The Tech Trek

Technology teams are often asked to solve something before everyone agrees on what the actual problem is.

Chad Carrington, CIO at Golden1 Credit Union, joins The Tech Trek to talk about listening to business partners, defining the outcome first, and resisting the instinct to jump immediately to a technology solution.

The conversation also looks at how AI is changing this skill. As tools make building easier and faster, people still need to articulate what they actually want, ask better questions, and understand what success looks like.

What You Will Hear

• Why listening and hearing are not the same thing
• When solving 85 percent of a problem may be enough
• Why technology teams need more time with business users
• How AI is increasing the value of asking better questions

Key Moments

01:20 Listening versus actually hearing the business problem
03:47 When immediate customer impact matters more than technical debt
06:31 Bringing the business into decisions about speed and tradeoffs
09:47 Why technologists need to spend more time with business teams
13:20 How easier tools change the importance of defining the problem
18:33 Why AI is still a tool, not a substitute for expertise

One Line That Stuck

“There’s a difference between listening and hearing.”

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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

Follow The Tech Trek for more conversations about AI, engineering, product, and technical leadership.

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.”

Follow The Tech Trek for more conversations with the people building and leading technology companies.