The Tech Trek

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.

Tom Drummond, Managing Partner at Heavybit, joins the show to break down what it takes to build and scale AI “picks and shovels” companies for the enterprise. We dive into the realities of selling into one of the hardest markets to reach, why differentiation matters more than ever, and how startups can wedge their way into massive opportunities despite fierce competition.Key Takeaways• Enterprise attention is more competitive than ever—breaking through requires clarity and category creation.• Cold email and traditional outbound are saturated—startups must iterate quickly on channels and messaging.• Landing enterprise deals often starts with developers and end users, not CIOs—grassroots adoption is powerful.• Narrow wedges matter—solve one painful, high-value problem better than anyone else, then expand.• Timing the industry cycle is critical—knowing when markets fragment and when they consolidate can define outcomes.Timestamped Highlights02:03 — Why enterprise attention has never been harder to win04:55 — Differentiation in a sea of lookalike AI infrastructure startups07:34 — Cold email vs content, billboards, and unconventional channels08:35 — The Pareto rule of enterprise revenue and why developer adoption is key11:47 — Competing with big tech incumbents: the power of the narrow wedge21:03 — Where the market is headed: cycles of expansion, contraction, and consolidationA line that stuck“You don't win by being another tool—you win by defining the category everyone else has to fit into.”Call to ActionIf you enjoyed this conversation, share it with a founder or tech leader who's navigating the enterprise market. Make sure to follow the show for more unfiltered conversations with people shaping the future of software and AI.

Jonathan DiVincenzo, co-founder and CEO of Impart Security, joins the show to unpack one of the fastest growing risks in tech today: how AI is reshaping the attack surface. From prompt injections to invisible character exploits hidden inside emojis, JD explains why security leaders can't afford to treat AI as “just another tool.” If you're an engineering or security leader navigating AI adoption, this conversation breaks down what's hype, what's real, and where the biggest blind spots lie.Key Takeaways• Attackers are now using LLMs to outpace traditional defenses, turning old threats like SQL injection into live problems again• The attack surface is “iterating,” with new vectors like emoji-based smuggling exposing unseen vulnerabilities• Frameworks have not caught up. While OWASP has listed LLM threats, practical solutions are still undefined• The biggest divide in AI coding is between senior engineers who can validate outputs and junior developers who may lack that context• Security tools must evolve quickly, but rollout cannot create performance hits or damage business systemsTimestamped Highlights01:44 Why runtime security has always mattered and why APIs were not enough04:00 How attackers use LLMs to regenerate and adapt attacks in real time06:59 Proof of concept vs. security and why both must be treated as first priorities09:14 The rise of “emoji smuggling” and why hidden characters create a Trojan horse effect13:24 Iterating attack surfaces and why patches are no longer enough in the AI era20:29 Is AI really writing production code and what risks does that createA thought worth holding onto“AI is great, but the bad actors can use AI too, and they are.”Call to ActionIf this episode gave you new perspective on AI security, share it with a colleague who needs to hear it. Follow the show for more conversations with the leaders shaping the future of tech.

Daniel Saks, co-founder and CEO of Landbase, joins The Tech Trek to unpack the real meaning of democratizing technology. From agentic AI that works for you—not the other way around—to rethinking workflows and change management, Daniel shares why this shift is bigger than the move from on-prem to cloud. For tech leaders, founders, and operators, this episode reveals how to reclaim time, scale smarter, and prepare for the next wave of AI-native business.Key Takeaways• AI is moving beyond hype—it's becoming the engine that executes real workflows and shifts power from systems to users• Businesses that recapture saved time will unlock significant cost efficiency and growth potential• The gap between idea and implementation is shrinking fast, but durable value will come from solving the hardest problems, not the easiest apps• Change management is now about building AI-native workflows and cross-functional systems, not just adopting tools• Sales and go-to-market leaders can gain an edge by mastering prompting and AI-driven enrichment todayTimestamped Highlights00:56 — Why Landbase built GTM-1 Omni to reimagine go-to-market execution01:40 — From on-prem to cloud to AI-native: the next major leap in democratizing technology04:34 — Why fears about AI replacing jobs miss the bigger story of new roles and industries emerging08:42 — How the pace of product cycles is collapsing and what that means for value creation13:25 — Inside Landbase's “AI Factory” model for automating workflows across functions16:39 — What people actually do with the time they reclaim through AI-driven automation19:23 — How AI is reshaping the role of the salesperson and why adoption speed mattersA line that stood out“You don't have to work for your software anymore—your software works for you.”Call to ActionIf this conversation gave you fresh ideas about how AI is reshaping business, share it with your team and subscribe to The Tech Trek on Apple Podcasts or Spotify. For more insights, follow along on LinkedIn.

Matt McLarty, CTO at Boomi, joins the show to break down what enterprise AI adoption really looks like in 2025. From navigating the hype cycle to identifying practical first steps, Matt shares what separates companies that are seeing value from those stuck in endless pilots. If you're a tech leader wondering how to move beyond experimentation and into measurable outcomes, this episode is your playbook.Key Takeaways• AI adoption is not binary—it's a spectrum, and success depends on linking it to business value, not just “using AI.”• Orientation matters: every company needs an honest assessment of where they are on their digital maturity curve before jumping in.• Small, low-risk bets build the organizational muscle memory required for bigger wins.• The fastest wins often come from augmenting existing automation rather than chasing moonshots.• Companies that succeed treat AI as a tool to solve business problems, not as an end goal.Timestamped Highlights01:38 – Why AI's hype cycle feels like “Mount Everest” compared to cloud and mobile04:50 – Why AI adoption can't be compared to past waves like blockchain or cloud07:36 – The hidden foundation: digital transformation work still matters11:11 – The inversion that changes everything: AI isn't the goal, business outcomes are16:26 – Defining “adoption” as a multi-dimensional spectrum, not a checkbox19:50 – How to recover if your first AI projects fall short28:04 – Building adaptability as a core enterprise competency31:25 – The common traits of companies succeeding with AI right nowA standout moment“AI isn't the end goal—it's just another tool. The real question is, what business problems can we finally solve with it?” – Matt McLartyCall to actionIf this episode gave you a clearer path toward enterprise AI adoption, share it with a colleague and follow the show so you never miss a conversation on where tech leadership is heading.

