricing grounded in the price of compute, to pricing grounded in the intelligence provided, or more specifically the economic value of that intelligence downstream.
The margins on that allow them to pay a hefty premium on compute and still come out ahead.
As they buy more compute at high prices, they're also pricing out competition from cheaper models. It's already become materially more difficult to get compute to run open weight models at competitive prices as a result of frontier labs in the last year.
Opus 4.7 has all the signs of a smaller model distilled from a newer pretraining run... except a smaller price.
Flash 3.5 raised in price pretty meaningfully over Flash 3
GPT 5.4 got a small price bump over gpt-5.3-Codex/gpt-5.2, then gpt-5.5 doubled pricing over gpt-5.4
Even open weights isn't immune: Kimi K2.6 was originally priced higher despite openly being 2.5 + more post-training, same with GLM 5.1 vs 5
-
All while rental prices are spiking month over month, and NVIDIA Inception discounted prices for buying are higher than undiscounted prices for buying 6 months ago...
I run a consumer AI product and the current reality of trying to get compute vs what it was 6-12 months ago is enough to justify it to anyone who has the money.
I think OpenClaw created a mania that was completely unfounded (Apple Silicon is worth dirt compared to literally anything from NVIDIA including consumer GPUs), but the prediction of compute becoming scarce was correct
Not necessarily. The GPU leases Spacex has made are month to month, so they are taking on all of the risk. If demand goes down, they're the ones stuck with the assets.
In the short term, compute becomes an appreciating asset.
In the medium term, everyone ramps up production. Huawei and other Chinese companies work really hard to develop in-house alternatives. At some point, the hype cycle will peak and less money will flow into datacentres (yes, this will happen. It always does. Even for technologies that change society. The bubble always bursts).
The question is not if this will happen. It will happen. It's just a question of when it happens and how big the magnitude of the cycle is.
Let's say it does all collapse. How would we know it's the 5-6% stake (which in my mind doesn't make them a "major shareholder") that was a circular deal that was the fall of the house of cards vs some other segment?
It doesn't even have to be circular. One company is juicing another company's valuation to make their stake worth more. Down the road they'll sell their stake, end the deal, and leave everyone else holding the bag.
Nothing about this deal is about better technology or talent. It's about an opportunity that's too juicy for Google to pass up on.
This might not be true. Someone was comparing Nvidia's production rate with known data center capacity, and they do not match. Their conclusion was that people (possibly even Nvidia) were hoarding GPUs- in the very short term this might be a good strategy, but GPUs go EOL fast. There are other stories about paused datacenter builds that match with this.
TSMC is definitely fully allocated, based on current 40 wk lead times for FPGAs..
All that means is that there's a bottleneck at the data center layer. When he says "dark GPUs" he's saying that there are no dark DEPLOYED GPUs.
This is a reference to the 1990's dot com bubble where internet infrastructure companies overbuilt network capacity, leading to the term "dark fiber". That was an indicator of a bubble because it showed that capacity was larger than demand. OP is saying that this is specifically NOT happening in the case of GPUs yet, indicating that demand still outstrips supply of compute.
>GPUs go EOL fast
We are seeing the opposite of what was expected, GPUs are actually getting more valuable because demand is so great, something that basically never happens. Even older chips have become more valuable.
>paused datacenter builds
It doesn't seem that datacenters have been paused because of lack of demand for AI, it seems mostly that there is a lot of pushback by cities to build these things and also there is a shortage of power to run them.
IMO none of these things point to a AI being a bubble (over-hyped, demand does not match the stated value). It mostly points to the opposite, there is massive demand for AI and every layer of the supply chain is struggling to keep up with that demand.
Adding to this, a lot of fiber installed in the 1990s is still dark. Multi-wavelength XYZ and other improvements mean the same fiber from 35 years ago can carry 100 or 1000x what it was originally designed for. Also, like Solar, all the cost is in labor. When they designed the Seattle/King County fiber network, they knew they would never have access/permits to go back and add more, so instead of running a single 12 fiber bundle the size of your pinkie, they ran 3 x 1024 bundles the size of your arm through the hollow bridges that span I-5 freeway and snakes through Seattle underground. Almost all of that sits dark today despite being in a very busy area, simply because fiber technology keeps getting better.
