Podcast
The Tech Trek
Hosted by Elevano · EN
The Tech Trek is a podcast about building and leading technology companies.
Each episode features founders, CTOs, engineering leaders, and operators sharing how they make decisions around product, engineering, AI, data, teams, hiring, and growth.
46episodes
Episodes
Newest firstAll episodes
What AI Agents Need Before Production
1w ago00:26:31Tap to summarizeAI agents are easy to demo. They are much harder to trust, maintain, govern, and put into production.In this episode of The Tech Trek, Amir Bormand talks with Lucas Thelosen, CEO and cofounder at Gravity, about the agent economy, AI analytics, and what changes when analysts move from doing every task themselves to managing AI systems that create more bandwidth.Lucas shares why Gravity built Orion, an AI analyst, after years working in analytics, product, and data teams at companies like Looker and Google. The conversation gets into the messy middle of AI adoption, why so many agent projects struggle to make it into production, and how context may become one of the most valuable assets a company owns.Practical Takeaways• Agent prototypes are easy. Production agents require support, maintenance, accuracy checks, and clear ownership.• Not every company should build every agent internally. If the capability is not core to what you sell, buying may be the faster path.• Context matters because it lets humans critique AI output with business judgment, not just technical review.• Analysts may shift toward data architecture, governed data models, and internal product management for analytics.• AI does not remove human responsibility. It raises the bar for review, delegation, and decision making.Timestamped Highlights00:40, What Gravity is building with Orion, an AI analyst designed around the work analytics teams already know well.02:28, Why mature companies still miss major insights, even when they already have data teams.03:43, The agent economy reality check, easy prototypes, hard production, and the gap between demo and durable system.06:28, Why companies still build agents internally, even when many projects never reach production.09:48, The case for experimenting now instead of waiting for the AI stack to settle.11:40, How AI shifts people from doing the work to managing the work.18:50, What the future analyst role may look like as AI takes on more of the execution layer.One Line That Stuck"Where previously you were the person doing the work, now you're the manager."Subscribe to The Tech Trek for more conversations on how technical teams are building, hiring, operating, and adapting around AI, data, platform, product, and engineering execution.
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Yahoo CTO on AI, Engineering Velocity, and Why the SDLC Has To Change
2w ago00:25:45Tap to summarizeYahoo is not just adding AI on top of existing products. It is using AI across product experiences, internal tools, engineering workflows, and modernization efforts.In this episode of The Tech Trek, Lee Zen, CTO at Yahoo, joins Amir Bormand to talk about modernizing at massive scale, moving from on prem infrastructure to the cloud, rebuilding internal tools with AI, and how engineering organizations need to rethink process when agents can move faster than people.Lee also shares how Yahoo views AI as a coworker, not just a tool, and why the next bottleneck in software delivery may be human judgment.Practical Takeaways• Modernization at scale often means operating in two worlds at once, keeping proven systems running while new cloud based services move faster.• AI can help teams move past legacy tools by reverse engineering requirements and rebuilding modern versions from scratch.• The real unlock is not only code generation. It is connecting agents to documents, chats, emails, production context, and internal knowledge with the right permissions.• As agents speed up execution, engineering teams need to rethink where human approval, judgment, and review should live.• The build versus buy equation is changing because some tools that were too expensive to build before may now be realistic to create internally.Timestamped Highlights00:31, Yahoo’s mission and why the internet still feels hard to navigate02:01, Where AI fits across Yahoo products and engineering work03:30, The challenge of moving from on prem data centers to cloud based infrastructure05:27, How Yahoo has used AI to rebuild internal tools and leave technical debt behind07:25, Why agents need access to engineering context, not just code10:20, AI as a coworker and the shift from human speed to machine speed16:27, Why parts of the SDLC may need to change as AI increases delivery speedOne Line That Stuck“AI as a coworker, not just as a tool.”The Tech Trek is for technical leaders thinking through how teams build, operate, modernize, and adapt as AI changes the work. Subscribe or follow for more conversations with engineering, product, data, and technology leaders.