Vipin Kumar, Head of CUSO IB Data Strategy and Analytics at Deutsche Bank, joins me to unpack one of the toughest problems in financial services: managing data quality in a highly regulated industry. From the outside, it might look like a box-checking exercise. In reality, it's a complex mix of legacy systems, global frameworks, regulatory controls, and the constant push to balance defensive compliance with offensive business value. Vipin makes it real with examples that connect directly to how we all experience data in daily life.Key TakeawaysData quality isn't just about accuracy—timeliness, completeness, and consistency all matter, especially when billions are on the line.Regulations push banks into “defensive” strategies, but there's growing opportunity to apply “offensive” strategies that use data for prediction, analytics, and competitive edge.Measuring effectiveness requires agreement between data producers and consumers, with preventive and detective controls working together.AI and machine learning are starting to automate checks, spot patterns, and even strengthen anti-money laundering defenses.Timestamped Highlights00:45 What data quality means in a regulated industry03:15 The challenges of managing fragmented legacy systems06:40 How producers and consumers measure effectiveness of frameworks09:30 The pizza delivery analogy for making sense of data quality14:20 Why accuracy is harder than timeliness or completeness16:50 The role of AI and machine learning in improving governance19:20 Shifting from defensive compliance to offensive strategy in banking22:40 Regulators testing AI-driven approaches to anti-money launderingMemorable Quote“Producer has preventive controls. Consumer has detective controls. True data quality happens only when both align 100%.” — Vipin KumarCall to ActionIf you enjoyed this conversation, share it with a colleague who thinks about data quality or governance. Don't forget to follow the show on Apple Podcasts or Spotify so you never miss an episode.

Marty Ringlein, co-founder and CEO of Agree.com, joins Amir to unpack why history always repeats itself in technology and what that means for the AI era. From the telephone to the automobile to ChatGPT, the biggest shifts have rarely been things people asked for—they were inventions that reshaped behavior once adopted. Marty explains why skepticism always comes first, how fear fuels resistance, and why optimism is usually rewarded. He also shares how Agree.com is rethinking contracts and payments by automating the painful parts of sales workflows.Key TakeawaysThe most transformative inventions weren't requested—they emerged through evolution and network effects.Human resistance to new tech often comes from energy costs of relearning, not the tech itself.AI isn't eliminating jobs—it's freeing people from low-value work so they can focus on bigger challenges.Every wave of disruption (printing press, cars, internet, mobile, AI) begins with fear, then proves to be a net positive.Timestamped Highlights00:51 — Why Agree.com calls itself “a better DocuSign” and how it integrates signatures, invoicing, and payments02:06 — The history of inventions nobody asked for and why they stuck05:41 — Human pessimism vs optimism when confronting new technologies09:05 — Why fears around AI echo the same debates once had about books, cars, and the cloud13:38 — How automation frees salespeople and engineers to focus on higher-value work18:51 — Are there technologies that have been net negative for society? Marty's take23:21 — Why every generation thinks “this time it's different”Memorable Quote“The biggest things that will change our lives are the ones we don't even know to ask for yet.” — Marty RingleinCall to ActionIf you enjoyed this episode, share it with a colleague who's navigating the AI conversation. Follow The Tech Trek for more conversations that cut through the noise on tech, leadership, and the future of work.

Simon Lam, VP of Engineering at M1, joins the show to unpack one of the trickiest topics in tech careers: how engineers can build influence without a formal leadership title. Too often, influence is mistaken for charisma or public speaking—but Simon explains why it's really about consistent impact, trust, and understanding how change happens inside teams. If you're an IC who feels stuck at the “senior wall” or a manager wondering how to better evaluate career growth, this conversation delivers clarity and actionable insight.Key Takeaways• Influence isn't charisma—it's the result of consistent impact and trust over time• Engineers can build influence at any stage, from junior to staff, by solving problems and being reliable• Career progression should tie back to impact, not just who speaks the loudest in the room• Change management offers a practical lens for understanding influence in technical settings• Dual career tracks mean engineers don't need to move into people management to keep advancingTimestamped Highlights01:39 Why influence is often misunderstood in engineering careers05:12 Influence vs charisma—and why you don't need to be an extrovert08:47 The virtuous cycle of impact leading to influence13:20 Are companies biased toward rewarding outspoken engineers?17:21 Practical ways ICs can start building impact today22:48 Why you don't need to manage people to have a leadership careerA line worth remembering“Consistent impact is how you build influence.” — Simon LamCall to ActionIf this episode sparked new ways to think about your own career, share it with a teammate who's navigating the same questions. Follow the show for more conversations with leaders shaping the future of engineering.

CJ King, CTO at Torc Robotics, joins the show to talk about the future of autonomous trucking at scale. Instead of asking “can we build one self-driving truck?” Torc is asking, “how do we safely put 10,000 on the road?” From supply chain transformation to regulatory hurdles, CJ breaks down what it really takes to bring production-ready autonomous semis into the market and why the ripple effects will reach far beyond trucking.Key Takeaways• Scaling autonomous vehicles isn't about prototypes—it's about building production-ready systems from the ground up.• Trucks face unique technical challenges, from 1,000-meter perception needs to fully redundant systems that can't rely on cloud compute.• Removing driver limitations could extend operations from 8 hours a day to 20, unlocking major gains in supply chain efficiency.• Regulatory collaboration is critical—success depends on alignment with federal and state agencies, law enforcement, and logistics partners.• Adoption will come in step-functions: once proven safe and reliable, logistics companies are ready to adopt at scale.Timestamped Highlights00:45 – Torc's focus on hub-to-hub autonomous trucking02:03 – Why scaling to thousands of trucks matters more than building one prototype06:48 – The unique technical problems of trucks vs. passenger cars09:25 – How extended operating hours reshape logistics and supply chains14:17 – Working with regulators and law enforcement to ensure safety and compliance17:42 – AV3.0, synthetic data, and billions of miles of training for safer systems22:31 – Building public trust and societal acceptance of autonomous trucking25:21 – Why large-scale adoption will happen in step functions, not tricklesA Line That Stuck With Us“Our bare minimum is to drive as good as a human—our mission is to be safer than one.” – CJ KingCall to ActionIf you enjoyed this episode, share it with someone who cares about the future of tech and logistics. Make sure to follow the show so you never miss conversations that dig into how technology is reshaping our world.

Joseph Krause, co-founder and CEO of Radical AI, joins the show to break down how scientific discovery is being reinvented. From the limitations of the traditional trial-and-error model to the rise of AI-driven self-driving labs, Joseph explains how science is moving from slow, serial processes to a parallel model that unlocks breakthroughs at scale. He also dives into the economics of materials, why big companies can't pivot fast enough, and how the role of scientists is being transformed.Key TakeawaysThe old model of science is serial: slow, linear, and limited by human capacity to read, experiment, and analyze.Negative results—failed experiments—are the true fuel for breakthroughs, but they're rarely captured or shared.Self-driving labs powered by AI create a “materials flywheel,” running 30,000+ experiments a year and learning continuously.Big corporations are trapped by the innovator's dilemma and talent challenges, leaving space for startups to lead.Scientists in the future will focus less on repetitive lab work and more on shaping hypotheses and applying intuition at scale.Timestamped Highlights02:00 How science traditionally works and why it's so slow05:50 Why mistakes and negative results matter more than we admit09:40 The fragmentation of research and why labs don't share data17:15 Inside a self-driving lab and how AI accelerates discovery23:40 Why big material companies can't innovate like startups35:40 The new role of scientists in an AI-powered discovery worldMemorable Line“You don't get a PhD to learn to pipette—you get it to think about how and why the world will change.”Call to ActionIf you enjoyed this conversation, share it with a colleague who geeks out on science and technology. Follow the show on Apple Podcasts or Spotify so you don't miss future episodes exploring where tech is headed next.

