The Tech Trek

Ogi Kavazovic, co-founder and CEO of House Rx, joins the show to unpack what most product leaders miss about building for enterprise software. Drawing from two decades in tech, Ogi breaks down how product management shifts when you move from B2C or “B to small B” to true enterprise—what he calls “B to Big B.” He explains why traditional user research frameworks don’t hold up, how buyer research should actually be done through sales and marketing motions, and how to keep engineering teams aligned when the product takes years to build.Key Takeaways• Building for enterprise (B to Big B) requires selling to buyers and users—two very different audiences with distinct needs.• Buyer research is not user research—it happens through early sales decks, vision slides, and iterative storytelling that test how well a concept resonates before code is written.• Pre-selling a “fantasy product” through slides helps validate the market fit and shapes the first version of your product strategy.• Engineering for enterprise software demands simulated iteration—testing features internally long before the MVP is complete.• Vision alignment between product, marketing, and engineering is crucial to avoid two-year build tunnels and ensure team motivation.Timestamped Highlights[03:12] The overlooked divide between B2B and true enterprise—why “B to Big B” changes everything for product teams.[10:47] How buyer research actually works and why it starts with slides, not software.[17:40] The difference between pitching VCs and pitching enterprise buyers—and why they care about totally different things.[22:29] The engineering challenge of building massive enterprise systems and why agile methods fall short.[30:11] How to keep teams motivated and moving forward when the product roadmap spans years.Standout Moment“You can pre-sell a product before it even exists. That sales and marketing artifact—the deck you built to sell your vision—can become the blueprint for your product strategy.”Pro TipsStart with conversations, not code. Use early customer and buyer meetings to validate your story through slides, then hand your engineers a vision they know can sell.Call to ActionIf you enjoyed this episode, share it with a fellow product leader or founder navigating enterprise challenges. Follow The Tech Trek for more conversations that connect people, impact, and technology.

Astasia Myers, General Partner at Felicis, breaks down how venture capital is betting on AI and why over 80% of their recent investments are in this space. But this isn’t just another “AI is the future” conversation. We dig into the real ROI happening right now in healthcare voice agents, why MIT says 95% of GenAI projects fail to reach production, and what needs to happen for that number to flip. If you’re building, investing, or just trying to understand where enterprise AI is actually working (not just hyped), this episode cuts through the noise.What You'll LearnThe labor replacement opportunity: Why outcome-based AI solutions are targeting the $35 trillion labor market instead of just software budgets and how that changes everything for startups and investors.Voice AI’s healthcare breakthrough: How voice agents are finally solving the operational bottlenecks in patient scheduling and communication, driving 24/7 availability with better NPS than human operators.Why 95% of GenAI projects still fail: The technical and infrastructure gaps that prevent most AI initiatives from making it to production and what’s needed to fix that in 2026.The new technical risk era: After years of focusing purely on market risk, VCs are back to evaluating deep technical challenges in agentic systems, browser automation, and continuous learning loops.The exceptionalism filter: How early-stage investors are separating signal from noise when everyone can spin up an AI startup and why founder insights and lived experience matter more than ever.Timestamped Highlights00:31 – What Felicis invests in and the types of AI companies dominating their portfolio right now02:58 – Why healthcare tech is finally ready for its AI moment after years of long sales cycles and unclear ROI08:15 – How outcome-based pricing is changing the VC evaluation playbook and unlocking 10x larger TAMs13:26 – The mythical one-person billion-dollar company: Is it real, and how would investors even spot it?17:18 – Voice AI as the gateway for enterprise adoption and why this modality is different from Siri and Alexa20:08 – Democratizing AI: What ChatGPT did for consumers and what needs to happen for enterprise buildersOne Thing Worth Remembering“These technologies can price towards the labor replacement markets, which is about 10x the size of the software market itself. The ROI right now is so tangible that it is a time to invest.”Subscribe and Stay in the LoopIf this episode gave you a new angle on where AI is actually delivering value, share it with a founder or investor who needs to hear it. Subscribe so you don’t miss the next conversation, and drop a comment if there’s a topic or guest you want us to tackle next.

