EXCLUSIVE: Inside The $2.2B AI Research Accelerator | Turing
Power Shift In AI Training: Turing, Scale AI, Mercor, Surge
AI has eaten the internet.
Data labeling is so over. & $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.
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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. Which brings us to today, with frontier labs like OpenAI, Anthropic, Meta, Google, Microsoft, Nvidia, & Amazon all relying on Turing for frontier data.
In this conversation we go deep into how models work, the steps to get to GPT-5, and why, despite the results of the MIT AI study, enterprise demand is still skyrocketing. Turing is at the front, helping close that “gap” – a huge opportunity 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.
The path to Superintelligence runs through evaluations, data, and accelerated research that pushes AI forward across the four pillars of Superintelligence:
Multimodality
Reasoning
Tool use
Coding
“The evaluation landscape is evolving towards increasingly complex, multi-step scenarios that mirror real-world applications. Turing is at the forefront, building specialized RL gyms across multiple domains to power large-scale reinforcement learning and drive meaningful progress.” - Jonathan
I had the fortunate opportunity to meet Turing in Paris at the Raise Summit back in July — thank you to Apoorv at Altimeter! — we quickly hit it off talking AI, bottlenecks, and high stakes competition.
Now, we’re happy to be working closely with the team. Turing is growing incredibly fast in the race towards Superintelligence with top AI researchers & experts. They’re on a mission to provide the highest quality data to train the next generation of world-changing models (you can often find them cited in newly released model papers).
They have a special, and uniquely positioned window, right now, to capture unrelenting demand by frontier labs and the $1B (arguable more) in revenue left on the table by Scale AI after their “acqui-hire.”
Technical Roots
With deep roots in AI research, Jonathan, CEO of Turing, studied at Stanford, working in the Stanford AI Lab and InfoLab, while his co-founder was at the Stanford NLP Lab. Both originally pursued academic paths before deciding to start a company, applying their research experience to real-world problems.
Turing’s early focus was on applying AI to global talent: finding, vetting, and managing software engineers at scale. As a result, Turing has built one of the world’s largest engineering platforms, connecting over 4 million developers to companies around the globe. This infrastructure, along with the company’s background in research, proved critical when the AGI wave exploded, positioning Turing to become the world’s leading research accelerator for the AI labs.. not bad.
Today, they’re at the center of it all. With the most qualified team & network.
The race is on.
Timestamps
(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
How Frontier Models Like GPT-5 Are Built
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
From Data Labeling to Frontier Research
The old model of mass data labeling no longer works.
“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.”
Turing works with OpenAI, Anthropic, Meta, Google, Microsoft, Nvidia, and Amazon to generate expert-level datasets.
“These models ate the internet when they were pre-trained. But the internet data is used up — it was used up like three years ago. Where’s the data gonna come from to keep the scaling laws going? That’s where Turing comes in.”
Where the Power Has Shifted
Power in AI training has moved away from labeling companies and talent marketplaces.
“Old school data labeling is over, and the era of a recruiting or a talent marketplace is also over. Labs need more than just data thrown over a wall. They need a true partner that can understand their research objectives and figure out what type of data is likely to be helpful. That’s what sets Turing apart.”
Debunking the Doomers
Some expect a sudden leap in AI capability. Jonathan sees steady progress instead.
“I don’t think rapid takeoff is how things will unfold. I think it’s gonna be steady, continuous progress every step of the way. GPT-5 is f***ing awesome — I think we’ve just gotten used to magic.”
He argues the real risk is enterprises failing to act.
“What’s the bigger risk? It’s not that AI takes off too fast. It’s that enterprises don’t deploy these systems quickly enough — and if they don’t, their competitors will.”
Enterprise AI Moats
Turing is now working with financial institutions, insurers, and pharmaceutical companies.
“If the job involves analyzing data and making relatively quantifiable decisions, it’s a great use case. For investment banks, private wealth management firms, and capital markets, AI is an existential risk to ignore.”
“The best enterprises know that it’s not about integrating general intelligence, it’s about building proprietary intelligence. Your data is your moat.”
Why Turing Is Positioned for the Future
Jonathan compares Turing to Nvidia.
“I think of Turing a lot like Nvidia — we’re the picks and shovels into the AGI industry. As long as compute scales and data scales, the demand for what we do is unlimited.”
And on the bigger picture:
“If we solve intelligence, we can solve diseases, we can solve aging, we can solve interstellar travel. It’s gonna be awesome.”
Road to ASI
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