Advertise on podcast: The Tech Trek

Some healthcare professionals want to brainstorm about AI. Others are reluctant to use it. And many simply want technology that helps them do their jobs. Dany Jradi, Head of Product at Abby Care, joins The Tech Trek to discuss what healthcare stakeholders actually care about when AI enters their workflows. For engineers and product teams, the technology itself can be exciting. For clinicians and operators, the outcome often matters more than how the system works. Dany explains why AI adoption remains uneven, even for tools like AI scribes that have been available for years. He also discusses why reviewing administrative documents requires a different level of scrutiny than providing clinical guidance. The conversation turns to family caregiving and the potential of daily care notes. Could AI help clinical teams identify changes in a patient's condition before those changes lead to a serious event? Key Takeaways Healthcare stakeholders often prioritize their work over how an AI system operates. AI adoption varies widely, even among technology savvy clinicians. Administrative automation and clinical guidance require different standards. Daily caregiver notes could reveal patterns that individual events miss. Episode Highlights 02:02 Why healthcare professionals respond differently to AI 04:20 What builders care about versus what stakeholders need 08:09 AI scribes and the uneven pace of adoption 11:44 Why clinical and administrative AI have different standards 15:42 Finding useful signals in daily caregiver notes 20:09 How AI could identify trends between major health events Follow The Tech Trek for more conversations about AI, product development, and building technology that solves real problems.

What happens when AI makes writing code faster, but your engineering process still operates on two week sprints? Junaid Warwani, VP of Engineering at Brellium, joins The Tech Trek to explain how he restructured engineering at his previous company after adopting AI development tools. His team moved away from traditional sprints, gave individual engineers ownership of priorities, and shifted toward weekly planning with faster feedback. But moving faster introduced new problems. Engineers had to manage more context, product managers needed different responsibilities, and the rest of the organization had to adapt. Junaid shares what worked, where the model fell short, and what he would change as he brings those lessons into his new role. Key Takeaways Why traditional sprint planning started losing its value as AI accelerated development. How one engineer can own delivery while product managers focus on what comes next. Why feedback loops can become a bigger bottleneck than writing code. How hiring priorities are shifting toward communication, customer understanding, and critical thinking. Episode Highlights 02:48 Why Junaid returned from parental leave ready to rethink engineering. 06:22 Rethinking the relationship between product managers and engineers. 10:45 Replacing traditional sprints with individual ownership and weekly priorities. 14:20 Why faster feedback matters more than faster code. 19:23 What Junaid now looks for when hiring engineers. 27:31 Why engineering leaders need to become better at change management. A Moment Worth Pulling Out "What we're doing does not make sense anymore." Junaid describes the realization that led him to rethink his team's development process. If you're reconsidering how your engineering team works with AI, this conversation offers practical lessons from a leader who has already experimented with a different model. Follow The Tech Trek for more conversations about technology, engineering, AI, and leadership.

What actually makes a company AI native? Is it using AI across every department, building more agents, or changing how the organization works? Eswar Vandenapu, Cofounder and CTO of NovaWorks AI, shares what his team is learning as it moves from basic AI adoption toward a shared context model that shapes how decisions are made. The discussion explores why giving everyone access to AI tools is only the beginning. Eswar explains how NovaWorks is using shared context to improve collaboration, why an agent that understands its environment can be more useful than dozens of specialized agents, and how the boundaries between product and engineering are changing. We also discuss the skills engineering teams need as AI handles more coding work, including systems thinking, domain expertise, and the ability to ask better questions. What You'll Learn Why AI adoption alone does not make a company AI native. How shared context changes collaboration and decision making. Why agent quality matters more than the number of agents. How AI is changing engineering roles, hiring, and technical skills. Episode Highlights 00:40 How NovaWorks is rethinking HR and workforce management 03:37 The journey from AI awareness to shared organizational context 07:54 Why building more agents is not necessarily better 11:07 How AI is changing the relationship between product and engineering 14:41 Why systems thinking and domain knowledge matter more in engineering 18:57 Learning to question AI rather than simply give it instructions One Line That Stuck "Quantity of the agents doesn't matter." Follow The Tech Trek for more conversations about engineering, AI, product development, and building technology companies.