John Fiedler, SVP of Engineering and CISO at Ironclad, joins the show to unpack the real challenges of technology leadership. From managing nonstop context switching to measuring success when you're no longer shipping code, John shares hard-earned lessons on how leaders can protect their time, set priorities, and thrive in the chaos. Whether you're moving from IC to manager or scaling as an executive, this conversation offers a candid look at what it truly takes to lead.Key Takeaways• Success in leadership isn't about features shipped—it's about execution, people, and culture.• Context switching is constant, but leaders can design their calendars to minimize the chaos.• Organizational size reshapes the challenge: startups reward speed, enterprises demand process.• Protecting your time isn't optional—leaders who don't own their calendars quickly burn out.• The leap from IC to manager requires starting fresh and mastering a new craft.Timestamped Highlights02:13 The hidden tax of context switching06:53 How John measures success as a leader without code10:45 What really slows executives down inside organizations15:51 How John protects his calendar and finds focus time24:47 The lessons every first-time manager needs to hearA Line That Sticks“If you don't control your calendar, your calendar will control you.”Call to ActionIf this episode resonated, share it with a fellow leader navigating the chaos. Subscribe to The Tech Trek on Apple Podcasts and Spotify for more candid conversations about scaling, leadership, and the future of technology.

Alex Salazar, co-founder and CEO of Arcade.dev, joins the show to unpack the realities of building enterprise agents. Conceptually simple but technically hard, agents are reshaping how companies think about workflow automation, security, and human-in-the-loop design. Alex shares why moving from proof-of-concept to production is so challenging, what playbooks actually work, and how enterprises can avoid wasting time and money as this technology accelerates faster than any previous wave.Key TakeawaysEnterprise agents aren't chatbots—they're workflow systems that can take secure, authorized actions.The real challenge isn't just building demos but getting to production-grade consistency and accuracy.Mid-market companies face the steepest climb: limited budgets, limited ML expertise, but the same competitive pressure.Success starts with finding low-risk, high-impact opportunities and narrowing scope as much as possible.Authorization is the biggest blocker today; delegated OAuth models are key to unlocking real agent functionality.Timestamped Highlights02:02 — Why agents are “just advanced workflow software” but harder to trust than traditional apps04:53 — The gap between glorified chatbots and real enterprise agents that take action09:58 — From cloud mistrust to wire transfers: how comfort with automation evolves14:00 — Chaos at every tier: startups, enterprises, and why the mid-market struggles most26:21 — The playbook: how to pick use cases, narrow scope, and carry pilots all the way to prod34:38 — Breaking down agent authorization and why most RAG systems fail in practice42:09 — Adoption at double speed: what makes this AI wave different from internet and cloudA Thought That Stuck“An agent isn't an agent until it can take action. If all it does is talk, it's just a chatbot.” — Alex SalazarCall to ActionIf this episode gave you a clearer lens on enterprise agents, share it with a colleague who needs to hear it. And don't miss future conversations—follow The Tech Trek on Apple Podcasts, Spotify, or wherever you listen.

Russ d'Sa, founder and CEO of LiveKit, joins the show to unpack the rise of voice AI and what it means for how we interact with technology. From the shift away from static decision trees to dynamic, LLM-powered systems, Russ explains why voice is emerging as one of the most natural interfaces for humans—and one of the most disruptive opportunities for builders. This episode goes beyond surface-level hype to explore real-world use cases, infrastructure shifts, and what's coming next as voice moves from novelty to mainstream.Key Takeaways• Voice AI has moved far beyond Siri and Alexa—LLMs enable open-ended, natural conversations without rigid decision trees.• Two main categories are emerging: open-ended voice experiences (like tutoring and therapy apps) and goal-oriented workflows (like healthcare intake, finance, and customer support).• The biggest barrier isn't just technology, but adoption behavior—older generations default to typing and screens, while younger users and voice-first cultures are accelerating change.• Infrastructure for voice and video AI requires a fundamental shift from stateless web servers to stateful, long-lived conversational systems.• The hardest technical challenge ahead: mastering conversational turn-taking so AI can interact as naturally as a human.Timestamped Highlights01:06 How LiveKit is giving applications the ability to see, hear, and speak04:18 The two main categories of voice AI use cases emerging right now09:53 Why adoption of voice AI depends as much on behavior as on technology14:20 Imagining a 24/7 voice-driven AI that replaces screens and UIs20:30 Why the internet's original infrastructure wasn't built for voice and video AI25:39 The challenge of memory, authentication, and group dynamics in AI conversationsA line worth remembering“If you have a computer that perfectly understands when to speak, when to listen, and adds value in the right moments—why would you ever use anything else?”Call to ActionIf you enjoyed this conversation, share it with a colleague who's curious about where AI is headed. Subscribe on Apple Podcasts or Spotify so you don't miss future episodes diving into the technologies shaping the next decade.

Sumit Arora, VP of Advanced Technology at Ascend Learning, joins the show to unpack the real challenges of turning AI prototypes into production-ready systems. From managing non-deterministic outputs to rethinking the relationship between engineering and product, Sumit shares hard-earned lessons on what it actually takes to build AI that works at scale. If you're navigating how to move beyond experiments and deliver AI products that stick, this episode will give you a clear look at the path forward.Key Takeaways• Scaling AI is not about building smarter prototypes—it's about mastering distributed systems, security, and availability.• The best AI teams combine deep systems engineering with practical product sense.• Traditional software requirements processes won't work for AI. Co-creation between product and engineering is essential.• Innovation pods—small, cross-functional teams—can accelerate experimentation without killing momentum.• Success at scale comes from modular, reusable AI systems that can plug into multiple contexts.Timestamped Highlights02:14 — Why building a working AI demo is easy, but scaling it into a reliable product is hard04:49 — Lessons from the big data revolution and how AI is moving even faster08:41 — The skill sets AI teams really need and why distributed systems expertise trumps pure ML13:13 — Designing user experiences for AI and why response times redefine UX expectations17:00 — The evolving relationship between product and engineering in the AI era23:10 — How innovation pods help organizations experiment without stalling production teams26:47 — Why modular, self-contained AI systems are the key to scaling across an enterpriseA Line That Stuck“You can't requirement doc your way to AI success. Product and engineering have to co-create and move fast.”Call to ActionIf you found this conversation useful, share it with a colleague, subscribe to the show, and leave a quick rating—it helps us bring more tech leaders and practitioners to the table.

