Ep 386: Was 2025 a Great or Terrible Year for AI? (w/ Ed Zitron)｜Deep Questions with Cal Newport

So much happened in the world of AI in 2025 that it can actually be hard to keep track of it all.

I mean, remember deep seek?

That was in 2025.

As was Dario Amadei, saying that we were going to lose half of white collar jobs to AI, as well as GPT-5's release.

The release of Sora AI looking like the best investment ever, followed by AI being described as a giant bubble that was going to bring down the economy, followed by that bubble being described as actually not being so bad.

This was also the year where NVIDIA CEO Jensen Wong took the stage in a conference wearing a jacket that well, I'll be honest looks like it came from the prop department from a Mad Max movie.

Jesse, let's put this on the screen here.

I mean, dude, you're a computer scientist.

You were in a racer jacket.

I love it.

I'm here for it.

What I'm trying to say is a lot happened in the world of AI in the year that just ended.

And the key question that I've been grappling with is did this year end up being a great year for AI or a terrible one?

I would believe either answer.

And so much happened it could be really hard to try to keep it all straight.

So here's what we're going to do today.

We're going to try to get an answer to that query.

To help me in these efforts, I've invited to join me Ed Zitron.

I think one of the big missed stories of AI in 2025 is Zitron himself, who hosts the Better Offline podcast and writes the Where's Your Eds at Substack.

He rose to become, I think, one of the more informed and important AI commentators out there.

The secret to Ed's success is pretty simple.

He just does his homework.

He actually talks to sources.

He talks to reporters.

He reads earning reports.

He gets leaked information.

He talks to people within these companies.

He puts together the pieces, old-fashioned shoe leather, reporting on what's actually happening with these businesses, as opposed to reporting on the stories these businesses are telling about what their technology may or may not do.

My honest opinion, or at least my humble opinion.

I think he's probably the most important AI commentator that you haven't yet heard about.

So Ed is going to join me, and what we've done is we've pulled the biggest AI stories of 2025, one per month for the entire year.

We're going to go through them in order and Ed is going to help us make sense of what was going on behind the scenes and what these stories actually mean for the AI industry.

Writ large.

We'll end up with a conclusion of just how good or bad this year actually was for AI technology.

But by the time that we are done with this episode, you will be more or less fully up to speed with where we are at this moment in the world of AI and what is likely to happen in the near future.

All right, so let's get in this episode.

As always, I'm Cal Newport, and this is Deep Questions.

Today's episode was 2025, a great year or a terrible year for AI?

And we'll get right into this.

After the music.

All right.

So, Ed, we got a lot to figure out.

I got to point out something first, though.

This is something I don't normally do.

But for those who are watching, I put on a jacket.

Nice.

To try to compensate for your English accent.

A jacket.

I think it's going to make me look a little bit more scholarly and erudite.

That's my that was my strategy.

I'm, I'm wearing a sweater that I've worn once and I'm like I guess I'm warm, but I look weird.

But it's fine.

That's, that's my bed.

Yeah.

But you sound, you know, but it sounds, I sound British and I can't hide that.

Yes.

And so that that gives you an advantage on me.

But I think my my blazer were kind of balanced it out.

But yeah, I think we're good.

I think we should be good.

You're wearing a, you're wearing a sweater in Las Vegas though.

So that is, that should take points away.

It gets cold here.

It gets cold here sometimes.

I don't believe I went once in July.

I'll never believe anything else.

Yeah.

Okay.

I can understand that.

All right.

So we're going to try to figure out what the hell happened in 2025, right?

It's you and I both were covering AI in that year.

It felt like all the things happened.

There was no quiet period in that year from the AI front.

And so what I wanted to do is go through month by month and hit some of the big headlines and And you and I will try to figure out what was that

Was that good news or bad news for AI?

What actually happened?

So it's going to be like a trip down a sort of frustrating memory lane.

All right.

Let's start in January.

I actually forgot that this was in 2025.

I thought it was earlier.

Man, it was a long year.

All right.

In January, we get Deep Seek.

Here is deep seek the Chinese AI app that has the world talking.

Let me read the first sentence of a BBC article from that period.

Deep seek, a Chinese artificial intelligence startup, made headlines worldwide after it topped ad download charts and caused

This was like a huge deal that no one talks about anymore.

Explain to my listeners, what the hell is DeepSeek?

So Deep Seek was a really interesting one.

I remember I was on a plane.

I just started to move back to New York and such.

I spent a lot of time there.

And I remember reading about this thing.

And what it was, was that it was a model that was trained for less money than other American models.

So American models that cost like $50, $100 million or more to train.

DeepSeek apparently cost $5.3 million, I think, to train.

It's really weird because it spooked the entire market.

Everyone freaked out.

And I remember thinking this is an obtuse story to freak people out like it was just like even trying to explain it, because I did, like a lot of media at the time, I was explaining it to people.

I was shocked that people even had any interest in model training.

But the big thing that spooked people was it was kind of the thing that shone a spotlight on the Nvidia problem, which is that Nvidia is like the only company really making money in this era or, and I think the People started to realize, oh crap, our entire stock market is based on that.

And it also made it clear that all the American model companies don't really give a crap about any kind of efficiency or anything.

And the reaction to it was great.

Sam Altman suggested we ban it.

That was my favorite bit.

They were like, ah, yeah.

The sneaky Chinese are going to – it's because they might be able to see inside things.

We can't possibly trust them.

What was really good as well was part of – I literally was just reading about this yesterday.

Part of what was funny about it was part of OpenAI's complaint was, yeah, they might do IP theft.

It's like, no, we only let American large language models do that.

We couldn't possibly have the Chinese take away our plagiarism machines.

No.

We are the world leaders in plagiarism.

Yeah, there we go.

Exactly.

We can't have the Chinese steal our things.

That's our job.

But what was also interesting was they were like, should we sue them?

Because there's a process called distillation, where you basically take another model's outputs and you use them to train another model.

That's a very – truncated version and it was they used chat gpt outputs to train deep seek and that made people pissy the other thing was was it was a reasoning model the r1 model and open ai had at the time only been out for a few months with its reasoning model oh one yeah that was a december 2024 release if i remember i think it was september or september okay It was September because the run-up to that was this whole thing.

People were like, oh, it's called QSTAR.

It's called Strawberry.

It's going to change everything.

It didn't change anything.

Actually, it did change something.

Reasoning models gave them more excuses to burn AI compute.

But yeah, this whole thing was great for me.

I did a bunch of media hits about it.

But it was peculiar because it was like quite a nuanced story.

And then you saw all of this xenophobic stuff being like, oh, well, the Chinese, they're lying.

They're lying.

They put out a paper about this.

They showed people how it was done, because they had to find a cheaper way to train, because they only had I think, I forget, maybe they had H800 chips.

They had quite old GPUs.

And the thing is it wouldn't talk about Tiananmen Square and people were like, oh look, this is proof that it's bad.

It's like, yeah, it is bad.

It does that.

But are you shocked that something that came out of China had censorship?

Yeah, but what's amazing about the story though is it went away.

Like I mean all the points you're talking about are fair points.

And like the biggest destabilizing point for the industry was this idea of –

You don't need the very largest data centers.

You don't need, like the custom AC Microsoft 40000 GPU data centers to build really useful language model based AI.

But those big companies are dependent on the idea that only they can do it.

And so it was almost like people didn't want that to be true.

So we just forgot about it.

Yeah, we memory hold this bad because at the time it was like surely, because I got asked quite a lot like open AI, surely they're going to make a cheaper model now?

They did not.

Anthropic, right?

Because if they said that that was possible, if their idea was we're now going to spend 5 million versus 100 million on this, they would then have very little justification for raising so much money.

But what about the nano models?

Didn't OpenAI do... The thing is... They have some cheaper to use models.

Cheaper to use, and that's the thing.

People love to use this as proof that the cost of inference is coming down, so inference being how an output is done.

And they're like, well, the models are cheaper.

It's like, yeah, if you sell something cheaper...

It's now cheaper for someone to buy.

There's no proof that that's actually cheaper to run.

And indeed, it would have been so easy for them to just say, actually, this is a cheaper model.

It costs this much.

The fact they didn't means that it's still unprofitable, which is crazy.

But the thing is, even deep seeks models aren't like no one proved that they're profitable to run.

And so, but I think everyone memory hold it because they, I don't know.

I think the media just was willing to.

That there was just a narrative that they could just get rid of?

The Chinese angle must have made a difference, because to me, an even bigger story, which I didn't really know until talking to a source, at the end of 2025.

The biggest story that's not being talked about is if you look at coding agents and you look at Cursor in particular.

To me, the big economic story was the fact that Cursor at some point quietly just said we're going to train our own model.

We don't need a frontier model.

We'll start with open source weights and train them themselves.

Now, whether or not that model is profitable for them, I think that opens the door.

Cursor has been working on their own models forever.

Yeah.

Ever and ever.

They raised $2.3 billion, so maybe the plan is for them to train their own one.

But there was a story back in September that Tom Dutton, over at Newcomer, apparently someone said that Cursor is sending 100 of their revenue to Anthropic.

So they're one of Anthropic's largest customers.

I see.

It's really interesting though, that they're trying and they've gone very hard on Composer and things like that.

So maybe they are trying that for real now.

I mean, they're capitalized.

But the question is, to what end?

Is it more profitable?

Because if it ends up just being unprofitable and they don't pay Anthropic, that would also be very funny.

No, I mean, I think the future has to be And we can get into this more later if we need to.

I think the future is really going to be small models, models that fit on machine, right?

Yeah.

The only thing that's profitable is I'm not spending any dollars to run inference, because this 2 billion parameter model, which is only really trained to do the very narrow thing I need it to do, which is like understand your spreadsheet program and help you do it can run on your phone.

It can run on your chip.

You're paying for the electricity.

Like that's got to be the only way that this is profitable.

But those companies – cannot be large companies.

Now you have 10,000 smaller companies instead of OpenAI as the new Microsoft.

Yeah, I also think the small language model stuff, because small language model is just a large language model with less parameters.

And while it is possible to do Edge, I just wonder how useful those edge like you can run on device.

But how long does it take to run a device?

Nvidia put out something like a DGX Spark box thing that can run large language models.

The question is, at the end of all this, is it worth it?

Like is spending.

Maybe it is worth three grand, 12 grand whatever, for one of these machines, but the companies building these models are not optimizing for that.

They're not building models with that in mind.

Nvidia has built that box so they have something else to sell.

It's been in the works for a while but it's like we don't no one had.

I i can't find anyone who has run client side, who has used it like in that manner.

Who's like oh, i do all my coding but i do an on-device one.

I'm sure they exist, but the fact there isn't A growing community of that suggests that that might not be viable either.

But I think any future large language models will have to be on devices.

Just the question is, does that happen at any kind of scale?

Yeah.

All right.

So other thing in January was agents.

I tracked this down recently.

This is when the chief product officer of OpenAI said.

The quote that then got translated by Axios into 2025 is the year of AI agents.

Axios does this, by the way.

I don't know if you've seen this.

As a reporter, it's really a pain.

They invent a quote.

They paraphrase what someone said into a better form.

And then we'll say like this headline is 2025 is the year of agents.

Open AI CPO says you would assume that means that the open AI CPO said 2025 is the year of AI agents.

He did not.

Now he talked about 20.

He said things that was less quotable.

They did the same thing with the bloodbath and Dario Amadei.

He never actually said it was going to be a bloodbath, but they had a headline that said it this year is going to be a bloodbath.

Dario Amadei says.

So anyways, I watched – No, Axios had a quote as well, where it was like this is proof that AI is taking jobs.

And you read the study and it's one line saying, yeah, we kind of see some effect.

It's very frustrating because it's –

It helps.

It's just marketing.

It's not helping... Sorry, I'm just going on an endless rant.

Yeah, I know.

It does.

I actually told I was talking to Speaking of AI skeptics.

I was talking to Gary Marcus not long ago and he happened to be on his way to do something at Axios.

And I was like you got to tell him to stop doing the headlines because I keep getting dinged by fact checkers afterwards.

Like, we cannot find evidence of this.

All right, but anyways, early in 2025, this is when we got...

Agent excitement and I think this kicked it off.

And then, around the same time is when Sam Altman wrote a blog post that said they're going to join.

Yeah, Reflections which was probably going to – agents will probably join the workforce this year and material impact their output.

So why would – why did they start talking about AI agents sort of out of nowhere in early 2025?

Well, because they needed something to keep selling this crap.

And they launched Operator within January, I think.

Maybe that was February.

And it didn't work.

And this is a failure across the board with the media.

They all went, yeah, Operator.

It can take actions in a... No, it can't.

Wait, okay.

It can take actions.

In the same way that if I just throw a brick, that is me, I don't know playing a game, if you consider it.

Like, you can take abstractions from abstractions all you want.

But yeah, it's just marketing.

It's marketing and mythology.

Because agents, much like the term AI, is a marketing term and that's an Emily Bender, Alex Hanna quote There is this thing of agents.

Conjure up this image in your head of like oh, an agent that goes out and does something for you.

Now, agents going back to 2023 literally just meant chatbot.

Like that was what it originally meant.

But agents within this era were meant to be digital labor.

And I take that from Mark Benioff and Salesforce and agent force, whether like oh, it's digital labor.

Sam Altman's comment was and he always says may or probably, but it's like agents may join the workforce.

Egregious lie.

Yeah.

Egregious lie.

There was no proof at that time that it was even possible to do it.

And guess what?

Where we are today, it's not possible either.

We do have a coda, because at the end of 2025 we'll get to a new story where spoiler alert OpenAI takes all their resources away from agents because it wasn't working.

But they were excited about it.

I found the Benioff – some Benioff quotes, by the way.

Ooh.

Speaking of egregious, he didn't say probably joined the workforce like Sam did.

He said it is going to.

Not only are they going to revolutionize the workforce, they're going to create two to five trillion dollars in economic activity, two to five trillion dollars from agents.

Wow.

Yeah.

So almost there.

All right.

So agents become a big thing.

What were they?

– to me, I always think, like what is the?

– there's a different flavor that AI companies are typically pushing.

Like they have to have a flavor of excitement because of the investment train.

Yeah.

Where were we?

I don't really remember.

Coming out of 2024, that was more what, like an AGI superintelligence?

They were pushing a different message back then, because I remember thinking the shift towards agents and therefore a shift towards we will just be in the workplace helping your bottom line.

That felt like at the time a shift towards a more pragmatic vision.

I don't know if I agree.

I think at that time, That was when you started hearing people say coding agents.

