LLM Inevitabilism

I think two things can be true simultaneously:

1. LLMs are a new technology and it's hard to put the genie back in the bottle with that. It's difficult to imagine a future where they don't continue to exist in some form, with all the timesaving benefits and social issues that come with them.

2. Almost three years in, companies investing in LLMs have not yet discovered a business model that justifies the massive expenditure of training and hosting them, the majority of consumer usage is at the free tier, the industry is seeing the first signs of pulling back investments, and model capabilities are plateauing at a level where most people agree that the output is trite and unpleasant to consume.

There are many technologies that have seemed inevitable and seen retreats under the lack of commensurate business return (the supersonic jetliner), and several that seemed poised to displace both old tech and labor but have settled into specific use cases (the microwave oven). Given the lack of a sufficiently profitable business model, it feels as likely as not that LLMs settle somewhere a little less remarkable, and hopefully less annoying, than today's almost universally disliked attempts to cram it everywhere.

LLMs need significant optimization or we get significant improvement on computing power while keeping the energy cost the same. It's similar with smartphone, when at the start it's not feasible because of computing power, and now we have one that can rival 2000s notebooks.

LLMs is too trivial to be expensive

EDIT: I presented the statement wrongly. What I mean is the use case for LLM are trivial things, it shouldn't be expensive to operate

Calling LLMs trivial is a new one. Yea just consume all of the information on the internet and encode it into a statistical model, trivial, child could do it /s

Imagine telling a person from five years ago that the programs that would basically solve NLP, perform better than experts at many tasks and are hard not to anthropomorphize accidentally are actually "trivial". Good luck with that.

Developers haven't even started extracting the value of LLMs with agent architectures yet. Using an LLM UI like open ai is like we just figured fire and you use it to warm you hands (still impressive when you think about it, but not worth the burns), while LLM development is about building car engines (here is you return on investment).

>programs that would basically solve NLP

There is a load-bearing “basically” in this statement about the chat bots that just told me that the number of dogs granted forklift certification in 2023 is 8,472.

LLM can give you thousands of lines of perfectly working code for less than 1 dollar. How is that trivial or expensive?

> all of the information on the internet

Total exaggeration—especially given Cloudflare providing free tools to block AI and now tools to charge bots for access to information.

Every booster argument is like this one. $trite_analogy triumphant smile

Yeah it solved NLP about 50% of the time, and also mangles data badly and in often hard-to-detect ways.

well I presented the statement wrongly. What I mean is the use case for LLM are trivial things, it shouldn't be expensive to operate

Thousands of lines of perfectly working code? Did you verify that yourself? Last time I tried it produced slop, and I've been extremely detailed in my prompt.

well I presented the statement wrongly. What I mean is the use case for LLM are trivial things, it shouldn't be expensive to operate

and the 1 dollar cost for your case is heavily subsidized, that price won't hold up long assuming the computing power stays the same.

> first signs of pulling back investments

I agree with you, but I’m curious; do you have link to one or two concrete examples of companies pulling back investments, or rolling back an AI push?

(Yes it’s just to fuel my confirmation bias, but it’s still feels nice:-) )

Sure, maybe solving NLP is too great a claim to make. It is still not at all ordinary that beforehand we could not solve referential questions algorithmically, that we could not extract information from plain text into custom schemas of structured data, and context-aware mechanical translation was really unheard of. Nowadays LLMs can do most of these tasks better than most humans in most scenarios. Many NLP questions at least I find interesting reduce to questions of the explanability of LLMs.

> Developers haven't even started extracting the value of LLMs with agent architectures yet

There are thousands of startups doing exactly that right now, why do you think this will work when all evidence points towards it not working? Or why else would it not already have revolutionized everything a year or two ago when everyone started doing this?

Theyre doing it so much it's practically a cliche.

There are underserved areas of the economy but agentic startups is not one.

Looking up a project on github, downloading it and using it can give you 10000 lines of perfectly working code for free.

Also, when I use Cursor I have to watch it like a hawk or it deletes random bits of code that are needed or adds in extra code to repair imaginary issues. A good example was that I used it to write a function that inverted the axis on some data that I wanted to present differently, and then added that call into one of the functions generating the data I needed.

Of course, somewhere in the pipeline it added the call into every data generating function. Cue a very confused 20 minutes a week later when I was re-running some experiments.

>evelopers haven't even started extracting the value of LLMs with agent architectures yet.

Which is basically what? The infinite monkey theorem? Brute forcing solutions for problems at huge costs? Somehow people have been tricked to actually embrace and accept that now they have to pay subscriptions from 20$ to 300$ to freaking code? How insane is that, something that was a very low entry point and something that anyone could do, is now being turned into some sort of classist system where the future of code is subscriptions you pay for companies ran by sociopaths who don't care that the world burns around them, as long as their pockets are full.

ML models have the good property of only requiring investment once and can then be used till the end of history or until something better replaces them.

Granted the initial investment is immense, and the results are not guaranteed which makes it risky, but it's like building a dam or a bridge. Being in the age where bridge technology evolves massively on a weekly basis is a recipe for being wasteful if you keep starting a new megaproject every other month though. The R&D phase for just about anything always results in a lot of waste. The Apollo programme wasn't profitable either, but without it we wouldn't have the knowledge for modern launch vehicles to be either. Or to even exist.

I'm pretty sure one day we'll have an LLM/LMM/VLA/etc. that's so good that pretraining a new one will seem pointless, and that'll finally be the time we get to (as a society) reap the benefits of our collective investment in the tech. The profitability of a single technology demonstrator model (which is what all current models are) is immaterial from that standpoint.

Most prominent example was this one: https://www.reuters.com/technology/microsoft-pulls-back-more...

Throwing genetic fallacies around instead of engaging with the comment at hand... :)

I cannot emphasize how much I agree with this comment. Thank you for writing it, I would never have had written it as well.

Most of them are a bunch of prompts and don't even have actual developers. For the good reason that there is no training system yet and the wording of how you call the people that build these system isn't even there or clearly defined. Local companies haven't even setup a proper internal LLM or at least a contract with a provider. I am in France so probably lagging behind USA a bit especially NY/SF but the word "LLM developer" is just arriving now and mostly under the pressure of isolated developers and companies like me. This feel really really early stage.

I don't have a subscription not even an Open AI account (mostly cause they messed up their google account system). You can't extract value of an LLM by just using the official UI, you just scratch the surface of how they work. And yet there aren't much developers able to actually build an actual agent architecture that does deliver some value. I don't include the "thousands" of startups that are clearly suffer from a signaling bias: they don't exist in the economy and I don't care about them like at all in my reasonning. I am talking about actual LLM developers that you can recruit locally the same way you recruit a web developer today, and that can make sense out of "frontier" LLM garbage talk by using proper architectures. These devs are not there yet.

Nah, if TSMC got exploded and there was a world war, in 20 years all the LLMs would bit rot.

Are you seriously comparing downloading static code from github with bespoke code generated for your specific problem? LLMs don't keep you from coding, they assist it. Sometimes the output works, sometimes it doesn't (on first or multiple tries). Dismissing the entire approach because it's not perfect yet is shortsighted.

Eh, I doubt it, tech only got massively better in each world war so far, through unlimited reckless strategic spending. We'd probably get a TSMC-like fab on every continent by the end of it. Maybe even optical computers. Quadrotor UAV are the future of warfare after all, and they require lots of compute.

Adjusted for inflation it took over 120 billion to build the fleet of liberty ships during WW2, that's like at least 10 TSMC fabs.

>> Developers haven't even started extracting the value of LLMs with agent architectures yet.

What does this EVEN mean? Do words have any value still, or are we all just starting to treat them as the byproduct of probabilistic tokens?

"Agent architectures". Last time I checked an architecture needs predictability and constraints. Even in software engineering, a field for which the word "engineering" is already quite a stretch in comparison to construction, electronics, mechanics.

Yet we just spew the non-speak "Agentic architectures" as if the innate inability of LLMs in managing predictable quantitative operations is not an unsolved issue. As if putting more and more of these things together automagically will solves their fundamental and existential issue (hallucinations) and suddenly makes them viable for unchecked and automated integration.

3 years into automating all white collar labor in 6 months.

The smartest and most well funded people on the planet have been trying and failing to get value out of this technology for years and the best we've come up with so far is some statistically unreliable coding assistants. Hardly the revolution its proponents keep eagerly insisting we're seeing.

Exactly. This is basically the argument of “AI as Normal Technology”.

https://knightcolumbia.org/content/ai-as-normal-technology

https://news.ycombinator.com/item?id=43697717

Between the ridiculously optimistic and the cynically nihilistic I personally believe there is some value that extremely talented people at huge companies can't really provide because they're not in the right environment (too big a scale) but neither can grifters packaging a prompt in a vibecoded app.

In the last few months the building blocks for something useful for small companies (think less than 100 employees) have appeared, now it's time for developers or catch-all IT at those companies and freelancers serving small local companies to "up-skill".

Why do I believe this? Well for a start OCR became much more accessible this year cutting down on manual data entry compared to tesseract of yesteryear.

"hard not to anthropomorphize accidentally' is a you problem.

I'm unhappy every time I look in my inbox, as it's a constant reminder there are people (increasingly, scripts and LLMs!) prepared to straight-up lie to me if it means they can take my money or get me to click on a link that's a trap.

Are you anthropomorphizing that, too? You're not gonna last a day.

They didn’t dismiss it, they just said it is not really that useful which is correct?

Thanks for the link. The comparison to electricity is a good one, and this is a nice reflection on why it took time for electricity’s usefulness to show up in productivity stats:

> What eventually allowed gains to be realized was redesigning the entire layout of factories around the logic of production lines. In addition to changes to factory architecture, diffusion also required changes to workplace organization and process control, which could only be developed through experimentation across industries.

is there a non-prompt way to interact with LLMs?

In an agentic setup the value is half the prompts half how you plug them together. I am opposing for instance a big prompt that is supposed to write a dissertation vs a smart web scraper that builds a knowledge graph out of sources and outputs a specialized search engine for your task. The former is a free funny intern, the latter is growth percentage visible in the economy.

They try to get value at their scale, which is tough. Your local SME definitely sees value in an embedding-based semantic search engine over their 20 years of weird unstructured data.

I didn't mean typical chatbot output, these are luckily still fairly recognizable due to stylistic preferences learned during fine-tuning. I mean actual base model output. Take a SOTA base model and give it the first two paragraphs of some longer text you wrote, and I would bet on many people being unable to distinguish your continuation from the model's autoregressive guesses.

I think that's more reflective of the deteriorating relationship between OpenAI and Microsoft than an true lack of demand for datacenters. If a major model provider (OpenAI, Anthropic, Google, xAI) were to see a dip in available funding or stop focusing on training more powerful models, that would convince me we may be in a bubble about to pop, but there are no signs of that as far as I can see.

>> There are many technologies that have seemed inevitable and seen retreats under the lack of commensurate business return

120+ Cable TV channels must have seemed like a good idea at the time, but like LLMs the vast majority of the content was not something people were interested in.

