Yep, We're At Peak AI
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Today I want to kick off by reading something I wrote march twenty twenty four and talked about the episode. K. I, what if what we're seeing today isn't a glimpse of the future, but the new terms of the present?

What if our official intelligence isn't actually capable of doing much more than what we're seeing today and one of they're no clear a timely and IT will be able to do more? What if this entire hype cycle has been built on hot air ghost by a compliant media, ready and willing to take career and belsher at their word? Reading that back, well, I think I might have been right.

And that's kind of what i'm gonna at today. I don't want to scream mustard. I'm not gonna get smog about IT, but this is what we're getting into today.

And in the next epo de that i'll come out on friday. Now i'll be linking to smart, of course, so quickly the episode notes, if you want to read them, but i'm going to get a lot into the spokesman. D, so I warned you in february that generate A, I has no killer apps and had no way of justifying its valuations.

I also want you in march that generate vi had already picked, and I pleaded with the tech industry in April to consider an eventuality where the jump between GPT four, which is the most current model, how tript for o to GPT five, was not significant, in part due to a lack of training data. One of the more obvious things, I shared more concerns in july that the transformer of based architecture, and depending generate A, I thinks like ChatGPT, was a dead end. And then there were really not many ways with progress past the products we d already seen back then, in part due to the limits of training data or commented and the limits of the models that use said training data.

In August, I summarized the pale horses of the AI apocalypse events. Many have now come to pass them, afraid that would signify the end. Well being nice though, it's not quite here yet and it's not obvious when IT will be.

But this can't loss ever. But I also added that GPT five would not change the game enough to matter. Let one add a new architecture to build future and more capable models or products of any kind.

Now throughout the things of written in and the things of spoke, and have repeatedly made the point that separate any core value proposition, training, data draw or unsustainable economics that i've got over quite a lot generate, if a is a dead end due to limitations of a robber lisc model that hycy ates. Now just to be clear with what that means, it's guessing what the next thing might be. And it's quite good at IT, but quite good as actually kind of shit.

And hello, SE nations of, of course, are whether authority of these state things that aren't true, like when ChatGPT tells you something, like, I don't know, there are two hours in strawberry. The whole nation problem is one that is nowhere closer to being solved. You may remember a few months ago when you had every take executives vat tim cooks such an adela bi will deal with the fluctuation problem.

It'll be alright. But I want to be clear, they have not solved IT, they have not really mitigate IT and there's no fixing IT, at least with the current technology, it's not going anywhere. And IT makes all of this stuff kind of a non starter for many business task.

I have since much expressed great dismay about the gradualist ness of the media about this and the weird acceptance of this inevitable way in which generate A I will change society, despite the fact there's not really a meaningful product that might justify any of this bullshit, this environmentally destructive nonsense, LED by a company that burns more than five billion dollars a year in big tech firms that are spending two hundred billion dollars on data centers for products that people don't want or even potentially use. And you're gonna context for everything i'm saying today. So it's worth going over how these models work and how they trained.

And I must be clear the reason number, repeating myself on so many levels here, it's it's just really important you to know how obvious the problems of general I have been since the beginning. It's really important. Let's go over how they work real quick.

A transformer based generate A I model, such as GPT, which is the technology behind ChatGPT, generates answers using inference, which means that draws conclusions based off of its training, which requires reading IT masses of training data, mostly text and images, straight from the internet. And both of these processes require you to use high n GPU graphics processing units and lots of them, tens, hundreds of thousands of them, while over hundreds. I'll get to that next episode.

Now the theory wasn't, might still be, that the more training data and computer you throw out these models, the Better they get. And i've protheus zed for a while that we'd have diminishing returns, both from running out of training data and based on the limitations of transformer best models. And wouldn't you know where I was? Bloody right.

I want i'm not gonna do many of these, but this one, really, this one, i'm right. On a few weeks ago, bloomberg reported the OpenAI, google and anthropic is struggling to build more fun day ee and the OpenAIA I o n m odel. Others SE, known as GPT five, did not hit the company's desired performance.

And that, and I quote again, a iran is so far not considered to be a bigger step up as IT was in GPT three point five to GPT4， its current model. You will be shocked to hear that the reason is that it's become increasingly difficult to fine new on tap sources of high quality human made training data that can be used to build more advanced eye systems. Something that I said in march, I said that would happen in march, and pissed off the people said I was a pessimist.

Well, who's a pessimist? now? Me, I guess that I I don't know. But I they also added one thing, which is that they believe, and I quote that the agi bubble is bursting little bit, which is something I said in july, agi isn't coming out of this shit, let's just be honest.

And I also want to stop and stare really hard at one particular point. And I quote again from bloomberg, these issues chAllenge the gospel that has taken hold in silicon valley in recent years, particularly in open eye released ChatGPT two years ago. Much of the tech industry is Better on so called scaling laws that say more computing power data and larger models will inevitably pave the way the greater leaps forward in the power of ai.

The only people taken, this is gospel of the members of the media, unwilling to ask the tough questions. And A I found is that don't know what the fuck they're talking about or the intend to misled you. Generate vice products have effectively been trapped in Amber for over a year. It's been blinkingly.