Yea, fiber is great. They can do hundreds of terabits/s per fiber today, and petabits/s is not far away. Bandwidth is fundamentally cheap enough that my ISP offers 50Gbps residential service!
I live in Oregon. The price was $900/month last time I checked. I believe they do provide a QSFP+ module to plug into your equipment. They also allow residential customers, at any tier of service, to announce their own IP blocks via BGP.
> ...GPUs are actually getting more valuable because demand is so great, something that basically never happens. Even older chips have become more valuable.
Huh, anybody want to buy a GTX 680? Or even a formerly-SLI'd pair?
Don't you think that under excess demand, production will ramp, competition will become available etc? These posts read like we're all out of fresh silicon or something.
Supply will catch up, it will just take 3-5 years, with the price rising the whole time. Basically a worse version of the Covid supply disruption where I sold my car for more than I bought it for years later.
The physical world can’t be patched overnight, and cutting edge manufacturing takes a long time. Fortunately we are in a very peaceful low tension world right now and no one would try burning down or blowing up one of those extremely important, irreplaceable fabs.
> IMO none of these things point to a AI being a bubble (over-hyped, demand does not match the stated value). It mostly points to the opposite, there is massive demand for AI and every layer of the supply chain is struggling to keep up with that demand.
Yes, the demand is there for the currently unsustainable price. Lets see what happens when the dumping of money into AI stops and the companies are forced to increase prices a lot.
> IMO none of these things point to a AI being a bubble (over-hyped, demand does not match the stated value).
I agree the demand is there, but hyperscaler capex is what now? 3% GDP? This is an absurd amount of money and people who question whether the ROI is there have a point just because of the order of magnitude of this spend number.
Indeed, that hardware was bought on old RAM, SSD, etc pricing. These are now 5x the price.
To reap massive profits before depreciation is just plain smart. LLM space, model generation is just plain crowded now too. And everyone thinks a crash is coming.
They could also build out their own end-user infra, but letting someone else which already sells direct to the public do so, is sensible.
I know of the desire to show profit for the IPO, but my point is, this is a good move on its own.
Presumably from internal all-hands presentation in Google: “Now we must double every 6 months… the next 1000x in 4–5 years.” reported by CNBC in November 2025, attributed to Amin Vahdat, Google Cloud VP / AI infrastructure lead.
Sundar Pichai at Q4 2025 earnings call: “We’ve been supply-constrained".
Satya Nadella, 2026: Microsoft would increase total AI capacity by over 80% in the year and roughly double total datacenter footprint over two years.
Microsoft CFO, 2026 earnings call: “We’ve been short now for many quarters. I thought we were going to catch up. We are not. Demand is increasing.”
So yeah, either top management of hyperscalers are doing a 'bit' for the last few years, or Aschenbrenner 'Situational Awareness' is going roughly as predicted and hyperscalers are desperate to acquire compute even at higher cost.
My read is that xAI built a lot of compute for their own use, but they didn't get any adoption so they are reselling the unused capacity to recoup at least some of the costs. So calling it a good bet is kind of misleading
Don't you mean gas turbine purchases and questionably legal operation? But yeah I feel exactly the same way. The AI part of xAI looks like a mess but it seems that they still managed to score a massive win.
> Don't you mean gas turbine purchases and questionably legal operation?
The point is it’s running. They built fast before the backlash got organized. Now everyone has to deal with delays and thoughtful permitting processes.
The point is they're in a business no one would claim is particularly profitable but claiming a valuation like they're in a totally different business - one where they're not even top 3.
Its not that there isn't value in that business, but it's not the AI business either. Its the one where Oracle is laying off staff to try and avoid a revenue crash on future commitments.
Both Google and Anthropic would be trying to can this sort of rental arrangement as fast as possible since it's a mind bogglingly expensive way to get something you already do in house.
It isn't normally particularly profitable but given their lucky timing they appear to temporarily be doing quite well. When their tenants eventually vacate either they make a move to reenter the race for the cutting edge and get lucky or else they revert to a "boring" cloud rental business with near cutting edge hardware. That seems like an extremely favorable mode of failure to me.
The "backlash" is the poorest residents one of the poorest large cities in America trying to fight for their right to clean air.
Your point might end at "it's running", but holistic thinkers have no problem considering the how they arrived there, given what it's doing to these folks for marginal benefit.