Sean McCarthy, co-founder and CEO of BackOps, shares how a career in sales prepared him to build an AI-driven logistics company from the ground up. In this episode, Sean reveals how observing real-world pain points at Amazon inspired BackOps' mission and why coming from a non-technical background can actually be a founder's advantage. This is a conversation about scaling, selling, and leading with insight — perfect for anyone thinking about making the leap from operator to founder.Key TakeawaysWhy non-technical founders are uniquely positioned to solve operational problems with AIThe mindset shift required to go from running sales to running an entire companyHow to validate an idea before leaving a stable, well-paying jobWhat it really takes to hand off sales when it's been your superpowerPricing insights that help ensure you're building a scalable businessTimestamped Highlights01:45 Sean's Amazon journey and what time spent in warehouses taught him about customer pain points04:14 The moment he saw the same issues plaguing both small and nine-figure sellers — and spotted an opportunity07:37 How becoming a CEO forced him to rewire his focus beyond sales and build internal infrastructure12:18 Why having a technical co-founder was non-negotiable — and how AI tooling is changing that equation15:18 The tough decision to leave Amazon and how he measured risk versus regret17:59 Learning to let go and trust others with the sales process while still staying close to customersMemorable Moment“Talk to the people that would actually buy your product. Measure the pain point. If it's a one or two out of ten, it's probably not worth building. If it's a nine or ten, and they'll pay for it, now you have something.”Pro TipsValidate early and price with intention. Don't just ask if someone would use your product — ask exactly what they'd pay for it. Those conversations can save months of wasted build time.Call to ActionIf this episode resonated, share it with a friend who's considering the leap into entrepreneurship. Follow the show for more conversations with founders, operators, and tech leaders building the next generation of companies.

Dmitri Sedov, Chief Data and Analytics Officer at Allvue, joins to explore why transformation isn't a destination but an ongoing journey. He shares how financial services are navigating the current wave of AI, what it means to balance short-term expectations with long-term strategy, and why open, modular ecosystems are critical to staying competitive. This conversation goes beyond buzzwords to uncover how leaders can embrace change without getting lost in it.Key Takeaways• Transformation isn't about a single endpoint—it's about staying curious, iterative, and open to multiple paths.• AI is accelerating faster than previous technology cycles, but its value lies in solving real customer problems, not chasing hype.• Companies that lagged in past waves of innovation may now have an opportunity to leapfrog forward.• The smartest moves aren't “rip and replace” but incremental improvements that build on existing engines.• Open, interoperable, and modular approaches reduce risk and keep technology flexible for the future.Timestamped Highlights01:26 – Why digital transformation is less about checking boxes and more about a mindset shift09:47 – The balancing act of managing short-term ROI while exploring long-term AI potential15:25 – A 24-hour hackathon prototype that changed how Dmitri thinks about multi-agent AI22:34 – Why interoperability and modularity matter more than big monolithic solutions27:43 – How to plan roadmaps when technology outpaces predictability31:43 – The future of “human in the loop” and what workforce transformation might look likeA line that stuck“Transformation is never about reaching one destination. The more you fixate on a single path, the more likely you'll be left behind.”Call to ActionIf this episode gave you new perspective on AI and transformation, share it with a colleague who's wrestling with the same challenges. And don't forget to follow the show so you're ready for the next conversation on building smarter, more resilient companies.

What if the key to real work-life balance isn't about escaping work, but transforming it into your outlet for curiosity and passion? In this episode, Hassaan Raza, co-founder and CEO of Tavus, explores how technology is removing friction in our daily lives and why teaching machines to be more human could unlock new levels of creativity, accessibility, and balance. From redefining what “work you love” actually means to the role AI will play in democratizing opportunity, this conversation challenges assumptions and paints a picture of a future where tech becomes a true partner to people.Key TakeawaysWork-life balance may not come from hobbies outside work, but from making work itself a fulfilling outlet.Technology's real value lies in removing friction and making tools more accessible to everyone, not just the highly technical.AI and human-computing advances could act as equalizers, giving people with different learning styles or backgrounds the same opportunities.Hollywood dystopias aside, machines can be designed to replace bad tools, not people—and that creates empowerment, not fear.The future of tech is less about replacement and more about enhancing human creativity, productivity, and quality of life.Timestamped Highlights00:33 — Hassaan explains Tavus' mission to teach machines how to be human.04:05 — Why some engineers care more about solving puzzles than the outcomes.07:08 — How passion-driven work blurs the line between career and hobby.12:12 — The obsession with removing friction and making machines easier to use.16:37 — Addressing fears about AI taking jobs and reframing the conversation.20:57 — How AI can open doors for non-traditional learners and democratize education.A Line That Stands Out“If you find work that you really love and are passionate about, that can be your outlet—you don't need balance because the work itself becomes your balance.”Call to ActionIf this episode gave you a fresh perspective on work, technology, and balance, share it with someone who's wrestling with the same questions. And don't forget to follow the show so you never miss the next conversation.

Dane Atkinson, CEO and founder of Odeko, joins the show to unpack the reality of evolving as a founder. He shares why the first idea you start with rarely survives, how to know when it's time to pivot, and why anchoring on a mission instead of a product keeps you in the game. This conversation dives into frameworks for making hard calls, the messy middle of startup life, and what it really takes to endure as a multi-time founder.Key Takeaways• Your first idea probably won't be the one that works—focus on the customer and the mission, not the concept.• Pivoting is brutal but necessary; small experiments can create the proof you need to shift direction.• Founders who learn from failure are more likely to succeed in their second or third ventures.• Having a North Star rooted in mission makes the day-to-day grind and tough decisions bearable.• The best outcomes come when investors give founders space to experiment and even fail.Timestamped Highlights00:43 – Why Odeko's mission is to help small coffee shops compete with giants01:44 – The flawed brilliance of Odeko's first AI-driven product and the hard pivot that followed05:28 – The painful trap of chasing product-market fit and the danger of sticking too long10:24 – Building proof for a pivot and the difference between charisma-driven sales and true demand14:04 – Why most successful founders are “multi-run players” and what VCs often miss about failure17:02 – How staying mission-driven keeps founders motivated through setbacksA line worth remembering“You can change the product, you can change the delivery, but if you have a North Star that matters, you'll always know how to steer the company back on track.”Founder TipTest new directions quietly alongside your current model. Early prototypes not only prove viability but also help you win over skeptical teammates, boards, and investors.Call to ActionIf this episode gave you something to think about, share it with a fellow founder or operator who's in the middle of their own evolution. And don't forget to follow The Tech Trek so you never miss the next conversation on scaling, leadership, and building companies that last.

What does a day in the life of a startup CTO really look like? Mo El Mahallawy, CTO and co-founder of Shepherd, shares the unfiltered journey from being engineer number one to leading a 50-person Series A company. He opens up about the hardest phases of building, what shifts as your company scales, and how he manages energy, priorities, and mental health along the way. This episode is a practical playbook for any founder, CTO, or tech leader navigating growth.Key Takeaways• The early days as a startup CTO are often the hardest—you're coding, recruiting, managing, and wearing every hat at once.• Growth means trading code for vision: shifting from building features to setting direction and enabling your team.• Time management is only half the battle—energy management and knowing when you do your best work is equally critical.• Planning is a mental health strategy: when you control your roadmap, you avoid the burnout of constant reaction mode.• Taking big swings matters more than just paying down tech debt—bets move the business forward.Timestamped Highlights02:41 – The origins of Shepherd and why Mo left Airbnb to build in insurance tech.07:28 – What makes the early-stage CTO role one of the toughest in startups.10:44 – Mo's brutally honest description of those days: “like chewing glass.”16:45 – How the CTO role evolves post-Series A and the challenges of stepping out of code.20:44 – Why energy balance beats pure time management.26:42 – Mo's take on mental health and how planning became his best defense against fatigue.A line worth remembering“Your life is going to suck, and then it's going to be great—but your job is to make this work.”Pro Tips• Use your strongest energy hours for high-leverage work, not busywork.• Build your network early at iconic companies—you'll rely on those relationships later.• Don't shy away from big bets; they create momentum that tech debt never will.Call to ActionIf you found Mo's story valuable, share this episode with someone thinking about becoming a founder or CTO. And don't forget to follow the show on your favorite podcast app so you never miss the next set of scaling playbooks.

