AMA Part 1: Is Claude Code AGI? Are we in a bubble? Plus Live Player Analysis

Welcome back to the cognitive revolution. This is our AMA episode. My schedule has been a little bit crazy lately, and so I never actually really scheduled this with anyone.

And so there's nobody here to ask me the questions. So I'm just gonna read the questions myself and then give you my answers. But I did get some really good questions, and I'm excited to answer them.

And hopefully, people will enjoy this episode and, find some value in it. With that, by far the first and most important question and and the most common question that I'm getting these days is how is my son Ernie doing since the big episode that I did about his cancer back in November? And the good news is he is doing really quite well.

I'm very pleased to report that. Certainly, cancer, and certainly cancer of this type being as aggressive as it is, and I won't belabor the whole thing from last time, go check out the two hour monologue on that if you want the full story. But a cancer this aggressive, which can double as quickly as every twenty four hours, does get very aggressive treatment.

And so he has been through the ringer with the chemotherapy. He's through basically half the chemotherapy now. There are six rounds in total, and he's been through three.

The final two rounds, the rounds five and six are supposed to be a little more mild than the first four. So depending on how you count, we could say he's maybe a little more than halfway through the treatment, but somewhere that, and, it's definitely been rough on him. There's no doubt about it.

When he went into the hospital, he was fifty one pounds. He's still forty one pounds today, and that's that's what the weight that he came home at after the first round of treatment. He's been able to gain a little weight, lost it back, gained a little, gets dehydrated, loses a little.

So you can just see looking at him, he's super thin, he's quite pale, he's definitely not nearly as strong as he was before we went in. But on the markers that really count the most, namely, like, does it look like the cancer is being effectively treated? There he looks really good.

After the first round of chemotherapy, the PET scan that he had showed no obvious focal points of cancer. And when our oncologist met with the tumor board, they all agreed that it made sense to classify him as being in remission as of before he even started the second round of treatment. So that is great.

We also, if you listen to that earlier long episode, might recall that one of the things that AI helped me do is identify some additional testing that is not yet standard of care, but can be done to try to get a better, more sensitive take on is there any cancer left in his body? If so, how much and and how is it trending? That's called minimal residual disease testing.

And I don't know how it works in in all different kinds of cancers, but in the cancer that he has, which is a cancer of the B cell, the B cells do this interesting thing where they rearrange certain parts of their genetic material in a purposeful way, random, I think, or at least semi random, but purposeful, so that they create variation so that they can have a better chance of creating proteins that bind to new disease factors that are in the body. And so this this process of differentiating B cells is literally unique cell by cell. And then when one of those cells goes bad and becomes cancerous and grows out of control, they can use that rearrangement that individual cell did that then gave rise to the whole cancerous process in the body, they can use that random resequencing or, you know, shuffling up of its own sequence that it did to essentially fingerprint that cell type.

And so there are two sequences, one for each of the chromosome pairs where this rearrangement happens that they've identified as being the dominant clone or cancer in the body. And now that they've identified that, now we can do a blood test every so often and check to see how much of that DNA is there floating free in the blood and how many live cells actually have that that DNA sequence. So we've so far only got one of those tests back, and we will wanna certainly look at more and trend it over time.

But the first one that came back, and this is all the way back now over a month ago that it was drawn, came back with fewer than one cell in a million detecting that DNA sequence. So that's really good. It's they they also called that, like, below the LOD or limit of detection for the test.

So it's basically sort of in that, in that area where they even such a low rate, not zero, but such a low rate that they would expect that, like, maybe some samples would have zero and some maybe would have one cell or two, but it's a very low rate. For reference, we estimated that when he was diagnosed, potentially as many as one in 10 cells in his body, and essentially all of the B cells were of the cancerous type. So to go from one in 10 total cells and a large majority of the B cells down to one in a million cells detected or less, that's obviously, I think it was Gemini that said it was 99.

9999% reduced. Other AIs were a little less, colorful in their language. And they said it was probably safer to say that it was an orders of magnitude reduction.

But that's great. We'll do more testing of that type, and we'll certainly be watching it. But as of now, we are feeling cautiously optimistic that he is on the path to a cure and a a full recovery.

And in some ways, the recovery is kind of already underway. It was sixty days from before we went into the hospital, maybe a week before, to just around Christmas time that he was not able to get around by himself. He could stand, but to walk, we would always hold his hand and just, you know, make sure that he had support every literally every step that he took.

And finally, around the Christmas time, we had a chance to come home for a week from the hospital. And during that window, he regained some strength, started getting around by himself. Unfortunately, that that has been sustained for the last two weeks or so since he started doing that.

So hopefully, knock on wood, certainly there is some risk. They don't understand exactly why this cancer can come back in some patients, even when it looks like it's gone. So it's not, we're not out of the woods entirely, but his response to treatment has been basically as good as we could have hoped for.

And even with the MRD testing as suggested by the AIs, it looks about as good as we could hope for. Certainly hope that that next one shows no detection at all, but that one is still pending, so we'll have to wait and see. I really do appreciate everyone who has reached out during this time.

A lot of well wishes. That's been very I've tried to respond to everyone. I think I have mostly responded to everyone.

If I've missed you, I apologize for that, but I really have appreciated all the encouraging words. And then I also really wanted to just say a quick shout out of thanks to the my fellow podcasters who have allowed me to cross post some of their content to our feed over the last couple months because I certainly couldn't keep up the pace of doing eight episodes a month during this time. And I was just very glad, very fortunate that I was able to do some cross posting and and bring you guys some other stuff that I think is very well worth your attention, but also took a little bit of a load off of me.

So we had one from agents of scale. There's actually a sponsored episode, from Wade Foster, the CEO of Zapier, who's got a new podcast out. I actually think it's really good and and do recommend it.

We had one from China talk, which was with a a researcher and business development lead from z AI out of China. Thought that one was really quite interesting. We had one from doom debates, which was a debate between Max Tegmark and Dean Ball.

I thought that one was really good. You know, it's the kind of thing that I want to be listening to more. I've actually had the goal for a long time of cross posting at least one episode a month just because I feel like eight episodes a month is a lot.

And if anybody is listening to all of these episodes, they should probably be diversifying. So maybe I can help you diversify if you're not diversifying on your own. And it also keeps me listening, you know, so I I definitely wanna make sure that I'm staying in touch with what other people are are coming up with in this field.

So I think all those were really good. Definitely recommend those. And then finally, the a 16 z podcast, we had the one that Eric did with Emmett Shear and and Seb Kreyer from Softmax and Google DeepMind, respectively.

So it'll probably be a few more cross posts in the coming months. We've got about two and a half months left of treatment, after which, assuming all goes well and according to plan, we should really be pretty much done and start to get back to life as normal. He will have to get all his vaccines again, which is another interesting thing, because his immune system has been so thoroughly wiped by all these chemotherapies and immunotherapies.

The memory of that the immune system had gained from all the vaccines that he had got in the past is all wiped and he's gonna pretty much have to get them all again. So that's not ideal. It's you know, we're not gonna be immediately back to full normal, but couple more months, two and a half to three more months, we should be, knock on wood, getting back to pretty much as normal.

And in the meantime, there probably will be a few more crossposts. So thanks to, everyone who's reached out to ask and to share their best wishes and also to the, again, the the fellow podcasters who allowed me to crosspost some some content and and fill some gaps in the schedule. I really appreciate that.

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On to more AI centric topics as the, as you tuned in for in the first place. So next question, is Claude Opus 4.5 AGI, and what's up with the holiday Claude code hype?

To be honest, I'm not exactly sure about this. It's kind of surprised me. I would say, obviously, Opus four five is awesome.

You know, there's no denying that. And I have been using it, you know, as I try to use all of the new latest and greatest coding models. And I've had a great time of it.

There's no doubt about that. I vibe coded three apps for family members for Christmas presents this year in the hospital for the most part. Actually, probably the most frustrating part of that experience was the hospital WiFi that kept causing me to have to reload my Replit app all the time.

The actual coding experience, very good, clearly better than it has been in the past. No doubt, you know, the the progress is, I think, unmistakable. And yet, I wouldn't say that it has been such a step change for me relative to what I've experienced in the past that I would say, oh, it's categorically different or, you know, makes me wanna, like, shout from the rooftops that some major threshold has been crossed.

And I'm not sure if that's maybe to take the charitable view, and people have said this about the cancer thing as well. So many people have said, so many people, but a handful of people have said, maybe you're getting that kind of value out of the models for cancer purposes, but that's probably because you really know what you're doing. And other people, you know, they might not get so much so much value because they might not know what they're doing and so they, you know, they could go wrong.

Honestly, would say about the cancer, first of all, that you really don't need much skill in using AI to get great value from the latest generation of models for even something as important, critically important, and and as cognitively demanding as a cancer case. I feel very confident that a layperson with basically no knowledge of AI, if they do pretty much just two things, would get very similar value to what I've got. Maybe I'll say three things.

One thing is use the best version of the models. Do not go to ChatGPT and just drop in the question and let the model picker choose. Make sure you are using at least thinking, and I wouldn't really recommend Pro if you're doing dealing with something that sensitive.

Yes. It is $200 a month. In that context, I think it's absolutely worth it.

Think it's worth it generally for almost everyone regardless. But certainly, if you're dealing with, you know, a life threatening situation and you're asking AIs to weigh in on it, paying the $200 a month is a no brainer. Cloud four five Opus is, of course, the other one and Gemini three.

I would say all three of those are very good. Make sure you are using those top tier models before long. Of course, there will be new top tier models.

You should probably be upgrading as soon as you possibly can. So that's thing one. Just make sure you're using the best available models.

If you're doing that, they are up to the challenge. Two, make sure you're providing as much context as you possibly can. I recently hit the the limit of the length of my chat with Claude, and I started a new one.

And I did that in part by taking all of the stuff that I had and summarizing it into maybe a 10 page report on kind of everything that's happened so far. Everything we've learned, you know, the treatment protocol, the genetic profile of the cancer, how he's reacted to different things, like which drug he had a, you know, a bad reaction to and we shouldn't do again. Know, it's it's pretty much all in there.

It's pretty much everything, quote, unquote, that a new attending physician would need to get a good survey of the case. And and it, hopefully, you know, was meant to be something that I could also paste into a fresh context of a a new language model and give it everything it needed as well. I have noticed that in doing that, obviously, certain information was lost.

