The Tech Trek

AI has learned from the digital world. Physical AI brings real world data into the picture.

Doron Hazan, Director of Products and AI at Wiliot, joins The Tech Trek to explain how physical AI connects AI systems with objects, environments, and supply chains.

The challenge is not simply processing data. It is collecting accurate, current information from the physical world.

Doron explains how ambient IoT, sensors, statistical inference, and cloud systems can help companies understand where assets are, what condition they are in, and what may happen next.

The conversation also covers the role of human judgment. Supply chains require many decisions, often with consequences that spread across the system. That makes guardrails and human involvement especially important.

Key Takeaways

• Physical AI connects AI systems with data from the real world.
• Better supply chain visibility starts with accurate, current physical data.
• Real time decisions matter, but decision accuracy matters more.
• Human judgment remains important when AI affects physical operations.

Highlights

01:53 What separates physical AI from traditional AI
03:18 Why real world data collection changes the problem
07:19 Supply chain visibility and practical use cases
11:57 How quickly physical AI systems can make decisions
12:48 Why guardrails matter in supply chain automation
15:28 Robotics, distributed physical AI, and connected systems

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Scott Hanson went straight from a PhD program at the University of Michigan to building a semiconductor company.

Today, as Founder and CTO of Ambiq, he is working on the same core idea that inspired the company years ago: putting intelligence into the devices around us. What changed is what those devices can now do.

Scott shares what it was like becoming CEO without prior industry experience, why moving into the CTO role was harder than expected, and how the rise of AI accelerated Ambiq’s original vision.

The conversation also looks at what happens as more AI processing moves closer to the device, from wearables and smart homes to factories, medical devices, infrastructure, and smart glasses.

Key Takeaways
• Founder roles may need to change as the company grows.
• Edge AI can reduce how much personal data needs to leave a device
• Low power computing expands where AI can operate.
• AI tools are changing engineering work from coding toward architecture and design.

Key Moments

01:57 Going directly from a PhD program into a startup
06:42 Why Scott moved from CEO to CTO
10:40 Smart dust and Ambiq’s original vision
13:43 Why AI is moving beyond the cloud
17:52 Privacy, security, and processing data locally
20:04 Industrial, medical, and smart glasses use cases

A Moment Worth Pulling Out

“Be present where your feet are.”

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Building an AI product is getting easier. Building an AI company that lasts is not.

Itamar Novick, Founder and General Partner at Recursive Ventures, joins The Tech Trek to explain what he looks for when investing at the earliest stages of AI companies. The conversation covers how lower development costs could change venture funding, why subject matter expertise matters more as software becomes easier to build, and what actually creates defensibility when competitors can move quickly.

Itamar also shares how Recursive Ventures thinks about founder anti patterns. Rather than trying to copy the paths of successful startups, he argues that founders can improve their odds by recognizing common mistakes that repeatedly create unnecessary risk.

Key Takeaways

• AI may let companies reach scale with much less outside capital.
• Subject matter expertise matters more when building software is no longer the main barrier.
• Proprietary data, feedback loops, hardware, and exclusive access can create stronger moats.
• Founders can reduce risk by learning to recognize repeatable startup mistakes.

Episode Highlights

00:38 What Recursive Ventures looks for in early AI companies
05:42 How AI could change the amount of capital startups need
10:04 Why subject matter expertise is becoming more valuable
12:02 What creates an AI moat when software is easy to copy
17:47 Why studying failure can be more useful than copying success
23:13 How AI could reshape venture investing itself

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AI coding agents can produce software faster, but they do not replace the judgment needed to understand the system.

Shaun Patterson, CTO at Titan, joins The Tech Trek to discuss how agentic coding is changing problem solving, development workflows, project management, and technical hiring.

Shaun explains why engineers still need a strong mental model of the systems they are building. AI can generate code, reproduce bugs, research implementation options, and automate repeated debugging work. But it can also keep working on the wrong problem long after a human debugger would have found the answer.

The conversation also gets into a bigger shift in software delivery. If agents can work across much larger pieces of a project, engineering teams may move from managing work at the story level to working at the epic level.

Key Takeaways

• AI speeds up implementation, but engineering judgment still matters.
• Repeated debugging work can become reusable agent skills.
• Faster implementation lowers the cost of testing different technical approaches.
• Hiring increasingly needs to measure how engineers work with AI.

Highlights

02:08 Why AI can abstract work, but not engineering wisdom
06:04 Turning repeated debugging sessions into reusable agent skills
09:47 Why faster development may change traditional project management
12:42 Moving engineering work from stories to epics
16:19 Where agentic coding still creates problems
19:29 How Titan evaluates engineers who use AI

One Line That Stuck

“It abstracts your thinking, but it doesn’t abstract your wisdom.”

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AI agents create a different security problem from traditional software. They can operate at software speed and scale while behaving in ways that are much less predictable.

Ev Kontsevoy, CEO and cofounder of Teleport, joins The Tech Trek to discuss what happens when companies deploy agents into security systems designed around humans, applications, and relatively static organizational structures.

The conversation gets into authentication, impersonation, infrastructure identity, access control, and a harder question: what actually defines the identity of an AI agent when its model, memory, skills, and capabilities can change?

Ev also explains why the combination of speed, scale, and unpredictable behavior changes the risk of mistakes. Later, he explores the tension between agents being useful because they can do new things and security systems that often depend on predictable behavior.

Key takeaways

• Agent identity gets harder when memory, models, and capabilities can change.
• Traditional access controls often reflect static organizational structures.
• Agents combine software speed with behavior that can be difficult to predict.
• Useful agent behavior can conflict with security systems built around anomaly detection.

Highlights

00:41 What Teleport does and why infrastructure identity matters
08:37 Why companies may already be behind on agent security
13:47 Why an electronic account is not the same as identity
15:11 What actually defines the identity of an AI agent?
22:49 Why agent speed and unpredictability change the risk equation
29:04 The conflict between useful agent behavior and anomaly detection

One Line That Stuck

“Agents are just as unpredictable as humans, but they are way, way, way faster.”

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