Cal Newport on Mythos and Anthropomorphization

I called directionally correct. It makes the general point that you see it as your job as reported to make, which is, hey, you need to be worried about this or this is a big deal, right, And so I think that is a problem. There's another issue I'm seeing, though I've sort of been refining my thinking on this, I'm also wondering if some of what I'm seeing in some of the reporting on this is just a embrace of the form of I'm going to give you a stress wave with no relief, just like we're all going

to take turns. Just I will choose an era you haven't thought about. How about mathematics are going to go away. Mathematicians are going to be okay, I'll take that one. Yeah, let's stress like yeah, and they But there there's this weird sort of passivity to it where it's like I'm just gonna sort of it's it's uh, I call it like head shaking numerism. You're just like, it's this, this feels just going away. What can we do like just

this sort of like passive head shaking. It's a very specific style you don't see a lot of other reporting historically. I think it takes on this resignation of I'm just gonna make the case that like you're screwed and then kind of give you a shoulder shrug and then we're and then then we're gonna drop the mic and walk off. And I'm kind of getting tired of this, Like I think there is a cost to stressing the hell out of people. I mean, I'm getting letters all the time

now from people. They'll say things like I feel like I'm trapped in a cage, just being hit with wave after wave of stress, and there's no outlet. There's no door or possibility in making things better. And I think the CEOs are doing it, and I think increasingly we're seeing commentators doing it as well. This is not good in many different ways, So I don't know. I'm adding that to my list. Some of it's directional true reporting, like they really are worried that people aren't worried enough,

and I think it's just sport. Now, can you find an area to come in and just write a headshaking article that's only trying to undermine the existence of this like important human activity or this job or our lives or whatever. It's a it's a very unusual style that quickly became a standard.

know what anyone's meant to do with this information. But it's just like, well there you haven't they have. We're all fugged. It's it's the it's the end the job, even though the data does not say that. Like I've read I think every AI jobs report now, every single one, and they're all the same. They are all right, now, AI can do this, and then you look at what it says. It's like it can do law, well, it

can't really do law. It can do one sigma within law kind of, and even then it isn't really obvious, and the people saying it can do that are partners at law firms that don't write motions but don't do like the grunt work. So it's it's almost it feels like the reporters have either given up or are just looking for clicks, and it's hard to tell. Sometimes.

the jobs are gone. If you really thought that our entire infrastructure is about the collapse, that superintelligence was going to emerge suddenly and be a threat to human existence, you want to just right sort of too cool for school headshaking resignation article. You would be like, we got to where are the Don Connors? Right Like, we need to get on the cool trench coats and get out there and go against the Skynet revolution, like you would be on your feet, you'd be you know, nothing would

be more important to you. So this is the this is my case about the text CEOs. I think there there's a moral hazard that I don't think that we're putting our finger on properly here. Right, So you have the text CEOs in the AI space that will just they'll just come out and just drop these bombs like, yeah, white collar blood. You know, I never actually said that that's Axios putting words in people.

making these big statements. These jobs are going away. The Internet as we know it is about the all fall apart because of mythos is going to have this, this new capability, the super intelligence is coming. I'm I don't even know what's going to happen. There's two possible things going on here, and both of them are morally bad. One is which is the one I think is true,

which is this is largely marketing. I look, this works, it gets reported, It keeps us seeming inevitable and important, in which case that's a huge moral hazard because you are making many many people normal people stressed the hell out, actively scaring them, actively scary them. The other option is you actually believe it's true. Well, this is an even larger moral trap that you've just fallen into because you are now perpetuating something that's going to cause exponentially more harm.

