AI is slowing down

Perhaps LLM's (or something better) will develop to be more efficient and quickly become something most people run on local hardware. Perhaps fad-obsessed management types will move onto the next big thing and AI will start being used more judiciously. Perhaps society will set sane regulatory limits that shape the direction AI is going in, from models that take jobs people want to models that, given the right hardware, can do the jobs few want.

Anthropic and OpenAI don't have to succeed for AI to succeed. If they turn out to be a bubble that bursts and torches a lot of investors, it might actually be a fundamentally good thing for everybody else.

We sure do need a reset.

Unless you are not company and you don't have some "enterprise deal". And you need an enterprise deal, as 1) it guarantees that your (and your customers) data will not be sold to someone else 2) you are scared that your competitor will have such deal and become much more productive.

This is what we have now. What will be the future?

Well, soon, if you want something like financial advice or medical advice or job search/CV polishing you will be told, that your $20/$200 is not covering that, you need to purchase additional model to have that. Will you do that? It depends how much you are desperate to get medical advice or find a job.

Anthropic Mythos is an example. Soon, if you are programmer and you will ask AI Agent to spot a bugs, AI Agent will tell you that you need to buy extra model for this. Same with performance analysis, same with the design using tool X, Y or Z.

This is pretty scary, as it will put our well-being, productivity in the hands of few corps. It will be event worst that Google Search monopoly we used to have (until AI chats broke this, replacing Google Monopoly with a few other vendors monopoly).

Can this be prevented? Surely. Hopefully we will have capable open models and consumer-level hardware will catch up. But I think this is the place where governments should step in, invest into alternative models which will be at least comparable with flagships.

Chinese models shows that this is doable, DeepSeek is worst than Chat GPT/Claude/Gemini, but not that much and is clearly better than Grok (which is a huge disappointment for me). I guess India would join this game (especially with nationalist like Modi as the leader).

Europe could join this game, the problem is it kills its capabilities with high energy prices and inability to come out with some reasonable, well financed solution. So the only thing EU was able to come up with is some set of regulations that are blocking fast AI development in Europe...

There is French Mistral, but it is French, it is under-financed, it is only-French, as France would not like to lose control over it.

Germany have totally different strategy, they invest into manufacturing oriented AI, what makes a lot of sense, but does not help with the dangers we are facing.

The rest of the Europe is just too poor to spend billions on AI.

There is still time to buckle up for Europe, but given the course of events, stupidity of Brussels elites who does not see the storm coming I am not optimistic.

If the AI companies need $X billion in revenue to stay afloat, it doesn't matter if 0.5% or 5% or 50% of that revenue is from transforming the State of the Art. It's 100% irrelevant: what matters is that, transformation or no, these companies won't have the income to pay their bills. And if they can't pay their bills, a whole lot of other companies can't either.

So again, transformation or no, it's still a house of cards waiting to collapse. The only thing that would change that is not more "transformation" ... it's a feature set that lets them multiply their current user base (or multiply how much they charge them) several times over.

There are definitely use cases for LLMs in coding. And at times, they can be wildly useful. But I feel like the industry atm wildly overestimates their broader/long term utility.

Anecdotally, I have not seen an explosion in quality/bespoke software since LLMs. In fact I've noticed the opposite to quite the extreme. Not only are new products worse in quality, but the quality of existing products is falling off a cliff.

I find it quite refreshing in some ways. Lots of people, when they start complaining about this or that aspect of this AI stuff, are wont to add in a little disclaimer that, despite all of the above, they actually really like AI and use it all the time. I assume this is to avoid the scenario of a bunch of pragmatic builders turning up and calmly shipping nuance in the comments (or whatever you call it these days when you get brigaded by a pile of angry keyboard warriors with chips on their shoulder) - and it sure is tiring having to wade through the equivocation.

That's a criticism that'd be hard to level at Zitron! Say what you like about the man, but he's unafraid to appear to take a side.

If inference becomes cheaper, it becomes cheaper for everyone.