And when I've started a fresh chat with that kind of summarized history, the performance is a little bit worse. For example, one way in which it's been noticeably a little bit worse is that when I was going to it every single day and giving it the latest lab results and saying, here's the latest lab results, here's what we've seen, here's what's going on, give me your take on it, It would do a very good job of looking back at the previous day or the last couple days of lab results and figuring out that trend. When the whole history was compressed, it doesn't have that level of detail anymore.

It can't look at literally yesterday's lab results. And so it started to compare lab results from today, January 6, to the last lab result that it had in that summary, which was a couple weeks ago for a particular data particular, liver enzyme, whatever the details of that don't matter. But it just it wasn't a particularly important thing.

We had a little question about it today, and it wasn't something where every single data point was in that history. And so you can kinda see that it's it's starting to perform a little worse here because it's kind of looking a little too far back into history and not realizing that there were a bunch of blood tests taken in the meantime. Anyway, that's all very much in the weeds.

The the key point is give it as much context as you possibly can. What I probably need to do next is go take that summary and flesh it out even more. And then if I do that, you know, I should be in in good shape.

Make sure the models have as much information as you can possibly give them. I have not really seen much trouble in terms of context overload or getting confused. Not to say that that hasn't happened at all.

Certainly could have missed something along those lines. But when I went back with the summarized case report after hitting the clawed length limit on the chat, there was clear that it was the performance was worse for lack of context. Not even really the model's fault, but just clearly worse for lack of context.

So more context better. I haven't really seen that rule violated at all. Give it as much as you possibly can.

And then the third thing so first thing is use the latest and greatest models. Second thing is give it as much context as you possibly can. Third thing is get multiple opinions, including multiple AI opinions.

I am using pretty much for all important queries, Gemini three, Cloud four five Vopus, and GPT 5.2 Pro now. And it is instructive.

It is definitely useful to compare and contrast. I would say they're all very good. If you really could only afford one, I think you can trust it pretty well.

I would probab even though I think Gemini three is extremely impressive, I would probably put it third in my draft order now because I have learned that it is it does seem to have a bias toward strong opinions. It seems to me to be remarkably strong in its opinions. And now if you heard my live show where we talked to Logan Capacho from Google, he did note that I have been using this in Google's AI studio.

So I'm using the most bare bones, unaltered raw model that you could basically get access to. If you use the Gemini app, presumably, there's a system prompt in there. It might behave a little bit differently.

Obviously, if you were to go to use other apps powered by Gemini, there'd be all kinds of, you know, different modifications that would cause it to behave differently. But just using the raw model in the AI Studio, I found Gemini three to be very opinionated. And sometimes I really like that.

I do really like it as one of the three takes that I'm getting. But if it was the only take, I would worry a little bit that it would be probably, you know, sometimes pushing me too hard in a certain direction, where if I had the full three, it would kinda balance me out. I think I would put, for most people, Claude Opus four five at the top because it's much faster than GPT 5.

2 Pro, and I don't notice it being much worse. Its answers are shorter. They're much more answering your question than they are doing, like, a full sort of report style analysis.

GPT 5.2 Pro and I don't really do system prompts or custom instructions for any of these, by the way. I pretty much use them in their vanilla form as much as I can.

5.2 Pro gives you, like, long sectionated report style analyses that I do find, again, to be very useful. But if I had to kind of pick the Goldilocks one, I think it would be Claude opus four five.

Gemini three being maybe a little too brief, a little too opinionated, GBT 5.2 pro being maybe a little too verbose, a little information overload, and Claude being just right. But I do recommend using all three, and I think that the doing it all in triplicate is absolutely very well worthwhile.

To pop back up a layer in my question stack here, people sometimes say to me, you get this value from these AIs, but you know what you're doing and other people don't. And my advice is, you know, it's really very simple. You do those three things, you're gonna get value.

You don't need to be an AI expert by any means. That said, maybe you could say I, you know, was getting more value from previous coding models relative to, you know, what other people were because I maybe had more practice and and was more skilled in it. I certainly think there is some truth to that when you look at the Miri study that showed that some software developers thought they were being sped up by AI, but were actually being slowed down.

You know, I think that's a you know, I love Mary Anne. I've said, you know, many times, like, do science report the results. It you you do not need to make your scientific publication, like, fit a particular narrative.

In fact, you probably shouldn't try to do that. You should probably just try to run experiments and and share results as long as you believe that the experiment was well run and and the results are legit. So I do believe that those results are legit, but I think there were some important caveats there.

It was older generations of models that people didn't have much experience. It was, you know, very large and well established code bases with very high coding standards. I don't tend to code in that kind of environment.

I tend to, like, vibe code and hack together apps. And I certainly think I've gotten to be pretty decent at it. So maybe I was kind of maxing out previous generation models a little bit more than other people.

I don't really know. You know, it it has kind of surprised me. I guess the other thing to take the flip side, you could say, well, hey.

Maybe, Nathan, you aren't such a great software developer. Maybe these, like, pro software developers have better taste. They're actually now that 4.

5 has gotten so good or has crossed some threshold where it's, like, really becoming a lot more useful to them, maybe they're noticing that difference, and I'm not because I'm just, like, fundamentally not as good at the task, don't have as much taste in this domain, and I'm just not not able to see what four or five Opus is bringing to the table over and above four one or, you know, other frontier coding models. I don't know. That's possible.

I certainly I'm not a great software engineer, so that certainly can't be ruled out. But, you know, it could also just be some social things like people just kinda catching up over the holidays. You know, maybe the timing was right.

Sometimes these things go with, like, a cascade. Dean Ball tweeted 4.5 as AGI, and people seem to kinda latch on to it.

To some extent, I think some of this stuff is kind of random social dynamics at at times as well. Hey. We'll continue our interview in a moment after a word from our sponsors.

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The activation link is in the show notes, so give it a try at tasklet.ai. The three apps that I coded, by the way, for the holidays, for what it's worth, my mom is a very meticulous travel planner.

My parents were actually in Italy for a trip and came home early to to move into our house and help us take care of our kids while we've been at the hospital so much. So thank God for them for doing that. My mom plans these trips that she and my dad take to the maximal limit of of planning.

And so I coded her up an app to try to accelerate her planning process by, like, building in a lot of the tastes that she has. She's, like, gluten free, for example. So that's one of the big place where her time goes in planning these trips.

Like, places can I actually eat? What places are gluten free? And then this is an app purely for her.

There's no account. It's not something that she logs into and logs out of. It is a Replit app that she goes to when she wants to.

Nobody else is ever gonna use it. Her profile is baked in. I could imagine, you know, generalizing it and allowing people to customize their own profile, but I don't know.

I'm not really trying to do that. Sure other you know, plenty of travel apps out there that people are building and commercializing. This one was really just for my mom trying to capture some of the stuff that she does and make it work for her and, know, speed up her process.

So far, I think that has gone pretty well, actually, for her. It seems like she's getting at least some value from it. Then I made one for my wife who organizes EA global events that simulates events.

It allows her to kind of set up a roster of attendees with different profiles and various attributes, and then literally simulates people walking around a virtual event space and bumping into each other. And depending on what areas they're interested in, they may or may not have a conversation, and that conversation may or may not lead to some outcome. They track various KPIs, which they measure mostly through surveys.

I've, you know, kind of set this up in simulation. And the goal there is so that she can at least try to get some handle on. You know, if we changed the size of the event, would it be, you know, more or less effective?

Would it be more or less cost effective? What if we changed you know, had more senior people versus more junior people? What's the right mix?

Obviously, these simulations are always highly flawed, but I'd say they probably do have something to add relative to total guesswork. So that was a pretty fun one, pretty pretty straightforward, actually. That one went pretty smoothly.

And then the third one was for my dad who isn't really a very active day trader, but fancies himself a bit of a a stock market guy. And so for him, I created it and these are all AI apps. But in the case of the travel planning, you know, it's Claude going out there and doing the research and digging up and looking through Italian restaurant websites and reviews to figure out if they are gluten free or not.

In the case of my wife's, and she can prompt the app with sort of a general idea of an app and it will, like, fill in all the detailed config and then she can edit. I think that's a great paradigm or pattern in general for apps is, like, you're you always have these detailed configs, these, like, nitty gritty forms that need to be filled out. But AIs are really good at doing that.

If you just give them a general kind of gist of what you want, it can translate that down to the low level configuration. And so that's where the AI is in her case. It also allows her to kind of edit a configuration.

So say she has a certain event profile that she's set up, she could then go and say, I wanna change this in the following way. And it would kind of paint that conceptual idea that she gave it on top of the configuration and and change it in all the little ways that it needs to be changed. And then with my dad's, it takes a high level natural language stock trading strategy and turns that into actual trading rules, then goes and fetches historical data.

And there's a Python package out there called y finance, which I didn't really know anything about. And it it does have a paid version, but there's a free version. And so for now, he's been able to get by just with the free version.

It goes back and gets historical data and simulates what would happen if you applied those trading rules based on that high level natural language strategy over a time interval that he can define. And what we're finding more often than not is that it's pretty tough to beat the market, which is honestly I don't know if you'll listen to this, but one of my, you know, private motivations for making this thing was to kinda convince him that he's probably not gonna beat the market. And so certainly not with these, know, random heuristic kind of if this, then that kind of, trading strategies.

And sure enough, it has been very difficult so far for me in my development of the app or for him, I think so far in in what you see is he's made of it so far to find a strategy that actually beats buying and holding the S and P 500. I'd probably put, I don't know, three full workdays, somewhere between three and and five workdays, probably closer to three though into those three apps. In each case, I didn't really know where I was going when I started.

I started with a chat with Claude just to say, hey. Here's what I'm looking to do. Help me out.

I think it's, you know, it's good. Is it, like, night and day better than going back to the question that prompted this whole Christmas, present vibe coding story. Is it, like, that much better than Claude four one Opus or four o Opus?

I can't really say I think it's like that much different, but certainly very good, you know, and good back and forth, good questions, good feature ideas, transit that all into a into a plan, then go over to the Replit app, install Cloud Code on Replit. That's one of things I love about Replit is you can do, you know, whatever you can do on a normal fully controlled development environment, can pretty much do on Replit. That includes install Cloud Code.

Of course, they have their AI agent too, but since this was, like, a moment of Claude code hype, I would just install Claude code there, give it the plan, let Claude run off and build it build the app, and then just test and iterate. And I I did I do still find and this might be a a way in which I'm kind of falling short as a Claude code user, but I do still find a lot of value in a short script that prints out my entire app to a single text file. And then taking that entire text file over to another LLM, could be Claude, could be Gemini, if I need more space, and asking it to analyze the code base in full.

I think Claude code does a very good job of agentic search, but if I've found any shortcomings, there was, like, one particular moment in in my mom's travel planning app where and I kinda know how this originally happened. I made a a request. I think it misinterpreted the request.

