Latent.Space 2024 Year in Review
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Happy holidays, friends. Thanks for all the love on the Latent Space Live episodes. Right after New Reaps, Swix and Alessio got back in the studio for a full end-of-year recap, which we published today.

This recap comes with a lot of references and visuals. So be sure to check out the attached YouTube for all the show notes. In other news, we have just announced the second AI Engineer Summit in New York City. We are bringing back the surprisingly successful AI Leadership track from World's Fair and also the single track AI Engineering track is now wholly focused on agents at work.

If you are building agents in 2025, this is the single best conference to attend. Applications are invite-only and we will sell out. Look for more sponsor and attendee information at apply.ai.engineer and see you there. Watch out and take care.

Hey everyone, welcome to the Latents-Based Podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host Zwickx for the 100th time today. Yay, and we're so glad that everyone has followed us in this journey. How do you feel about it? 100 episodes. Yeah, almost two years that we've been doing this. We've had four different studios. We've had a lot of changes. You know, we used to do this lightning round when we first started that we didn't like.

And we tried to change the question. Because every answer was cursor and perplexity. Yeah, exactly. I love mid-journey. It's like, do you really not like anything else? Like, what's the unique thing? And I think, yeah, we've also had a lot more research-driven content. You know, we had like Tridao, we had Jeremy Howard, we had more folks like that. I think we want to do more of that too in the new year. Like having some of the Gemini folks, both on the research and the applied side.

Yeah, but it's been a ton of fun. I think we both started, I wouldn't say as a joke, we were kind of like, oh, we should do a podcast. And I think we kind of caught the right wave, obviously. And I think your Rise of the AI Engineer post just kind of gave people somber to congregate and then the AI Engineer Summit. And that's why when I look at our growth chart, it's kind of like a proxy for like the AI engineering summit.

industry as a whole. Which is almost like, even if we don't do that much, we keep growing just because there's so many more AI engineers. Did you expect that growth or did you expect it would take longer for the AI engineer thing to become... Everybody talks about it today. Yeah. My sign of that we have won is that Gartner puts it at the top of the hype curve right now. So Gartner has called the peak in AI engineering. I did not expect to what level. I knew that I was correct when I called it because I did two months of work

going into that. But I didn't know how quickly it could happen. And obviously, there's a chance that I could be wrong. But I think most people have come around to that concept. Hacker News hates it, which is a good sign. But there's enough people that have defined it. GitHub, when they launched GitHub Models, which is the Hugging Face clone, they put AI engineers in the banner above the fold in big letters. So I think it's kind of arrived as a meaningful and useful definition. I think people are trying to figure out

where the boundaries are. I think that was a lot of the quote unquote drama that happens behind the scenes at the World's Fair in June, because I think there's, there's a lot of

doubt or questions about where ML engineering stops and AI engineering starts. That's a useful debate to be had. In some sense, I actually anticipated that as well. So I intentionally did not put a firm definition there because most of the successful definitions are necessarily underspecified and it's actually useful to have different perspectives and you don't have to specify everything from the outset. Yeah, I was at AWS reInvent and the line to get into like the

AI engineering talk, so to speak, which is, you know, applied AI and whatnot was like, there are like hundreds of people just in line to go in. I think that's kind of what enabled people, right? Which is what you kind of talked about is like, Hey, like you don't actually need a PhD. Just, yeah, just use the model. And then maybe we'll talk about some of the blind spots that you get as an engineer with the Alia posts that we also had on, on the sub stack, but.

Yeah, it's been a heck of a two years. Yeah, I was trying to view the conference as like, so NeurIPS is I think like 16, 17,000 people and the Latentspace Live event that we held there was 950 signups. I think,

The AI world, the ML world is still very much research heavy. And that's as it should be because ML is very much in a research phase. But as we move this entire field into production, I think that ratio inverts into becoming more engineering heavy. So at least I think engineering should be on the same level, even if it's never as prestigious. Like it'll always be low status because at the end of the day, you're manipulating APIs or whatever. Yeah.

but you're wrapping GPTs, but there's going to be an increasing stack and an art to doing these things well. And I think that's what we're focusing on for the podcast, the conference and all that.

basically everything I do seems to make sense. And I think we'll talk about the trends here that apply. It's this very strange mix of like keeping on top of research while not being a researcher and then putting that research into production. So like people always ask me like, why are you covering NeurIPS? Like this is a ML research conference. And I'm like,

Well, yeah, I mean, we're not going to understand everything or reproduce every single paper, but the stuff that is being found here is going to make its way into production at some point, you hope. And then actually, when I talk to the researchers, they actually get very excited because they're like, oh, you guys are actually caring about how this goes into production and that's what they really, really want.

the measure of success is previously just peer review, right? Like getting, getting sevens and eights on their academic review conferences and stuff like citations is one metric, but money is a better metric. Right. Yeah.

Yeah, there were about 2200 people on the live stream or something like that. So I tried my best to moderate, but it was a lot spicier in person with Jonathan and Dylan than it was in the chat on YouTube. I would say that I actually also created Lay in Space Live in order to address flaws that I perceived in academic conferences. It's not neuro specific. It's ICML, it's ICLR, it's neuro. Basically, it's very sort of oriented towards the sort of PhD students,

job market, right? Like literally basically everyone's there to advertise their research and skills and get jobs. And then obviously all the companies go there to hire them.

And I think that's great for the individual researchers, but for people going there to get info is not great because you have to read between the lines, bring a ton of context in order to understand every single paper. So what is missing is effectively what I ended up doing, which is domain by domain, go through and recap the best of the year, survey the field. And there are like NeurIPS had a, I think ICML had a like a position paper track. NeurIPS added a benchmarks

and data sets track. These are ways in which to address that issue. There's always workshops as well. Every conference has, you know, a last day of workshops and stuff that provide more of an overview, but they're not specifically prompted to do so. And I think really organizing a conference is just about getting good speakers and giving them the correct prompts.

And then they would just go and do that thing and they do a very good job of it. So I think Sarah did a fantastic job of the startups prompt. I can't list everybody, but we did best of 2024 in startups, vision, open models, post-transformers, synthetic data, small models, and agents. And then the last one was the, and then we also did a quick one on reasoning with Nathan Lambert. And then the last one, obviously, was the debate that

People were very hyped about. It was very awkward. And I'm really thankful for John Franco, basically, who stepped up to challenge Dylan. Because Dylan was like, yeah, I'll do it. But he was pro-scaling. And I think everyone who is in AI is pro-scaling. So you need somebody who's ready to publicly say, no, we've hit a wall. So that means you're saying...

Sam Altman's wrong. You're saying, you know, everyone else is wrong. It helps that this was the day before Ilya went on, went up on stage and then said, pre-training has hit a wall and data has hit a wall. So actually Jonathan ended up winning and then Ilya supported that statement. And then Noam Brown on the last day further supported that statement as well. So it's kind of interesting that I think the consensus kind of going in was that we're not done scaling. Like you should believe in a bitter lesson.

And then four straight days in a row, you had Sepp Hochreiter, who is the creator of DLSTM, along with everyone's favorite OG in AI, which is Juergen Schmidhuber. He said that pre-trading has hit a wall or we've run into a different kind of wall. And then we have, you know, John Franco, Ilya, and then Noam Brown, all saying variations of the same thing, that we have hit some kind of wall in the status quo of DLSTM.

what pre-trained scaling large pre-trained models has looked like. And we need a new thing. And obviously the new thing for people is some make, either people are calling it inference time computer, test time compute. I think the collective terminology has been inference time. And I think that makes sense because test time, calling it test meaning has a very pre-trained bias, meaning that the only reason for running inference at all is to test your model. That is not true. So I quite agree that OpenAI seems to have adopted

or the community since they adopted this terminology of ITC instead of TTC. And that makes a lot of sense because like now we care about inference. Even right down to compute optimality. Like I actually interviewed this author who we've recovered or reviewed the Chinchilla paper. Chinchilla paper is compute optimal training, but what is not stated in there is it's pre-trained compute optimal training.

And once you start caring about inference, compute optimal training, you have a different scaling law. And in a way that we did not know last year. I wonder, because John, he's also on the side of attention is all you need. Like he had the bet with Sasha. So I'm curious, like he doesn't believe in scaling, but he thinks the transformer. I wonder if he's still...

So, so, so he, obviously everything is nuanced and you know, I told him to play a character for this debate. Right. So he actually does. Yeah. He still, he still believes that we can scale more. Uh, he just assumed the character to be very game for, for playing this debate. So even more kudos to him that he assumed a position that he didn't believe in and still won the debate. Get back Dylan. Um,

Do you just want to quickly run through some of these things, like Sarah's presentation, just the highlights? Yeah, we can't go through everyone's slides, but I pulled out some things as a factor of stuff that we were going to talk about. And we'll publish the rest. Yeah, we'll publish on this feed, the best of 2024 in those domains.

And hopefully people can benefit from the work that our speakers have done. But I think these are just good slides. And I've been looking for sort of end of year recaps from people. The field has progressed a lot. You know, I think the max ELO in 2023 on LMSIS used to be 1,200 for LMSIS ELOs. And now everyone is at least at 1,275 in their ELOs. And this is across Gemini, ChatGPT, Grok,

O1 AI, which with their eLarge model and Anthopic, of course. It's a very, very competitive race. There are multiple Frontier Labs all racing, but there is a clear tier zero Frontier. And then there's like a tier one. It's like, I wish I had everything else. Tier zero is extremely competitive. It's effectively...

now three horse race between Gemini, Anthropic and OpenAI. I would say that people are still holding out a candle for XAI. XAI, I think for some reason, because their API was very slow to roll out, it's not included in these like, uh,

metrics. So it's actually quite hard to put on there. Like as someone who also does charts, XAI is continually snubbed because they don't work well with the benchmarking people. So it's a little trivia for why XAI always gets ignored. The other thing is market share. So these are slides from Sarah and we have it up on the screen. It has gone from very heavily open AI. So we have some numbers and estimates. These are from Ramp. This is an estimate of open AI market share in December, 2023.

And this is basically, what is it, GPT 3.5 and GPT 4 being 95% of production traffic. And I think if you correlate that with stuff that we asked Harrison Chase on the Langchain episode, it was true. And then Cloud 3 launched middle of this year. I think Cloud 3 launched in March. Cloud 3.5's sonnet was in June-ish. And you can start seeing the market share shift towards Anthopic.

very, very aggressively. And the more recent one is Gemini. So if I scroll down a little bit, this is an even more recent dataset. So RAM's dataset ends in September 2024. Gemini has basically launched a price war at the low end with Gemini Flash being basically free for personal use.

