Robots aren’t just software. They’re AI in the physical world. And that changes everything.

In this episode of TechFirst, host John Koetsier sits down with Ali Farhadi, CEO of Allen Institute for AI, to unpack one of the biggest debates in robotics today: Is data enough, or do robots need structured reasoning to truly understand the world?

Ali explains why physical AI demands more than massive datasets, how concepts like reasoning in space and time differ from language-based chain-of-thought, and why transparency is essential for safety, trust, and human–robot collaboration. We dive deep into MOMO Act, an open model designed to make robot decision-making visible, steerable, and auditable, and talk about why open research may be the fastest path to scalable robotics.

This conversation also explores:

• Why reasoning looks different in the physical world

• How robots can project intent before acting

• The limits of “data-only” approaches

• Trust, safety, and transparency in real-world robotics

• Edge vs cloud AI for physical systems

• Why open-source models matter for global AI progress

If you’re interested in robotics, embodied AI, or the future of intelligent machines operating alongside humans, this episode is a must-watch.

👤 Guest

Ali Farhadi

CEO, Allen Institute for AI (AI2)

Professor, University of Washington

Former Apple researcher

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00:00 – Plato vs Aristotle… in robotics?

00:55 – What “reasoning” means in the physical world

02:10 – How humans predict actions before they happen

03:45 – Why physical AI is fundamentally different from text AI

04:50 – The next revolution: AI in the real world

05:30 – What is MOMO Act?

06:20 – Chain-of-thought… for robots

07:45 – Trajectories as reasoning and robot transparency

08:55 – Trust, safety, and correcting robots mid-action

10:15 – Why predictability builds trust in machines

11:40 – What’s broken with data-only AI approaches

13:10 – Why reasoning + data isn’t an “either/or”

14:00 – Open sourcing robotics models: why it matters

15:20 – How closed AI slows innovation

16:45 – Global competition and open research

17:40 – What’s next for robotics reasoning models

18:20 – Can these models work across robot types?

19:30 – Temporal and spatial reasoning in MOMO 2

20:40 – Scaling robotics vs scaling LLMs

21:10 – Edge vs cloud AI for robots

22:20 – Specialized models, latency, and privacy

23:00 – Final thoughts on the future of physical AI