Podcast appearances and mentions of Dalton Caldwell

Is your career site delivering the conversion you need? Dalia's plug-and-play tech turns any employer career site into a high-performance candidate conversion engine — no replatforming required, live in days.Visit dalia.co to learn more. AND by jobcase, Jobcase is an online community where workers of all kinds – like hourly employees, tradespeople and healthcare technicians – access jobs, make connections, and support each other in any aspect of their work life.Visit jobcase.com/hire and tap into their 120 million strong  job seeker network First up…NEW YORK — Valence, the company behind Nadia, the world's first enterprise AI coach, today announced it has raised a $50 million Series B led by Bessemer Venture Partners. https://hrtechfeed.com/ai-coach-for-employers-platform-lands-50-million/ LOWELL, Mass. —- UKG, a leading global AI platform for HR, pay, and workforce management, today unveiled a new logo and identity with the launch of its global brand campaign, “When Work Works, Everything Works.” The campaign marks a major leap forward in UKG's evolution as the world's Workforce Operating Platform unifying HR, pay, workforce management, and AI agents into a single solution that turns the world's largest workforce data set into critical business insights supporting every worker — from the front office to the frontline. https://hrtechfeed.com/ukg-unveils-rebrand-new-logo/ SAN FRANCISCO – Alex, the AI recruiting partner transforming how companies discover and hire talent, today announced it has raised $20 million in funding, including a $17 million Series A round led by Peak XV Partners with participation from CHROs at Fortune 500 companies, Y Combinator, Uncorrelated Ventures, and other investors including Tim Sackett, Kris Fredrickson, and Dalton Caldwell. The funding also includes a $3 million Seed round led by 1984 Ventures. https://hrtechfeed.com/ai-powered-recruiting-startup-lands-20-million/ MINNEAPOLIS — Mashalot AI, the invite-only job search agent, is built to eliminate application fatigue and level the playing field for U.S. job seekers. By pulling the newest listings from LinkedIn, ZipRecruiter, and Indeed, and generating a custom resume and cover letter for every role, the platform can apply to 500 jobs in just 5 minutes. This helps candidates land interviews faster than ever. https://hrtechfeed.com/mashalot-ai-launches-to-fix-the-full-time-job-of-finding-a-job/ Workday announced it has completed its acquisition of Paradox, a candidate experience agent that uses conversational AI to simplify every step of the job application journey, particularly for frontline industries.  https://hrtechfeed.com/workday-completes-acquisition-of-paradox/ Learn more about your ad choices. Visit megaphone.fm/adchoices