What does it take to build a startup in a space where the ground shifts every 90 days? Rahul Sonwalkar, founder and CEO of Julius AI, joins the show to share how he navigates product market fit, resource constraints, and constant model evolution while scaling an AI-first company. Instead of relying on long-term roadmaps, Rahul runs Julius with rapid feedback loops, deep user focus, and a mindset that every team member contributes to AI development. This conversation is a playbook for founders and operators facing uncertainty and speed in equal measure.Key Takeaways• Why Rahul believes rigid long-term roadmaps can hold founders back in AI and fast-moving markets• How limited resources force sharper prioritization, and how to decide what makes the cut• The two-way product market fit Julius found by serving both data teams and business stakeholders• Why direct user conversations and dogfooding are non-negotiable for early-stage companies• A glimpse into the future of BI as AI agents that monitor and surface key metrics automaticallyTimestamped Highlights00:43 – Julius AI's origin story and how Rahul validated product market fit05:08 – The surprising way both data teams and business users bring Julius into organizations09:24 – Why Rahul avoids long-term roadmaps and favors month-to-month iteration13:55 – The role of feedback loops and customer support in shaping product direction17:27 – How to prioritize when you only have resources for two out of ten critical features19:39 – Rahul's vision for how BI and analytics will evolve with AI agentsA Standout Line“Overnight, things you thought were impossible for the next 12 months can suddenly become possible. That's why you can't have a rigid roadmap when building with AI.”Founder's LessonUse your own product daily. When the entire team feels the same friction as your users, the right priorities become obvious.Call to ActionIf you're a founder, operator, or investor looking for practical lessons on building in uncertain terrain, follow the show for more conversations like this. And if Rahul's approach resonated, connect with him on LinkedIn or explore Julius AI.

Matt Lynch, CTO and co-founder of Sage, joins the show to share how his team is using modern technology to transform elderly care. From rethinking outdated nurse call systems to capturing real-time data that improves both patient safety and caregiver retention, Matt breaks down the technical and human challenges of modernizing this critical industry. This is a story of mission-driven innovation, scale, and the surprising role data plays in quality of life.Key Takeaways• The aging population is outpacing caregiver availability, creating an urgent need for tech-driven solutions.• Most facilities still rely on fragmented or low-quality data, making it difficult to improve care.• Real-time documentation and streamlined workflows give caregivers credit for their work and reduce burnout.• New technology can deliver results at a fraction of the cost of outdated systems.• Adoption succeeds when tech is simple, resident-driven, and seamlessly fits into caregiver routines.Timestamped Highlights00:34 — Why Sage was founded: the gaps in elderly care tech that inspired three co-founders04:36 — The looming scale problem: a 40% surge in the aging population with flat caregiver growth06:56 — How poor data practices erode care quality—and what synchronous documentation changes12:05 — Why old systems cost 10x more and how modern tools flip the economics18:06 — Giving caregivers proof of their work and reducing turnover with better data21:54 — Lessons in building: why focusing on workflow software beat hardware reinventionA line worth remembering“Caregivers finally get credit for the work they're doing—and that completely changes how families and facilities work together.”Pro TipStart simple. In elderly care tech, the real breakthrough wasn't reinventing hardware but streamlining the caregiver workflow to make adoption natural.Stay ConnectedIf this episode resonated, share it with a friend in tech or healthcare. Don't forget to follow the show on Apple Podcasts and Spotify so you never miss new conversations with the builders shaping our future.

What does it really mean to build in stealth—and when does it help or hurt? In this episode, Amir sits down with Yoni Michael, co-founder of Typedef, an AI infrastructure startup that recently came out of stealth. Yoni shares why his team chose to stay under the radar early on, how they balanced secrecy with customer discovery, and the lessons they learned about finding product-market fit in a noisy AI landscape. If you're a founder or tech leader navigating early-stage strategy, this conversation offers practical insights you can apply right away.Key Takeaways• Stealth mode isn't all or nothing—there's a spectrum between total secrecy and open visibility• Execution and speed of iteration matter more than protecting “the idea”• Customer discovery should start before you even write code• Messaging is never final—test, refine, and keep adjusting as you learn from design partners• Investors expect shifts at the seed stage, but keeping them in the loop builds trustTimestamped Highlights00:38 — Why Typedef chose to launch in stealth and what they're building in AI infrastructure04:31 — The double-edged sword of operating in a crowded AI market09:22 — How Yoni approaches customer discovery without giving away too much13:55 — Shaping messaging and narrative before coming out of stealth19:49 — Managing investor expectations when your product vision evolves25:21 — How to connect with Yoni for advice and community in AI infraA line that stuck with us“Your competitive edge isn't the idea—it's the ability to execute and course correct fast enough to hit your runway.”Resources mentioned• Typedef: typedef.ai• FENEC open-source framework: [GitHub link from Typedef site]• Yoni Michael on LinkedIn: linkedin.com/in/yonimichaelPro TipsYoni advises founders to test messaging as early as possible—whether through decks, demo sandboxes, or LinkedIn posts. The feedback loop is as valuable as product feedback.Stay connectedIf this episode gave you something to think about, share it with a founder or tech leader who'd benefit. And don't forget to follow The Tech Trek on Apple Podcasts, Spotify, or YouTube so you never miss future conversations.

Simone Kalmakis, VP of Engineering at Viam, joins the show to share what it takes to lead teams that bridge hardware and software in the robotics space. With a background in software and machine learning, she now manages engineers with expertise ranging from code to mechanical systems. Simone talks about how she fosters collaboration across disciplines, adapts her leadership style, and keeps her teams aligned while building cutting-edge robotics solutions. If you want to understand what it's really like to lead at the intersection of AI, software, and hardware, this conversation is for you.Key Takeaways• Why managing hardware and software together requires humility, curiosity, and alignment• How integrating testing across both domains changes the development process• The role of “20% projects” in helping engineers learn and innovate faster• Why passion and purpose matter more than hours worked when it comes to work-life balance• Why strong engineers without PhDs can still excel in robotics and AITimestamped Highlights01:05 — How Viam bridges the gap between physical devices and AI04:04 — The challenges of testing hardware versus software, and why small changes have big consequences10:57 — How hobbyist projects and 20% time accelerated Simone's growth as a leader in robotics14:09 — What one-on-ones look like when leading both software and mechanical engineers16:14 — Attracting and retaining top robotics and AI talent in a competitive market22:54 — How passion and vision drive sustainable work-life balance in high-pressure environmentsA Line That Stuck With Us“Even the slightest change in hardware can have huge ramifications, and it's humbling to lead people whose expertise far outweighs your own.”Call to ActionIf you enjoyed this episode, share it with a colleague who's curious about robotics or leadership at the hardware-software frontier. And don't forget to follow the show so you never miss insights from top tech leaders.