It's not like xAI would go under if they had chosen a less populated location and waited to get permanent power.
> "backlash" is the poorest residents one of the poorest large cities in America trying to fight for their right to clean air
Sorry, I'm referring to the national pushback against datacenters being built in peoples' backyards. xAI didn't face backlash. At least not organised enough to stop them. Their competitors, today, are facing backlash sufficiently powerful to stop new datacenters from being put down.
Sure, they brought in artillery and a small freelance militia to shoot at the unionized workers, but the point is, the survivors are back working the mines...
This feels highly revisionist: they bet on becoming a frontier lab and were aiming for AGI.
If they were speculating on compute, it seems highly unlikely they'd have spent the operating costs for the last 3 years of model development and deployment instead of just getting even more compute.
And while there's no challenging the underlying proposition "AI has value", right now 95% of corporate users are still at the "throw everything at the wall and see what sticks" level in terms of model usage compute.
It's sheer brute force, tons of waste, seems like very little thought going in to fitting the implementation to the problem.
The value of compute can drop significantly in the event of users figuring out how to optimise for their particular need. And yep, there are wasteful applications that can burn whatever compute is available, but how much demand for that is there when it's properly priced?
Extreme example. Generating novel 4K VR video on demand. I'm certain there's a market for it, at $10/hour probably quite a healthy one, at $100/hour not so much.
In fact, for all these companies to do what they're going to do, they need a massive, massive massive amount of data centers, a highly improbable number of data centers that need to be built in an highly improbably short amount of time.
And the capitals about to dry off in about a year. So it's a race between these improbable timelines on data center construction, with capital evaporating.
- iran destabilization will shift middle east capital to military spending and infrastructure repair
- Ukraine war similarly is triggering an EU buildup and reduction in us dependency
- all the IPOs indicate the companies themselves know the private investment is coming to an end so they need the retail investors to keep the boondoggle moving
Nah, man, it's all fine, they're just going to take down the entire global financial system doing this crap, and by global, I mean <<everyone's>> pensions are going to take a hit, even "fully funded" pension systems.
> Both Alphabet/Google and Amazon have invested recently into Anthropic and are doing all sorts of financial chicanery
bko didn’t say there isn’t circular financing going on. They’re just saying this isn’t an example of it. They’re right.
It’s a potential conflict of interest. And if the agreement is fake—if Google cancels without paying the cash—it could be market manipulation. But the influencer space likes to latch onto jargon, and the one it’s overapplying right now is circular financing.
The comment you’re responding to and the comment above it are about circular financing. It’s reasonably to assume that’s the same chicanery you’re talking about; expecting everyone to watch a random video to understand your comment is unreasonable.
I listed a bunch of data points that make no sense (profits spiking 50% in a non-Christmas quarter for companies) and weren't directly tied[1] to the circular financing.
They were unrealized gains on non-marketable equities. It’s clearly disclosed and done according to GAAP. It’s put under other income precisely so analysts can strip it out when modelling long-term trends.
Like, yes, if SpaceX goes to zero Google would have to realize losses and probably lose a quarter or two of GAAP profits. (But not cash flows. Cash-flow wise, it may wind up being positive due to tax effects.) It’s a risk factor, of course, but far from making no sense.
None of which is particularly relevant to the deal at hand other than in raising a potential conflict of interest among related parties.
When I said "it makes no sense", I didn't mean "the accounting math doesn't work out". I meant "raising a potential conflict of interest among related parties".
This whole AI financing this is the motherlode of "potential conflict of interest among related parties".
And people who are obtuse enough to ignore this because it's not illegal right now will discover 5-10 years from now that laws are written in blood (or massive bankruptcies).
> whole AI financing this is the motherlode of "potential conflict of interest among related parties"
Sure? Lots of things are potential conflicts. In the Google and Anthropic deals, I'm not seeing evidence of problems.
And that's saying something, because we have a lot of evidence of actual circularity or related-party deals being done with no arms-length anything across AI, in many cases in ways that definitely do see like they are illegal.
> Sure? Lots of things are potential conflicts. In the Google and Anthropic deals, I'm not seeing evidence of problems.
And that's where my video comes in. Google and Amazon are very likely juicing up their share price. Of course, in this day and age we can't prove it's a pump and dump anymore...
