Is AI Profitable Yet?
isaiprofitable.com
isaiprofitable.com
I just wanna know how the OpenAI/Anthropic shell game works long-term. So both companies made equity deals with infrastructure providers; OpenAI on Azure, Anthropic on AWS, GCloud, and Colossus. They get a loan of compute credits and then pay for the compute with the credits. So the PaaS are effectively giving them free compute, then book it as revenue; and the AI provider lets them do inference and books that as revenue. So, it's like both types of company have a buffet, and let each other eat there for free. But somebody has to actually buy the pasta salad, with real dollars. Afaict, those real dollars are.... the cash reserves of the PaaS.
How long are they going to eat into that cash? Microsoft and AWS don't really have their own models, whereas Google and SpaceX do. And while Google has tons of cash, SpaceX is perpetually looking for cash. So the only player here that can actually afford to keep doing this, or leave the game entirely, is Google.
Either the actual revenue of paying customers ramp up or the bubble will pop at some point
I expect the paying customers will actually be companies buying ad, not people buying AI subscriptions
Everyone’s long term plan is hoping that they build out and survive long enough that, in the end, the market accepts them.
Even your local still-unprofitable restaurant is burning their grandparents’ inheritance money hoping that it works out.
But on the other hand, that’s what Theranos, WeWork, and Pets.com tried too.
When OpenAI goes public it will initially get a tsunami of cash, but it'll also be open to new risk due to the different operating model and transparency. Anthropic might not make it to an S-1 (this year). Even if they got a $30B infusion of cash each, based on their current spending projections, it doesn't cover half of what they need just to break even. In the meantime the PaaS's are holding the bag (and shedding cash).
So where's it going to end? To me, all of this (combined with inflation, degrading of reserve currency, war in middle east) is spookily similar to the railroad panic of 1873. Over-investment in new technologies leveraging too much from the largest financial institutions resulting in prolonged economic crisis. Our only saving grace now are laws ensuring banks have to cover their end; if your money's FDIC/SIPC insured you're safe. But all the businesses and individuals who aren't safe are gonna take a bath, which'll have systemic ripples. Afaict, Google is the only player who can survive all that and come out with profitable AI. (But I'm sure I've missed something because it seems too obvious)
> So they don't have investor money to burn (and when they do, they immediately burn it on new datacenters, which usually take years to build and aren't a certainty).
If AI models can get smarter and more practically useful via some combination of increased scale and more fine-tuned post-training on specific workloads (which is compute-heavy, even more than the usual kind of pre-training) these new datacenters are a fantastic investment.
They can get more efficient, but inference efficiency doesn't map linearly to cost efficiency. Firstly because software is a gas; if you give people more compute (for the same price), they immediately use it all up. But second, if you spend $50BN, you still have to make $50BN to break even. They could make inference cost $0.00000001, but that isn't going to cover their costs. That's what's driving their cost right now - they're trying to collect enough cash from people at the table to pay the bill, without the price scaring everyone out of the restaurant.
So they can't raise the price without scaring people off, and they can't lower the price and pay the bill.
People will want to pay that much once they're enabled to make the best and most efficient use of SOTA proprietary models for tasks that actually benefit from them, while using cheap third-party inference everywhere else. That's very different from what the leading AI firms are proposing right now and it does require some careful balance to get there from here, but it's absolutely doable.
It's simply not feasible.
The first computers were the size of buildings, now look where we are. I think same thing will happen to AI models. We will have a reasoning core installed on our phone connected to Google's Knowledge Graph or Ontology project via API. These companies just need to survive long enough to make themselves irreplaceable in the new ecosystem.
The frontier labs have fantastic margin on inference. You do not understand how fantastic. And they have license to change inputs at will based on profitability.
They are not only innovating on models and tooling, they are innovating on cogs (I wrote this btw, and I’m not going to stop writing this way because Claude discovered it’s brilliant).
Speaking of models, the cost of training is not scaling nearly as fast as demand for inference. Training used to be the biggest cost by far, now it’s not.