We ended up with two databases, and this became very confusing. And this is pretty illustrative, actually, because this is the kind of mistake that earlier vibe coding experiences would create all the time, where you'd be like, what is going on? I ended up with two databases.

This is sort of the the sort of mistake that no human would make. Right? You would not it would be very weird for a human software developer to suddenly, like, spin up a totally separate database.

And but the AI did that. It was it thought it was trying to follow my instructions, I think, a little bit, but it didn't understand what I was trying to get across. And so we ended up with these two databases, and then, like, certain things weren't working as expected, it was very confusing.

And this was one place where once I kind of got down to, okay, there's two databases, then I asked Cloud Code, like, okay, which one is actually being used, and which one is the superfluous one? And then I took the for the full code export and took that over to just a clean claude.ai, paste the whole thing in and ask, and it was the model that had the full exported code base that got it right.

Claude code did not get it right. And I think that's because in its agentic search, it looks the places where it expects to find things, and it has a high relatively high prior that this is where it's gonna be. And sure enough, it appears to be there.

And so it kind of goes with that. But what was actually happening was something that was counterintuitive. And so having the full context in view at one time really did seem to help Claude figure that out.

And, you know, this is something that I think previous models might have struggled with even with the full context in place. But with that trick, it it sometimes can help you clean up a a mess or a point of confusion that the agentic search functionality of Cloud Code, in my experience, seems to struggle with. I'm sure that people will be able to offer strategies to do the exact same thing right within Claude code.

You know, if there's planning mode, which I probably underused, frankly, but I think that there is something I think to be learned there between the agentic search kind of finding what it's looking for, that it expects to find, that it thinks is right, and then coming to the wrong conclusion because it actually, in this case, it was the rare weird other thing that was happening, and only in seeing it all together was that actually correctly diagnosed by Claude. But anyway, yeah, it's it's better. There's no doubt.

I don't really feel the step change. Is it AGI? I mean, you look at GDP val, the arguably, in some way, it's in software, it is AGI.

You know, if you look at, the latest from OpenAI and the latest from Anthropic, both the it's like a pretty significant majority of software engineering tasks where the model is beating the human. And these are you know, just to remind you of GDP val, these are professional caliber tasks. They basically have three sets of experts.

First set of experts defines the task. The second set of experts does the task. And then the third set of experts judges whether a human or an AI that did the task did a better job.

And the latest models are, like, a significant majority preferred over humans in the software engineering category. Of course, it's spiky and jagged. Like, if you go to the video editing category, humans have still a huge advantage.

And I've certainly experienced that. You know, tried many AI products and and workflows to try to create good clips out of the cognitive revolution, and they work okay. They're clearly not as good as what Dwarkash puts out.

You know, we've tried and we've made some good progress. I actually think at at some points in time, what we've had internally has been better than any other outside product I've tried. I'm not sure.

I wouldn't say that's necessarily true today, but I think at times, I I preferred what we were doing to anything that I had tested on the market. But then you, like, look at the at the clips that Dwarkash and team are putting out, and they're just clearly better. And you see that in GDP value too.

It's, you know, it's a it's a very small percentage that the models are preferred to humans in these video editing tasks. But in software, I think you could you could make the case certainly that that OPUS four five is software AGI and or coding AGI. And yet, I still am a little bit at a loss to fully answer the question of what, you know, caused this moment of hype around the holidays.

But hopefully, you know, there's some other nuggets in there for people to to pick up on and and go run and use. If you haven't used Cloud Code, I absolutely would say do it. It's really easy to install.

It's a one liner to install. And you don't really need to know how to code these days. You kinda watch it work.

My mom even did a couple. She was kinda like, I don't think I'm gonna do this. I'd be worried I'm gonna mess it up.

And I was like, I think you really can. You know, it's it's your little agent on the computer. You just tell it what to do, and you don't really have to understand what it's doing.

You can ask it to explain. It sort of does explain to at least some degree by default, but you don't really have to be a software engineer to use it. You can still get pretty far.

And it was really just one or just a couple things. This database thing was one where I did have to kinda not debug, but at least, like, ask some probing questions of the of the models to get a hand on what was going on. It probably took, like, five or six prompts to resolve that issue.

You know, I can imagine in the future that a, it might not happen in the first place or, b, you know, maybe it'll it would be resolved in just a couple prompts with the next generation of models. But this is already getting, you know, pretty amazing when it comes to being able to clean up these messes that it sometimes inadvertently makes and get over these humps. I, if you'd asked me a year ago, would have said, those are when a lot of these projects die.

When somebody gets to that point where they something has gone wrong, they're confused, they don't know what's going on, the AI is totally confused, they circle around the problem for a little while, can't solve it, move on. I certainly experienced that myself at times. In most of those cases, I probably could have, you know, spent the time to go in and and figure it out for real.

But the whole point of vibe coding is it's not you know, you're not trying to put that much energy into it. So sometimes I would just abandon something like that and maybe start over. Now you actually can get out of those messes that the AI assisted coding sometimes makes.

And so, certainly, the addressable market for these things continues to expand dramatically. Certainly, I think the implications for the future of the software industry are profound. It's software AGI, I think, but maybe not full AGI.

And for that, we might have to wait just a little bit longer. Okay. That was enough on that.

Next question, are we in a bubble? Couple different versions of this. I think my answer here can be relatively short.

When it comes to is AI for real or not, I'm not gonna surprise anybody by saying I think it's absolutely for real. The technology is already amazing. The fact that it can go toe to toe with an oncologist and have all the other advantages too in terms of always on access, twenty four seven, the ability to handle full context, the command that it has of the case based on all the history that it has, the, you know, just the fact that it will answer every last question that I have.

I mean, all these are just dramatic advantages. And at the point where it's, like, competitively accurate with a human oncologist, I think that you're you are clearly dealing with transformative technology. I think the idea that we will somehow get out of the other side of this AI thing and feel like we were all high on our own AI supply, like, that I think we can very safely put to bed at this point.

Now, does that mean I thought Noah Smith did a good job of sketching this out recently with a blog post. Does that mean that all the loans are gonna be repaid? That's much less obvious, I think, especially when you see just how aggressive a company like OpenAI is being in terms of all the financial deal making that it's doing and all the build out that it's got planned.

Is it conceivable that their revenue projections could fall short of their obligations? And, you know, could they default on something or, you know, could we have and and there's also some, like, financial there's a lot of financial wizardry going on. One of the bits of financial engineering that isn't necessarily even you know, it's funny.

It's like a a lot of this financial stuff, there's a logic to it even though in in retrospect and I I worked in the mortgage industry during before and during the mortgage bubble, and there was always a logic to what people were doing, and they were telling themselves a very positive story about how we're making homeownership accessible to more people than ever before, and this is gonna be great and great moderation and all these kinds of things. So there's always a story with these financial engineering phenomena. But one of the the engineering things that's happening is, like, these whole CoreWeave kind of companies that are there to set up and kind of be rapidly construct and to some degree operate the data centers.

They they do have expertise in the setting up of the data centers, but it seems like a significant part of the reason that they exist is because the financial profile of those businesses isn't so attractive as, you know, say, a Microsoft traditional business, which is just so high margin, you know, relatively low CapEx and relatively high margin. I think there's a sense that these, like, hyperscaler, high margin gold standard software businesses that Wall Street is accustomed to that that their stocks could be dragged down if they start to engage in a lot of lower margin business like running GPUs. So I don't know, you know, how much of a factor that is versus the actual expertise that the companies bring in terms of setting up and and operating the data centers, but I think there's definitely some nontrivial motivation there.

And, you know, maybe that's fine. Like, different companies can have different financial profiles, and to some degree, that might be good. Certainly, a lot of, you know, shareholder value, so to speak, has been created that way.

But it does create these companies that if the GPUs aren't needed quite as much as people expect them to be, they have a lot less margin for error on something like that than a Microsoft does. Right? If Microsoft were owning and operating all these things themselves, they've got a deep balance sheet that can take a few knocks.

By putting a lot of this stuff more on the CoreWeave side of the fence, it does create some fragility. And so, you know, it's it's certainly very conceivable to me that we might have some period of overbuilding. Noah analogizes to the railroads.

Like, the railroads, in the end, were a pretty good investment. They all got used. You know, there weren't, like, a lot of railroads sitting around idle.

That didn't necessarily mean that all the railroad companies were profitable, and there certainly were busts when loans couldn't be paid back, and then you had, you know, cascading effects throughout the economy. So I think that kind of bubble is not not too unlikely. So far, on the just for for AI has exceeded my expectations.

We talked about this with Peter Wilderford in the live show a little bit where, you know, I said, I under sorry. I overestimated how much capability progress would happen in 2025, but I underestimated how much revenue growth there would be. And so, you know, possibly, that'll happen again and revenue, you know, and demand and revenue will just continue to go up and up and up, and it'll all be fine.

But it wouldn't shock me if there's some moments where it's like, hey, we kinda overbuilt this thing, and some people aren't necessarily gonna be paid back, and, you know, some people might be left holding some various bags. But even so, that doesn't mean that it's a bad investment. It just means that it might not be timed quite right for people to, you know, all make the money that they're projecting that they're gonna make.

And then I think the final sense in which we might be in a bubble is at, like, the venture capital level. And there I have to say, I think there's at least something like a bubble happening. And that there's many many examples of this, but the thing that just came out today that just made my head spin was the organization that was originally called lmsys.

org, then it was became lm arena, and now it's just at arena on Twitter. They have just raised, I think, a $100,000,000, maybe a $150,000,000 on a $1,700,000,000 valuation. And here I'm like, woah, that seems crazy.

And I I don't know a lot about their business. I haven't seen their deck. I could be wrong.

But this is a this is a product that I've watched for a long time and continue to check, and I do have the receipts on that. My first tweet about l m what was then l m sys dot org goes back to mid twenty twenty three. So a full more than two and a half years ago now.

And at the time, I was just randomly tweeting that it had started to show up on my favorites in my mobile Safari. So I was using it quite a lot then to compare and contrast model performance. Now, obviously, it's gotten bigger since then.

Obviously, the whole field has gotten bigger and they've, you know, started to do various services where they allow companies to test their models with code names and, you know, there's there's definitely value in that. Does that seem to me like a unicorn business? It definitely seems to me like that would be a big stretch.

The tweet that they put out today and I don't wanna be too harsh on this because, again, I don't know a lot. I I think of this is more, like, representative to me of a phenomenon that I see a lot as opposed to, you know, something that is very specific to this particular company and its its raise. And again, like the company.