The compute is useless if nobody is left to pay for the compute, once all the AI companies die, from all that debt getting called in, once everyone realizes it's a scam. (AI isn't a scam, but the financial deals and promises of unrealistic recoupment are)
The thing I've never understood about the ai investment model is the upside. What's the point of valuations that only make sense if you've built a digital god, when at that point you've literally got a digital god. I can't imagine the tangible value of money being high in that scenario
Money is imaginary, it's just a placeholder, doesn't need to be tangible. In the case of AI it's the promise that you can replace humans with cheap fast robots, to do more things and cheaper. That's valuable. But the wealthy aren't considering that our economy depends on people (replacing all the people too fast would tank the economy from unemployment, and cause a revolt). So you might wonder, why don't they just move at a slower, sustainable pace? The answer is greed. Make as much as you can, as fast as you can, before the next guy does.
All this investment is completely driven by the companies leading the pack. OpenAI and Anthropic have been telling everyone they need to spend hundreds of billions in a few years. Of course they don't, they could do this over 10-15 years and still be profitable. But they're terrified they won't be able to dominate the market. So to dominate the market, they've estimated they need this growth to beat China (and each other). And the US technically has the capital to make this happen, but there's only so much money available to spend. By growing too fast, they spend money faster than they can make it, and the bills are so big that the investors go bankrupt.
That's what happened in the panic of 1873 (railroads instead of AI). That's what's going to happen here in the next 2-4 years.
> This question jumps past the more fundamental question of whether policymakers, and the government in general, should prevent people from making their own choices.
When your choices include terrorizing businesses and being a public nuisance to everyone else, then yes, government should prevent people from making those choices.
We already have laws for theft and similar crimes. You don't need a government creating more rules preventing entire categories of choices from being made, especially if they already can't enforce the laws on the books.
I'm not advocating for the US legal/criminal system at all actually. The prior comment was pointing to crimes already being committed by people who make or made certain choices. My only point was that further regulation may not be a great solution when the activities being done are already illegal and going unenforced.
Personally I'd rather gut the legal system and drastically raise the bar by which people are locked up as punishment, but that's beside the point.
I don't see how you are saying anything different.
You seem to agree that punishment and violence is the primary tool of American government, and then you want to use it to control more choices. Call me cynical, but I expect that's how it will be approached. Theft and vagrancy is already a crime. Maybe it was the punk music that led to those so let's criminalize that as well.
> you only think you're making a rational one and to outsiders and in retrospect
In retrospect? It's really not hard to determine before the fact that petty crime is not a road to good things.
We have ways to prevent people from going down this path. It's called enforcement. He was more or less allowed to steal and sleep in the parks. If there was strict enforcement, this wouldn't have been a medium term viable option. Doesn't have to be throw the person in prison for the rest of their life, but either accept help, go through the criminal justice system or figure out another way to contribute to society in a positive way. It sounds like the author at any point could have found some kind of employment, but chose this because it was viable. And society wasn't doing him any favors by looking the other way
Enforcement is right of boom, essentially a safety net for the negative external affects of a person having already made a series of choices that resulted in an enforceable outcome. My impression from the thread is a query to identify the things that can prevent an enforceable outcome in the first place.
While one might say strict enforcement would discourage particular behavior choices. I would not disagree and add that suppression of behaviors is not as effective as replacement of behaviors.
> We had weeks to ship what ended up being a million lines of code... Five months later, the repository contains on the order of a million lines of code across application logic, infrastructure, tooling, documentation, and internal developer utilities. Over that period, roughly 1,500 pull requests have been opened and merged with a small team of just three engineers driving Codex. This translates to an average throughput of 3.5 PRs per engineer per day, and surprisingly the throughput has increased as the team has grown to now seven engineers. Importantly, this wasn’t output for output’s sake: the product has been used by hundreds of users internally, including daily internal power users.
That's an insane level of throughput. What's a good baseline? Prior to agentic coding, whats the typical number of PRs engineers were expected to push? Maybe a 2-10?
Do people feel the software has gotten better in the last 6 months? The number of engs is prob the same so we should expect maybe 5x faster cycle in major software apps, but I don't see it. The AI apps do change very fast but given its a very new field, I'd expect as much. But outside of that, I don't see it.
Here's a fun one: firefox lists its current count at about 2.5M LOC, from roughly 1M commits during the years.