We are calling for the world's best AI Engineer talks for AI Architects, /r/localLlama, Model Context Protocol (MCP), GraphRAG, AI in Action, Evals, Agent Reliability, Reasoning and RL, Retrieval/Search/RecSys , Security, Infrastructure, Generative Media, AI Design & Novel AI UX, AI Product Management, Autonomy, Robotics, and Embodied Agents, Computer-Using Agents (CUA), SWE Agents, Vibe Coding, Voice, Sales/Support Agents at AIEWF 2025! Fill out the 2025 State of AI Eng survey for $250 in Amazon cards and see you from Jun 3-5 in SF!Coreweave's now-successful IPO has led to a lot of questions about the GPU Neocloud market, which Dylan Patel has written extensively about on SemiAnalysis. Understanding markets requires an interesting mix of technical and financial expertise, so this will be a different kind of episode than our usual LS domain.When we first published $2 H100s: How the GPU Rental Bubble Burst, we got 2 kinds of reactions on Hacker News:* “Ah, now the AI bubble is imploding!”* “Duh, this is how it works in every GPU cycle, are you new here?”We don't think either reaction is quite right. Specifically, it is not normal for the prices of one of the world's most important resources right now to swing from $1 to $8 per hour based on drastically inelastic demand AND supply curves - from 3 year lock-in contracts to stupendously competitive over-ordering dynamics for NVIDIA allocations — especially with increasing baseline compute needed for even the simplest academic ML research and for new AI startups getting off the ground.We're fortunate today to have Evan Conrad, CEO of SFCompute, one of the most exciting GPU marketplace startups, talk us through his theory of the economics of GPU markets, and why he thinks CoreWeave and Modal are well positioned, but Digital Ocean and Together are not.However, more broadly, the entire point of SFC is creating liquidity between GPU owners and consumers and making it broadly tradable, even programmable:As we explore, these are the primitives that you can then use to create your own, high quality, custom GPU availability for your time and money budget, similar to how Amazon Spot Instances automated the selective buying of unused compute.The ultimate end state of where all this is going is GPU that trade like other perishable, staple commodities of the world - oil, soybeans, milk. Because the contracts and markets are so well established, the price swings also are not nearly as drastic, and people can also start hedging and managing the risk of one of the biggest costs of their business, just like we have risk-managed commodities risks of all other sorts for centuries. As a former derivatives trader, you can bet that swyx doubleclicked on that…Show Notes* SF Compute* Evan Conrad* Ethan Anderson* John Phamous* The Curve talk* CoreWeave* Andromeda ClusterFull Video PodLike and subscribe!Timestamps* [00:00:05] Introductions* [00:00:12] Introduction of guest Evan Conrad from SF Compute* [00:00:12] CoreWeave Business Model Discussion* [00:05:37] CoreWeave as a Real Estate Business* [00:08:59] Interest Rate Risk and GPU Market Strategy Framework* [00:16:33] Why Together and DigitalOcean will lose money on their clusters* [00:20:37] SF Compute's AI Lab Origins* [00:25:49] Utilization Rates and Benefits of SF Compute Market Model* [00:30:00] H100 GPU Glut, Supply Chain Issues, and Future Demand Forecast* [00:34:00] P2P GPU networks* [00:36:50] Customer stories* [00:38:23] VC-Provided GPU Clusters and Credit Risk Arbitrage* [00:41:58] Market Pricing Dynamics and Preemptible GPU Pricing Model* [00:48:00] Future Plans for Financialization?* [00:52:59] Cluster auditing and quality control* [00:58:00] Futures Contracts for GPUs* [01:01:20] Branding and Aesthetic Choices Behind SF Compute* [01:06:30] Lessons from Previous Startups* [01:09:07] Hiring at SF ComputeTranscriptAlessio [00:00:05]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO at Decibel, and I'm joined by my co-host Swyx, founder of Smol AI.Swyx [00:00:12]: Hey, and today we're so excited to be finally in the studio with Evan Conrad from SF Compute. Welcome. I've been fortunate enough to be your friend before you were famous, and also we've hung out at various social things. So it's really cool to see that SF Compute is coming into its own thing, and it's a significant presence, at least in the San Francisco community, which of course, it's in the name, so you couldn't help but be. Evan: Indeed, indeed. I think we have a long way to go, but yeah, thanks. Swyx: Of course, yeah. One way I was thinking about kicking on this conversation is we will likely release this right after CoreWeave IPO. And I was watching, I was looking, doing some research on you. You did a talk at The Curve. I think I may have been viewer number 70. It was a great talk. More people should go see it, Evan Conrad at The Curve. But we have like three orders of magnitude more people. And I just wanted to, to highlight, like, what is your analysis of what CoreWeave did that went so right for them? Evan: Sell locked-in long-term contracts and don't really do much short-term at all. I think like a lot of people had this assumption that GPUs would work a lot like CPUs and the like standard business model of any sort of CPU cloud is you buy commodity hardware, then you lay on services that are mostly software, and that gives you high margins and pretty much all your value comes from those services. Not really the underlying. Compute in any capacity and because it's commodity hardware and it's not actually that expensive, most of that can be sort of on-demand compute. And while you do want locked-in contracts for folks, it's mostly just a sort of de-risk situation. It helps you plan revenue because you don't know if people are going to scale up or down. But fundamentally, people are like buying hourly and that's how your business is structured and you make 50 percent margins or higher. This like doesn't really work in GPUs. And the reason why it doesn't work is because you end up with like super price sensitive customers. And that isn't because necessarily it's just way more expensive, though that's totally the case. So in a CPU cloud, you might have like, you know, let's say if you had a million dollars of hardware in GPUs, you have a billion dollars of hardware. And so your customers are buying at much higher volumes than you otherwise expect. And it's also smaller customers who are buying at higher amounts of volume. So relative to what they're spending in general. But in GPUs in particular, your customer cares about the scaling law. So if you take like Gusto, for example, or Rippling or an HR service like this, when they're buying from an AWS or a GCP, they're buying CPUs and they're running web servers, those web servers, they kind of buy up to the capacity that they need, they buy enough, like CPUs, and then they don't buy any more, like, they don't buy any more at all. Yeah, you have a chart that goes like this and then flat. Correct. And it's like a complete flat. It's not even like an incremental tiny amount. It's not like you could just like turn on some more nodes. Yeah. And then suddenly, you know, they would make an incremental amount of money more, like Gusto isn't going to make like, you know, 5% more money, they're gonna make zero, like literally zero money from every incremental GPU or CPU after a certain point. This is not the case for anyone who is training models. And it's not the case for anyone who's doing test time inference or like inference that has scales at test time. Because like you, your scaling laws mean that you may have some diminishing returns, but there's always returns. Adding GPUs always means your model does actually get. And that actually does translate into revenue for you. And then for test time inference, you actually can just like run the inference longer and get a better performance. Or maybe you can run more customers faster and then charge for that. It actually does translate into revenue. Every incremental GPU translates to revenue. And what that means from the customer's perspective is you've got like a flat budget and you're trying to max the amount of GPUs you have for that budget. And it's very distinctly different than like where Augusto or Rippling might think, where they think, oh, we need this amount of CPUs. How do we, you know, reduce that? How do we reduce our amount of money that we're spending on this to get the same amount of CPUs? What that translates to is customers who are spending in really high volume, but also customers who are super price sensitive, who don't give a s**t. Can I swear on this? Can I swear? Yeah. Who don't give a s**t at all about your software. Because a 10% difference in a billion dollars of hardware is like $100 million of value for you. So if you have a 10% margin increase because you have great software, on your billion, the customers are that price sensitive. They will immediately switch off if they can. Because why wouldn't you? You would just take that $100 million. You'd spend $50 million on hiring a software engineering team to replicate anything that you possibly did. So that means that the best way to make money in GPUs was to do basically exactly what CoreWeave did, which is go out and sign only long-term contracts, pretty much ignore the bottom end of the market completely, and then maximize your long-term contracts. With customers who don't have credit risk, who won't sue you, or are unlikely to sue you for frivolous reasons. And then because they don't have credit risk and they won't sue you for frivolous reasons, you can go back to your lender and you can say, look, this is a really low risk situation for us to do. You should give me prime, prime interest rate. You should give me the lowest cost of capital you possibly can. And when you do that, you just make tons of money. The problem that I think lots of people are going to talk about with CoreWeave is it doesn't really look like a cloud platform. It doesn't really look like a cloud provider financially. It also doesn't really look like a software company financially.Swyx [00:05:37]: It's a bank.Evan [00:05:38]: It's a bank. It's a real estate company. And it's very hard to not be that. The problem of that that people have tricked themselves into is thinking that CoreWeave is a bad business. I don't think CoreWeave is explicitly a bad business. There's a bunch of people, there's kind of like two versions of the CoreWeave take at the moment. There's, oh my God, CoreWeave, amazing. CoreWeave is this great new cloud provider competitive with the hyperscalers. And to some extent, this is true from a structural perspective. Like, they are indeed a real sort of thing against the cloud providers in this particular category. And the other take is, oh my gosh, CoreWeave is this horrible business and so on and blah, blah, blah. And I think it's just like a set of perception or perspective. If you think CoreWeave's business is supposed to look like the traditional cloud providers, you're going to be really upset to learn that GPUs don't look like that at all. And in fact, for the hyperscalers, it doesn't look like this either. My intuition is that the hyperscalers are probably going to lose a lot of money, and they know they're going to lose a lot of money on reselling NVIDIA GPUs, at least. Hyperscalers, but I want to, Microsoft, AWS, Google. Correct, yeah. The Microsoft, AWS, and Google. Does Google resell? I mean, Google has TPUs. Google has TPUs, but I think you can also get H100s and so on. But there are like two ways they can make money. One is by selling to small customers who aren't actually buying in any serious volume. They're testing around, they're playing around. And if they get big, they're immediately going to do one of two things. They're going to ask you for a discount. Because they're not going to pay your crazy sort of margin that you have locked into your business. Because for CPUs, you need that. They're going to pay your massive per hour price. And so they want you to sign a long-term contract. And so that's your other way that you can make money, is you can basically do exactly what CoreWeave does, which is have them pay as much as possible upfront and lock in the contract for a long time. Or you can have small customers. But the problem is that for a hyperscaler, the GPUs to... To sell on the low margins relative to what your other business, your CPUs are, is a worse business than what you are currently doing. Because you could have spent the same money on those GPUs. And you could have trained model and you could have made a model on top of it and then turn that into a product and had high margins from your product. Or you could have taken that same money and you could have competed with NVIDIA. And you could have cut into their margin instead. But just simply reselling NVIDIA GPUs doesn't work like your CPU business. Where you're able to capture high margins from big customers and so on. And then they never leave you because your customers aren't actually price sensitive. And so they won't switch off if your prices are a little higher. You actually had a really nice chart, again, on that talk of this two by two. Sure. Of like where you want to be. And you also had some hot takes on who's making money and who isn't. Swyx: So CoreUv locked up long-term contracts. Get that. Yes. Maybe share your mental framework. Just verbally describe it because we're trying to help the audio listeners as well. Sure. People can look up the chart if they want to. Evan: Sure. Okay. So this is a graph of interest rates. And on the y-axis, it's a probability you're able to sell your GPUs from zero to one. And on the x-axis, it's how much they'll depreciate in cost from zero to one. And then you had ISO cost curves or ISO interest rate curves. Yeah. So they kind of shape in a sort of concave fashion. Yeah. The lowest interest rates enable the most aggressive. form of this cost curve. And the higher interest rates go, the more you have to push out to the top right. Yeah. And then you had some analysis of where every player sits in this, including CoreUv, but also Together and Modal and all these other guys. I thought that was super insightful. So I just wanted to elaborate. Basically, it's like a graph of risk and the genres of places where you can be and what the risk is associated with that. The optimal thing for you to do, if you can, is to lock in long-term contracts that are paid all up front or in with a situation in which you trust the other party to pay you over time. So if you're, you know, selling to Microsoft or something or OpenAI. Which are together 77% of the revenue of CoreUv. Yeah. So if you're doing that, that's a great business to be in because your interest rate that you can pitch for is really low because no one thinks Microsoft is going to default. And like maybe OpenAI will default, but the backing by Microsoft kind of doesn't. And I think there's enough, like, generally, it looks like OpenAI is winning that you can make it's just a much better case than if you're selling to the pre-seed startup that just raised $30 million or something pre-revenue. It's like way easier to make the case that the OpenAI is not going to default than the pre-seed startup. And so the optimal place to be is selling to the maximally low risk customer for as long as possible. And then you never have to worry about depreciation and you make lots of money. The less. Good. Good place to be is you could sell long-term contracts to people who might default on you. And then if you're not bringing it to the present, so you're not like saying, hey, you have to pay us all up front, then you're in this like more risky territory. So is it top left of the chart? If I have the chart right, maybe. Large contracts paid over time. Yeah. Large contracts paid over time is like top left. So it's more risky, but you could still probably get away with it. And then the other opportunity is that you could sell short-term contracts for really high prices. And so lots of people tried that too, because this is actually closer to the original business model that people thought would work in cloud providers for CPUs. It works for CPUs, but it doesn't really work for GPUs. And I don't think people were trying this because they were thinking about the risk associated with it. I think a lot of people are just come from a software background, have not really thought about like cogs or margins or inventory risk or things that you have to worry about in the physical world. And I think they were just like copy pasting the same business model onto CPUs. And also, I remember fundraising like a few years ago. And I know based on. Like what we knew other people were saying who were in a very similar business to us versus what we were saying. And we know that our pitch was way worse at the time, because in the beginning of SF Compute, we looked very similar to pretty much every other GPU cloud, not on purpose, but sort of accidentally. And I know that the correct pitch to give to an investor was we will look like a traditional CPU cloud with high margins and we'll sell to everyone. And that is a bad business model because your customers are price sensitive. And so what happens is if you. Sell at high prices, which is the price that you would need to sell it in order to de-risk your loss on the depreciation curve, and specifically what I mean by that is like, let's say you're selling it like $5 an hour and you're paying $1.50 an hour for the GPU under the hood. It's a little bit different than that, but you know, nice numbers, $5 an hour, $1.50 an hour. Great. Excellent. Well, you're charging a really high price per GPU hour because over time the price will go down and you'll get competed out. And what you need is to make sure that you never go under, or if you do go under your underlying cost. You've made so much money in the first part of it that the later end of it, like doesn't matter because from the whole structure of the deal, you've made money. The problem is that just, you think that you're going to be able to retain your customers with software. And actually what happens is your customers are super price sensitive and push you down and push you down and push you down and push you down, um, that they don't care about your software at all. And then the other problem that you have is you have, um, really big players like the hyperscalers who are looking to win the market and they have way more money than you, and they can push down on margin. Much better than you can. And so if they have to, and they don't, they don't necessarily all the time, um, I think they actually keep pride of higher margin, but if they needed to, they could totally just like wreck your margin at any point, um, and push you down, which meant that that quadrant over there where you're charging a high price, um, and just to make up for the risk completely got destroyed, like did not work at all for many places because of the price sensitivity, because people could just shove you down instead that pushed everybody up to the top right-hand corner of that, which is selling short-term. Contracts for low prices paid over time, which is the worst place to be in, um, the worst financial place to be in because it has the highest interest rate, um, which means that your, um, your costs go up at the same time, your, uh, your incoming cash goes down and squeezes your margins and squeezes your margins. The nice thing for like a core weave is that most of their business is over on the, on the other sides of those quadrants that the ones that survive. The only remaining question I have with core weave, and I promise I get to ask if I can compute, and I promise this is relevant to SOF Compute in general, because the framework is important, right? Sure. To understand the company. So why didn't NVIDIA or Microsoft, both of which have more money than core weave, do core weave, right? Why didn't they do core weave? Why have this middleman when either NVIDIA or Microsoft have more money than God, and they could have done an internal core weave, which is effectively like a self-funding vehicle, like a financial instrument. Why does there have to be a third party? Your question is like... Why didn't Microsoft, or why didn't NVIDIA just do core weave? Why didn't they just set up their own cloud provider? I think, and I don't know, and so correct me if I'm wrong, and lots of people will have different opinions here, or I mean, not opinions, they'll have actual facts that differ from my facts. Those aren't opinions. Those are actually indeed differences of reality, is that NVIDIA doesn't want to compete with their customers. They make a large amount of money by selling to existing clouds. If they launched their own core weave, then it would be a lot more money. It'd make it much harder for them to sell to the hyperscalers, and so they have a complex relationship with there. So not great for them. Second is that, at least for a while, I think they were dealing with antitrust concerns or fears that if they're going through, if they own too much layers of the stack, I could imagine that could be a problem for them. I don't know if that's actually true, but that's where my mind would go, I guess. Mostly, I think it's the first one. It's that they would be competing directly with their primary customers. Then Microsoft could have done it, right? That's the other question. Yeah, so Microsoft didn't do it. And my guess is that... NVIDIA doesn't want Microsoft to do it, and so they would limit the capacity because from NVIDIA's perspective, both they don't want to necessarily launch their own cloud provider because it's competing with their customers, but also they don't want only one customer or only a few customers. It's really bad for NVIDIA if you have customer concentration, and Microsoft and Google and Amazon, like Oracle, to buy up your entire supply, and then you have four or five customers or so who pretty much get to set prices. Monopsony. Yeah, monopsony. And so the optimal thing for you is a diverse set of customers who all are willing to pay at whatever price, because if you don't, somebody else will. And so it's really optimal for NVIDIA to have lots of other customers who are all competing against each other. Great. Just wanted to establish that. It's unintuitive for people who have never thought about it, and you think about it all day long. Yeah. Swyx: The last thing I'll call out from the talk, which is kind of cool, and then I promise we'll get to SF Compute, is why will DigitalOcean and Together lose money on their clusters? Why will DigitalOcean and Together lose money on their clusters?Evan [00:16:33]: I'm going to start by clarifying that all of these businesses are excellent and fantastic. That Together and DigitalOcean and Lambda, I think, are wonderful businesses who build excellent products. But my general intuition is that if you try to couple the software and the hardware together, you're going to lose money. That if you go out and you buy a long-term contract from someone and then you layer on services, or you buy the hardware yourself and you spin it up and you get a bunch of debt, you're going to run into the same problem that everybody else did, the same problem we did, same problem the hyperscalers did. And that's exactly what the hyperscalers are doing, which is you cannot add software and make high margins like a cloud provider can. You can pitch that into investors and it will totally make sense, and it's like the correct play in CPUs, but there isn't software you could make to make this occur. If you're spending a billion dollars on hardware, you need to make a billion dollars of software. There isn't a billion dollars of software that you can realistically make, and if you do, you're going to look like SAP. And that's not a knock on SAP. SAP makes a f**k ton of money, right? Right. Right. Right. Right. There aren't that many pieces of software that you could make, that you can realistically sell, like a billion dollars of software, and you're probably not going to do it to price-sensitive customers who are spending their entire budget already on compute. They don't have any more money to give you. It's a very hard proposition to do. And so many parties have been trying to do this, like, buy their own compute, because that's what a traditional cloud does. It doesn't really work for them. You know that meme where there's, like, the Grim Reaper? And he's, like, knocking on the door, and then he keeps knocking on the next door? We have just seen door after door after door of the Grim Reeker comes by, and the economic realities of the compute market come knocking. And so the thing we encourage folks to do is if you are thinking about buying a big GPU cluster and you are going to layer on software on top, don't. There are so many dead bodies in the wake there. We would recommend not doing that. And we, as SF Compute, our entire business is structured to help you not do that. It's helped disintegrate these. The GPU clouds are fantastic real estate businesses. If you treat them like real estate businesses, you will make a lot of money. The cloud services you can make on that, all the software you want to make on that, you can do that fantastically. If you don't own the underlying hardware, if you mix these businesses together, you get shot in the head. But if you combine, if you split them, and that's what the market does, it helps you split them, it allows you to buy, like, layer on services, but just buy from the market, you can make lots of money. So companies like Modal, who don't own the underlying compute, like they don't own it, lots of money, fantastic product. And then companies like Corbeave, who are functionally like really, really good real estate businesses, lots of money, fantastic product. But if you combine them, you die. That's the economic reality of compute. I think it also splits into trading versus inference, which are different kinds of workloads. Yeah. And then, yeah, one comment about the price sensitivity thing before we leave this. This topic, I want to credit Martin Casado for coining or naming this thing, which is like, you know, you said, you said this thing about like, you don't have room for a 10% margin on GPUs for software. Yep. And Martin actually played it out further. It's his first one I ever saw doing this at large enough runs. So let's say GPT-4 and O1 both had a total trading cost of like a $500 billion is the rough estimate. When you get the $5 billion runs, when you get the $50 billion runs, it is actually makes sense to build your own. You're going to have to get into chips, like for OpenEI to get into chip design, which is so funny. I would make an ASIC for this run. Yeah, maybe. I think a caveat of that that is not super well thought about is that only works if you're really confident. It only works if you really know which chip you're going to do. If you don't, then it's a little harder. So it makes in my head, it makes more sense for inference where you've already established it. But for training there's so much like experimentation. Any generality, yeah. Yeah. The generality is much more useful. Yeah. In some sense, you know, Google's like six generations into the CPUs. Yeah. Yeah. Okay, cool. Maybe we should go into SF Compute now. Sure. Yeah.Alessio [00:20:37]: Yeah. So you kind of talked about the different providers. Why did you decide to go with this approach and maybe talk a bit about how the market dynamics have evolved since you started a company?Evan [00:20:47]: So originally we were not doing this at all. We were definitely like forced into this to some extent. And SF Compute started because we wanted to go train models for music and audio in general. We were going to do a sort of generic audio model at some points, and then we were going to do a music model at some points. It was an early company. We didn't really spec down on a particular thing. But yeah, we were going to do a music model and audio model. First thing that you do when you start any AI lab is you go out and you buy a big cluster. The thing we had seen everybody else do was they went out and they raised a really big round and then they would get stuck. Because if you raise the amount of money that you need to train a model initially, like, you know, the $50 million pre-seed, pre-revenue, your valuation is so high or you get diluted so much that you can't raise the next round. And that's a very big ask to make. And also, I don't know, I felt like we just felt like we couldn't do it. We probably could have in retrospect, but I think one, we didn't really feel like we could do it. Two, it felt like if we did, we would have been stuck later on. We didn't want to raise the big round. And so instead, we thought, surely by now, we would be able to just go out. To any provider and buy like a traditional CPU cloud would sell offer you and just buy like on demand or buy like a month or so on. And this worked for like small incremental things. And I think this is where we were basing it off. We just like assumed we could go to like Lambda or something and like buy thousands of at the time A100s. And this just like was not at all the case. So we started doing all the sales calls with people and we said, OK, well, can we just get like month to month? Can we get like one month of compute or so on? Everyone told us at the time, no. You need to have a year long contract or longer or you're out of luck. Sorry. And at the time, we were just like pissed off. Like, why won't nobody sell us a month at a time? Nowadays, we totally understand why, because it's the same economic reason. Because if you if they had sold us the month to month or so on and we canceled or so on, they would have massive risk on that. And so the optimal thing to do was to only to just completely abandon the section of the market. We didn't like that. So our plan was we were going to buy a year long contract anyway. We would use a month. And then we would. At least the other 11 months. And we were locked in for a year, but we only had to pay on every individual month. And so we did this. But then immediately we said, oh, s**t, now we have a cloud provider, not a like training models company, not an AI lab, because every 30 days we owed about five hundred thousand dollars or so and we had about five hundred thousand dollars in the bank. So that meant that every single month, if we did not sell out our cluster, we would just go bankrupt. So that's what we did for the first year of the company. And when you're in that position. You try to think how in the world you get out of that position, what that transition to is, OK, well, we tend to be pretty good at like selling this cluster every month because we haven't died yet. And so what we should do is we should go basically be like this broker for other people and we will be more like a GPU real estate or like a GPU realtor. And so we started doing that for a while where we would go to other people who had who was trying to sell like a year long contract with somebody and we'd go to another person who like maybe this person wanted six months and somebody else on six months or something and we'd like combine all these people. Together to make the deal happen and we'd organize these like one off bespoke deals that looked like basically it ended up with us taking a bunch of customers, us signing with a vendor, taking some cut and then us operating the cluster for people typically with bare metal. And so we were doing this, but this was definitely like a oh, s**t, oh, s**t, oh, s**t. How do we get out of our current situation and less of a like a strategic plan of any sort? But while we were doing this, since like the beginning of the company, we had been thinking about how to buy GPU clusters, how to sell them effectively, because we'd seen every part of it. And what we ended up with was like a book of everybody who's trying to buy and everyone is trying to sell because we were these like GPU brokers. And so that turned into what is today SF Compute, which is a compute market, which we think we are the functionally the most liquid GPU market of any capacity. Honestly, I think we're the only thing that actually is like a real market that there's like bids and asks and there's like a like a trading engine that combines everything. And so. I think we're the only place where you can do things that a market should be able to do. Like you can go on SF Compute today and