> If you are a consumer and you have a Mac or an iPhone, what do you need from AI that Apple's new offering won't provide?

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.

>If you are a consumer and you have a Mac or an iPhone, what do you need from AI that Apple's new offering won't provide? Why would you pay for ChatGPT, or even tolerate its inevitably increasingly desperate ad placements?

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.

The obvious answer to where the AI Labs get customers is Cloud GPUs. Most users (globally) have cheap phones with poor CPUs and small amounts of RAM. They can't run usable models locally, and it's not clear from the Google-Apple deal if G is selling access to their cloud compute as part of that $1B, or just sharing the weights/IP.

Apple themselves have said there is usage limits, with a subscription upgrade for more usage. So clearly AI Labs are directly competing on that front, it's just a normal default/chosen decision. Considering there are defaults and still successful competitors (eg. safari v chrome), there's no reason to think that competition can't handle this too.

Edit: I want to add that Google is also probably willing to give the model away at a discount to its true value in exchange for guaranteeing that their primary competition (who has tons of cash) won’t have an economic incentive to enter the foundation model training arms race.

Most users who actually want these features for anything more serious than summarization and style updates will probably find value in a modest subscription or ad-supported tier of higher quality models, even if just for occasional usage. Apple can provide this, but once you're comparing features, for many Gemini/Claude/ChatGPT may be a better fit.

Oh, and I think there is an unfortunate but real risk that once again, apple totally over-promises here, and their AI models that they ship end up being pretty poor, and that drives users further into subscriptions.

Let's imagine for a second that this a few billion dollars per year to Google is correct. Why do you assume that it covers everything to be done by Google itself - from hosting to running actual servers? Apple may very well pay Google a licensing fee, take a trained LLM and run inference themselves locally or even at a yet another 3rd party for example a datacenter corporation or any mix of these. And then a true real cost of running just the inference on every Apple device would be separated into a completely different org payment flows, very obscured and higher than just a license fee.

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.

You are kind of glossing over the B2B market where contract pricing is basically just MBA vibes and the fact that people don't really care necessarily about the performance of the language model once it hits a baseline. They care about how it integrates into their lives. Precisely where first mover advantage comes into play. Having to train a language model all over again is it's own sunk cost.

It always seemed very natural to me that AI will move “down the stack”, where Open AI and Anthropic don’t really have a foot in the door.

Who makes consumer devices? Google

Who makes operating systems? Google

Who makes browsers? Google

Who makes the world’s most popular websites? Google

By the time 90% of average internet users get to chatgpt.com or whatever, they already went through several Google chokepoints, each layer is one more place Google can answer their questions.

And that’s not even getting into the chips, the data centers, the data, the talent, the consumer apps, the enterprise apps, the cloud platform, the brand, and of course the biggest cash printing machine in human history.

You would honestly have to be insane to bet against G.

Whats a bit wild to me is Google's only selling point for their Pixel phones are increasingly Gemini.

Now that you can get Gemini, operated by Apple (with the Apple privacy features that come along with that), why would you ever consider going Android/Pixel (outside of running GrapheneOS, but I'm talking regular consumers here)?

Google isn't even making anything on the deal with Apple. They pay $20B/year to be the default search engine. This is Apple just giving a $1B a year discount to that to be able to license Gemini.

This would be greate for google, because most people, specially in the apple environment don't much care to install new tools if they have a native tool that works reasonable well. If you have an ai assistant that's minimally competent in your desktop or phone, you will not care to go after chatgpt or alternatives, and google will receive tons of data to improve their models.

I expect that a lot of the money will be in Enterprise AI.

ChatGPT has >1B users globally a mere 3 years in. iPhone is at 1.5B mostly concentrated in rich areas.

> He would begin somewhere–statistics on AI demand, say–and then walk the calculations carefully over to the next step–maybe revenue needed for profitability by AI companies–and you could follow the argument.