I think the difference between all previous technologies is scope. If you make a super sonic jet that gets people from place A to place B faster for more money, but the target consumer is like "yeah, I don't care that much about that at that price point", then your tech sort is of dead. You are also fully innovated on that product, like maybe you can make it more fuel efficient, sure, but your scope is narrow.

AI is the opposite. There are numerous things it can do and numerous ways to improve it (currently). There is lower upfront investment than say a supersonic jet and many more ways it can pivot if something doesn't work out.

Cheaper models might be around $0.01 per request, and it's not subsidized: we see a lot of different providers offering open source models, which offer quality similar to proprietary ones. On-device generation is also an option now.

For $1 I'm talking about Claude Opus 4. I doubt it's subsidized - it's already much more expensive than the open models.

But the thing is, LLMs are already incredibly cheap to operate compared to the alternatives. Both for trivial things and for complex things.

I'm confused with your second point. LLM companies are not making any money from current models? Openai generates 10b USD ARR and has 100M MAUs. Yes they are running at a loss right now but that's because they are racing to improve models. If they stopped today to focus on optimization of their current models to minimize operating cost and monetizing their massive user base you think they don't have a successful business model? People use this tools daily, this is inevitable.

No, because if they stop to focus on optimizing and minimizing operating costs, the next competitor over will leapfrog them with a better model in 6-12 months, making all those margin improvements an NPV negative endeavor.

It’s just the natural counterpart to dogmatic inevitabilism — dogmatic denialism. One denies the present, the other the (recent) past. It’s honestly an understandable PoV though when you consider A) most people understand “AI” and “chatbot” to be synonyms, and B) the blockchain hype cycle(s) bred some deep cynicism about software innovation.

Funny seeing that comment on this post in particular, tho. When OP says “I’m not sure it’s a world I want”, I really don’t think they’re thinking about corporate revenue opportunities… More like Rehoboam, if not Skynet.

Making money and operating at a loss contradict each other. Maybe someday they’ll make money —but not just yet. As many have said they’re hoping capturing market will position them nicely once things settle. Obviously we’re not there yet.

> that's because they are racing improve models. If they stopped today to focus on optimization of their current models to minimize operating cost and monetizing their user base you think they don't have a successful business model?

I imagine they would’ve flicked that switch if they thought it would generate a profit, but as it is it seems like all AI companies are still happy to burn investor money trying to improve their models while I guess waiting for everyone else to stop first.

I also imagine it’s hard to go to investors with “while all of our competitors are improving their models and either closing the gap or surpassing us, we’re just going to stabilize and see if people will pay for our current product.”

I pay $300 to fly from SF to LA when I could've just walked for free. Its true. How classist!

It is absolutely possible for the unit economics of a product to be profitable and for the parent company to be losing money. In fact, it's extremely common when the company is bullish on their own future and thus they invest heavily in marketing and R&D to continue their growth. This is what I understood GP to mean.

Whether it's true for any of the mainstream LLM companies or not is anyone's guess, since their financials are either private or don't separate out LLM inference as a line item.

Are you saying they'd be profitable if they didn't pour all the winnings into research?

From where I'm standing, the models are useful as is. If Claude stopped improving today, I would still find use for it. Well worth 4 figures a year IMO.

I do wonder where in the cycle this all is given that we've now seen yet another LLM/"Agentic" VSCode fork.

I'm genuinely surprised that Code forks and LLM cli things are seemingly the only use case that's approached viability. Even a year ago, I figured there'd be something else that's emerged by now.

Well recently cursor got a heat for rising price and having opaque usage, while anthropic's claude reported to be worse due to optimization. IMO the current LLMs are not sustainable, and prices are expected to increase sooner or later.

Personally, until models comparable with sonnet 3.5 can be run locally on mid range setup, people need to wary that the price of LLM can skyrocket

> If they stopped today to focus on optimization of their current models to minimize operating cost and monetizing their user base you think they don't have a successful business model?

Actually, I'd be very curious to know this. Because we already have a few relatively capable models that I can run on my MBP with 128 GB of RAM (and a few less capable models I can run much faster on my 5090).

In order to break even they would have to minimize the operating costs (by throttling, maiming models etc.) and/or increase prices. This would be the reality check.

But the cynic in me feels they prefer to avoid this reality check and use the tried and tested Uber model of permanent money influx with the "profitability is just around the corner" justification but at an even bigger scale.

For me, if Anthropic stopped now, and given access to all alternative models, they still would be worth exactly $240 which is the amount I'm paying now. I guess Anthropic and OpenAI can see the real demand by clearly seeing what are their free:basic:expensive plan ratios.

> most people understand “AI” and “chatbot” to be synonyms

This might be true (or not), but for sure not on this site.

> Well worth 4 figures a year IMO

only because software engineering pay hasn't adjusted down for the new reality . You don't know what its worth yet.

It's not a great analogy. The only parallel with Concorde is energy consumption. I think a better analogy would have been VR.

> In order to break even they would have to minimize the operating costs (by throttling, maiming models etc.) and/or increase prices. This would be the reality check.

Is that true? Are they operating inference at a loss or are they incurring losses entirely on R&D? I guess we'll probably never know, but I wouldn't take as a given that inference is operating at a loss.

I found this: https://semianalysis.com/2023/02/09/the-inference-cost-of-se...

which estimates that it costs $250M/year to operate ChatGPT. If even remotely true $10B in revenue on $250M of COGS would be a great business.

They'd be profitable if they showed ads to their free tier users. They wouldn't even need to be particularly competent at targeting or aggressive with the amount of ads they show, they'd be profitable with 1/10th the ARPU of Meta or Google.

And they would not be incompetent at targeting. If they were to use the chat history for targeting, they might have the most valuable ad targeting data sets ever built.

They might generate 10b ARR, but they lose a lot more than that. Their paid users are a fraction of the free riders.

https://www.wheresyoured.at/openai-is-a-systemic-risk-to-the...

> Developers haven't even started extracting the value of LLMs with agent architectures yet.

For sure there is a portion of developers who don't care about the future, are not interested in current developements and just live as before hoping nothing will change. But the rest already gave it a try and realized tools like Claude Code can give excellent results for small codebases to fail miserably at more complex tasks with the net result being negative as you get a codebase you don't understand, with many subtle bugs and inconsistencies created over a few days you will need weeks to discover and fix.

But there are a ton of LLM powered products in the market.

I have a friend in finance that uses LLM powered products for financial analysis, he works in a big bank. Just now anthropic released a product to compete in this space.

Another friend in real estate uses LLM powered lead qualifications products, he runs marketing campaigns and the AI handles the initial interaction via email or phone and then ranks the lead in their crm.

I have a few friends that run small businesses and use LLM powered assistants to manage all their email comms and agendas.

I've also talked with startups in legal and marketing doing very well.

Coding is the theme that's talked about the most in HN but there are a ton of startups and big companies creating value with LLMs

Revenue is _NOT_ Profit

As you say, we will never know, but this article[0] claims:

> The cost of the compute to train models alone ($3 billion) obliterates the entirety of its subscription revenue, and the compute from running models ($2 billion) takes the rest, and then some. It doesn’t just cost more to run OpenAI than it makes — it costs the company a billion dollars more than the entirety of its revenue to run the software it sells before any other costs.

[0] https://www.lesswrong.com/posts/CCQsQnCMWhJcCFY9x/openai-los...

The difference is that the future is now with LLMs. There is a microwave (some multiple) in almost every kitchen in the world. The Concord served a few hundred people a day. LLMs are already ingrained into hundreds of millions if not billions of people’s lives, directly and indirectly. My dad directly uses LLMs multiple times a week if not daily in an industry that still makes you rotate your password every 3 months. It’s not a question of whether the future will have them, it’s a question of whether the future will get tired of them.

I heard majority of the users are techies asking coding questions. What do you sell to someone asking how to fix a nested for loop in C++? I am genuinely curious. Programmers are known to be the stingiest consumers out there.

my company has already fired a bunch of people in favor of LLMs so they are realizing all kinds of value

Can you explain this in more detail? The idiot bottom rate contractors that come through my team on the regular have not been helped at all by LLMs. The competent people do get a productivity boost though.

The only way I see compensation "adjusting" because of LLMs would need them to become significantly more competent and autonomous.

Obviously you don't need to train new models to operate existing ones.

I think I trust the semianalysis estimate ($250M) more than this estimate ($2B), but who knows? I do see my revenue estimate was for this year, though. However, $4B revenue on $250M COGS...is still staggeringly good. No wonder amazon, google, and Microsoft are tripping over themselves to offer these models for a fee.

According to fb's aggressively targeted marketing, you sell them donald trump propaganda.

Bolting banner ads onto a technology that can organically weave any concept into a trusted conversation would be incredibly crude.

And ARR is not revenue. It's "annualized recurring revenue": take one month's worth of revenue, multiply it by 12--and you get to pick which month makes the figures look most impressive.

I'm not sure that stereotype holds up. Developers spend a lot: courses, cloud services, APIs, plugins, even fancy keyboards.

A quick search shows that click on ads targeting developers are expensive.

Also there is a ton of users asking to rewrite emails, create business plans, translate, etc.

They didn't really need the cloud either and yet...

I mean, thats the point, they aren't the same. Concorde was one dimensional, AI is not.

True - but if you erode that trust then your users may go elsewhere. If you keep the ads visually separated, there's a respected boundary & users may accept it.

I don’t know your company but this thinking doesn’t necessarily follow logically. In a large company the value of developers is not distributed evenly across people and time, and also has a strong dependency on market realities in front of them.

While it’s true that lots of companies are getting some value out of LLMs, a much larger number are using them as an excuse for layoffs they would have wanted to do anyway—LLMs are just a golden opportunity to tie in an unmitigated success narrative.

CapEx vs. OpEx.

If they stop training today what happens? Does training always have to be at these same levels or will it level off? Is training fixed? IE, you can add 10x the subs and training costs stay static.

IMO, there is a great business in there, but the market will likely shrink to ~2 players. ChatGPT has a huge lead and is already Kleenex/Google of the LLMs. I think the battle is really for second place and that is likely dictated by who runs out of runway first. I would say that Google has the inside track, but they are so bad at product they may fumble. Makes me wonder sometimes how Google ever became a product and verb.

Only as much as replacing all your devs with a frog is "realizing value"

>Or why else would it not already have revolutionized everything a year or two ago when everyone started doing this?

The internet needed 20 years to take over the world. All of the companies of the first dot com bust are in the past. The tech is solid.

You'd probably do brand marketing for Stripe, Datadog, Kafka, Elastic Search etc.

You could even loudly proclaim that the are ads are not targeted by users which HN would love (but really it would just be old school brand marketing).

> I heard majority of the users are techies asking coding questions.

Citation needed? I can't sit on a bus without spotting some young person using ChatGPT

It's very important to note that advertisers set the parameters in which FB/Google's algorithms and systems operate. If you're 25-55 in a red state, it seems likely that you'll see a bunch of that information (even if FB are well aware you won't click).

I mean...

