The Tech Trek

Chris Morgan, VP of Data Science at Lincoln Financial Group, joins me to unpack what a real data culture looks like inside a complex, highly regulated business that has policies on the books for decades. We talk about how to turn Gen AI buzz into real value, why governance and quality suddenly matter to everyone, and how to tackle data technical debt without stalling delivery.Chris shares concrete ways he finds champions in the business, balances centralized and federated models, and keeps stakeholders excited about the future while he quietly fixes the messy data foundation underneath it all.Key takeawaysData culture is less about dashboards and more about curiosity, repeatable processes, and raising the analytical watermark across the company, not just in the data team.The teams that will win with Gen AI are the ones that can safely connect proprietary data to these models, which demands strong governance, clear definitions, and shared standards.A blended model works best for scaling data work, where a central function sets guardrails and standards while domain teams stay close to the business and own local decisions.Paying down technical debt works when it is framed in business terms, tied to revenue and risk, and treated as a regular slice of capacity instead of a one time side project.Education is now part of the job for data leaders, from internal road shows on Gen AI to simple stories that explain why foundational data work matters before you can ship shiny tools.Timestamped highlights00:04 Setting the stage Chris explains his role at Lincoln Financial and how data science supports life and annuity products that can live for decades.03:33 The Cobb salad story A simple grocery store analogy that makes data standards and shared definitions instantly clear to non technical stakeholders.06:06 Finding the right champions Why Chris prefers curious partners who will invest time with the data team over senior leaders who just want results without changing behavior.08:33 Governance as Gen AI fuel How regulatory pressure and the need to trust what goes into models are pushing data governance and quality into the spotlight.11:11 A practical way to attack data technical debt How Chris decides what to fix first, and why he tries to reserve a steady slice of team time for cleanup so progress is visible and sustainable.17:44 Managing Gen AI expectations From road shows to constant communication, Chris shares how he keeps enthusiasm high while also being honest about the timeline and effort.One line that sums it up“These generative models are going to become a commodity and what will separate companies is who can take the most advantage of their proprietary data.”Practical playbookStart small with data culture by picking one engaged business partner, one problem, and one outcome you can measure clearly.Reserve a consistent portion of team capacity for technical debt, even if it is only a small percentage at first, and make the tradeoffs visible.Use stories, analogies, and simple rules of the road so stakeholders can understand how data systems work without becoming experts in the tech.Call to actionIf this conversation helped you think differently about data culture and Gen AI inside your company, follow the show and leave a rating so more engineering and data leaders can find it. To keep the discussion going, connect with me on LinkedIn and share how your team is tackling data culture and technical debt right now.

Varun Puri, CEO and cofounder of Yoodli, joins the show to talk about using AI role play to transform how people practice for high stakes conversations, from sales calls to job interviews to tough manager chats. He breaks down how Yoodli went from a consumer public speaking tool to a serious enterprise platform used by teams at Google, Snowflake, Databricks, and more, all while staying anchored in one mission, helping humans communicate with confidence. We dig into product led growth, honest feedback loops, and why real human communication will matter even more as AI makes information instant.Key takeaways• Why Yoodli started with public speaking anxiety and grew into an AI role play simulator for any important conversation, not just conference talks or pitch decks• How watching real user behavior inside companies like Google pulled the team into enterprise without abandoning their consumer product• A simple approach to product feedback, talk to end users constantly, then prioritize changes by business impact, renewal risk, and how many people benefit• What it really takes to move from consumer to enterprise, new roles, new processes, and a very different mindset around reliability, security, and expectations• Why Varun draws clear ethical lines, using AI to coach and prepare people, not to replace human judgment in hiring, promotion, or high trust decisionsTimestamped highlights[00:35] What Yoodli actually does today, from solo practice to training sales and go to market teams inside large enterprises[01:43] The original vision, helping people who are scared of public speaking, and the insight that interviews, sales calls, and manager talks are all just role plays[03:37] How the team listens to end users, the channels they rely on, and why the consumer product is still their testing ground for new ideas and experiments[05:20] Following users into the enterprise, why it was an addition and not a full pivot, and how product led growth inside companies like Google works in practice[07:42] The early shock of selling to enterprises, learning about new roles, SLAs, InfoSec, and bringing in leaders from Tableau and Salesforce to build a real B2B engine[11:10] Two paths for AI in sales, tools that try to replace humans versus tools that make humans better, and why Varun has drawn a hard line on what Yoodli will not do[15:26] A future where information is commoditized and instant, and why communication and presence become the real edge for top performers in that world[20:48] Designing for trust and adoption, how Yoodli keeps practice private by default, when data is shared, and why control has to sit with the end userA line worth saving“In a world where AI makes everyone smarter and faster, the thing that will be at the biggest premium is how you communicate as a human with other humans.”Practical ideas you can use• Keep a consumer like surface in your product so you can experiment faster than your enterprise roadmap would ever allow• Treat feedback from large customers like a queue you rank by renewal risk, strategic value, and number of users helped, not as a list you must clear• Look for product led growth signals inside your user base, if thousands of people in one company are using you, someone there probably wants a team level solution• Draw explicit boundaries for your AI product, write down what you will not automate, so you can build trust with users and buyers over the long termCall to actionIf you care about the future of sales, interviewing, and communication in an AI rich world, this conversation is worth a listen. Follow the show, leave a quick rating, and share this episode with a founder, product leader, or sales leader who is thinking about AI in their workflow. And if you want feedback on your own speaking, check out what Varun and his team are building at Yoodli.

