Jonathan Siddharth
Co-founder & CEO, Turing. On 3 shows, 3 appearances. The Shows tab opens the full record on each.
founderexecutivescientistengineer@jonsidd ↗LinkedIn ↗turing.com ↗
Jonathan Siddharth is the co-founder and CEO of Turing, an AI infrastructure and research company providing training data for frontier models. Previously, he worked on natural language search at Powerset and co-founded Rover.
3shows
3appearances
71statements
7resolved
5supported
1contradicted
71%fully supported
6said about them ↓ Everything Jonathan Siddharth said on any show that made the record, most notable first. Each card names its show and opens the statement there.
Siddharth: Custom LLMs will allow investment firms to employ fewer analysts
“I think, like, the best firms are already doing this, ah, to help an investment analyst make better decisions faster, and eventually with significantly fewer people.”
Siddharth: Turing bridges frontier AI lab data and enterprise deployment
“Most data companies don't see deployment, and most deployment companies don't see data. So Turing is the one company that works with the Frontier AI Labs and Fortune 500 enterprises, and we are a trusted bridge between research and deployment.”
Siddharth: 10-trillion-parameter frontier AI models are now beginning to emerge
“The current series of frontier models are largely in the trillion parameter realm. Yeah. Now we are starting to see 10 trillion parameter models.”
Siddharth: AI scaling laws continue to hold across compute and data
“The scaling laws are continuing to hold, meaning bigger model with more data, with more compute, means that the models smoothly keep getting better. It's like the, when the pre-training loss keeps coming down, it's like the model's performance on also all thes…”
Siddharth: Turing is likely the largest coding data provider to AI labs
“We are probably the largest provider of coding data to all the labs.”
Enterprises are abandoning fine-tuning for frontier models with context management
“What I'm seeing more and more is there's a lot more people going into the no fine tuning camp than a couple of years ago for very high value enterprise use cases.”
Siddharth: AI disruption of legacy SaaS applies to legacy cybersecurity products
“Legacy SaaS software also applies to legacy cybersecurity products too.”
Turing avoids building proprietary foundation models to prevent competing with customers
“We would not build our own model. We don't want to compete with our customers, but we are building around the models so that enterprises can unlock the fullest value.”
Turing works with seven of the eight frontier AI labs
“We work with seven out of the eight frontier labs.”
Siddharth: Enterprise custom AI models are a permanent requirement
“I think it's a permanent requirement.”
Siddharth: Enterprise AI adoption will be slow in back-office, fast in front-office
“In back office automation, it'll probably be very slow, and it'll, it'll probably be the upstarts that'll do things well. I think the change management will be too slow. But I'm optimistic about front office, especially in financial services, life sciences, ph…”
Siddharth: Data-driven feedback loops will be the key moat in AI
“I think one moat will be data-driven feedback loops.”
Siddharth: Governments will build sovereign internal AI models
“I think it would make sense for governments to build their own internal versions of some of these models, which would require proprietary data again to be collected.”
Siddharth: Developers should keep frontier AI model technology closed for safety
“I feel like for frontier models, there is some value in keeping some of the technology closed.”
Siddharth: Turing works with seven of the eight frontier AI labs
“We work with seven out of the eight Frontier Labs. We work with OpenAI, Anthropic, Meta, Google, Microsoft, Nvidia, Amazon, anybody that's building a Frontier Foundation model, we are probably working with them already.”
Siddharth: Public internet data for pre-training AI ran out three years ago
“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, right?”
Siddharth: Verifiable domains allow self-play reinforcement learning to replace RLHF
“Now, for these verifiable domains like coding and math, instead of doing reinforcement learning with human feedback, you can do reinforcement learning. Because you can automatically check when you got the correct answer or not in these verifiable domains. And …”
Siddharth: AI will progress steadily rather than via rapid takeoff
“I don't think rapid takeoff is how things will unfold. I think it's going to be steady, continuous progress. Every step of the way, we're going to keep moving forward and it's going to be great, and it's great for a few reasons.”
OpenAI partnered with Turing to teach GPT-3 coding and tool use
“OpenAI came to us when they were training GPT-III and they wanted to teach GPT-III to code. So we collaborated with them on teaching the models to code and to do tool use, function calling”
AI reinforcement learning requires a 20% to 40% task success rate
“If you have tasks that are too easy for the model, there is no learning signal.
If it is too difficult, there is no learning signal.
So there is a sweet spot of complexity that you'd want the RL environments to be at.
Usually like, 20 to 40%, something in that…”
Siddharth: Non-binary knowledge work requires rubric-based AI evaluation
“Like with code or with math, it's relatively more binary, easy to verify. But how do you verify the quality of a board deck? Yeah. It's a, you have to be, you have to have like a good rubric based evaluator.”
Siddharth: AI scaling is constrained by compute, data, and algorithmic research
“And I think all the labs are scaling this up, and we are constrained by algorithmic research, compute, and data, and companies like Turing Advance the data pillar, NVIDIA advances the compute pillar, and of course the labs advance the algorithmic research pill…”
Siddharth: Verifiability makes coding ideal for reinforcement learning improvements
“Coding is one of those areas where, because it's verifiable, I think that there is a good path to using reinforcement learning to improve coding models quickly.”
The other half of the tape: Jonathan Siddharth's own voice is left out of every number here. Other people bring the name up 6 times in 4 episodes across the shows. every mention, with the transcript →
Who brings them up most Siddhartha Ahluwalia 3Molly O'Shea 2Vijay Krishnan 1
Every mention by year
tap a year for its mentions
the Neon Show 4
Sourcery 2
2026 4 mentions in 2 episodes 2 per episode
One line per show, most statements first. The link opens Jonathan's full record on that show: the calibration, argument clarity, speaking style and every statement made there.
| Show | Role there | Eps | Statements | Record | |
|---|---|---|---|---|---|
| NEON SHOWLEDGER | Co-founder & CEO, Turing | 1 | 28 | 75% 3/4 | full record on the Neon Show → |
 20VCLEDGER | Co-founder & CEO, Turing | 1 | 25 | 67% 2/3 | full record on 20VC → |
| SOURCERYLEDGER | Co-founder & CEO, Turing | 1 | 18 | — | full record on Sourcery → |