> I don't get that mentality. Millions of units, shares many parts with other models, OK margin. This is no reason to kill a product.
At 2% of sales, the opportunity cost is the reason to kill the product. Keep in mind this still needs design, manufacturing, marketing, and distribution resources.
> At 2% of sales, the opportunity cost is the reason to kill the product. Keep in mind this still needs design, manufacturing, marketing, and distribution resources.
All this is true for many other products, specifically cars. Ford makes the Bronco Sport anyway, and they make money on it.
I get it, Apple has much better margins than Ford. And maybe a iPhone 18 Mini would reduce total margin for them. But if they move a couple of million units, it absolutely would make money.
> Ford makes the Bronco Sport anyway, and they make money on it.
The current Bronco Sport generation was introduced in 2021 with minor changes in the ensuing model years. Ford USA sold 134k Bronco Sport in 2025, of 783k SUVs (17.2%) and 2.1M total USA vehicles (6.4%). the Ford E-series truck with 3.2% Ford Truck sales or 2.0% total vehicle sales may be a better comparison. It also is a design introducted in 2021.
phones are more likely mostly new designs in each model year, but we can wait for the teardowns to see.
Yes, as the owner of an LG OLED, I paid a premium for a good quality panel, and expect it to be mine.
Also, I anticipated ads on a smart TV (unfortunately it's inevitable), but (wrongly) assumed that such invasive tracking and "we own the glass" would be a bar too low even for the budget manufacturers.
I have an LG OLED as well. When I bought it, it was the previous year's flagship. I am also mad at this.
I also had an LG washing machine. The dispenser plastic drawer broke after a year of use. After over a week of multiple emails and calls with the shop, LG themselves and third-party spares shops trying to get a replacement, I bought a Bosch.
Your broken waking machine drawer is an orthogonal problem, and perhaps not specific to one corp. Plastic parts break but, OTOH, overbuilding consumes resources with diminishing utility. A deep spare parts market is a cost.
May i suggest basic home repair? Two techniques that work in many cases are:
- "welding" with a soldering iron, using cable tie as a filler rod. You'll want good ventilation and a sacrificial tip
- epoxy repair putty
Superglue (cyanoacrylate) tends to give disappointing results on its own, but can be a good first step before putty. Putty can be reinforced with some kind of fibre. Never use cyanoacrylate with the welding technique (cyanide).
I don't love the model profusion that makes spare parts markets inefficient, but free markets are inefficient all over the place. Price in the externalities.
I used to think this too, but e.g. Bosch and IKEA have great replacement parts websites accessible to the consumer. IKEA’s parts are mostly free, too! With modern logistics (automated warehouses, cheap shipping, ordering online) it is actually a solvable problem, if the company gives a damn about it.
I'll take that as a recommendation, thank you! Fwiw, i have found spares for many brands by searching model numbers and wading through spate parts aggregators. The market exists. It's often uneconomic :-(
> Your broken waking machine drawer is an orthogonal problem
Perhaps, but LG has proven problematic to deal with.
To give you an idea, I tried directly with them at first. They gave me an official website to search for the part. It wasn't available. They then game me 2 or 3 third-parties to search. None of them had the part available. I emailed and called. They would at best say "not available".
Eventually I got LG to escalate to a manager, who called me back the next day. The manager was very nice. Told me to go to that official website again. I couldn't find the part. She then instructed me how to navigate to a form on that same website where I could send a message requesting the part. Remember, this is an official LG website.
I sent the form. The next day I got a reply from a third party, telling me they didn't have the part and I should contact the manufacturer directly. Now this is with dirty laundry already accumulating and us risking running out of pants. There's no laundromat near me and I don't have a car, nor I have the time for this crap.
Eventually I found what _could_ be the right part. Only there was no way to be sure it was, and it had a 3-week wait and would cost about 100 quid.
At that point I sat down and calculated how much I paid for the machine, its expected lifetime, deducted the time I had it for to get an estimate "remaining value", and it was less than the time I was wasting with this ordeal, plus everything else if I had to arrange alternative washes for the time being and the price of the replacement part (assuming it was the right one), so it made sense to just buy a new machine and send that one for recycling, so I did that.
> May i suggest basic home repair?
Sure you can, but I don't have that machine any longer, would probably not get it to a good state, and would waste even more of my time.
It wasn't a simple plastic crack. The drawer was comprised of 3 bins (on the same part), 2 of them covered by rubbery lids. The rubber needs to seal the bin below, otherwise detergent and softener leak and make a mess (which was happening at this point). One of the bin walls somehow got completely deformed. The rubbery lid wouldn't seal, and to make it worse, it eventually split at the seal because of the pressure from the bin wall. It would have been a nightmare to repair to an acceptable state.
Also note that the bin lid is something that needs to be opened and closed frequently for top-ups and cleaning. No home repair would survive for long.
