The Tech Trek

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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AI agents are moving beyond helping with individual tasks. The bigger question is how much of a company they can actually run.

Ben Cera, founder of Polsia, joins The Tech Trek to discuss what happens when AI handles engineering, support, marketing, research, and other parts of company execution.

Ben explains how Polsia uses specialized agents that can take direction from a founder or decide what to work on autonomously. He also shares how he uses similar systems inside his own company, which he says has more than 10,000 paying customers and is approaching a $10 million run rate without a traditional full time team.

The conversation gets into where humans still matter, why AI mistakes may be acceptable, and how faster execution changes the way founders test ideas.

Key Takeaways

• AI agents can move from completing tasks to coordinating entire business functions.
• Faster execution gives founders quicker feedback on what works and what does not.
• Humans still matter most for judgment, direction, and authentic storytelling.
• Autonomy requires accepting some mistakes instead of demanding perfect AI output.

Highlights

02:43 What changes when AI becomes part of how a founder operates
04:03 Turning customer support into a system that can also fix problems
06:19 Running a company without a traditional full time team
13:47 Why founder judgment still matters when AI gives the options
18:49 How specialized agents coordinate engineering, marketing, and outreach
22:10 What happens when autonomous AI makes the wrong decision

One Line That Stuck

“You have to trust your gut and you have to be willing to make mistakes.”

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