In this episode, Amir sits down with Taofeek Rabiu, VP of Engineering at Etsy, to unpack a distinction that most organizations miss: being a people leader is not the same as being a people manager.If you have ever wondered why some teams thrive under pressure while others crumble, or why trust feels so hard to build in engineering orgs, this conversation has answers. Taofeek shares how leadership is not reserved for those with a manager title, why vulnerability is a strategic advantage, and how to spot the early warning signs of poor leadership before they drag down performance.What You’ll LearnLeadership exists at every level, not just in management roles. Individual contributors who mentor, influence, and model the right behaviors are leaders too — and organizations need to recognize and reward that.Trust is built through action, not talk. It grows when leaders show vulnerability, stay transparent about their thinking, and follow through on commitments. When you stop acting on what you hear, you break trust.Poor leadership has a smell. Teams that avoid hard conversations, struggle to navigate change, or fail to ramp new hires are showing symptoms of leadership gaps, not process problems.Feedback is about helping people see, not telling them what to do. The best leaders use curiosity to guide others toward realization and self-awareness.Effective leaders make high signal, low frequency decisions. The goal is not to make a thousand calls a day but to gather diverse perspectives and make the few decisions that truly move the team forward.Timestamped Highlights01:42 – Taofeek breaks down the difference between managing people (reviews, org charts, timesheets) and leading people (building trust, showing care, creating psychological safety).09:04 – What happens when managers focus only on mechanics. Taofeek describes the smells of poor leadership and how they surface in teams that can’t handle change.13:18 – How to give feedback when someone is not showing up as a leader. Taofeek explains his approach: start with curiosity, triangulate with skip levels, and guide people to their own realizations.17:47 – Who is responsible for building trust. Taofeek shares why it is on leaders to create the conditions, not on reports to earn it.22:04 – The moment Simon Sinek told Taofeek to stop saying people managers and start saying people leaders — and how that small shift in language changed his approach to leadership.24:29 – What feedback a VP of Engineering actually values. Taofeek shares how he uncovers blind spots and the kind of input that helps him grow.Words That Stuck“The team doesn’t trust you. You’re not providing a psychologically safe environment in which the team feels like they can course correct and flag things that they believe will lead to poor outcomes.”If This Resonates, Here’s What to DoTake one insight from this episode and put it into practice this week. Maybe it’s being more open in your next one-on-one, checking your follow-through, or asking your team a question you have been avoiding. Then share this episode with someone navigating the manager-to-leader transition. Subscribe to The Tech Trek for more conversations that help you grow as a leader, and connect with Taofeek on LinkedIn to keep the dialogue going.

Ion Feldman, CTO at Rightway, has learned to love one thing about scaling a company from a kitchen table to nearly 1,000 employees: his job completely changes every six months. In this episode, Ion shares what it means to lead engineering when the role refuses to stay still—from writing code in the early days to building product, security, and data teams, and now shaping AI infrastructure. He explains how to stay hands-on without micromanaging, why he deliberately works himself out of roles by hiring people better than him, and how to preserve startup urgency inside a heavily regulated industry. If you’ve ever wondered how CTOs balance technical depth with business strategy while keeping their team fast and focused, this conversation delivers.Key TakeawaysTreat change as part of the job.Ion’s leadership mindset centers on adapting to wherever the company needs him most—product, security, data, or AI. He views change as an opportunity to grow, not a disruption to avoid.Hire yourself out of the role.He dives deep into an area, builds it from scratch, then brings in experts who can take it to the next level. Once the right leadership is in place, he steps back completely and lets them own it.Hands-on time creates credibility.Ion makes sure every leader spends time building. Each quarter, his team takes a week off from meetings and Slack to focus on creating something new. It keeps them close to the work and sharp as technical leaders.AI adoption needs clarity and focus.Rightway avoids vague “use AI” goals by targeting clear use cases like unit test generation and onboarding to codebases. Sharing examples and results drives faster adoption than leaving teams to figure it out alone.Fail fast and move forward.Ion builds space for experimentation but expects quick recognition of failure. The goal is not to avoid mistakes but to learn, pivot, and evolve faster.Timestamped Highlights[02:10] The zero to one mindset – Why Ion thrives on constant reinvention and the satisfaction of building new functions from the ground up.[06:41] Three pillars of AI strategy – How Rightway is transforming work through AI enablement, applied projects, and bold experiments.[08:26] Delegating by design – How going deep before handing off creates clarity and trust across teams.[15:42] Skills that matter later – Ion reflects on learning public speaking and business fluency after years of technical focus.[17:48] Creating space for risk – How to give your team agency to take on big challenges and fail fast without fear.[21:22] Preparing successors – Why the best leaders hire people who will replace them and rethink everything they built.What Stuck With Us"I don't know, maybe I just get bored easily. I think a lot of people could view it as a burden and they want to stay in their lane of expertise, but I see it as an opportunity to learn and change things up."Pro Tips for Tech LeadersTake a week each quarter to build something with zero meetings or Slack. It reconnects you and your team with what you actually love about engineering.Wait to hire senior leadership until the need is undeniable. The role becomes meaningful, and you’ll attract higher caliber talent.Give your engineers specific AI examples and let them experiment from there. Adoption follows clarity, not mandates.

