Full episode · 58 min
Ed Zitron Unfiltered on OpenAI, Anthropic & Why the Whole Thing Is a Con
Ed Zitron argues that AI's apparent value rests on subsidized pricing, while returns and the cost of scaling remain unproven.
NPNewcomer Pod · Eric Newcomer
- Ed Zitron is a writer and podcast host.
The best minutes
- 12:01AI subscriptions hide the real cost of a unit of workPro pick 7 min
- 33:06Data center expansion depends on an enormous debt marketPro pick 2 min
- 41:37Executive demos can make AI look more capable than it is7 min
Also worth watching
- 8:56Anthropic’s reported profit coincided with a compute discount2 min
- 27:17Anthropic has not defined its reported run rate5 min
- 37:59Tech firms pushed AI into products before users asked for it4 min
- 50:49Some AI safety stories omitted how tests were staged3 min
Skip the extended model-cost debate after the Uber segment and the closing exchange about AI’s trillion-dollar cost.
Summary · 4 min read
Newcomer introduces Zitron as a podcast and newsletter writer whose critiques include 18,000-word takedowns. This is Zitron's second appearance on the Newcomer Podcast.
Ed Zitron argues that foundation-model companies have not shown a clear return on investment and that subscriptions conceal the high token costs of AI use. He says companies and users are being trained on subsidized pricing, while Eric Newcomer argues that AI already provides practical value and that lower model prices or business adaptation could matter. They dispute the comparison with Uber, the reliability and usefulness of coding tools, whether broad AI adoption was pushed on the public, and what level of accuracy is reasonable. They close by questioning whether present-day large language models justify the trillion dollars invested.
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- 1:38AI writing feels lifeless
Zitron says AI-generated prose feels lifeless and relies on recognizable linguistic patterns. He contrasts it with even boring human writing, which can still convey something human.
- 5:05AI's return remains unproven
Zitron argues that AI companies talk about theoretical potential rather than demonstrated returns. He says flat-rate subscriptions hide token costs and calls that pricing a deliberate con. He also says skeptics are treated as outsiders in Silicon Valley.
- 9:18Zitron opposes OpenAI and Anthropic IPOs
Zitron says regulators should not let OpenAI or Anthropic go public, arguing that their leaders sell theoretical capabilities. He claims a SpaceX filing projected Anthropic profitability in May and June, when Elon Musk discounted its compute. Newcomer presses him on what a public listing might reveal.
- 12:01AI subscriptions distort unit economics
Zitron contrasts Anthropic's spending with Uber's history, saying Uber burned about $32 billion while Anthropic had raised over $75 billion in the previous six months. He argues that flat subscriptions have trained users to consume AI without seeing its real cost; Newcomer says Uber also faced criticism before becoming profitable.
- 18:59More tokens can offset lower prices
Zitron says newer reasoning models can consume more tokens, so lower prices per token do not necessarily reduce total costs. Newcomer points to cheaper older models and open-source alternatives, while Zitron questions how widely companies can replace the models they use.
- 24:19Subscriptions conceal AI's full cost
Newcomer says his company used AI to build ticketing infrastructure, but Zitron argues that a subsidized subscription does not show its true cost. He cites Anthropic documentation estimating average Claude Code use at $13 a day in tokens and says Uber capped engineers at $1,500 a month.
- 29:11Run-rate revenue can obscure volatility
Zitron says annualized run rates can mislead when API revenue fluctuates and argues that Anthropic has not clearly defined its run rate. Newcomer says investors understand forward-looking revenue measures; Zitron counters that companies should report revenue and disclose their non-GAAP calculations.
- 32:00Zitron says data center financing is strained
Zitron acknowledges that he misjudged how quickly the AI boom would end. He points to Google's $84 billion equity raise as a possible bubble signal and says credit markets are drying up. He estimates the projected GPU buildout would require about $800 billion in debt.
- 35:11Zitron questions AI's value beyond code
Zitron asks why coding dominates claims about AI's usefulness. Newcomer says users need to explore the tools to find value, while Zitron compares their reliability unfavorably with the immediate impact he felt from his first iPhone. They also dispute how much current AI use is driven by user interest.