A few seconds of audio can now be enough to clone a voice. But the bigger challenge is knowing who, or what, is on the other end of an interaction. That question matters when someone calls a bank, joins a job interview, or sends an AI agent to act on their behalf. Vijay Balasubramaniyan, CEO and Co Founder of Pindrop, joins The Tech Trek to discuss how identity and security are changing as deepfakes and AI agents become easier to create. He shares why Pindrop's apparent overnight success took 15 years, how fraudsters are automating account attacks, and why companies need to think beyond a single identity check. The conversation also gets into hiring. Vijay describes fake applicants, different people appearing across interview rounds, and the case for verifying identity throughout the hiring process and after onboarding. His larger point is that stronger, ongoing identity signals could also make security less intrusive for legitimate users. Key takeaways - Deepfake detection needs to keep pace with tools that can clone voices from seconds of audio. - Companies must distinguish legitimate AI agents from agents attempting fraud or unauthorized access. - Recruiting teams are increasingly part of an organization's security defenses. - Continuous identity can protect interviews, accounts, and employee access without adding constant friction. Episode highlights - 01:38 Why Pindrop's success took years to build - 03:44 Voice cloning goes from 20 hours to three seconds - 06:44 How fraudsters use AI agents to make calls at scale - 11:51 Who takes responsibility when an AI agent makes a bad decision? - 16:28 Fake candidates and the risks hiding in hiring funnels - 21:34 How continuous identity could make security feel invisible A moment worth pulling out: “They are now part of security. They're the front end.” Vijay on recruiting teams. Follow The Tech Trek for more conversations about the decisions behind modern technology companies.

The goal is not to do less. It is to give agents the flexibility to handle more of the work customers actually need. Henry Hopcroft, Chief Product Officer at Gradial, joins The Tech Trek to discuss how building AI products is changing traditional approaches to product development, engineering, and customer delivery. Henry explains why his team has moved away from building individual agent features toward a smaller set of core capabilities. He also shares how Gradial tests agents against difficult enterprise environments, uses forward deployed engineers to learn directly from customers, and approaches product planning when AI capabilities are improving so quickly. What You'll Learn Why adding more features can limit an AI agent's flexibility. How real customer failures shape Gradial's agent evaluations. Why forward deployed engineers play a central role in product development. How coding agents are bringing product and engineering roles closer together. Episode Highlights 03:59 Why Gradial started deleting agent features. 07:31 Why traditional software testing falls short for enterprise AI agents. 08:35 Building a nightmare marketing stack to test agents. 12:03 How forward deployed engineers improve the core product. 15:56 Why great engineers increasingly need product judgment. 21:55 Why long term roadmaps are becoming harder for AI companies. A Moment Worth Pulling Out "Features can be something of an anti-pattern, I think, for agentic software." Follow The Tech Trek for more conversations about AI, engineering, product strategy, and building technology companies.

Jayeeta Putatunda, AI Engineering Lead at Turing, joins The Tech Trek to explore what happens after AI generates the first draft. She explains how excessive code can create maintenance challenges, why growing pull request volumes put pressure on code reviews, and how teams can build AI review agents around their own business rules. The conversation also explores how AI is changing product development. Teams can test ideas, create prototypes, and gather customer feedback faster. But taking those ideas into production still requires testing, security, and engineering discipline. Jayeeta also discusses LLM observability, the growing importance of domain expertise, and why sharing lessons across engineering teams matters as AI tools evolve. Key Takeaways More AI generated code can create additional review and maintenance work. Code review agents need business context, not just generic rules. Faster prototypes still require careful testing before production. Domain expertise and human judgment remain important in AI systems. Episode Highlights 00:34 Why AI generates more code and documentation than teams may need. 03:46 How growing PR volumes create new review challenges. 06:44 Measuring productivity beyond the number of features delivered. 12:10 Managing stakeholder expectations when prototypes become easier to build. 14:34 Why LLM observability matters in regulated industries. 18:44 The role of domain experts in building more specialized AI models. One Line That Stuck "Who's reviewing those 10 PRs?" Follow The Tech Trek for more conversations about engineering, AI, data, and technical leadership.