Jay Chia, cofounder of Eventual, joins the show to unpack what real empowerment looks like inside a fast growing startup. Most people confuse empowerment with initiative, but Jay explains how trust, vulnerability, and accountability work together to turn good teams into self directed ones. If you are scaling a startup or leading a growing engineering team, this conversation explores the human side of leadership, when to let go, when to step in, and how to help your team grow without losing alignment.What You’ll Learn• Why initiative and empowerment are different and how that distinction shapes your company culture• How to build trust so early employees can take ownership without constant oversight• Why vulnerability is the key to honest feedback and deeper one on ones• How to build a culture of experimentation that rewards progress, not perfection• When to intervene as a leader versus when to let your team learn through mistakesTimestamped Highlights03:20 The difference between taking initiative and true empowerment, and why fixing bugs is not ownership08:39 Using vulnerability to turn one on ones into real conversations12:20 Building an experimentation culture inspired by research driven teams17:53 How much room to give before stepping in, balancing trust, skill, and risk21:41 Why letting new managers bring their own cultural imprint can strengthen your companyA Line That Sticks“Empowerment is handing off the monkey. It is not just fixing the problem, it is owning the plan, asking for resources, and having the mandate to execute.”Practical Advice for Leaders• Start one on ones by being open first so your team feels safe to share what is really happening• Lower the barrier to experimentation and let people test ideas early. Progress beats polish• Build rituals, not just processes. Repetition creates trust and space for feedback• Encourage a mindset of asking for forgiveness, not permission. Autonomy grows from trustKeep the Conversation GoingIf this episode made you rethink how you empower your team, share it with another founder or manager who is building through similar challenges. Follow The Tech Trek for more conversations at the intersection of people, impact, and technology.

Rohan Kodialam, cofounder and CEO of Sphinx, is building AI agents that treat data as its own language—one most models and humans still fail to understand. In this episode, he unpacks why data science has lagged behind software engineering, how AI can finally close the gap between business questions and answers, and what happens when small teams gain the analytical power of a thousand person quant desk.What You'll Learn• How AI models that actually see data can unlock insights traditional transformers miss• Why enterprises must rethink dashboards and embrace real time ad hoc analysis• Where AI truly saves the most time across the data lifecycle and why modeling is not the hardest part• How decoupling statistics from business context gives teams freedom to focus on strategy and creativity• Why success in data science now means reclaiming human creativity while automating repetitive workTimestamped Highlights[01:44] Why data is fundamentally different from text and code and why most AI models struggle with it[06:39] The cultural problem with ad hoc being a dirty word in enterprises and why that mindset is changing[11:09] Where AI tools actually fit into the data science workflow[17:09] How to measure success when using an AI data scientist[21:04] What happens when a small team gains the data firepower of a hedge fund quant operation[24:37] Why bad data science is worse than none and why quality matters more than hypeA Thought That Stuck With Us“We are cutting the time to completion by 20x, 40x, even 50x and that remaining human review is not a bottleneck. It is the feature that keeps AI accountable.”Worth FollowingConnect with Rohan Kodialam on X (@KodialamRo) or LinkedIn and learn more about Sphinx AI and how they are transforming enterprise data science.If This ResonatedShare this with someone in the data world who is tired of waiting weeks for insights that should take minutes. Follow The Tech Trek for more conversations about how people and technology create lasting impact.

Karl Alomar, Managing Partner at M13 and former COO of DigitalOcean, joins The Tech Trek to share how being an operator changes the way you invest. He explains why M13 was built to be a truly founder-first VC firm—one that acts early, helps proactively, and builds deep relationships rooted in empathy and experience. From spotting great founders to balancing instinct and data, this episode explores how venture capital can drive better outcomes when it focuses on people as much as product.Key Takeaways• The most effective VCs act before problems surface, shaping a founder’s path rather than reacting to it.• Founder–market fit often comes down to whether someone is a specialist with deep expertise or an athlete who can adapt fast.• Empathy built through years of operating experience creates trust that fuels honest conversations and better decisions.• Great founders lead with vision—they can inspire, recruit, and align teams behind a clear story of what’s possible.• Even the best instincts and pattern recognition can’t outplay timing, luck, and market shifts—but reflection and learning can.Timestamped Highlights(01:20) How being an operator shaped Karl’s approach to venture capital(06:48) The three kinds of investors—and why empathy gives operators an edge(09:54) Creating a safe space where founders can share problems without fear(14:13) Identifying “athletes” and “specialists” when evaluating founders(20:33) Pattern matching, instincts, and the role of luck in investing(23:50) What M13 learns from postmortems on both wins and missesA Line That Stuck“To do it the right way, you have to be a proactive investor, not a reactive one.”Pro TipsKarl suggests founders build relationships with investors who understand their world and seek out those who can help them see around corners—not just react when things break.Call to ActionIf this episode resonated, follow The Tech Trek on Apple Podcasts or Spotify and connect with Amir Bormand on LinkedIn for more conversations at the intersection of people, impact, and technology.