You should be the very first person shutting down your company and trying to get the other ones that do it as well. So it's this weird world trap they've set up where whatever is actually going on here, if they're coming out here saying these things, it is bad. This can't possibly normalively speak. He be the right ethical behavior to be out there saying these scary things all the time, because either you need to be building the barricade,

or you're just scaring people for the marketing. Neither of these I think is something that's defensible.

across people like Kevin Bruce and Casey Newton. These are my words, not cows that they're aligning them that they're saying, we think this is going to happen, and we're here to tell you great news. This is good news for me the writer, because I will be safe somehow I will be fine. You will not. You should be scared. But it's also a good thing because economy, marketing market good,

and it is. It's a very incoherent message because it's like, to your point, Yeah, if this was a virus like a pandemic, you wouldn't be writing, Hey, millions of people are gonna die. What pretty good right, hey, or be good we'll have less people. That'd be good, right, It would be seen as peculiar.

who are the people doing the wrong thing. But it was very much coming from this angle of like, Okay, we need to do whatever it is, like, we need to be better about this. It's got to be vaccines, it's got to be masks, it's got to be pick your mitigation, whether you like it or not. It was very focused on what should we be doing or who is it that's getting in the way of a plan

that maybe would get us out of this. Which is where I think you're very right is that you did not see a lot of COVID pieces that were just well, I'm just going to kind of walk through like all the different ways you know, you might die and the morgues are going to fill up, and uh, you know that's COVID. But I also think what the other thing we saw on a lot of COVID coverage is something that we are seen in the AI coverage. That's where I saw a lot of the directionally true, not factually,

but directly true. There's definitely a period early on in COVID because I was following that coverage quite carefully where the papers were thinking, Okay, this is the right behavior and they're probably right about a lot of these things, but I just would notice this. There'd be a lot of like, Okay, we need people to buy into, for example,

the lockdowns or whatever. And there'd be a lot of directionally true reporting where maybe they would like put on a photo of a mass grave that was sort of unrelated the COVID, or you would see a lot of there'd be pushed back from like conservatives about schools, and then they put a lot of articles in the paper about teachers dying to COVID, even though it was they weren't in school, they got COVID elsewhere, And if you really pushed on it, it was because it's directionally true.

Like the general or more general truth here is like we need to be worried about this or these mitigations work. It doesn't matter if this if this photo is actually right, or if this teaching who died in Orlando, the fact that they hadn't yet been back in a school building yet it doesn't it's serving the directional truth. So it's like it highlights something. COVID highlights something we're seeing now that the reporters that are doing directional reporting like we

should be scared about it. I dare you not to be scared now, I dare not to be scared now. That's trying to ratchet it up. Yeah, but then you also get the contrast, which is this new style of just like headshaking resignation. And actually, I don't think the reporters think they're going to be safe. They're also like, writing's going to go away, the media is going to go away. So it's it's an almost like a nihilistic type of approach to this, like, yeah, I'm screwed, We're

all screwed. What are we going to do? And that is definitely different than we saw during that last crisis, which was obviously much more actually severe than what's happening now. So it's really confusing me, to be honest.

Fuck knows? Use jet GPT, I guess. And what's what's really confusing to me as well is you say, oh, these people don't think they'll be safe. For the most part, I just don't. I actually take back what I said. I think a lot of them just don't acknowledge it. They don't acknowledge the core ridiculousness of being like, well, everyone's jobs are going to get replaced, don't know, Like the Garfield meme with him looking at the Garfield with

the crossout and the TV. Yeah, flawlessly described there. It's just it's frustrating as well, because it is terrifying people without Like I'm not saying literally axios or however, but stories like this are what made that mentally unstable person throw a Molotov cocktail at Sam Mormon's house. Like it's obvious that these people are scared of the AI doom, partly because to your point, what the fuck are we meant to do about it? Because using the these tools

is not I don't really see how that works. Because if going along that line of logic, if the answer is you need to use this stuff now, but the eventual end point is that it's intelligent enough to do everything for you. How does using it now matter at all? Like what's the Surely chat GPT would be seen as like a rock versus a shotgun at that point, Like it's just technologically irrelevant if they get to AGI, which they probably won't, and it's just naturally illogical stuff.