Mike Collins, CEO of Alumni Ventures, joins Amir to unpack what it really means to democratize venture capital and why the next wave of value creation will happen in private markets long before it hits the public exchanges. He explains how Alumni Ventures lets accredited investors build a meaningful venture portfolio, why diversification and time matter more than stock picking, and how this model changes the game for both founders and individual investors.If you are a tech professional who cares about innovation, wealth building, and staying close to what comes next in AI, energy, health, and more, this conversation gives you a clear window into how the venture world actually works and how you can take part in it without becoming a full time investor.Key takeaways• Venture capital is a hits business, so the real game is building a broad portfolio, not trying to pick one or two magic startups• Diversification and time are the core levers for venture investing, especially for busy professionals who are not watching markets all day• Alumni Ventures acts as a large scale co investor with top venture firms, letting individual investors ride along with the same lead investors founders already want• Value creation is shifting to private markets, since many of the most important tech companies now stay private far longer than in past cycles• Alumni Ventures is building a global, tech enabled platform that aims to support founders and investors across regions, stages, and themesTimestamped highlights[03:01] Mike breaks down what venture capital really is and why random one off startup bets look more like gambling than investing[04:40] Why diversification is your superpower and how a portfolio of 30 to 200 startups changes the risk profile for individual investors[07:40] The rise of private value creation and why waiting for the open AI or Stripe IPO means missing the first big wave of upside[11:38] Venture as a time machine, looking five to seven years ahead at technologies the public will only hear about much later[17:48] How Alumni Ventures plays the role of co investor of choice for founders by bringing a global alumni network and real customer access[21:48] Roughly 300 deals a year and multiple themed funds, and what that volume unlocks for different types of accredited investors[25:31] The next ten years, going global, and why Mike wants Alumni Ventures to become the most valuable venture capital firm on the planetA line that stayed with me“Diversification is your superpower and time is really an asset.”Ideas you can use• Think of venture as a small but intentional slice of your overall portfolio, alongside public stocks, fixed income, and real estate• Treat venture like an ETF for innovation, where you build exposure to many teams across multiple years rather than buying a single hot deal• Use your curiosity as a filter, follow companies whose work you genuinely want to track over years, not daysCall to actionIf this episode helped you see the venture world in a clearer way, follow the show, leave a quick rating, and share it with a friend who cares about tech and investing. To stay close to upcoming conversations with founders and investors who sit at the edge of innovation, connect with Amir on LinkedIn and make sure you are subscribed so you never miss an episode.

Chinmay Barve, VP of Engineering at Nooks AI, joins the show to break down what the first sixty to ninety days look like when you step into a senior leadership role at a fast moving AI company. He explains how to build trust quickly, how to find real problems worth solving, and how to avoid the trap of either rushing change or waiting too long to act. This conversation is a practical playbook for engineering leaders who want early impact without losing alignment.Key Takeaways• The listening phase starts before day one and should shape how you enter the role• Early wins matter but only if they support the deeper problems you were hired to solve• Alignment with founders becomes the real foundation for fast progress• Sharing your thinking openly can build trust faster than any formal process• You need a clear personal compass so you know what parts of your approach are fixed and what parts can changeTimestamped Highlights00:36 How Nooks AI thinks about the next generation of sales productivity and why human guided AI matters in real workflows04:20 What leaders should really listen for during the first weeks on the job and why the listening starts before you join12:31 Why a new VP should enter with personal objectives while staying open to what the company needs most14:11 How to act fast without creating chaos and where to spot early wins that build confidence on both sides17:29 The value of a rough thirty sixty ninety plan and how daily syncs create deeper alignment right away20:34 What it looks like to foster trust through openness, vulnerability, and consistent shared reasoning with your teamA Line That Stands OutOnce you commit, go all in with conviction. Do all the real deciding before day one so you can show up fully aligned and ready to move.Pro Tips• Enter with a clear ambition that matches the founders vision so you are rowing in the same direction from day one• Look for low effort problems with high emotional or operational weight to build fast trust• Overshare your thinking at the start so the team can see how you reasonCall to ActionIf you found this useful, follow the show and share it with someone stepping into a new leadership role. You can also connect with us on LinkedIn for more conversations about people, tech, and real impact.