So margin is increasing, and guess what else is happening? Customers are finding value. And the customers that are finding value are also the ones who happen to have huge enterprise budgets.
And while this is happening, so is implicit collusion (and lock in, and hype, and all that). And so prices are going up.
They’re going to be just fine man, there is no inference bubble.
They can modulate supply. It’s all going to be fine. You should invest.
This. The gross margin on inference is at least 95% if not higher - several open weight models on my tiny consumer DGX Spark easily replace the 15 dollars a day I was paying in tokens for Claw usage with a dollar a day electricity. You add data centre overhead and depreciation, the theoretical net margin will trend lower but depreciation is always far more aggressive than actual product degradation. The old NVIDIA GPU on a 9 year old second hand gaming PC I bought still serves up a small Gemma 4 variant quite reasonably.
It is baffling that any government lets either themselves or their local companies use these tools. Utterly baffling. The potential for total security compromise through these models is ... essentially 100%.
But ... it's slightly cheaper.
Source?
The OpenAi filing will be very interesting indeed.
("trust me bro" statements from sama et al does not count, since I don't trust them)
Edit:
The best argument I have seen look at the price of inference from smaller companies running open models. And assuming they are profitable-ish. Their prices are lower than the OpenAi and Anthropics best models, so maybe they do make money on inference (ignoring all other costs)
But also, trust me bro.
Where I can find confirmation of that in public sources?
I know a traditional SaaS company I worked for that IPO’d years ago and still has no signs that they can be profitable (and many others like it) and nobody seems particularly concerned.
How so? Big corps got home safe. Not the people. People committed suicides and lost their livelyhoods.
The stock market. Stocks crash, companies go belly-up, tons of people get laid off, unemployment spikes, people die. I don’t give a shit about the companies themselves. I do give a shit about who they employ, both directly and downstream, and the job market that will result from many of them losing their jobs.
Privatize Profits and Socialize Losses is now Bog-Standard Operating Procedure.
Oracle likely structured everything the way that its gonna be everyone else problem before they go down. No?
So far capacity barely grown because its super slow to build, but prices skyrocketed x5 to x100.
If its blownup everyone will just return to selling hardware or capacity at 20% margin instead of 2000%.
Only major labs will collapse because they have nothing but models and losses. People working for them still gonna find a job just with $10,000 bunus instead of $1,000,000.
We can look at a “success story” like Uber and it is still net negative over its entire existence. This is a business that’s in a literal monopoly/duopoly status in most markets it operates in with vastly reduced regulatory burden compared to the industry disrupted. Literally the ideal scenario for printing money and yet it hasn’t made any. It’s the poster child for the unicorn exit that founders dream of.
The end result is that Uber and companies like it are a financial instruments that transfer dollars away from one set of investors to another set of investors.
If Uber hasn’t yet made its investment back, I struggle to wonder how some of these AI ventures will ever make that money back when their expenditures make Uber look like a small little side project.
Meta has spent almost 4 years worth of its net income for FY2025 on AI going by this website’s data, and counting.
We are decades since Web 2.0 took off, almost 20 years since the iPhone launched, 50 years of Apple Computer. Software isn’t some new industry anymore. There isn’t an industry left that hasn’t completed its digital transformation. These spray and pray economies would have died off years ago if it wasn’t for the fact that software companies have uniquely low cost structures where they don’t need to build factories or distribution networks to get their products to their customers. These low cost structures might just be concealing the fact that it’s not going to be a growth industry forever.
How has the sheer saturation of LLMs not resulted in profit? It has dominated the conversation, center stage, of every news outlet for like 4 years now. It is the most known-about thing currently out there.
And we haven't been able to convert that much captured attention into profitability yet? That seems... bad?
Maybe Reddit is an example? But my impression is that they ran a modest operation before going public.
ChatGPT is the 5th most visited website in the world. Gemini.Google.com is ranked above amazon.com. Where is the profit?
And I think we passed the threshold for crash down for AI, even if AI companies wont be that profitable. Nvidia/cloud providers will be profitable as long as there is demand for AI.