So far the data for productivity in coding is.. sus. The productivity gains outside of toy projects are mostly anecdotal and it's hard to tell if those accounts are even real humans or just astroturfing and bots. Almost every programmer I know personally has a pretty measured opinion on where these things are useful and where they're not. The breathless hype seems mostly from non coders.

These are three completely separate positions to have. You can think AI is incredibly useful and also dislike it because it will, for example, reduce your relative status in society. You can love the tech but think that Sam Altman is a dishonest person, etc. But for some reason, most anti-AI commentators feel compelled to present all three arguments.

Which is even sillier when you think about it, because if it's useless, then you really shouldn't care: the markets will eventually find out that it's useless, and everything will go back to normal, and the people you don't like will have lost money, so there's no point in being outraged. Of course, I don't really believe that they think it's useless. I do think they're worried about what it'll do to their prestige, though, and they're just hoping beyond hope that somehow everyone will one day "wake up" and share their belief that LLMs are just "stochastic parrots" with no utility, despite the fact that people are using them every day and can watch in real time as they improve.

This is often repeated but comes from ignorance mostly. You have * zero * reason to believe inference is costly other than just vibes. If you go by data and intuitions - the margins are high.

This kind of thinking really reinforces my belief that people have no idea and are using this whole [AI is not profitable and too costly] thing as a cathartic way to deal with immense progress.

Probably the same reason the Gemini app is still well behind ChatGPT in consumer usage and adoption despite being preinstalled on android phones worldwide ? Why are people using GPT on Windows. There's even a copilot button on new keyboards!

Or maybe its the same reason Microsoft Edge is not the most popular Windows browser ? Maybe its the same reason Instagram threads did not even dent Tiktok ?

You are asking the question the wrong way around. People use and like what they like and have a strong preference to continue doing so.

This is just human behaviour. You don't need mind blowing moat. You begin to have problems only when:

- Users are constantly using your product unsatisifed.

- There's a competitor(s) with a significantly better offering that people are talking about.

Will Apple's offering be providing any meaningful/significant benefit over just using GPT ? If not, don't expect any miracles.

Honestly, I don't think I need anything from AI at all. It's a convenience but it doesn't really enable anything I wasn't doing before. That's probably their biggest problem. The biggest thing is it enables non-coders to write code, but it's very debatable that that's a good thing excluding personal projects

I've been using Kagi Assistant for my AI needs, and have to say, Siri will probably replace it in the fall. The question will be, will I still want to keep Kagi for search, or will this new Siri get me where I need to be on all fronts? I need to start paying more attention to how often I actually use the search results vs just the AI summary.

There are things I didn't see Apple show and I wonder how Siri will handle it. One example would be basic coding. They mentioned LLMs in Xcode and Siri with the Shortcuts app and Safari Extensions, but I just had Kagi write up a webpage as a means to display a bunch of data it gave me. Gemini could also do this, so maybe it's not a problem for Siri, but it remains to be seen. There is also a question of what the experience will be like. ChatGPT, for example, handles writing up this code is a much nicer way than Kagi Assistant. Kagi feels more like the results I would have had from ChatGPT a couple years ago where it just dumps out the code in a block and any change is an entirely new code dump, meanwhile ChatGPT goes into a coding interface with a live editor. Going to Xcode feels like overkill, Siri will probably be not enough... so that's a gap in the market Apple may not serve. I assume there will be several things like this. The prosumer level of AI usage, if you will.

Would you care to wager on that?

Because I would gladly take the other side at even odds.

> consumer AI is worth, to Anthropic

Anthropic does not really care about consumer AI. I expect consumer is where their least profitably customers are.

My primary expectation is that Apple will mostly increase usage of AI by general consumers. To me, this reads like Instagram adding stories. Did it stop Snapchat's growth? Sure. But I would be cautious about claiming it will take too many users away from OpenAI. I think it will be a fairly different product offering.

If you're paying to use ChatGPT right now, you might be using it for hobby coding, projects, or image generation. If you're paying a lot for ChatGPT, you're almost certainly using it for personal programming projects.