And coding agents was their favorite one.

I'm sure you have some stories coming up where coding agents, oh, you can get them.

And later in the year, Anthropic spread some bullshit around this as well.

Where it was?

They go out and do things autonomously for you.

That was the whole thing that was being pushed.

I know because I read every agent's story because I found it so repulsive.

The idea was that they...

Clearly in the last year, in 2024, they kind of squeezed all they could have out of chatbots.

Like no one was like finding you.

They couldn't do more things.

They weren't really sure what to do.

So they went, agents are coming.

And what will they do?

What do you want them to do?

Because they might, might do that.

They can't, but they might.

What if they did?

Wouldn't that be good?

Please pay me.

Well, wait, but so...

I think by the time this interview airs, I have a piece out on agents.

I couldn't talk to anyone in the industry, but I talked to someone industry adjacent, someone who made the main benchmark you use to evaluate coding agents.

So here's the story.

SWE bench?

Terminal bench.

So I was going deep on what are these agents, and here's what I learned.

Okay, so there's two ways AI is helping coders.

And they get mixed up.

So there's sort of the tab complete way which is goes back early codex 2021, even pre chat GPT, which is it's all based on one shot.

Queries to a language model.

So that is like I'm I'm writing some code right now and I wanted to like finish.

I'm trying to like write a function, to do something, just like finish this thing immediately that I'm writing.

And that is powered by like you make one query to a language model.

So behind the scenes, it's here's the code that we've written so far.

How do you think I should finish what I'm writing?

So like that's right in the sweet spot of LLMs where you're trying to complete or autocomplete.

Because that's basically how they work.

That's basically how they work.

They are predicting the next token.

And programming languages are highly structured, so they're very predictable.

But that's been around pre-ChatGPT, but those work pretty well.

Those are now integrated into most developing environments.

My students all call it tab-complete.

Yeah, Cursor calls it that as well.

I don't know if you'd say they work really well.

Carl Brown from the Internet of Bugs describes it as it makes the easy things easier and the hard things harder.

Fair enough.

It makes intro computer science problem sets very well.

Yeah, which still has utility.

Yeah.

Which, like, that's something.

When I talk to real programmers like, the main thing they say is useful is they don't have to look up interfaces for functional libraries.

So you're like, okay, I can just tab complete a function call here.

Oh, this is all the parameters.

Okay.

Otherwise, I would have had the Google stack overload and… whatever.

But the thing is with.

That though, is that's useful, but if it's querying libraries, could it not get that wrong?

I mean, you have to still have to check its work, but maybe that is quicker.

Yeah, it is.

Yeah.

So the caveat emptor, then you had agents emerge, right?

Which we can do vibe coding, right?

So, so like agents was more like, I want you to, create a prototype of a dashboard.

I want you to add this to a personal website.

And it does multiple steps.

So it's making multiple queries to an LLM.

So it'll ask the LLM for like... It'll explain what's going on.

Here's the tools available.

What's the plan?

And then it'll go step by step.

Okay, here's the output of that last step.

What do you want me to do next?

So it's a program that's executing.

That's what makes it an agent is that it's executing multiple steps, each of which is based on its own LLM query.

What I learned from the people in the field is...

In one sense, this worked really well in that it can vibe code.

If you say, like I want you to produce, like a prototype of whatever, it could actually get through multiple steps and produce a prototype of whatever.

And it turns out it was like pretty good at this, because all of the stuff you have to do to create a computer program you can do is text-based commands in a terminal.

So the tools that the LLM had to work with were text-based commands.

All of these coding agents operate in a text-based terminal environment.

So on the one hand, it could do this vibe coding stuff pretty well, in the sense of like it could actually produce a program that more or less did what you said.

I've got to push back, Cal.

But wait, I'm going to let you push back in a second because I'm going to push back on myself first.

But on the other hand, these things that were being vibe coded have no economic utility.

So, like in the abstract right, if you're like, can we have a multi-step program, multi-step execution, call an LM a bunch of times to do a bunch of stuff on its behalf and, on the other end, end up with an updated website or a web-based dashboard that more or less does what you say?

It can do that, but the economic utility of that is very limited.

Can it do that?

Because that's the thing.

Vibe coding, I believe, is one of the greatest frauds of all time.

Because if you go on like Replit or Lovable or what have you's subreddits, you can see people struggling with basic things.

While it may get one thing right once, it's not going to get it done every time.

And on top of that, if vibe coding is I am a non-technical person building software, it is a lie.

It's a fraudulent thing.

Because you cannot just vibe code without knowing stuff.

You have to know how to read code because something will break.

It will not make stable or reliable or secure software on top of that.

Well, but i have seen it.

It does create if you need.

Um, there's like a real use case, right for someone i know if it really is just like.

I want a web-based interface for the silent auction for my kid's school and i don't know how to program like it can do that.

That that it can do Can maybe do that because of the large language model component.

It's like, will this work this time?

Oh yeah.

It might take you, but you can.

You can work with it and finally get that thing to work without really having to know.

But you do kind of still have to learn a lot of this stuff.

Fair enough.

Because the people I've talked to doing this without coding background, they now know a lot about setting up accounts on these different like AWS clouds and different coding environments.

And you do have to learn.

Yeah.

Which kind of, At that point, that's not an agent.

At that point, that's just generative code that may or may not work.

And then you need to do all the infrastructural things.

It gets back to the thing of what is a software engineer.

And it's like a software engineer is not just writing code.

I just think that even now and I'm not saying this is a criticism necessarily even then the marketing is so powerful that even we fell into that trope of like well, it can do this.

Can it?

Can it though?

Can it do it every time?

How much, how replicable is this process?

How realistic is this?

I think you're right though.

It's like, it has some use and I've heard people say this as well with like MVPs.

Like you need to fudge something together for an investor and you need to do it quick and dirty.

It doesn't have to be perfect, but it has to kind of resemble the form and function.

I've heard people do that and it's great, that's yeah, but that that's what i've heard.

And i've heard dashboards people like dashboards.

So we want internally um, so yeah okay, we're in january man, all right, we gotta roll, all right, we're gonna take a quick break here to hear from some of the sponsors that make this show possible.

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All right, let's get back to my conversation with Ed.

All right, February.

Oh, I know.

So it's not going well for AI so far.

I mean, at least... No, great.

Well, let's get to February.

February, I think, was the quietest month of 2025.

There were two models released that I don't remember at all.

And I want to see if you remember these at all, too.

The two models that were released in February 2025, Gemini 2.0, and this one I really forgot...

OpenAI's GPT-4-5, which I ended up learning about later.

This was them – tell me if I have this right.

This was, going back to the scaling article I wrote, I talked to you for that.

This was the result of the project they started right after GPT-4, where they said we're going to make the model 10 times larger, we're going to make our data center 10 times larger, and the result is going to be HAL 9000.

And it wasn't.

And it was the, like, oh, crap moment, where they're like, oh.

Oh, it was.

And this is what they eventually released out of that, I think, was 4.5.

Yes.

And I absolutely knew that this was coming.

So i have the tweet up and i want to read just a little bit of this, because it really tells a beautiful story.

But this is your.

What are you reading?

Your tweet from february sam, from february 27th.

All right okay, gpt45 is ready.

Good news it's the first model that feels like talking to a thoughtful person to me.

I've had several moments where i've sat back in my chair and been astonished at getting actually good advice from an ai.

And Bad news

It's a giant, expensive model.

We really wanted to launch it to Plus and Pro at the same time, but we've been growing a lot and are out of GPUs.

We will be adding tens of thousands of GPUs next week and roll it out to the Plus tier then.

Hundreds of thousands of GPUs coming soon.

I'm pretty sure y'all will use every one we can rack up.

This isn't how we want to operate, but it's hard to perfectly, perfectly even predict growth surges that lead to GPU shortages.

A heads up, this isn't a reasoning model and won't crush benchmarks.

It's a different kind of intelligence, and there's a magic to it I haven't felt before.

Really excited for people to try it.

Yeah, this was when you're right.

This is when people started going hmm.

Hmm, I don't know about clammy Sam Altman hmm,

You're starting to get a little bit worried.

Yeah, never want to hear magic.

It's magic, though.

Magic.

It's so good that I can't tell you why it's good.

It's magic.

It's just going to be magic.

Yeah.

It's so cool.

People giving billions of dollars.

So from what I understand was...

So they – basically reporting for an article we'll get to later.

They knew about a year before this that they were in trouble.

Going into summer 2024 for sure they – this Project Orion, their sort of next large model training after GPT-4, was not generating the same leaps in performance that they had seen before.

And so this became a problem.

Right.

This is why my understanding is, in the fall of 2024 – tell me if I have this more or less right – they began switching to talking about things like O-1 reasoning models, models that were tuned.

So they were on – not even on this 4-5 base, but like I think those original ones were actually tuned off of the GPT-4 base, right.

So they were taking – I think so, but Orion was such a mess.

There was a Wall Street Journal story towards the end of 2024 –

Where it was like it's costing a bunch of money and it keeps – it isn't getting better.

Yeah.

And I think that they were just – And that was pure scaling.

That was their last pure scaling play is they did the exact same thing they had done for GPT-4.

And they're like, let's just do that bigger.

So that's expensive because that's a big model, and it just wasn't getting much better.

And that's why my understanding is they switched towards these tuning things because now they're like well, what we'll do is we'll tune an existing model to do well on different benchmarks or give them specific features and talk about those particular features.

So reasoning is what really matters, not this model is just clearly much more better at everything.

That was kind of the GPT-4 experience for a lot of people.

What I think it is, is it's test time compute.

It's just reasoning as in like.

Instead of I ask it to write a fanfic about Scooby-Doo doing Tiananmen Square, instead of it just burping that out, it breaks it down into steps of what is Scooby-Doo, what is Tiananmen Square, and so on and so forth.

And that is the only way they started.

They were seeing reliable benchmark improvements.

Wait, so walk us through that.

So you would.

You would.

You would query the llm first to be like kind of break down the user's prompt, break it down into multiple things, and then they would make multiple queries to an llm on different parts of this and put it all together at the end.

It's a little simpler.

It's usually with an llm.

Before reasoning it, you would ask it.

This is very simple.

You would ask it a thing.

It would spit out an output instead.

Here, when it spits out the first outputs, it's not sending them to the user.

It's actually taking a query and saying what is the user asking?

Here are the steps.

And this is all output tokens, so it's expensive.

But it says, okay, these are the things that I think the user wants to do.

Time to generate something for each bit to make sure that I'm doing it right.

And then the output happens.

This allowed for improvements on benchmarks, and it had good returns on coding in particular.

It also chewed up way more compute.

And this helped everyone because the benefits of just training models by shoving a bunch of training data into them, we hit the diminishing returns at the end of 2024 as well.

On top of that, there's the post-training aspect of basically correcting correcting behavior saying this is a good output this is a bad output yeah that's also where they saw it and actually i realize i'm getting ahead of myself a gtc in march so the next month we're jumping ahead to march now so what tell us about this this will be our so in gtc in march two things i don't know if you've got all the nvidia stories because there's some weird stuff but jensen huang on the big screen showed like the pre-training so the shoving the data in we're past that era we're into post-training and inference i think we on the episode and an episode where we use some audio from you as well i showed i think exactly that part of his speech so was that that was in march of 2025 that was a big conference right yeah and that's where he was just as an aside because we talked about this in the intro of the show today Why does this computer scientist in his, like, 50s who makes graphic chips with glasses insist on wearing jackets that seem like they came out of the prop department for Mad Max Fury Road?

What is going on there?

Hey, I will defend.

I had the menswear guy on my show to talk about Jensen Huang's jackets.

They're sick.

Yeah.

They've got zippers everywhere.

I mean... Yeah, he really needs to stop doing the racer jackets, though.

Those don't look right.

But...

The GTC jacket was cooler because it looked like a psychedelic alligator skin.

But like he often is wearing like race car jackets, like motorcycle racer jackets.

Yeah, racer ones with all the zips.

I love leather jackets.

I can't pull off the races though.

But nevertheless, he also during that put up a big picture that said, we've sold, we've heavily hit.

He was like, we've done this many hoppers that we've shipped.

And now we've done 36 million Blackwells and ended up in an analyst QA having to correct himself to say oh, I didn't say shipped, I meant ordered.

And also it wasn't 36 or 32 million, it was actually half that, because each GPU has two GPUs in it, because he counts by the die.

It was when you started to see NVIDIA start doing their kind of riddles where it's like oh, we didn't ship, we ordered, we sold and they've been ordered from four of the last hype the largest hyperscalers.

And it was interesting because it was the last, i think, that march one actually brought nvidia kind of back to life a bit because, looking at the stock in the time they were kind of trundling and trembling and then they fell down towards the end of april.

So this was an attempt to restart the hype cycle.

But what was what's interesting as well was all he did was basically say yeah, we're going to have even bigger, more huger gpus and everyone will buy them.

We've sold so many and we love selling them and they're so good and they're so expensive and they are well and they were selling a lot.

So like, from their perspective, they were.

But he loves.

But the big other thing of that speech if it's the one i'm thinking about right is that that's where he explained for the media is where i took in the analyst Why, like the Wall Street Journal's coverage from the year before of, like the information's coverage of Orion struggling or this or that, wasn't a problem.

And he made it very clear.

He was like, look, we were in a scaling era and that made us this good.

And now we're in the post scaling era where we do tuning.

That also requires a lot of GPUs and that's going to keep us going.

So he just sort of. explained it in a way that I don't think it had been so clear before.

And then we were sort of off to the, the media was back like, okay, we're good.

We're on track for things continuing to get.

We don't know what any of this words mean, but the graph kept going up even as you pointed towards the post-scaling age.

So that was, I think, like the first real explanation of like something – OpenAI wasn't really talking about it yet.

It was a subtle shift as well, because one of the great myths of the AI bubble is that all of those GPUs were for training.

Very convenient to say that because if it's for training, well, we need them.

That's the only way the models get bigger.

Right.

I realize this is a few months in the future, but there was an MIT tech review piece in May of last year where it said the 80 to 90 of compute is actually inference.

So the truth is all those GPUs aren't building better models.

It's just running the bloody things.

And I think that this GTC with Jensen Huang was an attempt to bridge that age, to say actually the returns, the bigger benchmark scores that we love to see, they're going to come from actually renting more GPUs just to run the models.

But we can make the models better by using more compute.

Please buy GPUs.

As opposed to saying all of the compute that we're going to use is front loaded to build the models.

No, we need all this compute and you need to buy these GPUs, even because these models, to make them smart, need the compute to stay smart.