- 40:21Zitron calls AI adoption manufactured
Zitron argues that companies and media have pushed AI into everyday products and framed it as a threat to workers, rather than letting people choose whether to use it. Newcomer says interest in AI is also organic. They then debate whether executives use AI demonstrations as a reason to cut staff.
- 43:43Zitron separates generative AI from other AI
Zitron says executives can overestimate what AI demos prove and argues that leaders often make poor decisions about workers and products. When Newcomer points to uses such as drug discovery, Zitron says those techniques should not be conflated with generative AI.
- 48:03They dispute the standard for hallucinations
Zitron says models cannot learn from mistakes as people can and argues that hallucinations make them unreliable. Newcomer says requiring no errors sets an unrealistic bar, while Zitron responds that companies have marketed the systems as intelligent and capable of replacing workers. They also dispute how AI safety tests and their results are presented.
- 52:05Zitron links AI skepticism to its costs
Zitron says executives and investors have not done enough due diligence and points to data centers' effects on local environments. He argues that media coverage dismisses people who question AI. Newcomer presses him on what positive future he sees; Zitron says he loves technology but thinks the industry is damaging it.
- 56:12They question whether AI was worth the cost
Zitron asks whether today's large language models justify the trillion dollars spent if development stopped now. Newcomer points to expected company valuations, but Zitron clarifies that he is asking whether the money spent so far was worthwhile.
Published Jun 8, 2026.
Transcript · 51 min read
Newcomer Pod, hosted by Eric Newcomer, with Ed Zitron.
Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.
Today on the podcast, I finally bring Ed Zitron around on AI. Lifeless, it's empty, gonna look like a dog's asshole on Thanksgiving. You stupid, you moron, why don't you like data centers? Okay, maybe not quite, but I do have a lively debate with tech's loudest AI skeptic. So how are you going to get around to the return on investment thing? He's like, yeah, somebody will work it out. Sammy, clammy Sammy, this is your job. From his podcast, his newsletter, and his 18,000 word takedowns, He has made one thing very clear. AI is a bubble that's about to burst. I'm Eric Newcomer, author of the Newcomer Substack.
Let's get into it. Ed Zitron, welcome back to the Newcomer Podcast, round two. Thanks for having me. We're live, in person, just you and me, mano y mano. I asked Claude for, I don't know, what did I say? give some monikers for you all right we've got the hater laureate cassandra of compute the doom sommelier tech's designated mourner the bard of the burn rate chief bubble officer uh the last honest man it's that that's a charitable one i guess i just like i asked the dog would it give me just i'd be just as excited it wouldn't be as good though the dog i would care i would have more respect for it it's just like those are things that you get people saying in your mentions every so often it's like like a regular person like good on you like a normie but like it's like claude slop okay the hater laurier okay just like unex such it's so unexceptional that's the thing it's not just mediocre and all these different ways it's just like you read the text it just feels lifeless and if you go on substack it's i think it's mostly clawed written all subsack yes so many we saw yeah there were i forget the company's name but there's some pancrum right yeah yeah yeah and it's just it's very depressing and so it's lifeless
it's empty even human written linkedin slop evokes some kind of reaction even even if it's just reminded of the feeling of being at a conference you still there's still something human about it even if it's quite boring but you read the clawed slop as it's not just this it's it's this and that was the twist that was the turning point there's these little linguistic tropes it's just sad it's not even good it's not like we're still in this years in we're still like look yay we can kind of write like a person and it sucks and you can tell every time yeah it's just so boring so that that gives us a taste of where where you're coming from i to give context you know uh to people company this episode you know i host an ai conference i write in sub stack newsletter that is not ai slop ai copy written you know i pay you i pay thank thank you it's great um big fan of your newsletter as well and um though it obviously has a point of view we will get into here um yeah um we did we did one episode before nine months ago sort of disagreed for for context For the viewer on me, you know, I feel like I'm somewhere, you know, I host an AI conference.
Read the whole transcript
I wrote a case against OpenAI's valuation at, I think it was $157 billion, which is March past. I sometimes report data that you're happy to have. So I feel like I sit in some middle ground where sometimes we're allied, sometimes we're clashing heads. And here's the thing with you. I know we don't agree on everything, but you published Tom Dutan, who's a fantastic journalist, who is also quite critical. You yourself are one of the few people who's been even positive you are still willing to critique Anthropic, the Kutu, what's the Kutu? Kutu D, yeah. Where they said, oh, we think it'll be worth 1.99 trillion.