What does it really mean to be data-driven? Mark Gergess, VP of Data and BI at DoubleVerify, joins the show to unpack how data teams can go beyond dashboards to drive meaningful business action. From building an internal consulting lens to evaluating the latest AI tools, Mark shares how his team translates complex data flows into measurable revenue impact. If you've ever wrestled with the gap between insights and outcomes, this conversation will hit home.Key Takeaways• Being data-driven is about driving action, not just reporting numbers• Stakeholders don't care about your data problems—they care about business outcomes• The biggest challenge with AI adoption isn't the model, it's the use cases• Efficiency gains from AI should shift focus from ETL tasks to solving real business problems• Data culture health is measured by how naturally teams rely on data day-to-dayTimestamped Highlights01:17 How DoubleVerify helps advertisers build safer, more effective digital campaigns04:55 Why the definition of “data-driven” still varies and why it matters09:25 Measuring whether data efforts are moving the needle on revenue13:15 How to separate hype from value when evaluating AI and GenAI tools17:10 Lessons from the data science boom and why companies must go “all in” with AI25:31 Can AI act as your junior analyst? Where efficiency gains really show up27:01 How freeing up time changes the structure of data teams and boosts business impactA thought worth holding onto“It's not about dashboards. It's not about reporting. It's about doing something with the information.”Pro TipsMark recommends treating AI as a “junior analyst”—let it handle quick, lower-priority questions so your team can focus on bigger business challenges.Call to ActionEnjoyed the conversation? Share this episode with a colleague who talks about being “data-driven.” Subscribe on your favorite podcast platform and connect with me on LinkedIn for more insights from leaders shaping the future of data and technology.

Teddy Solomon, co-founder and CEO of Fizz, joins the show to share his journey from Stanford dropout to leading one of the fastest-growing Gen Z social platforms. In this conversation, Teddy breaks down what it's like to learn leadership on the fly, the misconceptions around Gen Z founders, and how he's scaling a product used by 95% of students at some of the top universities in the country. His story is a rare look at what it takes to build, adapt, and lead with composure at a young age.Key Takeaways• How Teddy turned a pandemic side project into the leading college social platform in the U.S.• Why staying “high with the lows and low with the highs” is his guiding principle for leadership and culture• The role of mentorship and humility in navigating early CEO decisions• How to earn respect and lead teams older and more experienced than you• Why real-world interaction still matters in an era dominated by digital connectionsTimestamped Highlights[01:00] Teddy explains how Fizz became the go-to social app for U.S. college campuses[02:45] The bold decision to drop out of Stanford after meeting a key mentor and investor[09:15] Why bringing in an external CEO was the best move for the company's survival[12:30] Lessons on leadership: performing your best in the worst moments[15:59] The reality of leading teams as a young founder[18:45] Challenging Gen Z stereotypes through action and connectionMemorable Line“You need to stay high with the lows and low with the highs. If you can perform at your best when things are at their worst, you give your company the best chance to survive.”Pro TipsFocus on results and culture. Age, background, and pedigree matter far less than the ability to create a strong team environment and deliver outcomes.Call to ActionIf you enjoyed this episode, share it with someone who's passionate about leadership and startups. Follow the show for more conversations with founders shaping the future of tech, and connect with us on LinkedIn for behind-the-scenes insights.

Desiree Lee, one of the Business CTOs at Armis, joins Amir to unpack one of the toughest realities in cybersecurity: the biggest risks aren't always technical, they're human. From phishing and deepfakes to the way AI is reshaping both attackers' and defenders' playbooks, Desiree shares hard-won insights on what companies should actually prioritize. If you're a tech leader navigating the expanding attack surface, this episode will sharpen how you think about security in the AI era.Key Takeaways• Most breaches stem from human behavior, not lack of technology.• Attackers adopt AI faster because there's no downside for them—defenders must catch up.• Fundamentals like patching and asset inventory still make or break resilience.• AI can reduce noise for security teams by spotting patterns in overwhelming data.• Small and midsize businesses will benefit from AI-driven tools that lower the barrier to effective security.Timestamped Highlights00:34 — How Armis evolved from asset inventory to full security solutions03:30 — Why security failures are more about psychology than technology07:32 — The deepfake CFO story and why training alone can't solve phishing risks09:18 — Why most enterprises struggle with basics like patching and automation11:41 — Where AI gives defenders an edge in processing massive data sets18:33 — Practical ways AI can ease alert fatigue and vulnerability management21:03 — The overlooked need to label assets by business criticalityA Moment Worth Remembering“There is no penalty on the attacking side for embracing AI. It's only good things for them. So they will adopt it quickly.” — Desiree LeeResources MentionedArmis: armis.comDesiree Lee on LinkedIn: linkedin.com/in/desireedleePro TipsTagging assets with their business criticality is one of the simplest, highest-impact steps companies can take. It turns asset inventories from static lists into real decision-making tools for AI-driven defense.Call to ActionIf you found this episode valuable, share it with a colleague who's thinking about security and AI. Subscribe on your favorite podcast platform so you never miss future conversations with tech leaders pushing the edge of what's possible.

Chris Ghanbarzadeh, Senior Director of Engineering at Game Changer, joins the show to unpack the shifting landscape of mobile development. From balancing quality with speed to navigating the rise of AI-assisted engineering, Chris shares how teams can adapt to new tools, methodologies, and user expectations. If you're building mobile products—or leading teams that do—this conversation will sharpen how you think about stability, velocity, and the future of software development.Key Takeaways• Why stability is the true driver of long-term speed in mobile development• How shifts away from native development are reshaping efficiency and delivery• The role of AI toolchains in accelerating experimentation and engineering workflows• Why leaders should avoid top-down mandates when introducing AI and instead create space for experimentation• How AI may reshape software methodologies, from Scrum to requirements gathering and beyondTimestamped Highlights01:45 – The eternal challenge of balancing quality with velocity in mobile engineering06:42 – Why “stability is speed” and how tech debt slows teams down09:15 – The move away from native development and how it impacts engineering teams13:27 – Experimenting with AI toolchains and finding the right balance of autonomy and structure18:05 – Standardization vs freedom: the future of AI tools in the enterprise22:23 – Will AI change the very way we build and support software?A standout moment“Stability is speed. If you're constantly chasing velocity without shoring up quality, you end up slower in the long run.”Resources Mentioned• Game Changer app – scorekeeping and live streaming for youth sportsPro TipsGive your teams space to experiment with AI tools. Celebrate learning—even when experiments fail—so engineers build confidence and marketable skills for the future.Call to ActionEnjoyed this conversation? Follow the show for more insights from engineering and tech leaders, and share this episode with a colleague who's navigating mobile development or exploring AI in their workflows.