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

> He would begin somewhere–statistics on AI demand, say–and then walk the calculations carefully over to the next step–maybe revenue needed for profitability by AI companies–and you could follow the argument.

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.)

I don't read Ed Zitron, aside from when he appears here on Hacker News, and I also find his tone to be over-the-top. I think we might agree on that much.

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.

Oddly suspicious how this comment which was not one of the first comments which does not address the content at all but the tone skyrocketed to the top.

Agreed. Phrases like "journalists are currently gooning over OpenAI and Anthropic" really put me off. It's a poor attempt at modern muckraking; cheeky yet offering little substance.

I particularly enjoy reading big banners asking me to pay for a newsletter subscription if I "liked" the content. Not if I found it interesting. Not if it actually provided any value whatsoever to me. No, you just have to "like" it. In other words, it is meant to be written in an engaging way and perhaps reinforce your believes like an echo chamber or even stir up certain strong emotions. Not to convey information. So, thanks, but no. I'm sure this opinion blog is very well written, but I don't think it is more well founded than anything else in this sea of opinions that sports a bigger garbage patch than the Pacific Ocean.

He does this on his podcast on a regular basis.

It's not entirely clear to me that the opposing argument is well-formed either. You constantly see numbers and statistics being wildly mis-used or overextrapolated.

His arguments on the numbers of AI are actually pretty solid.

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

Arguments have smells but rigour demands you investigate further. Zitron's smelly prose is, ironically, just the kind of stylistic distraction that AI can help condition; the further irony is that he will one day seem to have been right, for a year or two.

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.

If I thought there were some actual small cabal of people running the global economy, this is almost like a novel: massive amounts of money entered the economy starting in 2008 and 2020-2023, the rich became insanely wealthy. Their wealth is now all tied up in the 2020s version of the railroad/fiber, we're going to essentially erase trillions of dollars from the global economy and reset.

We sure do need a reset.

> undeniable, massive productivity gains.

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 $$$?

He has also consistently demonstrated, at least to me, that he doesn't really understand how inference works from a technical perspective, which weakens much of his core thesis for why there should be a collapse.

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.

Productivity is not value. It's quite possible for you to experience productivity improvements, and actual value to not be created. That is what I think the most robust data is showing.

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

>undeniable, massive productivity gains.

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

He’s been continuously predicting that the collapse was just around the corner, that progress was slowing, and that there was no market for inference, since 2024.

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.

I do not disagree with what you are saying, but I honestly still believe that most of the utility we experience are honestly gonna become very boring very soon that we can just run local... Even if it's a bit more slow who cares, can just run in background while you work on other stuff yourself, read up on things, review other work...

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...

They are absolutely deniable. Huge swathes of people deny them.

Even if we assume that everything you said holds true, how is that we as a crowd can make viable a service that eats some $300bn annually in infrastructure costs? Where would that money come from? Most tech companies these days are cutting their AI budgets because the per token pricing is killing them.

He has recently made the very good point that actually, the FAANG companies are struggling to put any ROI numbers on that incredible ground-level utility.

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.

> undeniable, massive productivity gains.

The jury is still out on that.

Agreed that he has an extreme POV (or more accurately that he trolls for views/subscriptions). But his central argument is valid: if AI underdelivers financially, this bubble will burst and this bubble is magnitudes larger than what we've seen before, so there could be very rough seas ahead.

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.

I quite like my mechanical spider from Wild Wild West and the coffee it makes with a 50% success rate

Every day people here debate whether or not there are any actual productivity gains from LLM, and it's only in the limited context of software development. While I understand that this place obviously skews heavily towards the software industry, the notion that LLMs are anywhere near as useful in other industries is hubristic (at best).

> through undeniable, massive productivity gains.

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?

