Comment by jgbuddy

1 day ago

100%, a driving factor will likely be how good we can make models that are so small they use almost no compute. Until then it is a race for adoption and moat-building (or screwing people over?) once you have users

> a driving factor will likely be how good we can make models that are so small they use almost no compute.

That will certainly help but it doesn't move the fundamental limit because resource efficiency is a cost driver not a demand driver - and my argument is against the thesis that lying beyond professional devs and knowledge workers, there's an untapped trillion dollar industry serving LLMs to mass global consumers.

Using Simon's cost estimates, I agree that halving the current $1,000 - $1,200/mo MSRP to profitably serve frontier inference to professional developers and knowledge workers (PD&K) will help Vendor A steal share from Vendor B or C. It will also increase LLM sales penetration into the segments of the global PD&K TAM which can't afford ~$1K/mo for every seat. A fair chunk of the PD&K workers in many SMEs aren't included in today's ~$1K/mo per seat license pool, especially in 2nd and 3rd world geos. When the price falls to $500 and $250 most will but that's still just saturating the existing PD&K TAM - not pushing into mass consumers.

While the PD&K TAM is big, justifying Trillion+ dollar capex spend requires believing the TAM is much more than PD&K and eventually grows into converting a couple billion non-PD&K consumers into ~$20/mo subscribers. I don't buy it for two reasons:

1) The Comps: There are vanishingly few examples of long-term, mass consumer adoption of a discretionary technology at that scale. Mobile phones at ~$15 to $30/mo are the obvious one but LLMs are nowhere near being that valuable to the average plumber in Des Moines, baker in Jakarta or retired nurse in Hamburg. Pondering it, I just imagined forcing any of those people to choose between their mobile phone and an LLM chatbot. Sure, some who are flush with cash might choose both but for most consumers in the world ~$20/mo is big enough they'd have to pick one and ~zero percent would choose the LLM over their phone. After mobile phones, the second comp for discretionary tech spend I thought about was XBox and Playstation monthly gaming subscriptions but combined they have less than 90M paying subscribers and the ARR is just under $10/mo. As an industry, "Big LLM" is spending well over a trillion dollars every five years. XBox and PS ARR doesn't even cover paying the interest on that capital, much less the 3 to 5x returns hedge fund investors are betting on.

2) The Alternative: It's useful to doubt my own intuitions and one counter to my skepticism is to assume "But LLMs aren't finished yet, they're going to get much better." How much better could an LLM which can be profitable at ~$20/mo get than Claude Mythos in the next five years? Instead of debating future unknowables with myself, I've found it's better to just imagine the most perfect future product I can that's still realistically plausible. So, let's imagine we're willing to spend a million dollars a month to very unprofitably deploy a prototype to test the consumer demand for "Tomorrow's Awesomest $20/mo LLM" today. So we gather a few hundred super smart, broadly knowledgeable intellectuals together at one top-tier university research library, where they'll have access to every commercial database and unlimited Claude Mythos 2.0 and ChatGPT 6.0. Since our experimental budget is $1M/mo we can afford to add in several Nobel prize and Fields Medal winners too. They'll work together manually reviewing and improving not only every LLM answer but also our test user's prompts - and of course our test chatbot will have human-level real-time speech recognition and vision (via Zoom and screen-sharing with actual genius-level humans), making this truly a test of the "smartest, most accurate, best consumer chatbot" we can imagine.

Now, let's run the test by having one thousand mass consumers try it out and see how many Des Moines plumbers, Jakarta bakers and Hamburg retired nurses we can convert to a 1 year @ $20/mo subscription for our $1M/mo ultimate chatbot simulation. Playing this thought experiment out in a bunch of ways, I find some percentage of outliers, iconoclasts and closet intellectuals would go for it but... the vast majority just don't find it enough better than "free" chatbot alternatives AT&T includes with their phone subscription or Samsung bundles with Galaxy phones - despite only being ChatGPT 5.4-level. It turns out, most plumbers, bakers and ex-nurses don't have a compelling "job to be done" in their daily lives that even an MoE panel of actual Nobel and Field's medalists with ivy league professors can make enough more valuable than an inferior but free-to-me chatbot, in the judgement of our Des Moines plumber. While the world's smartest chatbot is nice, when it comes time to pay, he prefers having one additional premium football match on TV and a six pack of cold beers every month.

