00:01:27 · Jonathan Siddarth, Molly O'Shea
[00:01:27] S1: Thank you for having me. [00:01:29] S0: Well, we met just a few months ago in Paris at the RAISE Summit. This was actually at Apoorv from Altimeter. Thank you, Apoorv. His event outside of the summit, you were one of the speakers on stage. What were you guys talking about? [00:01:45] S1: We were talking about training superintelligence. The race to AGI is on, and the data needs of these models have shifted. These models need incredibly complex data for training them to get better at coding, advanced reasoning, STEM, etcetera. And we were chatting about how the world looks so different today than it did like a few years back.
00:02:09 · Molly O'Shea
[00:02:09] S0: So I remembered that speech. It wasn't a speech. I remember that panel, and I was really taken aback because I've heard of Scale, I've heard of Mercor, I've heard of all these different companies in the AI research space. It's definitely evolved from just where it was before with data labeling to something a little bit more advanced as AI continues to evolve. But there was one thing that I thought was quite interesting, and I didn't know this, but, like, the scale at which these companies are running, specifically yours. And I had tweeted this out and it kind of went viral. But I tweeted out, just got back from RAISE Summit in Paris where AI's top names like Groq, Cerebras, Eric Schmidt, Lovable, and Windsurf. This was, like, right before the Windsurf thing, by the way. I don't know if you remember that. Yeah. All took the stage. Yet one unexpected player stood out as the next breakout AGI leader. Some stats. 300,000,000 plus in revenue and profitable, dollars $225,000,000 in funding at a last valuation of $2,200,000,000 a network of 4,000,000 engineers and customers that include OpenAI, NVIDIA, Anthropic, Google, Microsoft, Meta, Salesforce, Amazon, and I'm sure a lot more. So, Jonathan, what is Turing? So at Turing, we train superintelligence. We work with seven out of the eight frontier labs. We work with OpenAI, Anthropic, Meta, Google, Microsoft, NVIDIA, Amazon. Anybody that's building a frontier foundation model, we are
00:03:45 · Jonathan Siddarth
[00:03:45] S1: probably working with them already. And what's happening is as these models have become smarter and smarter, the data needed to improve them has become increasingly harder to generate. The earlier era was almost like commodity data labeling. That era is over. Now it's all about frontier data. These models need expert human data in every domain imaginable. They need data to train these models in reinforcement learning. And sometimes they need synthetic data. What we are doing is scaling up our data infrastructure at gigantic scale. So for these models, the cool thing is everybody is aware of all the research breakthroughs that are needed to move these models forward and all the labs do an amazing job at advancing the research frontier. You need tons and tons of compute and we have NVIDIA, Cerebras, Grok, like all these companies to thank for that. But they need a ginormous amount of data. And what's happened, Molly, is these models ate the internet when they were pretrained. But the internet data is used up. It was used up like three years ago. Where's the data going to come from to keep the scaling laws going? You need to have expert humans from every domain imaginable to work on a platform like Turing to break the models and figure out what type of incremental data will move them forward. The era of sweatshop data labeling is over. Now what the labs need is a strategic research accelerator. And Turing is the world's leading research accelerator working with all of these frontier labs to advance them along, I would say, the four pillars of superintelligence, which is multimodality, reasoning, tool use, and coding. If you solve these four things, you will get to ASI.
00:05:43 · Molly O'Shea
[00:05:43] S0: What's ASI?
00:05:45 · Jonathan Siddarth
[00:05:45] S1: Artificial superintelligence. I think of ASI and different people have different definitions. I mean, AGI was also very ambiguously defined. And I think of ASI as us having automated 90% of the tasks that 90% of humans do today in front of a computer. If you look at most types of knowledge work, like my job, your job, you're looking in front of a computer, analyzing what's on the screen, using different tools, and taking decisions. And this is the job of a CEO. It's the job of head of engineering, a director of performance marketing, an SDR. These types of tasks are going to be automated. The only constraint, the reason this hasn't happened yet, is the data that has to improve these models hasn't gone into them yet. And we are scaling like crazy. I think of this like a four dimensional Think of every industry you can imagine. Financial services, retail, health care, life sciences, that's dimension one. Next dimension, think of every function in that industry: software engineering, sales, marketing, etc. Third dimension, think of every role in the org chart in that function. Let's pick marketing. There's a CMO. There's a director of performance marketing. There's an SEO analyst. And fourth dimension, think of the workflow that a human performing that role goes through. Let's say we pick a Director of Performance Marketing and they have to figure out how to allocate marketing budget across different channels solving for CAC, LTV, efficiency of the channel, scalability of the channel, etc. This requires that human to do data analysis, to do AB tests, use tools like Facebook's performance marketing dashboard, LinkedIn's performance marketing dashboard, analyze which campaigns do well, and based on that update the website funnel. All of this is going to be automated. This is like, if you look at four dimensional matrix, that's $30,000,000,000,000 of knowledge work that is going to be automated. And these models will improve to automate all of this as long as we scale up compute and we scale up data. There is this joke among AI researchers. A researcher one says, Hey, is it true that LLMs cannot reason well out of distribution? Meaning are LLMs just interpolating based on data in the training set? How will they generalize? And Researcher two says then you bring all data into distribution.
00:08:17 · Jonathan Siddarth, Molly O'Shea, Molly O'Shea
Like all of humanity's human knowledge bring it into distribution. 7,000,000,000 humans on the planet doing all sorts of interesting things. I think most of them should be on platforms like Turing, generating data to train these models, so humans are a lot more leveraged in what they [00:08:35] S2: Sourcery is brought to you by Brex, the financial stack trusted by more than 30,000 companies, including one in three venture backed startups in The US. Nearly 40% of startups bail because they run out of cash. Brex is literally built to help founders avoid that. Unlike traditional banks that let your money sit idle, chipping away at it with fees, Brex is designed to help you spend smarter and move faster. Their all in one solution combines checking, treasury, and FDIC protection into one powerful account. You can send and receive money globally at lightning speeds, get 20 times the standard FDIC coverage through their partner banks, and even high yield from day one. With same day and even same hour liquidity, access your funds anytime. Companies like Scale AI, DoorDash, Service Titan, HIMS, Anthropic, Flexport, Robinhood, [00:09:27] S0: and Plaid trust and use Brex. [00:09:29] S2: Start today at brex.com/sorcery. That's brex.com/sorcery.