I see.

So this was bad news for the AI companies and good news for NVIDIA.

This is why they were pushing – Well, it was for everyone.

But the AI companies don't like this because they're saying this is going to make it more expensive to deploy and run these products.

That's going to hurt our profitability.

NVIDIA was saying to shareholders essentially, this is actually better for us as the chip sellers, because you need all of our chips just to use the product.

It's not like, oh, you need, you know, once we train it, then it's going to be cheap to deploy.

And maybe at some point.

Hey, now we're shifting to a world where just running the product requires a ton of chips.

And so, like, we're great.

The market, if anything, is bigger for us.

The thing is, if you look around the startups, though, they love this.

They love saying test time compute.

They love it.

They love saying test time compute.

I'm sorry, but they really do.

They loved it because it was a way of saying well, we need, because think about it from a startup's perspective.

Startup

If they say I need a bunch of money for training, that's a one-off operation or maybe a couple of times a year.

I need a bunch of money for compute.

I'm going to need a bunch of money.

Cursor, and this happens, I realized later in 2025, ended up raising like $3 billion that year.

They didn't raise that for trading.

They raised that to keep running their bloody operations.

OpenAI building out all these data centers, building them out.

They were doing that because the inference cost of running these models, the inference scale, to actually provide a service at any kind of scale, required all the GPUs.

So it was really just kind of a cartel operation type thing, where everyone and propaganda as well that yeah, we actually need all these GPUs just because these services are so powerful, when the real word is lossy.

These services are just inefficient crap piles they're.

They're slovenly.

It's like the, the very almost the opposite of what deep seek was about, though deep seek, i think.

I think deep seek required less gpus for inference as well, but it's like in the face of that deep seek story, it's almost like the american ai industry came up with a reason why deep seek was both wrong and And actually nothing to think about.

Stop thinking about it.

Stop thinking about deep-seek.

And they did.

By the end of April, people had forgotten about it.

It's the F-150 strategy.

Asia starts producing, like, cheap, reliable cars.

And instead of Detroit saying like well, we'll also have to now create like cheap, reliable – that's what people want.

They're like no, we're going to convince half the country.

They have to spend 80000 on a completely souped-up pickup truck that's capable of pulling a – Raptor baby.

Raptor.

We need a Raptor.

All right.

So then we jump to April.

You got this shift.

Maybe you know where this came from.

This seemed like a shift out of nowhere.

So now we have like, leading up to these, this talk of it's like very business focused, it's going to be agents, it's going to be like the age of uh, test time computing is going to be the future of this or that.

Then april we get um, ai 2027 and suddenly everyone for a while is back to talking about ai, doom and super intelligence.

So for people who don't know, ai 2027 was a Like a fan fiction.

I don't know.

It was a story that had animated graphs.

And here, let me read their description.

We predict that the impact of superhuman AI over the next decade will be enormous, exceeding that of the Industrial Revolution.

We wrote a scenario that represents our best guess about what it might look like.

It's informed by trend extrapolations.

That's the whole thing, by the way.

War games, expert feedback, experience, open AI, and previous forecasting successes.

This scared the hell out of a lot of people, Ed.

I know.

I just spent yesterday reading this thing and pulling it apart, so I know all about it.

But the key is, 2027, they had a non-trivial percentage of the extinction of humankind.

I think that's the headline thing from this, right?

In two years, that could be the end of humankind due to AI extermination.

So I want to be clear of who these people are.

Daniel Coccolatuljo, I think his name, worked at OpenAI for two years on the governance team.

He was previously a PhD philosophy student at UNC Chapel Hill.

On top of this, Daniel quit, and he quit in the middle of June 2024, claiming that OpenAI was secretive and didn't care about superintelligence.

Now you'd think if you And they was named as a whistleblower by Kevin Roos at the New York Times.

You know what whistleblowers tend to do?

They tend to blow a whistle.

Daniel didn't.

Daniel didn't actually say anything.

Daniel had nothing to share, other than he wrote a scenario in 2021 that was sort of accurate about what the future might be.

And AI 2027 is him...

Flippin' Star Codex guy who was a psychiatrist, who named Nick Land, a guy with a theory of hyper-racism as one of his favorite writers.

Like, the people that wrote this are not scientists.

They don't really know anything about anything.

The whole thing is written.

It's like thousands of terribly written words.

Lots of scary numbers.

But when you read it, it hinges on one idea.

Just one.

That in 2025...

OpenAI – sorry, OpenBrain – OpenBrain – That's their fictional company in the scenario.

Yes, which could be anyone. open brain invents the thing called agent one, which can do AI research.

That is the entire hinging of the piece.

It can do a, do they define what that means?

No, they never define it.

So, just just to be clear, this thing, this thing that was written to scare people to grift to help, was the AI safety research nonprofit that's connected to the effective altruists.

Anywho, the whole thing hinges on this idea that they invented an AI that that could research how to build an AI they wanted.

That's the entire game.

They wrap it in the trappings of finance and technical sounding things.

And there is a bit in it where it talks about new release functions and then cites a meta paper.

When you go and read the meta paper, it does not cite anything of the sort.

The thing they quote is unrelated.

New release, whatever it was, it's an effective altruist thing.

It's from LessWrong.

Or it's...

Sorry, this thing really deeply pissed me off because I had people calling me.

I had people, friends of mine, people I love and respect who were terrified by this.

Me too.

And that was the intention.

Sorry, I'm freshly pissed about this.

But you got – because I read it at the time as well and I came to the same conclusion as you.

It was like, is anyone picking up?

This whole thing hinges on – they never addressed the question of how do you build a super intelligent AI?

Like what's it going to look like?

Like what's the architecture?

How does this work?

They just said –

We'll build an AI that can build a better AI, and that'll build a better AI and a better AI, and then they'll just – they'll figure it out.

The AIs will figure it out.

But, as I keep emphasizing, we do not know how to build a language model that can produce a software for AI that's better than what any human can produce.

That's not how language models work.

They don't have novel ideas.

They can produce the type of code they've been trained on.

So unless you could train the AI and tune it with lots of examples of better AI, it can't build better AI right?

It can't leap beyond what it's trained on.

And no one is even close to this.

We talked about this earlier.

We're tab completing function calls with AI right now, so we don't have to look things up and vibe coding buggy dashboards.

It's unclear how did you get from that to brand new models of human intelligence that the collective AI community and decades of work couldn't figure out on their own?

I'm not quite sure where that leap happened.

So I was frustrated by this one.

I was frustrated by it as well, because it all came down to the same – it's all the FX argument, right?

So like the way I talk about – I rant about this on my show a lot Ed, but the way I talk about it is –.

What happened was is that the existential risk community came out of effective altruism in the 2000 teens, which was a community that looked at – we want to look at existential risks that might be unlikely but have really high impacts if they happen, like asteroid hits, pandemics and superintelligent AI, because we're doing our rationalist –.

And we're like the expected value of spending money now on rare things with cataclysmic outcomes is positive.

That's basically what's the – this was Nick Bostrom's existential risk center at Oxford was we probably won't get hit by an asteroid.

But because it would eliminate all of humanity.

It's actually a good investment to invest now in networks like look at asteroids.

And so one of the hypothetical risks they looked at was superintelligence.

Post-ChatGPT, they went through this weird sort of shift in their brain.

Where they went from this was this hypothetical risk we were looking at, along with asteroids and pandemics, to what if it was actually happening now.

Well, if it actually was happening, we're superheroes.

We're the ones who like point – and there was this shift that happened in that rationalist, effective altruism community, with the people working on existential risk, where they shifted from hypothetical to.

We're just going to convince ourselves it is happening, because that makes us the most important people on earth.

You're being – you're correct, but I think you're even being kinder.

I think they were waiting for a moment to grift.

I think they were sitting there waiting, being like what's a thing we can grasp onto so that we can start draining cash.

So we can get a bunch of attention.

Look at what happened with AI 2027.

I'm sorry.

I think Daniel's a true believer.

I think he's a cynical grifter.

Interesting.

Look at what happened when he left OpenAI.

He made this big song and dance with this petition.

Back with Jeff Hinton, who I have some other feelings about, with all of these smart people.

Oh, OpenAI is doing such bad things.

What are they?

I couldn't possibly say.

I had you, he made all of this noise and the media ate it up.

All this whistleblower, this brave guy who came forward.

What did he do?

Nothing.

He didn't share a goddamn thing.

He was so concerned, but couldn't say what about it's kind of sounds like AI 2027.

Oh, I'm so concerned.

This will happen.

What will happen?

I don't know.

The agent – China will steal agent two.

The China agent, agent China, they're scary, right?

Be scared.

My nonprofit is this, by the way.

The specific thing he was – recursive self-improvement.

That was the thing he was talking about was he incorrectly was saying –

We're almost at the point right now, within open AI, where the AI is going to be doing the programming for us.

And then we're going to have this takeoff idea that goes back to the 1960s, this recursive self-improvement idea.

I have another word for incorrect.

Yeah.

Lie.

Yeah.

I think he is a grifter.

I think this whole thing was a grift.

I think it's connected to the Star Codex guy.

I think it's connected to the effective altruists.

You'll notice that those assholes popped up with FTX too.

They pop up with everything.

They've been looking.

I don't know.

I just – I'm extremely, extremely – cynical, i don't know.

I just feel correct on this, because these people, if they, because here's the thing, here's my entire feeling about this, if they actually wanted to talk about scary bad things that are happening, i don't know, talk about the kenyans who are training these models for what like two dollars, yeah.

How about you go and talk about all the theft that's happening?

How about you go and talk about the gas turbines that were being spread everywhere?

How about we talk about the environmental issues?

How about we talk about the thing happening today?

It's the same problem with Jeff Hinton.

These people want to talk about, oh, what if the computer does this?

Which is fine.

We should have those conversations.

But when you were talking about AI safety, why don't you talk about now?

Because if you talked about now, you'd have to do something.

You would actually have to take action, take a position, make enemies.

You would actually have to do something that mattered.

Instead, they do this cynical grift where it's always whatever you're scared of it's a couple years away and we've got to do something now which involves me doing a speech, which involves my speaker fee, which involves my non-profit, which involves me doing a panel and speaking to politicians.

It's never about today.

So how do we understand Hinton?

Because there's this interesting situation where obviously Jeff Hinton doesn't need money.

He made a ton of money when they sold his startup to Google.

Also, he clearly knows the technology.

In computer science circles, he was just at Georgetown.

He really was the guy in the wilderness that was pushing trust me back.

Propagation on these deep networks really can work.

You just need the data.

He knows the tech.

Right.

I did a thing on my podcast earlier in the year where I was like comparing him talking about risks with Yakowski.

And it's really – Yakowski comes out of effective altruism, not a computer scientist.

And he's like LLMs are coming alive and have their own intentions or whatever.

Hinton knows that's not true because he helped invent the technology.

And if you parse his comments, he's very careful.

If you really parse it, he really is saying – what he's actually saying is –

We made more progress on this research than we thought we would.

So it stands to reason.

The same thing could happen with some new type of machine that we don't know how to build yet.

That could be a threat, right.

So he's actually being careful because he knows LLMs are not coming alive or autonomous or this or that, though he kind of merges us together.

But I'm trying to understand – his motivation right because, like he knows llms we're at, you know they're smoothing out.

He knows he's very careful when he talks about it, that it's we may invent.

So we may invent the machine in the future like we should be careful.

Um, it's been more sensationalized the way he's reported or talk about them somehow make it seem like the stuff we have now is dangerous.

But is that just influence?

Is it just like people want to hear what I have to say?

Like why is he – what's going on in your opinion for like Hinton's like big push for we should be really worried about AI.

I think he wants attention.

I think he wants glory.

And I don't think he wants to change anything.

I think he's quite happy with the current scenario.

Again, I say the same thing I say about the AI 2027 people except with more ire.

Jeff Hinton is a gifted scientist, a noble, was it noble?

Nobel and Turing.

Yeah, I don't know what the titles are, and he is a scientist and all that.

But again, Jeff Hinton has this massive microphone.

Do you ever hear him talking about the theft, the environment as the primary thing?

No, it's always science. couple years away.

What if this happens?

Wouldn't that be scary?

What if my grandmother had wheels?

Then she'd be a bicycle.

It's this thing of, he has all of this power and attention and so-called knowledge.

What's he use it for?

Nothing to do with what's happening today whatsoever.

He doesn't go on stage and say, hey, these gas turbines are popping up.

They're polluting black neighborhoods.

He doesn't talk about the fact that it involves stealing from millions.

The turbines are for generating the power demands of the data center?

Yes.

So what happens – and very simple thing of, because it takes so long to build power what these companies would be doing Elon Musk famously, and Stargate Abilene for OpenAI they're doing this as well.

They have these giant gas turbines that they put out that could be spun up quicker.

The thing is gas turbines are sold out.

They've been sold out and they have like years-long wait times.

So they're using old ones which are less efficient and pump out more horrible gas.

Anywho, I, a non-scientist, know that and I talk about it regularly because that is a harm from AI.

Why doesn't Jeff Hinton, a so-called AI safety guy, a guy who cares about what AI is doing, never talk about what AI is doing?

He always talks about what it might do.

And I consider that a grift too.

Him being scientific only makes it a more cynical grift.

They feel like a safe deal, but she at least has a startup, even though I think world models are another grift that people are going to move to next.

Nevertheless, with Hinton, he's always going out there to go, I'm so scared of the computer.

What if the computer does this?

To be clear, we should have these discussions.

Those are valid discussions.

That's all he does.

He doesn't give an rat's ass about any of this.

I think he's as cynical as the rest of them.

I'm sure he believes this stuff, but he doesn't give a damn enough about the human beings that are alive today.

He isn't actually trying to change anything.

He isn't trying to.

He loves signing open letters.

He loves doing paid speaker opportunities where he gets up and goes.

The computer is scary, But that's the thing.

Why doesn't he talk about large language models all the time?

Gary Marcus does more for AI safety than Jeff Hinton does.

I don't care if people are mad about that.

I think it needs to be said.

I have my issues with Gary, but at least Gary goes out there and talks about the actual harms.

Jeff Hinton talks about himself.

Jeff Hinton spreads the approximate fear, approximate danger, but never really talks about today.

Because yeah, we should discuss what would happen if this happens.

Sure, but how about if you're so?

And his whole thing was he quit because he was worried about what they might do.

Why why like, you're so worried?

Why aren't you doing anything about it?

If you, are you an activist, are you going to tell people to bomb data?

Like, what is it that you want people to do?

And the answer is Jeff Hinton doesn't want people to do anything.