The $100/month (and up) subscribers aren't going to churn because of this, and I would be extremely surprised if the $20/month users do in any meaningful way.

I'm not saying that this is what really happens. I'm saying that believing a CEO is as foolish and as grounded in reality as believing Ed Zitron.

Web SaaS gonna end up being seen as another failed play at slim clients and entirely centralized sources of pay to play access to eyeballs; more AOL-ification of networks

gobby ... British rabble-rouser. "Gob" is the Dick van Dyke approved word for mouth.

These articles are lengthy but, to my understanding, Ed's idea is...

* AI companies have committed to purchasing X amount of compute

* Data centers are being constructed to meet this demand, they'll need to charge amount Y

* AI companies do not have sufficient revenue to pay amount Y

IMHO this isn't surprising, personally the only real use-case for AI that I've seen is code generation or automated sales or scam calls. This doesn't seem like a big enough market for the huge dollar amounts I'm seeing thrown around.

I'm curious why you think Ed is so far off the mark on this. To me, it seems like we are headed for a big correction on the whole AI thing.

That's exactly what the first (titled) section does?

Which of the hyperlinks provided at the beginning sounded like what you wanted, and after you clicked it* how did it disappoint you?

The information you are describing is stuff I would not expect anybody to repeatedly duplicate across periodic blog-posts.

* (Yes, I'm being sardonic, but if you did bother to click them, then I'm legitimately interested in your answer.)

But, I also think he has missed the mark on a fair few things in terms of out comes. He may be proven right yet in terms of the general shape of things for some parts of the industry but also will have some big misses.

My general take away usually comes down to, places like OpenAI, Anthropic and Oracle have gone in a little to hard to fast and it may hurt them long term as they struggle to make it work in terms of economics. not that they can't just it will be difficult. But places like Microsoft, Google, Meta, Apple, Amazon; they have a very long runway to endure the growing pains and make it through to a long term business that no longer burns cash.

https://en.wikipedia.org/wiki/Tulip_mania

The money is indeed losing its mind over AI, and Zitron is a stopped clock. A correction is coming but the tool isn't going anywhere.

I am still to see a solid counter to what he brings up there.

Exactly what the AI evangelists are doing.

But it isn't like this hasn't been the long-running strategy for Google as well - provide more results on search so that people don't go to the site with ads, provide paid product results for shopping, to offer more services to keep people providing personal/behavioral queues to Google and more opportunities for ad placement.

If anything, AI turned up the heat such that the frog noticed what temperature the pot was. But that doesn't really put them in a better position to execute than Google.

Unless you are confident that Google will be antitrust forced to weakening their hold in some of those markets. A crack is all you need for the water to rush in and widen the crack.

Nah this is just Googler cope

Google missed the AI boat. Period.

Take any stock index, remove AI stocks, what do you see? That's right! Nothing...

So where is all the productivity going? Where is the value? Where are the massive unemployment stats or the millions of new startups making big $$$?

I do value having some naysayers in the mix generally, because we do need balanced critique in what is otherwise a very frothy hype cycle. I just don't think he's making sound arguments, and that's even assuming you even agree with his premises in the first place.

My biggest gripe with his napkin math is that he treats inference gross margins as something novel that you can't compare to normal SaaS margins. He's right in part: the constant carousel of R&D costs from model training, related infrastructure buildout, and other adjacent costs required to stay competitive do change the analysis a bit.

But he takes this way too far when he says this is structurally different from normal SaaS margins. The business model definitely doesn't look like Dropbox, but it absolutely looks a lot like AWS, especially early AWS, CDNs, telecom, etc. I can speak to the telecom bit personally, since it's been over half of my professional career as an engineer and, in this specific case, also as a founder. You can have a brutally capital-intensive infra business where profitability depends on utilization, oversubscription, peak-capacity planning, segmentation, and recovering capex over time.