I own an LG TV as well, and while I assume the Mi TV Box 4K tracks me instead, at least it's unpowered while the TV is off, and the TV itself is not connected to my network. I have updated TVs/soundbars/etc, but only through USB. When they remove that option is the day I stop being a customer, moving to one that still does.
Better yet would be never needing an update, but alas.
I have an LG OLED as well, and to be honest I don't care. It is pretty obvious that every TV manufacturer does it by the fact that they all have to make their TVs "smart", perhaps save for the tiny/unknown ones that don't have an ad department or a contract with an ad company. Five minutes of using any smart TV interface should be able to dispel the notion that it somehow exists for the benefit of the user, and not for other reasons. My TV simply never goes on the network, which makes this a non-issue
> Small models are rapidly growing in capability, require less compute to train and serve
According to Jevons' paradox a reduction in resource requirements (improved resource efficiency for the same payoff) leads to an increase in demand. This stops working when demand for compute is completely exhausted, but we are very far from that. There's even some very silly predictions floating around (see the latest Dwarkesh Patel podcast) that say compute will soon be most of the economy, even dictating market interest rates. Now, that has to be wrong, but the directional outlook is closer to correct than "very small and efficient models mean there will be ~0 demand for HPC-like compute".
I don’t think this line of thinking is particularly robust because it ignores how AI is being used and where the resource usage is coming from.
Right now there are a small number of very very resource intensive use cases that are being subsidized by OpenAI and Anthropic. There are people generating millions of lines of code because it’s basically free at the point of use, despite the code producing very little value. Anthropic and OpenAI frequently “reset” customer limits to allow them to use even more resources at no additional cost.
The majority of use cases across business are not generating millions of lines of code per employee. The majority of businesses need just a little bit of automation to radically improve the way they operate. A software engineer making endless projects because it’s free to do so might use hundreds of billions of tokens per year, but an entire manufacturing business could be revolutionized with a few million tokens per year.
I think 2 things can be true:
1. There is very little penetration of AI across the economy and huge room to grow in the number of businesses deriving economic value from AI
2. The compute usage today is vastly overrepresented by usage outliers who are not paying the cost of their usage and will stop when forced to pay the cost
We could see AI usage 10x while seeing compute decrease 10x if the type of usage shifts. Most businesses just need smarter macros.
> Anthropic and OpenAI frequently “reset” customer limits to allow them to use even more resources at no additional cost.
Surely this applies to fixed-price subscriptions, not per-token spend? Large enterprises (the "very very resource intensive" large-scale users) have to pay per token.
A lot of companies avoid paying for usage by encouraging their employees to use individual subscriptions. Outside of the short lived tokenmaxxing fever dream, enterprises are conscious of their usage with companies like Uber and Amazon reigning in their usage massively and companies like Ramp building their own routers for cost minimization.
Facebook is reportedly the company that spent $500 million in a single month on tokens. There are individual non-enterprise users rotating multiple subscriptions incurring $10k+ in tokens per subscription. Facebook’s $500 million month… is equivalent to ~10k individual subscriptions which could be as little as a few thousand of the heaviest users. That’s $500 million when billed on usage, or ~$2 million on plans.
The reason resets are such a big deal (people have set up websites to track them, tweets announcing them get millions of impressions) is because there are huge numbers of users pushing their plan limits every single day. If there was huge demand from usage-based customers (the large enterprises) that OpenAI and Anthropic couldn’t meet, they wouldn’t be handing out resets like candy.
I think a realistic belief is that Anthropic and OpenAI have vastly overstated demand and are using resets as a way to keep usage artificially inflated at a substantial financial cost. I’d guess fixed price plan users make up at least 95% of usage.
Jevons paradox is a veridical paradox, which, as you said, means that it's a true statement that merely looks wrong because it is counterintuitive.
I know that some people think that the word "paradox" should be only used to refer to antinomy paradoxes which are often called "true paradoxes" (such as "this sentence is false") which lead to a contradiction without requiring a flaw in reasoning.
Yeah, I think there's a tendency to underestimate how much demand is still gated behind cost constraints. The market for this is HUGE.
The PC era, call it 1975-2005, was one of the greatest wealth creation events in history, was characterized by the cost of the underlying commodity dropping mercilessly for the whole time. Each time it did, the space of problem you could solve with a PC would increase, to the point that by the end, they were both replacing mainframes and powering users who do nothing but chat and post cat pictures.
Could there be a correction in the short run? Quite possibly. I think an underestimated last mile problem is just the massive weight of bureaucracy and human process inertia. But in the long run, cheap, efficient intelligence is a new engineering capability that we've just begun to even explore.
> how much demand is still gated behind cost constraints. The market for this is HUGE.
I think this misses the actual limits here.