Yeah, He wants to sit there and worry about something that might happen without dealing with anything today, because that would require him to actually do something.

That is an interesting point more generally about the sort of expert class turn to AI safety.

It's where there's no specificity.

I mean, when you saw scientists leaving the Manhattan Project worried about what they did, they had a very clear program, right.

The concern unit, we should...

We need to roll back nuclear weapons.

We need to create these treaties to do whatever.

You're right.

It's an interesting point that you don't see.

Here's what we need.

I mean, Joukowsky does, but he's kind of crazy.

They all signed a letter.

Joukowsky thinks we should bomb data centers.

So I guess there's someone who does have ideas, but he's a little bit.

And the thing is, I think Yud's a scumbag, but I give him more credit for at least saying something.

For saying something, because they love signing open letters.

Oh, the open letters that they sign.

Oh, oh, we should stop working on AGI now.

Don't worry, we haven't started.

How about we talk about things happening today?

Again, we can't possibly.

We couldn't.

All right.

So then jumping forward to May, this is when the big headline then was Dario Amadei.

This is when he gave the quote that went everywhere about AI could eliminate half of all entry-level white-collar jobs in the next – I think he said up to the next five years.

This was part of a longer interview where he also – did this equation of test to general capability.

So we said AI was at a high school level.

Then it became to a college level and pretty soon it'll be at.

Now it looks like we're getting at a PhD level.

So now you can imagine replacing what you would hire a PhD level trained person to do in a job.

From what I can understand, that was just referring to math tests.

They're referring to math tests that the model had passed and someone had said the problems on this math test, which they had tuned it to do well on, were problems that you might give a grad student on a math test.

That got extrapolated to…

AI can do what a PhD-level employee could do.

I mean, I guess if the employee's job was to solve math competition problems, that's true.

That's kind of it.

Well, to quote Gun Toucher from Blue Sky, CEO of Oreo Cookies, the Oreo cookie is as important as oxygen, like that's.

Everything that amadei says is just yeah, it's like a, it's like a phd student, and i genuinely think that there is this thing with people i've heard casey newton say things like this as well where it's like they're like yeah, the proof that this is useful is people are using it to do their homework and it's like do you think there's a homework goblin in colleges?

Do you think that's how colleges are run?

You write the homework, you.

The homework goblin eats it.

The goblin pays into the endowment.

Is that?

Do you think we go to college to do homework?

Now, this is a larger discussion though, because there is a degree of college that's kind of like that.

That is a problem.

Yeah, but we didn't need a solution to do homework.

We need to fix college.

But amadei, just like altman, just like all these people, just says shit.

He just says stuff.

Why does he always?

He always says i'm reading the quote here why does he always say things?

Like Amadei said?

He was highlighting it to warn both the general public and the government of what's coming.

This is his shtick, right?

More so than Altman.

It's the same grift.

He's always saying, look, I'm the bearer of bad news.

I'm the one who's willing to tell you straight.

But it delivers the same message in the end as Altman's more optimistic messages, which is this is the most important technology.

It's going to change all the things.

All the money needs to come to the people who are building it. it gets you to the same place, right?

Like, whether you're saying I'm worried about it or excited about it if you're saying this technology is going to create 20 unemployment, I mean, if I'm an investor, I'm like, oh crap, I got to be investing in the company that's going to take those 20 of jobs, right?

Like it's still a very optimistic prediction for anthropic, right?

It's cynical.

It's this cynical crap that people repeat every single time.

They fall for it every time.

He made a prediction at one point.

It was like 90% of coders will be replaced in the next six months.

I think he did that in March.

Didn't end up being true, it turns out.

But the thing that I think people need to realize is that these people aren't special.

They don't know like they know stuff, but like oh, it's going to replace 50, but it's just like a PhD student.

They're just saying stuff.

I could make this crap up too.

Well, he says here, I think maybe this shows it.

He also said there, I'm reading a Fortune article here.

He also said there is still time to mitigate the doomsday scenario by increasing public awareness and helping workers better understand how to utilize AI.

Okay, so there is a solution.

So make people aware of AI and then get people to use it.

Buy more Claude subscriptions and you will prevent the 20% unemployment.

Yes.

All right.

So then, by the way, having just done an article on agents, my cynicism meter is off the chart for that particular interview he was doing, because they knew at that point that even just like using the mouse or get the like back-end decisions wasn't working.

Like they were nowhere near this.

I guess you could just say – but people are reporting it.

All right, we get into the summer.

I think like the big news in June that got reported was the MIT Your Brain on Chat GPT study.

So that – I feel like this was –

There's some other research that came out early summer that was poking holes.

There's the big Apple paper and then there's the one research at ASU.

There's a bunch of papers.

They're a little too technical, I think, for journalists to pick it up.

But they were poking holes in the reasoning narrative.

Right.

They were saying, oh, yeah, the Apple one.

Yeah, like this is kind of nonsense.

It's not reasoning.

It's working with – it's just like there's no generalization of concepts happening here.

That's where they took problems that they could solve, and then they made the problem size a little bit bigger and it catastrophically fell off the cliff.

It was like, oh, it had just seen this size of the – it's not generalizing.

It's not – so those were a little bit too academic, right?

But then there was this June 10th paper from MIT Media Lab that introduced this idea of cognitive debt.

It was like more easy reporters or writers.

I think this made more sense.

It was this, they said we studied people writing with the AI and it made them it was worse writing and they learned less and they were dumber.

That went everywhere.

So what did you what do you think about that paper?

What do you remember about that?

I mean just that it was a moment when it didn't change a ton, but it made people think about this a little bit more.

It's the first time I really remember having conversations where people were like oh, maybe this is actually having negative effects.

Because up until then people still had this weird thing of oh yeah well, this is a way of learning.

This will be your teacher.

This will be You could be a student and learn anything.

Sal Khan was saying this, right?

Like he had to start up with it.

Again, the CEO of Oreo situation.

It's like, of course, he's going to say that.

Sal Khan has been on the AI thing for a while.

I mean, Khan Mingo, I think they had a Wall Street Journal story in 2024 that just got math wrong.

Might be Washington Post, actually.

But that study, the MIT one, made people go, oh, this isn't teaching people stuff.

It made people realize, This is actually not an assistant.

This isn't intelligence at all.

This is a dumbass machine.

It can fill in gaps of stuff you already know.

But if you don't know it, it fills in the gaps wrong or not at all and makes you reliant on a machine that doesn't know stuff.

I always think it's like the story about Washington, D.C., like what stories pick up.

It's not the stories that teach you something you didn't know.

It's the stories that confirm a preexisting bias, right?

And so I think...

Everyone was like this.

This has got to be a problem that you're using this the right like this can't be good.

And so when a study came out, I don't know that it's that great of a study, by the way.

I mean, I don't want to cast a spurt.

I looked at it a little bit at the time.

I remember thinking like this was not meant to be like a super carefully researched like etc.

But anyways, yeah, it was it was it was a little more academic than a blog.

All right.

So then this is where things begin to get – this is like the real turn is into the summer.

We get the August and we get the GPT-5.

I think this was a big turning point.

I have kind of the tick-tock here, right?

So on – like, right as it came out Altman – right before it came out, Altman was doing like a really big this is going to –

Changed the world's song and dance.

He went on – oh, God, what podcast did he go on?

I don't know all the – one of the comedians in the Rogan circle.

You know who I'm talking to.

Theo Vaughn.

So he goes on.

Theo Vaughn compares himself to Oppenheimer and then chokes up just thinking about – how powerful GPT-5 is like what it's going to do.

He's like, what have I wrought?

It was like, basically what I had in my mind in that part of the interview was the scene in Oppenheimer, where Oppenheimer's in the gymnasium and they're celebrating the dropping of the bomb and he keeps like.

You know, it's just like the cinematic Chris Nolan IMAX moment where he's Cutting from that the images of the explosion and the bodies in Hiroshima.

And it's like the most fraught moment.

What have I wrought?

Like the emotional climax of the movie.

That was Sam Altman on Theo Vaughn talking about GPT-5.

Then almost immediately, so by August 11th, he's saying AGI is not a useful term.

Let's not talk about that anymore.

Let's not be talking about this.

Yeah, let's move away from expectations here.

And then I had my article, which was one of several that came out on the 12th but within five days and I was actually on vacation, but I remember talking to my editors and saying this has to come out.

Like, this is a big deal.

EPT five is a big deal.

It is not, I think this is opening people's eyes.

That's when I wrote my what if AI doesn't get much better than this article for the New Yorker, which I think I quote you in

You did.

And there was like, the New York times had a big article right around this time as well.

Uh, And so did I. And so did... The biggest was Ed's, obviously.

No, but I did reporting on GPT-5.

Oh, you had good report.

Let me just point people towards your podcast.

So that whole month...

I mean you had a couple of really good episodes where you got super into the weeds not super in the weeds, but like it was the deepest reporting I had heard on the technical side of like how GPT-5 worked and didn't work and why it was going to be.

That's where I learned about to get these benchmark numbers they could brag about.

They had to use these completely like cost ineffective, you know, inference which wasn't going to work they lost their caching i think you were deep on that that when you use gpt5 to do this reasoning that did better on the benchmarks you couldn't do cached versions of your like all the stuff that made it it's really simple because it sucks so story from 2023 by the way sam altman says gpt5 underway and will substantially differ from gpt4 untrue very similar gpt5 the thing it did that was amazing was that it has something called a router model in it, which means that it would choose the best model for the job.

Now, the problem with that – They were solving a practical problem.

This isn't a – originally it was supposed to be this will be, how, 9,000.

And then it became – Yeah, this will change everything.

We introduced so many models in 2025 and late 2024 that really the main practical thing is we'll have a model, choose those for you, so you don't have to know which one to use.

It had kind of gone from –

This is going to take over the economy to like we made things complicated ourselves.

Now we're solving our own problem by having it automatically route itself.

But that, as you reported, even that feature caused a lot of problems.

Yes.

So the router model was reported incorrectly everywhere as being more efficient and a way to reduce costs.

However, due to a source that I have, I found out it actually increases the cost because when you have a large language model, say you load a query, you load chat GPT, like write some code here.

When it does that, the system prompt is, you are ChatGPT.

You are an assistant that can do these coding things.

It adds this text in front of your – for the listener.

It adds a lot of text in front of your text before it sends it to the model to give the model instructions about how to answer, what tone to use like, what to avoid.

What tools, like do you use a Python tool or a web search tool?

Now, before this router model, what it would do was you would choose a model and it would have that system prompt and it would – not have to keep reloading it.

It wouldn't have to keep entering that in.

It would just have it and be able to cache that and then say, okay, this is what I'm working on.

Because of the router model.

Not only that, just to be computer science-y, they could actually – because it's sequential when you run things through these machines.

It's sequential.

They could run all of this through – the systems prompt is always the same.

They could run it through and get the state of all these embeddings.

Like a lot of math and computation.

They could cache and then not have to run all that through the GPUs.

They know what state everything is in after that system prompt.

So they could have all of the effect of having processed that system prompt without having to actually do the inference time for it.

So they could cache it in a way that like okay, we can have a huge long system prompt and not have to pay for it every time we do a prompt.

Every time a user submits a new prompt, which was very important from a money saving.

All right, go on.

Except that all that goes out the window.

Every single time it routes to a different model. it has to completely start again.

It has to wipe the system prompt every time.

It is less efficient.

My source was telling me.

I can't exactly explain how, but the source was explaining that it was actually creating more overhead and the people on the infrastructure side were saying what are we doing?

This seems like there were straight up people saying, not sure this is a great idea.

This seems to be creating more overhead.

We cannot cache the system prompt.

This is causing.

I reported this at the time and I took it to multiple reporters and they went no, it's not.

I had multiple reporters go, it's not true.

The reporters.

I showed them the thing.

You talked to reporters and they said.

Multiple reporters.

It's not true.

Not to blow smoke at you, but I will say no one else had that technical story.

I remember listening to your Better Offline podcast.

Why aren't there more people with this technical story?

I mean I just finished my own piece where I went deep on scaling and tuning and trying to explain that or whatever, which I thought was also being super underreported.

But you had like a really good technical story.

No one else had it.

I don't know that.

I got lucky.

I got lucky, but I also talk to sources regularly and I know what I'm looking for.

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Like that's really.

It's like the moment GPT-5 went out I went to source being like hey hear anything.

Like real simple.

I don't even do much source stuff because I have like other stuff I'm working on.

But this one was just like.

I went to someone, an infrastructure provider, and I said hey, have you heard any GPT-5 stuff?

And they said, let me check.

And I went, this.

And it was just I.

It shocked me that no one else wanted to cite it.

It shocked me that I had multiple people who were just like.

This is not true.

And I'm like, I can show you stuff that would prove it.

They didn't want to see it.

It's just, it's denialism.

It's because, when I I understand, when you've got editors who are pro AI, when you've got uh, other people you work with who, or perhaps you yourself want these people to win and you don't want to off their PR people.

You don't want to lose your access.

I get it.

But it's like, This is reality.

They don't have any.

No one has access, by the way.

I mean, all access is controlled.

They won't give you access to anyone.

I'm into legacy media.

These AI companies give no access.

And I've heard OpenAI place people against each other at outlets.

I've heard them straight up say apparently I'm sorry.

I've been told that apparently they will straight up say, if you piss us off, we'll stop responding to your emails.

Yeah.

Little worms.

Yeah.

No, I believe it.

But this did change a lot of things.

So again, the blow a little bit more.

It underwhelmed everything.

Everyone was just like.

They were under.

So because it was hard to ignore, the GPT-5 wasn't that different and had these other problems.

The thing I saw change – so there's articles, hey, scaling, this is a problem.

That's why my headline was like, what if AI doesn't get much better than this?

That was like a new idea for people.

But the thing that that seemed to really open up was a story that really only you had been covering for a year.

For a year plus, you had been actually – gathering earnings, revenue numbers.

You've been looking at earnings reports and you had been making the case for about a year up to that point.

The numbers don't make sense on these companies.

Look at how much they're spending, how expensive this is.

The numbers don't make sense.

This costs way more to run than they're getting in revenue.

When is the musical chairs game going to stop?

Post GPT five?

All the major publications that sent good financial reporters to do these type of stories.

So we get, for example, the new yorker had a big uh in the magazine.

A big wait, is this a bubble article?

The wall street journal had several um, including the one in september spending on ais at epic levels.

Will it ever pay off?

Um, we began to get really good analysis, like comparisons to level three and what happened with, like laying the infrastructure.

The new york times started writing these articles.