The math he presents gets even more questionable as we see explicit segmentation happening for cost-saving reasons. Many forward-thinking orgs are waking up to the fact that they don't need to use the best, most expensive model for every task. They can route easier tasks to cheaper models, use caching, batch non-urgent workloads, and reserve frontier models for the subset of work that actually needs frontier intelligence. That directly undermines his claim that providers always need to chase frontier intelligence in order to maintain current demand, utilization, and pricing curves.

https://unessays.substack.com/p/talk-is-cheap

The fact he’s never reflected on the glaring failures in his analysis tells what we need to know about his intellectual integrity. There’s truth in some of his words about financial risk, but if you can’t acknowledge that there’s upside too, you can’t evaluate risk properly either.

I find it difficult to take him seriously.

How can something so undeniable have zero scientific evidence? Are there any large peer reviewed or meta studies confirming your claim?

It's not that the utility of it put in question. What is however a giant question mark is how the heck any of the big AI companies are ever gonna get that ROI? Given how many of us are becoming more and more fine with local models that run just fine especially on a good enough computer which most developers have anyway...

The jury is still out on that.

Uber, for example, is so unclear there is any ROI, they are cutting their exposure pretty radically.

He points out that one single Anthropic customer — a payments provider — accidentally had to pay Anthropic $500M for one month of token spend.

That is half what Apple is reportedly paying Google for the supply side of their entire consumer AI strategy.

The question is: what does "underdeliver" mean here? the pro-AI arguments I am seeing in this thread are equating mass adoption to agentic coding. Er, I dont know of any trillion dollar cap companies that sell dev tools. The point is Zitron doesn't have to be 100% right for his central prediction to come true.

And where are those? They seem particularly hard to actually observe and only appear in anecdotes.

> I'm trying to believe

For every exponential increase in compute capacity you see linear gains in output accuracy. This is a death spiral. Anyways, you see "massive productivity gains" so why is "belief" a function of your viewpoint?

This, combined with his extreme ignorance, makes him unreadable. The only reason people read his stuff is because it validates and confirms their own anti-AI beliefs. It's why every time he publishes an article, it reaches the front page in an hour or less.

How are they undeniable? They're very deniable. One example is the (seemingly) increasing maintenance costs for AI-generated code[1]. Another is the cost incurred by everybody reading AI slop instead of actual communication.

I don't have hard data as to whether these cancel out the benefits, but it's not as rosy as some seem to think.

[1] After years of people understanding that LOC is not only a poor productivity metric but also a negative indicator of code quality (shorter code for the same thing is better), we now have people touting how many LOC their LLM agent is generating. It's like everyone forgot what LOC actually represents and what it means for long term maintenance costs.

No, he's not, he's making tons of money every month from his Substack subscriptions. In fact, the AI bubble popping would be the worse thing ever for him, he would be out of a job.

Just like the who have predicated the US dollar will collapse any-moment-now and which pushed gold for decades.

Funny how people always say "oh, you are an AI lab, of course you are going to hype AI", but never "oh, you make sooo much money from predicting the collapse of the AI bubble..."

Just because you keep repeating something doesn't make it an undeniable truth.

Companies love to cut costs, and just like they axe employee numbers at will, they will just as well make that kind of budget quickly dissapear the moment they realize they can go a different path for same or better value... Or simply because share holder short-term value demands it...

And as you have written on your blog it's a soft cap that can be exceeded with justification.

Another way you could take it is, avg Uber salary is what $300k/yr? Does Uber think LLMs make their engineers at most .5% more productive?

My company provides employees with API keys and soft limits, but as soon as you approach ~$400/month they ask that you get a Claude/Codex Max subscription instead. Curious if it's not the same case at Uber.

Predicting the timing of such a thing is notoriously difficult. I don't think being wrong about timing 2 years ago means there won't be a correction.

  When asked whether it will be possible to fix Sora's videos after they've been generated, Murati said "eventually," and then couched that by saying "that's what we're trying to figure out...how to use this technology as a tool that people can edit and create with." She promised that there would "eventually" be "more steerability, control and accuracy...and reflecting of intent of what you want." 