I think people don't understand the free tier. It's something like a billion tokens per day. Unless you're trying to abuse it, you cannot really exhaust your free tier on Gemini. They're really trying to get you to use it. They know they're in third place, fourth place, depending on how you count. And so they're going after the...

lower tier first, and then, you know, maybe the upper tier later. But yeah, Gemini Flash, according to OpenRouter, is now 50% of their OpenRouter requests. Obviously, these are the small requests. These are small, cheap requests that are mathematically going to be more. The smart ones, obviously, are still going to open AI. But, you know, it's a very, very big shift in the market. Like, basically, over the course of 2023 to...

going into 2024 opening has gone from 95 market share to reasonably somewhere between 50 to 75 market share yeah i'm really curious how ramp does the attribution to the model if it's api because i think credit card spin well but it's not the credit card doesn't say maybe maybe they'll maybe when they do expenses they upload the pdf but yeah the german i think makes sense i think that was one of my main 2024 takeaways that like the best small model companies are the large labs which is not something i would have thought that the open source kind of like

long tail would be like the small model yeah different sizes of small models we're talking about here right like so small model here for Gemini is 8B right Mini we don't know what the small model size is but yeah it's probably in the double digits or maybe single digits but probably double digits the open source community has kind of focused on the 1 to 3B size maybe 0.5B that's Moon Dream and

And that is small for you, then that's great. It makes sense that we have a range for small now, which is like maybe one to 5B. I'll even put that at the high end. And so this includes Gemma from Gemini as well. But it also includes the Apple Foundation models, which I think Apple Foundation is 3B. Yeah. No, that's great. I mean, I think at the start, small just meant cheap. I think today small is actually a more nuanced term.

discussion, you know, that people didn't, weren't really having before. Yeah. We can keep going. This is a slide that I mildly disagree with Sarah. She's pointing to the scale seal leaderboard. I think the researchers that I talked with in Europe were kind of positive on this because basically you need private test sets to prevent contamination and

And Scale is one of maybe three or four people this year that has really made an effort in doing a credible private test set leaderboard. Lama 405b does well compared to Gemini and GPT-40. And I think that's good. I would say that it's good to have an open model that is that big that does well in those metrics.

But anyone putting 405B in production will tell you, if you scroll down a little bit to the artificial analysis numbers, that it is very slow and very expensive to infer. It doesn't even fit on one node of a...

of H100s. Cerebras will be happy to tell you they can serve 405B on their super large chips, but if you need to do anything custom to it, you're still kind of constrained. So is 405B really that relevant? I think most people are basically saying that they only use 405B as a teacher model to distill down to something.

Even Meta is doing it. So when Lama 3.3 launched, they only launched in 70B because they use 4 or 5B to distill to 70B. So I don't know if like open source is keeping up. I think the open source industrial complex is very invested in telling you that the

If the gap is narrowing, I kind of disagree. I think that the gap is widening with O1. I think there are very, very smart people trying to narrow that gap and they should. I really wish them success. But you cannot use a chart that is nearing 100 in your saturation chart. And look, the distance between open source and closed source is narrowing. Of course it's going to narrow it because you're near 100. Yeah.

this is stupid um but in metrics that matter is open source narrowing probably not for 01 for a while and it is really up to the open source guys to figure out if they can match or not i think inference time compute is bad for open source just because you know doc can donate the flops at training time but he cannot donate the flops at inference time so it's really hard to like actually keep up

Big, big business model shift. So I don't know what that means for the GPU clouds. I don't know what that means for the hyperscalers. But obviously the big labs have a lot of advantage because it's not a static artifact that you're putting the compute in. You're kind of doing that still, but then you're putting a lot of computed inference too. Yeah, yeah, yeah. I mean, Lama 4 will be reasoning oriented. We talked with Thomas Shalom. Kudos for getting that episode together. That was really nice. Good, well-timed.

Actually, I connected with the AI meta guy at NeurIPS. And we're going to coordinate something for 9.4. And our friend Clara Shi just joined to lead the business agent side. So I'm sure we'll have her on in the new year. Yeah. So my comment on the business model shift, this is super interesting. Apparently, it is wide knowledge that OpenAI wanted more than $6.6 billion for their fund raise. They wanted to raise...

higher and they did not. And what that means is basically like, it's very convenient that we're not getting GPT-5, which would have been a larger pre-train. We should have a lot of upfront money. Instead, we're converting fixed costs into variable costs, right? And passing it on effectively to the customer. And it's so much easier to take margin there.

because you can directly attribute it to like, oh, you're using this more, therefore you pay more of the cost and I'll just slap a margin in there. So like that lets you control your gross margin and like tie your spend or your sort of inference spend accordingly. And it's just really interesting to, that this change in the sort of inference margin

paradigm has arrived exactly at the same time that the funding environment for pre-training is effectively drying up kind of I feel like maybe the VCs are very in tune with research anyway so like they would have noticed this but it's just interesting yeah

Yeah. And I was looking back at our yearly recap of last year and the big thing was like the mixed trial price fights, you know? And I think now it's almost like there's nowhere to go. Like, you know, Gemini Flash is like basically giving it away for free. So I think this is a good way for the labs to generate more revenue and pass down some of the compute to the customer. I think they're going to keep going. I think that

$2,000 chat GPT will come. Yeah, no, totally. I mean, next year, the first thing I'm doing is signing up for Devin, signing up for the pro chat GPT. Just to try. I just want to see what does it look like to spend $1,000 a month on AI? Yes. I think if your job is at least AI content creator or VC or someone whose job it is to stay on top of things, you should already be spending like $1,000 a month on stuff. And then obviously...

easy to spend, hard to use. Yeah. You have to actually use. The good thing is that actually Google lets you do a lot of stuff for free now. Um, so like deep research that they just launched uses a ton of inference and it's, it's free while it's in preview. Yeah. They need to put that in Lindy. I've been using Lindy lately. I've been a built a bunch of things once we had flow because I liked the new thing. It's pretty good. I even did a phone call assistant, um,

Yeah, they just launched a new voice. Yeah. I think once they get advanced voice mode-like capability, today it's still like speech-to-text, you can kind of tell. But it's good for like reservations and things like that. So I have a meeting prepper thing. Yeah.

It's good. Okay. I feel like we've covered a lot of stuff. Yeah. You know, I think we will go over the individual talks in a separate episode. I don't want to take too much time with this stuff. But suffice to say that there is a lot of progress in each field. We covered vision. Basically, this is all like the audience voting for what they wanted. And then I just invited the best speaker I could find in each audience, especially agents. Graham.

who I talked to at ICBL in Vienna. He is currently still number one. It's very hard to stay on top of Sweet Bench. He's, uh, Open Hand is currently still number one on Sweet Bench 4, which is the hardest one. He had very good thoughts on agents, which I, which I'll highlight for people. Everyone is saying 2025 is the year of agents, just like they said last year. Uh,

But he had thoughts on like eight parts of what are the frontier problems to solve in agents. And so I'll highlight that talk as well. Yeah. The number six, which is the how can agents learn more about the environment has been super interesting to us as well, just to think through.

Because, yeah, how do you put an agent in an enterprise where most things in an enterprise have never been public? You know, a lot of the tooling, like the code bases and things like that. So just indexing and RAG? Well, yeah, but it's more like you can't really RAG things that are not documented, but people know them better.

based on how they've been doing it, you know? So I think there's almost this like, yeah, the boring word is kind of like a business process extraction. It's like, how do you actually understand how these things are done? Um, and I think today the, yeah, the agents are the most people are building are good at following instruction, but are not as good as like extracting them from you. Um, so I think that will be a big unlock.

Cool. Just to touch quickly on the draft team thing. I thought it was pretty, I mean, we'll link it in the things, but I think the main focus was like, how do you use ML to optimize the systems instead of just focusing on ML to do something else? Yeah, I think speculative decoding, we had, you know, Eugene from RWKV on the podcast before, like he's doing a lot of that with Federal SAI.

Everyone is. I would say it's the norm. I'm a little bit uncomfortable with how much it costs because it does use more of the GPU per call. But because everyone is so keen on fast inference, then...

Makes sense. Exactly. Yeah, but we'll link that. Obviously, Jeff is great. Yeah, so Jeff's talk was more, it wasn't focused on Gemini. I think people got the wrong impression from my tweet. It's more about how Google approaches ML and uses ML to design systems and then systems feedback into the ML. And I think this ties in with Lubna's talk on synthetic data.

where it's basically the story of bootstrapping of humans and AI in AI research or AI in production. So her talk was on synthetic data, where like how much synthetic data has grown in 2024 in the pre-training side, the post-training side and the eval side. And I think Jeff then also extended it basically to chips.

chip design so he'd spend a lot of time talking about alpha chip and most of us in the audience are like we're not working hard right man like you guys are great tpu is great okay we'll buy tpus and then there was the ilia talk yeah but and then we have a essay tied to it what ilia saw i don't know if we're calling them essays what are we calling this but for me it's just like bonus for latent space supporters because i feel like they haven't been getting anything yeah and then i wanted a

more high frequency way to write stuff like that one I wrote in an afternoon I think basically we now have an answer to what Ilya saw it's one year since the blip and we know what he saw in 2014 we know what he saw in 2024 we think we know what he sees in 2024 he gave some hints

And then we have vague indications of what he saw in 2023. So that was the... Oh, and then 2016 as well, because of this lawsuit with Elon, OpenAI is publishing emails from Sam's... Like his personal text messages to Siobhan Zillis or whatever. So like we have emails from Ilya saying...

this is what we're seeing in OpenAI and this is why we need to scale our GPUs. And I think it's very prescient in 2016 to write that. And so like, it is exactly like basically his insights is him and Greg basically kind of driving the scaling up of OpenAI while they're still playing Dota. They're like,

No, like we see the path here. Yeah, and it's funny. Yeah, they even mentioned, you know, we can only train on 1v1 Dota. We need to train on 5v5 and that takes too many GPUs. Yeah, and at least for me, I can speak for myself. Like I didn't see the path from Dota to where we are today. I think even maybe if you ask them, like they wouldn't necessarily draw a straight line. Yeah, no, I definitely...

But I think like that was like the whole idea of almost like the RL. And we talked about this with Nathan on his podcast. It's like with RL, you can get very good at specific things, but then you can't really like generalize as much. And I think the language models are like the opposite, which is like, you're going to throw all this data at them and scale them up, but then you really need to drive them home on a specific task later on. And we'll talk about the OpenAI reinforcement, fine tuning announcement too, and all of that. But yeah, I think like,

Scale is all you need. That's kind of what LEI will be remembered for. It will be remembered for, yeah. And I think just maybe to clarify on like the pre-training is over thing that people love to tweet. I think the point of the talk was like, everybody, we're scaling these chips, we're scaling the compute, but like the second ingredient, which is data, it's not scaling at the same rate. So it's not necessarily...

retraining is over it's kind of like what got us here won't get us there in his email he predicted like 10x growth every two years or something like that and i think maybe now it's like you know you can 10x the chips again but i think it's 10x per year was it

I don't know. Exactly. And Moore's law is like 2x. So it's like, you know, much faster than that. And yeah, I like the fossil fuel of AI analogy. It's kind of like, you know, the little background tokens thing. And so the open AI reinforcement fine tuning is basically like instead of fine tuning on data, you fine tune on a reward model. So it's basically like instead of being data driven, it's like task driven. And I think

people have tasks to do, they don't really have a lot of data. So I'm curious to see how that changes how many people fine tune. Because I think this is what people run into is like, oh, you can fine tune llama. And it's like, okay, where do I get the data to fine tune it on? So it's great that we're moving the thing. And then I really like, he had this chart where the brain mass and the body mass thing is basically like mammals that scale linearly, like brain and body size. And then humans kind of like broke off the slope

So it's almost like maybe the mammal slope is like the pre-training slope and then the post-training slope is like the human one. Yeah, I wonder what the... I mean, we'll know in 10 years, but I wonder what the y-axis is for Ilya's SSI. We'll try to get them on. Ilya, if you're listening, you're welcome here. Yeah, and then he had, you know, what comes next? Like agent, synthetic data, inference computer. I thought all of that was like that. I don't think he was dropping any alpha there. Yeah, yeah, yeah. Any other new reps, highlights, or... I think that...