You end up with about 3 lines added per commit, which is not ridiculous when you consider that most would be editions rather than full additions.
Here, we have 1500 PRs and 1M LOC, which is about 650 added LOC per PR. Remember, not 650 lines total in the PR, but +650 balance after additions-removals.
Fun questions for attentive readers:
- What does a project growing at a rate of one full firefox-codebase worth of LOC per year look like, a decade down the line?
- What does the line count say about the verbosity of the tool, and what does it say about outcomes that the purpose of the project isn't clearly disclosed?
- Do we have reasons to care about LOC in a world where we don't write code manually? What happens to token usage numbers when the codebase is significantly larger?
- If it was confirmed that LLM usage blows up your line count, what's the implication for codebases that want to return to manual coding after months of usage? (Say, because the tool gets expensive).
> - Do we have reasons to care about LOC in a world where we don't write code manually? What happens to token usage numbers when the codebase is significantly larger?
Yes, at least to the extent that we care about context windows and tokens consumed by coding agents processing code that is ultimately irrelevant to their assigned task.
Anecdotally, I've found keeping file sizes small has been important for agentic coding not just to maintain human readability, but also for optimizing agent performance, precisely because it limits the amount of incidental context they load while working a problem, because they generally load entire files rather than just parsing the part relevant to their current assignment as a human might. That smaller file size thus reduces input noise and the LLM generates a tighter solution, which in turn reduces input noise for future solutions. Or at least this strategy avoids a death spiral into exploding context length.
I expect (but cannot currently prove) that keeping overall LOC down yields similar benefits even when file sizes are kept small because it spares the LLM from parsing potentially relevant files that prove irrelevant to its current task.
Seconded on smaller files. I feel like I tend to get better responses faster.
A notable flaw here is that I’ve not tried large vs small files in a large codebase. Most of my experimentation there has been on personal projects where even a small file contains a significant part of the project. I could see degradation when it has to load 5 files to figure out how something works.
Total LOC (tokens, really, literal lines probably don’t matter) is interesting as a factor. That might go some way towards explaining why LLMs are weirdly good at Clojure.
Eg last I checked Anthropics one-shot performance on Clojure was about the same as Python or Go despite almost certainly being less represented in training data. The combination of density and simple primitives might be easier for an LLM to wrangle, ameliorating the impact of a less popular language.
>Eg last I checked Anthropics one-shot performance on Clojure was about the same as Python or Go despite almost certainly being less represented in training data. The combination of density and simple primitives might be easier for an LLM to wrangle, ameliorating the impact of a less popular language.
There might be tons of confounding factors there. One that comes to mind is the quality of of data, it might perfectly be that the average clojure snippet is higher quality, due to the users demographics. Very few people start writing code with clojure, whether in college or during bootcamps.
Oh there absolutely are, I don’t mean to imply any certainty in that attribution.
Quality of data is totally one. Immutability may be another (it’s easier to reason about if you don’t have to track mutations to a variable). Another interesting one is Clojures emphasis on composability using basic primitives that are sort of hard to grok initially but unlock really cool stuff.
You can do some incredible stuff with recursive map and arrow functions in a few dozen characters.
Does the Firefox LOC include ALL forms of text: infrastructure (Firefox doesn’t have), documentation, developer scripts,tests, etc? How is the test coverage of Firefox?
They never specified what exactly the product was, without which it's impossible to judge the post.
For some reason most of the uses of "agents" are to build yet other AI products, it's turtles all the way down. Maybe that says more about the field of harnesses than it does about the power of "agents".
There is a sense in which it doesn’t matter at all; many of the limitations of agents in large codebases are just the context management challenges. So proving that you can cohere and progress at O(1m) is a useful scale observation. “Can I use agents in my 1m line codebase?”
There is of course another sense in which the output quality is the only thing that matters. “Can I use agents to build a 1m line codebase that I want to maintain going forward.”
I take this as being exclusively a tech demo of the former. Quality (feature velocity, bugs, scalability) is not demonstrated.
Feels like the active discovery going on is trying to understand what is computer vs what is AI, for every product.
Agents help a ton with the discovery, but the act of building a product needs a deeper level of thought and validation to make it actually better than what came before. So IMO what you see is people still learning what needs to be understood and crafted first hand to make a product better (including economics)
We've known for decades that output metrics like LOC/day are very bad measures of real productivity in software. But they seem to be back in vogue in the age of AI, because AI is so good at maxing these useless metrics, and we need to show how impressive our AI is and how impressive our usage of AI is.