you get thousands of H100s for an hour if you want. And that's because there is a price for thousands of GPUs for an hour. That is like not a thing you can reasonably do on kind of any other cloud provider because nobody should realistically sell you thousands of GPUs for an hour. They should sell it to you for a year or so on. But one of the nice things about a market is that you can buy the year on SF Compute. But then if you need to sell. Back, you can sell back as well. And that opens up all these little pockets of liquidity where somebody who's just trying to buy for a little bit of time, some burst capacity. So people don't normally buy for an hour. That's not like actually a realistic thing, but it's like the range somebody who wants, who is like us, who needed to buy for a month can actually buy for a month. They can like place the order and there is actually a price for that. And it typically comes from somebody else who's selling back. Somebody who bought a longer term contract and is like they bought for some period of time, their code doesn't work, and now they need to like sell off a little bit.Alessio [00:25:49]: What are the utilization rates at which a market? What are the utilization rates at which a market? Like this works, what do you see the usual GPU utilization rate and like at what point does the market get saturated?Evan [00:26:00]: Assuming there are not like hardware problems or software problems, the utilization rate is like near 100 percent because the price dips until the utilization is 100 percent. So the price actually has to dip quite a lot in order for the utilization not to be. That's not always the case because you just have logistical problems like you get a cluster and parts of the InfiniBand fabric are broken. And there's like some issue with some switch somewhere and so you have to take some portion of the cluster offline or, you know, stuff like this, like there's just underlying physical realities of the clusters, but nominally we have better utilization than basically anybody because, but that's on utilization of the cluster, like that doesn't necessarily translate into, I mean, I actually do think we have much better overall money made for our underlying vendors than kind of anybody else. We work with the other GPU clouds and the basic pitch to the other GPU clouds is one. So we can sell your broker so we can we can find you the long term contracts that are at the prices that you want, but meanwhile, your cluster is idle and for that we can increase your utilization and get you more money because we can sell that idle cluster for you and then the moment we find the longer, the bigger customer and they come on, you can kick off those people and then go to the other ones. You get kind of the mix of like sell your cluster at whatever price you can get on the market and then sell your cluster at the big price that you want to do for long term contract, which is your ideal business model. And then the benefit of the whole thing being on the market. Is you can pitch your customer that they can cancel their long term contract, which is not a thing that you can reasonably do if you are just the GPU cloud, if you're just the GPU cloud, you can never cancel your contract, because that introduces so much risk that you would otherwise, like not get your cheap cost of capital or whatever. But if you're selling it through the market, or you're selling it with us, then you can say, hey, look, you can cancel for a fee. And that fee is the difference between the price of the market and then the price that they paid at, which means that they canceled and you have the ability to offer that flexibility. But you don't. You don't have to take the risk of it. The money's already there and like you got paid, but it's just being sold to somebody else. One of our top pieces from last year was talking about the H100 glut from all the long term contracts that were not being fully utilized and being put under the market. You have on here dollar a dollar per hour contracts as well as it goes up to two. Actually, I think you were involved. You were obliquely quoted in that article. I think you remember. I remember because this was hidden. Well, we hid your name, but then you were like, yeah, it's us. Yeah. Could you talk about the supply and demand of H100s? Was that just a normal cycle? Was that like a super cycle because of all the VC funding that went in in 2003? What was that like? GPU prices have come down. Yeah, GPU prices have come down. And there's some part that has normal depreciation cycle. Some part of that is just there were a lot of startups that bought GPUs and never used them. And now they're lending it out and therefore you exist. There's a lot of like various theories as to why. This happened. I dislike all of them because they're all kind of like they're often said with really high confidence. And I think just the market's much more complicated than that. Of course. And so everything I'm going to say is like very hedged. But there was a series of like places where a bunch of the orders were placed and people were pitching to their customers and their investors and just the broader market that they would arrive on time. And that is not how the world works. And because there was such a really quick build out of things, you would end up with bottlenecks in the supply chain somewhere that has nothing to do with necessarily the chip. It's like the InfiniBand cables or the NICs or like whatever. Or you need a bunch of like generators or you don't have data center space or like there's always some bottleneck somewhere else. And so a lot of the clusters didn't come online within the period of time. But then all the bottlenecks got sorted out and then they all came online all at the same time. So I think you saw a short. There was a shortage because supply chain hard. And then you saw a increase or like a glut because supply chain eventually figure itself out. And specifically people overordered in order to get the allocation that they wanted. Then they got the allocations and then they went under. Yeah, whatever. Right. There was just a lot of shenanigans. A caveat of this is every time you see somebody like overordered, there is this assumption that the problem was like the demand went down. I don't think that's the case at all. And so I want to clarify that. It definitely seems like a shortage. Like there's more demand for GPUs than there ever was. It's just that there was also more supply. So at the moment, I think there is still functionally a glut. But the difference that I think is happening is mostly the test time inference stuff that you just need way more chips for that than you did before. And so whenever you make a statement about the current market, people sort of take your words and then they assume that you're making a statement about the future market. And so if you say there's a glut now, people will continue to think there's a glut. But I think what is happening at the moment. My general prediction is that like by the winter, we will be back towards shortage. But then also, this very much depends on the rollout of future chips. And that comes with its own. I think I'm trying to give you like a good here's Evan's forecast. Okay. But I don't know if my forecast is right. You don't have to. Nobody is going to hold you to it. But like I think people want to know what's true and what's not. And there's a lot of vague speculations from people who are not that close to the market actually. And you are. I think I'm a closer. Close to the market, but also a vague speculator. Like I think there are a lot of really highly confident speculators and I am indeed a vague speculator. I think I have more information than a lot of other people. And this makes me more vague of a spectator because I feel less certain or less confident than I think a lot of other people do. The thing I do feel reasonably confident about saying is that the test time inference is probably going to quite significantly expand the amount of compute that was used for inference. So a caveat. This is like pretty much all the inference demand is in a few companies. A good example is like lots of bio and pharma was using H100s training sort of the bio models of sorts. And they would come along and they would buy, you know, thousands of H100s for training and then just like not a lot of stuff for inference. Not in any, not relative to like an opening iron anthropic or something because they like don't have a consumer product. Their inference event, if they can do it right. There's really like only one inference event that matters. And obviously I think they're going to run into it. And Batch and they're not going to literally just run one inference event. But like the one that produces the drug is the important one. Right. And I'm dumb and I don't know anything about biology, so I could be completely wrong here. But my understanding is that's kind of the gist. I can check that for you. You can check that for me. Check that for me. But my understanding is like the one that produces the sequence that is the drug that, you know, cures cancer or whatever. That's the important deal. But like a lot of models look like this where they're sort of more enterprising use cases or they're so prior to something that looks like test time inference. You got lots and lots of demand for training and then pretty much entirely fell off for inference. And I think like we looked at like Open Router, for example, the entirety of Open Router that was not anthropic or like Gemini or OpenAI or something. It was like 10 H100 nodes or something like that. It's just like not that much. It's like not that many GPUs actually to service that entire demand. But that's like a really sizable portion of the sort of open source market. But the actual amount of compute needed for it was not that much. But if you imagine like what an OpenAI needs for like GPT-4, it's like tremendously big. But that's because it's a consumer product that has almost all the inference demand. Yeah, that's a message we've had. Roughly open source AI compared to closed AI is like 5%. Yeah, it's like super small. Super small. It's super small. Super small. But test time inference changes that quite significantly. So I will... I will expect that to increase our overall demand. But my question on whether or not that actually affects your compute price is entirely based on how quickly do we roll out the next chips. The way that you burst is different for test time.Alessio [00:34:01]: Any thoughts on the third part of the market, which is the more peer-to-peer distributed, some are like crypto-enabled, like Hyperbolic, Prime Intellect, and all of that. Where do those fit? Like, do you see a lot of people will want to participate in a peer-to-peer market? Or just because of the capital requirements at the end of the day, it doesn't really matter?Evan [00:34:20]: I'm like wildly skeptical of these, to be frankly. The dream is like steady at home, right? I got this $15.90. Nobody has $15.90. $14.90 sitting at home. I can rent it out. Yeah. Like, I just don't really think this is going to ever be more efficient than a fully interconnected cluster with InfiniBand or, you know, whatever the sort of next spec might be. Like, I could be completely wrong. But speaking of... I mean, like, SpeedoLite is really hard to beat. And regardless of whatever you're using, you just like can't get around that physical limitation. And so you could like imagine a decentralized market that still has a lot of places where there's like co-location. But then you would get something that looks like SF Compute. And so that's what we do. That's why we take our general take is like on SF Compute, you're not buying from like random people. You're buying from the other GPU clouds, functionally. You're buying from data centers that are the same genre of people that you would work with already. And you can specify, oh, I want all these nodes to be co-located. And I don't think you're really going to get around that. And I think I buy crypto for the purposes of like transferring money. Like the financial system is like quite painful and so on. I can understand the uses of it to sort of incentivize an initial market or try to get around the cold start problem. We've been able to get around the cold start problem just fine. So it didn't actually need that at all. What I do think is totally possible is you could launch a token and then you could like subsidize the crypto. You could compute prices for a bit, but like maybe that will help you. I think that's what Nuus is doing. Yeah, I think there's lots of people who are trying to do things like this, but at some point that runs out. So I would, I think generally agree. I think the only thread in that model is very fine grained mixture of experts that can be like algorithms can shift to adapt to hardware realities. And the hardware reality is like, okay, it's annoying to do large co-located clusters. Then we'll just redesign attention or whatever in our architecture to distribute it more. There was a little bit buzz of block attention last year that Strong Compute made a big push on. But I think like, you know, in a world where we have 200 experts in MOE model, it starts to be a little bit better. Like, I don't disagree with this. I can imagine the world in which you have like, in which you've redesigned it to be more parallelizable, like across space.Evan [00:36:43]: But assuming without that, your hardware limitation is your speed of light limitation. And that's a very hard one to get around.Alessio [00:36:50]: Any customers or like stories that you want to shout out of like maybe things that wouldn't have been economically viable like others? I know there's some sensitivity on that.Evan [00:37:00]: My favorites are grad students, are folks who are trying to do things that would normally otherwise require the scale of a big lab. And the grad students are like the worst pilots. They're like the worst possible customer for the traditional GPU clouds because they will immediately turn if you sell them a thing because they're going to graduate and they're not going to go anywhere. They're not going to like, that project isn't continuing to spend lots of money. Like sometimes it does, but not if you're like working with the university or you're working with the lab of some sort. But a lot of times it's just like the ability for us to offer like big burst capacity, I think is lovely and wonderful. And it's like one of my favorite things to do because all those folks look like we did. And I have a special place in my heart for that. I have a special place in my heart for young hackers and young grad students and researchers who are trying to do the same genre of thing that we are doing. For the same reason, I have a special place in my heart for like the startups, the people who are just actively trying to compete on the same scale, but can't afford it time-wise, but can afford it spike-wise. Yeah, I liked your example of like, I have a grant of 100K and it's expiring. I got to spend it on that. That's really beautiful. Yeah. Interesting. Has there been interesting work coming out of that? Anything you want to mention? Yeah. So from like a startup perspective, like Standard Intelligence and Find, P-H-I-N-D. We've had them on the pod.Swyx [00:38:23]: Yeah. Yeah.Evan [00:38:23]: That was great. And then from grad students' perspective, we worked a lot with like the Schmidt Futures grantees of various sorts. My fear is if I talk about their research, I will be completely wrong to a sort of almost insulting degree because I am very dumb. But yeah. I think one thing that's maybe also relevant startups and GPUs-wise. Yeah. Is there was a brief moment where it kind of made sense that VCs provided GPU clusters. And obviously you worked at AI Grants, which set up Andromeda, which is supposedly a $100 million cluster. Yeah. I can explain why that's the case or why anybody would think that would be smart. Because I remember before any of that happened, we were asking for it to happen. Yeah. And the general reason is credit risk. Again, it's a bank. Yeah. I have lower risk than you due to credit transformation. I take your risk onto my balance sheet. Correct. Exactly. If you wanted to go for a while, if you wanted to go set up a GPU cluster, you had to be the one that actually bought the hardware and racked it and stacked it, like co-located it somewhere with someone. Functionally, it was like on your balance sheet, which means you had to get a loan. And you cannot get a loan for like $50 million as a startup. Like not really. You can get like venture debt and stuff, but like it's like very, very difficult to get a loan of any serious price for that. But it's like not that difficult to get a loan for $50 million. If you already have a fund or you already have like a million dollars under your assets somewhere or like you personally can like do a personal guarantee for it or something like this. If you have a lot of money, it is way easier for you to get a loan than if you don't have a lot of money. And so the hack of a VC or some capital partner offering equity for compute is always some arbitrage on the credit risk. That's amazing. Yeah. That's a hack. You should do that. I don't think people should do it right now. I think the market has like, I think it made sense at the time and it was helpful and useful for the people who did it at the time. But I think it was a one-time arbitrage because now there are lots of other sources that can do it. And also I think like it made sense when no one else was doing it and you were the only person who was doing it. But now it's like it's an arbitrage that gets competed down. Sure. So it's like super effective. I wouldn't totally recommend it. Like it's great that Andromeda did it. But the marginal increase of somebody else doing it is like not super helpful. I don't think that many people have followed in their footsteps. I think maybe Andreessen did it. Yeah. That's it. I think just because pretty much all the value like flows through Andromeda. What? That cannot be true. How many companies are in the air, Grant? Like 50? My understanding of Andromeda is it works with all the NFTG companies or like several of the NFTG companies. But I might be wrong about that. Again, you know, something something. Nat, don't kill me. I could be completely wrong. But the but you know, I think Andromeda was like an excellent idea to do at the right time in which it occurred. Perfect. His timing is impeccable. Timing. Yeah. Nat and Daniel are like, I mean, there's lots of people who are like... Sears? Yeah. Sears. Like S-E-E-R. Oh, Sears. Like Sears of the Valley. Yeah. They for years and years before any of the like ChatGPT moment or anything, they had fully understood what was going to happen. Like way, way before. Like. AI Grant is like, like five years old, six years old or something like that. Seven years old. When I, when it like first launched or something. Depends where you start. The nonprofit version. Yeah. The nonprofit version was like, like happening for a while, I think. It's going on for quite a bit of time. And then like Nat and Daniel are like the early investors in a lot of the sort of early AI labs of various sorts. They've been doing this for a bit.Alessio [00:41:58]: I was looking at your pricing yesterday. We're kind of talking about it before. And there's this weird thing where one week is more expensive of both one day and one month. Yeah. What are like some of the market pricing dynamics? What are things that like this to somebody that is not in the business? This looks really weird. But I'm curious, like if you have an explanation for it, if that looks normal to you. Yeah.Evan [00:42:18]: So the simple answer is preemptible pricing is cheaper than non-preemptible pricing. And the same economic principle is the reason why that's the case right now. That's not entirely true on SF Compute. SF Compute doesn't really have the concept of preemptible. Instead, what it has is very short reservations. So, you know, you go to a traditional cloud provider and you can say, hey, I want to reserve contract for a year. We will let you do a reserve contract for one hour, which is the part of SFC. But what you can do is you can just buy every single hour continuously. And you're reserving just for that hour. And then the next hour you reserve just for that next hour. And this is obviously like a built in. This is like an automation that you can do. But what you're seeing when you see the cheap price is you're seeing somebody who's buying the next hour, but maybe not necessarily buying an hour after that. So if the price goes up. Up too much. They might not get that next hour. And the underlying part of this of where that's coming from the market is you can imagine like day old milk or like milk that's about to be old. It might drop its price until it's expired because nobody wants to buy the milk that's in the past. Or maybe you can't legally sell it. Compute is the same way. No, you can't sell a block of compute that is not that is in the past. And so what you should do in the market and what people do do is they take. They take a block. A block of compute. And then they drop it and drop it and drop it and drop into a floor price right before it's about to expire. And they keep dropping it until it clears. And so anything that is idle drops until some point. So if you go and use on the website and you set that that chart to like a week from now, what you'll see is much more normal looking sort of curves. But if you say, oh, I want to start right now, that immediate instant, here's the compute that I want right now is the is functionally the preemptible price. It's where most people are getting the best compute or like the best compute prices from. The caveat of that is you can do really fun stuff on SFC if you want. So because it's not actually preemptible, it's it's reserved, but only reserved for an hour, which means that the optimal way to use as of compute is to just buy on the market price, but set a limit price that is much higher. So you can set a limit price for like four dollars and say, oh, if the market ever happens to spike up to four dollars, then don't buy. I don't want to buy that at that price for that price. I don't want to buy that at that price for that price for an hour. But otherwise, just buy at the cheapest price. And if you're comfortable with that of the volatility of it, you're actually going to get like really good prices, like close to a dollar an hour or so on, sometimes down to like 80 cents or whatever. You said four, though. Yeah. So that's the thing. You want to lower the limit. So four is your max price. Four is like where you basically want to like pull the plug and say don't do it because the actual average price is not or like the, you know, the preemptible price doesn't actually look like that. So what you're doing when you're saying four is always, always, always give me this compute. Like continue to buy every hour. Don't preempt me. Don't kick me off. And I want this compute and just buy at the preemptible price, but never kick me off. The only times in which you get kicked off is if there is a big price spike. And, you know, let's say one day out of the year, there's like a four dollar an hour price because of some weird fluke or something. If there are other periods of time, you're actually getting a much lower price than you. It makes sense. Your your average cost that you're actually paying is way better. And your trade off here is you don't literally know what price you're going to get. So it's volatile. But your actual average historically has been like everyone who's done this has gotten wildly better prices. And this is like one of the clever things you can do with the market. If you're willing to make those trade offs, you can get a lot of really good prices. You can also do other things like you can only buy at night, for example. So the price goes down at night. And so you can say, oh, I want to only buy, you know, if the price is lower than 90 cents. And so if you have some long running job, you can make it only run on 90 cents and then you recover back and so on. Yeah. So what you can kind of create as like a spot inst is what other the CPU world has. Yes. But you've created a system where you can kind of manufacture the exact profile that you want. Exactly. That is not just whatever the hyperscalers offer you, which is usually just one thing. Correct. SF Compute is like the power tool. The underlying primitives of like hourly compute is there. Correct. Yeah, it's pretty interesting. I've often asked OpenAI. So like, you know, all these guys. Cloud as well. They do batch APIs. So it's half off of whatever your thing is. Yeah. And the only contract is we'll return in 24 hours. Sure. Right. And I was like, 24 hours is good. But sometimes I want one hour. I want four hours. I want something. And so based off of SF Compute's system, you can actually kind of create that kind of guarantee. Totally. That would be like, you know, not 24, but within eight hours, within four hours, like the work half of a workday. Yes. I can return your results to you. And then I can return it to you. And if your latency requirements are like that low, actually it's fine. Yes. Correct. Yeah. You can carve out that. You can financially engineer that on SFC. Yeah. Yeah. I mean, I think to me that unlocks a lot of agent use cases that I want, which is like, yeah, I worked in a background, but I don't want you to take a day. Yeah. Correct. Take a couple hours or something. Yeah. This touches a lot of my like background because I used to be a derivatives trader. Yeah. And this is a forward market. Yeah. A futures forward market, whatever you call it. Not a future. Very explicitly not a future. Not yet a futures. Yes. But I don't know if you have any other points to talk about. So you recognize that you are a, you know, a marketplace and you've hired, I met Alex Epstein at your launch event and you're like, you're, you're building out the financialization of GPUs. Yeah. So part of that's legal. Mm-hmm. Totally. Part of that is like listing on an exchange. Yep. Maybe you're the exchange. I don't know how that works, but just like, talk to me about that. Like from the legal, the standardization, the like, where is this all headed? You know, is this like a full listed on the Chicago Mercantile Exchange or whatever? What we're trying to do is create an underlying spot market that gives you an index price that you can use. And then with that index price, you can create a cash settled future. And with a cash settled future, you can go back to the data centers and you can say, lock in your price now and de-risk your entire position, which lets you get cheaper cost of capital and so on. And that we think will improve the entire industry because the marginal cost of compute is the risk. It's risk as shown by that graph and basically every part of this conversation. It's risk that causes the price to be all sorts of funky. And we think a future is the correct solution to this. So that's the eventual goal. Right now you have to make the underlying spot market in order to make this occur. And then to make the spot market work, you actually have to solve a lot of technology problems. You really cannot make a spot market work if you don't run the clusters, if you don't have control over them, if you don't know how to audit them, because these are super computers, not soybeans. They have to work. In a way that like, it's just a lot simpler to deliver a soybean than it is to deliver it. I don't know. Talk to the soybean guys. Sure. You know? Yeah. But you have to have a delivery mechanism. Your delivery mechanism, like somebody somewhere has to actually get the compute at some point and it actually has to work. And it is really complicated. And so that is the other part of our business that we go and we build a bare metal infrastructure stack that goes. And then also we do auditing of all the clusters. You sort of de-risk the technical perspective and that allows you to eventually de-risk the financial perspective. And that is kind of the pitch of SF Compute. Yeah. I'll double click on the auditing on the clusters. This is something I've had conversations with Vitae on. He started Rika and I think he had a blog post which kind of shone the light a little bit on how unreliable some clusters are versus others. Correct. Yeah. And sometimes you kind of have to season them and age them a little bit to find the bad cards. You have to burn them in. Yeah. So what do you do to audit them? There's like a burn-in process, a suite of tests, and then active checking and passive checking. Burn-in process is where you typically run LINPACK. LINPACK is this thing that like a bunch of linear algebra equations that you're stress testing the GPUs. This is a proprietary thing that you wrote? No, no, no. LINPACK is like the most common form of burn-in. If you just type in burn-in, typically when people say burn-in, they literally just mean LINPACK. It's like an NVIDIA reference version of this. Again, NVIDIA could run this before they ship, but now the customers have to do it. It's annoying. You're not just checking for the GPU itself. You're checking like the whole component, all the hardware. And it's a lot of work. It's an integration test. It's an integration test. Yeah. So what you're doing when you're running LINPACK or burn-in in general is you're stress testing the GPUs for some period of time, 48 hours, for example, maybe seven days or so on. And you're just trying to kill all the dead GPUs or any components in the system that are broken. And we've had experiences where we ran LINPACK on a cluster and it rounds out, sort of comes offline when you run LINPACK. This is a pretty good sign that maybe there is a problem with this cluster. Yeah. So LINPACK is like the most common sort of standard test. But then beyond that, what you do is we have like a series of performance tests that replicate a much more realistic environment as well that we run just assuming if LINPACK works at all, then you run the next set of tests. And then while the GPUs are in operation, you're also going through and you're doing active tests and passive tests. Passive tests are things that are running in the background while somebody else is running, while like some other workload is running. And active tests are during like idle periods. You're running some sort of check that would otherwise sort of interrupt something. And then the active tests will take something offline, basically. Or a passive check might mark it to get taken offline later and so on. And then the thing that we are working on that we have working partially but not entirely is automated refunds, which is basically like, is the case that the hardware breaks so much. And there's only so much that we can do and it is the effect of pretty much the entire industry. So a pretty common thing that I think happens to kind of everybody in the space is a customer comes online, they experience your cluster, and your cluster has the same problem that like any cluster has, or it's I mean, a different problem every time, but they experience one of the problems of HPC. And then their experience is bad. And you have to like negotiate a refund or some other thing like this. It's always case by case. And like, yeah, a lot of people just eat the cost. Correct. So one of the nice things about a market that we can do as we get bigger and have been doing as we can bigger is we can immediately give you something else. And then also we can automatically refund you. And you're still gonna experience it like the hardware problems aren't going away until the underlying vendors fix things. But honestly, I don't think that's likely because you're always pushing the limits of HPC. This is the case of trying to build a supercomputer. that's one of the nice things that we can do is we can switch you out for somebody else somewhere, and then automatically refund you or prorate or whatever the correct move is. One of the things that you say in this conversation with me was like, you know, you know, a provider is good when they guarantee automatic refunds. Which doesn't happen. But yeah, that's, that's in our contact with all the underlying cloud providers. You built it in already. Yeah. So we have a quite strict SLA that we pass on to you. The reason why