If AI companies aren't that profitable...then they're going to stop spending so much money on GPUs to train AI models. A gigantic amount of Nvidia's profits would go bust overnight.
Claude code and others are here to grow even if they don't do any further training.
The cost(and size) to train models is also increasing and is still 60% of the cards that Nvidia is selling. Losing 60% of your most profitable revenue stream I think would do bad for a company regardless of how much inference is increasing "dramatically"(all this means is the GPUs are dead sooner and the cost to do this massive inference increases too)
the market cap of a company is computed by the current price of a company's shares, the last price paid; not all the shares of the company were bought at that price, the ones who got shares cheaper are showing paper profits, unrealized. Those who have already cashed out have money in their bank accounts that was transferred from people who wanted to get in. If the company goes bankrupt, their shares will be worthless, but the money they paid for them still remains in the accounts of people who sold their shares: the money was not lost even if some people lost money.
I'm not going to keep going through it but the reason it works to value things the way we do is that the values are comparable and they frequently work out, so snapshots of the economy and the participants are comparable. But "losses" are not like taking gold and feeding it into some deep fold in the earth where it will disappear into the molten middle of earth.
Stock valuations are "expectations for the future". Those expectations weren't money, they were lottery tickes where the lottery consisted of human creativity and human effort. People buying and selling share are moving real money around to trade the expectations. The money didn't go anywhere, it's still there, it's just that expectations for the future have been reduced. It all boils down to humans trading some of their time and potential on a bet that things work out. Some people's effort gets more rewarded than others. Not every team wins the world cup, but people like to play and like to watch.
If they fail then the negative impact ripples through the economy due to misallocation of resources.
consider all the companies in a market and those that feed that market to be one virtual mega company, add up all the valuations and revenue streams, costs, etc and aggregate all the investors into one. Nothing changes about the picture I drew. We simplify models to make the real world understandable.
>negative impact ripples through the economy due to misallocation of resources
free or relatively free financial markets are the only way, the best way, the ne plus ultra of ways we know to allocate capital, we have no better way than for the owner of the capital and the reapers of the loss or reward to make a considered opinion that is risk "impedance" matched. By definition, the market does not "misallocate" capital, it optimally allocates it.
your theory is that we could somehow know the future, but that's a fallacy.
Free market efficiency is inherently tied to having multiple companies. Treating the entire economy as a single company gives nonsensical results because it fundamentally differs from what actually occurs. You might as well compare the economy to a game of tick tack toe, inherent complexity isn’t something you can simplify it has meaningful consequences.
Your ideas like many other ideas are simply wrong.
> could somehow know the future
Perfect accuracy isn’t the only possibility here, there’s levels of error.
Our system involves intermediaries between the actual owners of capital and the allocation of that capital who have very different incentives. When the worst possibility is missing a bonus there’s little difference between losing 10% of an investors money and 100%. That results in inefficiency through the misalignment of incentives.
That is actually true, and thus there’s no way to gloss over that truth without simply being wrong.
trust me bub, I've studied much more econ than you. If a competitive market sets the prices (check, that's what is happening), and you want to analyze statistics of a sector (check, that's what we are doing), you can take those competitive prices as "given" and hold them constant, and consolidate the assets of in industry into one virtual entity. No claims were being made about competition, the claim is that "it is validate to consolidate statistic of what you are trying to study.
"how much did the AI sector make last year? how much will it make next year?" is not answered by running a simulation of competitive marketplace with production functions.
>>could somehow know the future
>Perfect accuracy isn’t the only possibility here, there’s levels of error.
if you deviate from the market's prediction of the future, you are increasing your levels of error; why do that?
Then try and justify why you say shit this clueless:
> how much will it make next year?" is not answered by running a simulation of competitive marketplace with production functions.
