The Tech Trek

What happens when AI changes more than coding speed?

Chris Merrick, Co Founder and CTO at Omni, explains how his engineering team is adapting around AI, autonomy, product ownership, and a lighter operating model.

Omni has about 25 engineers working in groups as small as one to three people. Engineers are expected to make product decisions, move without waiting for detailed specifications, and show unfinished work during a weekly company demo day.

Those demos are also posted publicly. Customers watch them, join betas, and see features often move into production soon after.

Chris also shares how Claude Code changed development across the team. Commits to main increased roughly two to three times without comparable team growth, but the added output created another constraint: code review.

Takeaways

• Small teams can operate with less process when engineers have real ownership.
• Public demos create feedback loops between engineers, customers, and the rest of the company.
• AI can increase engineering output while moving the bottleneck somewhere else.
• Token costs still matter when AI usage scales across products and engineering teams.

Key Moments

01:15 Building AI into the engineering workflow
03:38 Why Omni works in teams of one to three engineers
06:48 The weekly demo day that replaces heavier process
10:29 Why Omni publishes unfinished product demos publicly
17:14 Measuring AI engineering productivity through commits to main
19:52 When token economics start affecting engineering decisions

One Line That Stuck

“I’m not married to the process. I’m married to the outcomes.”

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AI coding tools are helping engineers write code faster. But faster code does not automatically mean better business outcomes.
Vitaly Gordon, cofounder and CEO of Faros, joins The Tech Trek to talk about the gap between AI adoption and measurable results. The conversation looks at why engineering teams are spending more on AI, how enterprises are thinking about ROI, and why existing processes can become the bottleneck even when code generation speeds up.
Vitaly also explains why AI adoption inside large organizations looks different from simply giving engineers access to new tools. Teams still have to deal with security, legacy processes, risk, integration, and organizational change.
Key takeaways
• More AI usage does not automatically create more customer value.
• Engineering leaders need to connect AI spend to actual outcomes.
• Enterprise adoption requires process changes, not just new tools.
• AI ready engineers increasingly need both domain expertise and AI skills.

Key moments
00:36 Stop token maxing and start outcome maxing
02:51 Why FOMO accelerated AI adoption
06:32 Measuring ROI on engineering AI spend
09:19 Change management inside large engineering organizations
14:23 Why AI ready engineers are harder to hire
17:13 Will AI reduce engineering jobs?

Best Line
“Stop token maxing and start outcome maxing.”

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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

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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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