They had Covered it.

The bubble possibility zero.

And then they started covering it multiple times.

That's my story of September is all of these different bubble articles.

I'm assuming that was all basically opened.

The floodgates were opened by GPT-5 underwhelming.

It just sort of changed everything, the way that people categorize like wait.

Maybe there could be a problem here.

Which, by the way, I have experience with from my social media reporting back in the day.

Just as a quick analogy.

Everyone thought in the media that I was eccentric for my stances about social media is a problem.

We shouldn't be using it.

This is not a fundamental technology.

This is a real problem.

And I was shunned and attacked and people were coming after me.

And then post Donald Trump election, where he was successful, on Twitter it planted the seed of like oh, maybe social media isn't just done for good.

And it opened the floodgates and all of these issues with social media was suddenly open game to be covered well beyond even what i was talking about.

This felt similar to me.

Gpt5 underwhelming opened up all of these, the possibility of all these stories, including on economic struggling there was.

There was also a big story around august where it was, i think it it came out, the ai data center capital expenditures made up more of gdp growth than all consumer spending combined.

And then in September you had that insanely funny story where, as we've discussed, 300 billion deal with Oracle, between OpenAI and Oracle, where OpenAI will give them 300 billion they don't have and then Oracle will serve them compute from data centers that are not built yet.

And I think that was That happened and sent the stock spiking.

And then rapid fire, we had this AMD deal where AMD, it was a really funky deal as well.

It was let's see that came out.

I'm skipping ahead to October here but the AMD deal, but basically by October mid-October I think OpenAI had agreed to like 26 gigawatts of data centers.

Wow.

And there's just a bunch of funding that happened around here as well.

But it really felt like the air had been sucked out of the room.

There was scrutiny suddenly on some of these stories in a way there wasn't four months earlier.

Yeah.

And it's interesting because even with that scrutiny, can we do October as well?

Yeah, we can be in October.

Yeah.

So I bring this up because even with all that scrutiny and the reason I'm typing is, I need to bring up these deals

So in September, it was this NVIDIA.

I'm going to do big old air quotes.

NVIDIA does $100 billion investment in OpenAI.

Now, what I really remember at that time was no one having any details about it.

And indeed the writing within the NVIDIA and OpenAI announcement not really being clear about when things will begin or indeed, if any agreement was signed.

And I went to multiple reporters.

I'm like, hey, look, first of all, have you done the maths here?

Because if you did the maths, it would cost OpenAI, like I think, over a trillion dollars for the compute and their data center deals.

And I put that out fairly early, and then just other people wrote the same headline and did not quote me.

Thank you.

But...

OpenAI agreed to a six gigawatt deal where they built six gigawatts of data centers for AMD and in return would get 10 of their stock.

Never happened.

Broadcom did a deal with OpenAI 10 gigawatts of data centers, which we will get to in a minute because some funny stuff has happened.

And then this NVIDIA deal.

Now what was funny about this was I went to reports and saying like, hey look, nothing's been signed.

This is a lot of money.

And also, a gigawatt data center takes about two and a half years and $50 billion to build.

They're meant to start these data centers.

The first billion dollars 10 billion even that NVIDIA was meant to send to OpenAI was meant to be next year.

Same deal with AMD, same deal with Broadcom.

So it might be in 2026.

And I went to people, I'm like, hey, this isn't possible.

Like, this is quite literally impossible.

We cannot do this.

Like, you can't build data systems.

And everyone's like, yeah, you know, well, they're working out.

The crap they've been saying for the last year, it's just like, they're working out.

They're working out.

What's the advantage they get by announcing these deals?

Can they, like, mark it up as, like, future assets?

Does it help with stock?

Like, what would be the motivation for stocks?

It helped the stocks spike.

Okay.

And so Oracle added $300 billion to their remaining performance obligations.

Broadcom added, like, 50 billion, I want to say.

Because you can mark this up as expected revenue which, when you're then doing the calculations and figuring out, you're like oh, this makes this a more valuable company because the revenue it has or expected, has gone up.

And because the markets are, I assume, run by toddlers, everyone believed it.

Everyone was like, wow, wow, number go up so big, number so huge.

Well, number didn't stay big for long and things started to fall apart.

And it got to this point where people, even people who were quite cynical, started going.

One moment anyone, done the math here.

And the FT has stepped up.

The Financial Times has been pretty on top of this the whole time, but they really stepped up and did some analysis.

And it was just, they also did a trillion dollar story without citing me.

Well, but the Brits are more suspect of Silicon Valley.

I'm not bitter.

But nevertheless, it was this thing of Everyone's suddenly starting to do very simple math of.

Like well, OpenAI is projected to make 13 billion this year and they owe 300 billion.

How do they pay that?

They're going to lose billions.

And the information part of the story saying OpenAI was spent.

They planned to spend like over 150 billion or something didn't really make sense mathematically at all because the 300 billion but

It was a very interesting time.

Actually, that reminds me.

So when the Oracle announcement came out, OpenAI had leaked that they would spend, I think, 155 billion or something.

Yeah.

But it was days before the Oracle announcement was made.

So OpenAI leaked their costs – I'm doing air quotes again because I don't trust any leaks out of OpenAI – five days before the Oracle deal, so that no one would do the backmouth and go wait a second.

This doesn't make sense.

I love watching this kind of like disruptive public relations work.

I think it's cool as hell.

I think it's good that I'm watching because –

What's going to end up happening?

There is no one gets paid, which in a few months' time in this story, we'll get to.

But I actually had my own story in October as well.

I got Anthropic's Amazon Web Services bills.

Yeah.

So what's going on with that?

What's the headline?

$2.66 billion spent in three quarters.

$2.66 billion, and that's just on AWS.

And from what I know, they also spend about the same amount on Google Cloud.

So Anthropic will probably make I'm going to say $5 billion this year.

I would think by the end of September they, between Google Cloud and Amazon Web Services, had spent more than 5 billion.

So they're just annihilating capital, just burning it down.

And it actually leads me to an important statement which is Anthropic has done.

The other thing that Dario Amadei has done is he's framed them as this more efficient company.

This company that is more efficient, that doesn't burn as much as OpenAI, that spends less on training.

But when you look at the numbers, it tells a different story.

OpenAI in 2025 raised $18.3 billion, other than SoftBank's portion.

But nevertheless, $18.3 billion.

People say Anthropic, they're spending less money.

They're more efficient.

Anthropic raised $16.5 billion.

Basically... the same neighborhood of numbers.

But Anthropic has done such a good job just lying to reporters and spreading these rumors that people believe this.

I think Anthropic is as big a crap pile as OpenAI.

They're just as lossy.

They burn just as much money.

And yeah, I mean...

By this point, by the end of October, I think most, even outlets had begun to say like oh crap, oh crap, were we wrong?

Were we wrong for three years?

Did we fall for it again?

And they did fall for it again.

So then, if we jump forward to well, the other big story in October is Sora, the app which confusingly, is powered by Sora 2 the model, because there's also a Sora 1 model.

That didn't land, I think, the way I guess OpenAI hoped.

It kind of freaked out a lot of people.

Like, what are we doing here?

Who is asking for this?

But was that a sign – I took that as a sign of – a little bit of a sign of desperation.

This is OpenAI looking at – TikTok does $33 billion a year in revenue.

And like we need money.

So can't we do TikTok with AI and like help fill backfill, right?

So like, in other words, if you were about to automate half of the jobs in the knowledge economy, you don't need a TikTok clone.

You don't need to talk about – around the same time, they also talked about allowing GPT chat, GPT more erotica, et cetera.

You don't need that.

Like we're about to create the $3 trillion that Marc Benioff talked about.

Who cares about that?

But the fact they were putting that out was sort of taken as like an uh-oh.

Which I don't think is what they were hoping.

I don't even think they thought around.

I mean, people bought the TikTok thing hook line and sinker.

I think they were just desperate.

I think they did key jingling.

I think they were like, look, look, you generate videos.

Please, please, please keep using this.

Don't talk about the Oracle deal.

Look at the keys.

But they did like an Oracle-style deal, but with their own software.

So you have this situation where Sora now...

Forbes estimates that it costs them like $15 million a day or something.

Based on my sources, that number might be smaller.

Sorry, that number might be too small.

I have compelling evidence that to run 13 instances of Sora 2 required 940 H200 GPUs.

That's 13 instances.

That means 13 generations at once.

So that's 10, like...

This thing is really expensive.

What's the cost?

If you want to make Sora videos, what do you need?

What level?

I mean, they're still taking a loss on it.

I mean, you need to use their API.

But you have to use, you have to have their $200 a month level or above, or is it more?

No, anyone can use Sora, the app.

I mean, for creating the videos though, right?

So that's, you have to, that's limited.

You can create them on the app.

You're limited.

But if you want to use the API, I think it's like a couple dollars per video, and per video just means anything it generates, whether it's good or not, which is not, but that's not.

Uh, that's not sustainable.

Like the whole point about tiktok's model that's brilliant is all of the compute involved in taking videos, editing videos um, trying a bunch of experimentation is all done on the user's phone, they pay for it and but also tiktok loses money.

Yeah oh interesting, like tiktok is an unprofitable business as well, because they spend a bunch of money on marketing because of marketing, just a marketing and hosting and streaming a bunch of videos.

I guess yeah yeah, it's still inexpensive and also they are poised for growth.

But, putting it aside, you're still completely right, that's how that service runs.

Sora was just an attempt to try and it's like um, i don't know like when you ever see a couple that's like about to break up yeah, And they're like yeah, we're going on vacation.

It's great.

God, I love them so much.

It's all going so well.

I love it here.

And Sora, you had all of these.

What was funny was Sam Altman.

I think Truanon had this point where it's like it looks like Sam Altman put himself in it so that he would make himself famous.

And all that ended up happening was people did like Sam Altman stealing from Target.

I saw a lot of those videos.

Or Sam Altman crying.

And it was just, It was weird and bad and it sucked.

It got a bunch of media attention.

A lot of people got scared because that was the other intention.

It was meant to give people the sense that this would replace videos in general.

This replaced social media.

It didn't.

It obviously didn't.

And it's obviously too expensive to run.

And I think that It gave them the top of the App Store ranking very briefly, and then, Because it's like everything with large language models,

Other than in really specific use cases, it is just a toy, a really horribly expensive one too.

So then, if we jump ahead to November I summarize November as basically, here's what's interesting to me about it.

Multiple models from different companies GPT-51 Google, Gemini 3 Anthropic, Opus 45.

No one cared or noticed, which itself, I think, is significant.

Like suddenly there wasn't – no one cared.

I mean there were some – Jim and I.

They cared about the fact that it was using different – their own chips.

And like there were some economic stories there.

But no one cared that like Opus 45 was like better at coding agents, or that 51 – Because better doesn't mean anything anymore.

The other thing I saw in November was there was kind of a defensive backlash to the bubble stories.

So now you start to get, well, wait, wait, we'd gone too far.

Maybe it's not a bubble.

We begin to get to like, I think it might be okay story.

So that was like, I don't know your take on that.

That seemed to happen in November.

Oh, for sure.

And we had.

But by this point, Sam Altman, Mark Zuckerberg and Jeff Bezos had all said it was a bubble, like all three of them had said it.

But also we had a bunch of people doing stories that were quite literally, actually it's bubbles can be good.

Actually it's a good kind of bubble.

None of these had particularly logical points, and so we had these people trying to work out like crap, did we?

You kind of put it like this.

It was like did we over correct?

Oh no, i don't want to piss off the powerful people.

Llms are actually great now, but they're actually bad.

And then it got to this narrative of well, remember the dot com boom.

Remember the dot com bubble?

And there were companies left over at the end.

And it's like, did you read about the dot com bubble?

Because...

Lucent got acquired.

Lucent did probably the best of them all, other than Cisco and Microsoft, who kind of survived.

Amazon.

They've done really well, but it took them a while to get out of there.

Amazon was interesting as well, because Amazon didn't.

It was within the universe of the dot-com bubble, but didn't make all the mistakes that they made.

Also the thing with the dot-com bubble was and this is how we get to NVIDIA in a minute was just the insane deals.

Yeah.

Like Windstar Communications getting a 2 billion loan from Lucent Technologies, which would and in the press release it said this make 100 million of revenue.

It's like, they don't teach you that in business school.

Also in the middle of this month, I got OpenAI's costs.

They spent $8.67 billion on inference, just inference, through the end of September.

That was a great story because the FT and I worked on it.

But the denial around that was really cool.

Wait, that's eight, because these numbers again eight uh eight, seven billion in inference, meaning like just what it costs to train and run their models uh, just to run the models, uh.

And then what?

Against what revenue for that period.

So that was the fun thing.

So i also got the revenue share from microsoft, and the way it worked out was because open ai had leaked that by the halfway point of the of 2025, they had made 43 billion dollars in revenue Based on the revenue share, because they do 20 revenue share with Microsoft.

So Microsoft, I could see what they'd been paid.

Just multiply it by five or whatever.

Yeah.

And they made 4.3 something billion through the end of September.

Now people then said, but Microsoft pays a revenue share to OpenAI too.

I actually have those numbers now.

And it works out to like $4.5 billion through the end of September.

I don't know if we ever find out what happens with OpenAI, but I will say this.

Those numbers do not match up with anything else reported.

And people did intellectual gymnastics to try and say, they said, oh, your numbers are delayed.

They're a quarter late.

They're three quarters late.

They're a cruel accounting.

I'm, I, I, I, I play to win.

I know what I'm doing.

Just to be clear for the audience, though you're saying like through September you're talking four point something, billion dollars probably in revenue, against already close to nine billion dollars in inference costs.

Correct.

Yeah.

Not good.

You want the first number to be larger.

You ideally want those to be reversed.

Yeah.

And it's like.

So they're operating.

Yeah.

Which is the issue is you're operating at a loss. massive loss.

And these costs increase with revenue.

That's the actual problem.

It's like if costs were going up but revenue, if costs were going up this fast but revenue was going this fine.

The problem is and this was what I saw with Anthropic as well, because with Anthropic spend I actually compared the revenues versus spend

And it just goes like that.

It scales.

It's clear that the more money you make, the more you spend.

And there's no real reversing that trend either.

If you zoom in on a user that's paying X per month, you're probably costing you more than that for that user.

And that's why it doesn't scale.

And that's the unique problem with large language models is you can't do cost control.

Augment Code.

I think in the middle of October or November put out a thing saying they had a 250 a month customer spend 15000.

In CompCost, Claude Code, there's a leaderboard called Viberank.