The problem isn't demand it's, "how much people are willing to spend on it".
Cheap AI has to be served on cheap compute, and if inference gets cheap enough to unlock massive usage numbers, by definition it also doesn't require anywhere near as much infrastructure per unit of demand.
Take DeepSeek serving ~100T tokens/day, depending on workload and utilization, you're potentially talking about only a few thousand last-gen GPUs. With current-gen GPUs maybe closer to ~1,000, and with Rubin even fewer I will be damned if I could get my hands on one.
That's the part I think people are missing when they extrapolate token demand into enormous infrastructure or AI revenue.
Yes usage will explode. But if the cost per unit collapses, the revenue doesn't necessarily go up with it.
You can't simultaneously argue that intelligence becomes so cheap that everyone uses enormous amounts of it, while also assuming customers will somehow spend trillions of dollars a year consuming it.
There is no obvious $1T customer-facing AI revenue number at the end of this rainbow in the short/medium term.
The average person isn't going to spend anything remotely comparable to what they spend on a car every year for an AI service. Even businesses have budgets now, huge demand doesn't matter if the willingness to pay isn't there.
The only path I can see to numbers like that is AI consuming existing business domains, even then it's very thin.
Say SaaS + legal + consulting + BPO + various other service industries collectively represent something like $10-20T globally.
Even if AI eventually replaces an enormous portion of that, it's probably not doing so at the same price. Why would customers switch otherwise?
Either the AI product has to be dramatically better, which is difficult for mature workflows, or dramatically cheaper which is much more plausible.
If it replaces $10-20T of existing services at roughly 1/10th or 1/100th (more likely) the cost, then you're looking at maybe a ~$1T AI revenue opportunity after replacing an absurdly large fraction of the existing service economy.
Who are now unemployed and can't pay for shit.
And that's before competition.
I think it's crazy to assume AI companies won't compete aggressively on price. As capabilities diffuse, smaller models catch up, inference hits pareto frontier the open-source alternatives have already improved and caught up, margins on routine intelligence should compress "hard" (emphasis on "hard").
We've already seen how difficult adoption can be even when the technology looks impressive on paper. Cheap here means 100x cheaper for 10x more demand that's a net 10x loss before any software or hardware optimizations.
So yes, I completely agree that cheap intelligence can bring an enormous amount of new usage.
"I just don't think usage means revenue." (you can plaster it on a wall if you want to, "usage doesn't mean revenue", if you want to find that out I have foss software bridge to sell)
The PC analogy actually reinforces this if you really think about it.
Compute became "vastly more useful" while the cost per unit of compute collapsed. Society captured enormous value, but all computer companies are literal failing giants without the AI hype. Value got caught by people who provided productionization.
Now if people expect AI to self productize itself I am happy to tell your try it. We all saw how OpenAI fell behind Anthropic because they thought that would work...
Google couldn't productize the search, instead they sold the eye balls and web-real-estate. Maybe that's the AI business model, but that's not $1T worth given you need to unglue people from other stuff.
Unless we get something approaching genuine ASI producing so much additional economic value that entirely new trillions, I don't see a path to $1-2T in direct AI revenue from customers.
The market simply can't absorb that level of spending.
Demand can be effectively infinite at the right price. But I think people are delusional on HN and SF if they think that number is in Trillions like the investments seem to suggest.
I am not saying Nvidia will fall tomorrow but someone will have to pull the breaks before this car goes to hell.
If AI compute is a transformative technology compared to industrialization (that's a huge "if", essentially positing a singularity-like outcome), that $1T-$2T/yr at current prices might be a tiny fraction of future GDP (real incomes), thus actually quite sustainable.
"The problem isn't demand it's, "how much people are willing to spend on it".
Lol its not even that - its what can I do with it? Which eventually has to show up somehow in the financials - from a macroeconomic stand point. Software production is microeconomic.
> The problem isn't demand it's, "how much people are willing to spend on it".
This is the right way to look at it, but a few of your estimates are a bit off. AI is being sold as an accelerator (or, if you're in a dystopian mood, total replacement) of knowledge workers. Currently knowledge worker salaries are $50 - 70 trillion a year globally, $10 - 11T in the US alone: https://gist.github.com/danielmiessler/2dc039762a202b083753b...
> Even if AI eventually replaces an enormous portion of that, it's probably not doing so at the same price. Why would customers switch otherwise?
AI is wayyyyyyy easier to wrangle than humans; no sick leaves, health insurance, perks, HR issues... heck they don't even sleep! If companies could replace us with robots, they would do so in a heartbeat. Capitalism!
So in a "what the market will bear" sense, we have an upper bound on the TAM. Indeed, I expect this is where Anthropic's ridiculous "$30 trillion" number is coming from... except now we see how they came to it.