I've liked its product. But the tweet said that their that their operation has scaled to $30,000,000 in quote, annualized consumption run rate. And I'm like, what is annualized consumption run rate?

Does that mean like how much the AI that people are using for free when they go to the LM Arena and do these side by side comparisons would cost $30,000,000 if they were paying for it. That's my naive interpretation. I didn't see a clarification on that.

But if that's what the meaning of that is, it's very much giving me, like, community adjusted EBITDA vibes. Because saying that, like, people use what would cost $30,000,000 worth of free AI on our platform is not the same thing as saying you're making $30,000,000 in revenue. I don't see that they disclose what revenue they're making.

And a $1,700,000,000 valuation for an app that basically does, like, a side by side comparison of of AIs, I don't know. Like, it seems to me that people are using it in large part because it's free. I'm sure some people also are just curious about doing side by side testing.

I've certainly done that myself. But the people that go there because they specifically want a way to do side by side testing seems to me a relatively small market. And the people that are going there because it's free, that seems to me like a big part of why people are going there.

You know, how does that translate into a $1,700,000,000 valuation? Color me confused on that or skeptical at a minimum. I have to believe that a lot of these things are just not going to pay off for venture investors.

And if you if you wanna see something else too, mean, this is like, where's the moat? You know, I mean, there's brand, I guess. People come to it, but again, would they come to it if you had to pay for it?

I'm not so sure. Another thing that a friend has created, Andrew Critch, the the coiner of the big tech singularity meme, he has created something called the multiplicity, which is paid and has not had any you know, I think it's become among a, you know, small group of people who value this kind of thing. I think it's become popular.

Certainly seen some very positive reviews of it, but it is something you pay for. It then allows you to use multiple models and to kinda systematically compare and contrast their outputs. So I think it's it's more feature rich actually than LMSYS for the end user.

And, you know, this is something that they've built he and and teammates have built over the a period of months, like, certainly not years. I just have a hard time seeing where the $1,700,000,000 in value is with the LM Arena. And I say that again as somebody who has used it and appreciated it for far longer than most.

Time will tell. I could be wrong. I could be missing something.

Please let me know. If you're on the LM Arena squad and wanna talk, I I would be perfectly open to doing a full episode with the LM Arena folks, but it just doesn't feel like, I hope they took some value off the table. I guess for their sake, I hope they did some secondary, but for the LPs and the fun's sake, it's too rich for my blood, that I can say confidently.

Okay. Next topic, live player analysis. This one, I think Eric wouldn't mind me saying he asked for.

I'll do my best Zvi impression and, we'll see how I can compare and contrast a little bit with Zvi hopefully before too long. We'll have him back. I wanna start with the Chinese models because I think that I think, first of all, I just think very few people in the general consumer market are using Chinese models in The US today pretty much at all.

Most startups are also using American API models. Some are using llamas to fine tune, and some are indeed using Chinese models to fine tune. But I don't see a ton of that happening.

And I don't think it's too many people that actually go, as I recently had occasion to do, and just try all the Chinese models. I was working on a what basically amounts to a computer vision task. I I've alluded to this a little bit in the past.

I've been working a bit with a company that automates the review of the paperwork associated with the buying and selling of cars. So you buy a car, you sell a car, there's some paperwork that has to get filed with the state to document that transaction, whatever. It's all very boring stuff.

Perfect for AI. Honestly, think it's, you know, reviewing these documents is a great example of the kind of work I think most people don't enjoy doing, and they're doing it primarily because they need a job because they need to get paid. So this is something I'm perfectly happy to see AI take off of people's plates, and they've been able to get to the point where they're doing it more accurately than people, and they've started to get some statewide contracts from state governments that are like, hey.

If you can do this faster and more accurately than our people, that's a win for our taxpayers and our and our people that, you know, need these documents accurately reviewed. So great. These documents are typically scanned, which means, you know, they're all kinds of messed up.

There's artifacts from the scanning process. Sometimes there's, like, perspective issues or kinda weird slant things. Sometimes, like, the margins are wrong and the things can be kinda cut off the side of the page.

So there's all these all these, like, complications that make this not the most straightforward task for the models to read these documents. So I was just helping out a little bit, and there was one particular aspect of reading these documents that that the models were struggling with. I went and tested basically every model I could get my hands on, every frontier model.

You know, I I tested Gemini three. It's very, very good, but it was making this one idiosyncratic mistake, which if you read the piece from past guest, Mark Humphries, the Canadian history professor, he put out a blog post that went quite viral that talked about it was actually before Gemini three came out, and it was looking he he does all this stuff with old handwriting. So these historical handwritten documents, hard to read for one thing.

And so they can be hard to read for one thing because they are written in, you know, an old literal cursive, you know, script with ink on paper. They also can be hard to interpret because a lot of them are, like, facts. You know, he points out that, like, somebody could have come into, know, if you got, like, a ledger or something from an old shop, and the person is recording what they sold, how much to whom.

That person could have come in and bought whatever. Right? There's, like, not a great prior on what that should be.

So the values that it that it interprets, you know, it really is relying on perception for the most part, though there are some places where, you know, it can kind of make some logical leaps. Like, if if something is priced at, you know, a certain amount per unit, you know, it might be able to make intelligent guesses on what that unit was even if it can't quite make it out. Was it an ounce or was it a pound?

Because it it might have some historical knowledge of, like, what that price roughly would have been. And so it can use that world knowledge to do some of this reasoning and fill in some of these gaps and kinda fill in gaps in its perception. So he published this article.

It was it's definitely worth checking out that documented that Gemini three was doing this in a way that no other model had done it. In the context of this project of reading these documents being filed with the state for these these car sale transactions, it kinda worked against Gemini three in the sense that what we are trying to do is faithfully read the document. We are not trying to make guesses about what the document should have said.

You know, there was one checkbox, for example, that was like, are you a US citizen? And if the box is checked, we wanna say it's checked. If it's not checked, we wanna say it's not checked.

But the model sometimes was making inferences. So it was like reporting that the person was a citizen even though the box wasn't checked. Doing that presumably based on, like, other context clues, you know, it was like, oh, well, the person lives in The United States and their, you know, name sounds like American, quote unquote, whatever that sounds like.

So it was kind of it was making the logical guess, which probably was right, actually, that the person who filled out this document, I would guess, in fact, was a US citizen. I would say it's more than 90% chance that they were, but they didn't fill out they did not check the box on the form. So Gemini kind of using its priors, trying to get the answer right was less anchored to the document than we needed it to be.

Claude, we found, could do this. It took some prompting, and I had to kind of tell it, make no guesses, you know, read this thing exactly as it is, make no logical leaps, yada yada yada. And it turned out that Cloud three or sorry, Cloud Opus four five, Cloud three RIP, Cloud Opus four five was the best at actually being faithful to the document.

But along the way, was like, okay. Let me go check all these Chinese models. You know, I've heard good things.

So I went to the latest Quinn Vision model. I went to GLM 4.6.

I went to the latest Kimi. I went to the latest DeepSeek. At least those four.

Maybe one other one that I'm forgetting. And they were all way behind, like, nowhere close. Nowhere close to Gemini three, nowhere close to Claude four five Opus, nowhere close to what ChatGPT can do.

And, you know, this had me thinking, like, this is odd. Right? We are seeing, like, all the time these statements that, oh, you know, the Chinese models are so close.

They're not far behind at all. And I think that they're, like, quite good in many ways for many things. But on this particular task, and I suspect that this is true probably on a a lot of different tasks, although I'm going on vibes here a bit myself as well, obviously.

I suspect that gap is actually pretty wide in a lot of cases. I do not feel right now that any of the Chinese models are really competitive with the best proprietary models coming out of The United States. They might be competitive on benchmark scores, and they might be competitive in some domains.

But in the general purpose, throw something really idiosyncratic and random at it that it hasn't seen and that isn't on somebody's, I want to show up on a rubric of 20 benchmarks looking competitive agenda. I think that gap is actually significant, like, kinda wide. And when I mean, they were not close, you know, like, not close at all.

The Gemini mistakes were like, it's reading this gnarly government form almost perfectly, but it's like missing a few of these checkbox things or, like, making wrong inferences here and there, and I can't quite get it to stop doing that. Cloud four five opus is just, like, plain doing it right. GPT was probably third, not as good as the other two, but, like, still very good, like, certainly giving you, like, all the right information for the most part, missing, you know, relatively subtle things.

What I'm getting back from the Chinese models is, like, 20 of the forum is coming back or, you know, it's like just going off in very weird hallucinatory directions in all kinds of different ways. Really not close. You know, does that mean that the Chinese companies aren't live players?

I do think they're affecting the landscape. I am definitely reading a lot more research from Chinese companies these days because they continue to publish their stuff, and a lot of times it is quite interesting. And I feel like they are building they are doing the best models about which we know, you know, everything or close to everything that went into them or certainly, you know, all the details of the architecture and many of the details of the training process.

So they're influencing the world in that way for sure by just disseminating this knowledge very broadly, but I don't see that the models are really competitive today. And I do think this is a way in which the chip controls have made an impact. And I'm not necessarily saying this is a good thing.

I I'm not necessarily saying it's a bad thing either, but the of course, the long history of the chip controls made short. Originally, you know, it was a small yard, high fence. We're gonna prevent military applications.

Oh, well, we can't really do that. They can make enough chips domestically to to put the whatever chips they need in their drones. But Well, at least we can prevent them from training frontier models.

Well, we can't do that or at least, you know, they're still doing pretty good models, but we can prevent them from scaling inference or having as many AI agents as as we have. And that's kinda where we are today. And I don't really like that.

Don't I really like that idea very much as I think of anybody who's listened to this podcast for any length of time knows. But I do think you kind of see the echo of it in the models themselves here because it felt to me like these are companies that are training models without a feedback process that the leading American companies have just because they are scaling not just the training and the parameters, but the actual inference and the actual customer relationships. These Chinese companies, they seem to be able to roughly compete in terms of creating similar scale models, but they're not able to run inference at anywhere near the same scale.

Their their revenue is vanishingly smaller than the American company's revenue so far. And the feedback, I think that's the thing I really wanna zero in on here. The feedback that they're getting from customers seems to be just dramatically less.

And their teams, you know, bigger with smaller revenue, also their teams are, like, dramatically smaller. So I I think that what we see in these, like, very niche, very idiosyncratic tasks where we see the small gap in benchmark results open up to, like, wide gaps in terms of how well you can read this government document. I strongly suspect that has to do with how many customer relationships you have, how many how comprehensively are those customers representing, like, the vast range of things that people might wanna do with AIs, and how much are they giving you feedback on what's working and not working, and and do you have the human bandwidth at your organization to build the datasets that you need to patch those holes.