There was Comparatively A lot more work Oh by the way I need to plug That my friend Yi Made this like Little nice Yeah that was really nice Of Of like all the She called it Must read papers of 2024 So I laid out Some of these at NeurIPS And it was just gone Like everyone just picked it up Because people Are dying for like Little guidance And visualizations Of Of each paper And so I thought it was Really super nice That we got that Should we do a Latentspace book?

I thought about it. For each year, we should. Yeah. Yeah. Okay. Put it in that will. Hi, Will. By the way, we haven't introduced you. These are new, you know, Jorgen and Jamie. You need to pull up more things. One thing I saw that... Okay. What does Quixie...

Okay, one fun one and then one more general one. So the fun one is this paper on agent collusion. This is a paper on steganography. This is secret collusion among AI agents, multi-agent deception via steganography. I tried to go to NeurIPS in order to find these kinds of papers because the real reason, like,

New York this year has a lottery system. A lot of people actually even go and don't buy tickets because they just go and attend the side events. And then also the people who go and end up crowding around the most popular papers, which you already know and already read them before you showed up to New York. So the only reason you go there is to talk to the paper authors. But there's like something like 10,000 other papers out there that are just people

people's work that they did on the air and they failed to get attention for one reason or another and this was one of them. It was like all the way at the back. And this is a DeepMind paper that actually focuses on collusion between AI agents by hiding messages in the text that they generate. So that's what steganography is. So a very simple example would be the first letter of every word. If you pick that out, you know, it decodes a different message than steganography.

than that. But something I've always emphasized is to LLMs, we read left to right. LLMs can read up, down, sideways, you know, in random character order. And it's the same to them as it is to us. So if we were ever to get, you know, self-motivated, unaligned LLMs that we're trying to collaborate to take over the planet, this would be how they do it. They spread messages among us in the messages that we generate. And he developed a scaling law for that. So he marked, I'm showing it on screen right now,

the emergence of this phenomenon. Basically, for example, for cipher encoding, GPT-2, LAMA-2, mixed route, GPT-3.5, zero capabilities and sudden emergence of GPT-4. And this is the kind of Jason Wei type emergence properties that people kind of look for. I think what made this paper stand out as well, so he developed a benchmark for steganography collusion and he also focused on shelling point collusion, which is very low coordination. Like,

for agreeing on a decoding encoding format, you kind of need to have some agreement on that. But, but shelling point means like very, very low or almost no coordination. So for example, if I, if I ask someone, if the only message I give you is meet me in New York and,

and you have no idea where or when, you would probably meet me at Grand Central Station. Grand Central Station is a shelling point, and probably somewhere during the day. The shelling point of New York is Grand Central. To that extent, shelling points for steganography are things like the common decoding methods that we talked about. It will be interesting at some point in the future when we are worried about alignment. It is not interesting today, but it's interesting that DeepMind is already thinking about this. Mm-hmm.

Interesting. I think that's like one of the hardest things about NeurIPS. It's like the long tail. Very long tail. I found a pricing guy. I'm going to feature him on the podcast. Basically, this guy from NVIDIA worked out the optimal pricing for language models.

It's basically an econometrics paper at NeurIPS where everyone else is talking about GPUs. And the guy with the GPUs is talking about economics instead. That was the sort of fun one. The broader focus I saw is that model papers at NeurIPS are kind of dead.

no one really presents models anymore. It's just data sets because it's all the grad students are working on. So like there was a data set track and then I was looking around like, I was like, you don't need a data set track because every paper is data sets paper. And so data sets and benchmarks, they're kind of flip sides of the same thing. So yeah, if you're a grad student, you're a GPU board, you kind of work on that. And then the,

the sort of big model that people walk around and pick the ones that they like, and then they use it in their models. And that's, that's kind of how it develops. I feel like, like, like you didn't last year, you had people like Hao Tian who worked on Lava, which is take Lama and add Vision. And then obviously XAI hired him and he added Vision to Grok. He's the Vision Grok guy. This year, I don't think there was any of those.

Yeah. What were the most popular like orals last year? It was like the mixed Monarch, I think was like the most attended. Yeah. Uh,

I need to look it up. Yeah, I mean, if nothing comes to mind, that's also kind of like an answer in a way. But I think last year there was a lot of interest in like forgery models and like different architectures and all that. I will say that I feel the oral picks this year were not very good. Either that or maybe it's just a highlight of how I have changed in terms of how I view papers. So like in my estimation, two of the best papers are

in this year for datasets or data comp and refine web or find web. These are two actually industrially used papers, not highlighted for all. I think DCLM got the spotlight, find web didn't even get the spotlight. So like, it's just that the picks were different. But one thing that does get a lot of play that a lot of people are debating is the road less scheduled. This is the schedule-free optimizer paper from Meta, from Aaron DeFazio.

And this year in the ML community, there's been a lot of chat about shampoo, soap, all the bathroom amenities for optimizing your learning rates. And most people at the big labs who I asked about this say that it's cute, but it's not something that matters. I don't know. But it's something that was discussed and very, very popular. Four words of AI recap, maybe just quickly. Where do you want to start? Data? Yeah.

Yeah. So to remind people, this is the four wars piece that we did as one of our earlier recaps of this year. And the belligerents are on the left, journalists, writers, artists, anyone who owns IP, basically, New York Times, Stack Overflow, Reddit, Getty, Sarah Silverman, George R.R. Martin. Yeah. And I think this year we can add Scarlett Johansson to that side of the fence. So anyone suing, open the eye, basically. Yeah.

I actually wanted to get a snapshot of all the lawsuits. I'm sure some lawyer can do it. That's the data quality war. On the right-hand side, we have the synthetic data people. I think we talked about Luminous Talk, really showing how much synthetic data has come along this year. I think there was a bit of a fight between Scale AI and the synthetic data community because Scale published a paper saying that synthetic data doesn't work. Surprise, surprise, Scale is the leading vendor of non-synthetic data.

Only cage-free annotated data is useful. So I think there's some debate going on there, but I don't think it's much debate anymore that at least synthetic data for the reasons that are blessed in Lubna's talk

Makes sense. I don't know if you have any perspectives there. I think, again, going back to the reinforcement fine-tuning, I think that will change a little bit how people think about it. I think today people mostly use synthetic data for distillation and kind of like fine-tuning a smaller model from like a larger model. I'm not super aware of how the frontier labs use it outside of like the rephrase, the web thing that Apple also did. But yeah, I think it'll be...

useful. I think like whether or not that gets us the big next step, I think that's maybe like TBD, you know, I think people love talking about data because it's like a GPU poor thing, you know, I think synthetic data is like something that people can do, you know, so they feel more opinionated about it compared to, yeah, the optimizers stuff, which is like,

they don't really work on. I think that there is an angle to the reasoning synthetic data. So this year we covered in the paper club, these star series of papers. So that's star Q star V star. It basically helps you to synthesize reasoning steps or at least distill reasoning steps from a verifier. And if you look at the OpenAI RFT, it's,

API that they released or that they announced, basically they're asking you to submit graders or they choose from a preset list of graders. Basically, it feels like a way to create valid synthetic data for them to fine tune the reasoning paths on. So I think that is another angle where it starts to make sense.

And so like, it's very funny that basically all the data quality wars between let's say the music industry or like the newspaper publishing industry or like the textbooks industry on the big labs, it's all of the pre-training era. And then like the new era, like the reasoning era, like nobody has any problem with all the reasoning. Especially because it's all like sort of math and science oriented with very reasonable graders. I think the more interesting next step is how does it generalize beyond STEM and

We've been using O1 for AI news for a while. And I would say like for summarization and creative writing and instruction following, I think it's underrated. I started using O1 in our intro songs before we killed the intro songs. But it's very good at writing lyrics. You know, it can actually say like,

I think one of the O1 Pro demos that Noam was showing was that you can write an entire paragraph or three paragraphs without using the letter A. So literally just anything in the token-- not even token level, character level manipulation and counting and instruction following, it's very, very strong. So no surprises when I ask it to rhyme.

And to create song lyrics is going to do that much better than in previous models. So I think it's underrated for creative writing. What do you think is the rationale that they're going to have in court when they don't show you the thinking traces of O1, but then they want us to... Like they're getting sued for using other publishers' data, you know? But then on their end, they're like, well, you shouldn't be using my data to then train your model. So I'm curious to see how that...

Yeah, I mean, OpenAI has many ways to punish people without taking them to court. Already banned ByteDance for distilling their info. And so anyone caught distilling the chain of thought will be just disallowed to continue on the API and it's

Fine. It's no big deal. Like, I don't even think that's an issue at all just because the chain of thoughts are pretty well hidden. Like, you have to work very, very hard to get it to leak. And then even when it leaks the chain of thought, you don't know if it's the real one. So there's much less concern here. Yeah, yeah, yeah. The bigger concern is...

is actually that there's not that much IP hiding behind it. That Cosign, which we talked about, we talked to him on Dev Day, can just fine-tune 4.0 to beat 0.1. That Cloud Sonnet so far is beating 0.1 on coding tasks without...

at least a one preview without being a reasoning model and same for Gemini Pro or Gemini 2.0. So like how much is reasoning important? How much of a moat is there in this like proprietary sort of training data that they've presumably accomplished? Because like even DeepSeek was able to do it and they had, you know, two months notice to do this, to do R1. So it's actually unclear how much moat there is. Obviously, you know, if you talk to the Strawberry team, they'll be like, yeah, I mean, we spent the last two years doing this.

So we don't know. And it's going to be interesting because there'll be a lot of noise from people who say they have inference time compute and actually don't because they just have fancy chain of thought.

And then there's other people who actually do have very good chain of thought and you will not see them on the same level as OpenAI because OpenAI has invested a lot in building up the mythology of their team, which makes sense. Like the real answer is somewhere in between. Yeah. I think that's kind of like the main data war story developing. Yeah.

- GPU poor versus GPU rich? - Yeah. - Where do you think we are? I think there was, again, going back to like the small model thing, there was like a time in which the GPU poor were kind of like the rebel faction working on like these models that were like open and small and cheap. And I think today people don't really care as much about GPUs anymore. You also see it in the price of the GPUs. Like, you know, that market is kind of like plummeted because there's, people don't want to be, they want to be GPU free.

They don't even want to be poor. They just want to be, you know, completely without them. Yeah. How do you think about this war developing? You can tell me about this, but I feel like the appetite for GPU rich startups, like the, you know, the funding plan is we will raise 60 million and we'll give 50 of that to Nvidia. That is gone, right? Like no one's pitching that. This was literally the plan, the exact plan of like, I can name like four or five startups, you know, this time last year. So yeah, GPU rich startups gone. But I think like,

the GPU ultra rich, the GPU ultra high net worth is still going. So now we're, you know, we had Leopold's essay on the trillion dollar cluster. We're not quite there yet.

We have multiple labs, you know, XAI, very famously, you know, Jensen Huang praising them for being best boy number one in spinning up 100,000 GPU cluster in like 12 days or something. So likewise at Meta, likewise at OpenAI, likewise at the other labs as well. So like the GPU ultra rich are going to keep doing that because I think partially it's an article of faith now that you just need it. You don't even know what you're going to use it for. You just need it. And it makes sense that

that especially if we're going into more researchy territory than we are. So let's say 2020 to 2023 was let's scale big models territory because we had GPT-3 in 2020. And we were like, okay, we go from 175B to 1.8T. And that was GPT-3 to GPT-4. Okay, that's done. And as far as everyone is concerned, cloud, you know, Opus 3.5 is not coming out. GPT-4.5 is not coming out.

and Gemini 2, like we don't have pro or whatever. We've hit that wall, whatever that wall is. Maybe I'll call it like the 2 trillion parameter wall. Like we're not going to 10 trillion. Like it's just like, no one thinks it's a good idea, at least from training costs, from amount of data, or at least the inference. Like, would you pay 10X the price of GT4? Probably not.