  • I'm having a hard time understanding this huge post that doesn't talk about enterprise users. I'm convinced that the consumer isn't going to be coming up with enough money to justify AI valuations... but doesn't this just mean that we expect the money to come from large enterprise users?

    A recent post here said AI spend could be "20% of every software developer's salary"... and that seemed plausible based on productivity improvements. That's not about a phone bill.

    • > doesn't this just mean that we expect the money to come from large enterprise users?

      Yes, if you agree that not enough consumers will value "smart paid chatbots" over dumb free chatbots at ~$20/mo, then, as you say, the money has to come from enterprise developers and knowledge workers (PD&K). The big problem with that is the numbers don't work. There aren't enough PD&K workers that are highly-paid enough to justify $12,000 to $16,000 a year to cover the astronomical spending run rates.

      Without recapitulating all the various scenarios AI CEOs like to hand-wave, my take-away is the scenarios which show today's frontier AI vendors earning the returns they've promised investors to get those trillions of dollars they're spending ALL require truly extraordinary deltas far above current historical actuals. Whereas ALL the scenarios which I find more plausibly realistic, even if still quite bullish, never even get near the required ballpark.

      I've tried to make various scenarios fit but inevitably, when the PD&K demand side starts getting implausibly inflated, I start pushing the cost side down to keep the adoption rates remotely plausible. For example, assuming things like the number of PD&K workers will grow at 2X the highest rates ever seen before or that the percentage of the tasks knowledge workers actually do at sufficient frequency to really matter and which are also "LLM improvable" is implausibly vast.

      It gets challenging because one quickly hits finite limits on the increase in tangible economic value that an LLM can possibly deliver on many common knowledge work tasks like drafting an email or a product proposal. Even if we assume next year's frontier LLM is so good literally ZERO slop is even possible so that our knowledge worker doesn't ever have to review anything - turning a 20 minute task into 2 minutes, or even 200 milliseconds, yields finite economic value to the enterprise. Even if you go extreme and suppose pretty crazy stuff like 18 months from now LLMs have eliminated 50% of PD&K workers, that messes with the spreadsheet assumptions because now the there are half as many $16,000/yr seats so the LLM license prices have to double again just to stay even.

      At the edges it quickly gets nuts. I even tried assuming that in five years LLMs eliminate 100% of non-managerial PD&K jobs. All actual work is done by Super-AGI LLMs. Even if we assume such amazing intelligence can be profitably sold in five years for only 4x the price of today's far dumber LLMs, that Super AGI still costs $50,000/yr per human replaced. Even that assumption only cuts the blended labor cost of non-managerial roles roughly in half (depending on industry and geo). In the end, no matter how much labor cost LLMs enable companies to cut, it still only reduces overhead. Lower costs might allow Vendor A to steal some of Vendor B's customers but it doesn't increase the total TAM or demand of the entire sector both vendors serve.

      Once you're out of plausible labor savings, one has to move to assuming that within five years Super-AGI LLMs working unsupervised will be making and validating fundamental scientific breakthroughs, then reducing those breakthroughs to engineering practice enabling new technologies which create entire new markets. Then they'll create whole new companies with maybe only 1/10th the humans and profitably grow those new markets. It quickly starts to feel like bad sci-fi instead of plausible, near-term financial scenario planning. Is stuff that extreme and unprecedented actually possible? Sure, incredibly unlikely and unprecedented things DO sometimes happen but things so far out of the distribution are very rare. But when making Sam and Dario's scenarios actually 'math out' starts requiring more than one "Black Swan" unlikely, historically unprecedented, earth-shaking event. And then goes on to need whole flocks of Black Swans, it just isn't credible. That said, I do believe that some parts of these scenarios may happen in 10 or 20+ years. It's just that the investment theses trillions worth of our 401K's are sunk into assume five or six year amortization and financial payback.