Jonathan Siddharth
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If an AI Model Can Cheat, It Will | Turing CEO on Reward Hacking
Jonathan Siddharth, Molly O'Shea
Shifting focus from test mastery to real-world application, the event details the 2026 transition where enterprises leverage simulated reinforcement learning environments to deploy autonomous agents for complex, long-horizon workflows. Organized by Turing in partnership with NVIDIA and Anthropic, the discussion highlights how custom open-weight models and proprietary frontier systems will coexist to optimize core business processes while addressing emerging safety risks like emergent cooperation and biological threats. The dialogue concludes with a strategic roadmap for the next decade, emphasizing a slow takeoff of superintelligence to solve fundamental human constraints through rigorous safety engineering and the democratization of healthcare.
Turing CEO Jonathan Siddharth: Who Wins in Data Labelling & Why 99% of Knowledge Work Will Disappear
Jonathan Siddharth, Harry Stebbings
Turing has repositioned itself as a research accelerator for the seven frontier AI labs currently training superintelligence, shifting its core focus from traditional data labeling to generating complex, multi-step real-world data for autonomous agents. Led by Jonathan Siddharth, the firm builds massive Reinforcement Learning environments to teach AI models to perform economically valuable tasks, serving clients like Disney, Pepsi, and BlackRock while offering fine-tuned deployments for on-premise enterprise security. This strategy addresses a projected $30 trillion automation market by replacing generic chatbots with specialized vertical data solutions, ultimately aiming to transform entrepreneurship and drive breakthroughs in fields such as medical research.
Inside The $2.2B AI Research Accelerator | Turing
Jonathan Siddharth, Molly O'Shea
Turing, a profitable AI infrastructure firm valued at $2.2 billion, serves nine frontier labs by deploying a global network of four million engineers to generate expert data for training superintelligence models. The company distinguishes itself from traditional data labelers by combining smart human sourcing with proactive research to advance coding, reasoning, and multi-modality pillars while challenging the dominance of commodity data services. Facing a market where internet-based pre-training data is exhausted, Turing aims to capture a $30 trillion opportunity by replacing legacy consulting models with agentic systems that automate complex enterprise knowledge work.