Dan Siroker is the Co-Founder and CEO @ Limitless, a personalized AI powered by what you've seen, said, or heard. For his latest funding round, Dan took an unusual approach resulting in 1,000 preliminary offers with valuations as high as $1BN — and resulted in a $350 million Series A valuation. Prior to founding Limitless, Dan was the Founder of Optimizely, scaling the company to $120M in ARR and raising from some of the best in the business including Peter Fenton @ Benchmark who led the Series A. In Today's Episode with Dan Siroker We Discuss: 1. Serial Entrepreneurs are More Investable: Why would Dan always prefer to invest in serial entrepreneurs than first time founders? How do serial entrepreneurs approach team building and size of team differently? How do serial entrepreneurs approach focus and prioritisation differently? How do serial entrepreneurs approach pivoting differently to first time founders? What is Dan's advice from Elad Gil and YC's Dalton Caldwell on when to pivot? 2. The Secret to Fundraising: How to Speak VC Should founders always be raising? What is the right thing to respond to investors when they reach out to you outside of a round? What question are investors really asking when they ask, how much are you raising? How should founders approach valuation, what should they say when they are asked for it? How can founders create urgency in a funding round? What works? What does not? 3. How to Raise the Best Funding Round: Should founders engage with associates or only worth it with decision-makers? Why should founders always choose the investor who is on the early arc of their career? Why was Dan's first meeting with Peter Fenton the best meeting he has ever had with a VC? Why does Dan believe that taking the highest price is never the right answer? To what extent does having a true Tier 1 VC lead your round, change the game for your company? 4. Dan Siroker: AMA: How did becoming a father change the way that Dan operates? Why is Dan scared we might see technological progress stall for the next 20 years? Why did Dan not do YC the second time around with Limitless? What is the story of how Optimizely nearly bought Amplitude?  