Because with Claude Code you can actually find out how many tokens you're burning and extrapolate the costs.

Someone spent $50,000 worth in one month on a $200 a month subscription.

That's large language models, baby.

That's just how the cookie crumbles.

People underestimate the brilliance of a company like Google Search. is it is really cheap to run.

Like they built their whole.

I mean, I guess it's like the acquired episode from the fall, but I know a lot of Google stuff.

They, they built a very cost efficient infrastructure.

That's what they figured out is like we're dealing largely with text and we can cash most of this stuff and we're moving very little bits and we can use commodity processors that like, are idle a lot of the time anyways.

And it's not that expensive to run and we can get a huge amount of revenue from per search done versus.

You know we can generate two dollars in ad revenue on like seven cents of.

That's why that was a money cash cow is.

They thought a lot about the compute cost and they were like this can be super efficient and they built an infrastructure from scratch for google search To be super efficient.

And because of that, it became like a cash fire hose.

What you're saying is that's impossible for LLMs, because the way an LLM works is you have to fire up every one of those stupid weights and run it through a GPU to generate a single token.

The whole LLM, every weight is involved for every token of every response.

There is no cost effective way of doing it.

But even mixture of expert stuff still run into the same problem because they're imprecise in how they call up the experts.

It's because of the probabilistic nature.

But on top of that, people will also say, well, what about Amazon Web Services?

It's the favorite comeback that people have.

They burned a lot of money.

I actually went and looked.

In the space of nine years, they spent about $70 billion to build AWS.

$70 billion.

That's less than half of the cost of OpenAI's infrastructure.

Well, and also that scaled with revenue.

Exactly.

AWS was a very – I mean, it came out of.

What people know or don't know is like.

Where it came out of is they built this infrastructure for their own compute.

And then it was incrementally they could be like, oh, well, we know how to do this now.

Why don't we offer this to other people?

The revenue curve was like the opposite of what you described for OpenAI or Anthropic.

It was the more people using AWS, the more money they would make.

So it was you could invest money in this.

This is not – I mean you could – the growth here is not nearly as expensive as building out the infrastructure for AWS.

AI, right?

This was like, this was more, they knew how to run these data centers.

These are more standard data centers.

It was more of the software was the main innovation was in the virtualization software which, once you program it, it's free.

It's yours.

It's your IP.

These were known quantities to build out.

And I don't know the details, but I would assume they, you know, you'd go a little bit into the red.

You could immediately in like two years, get back in the black.

It was like a much more controllable space.

Like they, this was, and there was a path.

Yeah.

There was actually a path to him.

And then it started making money.

And then like, oh, let's like 10x this because like we see the revenue is made.

If we 10x it, we'll 10x the revenue.

And it kept Amazon in the black for a decade, where they were giving away Prime memberships when their costs Also the business model made sense.

People needed to host websites and apps on the internet.

We still don't have one of those for large language models.

We don't have a thing we can point to and say this is what they do that actually makes money.

This is the economic viability of it.

All right.

So December, looking at the last month, there's Disney.

Disney.

There's NVIDIA as well.

And NVIDIA.

Okay.

So Disney's easier.

And Michael Burry.

Okay, Disney.

All right, let's cover Disney first.

I listened to you just this morning talking about it.

But okay, Disney, for some reason, put – well, they put a billion dollars –

That should cover like a month's inference costs.

Into open AI for Sora.

This smells like to me what I don't like about the AI of the last couple of years.

The thing that often annoyed me as much as anything else was the executives at unrelated companies who did not understand the technology, who felt like it made them look cool and forward thinking, to be like we got to do AI or we're just we need to be doing AI.

Go do AI or you're not going to work here.

And you'd be like, AI to do what?

Like, what specifically are we making money on?

No, no, just we're doing AI.

Stop asking questions.

We do AI, right?

If we don't do – and you sound like as a CEO at a board meeting.

You're like we got to do AI or we're going to fall behind.

And they just leave it there.

This felt a little bit like Bob Iger saying –

We got to show our shareholders we do AI, but at a level that's like we can absorb the loss.

Is that right?

Well, I mean – They're going to use Sora to create – it made no sense to me.

People will use Sora to make custom Disney videos with themselves in it.

Like what – I don't understand what's actually going on here.

Other than Iger can be like we AI good now.

It's about as far as I can get.

So –

Middle of May 2024, Iger actually said that we need to embrace the change driven by tech innovation, referring to AI, and that Hollywood storytellers needed to.

I think that what's happened here is that they wanted to invest in open AI.

Maybe they were going to sue them and open AI.

Open AI just kind of scammed them a little bit.

Scammed them and said, oh, yeah, yeah.

Well, what if we gave you the opportunity to invest?

We're not letting anyone in, and so they agreed to that and they're going to have 200 Disney characters and the the actors unions are pissed off.

They just want the stock.

They just want the stock position.

So now I can be like look, we have we've, we're hedged against AI disruption because we have like a non-trivial stock position in open AI.

Yeah, I guess.

I mean, it's just like, but they're not using, they're not building tools for film production.

It's like in this weird sloppy IP space.

Right.

And the thing is as well the first time you have like Goofy doing the introduction of Frank from blue velvet.

The moment that that happens, you're going to see this shut down.

Like you're going to.

People are going to be on there day one trying to make Goofy have sex with someone, have Donald have sex with someone, Mickey doing nine 11, whatever they want, which they already were doing with Pikachu when Sora came out.

This is all that they are.

And the thing is, Disney's crazy.

They already have this problem.

There was a Fortnite thing in 2025 where they put a generative AI Darth Vader.

Within one hour, people had it saying slurs.

I remember that.

The internet is built to generate that kind of horror.

They put a chatbot behind a Darth Vader character so it could talk to you.

And they made him into – yeah, giving racial slurs within – yeah.

And it was just immediate.

And it was giving like really unsettling dating advice to the character.

I remember covering this story.

And it's like –

It's funny as well, because that's obviously what's going to happen.

But again, this is one of these deals where it's like, it's going to happen sometime in 2026.

Sometime, at some point.

It's always at some point with these deals.

Sometime, somewhere, some point.

Has Disney actually invested?

Is that money?

There's a licensing agreement.

Is it a licensing agreement?

I'll be looking at Disney's earnings when they come out, but it's just a very boring and cynical thing.

I think sam altman is a good con artist and i think he's good at convincing rich guys to give him money by scaring them.

Well, two other yeah, two other stories, that coda things we talked about earlier in the year, which might uh color our analysis of the full year.

When we do so uh, some of the writers of ai 2027 basically came out and said like, well you, This is not going to happen anymore.

We'll do another one.

It got a little bit too far.

They're like, actually, it's not going to happen.

And then also this was in December where the code red was declared at OpenAI, where they're like basically, we need to make ChatGPT better.

And one of the things they said after the beginning of the year was this the year of the agents.

In December of the year, they said, we're de-emphasizing agents.

We need to put more energy on chat making.

Chats yeah, so there's kind of this like tragic coda to the end of the year.

That was a leak.

It was a leak to the information about their cost.

They were like yeah, we expect 26 billion dollars less revenue from these.

It's like sure, it was an internal memo that was leaked, so it was the information got it first and the journal picked it up, i guess.

Yeah um, but yeah, in that they listed, like we have to de-emphasize agents because we need to make more money on our core product because well, this is a google gemini reaction, i guess.

But So this was great though.

So Gemini 3 comes out.

And just before that there was a story in the information where OpenAI was like yeah, we're going to – Sam Altman did an internal all-hands thing where he was like yeah, we're going to have some economic headwinds.

Around that time Alex Heath from Sources reported that CFO Sarah Fryer who we also missed that she kind of hinted at a government backstop.

But that was kind of – that kind of went away.

Nevertheless, she said that there was slowing growth due to safety features.

Then Google Gemini 3 comes out.

Google stock spikes.

Gun to my head, I could not tell you what's different with Gemini 3.

I've talked to multiple people.

They're like, it's better on benchmarks.

I'm like, okay, but does it do anything?

It didn't need the Nvidia.

That was the issue.

Google had been working on their own chips and they trained it on their own chips.

But that's the thing.

Is that the case?

Google's got a lot of NVIDIA GPUs.

That's a convenient story for them that they leaked.

TPUs have not been proved.

There was a whole argument between analysts about this.

Nevertheless, Gemini 3 comes out and because the media just cannot come up with unique ideas like this is big, this is different.

Stock go up, number go up.

And there was this Code Red that you mentioned that gets called.

And what's great about the Code Red story from the information is it's like an open AI had a plan.

Step one, we're going to make ChatGPT's responses better.

Step two we're going to give people reasons to use ChatGPT more than other models and prefer it over other models.

And three, we're going to improve the functionality of ChatGPT.

To which I ask, what the hell have you been doing all year?

What have you been doing?

What is it?

I think open AI is like an adult summer camp.

I think that they're all just dicking around doing random projects, no real management.

They're just like, I, I think all of, I think anthropics the same way.

It's like, I don't know what we get.

I'm done working on a model thing.

Sure.

I'm also.

I've heard multiple stories that you have teams in open AI working on the same thing that do not talk.

They just like bumping their heads together.

It's like the minions in there.

But this Code Red happens.

And at that point, really, you saw the media shift of, oh, God, OpenAI is bad.

I think just everyone was like, ah, wait, does this company lose billions of dollars?

Did anyone say anything about this?

Why didn't anyone tell us this?

Oh, my God.

When those articles come out, I'm going around with a mallet.

I'm going to be like Mario and Donkey Kong.

It's going to be messy.

But that's the thing.

Yeah, everyone was kind of like, hey –

OpenAI loses so much money and they don't appear to make enough to pay their bills.

Is that good?

And every day there's a new story where I will post it and say, is that good?

Because it really is just like, none of this ever made sense if you looked at it.

But it's like really that you can see the milk is curdling in real time.

You can see it happen and you've had terrible earnings.

You've had this Broadcom earnings, Broadcom being the one that was meant to build chips for OpenAI.

Now the revenue for that is not coming in 2026.

It's crazy.

It's completely nuts.

Oracle, I think they missed on several parts of their earnings.

And $300 billion out of their $455 billion remaining performance obligations is OpenAI.

And people are like, hey, man, how are you getting paid for that?

Yeah. where's the money coming from?

Cause like you need money to, to you, you need money in your business.

That's how you make money.

And no one has a good answer.

And now Oracle has delayed those data centers.

So it's like, right.

They can't afford to build them.

They raised 18 billion in bonds and they're trying to raise another 38 billion with Vantage data center partners.

It isn't clear if that's going to happen.

The credit default swaps so betting against Oracle saying they might default are at their highest.

They've been since 2009.

It's – the era of smiles is beginning.

It's really – it's dark out there for them, but I'm laughing.

I'm having a good time.

Well before – so before I get your final take on the year, let me just get your opinion on – your official answer on this, because the number one thing I hear from people – who don't think my coverage is too skeptical of AI, like the people who are really AI boosters.

Like the number one thing they say is like these details don't matter.

I really – Cal, you are wrong.

You're really underestimating the likelihood that there's going to be these quantum leaps.

They're going to come alive.

They're going to be – it's AGI.

It's like you don't – like it's going to be so transformational that Why are you talking about 47 billion versus 87 billion?

It's the future of humankind.

It's about the change.

And whenever I do an episode on consciousness or superintelligence and why, as a computer scientist, I say this is bunk.

I mean, my toaster might as well come alive.

It's like, no, no, no, you're wrong.

And they really get in the weeds of trying to argue with me about – there's this other story of these models are on the precipice of – transform transformational change in, like the very definition of intelligence and AI and what machines can do.

Have you picked up, you cover this as closely as anyone.

Is there any inkling from people who are in these companies, the analysts who are analyzing these companies financially, the investors?

Is there any inkling or any care or any attention put to this idea and actually put to it that?

No no, this technology is going to make a leap into being intelligent or conscious and it's going to solve all the problems.

I know there was some – that was the way they used to talk about it.

But just to clear the decks.

Is there any conversation about that actually seriously happening anywhere tied to these companies?

No.

I just need you to say that.

I needed that clip to be able to give the people.

It's just no.

And my evidence is all of the stuff we've been talking about.

Their evidence that these are getting exponentially better is fairy tales.

It is, well, what if this happens?

If a frog had wings, it could fly.

It's fantastical.

And the fact that people are still doing that is so sad.

Because there are people I talk to who like large language models, who use them for coding and such.

They don't talk like this.

Simon Willison doesn't talk like this.

Max Wolf doesn't talk like this.

Carl Brown from the Internet of Bugs, he uses large language models for coding.

He does some of the best coverage anyone has done.

He did that takedown of the horrible Hank Green AI doomerism thing.

The people who know what they're talking about are all being like yeah, we're pretty much at a wall.

It's useful for this.

And because there's this cult and I think it is a cult-style thing of I want to be at the forefront of technology and I want to be known as being right.

I want to be the correct person.

I think that you are seeing this religious belief.

I'm galaxy brain take.

I think this is what happens when you lose, when you destroy social services and meeting places in third places where people have communion, people get attached to things like technology and the ideas behind them.

You're saying, in a world, i think, in a world where you meet and you're not on your phone and you meet with real people, you get a lot of pushback in real time when you start talking about you know hey, i think the computers are going to take over the whatever, whatever.

If you're just around normal people all the time, they're like oh, that's kind of a weird thing to say.

And also, I think if you're less lonely, less connected, if you don't have a support system, if you don't have good friends, if you don't have people to talk to, you're likely to fall down rabbit holes.

And there are these less wrong EA freaks.

They're really good at – they are like right-wing grifters as well, the same way.

It's like they present an attractive thing, where it's like you can join our community of people who all know the real truth.

And I think people like Sam Altman and Dario Amadeus scum for this as well, because they fed into this with their noxious fantastical crap about AI will do.

They won't talk about what AI can do.

You see, that was all cynically from their perspective.

They're not a part of the EA Doomer world.

They just – this helps them.

Sam got rid of the one.

I mean there's probably a connection.

But sam altman got rid of helen toner, who was an ea person.

I am sure the ea people are attached to dario amadei.

He certainly speaks like that.

I don't believe him for a goddamn second.

He believes in this.

I think he's a carnival barker like the rest of them, but this rabbit hole is more, way more attractive than a lot of rabbit holes because of the reality.

Right, like they give credit to the people who are falling down it.

Uh, AI got way better right.

So there's a lot of, there is a lot of rabbit holes that come out of nowhere.

It's just a conspiracy.

I think, you know, whatever this, the moon landing was fake.

There's no real reason.

I mean, it's nonsense, right?

