AI has eaten the internet, data labeling is so over, and $30 trillion of human work is on the verge of automation. Jonathan Siddharth, Founder & CEO of Turing, joins Sourcery to break down the power shift in AI training — from commodity data labeling to expert research — positioning Turing apart from AI data providers like Scale AI, Mercor, & Surge.
Turing has become a hidden force in the AI race, hitting $300M in ARR in 2024 (~3x YoY), achieving profitability, and raising $111M at a $2.2B valuation in March. That growth cements its position as one of the fastest-growing AGI infrastructure companies.
Today, frontier labs like OpenAI, Anthropic, Meta, Google, Microsoft, Nvidia, & Amazon rely on Turing for the frontier data that pushes AI forward across the four pillars of superintelligence:
• Multimodality
• Reasoning
• Tool use
• Coding
We explore Turing’s expansion into the enterprise, closing the “gap” – where Fortune 500s in finance, insurance, and pharma are racing to build proprietary intelligence on their own data, creating durable moats in the $30T knowledge work economy.
PS Jonathan also explains how labs like OpenAI train models:
• Pre-training on filtered internet corpora (Common Crawl, GitHub, books, video)
• Post-training with supervised fine-tuning (human Q&A datasets)
• Reinforcement learning (RLHF + verifiable domains) to align models with human preferences
• Model-breaking data from Turing’s 4M+ engineers to close gaps and advance systems like GPT-5
1. Jonathan Siddharth: https://www.linkedin.com/in/jonsid/
2. Molly O’Shea: https://x.com/MollySOShea
3. Sourcery: https://x.com/sourceryvc
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(00:00) AI Ate The Internet
(00:49) Training superintelligence: the race to AGI
(02:31) Viral tweet
(03:24) What Turing actually does
(04:43) The internet data is “used up” — where will new data come from?
(05:34) Four pillars of superintelligence: multimodality, reasoning, tool use, coding
(06:07) Automating $30T of global knowledge work
(09:18) The $1B revenue opportunity
(10:59) Why Turing is a research-first accelerator, not a data labeler
(13:45) Jonathan’s Stanford AI Lab roots and founding DNA
(17:57) How models are built: pre-training vs. post-training
(20:14) RLHF, reinforcement learning, and “breaking the models”
(25:19) GPT-5 and the myth of rapid takeoff
(30:46) Safety debates and human-in-the-loop systems
(34:53) Closing Enterprise Gap: finance, insurance, & pharma
(39:23) Why proprietary enterprise data is the next moat in AI