I don't want to say two. That would be ludicrous. Yeah, that was just that we couldn't possibly value it. a company that burns billions of dollars that much. And then Elon Musk comes along and is like, watch this, it's so based. And it's, but you at least try. You at least try and find some middle ground of, okay, you think that this will work, I don't, whatever. But you at least try and found that in something. And even then you bring in some critical. And I think that's healthy. I think it's the healthier way of approaching this for someone who is going to be pro AI or at least willing to accept a pro AI opinion.
I never want to have a shtick. I feel like my disposition is sort of I always like to poke and prod at things. So I think this, you know, we'll get into it and I'm sure we'll push back. But at first, I just sort of want to give you a chance, like lay out your case for a couple of minutes. Like, where are you today? Like what is there? There's sort of this like. It's a fraud. It doesn't work. Nobody likes it. You know, there's sort of the self-dealing aspect. There are lots of pieces to it. Like, where are you right now on the, like, it sucks, nobody wants it versus these numbers are made up? Or like, what is your core case against the foundation model companies today?
So we're really talking about like OpenAI Anthropic, like the case against them. So fundamentally, there is no ROI in AI. You can dress it up however you want. Look at how Anthropic and OpenAI discuss their products in the sense that they don't. They're always talking in theoreticals, in, well, the potential's there. Now, everyone's always talking about the fact that we are still having a conversation about AI's value and whether AI is real or not is kind of the foundation of my argument in that if this was real, we would definitively know. We wouldn't have people saying emphatically, like, AI is here and it's real.
You don't need to say that. You don't need to say that if something is real. so i have never disputed that llms can do some code but i think people have over become a little overwrought about what kind of coding they can do and how reliable or stable or functional that coding is but the biggest thing by far is that the con is the financial side the con is the fact that most people's experience of ai is unrealistic when it comes to the cost so you using chat gpt i believe the subscriptions will go away i think we are just at the dawn of token-based billing meaning that most people's use of clawed code for example is based on a relatively unlimited you can burn between eight and thirteen and a half dollars for every dollar of your subscription in tokens so most people's experience of ai is separate from the cost divorced from the cost and this was a deliberate con on the part of sam altman and dario amade because they both knew that if they actually made people pay on a per million token basis to use these things they go wait how much what whoa this this thing messed up and i still had to pay and you're kind of seeing that already with the enterprise customers the ubers and walmarts of the world who are starting to
have to pull back this is the token maxing soul searching we're seeing going well the thing is uber isn't wasn't token maxing they i don't think they had a leaderboard i think i think they might have at some point but they burned through their entire annual token budget in four months in the space of the first quarter and then their COO Andrew McDonald said it's getting hard to justify because you can't connect it to outcomes this is we are years in we can't we why are we all acting like this is going well it isn't and so the experience of AI the value judgments of AI are all based on phony metrics an outright con about the costs and quite frankly a very weird psyop the whole thing is a psyop the cult of personality around ai the very much if you are in the out group you don't like it you're you're othered you're different you're a you're a hater you're a skeptic and it's frustrating because silicon valley is meant to be a place of meritocracy it's meant to be a place where you're evaluated based on your value theoretically speaking remember the i don't know if you ever saw the github thing the meritocracy doormat they had no i look like they had like a white house yeah it looked like a cia thing which
is also good we love that but the thing is silicon valley is meant to be a meritocracy yeah everyone coddles these things everyone talks about these things like a gifted child like they're afraid that they'll get turned into an ear of corn like that twilight zone episode for saying the wrong thing and that's oh i watched your bloomberg interview recently and you know i used to work at Bloomberg. I'm very sympathetic to them. But their response to some of your criticism was very, but they're smart people who believe this. Like surely it can. I felt a little bit like, is that the best AI defense they're going to muster? So I just want to make sure. I mean, I think, you know, argument number one is that the AI companies talk about what they can do in the future, not what they can do today. Your assessment of what they can do today is not that exciting. To the extent companies are using it, it's often subsidized and or there's sort of this delusional sense of like, clearly we're supposed to get some value out of it. We're
“anthropic leaking that they are profitable in the two months that Elon Musk discounted their compute.”