I think I've heard you say this recently and I agree with it as well, Like I don't think we should be chatting with technology. He should not be chatting in a sort of anthropomorphized, humanized way. Doesn't mean you can't do natural language processing. I mean Google's natural language processing. You're writing your Google searches in natural language, but no one's having a conversation with Google's. You list the keywords as quickly as possible, and Google's pretty good at figuring out,

you know, population Spain nineteen eighty two. Any presenter, and you get that information. You're not like, hey, so I'm wondering what the population is of Spain in nineteen eighty two, can you help me find that question mark? There's something odd about that anthropomorphized conversational interface. I guess we saw a lot of Star Trek growing up, and that's, you know, what we think the future is supposed to be like, but it has all sorts of problems.

let's say, Chut GPT has grown comes from search. I think it is the core of it, because Chad GPT and Claude and all them are better at understanding what you asked for, not saying that they output is necessarily great, but just they understand the inference they make from what you say is better than Google, or at least better than Google has been. I feel like it was better before, and I think that had Google not kind of boothed it on this one, we wouldn't be in this spot.

But even then, using Google now it forces you. It forces the AI summariz and you could do minus AI and all that. But sometimes I don't remember too, and it's just it's just turned search into this nightmare. But nevertheless, back to what you were saying, I agree. I think the anthropomorphization needs to go. I think that these things need to respond like terminal windows or what have you. They need to respond like computers and go, okay, here you go. Just don't need all that clutch. I don't

need to be told, oh, what a great idea. I know I had it, or indeed, if I'm being told that, I need to be told if it's a bad idea. But I don't even necessarily need a nonswer. I just need stuff to look at so that I can come to my own conclusions.

And then if you do that auto aggressively, so you call it again and again and again, adding the answer to the input so it grows out an answer, what you're going to get is a text expansion. You've given me a text that I'm trying to expand as if there is a real text that exists and I'm trying to match it. You get that like kind of indirectly. So really it's idiom is the type of text that's trained on, which for the most part is more sort

of pro style text. So you can tune it away from it like you can tune its mood, you can tune its synofancy. But it might be hard to actually tune an LLM because it deals with human written pros as its main training data. It might be harder than we think to tune that away from being verbose and that just give a table. Now, I guess you could take its output and then maybe run that through another thing that then strips away the other piece. It's like,

it's possible. But I think the anthropomorphized verbosity we see in language models is also that's kind of the native tongue of this particular, which is why we still have a lot of chatbots being emphasized, and tools that are built upon LLM as the digital brain are still way more scarce than you would imagine outside of maybe computer programming en coding harnesses, we just don't have a lot of other examples where we just use the LLM as

a general person's digital brain, because I think this verbosity is okay, humans can interpret that, but it's not great. If the LM is just a digital brain that's interfacing between you and another computer, it doesn't need to hear that their idea is great or wants to try to parse the different types of text. So there's some interesting things going on there about the fundamental nature of these things.

this fun. That fund was fund that was founded after this happened, And it goes, okay, well maybe it's this one different fun three years old. Doesn't it not involved? Do you have proof of that?

fire the thanking me accountable. That was I did the opposite thing, you know, Yeah, that didn't happen in Star Trek.

the ability to like, oh, they'll use computers. Computer use is basically non functional in AI, and it takes insane amounts of compute. It feels like a conversation keeps happening in theory, in the media on social media about something that's possibly completely impossible, but the centency they discuss it with is insane. To me. This whole agent conversation, I've never seen anything like in my life.

but this was purely decentralized. I got worse versions of software to be decentralized.

do XYZ. Mythos is a language model. You can give it an input and it can give you a token. You're talking about a program that is calling Mythos and then taking actions based on what it called. And this is really what we're talking about with agents is the digital brains are lms. And then you write a program that will say to the LLM, give me a plan for doing X. And then the LM spits out what seems like a reasonable text, that seems like a reasonable plan,

and then you execute that plan. The program executes that plan on behalf of the LLM. And I wrote about this crypt Yeah, and I wrote about this earlier this year. Llms are bad, you know, as a digital brain are bad planners. It's not really you're not going to get consistently usable plans because what an LLM is actually trying to do is finish the store you gave it. So all it wants to do is produce a story that