  You'll "eventually" be able to add audio to Sora videos, and when asked when Sora's generative videos will be available to the public, she once again said "eventually," and when pushed said that Sora's launch would "definitely be this year, but could be a few months."

  Murati, living in a world of "eventuallies," provided no technical insights, no specifics, and very few details.

Please speculate on why OpenAI wouldn't just leave it up (whether or not they were able to improve it).

Tim Lee also pointed out that when Ed has posted details on some of his analysis, they have had some....oddities: https://x.com/binarybits/status/2034377838883700953

Instantly close the tab as soon as the popup to subscribe to his newsletter pops up.

So, judge the book by it's cover?

> arguing that AI is failing, is a waste of money, is bad, will never work, etc.

Then the opposite should be easy to prove. AI is succeeding, is efficient, is universally good, and is working everywhere it's tried. Are those true?

Hah, I'm actually working on just this problem.

Cost isn't the issue. There are only so many coherent (in context) responses and scenarios, that you don't need an LLM to generate text in the game, in real time. Instead, you can have LLMs build a vast corpus of "atoms" (dialog messages, fragments, cues, etc.) that can be stringed together in a deterministic way in response to player input. These can also be pre-screened and subjected to various tests prior to implementation.

To a player interacting in the game, a system like this would seem functionally indistinguishable from generated text within the game's designed interaction envelope. And it has huge advantages: Although it can expose seams if the player breaks character and decides to probe it, it won't be exploitable the way an LLM would be.

Far more interested in dialog and characters developed by a writer - simulation is boring

And we’re only about a year into the agentic coding era portion of that era.

I have deployed LLM-based NPCs in production for a reasonably popular game (100k DAU), so this is news to me.

Worthless statement. Wow, you suspect something can make things better, worse, or both? That's a keen insight there.

> For reference, radio waves were discovered in 1886, Marconi used them for communications in 1895, and while telephone and radio coexisted for many decades, it wasn't until the 1995 that mobile phones and wireless technologies started picking up.

We are still so early.

I mean, we have advertised them in multiple super bowls, have companies that basically own tech news (incredulous journalists will repeat any stupid insane shit a CEO wants to say), that say they're valued at over a trillion dollars and nobody with the power to argue those finances seems willing to do anything but agree. We have built hundreds and hundreds of acres of data centers (and made deals for data centers that are never going to happen) that demand *billions* per month. They are devouring all the silicon to where people are visibly seeing the price of hardware double, triple, more in price. Work places insist on employees using AI (then pulled back because it turns out this stuff costs money and it's not fun anymore when it's not subsidized).

But we just need more time, more eyes, more people looking at it.

Where in the radio wave timeline did this happen?

Publications love a doom and gloom rant, which is why he seems to have built an entire career on hysterical anti-ai screeds. It doesn’t mean that he’s right though.

The problem is when untrusted authors take positions, then it circulates widely, then people discredit the author and by proxy the position, when the position could be correct.

The article has a number of emotional appeals in it. Something more focused on raw numbers would foster more curious discussion.

I think the most compelling part of the article is that these numbers point to a situation where the level of investment required seems unsustainably high by plain dollars.

You don’t really have to agree with the author to see how it plays out. OpenAI and SpaceX and Anthropic need to go public this year to avoid running out of money. There’s no more private money, not enough to fund them. The IPO is the last funding round.

They can continue growing extremely quickly and AI can still be highly useful and maybe be transformative, but still not have the money to fund that growth.

That part he wrote about an AI company gone bust canceling their Oracle contract made Oracle feel like a Nortel analogy to me. If they have a sudden lapse with a big chunk of their customers they are writing down triple digit billions of dollars.

IF I WROTE AN ENTIRE BLOG POST IN ALL CAPS ABOUT HOW AI IS LITERALLY SATAN PEOPLE WOULD JUST THINK I AM A CRAZY PERSON

If that's his message, why is he going on about ecconomic sustainability? Whether or not you have a coherent business model has nothing to do with how morally corrupt you are.

