Context windows, computer constraints, and energy consumption with Sarah and Elad

This week on No Priors hosts, Sarah and Elad are catching up on the latest AI news. They discuss the recent developments in AI like Meta’s new AI assistant and the latest in music generation, and if you’re interested in generative AI music, stay tuned for next week’s interview! Sarah and Elad also get into device-resident models, AI hardware, and ask just how smart smaller models can really get. These hardware constraints were compared to the hurdles AI platforms are continuing to face including computing constraints, energy consumption, context windows, and how to best integrate these products in apps that users are familiar with. Have a question for our next host-only episode or feedback for our team? Reach out to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil Show Notes: (0:00) Intro (1:25) Music AI generation (4:02) Apple’s LLM (11:39) The role of AI-specific hardware (15:25) AI platform updates (18:01) Forward thinking in investing in AI (20:33) Unlimited context (23:03) Energy constraints

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:05Sarah Guo?

  Hey listeners, you are here for another episode of No Priors, just with me and Lod. And there's been a lot going on in earnings and in the technical world. So I think we will start with maybe just one fun thing that seems to have taken flight in terms of music generation. Lod, what do you make of the popularity that Suno and Udeo have found?

• 0:31Speaker 01

  When you said fun things, I thought you were going to talk about my hats, your hats.

• 0:35Sarah Guo?

  We can talk about your hats.

• 0:36Speaker 01

  I have my Bitcoin having hat that I got from Coinbase. As you see, Bitcoin having. And then Zayd has these Make AI Great Again hats. He's actually selling, I think it's going to fund his data labeling habit.

• 0:49Sarah Guo?

  That's a lot of hats.

• 0:50Speaker 01

  I know, yeah, that's all I got, that's all I got left there.

• 0:53Sarah Guo?

  If anybody has read Jared Kushner's book, there's a great bit about how much money they're making from all the MAGA swag. And so, listeners, Lod and I are making hats and tequila for the guests.

• 1:07Speaker 01

  Yeah.

• 1:08Sarah Guo?

  But for the low, low price of one H100 GPU, we will give each of those to you too.

• 1:14Speaker 01

  Or a Bitcoin, either way.

• 1:16Sarah Guo?

  Okay, I'll take the Bitcoin instead, yeah.

• 1:17Speaker 01

  Me too, I know, yeah. I think we all would at this point. We can check in again in a couple of months when the D100s come out, or whatever time period. So, yeah, so, you know, as you know, there's been some really interesting things happening on the music generation side. And so there's both Suno and Udeo, and both seem to be kind of taking people by storm in terms of really interesting music-based models. And it feels like one of those things where it's early, but it's really giving a glimpse of what's coming in terms of the ability to create other types of content. Obviously, the very first content wave in some sense was simple text-based things on GPT-3 like Jasper. And then we hit an image gen wave. And that was Midjourney and Stable Diffusion and things like that. And then we had obviously chat come out as sort of a new type of format and interaction modality.

• 2:02Speaker 01

  And then we had video with things like Pika. And so it just feels like sequentially, we're hitting these different formats.

• 2:08Speaker 01

  And then obviously Suneo from OpenAI. And now we have these really interesting music models where you can specify

• 2:16Speaker 01

  the type of music that you want. You can write the lyrics. It'll add vocals. And so, you know, these really seem to be the two models initially, at least the people are really adopting. And so it just seems like an interesting moment in time from the perspective of look at all these different creative things that people are now empowered to do. And look at the different ways to engage. And of course, you could imagine going forward in time and saying, okay, at some point there'll be voice cloning where, and I think, I think it was Drake who put out a song, right? Where he had two or three other rappers that he just voice cloned in. And you can imagine a world where you could use anybody's voice, assuming there's permissions and everything else to generate your own songs and content and all things. So it just seems like a very exciting future world between UDOs, you know, and some of those other companies.

• 2:59Sarah Guo?

  Yeah, I think one of the things that's not obvious here is in like media platforms in general, they're like, the ratio varies, right? But there are a lot more readers on X or consumers, like people who scroll a feed on TikTok or not TikTok anymore, I suppose, but whatever it is than than creators. And so I think like one thing that is just like unknown is how many people actually want to create music. If you make it a lot easier to, you know, create something that's any good, right? And if if the music we get changes. And so I was talking to one of these founders and he was like, everybody should have a personalized soundtrack for their life.

• 3:43Sarah Guo?