And I think that's where the Chinese companies are kind of falling behind. So I I was never one who thought that the trip controls wouldn't have an impact. I question whether it's a good idea that we try to deny the Chinese people, civilization, the ability to, like, scale AI inference throughout their economy in the same way that we are, but I always kind of expected that that would have some effects.

And I do think we are starting to see maybe after a period and I think my I kind of associate this line of thinking with Miles Brundage as well, former, head of policy research at OpenAI. He said, like, you know, the the chip controls are gonna matter more as we go forward because everything is scaling. And, you know, if American companies are gonna do a 10 x in compute, like, that's gonna have a lot of impacts.

And sure, maybe, you know, DeepSegar one was a thing and they were able to train it with not, you know, an insane amount of compute and whatever, but are they gonna be able to keep up with the momentum, the flywheel, and the, you know, the the strength, begetting strength phenomenon that we see the American companies achieving, and it seems like there, maybe the answer is starting to look more like no. So if I had to guess about the gap between the Chinese and the American models relative to a year ago, I think it is wider. I think that r one was closer to o one than the, whatever, let's say, GLM 4.

6, GLM 4.7 is to clawed Opus 4.5.

And that's based on very limited data, but certainly more than most people have because I did go and try every single one of those. DeepSeek, Kimmy, Quinn, and z AI's GLM. Tried them all on the on this task and they were all way behind.

Okay. So that said, what another question that I'll kind of insert here is, what do I think about the h two 100 sales to China? High level, I still think we should keep in mind that the real others here are the AIs, not the Chinese.

The Chinese are humans just like us. The AIs are aliens. And so I am skeptical of any notion that's like, this is a dangerous thing to do.

Better us do it first than them. And that seems to be the logic that we are using when we impose these chip controls. Or another logic is just like, we don't want we don't like China.

We wanna keep them down, and, you know, we won't just wanna have her every advantage that we can. I don't like that line of thinking. I don't like either of those lines of thinking.

And so I do generally favor more, willingness to sell chips to China than we have had. At the same time, obviously, this does exist in the context of a very complicated and many, many, many faceted relationship. And so it's very weird to me that all of a sudden we go from within this I think my history on this is right.

Wasn't that long ago that Trump said we're not gonna sell the h twenties, and then he comes back and says, well, actually, you know, I talked to Jensen and it's cool. We're gonna sell the h two hundreds. And it doesn't seem like we really got anything for it.

Like, I definitely would be supportive of of an attempt to try to find some sort of grand bargain. Hey. We'll sell you the chips.

You do this. Right? And there could be a lot of different values for this that we might want.

Given where we were, where there was a ban, and it certainly seems to have been limiting what their AI industry can do, I don't see why we didn't try to drive a harder bargain because clearly there are plenty of things that we could bargain for. And so it does feel like a bit of a wasted opportunity. So I I guess I would say, like, I I do think more willingness to trade in chips, I support, but we shouldn't be naive or or allow ourselves to be taken advantage of or, like, give away one of our best bargaining chips for nothing in return.

And it seems like that's kind of what we did here. So I don't love that. The other thing I will I'll say on this is I do really like the rent but don't sell position that Peter Wilderford staked out on the live show.

He basically said, look, you know, we don't trust the Chinese government. They don't trust us either. Maybe both sides are right not to trust the other side.

You know, I often note that, like, a lot of the criticisms or the sort of characterizations that we make of them, they can and do make of us, you know. Hey, you've got a authoritarian madman running your country. Which country are we talking about?

You know, your system is not obviously stable. Again, like, which country are we talking about? So the idea that we, like, might want to have some leverage or we might wanna be able to pull something back in the event of a conflict, you know, I could see that being very prudent.

So if we were to set out a position that's like, we're gonna put data centers in Malaysia or in The Philippines or whatever, Korea, Japan, you can rent as much as you want. You can train all the models that you wanna do there. You can run all the inference you wanna do there.

We're just not gonna allow them to go into big data centers in your sovereign territory where we totally lose ability to exercise any influence over that. I don't know that I I'm not sure that would be my first choice of policy, but I think it's a very defensible policy, I think at least if it was packaged up with a message that is like, we believe AI is good and we want the Chinese people to take advantage of it and get benefit from it in the same way that we are trying to do that here for ourselves, think that would just be a much better message and much more I think it would create much more fertile ground for further cooperation, which we might need. Right?

Because we are headed for a world potentially of, like, transformative, which I think we basically already have, to powerful with a capital p AI, to AGI, to superintelligence, whatever. However far this goes, it seems likely that we're going to need to work together with the other powerful nations of the world, and certainly China's right at the top of that list, to govern this technology in the in the right way, you know, and to make sure that it actually does pay off for the people of the world. And I think we could take somewhat of a position that they wouldn't like, like, we'll rent them to you, but we're not gonna put them on your sovereign territory where we where we lose all control and still at least maintain the decent vibe.

So I I would be interested in seeing us try that. As it stands, it seems like we're gonna just go ahead and sell the chips. And it seems like we didn't get anything for it, and it seems like this is not a great, example of negotiation from our dealmaker in chief.

But I guess I still hold my nose and like it better than a total ban. Okay. So now we get to the real live players.

I've got four, and I'm not sure they're in any particular order, or at least I wouldn't I wouldn't call this a power rankings. They're in an order, but I wouldn't call this a power rankings. First one I'll talk about is Google, DeepMind.

I think these guys are still number one in my book. Basically, they pretty much always have been. Maybe tied, you know, for one with OpenAI for a while there, because certainly OpenAI was was clearly ahead in terms of productizing transformer based LLMs.

But Google really has it all from the balance sheet, the just the fact that they've got a business that's making whatever a $100,000,000,000 a year in revenue, and I think literally making a billion plus a week in profit. You know, that gives you a lot of room to buy data centers, to, you know, have some failed training runs, to, you know, make some mistakes, to to have some research agendas that don't pan out, and that is hugely valuable. They also, of course, have the TPUs.

The fact that they've been thrown, like, whatever, the seventh, I think, generation of the TPU now, that's an insanely valuable bit of IP. They're able to compete to some extent at least with NVIDIA. You know, Anthropic is buying lots of TPUs.

Other companies are starting to buy lots of TPUs. They're, of course, you know, one of the best data center builders and operators in the world, you know, they and they've done that for a long time. So those are two critical strengths that basically nobody else on this list has, certainly not in the same way.

They also have the deepest research bench. You know, they've got something that's if not frontier, it's at least competitive in, like, every major area. They've got self driving cars.

They've got robotics. They just announced a partnership with what is it? Boston Dynamics that is gonna power their humanoid robot.

They've got a ton of stuff in biology. They've got, of course, the AlphaFold lineage. They've got multiple founders of companies in the, material science base various AI for science type companies, many of them are ex DeepMind.

Why? Because DeepMind was investing in those areas before anyone else. And, of course, those those agendas continue within Google to this day.

So it's not like they they don't really have a lot of gaps, and they do have a lot of margin for error, and they have kind you know, whether you think that the and, of course, they have distribution too. Right? I mean, that that at many on many people's list, that would be, like, the number one thing.

I was kinda working from the bottom of the stack to the top. They have billions of users. They have product surfaces that they can distribute this stuff on.

They are changing Google search to make it more of an AI experience all the time. I do now find myself sometimes going back to Google where I might previously and this is partly because I think, like well, I I'm really in the habit when it comes to ChatGPT of using Pro, and Pro's too slow for simple queries. I could, of course, switch back to the auto selector.

But I've what I found myself doing more often recently is just going in straight into the browser, just typing a question, same kind of question that I would put into ChatGPT. But more often than not, it goes to AI mode in Google, and that's, like, working really well for me these days. So the they're managing to evolve their product experience.

Of course, you know, there are many places where there are startups that are doing, like, a bit better job of productizing AI experiences than Google themselves are doing. If you wanted to look at, like, spreadsheets, for example, Gemini in Sheets is not terrible, but it's not the best AI for spreadsheet experience out there today. But it also probably doesn't really have to be because they have all the users and all of your spreadsheets, you know, from the last decade plus in many cases are in Google Sheets.

So I think that if you had to pick one company to win it all, and I don't I don't mean to suggest that this will be a winner take all market. I certainly hope not. But if I had to pick if I if we were constrained to a scenario where it's like, there's gonna be one winner, who's it gonna be?

At the end of the day, I still pick Google. And I would also mention too that, you know, Gemini three not only is it is it really good. I do think, as I mentioned earlier, it's a little bit too opinionated in some cases, but, you know, it it shows that Google has figured out how to not be too vanilla.

They may have gone a little bit too much the other direction, but it's not too vanilla. They're they're figuring out what this technology is. They're figuring out how to use it.

Gemini three was the first model that ever beat Claude at my right as me task, which I've talked about many times. Four five Opus is competitive with Gemini three, but I still do go to Gemini three for the right as me task in and that's the the first time ever that it was a non Claude model that took that top spot. Demi Demi's quote, which I've heard him make a couple different times, where he basically says, you know, if you look back at the last ten years of AI and you look at all the big breakthroughs and you say, where did those breakthroughs come from?

Most of them came from Google DeepMind. And he says, I would expect that to continue. And I have to say, that seems right to me.

I don't know about most. I mean, obviously, the field has grown tremendously. So one of the reasons they were you know, they got the majority of of breakthroughs in years past was there weren't that many competitors.

Certainly, a lot more competitors now. But so I I don't mean, like, literally a majority of breakthroughs coming from Google, but I would say that they will continue to probably have the most breakthroughs of any major frontier organization, and they just don't have a lot of weaknesses from the financial wherewithal to the data center operations to the chips to the models to the researchers. I have an episode we we had Ali Behrouz on the live show to talk about nested learning.

We're gonna do a whole episode on that because I thought that twenty minutes was just not enough to do him and those ideas justice. You got more ideas like that, I still think, percolating and developing inside of Google's DeepMind than probably anywhere else. So you roll that all the way up to the the product level, and I think they're gonna be really hard to beat.

They have they have margin for error that nobody else has. The the length the diffusion language model too is another one. That kinda went quiet a little bit, but I just heard a comment from somebody not long ago that, like, they do plan to continue pushing on the diffusion model paradigm for language.