If AI makes workers even 1% more efficient, that's a $500 - 700 billion value annually. In reality AI makes workers way more efficient (studies from the ancient era of 2024 showed about a 30% boost) so AI companies could realistically charge that much more. But then all the other factors you mentioned -- smaller models, competition, self-hosting, etc -- come into play, which put a downward pressure on revenues.
It's impossible to predict how these dynamics will play out, but the numbers involved are astronomical. This is why everyone from the frontier labs to Big Tech to VCs to nation states are scrambling to get in on it.
1. Depending on the data source you look at, about 50 - 60% of people use AI at work but only for 5 - 15% of work hours. That leaves about 2x (from users) times 7 - 20x (from work hours) for growth. Furthermore agentic usage is much more token-intensive than regular prompts, that's another unknown multiple that will get applied.
Small models will make a dent for sure, but even they need to run on hardware. It's not clear how much their lower resource requirements will cancel out the scope for growth, but I think it will take time for that dynamic to play out; people are only just starting to ease up on tokenmaxxing. Anthropic revenues would be the canary in the coalmine, and thankfully they'll be IPO'ing soon.
2. All the relevant fabs (mainly, TSMC) are extremely capacity-constrained, so who actually gets the chips depends on who has the best vendor relationships... and who can pay the most for them. Even Apple, famed for its supply chain mastery, is having trouble these days.
I would assume TSMC will try to keep all its customers happy but will prioritize supplying the customer that will pay it the most money, and these days that's Nvidia. Simply because that's where ~all the AI boom money is flowing. Heck, you could even imagine some form of revenue share to keep the spice errr chips flowing...
3. Memory constraints affect all vendors, they will just pass those costs on to customers, like Nvidia with its recent 15% price bump. Notably the bump was announced BEFORE the earnings; I wonder if the effects of that was reflected in these projections.
Nvidia is in the same position with acquiring chip supply that Google is with acquiring search traffic: monopoly profits shared with suppliers make it very hard for other companies to compete.
Every quarter I see a similar analysis, similar projection. Yet, they keep posting these insane numbers. Everyone knows it’s a bubble, the problem is determining the top. Nvidia is continuously showing the top is far far higher than everyone imagines.
I don't get why people say Nvidia is a bubble. They are selling products now, not in the future! If AI market collapses (I doubt it will happen), they will still be selling GPUs. They will make less money, but that is expected
the bubble doesnt mean they are worthless only that after a pop the value of stock will drop significantly. it is out of their control if people overvalue the stock, and thats what creates the bubble which will eventually need to pop for self correction - but might trigger a massive oversale bringing the stock below actual value and causing all sorts of problems that will challenge the solvency of the company (basically challenging their liquid funds vs how much debt that they backed to their stock value). if they survive that then a bounce back is expected and buying while they were low would get you profit again. if they overleveraged themselves during the bubble bc they bought into the hype themselves, then they could face serious financial troubles and be susceptible to getting bought out.
But isn't that supposed to happen when you create a hit product? I imagine some people said similar things about Apple when iPhone started selling like water, but I don't think that it was the majority like here
Bubble means inflated not fake, i.e when they make less money their stock will crash and bubble will pop, which is what people buying the stock today are concerned with , is this going to hold
>Small models are rapidly growing in capability, require less compute to train and serve
Must be very clear that China’s undercut strategy, which is a well-known and studied tactic that they’ve used for a long time, it is absolutely dominating this point.
Right now you can LLM, code, make songs, images, and esp video on gaming hardware in your PC that would’ve been absolutely datacenter shit last year.
So the question will be does the scaling continue to benefit efficiency or ability?
If ability (needs datacenter storage and performance), how much better can the code get? How much more realistic in the images videos get? There are definitely strides to be made everywhere, but man, just like the bottleneck wasn’t coding, I’m not sure the creation bottleneck is rendering.
IMO Opus 5 wasn't that great at launch, but 4.8 was definitely downgraded just prior.
Agree that Sol is a great model. I find that it's improved a bit but I mostly attribute this to its eagerness to use the harness' memory features. (I'm using it in Hermes Agent, FWIW)
Bending Spoons' entire business model is buying businesses that are failing/non-profitable despite having customers. Is it much of a surprise that the first thing they do is to massively increase pricing to make RoI?
Airtable and Harvest weren't failing. They just weren't as profitable as hoped, and appear to have eaten most of their TAM and were wasting the engineering dollars being spent building features to increase the TAM.
Bending Spoons business model is less buying failing businesses and more buying runouts; ending ongoing investment in them / shifting to maintenance; and and hiking prices to grab as much cash as possible. It's Broadcom's business model (see vmware) just pointed at b2c or software in the smb not enterprise category.
At 2% of sales, the opportunity cost is the reason to kill the product. Keep in mind this still needs design, manufacturing, marketing, and distribution resources.
I say all of this as a fan of the Mini series.
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