Obviously, if you fuck can use them and pissed off a short wear so much, there would be no meaningful industry defining products out of this because and I quote darna mugu, the economist that MIT back in may, more powerful models do not unlock new features or really change the experience, nor what you can build to a transformer based models is really a worthwhile product. Or but another way, a slightly Better White elephant is still a White telephone, despite the billions of dollars burned and thousands of glossy headlines, is difficult to point to any truly important generate a ae product, even apple intelligence. The only thing that apple really had to add to the latest iphone IT sucks.

It's not useful. I I can make a special emerging. now. I now get summaries of my text that are completely or gally incorrect or just summarized a giant meaningful paragraph into a blob of a sentence is so stupid.

And just as aside question, what the hell is apple going to put in the next iphone? I buy one of these every year. I'm a little pig ongoing coin, but still I don't even know why I up right again, the camera is already about as good as IT it's gna get anyway. There are people that use ChatGPT two hundred million them a week, allegedly losing the company money with every prompt other way. But there's little to suggest that there's widespread adoption of actual general aai software. The information reported in september that between zero point one percent and one percent of the four hundred and forty million of microsoft business customers were willing to pay for its AI power copilot, and in late october, mysore claimed that he was on pace to make A I A ten billion dollar a year business, which sounds really good until you think about IT for roughly ten seconds.

First of all, microsoft does not have an A I business unit, which means that this annual ten billion dollars or ten of billion a quarter revenue figures spit across providing cloud computer services on as you selling copilot the dumb people with microsoft 3 sixty five subscriptions so didn't get hub copilot basically anything else with A I on IT microsoft Cherry picking a number based on non specific criteria and claiming it's a big deal when it's actually pretty pathetic considering that microsoft capital expenditure will likely hit over a sixty billion dollars in twenty four with no sign they are gonna slow down now that sticky word revenue， not profit. Those are two very different things. How much is microsoft spending to make ten billion dollars a year? OpenAI currently spends two dollars and thirty five cents to make a dollar.

A microsoft fo. Amy hood d said the OpenAI would cut into microsoft profits in their last learning school, losing at a remarkable one point five billion dollars, mainly because of the expected loss from a company that has only have a lost money. Now a year ago, in october twenty twenty to three, the all street general report at the microsoft was losing average of twenty dollars per use of per month on get hub copilot, a product with over a million users.

If this is true, by the way, this suggests losses of at least two hundred million a year. They have one point eight million users, allegedly. This is based on documents on reviewed.

It's not great either way that two hundred million dollars is a lot of money to lose. I would personally like to make two hundred billion dollars rather than lose, then asked me that I didn't want microsoft. Now microsoft is still yet to break out exactly how much generate F, A.

I is increasing revenue in the specific business units they have. Generally, if a companies doing well at something, they take great pains to make net clear. Instead, microscope in August to revamp its reporting structure to give Better visibility into cloud consumption revenue, which is something you do if you say anticipate you're gonna have your worst day of trading in years after your next earnings, as microsoft did in october. Very good. It's all going well.

Now I must be clear that every single one of these investments and products is are being hyped with the whispers that they would get exponentially Better over time and that eventually the two hundred billion dollars in capital expenditure would spit out this remarkable productivity improvement, this crazy new product that will change our lives, fascinate in new things that consumers and enterprise of buy and droves and talk about how much they loved. Instead, big tech has found itself peddling increasingly more expensive iterations of near identical large language models and shirty products attached to them, a direct result of all of them having to use the same training day a, which they're now running out of. But if you're running out of stuff and you can't find stuff to buy, I really recommend the following advertisement. I'm sure I will totally jail with my beliefs. The things i'm talking about right now won't be embarrassing at all.

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And with back now, there's another assumption that people have about these so called scaling laws that's been the by simply building bigger data set is with even bigger, more powerful gp s, the expensive, power hungry graphics processing units that used to both train and run these models and throwing as much training data them as possible.

They would simply start doing new things that have new capabilities, despite there being little proof that they would do so in any my shape, perform microsoft matter, amazon, google of all, burn billions in the assumption that doing so would create something, you know, a thing, a good thing, like a human level of official general intelligence, or or a product to the make more money than IT costs the people liked. It's become kind of obvious that that isn't gonna happen as we speak. Members of the media, who should know Better, are already desperately trying to prove that this is not a problem.

The information, in a similar story to bloomberg attempted to put lipstick on the pig of generate AI, framing the lack of meaningful progress of GPT vivers fine, because open an eye can now combine its GPT five model with its one reasoning model, which is the one that can't count the number of us. And strawberry, by the way, which will then do something, something, something good. Something's gone to happen.

Like sammon said, I could write a lot more very difficult code, know something on the career layer who intimated the GPT5 may function like a virtual brain in may。 Like these people are liars as they lives. They lying to you. They were lying then. They're lying now.