  LLMs have not yet discovered a business model that justifies the massive expenditure of training and hosting them,

The only way one could say such a thing is if they think chatbots are the only real application.

That's still not profit.

Astonishing that that concept survived getting laughed out of the room long enough to actually become established as a term and an acronym.

But assuming no new models are trained, this competitive effect drives down the profit margin on the current SOTA models to zero.

It's 3 years away.

You don't get to pick the month. At least not with any half-serious audience.

This is a bit developer centric, I am much more impressed by the opportunities I see in consulting rather than applying LLMs to dev tasks. And I am still impressed by the code it can output eventhough we are still in the funny intern stage in this area.

This means I believe we currently underuse LLM capabilities and their empirical nature makes it difficult to assess their limitations without trying. I've been studying LLMs from various angles during a few months before coming to this conclusion, as an experienced software engineer and consultant. I must admit it is however biased towards my experience as an SME and in my local ecosystem.

It's a good point. Any business can get revenue by selling Twenty dollar bills for $19. But in the history of tech, many winners have been dismissed for lack of an apparent business model. Amazon went years losing money, and when the business stabilized, went years re-investing and never showed a profit. Analysts complained as Amazon expanded into non-retail activities. And then there's Uber.

The money is there. Investors believe this is the next big thing, and is a once in a lifetime opportunity. Bigger than the social media boom which made a bunch of billionaires, bigger than the dot com boom, bigger maybe than the invention of the microchip itself.

It's going to be years before any of these companies care about profit. Ad revenue is unlikely to fund the engineering and research they need. So the only question is, does the investor money dry up? I don't think so. Investor money will be chasing AGI until we get it or there's another AI winter.

I imagine they HOPE they'll realize value. A lot of people are acting on what might be, rather than what is, which makes sense given that the AI "thought leaders" (CEOs with billions invested that need to start turning a profit) are all promising great things soon™.

I know. It's a doubly-dubious figure.

We're not talking about a half-serious audience: we're talking about the collection of reposters of press releases we call "the media".

The best they've come up with is the LLM chatbot, which both OpenAI and Anthropic have as their flagship product because many people find it extremely valuable. Many people I know routinely use ChatGPT to help them write things, even those who were already good at writing, and if you don't think that's true at your workplace I strongly suspect it's because people aren't telling you about it.

Like that’s ever stopped the adtech industry before.

It would be a hilarious outcome though, “we built machine gods, and the main thing we use them for is to make people click ads.” What a perfect Silicon Valley apotheosis.

Investments are mostly in model training. We have trained models now, we'll see a pullback in that regard as businesses will need to optimize to get the best model without spending billions in order to compete on price, but LLMs are here to stay.

One thing we're seeing in the software engineering agent space right now is how many people are angry with Cursor [1], and now Claude Code [2] (just picked a couple examples; you can browse around these subreddits and see tons of complaints).

What's happening here is pretty clear to me: Its a form of enshittification. These companies are struggling to find a price point that supports both broad market adoption ($20? $30?) and the intelligence/scale to deliver good results ($200? $300?). So, they're nerfing cheap plans, prioritizing expensive ones, and pissing off customers in the process. Cursor even had to apologize for it [3].

There's a broad sense in the LLM industry right now that if we can't get to "it" (AGI, etc) by the end of this decade, it won't happen during this "AI Summer". The reason for that is two-fold: Intelligence scaling is logarithmic w.r.t compute. We simply cannot scale compute quick enough. And, interest in funding to pay for that exponential compute need will dry up, and previous super-cycles tell us that will happen on the order of ~5 years.

So here's my thesis: We have a deadline that even evangelists agree is a deadline. I would argue that we're further along in this supercycle than many people realize, because these companies have already reached the early enshitification phase for some niche use-cases (software development). We're also seeing Grok 4 Heavy release with a 50% price increase ($300/mo) yet offer single-digit percent improvement in capability. This is hallmark enshitification.

Enshitification is the final, terminal phase of hyperscale technology companies. Companies remain in that phase potentially forever, but its not a phase where significant research, innovation, and optimization can happen; instead, it is a phase of extraction. AI hyperscalers genuinely speedran this cycle thanks to their incredible funding and costs; but they're now showcasing very early signals of enshitifications.

(Google might actually escape this enshitification supercycle, to be clear, and that's why I'm so bullish on them and them alone. Their deep, multi-decade investment into TPUs, Cloud Infra, and high margin product deployments of AI might help them escape it).

[1] https://www.reddit.com/r/cursor/comments/1m0i6o3/cursor_qual...

[2] https://www.reddit.com/r/ClaudeAI/comments/1lzuy0j/claude_co...

[3] https://techcrunch.com/2025/07/07/cursor-apologizes-for-uncl...

It’s a KPI just like any KPI and it’s gamed. A lot of random financial metrics are like that. They were invented or coined as a short hand for something.

Different investors use different ratios and numbers (ARR, P/E, EV/EBITDA, etc) as a quick initial smoke screen. They mean different things in different industries during different times of a business’ lifecycle. BUT they are supposed to help you get a starting point to reduce noise. Not as a the 1 metric you base your investing strategy on.

> model capabilities are plateauing at a level where most people agree that the output is trite and unpleasant to consume.

What are you basing this on? Personal feelings?

> Can you explain this in more detail?

Not sure what GP meant specifically, but to me, if $200/m gets you a decent programmer, then $200/m is the new going rate for a programmer.

Sure, now it's all fun and games as the market hasn't adjusted yet, but if it really is true that for $200/m you can 10x your revenue, it's still only going to be true until the market adjusts!

> The competent people do get a productivity boost though.

And they are not likely to remain competent if they are all doing 80% review, 15% prompting and 5% coding. If they keep the ratios at, for example, 25% review, 5% prompting and the rest coding, then sure, they'll remain productive.

OTOH, the pipeline for juniors now seems to be irrevocably broken: the only way forward is to improve the LLM coding capabilities to the point that, when the current crop of knowledgeable people have retired, programmers are not required.

Otherwise, when the current crop of coders who have the experience retires, there'll be no experience in the pipeline to take their place.

If the new norm is "$200/m gets you a programmer", then that is exactly the labour rate for programming: $200/m. These were previously (at least) $5k/m jobs. They are now $200/m jobs.

Even if the profit margin is driven to zero, that does not mean competitors will cease to offer the models. It just means the models will be bundled with other services. Case in point: Subversion & Git drove VCS margin to zero (remember BitKeeper?), but Bitbucket and Github wound up becoming good businesses. I think Claude Code might be the start of how companies evolve here.

How many of those MAUs are crappy startups building a janky layer on top of the OpenAI API which will cease to exist in 2 years?

google did it. LLms are the new google search. It'll happen sooner or later.

That's calculating value against not having LLMs and current competitors. If they stopped improving but their competitors didn't, then the question would be the incremental cost of Claude (financial, adjusted for switching costs, etc) against the incremental advantage against the next best competitor that did continue improving. Lock in is going to be hard to accomplish around a product that has success defined by its generalizability and adaptability.

Basically, they can stop investing in research either when 1) the tech matures and everyone is out of ideas or 2) they have monopoly power from either market power or oracle style enterprise lock in or something. Otherwise they'll fall behind and you won't have any reason to pay for it anymore. Fun thing about "perfect" competition is that everyone competes their profits to zero

So the "multiply by 12" thing is a slight corruption of ARR, which should be based on recurring revenue (i.e. subscriptions). Subscriptions are harder to game by e.g. channel-stuffing and should be much more stable than non-recurring revenue.

To steelman the original concept, annual revenue isn't a great measure for a young fast-growing company since you are averaging all the months of the last year, many of which aren't indicative of the trajectory of the company. E.g. if a company only had revenue the last 3 months, annual revenue is a bad measure. So you use MRR to get a better notion of instantaneous revenue, but you need to annualize it to make it a useful comparison (e.g. to compute a P/E ratio), so you use ARR.

Private investors will of course demand more detailed numbers like churn and an exact breakdown of "recurring" revenue. The real issue is that these aren't public companies, and so they have no obligation to report anything to the public, and their PR team carefully selects a couple nice sounding numbers.

That's fixable, a gradual adjusting of the free tier will happen soon enough once they stop pumping money into it. Part of this is also a war of attrition though, who has the most money to keep a free tier the longest and attract the most people. Very familiar strategy for companies trying to gain market share.

The number of things it can actually do is significantly lower than the number of things the hype men are claiming it can do.

$200 does not get you a decent programmer though. It needs constant prompting, babysitting, feedback, iteration. It's just a tool. It massively boosts productivity in many cases, yes. But it doesn't do your job for you. And I'm very bullish on LLM assisted coding when compared to most of HN.

High level languages also massively boosted productivity, but we didn't see salaries collapse from that.

> And they are not likely to remain competent if they are all doing 80% review, 15% prompting and 5% coding.

I've been doing 80% review and design for years, it's called not being a mid or junior level developer.

> OTOH, the pipeline for juniors now seems to be irrevocably broken

I constantly get junior developers handed to me from "strategic partners", they are just disguised as senior developers. I'm telling you brother, the LLMs aren't helping these guys do the job. I've let go 3 of them in July alone.

So has mine, and quite predictably our product has gone into the shitter and breaks constantly, requiring reverts almost daily. They've armed a couple of Juniors with Cursor and given them the workload of all those people they fired / have quit since the firings, some of which have been at the company for years and held a lot of institutional knowledge that is now biting them in the ass.

Now sure, "Just don't fire the useful people and get rid of the juniors and supercharge the good devs with AI tooling" or whatever, except the whole reason the C-level is obsessed with this AI shit is because they're sold on the idea of replacing their most expensive asset, devs, because they've been told by people who sell AI as a job that it can replace those pesky expensive devs and be replaced by any random person in the company prompting up a storm and vibecoding it all.

Churn rates are up, we're burning unfathomable amounts of money on the shitty AI tooling and the project has somehow regressed after we've finally managed to get a good foothold on it and start making real progress for once. Oh and the real funny part is they're starting to backpedal a bit and have tried to get some people back in.

I expect to hear a LOT more of this type of thing happening in the near future. As the idiots in charge start slowly realizing all the marketing sold to them on LinkedIn or wherever the fuck it is they get these moronic ideas from are literal, actual literal lies.

how is it "trusted" when it just makes things up

OpenAI has half a billion active users.

You don't need every individual request to be profitable, just the aggregate. If you're doing a Google search for, like, the std::vector API reference you won't see ads. And that's probably true for something like 90% of the searches. Those searches have no commercial value, and serving results is just a cost of doing business.

By serving those unmonetizable queries the search engine is making a bet that when you need to buy a new washing machine, need a personal injury lawyer, or are researching that holiday trip to Istanbul, you'll also do those highly commercial and monetizable searches with the same search engine.

Chatbots should have exactly the same dynamics as search engines.

This seems like one the only sane arguments in this whole sea of articles.

My take since day one:

(1) Model capabilities will plateau as training data is exhausted. Some additional gains will be possible by better training, better architectures, more compute, longer context windows or "infinite" context architectures, etc., but there are limits here.