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Why AI Founders Need to Say No Faster
2w ago00:19:49Tap to summarizeMike Choi wanted to work at Apple for years. Then he got there and had the moment many ambitious builders eventually hit.Is this the thing I was sprinting toward?In this episode of The Tech Trek, Mike Choi, co founder at Koah, shares his path from Korea to the United States, mandatory military service, Apple, Twitter, and eventually building Koah, an AI monetization company helping AI app builders create sponsored experiences.The conversation is less about the glamour of startups and more about what founder work actually demands: making decisions without complete information, learning from Big Tech without copying it, and staying focused when AI moves faster than your team can absorb.Practical Takeaways• Big Tech can teach you strong operating patterns, but startups force you to build your own style.• Founder decisions rarely come with complete data. Moving creates the next data point.• In AI startups, speed can become a distraction if every new tool or feature changes the plan.• Clear vision helps teams make decisions without waiting on the founder.• Knowing when to share an idea matters as much as having the idea.Timestamped Highlights00:38, Mike explains Koah and why AI products need new monetization models.02:25, Mike shares how his father’s Korean Air Force service brought him to the United States as a child.05:01, Mandatory military service, pausing college, and learning to code around strong engineers.07:29, The long term goal of working at Apple and the unexpected feeling after getting there.10:57, Why Mike chose to build from scratch instead of staying on the Big Tech path.14:05, What Big Tech did and did not prepare him for as a founder.17:03, The founder lesson of making decisions before the full picture is clear.19:35, Why AI startups move so fast and how shiny object syndrome drains energy, time, and attention.One Line That Stuck“Just make the decision, produce data points that way through actions, and make a better decision tomorrow.”Subscribe to The Tech Trek for more conversations on how modern technical teams are building, hiring, operating, and adapting around AI, data, platform, product, and engineering execution.
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Why Sovereign AI Matters Now
3w ago00:29:04Tap to summarizeAI is moving fast, but the bigger question for companies and governments may be control. Who owns the data, the workflow, the output, and the risk?In this episode, Amir talks with Shaun Modi, cofounder and CEO of Capitol AI, about sovereign AI, shadow AI, model dependency, government use cases, and why organizations need repeatable, governed, auditable workflows before AI becomes part of core operations.Shaun also brings a design lens to the conversation, connecting AI adoption to user experience, voice interfaces, and the next wave of AI in the physical world.Practical Takeaways• Sovereign AI means having control over your data, outcomes, upside, and risk.• Shadow AI creates short term productivity, but can also create silos, governance gaps, and data exposure.• Organizations need repeatable, governed, auditable AI workflows, especially in regulated environments.• Model independence matters because model costs, performance, and capabilities keep changing.• Design will come back into focus as AI systems become more powerful and more embedded in work.Timestamped Highlights00:40, What Capitol AI does and why decision ready artifacts matter01:50, Shaun defines sovereign AI in plain language02:36, The intelligence paradox, more data, less control04:15, Why shadow AI can become a governance and accuracy problem10:29, Zero data retention, model independence, and evaluation criteria17:53, Why AI user experience may be entering a new design cycle25:56, Where AI may create major impact in the physical worldOne Line That Stuck“Companies and governments have more data than ever, but they are losing control over the outcomes.”Practical Takeaways For TeamsIf AI is moving into real business processes, start by asking what needs to be controlled. Data rights, model choice, accuracy standards, workflow governance, and auditability all matter more once AI is producing work that affects customers, citizens, or critical operations.Follow The Tech Trek for more conversations on how technical teams are building, operating, and adapting around AI, data, product, and engineering execution.
