The Tech Trek

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.

Allan Leinwand, CTO at Webflow, joins me to explore how AI is reshaping engineering workflows, from code generation to team structure. We dig into how AI tools are boosting productivity, enabling faster onboarding for junior engineers, and freeing up senior talent to focus on distributed systems and business-critical challenges. Allan shares real examples of automation in action, how his team measures success, and why the future of software engineering will be even more dynamic than its past.Key Takeaways• AI-powered tools like code generation and multimodal debugging are changing how engineers interact with code• Junior engineers can now ramp up and make meaningful contributions faster than ever before• Senior engineers are moving closer to the business by tackling architectural and scalability problems• Automation is cutting down repetitive tasks, increasing flow time, and boosting ship rates• AI is influencing not just engineering, but also product workflows and even how methodologies like Scrum might evolveTimestamped Highlights01:45 – Inside Webflow’s AI-powered engineering stack and tools every developer gets03:57 – How AI is shifting the engineer–code relationship from typing to prompting and reviewing07:15 – Why junior engineers are thriving in the age of AI13:37 – Senior engineers focusing on distributed systems and architectural challenges16:15 – Automating “paper cut” bug fixes with AI agents and background processes21:09 – AI’s role in expanding software creation to non-engineers and influencing product workflowsQuote of the Episode“The relationship with code is changing. We can talk to the code base, use AI to fix bugs, and still have humans in the loop to make sure it’s the right answer.” — Allan LeinwandResources Mentioned• Webflow — https://webflow.com• Webflow Forums — https://forum.webflow.comCall to ActionIf you found this conversation valuable, share it with another tech leader who’s navigating AI adoption. Follow the show for more insights from engineering leaders shaping the future of work.

Nikola Borisov, CEO and co-founder of Deep Infra, joins the show to unpack the rapid evolution of AI inference, the hardware race powering it, and how startups can actually keep up without burning out. From open source breakthroughs to the business realities of model selection, Nikola shares why speed, efficiency, and strategic focus matter more than ever. If you’re building in AI, this conversation will help you see the road ahead more clearly.Key Takeaways• Open source AI models are advancing at a pace that forces founders to choose focus over chasing every release.• First mover advantage in AI is real but plays out differently than in consumer tech because models are often black boxes to end users.• Infrastructure and hardware strategy can make or break AI product delivery, especially for startups.• Efficient inference may become more important than efficient training as AI usage scales.• Optimizing for specific customer needs can create significant performance and cost advantages.Timestamped Highlights[02:12] How far AI has come — and why we’re still under 10% of its future potential[04:11] The challenge of keeping pace with constant model releases[08:12] Why differentiation between models still matters for builders[14:08] The hidden costs and strategies of AI hardware infrastructure[18:05] Why inference efficiency could eclipse training efficiency[21:46] Lessons from missed opportunities and unexpected shifts in model innovationQuote of the Episode“Being more efficient at inference is going to be way more important than being very efficient at training.” — Nikola BorisovResources MentionedDeepInfra — https://deepinfra.comNikola Borisov on LinkedIn — https://www.linkedin.com/in/nikolabCall to ActionIf you enjoyed this conversation, share it with someone building in AI and subscribe so you never miss an episode. Your next big idea might just come from the next one.

Zach Lloyd, CEO and founder of Warp, joins The Tech Trek to unpack what it really takes to build tools that transform the developer experience. From rethinking the terminal to balancing product focus with user growth, Zach shares hard-earned lessons from scaling products that developers actually want to use. This is a conversation about building with empathy, understanding workflows, and making deliberate trade-offs that move the needle.Key Takeaways• Why deep focus on the developer workflow leads to products that stick• The importance of balancing big-picture vision with small, iterative improvements• How to make trade-offs between growth experiments and core product quality• Why some of the most powerful product ideas come from rethinking “old” tools• The role of design and speed in shaping developer adoptionTimestamped Highlights[03:15] The inspiration behind Warp and why the terminal needed rethinking[09:42] Balancing user requests with long-term product vision[14:10] How small quality-of-life improvements can have outsized impact[21:55] Deciding when to invest in growth versus core product work[28:30] Lessons from building for an audience of highly opinionated users[36:05] Why the future of dev tools will blend speed, design, and collaborationQuote of the Episode“The best products come from understanding the real workflow pain and then removing it in a way that feels almost invisible to the user.”Resources MentionedWarp: https://www.warp.devIf you enjoyed this conversation, follow The Tech Trek on your favorite podcast platform and connect with me on LinkedIn for more insights from the leaders shaping the future of technology.