In this episode of The Tech Trek, Amir sits down with Michi Kono, CTO of Garner Health, to unpack what it really takes to scale engineering leadership inside a fast growing startup. Michi shares how he balances structure and speed, why formalizing processes too early can slow innovation, and how “the Garner way” blends lessons from big tech with first principles thinking. This is a conversation about leadership maturity, cultural design, and building systems that evolve with your company’s growth.Key Takeaways• Leadership scale comes from knowing when to formalize processes, not just how.• “Six months is never”: waiting on fixes usually means they will never happen.• Feedback is a gift, and it is on leaders to create the safety for it to flow upward.• Borrowing from big tech only works when you adapt the principles, not the playbook.• Engineering leaders should measure success by business outcomes, not just delivery speed.Timestamped Highlights01:46 The first signals Michi looked for when stepping into the CTO role03:49 Turning ad hoc collaboration into structured dependency management06:36 Why delaying operational fixes is a silent killer for scaling teams08:38 Building standards only when they solve real, visible problems12:13 The art of forecasting leadership hiring and team design14:54 Lessons borrowed from Meta, Stripe, and Capital One, and when not to use them17:31 Defining “the Garner way” through first principles20:59 Judging engineering performance through business impact25:00 Creating true psychological safety for feedback across all levelsA Line That Stuck“If we can’t execute on the roadmap that lets us actually build a successful business, then I failed as a leader. There are no excuses.”Pro TipsWhen you inherit a growing engineering organization, start by mapping dependencies, not hierarchies. Clarity around how teams interact is more valuable than adding headcount too early.Call to ActionEnjoyed this episode? Follow The Tech Trek on Apple Podcasts and Spotify, and connect with Amir on LinkedIn for more conversations on scaling teams, leadership, and engineering culture.

Vibe coding isn’t just a new buzzword—it’s a complete shift in how engineering teams build, ship, and think. Zach Wills, Director of Engineering at Luxury Presence, joins to share how his team is rewriting the rules of software delivery using AI-assisted workflows. From Greenfield experiments to Brownfield transformations, Zach breaks down the frameworks, lessons, and mindset shifts reshaping what it means to be an engineer.Key TakeawaysWhy vibe coding feels less like automation and more like a new management skill for engineersThe real differences between Greenfield and Brownfield AI-assisted projects—and how to avoid the biggest trapsHow “trusting the autonomous loop” became a core principle for speed and qualityThe cultural shift that happens when developers stop typing every line of codeWhy teams that embrace AI early will outpace their competition, not replace their peopleTimestamped Highlights02:20 — The moment vibe coding clicked and how it compressed days of work into hours06:45 — Testing AI in a five-year-old codebase with tens of thousands of commits10:45 — Engineers are becoming more like managers of autonomous agents14:40 — The hidden emotional impact of giving up “manual” coding17:30 — Inside Zach’s eight-rule framework for productive AI workflows25:25 — Why SDLC as we know it is breaking apart—and what replaces it30:00 — Why fearing AI misses the point entirelyMemorable Line“If AI can do something I was doing yesterday, I never want to do that thing again. My value comes from what only I can do.”Pro TipStart small but think organizationally. Train your engineers to lead AI, not just use it. The biggest unlock isn’t speed—it’s mindset.Call to ActionIf this conversation sparked new ideas about how your team could work smarter, follow The Tech Trek wherever you listen and connect with Amir on LinkedIn for more behind-the-scenes insights.