(2) Training on synthetic data beyond a very limited amount will result in overfitting because there is no new information. To some extent you could train models on each other, but that's just an indirect way to consolidate models. Beyond consolidation you'll plateau.

(3) There will be no "takeoff" scenario -- this is sci-fi (in the pejorative sense) because you can't exceed available information. There is no magic way that a brain in a vat can innovate beyond available training data. This includes for humans -- a brain in a vat would quickly go mad and then spiral into a coma-like state. The idea of AI running away is the information-theoretic equivalent of a perpetual motion machine and is impossible. Yudkowski and the rest of the people afraid of this are crackpots, and so are the hype-mongers betting on it.

So I agree that LLMs are real and useful, but the hype and bubble are starting to plateau. The bubble is predicated on the idea that you can just keep going forever.

Most of the comments here feel like cope about AI TBH. There's never been an innovation like this ever, and it makes sense to get on board rather than be left behind.

That assumes that everyone is willing to pay for it. I don't think that's an assumption that will be true.

That's a great question to ask the people who seem to trust them implicitly.

Yes, but for a while google was head and shoulders above the competition. It also poured a ton of money into building non-search functionality (email, maps, etc.). And had a highly visible and, for a while, internally respected "don't be evil" corporate motto.

All of which made it much less likely that users would bolt in response to each real monetization step. This is very different to the current situation, where we have a shifting landscape with several AI companies, each with its strengths. Things can change, but it takes time for 1-2 leaders to consolidate and for the competition to die off. My 2c.

Competition is almost guaranteed to drive price close to cost of delivery especially if they can't pay trump to ban open source, particularly chinese. With no ability to play the thiel monopoly playbook, their investors would never make their money back if not for government capture and sweet sweet taxpayer military contracts.

15% of people aren't smart enough to read and follow directions explaining how to fold a trifold brochure, place it in an envelope, seal it, and address it

you think those people don't believe the magic computer when it talks?

I imagine they would be more like product placements in film and TV than banner ads. Just casually dropping a recommendation and link to Brand (TM) in a query. Like those Cerveza Cristal ads in star wars. They'll make it seem completely seamless to the original query.

I understand the importance of having data, and that any measurement can be gamed, but this one seems so tailored for tailoring that I struggle to understand how it was ever a good metric.

Even being generous it seems like it'd be too noisy to even assist in informing a good decision. Don't the overwhelmingly vast majority of businesses see periodic ebbs and flows over the course of a year?

Technology is an exponential process, and the thing about exponentials is that they are chaotic. You cant use inductive reasoning vis a vis war and technology. The next big one could truly reset us to zero or worse.

A lot of people use it for cooking and other categories as well.

Techies are also great for network growth and verification for other users, and act as community managers indirectly.

Many obviously disagree that it's correct

Absolutely, free-tier AI won’t stay "free" forever. It’s only a matter of time before advertisers start paying to have their products woven into your AI conversations. It’ll creep in quietly—maybe a helpful brand suggestion, a recommended product "just for you," or a well-timed promo in a tangential conversation. Soon enough though, you’ll wonder if your LLM genuinely likes that brand of shoes, or if it's just doing its job.

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If interactions with your AI start sounding like your conversation partner shilling hot cocoa powder at nobody in particular those conversations are going to stop being trusted real quick. (Pop culture reference: https://youtu.be/MzKSQrhX7BM?si=piAkfkwuorldn3sb)

Which may be for the best, because people shouldn’t be implicitly trusting the bullshit engine.

This echoes a lot of the rhetoric around "but how will facebook/twitter/etc make money?" back in the mid 2000s. LLMs might shake out differently from the social web, but I don't think that speculating about the flexibility of demand curves is a particularly useful exercise in an industry where the marginal cost of inference capacity is measured in microcents per token. Plus, the question at hand is "will LLMs be relevant?" and not "will LLMs be massively profitable to model providers?"

The huge leap that is getting pushback is the sentiment that LLMs will consume every use case and replace human labor. I don't think many are arguing LLMs will die off entirely.

> 2. Almost three years in, companies investing in LLMs have not yet discovered a business model that justifies the massive expenditure of training and hosting them, the majority of consumer usage is at the free tier, the industry is seeing the first signs of pulling back investments, and model capabilities are plateauing at a level where most people agree that the output is trite and unpleasant to consume.

You hit the nail on why I say to much hatred from "AI Bros" as I call them, when I say it will not take off truly until it runs on your phone effortlessly, because nobody wants to foot a trillion dollar cloud bill.

Give me a fully offline LLM that fits in 2GB of VRAM and lets refine that so it can plug into external APIs and see how much farther we can take things without resorting to burning billions of dollars' worth of GPU compute. I don't care that my answer arrives instantly, if I'm doing the research myself, I want to take my time to get the correct answer anyway.

I don't really buy your point 2. Just the other day Meta announced hundreds of billions of dollars investment into more AI datacenters. Companies are bringing back nuclear power plants to support this stuff. Earlier this year OpenAI and Oracle announced their $500bn AI datacenter project, but admittedly in favor of your point have run into funding snags, though that's supposedly from tariff fears with foreign investors, not lack of confidence in AI. Meta can just finance everything from their own capital and Zuck's decree, like they did with VR (and it may very well turn out similarly).

Since you brought up supersonic jetliners you're probably aware of the startup Boom in Colorado trying to bring it back. We'll see if they succeed. But yes, it would be a strange path, but a possible one, that LLMs kind of go away for a while and try to come back later.

You're going to have to cite some surveys for the "most people agree that the output is trite and unpleasant" and "almost universally disliked attempts to cram it everywhere" claims. There are some very vocal people against LLM flavors of AI, but I don't think they even represent the biggest minority, let alone a majority or near universal opinions. (I personally was bugged by earlier attempts at cramming non-LLM AI into a lot of places, e.g. Salesforce Einstein appeared I think in 2016, and that was mostly just being put off by the cutesy Einstein characterization. I generally don't have the same feelings with LLMs in particular, in some cases they're small improvements to an already annoying process, e.g. non-human customer support that was previously done by a crude chatbot front-end to an expert system or knowledge base, the LLM version of that tends to be slightly less annoying.)

You need to train new models to advance the knowledge cutoff. You don't necessarily need to R&D new architectures, and maybe you can infuse a model with new knowledge without completely training from scratch, but if you do nothing the model will become obsolete.

Also the semianalysis estimate is from Feb 2023, which is before the release of gpt4, and it assumes 13 million DAU. ChatGPT has 800 million WAU, so that's somewhere between 115 million and 800 million DAU. E.g. if we prorate the cogs estimate for 200 DAU, then that's 15x higher or $3.75B.

I agree, its easily fixable by injecting ads into the responses for the free tier and probably eventually even the lower paid tiers to some extent

They aren't trusted in a vacuum. They're trusted when grounded in sources and their claims can be traced to sources. And more specifically, they're trusted to accurately represent the sources.

The existence of the LLMs will themselves change the profile and proclivities of people we consider “programmers” in the same way the app-driven tech boom did. Programmers who came up in the early days are different from ones who came up in the days of the web are different from ones who came up in the app era.

There will be a respected boundary for a time, then as advertisers find its more effective the boundaries will start to disappear

> At least not with any half-serious audience.

So I guess this rules out most SV venture capital

But if Claude stopped pouring their money into research and others didn't, Claude wouldn't be useful a year from now, as you could get a better model for the same price.

This is why AI companies must lose money short term. The moment improvements plateau or the economic environment changes, everyone will cut back on research.

and they wouldn't even have to make the model say the ads. I think that's a terrible idea which would drive model performance down.

Traditional banner ads, inserted inline into the conversation based on some classifier seem a far better idea.

The point is that if they’re not profitable they won’t be relevant since they’re so expensive to run.

And there was never any question as to how social media would make money, everyone knew it would be ads. LLMs can’t do ads without compromising the product.

Literally nobody would talk to a robot that spits back ads at them

> There's never been an innovation like this ever

There have been plenty of innovations like this. In fact, much of the hype around LLMs is a rehash of the hype around "expert systems" back in the '80s. LLMs are marginally more effective than those systems, but only marginally.

> It needs constant prompting, babysitting, feedback, iteration.

What do you think a product manager is doing?

Nope, lots of idiots just take them at face value. You're still describing what rational people do, not what all actual people do.

Just wait until companies start calculating it on future revenue from people on the trial period of subscriptions... I mean, if we aren't there already.

Any number that there isn't a law telling companies how to calculate it will always be a joke.

> LLMs might shake out differently from the social web, but I don't think that speculating about the flexibility of demand curves is a particularly useful exercise in an industry where the marginal cost of inference capacity is measured in microcents per token

That we might come to companies saying "it's not worth continuing research or training new models" seems to reinforce the OP's point, not contradict it.

Fair enough.

Sure you can't plan for black swan events, so the only choice you have is to plan for their absence. If we all nuke ourselves tomorrow well at least we don't have to worry about anything anymore. But in case we don't, those plans will be useful.

You’re not thinking evil enough. LLMs have the potential to be much more insidious about whatever it is they are shilling. Our dystopian future will feature plausibly deniable priming.

That paragraph is quite clear.

OpEx is larger than revenue. CapEx is also larger than the total revenue on the lifetime of a model.

Well, they haven't really tried yet.

The Meta app Threads had no ads for the first year, and it was wonderful. Now it does, and its attractiveness was only reduced by 1% at most. Meta is really good at knowing the balance for how much to degrade UX by having monetization. And the amount they put in is hyper profitable.

So let's see Gemini and GPT with 1% of response content being sponsored. I doubt we'll see a user exodus and if that's enough to sustain the business, we're all good.

I can run an LLM on my RTX3090 that is at least as useful to me in my daily life as an AAA game that would otherwise justify the cost of the hardware. This is today, which I suspect is in the upper part of the Kuznets curve for AI inference tech. I don't see a future where LLMs are too expensive to run (at least for some subset of valuable use cases) as likely.

> “most people agree that the output is trite and unpleasant to consume”

That is a such a wild claim. People like the output of LLMs so much that ChatGPT is the fastest growing app ever. It and other AI apps like Perplexity are now beginning to challenge Google’s search dominance.

Sure, probably not a lot of people would go out and buy a novel or collection of poetry written by ChatGPT. But that doesn’t mean the output is unpleasant to consume. It pretty undeniably produces clear and readable summaries and explanations.

Yup. Lots of products in the education space. Even doctors are using LLMs, while talking with patients. All sorts of teams are using the adjacent products for image and (increasingly) video generation. Translation freelancers have been hit somewhat hard because LLMs do "good enough" quite a bit better than old google translate.

Coding is relevant to the HN bubble, and as tech is the biggest driver of the economy it's no surprise that tech-related AI usages will also be the biggest causes of investment, but it really is used in quite a lot of places out there already that aren't coding related at all.

Those that aren't willing to pay for it directly, can still use it for free, but will just have to tolerate product placement.

