From early experiments with dismantled electronics to building AI systems that power real-world machines, Ash Saxena has spent decades at the intersection of research, entrepreneurship, and applied intelligence. Now, as Founder & Chief AI Officer of TorqueAGI, he is focused on one of the most ambitious challenges in technology: enabling robots to perform meaningful work in the physical world.

Ash brings a rare depth of experience, from his PhD work at Stanford alongside Andrew Ng to founding and scaling multiple AI-driven companies. His perspective cuts through the noise of today’s AI hype cycle, offering a grounded view on what is actually working, what is misunderstood, and where the real opportunities lie in robotics and embodied intelligence.

We explore how the shift from data-driven AI to reasoning-based systems is reshaping robotics, why most companies are approaching the problem the wrong way, and what it takes to move from impressive demos to reliable deployment in the real world.

Highlights:

• Ash’s journey from building robots as a child to leading AI innovation across academia and industry, including early work on deep learning for robotics
• Key inflection points that led him to found multiple companies, including applying AI to unlock access to credit through Catapult
• Why “technology-first” companies often fail and the importance of aligning AI with real customer demand and ROI
• The evolution of AI from statistical models to deep learning to today’s foundation models and reasoning-based systems
• Why the biggest shift in AI is not better models, but dramatically faster time to deployment from years to days or weeks
• What Torque AGI is actually building: end-to-end robotic “skills” that combine foundation models, agents, and real-time infrastructure
• Why data collection at massive scale may not be the answer and how useful systems can be built with far less data than expected
• The gap between AI demos and real-world deployment, and why most demonstrations fail outside controlled environments
• A pragmatic roadmap for robotics adoption, from simple tasks today to more complex industrial automation over the next decade
• Where Torque AGI fits in the stack as a modular layer that translates AI models into actionable robotic capabilities
• The importance of interpretability, safety, and measurable performance when deploying AI into physical systems
• The core technical bottleneck in robotics today: bridging deep learning with real-world physics and constraints
• Why industrial robotics will see massive value creation in the next 5 to 10 years, while humanoids remain further out
• A contrarian take on general-purpose systems: general AI will matter more than general-purpose robots
• Where the industry is overhyping progress, especially around humanoid demos, and what is actually working today
• Why AI-driven upgrades to existing robots could unlock 10x to 40x increases in productivity without new hardware
• How to stay disciplined as a founder in a hype-driven market by focusing on real customer outcomes instead of funding cycles
• What a successful deployment looks like, from quick demos to full operational integration in messy real-world environments

Learn more about TorqueAGI: LinkedIn | Twitter | Website

Connect with Ash Saxena: LinkedIn | Stanford

Connect with Greg Toroosian: https://www.linkedin.com/in/gregtoroosian/