Digital IDs are about to reshape how we prove who we are online. Peter Horadan, CEO at Vouched, joins the show to break down what this shift really means for trust, privacy, and the rise of agent driven systems. He explains why digital IDs will remove huge amounts of friction, stop common fraud paths, and change how we secure everything from bank accounts to AI agents acting on our behalf.This is a clear look at what is coming in the next two years and why it matters to every engineering and product leader.Key Takeaways• Digital IDs will move identity checks from risk based guesses to near perfect certainty which changes how products verify users• Fine control over what you share will unlock new applications and ease concerns about oversharing personal data• Agent driven workflows need a clear way to separate human actions from agent actions so that permissions, auditing, and safety scale• Identity standards for agents will remove phishing and reduce fraud by creating traceable reputations for good and bad agents• Regulation and real world use are not fully aligned yet which creates gaps around privacy, liability, and legal agreementsTimestamped Highlights00:53 How digital IDs work on your phone and why they remove friction across services04:14 What becomes possible when you can share only the specific parts of your ID07:22 Why physical ID checks are easy to fake and how digital IDs solve this12:16 How agents act on your behalf and why that breaks old security patterns17:40 Why agents need their own identity and reputation systems22:01 Legal gray zones around AI, privacy, accountability, and real world contracts27:12 The tipping point where digital IDs become standard for most online servicesA line that captures the episode“Everything we do today to identify people online is risk based. Digital IDs move us to absolute proof.”Pro Tips from Peter• Expect digital ID flows to replace password resets across most valuable services• Treat agent permissions like API scopes and give only what is needed• Plan for separate logging of human actions and agent actions in your systemsCall to ActionIf this episode gave you a clearer picture of where identity and agent driven systems are headed, follow the show and share it with someone building in security, AI, or product. You can also follow along on LinkedIn for more discussions that connect people, impact, and technology.

Most people still think of AI in medicine as a novelty. Matt Pavelle sees it as the new first step in patient care.In this episode, Matt breaks down how Doctronic built an AI doctor that can gather history, follow clinical guidelines, produce full treatment plans, and then hand everything to a real physician who can review it in minutes. It is private by default, aligned with top primary care doctors, and already helping millions of people move faster through the healthcare system without lowering the standard of care.We talk through how this changes access, trust, and the way care teams work. And we open up what this means for the future of primary care as capacity continues to fall and patient demand keeps rising.Key takeaways• The AI is trained on physician written clinical guidelines which gives it a clear path for gathering symptoms, sorting possible conditions, and building treatment plans that match top doctors at a high rate.• Privacy and trust were built in from the start. The chat is anonymous, data is not used for training, and everything is run with HIPAA level protection even when it is not required.• Capacity pressure is the real problem in primary care. Offloading the easy eighty percent of cases lets doctors focus on the harder ones and gives them more time with each patient.• The system writes notes, gathers history, and completes insurance paperwork which cuts down on burnout and improves the patient experience.• This model can scale to wearables, home devices, labs, and specialists which could raise the standard of care for people who normally wait weeks for answers.Timestamped highlights00:40 Doctronic explained and why a full visit can take only a few minutes03:44 How medical knowledge moved from books and search results to AI that can guide real care08:13 A look at the micro agent system and how the team measures accuracy against real doctors11:27 The shortage of primary care doctors and why capacity pressures make AI support necessary17:20 How anonymous design and strong privacy choices help people trust the system26:05 Adoption numbers, fast growth, and what millions of consults are teaching the teamA line that captures the episodeWe want to be that first step in patient care every time you need that first step.Pro tips for builders and leaders• Ground your product in real domain guidelines so the AI follows the same reasoning paths as experts.• Treat privacy as a design choice. Make it clear, simple, and part of the value of the product.• Focus on the work that slows experts down. The biggest wins come from reducing the load, not from replacing the expert.• Make the handoff between AI and human seamless so the expert starts with context instead of starting over.Closing noteIf you enjoyed this conversation, follow The Tech Trek, leave a quick rating, and share this episode with someone curious about the future of patient care and AI.