“trusted” in computer science does not mean what it means in ordinary speech. It is what you call things you have no choice but to trust, regardless of whether that trust is deserved or not.

Hundreds of millions of people watch TV and listen to Radio that is at least 30% ad content per hour.

It feels like there's a lot of shifting goalposts. A year ago, the hype was that knowledge work would cease to exist by 2027.

Now we are trying to hype up enhanced email autocomplete and data analysis as revolutionary?

I agree that those things are useful. But it's not really addressing the criticism. I would have zero criticisms of AI marketing if it was "hey, look at this new technology that can assist your employees and make them 20% more productive".

I think there's also a healthy dose of skepticism after the internet and social media age. Those were also society altering technologies that purported to democratize the political and economic system. I don't think those goals were accomplished, although without a doubt many workers and industries were made more productive. That effect is definitely real and I'm not denying that.

But in other areas, the last 3 decades of technological advancement have been a resounding failure. We haven't made a dent in educational outcomes or intergenerational poverty, for instance.

ARR traditionally is _annual_ recurring revenue. The notion that it may be interpreted as _annualized_ and extrapolatable from MRR is a very recent development, and I doubt that most people interpret it as that.

…for starters, you can sell them the ability to integrate your AI platform into whatever it is they are building, so you can then sell your stuff to their customers.

The point I'm making is that, even in the extreme case where we cease all additional R&D on LLMs, what has been developed up until now has a great deal of utility and transformative power, and that utility can be delivered at scale for cheap. So, even if LLMs don't become an economic boon for the companies that enable them, the transformative effect they have and will continue to have on society is inevitable.

Edit: I believe that "LLMs transforming society is inevitable" is a much more defensible assertion than any assertion about the nature of that transformation and the resulting economic winners and losers.

> It needs constant prompting, babysitting, feedback, iteration. It's just a tool. It massively boosts productivity in many cases, yes.

It doesn't sound like you are disagreeing with me: that role you described is one of manager, not of programmer.

> High level languages also massively boosted productivity, but we didn't see salaries collapse from that.

Those high level languages still needed actual programmers. If the LLM is able to 10x the output of a single programmer because that programmer is spending all their time managing, you don't really need a programmer anymore, do you?

> I've been doing 80% review and design for years, it's called not being a mid or junior level developer.

Maybe it differs from place to place. I was a senior and a staff engineer, at various places including a FAANG. My observations were that even staff engineer level was still spending around 2 - 3 hours a day writing code. If you're 10x'ing your productivity, you almost certainly aren't spending 2 - 3 hours a day writing code.

> I constantly get junior developers handed to me from "strategic partners", they are just disguised as senior developers. I'm telling you brother, the LLMs aren't helping these guys do the job. I've let go 3 of them in July alone.

This is a bit of a non-sequitor; what does that have to do with breaking the pipeline for actual juniors?

Without juniors, we don't get seniors. Without seniors and above, who will double-check the output of the LLM?[1]

If no one is hiring juniors anymore, then the pipeline is broken. And since the market price of a programmer is going to be set at $200/m, where will you find new entrants for this market?

Hell, even mid-level programmers will exit, because when a 10-programmer team can be replaced by a 1-person manager and a $200/m coding agent, those 9 people aren't quietly going to starve while the industry needs them again. They're going to go off and find something else to do, and their skills will atrophy (just like the 1-person LLM manager skills will atrophy eventually as well).

----------------------------

[1] Recall that my first post in this thread was to say that the LLM coding agents have to get so good that programmers aren't needed anymore because we won't have programmers anymore. If they aren't that good when the current crop starts retiring then we're in for some trouble, aren't we?

Maybe he's referencing how people don't like when other humans post LLM responses in the comments.

"Here's what chatGPT said about..."

I don't like that, either.

I love the LLM for answering my own questions, though.

I'm not really countering that ChatGPT is popular, it certainly is, but it's also sort of like "fastest growing tire brand" that came along with the adoption of vehicles. The amount of smartphone users is also growing at the fastest rate ever so whatever the new most popular app is has a good chance of being the fastest growing app ever.

> This echoes a lot of the rhetoric around "but how will facebook/twitter/etc make money?"

The answer was, and will be ads (talk about inevitability!)

Can you imagine how miserable interacting with ad-funded models will be? Not just because of the ads they spew, but also the penny-pinching on training and inference budgets, with an eye focused solely on profitability. That is what the the future holds: consolidations, little competition, and models that do the bare-minimum, trained and operated by profit-maximizing misers, and not the unlimited intelligence AGI dream they sell.

> they're trusted to accurately represent the sources.

Which is still too much trust

Not writing and committing code with GitHub Copilot, I'll tell you that. These things need to come a _long_ way before that's a reality.

...I do wonder what percent of ChatGPT usage is just students cheating on their homework, though.

> (the supersonic jetliner) ... (the microwave oven)

But have we ever had a general purpose technology (steam engine, electricity) that failed to change society?

I was chatting with Gemini about vacation ideas and could absolutely picture a world where if it lists some hotels I might like, the businesses that bought some LLM ad space could easily show up more often than others.

Consider the general research - in all, it doesn't eliminate people, but let's say it shakes out to speeding up developers 10% over all tasks. (That includes creating tickets, writing documentation, unblocking bugs, writing scripts, building proof of concepts, and more rote refactoring, but does not solve the harder problems or stop us from doing the hard work of software engineering that doesn't involve lines of code.)

That means that it's worth up to 10% of a developer's salary as a tool. And more importantly, smaller teams go faster, so it might be worth that full 10%.

Now, assume other domains end up similar - some less, some more. So, that's a large TAM.

It very much does not assume that, only that some fraction will have become accustomed to using it to the point of not giving it up. In fact, they could probably remain profitable without a single new customer, given the number of subscribers they already have.

Your argument requires "Claude can replace a programme" to be true. Thus, your argument is false for the foreseeable future.

Neal Stephenson has a recent post that covers some of this. Also links to teachers talking about many students just putting all their work into chatgpt and turning it in.

https://nealstephenson.substack.com/p/emerson-ai-and-the-for...

I just hope that if it comes to that (and I have no doubt that it will), regulation will catch up and mandate any ad/product placement is labeled as such and not just slipped in with no disclosure whatsoever. But, given that we've never regulated influencer marketing which does the same thing, nor are TV placements explicitly called out as "sponsored" I have my doubts but one can hope.

> especially if they can't pay trump to ban open source?

Huh? Do you mean for official government use?

I see a real window this time to sell your soul.

Social networks finding profitability via advertising is what created the entire problem space of social media - the algorithmic timelines, the gaming, the dopamine circus, the depression, everything negative that’s come from social media has come from the revenue model, so yes, I think it’s worth being concerned about how LLMs make money, not because I’m worried they won’t, because I’m worried they Will.

> LLMs can’t do ads without compromising the product.

Spoiler: they are still going to do ads, their hand will be forced.

Sooner or later, investors are going to demand returns on the massive investments, and turn off the money faucet. There'll be consolidation, wind-downs and ads everywhere.

> And since the market price of a programmer is going to be set at $200/m

You keep saying this, but I don't see it. The current tools just can't replace developers. They can't even be used in the same way you'd use a junior developer or intern. It's more akin to going from hand tools to power tools than it is getting an apprentice. The job has not been automated and hasn't been outsourced to LLMs.

Will it be? Who knows, but in my personal opinion, it's not looking like it will any time soon. There would need to be more improvement than we've seen from day 1 of ChatGPT until now before we could even be seriously considering this.

> Those high level languages still needed actual programmers.

So does the LLM from day one until now, and for the foreseeable future.

> This is a bit of a non-sequitor; what does that have to do with breaking the pipeline for actual juniors?

Who says the pipeline is even broken by LLMs? The job market went to shit with rising interest rates before LLMs hit the scene. Nobody was hiring them anyway.

Let's not ignore the technical aspects as well: LLMs are probably a local minima that we've gotten stuck in because of their rapid rise. Other areas in AI are being starved of investment because all of the capital is pouring into LLMs. We might have been better off in the long run if LLMs hadn't been so successful so fast.

> AI apps like Perplexity are now beginning to challenge Google’s search dominance

Now that is a wild claim. ChatGPT might be challenging Google's dominance, but Perplexity is nothing.

I'm not even in USA and I've never been in USA in my entire life.

> That is a such a wild claim. People like the output of LLMs so much that ChatGPT is the fastest growing app ever.

The people using ChatGPT like its output enough when they're the ones reading it.

The people reading ChatGPT output that other people asked for generally don't like it. Especially if it's not disclosed up front.

> That is a such a wild claim.

Some people who hate LLMs are absolutely convinced everyone else hates them. I've talked with a few of them.

I think it's a form of filter bubble.

> People like the output of LLMs so much that ChatGPT is the fastest growing app ever

While people seem to love the output of their own queries they seem to hate the output of other people's queries, so maybe what people actually love is to interact with chatbots.

If people loved LLM outputs in general then Google, OpenAI and Anthropic would be in the business of producing and selling content.

You sell them Copilot. You Sell them CursorAI. You sell them Windsurf. You sell them Devin. You sell the Claude Code.

Software guys are doing much, much more than treating LLM's like an improved Stack Overflow. And a lot of them are willing to pay.

There's another specific class of person that seems helped by them: the paralysis by analysis programmer. I work with someone really smart who simply cannot get started when given ordinary coding tasks. She researches, reads and understands the problem inside and out but cannot start actually writing code. LLMs have pushed her past this paralysis problem and given her the inertia to continue.

On the other end, I know a guy who writes deeply proprietary embedded code that lives in EV battery controllers and he's found LLMs useless.

I predict this comment to enter the Dropbox/iPod hall of shame of discussion forum skeptics.

I don't even get where this argument comes from. Pretraining is expensive, yes, but both LoRAs in diffusion models and finetunes of transformers show us that this is not the be-all, end-all; there's plenty of work being done on extensively tuning base models for cheap.

But inference? Inference is dirt cheap and keeps getting cheaper. You can run models lagging 6-12 years on consumer hardware, and by this I don't mean absolutely top-shelf specs, but more of "oh cool, turns out the {upper-range gaming GPU/Apple Silicon machine} I bought a year ago is actually great at running local {image generation/LLM inference}!" level. This is not to say you'll be able to run o3 or Opus 4 on a laptop next year - larger and more powerful models obviously require more hardware resources. But this should anchor expectations a bit.

We're measuring inference costs in multiples of gaming GPUs, so it's not an impending ecological disaster as some would like the world to believe - especially after accounting for data centers being significantly more efficient at this, with specialized hardware, near-100% utilization, countless of optimization hacks (including some underhanded ones).

For one, it's not like we're at some CS conference, so we're engaging in ordinary speech here, as far as I can tell. For two, "trusted" doesn't have just one meaning, even in the narrower context of CS.

> most people agree that the output is trite and unpleasant to consume

This is likely a selection bias: you only notice the obviously bad outputs. I have created plenty of outputs myself that are good/passable -- you are likely surrounded by these types of outputs without noticing.

Not a panacea, but can be useful.