From a farm in Adelaide to the front lines of AI-powered personalization.Tullie Murrell, CEO and co-founder of Shaped, shares how he went from researcher to founder and built a platform helping businesses deliver the kind of intelligent recommendations once reserved for big tech.We explore the mindset shifts, technical leaps, and founder lessons that shaped his path—from Meta’s AI labs to democratizing personalization for everyone else.Key Takeaways• The best founders know when to trade technical depth for go-to-market mastery. Tullie learned that 70% of startup success lives outside the codebase.• Real personalization is no longer just for Meta, Amazon, or TikTok—new model architectures are closing the gap for everyone.• Flexibility early in your career opens unexpected doors. Choosing Meta over Google gave Tullie room to explore and evolve.• AI research isn’t just about papers—it’s about transforming how people experience products and decisions in real time.• The future of personalization sits at the intersection of generation and intent—content created and adapted for each individual moment.Timestamped Highlights00:35 — What Shaped does and how it’s redefining AI-driven recommendations03:00 — From a farm in Australia to computer science and a path to Silicon Valley07:30 — Why joining Meta offered more freedom than Google13:25 — The insight that sparked Shaped: how Meta’s personalization drove massive engagement19:00 — Leaving Big Tech, embracing discomfort, and starting over as a founder22:45 — The moment he realized go-to-market mattered more than code29:00 — How new AI breakthroughs are rewriting what’s possible in personalization33:55 — Real-time generation meets personalization: where we’re headed nextA standout moment“Most founders think success is 70% product and 30% go-to-market. I learned it’s the other way around.”Pro TipIf you’re a technical founder, study go-to-market strategy as hard as you studied your first programming language. It’s the difference between a great product and a great company.Call to ActionIf you enjoyed this episode, share it with a founder or engineer exploring their next leap. Subscribe to The Tech Trek on Apple Podcasts or Spotify, and follow Amir on LinkedIn for more conversations at the edge of tech, leadership, and innovation.

Jason Eubanks, Co-Founder and CEO of Aurasell, shares the path that led him from a small town in rural Ohio to building one of the most ambitious AI-driven CRM platforms on the market. His journey reveals how limited opportunity can spark relentless ambition and how early lessons in persistence shaped the mindset of a founder willing to take on giants.Key Takeaways• A clear purpose often starts from simple beginnings that demand creativity and discipline.• The hardest experiences can build the confidence to face uncertainty without fear.• Great products are born when you question accepted norms and rebuild from first principles.• Growth happens when you move before comfort arrives.• Progress depends on focusing on the next meaningful step rather than the entire mountain ahead.Timestamped Highlights[01:49] Growing up in a small Ohio town where college was rare[05:58] Discovering technology after realizing civil engineering wasn’t the right fit[11:17] Researching careers in a library and choosing a future in tech and sales[17:16] Early family struggles that shaped resilience and perspective[22:57] Building Aurasell to challenge entrenched enterprise software[26:57] The lesson every ambitious professional needs to hear about taking risks earlyA Line That Stuck“I’ve already seen what it’s like to lose everything. So when you’ve been there, the idea of taking a big risk doesn’t feel so scary anymore.”Pro TipsSeek situations that stretch you. Every challenge adds another layer of experience that will serve you later.Call to ActionIf this story pushed you to think differently about risk and growth, follow the show for more founder conversations that reveal what it takes to build something lasting in tech.

Some companies thrive while others quietly lose their edge.For Tanay Kothari, CEO of Wispr Flow, the difference comes down to one idea: people are your responsibility.In this conversation, Tanay shares how that realization changed everything about the way he leads. From early missteps as a young manager to building a company rooted in empathy and accountability, he shows that the strongest cultures are designed with intention, not left to chance.You’ll come away with a practical look at how to build a team that performs at a high level because they feel valued and trusted.Inside the ConversationTanay explains how he built systems that make empathy operational. He spends time understanding each person’s strengths, shapes feedback and growth paths around them, and invests in training people managers who can multiply impact. He also shares why he still keeps a founder’s eye on product quality, customer connection, and hiring as the company grows.Takeaways• Culture doesn’t scale on its own, it must be built with care• Empathy can drive performance without lowering expectations• The three areas Tanay never delegates as a founder• How to recognize when a culture is truly working• What happens when leaders trade control for curiosityTimestamped Highlights00:43 The mission behind Wispr Flow and the future of voice technology01:50 Why treating people as your responsibility changes everything03:39 Building around individual strengths and learning styles06:23 The importance of developing great managers10:35 Small but powerful signals of a thriving culture12:41 The lesson that reshaped Tanay’s approach to leadership15:50 Turning frustration into growth and creating top performers19:30 Interviewing for passion, not just technical skill21:58 The three things a founder should never hand offA line that says it allCulture isn’t a vibe, it’s a decision you make every single day.Call to ActionGreat companies are built by leaders who care as much as they execute. Follow The Tech Trek for conversations that help you grow as both.