sounds reasonable. So it's giving you reasonable sounding plans like, yeah, that's what a plan for doing this would more or less sound like. But what it's not doing is actually doing step by step evaluations. It doesn't have a clearly isolated goal that it's trying to measure how close you're getting to it. It doesn't have a world model to evaluate what's going to happen with the steps that are going

to unfold next. And so in almost every context, it turns out, oh digital brain, by itself being an LLM doesn't lead to good agents in programming. It seems to work a little bit better. But I do think Gary Marcus, I don't info is a scoop. But Gary Marcus captured in a recent newsletter something really important when Anthropic leaked the code for their cloud code coding harness that sits

on top of their lllms to do coding. It turns out they've added a huge amount of old fashioned hand coded symbolic AI style rules and pattern recognizers and special if thens. So they've just been sitting there tuning this program for specifically doing computer programming, and the LM is being a little bit more isolated to just the code production, so they've kind of just gone back to old fashion. This is like an old fashion system that is plussing

up an LLLLM. But I'm with you, Yeah, it's very hard just asking an LLM tell me give me a plan for doing X for almost any scenario of X. You really can't trust a plan from a model whose goal is primarily to finish text. To finish the story you gave it in a reasonable style way. That's not how we plan. That's not how we think about planning, and it doesn't give you consistently usable plans. So yeah, but you're right, it's magic. Like the agents are coming.

They've been saying this. I mean, I wrote the article I wrote you know in January what happened to the Year of the Agent. Twenty twenty five was the year of the Agent. All we had was coding agents. That's the only thing that we worked on that whole year. It was a post. I'm mean, I have the receipts. Early twenty twenty five, all of these executives saying, your work is a knowledge worker now as a computer programmer, but just as a knowledge worker is going to be

largely done with agents. You're going to have agents are going to be a major part of your workforce in just a normal office setting. And none of that happened because it turns out to ask in an LLM, give me a plan for doing X, does it often actually produce a workable plan.

But what's more insane to me is that the conversation around agents is if they're already here, I'm about to read you something from Box CEO Aaron Levy. The CEO of a public company. One corollary to the fact that AI agents take real work to set up in a company at scale is that the role of the forward deployed engineer or whatever it gets called in the future

isn't going away anytime soon. When a vendor sells any kind of agents into an organization, you're no longer just selling a software tool that gets implemented and you're done. You're fundamentally selling some sort of actual workflow being done by your technology. What are you fucking talking about? What do you talk to You were a cloud storage and collaboration. What do you sell And the answer is nothing. They don't sell any agents. Agents do Oh, agents are going

to do this. What you are describing is a different kind of technology. Just that's it, Like it's something else that doesn't exist. But this is everywhere you go. You look at any consultancy right now, any conference right now, they will be a speech about agents. Even Meredith Whitaker, who I deeply, deeply respect, went on stage last year, is like, yeah, AI agents using money, they're booking plane tickets. No they're not. That's not happening. And I said, I

say this again, deeply respect Meredith. I said this online. People flip their shit, I mean, and it's like, oh, she's directionally correct. Yeah, she's directionally correct. It's like, let's be scared of the things that exist, because I think it's perhaps scarier for a different reason that we have large swarts of the tech industry talking about something that doesn't exist, like just like agents don't like, they don't they don't exist, they don't like people are talking about

the agentic Internet. I keep reading about even on the Verge. I read about it. I read it all over the shop where it's like, oh, yeah, well the Internet needs to be rebuilt for agents to use. It's like, what do you mean? And they never say because the answer is when we come up with something else, because I don't even think neurosymbolic makes sense for this. I mean neurosymbolic being the one where it's they have a deterministic

system that they access it from what I understand. Like the other thing as well, now that I think about it out loud, is how would they actually browse the Internet? Where are they being housed? Are we using GPUs to make them browse the Internet? That's insanely, insanely, that's very very convoluted and probably quite expensive to do. And to what end?