Profits next year very much depend on the number of companies involved 1 vs 100 is not going to give the same results. Like I hope you realize how false what you just said was. Because if you actually believe this there’s literally no point in talking with you.
in a competitive marketplace, economic profit will go to zero. so whether an AI company buy or rents outside infrastructure, or builds it itself doesn't matter, it makes no difference. Therefore, if your argument is "outside infrastructure X", you can see the meaning of that by looking at "assume the company bought up the whole industry including outside infrastructure, then go back and look what I said and it still applies" A company monopolizing outside infrastructure for its own use would not abuse its monopoly against itself, but even if it did, makes no difference the extra profit and extra loss would balance out. Would it abuse its monopoly against downstream customers? if we use the existing market prices unchanged in our example, that is analyzing the case where it does not, which is the case that is comparable to the current situation.
or to put it another way, let's say these are all publicly traded companies competing. What if I told you "hey, i've investigated the ownership of all these public shares, guess what, Elon Musk owns them all, he owns every share of every company in AI, and all the infrastructure suppliers. Does that change the analysis from what we see in the marketplace? no, it doesn't. You want to draw a bigger circle around more affected parties, the suppliers to the infrastructure suppliers: OK, Elon owns those too it turns out.
nobody is analyzing the future here, we're talking about the case of AI going bust, not trying to predict AI going bust.
if were were going to project the future, we still would not do it with a simulation using functions to model companies to try to come up with meaningful profit numbers, we would project profits (and costs and revenues) based on margins of similar industries
Lots of words to say you don’t understand what you’re talking about.
At the most simple level monopolies extract profits through raising prices above that of a competitive market. This price increase reduces total sales even as it drives up profits.
As such the existence or non existence of a monopoly would change how much energy/etc AI was consuming among a host of other effects completely independent of how much utility it provided.
> nobody is analyzing the future here
That’s genuinely funny.
if you want to sound like you know what you are talking about, don't talk about "total sales", you want to talk about quantity demanded at the market clearing price vs the monopoly price, and the effect that has on producer surplus.
and what is bad about a monopoly in economic terms is not that they extract a higher price and profit from their smaller number of customers (those customer choose to purchase the product because it's worth it to them), it's the dead weight loss which represents unmet demand which slows the economy overall
We can’t look at next year’s power bill today. Hell finding their power bills from 1930 isn’t trivial either, thus we model the universe and test those models rather than just making up nonsense models that look pretty.
> that's the impact they have on the power grid.
That’s only the direct effect, the indirect effects get way more complicated.
The fact you’re constantly demonstrating profound ignorance is why I am treating you like a 5 year old. You understood what I said and had no defense, thus my use of simple terms was entirely justified. Using more words to say exactly what I just said doesn’t change my opinion. Instead try and apply that line of reasoning to your earlier statements and find the issue, that would demonstrate some actual understanding.
PS: The economic harm from monopolies extends to R&D etc but …
Let me guess, you sit down next to Kobe Bryant and start by saying you're going to tell him about winning basketball?
you tried to dismiss me by saying "oh but you're doing well" as if that meant anything. You brought it up, not me, but inasmuch as it does means anything, it suggests I'm winning the race that you purport to be an expert at.
I do not come from wealth, my family is largely working class. I have grown my wealth dramatically because I understand how the market works. I didn't know a priori what would happen, I just took what they taught me in school and applied it with extreme discipline and without fear. Turns out that works.
>The equity of the economy is not very similar at all to a game
the economy is about efficiency, and supply meeting demand, and fair exchange of factors and products for pareto optimality. that is what equity should mean but it's not what you mean by it. Your equity lifts only some boats and at the cost of lowering and even sinking others. Nobody can prove except by simple observation that your equity does not in fact lift boats.
You assume too much, that I am going to argue for centrally planned economies or something. I never claimed or implied I was an expert, or to what degree I’m “winning the race” (what a horrible way to think about human society!). I think it’s either an absurd failure of imagination or simple invested ideology, that we have to have either hardcore “free” markets (free for who?) or strict Soviet-style planning (typically with the assumption that we have only the knowledge and technology from that period too, for some reason). I think we can do a lot better than both.