I think this can't be understated. It also destroyed search. I listened to a podcast a few years ago with an early googler who talked about this very precipice in early google days. They did a lot of testing, and a lot of modeling of people's valuation of search. They figured that the average person got something like $50/yr of value out of search (I can't remember the exact number, I hope I'm not off by an order of magnitude). And that was the most they could ever realistically charge. Meanwhile, advertising for just Q4 was like 10 times the value. It meant that they knew that advertising on the platform was inevitable. They also acknowledged that it would lead to the very problem that Brin and Page wrote about in their seminal paper on search.

I see LLMs inevitably leading to the same place. There will undoubtedly be advertising baked into the models. It is too strong a financial incentive. I can only hope that an open source alternative will at least allow for a hobbled version to consume.

edit: I think this was the podcast https://freakonomics.com/podcast/is-google-getting-worse/

Targeted banner ads based on chat history is last-two-decades thinking. The money with LLMs will be targeted answers. Have Coca-Cola pay you a few billion dollars to reinforce the model to say "Coke" instead of "soda". Train it the best source of information about political subjects is to watch Fox News. This even works with open-source models, too!

The thing about facebook/twitter/etc was that everyone knew how they achieve lock-in and build a moat (network effect), but the question was around where to source revenue.

With LLMs, we know what the revenue source is (subscription prices and ads), but the question is about the lock-in. Once each of the AI companies stops building new iterations and just offers a consistent product, how long until someone else builds the same product but charges less for it?

What people often miss is that building the LLM is actually the easy part. The hard part is getting sufficient data on which to train the LLM, which is why most companies just put ethics aside and steal and pirate as much as they can before any regulations cuts them off (if any regulations ever even do). But that same approach means that anyone else can build an LLM and train on that data, and pricing becomes a race to the bottom, if open source models don't cut them out completely.

There are pretty hidden assumption in this comment. First of all, not every business in the AI space is _training_ models, and the difference between training and inference is massive - i.e. most businesses can easily afford inference, perhaps depending on model, but they definitely can.

Another several unfounded claims were made here, but I just wanted to say LLMs with MCP are definitely good enough for almost every use case you can come up with as long as you can provide them with high quality context. LLMs are absolutely the future and they will take over massive parts of our workflow in many industries. Try MCP for yourself and see. There's just no going back.

> I just wanted to say LLMs with MCP are definitely good enough for almost every use case you can come up with as long as you can provide them with high quality context.

This just shows you lack imagination.

I have a lot of use cases that they are not good enough for.

It wouldn't be general purpose if it fails to bring change. I'd take every previous iteration of "AI" as example, IBM Watson, that stuff

LLMs with tools*

MCP isn’t inherently special. A Claude Code with Bash() tool can do nearly anything a MCP server will give you - much more efficiently.

Computer Use agents are here and are only going to get better.

The conversation shouldn’t be about LLMs any longer. Providers will be providing agents.

What does it tell you then, that the interpretation of "A" as "annualized" is the interpretation Anthropic, to name one, has chosen?

You aren’t extrapolating enough. Nearly the entire history of computing has been one that isolates between shared computing and personal computing. Give it time. These massive cloud bills are building the case for accelerators in phones. It’s going to happen just needs time.

Try again.

Then cut off the free riders. Problem solved overnight.

> This echoes a lot of the rhetoric around "but how will facebook/twitter/etc make money?" back in the mid 2000s.

The difference is that Facebook costs virtually nothing to run, at least on a per-user basis. (Sure, if you have a billion users, all of those individual rounding errors still add up somewhat.)

By contrast, if you're spending lots of money per user... well look at what happened to MoviePass!

The counterexample here might be Youtube; when it launched, streaming video was really expensive! It still is expensive too, but clearly Google has figured out the economics.

I meant it in the ordinary speech sense (which I don't even thing contradicts the "CS sense" fwiw).

Many people have a lot of trust in anything ChatGPT tells them.

"Here's what chatGPT said about..." Is the new lmgtfy

> While people seem to love the output of their own queries they seem to hate the output of other people's queries

Listening or trying to read other peoples chats with these things is like listening to somebody describe a dream. It’s just not that interesting most of the time. It’s remarkable for the person experiencing it but it is deeply personal.

> You need to train new models to advance the knowledge cutoff

That's a great point, but I think it's less important now with MCP and RAG. If VC money dried up and the bubble burst, we'd still have broadly useful models that wouldn't be obsolete for years. Releasing a new model every year might be a lot cheaper if a company converts GPU opex to capex and accepts a long training time.

> Also the semianalysis estimate is from Feb 2023,

Oh! I missed the date. You're right, that's a lot more expensive. On the other hand, inference has likely gotten a lot cheaper (in terms of GPU TOPS) too. Still, I think there's a profitable business model there if VC funding dries up and most of the model companies collapse.

At some point, Groupon was the fastest growing company ever.

Yup, and I wouldn't be willing to bet that any firewall between content and advertising would hold, long-term.

For example, the more product placement opportunities there are, the more products can be placed, so sooner or later that'll become an OKR to the "content side" of the business as well.

It won’t be ads. Social media target consumers, so advertising is dominant. We all love free services and don’t mind some attraction.

AI on the other hand target businesses and consumers alike. A bank using LLM won’t get ads. Using LLM will be cost of doing business. Do you know what they means to consumers? Price for ChatGPT will go down.

He links to Reddit, a site where most people are aggressively against AI. So, not necessarily a representative slice of reality.

We actually aren't too far off from that reality. There are several models you can run fully offline on your phone (phi-3, Gemma-3n-E2b-it, Qwen2.5-1.5b-instruct all run quite well on my Samsung S24 ultra). There are a few offline apps that also have tool calling (mostly for web search but I suspect this is extendable).

If you want to play around a bit and are on android there is PocketPal,ChatterUI, MyDeviceAI, SmolChat are good multi-model apps and Google's Edge gallery won't keep your chats but is a fun tech demo.

All are on github and can be installed using Obtainium if you don't want to

> I am much more impressed by the opportunities I see in consulting rather than applying LLMs to dev tasks.

I expect there'll be a lot of consulting work in the near future in cleanup and recovery from LLM-generated disasters.

Had someone put up a project plan for something that was not disclosed as LLM assisted output.

While technically correct it came to the wrong conclusions about the best path forward and inevitably hamstrung the project.

I only discovered this later when attempting to fix the mess and having my own chat with an LLM and getting mysteriously similar responses.

The problem was that the assumptions made when asking the LLM were incorrect.

LLMs do not think independently and do not have the ability to challenge your assumptions or think laterally. (yet, possibly ever, one that does may be a different thing).

Unfortunately, this still makes them as good as or better than a very large portion of the population.

I get pissed off not because of the new technology or the use of the LLM, but the lack of understanding of the technology and the laziness with which many choose to deliver the results of these services.

I am more often mad at the person for not doing their job than I am at the use of a model, the model merely makes it easier to hide the lack of competence.

People "like" or people "suffice" with the output? This "rise of whatever" as one blog put it gives me feelings that people are instead lowering their standards and cutting corners. Letting them cut through to stuff they actually want to do.

This isn't some niche outcry: https://www.forbes.com/sites/bernardmarr/2024/03/19/is-the-p...

And that was 18 months ago.

Yes, believe it or not, people eventually wake up and realize slop is slop. But like everything else with LLM development, tech is trying to brute force it on people anyway.

Given recent studies, that does seem to reflect reality. Trust in AI has been waning for 2 years now.

Well, given the answers to the former: maybe we should stop now before we end up selling even more of our data off to technocrats. Or worse, your chatbot shilling to you between prompts.

And yes these are still businesses. If they can't find profitability they will drop it like it's hot. i.e. we hit another bubble burst that tech is known to do every decade or 2. There's no free money anymore to carry them anymore, so perfect time to burst.

lmgtfy was (from what I saw) always used as a snarky way to tell someone to do a little work on their own before asking someone else to do it for them.

I have seen people use "here's what chatGPT" said almost exclusively unironically, as if anyone else wants humans behaving like agents for chatbots in the middle of other people's discussion threads. That is to say, they offer no opinion or critical thought of their own, they just jump into a conversation with a wall of text.

He links to a post about a teacher’s expertise with students using AI. The fact that it’s on Reddit is irrelevant.

> I also imagine it’s hard to go to investors with “while all of our competitors are improving their models and either closing the gap or surpassing us, we’re just going to stabilize and see if people will pay for our current product.”

Yeah, no one wants to be the first to stop improving models. As long as investor money keeps flowing in there's no reason to - just keep burning it and try to outlast your competitors, figure out the business model later. We'll only start to see heavy monetization once the money dries up, if it ever does.

>AI on the other hand target businesses and consumers alike.

Okay. So AI will be using ads for consumers and make deals with the billionaires. If window 11/12 still puts ads in what is a paid premium product, I see no optimism in thinking that a "free" chatbot will not also resort to it. Not as long as the people up top only see dollar signs and not long term longevity.

>Price for ChatGPT will go down.

Price for ChatGPT in reality, is going up in the meanwhile. This is like hoping grocery prices come down as inflation lessens. This never happens, you can only hope to be compensated more to make up for inflation.

>what has been developed up until now has a great deal of utility and transformative power

I think we'd be more screwed than VR if development ceased today. They are little more than toys right now who's most successsful outings are grifts, and the the most useful tools are simply aiding existing tooling (auto-correct). It is not really "intelligence" as of now.

>I believe that "LLMs transforming society is inevitable" is a much more defensible assertion

Sure. But into what? We can't just talk about change for change's sake. Look at the US in 2025 with that mentality.

You still have faith in society after decades of ads being spit at them.

I mean, it adjusted down by having some hundreds of thousands of engineers laid off in he last 2+ years. they know slashing salaries is legal suicide, so they just make the existing workers work 3x as hard.

That's fine, that's what I want ;) I just grow tired of people hating on me for thinking that we really need to localize the models for them to take off.

LLMs are amazing at anything requiring text analysis (go figure). Everyone I know doing equity or economic research in finance is using it extensively for that, and from what I hear from doctors the LLMs are as good as that in their space if not better

Yeah callousness does seem to be the leaking area of improvement.

It sounds quite scary that an LLM could be trained on a single source of news (specially FN).

Great, we've got mediocre writing from unprofitable companies that are subsidizing the cost of this technology.

You posted an article about investors trust in AI companies to deliver and societies strong distrust of large corporations.

You article isn’t making the point you seem to think it is.

If you're going to champion something that comes from a place of extreme political bias, you could at least acknowledge it.

If you believe this, people believe everything they read by default and have to apply a critical thinking filter on top of it to not believe the thing.

I know I don't have as much of a filter as I ought to!

https://www.lesswrong.com/s/pmHZDpak4NeRLLLCw/p/TiDGXt3WrQwt...

> I constantly get junior developers handed to me from "strategic partners", they are just disguised as senior developers. I'm telling you brother, the LLMs aren't helping these guys do the job. I've let go 3 of them in July alone.

I find this surprising. I figured the opposite: that the quality of body shop type places would improve and the productivity increases would decrease as you went "up" the skill ladder.