Like you want something else that is at least more useful. So it makes sense that people are pivoting in terms of their inference paradigm. And so when it's more researchy, then you actually need more just general purpose compute to mess around with at the exact same time that production deployments of the previous paradigm are still ramping up.

pretty aggressively. So it makes sense that the GPU rich are growing. We have now interviewed both together and fireworks and replicates. We haven't done any scale yet, but I think Amazon may be kind of a sleeper one, Amazon in a sense of like they at reinvent, I wasn't expecting them to do so well, but they are now a foundation model lab. It's kind of interesting. I think, you know, David went over there and like started just creating models. Yeah.

yeah i mean that's the power of prepaid contracts i think like a lot of aws customers you know they do this big reserve instance contracts and now they gotta use their money that's why so many startups get bought through the aws marketplace so they kind of bundle them together and prefer pricing okay so maybe gpu super rich doing very well gpu middle class dead

and then if you pour i mean my thing is like everybody should just be gpu rich huh there shouldn't really be even the gpu pores it's like does it really make sense to be gpu poor like if you're gpu poor you should just use the cloud yes you know and i think there might be a future once we kind of like figure out what the size and shape of these models is where like the tiny box and these things come to fruition where like you can be gpu poor at home but i think today is like

Why are you working so hard to get these models to run on very small clusters where it's so cheap to run them? Yeah. I think mostly people think it's cool. People think it's a stepping stone to scaling up. So they aspire to be GPU rich one day and they're working on new methods. Like news research, probably the most deep tech thing they've done this year is Distro or whatever the new name is.

There's a lot of interest in heterogeneous computing, distributed computing. I tend generally to de-emphasize that historically, but it may be coming to a time where it is starting to be relevant. I don't know. You know, SF Compute launched their compute marketplace this year. And like, who's really using that? Like, it's a bunch of...

small clusters, disparate types of compute. And if you can make that useful, then that will be very beneficial to the broader community, but maybe still not the source of frontier models. It's just going to be a second tier of compute that is unlocked for people. And that's fine. But yeah, I mean, I think this year, I would say a lot more on device. I now have Apple intelligence on my phone, doesn't do anything apart from summarize my notifications, but still not bad.

Like it's multimodal. Yeah. The notification summaries are so-and-so in my experience. Yeah. But they add juice to life.

And then Chrome Nano, Gemini Nano is coming out in Chrome. They're still feature flagged, but you can try it now if you use the alpha. And so I think we're getting the sort of GPU-poor version of a lot of these things coming out. And I think it's quite useful. Windows as well, rolling out RWKB in sort of every Windows department is super cool. Yeah.

And I think the last thing that I never put in this GPU poor war, that I think I should now, is the number of startups that are GPU poor but still scaling very well as sort of wrappers on top of either a foundation model lab or a GPU cloud. A GPU cloud, it would be Suno. Suno, Ramp has rated as one of the top ranked, fastest growing startups of the year. I think the last public number is like zero to 20 million this year in ARR. And Suno runs on Modo.

So Zuno itself is not GPU rich, but they're just doing their training on Moto, who we've also talked to on the podcast. The other one would be Bolt, straight cloud rapper. And again, now they've announced 20 million ARR, which is...

which is another step up from our 8 million that we put on the title. So yeah, I mean, it's crazy that all these GPU pourers are finding a way while the GPU riches are also finding a way. And then the only failures, I kind of call this the GPU smiling curve, where the edges do well because you're either close to the machines and you're like number one on the machines or you're like close to the customers and you're number one on the customer side. And the people who are in the middle inflection,

didn't do that great. I think Character did the best of all of them. Like you have a note in here that we apparently said that Character's price tag was 1B. Did I say that? Yeah. You said Google should just buy them for 1B. That's a crazy number. Then they paid 2.7. I mean, what do you pay for? No. I don't know what the bidding war was. Maybe the starting price was 1B. Yeah.

I mean, whatever it was, it worked out for everybody involved. Multi-modality war. In this one, we never had text-to-video in the first version, which now is the hottest. Yeah, I would say it's a subset of image, but yes. Yeah. Well, but I think at the time, it wasn't really something people were doing. And now we had...

VO2 just came out yesterday. Sora was released last week. Have you tried Sora? I've not tried Sora because the day that I tried, it wasn't. I think it's generally available now. You can go to Sora.com and try it. They had the outage, which I think also played a part into it.

Small things. Yeah. What's the other model that you posted today that was on Replicate Video or One Live? Yeah, just a lot. Nondescript name, but it is from Minimax, which I think is a Chinese lab. The Chinese labs do surprisingly well at the video models. I'm not sure it's actually Chinese. Don't hold me up to that. Yep, China. Yeah.

it's good so high law yeah Chinese love video what can I say they have a lot of training data for video or a more relaxed regulatory environment well sure in some way

Yeah, I don't think there's much else there. I think on the image side, I think it's still open. Yeah, I mean, 11 Labs, now Unicorn. So basically, what is multi-modality war? Multi-modality war is, do you specialize in a single modality? Or do you have God model that does all the modalities? So this is definitely still going in a sense of 11 Labs, now Unicorn, Unicorn.

Pika Labs doing well, they launched Pika 2.0 recently. HeyGen, I think has reached a hundred million ARR. Assembly, I don't know, but they have billboards all over the place. So I assume they're doing very, very well. So these are all specialist models and specialist startups. And product, especially. Yeah. And then there's the big labs who are doing the sort of all-in-one play. And here I would highlight Gemini 2 for having native image output.

Have you seen the demos? No. Yeah, it's hard to keep up. Literally, they launched this last week. And shout out to Paige Bailey, who came to the Lint Space event to demo on the day of launch. And she wasn't prepared. She was just like, I'm just going to show you. So they have voice. They have, you know, obviously image input. And then they obviously can co-gen and all that. But the new one that OpenAI and Meta both have, but they haven't launched yet, is image output.

So you can literally, I think their demo video was that you put in an image of a car and you ask for minor modifications to that car. They can generate you that modification exactly as you asked.

So there's no need for the stable diffusion or comfy UI workflow of like mask here and then like infill there, in paint there and all that stuff. This is small model nonsense. Big model people are like, huh, we got you in as everything in the transformer. This is the multimodality war, which is, do you bet on the God model or do you string together a whole bunch of small models like a chump?

Yeah. I don't know, man. Yeah. That would be interesting. I mean, obviously I use mid journey for all of our thumbnails. Um, still, they've been doing a ton on the product. I would say they launched a new mid journey editor thing. They've been doing a ton because I think, yeah, the model, it's kind of like maybe, you know, people say black forest, the black forest models are better than mid journey on a pixel by pixel basis. But I think when you put it, put it together. Yeah.

Have you tried the same prompt on Black Forest? Yes, but the problem is just like, you know, on Black Forest, it generates one image and then it's like, you got to regenerate. You don't have all these like UI things. Like what I do- Skill issue, bro. No, but it's like time issue. You know, it's like on mid journey- Call the API four times. No, but then there's no like variate. Like the good thing about mid journey is like-

You just go in there and you're cooking. There's a lot of stuff that just makes it really, really easy. And I think people underestimate that. Like, it's not really a skill issue because I'm paying mid-journey. So it's a blackboard skill issue because I'm not paying them. Correct. You know? Yeah. So, okay. So this is a UX thing, right? Like you understand that at least...

We think that Black Forest should be able to do all that stuff. I will also shout out ReCraft has come out on top of the image arena that artificial analysis has done. It has apparently taken Flux's place. Is this still true? So artificial analysis is now a company. I highlighted them, I think, in one of the early AI news of the year. And they have

a whole bunch of arenas. So they're trying to take on LM Arena, Anastasios and crew, and they have an image arena. Oh yeah, ReCraft V3 is now beating Flux 1.1, which is very surprising because Flux and Black Forest Labs are the old stable diffusion crew who left stability after the management issues. So ReCraft has come from nowhere to be the top image model. Very, very strange.

I would also highlight that Grok has now launched Aurora, which is very interesting dynamics between Grok and Black Forest Labs because Grok's images were originally launched

in partnership with Black Forest Labs as a thin wrapper. And then Grok was like, "No, we'll make our own." And so they've made their own. I don't know, there are no APIs or benchmarks about it. They just announced it. So yeah, that's the multi-modality war. I would say that so far the small model, the dedicated model people are winning because they are just focused on their tasks, but the big model people are always catching up.

And the moment I saw the Gemini 2 demo of image editing, where I can put an image and just request it and that's how AI should work. Not like a whole bunch of complicated steps. So it really is something. And I think one frontier that we haven't seen this year, like obviously video has done very well and it will continue to grow. You know, we only have the release of Sora Turbo today, but at some point we'll get full Sora.

Or at least the Hollywood labs will get full SORA. We haven't seen video to audio or video synced with audio. And so the researchers that I talked to are already starting to talk about that as the next frontier. But there's still maybe like five more years of video left to actually be SORA. I would say that Gemini's approach compared to OpenAI, Gemini seems, or DeepMind's approach to video seems a lot more fully fledged than OpenAI is.

Because if you look at the ICML recap that I published that so far nobody has listened to.

People have listened to it. It's just a different, definitely a different audience. It's only seven hours long. Why are people not listening? It's like everything in one thing. So DeepMind is working on Genie. They also launched Genie 2 and Video Poet. So like they have maybe four years advantage on world modeling that OpenAI does not have. Because OpenAI basically only started diffusion transformers last year when they hired GoPeebles.

So DeepMind has a bit of advantage here, I would say, in showing... Like the reason that VO2... Well, one, they cherry-picked their videos, so obviously it looks better than Sora. But the reason I would believe that VO2, when it's fully launched, will do very well is because they have all this background work in video that they've done for years. Like last year's New Reps, I already was interviewing some of their video people. I forget their model name, but for people who are dedicated fans, they can go to New Reps 2023 and see that paper. Yeah.

And then last but not least, the LLMOS slash RegOps, formerly known as RegOps War. I put the latest chart on the Braintrust episode. I think I'm going to separate these essays from the episode notes. So the reason I used to do that, by the way, is because I wanted to show up on Hacker News. I want the podcast to show up on Hacker News. So I always put an essay inside of there because Hacker News people like to read essays.

and not listen. Yeah. So episode essays, I don't know. We're just doing them separately. You say Langchain Laman next to growing. Yeah. So I looked at the PyPy stats, you know, I don't care about stars. On PyPy you see Do you want to share your screen? Yes. I prefer to look at actual downloads, not at stars on GitHub. So if you look at, you know,

-Langchain still growing. These are the last six months. Lama index still growing. What I've basically seen is like things that one, obviously these things have a commercial product. So there's like people buying this and sticking with it versus kind of hopping in between things versus, you know, for example, crew AI, not really growing as much. The stars are growing. If you look on GitHub, like the stars are growing, but kind of like the usage is kind of like flat in the last six months. -Have they done some kind of a reorg where

they did like a split of packages and now it's like a bundle of packages. Sometimes that happens, you know. I didn't see that. I can see both. I can see both happening. The Koo AI is very loud, but not used. And then... Yeah, but anyway, to me, it's just like... Yeah, there's no split. I mean, similar with AutoGPT, it's like there's still a waitlist for AutoGPT to be used.