Crypto follows patterns—just like every major wave of innovation. In this episode, Brad Holden of Protocol VC breaks down what really drives those cycles, how investors separate substance from hype, and where crypto and AI are beginning to converge.From evaluating early founders to understanding when to double down or step back, Brad shares how top VCs navigate frontier tech markets and what makes a company endure beyond the hype cycle.Key Takeaways• Crypto’s ups and downs follow predictable adoption cycles—and understanding that rhythm matters.• Founders who focus on real problems, not hype, stand out in crowded markets.• AI and blockchain are intersecting through decentralized compute and data transparency.• Great founders show conviction, grit, and self-awareness—qualities investors notice immediately.• The strongest pitches come from founders who lead with their own vision, not what investors want to hear.Timestamped Highlights01:20 — Why crypto moves in repeating cycles and what drives each one03:40 — How blockchain transparency helps investors see real traction06:00 — Evaluating crypto startups: solving problems vs. chasing novelty10:49 — How blockchain complements and verifies AI13:05 — The hidden risk of building around hype15:53 — Why over-customizing your pitch can backfire17:50 — How top VCs view pivots and founder adaptability25:28 — The traits that signal long-term founder successA line worth remembering“Being too early is just another way of being wrong—but betting on the right founder can make up for almost anything.”Call to ActionIf you want to understand where crypto and AI actually intersect—and what real investors look for behind the scenes—follow The Tech Trek on Spotify or Apple Podcasts and join the conversation on LinkedIn.

Edward Khoury, CTO at Jump, joins Amir to unpack what it really means to lean into discomfort as AI transforms engineering. From redefining craftsmanship in the age of AI-generated code to helping teams evolve their skill sets, Edward shares how he’s creating space for experimentation without losing focus on delivery, culture, or shareholder value.This is a conversation about leadership in motion—where the future of engineering isn’t just about writing code faster, but about reshaping how teams learn, build, and think.Key Takeaways• Why leaders must intentionally give engineers time and space to experiment with AI tools• How to balance individual learning with organizational goals and KPIs• The rise of the “product-focused engineer” and what it means for the next generation of builders• Why platform engineering is becoming critical for scaling AI adoption• How embracing discomfort leads to resilience and competitive advantageTimestamped Highlights1:29 — What “leaning into an uncomfortable world” means for engineers today3:40 — Creating space for experimentation while keeping delivery on track6:06 — Balancing freedom to explore with standardization and shared learning8:34 — Navigating the fear that AI will replace engineering roles14:11 — How productivity gains will shift bottlenecks from engineering to product20:31 — Teaching engineers to think like product owners23:45 — Why user adoption will become the next big challenge as development accelerates26:58 — How AI tooling is already shaping hiring plans and org designOne Idea That Stuck“You can’t push everyone through the door—you just have to open it.”Pro TipsEdward suggests pairing engineers with product partners earlier in the process—not after specs are written—to help them understand business context and build stronger product intuition.Call to ActionIf this episode made you think differently about leadership in engineering, share it with a teammate who’s navigating AI adoption. Subscribe to The Tech Trek on Apple Podcasts or Spotify, and follow Amir on LinkedIn for more conversations with the builders shaping the future of tech.

Rick Doten, cybersecurity startup advisor and AI researcher, joins the show to unpack how AI-assisted development is reshaping software—and what it means for security. From startups rushing to ship faster code to the unseen risks of “vibe coding,” Rick explains how engineering teams can balance innovation with secure, resilient design.If your dev team is using AI tools to boost velocity, this conversation might change how you think about your SDLC, code review, and even your threat model.Key Takeaways• AI-assisted coding speeds up output but can multiply security risks if context isn’t baked in.• Startups often trade speed for security early on—and that can be expensive to unwind later.• Traditional fundamentals like OWASP and BSIMM still apply, even as architectures evolve with agents and MCP.• AI creates a widening gap between companies that can secure their models and those that can’t.• “Vibe coding”—non-devs using AI to build—introduces a new wave of shadow code leaders must prepare for.Timestamped Highlights[02:09] The real range of how startups are using AI-assisted tools—and why security is often an afterthought.[05:12] Why AI-generated code is not just another form of third-party code.[09:40] The hidden risk: code volume grows faster than your ability to secure it.[15:51] How AI is widening the gap between resource-rich enterprises and everyone else.[18:25] The new fragility of systems—where architecture and resilience start to break.[22:07] Rethinking SDLC: integrating AI tools without losing security fundamentals.[25:29] “Vibe coding” and what happens when non-engineers start shipping code.Memorable Insight“AI isn’t lazy like humans—it doesn’t just fix one thing. It rewrites everything. That’s why every line has to be re-scrutinized.”Pro TipsIf your startup doesn’t have a dedicated security function yet, start with the basics: integrate OWASP checks into your CI/CD, use non-human accounts correctly, and automate code review gates early. Don’t wait until production to harden your systems.Call to ActionIf this episode sparked ideas for your dev or security team, share it with someone who’s experimenting with AI-assisted tools. Follow The Tech Trek for more conversations at the intersection of engineering, AI, and leadership.