Sek Chai, CTO and cofounder of Latent AI, joins The Tech Trek to talk about what it actually takes to get AI running on the edge. We explore the real-world constraints of power, compute, and hardware diversity, why an agent-assisted workflow can accelerate MLOps, and how to choose models that are good enough to ship. Sek also breaks down lessons from selling into the federal market and explains why a clear guiding principle beats chasing every shiny opportunity.Key TakeawaysEdge AI is a different game than the cloud. Power limits, hardware diversity, and deployment realities have to shape the design from day one.The best model is the smallest one that delivers the capability and latency you need. Bigger isn’t always better.An AI agent that understands your data, model, and hardware personas can move teams from idea to deployment much faster.Whether you’re selling to federal or commercial buyers, lead with capability, then meet security and compliance needs.A strong tenet should guide product direction and market focus more than raw market size.Timestamped Highlights00:30 Why edge optimization matters and what Latent AI does01:09 The messy reality of heterogeneity and power constraints in edge deployments02:54 Why most edge AI projects never ship and how an agent can change that05:03 Mapping MLOps personas and tailoring the workflow for each11:49 Selling to both federal and commercial buyers without losing focus15:55 Building a company around a tenet rather than chasing every marketQuote of the Episode“It’s not the model that you’re really chasing after. It’s that capability.”Pro TipsDefine capability and constraints first—latency, frame rate, and power budget—then pick and optimize the model.Collect and use telemetry from experiments and deployments to guide model and hardware choices.If federal markets are in play, bake security and compliance into your early prototypes.Call to ActionEnjoyed this episode? Follow The Tech Trek, rate us on Apple or Spotify, and share it with someone working on an edge AI project.

Berit Hoffmann, CEO and co-founder of Korl, joins The Tech Trek to share her candid journey from big tech leader to late-stage startup founder. With a resume that includes Google, Dell, and Sisu, Berit could have landed any top role—but she chose the riskier path of building her own AI company while raising two kids and fundraising while seven months pregnant. In this episode, she opens up about the internal tug-of-war, the realities of balancing family and founder life, and how she’s navigating the fast-moving, hype-driven world of AI. If you're a tech professional wondering when—or whether—to make your own leap, this one’s for you.Key Takeaways:Experience doesn’t remove fear—but it can sharpen your confidence in taking big risksAI founders must constantly recalibrate as models evolve and moats evaporateThe best startups fall in love with the problem, not the initial solutionYou don’t have to wait for perfect timing—it might never comeExecution and clarity win over buzzwords in a crowded AI marketTimestamped Highlights:00:44 — What Korl actually does and why it's different from other AI presentation tools02:30 — Why Berit waited to found a startup and how early roles shaped her confidence07:03 — The hidden opportunity costs and fears of starting later in life11:38 — Her zero-to-one playbook: validate the problem deeply before writing a line of code15:50 — Fundraising in the age of AI hype and navigating the balance between clarity and buzz20:33 — How she processes new AI releases and adapts strategy without spinning out24:45 — What it was really like to raise VC funding while visibly pregnant30:11 — Her honest take on founder-parent balance: sometimes 80% has to be enoughQuote of the Episode:“There’s still such a gap between what many AI tools promise and what they actually deliver. Closing that gap is all about execution—and that’s where startups win.”Resources Mentioned:Koral: https://www.getkoral.comConnect with Berit on LinkedIn: https://www.linkedin.com/in/berithoffmann/Call to Action:Enjoyed the conversation? Follow The Tech Trek for more real stories from tech builders and startup leaders. Share this episode with someone who's debating their next leap—you never know what might spark them to go for it.