It can have a text interface so that an LLM, like they do the coding agents do, can give you know, description of how to do something in Excel in text without having to actually move a mouse or click things around. And then that these evolve to say, okay, well, what's the one type of instruction that we're good at producing? Because they get when elms produce plants, they're they're directionally

correct plans. They don't actually get the thing done. But they said, oh, what lms are good at is producing code that compiles and we can actually like check that it works. And so this is where this whole vision has changed is that all applications and Internet websites should have a code accessible API that you can expose, and that an LM can write a program that will then act says that API. So we don't need to teach the LM how to use Excel. It'll write a It'll

write a Python program that'll call hooks into Excel. The problem with this is no one wants to open up their application to just agents in general. If I'm Microsoft, is like, I don't want I want to write a custom tool for my program. Why would I expose my program for anyone else, anyone else to use it? But your your original question, it's a big one to what end. Like I've been writing about this recently, especially with uh

work and AI. You got to find the real bottlenecks, right, Yeah, it's it's the drunk looking for the keys under the under the street light. There's a lot of this going on where this is what we can do with AI right now, then this now becomes like the key to productivity. But the real bottlenecks in people's work is often not the things that we're trying to aim AI at. Like I don't know people are super frustrated at booking a plane ticket online.

that result. This is revolutionizing research. And I'm saying, well, it's not. The bottleneck for social science researchers is not analyzing data and producing plots. You're not sitting there doing that eight hours a day every day. And if I could do this twice as fast, I'll produce twice as many papers. I might write one paper in a three month period. Yeah. In there, there's like four hours I

spent making a plot. And sure it'd be nice if that four hours became thirty minutes, but that's four hours out of like a multi month process of sort of thinking about this paper is, by the way, like a graph. Oh right, yeah, the computer science turn. But yeah, it's like that's nice, that got a little bit faster. But that's not the bottleneck. That's not what's going to unlock a lot more research. It's like, man, I would write more papers if it wasn't for how long it took

me to draw a graph. And if you could buy.

I can get three papers. Out of all of that work, there's one day in there where you're crunching the numbers and making a plot, and it's nicer if you could do a little bit faster. But it's not a productivity bottleneck. It's a marginal efficiency. I think there's a lot of that going on right now with AI and productivity, as we look at what the AI can do and then try to make that thing and somehow being the key to getting things done.

would possibly help me here. And they're not working on that.

know how to do that. And they tried it. I mean, this is Microsoft Copilot. But it turns out we underestimated the degree to which when we as humans are interacting with a chatbot that were incredibly gracious, were able to adjust and kind of get the gist of what it means and filter out the part of the chatbot response is not really relevant or ask the follow up question. And when they tried to just use LLLM responses to automate actions within programs there it's just not accurate enough.

So they wanted that to be the case that you could just be talking to a riverside bot and you never would have to press a button ever. Again in Riverside, it's just not accurate enough. Llm's it's fine for human conversation, it's it's just not accurate enough in this general case.

it's it's just very like Salesforce today. I don't know if you saw, they announced that they're doing Salesforce headless three sixty. Mark Benioff needs to fire everyone in marketing. But they've made it so that you can do everything with Salesforce fire and API, which is I mean, the first question I always ask is what does Salesforce do? Because I've talked to so many people and they can't tell me. There's like twenty one different features. No one

knows what they do. But it's like it's just a very bizarre thing it's very much a cart horse thing. But also what agent Like, That's what this is the thing that really drives me insane. They're talking about we've built this API for agentic web for agents to use it, which one? What agent? What are you talking about whether it will be in the future. What do you you change something materially with your publicly traded company worth three

hundred billion dollars because it might happen. Well, we're getting ahead of it. What the And it's you talk to members of the media about this and they just go, yeah, you know, yeah, you know, it will happen. It's obviously going to happen. They wouldn't put this much money behind it if it wasn't going to It's like, I don't know, especially with Salesforce, And I'm like, you don't think Salesforce

would spend a bunch of money for no reason? Well, but you've not been following Salesforce at all then, I mean, yeah, gone, yeah.

web three. These people will meet and talk about anything. Oh, it's scary and they're not talking about it, which means it's powerful. How is it powerful? What does it do? Because I think you probably saw this as well. It didn't list how many false positives they were. It also didn't mention that the free bsd bug that they talk about that they found the wasn't actually exploitable. I think it was something about like the about the level, like

the level it was that I forget. I'm not I don't do programming other than other than very simple Python, the dog's Python.