Yeah, they're still kicking. They announced some stuff recently. But I think that's another one. It's the fastest growing project in the history of GitHub. But I think, you know, when you maybe like run the numbers on like the value of the stars and like the value of the hype, I think in AI you see this a lot, which is like a lot of stars, a lot of interest at a rate that you didn't really see in the past in open source where nobody's running to start, you know, a NoSQL database. It's kind of like just the people that actually use it. Yeah.

I think one thing that's interesting here, one obviously is that in AI, you kind of get paid to promise things. And then to deliver them, people have a lot of patience. I think that patience has come down over time. One example here is Devin, right? This year, where a lot of promise in March and then it took nine months to get to GA. But I think people are still coming around now on Devin. Devin's product has improved a little bit and even you're going to be a paying customer. So I think something Devin-like will work. I don't know if it's Devin itself.

The AutoGBT has an interesting second layer in terms of what I think is the dynamics going on here, which is a very...

AI specific layer. Over promising under delivering applies to any startup. But for AI specifically, there's this promise of generality that I can do anything, right? So AutoGPT's initial problem was make me money, like increase my net worth. And I think that means that there's a lot of broad interest from a lot of different people who are trying to do all different things on this one project. So that's why there's concentrates a lot of stars. And then obviously, because it does too much, maybe, or it's not focused enough, then it fails it.

So that would be my explanation for why the interest to usage ratio is so low. And the second one is obviously pure execution. Like the team needs to have a vision and execute like half the core team left right after A Engineer Summit last year. That will be my explanation as to why like this promise of generality works because

Basically only for ChatGPT. Right. And maybe for this year, Notebook LM. Like sticking anything in there, it'll mostly be correct. And then for basically everyone else, it's like, you know, we will help you complete code. We will help you with your PR reviews, like small things. Yeah. All right. Code interpreting. We talked about a bunch of times we soft announced the E2B fundraising on this podcast. Yeah.

Code Sandbox got acquired by Togetter AI last week, which they're now also going to offer as an API. So more and more activity, which is great. Yeah, and then in the last two episodes ago with Bolt, we talked about the web container stuff they've been working on.

I think like there's maybe the spectrum of code interpreting, which is like, you know, dedicated SDK. There's like, yeah, the models of the world, which is like, hey, we got a sandbox. Now you just kind of run the commands and orchestrate all of that. I think this is one of the, I mean, easy to be screwed. That's just been crazy. Just because, I mean,

everybody needs to run code, right? And I think now all the products and everybody's graduating to like, okay, it's not enough to just do chat. So perplexity, which is a easy to be customers, they do all these nice charts for like finance and all these different things. It's like the products are maturing. And I think this is becoming more and more of kind of like a hair on fire problem, so to speak. So yeah, excited to see more. And this was one that really wasn't on the radar when we first wrote the four wars. Yeah.

Yeah, I think mostly because I was trying to limit it to rag and ops. But I think now that the frontier has expanded in terms of the core set of tools, core set of tools would include code interpreting, like tools that every agent needs, right? And Graham in his state of agents talk,

had this as well, which is kind of interesting for me because like everyone finds the same set of things. So it's basically like someone, everyone needs web browsing, everyone needs code interpreting, and then everyone needs some kind of memory or planning or,

whatever that is. We'll discover this more over time, but I think this is what we've discovered so far. I will also call out Morph Labs for launching a time travel VM. I think that basically the statefulness of these things needs to be locked down a lot. Basically, you can't just spin up a VM and run it

run code on it and then kill it. Because sometimes you might need to time travel back, like unwind or fork to explore different paths for sort of like a tree search approach to your agent development. I would call out the newer ones, the new implementations as the emerging frontier in terms of like what people kind of are going to need for agents to do very fan out approaches to all these sort of code execution.

And then I'll also call out that I think ChatGPT Canvas with what they launched in the 12 days of shipments that they announced has surprisingly superseded Code Interpreter. Like Code Interpreter was last year's thing. And now Canvas can also write code and also run code and do more than Code Interpreter used to do. So right now it has not killed it. So there's a toggle box there.

for Canvas and for Code Interpreter when you create a new custom GPT. You know, my old thesis is that custom GPT is your roadmap for investing because it's what everyone needs. So now there's a new box called Canvas that everyone has access to. But basically, there's no reason why you should use Code Interpreter over Canvas. Like Canvas has incorporated the diff mode that both Anthropic and OpenAI and Fireworks has now shipped.

that I think is going to be the norm for next year. That everyone needs some kind of diff mode code interpreter thing. Like, AIDR was also very early to this. The AIDR benchmarks were also all based on diffs and Coursera as well. MARK MANDEL: You want to talk about memory? FRANCESC CAMPOY: Memory. You think it's not real.

Yeah, I just don't. I think most memory product today, just like a summarization and extraction. Yeah. I don't think there's... They're very immature. Yeah. There's no implicit memory, you know? It's not explicit memory of what you've written. There's no implicit...

extraction of like, oh, you said no to this, you said no to this 10 times, so you don't like going on hikes at 6 a.m. Like it doesn't, none of the memory products do that. They'll summarize what you say explicitly. When you say memory products, you mean the startups that are offering memory as a service? Yeah, or even like, you know, Lindy has like memories, you know, it's like based on what I say, it remembers it.

So it's less about making an actual memory of my preference. It's more about what explicitly said. And I'm trying to figure out at what level that gets solved. You know, like is it, do these memory products like the MGBTs of the world create a better way to implicitly extract preference or can that be done very well? You know, I think that's why I don't think it's not that I don't think memory is real. I just don't think that like,

the approaches today are like actually memory or what you need a system to have yeah I would actually agree with that but I would just point it to it being immature rather than not needed like it's clearly something that we will want at some point and so the people developing it now are

you know, not very good at it. And I would definitely predict that next year will be better. And the year after that will be better than that. I definitely think that last time we had the Shunyu pod with Harrison as guest host, I over-focused on LangMem as a separate product. He has now rolled it into LangGraph as a memory service, the same API. And I think that everyone will need some kind of memory. And I think that this is...

has distinguished itself now as a separate need from a normal RAG vector database. Like you will need a memory layer, whether it's on top of a vector database or not, it's up to you. A memory database and a vector database are kind of two different things. Like I've had to justify this so much actually that I have a draft post in the latent space dashboard that I'm

basically says like, what is the difference between memory and knowledge? To me, it's very clear. It's like knowledge is about the world around you. And like, there's knowledge that you have, which is the rag corpus that you're maybe your company docs or whatever. And then there's external knowledge. There's this stuff that you Google. So you use something like X, whatever.

And then there's memory, which is my interactions with you over time. Both can be represented by vector databases or knowledge graphs. It doesn't really matter. Time is a specifically important one in memory because you need a decay function. And then you also need like a review function

A lot of people are implementing this as sleep. Like when you sleep, you like literally you sort of process the day's memories and you come up with new insights that you then persist and bring into context in the future. So I feel like this is being developed. LandGraph has a version of this. ZEPP is another one that's based on Neo4j's Knowledge Graph that has a version of this. MGPT used to have this, but I think I feel like Leda, since it was funded,

by Quiet Capital has broadened out into more of a sort of general LLMOS type startup, which I feel like there's a bunch of those now. There's all hands and all this. Do you think this is an LLMOS product or should it be a consumer product? I think it's a building block. I think every, I mean, just like every consumer product is going to have a, going to eventually want a gateway, you

you know, for, for managing their requests and ops tool, you know, that kind of stuff. Um, code interpreter for her, maybe not exposing the code, but executing code under the hood for sure. So it's going to want memory. It's going to want long live memory. So as a consumer, let's say you are a new doc computer who, um,

you know, they've launched their own little agents or if you're friend.com, you're going to want to invest in memory at some point. Maybe it's not today. Maybe you can push it off a lot further with like a million token context. But at some point, you need to compress your memory and to selectively retrieve it. And that's

then what are you going to do? You have to reinvent the whole memory stack and these guys have been doing it for a year now. - Yeah, to me it's more like I want to bring the memories. It's almost like they're my memories, right? So why- - So you selectively choose the memories to bring in. - Yeah, why does every time that I go to a new product, it needs to relearn everything about me? - Okay, you want portable memories. - Yeah, is it like a protocol? Like how does that work?

Speaking of protocols, Anthropix Model Context Protocol that they launched has a 300 line of code memory implementation. Very simple, very bad news for all the memory startups.

But that's all you need. And yeah, it would be nice to have a portable memory of you to ship to everyone else. Simple answer is there's no standardization for a while because everyone will experiment with their own stuff. And I think Anthropic's success with MCP suggests that basically no one else but the big labs can do it because no one else has the sway to do this. Then that's how it's going to be. Yeah.

Unless you have something silly like... Okay, one form of standardization basically came from Georgi Gurgenov with Lama CPP. And that was completely open source, completely bottoms up. And that's because there's just an infinite amount of work that needed to be done there. And people build up from there. Another form of standardization is ComfyUI from Comfy Anonymous. So that kind of standardization can be done. So someone basically has to create that for the roleplay community, right?

Because those are the people with the longest memories. Right now, the roleplay community, as far as I understand it, I've looked at Soli Tavern, I've looked at Kobold. They only share character cards. And there's like four or five different standardized versions of these character cards, but nobody has exportable memory yet. If there was anyone that developed memory first that became a standard, it would be those guys. Cool. Excited to see what people built.

Benchmarks. One of our favorite pet topics. Yeah. So basically I just wanted to mention this briefly. I think that in an end of year review, it's useful to remind everybody where we were. So we talked about how in LMS's ELO, everyone has gone up and it's a very close race. And I think benchmarks as well. I was looking at the OpenAI live stream today when they introduced O1 API with structured output and everything.

And the benchmarks they're talking about are like completely different than the benchmarks that we were talking about this time last year. This time last year, we were still talking about MMLU, a little bit of, there's still like GSM 8K. There's stuff that's basically in V1 of the Hugging Face Open Models leaderboard, right? We talked to Clementine about the decisions that she made to upgrade to V2.

I would also say LMSYS, now LM Arena, also has emerged this year as the leading battlegrounds between the big frontier labs.

But also we have also seen like the emergence of SweetBench, LiveBench, MMYU Pro and AIMEE, AIMEE specifically for one. It will be interesting to see like top most cited benchmarks of the year from 2020 to 2021, two, three, four, and then going to five. And you can see what has been saturated and solved.

and what people care about now. And so now people care a lot about frontier math coding, right? There's literally a benchmark called frontier math, which I spent a bit of time talking about at NeurIPS. There's Amy, there's LiveBench, there's MOU Pro, and there's SweetBench. I feel like this is good. And then...

There was another one, this time last year it was GPQA. I'll put math and GPQA here as sort of top benchmarks of last year. At NeurIPS, GPQA was declared dead, which is very sad. People are still talking about GPQA Diamond. So literally the name of GPQA is called Google-proof question answering. So it's supposed to be resistant to saturation for a while. And Noam Brown said that GPQA was dead.