  But in the voice of Taylor Swift and the style of Taylor Swift. So.

• 3:48Speaker 01

  Yeah, I already have one of those, but yeah.

• 3:50Sarah Guo?

  So what is yours?

• 3:51Speaker 01

  I can't really share publicly, but we can talk about it later.

• 3:54Sarah Guo?

  Okay, okay. Yeah, it's gonna be a little bit, it's gonna, I think it's gonna be a little bit of a personal thing.

• 3:59Speaker 01

  Yeah.

• 4:01Sarah Guo?

  What else is going on?

• 4:02Speaker 01

  I don't know. I mean, what about local LLMs and the Apple release? You want to talk about that?

• 4:05Sarah Guo?

  Yeah, so it's it's interesting. Apple has entered the chat with a release of, you know, relatively small models that are now on Hugging Face and such. And I think it's just worth talking about the fact that. What some of the initial open source releases mixed straw llama did was create, you know, pretty impressive reasoning capabilities that were open that developers could use. But there's been huge demand for models that actually have, you know, that level of capability, or at least useful capability in a one and three billion

• 4:46Sarah Guo?

  parameter size that'll fit on edge devices. And of course, like if you if you enable that, you have a very different latency paradigm in terms of what experiences for consumer are possible and then not paying for the compute of inference all the time means you can do things much more easily that are ongoing and passive and proactive. And so I think there's a lot of developer demand. And I think it like foreshadows, we should see Apple creating interfaces for running models locally as part of their ecosystem. That's my general prediction.

• 5:21Speaker 01

  Yeah, I think it's kind of interesting because I've seen a number of people building apps recently that have been these Mac or iPhone OS resident sort of exposure to LLMs. And in some cases, it's let me index everything on your computer and let you interact with it or search on it or, you know, build an embedding on top of it or set our embeddings. Or let me just integrate chat GPT or other things into your desktop. And there used to be like search bar apps and things like that and sort of prior generations. And so I think a lot of that is going to be really interesting from user experience perspective. And one can imagine a couple of years from now, that's just going to be something that comes standard on your device, either direct from Apple or through some sort of partnership or something else.

• 6:01Sarah Guo?

  Do you believe any of those things can persist independently? Right? Because I'm reminded of like, the launcher platforms, like, you know, if you think about Android as a more permissive platform, people trying to choose core change sort of core user experiences like that at what felt like an operating system level was was kind of tough.

• 6:19Speaker 01

  I think it depends on whether or not you truly take advantage of the browser and you're also accessing browser related third party applications that you're just getting through the web and indexing them or not. And so really it comes down to like, what's the footprint of content that you're searching over in some sense? Yeah, that to me is the biggest pivot point in terms of whether you'll end up with something that's going to be specific to the OS company or broader. I think in general, platforms tend to integrate the most valuable things into themselves. And so it's always risky. They do that as a strategy and every once in a while you see a company that has breakout success even though it's built on top of a platform. So I think the oddest example of that is Viva. Do you know Viva?

• 7:02Speaker 01

  They're like a $40 billion vertical SaaS company focused on pharma.

• 7:07Sarah Guo?

  Oh, yes. Yeah.

• 7:08Speaker 01

  Yeah, was built on top of Salesforce. It was just a Salesforce app. And they were just selling this into pharma and then it started working and they eventually swapped out Salesforce in the backend. But they were literally just some like thin layer on top of Salesforce for years. And so that's, that's, that's the only example I can really think of as something that's getting truly massive on top of a platform that was intensive soon by that platform. But I'm sure there's other examples too.

• 7:34Sarah Guo?

  Yeah, I think they're like all kinds of apples and oranges. Yeah, Viva, sorry, so out of context, I'm like, what does that have to do with AI, man? And like, uh, operating systems. Yeah, I think choosing the, the vertical thing makes sense to me there, right? You've got compliance around workflows for selling to doctors for life sciences that a Salesforce like didn't really have the expertise or maybe they just didn't see how big it was to attack.

• 8:01Speaker 01

  Well, they verticalized. I mean, Salesforce always had these verticals, right? Let's see, what's Salesforce market cap? It'd be interesting to see like relative to Viva, how much value? Salesforce is 266 billion. So yeah, Viva is like 20% of Salesforce or something like that.

• 8:16Sarah Guo?

  Yeah, that was worth investing in.