I really like some good drama slop that reads like a thriller, it is entertaining. I don't take any of it THAT serious, but lately with the IPOs that are about to hit the indizes, he has gained a lot of attention. If you look around the internet, most people publish a negative angle on something and then extrapolate it into some grand conspiracy, which is really captivating. Its crazy when you enter some echo chamber you never engage with (movies, gaming, art/comics) and they have their own head cannon for why the world is bad and collapsing. It puts your echo chamber into perspective to see the same patterns of argumentation and presentation spin out in a different way

> undeniable, massive productivity gains.

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

Yes. Zitron has been predicting and begging for collapse since 2024. It's not just his brand at this point. It's his entire identity. As such, he cannot back down, he cannot question himself, and he cannot accept any other viewpoint. And he will keep moving his goal posts until something happens that can make him go "aha! I told you guys!!"

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.

> undeniable, massive productivity gains

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.

> Zitron is begging for a collapse at this point

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..."

Not sure where I heard this, but I'm reminded of a story about someone predicting the dotcom crash early, circa 1998. For 2 years they were demonstrably crazy, and missed out on massive stock market gains. Then they were right. (And yes, tech slowly bounced back after that.)

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.

Can you point to anything specific from the article that you'd describe as consistently wrong? Not disagreeing with you, but nothing popped out to me after skimming the article.

The quality of AI doomerism takes is matched only by the quality of AI boosterism takes. Ed's kind of interesting as a temperature sensor but I don't feel like you can really take anything he writes seriously.

I highly recommend folks read Wired's profile on him: https://www.wired.com/story/ai-pr-ed-zitron-profile/

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

Yeah they seem clickable because anything Anti-AI is a bit soothing right now, but he is constantly wrong and usually is pushing the angle of "these businesses aren't even profitable!"

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

What if you phrase the question from "will AI ever be useful" (a term as utterly vague as "IT") to "will it ever be able to promise the financial gains these companies are hoping? Especially with local models eating their lunch :shrug:

And its been 3 years of AI boosters telling me that my job as a litigating attorney will not exist in 2 months. Yet here I am, gainfully employed.

He also does PR for AI companies and only really acknowledges this in interviews. As far as I know he never discloses it in his rants.

> Before you spend 20 minutes reading this article, it's worth understanding that the writer has been posting popular but consistently wrong

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?

I don't think whether "LLMs are actually changing the State of the Art" or not matters for anything he wrote.

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.

He's got subscribers. Maybe the attitude is one he's found plays well with them.

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.

It's pretty likely that inference will get substantially cheaper. His argument is that for these companies to be profitable some very major and (pre 2022) unprecedented things have to happen. Which I tend to agree with, except I think they will happen, seeing as how they've been happening for a few years.

> until inference becomes much cheaper these companies cannot be profitable. Some mega-players will pay the API token price, but most will not.

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.

> For example, LLMs to drive NPC dialog and world mechanics in games is not a thing due to cost.

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.

> LLMs to drive NPC dialog

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

> The way I see it, AI is going to change the world radically. It could be for the worse, the better, or a mix of both, but in my mind there's no doubt.

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?

For the purposes of content creation they don't even have to be right once

Maybe in USA in big tech where companies give absurd wages to engineers anyway in some states, that might be acceptable. But to make their ROI they need that (and more) to be spend world wide... no way that is gonna be a budget that is gonna fly in the long term...

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...

It's also not $1,500 per month per engineer. It's that per month per engineer per tool. Which means it could easily be at least $3,000 (Claude Code and Cursor) or $4,500 if Codex was also an option on top of those two.

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

Developers at big companies need to pay per token, they don't have subscription available. So in case you use that, you likely spend way more than $20 in tokens.

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.

Given the way tone has been intentionally abused, particularly in this industry, I’ll take a few f bombs and the truth.

Agreed. If the arguments seem sound and well supported, then all we can do is attack the tone.

Ed's posts are peak preaching to the choir, they're usually factually correct but he is really bad at convincing anyone who doesn't already strongly agree with him.

