The article claims that AI services are currently over-utilised. Well isn't that because customers are being undercharged for services? A car when in neutral will rev up easily if the accelerator pedal is pushed even very slightly, because there's no load on the engine. But in gear the same engine will rev up much less when the accelerator is pushed the same amount. Will there be the same overutilisation occurring if users have to financially support the infrastructure, either through subscriptions or intrusive advertising?
I doubt it.
And what if the technology to locally run these systems without reliance on the cloud becomes commonplace, as it now is with open source models? The expensive part is in the training of these models more than the inference.
> Will there be the same overutilisation occurring if users have to financially support the infrastructure, either through subscriptions or intrusive advertising?
> I doubt it.
I agree. Right now a lot of AI tools are underpriced to get customers hooked, then they'll jack up the prices later. The flaw is that AI does not have the ubiquitous utility internet access has, and a lot of people are not happy with the performance per dollar TODAY, much less when prices rise 80%. We already see companies like Google raising prices stating it's for "AI" and we customers can't opt out of AI and not pay the fee.
At my company we've already decided to leave Google Workspace in the spring. GW is a terrible product with no advanced features, garbage admin tools, uncompetitive pricing, and now AI shoved in everywhere and no way to granularly opt out of a lot of it. Want spell check? Guess what, you need to leave Gemini enabled! Shove off, Google.
I am totally on the other end of the spectrum. For $20 a month, the amount of value I get from ChatGPT is incredible. I can talk to it in voice mode to help brainstorm ideas, it teaches me about different subjects, it (+ claude code) helps me write boilerplate code so I can spend more time doing things I enjoy.
I'm going through the process of buying a home, and the amount of help its given is incredible. Analyzing disclosures, loan estimates, etc. Our accountant charges $200 an hour to basically confirm all the same facts that ChatGPT already gave us. We can go into those meetings prepped with 3 different scenarios that ChatGPT already outlined, and all they have to do is confirm.
Its true that its not always correct, but, I've also had paid specialists like real estate agents and accountants give me incorrect information, at the cost of days of scheduling, and hundreds of dollars. They also aren't willing to answer questions at 2am in the morning.
> At $249/month the market adoption will crash resulting in somewhere in the middle pricing that the market can bear
Or much like what is going to happen with Alexa, it just dies because the cost of the service is never going to align with “what the market can bear”. Even at $75/mo, the average person will probably stop being lazy and just go back to doing 10 minutes worth of searching to find answers to basic questions.
I feel like I can get all of that for free already. Not sure why I would pay a monthly subscription when I'm already getting Gemini across the Google ecosystem.
Lol what. Analyzing disclosures? What information of use could it possibly synthesize from a three column table with checkmarks in [no disclosure]? For your sake I sincerely hope you aren't using ChatGPT when you should be getting inspections.
Inspections are fairly worthless if you are remotely handy and competent at basic stuff. I don’t need someone to go through and catalog the make and models of all the appliances, and I can visually inspect my own water heater and furnace pretty trivially.
You can get “real” inspections done but they cost thousands of dollars and take a full or more day to do with multiple subject matter experts. Almost no one does this.
Waiving inspection other than for major material defect is what I’ve done for all my real estate transactions. I’m not putting in an offer to nickel and dime someone over trivial bullshit like a busted GFCI circuit. My offer simply accounts for the trivial odds and ends I’ll have to take care of. Plus I’d much rather get the work done myself since I don’t trust a seller to do anything but the bare minimum.
Every one of my friends who have had five figures or more of surprise repair work on homes they purchased all had an inspection done. None of those could have found the various hidden damages for those buildings short of destructive stuff like pulling drywall out or lifting up shingles from a roof. Don’t worry though, the inspectors found stuff like a bathroom faucet with a crack in the knob.
Liability for what? The only real liability in my state is for outright misrepresentation or fraud via failing to disclose. The disclosure form covers anything material I'd care about. Even then - good luck actually proving anything short of exceptional circumstances.
If you look at the standard offer document for waiving inspection it's pretty easy to walk it back. You're simply waiving a contingency - you can typically still inspect the property itself. I'm sure if you get way off the beaten path you are correct, but almost no one is engaging in totally non-standard contracts where I'm at.
I'm curious what liability you think would apply for an inspection that misses whatever it may be that ends up in dispute after the transaction closes - since the whole point in the inspection is finding that beforehand? If I find a material defect in the foundation after I close - it won't matter if I had an inspection or not. Unless I can prove the seller knew about it and failed to disclose.