answers when the yes i'm looking forward to it and you know we'll talk about the uber analogy because i covered uber yes you were one of the and you were actually one of the few people who were really critical of them. I was very critical, but in some ways I learned the opposite lesson that you did, which is they ended up turning a profit. People over extrapolated from some of the private financials and drew these complicated models that sure, like Uber was spending more
aggressively and they were, they were like Uber pool never worked. Right. So there were things about Uber that didn't work that were worthy of criticism, but I just, there are just limits to how much you can sort of do financial calculuses off of these like limited private numbers that we see. Do you want to wait to discuss that? No, I don't. Let's talk about the Uber case, because it forms both of our views so deeply.
“there were people who were paying 39 bucks a month and being able to burn 1500 to 3000 dollars worth of tokens”
today will be cheaper in the future right that we see that over and over again what do you mean Like the models of 12 months ago fall in price. The new models obviously are new, have new prices, but you're able – the old models become either offerings from Anthropic and OpenAI themselves and or open source competitors basically copy versions of that model and offer them at a discount. So I can see the argument that – and you've talked about this sort of like the CapEx expenditure.
The situation with Uber and OpenAI and Anthropoc are very different in the sense that OpenAI's costs are because they're spending a lot training a new model. Well, but before we get on from the token thing, there is an important distinction I need to make, which is models are burning more tokens. So even if the cost of intelligence comes down, the biggest myth of it all, even if a model might be cheaper. In general, they are burning more tokens, Opus 4.8, but more tokens than ever. And every time Anthropic has released a new model, it seems to get worse. I don't know if you noticed that. They seem to be more convoluted.
They do things differently. Again, burning tokens so you can... You're saying every Anthropic model is getting worse. If you go and look at Twitter right now and see how people are reacting to 4.8, see how they react. But they're comparing it to Codex. No, they're comparing it to Opus 4.6, man. Like, they're literally comparing it. they've and but this is the thing good example actually even if it's better it is still different which means you have an adjustment to it which means you are going to burn tokens but anyway they are burning more tokens chain of thought reasoning models burn more tokens so even if the model is cheaper on a per million token basis you're still burning way more tokens so you're burning more tokens and spending more money same thing with open source models this has been a consistent problem it's a problem with grok it's a problem with all of them and the thing is but If you're happy as a company with the model today, you can basically find a version of that model.
You can make a version of that model yourself. What do you mean to make a version? People can build their own models. There are open source models that people use that they sort of fine tune themselves. There are companies that build their in-house models. It's not like you're forced by Anthropic and OpenAI to shift off the model architecture that you like. The thing is, you're speaking in generalities here. Who has successfully done that? Microsoft just farted out six new models from Gooner himself, Mustafa Suleyman, who is apparently quite a horrible boss. I hear he goes nuts on Slack. But put it all like gossip aside.
Gossip is like gossip aside. Obviously, there are a lot of Chinese companies that have produced models. DeepSeek is running the Chinese wear them down process. They discounted permanently just to drag down American models. That's tactical. I love that. It's just funny to watch them do that because it's the kind of thing American AI labs have no idea how to do, which is political rat effing. I'm not going to swear today. But back to the core thing, which is it's kind of like the mileage thing. Okay, gas is cheaper, but you're driving an extra 50 miles, so it's not. It's still the cost of the journey is more expensive.
So every model is burning more because the more complex they get, the more things that the whole mixture of experts thing that was meant to bring cost down. It is not more expensive to do the same task. it is more it gets more expensive because people get more ambitious about what task they're going to do no it's actually that because everything i've read is the go on the clawed code sub i love going on these subreddits because you can get a real taste test for it they will find these things new models going in crazy loops the things that could they could do before magically don't work this is a common experience with large language models speak to these users you're looking yeah certainly it is true that when new models come out they have like all these different edge cases and their personalities change their sycophancy level changes so there is clearly a reaction where people are like i used it for this one thing it doesn't deliver on this anymore but like the companies are clearly have every incentive and are measuring their models to make them better over time like they score them i mean just pushing back there are they because are they incentivized or are they delivering i don't either because i don't think they're incentivized to i
actually don't think anthropic has any incentive to make the models more efficient same with open ai the markets are rewarding them for being big lossy messy companies and on top of that they have the various business idiots and various companies who will zillow i've been talking to people at zillow for a few weeks crazy ai psychosis over there burning over a million dollars a month in tokens but the thing is these companies are not incentivized to be more efficient and they're not incentivized to bring costs down because people anthropic believes that they have the mandate of heaven i believe that they're a deeply cynical organization and also they're burning more tokens like they obviously are doing it you can people will say oh we're just picking out a few examples are are any of the people Who are excited about this, actually talking about the real cost? Probably not.