So now we only care about SweetBench, LifeBench, MMORPG Pro, AME. And even SweetBench, we don't care about SweetBench proper. We care about SweetBench verified. We care about the SweetBench multimodal. And then we also care about the new Kowinski Prize from Andy Kowinski, which is the guy that we talked to yesterday, who has launched...

a similar sort of Arc AGI attempt on a three bench type metric, which arguably is a bit more useful. OpenAI also has MLE bench, which is more tracking sort of ML research and bootstrapping, which arguably like this is the key metric that is most relevant for the Frontier Labs, which is when the researchers can automate their own jobs. So that is a kink in the acceleration curve if we were ever to reach that. Yeah, that makes sense.

I'm curious. I think Dylan at the debate, he said SweetBench 80% was like a soap for end of next year. As a kind of like, you know, watermark that the models are still improving. Keeping in mind we started the year at 13%. Yeah, exactly. And so now we're about 50 open hands is around there.

And yeah, 80 sounds fine. Uh, Kominsky prize is 90. Yeah. And then as we get to a hundred and the open source catches up. Oh yeah. Magically going to close the gap between the closed source and open source. So basically I think my advice to people is a key track of the slow cooking of benchmarking.

because the labs that are not that frontier will keep measuring themselves on last year's benchmarks. And then the labs that are actually frontier will tell you about benchmarks you've never heard of. And you'll be like, oh, okay, there's new territory to go on. That would be the quick tip there. Yeah, maybe I won't belabor this point too much. I would also say maybe VO has introduced some new video benchmarks, right? Like basically every new frontier capabilities, and this is the next section that we're going to go into, introduces new benchmarks.

we also briefly talked about ruler as like the the new sort of uh you know last year we was like needle in a haystack and ruler is basically a multi-dimensional needle haystack yeah we'll link on the episodes and the yeah this is like a review of all the episodes that we've done yeah which i have in my head this is one of the slides that i did on my dev day talk so so we're moving on from benchmarks to capabilities and i think i have a useful categorization i've been kind of sell i'd be curious on your feedback or edits i think there's

basically like I kind of like the thought spot model of what's mature what's emerging what's frontier what's niche so mature is like stuff that you can just rely on in production it's solved everyone has it so what's solved is general knowledge MMLU and then what's solved is kind of long context everyone has 128k today O1 announced 200k which is very expensive I don't know what price is that

What's solved? Kind of solved is RAG. There's like 18 different kinds of RAG, but it's mostly solved. Bash transcription, I would say Whisper is something that you should be using on as much as possible. And then code generation and kind of solve. There's different tiers of code generation and I really need to split out. Yeah. Single line autocomplete versus multi-file generation. I think that is definitely emerging. So on the emerging side, tool use, I would still consider emerging, maybe more mature already, but they only launched for ShuttlePub this year. Yeah.

Yeah, yeah, yeah. So next year, I think the merging is fine. Vision language models, everyone has vision now, I think, including O1. So this is clear. A subset of vision is PDF parsing. And I think the community is very excited about the work being done with Colpoly and Colquen. What's for you the break point for vision to go to mature? I think it's basically now. Yeah.

This is maybe two months old. Yeah, yeah, yeah. Nvidia, most valuable company in the world. Also, I think this was in June.

Then also they surprised a lot on the upside for their Q3 earnings. I think the quote that I highlighted in AI News was that it is the best, like Blackwell is the best selling series in the history of the company. And they're sold, I mean, obviously they're always sold out, but for him to make that statement, I think it's another indication that the transition from the H to the B series is going to go very well. Yeah, I mean...

If you had just bought NVIDIA when ChaiGBT came out, that would be insane. Which one more? NVIDIA or Bitcoin? I think NVIDIA. I think NVIDIA. Well, the question is, people ask me, what's the reason to not invest in NVIDIA? I think it's really just like they have committed to this. They went from a two-year cycle to one-year cycle, right? And so it takes one misstep to delay. There have been delays in the past. And when delays happen, they're typically very good buying opportunities. Anyway.

Hey, this is Fix from the editing room. I actually just realized that we lost about 15 minutes of audio and video that

was in the episode that we shipped and I'm just cutting it back in and re-recording. We don't have time to re-record before the end of the year. It's December 31st already. So I'm just going to do my best to re-cover what we have and then sort of segue you in nicely to the end. So our plan was basically to cover like what we felt was emerging capabilities, frontier capabilities, and niche capabilities. So emerging would be tool use, visual language models, which you just heard,

The real-time transcription, which I have on one of our upcoming episodes, the B, as well as you can try it in Whisper WebGPU, which is amazing. I think diarization capabilities are also maturing as well, but still way too hard to do properly. Like we had to do a lot of stuff for the latent space transcripts to come out right.

I think maybe, you know, Dwarkesh recently has been talking about how he's using Gemini 2.0 Flash to do it. And I think that might be a good effort, a good way to do it. And especially if there's crosstalk involved, that might be really good. But there might be other reasons to use normal diarization models as well.

Specifically, Pyanote. Text and image, we talked about a lot, so I'm just going to skip. And then we go to Frontier, which I think basically, I would say, is on the horizon, but not quite ready for broad usage. It's interesting...

to show off to people, but we haven't really figured out how the daily use, the large amount of money is going to be made on long inference, on real-time interruptive, sort of real-time API voice mode things, on on-device models, as well as all other modalities. And then niche models, niche capabilities. I always say base models are very underrated. People always love talking to base models as well. And we're increasingly getting less access to them

It's quite possible that Sam Altman for 2025 was asking about what people want him to ship or what people want him to open source. And people really want GPT-3 base. We may get it. We may get it. It's just for historical interest at this point. But we may get it. It's definitely not a significant IP anymore for him.

So we'll see. You know, I think OpenAI has a lot more things to worry about than shipping-based models, but it would be a very, very nice thing to do for the community. State-based models as well. I would say like the hype for state-based models this year, even though, you know, the post-Transformers talk at Lenspace Live was extremely hyped and very well attended and watched. I would say like it feels like a step down this year. I don't know why.

It seems like things are scaling out in state-based models and RWKBs. So Cartesia, I think, is doing extremely well. We use them for a bunch of stuff, especially for small talks and some of our sort of notebook LN podcast clones. I think they're a real challenger to 11 labs as well. And RWKB, of course, is rolling on Windows.

So, I'll still say these are niche. We've been talking about them as the future for a long time. And I mean, we live technically in a year in the future from last year, and we're still saying the exact same things as we were saying last year. So what's changed? I don't know. I do think the XLSTM paper, which we will cover when we cover the sort of NeurIPS papers, is worth a look. I think they are very clear eyed as to how they want to fix LSTM.

Okay, so and then we also want to cover a little bit like the major themes of the year and then we wanted to go month by month. So I'll bridge you into it back to the recording, which we still have the audio of. So one of the major themes is sort of the inference race at the bottom. We started this last year, this time last year with the mistrial price war of 2023 with mixed trial going from $1.80 per token down to $1.27 in the span of like a couple weeks.

And I think a lot of people are also interested in the price war, sort of the price intelligence curve for this year as well. I started tracking it, I think, roundabout in March of 2024 with Haiku's launch.

And so this is, if you're watching the YouTube, this is what I initially charted out as like, here's the frontier. Like everyone's kind of like in a pretty tight range of LMS ELO versus the model pricing. You can pay more for more intelligence and it'll be cheaper to get less intelligence, but roughly it correlates to

aligned and a trend line. And then I could update it again in July and see that everything had kind of shifted right. So for the same amount of ELO, let's say GPT-4 2023 would be about sort of $11.75 in ELO. And you used to get that for like $40 per token, per million tokens. And now you get Cloud 3 Haiku, which is about the same ELO, for $0.50.

And so that's a two orders of magnitude improvement in about two years. Sorry, in about a year.

But more importantly, I think you can see the more recent launches like Cloud 3 Opus, which launched in March this year, now basically superseded completely, completely dominated by Gemini 1.5 Pro, which is both cheaper, $5 a month, $5 per million, as well as smarter. So it's about slightly higher than $12.50 in ELO. So the March frontier and shift to the July frontier is roughly one order of magnitude improvement per sort of ISO ELO.

And I think what you're starting to see now in July is the emergence of 4.0 Mini and DeepSeq v2 as outliers to the July frontier, where July frontier used to be maintained by 4.0, Lama 4.5.

Gemini 1.5 Flash and Mistral Nemo, these things kind of break the frontier. And then if you update it like a month later, I think if I go back a month here, you update it, you can see more items start to appear here as well with the August frontier with Gemini 1.5 Flash coming out with an August update as compared to the June update being a lot cheaper and roughly the same, you know. And then we update for September.

And this is one of those things where we really started to understand the pricing curves being real instead of something that some random person on the internet drew on a chart. Because Gemini 1.5 cut their prices and cut their prices exactly in line with where everyone else is in terms of their ELO price charts. So if you plot, by September, we had the O1 preview and pricing and costs and ELOs.

So the frontier was 01 Preview,

GPT-40, 01 Mini, 40 Mini, and then Gemini Flash at the low end. That was the frontier as of September. Gemini 1.5 Pro was not on that frontier. Then they cut their prices, they halved their prices, and suddenly they were on the frontier. And so it's a very, very tight and predictive line, which I thought was really interesting and entertaining as well. And I thought that was kind of cool. In November, we had 3.5 Haiku New.

And obviously we had Sonnet as well. Sonnet is not... I don't know where there's Sonnet on this chart, but Haiku New basically...

was 4x the price of old Haiku. Sorry, 3.5 Haiku was 4x the price of 3 Haiku. And people were kind of unhappy about that. There's a reasonable assumption, to be honest, that it's not a price hike. It's just a bigger model. So it costs more. But we just don't know that. There was no transparency on that. So we are left to draw our own conclusions on what that means. That's just is what it is. So yeah, that would be the sort of

price ELO chart. I would say that the main update for this one, if you go to my LLM pricing chart, which is public, you can ask me for it or share it online as well. The most recent one is Amazon Nova, which we briefly, briefly talked about on the pod, where they've really sort of come in and basically offered Amazon Basics LLM, where Amazon Pro, Nova Pro, Nova Light, and Nova Micro are the efficient frontier for their intelligence levels of 1200 to 1300.

You want to get beyond $1,300, you have to pay up for the 01s of the world and the 40s of the world and the Gemini 1.5 Pros of the world. But 2Flash is not on here and is probably a good deal higher. Flash Thinking is not on here as well as all the other QWQs, R1s, and all the other sort of thinking models. So I'm going to have to update this chart. It's always a struggle to keep up to date. But I want to give you the idea that basically for through the month, through the, through the, through,

through 2024 for the same amount of elo what you used to pay at the start of 2024 um you know let's say you know 50 40 to 50 dollars per million tokens uh now is available approximately at with amazon nova uh approximately at i don't know 0.075

per token. So like 7.5 cents. So that is a couple orders of magnitude at least. Actually, almost three orders of magnitude improvement in a year. And I used to say that intelligence, the cost of intelligence was coming down one order of magnitude per year, like 10x. That is already faster than Moore's Law. But coming down three times this year is something that I think not enough people are talking about. And so

Even though people understand that intelligence has become cheaper, I don't think people are appreciating how much more accelerated this year has been. And obviously, I think a lot of people are speculating how much more next year will be with H200s becoming commodity, Blackwells coming out. It's very hard to predict. And obviously, there are a lot of factors beyond just the GPUs. So that is the sort of thematic overview. And then we went into sort of the...