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Healthcare AI Starts With Boring Problems
3w ago00:28:00Tap to summarizeMost healthcare AI stories start with diagnosis. Edmund Jackson thinks that misses the real bottleneck.In this episode of The Tech Trek, Edmund Jackson, CEO and founder of Unity AI, joins Amir to talk about AI for healthcare operations. The conversation gets into why scheduling, staffing, follow up, payer coordination, and interoperability are often where healthcare breaks down, and why solving those operational problems may matter more than chasing the flashiest use cases.Edmund brings a healthcare first view to AI. His argument is simple: healthcare is not slow because people are ignoring technology. It is slow because the real workflows are complex, regulated, high context, and hard to capture cleanly in software.What You’ll Take Away• Why healthcare experience matters when choosing which AI problems are actually worth solving• Why diagnosis is not always the best starting point for healthcare AI• How scheduling becomes much more complex when patients, payers, clinics, staff, protocols, and follow up all have to line up• Why AI can help clinics save time while moving human staff toward higher value patient interactions• Why interoperability is still hard, even with standards like FHIR gaining momentumTimestamped Highlights00:29, What Unity AI does and why healthcare operations is the focus01:11, Why healthcare AI needs people who understand the domain, not just the technology02:26, The danger of solving the hardest or flashiest problem instead of the most pragmatic one05:15, Why AI may finally help healthcare handle personalization and operational complexity at scale07:47, Why scheduling a healthcare visit is nothing like scheduling a delivery or restaurant order10:42, How operational AI can save time and reduce downstream chaos in clinics24:33, Why healthcare data is much harder to structure than financial dataOne Line That Stuck“Software is like children. Making it is all fun and games. Maintaining it is a whole other question.”Practical Takeaways• Start with the workflow that actually blocks progress, not the one that sounds most impressive• In healthcare, operational context is often the product• AI should create more room for humans to handle the interactions that require judgment, care, and clinical responsibility• More software is not always the answer, especially in regulated environments where maintenance, compliance, and security matterSubscribe to The Tech Trek for more conversations with founders, operators, and technical leaders building through AI, data, product, platform, and engineering execution.
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AI Coding Agents Are Changing Engineering Teams
4w ago00:26:44Tap to summarizeDeepak Bapat, CTO and co founder at Tabs, joins The Tech Trek to talk about how his team is using tools like Claude Code and Cursor, where AI is helping, and why systems thinking may matter more than raw coding ability as engineering work shifts.Practical Takeaways• AI coding agents are already producing useful production work, but judgment still matters.• Tool choice may be less important than standardizing the expected output.• Messy repos can make AI generated work harder to trust, so cleanup and patterns matter.• The future engineer may look more like a product engineer with strong systems thinking.• Teams may move from debating features to rapidly building multiple versions and testing what works.Timestamped Highlights00:37What Tabs does and why contracts create hard revenue workflow problems for B2B finance teams.02:16Deepak compares pre AI engineering work with the current shift toward AI assisted development.05:09How the Tabs engineering team uses Claude Code, Cursor, and other coding tools in real work.08:13Why inconsistent codebases create more risk when teams add coding agents14:00The idea that teams can build the same feature multiple ways in one afternoon.20:53Deepak’s view on whether the future team needs separate PMs and engineers, or more product engineers.23:38A future where software can become more bespoke to each customer because AI changes the cost model.One Line That Stuck“You can build on three different work trees the same feature in three different ways and see which one you like, and you can do it all in an afternoon.”Practical Moves From The Conversation• Keep humans close to the review process, especially when the last five percent still requires taste and judgment.• Clean up inconsistent code patterns before letting agents operate broadly across the repo.• Hire for adaptability, systems thinking, and problem solving, not just past tool familiarity.• Use AI to explore more product options faster, but do not remove the need to ask whether the feature should exist.Subscribe or follow The Tech Trek for more conversations on how technical teams are building, hiring, and operating as AI changes the work.