I’ve been vibe coding a lot over the past year or so, and I think I’m going to stop. In fact, I sort of want to challenge myself to see, can I go back to a sort of the fork in the road with the old copilot autocomplete workflow and really maximize that. Be in the drivers seat for most of the code being written, but find ways to use AI to really enhance the flow state / remove blockers. Tools only minimal actual code generation.
One workflow I like is writing a comment for what I’m about to do and then waiting a few seconds and then tab through the auto-completions. Then I check what the agent came up with, make some edits, and then on to the next block. That works well, I feel in control but don’t have to type as much.
I do use claudecode totally hands off too however. Mostly for UI tasks. Like themifying css or data grids and CRUd with all the bells and whistles, I hate that stuff and cc gets it done in minutes and mostly right. It’s also super nice to say things like “user profile in the upper right hand corner” without having to fight css.
/if it’s not clear, I hate dealing with css and related frameworks.
I would be very impressed with someone who's been vibecoding "a lot" for about a year who could then go back to being fully in the loop for even 50%. I would even say I'd expect withdrawal symptoms at that point.
The dopamine hits are core to why people even do vibecoding (or vibecoding-in-a-dress/spec-driven development) and why they tend to overestimate its output so much. Hell, it's core to all forms of LLM-assisted development (because it feels like magic), but most of the other forms are more value, less delusion.
The dopamine hit is real, I feel like that was identified early on by OpenAI and probably lit a fire to get ChatGPT in the hands of the public. Bf Skinner (I think) is the one who narrowed in on variable ratio reward systems to maximize operant conditioning. An LLM, with hallucinations and imperfections, is the perfect variable ratio reward system. It’s no wonder they’re getting pushed so hard along with a consumption based pricing model. Whether you’re a human, rat, plant, bacteria there’s no real defense against that kind of conditioning.
I actually don’t find vibe coding satisfying is one of the many reasons I’m going back. I feel a little of what you’re talking about, but I’m a nerd. I like to code.
But I’m not dismissing your concern. Because it is one of the reasons I’m making this decision. I’m a professional. I’m not just here to feel good I’m here to do a good job over the course of a career. I think all in, when you think about writing good maintainable, software, learning, staying mentally sharp, and speed put together. Vibe coding could be less effective and maybe even in the aggregate “slower”.
It feels like the update cadence has indeed sped up. But not necessarily quality.
Looking at MS Office I notice a lot of small changes recently that are mostly annoying. Things like Word comments losing the focus after you @-tagged a colleague, needing to click the Outlook search field twice before you can enter text, Outlook mobile date picker losing its ability to show your and attendee's availability.
So it looks like lots of throughput, but unfortunately breaking features that work. Or wasting time on things that don’t matter such as the status bar of OneDrive search circling around the input field.
This almost reeks of "I've never cleaned up our code base because there is too much code, and didn't even bother having agents/LLM cleaning them up".
You almost never need a million lines of code - this includes your software, infra, testing and operational tools. You didn't ship the linux kernel in 3 weeks and you know it. The code is already speghetti and it achieve the basic functions OK but it will harder and harder to simplify and untangle and maintain.
Even the linux kernel doesn't need millions of lines of code; most of the actual LOC is device drivers, and you don't need all of them, you just need the ones for the devices you have.
As a point of reference, 1MLOC is about the size of the entire Python standard library including tests, as well as stuff like IDLE. (Well, the Python part of the code. There's about half that much again of C in Modules/ .)
I ported/rewrote a million-LOC medical imaging workstation app over the course of 2 years with a team of 6. We had a full feature matrix with an extensive manual testing plan from previous work.
The average efficiency improvement is closer to something like 2-3x per Anthropic’s numbers and this is only the rate at which software can advance. Do you expect to notice if 12 months of software engineering on a project you’re following gets done in 6 months? I suspect not.
The root cause is that the acceleration is pareto distributed so the modern engineering team at the moment looks like one 10x engineer, one 5x engineer, and the rest are approximately 1.5x engineers.
> should expect maybe 5x faster cycle in major software apps
To what end and what would that even look like though? Enshittifying everything at maximum speed? The apps/platforms I use regularly - GitHub, Spotify, Google maps (just to name a few), have gotten noticeably shittier in recent times.