The annual overview, this is basically us going through the AI news releases of the year and just picking out favorites. I had Will, our new research assistant, help out with the research, but you can go on to AI news and check out all the top news of the day. But we had a little bit of an AI rewind thing, which I'll briefly bridge you in back to the recording that we had.

So January, we had the first round of the year for Perplexity. And for me, it was notable that Jeff Bezos backed it. Jeff doesn't invest in a whole lot of companies, but when he does, you know, he backed Google back in the day. And now he's backing the new Google, which is kind of cool. Perplexity is now worth $9 billion. I think they have four rounds this year. Okay.

Will also picked out that Sam was talking about GPT-5 soon. This was back when he was, I think, at one of the sort of global summit type things, Davos. And yeah, no GPT-5. It's actually, we got 01 and 03. In February, you know, people were, we were still sort of thinking about last year's Dev Day. And this is three months on from Dev Day. People were kind of losing confidence in GPTs.

And I feel like that hasn't super recovered yet. I hear from people that there are still stuff in the works and you should not give up on them. And they're actually underrated now, which is good. So I think people are taking a stab at the problem. I think it's a thing that should exist. And we just need to keep iterating on them. Honestly, any marketplace is hard. It's very hard to judge given all the other stuff they've shipped.

ChatGT also released Memory in February, which we talked about a little bit. We also had Gemini's diversity drama, which we don't tend to talk a ton about in this podcast because we try to keep it technical. But we also started seeing context window size blowout. So this year, I mean, it was Gemini with 1 million tokens.

But also, I think there's 2 million tokens talked about. We had a podcast with Gradience talking about how to fine tune for 1 million tokens. It's not just like what you declare to be your token context, but you also have to use it well. And increasingly, I think people are looking at not just Ruler, which is sort of multi needle in a haystack we talked about, but also Muser and like reasoning over long context, not just...

being able to retrieve over long context. And so that's what I would call out there. Specifically, I think magic.dev as well, made a lot of waves for the 100 million token model, which was kind of teased last year, but whatever it was, they made some noise about it. Still not released, so we don't know, but we'll try to get them on the podcast. In March, Cloud3 came out, which huge, huge, huge for a topic. This basically started to mark the shift of market share that we talked about earlier in the pod, where

Most production traffic was on OpenAI and now Anthropic had a decent frontier model family that people could shift to. And obviously now we know that Sonnet is kind of the workhorse, just like 4.0 is the workhorse of OpenAI. Devon came out in March and that was a very, very big launch. It was probably one of the most well-executed PR campaigns ever.

maybe in tech, maybe this decade. And then I think there was a lot of backlash as to what specifically was real in the videos that they launched with and

And then they took nine months to ship to GA. And now you can buy it for $500 a month and form your own opinion. I think some people are happy, some people less so, but it's very hard to live up to the promises that they made. And the fact that some of them, for some of them, they do, which is interesting. I think the main thing I would caution out for Devin, and I think people call me a Devin show sometimes because they say nice things. Like one nice thing doesn't mean I'm not, I'm a show.

basically is that like a lot of the ideas can be copied. And this is the, always the threat of quote unquote GPT wrappers that you achieve product market fit with one feature. It's going to be copied by a hundred other people. So of course you've got to compete with branding and better products and better engineering and all that sort of stuff, which Devin has in spades. So we'll see April. We actually talked to you do and Suno.

We talked to Suno specifically, but Udio also got a beta access to AI Music Generation. We played with that on the podcast. I loved it. Some of our friends of the pod played in their cars. I rode in their cars while they played our Suno intro songs, and I freaking loved using O1 to craft the lyrics and Suno and Udio to make the songs. But ultimately, a lot of people... Some people were skipping them. I don't know what exact percentages, but those...

you know, 10% of you that skipped it. You're, you're the reason why we cut the intro songs. Um, we also had Lama three release. So, you know, I think people always want to see, uh, you know, like a, a good frontier, uh, open source model and Lama three obviously delivered on that with the AB and 70 B the 400 B came later. Then, um, may GPT four Oh released, uh,

and it was kind of a model efficiency thing, but also I think just a really good demo of all the things that 4.0 was capable of. This is where the messaging of OmniModel really started kicking in. Previously, 4 and 4 Turbo were all text and not natively

I mean, they had vision, but not natively voiced. And I think everyone fell in love immediately with the Sky voice. And Sky voice got taken away before the public release. And I think it's probably self-inflicted. I think that the version of events that has Sam Altman basically putting a foot in his mouth with a three-letter tweet causing confusion

decent grounds for a lawsuit where there was no grounds to be had because they actually just used a voice actress that sounded like Scarlett Johansson is unfortunate because we could have had it and we don't. So that's what it is. And that's what the consensus seems to be from the people I talk to. People be pining for the Scarlett Johansson voice.

in June, Apple intelligence at WWDC. And we haven't, most of us, if you update your phones, have it now, if you're, if you're on an iPhone and I would say it's like decent, you know, like I think it wasn't the game changer, that thing that caused the Apple stock to rise like 20%. And just cause they, everyone was like going to update, upgrade their iPhones just to get Apple intelligence. It did not become that, but yeah,

It is probably the largest scale rollout of transformers yet after Google rolled out BERT for search.

And people are using it. And it's a 3D foundation model that's running locally on your phone with LORAs that are hot swaps. And we have papers for it. Honestly, Apple did a fantastic job of doing the best that they can. They're not the most transparent company in the world and nobody expects them to be. But they gave us more than I think we normally get for Apple tech. And that's very nice for the research community as well.

NVIDIA, I think we continue to talk about. I think I was at the Taiwanese trade show, CompDex.

um and saw saw him signing you know women body parts and i think that was maybe a sign of the times maybe a sign that things have peaked but uh things are clearly not peaked because they continued going um ilia and and then and then that bridges us back into the episode recording i'm gonna stop now and stop yapping but uh yeah we you know we recorded a whole bunch of stuff we lost it and we're

scrambling to re-record it for you but also we're trying to close the chapter on 2024 so now I'm going to cut back to the recording where we talk about the rest of June July August September and the second half of 2024's news and we'll end the episode there Ilya came out from the woodwork yeah saw a term sheet

raised a billion dollars. Dan Gross seems to have now become full-time CEO of the company, which is interesting. I thought he was going to be an investor for life, but now he's operating. He was an investor for a short amount of time. Very short amount of time. What else can we say about Ilya? I mean, I think this idea that you only ship one product and it's a straight shot at superintelligence

Seems like a really good focusing mission, but then it runs counter to basically both Tesla and OpenAI in terms of the ship intermediate products that get you to that vision. Well, I think the question is OpenAI now needs more money because they need to support those products. And I think maybe their bet is like 1 billion we can get.

to the thing. Like, we don't want to have to have intermediate steps. Right. Like, we're just making it clear that, like, this is what it's about. Yeah, but then, like, where do you get your data? You know, where you... Yeah, totally. Well, that's the...

So I think that's the question. I think we can also use this as part of a general theme of the safety wing of OpenAI leaving. Yeah. It's fair to say that Jan Leike also left and basically the entire Super Alignment team left. Yeah, then there was Artifacts, kind of like the Chajupiti Canvas team.

Equivalent. I think more code oriented. Yeah. No one has a Canvas clone yet apart from OpenAI. Interestingly, I think the same person responsible for artifacts and Canvas, Karina, she left Anthropic after this to join OpenAI on the rare reverse moves. Yeah. And then we had AI Engineer World's Fair in June. I was over 2,000 people, not including us. I would love to attend the next one.

If only we can get tickets. Yeah, but I think a really good demo. We now have it deployed for everybody. And Gemini actually kind of beat them to the GA release, which is kind of interesting. I think that everyone should basically always have this on as long as you're comfortable with the privacy settings because then you have a second person kind of looking over your shoulder. And like this time next year, I would be willing to bet that I would just...

have this running on my machine. And, you know, I think that assistance always on that you can talk to with vision that sees what you're seeing. I think that is where at least one hour software experience to go. Then it will be another few years for that to happen in real life in outside of the screen. But for screen experiences, I think it's

basically here, but not evenly distributed. And, you know, we've just seen the GA of this capability that was demoed in June. And then July was Lama 3.1, which, you know, we've done a whole podcast on, but that was great.

July and August, kind of quiet. Yeah, August was such an upwards. Structure outputs, we also did a full podcast on that. And then September, we got 01. Yes. Strawberry, aka Q-Star, aka we had a nice party with strawberry glasses. Yes. I think very underrated. Like, this is basically from the first internal demo of Strawberry was, let's say, November 2023. So between November to September, that's...

like the whole red teaming and everything honestly a very good ship rate like i don't know if like people are giving openai enough credit for like this all being available in chat gbt and then shortly after in in api i think maybe in the same day i i don't know i don't remember the exact sequence already but like this is like the frontier model that was like rolled out very very quickly to the whole world and then we immediately got used to it immediately said it was shit because i'm still using sonnet whatever but like still very good and then obviously now we have

01 Pro and 01 Full. I think like in terms of like biggest ships of the year, I think this is it, right? Yeah. Yeah, totally. Yeah. And I think it now opens a whole new Pandora's box for like the inference time compute and all of that. Yeah. It's funny because like it could have been done by anyone else before. Yeah. Literally, this is an open secret. They were working on it ever since they hired Gnome. But no one else did. Yeah. Yeah.

Another discovery, I think Ilya actually worked on a previous version called GPT-0 in 2021. Same exact idea. And it failed. Yeah. Whatever that means. Timing. Voice mode also. Voice mode, yeah. I think most people have tried it by now because it's generally available. I think your wife also likes it. Yeah. Yeah. She talks to it all the time. Okay. Canvas in October. Okay. Another big release. Have you used it much?

Not really, honestly. I use it a lot. What do you use it for mostly? Drafting anything. I think that people don't see where all this is heading. Like OpenAI is really competing with Google and everything. Canvas is Google Docs. And like it's a full document editing environment with an auto-assister thing at the side that is arguably better than Google Docs

at least for some editing use cases, right? Because it has a much better AI integration than Google Docs with Gemini on the side. And so OpenAI is taking on Google and Google Docs. It's also taking it on in search. They launched their little Chrome extension thing to be the default search.

And I think like piece by piece, it's kind of really tackling on Google in a very smart way that I think is additive to workflow and people should start using it as intended because this is a peek into the future. Maybe they're not successful, but at least they're trying. And I think Google has gone without competition for so long that anyone trying will at least receive some attention from me. Yeah. And then, yeah, computer use also came out. Yeah. Yeah, that was a busy... It's been a busy...

A couple months. Busy couple months. I would say that computer use was one of the most upvoted demos on Hacker News of the year. But then comparatively, I don't see people using it as much. This is how you feel the difference between a mature capability and an emerging capability. Maybe this is why vision is emerging. Because I launched computer use, you're not using it today. But you use everything else in the mature category. And it's mostly because it's not precise enough or it's too slow or it's too expensive.

And those will be the main criticisms. Yeah, that makes sense. It's also just like overall uneasiness about just letting it go crazy. I don't care. Yeah, no, no, totally. But I think a lot of people do. November. R1. So that was kind of like the open source of one competitor. This was a surprise. Yeah, nobody knew it was coming. Everyone knew like F1, we had a preview at the Fireworks HQ. And then I think some other labs did it. But I think R1 and QWQ, Quill did it.

from the Quent team, both Alibaba affiliated, I think, are the leading contenders on that front. And we'll see. We'll see. What else to highlight? I think the Stripe agent toolkit. You like that one? It's a small thing, but it's just like people are like, agents are not real. It's like when you have, you know, companies like Stripe and like start to build things to support it. It might not be real today, but obviously they don't have to do it because they're not an AI company. But the fact that they do it

Shows that there's one demand and two, there's belief on their end. This is a broader thing about, a broader thesis for me that I'm exploring around. Do we need special SDKs for agents? Why can't normal SDKs for humans do the same thing?