AI teams can build solutions faster than many organizations can make decisions. That gap is changing how engineers work with the business and what companies need from technical leaders. Ash Pembroke, Managing Director of AI Strategy at Accordion, joins The Tech Trek to discuss the growing role of forward deployed engineers. She explains why technical depth alone is not enough and how successful teams combine engineering, product thinking, and business knowledge. The conversation covers leading lean engineering teams, finding the right mix of skills, working alongside business stakeholders, and navigating the tension between rapid AI development and slower organizational adoption. Key Takeaways Strong forward deployed engineers combine technical expertise with curiosity and business understanding. Lean teams need complementary skills rather than one engineer who can do everything. AI agents can extend existing SaaS systems, but replacing those systems requires careful decisions. Faster development does not automatically mean faster adoption or better business decisions. Episode Highlights 01:30 What makes a strong forward deployed engineer? 03:18 Leading small engineering teams with overlapping responsibilities 08:25 How AI agents are changing the role of traditional SaaS 16:50 Moving from software engineering or product into forward deployed roles 25:13 Why business decisions can become the bottleneck to AI adoption 33:48 Giving business teams the ability to automate their own workflows safely A Moment Worth Pulling Out "Nobody knows the work better than the person doing the work." Ash makes the case for giving employees the tools, access, and guardrails to improve their own workflows. Follow The Tech Trek for more conversations on AI, engineering, and technical leadership.

AI can generate answers in seconds. But what happens when the information needed to make a decision isn't available online? Sagar Kadakia, founder and CEO of Qualitate, believes reasoning is becoming abundant while real world evidence remains scarce. His company uses AI moderators to interview experts, capture their knowledge, and make that information accessible to businesses. In this episode of The Tech Trek, Sagar explains how AI can change the economics of primary research. We discuss why human knowledge still matters, how AI interviews work, and what happens when companies can gather expert insight faster and at lower cost. The conversation also explores AI's impact on jobs, why Sagar is hiring more engineers, and how early AI adoption could create lasting advantages for businesses. Key Takeaways AI reasoning is becoming widely available, but firsthand human knowledge remains difficult to access. AI moderators can reduce the cost and complexity of collecting expert insights. AI may affect coordination roles differently from engineering, sales, and physical services. Companies adopting AI early could build advantages that compound over time. Episode Highlights 00:55 | How Qualitate captures human knowledge using AI 04:20 | Why reasoning is abundant but real world evidence is scarce 08:20 | How AI could reshape research and consulting services 12:00 | Inside an AI moderated expert interview 23:00 | Which jobs could change as AI adoption increases 28:00 | Why AI adoption could separate competing companies One Line That Stuck "Reasoning is no longer scarce." Follow The Tech Trek for more conversations about AI, engineering, technology leadership, and company building.

AI can help a small company move faster. But a leaner team also has fewer people to rely on when someone leaves. Kevin Bradt, founder and CEO of CasaPerks, joins The Tech Trek to discuss building an AI first company as a nontechnical founder. Drawing on his experience in hospitality and multifamily housing, Kevin explains how he uses AI to rethink business operations, engineering, hiring, and customer delivery. Kevin shares how CasaPerks uses AI to support software development and quality assurance, accelerate product requests, and generate business insights. He also explains why domain expertise and customer relationships may become more valuable as AI makes software development more accessible. What You'll Learn How AI changes staffing decisions across a growing company. Why smaller engineering teams need stronger retention and specialist coverage. How automated QA helps CasaPerks deliver features faster. Why Kevin invests more heavily in customer support while automating internal work. Episode Highlights 00:55 How hotel loyalty experience led to CasaPerks 03:00 Thinking like a business engineer 05:34 Building lean teams without losing coverage 07:18 Casa Flow and automated development workflows 12:31 How faster feature delivery helps win enterprise customers 17:18 Using automation to help residents after a flood One Line That Stuck "We're a service company first, and how we deliver service is through the medium of technology." Follow The Tech Trek for more conversations about AI, engineering, leadership, and building technology companies.

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

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." Follow The Tech Trek for more conversations about building and operating modern technology teams.

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. 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 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." 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 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.

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.” Follow The Tech Trek for more conversations with technical leaders building and operating modern teams.