• 8:17Speaker 01

  Yeah, I think people also forget that all of Microsoft Office at some point were third-party applications. So in the eighties, there were separate companies like Lotus and others that were providing what turned into Excel. There was a PowerPoint company that was very popular, you know, before Word, there was documents that people would buy as separate applications and then Microsoft just ended up subsuming them into its, its own distribution platform. And so again, it's a very standard kind of traditional thing to have happen. The default case would be that those things eventually become part of the operating system

• 8:50Speaker 01

  or, you know, the operating system company. But again, you never know. So I always think it's interesting to see what people do and. Can they make it cross-platform? Does that matter in the long story? The one other thing that I think is interesting about small models is the question of how how smart can small models models get and how much can you actually pack into them? And to some extent, if you look at an LLM, there's like three or four pieces of capability that people care about. There's sort of the reasoning part of it. There's a set of capabilities in terms of what it can do from a synthesis or other perspective. There's the multimodality, the knowledge base or knowledge set that's actually resident in the model. And you can only stuff so much into, you know, a 3B model or a 7B model or whatever size you end up eventually running on device.

• 9:33Speaker 01

  And so that's the other question is what are the capabilities that you can actually have that are device resident versus which you always have to go out to the cloud for? And obviously, devices always expand their capabilities over time because of the microprocessors they have and everything else. But fundamentally, there are going to be limitations. And so the question is, where's that line? And you could probably

• 9:51Speaker 01

  define that analytically and just say, okay, today, this is all the stuff that's going to get cut off if you just do it on device and here are the things that you can do. And as we move up in terms of device capabilities and their ability to

• 10:05Speaker 01

  provide access to larger and larger LLMs that are resident, what are the capabilities that come with that? And so I haven't really seen that analysis, but that seems like something we should be reasonably straightforward to do in terms of just trying to understand what can you really do on device versus not? And therefore, what apps or products can you build that are going to be, you know, device resident?

• 10:22Sarah Guo?

  Yeah, well, I think there's like an obvious analogy to draw to just like the weight of like how much computing is done like on client or in cloud client server if you're really old, right? And I don't think it's like obvious that there is any sort of principled answer here, except it's going to vary by application and how quickly, as you said, like capabilities actually fit into a whatever hardware people already have.

• 10:51Sarah Guo?

  Because now there are companies doing AI-specific hardware already, right? And if you're trying to make something that feels very AI-native work, there is a question of like, okay, well, you know, do we try to make the model work? What hardware do we, what compute do we want in the device itself? How much preprocessing do we do to, you know, send less data over the network to a large model in the cloud? And so I definitely think there's going to be like a distribution of that compute, just like the religious wars about like, you know, what gets done, you know, on client and server in your CDN, whatever.

• 11:29Speaker 01

  But are one circumstances, do you think that's not just your phone and your watch or something? Like, where do you think the new AI hardware will matter? At least from a consumer perspective?

• 11:37Sarah Guo?

  One of the most interesting theories, and we can actually talk about like the Meta AI launch as well, is whether or not you want some sort of passive device that is like continually collecting data about the world from a vision environment perspective, which is why you get like Ray-Ban Meta Glasses instead of just like a phone or a watch that's sitting in your pocket, right?

• 12:00Speaker 01

  Sure. I guess you could also just fix small cameras to other devices as well. So it just comes down to, do you need a new form factor or not? Although I think it's an interesting area and interesting direction. I guess relatedly, like, when do you need that extra data, that information? And under what circumstances will you use it? I mean, it's super interesting, right? I think in general, if I look at the early mobile device products that emerged from it, a lot of them just took advantage of the accelerometer. If you were doing fitness or GPS, if you were doing everything from Uber to a variety of other applications. So I do think those sorts of new capabilities from a primitive's perspective are super interesting. So, but again, it just comes down to, okay, what are you going to use it for and how and when?

• 12:44Speaker 01

  You know, there's also really interesting, unexpected applications that visioned other areas right now. So I don't know if that's, you know, the meta thing I think is super interesting. Have you tried the product?

• 12:53Sarah Guo?

  Yeah.

• 12:54Speaker 01

  Yeah, I was very impressed. Yeah.

• 12:56Sarah Guo?

  Yeah, they they entered the race with like multiple different products, like different modalities upfront. It's interesting that they haven't actually, they haven't pushed it that aggressively into their existing surfaces yet, because they have like unlimited distribution, right? And it's just meta.ai, like independent product. But I imagine they're going to, I imagine they're going to phase into it.