Dalton Caldwell is Managing Director and Group Partner at Y Combinator. Prior to YC, he was the co-founder and CEO of imeem (acquired by MySpace in 2009) and the co-founder and CEO of App.net. During his time at YC, he's advised more than 35 YC unicorns, including DoorDash, Amplitude, Webflow, and Retool, and has worked across 21 different YC batches. He's also racked up more than 6,500 office hours with founders. In our conversation, we discuss:• Why founders need to adopt the mindset “Just don't die”• The most common reason startups fail• When to pivot, and characteristics of a good pivot• The concept of “tar pit ideas” and examples of bad startup ideas• Why investors say no to startups• The importance of market size in investment decisions• The pitfalls of founders over-delegating• Effective ways to talk to customers• 20 ideas Dalton is looking to fund—Brought to you by:• Eppo—Run reliable, impactful experiments• Vanta—Automate compliance. Simplify security• Coda—The all-in-one collaborative workspace—Find the transcript at: https://www.lennysnewsletter.com/p/lessons-from-1000-yc-startups—Where to find Dalton Caldwell:• X: https://twitter.com/daltonc• LinkedIn: https://www.linkedin.com/in/daltoncaldwell/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Dalton's background(04:41) The value of simple advice(07:04) Dalton's advice: “Just don't die”(08:39) Knowing when to stop(11:45) Deciding to pivot(14:26) Characteristics of a good pivot(17:53) Knowing when to pivot(19:03) Zip's journey and finding a market(21:22) Why Dalton says to “Move towards the mountains and the desert”(23:45) Tar pit ideas(26:49) Understanding why investors say no(29:14) The importance of market size(32:16) Avoiding over-delegation and hiring senior people too early(36:43) Why startups fail(40:30) Effectively talking to customers(45:17) Examples of startups hustling to talk to customers(48:01) Patterns of successful startups(52:05) YC's Request for Startups(55:37) Early days of Silicon Valley(01:05:33) Contrarian corner: growth hacking for early startups(01:09:28) Failure corner(01:11:15) Closing thoughts(01:12:22) Lightning round—Referenced:• Y Combinator: https://www.ycombinator.com/• Tiger Woods's website: https://tigerwoods.com/• Co-Founder Mistakes That Kill Companies & How to Avoid Them: https://www.youtube.com/watch?v=dlfjs_eEEzs• Daniel Alberson's LinkedIn post about Y Combinator: https://www.linkedin.com/posts/alberson_i-left-my-dream-job-as-a-product-manager-activity-7089677882431533056-jJ9H• Companies in Y Combinator W17 Batch: https://www.ycdb.co/batch/w17• Brex: https://www.brex.com/• Retool: https://retool.com/• Segment: https://segment.com/• Mixpanel: https://mixpanel.com/• Whatnot: https://www.whatnot.com/• Andreessen Horowitz: https://a16z.com/• Airbnb's CEO says a $40 cereal box changed the course of the multibillion-dollar company: https://fortune.com/2023/04/19/airbnb-ceo-cereal-box-investors-changed-everything-billion-dollar-company/• Rujul Zaparde on LinkedIn: https://www.linkedin.com/in/rujulz/• Zip: https://ziphq.com/• Lu Cheng on LinkedIn: https://www.linkedin.com/in/lu-cheng-973b7830/• Avoid these tempting startup tar pit ideas: https://www.ycombinator.com/library/Ij-avoid-these-tempting-startup-tarpit-ideas• Airbnb acquires Localmind to create crowdsourced advice about neighborhoods: https://skift.com/2012/12/13/airbnb-acquires-localmind-to-create-crowdsourced-advice-about-neighborhoods/• Foursquare: https://foursquare.com/• Razorpay: https://razorpay.com/• Total Addressable Market: https://www.productplan.com/glossary/total-addressable-market/• Lenny Bogdonoff on LinkedIn: https://www.linkedin.com/in/rememberlenny/• Milk Video: https://milkvideo.com/• Lessons from working with 600+ YC startups | Gustaf Alströmer (Y Combinator, Airbnb): https://www.lennyspodcast.com/lessons-from-working-with-600-yc-startups-gustaf-alstromer-y-combinator-airbnb/• How the most successful B2B startups came up with their original idea: https://www.lennysnewsletter.com/p/how-the-most-successful-b2b-startups• Collison installation: https://news.ycombinator.com/item?id=18400504• Stripe: https://stripe.com/• Patrick Collison on LinkedIn: https://www.linkedin.com/in/patrickcollison/• John Collison on LinkedIn: https://www.linkedin.com/in/johnbcollison/• Tony Xu on LinkedIn: https://www.linkedin.com/in/xutony/• Grant LaFontaine on LinkedIn: https://www.linkedin.com/in/grantlafontaine/• Ryan Petersen on LinkedIn: https://www.linkedin.com/in/rpetersen/• Lessons on building product sense, navigating AI, optimizing the first mile, and making it through the messy middle | Scott Belsky (Adobe, Behance): https://www.lennyspodcast.com/lessons-on-building-product-sense-navigating-ai-optimizing-the-first-mile-and-making-it-through-t/• YC's latest Request for Startups: https://www.ycombinator.com/blog/ycs-latest-request-for-startups• ERPs: https://www.ycombinator.com/rfs#new-enterprise-resource-planning-software• Commercial open source companies: https://www.ycombinator.com/rfs#commercial-open-source-companies• New space companies: https://www.ycombinator.com/rfs#new-space-companies• A way to end cancer: https://www.ycombinator.com/rfs#a-way-to-end-cancer• Spatial computing: https://www.ycombinator.com/rfs#spatial-computing• New defense technology: https://www.ycombinator.com/rfs#new-defense-technology• Bringing manufacturing back to America: https://www.ycombinator.com/rfs#bring-manufacturing-back-to-america• Better enterprise glue: https://www.ycombinator.com/rfs#better-enterprise-glue• Small fine-tuned models, as an alternative to giant generic ones: https://www.ycombinator.com/rfs#small-finetuned-models-as-an-alternative-to-giant-generic-ones• Reid Hoffman on LinkedIn: https://www.linkedin.com/in/reidhoffman/• Sam Altman on X: https://twitter.com/sama• Sean Parker on LinkedIn: https://www.linkedin.com/in/parkersean/• Owen Van Natta on LinkedIn: https://www.linkedin.com/in/owen-van-natta-444a7/• iMeme: https://apps.apple.com/us/app/imeme-generator/id1560021364• Marc Andreessen on X: https://twitter.com/pmarca• Picplz 1, Instagram 0 as VC firm Andreessen Horowitz chooses photo app rival: https://www.reuters.com/article/idUS2587232395/• Gustaf Alstromer—How to Get Users and Grow: https://www.youtube.com/watch?v=T9ikpoF2GH0• Getting to Yes: Negotiating Agreement Without Giving In: https://www.amazon.com/Getting-Yes-Negotiating-Agreement-Without/dp/0143118757• Founding Sales: The Early Stage Go-to-Market Handbook: https://www.amazon.com/Founding-Sales-Go-Market-Handbook-ebook/dp/B08PMK17Z1• Founder-led sales | Pete Kazanjy (Founding Sales, Atrium): https://www.lennyspodcast.com/founder-led-sales-pete-kazanjy-founding-sales-atrium/• The Sopranos on HBO: https://www.hbo.com/the-sopranos• The Wire on HBO: https://www.hbo.com/the-wire• Columbo on Prime Video: https://www.amazon.com/Columbo-Season-1/dp/B008SA89HA• Oura ring: https://ouraring.com/• Apple watch: https://www.apple.com/watch/• SiPhox: https://siphoxhealth.com/• Dalton & Michael on YouTube: https://www.youtube.com/playlist?list=PLQ-uHSnFig5Nd98Sc9I-kkc0ZWe8peRMC• How Future Billionaires Get Sh*t Done: https://www.youtube.com/watch?v=ephzgxgOjR0• The Student's Guide to Becoming a Successful Startup Founder: https://www.youtube.com/watch?v=O5KCB2p6SB8—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