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Agentic AI Has a Data Layer Problem
4w ago00:29:42Tap to summarizeAgentic AI is not just a model problem. It is exposing gaps in how teams store, share, retrieve, and coordinate context across applications, agents, and people.In this episode, Amir talks with Karthik Ranganathan, cofounder and co CEO at Yugabyte, about why databases are under new pressure as AI moves from model serving into agentic workflows. They discuss Yugabyte’s evolution, the limits of today’s data infrastructure, and why memory, knowledge, and shared context may become central to how agentic systems actually work.Practical takeaways• Agentic workloads push databases beyond simple relational access because agents may need relational, vector, graph, NoSQL, scale, and multi tenant support in the same workflow.• A query can be optimized inside each data store and still be slow, expensive, or wasteful when the work spans multiple systems.• Context sounds simple to humans, but it becomes messy when it includes private memory, shared project knowledge, conversation history, team collaboration, and agent actions.• Human handoffs can erase much of the speed promised by agents when teams have to copy outputs, re explain reasoning, and manually reconcile conflicts.• Yugabyte is working on Meko as a data infrastructure layer for agents, with a focus on memory, knowledge, context quality, and shared learnings.Timestamped highlights00:43What Yugabyte does and why critical data needs to survive infrastructure change04:08How databases evolved from mainframes to internet apps, mobile, cloud native systems, and now AI09:42Why agentic workloads create new demands across relational, vector, graph, NoSQL, and multi tenant data12:29Why the current agentic data stack is still in the messy middle15:24Why context becomes hard when agents, people, teams, and permissions collide21:50How agent collaboration can fall back to human speed24:34How eeko aims to capture memory, knowledge, learnings, and reasoning across agent workflowsOne Line That Stuck“We have killed the velocity of agentic development and brought it back to human speed.”Practical signals for teams building with agents• Do not treat context as one generic blob.• Decide what should stay private, what should be shared, and what should become reusable project knowledge.• Watch for hidden cost when agents query across separate systems.• Pay attention to agent collaboration, not just single agent output.• Build for memory and knowledge flow before team size makes the gaps harder to fix.Follow The Tech Trek for more conversations with technical leaders building modern teams, products, and infrastructure around AI, data, and engineering execution.
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When Agentic Coding Changes The Team
May 2900:37:47Tap to summarizeAgentic coding is not just making engineers faster. It is changing how teams triage bugs, prototype features, involve product, and think about hiring.Scott Weller, CTO and founder at EnFi, joins The Tech Trek to talk about how his team is building around agentic software development while operating in financial services, where trust, accuracy, and human judgment still matter. EnFi uses AI agents to work through complex financial data rooms, extract knowledge, and support faster analysis in commercial lending.In this episode, Scott breaks down how EnFi moved from simple coding assistance to a broader development harness, why Slack became a central interface for agents, how product and business leaders can now participate earlier in feature creation, and why engineering interviews need to change when AI is part of the actual job.Practical Takeaways• Start with specific productivity goals before trying to rebuild the whole development process.• Agentic tools work better when they connect to the team’s real workflow, shared context, and software lifecycle data.• Faster code generation changes the cost model, but it also creates new problems around review, testing, prioritization, and decision fatigue.• Product, sales, and executive teams may be able to prototype ideas faster, but engineering still has to make the work production ready.• Hiring needs to test how people solve problems with AI, not whether they can perform the old interview format without help.Timestamped Highlights00:38, What EnFi is building around financial data, AI agents, and commercial lending02:13, Why software teams may need to forget part of their old development process04:45, How EnFi started with productivity gains before building a broader development harness09:53, Why merge requests went up, and why that alone is not the same as better outcomes10:30, How Slack became the entry point for an agentic development harness14:10, What happens to agile ceremonies when teams can create discovery builds much faster25:08, Scott’s view on whether AI reduces engineering headcount or changes the work engineers do31:00, How EnFi is changing technical interviews for an AI assisted engineering environmentOne Line That Stuck“We do not care if you use AI to solve the problems, we just want to know you can solve the problem.”Practical Takeaways For Technical TeamsPut agents close to where work already happens.Keep humans in the loop for review, testing, and production judgment.Treat AI generated code as cheaper to create, not free to maintain.Build stronger test harnesses instead of slowing everything down with excessive process.Update interviews to reflect how engineering work is actually getting done.Subscribe to The Tech Trek for more conversations with technical leaders building, hiring, and operating through the next stage of AI, data, product, and engineering execution.