And if I ever sell any properties - I will be pretty loath to sell to anyone demanding an inspection contingency. They are almost always used for nickel and dime BS that I really don't have time for. If you walk the place, get your inspector to do so too, and come up with a punch list and still want to make an offer, discount it appropriately and fix it yourself after you close. It's nearly always either pointless or used as a negotiation tool after the fact due to the fact buyers can expect a seller to not want to walk away from the middle of a transaction (sunk cost/time). I'd much rather take an offer at 5% less up-front than deal with someone wasting 30-45 days on the market and my time dealing with trivial items.
> Will there be the same overutilisation occurring if users have to financially support the infrastructure, either through subscriptions or intrusive advertising? > I doubt it.
Yea, I think this is wrong. The analogy is more like the App Store, in that there is very little to do currently other than a better Google Search with the product. The bet is that over time (short time) there are much more financially valuable use cases with a more mature ecosystem and tech.
Unlike the smartphone adoption era where everything happened rather rapidly, we're in this weird place where labs have invented a bunch of model categories, but they aren't applicable to a wide variety of problems yet.
The dial up -> broadband curve took almost a decade to reach penetration and to create the SaaS market. It's kind of a fluke that Google and Amazon came out of the dial up era - that's probably what investors were hoping for by writing such large checks.
They found chat as one type of product. Image gen as another. But there's really not much "native AI" stuff going about. Everyone is bolting AI onto products and calling it a done day (or being tasked with clueless leadership to do it with even worse results).
This is not AI. This is early cycle WebVan-type exploration. The idea to use AI in a given domain or vertical might be right, but the tools just don't exist yet.
We don't need AI models with crude APIs. We need AI models we can pull off the shelf, fine tune, and adapt to novel UI/UX.
Adobe is showing everyone how they're thinking about AI in photoshop - their latest conference showed off AI-native UX. And it was really slick. Dozens of image tools (relighting, compositing, angle adjustment) that all felt fast, magical, and approachable as a beginner. Nobody else is doing that. They're just shoving a chat interface in your hands and asking you to deal with it.
We're too early. AI for every domain isn't here yet.
We're not even in the dialup era, honestly.
I'd expect the best categories of AI to invest in with actually sound financials will be tool vendors (OpenRouter, FAL, etc.) and AI-native PLG-type companies.
Enterprise is not ready. Enterprise does not know what the hell to do with these APIs.
I've got 4 different chat windows open on 4 free plans. That's not counting the free IDE-autocomplete I use.
In any given day I never have no access to free LLM help.
Since all the models are converging onto the same level of performance, I mostly can't even tell responses from ChatGPT and Claude apart.
> Right now a lot of AI tools are underpriced to get customers hooked, then they'll jack up the prices later.
Good luck with that. I mean it.
The ChatAI TAM is now so saturated with free offerings that the first supplier to blink will go out of business before they are done blinking.
I see people (like sibling reply to parent) boasting about the amount of value they get from the $20/m subscription, but I don't see how that is $20 better than just using the free ChatAIs.
The only way out of the red for ChatAI products is to add in advertising slowly; they have to boil the frog. A subscription may have made sense when ChatGPT was the only decent game in town. Subscriptions don't make sense now - I can get 90% of the value of a ChatAI for 0% of the cost.
> The article claims that AI services are currently over-utilised. Well isn't that because customers are being undercharged for services?
Absolutely, not only are most AI services free but even the paid portion is coming from executives mandating that their employees use AI services. It's a heavily distorted market.
And a majority of those workers do not reveal their AI usage, so they either take credit for the faster work or use the extra time for other activities, which further confounds the impact of AI.
This is also distorting the market, but in other ways.
We're talking miraculous level of improvement for a SOA LLM to run on a phone without crushing battery life this decade.
People are missing the forest for the trees here. Being the go to consumer Gen AI is a trillion+ dollar business. How many 10s of billions you waste on building unnecessary data centers is a rounding error. The important number is your odds of becoming that default provider in the minds of consumers.
I used ChatGPT for every day stuff, but in my experience their responses got worse and I had to wait much longer to get them. I switched to Gemini and their answers were better and were much faster.
I don’t have any loyalty to Gemini though. If it gets slow or another provider gives better answers, I’ll change. They all have the same UI and they all work the same (from a user’s perspective).
There is no moat for consumer genAI. And did I mention I’m not paying for any of it?