Ultimately i agree with the GP post, the article reads like something preaching to the choir. If you already agree it seems natural. If you don't agree it looks like an incoherent rant that is not particularly convincing.

This is nothing new for Zitron. The last article of his I saw:

> I also severely doubt that Anthropic managed to make the cost of running its services profitable in the space of six months.

> [Per The Information in January], Anthropic missed on its gross margin projections, saying that its inference costs were 23% higher than the company had anticipated.

> How did Anthropic, which faced a massive influx of new business to the point that Anthropic was forced to buy more compute from Elon Musk, magically become profitable? Other than that discount, of course.

If you follow the link to The Information, you’ll see that it’s a paid article with the headline “Anthropic Lowers Gross Margin Projection as Revenue Skyrockets”. But what happens when you actually read the article?

> Anthropic last month projected it would generate a 40% gross profit margin from selling AI to businesses and application developers in 2025, according to two people with knowledge of its financials. That margin was 10 percentage points lower than its earlier optimistic expectations, though it’s still a big improvement from the year before.

— https://archive.is/aKFYZ

So, according to Zitron’s own source, Anthropic are actually earning 40% gross profit margin on inference, and that is a dramatic jump upwards! This totally contradicts his position that it’s an implausible “swindle” for Anthropic to claim profitability. He’s counting on the fact that most of his readers don’t subscribe to The Information and will only see the headline, or that they will just see a citation and trust that it backs him up without checking.

Specifically for image generation. They haven't indicated you have limits for Siri interactions.

OK, that I would concede is a possibility. Though Gemini is clearly capable, and the (alleged) story is that they have licensed a one-trillion parameter form of Gemini. I don't think they are making the same mistake.

ETA: I also concede they could make a different mistake ;-)

The best solution, from an efficiency point of view, is to use smaller models on datacenters, requiring much less of them.

Does the truth normally lie somewhere in the middle of it all?

As for how to spend that much -- not that hard, to be honest. Just give it a lot of context and some relatively open-ended problem and it will easily eat through tons of tokens.

I have $200 subscription for Codex and it is crazy what it can do in terms of debugging. I have a pretty complex Electron setup with some native code linked via Node addons, a few App Extensions and it can easily read the source code to see how the builder works internally (e.g. if your end Info.plist is not correct), debug the xcodebuild output to see at which step something is not linked correctly (like after XCode major version bump), etc.

It is not a silver bullet but if you are not the one paying for it, there is no downside to throw a problem at it and see if it can come up with a fix.

> And, if they are pouring thousands into LLMs per developer, have they considered looking at alternatives like having LLMs running locally on own hardware with their own agent harness?

I am curious about that myself. I have a good machine now (Macbook Pro M5 Pro with 48GB memory), so I'll give it a try; I don't have high expectations so if it is actually helpful would be very neat.

The internet was a bubble: you could make a web page and sell it for millions because next year it was going to be worth billions. And then internet grew up.

AI is technology that's still beginning to find its place to settle. It's far from mature and that's perfectly fine. We'll have reached a reasonable plateau once the technology and the related stack stops changing every month and instead develops incrementally and boringly over the span of few years. That's like internet in the 2008-2010, and many investors will have a collection of new burn marks by that time.

Not only financially there's an unsustainable push for AI by the zealots du jour who are more often than not managers rather than engineers. AI is championed most ruthlessly as a silver bullet revolution by people who least grasp the limitations of AI. It'll take some time to figure out the dreamed-up proceeds won't be there, and "then what?".

I predict that the real bottlenecks of development will re-emerge as soon as the limitations of AI will manifest out of the hype. They bottlenecks are human-based, in development processes and in human interactions. A large part of development is trying to understand what we want and what we need and you can't offload that to AI.

If you're in the Google ecosystem like Gmail and Calendar, it is exceptionally refreshing to be able to use an assistant that uses that ecosystem, instead of iOS requiring you to use Mail or its own Calendar app.

I don't think there's any real gap between Pixel and iPhone on the things that matter: UX jank, battery life, camera. Even the messaging issue in the US has closed with encrytped RCS support between them launching. So now it's just an ecosystem question, which might be why Gemini is mentioned so much with Pixel.

