The Tech Trek

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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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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AI coding agents can help engineering teams ship more code. But the bigger change may be what engineers spend their time doing.

Viren Baraiya, Co-Founder and CTO of Orkes, joins The Tech Trek to discuss how AI is changing workflow orchestration, engineering productivity, project delivery, and hiring. As agents take on more implementation work, engineers are spending more time on design, architecture, review, and verification.

Viren shares how his team measures the return on AI through product velocity, stability, and the ability to build things that previously required more time or outside resources. He also explains how Orkes manages model costs by using stronger models for difficult reasoning and smaller models for implementation.

Key Takeaways

• Coding agents increase output, but they also increase the need for verification.

• Engineers are shifting from pure implementation toward design, review, and orchestration.

• Repeated AI tasks can become reusable workflows that reduce ongoing token usage.

• Hiring should test how engineers actually work with agents, not just manual coding.

Highlights

01:21 Why agents are workflows and where orchestration fits into AI systems

05:19 How AI changed feature velocity, testing, and customer engineering at Orkes

07:44 Measuring AI ROI through velocity, stability, and new product capabilities

09:19 Why engineers increasingly look more like tech leads

12:56 Turning repeated AI requests into reusable workflows to reduce token usage

19:34 Why Orkes changed engineering interviews to include agentic coding

One Line That Stuck

“That has become a more important skill than actually writing the code now.”

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AI is doing more than helping engineers write code faster. It is starting to change who can participate in software development, how teams divide work, and where technical talent creates the most value.

Shaosu Liu, Co Founder and CTO at Loop, explains how his team is using AI agents across the software development lifecycle while also enabling highly technical people outside traditional software engineering roles to build customer specific workflows. The result is a different model for scaling technical work, one that matters for founders and technical leaders thinking about team design in the age of AI.

Loop is also using forward deployed engineers as core product engineers who can work directly with customers, understand difficult edge cases, and turn those requirements into product improvements.

Takeaways

• AI agents can now support much more than coding, including specifications, testing, deployment, validation, rollout, and production management.

• Giving technical people better AI tools can expand who is capable of contributing to software development.

• The final few percent of a customer workflow may consume most of the manual effort. AI makes that customization more practical.

• Forward deployed engineers need both technical ability and the judgment, communication skills, and confidence to work directly with customers.

Key Moments

04:37 How Loop uses AI across the software development lifecycle

07:05 Why people outside traditional engineering roles are writing significant amounts of code

09:03 Why automating the final few percent of a workflow can remove most of the remaining manual work

11:01 Why forward deployed engineers matter when customer requirements get complicated

16:37 Why forward deployed engineering is often a path to another role rather than a long term career

19:50 How Loop evaluates technical ability and customer facing skills when hiring

One Line That Stuck

“That last 5% automation ends up saving 100% of time.”

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