Moinul Khan, co-founder and CEO of Aurascape, joins the show to unpack what it takes to build a cybersecurity startup in the age of AI. With decades of experience at companies like Zscaler, Palo Alto Networks, and FireEye, Moinul shares why AI demands an entirely new security stack, how agentic AI is changing the game, and why prevention—not dashboards—must be at the heart of real solutions. If you're a tech leader navigating the future of AI and security, this is a conversation you won't want to miss.Key Takeaways• Traditional security stacks can't keep up with dynamic, evolving AI tools• Prevention-focused solutions matter more than dashboards or API visibility• Agentic AI is both an opportunity and a security challenge that startups must address• CISOs are rethinking consolidation and becoming more open to best-of-breed solutions in AI security• Building with a long-term prevention mindset creates stronger, more resilient startupsTimestamped Highlights00:37 — Aurascape's mission to deliver an all-encompassing AI security solution02:27 — The “aha” moment: why legacy firewalls and proxies can't secure AI08:23 — How Aurascape's vision has evolved from public AI tools to securing private and third-party applications13:17 — Agentic AI, MCP protocols, and why startups need to secure the next wave of AI agents16:44 — Best-of-breed vs consolidation: where the security market is really heading20:37 — Advice for founders: why prevention-first is the only real path to solving security problemsA standout moment“If you try to patch what you have built in the last 20 years, you will fail. If you want to secure AI, you have to build your entire stack from the ground up.” — Moinul KhanResources MentionedAurascape.aiPro TipDon't build for a quick exit. Focus on prevention, even if it's the harder road—it's what truly solves customer problems in cybersecurity.Call to ActionIf you enjoyed this episode, share it with someone exploring AI security. Subscribe or follow the show for more conversations with the builders shaping the future of tech.

Cat Miller, CTPO of Talkiatry, shares her unconventional path from coding to leadership, including a detour into acting before rising into executive roles. She talks candidly about the realities of building a tech career you actually want, navigating transitions from engineering to management, and what it takes to succeed at the VP and C-level. This conversation is packed with lessons for anyone in tech who's asking themselves, “What's next for me?”Key Takeaways• Career paths in tech don't have to be linear. Detours can provide perspective that makes you a stronger leader.• Early dissatisfaction with day-to-day coding doesn't mean you don't belong in technology—it often evolves into broader roles.• Doing your current job really well is often the fastest way to position yourself for growth when opportunity knocks.• Building a network of peers and mentors is essential when stepping into senior leadership.• Self-awareness and documenting your wins helps you stay grounded and measure progress at the executive level.Timestamped Highlights01:29 — Why coding wasn't fulfilling long-term and how Cat thought about her next move04:54 — Leaving a stable job to explore acting, and how planning made the risk manageable10:35 — Returning to tech and finding the “perfect fit” role that shifted her career trajectory16:35 — Rethinking work-life balance and how mission-driven work changes the equation19:17 — Lessons from moving from VP to C-suite and the role luck and preparation both play24:40 — Why building a strong peer network is critical once you reach the CTO level29:14 — Tracking wins and staying accountable for your own performance as a leaderMemorable Line“It just feels really gross to be bad at your job. So why wouldn't you always do your best to be good at it?”Pro TipsWrite down your team and personal wins each quarter. It helps you see progress that isn't always obvious in the day-to-day grind.Stay ConnectedIf this episode resonated with you, share it with someone who's navigating their own tech career path. Follow the show for more conversations with leaders who've carved their own way forward.

What does it take to reimagine how hardware products are built in a world moving at the speed of AI? Michael Corr, founder and CEO of Duro, shares how he turned two decades of experience in engineering and manufacturing into a modern platform that helps hardware teams move faster and smarter. From journaling early product ideas to navigating the relentless pace of innovation, Michael reveals what it really means to be a founder when the path is anything but straight.Key takeaways• Why traditional hardware manufacturing processes create hidden risks—and how software can solve them• The journaling habit that helped shape Duro's first product features• How to balance investor demands with long-term product vision• The danger of chasing every shiny object as a CEO and how to filter noise for your team• Why adaptability matters more than rigid 5-year plans in today's tech landscapeTimestamped highlights00:36 — How Duro is reinventing product lifecycle management for hardware teams05:39 — “If I were king for a day…” the origin story of Duro06:52 — The role of note-taking and journaling in building a company from scratch09:31 — Staying true to a mission while adapting to market and investor pressures14:54 — The trap of chasing every customer request and how to avoid burning out your team19:03 — Why looking beyond 18 months is mostly speculation in a fast-changing industryMemorable insight“All we can really focus on is the next 12 to 18 months—everything beyond that is just speculation.”Resources mentionedDuro website: getduro.comPro tipWhen you're leading a fast-moving company, not every customer request deserves a green light. The best founders know when to say no, even to a big check, to protect long-term focus.If you enjoyed this episode, follow the show for more conversations with tech leaders shaping the future of software, hardware, and everything in between.

Sean Neville, co-founder and CEO of Catena Labs (and co-founder of Circle), joins the show to explore the rise of AI economics and what it means for the future of payments, trust, and financial systems. From stable coins powering machine-to-machine transactions to identity layers for AI actors, Sean unpacks the building blocks that could reshape how value flows online. This episode is for anyone curious about the intersection of AI, crypto, and the next era of digital finance.Key Takeaways• AI actors are evolving into full economic participants, capable of executing payments and workflows.• Stablecoins provide a more efficient and borderless payment rail compared to legacy systems, especially for AI-driven transactions.• The biggest hurdle isn't technology—it's trust, identity, and accountability in agent-to-agent interactions.• B2B use cases are likely to adopt AI-powered payments faster than consumer markets due to inefficiencies in existing flows.• AI to human payouts and human to AI pay-ins will likely arrive before true AI-to-AI payment systems go mainstream.Timestamped Highlights00:33 — What Catena Labs is building: a regulated AI-native financial institution03:16 — Why the internet is becoming agent native and what that means for AI economics05:35 — The trust hurdle: how AI can move from 60% reliable to 99.9% through tuning and workflows08:38 — Why legacy payment rails aren't built for AI actors and how stablecoins change the game11:59 — The missing piece: agentic identity and why it matters for accountability15:48 — Could AI actors one day open bank accounts? Building toward semi-autonomous financial participation19:06 — Why B2B transactions will likely see AI payments before consumers do24:26 — Stablecoins vs. crypto: why digital dollars are the foundation for AI-native paymentsMemorable Line“If I can't trust a chatbot to get a chocolate cake recipe right, how can I trust it with my money? Yet at the same time, this is the worst it will ever be—it's only getting more capable at an unprecedented pace.” – Sean NevilleResources MentionedCatena Labs – catenalabs.comSean on X – @PSNevilleCall to ActionIf this conversation got you thinking about the future of AI and finance, share it with a colleague who's curious about the space. Don't forget to follow the show so you don't miss the next episode.

