Episode #520: Training Super Intelligence One Simulated Workflow at a Time

Jan 5, 2026

In this engaging discussion, AI practitioner Josh Halliday, who specializes in training advanced models and synthetic data, shares his expertise in reinforcement learning environments. He highlights the evolution of AI training, emphasizing the transition to high-quality, expert-verified data as the main bottleneck in development. Josh also delves into the fascinating uses of gaming engines for AI training, the challenges of data collection, and the philosophical implications of our dependence on AI. He predicts rapid advancements in robotics and emerging job opportunities in AI training.

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00:00 / 00:00

Simulations Fill Dangerous Data Gaps

Unity's real-time 3D engines let teams generate synthetic data for dangerous or expensive edge cases.
High-fidelity simulated scenarios replace impossible real-world data collection for training vision models.

00:00 / 00:00

RL Teaches By Iterative Failure

Reinforcement learning trains agents by trial and error with reward signals.
Agents reset after failures and iterate until they reliably solve the task.

00:00 / 00:00

Render Fidelity Matters For Vision Models

Ray tracing and path tracing mimic real light paths to increase visual fidelity in simulations.
Higher visual realism reduces model confusion in safety-critical vision tasks.

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In this episode of the Crazy Wisdom podcast, host Stewart Alsop sits down with Josh Halliday, who works on training super intelligence with frontier data at Turing. The conversation explores the fascinating world of reinforcement learning (RL) environments, synthetic data generation, and the crucial role of high-quality human expertise in AI training. Josh shares insights from his years working at Unity Technologies building simulated environments for everything from oil and gas safety scenarios to space debris detection, and discusses how the field has evolved from quantity-focused data collection to specialized, expert-verified training data that's becoming the key bottleneck in AI development. They also touch on the philosophical implications of our increasing dependence on AI technology and the emerging job market around AI training and data acquisition.

Timestamps

00:00 Introduction to AI and Reinforcement Learning
03:12 The Evolution of AI Training Data
05:59 Gaming Engines and AI Development
08:51 Virtual Reality and Robotics Training
11:52 The Future of Robotics and AI Collaboration
14:55 Building Applications with AI Tools
17:57 The Philosophical Implications of AI
20:49 Real-World Workflows and RL Environments
26:35 The Impact of Technology on Human Cognition
28:36 Cultural Resistance to AI and Data Collection
31:12 The Bottleneck of High-Quality Data in AI
32:57 Philosophical Perspectives on Data
35:43 The Future of AI Training and Human Collaboration
39:09 The Role of Subject Matter Experts in Data Quality
43:20 The Evolution of Work in the Age of AI
46:48 Convergence of AI and Human Experience

Key Insights

1. Reinforcement Learning environments are sophisticated simulations that replicate real-world enterprise workflows and applications. These environments serve as training grounds for AI agents by creating detailed replicas of tools like Salesforce, complete with specific tasks and verification systems. The agent attempts tasks, receives feedback on failures, and iterates until achieving consistent success rates, effectively learning through trial and error in a controlled digital environment.
2. Gaming engines like Unity have evolved into powerful platforms for generating synthetic training data across diverse industries. From oil and gas companies needing hazardous scenario data to space intelligence firms tracking orbital debris, these real-time 3D engines with advanced physics can create high-fidelity simulations that capture edge cases too dangerous or expensive to collect in reality, bridging the gap where real-world data falls short.
3. The bottleneck in AI development has fundamentally shifted from data quantity to data quality. The industry has completely reversed course from the previous "scale at all costs" approach to focusing intensively on smaller, higher-quality datasets curated by subject matter experts. This represents a philosophical pivot toward precision over volume in training next-generation AI systems.
4. Remote teleoperation through VR is creating a new global workforce for robotics training. Workers wearing VR headsets can remotely control humanoid robots across the globe, teaching them tasks through direct demonstration. This creates opportunities for distributed talent while generating the nuanced human behavioral data needed to train autonomous systems.
5. Human expertise remains irreplaceable in the AI training pipeline despite advancing automation. Subject matter experts provide crucial qualitative insights that go beyond binary evaluations, offering the contextual "why" and "how" that transforms raw data into meaningful training material. The challenge lies in identifying, retaining, and properly incentivizing these specialists as demand intensifies.
6. First-person perspective data collection represents the frontier of human-like AI training. Companies are now paying people to life-log their daily experiences, capturing petabytes of egocentric data to train models more similarly to how human children learn through constant environmental observation, rather than traditional batch-processing approaches.
7. The convergence of simulation, robotics, and AI is creating unprecedented philosophical and practical challenges. As synthetic worlds become indistinguishable from reality and AI agents gain autonomy, we're entering a phase where the boundaries between digital and physical, human and artificial intelligence, become increasingly blurred, requiring careful consideration of dependency, agency, and the preservation of human capabilities.