What happens when a telehealth CTO takes AI beyond code generation and into the heart of the software development lifecycle?Matt Buckleman, Co-founder and CTO of Hone Health, joins to share how his team uses AI not just to accelerate development, but to rethink workflows—from documentation and traceability to sentiment analysis across teams. This episode dives deep into how he’s blending engineering fundamentals with modern AI agents to create a smarter, more adaptive SDLC.Key Takeaways• Why AI’s biggest near-term value isn’t in code generation—it’s in improving process and communication.• How Hone Health evolved its SDLC from three engineers on Slack to a 30+ person organization using agent-based automation.• The hidden advantage of consistent naming conventions and traceability when applying AI to production systems.• How AI can automate the “soft” but essential parts of software delivery, like documentation, requirements gathering, and developer sentiment tracking.• What it takes to create feedback loops that make AI genuinely useful inside technical workflows.Timestamped Highlights[02:09] Flexible, anti-dogmatic SDLC: why strict process frameworks can slow learning.[09:00] When more engineers doesn’t equal more output—the hidden cost of coordination.[13:00] AI for experts vs. juniors: why prompting mirrors domain mastery.[18:38] Offloading the unglamorous work: how LLMs now handle code comments, documentation, and swagger generation.[23:50] Shared ownership and experimentation: how Hone’s engineering team pilots new AI tools.[28:40] Turning meeting transcripts into smarter requirements: how agents refine specs automatically.[32:00] Using sentiment analysis to spot risk and burnout across engineering projects.Memorable Line“LLMs are great at patterns in text—and that makes them better than people at understanding what’s really happening inside your workflow.”Call to ActionIf you enjoyed this conversation, follow The Tech Trek on Spotify or Apple Podcasts for more real-world discussions at the intersection of AI, engineering, and leadership. Share this episode with a teammate rethinking their own SDLC.

Yosi Dediashvili-Drossos, Co-Founder and CTO of City Hive, joins Amir to unpack how a hyper-focused approach helped transform a niche idea into the dominant e-commerce platform for the liquor industry. From bootstrapping into a complex, highly regulated space to giving small brands a voice, Yosi shares how City Hive built the connective tissue across the entire alcohol supply chain—bridging brands, distributors, and local retailers through data, trust, and mission-driven execution.Key Takeaways• Why narrowing your focus often creates more growth than going broad• How City Hive turned regulatory complexity into a competitive advantage• The power of connecting all layers of an industry—brands, distributors, and retailers—through one platform• Why small, single-SKU brands now have a real chance to compete• What founders need to know before tackling a regulated industryTimestamped Highlights00:36 – The origin story: building an e-commerce engine for liquor stores04:00 – When niche focus becomes a gateway to full-scale growth06:49 – Why the liquor supply chain is one of the most fragmented in the U.S.10:22 – The uphill battle for small brands trying to reach consumers12:16 – Empowering micro-brands through digital visibility and data16:42 – How narrowing your scope can actually open new opportunities19:48 – Lessons from scaling in a regulated market22:49 – Yosi’s advice for founders navigating complex industriesStandout Moment“You can’t solve everything at once. Focus on the next real problem that’s in front of you—if you do that well, you’ll eventually build something that can solve the bigger picture.”Pro TipsFor founders entering regulated markets: Don’t start by trying to fix the system. Start by understanding one piece of it deeply enough that you can actually move it forward.Call to ActionIf you enjoyed this episode, follow The Tech Trek for more conversations with founders building technology that powers real-world industries. Share this episode with someone tackling a complex market—there’s a lot they’ll take away.