I agree with you that there's, you know, public companies, all sorts of companies have an incentive to show that they're getting the most out of AI. And therefore, they have a reason to spend aggressively independent of what they're delivering. I think you underestimate that companies do find value in AI. Like, I feel like you... When? Well, literally my own company, you know, our ticketing infrastructure was vibe-coded. Like, we could have paid, you know, for HubSpot or whatever, some SaaS product, and you build your own version so that you can quickly change it. Quick question. Did you pay on a per-token basis?
No, I doubt it. You're like, it's just not. That's the thing. You are living in a dream, but who is the dreamer here? Like, this is the thing. you are experiencing it through the larger con which is yeah it burped out some open source software you were like it burped out a copy of an open source crm you have it you have it privately so there's not really i mean i would make sure it's checked by a real coder but you also did not pay for it you paid for the subsidized rate i'm guessing the 20 a month plan maybe the 100 a month i'm sure it's 100 but that's the thing though this is my point you didn't experience the reality of ai you were you're the victim of a con you have been convinced that something is a price that it is not and anthropic only moved companies to token-based billing along with open ai who do it much quieter about three months ago it was announced by the information in april but it's been happening longer and already they're screaming they're like i don't know about this because it's one thing to say wow i'm spending 200 bucks a head and they get rate limits and they can go nuts nuts bananas on this it's another when you have people spending fifteen hundred two thousand dollars a month uber just had to limit their engineers to fifteen hundred dollars a
month which makes me wonder how much were they burning before because that's the thing at two hundred bucks a month very different product to one that is a variable cost of hundred bucks a day two hundred bucks a day anthropic's own documentation says that they think that people spend an average of 13 bucks a day in tokens on Claude Code. That's not sustainable for the average person. And I think, ethically speaking, any journalist writing about AI should not use the subscriptions. I think they should only use tokens. No, no, why are you laughing? I'm not even being, I'm not being aggressive here. I'm saying, why aren't they doing that? Because to have a realistic perspective on the actual efficacy of these models, you have to experience the horrors of this thing just went on a little loop and burned five bucks yeah most consumers are not going to want consumers don't want an experience where the product could accidentally you know way overspend and they'll and enterprises are sophisticated i mean enterprises are not sophisticated not always but at least i mean in general i mean but the point i'm making is i think token-based billing is coming for everyone i think it is an inevitability the economics do not make
sense for subscriptions everyone does this funny fusion dance of oh inference is profitable no one has proof of that maybe maybe if they didn't pay any training there would be economics that may be lined up but in truth inference and training are one and the same at this point you have to keep
“Anthropic has never defined what they mean by run rate.”
Like, you've been negative in a period where everybody else has gotten more positive.
Oh, I definitely flubbed how quickly it would end. I will fully admit. Like, 2024, I was like, it landed three quarters. I was just wrong. So what? Like, over and over again, you're like, this is the peak. This is the peak, right? And then early in 2025, I very specifically was like, I'm not going to call the peak anymore. you just did call the peak again right didn't you do like it's a bubble the top is the ipos 100 i mean paul kudruski agrees with me he's excellent like you just had him on the show like the google equity raise google raising 84 billion dollars on the public markets like a goddamn middle low tier cap company come on man but the thing that is i agree with you that is a potential bubble sign i think though but not in the cynical way you're putting it sort of like google's like wow people really think we're worth a lot of money like let's get some cash no i mean it's their share they're allowed to get money you know i mean they're allowed to do it that's perfectly legal i'm just saying that they're doing it because the credit markets are drying up that's 100% what's happening well their equity is super inflated i guess we'd both say so they can get a good deal raising money at a high equity price sure but also the credit markets are running
“we're going to need like 800 billion dollars worth of debt”
Then why, here's the thing though, then why are we only really talking about code? Like that's, that's a very, like we really only talk about code and there's, it's because there's a ton of training data for code. There's a lot of people that can post train the models.