It's complicated because again you can't really trust the system cards are just gonzo that Anthropic puts out and it's not publicly available. But there were I think a few very telling things. So there's two features they say Mythos has. One is finding vulnerabilities and source code, and two is writing programs to exploit them. It's first really important that people understand this has been something that people have been doing with lollm since the beginning of publicly available lllmbs.

Right there is not only is there nothing new about that, but I found they put this on my podcast almost word for word from the Anthropic system card them they said in the the Opus four to six rather systems card, right, a publicly available model that's already been out for many months, almost for word for what they said about Mythos, except

for no coverage of it and no fear. They said, we have found five hundred zero day vulnerabilities, including some that had been in existing for decades without having been discovered. That is what they said about what Opus four to six could do for Mythos. They said the same thing. They just replaced the word five hundred one thousands, but when OPIS four to six came out, there was no Oh my god. They have found many hundreds of zero day exploits, many of which have been around for decades.

Because they didn't push that marketing button, no one particularly cared about it. I went back to my podcast and showed multiple papers. This has been a huge concern, and it's a real concern. By the way, right, is that partially what slows down slightly cracking breaking into systems is the fact that it's annoying and hard, and lllms have made it easier. GPT four was good at finding exploits, right, and this was a big deal. They were like GPT

three five wasn't great at at GPT four is. And then as we got the more recent models, they've been much better are at writing code to exploit them because we had better agents for it, and they're more they're better able to produce multi step software goals and so they can better build software to exploit them. This is a real issue, but it's not new with mythos, right, Yeah, Mythos was presented as if some rubicon had been passed. But there was a couple things I noticed, right, off

the bat one. They made the mistake of listing a bunch of the exploits that they vulnerabilities they had found to try to brag, look at this thing in FreeBSD, look at this thing at FFP and G or whatever. They showed all these exploits they found, they didn't count on a lot of security researchers said well, wait a second, why don't I get like a much smaller, cheaper model in and at that same source code and say can you find any vulnerabilities? They could find the same ones.

So the evidence that it's finding vulnerabilities better, we don't have any way of knowing that's true. And if anything, we actually are gain a lot of reports that they were paying big bounties for security researchers. I'm going to give you access to mythos. I'm going to pay you for any bugs you can report you found with it. So they had security researchers, just who knows how many false positives were coming out of that. And then on

the exploitation side, we only really have one study. It comes from AISI, who I do not trust, but it's the only independent study. The fact that they gave them access itself should make us maybe a little bit suspect, but it basically just showed like normal progression, no massive leap. Model by model gets a little bit better on some of these tests and benchmarks, and Mythos has no out of scale leap. It's just like on some it's about

the same, on some it's a little bit better. And yet it got covered as if we had just turned on you know, Whopper from the movie War Games, like we had just some new entity that was like on its own undermining security. And I do not think that. I think that was highly credulous coverage of what almost certainly is just like a standard, slight jagged move forward on these various capabilities that we've been seeing for the last three years.