• 13:19Speaker 01

  Yeah, it seems like you would potentially, and who knows what their plans are, have like a, you know, a channel or bot on WhatsApp or on, you know, Messenger or multiple different services that you could just start interacting with to do things for you. And that could effectively be a way to encapsulate meta.ai as like a, just another line item or another account that you're interacting with in chat or another sort of properties they own. But I thought it was really well done. I've been making different images with my kids on the different services, you know, Playground and OpenAI and Meta and everything else. And they really enjoyed the one-click animation. It's like a feature.

• 14:00Sarah Guo?

  Yeah, I think it's it's an impressive product launch. And then on the open source side, they've also made a splash. Where I think one of the things that surprised people is,

• 14:13Sarah Guo?

  there's an argument generally in research about like, how much do you want to try to change? Like the efficient frontier of model training? Or like, do you just, you know, ride the curve and scale up? And and if you are Meta and you have somewhere between, you know, 22,000 GPU clusters and 350,000 GPUs available,

• 14:35Sarah Guo?

  then continuing to train past like supposedly optimal points like does improve performance, apparently, and doesn't just fully asymptote as soon as maybe a lot of the research community predicted. And so I think it just is a, is actually a point in favor of the very large firms who are really willing to invest against this and don't have, like, it begs the question of how important is efficiency? I think efficiency and creativity and architectural approach is going to end up being. Really important for lots of different use cases. Like over time, applications are going to want efficient inference. And it's like, really large models are impossible today to serve for the vast majority of use cases from a cost and speed perspective.

• 15:19Sarah Guo?

  But if all you're trying to do is a technical demonstration, like this is very impressive.

• 15:24Speaker 01

  Yeah, it did really nice work. I guess the other thing on the sort of data compute platform side, while we're talking about all these different platforms is

• 15:32Speaker 01

  Snowflake and Databricks and sort of outlier companies. Do you think you need to own the model as a data or compute platform? Or how do you think about these various open source models that these folks are starting to launch now?

• 15:42Sarah Guo?

  One thing that's tricky as an investor, as an upper in the space is like, it begins to look like all one blended landscape of competition. So the Snowflake platform hosts a bunch of different models from other players, including, for example, Mistral. And they're also training their own. And their models are available on, for example, like Microsoft Azure, right? And so you've got a huge number of players all in co-opetition somewhere between models and inference. And it's like, I can't tell you how I think it is going to work. I do think that in the immediate term, like it is less obvious to me that they necessarily, like these, these compute platforms necessarily need to own the models,

• 16:27Sarah Guo?

  especially if there's a, a big landscape of open source out there versus need to demonstrate to customers that, and actually have the expertise of, let's say, like training models and fine tuning them and deploying them and customizing them to different use cases. So I think there's a piece of it that is actually like learning something that they can deliver to their customers and marketing too.

• 16:55Speaker 01

  Yeah, it definitely feels like in the long run, there's a bit of a capital scale question, which is if, if you're focused on the frontier models at least, you need more and more scale in terms of compute or other things, which means you need more and more money to invest behind it. And that's why a lot of the models seem to have sponsors in the hyperscalers or other sort of large corporations. And so the question is, how long do some of these other players want to keep up? In terms of trying to build things that are bigger and bigger, or do they just focus on a specific subclass of models that are more kind of medium to small? That have more specialized use cases. And so one could argue that in the long run, that's where those types of models should go. Is the inference platforms probably provide things that are more in that range and then the hyperscalers and their partners.

• 17:37Speaker 01

  You know, a handful of them will be at the frontier because those are the ones that have business models. They can actually afford the subsidization of these massive frontier models with the idea that. The ROI pays for itself dramatically later. As you scale into more and more intelligence and more and more applications and things like that. So. It's kind of investing far ahead and the question is how far ahead can certain companies invest?

• 17:59Sarah Guo?

  I'm sure you've been getting this question, but I get like a stressed out question from investors. Of essentially like, how much money should the world spend on this? And like, is it? Is it rational? And I, I, I think this is an impossible question because should is. And it like, this is a, this is a question of people's business decisions, like a very small number of individuals, actually, right? And something that feels unique in AI to me is you, you have these folks who like. They really believe in technology and, you know, strong intelligence. They can. Reason about exponential growth. That's something like Silicon Valley people are very proud of, but also all of these people are very capable of. And they have like very high risk appetite. And then, you know.

• 18:43Sarah Guo?