Your impressive-sounding words about efficiency quickly fall apart for anyone who has actually looked at the dirty end of capitalist processes. Inefficiencies abound; the market optimises for only money which a lot of the time is a stupidly poor abstraction of the stuff of life that actually matters. And that abstraction enables and justifies untold cruelty and exploitation.
If you were a sort of capitalist-pessimist, saying that you didn’t like it but this seemed to be the least-worst option, I’d think you were woefully unambitious, but at least some way understandable. But you arrogantly defend this system, and brag about how your massive brain managed to exploit it. Welcome to HN, I guess.
so, according to you, you are not arrogant, you just legitimately know what's best for everybody else, and you are virtuous to boot?
writing tip: take all the emotionally charged words out of your prose, they don't have the effect you think they do.
I won’t be taking “writing tips” from you; my words are an expression, not pure calculated rhetoric. I never thought I was going to change your mind, I wanted you to face a teeny tiny bit of resistance.
Expressing an opinion and making observations is not arrogance. I said “I think” and “I’m against”, I never claimed any virtue, I didn’t brag, and I didn’t tell you that you don’t know what you’re talking about. According to me, I just have a view about part of the way the world works, that’s it. Reading tip: get better at it.
AI usage seems to have plateaued overall [2], except for niche use cases like coding, that is why companies are forcing it on their employees to justify ROI [3] or creating "products" w/ AI features [4] or embedded addiction.
[1] https://news.ycombinator.com/item?id=48241012
[2] https://news.ycombinator.com/item?id=48179021
I sure hope more people think like this, because it's going to leave a lot of money on the table (for me)
I don't know what your statement is but if you are an employee, then as your employer is forcing you to tokenmax and forcing you to use slop and creating leaderboards for these token spend which will all end up forcing the company to bleed money afterwards they might even lay off people.
If you are an employer then there are still long term issues associated. For example, cloudflare is a company which hasn't been in profit but it has burnt through 5 million dollars per month for AI as it first created an incentive (shrewd even) for employees to use it (for everything) only to please the investors but in the end, its still unclear how profitable all of it is for cloudflare.
Perhaps I have misunderstood you but I really don't understand how its going to leave a lot of money on the table, the only thing I see is a race to the bottom.
And if they are right then what? You won't get a lot of money?
Seems like a weird mix of inflated ego and lack of business understanding by you on this comment.
https://www.theinformation.com/articles/anthropic-openais-sh...
The weird thing is that so many people believe that inference is unprofitable. There are large open weights models that companies run at a profit while charging far less than what OpenAI and Anthropic charge. Deepseek V4 just made their 75% off deal permanent and it was already very cheap.
Yes, you have to consider costs of training the models, but as usage grows it’s going to become a smaller and smaller part of the business.
I think we will see some data center businesses and AI companies blow up, but I think the people expecting the entire AI scene to blow up because prices quadruple are going to be disappointed.
You have no idea whether those companies are making a profit.
1. All it takes is one of them operating a loss to gain market share to force the other ones to lower prices to compete.
2. There’s not reason to expect that these relatively small companies are correctly pricing GPU depreciation.
I doubt the various providers on OpenRouter are benevolently operating at a loss because they’re so generous.
You can also calculate the cost to run these models yourself. They are open weight and the hardware required to run them is not a secret. They can be modeled and many have done the business modeling.
I’m always surprised at how many Hacker News commenters are unaware that a lot of financial modeling and analysis has been done on these companies and models. It’s naive to think the the hottest topic in tech has not already been dissected and analyzed by the finance industry at every level.
If you want to link to a specific cost analysis that was performed by someone without a vested interest in generating hype then do it and we’ll discuss that.
Because what you wrote sounds an awful lot like “let me tell you a lot of very smart people are saying it.”
AWS already have a strategy in place for what you describe. They are very liberal in giving out credits. They don’t do it by subsidising prices.
They might be panicking because they don’t have good models of their own. Or they might just be price matching other open source inference providers. They have cut prices to keep up with competition many times over the years.
