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.
00:09:29 · Molly O'Shea, Molly O'Shea
[00:09:29] S2: Start today at brex.com/sorcery. That's brex.com/sorcery. [00:09:37] S0: I kind of think that's why Nat Friedman started to, he started to translate all those scrolls. He started, and that was one of his projects. He's translating scrolls because we ran out of data, we ran out of human knowledge, and now we have to go back in time and decipher these things. So right now there's a lot of competition in the space and it seems like the hottest thing to look at. Ever since the Scale AI acquisition investment, whatever you wanna call it, there's essentially a billion dollars up for grabs. And I want to know between the three leaders, of which you are one of them, how do you differentiate?
00:10:21 · Jonathan Siddarth, Molly O'Shea
[00:10:21] S1: This market is massive and growing. There's unlimited demand for high quality data, unlimited demand. I see two axes that you have to be really good at to win in this market. One axis is finding really smart humans really, really fast. And there are a few companies that do a good job at that. Turing being one of them. There is another axis which is you have to be at the forefront of data research. You have to know what type of data is likely to be helpful to the model for it to advance in coding, STEM, advanced reasoning, etc. Turing is great at that. We are the one company in this category that's not just good at finding people and generating data but being proactive about figuring out what type of data these models are likely to need. And as the models get getting smarter and smarter every quarter, you have to innovate on the research side, too. So on that two by two, I would say Turing is the one company up and to the right. There are plenty of companies that are good at, okay, we have a large talent network, we work with university students, or we are good at finding people. We think that's necessary, that's not sufficient. You have to ensure that the quality of the data is good and that it can improve the model's performance. [00:11:39] S0: Taking a step back, can you explain the evolution of this industry and models? You gave a really good explanation on This Week in Startups with Alex, and I think this was, like, phenomenal. Like, it was clipped and everything. Could you explain the evolution of that?
00:12:01 · Jonathan Siddarth
[00:12:01] S1: This industry had a huge shift after the reasoning models came out late last year, O1 being the first. The shift is, before the reasoning models, this industry needed simple data, gobs and gobs of simple data. You needed a data factory. Or this industry needed somebody to find people really fast. After the reasoning models came out, the game completely changed. This is like after 'one and DeepSeek. Now what the labs need is not a data factory. It's not a talent marketplace. The labs need a strategic research partner, somebody who can collaborate directly with their researchers to understand where the models are weakened today and strategically custom engineer data to improve the model's performance. They need a research accelerator. In house researchers collaborate with teams in coding, multimodality, STEM, RLGIMs, etc. Generate data that will improve these models. And the data that the models need now needs to be hard. That is model breaking. It has to literally break the model. So you need humans who are smarter than the models. It has to be realistic that reflects how real humans use these models to do real work. Only then AI will actually move the GDP of the world. It has to mirror real world use, not esoteric academic use cases that test whether you've hit the singularity or not. And third, the data needs to be diverse to cover every single type of knowledge work imaginable. So you need hard data, realistic data, and diverse data. That's how it shifted.
00:13:41 · Molly O'Shea
[00:13:41] S0: Because research has become such an important part of this next era of AI, it would be would be wrong not to bring up the fact that this is your background. So you were in research and in AI development before this. So how did that lead to founding Turing?