  They can control and marshal a huge amount of resources, $10 billion plus spend, $20 billion plus spend if you're. Meta every year on GPUs, 30, I think. 30 to 35 this year. They may have changed the estimate there against these bets. And I actually think it's interesting to put in context, like this isn't. Completely unheard of in terms of investment. Toward some future goal, like, you know, you and I have talked about chip fabs before and how much they cost. But if you think in aggregate, a handful of players in terms of the hyperscalers are spending. Almost $200 billion this year on, on compute for AI.

• 19:25Sarah Guo?

  And then you like, try to think about the, the other types of investment in a new ecosystem, like. Oil majors spend $80 billion a year. Broadband providers, you know, last few, like it's about $100 billion a year of spend in infrastructure, right? Like capex for high speed internet. This is like going back a decade or two, but if you look at railroad freight, like there are years where you're spending a huge number on railroad CapEx. And so I, I think like the different dimensions you can think about spent are like, well, what is it in overall, overall context? And then, you know, what is, what does that get you over time? I think both these questions are pretty, pretty hard to answer.

• 20:09Sarah Guo?

  But it's not unheard of in terms of scale.

• 20:11Speaker 01

  Yeah, 100%. And I, again, it depends on your belief in terms of where all this goes, but I think it's notable that the hyperscalers are doing it. And then the one other potential source of immense scale in the long run may be sovereigns. As, as people want to customize models that are specific to their region, or customs, or language, or culture, or whatever it may be.

• 20:30Sarah Guo?

  So there are a few areas of research that have been like debated hotly recently. And one is this idea increasingly of like unlimited context, which I think is like mostly a term for like actively managing context. But you're an investor in Magic. Magic is one of the key players here. Can you, can you talk about this?

• 20:50Speaker 01

  Oh, sure. I mean, Magic, I think was one of the first companies to launch a really long context window. I think they launched a 5 million token context window, you know, six or 12 months ago now. So it's been a while. And then when Google came out with Gemini 1.5, I think it was a million token context window. And then it seems likely that a lot of people will end up in the 10 million plus range in the next a year or two.

• 21:12Speaker 01

  Or, you know, some reasonable timeframe ahead. And what's happening is with longer, longer and longer context windows, the way that you think about what you put into a prompt changes pretty dramatically because you can start, start dropping everything from entire code repos to all sorts of documents. If you're dealing with legal on through to, you know, you kind of name it. You can also drop in all the context of a giant customer support queue, you know, because, you know, some of these things eventually get big enough that you can do really significant things with them. I think one of the most striking examples of long context window being important is actually some biology models that have come out recently, where just increasing the context window for things like protein folding seems to really make a big difference in terms of your end result, in terms of the fidelity of that folding.

• 21:56Speaker 01

  So I think this is going to end up being one of those really significant things. And it reminds me a little bit of microprocessors or bandwidth in the 90s where each generation of Microprocessors that came out had a big step up in things that you could do with it or bandwidth, you know, instead of a dial-up modem, some of you had a fatter connection and then eventually had broadband. And now, you know, people have fiber in some cases.

• 22:21Speaker 01

  And so it feels like a similar thing where there will be a really long period, a long period in this, and AI means like two weeks or whatever. There'll be a really long, I mean, it's gonna be a long time. There's a really long period where bigger and bigger context windows will matter. And then there'll be some shift where, okay, now we've kind of maxed out what we're really going to be able to do with it. But it seems like a significant shift in thinking around how important this is going to be. And again, in areas that I didn't expect necessarily, like biology. I guess the other thing that people have been looking at, you know, we talked a little about context window. We talked a little about compute platforms. We talked a little about the capabilities of small models and where are they constrained? The other potential feature constraint is energy.

• 23:02Speaker 01

  So I'm just curious how you think about, you know, if you have a 500 megawatt or gigawatt data centers, like, does a way we think about energy shifts, like there have been things like job ads posted on the Microsoft website for like nuclear engineering and things like that. And so what do you think happens from an energy constraint perspective? Because you kind of look at it, the first constraint was like chips and then it was packaging for the chips. And then there's probably going to be some data center constraint where everybody miscalculates how many data centers we actually need. And then energy is sort of related to that. So I'm just curious how you think about future energetic needs.

• 23:37Sarah Guo?