Now I couldn't possibly leave out j felly cheerly, the casey new and who wrote on platformer a few weeks ago, that diminishing returns in training models may not matter as much as you would guess, with his evidence being the anthropic who he also claims has not been proved to hyperbole. Do not think the scaling laws are ending. Now the original scaling laws paper in para, a dacy of tropic. Important to know, and to be clear, in a fourteen thousand word ipad, the casing, you and for no reason wrote two pieces about anodic. A dari O E said that, and I quote A I accelerated neuroscience is likely to vastly improved treatments for, or even cure most mental illness, which is the kind of hyper ability that should be.

Have you tarred and feared and put in a jail? I'm not seriously saying you put him in jail, but why are we? Why are we trusting these people? Why are we listening to them? Why we treating them as if they are telling the truth, or even that they know what's going on? But let's summarize the main technology behind the entire, and I say this in quotation Marks, by the way, artificial intelligence firm is generate A A, I transform voice models like OpenAI GPT four and soon GPT five, and said, technology has speaks with diminishing returns from the only ways, making them Better, feeding the training data, throwing tons of computer them, suggesting that we may have, as I said before, reach P, K, I, general.

If AI is incredibly unprofitable, open eye, the biggest player in the industry, is on course to lose more than five billion dollars this year with competitor anthropic, which also makes its own transformer based model claud on course to lose more than two point seven billion dollars this year. They just raised another four billion. Every single big tech company is thrown billions of dollars, as much as seventy five billion dollars.

In amazon's case, in twenty twenty four alone, a building the data, that is, an acquiring the GPU, the population said data is specifically so they can train their models and other people's models of customers that would integrate genre AI into their businesses, something that does not appear to be happening at scale. And these investments could theoretically be used for all the products, but these day isn't as a heavily focused on generate A I business inside of reports, the microsoft tends to a mass one point eight million gp s by the end of this year, costing IT tens of billions of dollars. Were still many of these companies integrating generate.

I do so by connecting to models made by either OpenAI anthropic, both of women running on profitable businesses and likely charging no way near enough to cover their costs. As i've said before in my article, supply my eye crisis in the event that these companies start charging what they actually need to their real costs apotheosized IT will multiple the costs of their customers to the point that they can't afford to run their businesses, or the very least will have to remove or scale back generative A I functionality and their products. It's just it's such a waste.

The entire take industry is become warrant IT around this debt technology that requires boning billions and blissful dollars to provide an essential products that cost them more money to serve than anybody ever would pay. Their big strategy has to be to throw more money at the problem until one of these transformer based models created something useful despite the fact that every iteration of GPT and other models is being well iterative. And it's weird.

You think at some point that go share, do we actually have the ability to build products with this? What are the products? Maybe we should work out the products first before we throw all the capex at IT.

But wait, no, over yonder. I couldn't possibly not do this because the other big tech company that also has no ideas, they're doing this. And if I don't do this, my investors are going to be angry at me.

And then what will I do? Oh oh no. What could I possibly do if the investors I don't fucking? Now that's your problem. Why waste this much money? It's just has never been any proof other than these benchMarks that are are really easy to game and also only show is this vae power of these models.

It's been obvious ous that GPT or other models wouldn't become conscious that they not going to to do more than they do today or three months ago, even a year ago. Hesitate to give gary mark is credit, but in twenty twenty three, he was saying that, if not earlier, many people have as well. And it's just really, really, really, really frustrating.

Better offline isn't even a year old, but when we put out our P, K, I, M. So I got so much flag, I got so much shit for being a hate other. I didn't really understand things, that my fly was open in my instagram picture, that I didn't get IT.

And in mere months I would be proven wrong. Well, the we are. How wrong am I? Now, what happens next? Exactly where all these hundreds of billions of dollars go.

What happens to open a eye when IT collapse is what does microsoft do with all of these gp s because you can't just move them into other shit. You know, from what I hear, they don't really have a plan. And that's the scarious thing.

Because what happens to a stock market is dependent on big tech companies for growth when the big tech companies can't walk out a way to grow anymore. And in fact, they are big path of trying to grow more. Was the burn a shit ton of money on things that people hate to destroy our environment? I know, I know i'm angry.

I know I should calm down. I should. But as I said, in the broad society, this money could go elsewhere.

More things could be done. IT would enter a fellow period of tech. But we don't just have to burn all this money.

We don't have to do that. Why not make the products you have already Better? Because stapling generate AI on them.

I think IT makes them worse. But there are more problems ahead. There are problems around the infrastructure.

And in the next episode, i'm going to break down these worrying problems, and i'm gonna kind of tell you what happens next. This best I can. I really appreciate your faith in me.

And there are many people who also contact to be inside. And now you're bang on. Keep going.

I'm glad they did. I'm very grateful for your audience. I love you all much like he said in the man.

Thank you for listening to Better roofline. The editorial um composer of the battle offline theme song is matter salesa pe. You can check out more of his music and audio projects.

A meta sale, sky dot com M A T T O S O W S K I dot com, you can email me are easy at Better off line dot com or visit Better off line 点 com to find more podcast links。 And of course, my newsletter, I also really recommend you go to chat that with your ed or ant to visit the discord and go to ask lash Better roof line to check out our red IT. Thank you so much for listening.

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