Whether they are doing it or not, you don’t know they aren’t, and it’s plausible that they are. So the claim that starts with “we know that people are making a profit selling open source inference at X price therefore Y” is unfounded.
(Is there a more extreme example so far of this than AI companies, just in terms of raw losses? As far as I know, Netscape's lifetime losses as an independent company "only" total a bit over $100 million dollars, which is a lot, it just doesn't look like all that much when put into perspective...)
In 10 years, we've spent nearly 3x the cost of the entire US interstate highway system on AI.
Some helpful visualizations: https://www.aljazeera.com/news/2026/2/19/visualising-ai-spen...
I do wonder why Nvida is included, though. If you include the company that all of the frontier models are pouring money into, of course the net (expenditure - profits) of the collective is going to be closer to zero :-)
If Nvidia is included, does that mean that the money Amazon, Microsoft, and Oracle get for selling compute to the frontier models are included in their revenue?
Because for Amazon in particular, the situation this pages shows is actually much WORSE than I expected. I thought they were making a killing selling compute for model training.
Given how the curves look like in terms of ramping of spend, these are very healthy numbers.
https://cloud.google.com/tpu/pricing https://en.wikipedia.org/wiki/Tensor_Processing_Unit#Second_...
No one really knows how quickly AI hardware investments will become obsolete and thus how long it should be amortized, but 2-3 years would be extremely conservative, and in fact used H100 (discontinued/2 generations old) prices are higher today than they were when the equipment was new several years ago.
There are actual risks that this trend doesn't continue, but as long as the trend continues, it is pretty good for revenue. "AI shown to hit a wall/doesn't actually deliver/stops growing so fast", "massive improvement in hw efficiency or tech such that all the old stuff becomes obsolete", "bottleneck on power/regulations/etc such that no one wants anything but the most efficient cutting edge stuff" would be the ways it could end and then all these factors reverse. Right now, power is so constrained that old, inefficient power generation is actively being turned back on or set up at new sites (e.g. old aviation turbines which are very inefficient compared to combined cycle).
For example: I have index funds which have some of these stocks. So I, by process of revealed-preference, don't think it's a bubble, or I think I will keep my money in through the bubble's pop. I don't have that much else to say!
For the record: I would respect the creator of this site equally or more if he/she said, "I'm shorting these stocks and this is why."
The core bottlenecks are power and computing capacity, and they actually trace back to the exact same issue. It all comes down to the physical energy it takes to flip or move a single bit inside the ram or disk storage. This concept is subject to fundamental physical barriers.
There are a few ways to tackle this, like improving power efficiency, reducing model size, or pushing hardware further. However, achieving orders-of-magnitude improvement in any of these areas will cost a massive amount of time and money. I wonder if governments, corporations, and investors have the patience to wait for these tech breakthroughs.
The only way to get consistently rich in any bubble economy.
Would be weird if they're raising $10 billion after spending only 0.3
https://newsletter.semianalysis.com/p/deepseek-debates
Probably more like 3-4 billion by now?
Yet this site suggests that tokens are very unprofitable
Out of all the companies, considering their own silicon etc. I wouldn't be surprised. Though I do wonder in terms of total CapEx and R&D where it would be at...
Building a datacenter that will produce hundreds of billions of dollars worth of tokens over a multi-decade life shouldn't surprise anyone that it's in the red in year 1 or 2. There's a lot of front loaded capex in this business. If someone built a tractor factory you wouldnt expect 1 year payback.
But the site sort of implies that these companies are selling tokens for less than it takes to inference them. As if this is some sort of COGS ledger. Especially by throwing Nvidia in there. Don't take it too seriously.
They're soaking up the investor bonanza into AI - Gemini ain't making them money.
For context Cloud Compute made 20bn in Q1, Other services made 90bn.
Comparing to ad revenue from a company like meta, the story that Gemini tokens are a strong cash drag on Google just doesn't add up. It seems at worst they are losing like 50 cents/1M tokens (including r&d spend, data centers, etc..), and very possible they are actually profitable per token.