But given how dependent OpenAI are on Samsung, it's hard to believe they will see a radically better deal in material terms.

This is fire erasure

/s

But it's famous for having collapsed after their IPO. It took 4 years to get back at the same nominal valuation (not inflation corrected), and after all the 2020s inflation it is still at 2x the initial price.

It's a pretty classic business strategy, and not directly comparable to any of the AI companies. There's a reason people compare the current situation to the dotcom era and not Uber. Also, don't take Uber as an example of a slam-dunk VC success story and leave it at that -- plenty of dumb ideas get pitched and funded and go bankrupt for every Uber.

How's that?

Yes! Email me at ed@ezpr.com. I have an extremely high bar both for advertisers and the cost of advertising on here - I have 84,000 subscribers and a 55-60% open rate, as well as an 8-11% clickthrough rate.

I do not do any kind of outcome-based advertisement (IE: X number of people click through and you pay me Y), so any kind of agreement would effectively be a sponsorship. I have an engaged reader base and you will have to pay to get in front of them, as I also do not need advertising to support this newsletter."

Maybe the "AI" companies could pay for sponsorship

Would he take the money and run their ads

So it's okay for everyone's who's due to retire in the next 13 years to have their 401k or equivalent wiped out when the correction happens?

-------

> While complex, generative AI is a technology that probabilistically generates answers, and has no "intelligence." It is inherently limited by its architecture, and in turn can only get "better" in a linear fashion. I see no signs that the transformer-based architecture can do significantly more than it currently does.

He wrote this in 2024 before reasoning models came out. Remember how ChatGPT was in 2024? Do you think this person is someone who gets predictions right?

> Furthermore, I hypothesize a race to the bottom in generative AI will significantly hamper OpenAI's ability to expand revenue, compounded by the fact that we're approaching the limits of transformer-based architecture.

He wrote this in 2024 and since then Anthropic's revenue increased by 160x to $40 B dollars a year and OpenAI's increased by 6x. Do you think this person gets predictions right still?

> I believe we're reaching the upper limits about what generative AI can do and how accurate its outputs can be,

He wrote this in 2024, do you really think we have reached upper limits? Huh?? What I'm using today is significantly more accurate and 2 tiers above what we had.

> And if there are true industry-changing possibilities waiting for us on the other side, I am yet to hear them outside of the fan fiction of Silicon Valley hucksters.

He says this about AI when we have with all honesty have had industry changing possibilities like agentic coding.

> There are indications that consumers have also lost interest. As pointed out by Alex Kantrowitz’ Big Technology newsletter, traffic to ChatGPT on both mobile and web has started to stagnate, if not decline. In January 2024, ChatGPT had 1.6 billion visits — 11% below the all-time peak of 1.8 billion. This makes it only modestly more popular than Bing, which had 1.3 billion unique visits during that period. On the mobile front, ChatGPT has an estimated 6.3 million US users — or 1.7 times less than the total of new Snapchat users added during Q4 2023.

He agrees with the claim that the consumer interest has declined. Since he said this, there was a 9x growth in active users.

-----

https://www.youtube.com/watch?v=_wStScmT748&t=1s

"AI Bubble Already Bursting?" (8 months back)

https://www.youtube.com/watch?v=T8ByoAt5gCA&t=1s

"A.I bubble is bursting with Ed Zitron" (1 year back)

He's been constantly crying bubble for years now.

-----

> AI video won’t get truly fixed just by waiting a year.

This is what he had said in 2024, and you just need to compare video from then and now to check whether the predictions came true. Why would anyone trust what this guy has to say?

And for those who are all "but dur CCP get all ur data" you can use things like AWS Bedrock (at least for earlier versions of Deepseek and Qwen for now) and have more familiar people get all your data. Or buy (at obnoxiously inflated prices) your own HW and not send your data to anyone.

> It's like someone arguing that cheese isn't real

I agree with your first statement (any being you) because of your second statement.