I mean, I do think like a key, there's clearly a receptivity among like consumers to the argument you're making. And my view on it is that AI requires like proactive work. You know, you have to go out, you have to use the models, you have to find things you want from them. Still, I mean, when we talked last time, you were really emphasizing that OpenAI was like the only one. And now the top three apps, I believe, are like ChachiBT, Claude and Gemini in some order. So it's not just one, like you've argued. in the past. So that is another thing. But the core issue is that there's a large audience of people that are like, I'm not, I don't, it's stupid. I used it a couple months ago. It like hallucinated. And they're not like investing like the energy and time to like get value out of the models. To the extent they experience it, they get it through Google and they underestimate the extent that they're getting LLM answers in the first place. So they're sort of using Google results, forgetting how the product used to work, how it is now. And sort of, it's pretty easy if you don't pick up the app use it and spend time on it to say nothing's happening and so people like this story of they don't derive any value but then i feel like i'm using a fairly like it's
not like i'm building you know like i have like my own server at home you know i'm using cloud in a pretty straightforward way and i find that i derive a lot of value from the subsidized thing but also were you what just this is not an insult were you describing embracing no no no i really want to be clear this is not sure doesn't sound very intelligent you are you're front loading this by saying well they haven't made the effort to make these things useful that doesn't sound like ai to me that sounds like a kind of scam it sounds it sounds like something that some that someone on these model companies would say because they know that they can't just give you something use it when i used the first iphone it was one of the first people to buy it state college pennsylvania Yeah, singular, wireless.
That thing immediately, we're like, holy crap. Like, this is the future. Using AI to this day, and I've played around with it. I've used it. I've really tried. I've thrown a few models in it. Messes things up all the time. Still does it. This is the same fight we had last time where, I mean, you sort of presented AI as if it had been sold to the world and we've been, like, conned into it. Yes. But the iPhone was significantly marketed. Like, it took the world a long time. Like, most people did not use the first iPhone. There were a million of them in the first year, man. Most of the... Yeah, because it was limited to a single carrier.
I mean, I'm sure ChatGPT has more users than the first iPhone did. Yes, but it was limited to a single carrier, singular wireless, and didn't come to Verizon, I think, for at least, I think it was six months to a year.
“we have had it shoved in google we've had it shoved in every app we use every single goddamn app summaries generations where we don't need them shoved”
from meta and oracle right now oracle right now i think like 40 of their renewals aren't happening because they fired the people and forgot to reassign them you have jobs that aren't getting
“all you need for an executive is to cobble something together looks close enough”
If these models were literally perfect, if they never made mistakes, if they were always good, we'd be talking about something else. What would it take to change your point of view? Like, what could come true? They would have to solve all hallucinations forever, which they are completely incapable of. But humans make tons of mistakes. Like, it is very inherent to reasoning, like, one of us is engaged in logical fallacies here.
Like, it is normal for human beings to think incorrectly. The mistakes a human being makes, they learn from. When a human being makes a mistake, even if they don't know they've made it, when they find out the error of their ways through trial and error, they can fix it. Some imbeciles don't. Some people don't, but they can learn. Models have no capacity for learning. They have none. The mistakes they make are mathematical certainties. they are not the mistakes a human being can make can be mitigated they can be learned from they can be trained into in a way that models cannot we do not learn just from having knowledge your bar is no hallucination like none no mistakes of any kind why should that this just seems like humans make mistakes like even the best mathematician this is meant to be artificial intelligence they've sold this is the most powerful thing ever that's going to replace 50 of white collar workers why do i have to lower my standards when they keep raising them This just seems like you don't want to have machines involved.
Like, it seems like a truly impossible bar. I use the computer all the time. Then what's the problem with AI? Why do I have to accept mediocrity? Like, that's my question. This just is, I'm so dug in that nothing could change my point of view. We're talking about artificial intelligence, man. And you're saying, can't it be a little stupid? You're saying, can it be perfect? You're saying the date is perfect, I will accept it. They're talking about replacing 50% of workers. But most of those workers, myself included, make many mistakes. Like the bar for perfect just seems like totally outlandish to me.