Which is much better than anthropic and openai.
This will always be negative for any new business as you are effectively depreciating the assets straight away. Like if you build a hotel and deduct the cost of building it from room income - it would take years before you get the money back but may be quite profitable with GAAP accounting.
GAAP accounting (Generally Accepted Accounting Principles) is what's used for official reporting and tax returns but excludes any increases in IP value or goodwill unless there's a buyout. If you included those the likes of OpenAI or Anthropic would have done pretty well. I'm not sure there's a word for that but basically value of the business less the money that's gone in. It doesn't get reported because 'value of the business' is guesswork and can be prone to BS but is pretty important to real world outcomes. AI is probably doing well on that one. Maybe why
>Is AI Profitable Yet? NO. Everyone's Broke.
doesn't fit with the top companies on the list having many billions in the bank.
Maybe most of them or all of them lose on their bets, but there's potential for a future where revenue grows beyond the immense capex and research investments.
Oracle though... Immensely risky capex to service a startup industry with what will soon be a commodity...
[1] https://www.wired.com/story/spacex-ipo-anthropic-compute-fin...
https://www.cnbc.com/2026/05/20/anthropic-revenue-explosive-...
Also many of these companies like Amazon, Google, and Meta drive a lot of incremental value due to both AI powered content suggestion and AI powered ad suggestion. Personalized ads has driven a ton of revenue.
Sure they're torching money on building consumer LLMs, but they seem to be doing very well optimizing things like ad ranking
https://engineering.fb.com/2025/11/10/ml-applications/metas-...
https://engineering.fb.com/2026/03/31/ml-applications/meta-a...
1/ User targeting is complex - you can charge more for ads if the users you're showing the ads to click
2/ Ads impact user retention - you need to balance making money and keeping users around
3/ AI generated ads - this is a pretty big thing now, where instead of bringing your own media, you just describe your target audience and the AI will A/B test media + CTAs for you
4/ Integrity - you want to vet the ads against laws/site policies
Probably forgetting a few, but there's a reason the ad industry employs so many
As for integrity ad platforms don't have any. Most of the ads seem to be for scams. The first search result for OBS is usually malware. Scammers have a low cost to advertise because they use stolen credit cards. Advertisers don't mind because the charge back doesn't cost them, COGS is near 0.
The startup blitz-scaling-market-capturing playbook makes makes sense when you spend to scale, not when you spend because you scale, yeah, I understand that step 2 is "and now you squeeze the users", but it will need to be by such a bigger factor...
1. Outspend and outlast your competition until you have market dominance. Win over and lock in your customers with sweetheart deals.
2. Enshittify and squeeze your customers to pay back your debt.
If you're using AI, you're not paying the true cost right now because we're in phase 1. Be ready for phase 2.
The big labs are actively moving into the application layer, where they’ll have more pricing power. Maybe that layer will end up with a Mac (Anthropic) vs Windows (OpenAI) vs Linux (open-source) dynamic as well if they can create a moat. But so far it’s pretty easy to move between providers.
In that case, AI companies will never get their money back, leading to a huge crash.
Oh it doesn't fit the narrative. Never mind then.
You can turn your drive-by dismissal into something really informative if you want to.
Second, even if you take CEOs' words at face value, they didn't distinguish the capex for hardware, electricity, software and salary. You can make up whatever the percentage for hardware and the depreciation rate you believe and fit an arbitrary narrative.
Only the later have something to lose if AI bubble gone by tomorrow. Everyone else will just stay with grown capacity and reuse infrastructure for whatever.
Not listing other hardware companies is just dishinest. AI is not a crypto mining where resources are just burned.
AI is exactly like crypto mining in that Nvidia is the one who profited from both
No matter what happen with the AI bubble text, image and video and other generative neural networks are here to stay.
Whatever you like it or not this tech already changed a lot of industries and there is no going back.
And then Bing gets a pretty big upgrade: ChatGPT. And here we are.
Dark fiber, for example, had a much more compelling use case.