And they had a chart working Christ and they had a chargament and bad line went up, and it went up in like January and it goes up up And if you read this article about this study, they're like, something's going on. Scheming has been increasing rapidly recently, and they like gave some examples of it or whatever. And so I look at this. It's like, well, I want to look at what is going on here? So I

look at this chart. What are they charting? Oh, they're charting tweets per day that they've detected, tweets about AI doing things that you didn't want it to do. And I said, huh, so when does this line start going up? The week that open Claw was released to the public and everyone just started building their own bad agents and then tweeting about how bad they were and you know what word was not mentioned in that article, open claw. And even though the examples they were giving, so they

just said, scheming just started rising. I guess ai is becoming Cynthia. And all they were measuring.

don't think it's being made. And it reminds you of the Sherlock Holmes story of the dog that didn't bark right, where the actual piece of evidence that mattered is not what you heard, but what you didn't here. This is what I think the real story here is is you did not hear Dario Amaday in the lead up to the Mythos release in the last year, let's say, or

the last two years. You did not hear him talking about what we're working on and why AI is important is because we're going to be able to find vulnerabilities and software that have been long hidden. We're going to build the ultimate cybersecurity machine. This was not discussed. That's old fashioned stuff, that's boring stuff, that's stuff that we were worried at even GPT two. People are worried about that. What we've been hearing about steadily was jobs are going

to be automated. We're going to have like whole creative industries wiped out. We might have synthients coming and at the very least like AGI and these massive disruptions. This is what they've been focusing on again and again. And then their biggest best model, right, their newest, greatest, bestest model that they trained forever and use all the electricity. What did they say about it? None of those things.

They didn't talk about. Any of the things they said the key to AI was the things they were afraid of, the things they're excited about. Instead, they went back and talked about a boring, parochial, old feature that has been an issue that nerdy security researchers have been talking about for a half decade now. That to me is if I was an investor, I would say, take off your like Greek helmet, coseplay, Mythos is coming to destroyer. Hold

on a second, Is this better at automating jobs? Is this better like producing code?

this model. They would much much much rather be able to brag this model is now much better at any of those things that they've been saying is the key to the AI future. And you didn't hear them talk much at all about any of those.

If they were like, we created the Slack clone. Here it is and it was bug free, Like if it actually just worked and we're like we now we have done this, because theoretically, if this SaaS pocalypse story was true, which it's not that AI is going to replace all software if they actually did that. If they because someone from Anthropic just left the board of Figma and they created a Figma clone and the stock went down because

the market's run by toddlers. If they were like, we've released a clone of Microsoft Word, it's yeah, like we've done Anthropic Word and we now sell that as part of our subscription. That would actually be quite something. But the thing is they're not. It's kind of gets back to the old talking point of if they made Agi, why would they sell it. Wouldn't it be a massive competitive advantage to keep this? And I think you're right. I think maybe mythos is not as powerful as they

say and they've just had to dress up. But it gets back to the thing of the direction the true media coverage. It's like, well, this is scary, right, I mean that system caused like one hundred and eighty pages long. I don't good all day. I have to write three one hundred wood blogs a week. I couldn't possibly spend time reading this.

Coast computer scientists are all baffled by. You talk to any East Coast computer scientists, they're all baffled by. Like oftentimes there's claims that are just not true or widely exaggerated. Why are we so credulous? I mean, it'd be one thing if it was like a government agency. We didn't realize was like trying to, you know, protect the fact that there was UFOs and they're just straight uply and we've ever encountered that before. Like I didn't realize that,

you know, no, it's a business right. The credulity with which we're taking these claims like mythos is I think the most important story there is. Yeah, this is another example of what I wrote last summer about AI's had a bit of a wall in the sense that all of the improvements that have come really since over the last two years have almost all been either on post training or more importantly, on the harnesses that you built. So it comes in the software you're building tas harness.

So that's where all the progress has come. All of the progress and coding agents since about a year has come, especially sorrying this fault has come from better rappers, better harnesses. It's all in let's build better. Just hand coding, no machine learning, no intelligence, no skynt here, but just hand coding. These programs that will call ms. Let's just keep tuning

and tweaking those to be better and better. And of course the programmers building those particular programs, they're building them to do their type of work, so it's a field they understand really well, so they can really just sit here and twist and tune. And also, like programmers are very adaptable, they like tools and they'll adapt around the weaknesses or not. So it's like kind of like a

best case scenario. But this is another indication of we're not getting these fundamental giant leaps in the capabilities of the digital brains. It's either some bench maxine, like we tuned it to do better on a particular benchmark, or we've built better programs around it. So when you put the money that they put in the Mythos and if really the best thing you had to emphasize when it was done is we have a cybersecurity benchmark where Opus four to six was at sixty six point seven and

this is eighty three point one. That doesn't necessarily going to justify what's going on or that AISI has this. There's only one thing in there where they see a leap from Mythos at a particular contrived security scenario. They came up with, and this big leap that got them all worried was Opus four to six could on average complete sixteen out of thirty two steps in this in this challenge, and Mythos on average could do twenty two steps out of thirty two.

