The Tech Trek

The original idea may not be what makes a startup successful. As AI changes markets and creates new opportunities, the ability to learn and change direction becomes increasingly important.

Tim Chen, Managing Partner at Essence Venture Capital, invests in highly technical founders building infrastructure and developer tools. A former engineer and founder himself, Tim started his fund to provide the kind of support he wished he had when raising capital.

In this episode, Tim explains why his investment approach has evolved as AI accelerates change. He shares how he evaluates technical teams, what makes a pivot worth pursuing, and why he believes specialization helps investors stand out.

The conversation also explores his decision to operate as a largely solo investor, the importance of founder autonomy, and what it means to build a venture fund around a specific area of expertise.

Key Takeaways

Why a team's ability to learn matters beyond its initial product idea.
How AI is changing investment decisions in infrastructure and developer tools.
Why specialist investors can offer founders more relevant connections and guidance.
How Tim approaches pivots without making decisions for founders.

Episode Highlights

00:30 Why Tim started Essence Venture Capital.
03:32 The advantages of making independent investment decisions.
12:08 How specialist investors demonstrate value before investing.
17:03 What the Tabular investment revealed about technical opportunities.
18:04 Why some of Tim's strongest investments have involved major pivots.
22:33 How Tim evaluates the reasoning behind a founder's decision to pivot.

A key quote:

"Don't be too religious about the approach you take because you most likely are wrong."

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An AI agent can be fast, available, and still get the answer wrong. That changes what engineering teams need to measure.

Jeetendra Soneja, VP of Engineering, AI Platform at iHerb, joins The Tech Trek to discuss why AI observability goes beyond traditional software monitoring. Teams need to understand not just latency and errors, but also response quality, groundedness, cost, and whether an agent completes its intended task.

We also explore how to measure AI ROI without relying on misleading productivity metrics. Jeetendra shares his perspective on risk based guardrails, the skills engineers need, and why observability should begin during development rather than waiting until production.

Key Takeaways

Monitor agent quality and task completion alongside latency, availability, and cost.
Build evaluations around the specific business problem the AI system is solving.
Connect AI spending to business outcomes, not just token usage or code output.
Start with basic observability, then expand controls as AI adoption grows.

Episode Highlights

01:00 Why AI observability requires more than traditional monitoring.
06:00 The metrics that matter for cost, quality, and reliability.
08:00 How to measure AI ROI beyond productivity claims.
11:00 Setting risk tolerances and choosing appropriate guardrails.
16:00 The engineering skills becoming more valuable with AI.
22:00 Proving value before scaling AI systems.

One Line That Stuck

"It's one thing to get it done, it's another thing to get it done economically."

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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 Takeaways

AI 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 Highlights

02:15 | Why Bali considers his entire company an engineering team
04:00 | How AI changes the value of programming skills
08:30 | Judgment, first principles, and evaluating AI output
11:45 | Why using AI is now expected in engineering interviews
14:25 | Generalists, specialists, and the need for edge AI expertise
16:15 | AI adoption, privacy, and skepticism in public safety

One 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.

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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 Highlights

00:32 How Coval tests voice agents before and after deployment
01:26 What autonomous vehicle simulation teaches us about voice AI
03:24 Why better speech recognition wasn't enough
08:00 Could voice AI change how we use screens?
14:43 How AI is changing engineering interviews
16:43 Where a founder uses AI and where human judgment matters

A Moment Worth Pulling Out

"We had the eyes and the mouth, but we didn't have the brain."

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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.”

Follow The Tech Trek for more conversations about how modern engineering and product teams are changing.