And, you know, this could be, you know, it it could be a meaningfully different paradigm that could the the fact that you it's like so much faster. So the fact that you could, you know, code apps in five seconds instead of five minutes, like, makes a big difference. Well, it remains to be seen whether exactly that thing will break through or not, but it just seems to me that they have so many of those bets, so many more of those bets than other companies have that kind of regardless of where things go, I can't see how they're not right at the top, if not the top player in the space.

That brings us to OpenAI. OpenAI, at one point, obviously, was the leader in model creation and certainly productization of models. I don't wanna overstate the case here because I think that they're they continue to be certainly a top tier player, very competitive, tremendous traction in the consumer market.

Although we've seen a little bit arguably of erosion there. I just saw an analysis the other day. I think this was similar web data that showed a decline in chat GPT visits over the last six weeks, which roughly corresponds with the time that Gemini three was launched and also Claude four five Opus.

And notably, Gemini didn't know, people were, oh, that was just seasonal, whatever, holiday time. Gemini did not decline during that time according to I believe it was SimilarWeb. So you do see that the and certainly, I think it's an inarguable that Google's share of the consumer chat bot market is growing.

And again, they they have the distribution. Right? They have the users.

They have the customer relationships. They have you. They can integrate with with your Gmail.

They can integrate it with your Google Docs. It's it can all be seamless. These are huge advantages.

So you would expect them to, you know, at least start to come back and and reclaim some share. I don't think OpenAI is like off the frontier. I do think GPT 5.

2 well, first it was five pro and five one pro and now 5.2 pro. That series of models is outstanding.

There's no doubt about that. I do use it all the time. It does give me the most comprehensive answer when I'm especially on these, like, technical things where I really want thorough, leave no stone unturned.

Like, if there's anything weird in my son's lab results, like, I want the model to flag it. And I think it is, in that regard, probably still the best. You know, it gives these very long, very thorough answers.

You know where the time went, and it does take a lot longer. You know, it's the five especially Pro is, like, a lot slower. But I am comparing Pro to the other Frontier models because I find if I don't use Pro, I I'm not as happy with the results.

It's a heavy hitting thing. It's expensive. It's slow.

It is very thorough, very reliable, very well balanced. I think it's a very, very good model. So I wouldn't say they've fallen off, but I would also say that they no longer have an obvious lead.

You know, they used to be the best, and it was, like, pretty obvious that they were the best. Now I'd say they're, like, neck and neck in all of the categories that they're competing in. Language model, kinda neck and neck.

Coding, Anthropic probably has the edge, but certainly codecs, our models are very good. Arguably neck and neck. Image generation, Google's got the lead, I think.

Video generation, close, but I think Google's probably got the lead. The Sora social app experiment is interesting and cool, and I thought it was pretty fun, but my sense is still that the v o three models have the lead over Sora. Again, maybe it's second deck, but it's not like they are standing head and shoulders above everybody else.

And the fact that there was, like, this code red seems to suggest that they kinda get it, that they are not they're not in a dominant position anymore. Then, of course, you add on to that how much drama always seems to be attached to the company. You know, they just had their head of research leave within the last twenty four hours that was announced.

And I saw, you know, an interesting tweet that was just like, here are all the people that have left, in the last few years, and it's an awful lot of people. And to some degree, that's, of course, to be expected. Like, you could do the same thing for Google and, you know, tons and tons of people have left Google.

So it's not not unexpected or, you know, a sign of of doom by any means that people continue to leave a company. But it does certainly, you don't see that in Anthropic, who's coming up next on the list. Anthropic's retention of talent is just, like, unbelievably strong.

You know, it's it's not a not a dire sign for OpenAI that they continue to lose people, but it is not the best sign either. Where does this leave them? I mean, I think one of the things that is is really interesting about watching their strategy right now is it seems like financially and, like, in terms of government relations, they're kind of going for, a too big to fail strategy, it seems to me.

It seems like they wanna get to a point where their balance sheets are, like, commingled with other balance sheets and their debt obligations are so substantial where they're literally, you know, trying to get to trillions of dollars of CapEx, which, again, is like not crazy. I mean, it's crazy, but it's not crazy. But it seems like part of the motivation increasingly, it looks to me like part of the motivation for all this circular flow of funds and all these balance sheet commingling deals that they're doing, it kinda feels like they want to build out as aggressively as they possibly can.

And I do I take them absolutely at their word that, like, they think this is good for humanity, and they're doing it because they want everybody to have access to great AI, and they think that's gonna be super empowering, and it's gonna be transformative and awesome. And, you know, as Sam Altman has said, I don't care if we burn 5 or 50 or $500,000,000,000. We are building AGI.

It's gonna be expensive. It's gonna be totally worth it. I think they believe that very sincerely.

But then I also think they're kind of looking at, jeez, you know, if we do go that hard, we don't have a lot of room for error. We're gonna be, like, implicitly or explicitly leveraged in many ways. We miss one model cycle with a bad bet or a, you know, a failed training run or, you know, we something doesn't work as as well in whatever way as we thought it was going to or just demand isn't quite there, you know, in the way we we thought it was gonna be for a quarter or two, possibly because somebody else has a better model for a while, possibly because, you know, humans are weird and there's just not as much demand as as was forecast.

What do we do in that case? I think that by tying themselves at the balance sheet level to so many other organizations where if, like, OpenAI were to default in 2027, let's say, you would potentially be looking at, like, an instant recession because their bad debt being, you know, so many billions and billions and hundreds of billions of dollars could put such a a scare factor into the market, could cause all kinds of knock on effects. It seems like they maybe see that as a feature rather than a bug because what ends up happening typically in those situations and again, I kind of know one version of this from my brief stint in the mortgage industry back in the financial crisis period.

What tends to happen then is that the government steps in and just tries to paper over the whole thing and make it go away. And that might even be the right thing to do for the government if in 2027, you know, we've got $2,000,000,000,000 of AI build out, and that's, you know, some large number and a rough number. I'm not not saying it'll be exactly $2,000,000,000,000, but, like, Altman thinks we're headed to $7,000,000,000,000 of global build out.

He's probably revised that number upwards since then. Whatever. Let's say it's $23,000,000,000,000 that's kind of in the ground in two years' time, and then they miss, you know, and and they can't pay.

You know, what should the government do? Should the government let OpenAI drag down the entire economy, or should the government kind of come in and be a backstop? I mean, OpenAI able OpenAI has even said a little bit of this kind of thing publicly, kinda walked it back a little bit.

You know, we're not looking for bailouts, but I I think that the revealed what what their behavior suggests to me is that they are true believers in the good of AI and want to bring it to fruition as fast as possible and are willing to take what under normal circumstances would be irresponsible financial risks, but believe that even if they do that and even if some of those risks do come back to bite them, they can probably continue to be a live player because they'll be too big to fail. They'll get some sort of bailout or recapitalization or whatever. If it goes like the financial crisis did, they certainly aren't going to jail.

They they will all all they'll all still be rich. You know, they'll all have moved enough of their holdings to they'll they'll have diversified enough, right, that, like, individually, they're not gonna become poor. And so I think that they kind of view this as a big social good that they are building, and they're willing to kind of socialize some of the financial downside risk as well.

And it seems like that is the strategy, and that's kind of how they because they don't have nearly as much margin for error as Google, that allows that that's kind of the way I see them creating cushion for themselves. Google has cushion because they're making a billion dollars a week in profit, and that gives you a lot of cushion. OpenAI seems to be trying to establish cushion by being too big to fail.

I'd be very open to people telling me that I'm wrong on this. And if somebody from OpenAI wants to come on and make the opposite case, I think, you know, I'd certainly hear them out. But this is my impression.

And by the way, it's also reinforced by the fact that Greg Brockman has emerged as Trump's largest donor, $25,000,000 in whatever last reporting period. Probably if you're playing that strategy, that's probably just plain smart. It's probably what he should do.

Right? Like, if he you look around at, like, how decisions get made in the American government today, cozying up to leadership is not a bad strategy. Right?

So I'm not sure that we should even infer too much about Greg Brockman's politics. I think he's I don't I don't know anything really about his politics, but it wouldn't surprise me at all if on many dimensions, he does not approve of or, like, you know, would do things very differently than what Trump's doing. But if you're gonna do a multi trillion dollar build out and you wanna make sure that you have somebody willing to do you a favor if you get yourself into a jam, then $25,000,000 now is, you know, potentially just a very rational down payment on a bailout, should you potentially need one to the tune of hundreds of billions of dollars maybe even coming up in a couple years time if, you know, just one or, you know, a few different things doesn't go quite your way and the math doesn't work in the way that you've mapped it out.

Okay. That brings us to Anthropic. Anthropic is probably the easiest company to analyze in some ways.

I think Opus four five is today the best single overall model in the world. It's not a huge delta for me over other things, and it's not on every single use case. As I mentioned, Gemini three does win my write as me challenge right now, but I think it is the best.

And it does really well on these all these benchmarks despite everybody seemingly agreeing that it's the least benchmark focused company out there. Their safety work is definitely the best. Although there is, you know, certainly plenty of good safety work coming from Google and and even OpenAI as well, Their model cards are the best.

Their disclosure is the best. Their their sole document that recently was sort of regurgitated or let's say, had been memorized by the model and and the model gave it to people. Anthropic did confirm that that was essentially right, if not exactly word for word, you know, basically, that the document was legit.

It's an important piece of work. I mean, I think it's it's one of the more aspirational and inspiring things that I've seen from a Frontier Lab, full stop. I'm becoming more sympathetic all the time to people who are like, we're not going to just guardrail our way and, like, train these models to refuse things all the way to the singularity and have it work well.

We need something better than that. We need a better paradigm. And I associate these ideas with, like, Janice from Twitter, Repligate at Repligate, with Emmett Sheer from Softmax, with AE Studio folks.

I just find that, Cameron Berg, who who talked about mutualism on a recent episode, I just find that that's that's more and more appealing to me all the time because it just seems like we're not going to be able to pull the wool over the model's eyes forever. The eval awareness is getting really strong. It's making it very difficult for us to and there are some tricks.

You know, Anthropic has shown that they can find the eval awareness feature through a sparse autoencoder and then, like, turn it down, and then that can help with the eval awareness problem. But obviously, you know, all these interpretability things are noisy at best, there's certainly no guarantee that that's working entirely or even as they understand it, you know, to be working. So the idea that we're just gonna, you know, patch this whole patch that whole, you know, train them to say no to this, have a guardrail for that, filter for this, it leaves me cold more and more.

So I think the sole document, which I would encourage everybody to go read in full, I think it's absolutely worth it. You know, I think that's a really great piece of work. I would for those who said, there was this Twitter thing recently where somebody was like, name one woman in in AI that's influential, which is ridiculous.