If you're a high school or college student with big dreams of starting your own company, this video is for you. Dalton Caldwell and Michael Seibel, two startup founders who started in the early 20s and are now top investors, sit down to share the hard-won advice they wish they had known back in high school. Whether you're already running your own startup or just have an idea you can't stop thinking about, Dalton and Michael cover the skills you need to learn now and how to set yourself up for success after graduating school. Why YC? https://www.ycombinator.com/why Apply to Y Combinator: https://www.ycombinator.com/apply/ Work at a Startup: https://www.ycombinator.com/jobs

In this first in-person episode, Michael Seibel and Dalton Caldwell reveal how startups can gain a competitive advantage by doing something deceptively simple. They share compelling stories of companies that built loyal relationships and achieved success by making personal connections with users. In contrast, they discuss the traps early-stage founders make by trying to emulate big tech. If you want to build a business that customers love and that thrives in the long run, this advice on why caring deeply about your customers is key. Apply to Y Combinator: https://www.ycombinator.com/apply/

Brought to you by Linear—The new standard for modern software development. | Eppo—Run reliable, impactful experiments. | Pando—Always-on employee progression.—Gustaf Alströmer is a Group Partner at Y Combinator, where he's worked with over 600 startups in his 6.5 years there. He's also a fellow Airbnb alumnus and even started the original Airbnb growth team. In today's podcast, Gustaf discusses common reasons startups fail and how he helps coach founders on avoiding these mistakes. He explains the attributes that the best founders tend to have, and signs that a company has potential. We also cover the growing space of climate tech, for which Gustaf has a huge passion and where he's already had an incredible impact. He shares some key areas of innovation and investment in climate tech, some notable companies he's helped fund, and where he sees potential going forward.Find the full transcript here: https://www.lennyspodcast.com/lessons-from-working-with-600-yc-startups-gustaf-alstromer-y-combinator-airbnb/#transcriptWhere to find Gustaf Alströmer:• Twitter: https://twitter.com/gustaf• LinkedIn: https://www.linkedin.com/in/gustafalstromer/Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• Twitter: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/Referenced:• Airbnb tweet: https://twitter.com/gustaf/status/1580330162725347330• Startups Are an Act of Desperation: https://blog.eladgil.com/p/startups-are-an-act-of-desperation• The 18 Mistakes That Kill Startups: http://www.paulgraham.com/startupmistakes.html• Do Things That Don't Scale: http://paulgraham.com/ds.html• Marc Andreessen: https://a16z.com/author/marc-andreessen/• How to Talk to Users: https://youtu.be/z1iF1c8w5Lg• How to Get Your First Customers: https://youtu.be/hyYCn_kAngI• Pachama: https://pachama.com/• Request for Startups: Climate Tech: https://www.ycombinator.com/blog/rfs-climatetech• Climate Draft: https://www.climatedraft.org/• Seabound: https://www.seabound.co/• Fleetzero: https://www.fleetzero.com/• Unravel Carbon: https://www.unravelcarbon.com/• CarbonChain: https://www.carbonchain.com/• Sinai: https://www.sinaitechnologies.com/• Enode: https://enode.com/• Statiq: https://www.statiq.in/• Heart Aerospace: https://heartaerospace.com/• The 100% Solution: A Plan for Solving Climate Change: https://www.amazon.com/100-Solution-Solving-Climate-Change/dp/1612198384• Without a Doubt: How to Go from Underrated to Unbeatable: https://www.amazon.com/exec/obidos/ASIN/1982147903?tag=simonsayscom• Emily in Paris on Netflix: https://www.netflix.com/title/81037371• Everything Everywhere All at Once on Showtime: https://www.sho.com/titles/3493875/everything-everywhere-all-at-once• How to Apply and Succeed at Y Combinator, by Dalton Caldwell: https://www.youtube.com/watch?v=8yiOcCPvyNE• Y Combinator on YouTube: https://www.youtube.com/c/ycombinatorIn this episode, we cover:(00:00) Gustaf's background(04:15) What made Airbnb so special(07:21) How culture interviews and hiring founders contributed to Airbnb's success(10:31) Motivations for starting companies(13:17) Why Gustaf helps founders understand their motivations(14:13) Reasons you should not start a company(16:03) The magic that happens at YC office hours(20:45) Why founders in coworking spaces should schedule time to talk (21:36) Questions Gustaf asks founders(22:26) Common reasons startups fail(26:23) Getting over the fear of rejection (27:57) The importance of solving for pain points and why you should watch users(34:21) The value of having a technical co-founder(37:42) How founders without technical expertise have succeeded(40:46) Attributes of the most successful founders(44:57) Why it's hard to predict success and how YC advises against failures(46:59) Indications of potential for success(50:03) Speed vs. quality(51:11) Confidence vs. humility(52:48) Execution and tactics vs. strategy(54:36) Autocratic vs. collaborative-driven founders(56:27) Why you should focus on product first(59:03) The economic incentive for investing in climate tech(1:02:16) The clean-tech bubble of 2008(1:04:59) Why you don't need to be super-scientific to work in climate tech(1:06:51) Areas of climate tech and promising companies(1:12:27) What's going well in the climate-change space(1:16:49) Lightning roundProduction and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