It should be best. Based on their promises, no, it doesn't. Based on what they've been talking about. If they had solved this from the beginning, it's like, hey. Who is they? OpenAI. These companies disagree. They dislike each other. They're competing. Yeah, and they're all lying about what they're all talking about some theoretical future. Demise, Sam Altman, Dario, they're all doing the same crock of crap where they're just saying this will do, this will do. when it does this if it like they're all talking in theoreticals if they have from the beginning solved this as a kind of experimental project that we need to be really careful with because it can make so many mistakes and they were like that's basically in 2023 that's how it was that's not there were people who didn't want to release man go back and read the stories i'm so sorry i mean there were people who are optimistic the employees themselves are true believers like there's a whole you know but it was it was called gpt3 like it wasn't branded in the most like Like in this amazing, I feel like you tell this story of like great marketing, they're bad marketers.
“they had actually prompted it, asked it what it would say, preferentially chosen the responses.”
It's been sold as intelligent. It is not. Like, it's sold on a lie. And it's now inflated markets. When we talked nine months ago, you were very negative. My view is that the models have only continued to improve. I think the funny dynamic is that your crowd continues to not see that anything is happening. Who is my crowd? I don't know. I don't know. Anonymous YouTube fans. Like, you're a very popular guy.
Right. Right. Yeah. I think you are selling something to a media consumer that they want to hear that nothing is happening. But you've been doing it year after year after year. I think what is strange is that optimism is framed as never, not saying yours here, The optimism, which I actually don't think most of the AI industry is doing, blind hope in technology and appreciation of capitalism is framed as a moral good, that all investment is progress, and skepticism and scrutiny is framed as other and different. Yeah, the fact that you say you're people, but you can't even designate the group makes me wonder who that actually refers to.
Regular folks? Who am I? I don't know. You know your audience better than me. use the term no no i'm like who are your i just see them online they're a bunch of anonymous accounts i talk to a ton of people in the tech industry i have a ton of tech works yeah janitors line line cooks yeah i have teachers ton of teachers ton of people in academia i talk to people in the industry all the time so yeah man i think that the thing i represent is an a person who loves technology watching the tech industry ruin it i just think our you know we hunger for what are we creating as a society what is our society moving towards in a world that is post religion for the most part is unsure of what armenia is supposed to be is looking for some sort of forward direction i don't think ai is perfect post religion what are you talking about i i think church church going is down but There are many spiritual people, tons of them.
What is your- Anthropic just meant the Pope, man. What are you talking about? Like, here's the thing. I'm just saying the critique of negativity is we need some sort of like, if not this, what are you proposing? Do you think that the AI industry is a hopeful thing? We're going to- Yes, yes. Dotting the world with data centers for a product, ruining local environments, spending more money than there's ever been spent on anything for a still theoretical outcome, for a still wholly theoretical outcome. Do you think that what we have today with large language models is worth a trillion dollars? God's honest truth.
Worth a trillion dollars. Because that's what it's cost. All of AI? Yes, with large language models specifically, because that's where the trillion dollars went. Yeah, I mean, we have multiple companies that are going to get valued, certainly. So strictly on an equity basis, you believe. Right, right, right. Yeah, that's how things are valued. What are you asking me? Like, are they going to be valued over the next five years for more than a trillion dollars? It seems very likely. OK, but that's not where the trillion dollars and the trillion dollars of CapEx, like the trillion dollar. It's more than a trillion when you include investments by venture capital.
You include the debt as well. Right, right, right. Obviously, the market is saying it's worth way more than a trillion. OK, I don't care about the market. I'm asking you. Yeah. You think that this is worth it. Say it stopped today. And this was where we did. This is theoretical. Say nothing else happened. would you say that this was worth it the trillion dollars that's being spent what we have today forget the air that it was good that it was money well spent is that what you're asking yeah yes thanks for sticking around with our second face off with ed zitron i i think he held too high high of a bar for perfection there at the end uh this is the newcomer podcast if you want more uh please check out the substack newcomer.co if you really want more i have another show cerebral Valley show where I chop it up on AI with my Cerebral Valley co-hosts. It's on YouTube and everywhere else you get your podcasts. Like, comment, subscribe. We're building this channel out. Tell us who you want to hear from next. Tell them to come on the pod. Thanks so much.
We'll see you next week.
