What happens to engineering teams when everyone can code?Bali Raghavan, cofounder and CTO at Hero Safety, joins Amir Bormand to discuss how AI is changing team structures, engineering skills, and hiring. At Hero Safety, designers, product managers, and operations teams are using AI to build and solve problems that once required engineering support.That shift changes what companies need from engineers. Bali explains why systems thinking, architecture, judgment, and problem solving are becoming more valuable. He also shares why he now expects engineering candidates to use AI during interviews.The conversation explores where specialized expertise remains essential, including computer vision and edge AI, and how public safety organizations are approaching AI adoption with both interest and skepticism.Key TakeawaysAI is expanding who can build software across a company.Engineering judgment and architecture matter more as code generation becomes easier.Coding interviews should reflect how engineers actually use AI at work.Specialized skills remain essential for complex AI systems and edge computing.Episode Highlights02:15 | Why Bali considers his entire company an engineering team04:00 | How AI changes the value of programming skills08:30 | Judgment, first principles, and evaluating AI output11:45 | Why using AI is now expected in engineering interviews14:25 | Generalists, specialists, and the need for edge AI expertise16:15 | AI adoption, privacy, and skepticism in public safetyOne Line That Stuck"The bug has become a feature."Bali explains how AI use went from something employers tried to prevent in interviews to something they now want to evaluate.Follow The Tech Trek for more conversations about engineering leadership, AI, and building technology companies.

Getting a voice agent to talk is one challenge. Knowing whether it can handle real conversations is another.Brooke Hopkins, founder of Coval, joins The Tech Trek to discuss how teams test, evaluate, and scale voice AI agents. Drawing on her experience leading evaluation infrastructure at Waymo, Brooke explains why testing voice agents shares surprising similarities with testing autonomous vehicles.The conversation explores why older voice assistants struggled, how better reasoning models changed what's possible, and what it takes to earn users' trust.Brooke also shares how her engineering team works with multiple AI agents, why technical interviews need to assess AI skills, and how she balances automation with human judgment as a founder.Key Takeaways- Voice AI evaluations must account for interruptions, background noise, and complex conversations.- Simulations identify failures before deployment, while production QA reveals what testing missed.- Engineers need new ways to divide work when running several AI agents in parallel.- Hiring should assess problem solving, system design, and how candidates use AI.Episode Highlights00:32 How Coval tests voice agents before and after deployment01:26 What autonomous vehicle simulation teaches us about voice AI03:24 Why better speech recognition wasn't enough08:00 Could voice AI change how we use screens?14:43 How AI is changing engineering interviews16:43 Where a founder uses AI and where human judgment mattersA Moment Worth Pulling Out"We had the eyes and the mouth, but we didn't have the brain."Follow The Tech Trek for more conversations about engineering, AI, and building technology companies.

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 Moments01:15 Building AI into the engineering workflow03:38 Why Omni works in teams of one to three engineers06:48 The weekly demo day that replaces heavier process10:29 Why Omni publishes unfinished product demos publicly17:14 Measuring AI engineering productivity through commits to main19:52 When token economics start affecting engineering decisionsOne Line That Stuck“I’m not married to the process. I’m married to the outcomes.”Follow The Tech Trek for more conversations about how modern engineering and product teams are changing.

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 moments00:36 Stop token maxing and start outcome maxing02:51 Why FOMO accelerated AI adoption06:32 Measuring ROI on engineering AI spend09:19 Change management inside large engineering organizations14:23 Why AI ready engineers are harder to hire17:13 Will AI reduce engineering jobs?Best Line“Stop token maxing and start outcome maxing.”Follow The Tech Trek for more conversations with technical leaders building and operating modern teams.

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 questionsKey Moments01:20 Listening versus actually hearing the business problem03:47 When immediate customer impact matters more than technical debt06:31 Bringing the business into decisions about speed and tradeoffs09:47 Why technologists need to spend more time with business teams13:20 How easier tools change the importance of defining the problem18:33 Why AI is still a tool, not a substitute for expertiseOne Line That Stuck“There’s a difference between listening and hearing.”Follow The Tech Trek for more conversations with the people building and leading modern technology teams.

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.Highlights01:53 What separates physical AI from traditional AI03:18 Why real world data collection changes the problem07:19 Supply chain visibility and practical use cases11:57 How quickly physical AI systems can make decisions12:48 Why guardrails matter in supply chain automation15:28 Robotics, distributed physical AI, and connected systemsFollow 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 Moments01:57 Going directly from a PhD program into a startup06:42 Why Scott moved from CEO to CTO10:40 Smart dust and Ambiq’s original vision13:43 Why AI is moving beyond the cloud17:52 Privacy, security, and processing data locally20:04 Industrial, medical, and smart glasses use casesA Moment Worth Pulling Out“Be present where your feet are.”Follow The Tech Trek for more conversations with the people building and operating modern technology companies.

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 Highlights00:38 What Recursive Ventures looks for in early AI companies05:42 How AI could change the amount of capital startups need10:04 Why subject matter expertise is becoming more valuable12:02 What creates an AI moat when software is easy to copy17:47 Why studying failure can be more useful than copying success23:13 How AI could reshape venture investing itselfFollow The Tech Trek for more conversations with founders, investors, and technology leaders building what comes next.

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.Highlights02:08 Why AI can abstract work, but not engineering wisdom06:04 Turning repeated debugging sessions into reusable agent skills09:47 Why faster development may change traditional project management12:42 Moving engineering work from stories to epics16:19 Where agentic coding still creates problems19:29 How Titan evaluates engineers who use AIOne 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.

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.Highlights00:41 What Teleport does and why infrastructure identity matters08:37 Why companies may already be behind on agent security13:47 Why an electronic account is not the same as identity15:11 What actually defines the identity of an AI agent?22:49 Why agent speed and unpredictability change the risk equation29:04 The conflict between useful agent behavior and anomaly detectionOne Line That Stuck“Agents are just as unpredictable as humans, but they are way, way, way faster.”Follow The Tech Trek for more conversations with the people building and leading technology companies.