  I think like one supposed coming limit to scale is going to be energy. And that nobody's built like a 500 megawatt gigawatt data center yet. And if you think of it as the equivalent of like a nuclear power plant's worth of energy going toward a single data center, it is quite large. And I mean, the, the basic understanding here is today to train these large models, you, you need all of the GPUs co-located because there is enough data transfer between different chips, right? Between your nodes. And there's a physical constraint on that in that you need to get that much power to a data center. And I think it's kind of, it's kind of interesting because Sam made the point recently

• 24:21Sarah Guo?

  that when you have constraints that require permitting and physical world changes, like you will see a slowdown in terms of how quickly you can go deploy this, right? It's no longer a software engineering problem only. And I think the fear, I think we're likely to work through a lot of these things. But the fear is that some of the potential limits to progress are going to be like, we can't just throw more compute at the problem because it's like physically hard to throw more compute at the problem, energy data centers, right? And then also, you know, the concept of the data wall, right? Like the, the number of cheap available tokens on the internet we have used.

• 25:05Sarah Guo?

  And now we have to go figure out how to go get more, collect more in the world, or more likely like, you know, or in combination with generating synthetic data. That still feels like a bits, not atoms problem and you can solve pretty fast. But you can even see it in the designees for the new DHS safety board, that the AI safety board that just got announced, right? Some of the players are very obvious. So the, you know, Sam and Satya's of the world, but it also includes people who work on energy to this point and infrastructure security.

• 25:44Speaker 01

  Yeah, it makes a lot of sense. I mean, I actually found that the news around Microsoft investing 1.5 billion in Abu Dhabi's G42 was super interesting from the perspective of you're effectively setting up a big AI data center. A, you're, you're starting to sort of democratize or broaden global access to it. But B, you're doing it, you're an energy center. And so, you know, I think that's really interesting. I think, you know, in general, if you view the last 50 or 70 or 100 years as two sort of competing philosophies around progress and anti-progress or abundance and scarcity, you know, one of the biggest wins on the scarcity side was really shutting down nuclear

• 26:29Speaker 01

  power in the seventies, at least in the US, or the ability to add new nuclear power since, you know, 17 or 18% of US power generation today is still nuclear. It's like 70% in France, it's 30-ish percent in Japan. So it's still quite significant in some places, but it could have been 70% in the US and a bunch of other countries. And if you would have thought of that large abundant cheap. Resource and now how that ties into things like compute and other areas. It's a real game changer, right? In terms of capabilities. And so there are solutions. The question is, are we going to adopt those solutions at any point? But these are actually very solvable problems if we choose to solve them. Which is why I thought that job posting on the Microsoft website was so interesting.

• 27:11Sarah Guo?

  Yeah, well, and I'd love to, I'd love to see that reversed, even beyond just like general, general energy needs. If you, if you do think of AI as a strategic issue and a national security issue, not using every energy resource we have and every energy technology we have. Like, like you and I have talked about, you know, generations of nuclear technology that have existed, not, not deployed in the United States for, for really like policy reasons. It is yet another dependency that we're creating for ourselves on other countries without needing to, right? It makes the, it makes the sort of energy dependency even worse to have AI rely on it.

• 27:55Speaker 01

  Yeah, all of geopolitics would be dramatically different if we just had a lot of widespread nuclear power. And it's, it's interesting to think of that. And you think of some of the biggest producers being Russia and Venezuela, obviously the Gulf, but you know, Iran, et cetera. And you think of geopolitical policy. It's kind of interesting to think about how the world would be different if we weren't dependent or as dependent on some of these sources. So anything else we should talk about?

• 28:20Sarah Guo?

  I think we're good.

• 28:20Speaker 01

  Do you want to see my hats again?

• 28:22Sarah Guo?

  Yeah, let's see the hats.

• 28:24Speaker 01

  That's the other piece of infrastructure where people spend a lot of money in the past and just sort of hat making, you know?

• 28:29Sarah Guo?

  Yeah.

• 28:30Speaker 01

  Millions of dollars a year going to that infrastructure.

• 28:32Sarah Guo?

  The trade is good for anybody listening. We will offer you one branded bottle of tequila or mock keela or whatever you'd call it and a, a cool No Priors hat for H100s.

• 28:46Speaker 01

  Just let us know.

• 28:47Sarah Guo?

  Okay, that's all we got. Find us on Twitter at NoPriorsPod. Subscribe to our YouTube channel if you want to see our faces. Follow the show on Apple Podcasts, Spotify, or wherever you listen. That way you get a new episode every week. And sign up for emails or find transcripts for every episode at no-priors.com.