Agreed. I am open to the possibility of the bubble bursting or whatever, but this piece is like 3,000 words and cites everything as evidence the sky is falling. It's just as bad as the pro-AI grifters, just in the other direction.

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

Given this, his righteous anger towards craven boosters and grifters is pretty funny. Pot calling the kettle black.

Totally agree, remember WeWork's S1 and the fall that followed. Don't think it's the same case, but it'll clarify a lot of things

>LLM-type AI exacts huge costs because it is terrible at reporting "I don't know". When it doesn't know, it generates noise and polishes it.

>If a "confidence too low for output" signal could be extracted, this whole technology would be a lot more useful

Anthropic's interpretability research explored this topic a bit in 2025. Apparently, the signal is extractable:

  It turns out that, in Claude, refusal to answer is the default behavior: we find a circuit that is "on" by default and that causes the model to state that it has insufficient information to answer any given question. However, when the model is asked about something it knows well—say, the basketball player Michael Jordan—a competing feature representing "known entities" activates and inhibits this default circuit (see also this recent paper for related findings). This allows Claude to answer the question when it knows the answer. In contrast, when asked about an unknown entity ("Michael Batkin"), it declines to answer.

  By intervening in the model and activating the "known answer" features (or inhibiting the "unknown name" or "can’t answer" features), we’re able to cause the model to hallucinate (quite consistently!) that Michael Batkin plays chess.

  Sometimes, this sort of “misfire” of the “known answer” circuit happens naturally, without us intervening, resulting in a hallucination. In our paper, we show that such misfires can occur when Claude recognizes a name but doesn't know anything else about that person. In cases like this, the “known entity” feature might still activate, and then suppress the default "don't know" feature—in this case incorrectly. Once the model has decided that it needs to answer the question, it proceeds to confabulate: to generate a plausible—but unfortunately untrue—response.

Isn’t the thing that an LLM never knows (at all)? It just guesses words based on the context and previous words and often gets lucky.

It isn’t thinking or knowing and then expressing the resulting understanding but just spitting out contextual words and hoping it reaches a conclusion or ending of some sort.

The top twenty comments are negative about Ed. I think maybe HN just likes being skeptical.

Anthropic's S-1 is not public yet

Funny thing, the uber's investor results from last year only mentions "profit" once, in a motivating paragraph where they say they will be great.

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.

Uber used the classic triple-E philosophy of Microsoft and entered a market that was ripe for disruption -- many cities lacked reliable taxi service entirely, others were cartels that fixed prices. They undercut prices to an extreme degree, subsidized fares, and when it either drove local taxi companies out of business and spurred widespread adoption as the default, it had a captive market and duopoly with Lyft which allowed them to raise fares without losing any market share whatsoever.

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.

I mean, do you really want to compare AI to the "do crimes hard and fast enough we become a monopoly before anyone can properly respond" model.

Concur.

"Can I Advertise On Your Newsletter?

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

Well there are a lot of commenters so presumably some interest. I just had a look at the Bloomberg bit https://youtu.be/zbKDmkJPVvI and didn't see sweaty boosters rattled, just Ed doing his usual spiel - they are loss making and so it's all a big con. Which is kind of unproven on the big con bit.

Lol... in this case, cheese imports from China are much cheaper, just not quite as good.

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.

> What's the point of arguing with any of this.

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

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

I'm collecting many kinds of predictions Ed Zitron made so that you can see for yourself whether he has a good track record.

-------

> 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.

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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.

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> 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?

Uhh, citations for all of these claims please.

The only "bubble" with AI is that the initial build out is cyclical, and many of the high flying chip stocks with no software arms (ala Nvidia's CUDA) will come back to Earth. I think anyone that thinks AI is going away or won't have massive impact (though maybe not in the doomsday scenario) are in complete denial.

> The current wave of AI unlocked language - the tools are now speaking and understanding. This, on its own, is astonishing progress. Language is the foundation of our culture and society; it is the very technology that got us, as a species, to where we are today.

This is fire erasure

/s