>GitHub, Spotify, Google maps (just to name a few), have gotten noticeably shittier in recent times.
What if AI lets you create new versions of those tools, but without the enshitification?
I say that being in the "soaking" stage of using AI to rebuild a shitty software project in 70KLOC over about 2 weeks of spare time, so this may not be as theoretical as you might think.
> What if AI lets you create new versions of those tools, but without the enshitification?
I'm not sure I fully understand what you're saying here. Isn't the value of these tools almost entirely independent of their actual software? That is, we have many good open source, self-hostable forges (Forgejo, sr.ht, etc.), lots of great music player software (Jellyfin, Symphonium, etc.), and decent maps software (OsmAnd and Organic Maps). People use GitHub, Spotify, and Google Maps -- perhaps even _put up_ with their often bad/glitchy software -- because of network effects (all three) and content/licensing partnerships (Spotify/GMaps). That proprietary data isn't something AI can help you with, right?
It really depends on the use-case. For example, my most starred github repo is a tool to convert Spotify playlists to YouTube Music (that was done pre-AI). Github depends on what issues you have with it, what your use case is, and whether you can leverage some of the network effects via API from the github source. Maps, same story.
Confirmation bias. The internet has complained about software updates decades before LLMs became ubiquitous. I made a career fixing human slop by domain experts.
We easily forget that the great majority of software engineering is fixing the mistakes of other highly capable software engineers.
It's just so easy to blame the machine instead of admitting no one here is an expert on anything and they count their hits and not misses. If they did, we would find the probability of making a mistake to be higher than a fronter coding agent.
It's a hard headed crowd and everyone, LLM pilled or not, suffers from the Dunning-Kruger. All of us.
Just look at the comments. Everyone is perfect when they do things themselves.
I have been building an entire operating system ( not figuratively)
Prior to ai autocomplete 500 loc a day and then with ai autocomplete I could do 2500 a day and now 50k is pretty normal. Walking around tech week with my phone yielded 150k this week
It is likely better because AI agents make access to domain knowledge easier. However, I would wager that the problem is people don’t remember the code well. The problems are going to be long-term as the pace of change increases.
If you think about it, successful products rely on designing well-thought-out experiences, customer discovery (see all the Forward-Deployed Enginneer job listings at OpenAI) so the code velocity somewhat becomes irrelevant.
If you’re solving the right problem and you’ve got a good team then competitive advantage comes from somewhere OUTSIDE of code velocity.
The more important question I think is does faster code yield more value long-term? At the moment, it’s like yeah we do 3.5 pull requests per day.
I’m thinking, great, good for you. You could also combine three pull requests into one and then you’re doing 1 per day. This is quantitative data that doesn’t really mean anything tangible.
For instance:
> So the speculative discourse only works as long as investors subsidize the use of the technology. When that subsidy stops, these AI firms have to actually deliver value, or customers won’t buy it.
Are investors subsidizing the technology? There's upfront build out, but Anthropic is profitable and I believe the big labs are profitable on inference.
In comparison consider the real estate bubble in 2008. Why was real estate going up so much? Was there a surge of people coming into the country driving up demand? Were people using these houses? No it was purely inflationary driven by cheap money and financial engineering.
It also relies on arguments that this technology is all speculative, like one day we'll figure out how to use AI and demand will be high. But the demand is already there. Everybody is using AI, revenues are insane, it's the fastest growing product in history. It spans consumers and increasingly businesses.
The comparison to dot-com crash is also superficial. Was dot-com a bubble? I don't know, if you were transported to peak 2000 hype, would you argue "you guys are in a bubble, and the internet impact on the economy will be no greater than that of the fax machine"? No, of course not. In this case the supply outstripped demand. The build out and hype was early. But with AI you're seeing real usage.
It's not contagion. People are using this technology, not because it's cool and (apart from a few examples) because their bosses are forcing them to hit token metrics, but because it's actually useful and people are finding more uses every day.
It's just lazy. There is real risk that the build out is too much. But to make that argument you would have to say that the model intelligence would asymptote or become increasingly expensive such that its not worth it. Or that demand for broad intelligence is capped somehow. But saying we're in a bubble just because number goes up is not a strong argument.
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