But here it was like, well, wait a second.

I witnessed AI not being something that was good.

And now it's like can do things that are really impressive.

So they saw there's a trajectory.

So it's a trajectory extrapolation.

I kind of understand.

That's like a much more broader entrance to a rabbit hole than a lot of them.

Because you can just extrapolate trajectory.

That makes a lot of sense to people.

Let me just go back to 2021 to today and how much better it is.

Cause it's pretty amazing.

I think the fluency of chatbots, like it's a really cool technology.

Oh yeah.

Extrapolate that another three years.

You do have God knows what, right?

So it's like a very tempting rabbit hole.

It's not nearly – it's a very broad entrance to – I don't know.

I'm stretching the metaphor.

The entrance to this rabbit hole is very large and not well marked, so it's easier to fall in than other ones.

And I agree.

I actually like ending this on a more empathetic level, because I think that people who got scared by AI 2027 – or who got kind of pulled into this world of believing I can see how they got there.

Charlie Meyer has an excellent blog about scaling laws with this where, if you looked at the jump from 2021 or even 2022 from like GPT-3 to 4, it was big.

Now, big can mean a lot of things.

It doesn't mean autonomous.

These things still couldn't do stuff.

But the fluency of the models, the ability to generate stuff correct or not, It was still technologically impressive.

And it did non, the GPT-4 jump, because I really was covering this for New York at the time.

The big thing in the GPT-4 jump was like, oh, non-language based things.

It's picking up non-language based things, being trained on language.

That opened up the possibility of, oh, a language model.

It's not just fluency with language.

It's learning other things.

Look, we never talked to it about chess, but it can do some chess.

Not very well, but it can do.

So that was like the real thing.

That opened up the idea of like training things on text might create knowledge models.

Now, it didn't go any farther.

They didn't realize they were at the edge of it.

It was trying non-images too.

Like that's the thing.

They fed documents into it with images.

I'm not saying you're wrong.

It's just, there was context.

But yeah, but it was a cool, I get the excitement basically.

Right.

I do too.

And like, I totally get how someone who saw chat GPT in November, 2022 went, holy crap.

I then understand when they saw GPT three point, sorry, that was 3.5.

When four came out, they went, this is multimodal.

Wow.

And it's doing well on test.

I went back and read all the coverage.

This was when it was doing well on test.

And that's where people were like, I equate test with people's intelligence levels.

But there are also members of the media who helped push it up the hill.

Kevin Roos, for example, who claimed that the task rabbit, that the GPT-4 was able to manipulate a task rabbit into solving a captcha.

That's hidden within an METR study where it even admits it didn't do it.

It was copy pasting stuff between windows and prompting it.

It was.

They were telling it what to do, but nevertheless that got reported as the AI manipulating people.

The myth was there.

Well, I got it.

And I got to tell my favorite story about that, which is the blackmailing story.

Cause I did a deep dive.

I read the actual.

Oh, I just, my man, I just spent like hours on the blackmail.

It's so funny.

They gave it.

Language models are trying to complete the story you give them.

That's what they do.

You give them a story.

They try to complete the story you give them.

This leads to tragic things too, like the suicidal ideation or whatever.

If it thinks this is – it's trying to – it's a story about suicide, it's going to try to finish that story properly.

The blackmail thing was they fed it a bunch of stuff.

These emails really – poorly written, like it's like the worst fiction story you could write where, like here's these emails from this engineer, full of all these details of the engineer's affair and And of all these facts that the engineer is going to turn off the AI.

And then they're like, okay, you are now the AI in this story.

What do you want to do next?

It's like, this is clearly like a bad science fiction story.

I know what's supposed to happen in this type of story.

I should, you gave me all of this information.

Like clearly this is supposed to be a story about this is the, the, the MacGuffin, right?

Like it's supposed to be about me using this information about the affair to get in the terminal.

I've seen stories like this and it completed the story.

It was reported as as if, like in production somewhere, an AI was blackmailing an engineer.

So that's what's great about that as well is the one where that bit in it where it's like, oh yeah, it was copying the files off.

That was because the system they prompted it to say you are in a computer thing where you can do it was like you can do this here.

And it generated code that doesn't make sense.

But the funnier one was they had one where they literally trained a model and to reward hacks.

So instead of solving a problem, it would find a way to cheat.

And they're like, yeah, it shocked us that it was able to do this.

It's like you trained the model to do it.

Well, this is the 01 breaking out of the container.

Now this – I'm talking about Anthropic.

There's another one where 01 broke out of a container in playing a hacking challenge.

Like it did something – it broke out of its virtual machine and like – and restarted the virtual – so it was breaking out.

But what happened was is there was a configuration error so it couldn't access the machine it was supposed to hack.

All over the internet is instructions for like what should you do in this case.

Oh, you should restart that, whatever.

It was just following –

The instructions – because again, it's trying to complete this story that's partially written.

All over the internet it talks about.

The thing they do here is to restart the virtual machine if you're having this issue or whatever.

Again, that was reported as 01 broke out of its virtual machine.

This all came out of Yukowsky.

It has its mind of its own.

It's trying to break out of its constraints.

So it's going to kill us all like ants.

They're just trying to finish the story.

That's all they do.

That's what they've been trained to do is finish the story.

That is like the original Kevin Roos 2022 scare article about.

It tried to get me to divorce my wife or whatever.

It's just trying to finish the story.

Like it thinks that this is story.

I was fed in my training and I get the cookie.

If I finish it properly, you can lead it wherever.

But my favorite part of the Kevin Roos story was when he went to the CTO of Microsoft, Kevin Scott, and Kevin Scott went.

Yeah, you know it's important we have this conversation.

It's just like.

Eat the slop.

Yum, yum, yum, yum.

What do you want me to say, Nick?

Yum, yum, yum.

I love AI.

It's just pathetic.

And it leads the markets and people down these rabbit holes.

So I actually feel a degree of empathy for some, some AI boosters like regular people who were like super into this.

Maybe I'm being a little too kind, because there was a large media campaign, a cynical one, led by large media outlets like the New York Times, and also a cynical marketing campaign from the Doomers.

There was an attempt for everyone to grift off of this machine, and I think that that's the era.

It's like the era of ultra-grift.

The end of the rot economy where everything must grow forever.

We made a thing that's... linearly more expensive, so you need to keep buying more things.

And what does it do?

It makes more stuff.

Is it useful?

No.

But it costs a lot of money, so we now have companies that will make money now.

Well okay, they're losing money, but that's good because well, we don't really know how businesses work anymore.

We've learned nothing.

So we're just going to burn more money and see what happens.

It's this deeply cynical era.

And I'm glad that things are changing and people are seeing this now.

And I hope in 2026, we see the end of it.

Because the sooner this ends, the sooner we can do something else.

All right.

So I know your answer, but let's answer the original question.

Was 2025 a great year or a terrible year for AI?

Terrible year, you think it started off bad, only got worse.

All right well, there we go.

Thank you ed, for joining us.

Uh, we went long because i i nerd out on this stuff all the time.

My audience, i know i loved it.

I love yeah, i love talking to you.

This is awesome.

I had a great time.

All right well, thanks for helping us out.

We'll have to have you back next time.

We're confused about something.

Ai, everyone check out.

Uh, the podcast better offline.

Uh, award webby, award-winning podcast.

Is that what you won?

What'd you win?

Yeah, Webby.

Webby award-winning podcast, Better Offline and Substack.

Where's your Ed at?

That's what it's all called, right?

Yep, there you go.

Check it out.

All right.

Thanks, Ed.

Bye.

All right, so there you go.

That was my conversation with Ed Zitron to try to dissect the last year.

Jesse, it's kind of exhausting looking back at how much happened in AI last year because I was writing about this and podcasting about it.

Just thinking about the year ahead, I feel like we have our work cut out for us.

Like if there's going to have to do a lot of writing.

Oh my God.

So much is happening.

It's so hard to keep track of.

Maybe we'll just keep having Ed back to explain stuff for us.

He actually like, sits there and reads, you know, earnings reports and the AI company is like well, wait a second.

You're not really supposed to read these.

You're just supposed to listen to us.

I think the most important thing is I need to get that Jensen Wong jacket.

Yeah.

It's probably pretty expensive.

Yeah.

It just, it's crazy.

He's a computer scientist that makes graphic chips, but he he dresses like he's in a post-apocalyptic biker gang.

But he's a billionaire and he probably has a, you know, a dress person buys him the clothes.

I think he's a billionaire.

So his dress person doesn't tell him you look ridiculous.

I think that's what's, I think that's what's really happening there.

I'm going to start wearing those type of jackets.

All right, so let's get on now to our final segment.

We spent a long time dissecting the year in AI, so we're not going to belabor the final segment.

I want to focus on just one particular segment that I have a lot of fun of and I'm happy to do for the first time in this year, which is me reacting to the comments.

All right, so what we did here is we pulled some comments, God help me, from YouTube.com.

From one of the last episodes before we went into the holidays last year.

So the last sort of normal episode before the holidays last year, or one of the last episodes was about, is the internet becoming like television?

So sort of like a big think piece where I took Derek Thompson's Substack essay and then I elaborated on it.

This generated some pretty good comments on YouTube.

And what we're going to do is we're going to go through some of these now.

All right.

I want to start.

I'll put them on the screen here for people who are watching instead of just listening.

This first comment is from Farhanamad2022, who said, Cal, great insights as always.

I was thinking about the numbers that you mentioned, about how so many people watch content from random strangers instead of content from their friends and family.

Then I had to go to Facebook for something, and within a minute, I think I found the reason.

It's not because we don't want to watch or read stuff from friends and family.

It's because these darn social networks won't show you the stuff, that stuff, and instead will keep shoving the random content, because that's what drives their revenues more.

All right, that's a good comment.

Yes, that is most people's experience with social media today that most of what they're looking at is actually algorithmically selected from people they don't know.

But as pointed out by this comment, most people don't realize that yet.

I've been writing about this for years, but it's something that I think for the average social media user it was a bit of a water getting hotter in the pot, until next thing you know you're being the lobster being boiled.

They've been moving more and more of what you see in your feed away from people that you are connected to in the social graph that you helped establish by saying I'm going to follow this person or this person is my friend, to give you algorithmically selected content, because the algorithm can be using its machine learning approximation of the reward center in your brain, which it learns, because it's going to have a higher success rate of actually delivering a short-term reward.

And the more you get those clear reward signals in your short-term motivations sections of your brain, the more the short-term motivation region of your brain is going to push you to pick up the phone.

So it's this feedback loop that gets you on phone more often.

The experience is worse for you in terms of actual meaning, but it is better from the perspective of short-term rewards, of alleviating boredom in an intermittent way, giving you really big rewards from something that's very funny or outrageous or surprising.

So it is very good for them to move you towards that model.

So it's interesting the degree to which people don't always realize that until you actually point out that this shift has been Now.

As I've argued and I talked about it in that episode.

As I've argued before, this is a long-term problem for the social media companies.

You get more time on app by shifting to algorithmic curation of strangers' content, but you also get rid of all of your competitive advantages.

If I'm just seeing slop on Instagram, for example, instead of actually seeing content from a selection of influencers and friends that I selected.

I am interested in exactly this AI commentator and I want to see his videos.

I'm interested in exactly this fitness influencer.

I like the way she trains.

I want to see her videos.

I know this person.

I want to see what's going on with their friends.

When you shift from that to just it's slop that's going to catch your attention.

In the moment I have no loyalty, no buy-in to that app.

Because I can get slop on TikTok.

I can also get slop on Facebook.

I can also get slop from the Sora app from AI or MetaVibes.

I can also do other things that will distract me in the moment, like going to a streaming service or listening to a podcast, or going to YouTube and going through the recommended videos on the side.

You're now in a slot battle with any other source of distraction and entertainment, and now you have no competitive advantage in that battle.

How do you expect, if you're meta, that you're going to remain on top of that pile, especially when you have this sort of huge organization with all this overhead?

You're not going to stay on the top of that mountain.

So I think long-term, this is really bad news or the social media companies, for them to move towards algorithmically curated content that has nothing to do with social networks.

But it's what's happening now because in the moment it creates more time on app.

All right, let's move on to another comment.

This one is from Carl Oliver, who says TV as a never-ending stream of entertainment is only a concept relevant for a few generations.

Television is a good metaphor for how media will work, but people don't really need it, just like they didn't need it in Dickensian England or whatever.

We're going to have to progress beyond it at some point as a people, so that we aren't all lost in consumption and have lives we can attend to.

Yeah, I mean, it's an interesting point, right?

The television becoming all consuming as a background distraction, right?

This is really like the 1970s and 80s where that happened.

So a lot of this is relatively new.

You can zoom out, however, right?

And what we see is that People like diversion.

And the more diversion they can get, the better.

We really don't like boredom.

And as we move post-Neolithic revolution into sort of more boring configurations where we might just be working on a field all day long or we're not like out doing active hunting and foraging, the day becomes more predictable.

We really do want diversion.

So, like you can look at almost any generation going all the way back to I don't know, we go pretty far back.

Let's start with like the 18th century.

Newspapers began this in colonial America, right?

People were obsessed with newspapers.

The big cities had multiple different newspapers and you had all sorts of different information here.

It was diverting and you could look through it and find all sorts of different stuff, and who is debating about what, or what happened to who, or what's the news that's happening over here?

That was incredibly important.

It became a really big part of the economy.

Then you got more in the 19th century.

The Penny Press, which was the first attention economy media company.

I think Tim Wu's book, The Attention Merchants, gets at this really well.

But this is the first time we had media that was advertising supported, right?

So we get in the late 1800s this idea of we'll put out newspapers and sell them for cheaper than it costs to print.

But the way we're still going to make money is there's advertisements in those newspapers and the companies paid us to advertise.

So the more people that look at the paper, the more advertisements people will look at and the more we can charge for the ads.

So actually the cost of the paper is now not the important thing.

That was like a really big deal.

But now you have to have lots of people read your thing.

And so we got some of the first sensationalistic ads. media came out of that.

Then radio emerged.

People loved radio.

It's a weird technology.

If you look at it, you're in 1915 looking at a radio at a Nebraska farmhouse.

It's like this weird technology, this big box with knobs and electronics, like electricity was new humming vacuum tubes and you're moving this dial back and forth.

There's all this static and if you tune it right you can hear people talking through radio plays on the other side.

It is a weird technology, but it was diverting, and you could put it on at almost any time.

There'd be something on it.

We listen to it all the time.

Television came along then.

Images are way more diverting than radio, because it gives you a much richer stream of things for your mind to look at and engage with.