Sara Wyman, founder and CEO of Stackpack, joins me to share her journey from investment banking and a Wharton MBA to launching a company that's redefining how finance and operations teams manage vendors. From surviving the Bear Stearns collapse to scaling Etsy and Affirm through IPOs, Sara's career has been built on spotting patterns and acting with conviction. In this episode, she breaks down how she validated her idea with 75 CFOs before writing a line of code, why timing and conviction matter more than a perfect resume, and what it really takes to leave the safety of corporate life to build something of your own.Key Takeaways• Why solving a problem you've lived through yourself is the best foundation for a startup• How interviewing potential customers before building can double as both research and sales• Why founders should outsource what they're not great at instead of spinning wheels• The hidden advantage of years of work experience when stepping into a founder role• Why pace setting—not just hiring—is one of the founder's most critical responsibilitiesTimestamped Highlights00:39 — What Stackpack does and how it helps finance teams gain full visibility into spend and contracts02:10 — Lessons from investment banking, the Lululemon IPO, and the realization she wanted to be the CEO, not the banker04:30 — Spotting the problem of vendor chaos and validating it through 75+ CFO conversations07:12 — The leap from corporate security to founder risk and why timing mattered more than age12:47 — A different founder path: starting with customers and funding before building the team17:15 — Why the stereotype of the 24-year-old coder isn't the reality of most successful exits19:45 — Hard-earned lessons: outsource what you don't excel at and embrace the founder role as a pace setterA Standout Moment“If you're not awesome at something, outsource it or find the person that is. You don't get bonus points for struggling through work that isn't your strength.”Pro TipTalk to customers before you build. Sara's early interviews not only validated her idea but converted into her first paying design partners.Call to ActionIf Sara's journey resonated with you, share this episode with someone considering the founder path. Don't forget to follow the show on your favorite platform so you never miss stories like this one.

Troy Astorino, co-founder and CTO at PicnicHealth, joins Amir to unpack one of healthcare's most stubborn problems: fragmented medical records. Troy shares how Picnic Health is using AI to unify patient data, cut through friction, and improve both individual care and clinical research. This conversation dives into the technical, regulatory, and human sides of healthcare data—and why accuracy matters more than ever.Key Takeaways• Why interoperability in healthcare has failed despite billions invested• How AI transforms messy, inconsistent records into unified patient data• The critical role of low-friction design in patient adoption• Balancing accuracy, human oversight, and scalability in medical AI• What recent FDA guidance signals about the future of AI in healthcareTimestamped Highlights00:40 — How Picnic Health helps patients and researchers get all their records in one place05:16 — Why data portability across EMRs is still broken despite decades of effort09:40 — Friction as the biggest barrier to patient adoption (and why it matters for outcomes)10:42 — Inside Picnic's AI pipeline: from raw documents to unified patient profiles17:18 — Tackling accuracy: expert-level thresholds, guardrails, and continuous auditing24:47 — Why AI is judged against perfection while humans get a pass on errors29:45 — The FDA's evolving approach to regulating AI in healthcareA thought that stands out:“Having systems that don't just work in theory but actually work in practice—because they're low friction—is critical for real usage in healthcare.”Resources Mentioned• Picnic Health: https://picnichealth.com• FDA Draft Guidance on AI in Healthcare (2024)• HL7 standards overview (for context on interoperability)Pro Tips for Tech LeadersThink about adoption the way Picnic Health does: remove friction first. Even the most sophisticated AI solution fails if the user experience creates barriers. Start with the end user, not the system.Call to ActionIf you found this conversation valuable, share it with someone working in health tech or data science. Subscribe to The Tech Trek on Apple Podcasts and Spotify so you never miss new insights on where tech and leadership intersect.

Pritesh Patel, Director of AI at Fisher Phillips, joins The Tech Trek to unpack how AI is reshaping knowledge-based businesses and what that means for industries like law, consulting, and beyond. From shifting revenue models to practical adoption challenges, Pritesh shares how firms can embrace AI early, stay competitive, and unlock new opportunities. This episode is a roadmap for leaders who want to move from incremental efficiency to real transformation.Key Takeaways• AI is disrupting the traditional “revenue per person” model, pushing knowledge firms toward more outcome-driven approaches• Early adoption matters: experimenting now gives companies a competitive edge rather than playing catch-up later• Success in AI transformation starts with deeply understanding business outcomes, not just implementing new tools• Human expertise will remain essential, but AI will free professionals to focus on higher-level, creative problem-solving• Iteration speed is a critical advantage: nimble firms can innovate faster than larger, slower-moving competitorsTimestamped Highlights01:32 – Defining knowledge-based businesses and why AI is changing the game04:33 – How old business models are being disrupted by automation and new expectations08:55 – Translating technical expertise into outcomes that resonate with non-technical stakeholders14:23 – A framework for identifying high-impact opportunities before choosing a technology solution16:34 – Building an innovation engine through fast prototyping and iteration21:16 – The role of trust, validation, and regulation in the future of AI-powered knowledge workQuote of the Episode“You don't want to be in a situation where you're adapting late because of competition. If you start early, you can shape the future of your industry instead of reacting to it.” — Pritesh PatelPro Tips• Focus first on business outcomes, not technology. Identify the most impactful functions, then explore how AI can enhance them• Use prototyping to spark ideas and build momentum. A working demo creates buy-in faster than presentationsCall to ActionIf this conversation sparked ideas about how AI could reshape your business, share the episode with a colleague who would benefit. Subscribe to The Tech Trek for more conversations with leaders driving the future of technology, and connect with us on LinkedIn to continue the discussion.

Udhay Durai, Executive Director of Data Platform and Engineering at Evolus, joins the show to unpack his journey from consulting to leading enterprise data teams. He shares how the high-pressure, quick-delivery mindset from consulting can be a secret weapon in a corporate setting, and what changes when you shift from delivering outputs to owning long-term outcomes. From navigating different types of pressure to building sustainable systems that scale, Udhay offers candid insights for anyone considering a similar transition.Key Takeaways• The consulting mindset of speed and adaptability can be a major advantage in enterprise roles when paired with long-term thinking• Pressure exists in both consulting and full-time roles, but the nature of that pressure—and how you manage it—differs greatly• Consultants focus on outputs, while enterprise leaders are measured on outcomes that stand the test of time• Generalist experience across domains can complement deep subject matter experts in a corporate team• Bringing incremental change and a “flywheel” approach from consulting can accelerate enterprise delivery without sacrificing reliabilityTimestamped Highlights01:34 — Why quick wins and stakeholder empathy are essential in consulting03:28 — How the pressure changes when you own the platform instead of just delivering a project05:32 — Outputs vs outcomes and why the shift matters in enterprise leadership09:48 — Turning generalist consulting experience into an asset in a full-time role11:43 — The biggest mindset and skill gaps to address when making the switch13:42 — Adapting consulting habits for long-term success in product companiesQuote of the Episode“Pressure is there in both consulting and enterprise. The difference is in consulting you deliver outputs—enterprise leaders deliver outcomes.”Resources MentionedUdhay Durai on LinkedIn — https://www.linkedin.com/in/udhay-duraiCall to ActionIf this episode gave you new perspective on career transitions, share it with a colleague or friend who's considering a similar move. Follow the show for more real-world tech leadership conversations.