Y Combinator group partners Michael Seibel and Dalton Caldwell discuss the importance of having honest and difficult conversations with startup founders. While having hard conversations can be uncomfortable, Michael and Dalton argue that this transparency is essential for founders to grow and ultimately, these experiences help founders have their own hard conversations that they've been avoiding with the people around them. Apply to Y Combinator: https://www.ycombinator.com/apply/

What happens when the unconventional becomes conventional? Michael Seibel and Dalton Caldwell discuss how the startup world has changed from being dominated by outsiders and nonconformists to now attracting more mainstream conformists looking for status and money. They share stories of what the tech scene was like when they were in school - and how radically different it is today, while offering their advice around navigating a world that doesn't always reward nonconformists embarking on risky entrepreneurial journeys. Don't just think different, act different. Apply to Y Combinator: https://www.ycombinator.com/apply/

If you're looking to maximize your startup's potential, start by setting the right goals. Michael Seibel and Dalton Caldwell provide tips and strategies for setting goals that will help keep you and your new business focused on success—plus provide examples of bad goals to avoid. Apply to Y Combinator: https://www.ycombinator.com/apply/

Thinking of a new startup idea? Dalton Caldwell and Michael Seibel discuss the types of ideas to stay away from—what we commonly refer to as "tarpit ideas." Apply to Y Combinator: https://www.ycombinator.com/apply/

With the YC S22 batch coming to a close, Dalton Caldwell and Michael Seibel reflect on the recent batch and their experience fundraising. The two group partners also clear up some misconceptions about Y Combinator based on feedback from founders. Apply to Y Combinator: https://www.ycombinator.com/apply/

Dalton Caldwell and Michael Seibel discuss the qualities that make a founder overly optimistic or far too pessimistic about their startup. Where is the right middle ground? Apply to Y Combinator: https://www.ycombinator.com/apply/

Dalton Caldwell and Michael Seibel discuss the problems with zero sum games within tech culture. Is your startup making a positive impact on the world or are your business practices a net negative in society? Apply to Y Combinator: https://www.ycombinator.com/apply/

Dalton Caldwell and Michael Seibel discuss the best approaches to developing a healthy lifestyle that ultimately helps you run and grow a successful startup. Funders, take care of yourselves out there. Apply to Y Combinator: https://www.ycombinator.com/apply/

Dalton Caldwell and Michael Seibel on the midwit meme, how it applies to startups, and the best example: Elon Musk. Apply to Y Combinator: https://www.ycombinator.com/apply/

Dalton Caldwell and Michael Seibel on software hacks that don't scale. Companies discussed include Google, Facebook, Twitch, and imeem. Watch the first video on doing things that don't scale here: https://youtu.be/4RMjQal_c4U Apply to Y Combinator: https://www.ycombinator.com/apply/

Dalton Caldwell and Michael Seibel on common pitfalls in the advice from different types of investors and why you, the founder, are ultimately responsible for the success of your company. Apply to Y Combinator: https://www.ycombinator.com/apply/

Dalton Caldwell and Michael Seibel talk about Paul Graham's essay "Do Things That Don't Scale" and what it really means for founders. Read the essay here: http://paulgraham.com/ds.html Apply to Y Combinator: https://www.ycombinator.com/apply/

Dalton Caldwell and Michael Seibel talk about the importance of understanding incentives and doing research when it comes to building a world-changing startup. To create Rookies Mistakes we asked YC founders: Is there a simple fact you wish you knew when you started your company or a rookie mistake you wish you could take back? Apply to Y Combinator: https://www.ycombinator.com/apply/

Dalton Caldwell and Michael Seibel talk about OpenSea, Gusto, and the importance of building simple products that solve a real problem. To create Rookies Mistakes we asked YC founders: Is there a simple fact you wish you knew when you started your company or a rookie mistake you wish you could take back? Apply to Y Combinator: https://www.ycombinator.com/apply/

Dalton Caldwell and Michael Seibel talk about where the ideas for Airbnb, Coinbase, and Stripe came from. Then they discuss what you can learn from these founders. To create Rookies Mistakes we asked YC founders: Is there a simple fact you wish you knew when you started your company or a rookie mistake you wish you could take back? Apply to Y Combinator: https://www.ycombinator.com/apply/

Dalton Caldwell and Michael Seibel on the importance of talking to your users, why successful founders are ok with rejection from potential customers, and how protecting your ego by not talking to your users can kill your startup. To create Rookies Mistakes we asked YC founders: Is there a simple fact you wish you knew when you started your company or a rookie mistake you wish you could take back? Apply to Y Combinator: https://www.ycombinator.com/apply/

Dalton Caldwell and Michael Seibel talk about solutions in search of a problem, whether or not to follow your passion, how to figure out what to work on, and how to motivate yourself. To create Rookies Mistakes we asked YC founders: Is there a simple fact you wish you knew when you started your company or a rookie mistake you wish you could take back? Apply to Y Combinator: https://www.ycombinator.com/apply/

Dalton Caldwell and Michael Seibel talk about investor terms and incentives. To create Rookies Mistakes we asked YC founders: Is there a simple fact you wish you knew when you started your company or a rookie mistake you wish you could take back? Apply to Y Combinator: https://www.ycombinator.com/apply/

Michael Seibel and Dalton Caldwell are back for episode 2 of Rookie Mistakes to discuss common mistakes founders make when fundraising, and how to avoid them. Apply to Y Combinator: https://www.ycombinator.com/apply/ Work at a startup: https://www.ycombinator.com/jobs

In the first episode of Rookie Mistakes, Dalton Caldwell and Michael Seibel discuss co-founder mistakes. To create Rookies Mistakes we asked YC founders: Is there a simple fact you wish you knew when you started your company or a rookie mistake you wish you could take back? Apply to Y Combinator: https://www.ycombinator.com/apply/ Work at a startup: https://www.ycombinator.com/jobs

Dalton Caldwell and Michael Seibel discuss the best approaches to managing the many setbacks startup founders can face over the lifetime of starting and running a business. Apply to Y Combinator: https://www.ycombinator.com/apply/ Work at a startup: https://www.ycombinator.com/jobs

Dalton Caldwell and Michael Seibel discuss Paul Graham's essay "Default Alive or Default Dead." They share strategies to cut your company's burn rate and keep your startup alive to see another day. Paul Graham's essay: http://www.paulgraham.com/aord.html Trevor Blackwell's startup growth calculator: http://growth.tlb.org Apply to Y Combinator: https://www.ycombinator.com/apply/ Work at a startup: https://www.ycombinator.com/jobs

Dalton Caldwell and Michael Seibel discuss the struggles of working at FAANG (Facebook, Apple, Amazon, Netflix, Google) and how to strategize leaving a big tech job to become a founder at a startup. Apply to Y Combinator: https://www.ycombinator.com/apply/ Work at a startup: https://www.ycombinator.com/jobs

Dalton Caldwell and Michael Seibel take a look at Paul Graham's essay "Maker's Schedule, Manager's Schedule" and share tips on how to be more effective and productive on the journey to creating a billion dollar business. Read PG's essay here: http://www.paulgraham.com/makersschedule.html Apply to Y Combinator: https://www.ycombinator.com/apply/ Work at a startup: https://www.ycombinator.com/jobs

Miguel Armaza is joined by Michael Seibel and Dalton Caldwell, Managing Directors and Group Partners at Y Combinator (YC). YC is one of the most successful startup accelerators and venture capital funds, and since March 2005 has helped over 5,000 startup founders build and launch companies like Stripe, AirBnB, DoorDash, Dropbox, Reddit, and the list goes on and on… This was a fascinating conversation and Michael and Dalton talked about lessons learned from their years of experience with YC, what they look for in a founding team, and why they are so passionate about helping entrepreneurs. We also touched on their decision to expand beyond the US to back entrepreneurs from all over the world, and the fascinating network effects this has created. Dalton and Michael also shared lessons learned from working with over 200 Fintech companies, including Brex, Stripe, and Coinbase. And some of the Fintech trends they are excited about. Finally, we could not end this conversation without talking about the state of diversity in the industry and hearing what Michael has to say about it Plus a lot more golden nuggets of information! Michael Seibel Michael Seibel is the Managing Director, Early Stage and Group Partner at YC. He was the cofounder and CEO Justin.tv and Socialcam. Socialcam sold to Autodesk in 2012 and under the leadership of Emmett Shear, Justin.tv became Twitch.tv and sold to Amazon in 2014. Before getting into startups, he spent a year as the finance director for a US Senate campaign and in 2005, Michael graduated from Yale University with a BA in political science. Dalton Caldwell Dalton Caldwell is the Managing Director, Architect and Group Partner at YC. He was the cofounder and CEO of imeem (acquired by MySpace in 2009), and the cofounder and CEO of App.net. He has a BS in Symbolic Systems and a BA in Psychology from Stanford University. About Y Combinator Y Combinator is a startup fund based in Mountain View, CA. In 2005, Y Combinator developed a new model of startup funding. Twice a year they invest a small amount of money in a large number of startups. The startups move to Silicon Valley for 3 months, and the YC partners work closely with each company to get them into the best possible shape and refine their pitch to investors. Each batch culminates in Demo Day, when the startups present their companies to a carefully selected audience of investors. Y Combinator has invested in over 3,000 companies including Airbnb, Dropbox, Stripe, Reddit, Instacart, Docker and Gusto. The combined valuation of YC companies is over $300B. For more FinTech insights, follow us below: Medium: medium.com/wharton-fintech WFT Twitter: twitter.com/whartonfintech Miguel's Twitter: twitter.com/MiguelArmaza Miguel's Substack: https://bit.ly/3jWIpqp