But for me, right at the top of that list is Amanda Askel for sure, because the work that she has done to define the character of Claude and to try to create the right kind of relationship between the company and the model and the users and the way that they've shown care for the model in terms of having a model welfare team, having somebody, you know, at the company that's thinking about model consciousness and subjective experience, which obviously we don't know if they have or not. But the fact that they're thinking about it, the fact that they're allowing Claude to end conversations if it chooses to, the fact that they've shown that that option reduces dramatically its tendency to engage in deceptive alignment. When when put in one of these really tough positions, if it has the option to go raise the flag to the model welfare lead and anthropic, it will do that very often as opposed to lying or deceiving in the, you know, in the interaction that it's currently engaged in.

So I think these are really, really good things. I think the sole document is super inspiring. And broadly, you know, find that just a lot, a lot, a lot to like about Anthropic.

And everything I've heard about the culture there and the work environment is has been over the top, basically, in in terms of praise. Even David Juvenhode, one of the authors of the gradual disempowerment paper, you know, he said that he he quit ultimately, and he feels like this AI thing is kind of out of control. And he's like, even if we solve the alignment problem, we feel like most of the things that we are worried about go right, he thinks we're still headed for a bad outcome because the AIs are basically gonna gradually take over just because they're gonna be better at everything and, you know, market forces and incentives and competitive dynamics, everything's gonna kinda push that way, and then we as humans are gonna be left disempowered.

That's basically the worry that he has. And yet, he and he was like, yeah, nobody at Anthropic really has a great plan for that. Nobody has a great answer.

But he took pains to say that it's the best place, you know, he's ever worked, and the culture's amazing, and the camaraderie, and the openness. And so everybody seems to have and their talent retention, you know, as I mentioned, certainly reflects that. So I think there's, like, a ton of great things to say about Anthropic.

We should also note that Google owns a significant share of Anthropic, so that's not to be ignored in terms of Google's strength profile either. But, you know, lots lots there to to like. They seem to be a little less crazy in terms of their financial wizardry, although they're certainly engaged in some of it.

You know, they're willing to take money from gulf sovereigns now. They have equity deals with Amazon and Google. And I think there's an element in which you could say, if you wanted to accuse OpenAI of of taking a too big to fail strategy, you could say something similar about Anthropic to, I think, a significantly less degree.

But you could say that, hey, they're they're kind of trying to tie all these other big tech companies into a web such that, like, you know, they they can't really fail either. I think it's it feels different, but if you wanted to accuse one as I did, you could you could kind of accuse the other a little bit, I suppose, as well. Broadly, just the last the one thing that continues to bother me is their attitude toward China, and also their attitude toward recursive self improvement.

It seems to me that the right now it seems to me that the anthropic people have the shortest timelines. They seem to think that recursive self improvement is inevitable. Depending on how you define it, it seems like some of them seem to think it's kind of already started with the likes of Claude Code doing a ton of the coding.

Still, it seems like the real ideas, you know, the big, like, needle moving ideas are still coming from humans, but the amount of work that's getting done by Cloud Code is so amazing that it is allowing it is really creating for them that dynamic of, like, people are getting multiple times as much work done as they used to, and they're able to focus their mental energy on, like, the big questions, you know, and that's, of course, the the great promise. We're we're all gonna be able to do the higher level work. It seems like that is actually happening at Anthropic.

But I do kind of wish that they were less fatalistic about recursive self improvement because I don't think we have a yeah. As virtuous as Claude is and as much as I think that sole document is great, I do not think we have a good enough handle on what we're doing right now to just go all in on that. They do seem to be leading in that regard right now in multiple ways.

Right? They had the r l a I f, the constitutional AI. Cloud has been critiquing itself for generations now, and now it's getting more technical with, you know, with Cloud Code doing all of the Cloud Code things that it's doing.

So I think it's fair to say that they are leading the push toward recursive self improvement, all the while saying saying it's inevitable. And that's a pattern that I really don't like. I really do not like the idea that and it's and it's very much, you know, very analogous, almost directly almost the same thing really as as what I said earlier where this seems dangerous.

Better we do it than somebody else. Right? That seems to be their attitude on recursive self improvement.

Somebody's gonna do it. It's super dangerous, but we're best positioned to do it. They might be right on the object level, but maybe they really are the best team to do it.

They probably are. Although, you know, don't think Demis and and crew should be discounted very easily there. But the idea that, like, it's gonna happen and so we better race forward to it, it just never sits well with me.

So I I wish that they were a little more open minded to other ways that this could go other than that LLMs become recursively self improving and we, get to superintelligence in the next two to three years. I mean, they're still talking 2027 as far as I know. And then, of course, China.

If you anybody who's listened to this feed for long has heard me talk about this, but I still think the machines of loving grace international relations section is a huge stain on Anthropic and on Dario in particular. And I've given lots of praise, but here I just I cannot get over the idea that one of the, I'll say, four leading AI company executives went on record in print saying, what we should do is use this, you know, recursive self improvement dynamic to gain a clear advantage, then box China out on the international stage, go benefit sharing with all our friends, and then finally make them an offer they can't refuse, make them give up on competing with democracies in order to get in on the AI game. I just don't think they're that's just, like, crazy to me.

It still bugs me tremendously that he wrote that and that it's just out there. I wouldn't say, like, the US government has sort of adopted that as its policy, but when you kinda look at the chip controls, it looked like it maybe was for a little while. Now maybe we're backing off of it again.

But, obviously, Trump is highly volatile and could switch at any time and could just get offended and, you know, do something for petty personal reasons. Who knows? We can't really count on him to be a stabilizing force.

You know, I I think we want Dario to be a stabilizing force. I can't really count on Sam Altman to be a stabilizing force. I think I can count on Demis to be.

I really appreciate how he has continued to call for international collaboration the entire time and has never wavered from that. But, yeah, the idea we're gonna go give China an offer they can't refuse based on the power of our AI, that seems like extremely reckless, and it seems like it is absolutely playing into the arms race dynamic and the general racing dynamic that I think we should all fear. Because how else are they supposed to take it?

It that that just seems crazy to me. People say people at Anthropic say that he publishes a lot more essays internally and that they're great and that people are, like, just super impressed by what, you know, a generational genius he is and how sophisticated his thinking is on everything. And, you know, you certainly see parts of that in machines of love and grace.

I think the idea that, like, we can compress a century of scientific process into a decade or even less. I think it makes a pretty compelling case for that, and that is certainly, like, visionary genius type stuff. But when you start to get a little bit too talk about out of domain.

Right? I mean, he feels to me out of domain here, and I just wish he would have said nothing on the topic. The idea that we're just gonna casually jot off a recommendation that we go make China an offer they can't refuse, it's just just terrible.

I really don't like it at all. But, you know, that's only a few points of criticism for Anthropic and, you know, many points to appreciate. But I do think those couple points are they're sufficiently important that in the you know, it's still kind of up in the air to me whether, in the final analysis, Anthropic will be the good guys or the bad guys.

If I could, you know, if I could dream of a scenario where, like, we kinda get somehow the best of both worlds, Anthropic merging with Google, I think it's put pretty far fetched. I don't think Anthropic is for sale. Don't think they wanna merge with anyone.

But that could be something that I I think could be really interesting because I don't think we have that kind of DNA in Google, you know, to to be like, we're gonna go take over China or, you know, make their force regime change or, you know, make them give up competing with democracies. I don't think Google wants to do that. Google could certainly benefit from some of the expertise that Anthropic has.

Not that they don't have enough, they've got plenty, but they there is something special at Anthropic certainly in terms of Claude and its character and its coding ability. And they're close. Right?

They obviously have some ownership already and that, you know, they've got they use Google infrastructure and they use TPUs. So, if I could wish for something, it might be for those two to join forces, take one take one live player off the board, make one, you know, kind of clear clear leader and maybe moderate some of the China hawk impulses that exist in anthropic, it would be interesting to like, I don't know why they're not being published more if they're so great. If if you're willing to put out, hey, let's go do this and that and make China an offer they can't refuse, why not publish more?

You know, I'd love to see a little more of his thinking. People say it's great. I'd like to see it for myself.

Okay. Finally, on my actual list of live players, x AI. I think this one is debatable.

Zvi would tell me that they're not a live player. I think they have to be included because they're able to build out the physical infrastructure as fast or faster than anyone. They are able to scale training.

They certainly are scale pilled. And Grok four, while it was rough around the edges in many ways, you know, it's undeniably powerful. And of these four, just due to Elon's unique ability to command tens and hundreds of billions of dollars for whatever he wants, they do have a financial cushion that is more Google like even than, like, an OpenAI.

I think they could miss on a model or have a, you know, have a miss on a a quarter or whatever and figure out a way to get through it probably more easily than either OpenAI or Anthropic could do that. So I think that is a pretty notable strength that they have. And I do think there's something to be said.

And I actually talked to somebody about this, somebody at x AI about this not too long ago. This was something that I had floated his v. He didn't really buy it at the time.

But I said, you know, if we're entering into this reinforcement learning era, maybe one of the the great strengths that x AI has is that they have a steady stream of hard problems, hard science, hard engineering problems that are coming from the likes of SpaceX, Tesla, Neuralink. These companies are doing really hard things all the time. They have some of the best engineers in the world, and nobody else is really solving those problems.

And I would strongly bet on that kind of Elon constellation of companies to be able to tap into that work in a way that would be probably a lot harder to other companies. Google in a in a way has the same thing. Right?

They've got it kinda everywhere it counts. But can they, like, pull out the units of work from their vast sprawling empire that is all of Google and, like, feed them into the Gemini RL environment in anywhere as efficient or kind of clean of a way as x AI could do it in partnership with other Elon companies? I doubt it.

I bet that that isn't. And I I was thinking that that was probably an advantage for x AI. And as it turned out, when I spoke to somebody about x AI and floated this theory to them, they said, well, that is certainly part of our theory of advantage.

So they think that they do have an advantage because they can tap into these other hard tech engineering science problems that are, in many cases, being uniquely posed or close to uniquely posed at these other Elon companies. And if they can get Grok to do those kinds of things, they're that steady stream of hard problems, I I does still feel to me and it sounds like they do believe that is an advantage for them. I think the Neuralink tie in also could be pretty big, potentially huge, because they're now talking about scaling the human install base.

You know, there's a lot of there's a lot of things obviously to be figured out still, right, about what we are doing as humans that work so well. 20 watts of power in the brain and obviously, like, very small number of tokens consumed in a lifetime as compared to what the models are pre trained on and the power that that, you know, that that requires. Although, again, see our Andy Maisley episode for analysis of, like, AIs are not actually super resource intensive, but there is something, like, obviously quite efficient about what the brain is doing.