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Data Teams Are Moving Beyond Dashboards
May 2700:23:27Tap to summarizeAI adoption looks very different when mistakes can create legal, financial, and reputational risk.Vijay Gandra, Global CDO at Acrisure, joins The Tech Trek to talk about AI transformation inside a regulated industry, where explainability, data quality, governance, cost, and team readiness matter just as much as model capability.The conversation covers the trust gap in AI, how data teams are shifting from dashboard production to conversational data access, when to buy versus build, and why AI proof of concepts need to be judged by business value, operational efficiency, and customer impact.Practical Takeaways• Regulated industries cannot treat AI as a black box. Decisions need traceability, consistency, and often a human review layer.• Data quality has to be addressed from the start. AI can amplify bad data as easily as it can create value.• Data teams are moving beyond dashboard factories toward conversational data access and generative interfaces.• Most companies can likely use existing AI tools for many needs, but sensitive IP and core business logic may require internal capabilities.• AI cost will become a bigger production question as companies move from experimentation to scaled deployment.Timestamped Highlights00:47, Acrisure’s shift from insurance brokerage toward fintech and financial tools.01:44, Why regulated industries face a trust gap with AI and need explainable decisions.04:41, How data teams are evolving from dashboards to conversational data enablement.08:28, The build versus buy question and where internal AI tools may still make sense.10:52, Why AI experimentation can get expensive before companies know what works.16:15, How to evaluate AI proof of concepts based on customer value, efficiency, and business impact.18:14, Why data governance and data quality need to be treated as day one requirements.One Line That Stuck“In an industry like this, a 5 percent deviation is not just a simple glitch. It is actually a legal liability.”Subscribe to The Tech Trek for more conversations with technical leaders building, operating, and adapting modern teams around AI, data, platform, product, and engineering execution.
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AI Is Changing Coding, Not Engineering
May 2200:33:27Tap to summarizeLeonid Belkind, co founder and CTO at Torq, joins The Tech Trek to talk about what changes when an engineering organization does more than experiment with AI tools. Torq builds agentic security operations, and Leonid shares how his team is using AI across engineering, product, hiring, customer success, and go to market work.This conversation gets past the shallow version of “AI makes coding faster.” Leonid makes a clear distinction between coding and software engineering, and explains why the best teams are using AI to shift cognitive load, not remove judgment.Practical takeaways• AI does not erase software engineering. It changes where engineering judgment shows up.• Strong engineers still produce better AI generated work because they know what to ask, what to test, and what tradeoffs matter.• Hiring processes need to reflect how engineers actually work now, including how they use AI to build, explain, and defend technical decisions.• Productivity should not only be measured by speed. Leonid talks about throughput, maturity of delivery, and whether teams can produce more without lowering quality.• AI adoption becomes more powerful when it moves beyond engineering into product, customer success, revenue operations, and talent.Key moments00:32What Torq means by agentic security operations and why different tasks need different AI approaches.01:49Why building AI native products with AI native methods creates a useful feedback loop for engineering teams.05:28How AI shifts cognitive load so engineers can spend more attention on user experience, architecture, and product value.10:34The difference between software engineering and coding, and why that distinction matters more now.15:13How Torq has changed technical interviews to evaluate AI assisted engineering instead of pretending AI does not exist.21:51How one R&D group measured meaningful delivery gains after adopting AI more deeply.24:25Why AI adoption is moving into product, customer success, revenue operations, and talent teams.One Line That Stuck“Software engineering as a discipline is not going away. It just changes a phase a bit.”Practical moves to stealFor hiring, Leonid suggests giving candidates more complex take home work because AI is now part of the real engineering workflow. The evaluation then shifts to the candidate’s ability to explain the architecture, defend decisions, describe how AI was used, and show how they tested and constrained the output.That is a much better signal than asking someone to work as if the tools do not exist.Subscribe or follow The Tech Trek for more conversations with technical leaders building, hiring, and operating through the next shift in software, data, AI, and engineering execution.
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