It’s like quick commerce, sure it’s easy to get users by offering them something expensive off of VC money. The second they raise prices or offer degraded experience to make the service profitable, the users will leave for another alternative.
> The important number is your odds of becoming that default provider in the minds of consumers.
I haven't seen any evidence that any Gen AI provider will be able to build a moat that allows for this.
Some are better than others at certain things over certain time periods, but they are all relatively interchangeable for most practical uses and the small differences are becoming less pronounced, not more.
I use LLMs fairly frequently now and I just bounce around between them to stay within their free tiers. Short of some actual large breakthrough I never need to commit to one, and I can take advantage of their own massive spends and wait it out a couple of years until I'm running a local model self-hosted with a cloudflare tunnel if I need to access it on my phone.
And yes, most people won't do that, but there will be a lot of opportunity for cheap providers to offer that as a service with some data center spend, but nowhere near the massive amounts OpenAI, Google, Meta, et al are burning now.
As a regular user, it becomes increasingly frustrating to have to remind each new chat “I’m working on this problem and here’s the relevant context”.
GenAI providers will solve this, and it will make the UX much, much smoother. Then they will make it very hard to export that memory/context.
If you’re using a free tier I assume you’re not using reasoning models extensively, so you wouldn’t necessarily see how big of a benefit this could be.
They all offer some "memory" cross chat now and they're all more annoying than helpful. Not really compelling. You can pretty easily export your chat if you want.
In fact it's apparently $5.2 trillion by 2030 [0] (out of $6.7T total data center spend; meaning all of "traditional IT needs" are less than a quarter of the total). That's the total if you add up all of the firms chasing this opportunity.
I do wonder, if you (and the commenter you replied to) think this is a good thing, will you be OK with a data center springing up in your neighbourhood, driving up water or power prices, emitting CO2? Then if SOTA LLMs become efficient enough to run on a smartphone will you be OK with a data center bailout coming from your tax dollars?
My hot (maybe just warm these days) take is, the problem with voice assistants on phones is they have to be able to have reasonable responses to a long tail or users will learn not to use them, since the use cases aren’t discoverable and the primarily value is talking to it like a person.
So voice assistants backed by very large LLMs over the network are going to win even if we solve the (substantial) battery usage issue.
Why even bother with the text generation then? You could just make a phone call to an LLM with a TTS frontend. Like with directory enquiries back in the day. Which can be set up as easily as a BBS if you have a home server rack like Jeff Geerling makes youtube videos about.
Yes, over-utilization is a natural response to being undercharged. And being undercharged is a natural result when investors are throwing money at you. During bubbles, Silicon Valley often goes to "lose money, make it up with scale". With the vague idea that after you get to scale, THEN you can figure out how to make money. And fairly consistently, their idea for how to make money is "sell ads".
Past successes like Google encourage hope in this strategy. Sure, it mostly doesn't work. Most of of everything that VCs do doesn't work. Returns follow a power law, and a handful of successes in the tail drive the whole portfolio.
The key problem here doesn't lie in the fact that this strategy is being pursued. The key problem is that it is rare for first mover advantages to last with new technologies. That's why Netscape and Yahoo! aren't among the FAANGs today. The long-term wins go to whoever successfully create a sufficient moat for themselves to protect lasting excess returns. And the capabilities of each generation of AI leapfrogs the last so well that nobody has figured out how to create such a moat.
Today, 3 years after launching the first LLM chatbot, OpenAI is nowhere near as dominant as Netscape was in late 1997, 3 years after launching Netscape Navigator. I see no reason to expect that 30 years from now OpenAI will be any more dominant than Netscape is today.
Right now companies are pouring money into their candidates to win the AI race. But if the history of browsers repeats itself, the company that wins in the long-term would launch in about a year from now, focused on applications on top of AI. And its entrant into the AI wars wouldn't get launched until a decade after that! (Yes, that is the right timeline for the launch of Google, and Google's launch of Chrome.)
Investing in silicon valley is like buying a positive EV lottery ticket. An awful lot of people are going to be reminded the hard way that it is wiser to buy a lot of lottery tickets, than it is to sink a fortune into a single big one.
> Today, 3 years after launching the first LLM chatbot, OpenAI is nowhere near as dominant as Netscape was in late 1997.
Incorrect. There were about 150 millions Internet users in 1998, or 3.5% of total population. The number grew 10 times by 2008 [0]. Netwcape had about 50% of the browser market at the time [1]. In other words, Netscape dominated a small base and couldn’t keep it up.