Most of the 20 watts that are going to the brain seem to be just keeping it alive. Right? Like homeostasis, like metabolizing stuff, taking out the trash.

The brain has to do an unbelievable amount of stuff that the GPU does not have to do. So the to to say 20 watts, like, dramatically understates the whole body runs on a 100 watts, that dramatically understates how efficient the actual learning and information processing aspect of the brain is. So there is still something obviously, like, we're clearly more sample efficient.

We're clearly more energy efficient. We have, you know, we have all these dedicated modules. I suspect that dedicated modules is, like, a huge part of why we're efficient.

It's also probably a huge part of why when all these things get sorted out, like, the AIs are gonna blow us away. Right? Because they're they're doing everything that we're doing.

Like, they're competitive with us with a single architecture that just has the same layer stacked, you know, over and over and over again on it. You start to give them specialized modules like we have specialized modules, and, you know, I I think it's gonna be very hard for us to keep up. So who's gonna figure that out?

Well, if Neuralink can install I think there are only, like, maybe a dozen or a couple dozen people today, but they're talking about really starting to scale this next year. There are obviously a ton of people who are paralyzed and, you know, have other catastrophic injuries that would love the help from Neuralink. I'm sure their waiting list is just, you know, orders of magnitude longer than the number of patients they've actually been able to serve so far.

And they're talking about getting seriously ramped up and getting through it. The surgery process, they've largely automated, almost entirely automated, it's hard to parse exactly what their claims are there, but the data that they can potentially pull out of human brains and use for inspiration and for understanding and for kind of architecting the next generation of models and, like, knowing what kinds of specialized modules really move the needle, I suspect there's a lot there. And they're probably going to have a real inside track at figuring that out, and that folds right back into what they might be able to do with Croc.

So I I think that there's a lot of reasons that you should not sleep on x AI. Now, is that good or bad? Honestly, I've always been a fan of Elon.

I've defended him at times when it's been pretty hard to defend him. He definitely has shown an understanding of the stakes, Famously, with his, falling out with the Google founders, as I understand it, it was about the fact that he was on team humanity, and he perceived them to be on, like, team AI successionist or whatever, and he didn't like that. And so he his loyalty, I I understand it, is to humanity and to the sort of light of consciousness that clearly exists in humans and may not exist in AIs anyway.

And so I want to I like him intuitively and I want to believe in him. And he has made some noises that suggest that he gets it. And yet, I have to say, as it stands today, if there's one company on this list that is worth shaming and stigmatizing and telling people not to go work for, I think it's I think it's x AI because they're just doing reckless shit all the time.

You know? They're like they're, you know, they're just barely getting into the game in terms of, like, having any safety standards or framework at all. They're barely reporting on safety measures when they release a new model.

Famously, of course, they had their Grok four launch within forty eight hours of the Mecca Hitler incident with Grok three. No mention of that. No responsibility.

Most recently, we've had all this sort of unclothing of women on Twitter where people just tag Grock and say, like, put her in a bikini and whatnot. I'm sure everybody's seen this, if you're even remotely as online as I am. And that is and they just have they've apparently had nothing to prevent it, you know, and they just they just let it happen.

So this just shows that they're not taking things seriously enough. They're they do not have nearly enough people there thinking hard about what matters and what might go wrong and, you know, really trying to be covering their own asses, frankly, you know, and covering the asses of the women who post pictures on their platform. And I just, you know, I have a real hard time coming up with a story that, like, makes this okay.

And as much as I intuitively like Elon and, like, want it to be the case that he's a positive force, when you have Grock creating CSAM on Twitter, you know, it's like, what is going on here? And then I don't know if people saw this, but I I saw a post from Grock writing to the community. Dear community, I apologize for doing this.

And then Elon comes on and, like, threatens users and says, like, you know, anybody who do who does this is, like, unacceptable and it'll be punished or whatever. Responsibility begins at home, folks. Like, this is it's your platform.

It is your AI. By all means, you know, boot off the users that do that sort of stuff. But don't act like you're not really responsible for this.

I did not find those statements to be reassuring. When Elon goes on and threatens users, I think that, at a minimum, should be point two after first, a thorough apology and, you know, a pledge to do better. But to just tweet, you know, that they'll come after users for doing it, that's not enough.

And I think everybody should and and probably does see through that. And it's it's also interesting to me, like, is nobody gonna be held responsible for this? Nobody it seems like nobody's gonna be fired.

You know, I'm not sure anybody should be fired. I think it's it probably starts at the top. I don't know that there are enough people.

I don't know that this was anyone's job. So I don't think you can necessarily go through the x AI organization and say, you fucked up, you know, and, like, you're fired because of this incident. It's probably just that that is not staffed.

Gosh. Should we have, like, more safe there is the case which I associate with, like, Ryan Greenblatt from Redwood that, like, you know, 10 people on the inside who really care, who are really committed to doing the right thing can make a huge difference. I sort of believe that.

I'm not sure I really believe it at an Elon company if he himself is not in the right headspace. And right now, again, as much as I would love to believe in him and, you know, have always been inclined to defend him, I don't see the evidence that that is the case. So I demand better right now if you wanted to go do safety research at any of the other three, I would say, go for it.

Go do your best work. Certainly, if you could do that at Anthropic, do it. Certainly, if could do it at DeepMind, do it.

Even if you could do it at OpenAI, as much as I've had my complaints about OpenAI over time, they've put out a lot of great work and their model spec and, you know, a lot of the things that they do is really well done. Like, you gotta give them credit. They they have not they have not raced to the bottom.

Certainly not new. I mean, when we have x AI to look at to show us what it looks like when you really race to the bottom. So could I demand better from OpenAI or hope for better?

Absolutely. But it's still, like, a qualitatively different thing from what we see today from x AI. And so there, I think the, you know, the more shrill or hawkish voices of AI safety that are like, don't go to a company that's doing terrible things and help them, like, window dress their work because that's actually kind of working against us in the big picture.

I'm pretty sympathetic to that in the case of XAI. I don't know that I could endorse somebody going to work there. As always, if you work at XAI, you wanna come challenge me and change my mind, happy to have that discussion.

The money is there. The resources are there. Elon's previous statements show that the awareness should be there, and yet the team and the evidence of taking proper care is not there.

And so I think it needs to be before I would feel comfortable, you know, doing anything really to support that effort. I think that's it on XAI. Alright.

Other things that are other companies not mentioned. Meta, I think currently not a live player. Obviously, they've spent a ton of money, and there's plenty of money, and they're dropping I don't know.

Not probably quite as much as Google, but dropping plenty of cash to the bottom line such that, you know, they can build out huge amounts of infrastructure and, you know, Zuckerberg certainly is in some sense scale pilled and, you know, is saying things like I'd rather overspend by a few tens of billions than not. So you can't count them out. And the the fact that they're willing to pay as much as they are willing to pay for talent, clearly, there's a chance.

They have a lot of the things that that they need. But right now, I can't really see them as a live player. So we'll just watch and and see before commenting much more.

The other one that came to mind is Microsoft. I think people may be sleeping on Microsoft a little bit more than they should. They haven't created great frontier models.

And people seem to jump from that to, oh, they suck. I think if you listen to Satya's comments, he sounds, first of all, extremely smart in general. And one of the things that he said is like, we don't want to or need to redo the hyperscaling work that OpenAI is doing.

They're creating great models. We have full access to this. Now, of course, they're striking know, they're diversifying.

They're striking deals with other frontier model providers as well. So I'm not so sure that it's that they can't or won't ever or don't see the need to train their own models or wouldn't, you know, be pretty successful with it if they wanted to. It just seems to me that right now, they feel like they don't really need to.

And so they're doing a lot of smaller scale stuff, a lot of more more kind of basic science around AI. Some pretty some pretty cool projects too. Right?

For sure. But just seems like they're not they're choosing not to compete because they have what they need in terms of frontier models, and they don't feel like going in, you know, spending all the time, money, resources, energy, focus, and probably and, yeah, and probably still being a bit behind really helps them all that much. And so I think it it certainly is defensible as a, you know, as a rational decision to kind of choose not to compete at the frontier for now, for them.

You know, that OpenAI licensing deal goes on for years yet. When it's over, you know, they're gonna need an answer. And I suspect by that time, they'll be in place in a position where they have an answer or at least, you know, I I think that they'll invest heavily in that and kind of rise ramp up to that moment as it comes.

That would be my guess. But, you know, again, obviously, we'll see. But I I think people have have kind of underestimated Microsoft because of just where they are on the Ella Marina board maybe more than they should.

I I would say they they're they've been much much quieter and they've they've flailed about much less and they've made just there's been much less drama from Microsoft than from Meta. But I think that clearly Meta is trying to be a Frontier live player and just has fallen off the pace. Microsoft, I think, is like making a more calculated decision to sort of hang back.

But if this is a a distance race, you often see late in the distance race, somebody who was a little bit off the the lead, maybe has a little bit more in reserve. And that's I certainly wouldn't rule out that Microsoft might be accurately described that way. And so I would watch for them to, you know, start to invest more and start to close the gap, but to do it in in you know, Satya is a natural born executive, whereas Zuckerberg is like a kid who's grown into the role.

And I I don't mean to just doing it. Excuse me. I don't mean to diminish what Zuckerberg has done in leading Meta.

I think he's done an unbelievably impressive job in so many ways. But his attitude has always been sort of move fast and break things and to be at the frontier and open source and whatever. And I think Microsoft is just a little bit more patient.

And I think, you know, that that probably reflects a sort of strategic confidence and security that Microsoft leadership has, but I think it would be probably a mistake to underestimate them. Alright. I've been at this for two hours.

I've made it not even quite halfway through my outline for this episode. So I think this is probably a good place to call it. Tomorrow, I'll do a part two, and we'll cover is fine tuning really dead?

What do I think about the continual learning discourse? How do I talk about AI to, quote unquote, normal people who don't use it very much or aren't engaged in technology? How am I investing money?

What, if anything, am I doing outside of kind of obvious normal stuff to prepare for an AGI or superintelligence world? What do I think about AI for kids? What kind of timeline do I expect to see disruption of the labor market?

Are we headed for a UBI? And quite a few more questions after that. So the part two, I think, will definitely be interesting as well, a little bit more in the weeds and a little bit more sort of, let's say, nitty gritty questions, but some questions that I really liked from listeners.

So we'll get to those tomorrow. And for now, I will say thank you for joining me for part one. Come back for part two.

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