Chris Church, VP of Engineering at Rainforest, breaks down why a zero bug policy is more than a technical choice. It is a mindset, an operating model, and a culture shift that shapes how engineering teams build, release, and support software at scale.In this conversation he goes inside the habits that actually make quality a strategic advantage and explains how small releases, strong visibility, and healthy engineering practices create real impact over time.Key Takeaways• Quality is not a feature. It is the foundation of trust, especially in a payments environment where even small defects can erode confidence.• Small releases reduce risk because teams can actually reason about the changes they ship. Frequency builds confidence and reliability.• Visibility is non negotiable. You cannot fix what you cannot see, so strong monitoring and clear alerts must exist before a quality culture can grow.• Teams need real capacity set aside for fixes and improvements. Without that buffer, bugs turn into a silent tax that slows down the entire org.• You can adopt a zero bug mentality even in a mature codebase, but you must commit to a long game of continuous improvement.Timestamped Highlights00:33What Rainforest actually does and why their customers rely on embedded payments01:44Chris explains what a zero bug policy means in practice for a fintech engineering team03:06Why the policy must be strict and why a backlog of broken things creates a false sense of safety06:13How Rainforest structures ownership, on call rotations, and incident response to support quality10:51Smaller releases, lower risk, and why the size of a change has a direct impact on failure modes12:59Why test coverage and automation must start early and why teams struggle when they try to catch up later14:27How to adopt this mindset if your org is nowhere near zero bugs and where to begin23:44The biggest gotchas teams underestimate when they start this journey and why progress requires patienceOne line that stands out“People overestimate what they can fix quickly and underestimate what they can improve over the long run.”Pro Tips• Start by making your system noisy. More visibility will feel painful at first, but it becomes the foundation for every improvement.• Reserve capacity for fixes before planning feature work. If you wait until later, that time will never appear.• Break tech debt into specific problems. Vague labels hide real risks and slow down prioritization.Call to ActionIf you found value in this conversation, follow the show and share it with someone who cares about engineering quality, team culture, and building software that lasts. You can also connect with me on LinkedIn for more conversations that explore people, impact, and technology.

Snehal Antani, co founder and CEO of Horizon3 AI, joins the show for a conversation about how veterans bring rare leadership strengths to fast moving companies. He pulls back the curtain on the world of special operations, shares what industry leaders often miss when interviewing former service members, and explains why these leaders are some of the most prepared problem solvers you can hire.This episode helps any listener understand the real strengths behind military experience and how those strengths translate into modern tech and business environments.Key Takeaways• Veterans succeed in high pressure environments because they train as learn it alls and solve problems as a team• The best performing military units succeed due to empowerment, shared understanding, and clear cadence• Many veterans underestimate their own leadership ability when entering industry and need support reframing their experience• Hiring managers often miss top talent because they use filters that do not map well to military backgrounds• Reference based hiring and early transition planning create a smoother path for veterans entering tech rolesTimestamped Highlights00:41 Snehal describes the world inside JSOC and what makes special operations leaders exceptional04:45 Why many transitioning service members experience imposter syndrome and how to shift that mindset10:17 How geography affects familiarity with military culture and shapes hiring outcomes14:33 A look at why Israeli veterans become top founders and what the United States can learn from that19:19 How military roles connect directly to major sectors like logistics, telecom, infrastructure, and talent management24:24 The real reason many veterans struggle to land interviews and why referral networks matter so much28:40 Practical resources and programs that help veterans navigate transition with clarity and confidenceA line that captures the heart of the episode“You are the most cycle tested leader in the world. Those skills are not taught in school. They are earned.”Practical advice from the conversation• Translate military jargon into industry language and speak to the business outcomes you created• Build and maintain a strong network long before you transition• Start planning two to three years out and use programs like SkillBridge to build experience and confidence• Hiring teams should look beyond titles and focus on the pressure tested leadership traits that veterans bringCall to actionIf this conversation helped you, follow the show and share the episode with someone who would benefit. You can also connect with us on LinkedIn for more leadership insights and real stories from people shaping tech today.