ChatGPT has about 800 millions monthly users, or already 10% of total current population. Granted, not exclusively. ChatGPT is already a household name. Outside of early internet adopters, very few people knew who Netscape or what Navigator was.
Furthermore my point that the early market leaders are seldom the lasting winners is something that you can see across a large number of past financial bubbles through history. You'll find the same thing in, for example, trains, automobiles, planes, and semiconductors. The planes example is particularly interesting. Airline companies not only don't have a good competitive moat, but the mechanics of chapter 11 mean that they keep driving each other bankrupt. It is a successful industry, and yet it has destroyed tons of investment capital!
Despite your quibbles over the early browser market, my broader point stands. It's early days. AI companies do not have a competitive moat. And it is way to premature to reliably pick a winner.
Netscape in 1997/1998 had about 90% of the target market.
OpenAI today has about 30% of the target market, maybe? (seeing as how every single Windows installation comes with copilot chat already, it's probably less. Every non-tech user I know has already used copilot because it was bundled and Windows prompted them into using it with a popup. Only one of those non-tech users even heard of OpenAI, maybe 50% of them have heard that there are alternatives to Copilot, but they still aren't using those alternatives)
Not many people buy Windows, they buy laptops that happen to have Windows installed. IMO this is a worthwhile distinction because most people don’t really care about operating systems anyway, and would happily (I suspect, at least) use an Open Source one if it came installed and configured on a device that they got in a store.
Installing an OS is seen as a hard/technical task still. Installing a local program, not so much. I suspect people install LLM programs from app stores without knowing if they are calling out to the internet or running locally.
Besides the fact that this article is obviously AI generated (and not even well, why is there mismatches in british/american english? I can only assume that the few parts in british english are the human author's writing or edits), yes "overutilization" is not a real thing. There is a level of utilization at every price point. If something is "overutilizated" that actually means it's just being offered at a low price, which is good for consumers. It's a nice scare word though and there's endless appetite at the moment for ai-doomer articles.
Sorry but it's highly suspect to be spelling the same word multiple different ways across paragraphs. You switch between using centre/center or utilization/utilisation? It is a very weird mistake to make for a human.
I mix British and American English all the time. Subconsciously I type in British English but since I work in American English, my spell checkers are usually configured for en-US and that usually means a weird mix of half and half by the time I've fixed the red squiggles I notice.
> You sometimes see this with real live humans who have lived in multiple counties.
Also very common with... most Canadians. We officially use an English closer to British English (Zed not zee, honour not honor) however geographically and culturally we're very close to the US.
At school you learn "X, Y, Zed". The toy you buy your toddler is made for the US and Canadian market and sings "X, Y, Zee" as does practically every show on TV. The dictionary says it's spelled "colour" but most of the books you read will spell it "color". Most people we communicate with are either from Canada or the US, so much of our personal communication is with US English.
But also there are a number of British shows that air here, so some particularly British phrases do sneak in to a lot of people's lexicon.
See a similar thing in the way we measure things.
We use celsius for temperature but most of our thermostats default to Fahrenheit and most cookbooks are primarily in imperial measures and units because they're from the US. The store sells everything in grams and kilograms, but most recipes are still in tablespoons/cups/etc.
Most things are sold in metric, but when you buy lumber it's sold in feet, and any construction site is going to be working primarily in feet and inches.
If anything I expect any AI-written content would be more consistent about this than I usually am.
One of my least favorite things to come from AI is labelling any writing someone doesn't like as "obviously AI generated". I've read 3 of these kinds of comments on HN just today.
As non native English speaker I mix British and American English all the time, and you should hear me speaking. I mix in some novel accent too. Anyway, the author answered in a sibling reply.
Will the OpenRouter marketplace of M clouds X N models die if the investor money stops? I believe its a free and profitable service, offered completely pay as you go.
I don't. This is simply the "drug dealer" model where the first hit is free. They know that once people are addicted, they will keep coming back.
The question of course is, will they keep coming back? I think they very much will. There are indications that GenAI adoption is already increasing labor producitivity labor improvements at a national scale, which is quite astounding for a technology just 3 years old: https://news.ycombinator.com/item?id=46061369
Imagine a magic box where you put in some money and get more productivity back. There is no chance Capitalism (with a capital "C") is going to let such a powerful growth machine wither on the vine. This mad AI rush is all about that.
I doubt it.
And what if the technology to locally run these systems without reliance on the cloud becomes commonplace, as it now is with open source models? The expensive part is in the training of these models more than the inference.