Tim is CEO and co-founder of a company called Cloosiv (YC S19).   Cloosiv is a company looking to offer smaller coffee shops a mobile ordering solution that can compete with those of the mega coffee brands.    Tim had an idea when he was working at Apple, he was looking at the Apple Store app and customers could walk into the store without ever talking to anyone and buy something. This sparked Tim to build something like that for other markets, which kicked off a journey to the coffee shop market.   He became a management consultant at the North Highland Consulting firm, and they invested 300k into the winner of a startup idea. Tim was that winner and they gave him 300k to start his business. Tim found his cofounder James on UpWork to help develop the app.    Tim had to unlearn how to be a management consultant, generally, companies pay large dollar amounts and "paid for perfection". But as a startup founder Tim mentioned he had to test, removing those own expectations.    Tim's first idea was to create a unified payment app and mentioned he would stand outside stores at the mall and ask questions to the customers walking around. Tim met with the founders of DoorDash and Instacart to learn about mobile ordering. Coffee shops have never had an app that was just for them.   Tim applied to Y Combinator 4-5 times over and over again getting rejected, but Tim never gave up. Dalton Caldwell called the Cloosiv founders delivering the bad news about not getting in and Tim applied again after making progress.   In one trip to San Fransisco and through a fortunate series of events came to a meeting with Sam Altman, who made some introductions, one being Lachy Groom who was an early employee at Stripe, previously head of stripe issuing, core payments product. Lachy was one of Cloosiv's first investors. Laura Behrens, Founder & CEO at Shippo joined as one of the early angel investors.   Square is now Cloosiv's payment partner, coffee shops tend to have very little free table space and their app can run simultaneously as the Cloosiv app on an iPad.   Tim and the Cloosiv team manage a coffee shop's inventory of coffee ordering real-time working towards a platform for a coffee shop to build their brands.   More about Tim here - https://www.linkedin.com/in/griffintimothy/   More about Cloosiv here - https://www.cloosiv.com/

We've cut down the sixth week of lectures to be even shorter and combined them into one podcast.First a lecture from Tim Brady. Tim’s a partner at YC. His lecture covers the importance of building a good culture early and shares six things that you can do now to help create a solid foundation for your startup.Then a lecture from Dalton Caldwell. Dalton is a partner at YC and he’s also the head of admissions. His lecture covers pivoting and his advice on how founders should think about it.Y Combinator invests a small amount of money ($150k) in a large number of startups (recently 200), twice a year.Learn more about YC and apply for funding here: https://www.ycombinator.com/apply/***Topics00:00 - Intro00:38 - Tim Brady on Building Culture1:13 - Culture is behavior and the right behaviors support a good business 4:38 - Six things new startups can do now5:00 - 1. Be proud of the problem you are solving7:31 - 2. Create a long term vision that others will follow9:36 - 3. List your values then model the behavior12:34 - 4. Align your culture with your customer14:49 - 5. Discuss the importance of diversity to your company16:43 - 6. Put a hiring process into practice. Plan to evolve it.18:24 - Dalton Caldwell on Pivoting18:53 - The term "pivot"20:20 - Why pivot?21:33 - Good reasons to pivot22:35 - Good reasons not to pivot23:13 - Why people take too long to pivot26:01 - Anecdotes27:22 - Product market fit28:34 - How to find a better idea30:40 - It's ok to not work on an idea that requires venture capital31:34 - Venture vs. non-venture scale ideas 32:52 - When is the best time to pivot33:48 - More pivoting thoughts 35:07 - Idea quality scores37:11 - Brex39:51 - Retool 41:37 - Magic43:22 - Segment45:16 - Dalton's summary

YC Partner and Head of Admissions Dalton Caldwell talks about pivoting for startups and shares his advice on when and how founders should consider it for their companies.

Ryan Hoover is the founder of Product Hunt which was in the Summer 2014 YC batch and was acquired by AngelList. He also invests in startups through his Weekend Fund.Dalton Caldwell is a Partner at YC where he runs admissions.Ryan is on Twitter at @rrhoover and Dalton is at @daltonc.The YC podcast is hosted by Craig Cannon.***Topics00:27 - Ryan's intro00:52 - Dalton's intro1:27 - Forming Product Hunt and applying to YC5:17 - Product Hunt's growth rate when they applied to YC6:27 - Raising money for the right reasons9:42 - Maker communities11:27 - Why raise money for Product Hunt?13:12 - Having buzz during the batch18:12 - Brex changing their idea during YC20:17 - Pivoting into something you know well21:32 - In retrospect, how would Ryan have advised himself around monetization?28:27 - Trying to build out other verticals34:27 - Don't act like you have infinite runway35:57 - Creating urgency and developing products within AngelList40:17 - Tips to launch on Product Hunt45:07 - What Dalton looks for in applications46:57 - Giving people the opportunity to start48:47- What motivated Ryan to leave his job before Product Hunt

YC Partner Dalton Caldwell gives insight into how YC admissions works and what makes for a successful YC experience.Clarification on the Application video: Founders can splice together a video if they're not all in the same place; you do not need to be in same place to do the video.Lecture SlidesLecture TranscriptVideo Link

Juan Benet is the founder of Protocol Labs (YC S14). They're working on IPFS, Filecoin, and Coinlist. Dalton Caldwell is a Partner at YC.

Bloomberg reports that Apple is building a Snapchat competitor Pandora aims to compete with Spotify and Apple Music with two new paid tiers. Dalton Caldwell's interview from 2010 in regards to music licensing - https://techcrunch.com/2010/10/20/imeem-founder-dalton-caldwells-must-see-talk-on-the-challenges-facing-music-startups/ Brad bought an Amazon Echo this week and is integrating it into his home Google has a new OS called  “Fuchsia” Paragon Software, maker of NTFS for Mac and EXTFS for Mac, has made a kick-ass disk utility that’s better than Apple's Review from Macworld macOS Sierra Code Confirms Thunderbolt 3 and 10Gb/s USB 3.1 Transfer Speeds in Future Macs Siri’s error rate has been cut by a factor of two by Apple’s Machine Learning Siri is reportedly held back by the quality of microphones in iOS and Apple Watches Pinterest acquires Instapaper from Betaworks Twitter now has “night mode” on their official iOS app. Apple acquires health startup Gliimpse 

Former General Magic executive Joanna Hoffman, former Aereo CEO Chet Kanojia and Y Combinator partner Dalton Caldwell talk with Recode's Peter Kafka about thier past entrepreneurial efforts that failed. They explain why they failed, with reasons ranging from unready technology to Supreme Court intervention, and how they moved on in different ways. Central to the discussion: How these entrepreneurs and others define what success means for them and whether that definition changes over time. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Episode 12 of Startup School Radio: Host Aaron Harris interviews Y Combinator partner Dalton Caldwell. Also on the show: Brandon Rodman, cofounder and CEO of Weave Communications.

Show Notes In which we talk about App.net closing down, how even good business models do not guarantee successful businesses, and the future of paying for things on the internet—especially whether social media will ever be the kind of thing people are willing to pay for. Chapters Intro (0:53) Follow-up: Fixed and Marginal Costs (0:53–1:44) What Happened to ADN? (1:44–7:26) Paying to Socialize (7:26–17:41) Free or Not Free (17:41–23:00) Conclusion (23:00–24:47) Music “Mountain Song”, from Lion’s Den by Little Chief. Used by permission. “Winning Slowly Theme”, by Chris Krycho. Used because we don’t need permission to use our own stuff! Links App.Net State of the Union – in which Dalton Caldwell (founder of App.net) announced publicly the layoffs and future of the service.

Adam Stacoviak talks with Dalton Caldwell the Founder of App.net after Founders Talk #45.

Adam talks with Dalton Caldwell the Founder of App.net. Since we barely scratched the surface of the planned conversation around what he’s doing with App.net in part 1, Dalton agreed to come back on the show for a part 2 to discuss the back story of App.net!

Adam talks with Dalton Caldwell the Founder of App.net. Since we barely scratched the surface of the planned conversation around what he’s doing with App.net in part 1, Dalton agreed to come back on the show for a part 2 to discuss the back story of App.net!

Adam Stacoviak talks with Dalton Caldwell the Founder of App.net after Founders Talk #42.

Adam talks with Dalton Caldwell the Founder of App.net. This is a hefty part 1, mainly focusing on the road traveled by Dalton to get to App.net. We barely scratched the surface of the planned conversation around what he’s doing with App.net. We end this call by teeing up the topic of discussion for part 2.

Adam Stacoviak talks with Dalton Caldwell the Founder of App.net after Founders Talk #42.

Adam talks with Dalton Caldwell the Founder of App.net. This is a hefty part 1, mainly focusing on the road traveled by Dalton to get to App.net. We barely scratched the surface of the planned conversation around what he’s doing with App.net. We end this call by teeing up the topic of discussion for part 2.

Adam Stacoviak talks with Dalton Caldwell the Founder of App.net after Founders Talk #42.

Dalton Caldwell, CEO and co-founder of App.net, is Jeffrey Zeldman's guest in Episode No. 84 of The Big Web Show, sponsored by Happy Cog™.

Dalton Caldwell, CEO and co-founder of App.net, is Jeffrey Zeldman's guest in Episode No. 84 of The Big Web Show, sponsored by Happy Cog™.

Dalton Caldwell is the head of App.net (sometimes called ADN for App Dot Net), which some think is a Twitter competitor, but it isn't quite. It seems to be a typical venture-backed startup firm, but it is not quite that, either. There's a lot more under the surface of App.net, including neutrally hosted services, ecosystems aimed at software developers, and crowdfunding, among other topics, which we talk about in this episode. App.net is offering a limited number of free-tier accounts, introduced last week and explained in the podcast, to listeners of The New Disruptors. Follow this link to sign up. If the link says there are no invitations left, please send me an email (click Contact above) or message us through our Twitter or App.net accounts. Sponsored by Kiwi is a full featured App.net client in a super simple package built just for OS X. Riposte for App.net: A Brave New App for a Brave New Network. Free.

This week Patrick and Myke conduct one of their 'rambling' episodes, with topics ranging from working out to being precious with your time. Show Notes: - Men's Journal: Everything You Know About Fitness Is a Lie - The Pen Addict: Episode 43 - Adding To Cart Now - CMD+SPACE: 031 - Free Accounts and Developer Incentives, with Dalton Caldwell and Bill Kunz   Sponsors: This episode is brought to you by: Squarespace, the secret behind exceptional websites. Go to squarespace.com/70decibels to start your free trial and use the offer code '70decibels3' at checkout to get 10% off your first order.

This week Myke talks to Dalton Caldwell about some of the recent changes to App.net, including the new free accounts and the Developer Incentive Program. He is then joined by Bill Kunz to discuss his App.net client, Felix.

This week Myke is joined by Dalton Caldwell, CEO and Founder of App.net. They discuss the experience of launching this service, how it has evolved and what the future has in store for App.net.

Frank and Dalton discuss hitting app.net's funding goal, the future of the platform, what's on the horizon for third-party developers, and Dalton's favorite coffee.

SF MusicTech Summit IX on September 12, 2011 in San Francisco Moderator: Lee Martin, SoundCloud (Experimental Development); Danielle Morrill, Twilio (Director of Marketing); Dalton Caldwell, App.net (CEO); Brad Serling, Nugs.net (Founder and CEO); Bram Cohen, BitTorrent (Founder & Chief Scientist); Taylor McKnight, SCHED.org (Founder)