Invest Like the Best with Patrick O'Shaughnessy
00:00:01 · Patrick O'Shaughnessy
[00:00:01] S0: Ramp is the only platform built to make your finance team leaner, faster, and better, saving businesses 5% annually on average so you can stay focused on growth. Ramp just opened for business in The United Kingdom. So if you're running running a business in The UK, you can now use Ramp's AI powered finance platform to manage cards, expenses, bills, approvals, and accounting all in one place. I run my business on Ramp and so should you. Learn more at ramp.com/invest. Rogo is the AI platform purpose built for financial institutions serving tens of thousands of bankers and investors at hundreds of leading firms worldwide. Rogo's AI agents autonomously execute large chunk of your firm's workflows. They can screen deals, draft SIEMs, run buyer outreach, and due diligence on data rooms, all with full security and compliance handled. Every action feeds Rogo intelligence, the firm's context layer, turning years of deal experience into a secure, governed institutional memory that makes each new transaction stronger than the last. To learn more, visit rogo.com/invest. OpenAI, Cursor, Antti, and Vercel all have something in common. They all use WorkOS. To achieve enterprise adoption at scale, you have to deliver on core capabilities like SSO, SCIM, RBAC, and audit logs. Instead of spending months building these mission critical capabilities yourself, you can just use WorkOS APIs to gain all of them on day zero. Visit workos.com to get started. Hello and welcome everyone. I'm Patrick O'Shaughnessy, and this is Invest Like The Best. This show is an open ended exploration of markets, ideas, stories, and strategies that will help you better invest both your time and your money. If you enjoy these conversations and wanna go deeper, check out Colossus, our quarterly publication with in-depth profiles of the people shaping business and investing. You can find Colossus along with all of our podcasts at colossus.com.
00:01:54 · S1
[00:01:54] S1: Patrick O'Shaughnessy is the CEO of Positive Sum. All opinions expressed by Patrick and podcast guests are solely their own opinions and do not reflect the opinion of Positive Sum. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of PositiveSum may maintain positions in the securities discussed in this podcast. To learn more, visit psum.vc.
00:02:22 · Patrick O'Shaughnessy
[00:02:22] S0: My guest today is Neil Moba, the founder of Sale Research. Sale is building what Neil calls a token factory, an inference company designed for a specific kind of future, one where AI agents run-in the background for hours or days at a time rather than answering a human in real time. In that world, latency matters less and cost matters much more. And Neil has built the entire company around driving the cost of a token as low as it can possibly go. What makes this conversation special is it's one of the most detailed tours I've ever done through the full stack of intelligence, the software, the chips, the power, and how the three connect. Along the way, we cover the trade off between speed and cost that lives inside of every GPU, his scavenger strategy for buying the chips and power no one else wants, his contrarian view on NVIDIA, and why the premium the Frontier Labs charge for being three to six months ahead may not last. Please enjoy my conversation with Neil Mova. I think it's important early in these conversations to just say the thing, literally what you're building and what it does today. So maybe just orient us there with a brief description, like literally what the system is that you're building and why it should exist.
00:03:24 · Neil Movva, Patrick O'Shaughnessy
[00:03:24] S2: Sale Research is a token factory. We have an API where anyone can send us requests, where they can use large language models, open source large language models for any task they want. We will serve those tokens to them at a price that is unbeatable in the market. We also support their ability to build agents on top of this. We host what we call Sailboxes, which are long running agent virtual machines hosted in the cloud that are designed for agents that run for hours, days, or weeks. [00:03:48] S0: So I should think about you as a peer company to others that serve different kinds of inference. You're serving one specific kind of inference and your goal is to be the absolute cheapest provider and enabler of a certain kind of use of intelligence. [00:04:01] S2: Exactly. The theme of our company is abundance. We want to deliver this new commodity of intelligence to as many people as possible at a cost that is sustainable for almost every industry. We think that whenever you make something 10 times cheaper, it's a new product category. We aspire to do that for tokens. We think it's so profound that the machine can think, and now our job is to make as many machines as possible in the world work towards thinking. [00:04:22] S0: So if you think about the theme of the day being token costs, is token cost the right way
00:04:22 · Patrick O'Shaughnessy, Neil Movva
[00:04:22] S0: So if you think about the theme of the day being token costs, is token cost the right way [00:04:27] S2: to think about this? Is there some other way you'd put it? To start with, absolutely, token cost. Today, my North Star is I wanna have the lowest cost per token in the industry and do that by a mile. I don't think tokens are the final unit of work or intelligence, but they are what we use today. After tokens, you start to move more towards more outcomes, which is like a vague direction. You can imagine, for example, today when you consume tokens through an agent, you don't actually control how many tokens the agent reasons for. It can reason for a certain amount of time or it can call a certain number of tools. And increasingly, think we will have agents do some unit of work, take as many shots on goal as they can. And however many tokens they used to get there is going to be a dependent variable depending on the task. So you think about like agents that self administer a token budget as opposed to a company setting a budget for how many tokens engineers can spend per month. [00:05:15] S0: Why is there an opportunity that you can tackle? It seems like the entire world is oriented around more, better, faster, cheaper tokens right now. It seems like the world is trying to solve this problem very aggressively. What was the unique opening that you saw that's maybe the market's not being efficient in its attempt to tackle this?
00:05:33 · Neil Movva, Patrick O'Shaughnessy
[00:05:33] S2: So I think there's two things that are tailwinds for our company. One is gotta be the rise of open source. I have to tackle that first. I think we're starting to see an increasing number of our customers and the broader market care about owning intelligence. They want to have control sovereignty over the thing that they depend on. That created a much more robust market for our customized models or even just like these vanilla open source models that no one can ever take away from you. You always have the weights, you always have the right to deploy them however you like. In that world, there's been a reasonably robust market for the past couple years serving these models at large scale. The challenge is, all those companies, you could take your pick, base 10, fireworks together, they all focus on low latency inference. And they were pulled in that direction by one very important customer, Cursor. I think that that was the right choice about a year ago. And as of six months ago, it started to look like maybe low latency wasn't the only thing you wanted from an agent. You wanted more persistence, more long horizon tasks. And now it's, to me, very obvious that the future of agentic inference is long horizon tasks. You're gonna run the machine for hours or days at a time. It doesn't matter if it's been set tokens at a 100 tokens per second. Maybe 10 is just fine. That comes with corresponding advantages and efficiency. [00:06:44] S0: Why are you so confident in that? To me, it seems like I want everything as fast as possible.