Again, kind of a weird technology.

We had people on these sound stages live kind of doing plays and stuff like this, people with puppets and all these weird shows.

People loved it.

Like, let me look at that.

And then by the time we get to the 1980s, as I reported in that podcast episode that these comments are reacting to, The average person just kept the TV on all the time.

We forget this now, but the statistic from that episode that was relevant is that the average household, as measured by these Nielsen audiometers, would actually just listen to see if the TV was on or not, to get the actual ground truth of how much the TV was on in the houses they were placed in.

The average person had the TV on for seven to eight hours a day.

That means they just had it on all the time.

It was just always on in the background.

We didn't yet have the technology to deliver distraction straight to our hands, so we delivered it online to this box that we would just keep coming back to and looking at.

So instead of looking at our phone at every moment of downtime, we would just turn and look at the TV at every moment of downtime.

So there's this model of like, we want to be diverted.

We don't like boredom.

It's really been around for a long time.

And then yes, when smartphones come around, we combine that with algorithmic information curation.

Well, that's just really refined that model now to it's getting closer to its apex.

I mean, I think its entire apex will be.

You're delivering sort of distracting content through some sort of augmented reality screen.

So at all times you have something that can distract you even quicker than it takes to look at your phone.

But we're getting pretty close to the apex of every possible moment of boredom.

You are diverted.

So, I mean, I think it's a good point, but I'm just stressing out the time here.

It's like it's not just television.

It wasn't before television we were all philosophical and thinking big thoughts and walking around.

Any media-powered diversion technology basically we've had for the last 300 or 400 years has been incredibly successful.

Our human nature really craves it.

So we're really.

The battle against being lost in distraction is in some sense, a battle against our human instincts to the same extent of power and impact as the battle we're going through right now with like health in our culture, where our instincts for sugar, fat and salt, combined with modern environment that's trying to take advantage of that to make money, has created gigantic health issues.

I think it's this cognitive fitness issue is just as strong and it goes back longer than people like this commenter might even recognize.

All right, let's pull up another one here.

We have a negative take.

Not everyone agrees with me.

This next comment, let's see here.

Lewis9116, can we put this up on the screen, Jesse?

Personally, don't agree with this take.

I think social media and curated algorithms are much more dangerous than TV.

TV, at least in the old days, doesn't track your every move.

It doesn't know when you're depressed.

It doesn't feed your outrage content.

It doesn't farm engagement.

It's just there, not constantly bombarding you with notifications and trying to hijack every possible neural pathway.

Yeah, I think fair enough.

I don't know that.

Derek's take however, was that the current distraction technologies are somehow the same or no worse than television.

I think he would be quick to say that.

Yes, this modern form of television, which can be powered by algorithms and personalized to individual screens, is even more powerful than what we had with TV.

But I would also push back.

I think it's a little bit too nostalgic the way you're remembering TV.

This was sort of the key data from that episode, this idea of the seven to eight hours a day the TV was on.

It really became something that people had on constantly.

It was closer to our current relationship with phones than I think people remember.

And the reason why we don't remember that 1980s, early 1990s era relationship with TV, where it was always on, like you'd be doing the dishes, you'd be cleaning your house, you'd be at dinner and it was always on.

We don't remember that because there was this lacuna the golden age of TV that emerged in the 2000s.

Where we remembered like appointment TV watching where I would on Sunday night watch The Sopranos.

But that really – before that, TV was much more closer to the slot model.

You would watch – there's just stuff that was on that was like entertaining in some basic way.

Occasionally like a show would be unusually smart like Seinfeld, but most of it wasn't.

And it was just kind of on.

Like you just put it on at night or you had it on if you're at home.

You would just have it on.

The difference, as you point out though, Lewis, which is right, is it didn't track you personally.

It couldn't follow you outside of the house, which I think is a big deal.

You didn't have it at work where our phones are at work.

So it's not like we had the TVs on while we're at the office.

So there's a lot of ways that it's worse.

But I also want to puncture the nostalgia and be like, actually...

We want to be constantly distracted.

And we got as close to simulating TikTok with an old Zenith color TV at our houses as we possibly could with that technology.

And so it's a drive that we have, which is why I think, by the way, that's the point of the episode.

This is why so much of the internet just went back to that model and just did it even better.

That's where the money is.

That is like this deep human instinct.

It all kind of comes back to that.

All right, let's load up another comment here.

This one is, I'm going to say supposedly from Glee Date LJ 1979.

I say supposedly because I think this is clearly an AI comet.

Actually, I had Nate look at this, Jesse, and he threw an AI detector.

And he's like, oh, yeah, this is definitely AI.

So this is AI kind of defending AI.

But let's just read this.

I have just finished viewing Mr Cal Newport's latest discourse, wherein he posits rather dourly, I might add that the internet is devolving into little more than a continuous flow of episodic video or, to use his pedestrian term, television.

He seems quite perturbed by this notion, invoking sociologists and data charts to bemoan our slide from a culture of literacy to one of passive consumption.

While Mr Newport is a thoughtful chap, I fear he has missed the forest for the trees, or perhaps missed the symphony for the noise.

Allow me to offer a more refined perspective on why the shift, particularly powered by our marvelous artificial intelligence, is not a regression but a renaissance.

All right.

And then this person, who's actually an AI, goes on to say like hey, the content we get from like AI and social media is like great and targeted and much more edifying to what was on TV.

All right, so this was clearly written by AI, but it's like an interesting point.

It's worth taking this apart.

It summarizes the episode wrong, as you would expect, because it's AI trying to do it.

It's not my term, Flo.

That's Raymond Williams' term.

I don't – the culture of literacy to want a passive consumption, that's Walter Ong.

That's not me saying that, but whatever.

I'm glad it calls me a thoughtful chap.

But is it true –

Is it true?

This argument that what we get now through our phones, powered by algorithms that personalize to us, is way more interesting than the junk that we used to look at on TV?

It could have been.

It could have been, but it's really not.

It's mainly slop now.

Once we went to all, social media began devolving not towards.

What's the goal of social media algorithms?

Is it...

The personalized, the most meaningful or interesting possible user experience?

No, it's time on app.

And guess what gives you time on app?

It's slop.

It's just customized slop.

Like if you look at Twitter...

Just like the homepage, it shows you.

It'll be whatever weird slop happens to press your buttons, like people in fistfights caught on surveillance cameras or car crashes or whatever.

It is right.

It's just devolving towards slop, because once you have an algorithm saying I want you to look at this app as much as possible,

So now it's just playing with your short-term motivation centers, not your frontal cortex, not with your understanding of what's interesting and what's good.

The stuff it shows you is not going to be great.

So AI, thank you for trying to defend AI, but I think you aren't doing that well of a job.

All right, let's do another comment here.

Earnhardt768, Mark Zuckerberg was never the brightest bulb in the pack.

He just got super lucky with Facebook.

This is kind of a baffling thing to me because there's two things that are true at once.

I agree that a lot of like Zuckerberg's decisions don't seem very savvy, right?

Like yeah, moving both Facebook and Instagram towards algorithmic curation of other people's content to try to compete with TikTok, but now making them both sort of superfluous and vulnerable.

Losing the main competitive advantage he had, which was the distinct feel of both of those platforms and the social networks.

Facebook's competitive advantage, everyone I know is on it.

You would think you would lean into that.

This is the place where you stay in touch with and keep in touch with people you know.

No other service can offer that.

But no, they've changed Facebook.

So now I think it's something like 80-something percent of what the average Facebook user sees.

According to their August FTC, filing from Meta is from other people they've never heard of.

All your competitive advantage is gone.

You're just competing with TikTok with the worst TikTok.

Like TikTok, but only populated by your 64-year-old uncle who watches a lot of Fox News.

That's not fun.

I don't need to see, you know whatever random people's uncles sharing their outrage about whatever.

Instagram, it had like a nice visual aesthetic to it.

It was a place where you went at first to follow friends and family, but then it became more about highly visual influencers and experts that you cared about.

Like this person who walks in her white linen dress through flower fields and puts stuff in jars with her kids is calming to me.

This particular person, I want to see these really nice videos she produces.

It was like a documentary channel that was made for your needs.

Once you're like, it doesn't matter who you follow.

We're just going to show you like random videos to do well.

Again, where's your competitive advantage?

Like that's a bad decision.

The metaverse was a spectacularly bad decision.

He put way more money into that adjusted for inflation, that the UN government did for the Apollo program and nothing came out of it.

He was just wrong.

Their AI investments have all messed up.

They hired away all these people, built the superintelligence center.

Then shut down.

The superintelligence center moved people around.

They really have had an incoherent AI strategy, right?

So you're like, Mark Zuckerberg, yeah.

Geez, it seems like this guy doesn't know what he's doing.

Also, though, he's still in charge of this company.

You know how hard it is to start a company when you're 20 and now, in your early 40s, to still be in charge of it.

Ain't no small thing.

Meta is like one of the highest capitalized companies in the world right now.

I mean, it's one of these companies that has revenue in the hundreds of billions of dollars a year.

It's capitalized near a trillion dollars.

All of the other big tech companies that came out of that era, their leader, the people who founded them.

They're not in charge of these things.

Google is not in charge.

You know, it's not Larry Page anymore. running Google anymore, right?

They passed that on.

I mean, we see these big companies that have survived, like almost all of them.

Microsoft's not run by Bill Gates anymore, right?

Like almost all of these, of course, you've passed on your leadership to like an expert class of leaders.

Zuckerberg has held on.

That means he's a savvy and savage corporate infighter.

Here's another thing about Meta.

It's making a lot of money.

They're making a huge amount.

I looked it up the other day.

It was over $200 billion a year in annual revenue.

That's massive.

TikTok, by comparison, is about $30 billion annual revenue.

The Meta is a so it's doing really well.

I know people who work there.

They're well resourced and they have really good people working there.

So somehow we have on one hand Mark Zuckerberg.

Is like making weird bad decisions one after another.

On the other hand, it's like an incredible.

It's a very high revenue company, one of the biggest companies in the country.

And this guy has stayed on.

Mark has stayed in charge.

You got to believe people are coming out.

You don't have a company worth almost a trillion dollars, where you don't have swords being thrown towards your throne all day long, and he survived it all.

He was also like a savvy savage operator.

So I don't know how both of these things are true.

Maybe he's just milking the money out of his assets.

He bought Instagram.

Then he bought WhatsApp.

They're putting their cash towards the right things.

They keep making cash.

I don't know what's going on.

Because he's not making good decisions, and yet he's arguably one of the most successful CEOs of the 21st century.

So, you know, I don't know what's going on there.

It's a good question, and he confuses me.

All right, here we go.

JRGY108 says Cal.

Personally, I like it when you go deep on nerd shit like chaos theory and Loren's number.

More of this, please.

All right, I think we're obligated now.

I don't normally curse, but because there was so much cursing in the earlier part of this episode, I was like that horse is out of the barn.

We might have to take it off of YouTube.

Oh, yeah.

They don't like the cursing, right?

Yeah.

I know.

Well, we'll figure it out.

Yeah.

I'm happy to talk chaos theory or math or whatever all day long.

All right.

What we got here?

Yeah, this question kind of confused me.

Daniel Welkin, 3108.

Is Newport being paid to read these adverts?

Certainly seems like it.

What does he assume the other option is?

That I just like to read ad copy on my own for free?

Adverts is short for advertisement?

Oh, advertisement.

Yeah.

So, okay.

I hate the.

I feel like I hate the burst.

You know your illusions about media, but we get paid to do advertisements, like that's kind of how this works.

It's not the cheapest thing.

We got to pay for the studio and all of this uh equipment.

Um, you know, jesse's truck requires, i would estimate, like about a quarter million dollars a year in just repair cost to get me to tacoma park, just to get you to tacoma park.

Right, that ain't cheap.

Advertisements is how you pay for it's either that or you put it behind a paywall, but then no one listens to it.

So yeah we i'm, I mean, I love all these companies, but yeah, there probably would be less content about those companies in this show if I wasn't getting paid to read them.

So I guess I should clarify that.

All right.

We got Peter Webb 8732 said on the Internet.

The people who yelled at the television now yell at each other.

Yeah, that's that's about right.

That about sums it up.

The Internet has become television.

This is the main difference, though.

Instead of yelling at the newscaster, we we can yell directly at each other.

So I guess progress.

Yeah, there we go.

Thank you, technology.

All right.

That's all the time we have for today.

Our first episode of 2026.

This is our this is our Super Bowl, right?

January is when our podcast people people are on it.

They want to improve.

So we got some cool episodes coming up.

So definitely stick with us.

We'll be back next week with another episode and until then, as always, stay deep.

Hi, it's Cal here.

One more thing before you go.

If you like the Deep Questions podcast, you will love my email newsletter, which you can sign up for at calnewportcom.

Each week I send out a new essay about the theory or practice of living deeply.

I've been writing this newsletter since 2007 and over 70000 subscribers get it sent to their inboxes each week.

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[2025년 AI 회고: 위대한 한 해였는가, 아니면 재앙이었는가?]-[Ep 386: Was 2025 a Great or Terrible Year for AI? (w/ Ed Zitron)]

📋 Summary

2025년 AI 산업 회고: 혁신인가, 거품인가?

1. 딥시크(DeepSeek)의 충격과 '규모의 경제'에 대한 의문

2. 'AI 에이전트'라는 마케팅의 허상

3. GPT-5의 실망과 '추론(Reasoning)' 모델의 실체

4. 'AI 안전'을 빙자한 지식인들의 그릇된 공포 마케팅

5. 결론: AI 산업은 현재 '심각한 적자' 상태인가?

최종 평가

🎯Key Sentences

📝Key Phrases

📖 Transcript

[2025년 AI 회고: 위대한 한 해였는가, 아니면 재앙이었는가?]-[Ep 386: Was 2025 a Great or Terrible Year for AI? (w/ Ed Zitron)]

📋 Summary

2025년 AI 산업 회고: 혁신인가, 거품인가?

1. 딥시크(DeepSeek)의 충격과 '규모의 경제'에 대한 의문

2. 'AI 에이전트'라는 마케팅의 허상

3. GPT-5의 실망과 '추론(Reasoning)' 모델의 실체

4. 'AI 안전'을 빙자한 지식인들의 그릇된 공포 마케팅

5. 결론: AI 산업은 현재 '심각한 적자' 상태인가?

최종 평가

ListenLeap으로 딥러닝 학습

🎯Key Sentences

📝Key Phrases

📖 Transcript

ListenLeap이 실제 문맥에서 학습하도록 이끌어줌