Stripe Agent Toolkits happens to be a wrapper on the Stripe SDK. It's fine. It's just like a nice little DX layer. But it's still unclear to me. I have been asked my opinion on this before, and I think I said it on a podcast, which is the main layer that you need is separate off-rolls so that you don't assume it's a human doing these things. And you can lock things down much quicker, or you can identify...

whether it is an agent acting on your behalf or actually you. And that is something that you need. I had my 11 labs key pwned because I lost my laptop and I saw a whole bunch of API calls and I was like, oh, is that me or is that...

It turned out to be a key that I committed onto GitHub and that I didn't scrape. And so sourcing of where API usage is coming from, I think you should attribute it to agents and build for that world. But other than that, I think SDKs, I would see it as a failure of...

tech and AI that we need every single thing needs to be reinvented for agents I agree in some ways I think in other ways we've also like not always made things super explicit there's kind of like a lot of defaults that people do when they design APIs but like

I think if you were to redesign them in a world in which the person or the agent using them as like almost infinite memory and context, you would maybe do things differently. But I don't know. I think to me, the most interesting is like REST and GraphQL is almost more interesting in the world of agents because agents could come up with so many different things to query versus like before I always thought GraphQL was kind of like not really necessary because like, you know what you need, just build the REST endpoint for it. So yeah, I'm curious to see what else comes up.

And then, yeah, the search wars. I think that was, you know, search GPT, perplexity. Dropbox. Dropbox Dash. Yeah. We had Drew on the pod and then we added the Pioneer Summit. The fact that Dropbox has a Google Drive integration, it's just like if you told somebody five years ago, it's like Dropbox doesn't really care about those here files. You know, it's like that doesn't compute. So, yeah, I'm curious to see where it goes.

where that goes cool this whole space and that brings us up to December still developing I'm curious what the last day of opening ice shipments will be I think everyone's expecting something big there I think so far has been a very eventful year definitely has grown a lot we were asked by Will actually like whether we made predictions I don't think we did but not really I think well I think we definitely talked about agents yes and I don't know if we said it was the year of the agents but we said next year is the year no no but

Well, you know, the anatomy of autonomy, that was April 2023, you know? So obviously there's been belief for a while. But I think now the models are, I would say maybe the last, yeah, two months. Yeah. I made a big push in like capability for like 3.6, 0.1. Yeah, I mean, Ilya saying the word agentic on stage at NeurIPS, it's a big deal. Satya, I think also saying that a lot these days. I mean, Sam has been saying that for a while now.

So DeepMind, when they announced Gemini 2.0, they announced DeepResearch, but also Project Mariner, which is a browser agent, which is their computer use type thing, as well as Jules, which is their code agent. And I think that basically complements with whatever OpenAI is shipping next year, which is Codename Operator, which is their agent thing.

It makes sense that if it actually replaces a junior employee, they will charge $2,000 for it. Yeah, I think that's my whole... I did this post that it's been on my Twitter, so you can find it easily, but about skill floor and skill ceiling in jobs. And I think the skill floor more and more... I think 2025 will be the first year where the AI sets the skill floor of a role. I don't think that has been true in the past, but yeah, I think now really like... If Devin...

works if all these customer support agents are working so now to be a customer support person you need to be better than an agent because the economics just don't work i think the same is going to happen to in software engineering which i think the skill floor is very low you know like there's a lot of people doing software engineering that are really not that good so i'm curious to see it the next year we recap what other jobs are gonna have that change yeah

Every New Reps that I go, I have some chats with researchers and I'll just highlight the best prediction from that group. And then we'll move on to end of your recap in terms of, we'll just go down the list of top five podcasts and then we'll end up. So the best prediction was that there will be a foreign spy caught at one of the major labs. So this is part of the...

consciousness already that, you know, like, you know, whenever you see someone who is like too attractive in a San Francisco party where it's like the ratio is like a hundred guys to one girl. And like suddenly the girl's like super interested in you. Like, you know, it may not be your looks.

So there's a lot of state-level secrets that are kept in these labs and not that much security. I think if anything, the situational awareness essay did to raise awareness of it, I think it was directionally correct, even if not precisely correct. We should start caring a lot about this. OpenAI has

hired a CISO this year. And I think like the security space in general, oh, I remember what I was going to say about Apple Foundation Model before we cut for a break. They announced Apple Secure Cloud, Cloud Compute. And I think we are also interested in investing in areas that are basically secure cloud LLM inference for everybody. I think like what we have today is not secure enough because it's like normal security when like this is literally a state level interest. Agreed.

top episodes? Yeah. So I'm just going through the sub stack. Number one, the David Luan one. It's the most popular in 2024. Why Google failed to make GPT-3. I will take a little bit of credit for that, for the naming of that one, because I think that was the hack news thing. It's very funny because like, actually, obviously he wants to talk about a debt, but then he spent half the episode talking about his time at OpenAI. But I think it was a very useful insight that I'm still using today, even in like the earlier posts.

I was still referring to what he said. And when we do podcast episodes, I try to look for that. I try to look for things that we'll still be referencing in the future. And that concentrated badness, David talked about the brain compute marketplace. And then Ilya in his emails that I covered in the What Ilya Saw essay had the opening eyesight of this, where they were like...

One big training run is much, much more valuable than the hundred equivalent small training runs. So we need to go big and we need to concentrate bets, not spread them. Number two, how Notebook Alarm was made. Yeah. That was fun. Yeah. And everybody, I mean, I think that's like a great example of like just timeliness. You know, I think it was supplement for everybody. They were great guests.

It just made the rounds on social media. Yeah. And that one, I would say, Risa is obviously a star, but she's been on every episode, every podcast. But Isama, I think, you know, actually being the guy who worked on the audio model, being able to talk to him, I think was a great gift for us. And I think people should listen back to how they trained the NovoColor model, because I think you put that level of attention on any model, you will make it sold out.

Yeah, that's true. And specifically, like, they didn't have evils. They just... Vibes. Yeah, a group session with vibes. The ultimate got to prompting. Yeah. That was number three. I think all these episodes that are like,

summarizing things that people care about, but they're desperate. I think I always do very well. This helps us save on a lot of smaller prompting episodes, right? If we interviewed individual paper authors with like a 10 page paper, that is just a different prompt, like not as useful as like an overview survey thing. I think the question is what to do from here. People have actually, I would say I've been surprised by how well received that was.

Should we do ultimate guide to other things? And then should we do prompting 201? Right. Those are the two lessons that we can learn from the success of this one. I think if somebody does the work for us, that was the good thing about Sander. Like he had done all the work for us. Yeah, yeah. Sander is very, very fastidious about this. So he did a lot of work on that. You know, I'm definitely keen to have him on next year to talk more prompting. Okay. Then the next one is the not safe for work one. No. Or structured outputs. Yeah.

The next one is Braintrust. Really? Yeah. Okay, we have a different list then. I'm just going on the sub stack. I see. So that includes the number of likes. But I was going by downloads.

it's fine. I would say this is almost recency bias in the way that like the audience keeps growing and then like the most recent episodes get more views. So I would say definitely like the NSFW one was very popular. What people were telling me that we liked because it was something people don't, don't cover. Yeah. Um,

Yeah, structural outputs. I think people like that one. I mean, the same one. Yeah, I think that's like something I refer to all the time. I think that's one of the most interesting areas for the new year. No, the simulation. Oh, WebSIM, Wilson. Really? Yeah. Not that use case, but like how do you use that for like model training and like agents learning and all of that? Yeah. So I would definitely point to our newest seven hour long episode on...

on simulative environments because it is the, let's say the scaled up, uh, very serious AGI lab version of WebSim and MobileSim. If you take it very, very seriously, you get Gini too, which is exactly what you need to then build Sora and everything else. Uh,

So yeah, I think similar to AI, still in summer. Still in summer. Still coming. And I was actually reflecting on this. Would you say that the AI winter has come and gone or was it never even here? Because we did a Winds of AI Winter episode and I was trying to look for signs. I think that's kind of gone now. Yeah, I would say...

It was here in the vibes, but not really in the reality. You know, when you look back at the yearly recap, it's like every month there was like progress. There wasn't really a winter. It was maybe like a hype winter, but I don't know if that counts as a real winter. I think the scaling has hit a wall thing has been a big driving discussion for 2024. Yeah. And...

And, you know, with some amount of conclusion in NeurIPS that we were also kind of pointing to in the Winter on AI Winter episode. But like, it's not a winter by any means. We know what winter feels like and this is not winter. So I think things are going well. I think every time that people think that there's like not much happening in AI, just

Think back to this time last year. Right. And understand how much has changed from benchmarks to frontier models to market share between OpenAI and the rest. And then also cover like, you know, the various coverage areas that we've marked out, how the discussion has evolved a lot and what we take for granted now versus what we did not have

Yeah. And then just to like throw that out there, there have been 133 funding rounds over 100 million NAI this year. Does that include Databricks, the largest venture round in history? $10 billion. Sure.

Sheesh. Well, that mosaic now has been bought for two something billion because it was mostly stock, you know? So price goes up. I see. Theoretically. I see. So you just bought at a valuation of 40, right? Yeah. It was like 43 or something like that. And at the time, I remember at the time there was a question about whether or not that valuation was real. Yeah. Well, that's why everybody... Snowflake was down. Yeah. And like Databricks was a private valuation that was like two years old. It's like, who knows what this thing's worth. Now it's worth 60 billion. It's worth more. Yeah.

It's word born. That's what it's word. It's word born. That's what you thought. Yeah. It's been a crazy year, but I'm excited for next year. I feel like this is almost like, you know, now the agent thing needs to happen. And I think that's really the unlock. Yeah. I mean, I have to agree with you. Next year is the year of the agent in production. Yeah. I don't, I don't, you know, it's almost like I'm not a hundred percent sure it will happen, but like it needs to happen. Otherwise it's definitely the winter next, next year.

Any other parting thoughts? I'm very grateful for you. I think you've been a dream partner to build Lane Space with. And also the Discord community, the Paper Club people have been beyond my wildest dreams, like so supportive and

and successful. Like it's amazing that, you know, the community has, you know, grown so much and like the, the vibe has not changed. Yeah. Yeah. That's true. We're almost at 5,000 people. Yeah. We started this discord like four years ago. Yeah. And still like people get it when they join there, like you post news here and then you discuss it in threads and,

You know, you try not to self-promote too much and mostly people obey the rules and sometimes you smack them down a little bit, but that's okay. We rarely have to ban people, which is great. But yeah, man, it's been awesome, man. I think we both started...

not knowing where this was going to go. And now we've done a hundred episodes. It's easy to see how we're going to get to 200. I think maybe when we started, it wasn't easy to see how we would get to 100, you know? Yeah. Excited for more. Subscribe on YouTube. We're doing so much work to make that work. So it's very expensive for, for an unclear payoff as to like what we're actually going to get out of it. But hopefully people discover us more there. Like I, I do believe in YouTube as a podcasting platform, much more so than Spotify. Yeah.

Totally. Thank you all for listening. See you in the new year. Bye-bye.