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20VC (20 Minute VC)

20VC: Scale, Surge, Turing, Mercor: Who Wins & Who Loses in Data Labelling | Is Revenue in Data Labelling Real or GMV? | Why 99% of Knowledge Work Will Go and What Happens Then? | Why SaaS is Dead in a World of AI with Jonathan Siddharth @ Turing

68 min episode · 2 min read
·
Jonathan Siddharth

Episode

68 min

Read time

2 min

Topics

Startups, Fundraising & VC, Design & UX

AI-Generated Summary

Key Takeaways

  • Data Evolution: AI training shifted from simple labeling tasks like sorting numbers to complex multi-step workflows requiring expert humans across verticals. Models now need data showing how to operate computers, call APIs, and execute real business workflows through reinforcement learning environments, not just imitation learning.
  • RL Environment Architecture: Turing creates mini world models with clones of business applications using synthetic data, where AI agents try different trajectories to complete tasks. The curriculum difficulty must balance between too easy (no learning) and too hard (no progress), similar to AlphaZero's self-play approach in mastering Go.
  • Custom Model Economics: Enterprises need smaller fine-tuned models (500M to 10B parameters) for specific workflows like insurance underwriting, trained on proprietary data that stays on-premises. These specialized models outperform trillion-parameter world models for narrow tasks while protecting competitive data from reaching frontier labs or competitors.
  • Enterprise Deployment Reality: Successful AI implementation requires first-mile schlep (consolidating fragmented data from spreadsheets and departed employees into structured formats) and last-mile schlep (building cursor-like interfaces for partial autonomy, training humans, collecting feedback). Ninety-five percent of pilots fail due to skipping these steps.
  • SaaS Disruption Thesis: Traditional SaaS dies because building AI applications on LLMs becomes trivially easy, foundation models move into apps layer with agentic capabilities, and software designed for human GUI navigation becomes obsolete. Companies will build custom solutions internally rather than subscribe to 80-100 third-party products.

What It Covers

Jonathan Siddharth explains how Turing shifted from talent marketplace to research accelerator, generating complex data through reinforcement learning environments to train frontier AI models for seven of eight major labs at $350M ARR.

Key Questions Answered

  • Data Evolution: AI training shifted from simple labeling tasks like sorting numbers to complex multi-step workflows requiring expert humans across verticals. Models now need data showing how to operate computers, call APIs, and execute real business workflows through reinforcement learning environments, not just imitation learning.
  • RL Environment Architecture: Turing creates mini world models with clones of business applications using synthetic data, where AI agents try different trajectories to complete tasks. The curriculum difficulty must balance between too easy (no learning) and too hard (no progress), similar to AlphaZero's self-play approach in mastering Go.
  • Custom Model Economics: Enterprises need smaller fine-tuned models (500M to 10B parameters) for specific workflows like insurance underwriting, trained on proprietary data that stays on-premises. These specialized models outperform trillion-parameter world models for narrow tasks while protecting competitive data from reaching frontier labs or competitors.
  • Enterprise Deployment Reality: Successful AI implementation requires first-mile schlep (consolidating fragmented data from spreadsheets and departed employees into structured formats) and last-mile schlep (building cursor-like interfaces for partial autonomy, training humans, collecting feedback). Ninety-five percent of pilots fail due to skipping these steps.
  • SaaS Disruption Thesis: Traditional SaaS dies because building AI applications on LLMs becomes trivially easy, foundation models move into apps layer with agentic capabilities, and software designed for human GUI navigation becomes obsolete. Companies will build custom solutions internally rather than subscribe to 80-100 third-party products.

Notable Moment

Siddharth reveals he spends every weekend manually selecting 15-20 clips per podcast episode, taking three hours per show. He acknowledges this exact workflow could be automated with fine-tuned models trained on his past clip selections, demonstrating the model capability overhang he describes.

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Episode Transcript

I think the era of data labeling companies is over, and it's now the era of research accelerators. All knowledge work is going to be automated. It's only a matter of time. I don't see an AI bubble. These models are incredibly powerful today. SAS as we know it, I think is over. I think it's completely over. This is 20 VC with me, Harry Sebbings, and I'm so excited for the show today. So I'm fascinated by the data labeling market. Macaw, Surge, Turing, Invisible. There are seven players that I know of that do over a 100,000,000 in annual recurring revenue. All of them have more than 50% of their revenue from two customers. The way they use the term revenue is also sometimes super unclear versus GMV. There are bluntly just a lot of questions. Today, I do not pull any punches with the man leading one of these companies, Jonathan Sedat, founder and CEO of Turing, a company that he has scaled to over 350,000,000 in annual recurring revenue, raising $225,000,000 in the process, and now being a profitable company. This is an amazing discussion, and as I said, the tough questions get answered. But before we dive into the show today, are you drowning in AI tools? ChatGPT for writing, Notion for docs, Gmail for email, Slack for comms, and you're constantly copy pasting between them all losing context and losing time. This is the AI productivity tax, and it's killing your output. At twenty VC, we're all about speed of execution and Superhuman is the AI productivity suite that gives you superpowers everywhere you work. With the intelligence of Grammarly, mail, and coder built in, you can get things done faster and collaborate seamlessly. Finally, AI that works where you work, however you work. Superhuman gets you from day one with zero learning curve and is personalized to sound like you at your best, not like everyone else using generic AI. Get AI that works where you work, unlock your superhuman potential. Learn more at superhuman.com/podcast. That's superhuman.com/podcast. And just like Superhuman gives you superpowers in your inbox, Vantaa Vanta gives your company superpowers in security and compliance. Customer trust can make or break your business, and the more your business grows, the more complex your security and compliance tools get. It can turn into chaos, and chaos isn't a security strategy. That's where Vanta comes in. Think of Vanta as your always on AI powered security expert who scales with you. Vanta automates compliance, continuously monitors your controls, and gives you a single source of truth for compliance and risk. So whether you're a fast growing startup like Cursor or an enterprise like Snowflake, Vanta fits easily into your existing workflows so you can keep growing a company your customers can trust. My listeners can get $1,000 off Vanta by going to vanta.com/20vc. That's vanta.com/20vc20vc for $1,000 off Vanta. And like Vanta builds trust with automated security, AngelList drives momentum by helping you …

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