to five billion parameter open weight models. Put that in perspective, right, a model like Mythos is going to have hundreds of billions, if not a trillion parameters, and they use a three to five billion parameter off the shelf. You could run this model on a chip inside your ten trillion, ten trillion, oh, ten Trillionzy, that's crazy, love the number, bro? Is that true?

wait a second. If they could do most of this stuff with a free, cheap model that I could just run on a machine at home, That's what keeps I think, Dario Amiday up at night. That's what keeps Sam Altman up at night. It's the future. Look, I've been pitching this right. I think the useful and the only ethical and sustainable future for AI is what I call distributed AGI.

And I think it's just what the future is going to be, which is you have specialized applications for different things where oh, we want to do this thing over here. We built something that has some AI in it, and maybe it has an LLM or it's a modular architecture, and it has a billion parameter model in there in a world model, and it's really good at doing this thing, and it's small and it mainly runs on chip. And now this program can do this thing that I used

to have to do. And you multiply that across ten thousand different use cases and you're like, oh, we kind of have AGI, right, there's all these different things that have AI tools that like do pretty well. That's like a completely probably the most probable future. It's a future I really like for a lot of reasons. There'll be a lot of things that we can't make progress on a lot of things we will, but it's a much more heterogeneous future. There's no giant hell nine thousand brains

as economically more interesting and diverse. It doesn't have all the sustainability issues. That has to be the future. But the problem about that future, if you're Sam Altman or dearI Amiday, is that their entire moat is unless you need ten trillion parameters. They want that to be the key to the AI future because that moat is something that no one can cross. And if that's not the moat, if it's just oh, if I want to build a

poker playing AI, that's really good. I just need people who are good at poker and to spend a couple of years and figure out a cool custom system, and that thing now does well. If that's the future, you don't need open AI, and you don't need Anthropic, and I think that probably might be the future, and I

think that's terrifying. And they're trying to race to an IPO and they're marketing out of their butts, Like what can we do to kind of keep things going so at least we can get our stock on the market. That's what would keep me up at night if I was them, is actually the future. There might be a lot of AI in the future, and it's not going to be nearly as sexy as there hoping.

a nuke to kill a single gopher. Yeah, Like you're just like, we're going to throw everything we have at it, to the point that I don't know if you've been seeing the amount of trouble Anthropic has had keeping its service online and how they're making the models dumber. It just feels like we're in this weird hysterical moment where no one knows why they're doing this, but everyone's ready

to accept whatever anyone says. Like it's just like, oh, we're all doing this insane thing, so we're just going to repeat what kind of informs the bias and makes us look less dumb the more excited we are.

you're positioning on the benchmark leader boards. Like, so you do this, You build these giant models, and you spend all this money electricity, and they're so big they're not even economically viable to like have people use, which might really be what's going on with mythos is like, yeah, we have to make this seem super premium because otherwise people are going to get charged five thousand dollars a month.

And just like if you're red Bull or Ferrari, your FRK F one car doing well on this leaderboard just lets people know this company builds good cars and then you can sell your normal cars. I think that's a lot of what's going on here is that they want to be high on that leaderboard means we know how to do AI. We AI smart, even though the future of actual consumer deployed products is going to be much more like a Honda Odyssey minivan than it's going to be like a top Formula one car.

I'm normally like the very very cut of staid professor New Yorker writer, just like Well, on the one hand, on the other you bring this out of me. I love it.