Turing CEO Jonathan Siddharth - The $30 Trillion Knowledge Work Market, Training Frontier AI Models and Building Stage Five Culture

**Auren Hoffman** (0:02)
Hello, fellow data nerds. Welcome to World of DaaS. I'm your host, Auren Hoffman, CEO of NQB8 and GP of Flex Capital. Discover more episodes, get weekly data as a service news, original content, articles on data, and more at worldofdaas.com. That's worldofdaaS, daas.com. Hello, fellow data nerds. My guest today is Jonathan Siddharth. Jonathan is the co-founder and CEO of Turing, a $2.2 billion AI company that provides coding and reasoning data to train frontier models for OpenAI, Google, Meta, Anthropic, and many, many others. He's a second time founder. His first company, Rover, was acquired by Revcontent in 2016 Jonathan, welcome to World of DaaS.

**Jonathan Siddharth** (0:47)
Thank you, Auren, for having me. I'm a fan of the show and excited to be here.

**Auren Hoffman** (0:51)
I'm super excited as well. Now, you said like the bottleneck for AGI progress and just AI progress in general. It used to be compute and data, but now it's human intelligence. When did that bottleneck happen? And walk me through why human intelligence is the bottleneck now.

**Jonathan Siddharth** (1:08)
So, there's something very exciting happening as we're heading on this race towards super intelligence. So, to get to super intelligence, you need three things. You need innovation in algorithms, model architectures, which the frontier labs do a phenomenal job of. You need innovation and scale up on the compute side.

**Auren Hoffman** (1:26)
And power and all that stuff.

**Jonathan Siddharth** (1:29)
Power, energy, yeah, absolutely. And then you need innovation and scale up on the data side. So, Turing powers that data pillar. And our mission is to accelerate super intelligence in a way that drives real economic progress. We wanted to show up in the world in terms of something that moves the GDP of the world. So, we want to accelerate super intelligence in a way that drives real economic progress. And we do that in two ways. On one hand, we work with all the frontier AI labs to build high quality data to help them improve their models to do a wide variety of cognitive tasks. And on the other hand, we are working with enterprises. We work with some of the largest financial institutions in the world, media companies in the world, to help them build what we call proprietary intelligence, basically end-to-end AI systems to solve real enterprise problems, automate enterprise workflow.

**Auren Hoffman** (2:23)
Is there a particular... So OpenAI has been talked about, they're hiring investment bankers to come in and go through the actual deal process or something, and other folks have brought in emergency doctors to help understand. Are we talking about like that type of thing to get super high level?

**Jonathan Siddharth** (2:42)
That is exactly right. I think of it, Auren, as a four-dimensional matrix. Think of the first dimension as every industry you can think of, financial services, life sciences, health care, retail, etc. The second dimension is every function in an organization, software engineering, product management, sales, marketing. The third dimension is every role in the org chart in that function. For example, if we index into finance, there is the CFO, there's the director of FP&A, there's the head of accounting. The fourth dimension is every workflow that a human does. For example, a CFO goes through the workflow of preparing board materials or goes through the workflow of preparing monthly finances.

**Auren Hoffman** (3:18)
And why can't we just watch their computer while they work?

**Jonathan Siddharth** (3:22)
What makes you think we don't?

**Auren Hoffman** (3:24)
Okay, maybe we do.

**Jonathan Siddharth** (3:25)
But this four-dimensional matrix that I mentioned accounts for $30 trillion of digital knowledge work. $30 trillion. This is my job, your job, every human's job in front of a computer. And if you add in robotics, we're talking $110 trillion market. Let's look at the digital knowledge work, work humans do in front of a computer. A human looks at a computer, looks at the screen, analyzes data, is thinking, using different tools, using a keyboard and a mouse to drive action like pixels in, act, and out. I see the fundamental building blocks here to be multimodality, your ability to process audio, video, image, input, including textual input, reasoning, tool use, and coding. My hypothesis is if you master all four, multimodality, reasoning, tool use, and coding, you can do anything in front of a computer.
What we are doing at Turing is massively scaling up the data factory that can generate the data to power the models to automate every cell in this four-dimensional matrix. You can think of a human doing a role as a composite of workflows, and you can think of a function as a composite of humans in that org chart, and you can think of a company as a composite of all the functions. We have these two wonderful learning paradigms for imitation learning and reinforcement learning. Both have very different data needs. We've built a talent platform, and on top of that, we've built a data platform that powers generating data for imitation learning and RL and reinforcement learning for every cell in this four-dimensional matrix. We are hiring investment bankers, doctors, lawyers. We have to replicate their entire work environment. When we get into RL, if you're interested in RL, we can talk more about it later, but it's such a fascinating area. These models are so data-starved today because all the easy gains from eating the Internet, eating all the books that humanity has ever created, that's done. That knowledge has already gone in. And companies like Turing are creating data with these expert humans working inside realistic work environments to generate data for these models for these different learning paradigms. And who knows what new learning paradigms we'll invent in the coming years.