I've worked on/inherited a few projects from the Big Name body shops and, frankly, I'd take some "vibe coded" LLM mess any day of the week. I really figured there was nowhere to go but "up" for those kinds of projects.

You're either overestimating the cost of inference or underestimating the cost of running a service like Facebook at that scale. Meta's cost of revenue (i.e. just running the service, not R&D, not marketing, not admin, none of that) was about $30B/year in 2024. In the leaked OpenAI financials from last year, their 2024 inference costs were 1/10th of that.

No… dude… it’s a new household name. We haven’t had those in software for a long time, maybe since TikTok and Fortnite.

By what relevant metric?

The userbase has grown by an order of magnitude over the past few years. Models have gotten noticeably smarter and see more use across a variety of fields and contexts.

Maybe I’m naïve/ignorant of how things are done in the VC world, but given the absolutely enormous amount of money flowing into so many AI startups right now, I can’t imagine that the gravy train is going to continue for more than a few years. Especially not if we enter any sort of economic downturn/craziness from the very inconsistent and unpredictable decisions being made by the current administration

> do not have the ability to challenge your assumptions or think laterally.

Particularly on the challenging your assumptions part is where I think LLMs fail currently, though I won't pretend to know enough about how to even resolve that; but right now, I can put whatever nonsense I want into ChatGPT and it will happily go along telling me what a great idea that is. Even on the remote chance it does hint that I'm wrong, you can just prompt it into submission.

None of the for-profit AI companies are going to start letting their models tell users they're wrong out of fear of losing users (people generally don't like to be held accountable) but ironically I think it's critically important that LLMs start doing exactly that. But like you said, the LLM can't think so how can it determine what's incorrect or not, let alone if something is a bad idea or not.

Interesting problem space, for sure, but unleashing these tools to the masses with their current capabilities I think has done, and is going to continue to do more harm than good.

Yeah I don't even read those. If someone can't be bothered to communicate their own thoughts in their own words, I have little belief that they are adding anything worth reading to the conversation.

Google does put AI output at the top of every search now, and sometimes it's helpful and sometimes it's crap. They have been trying since long before LLMs to not just provide the links for a search but also the content.

Google used to be interested in making sure you clicked either the paid link or the top link in the results, but for a few years now they'd prefer that a user doesn't even click a link after a search (at least to a non-Google site)

> LLMs do not think

Yep.

More seriously, you described a great example of one of the challenges we haven't addressed. LLM output masquerades as thoughtful work products and wastes people's time (or worse tanks a project, hurts people, etc).

Now my job reviewing work is even harder because bad work has fewer warning signs to pick up on. Ugh.

I hope that your workplace developed a policy around LLM use that addressed the incident described. Unfortunately I think most places probably just ignore stuff like this in the faux scramble to "not be left behind".

You may want to pay for Claude Max outside of the Google or iOS ecosystem and save $40/month.

ChatGPT also makes money via affiliate links. If you ask ChatGPT something like "what is the best airline approved cabin luggage you can buy?" you get affiliate links to Amazon and other sites. I use ChatGPT most of the time before I buy anything these days… From personal experience (I operated an app financed by affiliate links). I can tell you that this for sure generates a lot of money. My app was relatively tiny and I only got about 1% of the money I generated but that app pulled in about $50k per month.

Buying better things is one of my main use cases for GPT.

> a much larger number are using them as an excuse for layoffs they would have wanted to do anyway

It's a simple formula. Layoffs because of market conditions or company health = stock price go down. Layoffs because "AI took the jobs" = stock price go up.

Why communicate your own thoughts when ChatGPT can give you the Correct Answer? Saves everybody time and effort, right? I guess that’s the mental model of many people. That, or they’re just excited to be able to participate (in their eyes) productively in a conversation.

You would think so. Investors are eventually going to want a return on their money put in. But there seems to be a ton of hype and irrationality around AI, even worse than blockchain back in the day.

I think there's an element of FOMO - should someone actually get to AGI, or at least something good enough to actually impact the labor market and replace a lot of jobs, the investors of that company/product stand to make obscene amounts of money. So everyone pumps in, in hope of that far off future promise.

But like you said, how long can this keep going before it starts looking like that future promise will not be fulfilled in this lifetime and investors start wanting a return.

I’d like to see a statistically sound source for that claim. Given how many non-nerds there are on Reddit these days, it’s unlikely that there’s any particular strong bias in any direction compared to any similar demographic.

I think the thing people hate about that is the lack of effort and attention to detail. It’s an incredible enabler for laziness if misused.

If somebody writes a design or a report, you expect that they’ve put in the time and effort to make sure it is correct and well thought out.

If you then find the person actually just had ChatGPT generate it and didn’t put any effort into editing it and checking for correctness, then that is very infuriating.

They are essentially farming out the process of creating the document to AI and farming out the process of reviewing it to their colleagues. So what is their job then, exactly?

These are tools, not a replacement for human thought and work. Maybe someday we can just have ChatGPT serve as an engineer or a lawyer, but certainly not today.

> People like the output of LLMs so much that ChatGPT is the fastest growing app ever

And how much of that is free usage, like the parent said? Even when users are paying, ChatGPT's costs are larger than their revenue.

> Models have gotten noticeably smarter and see more use across a variety of fields and contexts.

Is that really true? The papers I've read seem to indicate the hallucination rate is getting higher.

It's even worse than you suggest, for the following reason. The rare employee that cares enough to read through an entire report is more likely to encounter false information which they will take as fact (not knowing that LLM produced the report, or unaware that LLMs produce garbage). The lazy employees will be unaffected.

> There are many technologies that have seemed inevitable and seen retreats under the lack of commensurate business return (the supersonic jetliner)

I think this is a great analogy, not just to the current state of AI, but maybe even computers and the internet in general.

Supersonic transports must've seemed amazing, inevitable, and maybe even obvious to anyone alive at the time of their debut. But hiding under that amazing tech was a whole host of problems that were just not solvable with the technology of the era, let alone a profitable business model. I wonder if computers and the internet are following a similar trajectory to aerospace. Maybe we've basically peaked, and all that's left are optimizations around cost, efficiency, distribution, or convenience.

If you time traveled back to the 1970s and talked to most adults, they would have witnessed aerospace go from loud, smelly, and dangerous prop planes to the 707, 747 and Concorde. They would've witnessed the moon landings and were seeing the development of the Space Shuttle. I bet they would call you crazy if you told this person that 50 years later, in 2025, there would be no more supersonic commercial airliners, commercial aviation would basically look the same except more annoying, and also that we haven't been back to the moon. In the previous 50 years we went from the Wright Brothers to the 707! So maybe in 2075 we'll all be watching documentaries about LLMs (maybe even on our phones or laptops that look basically the same), and reminiscing about the mid-2020s and wondering why what seemed to be such a promising technology disappeared almost entirely.

I would pay $5000 to never have to read another LLM-authored piece of text ever again.

> LLMs do not think independently and do not have the ability to challenge your assumptions

It IS possible for a LLM to challenge your assumptions, as its training material may include critical thinking on many subjects.

The helpful assistant, being almost by definition a sycophant, cannot.

Models from a few years ago are comparatively dumb. Basically useless when it comes to performing tasks you'd give to o3 or Gemini 2.5 Pro. Even smaller reasoning models can do things that would've been impossible in 2023.

This is a baffling response. The politics are completely irrelevant to this topic. Pretty much every American is distrustful of big tech and is completely unaware of what the current administration has conceded to AI companies, with larger scandals taking the spotlight, so there hasn't been a chance for one party or the other to rally around a talking point with AI.

People don't like AI because its impact on the internet is filling it with garbage, not because of tribalism.

Last year, ChatGPT was 75% of OpenAI's revenue[1], not the API.

[1]: https://www.businessofapps.com/data/chatgpt-statistics/

> Obviously you don't need to train new models to operate existing ones.

For a few months, maybe. Then they become obsolete and, in some cases like coding, useless.

To be fair, ads always compromise the product.

What specifically do you find to be mediocre? I feel like LLMs write better than most people I know, myself included.

There could be a mismatch on what the state of the art really is these days. In my experience, since the release of GPT-4 and especially 4o, ChatGPT has been able to do the vast majority of concrete things people tell me it can't do.

I think this is both right and wrong. There was a good book that came out probably 15 years ago about how technology never stops in aggregate, but individual technologies tend to grow quickly and then stall. Airplane jets were one example in the book. The reason why I partially note this as wrong is that even in the 70s people recognized that supersonic travel had real concrete issues with no solution in sight. I don't think LLMs share that characteristic today.

A better example, also in the book, are skyscrapers. Each year they grew and new ones were taller than the ones last year. The ability to build them and traverse them increased each year with new technologies to support it. There wasn't a general consensus around issues that would stop growth (except at more extremes like air pressure). But the growth did stop. No one even has expectations of taller skyscrapers any more.

LLMs may fail to advance, but not because of any consensus reason that exists today. And it maybe that they serve their purpose to build something on top of them which ends up being far more revolutionary than LLMs. This is more like the path of electricity -- electricity in itself isn't that exciting nowadays, but almost every piece of technology built uses it.

I fundamentally find it odd that people seem so against AI. I get the potential dystopian future, which I also don't want. But the more mundane annoyance seems odd to me.

Yep. Remember when Amazon could never make money and we kept trying to explain they were reinvesting their earnings into R&D and nobody believed it? All the rhetoric went from "Amazon can't be profitable" to "Amazon is a monopoly" practically overnight. It's like people don't understand the explore/exploit strategy trade-off.

If I cared about the output from other people's queries then wouldn't they be my queries? I don't care about ChatGPTs response to your queries is because I don't care about your queries. I don't care if they came from ChatGPT or the world's foremost expert in whatever your query was about.

There are sound math reasons for skyscrapers topping out, mostly due to elevator capacity and the inability to effectively get people in and out of the floorspace as you go past a few hundred ft. There's no construction engineering reason you can't go taller - the Burj Khalifa, for example, is three times taller than a typical Western major city skyscraper - it just doesn't make economic sense unless you're a newly rich nation looking to prove a point.

>This is a baffling response.

Likewise.

95+% of the time I see a response like this, it's from one particular side of the political aisle. You know the one. Politics has everything to do with this.

>what the current administration has conceded to AI companies

lol, I unironically think that they're not lax enough when it comes to AI.

From a system optimisation perspective, SSTs solved the wrong problem.

Want to save people time flying? Solve the grotesque inefficiency pit that is airport transit and check-in.

Like, I'm sorry, STILL no high speed, direct to terminal rail at JFK, LAX and a dozen other major international airports? And that's before we get to the absolute joke of "border security" and luggage check-in.

Sure, supersonic afterburning engines are dope. But it's like some 10GHz single-core CPU that pulls 1.2kW out of the wall. Like it or not, an iPhone 16 delivers far more compute utility in far more scenarios.

It still doesn't pass the Turing test, and is not close. Five years ago me would be impressed but still adamant that this is not AI, nor is it on the path to AI.