What happens when a 17-year Google veteran starts over with a 10-person AI startup? David Petrou, founder and CEO of Continua AI, joins Amir to unpack what it really takes to go from Big Tech stability to startup chaos. They dive into what to keep, what to unlearn, and how to build a high-performing team when everyone has to wear ten hats.From career ladders to “vibe coding,” David shares a candid look at the tradeoffs, mindset shifts, and hard lessons behind scaling something new in AI.Key Takeaways• Career ladders are a luxury—startups win by hiring for adaptability and shared ownership, not rigid progression.• Moving from Big Tech to startup means trading resources for speed—and rediscovering why building things is fun again.• Productivity at small teams thrives on decisive action and ruthless prioritization, not endless debate.• AI is transforming software development—but human experience still defines whether the tools actually deliver.• The best retention strategy in a startup: keep the work interesting and the problems worth solving.Timestamped Highlights[00:48] How Continua AI brings “social AI” into group chats[05:35] Why hiring for collaboration beats hiring for raw talent[08:51] The real gap between Big Tech engineers and startup engineers[11:19] What David had to unlearn after 17 years at Google[18:58] How limited resources force sharper technical decision-making[22:32] Productivity at early-stage startups—making faster decisions and moving forward[26:41] “Vibe coding,” AI-assisted development, and why experienced engineers adapt fasterMemorable Moment“It’s much better to be a few degrees off from optimal and moving fast than stuck in indecision for two weeks.” — David PetrouPro TipsWhen hiring for an early-stage startup, focus less on titles or ladders and more on whether the person thrives without structure. The ability to figure things out independently is the best predictor of success.Call to ActionIf this episode gave you a fresh take on startup leadership, share it with someone thinking about making the leap from Big Tech to founder life. Follow The Tech Trek for weekly insights from leaders shaping the future of tech and AI.

When you step into a new leadership role, do you prefer to build a team from the ground up—or inherit one that already exists?Ashwin Baskaran, VP of Engineering at Mercury, joins the show to unpack what really changes between these two scenarios—and what stays the same. From managing team dynamics to molding culture and earning trust in the first 90 days, Ashwin shares practical frameworks every engineering leader can apply.Key Takeaways• Building and inheriting share more similarities than most leaders realize—the principles of empathy, awareness, and low ego are universal.• When inheriting a team, awareness is your first superpower. Learn the organization before making moves.• Building from scratch gives freedom, but also more ways to make mistakes if you over-index on hiring people who think like you.• The best leaders telegraph intent early and seek alignment through action, not reassurance.• Feedback should be about context and priorities, not personal validation—it builds credibility and trust faster.Timestamped Highlights00:45 — The hidden overlap between building and inheriting a team03:25 — Why self-awareness and low ego are critical when replacing a leader06:51 — How “building” can lead to blind spots if you hire for similarity11:38 — Finding alignment between company values and your leadership style15:25 — How to read the room and earn feedback in your first 90 days21:47 — What to look for when interviewing for a role where you’ll inherit a teamA Line That Stuck“You want to find a problem that the team and company care about—and solve it in a way that feels aligned with their values.”Call to ActionIf this conversation helps you think differently about leadership transitions, share it with someone who’s stepping into a new role. Subscribe to The Tech Trek for more conversations that bridge technical leadership with real-world growth.

Jarah Euston, Co-Founder and CEO of WorkWhile, joins the show to share how she’s building a worker-first labor marketplace that puts money back into the pockets of frontline employees. Drawing from her own early experience in hourly jobs, Jarah explains why this massive yet underserved workforce deserves better tools, more respect, and faster access to earnings. We dive into automation, AI, re-skilling, and why the future of work isn’t just about robots replacing people but about using technology to unlock opportunity for 80 million Americans.Key Takeaways• Why hourly workers are overlooked in tech innovation and what WorkWhile is doing to change that• How automation can cut overhead and actually raise wages instead of lowering them• Why entry-level white-collar roles may be more at risk from AI than frontline jobs• The importance of re-skilling and flexible training for workers who can’t stop earning to learn• How instant pay and eliminating predatory fees can transform financial stability for familiesTimestamped Highlights01:26 — Jarah’s early jobs in retail and fast food and how they shaped her perspective06:56 — Why frontline workers are less likely to be displaced by AI than software engineers11:23 — Building against the grain: focusing on people instead of replacement tech13:31 — Why robotics companies still hire frontline workers alongside automation17:47 — Launching the American Labor Utilization Rate to track real work happening now21:44 — Three pillars of WorkWhile’s mission: earning, upskilling, and financial access25:17 — How word of mouth drives organic growth among workers and familiesMemorable Line“Even the companies building the future of automation still need people—and they’ve been our customers since day one.”Call to ActionIf this conversation opened your eyes to the future of frontline work, share it with someone who should hear it. Subscribe to the show for more conversations with founders and leaders reshaping technology and work.