If I want the "correct answer" I'll research it, maybe even ask ChatGPT. If I'm having a conversation I'm interesed in what the other participants think.

If I don't know something, I'll say I don't know, and maybe learn something by trying to understand it. If I just pretend I know by pasting in what ChatGPT says, I'm not only a fraud but also lazy.

SST came and went in an era when none of that security theater existed to begin with

> That is a such a wild claim. People like the output of LLMs so much that ChatGPT is the fastest growing app ever.

And this kind of meaningless factoid was immediately usurped by the Threads app release, which IMO is kind of a pointless app. Maybe let's find a more meaningful metric before saying someone else's claim is wild.

Especially if it's not disclosed up front, and especially when it supplants higher-value content. I've been shocked how little time it's taken for AI slop SEO optimized blogs to overtake the articles written by genuine human experts, especially in niche product reviews and technical discussions.

However, whether or not people like it is almost irrelevant. The thing that matters is not whether economics likes it.

At least so far, it looks like economics absolutely loves LLMs: Why hire expensive human customer support when you can just offload 90% of the work to a computer? Why pay expensive journalists when you can just have the AI summarize it? Why hire expensive technical writers to document your code when you can just give it to the AI and check the regulatory box with docs that are good enough?

> LLMs can’t do ads without compromising the product.

It depends on what you mean by "compromise" here but they sure can inject ads.. like make the user wait 5 seconds, show an ad, then reply..

They can delay the response times and promote "premium" plans, etc

Lots of ways to monetize, I suppose the question is: will users tolerate it?

Based on what I've seen, the answer is yes, people will tolerate anything as long as it's "free".

Social networks will have all of those effects without any effort by the platform itself because the person with more followers has more influence so the people on the platform will do all they can to get more.

I'm not excusing the platforms for bad algorithms. Rather, I believe it's naive to think that, but for the behavior of the platform itself that things would be great and rosy.

No, they won't. The fact that nearly every person in the world can mass communicate to nearly every other person in the world is the core issue. It is not platform design.

> even in the 70s people recognized that supersonic travel had real concrete issues with no solution in sight. I don't think LLMs share that characteristic today

I think they pretty strongly do

The solution seems to be "just lower your standards for acceptable margin of error to whatever the LLM is capable of producing" which should be concerning and absolutely unacceptable to anyone calling themselves an Engineer

Lots of things had household recognition. Do you fondly remember the Snuggie? The question is whether it'll be durable. The lack of network effects is one reason to be skeptical.

But their research costs are extremely high, and without a network effect that revenue is only safe until a better competitor emerges.

I'm no expert, but the most frequent recommendations I hear to address this are:

a) tell it that it's wrong and to give you the correct information.

b) use some magical incantation system prompt that will produce a more critical interlocutor.

The first requires knowing enough about the topic to know the chatbot is full of shit, which dramatically limits the utility of an information retrieval tool. The second assumes that the magical incantation correctly and completely does what you think it does, which is not even close to guaranteed. Both assume it even has the correct information and is capable of communicating it to you. While attempting to use various models to help modify code written in a less-popular language with a poorly-documented API, I learned how much time that can waste the hard way.

If your use case is trivial, or you're using it as a sounding board with a topic you're familiar with as you might with, say, a dunning-kruger-prone intern, then great. I haven't found a situation in which I find either of those use cases compelling.

This is why once you are using to using them, you start asking them for there the plan goes wrong. They won't tell you off the bat, whuch can be frustrating, but they are really good at challenging your assumptions, if you ask them to do so.

They are good at telling you what else you should be asking, if you ask them to do so.

People don't use the tools effectively and then think that the tool can't be used effectively...

Which isn't true, you just have to know how the tool acts.

Any retries before nailing the prompt are still going to be billed, so this supports GP position about LLMs being expensive for trivial things.

Based on your response and logic - no dem should read stuff written by repub voters, or if they do read it, dismiss their account because it cannot be … what?

Not sure how we get to dismissing the teacher subreddit, to be honest.

Obviously YMMV, but it is extremely useful for me and for many people out there.

correct and companies will be exposing their data via mcp instead of standard rest apis.

Yes. I verified it myself. Best results from Opus 4 so far, Gemini might be OK too.

That checks out with my experience. I don't think it's just reading either. Even deeper than stranger danger, we're inclined to assume other humans communicating with us are part of our tribe, on our side, and not trying to deceive us. Deception, and our defenses against deception, are a secondary phenomenon. It's the same reason that jokes like "the word 'gullible' is written in the ceiling", gesturing to wipe your face at someone with a clean face, etc, all work by default.

That makes no sense. MCP at best is a protocol transpilation at runtime. It is not redefining things like DB drivers or connections. And I did not say rest apis enable agents. Computer use tooling does. APIs and everything else that already exists.

MCP is more like graphql. Not a new network paradigm.

The design of MCP right now is not very optimal esp when you can equip an agent with one tool vs 5-20 that bloat it's reasoning every prompt.

> The reason why I partially note this as wrong is that even in the 70s people recognized that supersonic travel had real concrete issues with no solution in sight. I don't think LLMs share that characteristic today.

The fundamental problem has already been mentioned: Nobody can figure out how to SELL it. Because few people are buying it.

It's useful for aggregation and summarization of large amounts of text, but it's not trustworthy. A good summary decreases noise and amplifies signal. LLMs don't do that. Without the capability to validate the output, it's not really generating output of lasting value. It's just a slightly better search engine.

It feels like, fundamentally, the primary invention here is teaching computers that it's okay to be wrong as long as you're convincing. That's very useful for propaganda or less savory aspects of business, but it's less useful for actual communication.

That's pretty much what search engines are nowadays

that's not a technology problem, many airports are super efficient, e.g. Singapore. Public transport in the US is held back by other forces.

It did for international, maybe not at the dawn of SSTs but after a string of hijackings in the 70s/80s they brought it in. Not for US internal flights, it's true.

Makes you wonder whether the affiliate links are actual, valid affiliate links or just hallucinations from affiliate links it's come across in the wild

> 2. Almost three years in, companies investing in LLMs have not yet discovered a business model that justifies the massive expenditure of training and hosting them,

I always think back to how Bezos and Amazon were railed against for losing money for years. People thought that would never work. And then when he started selling stuff other than books? People I know were like: please, he's desperate.

Someone, somewhere will figure out how to make money off it - just not most people.

what I struggle with is that the top 10 providers of LLMs all have identical* products. The services have amazing capabilities, but no real moats.

The social media applications have strong network effects, this drives a lot of their profitability.

* sure, there are differences, see the benchmarks, but from a consumer perspective, there's no meaningful differentiation

It makes it all the dumber that we have the tech and still can't manage to implement the solution.

Like an org with crappy management and team structure shipping bloated, buggy code even though they've the budget to hire great engineers and the problems they're solving are largely known and well-trodden.

The problem with supersonic commercial jets was mainly one of marketing/politics. The so called "sonic boom" problem was vastly overhyped, as anyone who lives near an air force base can tell you.

The conspiracy theorist tells me the American aerospace manufacturers at the time (Boening, McDonnell-Douglas, etc.), did everything they could to kill the Concorde. With limited flyable routes (NYC and DC to Paris and London I think were the only ones), the financials didn't make sense. If overland routes were available, especially opening up LA, San Francisco and Chicago, it might have been a different story.

AWS is certainly super profitable, if the ecommerce business was standalone, would it really be such a cash-gusher?

why would you make an agent click around a web browser like a human when it could self discover the api and call it directly?

Was the problem that supersonic flight was expensive and the amount of customers willing to pay the price was even lower than the number of customers that could even if they wanted to?

This is an interesting take - is my "attention" really worth several thousand a year? In that my purchasing decisions being influenced by advertising to that degree that someone is literally paying someone else for my attention ...

I wonder if instead, could I sell my "attention" instead of others profitting of it?

Eventually the economics will correct themselves once people yet again learn the old "you get what you pay for" lesson (or the more modern FAFO lesson)

Why? So you could discard it faster?

Read things from people that you disagree with.

self discovery via primitives is what works well today. I never discouraged that, only discouraged MCP sensationalism.

However, an agent that can see the screen and immediately click through whatever desired UI modality is immensely more efficient than swimming through protocols.

There is at least one frontier lab who has prepared enough foresight that agents running on VDI infrastructure is a major coming wave.

From what I had read in passing and remember.

  - They were loud (sonic booms were nasty).

  - They were expensive to maintain and operate. Guzzlers. (Britain and France clung to them as a matter of pride/ego)

  - They were narrow and uncomfortable. I have seen videos where there is space only for one stewardess to walk. I had been inside of one in Seattle museum. Very cramped.

  - As you mentioned, ticket cost was high.

  - I suspect people traveled in these mostly for bragging rights.

It made me switch away from google. The push I needed

Low effort Youtube shorts with AI voice annoy the crap out of me.

After all this hype, they still can't do text to speech properly. Pause at the wrong part of the sentence all the time.

> Nobody can figure out how to SELL it. Because few people are buying it.

Just picking one company who basically just does AI, OpenAI. They reported it has 20 million PAID subscribers to ChatGPT. With revenue projected above $12b dollars (https://www.theverge.com/openai/640894/chatgpt-has-hit-20-mi...).

I think what you meant to say is that costs are high so they can't generate large profits. but saying that they can't figure out how to sell it seems absurd. Is it Netflix level of subscribers, no. But there can't be more than a couple of hundred products that have that type of subscription reach.

Yeah, and with LLMs the thing I can't shake, however, is that this time it's pretty strongly (maybe parasitically) latched onto the aggregate progress of Moore's law. Few other technologies have enjoyed such relatively unfettered exponential improvement. It's like if skyscraper materials double in strength every n years, and their elevators approach teleportation speed, the water pumps get twice as powerful, etc., which would change the economics vs the reality that most of the physical world doesn't improve that fast.

Ok but isn’t 20 million subscribers out of what, 800 million or 1 billion monthly users or whatever they’re claiming, an absolutely abysmal conversion rate? Especially given that the industry and media have been proclaiming this as somewhere between the internet and the industrial revolution in terms of impact and advancement? Why can they not get more than 3% of users to convert to paying subscribers for such a supposedly world changing technology, even with a massive subsidy?

Amazon is successful because of the insanely broad set of investments they've made - many of them compound well in a way that supports their primary business. Amazon Music isn't successful, but it makes Kindle tablets more successful. This is in contrast to Google, which makes money on ads, and everything else is a side quest. Amazon has side quests, but also has many more initiatives that create a cohesive whole from the business side.

So while I understand how it looks from a financial perspective, I think that perspective is distorted in terms of what causes those outcomes. Many of the unprofitable aspects directly support the profitable ones. Not always, though.

Has any SAAS product ever reduced their subscription cost?

Because they give too much of it away for free? Most casual use fits into the very generous free tier.

Strong agree. If you simply ask an LLM to challenge your thinking, spot weaknesses in your argument, or what else you might consider, it can do a great job.

This is literally my favorite way to use it. Here’s an idea, tell me why it’s wrong.