Chandni Jain, VP of Engineering at Checkr, joins the show to talk about what it takes to build a real culture of ownership. She explains how clarity, trust, and true empowerment help teams move faster and work better together. You will also hear how leaders can bring out stronger initiative and confidence in their people.This episode gives a simple and useful guide for anyone who wants to lead with intent and build teams that think and act like owners.Key Takeaways• Ownership grows when clarity, context, and empowerment all work together• Strong accountability does not require fear. It comes from trust and clear expectations• Teams follow what leaders show, so leaders need to model ownership every day• Feedback works only when trust comes first• New managers grow fastest when they balance technical skills with people leadershipTimestamped Highlights00:26 Why ownership begins with customer outcomes02:02 How accountability, empowerment, and safety support each other04:08 The difference between blame and real accountability11:36 How to give people space to lead without losing direction14:37 What new managers struggle with and how to guide them16:49 A four part checklist for building stronger ownership20:16 Why recognition matters and how it lifts the whole orgA standout moment“Ownership begins with you as a leader. The team mirrors what they see.”Pro Tips• Give clear context early and often so people know what they own• Celebrate small wins to encourage more initiative• Focus on outcomes, not tasks. It changes how people think and deliver• When someone steps up, give them more room to growCall to ActionIf this episode helped you see leadership and ownership in a new way, follow the show and share it with someone who might find it useful. For more conversations on people, impact, and technology, subscribe and stay connected.

Shawn Taikratoke, CEO and co founder of Mozee, joins the show to unpack one of the biggest questions in mobility today. How close are we to real autonomous transportation and what will actually move the needle in our cities. Shawn breaks down why the future is not a single robotaxi dream, but a more human centered shift in public transit that solves the first and last mile in a smarter way. If you care about how people move, how cities evolve, or how autonomy will reshape everyday life, this one is worth your time.Key Takeaways• The biggest transportation barriers are not technical. They come from how cities were built and how people actually move in short distances.• Robo taxis will play a role, but public transit needs a more flexible and human centered model before adoption changes.• Many Americans still have no access to reliable transit, which creates ripple effects in work, health, and community access.• Real adoption will come when mobility becomes easier and cheaper than using your own car.• Cities want smarter transit, but they need partners that help them bridge gaps without major infrastructure costs.Timestamped Highlights00:44 What Mozee was built to solve and why they avoided the pure robotaxi route03:26 Why autonomy still scares most people and how public perception is shaping rollout06:57 How regional culture and city layout shape transportation adoption10:24 The vision for a mesh network of shared autonomous shuttles16:24 How smarter first mile and last mile service can shift car dependence21:52 What it takes to move from a handful of vehicles to true scale27:54 Why Shawn moved from the robotaxi hype to solving public transit gaps insteadA standout thought“Progress is rarely a straight line. The products that last are the ones that stay human centered.”Pro Tips from the Conversation• Transit solutions that work do not start with tech. They start with how people move in the real world.• Scale only matters when it meaningfully makes someone's day easier.• If you want to understand mobility problems, talk to city officials. They know exactly where the gaps are.Call to ActionIf this episode pushed your thinking about mobility and smart cities, follow the show and share it with someone who is curious about the future of how we move. New episodes every week with leaders shaping technology, people, and impact.

Mike Hanson, CTO at Clockwise, joins the show to break down how our relationship with computers is changing as language based systems reshape expectations. We explore why natural storytelling feels so intuitive with today's AI tools, how context is becoming the new currency of great software, and why narrow AI is often more powerful than the industry hype suggests.This conversation gives tech leaders a grounded look at what is real, what is noise, and what is coming fast.Key Takeaways• Natural storytelling is becoming the default way people communicate with AI, and products must adjust to that shift.• Context is the driving force behind great interaction design and LLM powered systems now surface and use context at a scale traditional UIs never could.• Most real world gains come from narrow AI that solves focused everyday problems, not from broad AGI promises.• Multi agent systems and multiplayer coordination are emerging as the next frontier for enterprise AI.• The biggest risk is not model weakness but user uncertainty about when an answer is trustworthy.Timestamped Highlights01:21 What Clockwise is building with its scheduling brain and how natural language creates new value04:13 Why humans default to storytelling and how LLMs finally make that instinct useful08:00 The rising expectation that software should understand context the way people do12:13 The shift away from feed centric design and toward multi person coordination in AI systems17:31 Why narrow AI delivers real value while wide AI often creates anxiety23:52 A real world example of how AI can remove busy work by orchestrating tasks across tools26:24 Why we do not need AGI to meaningfully improve everyday productivityA standout thoughtPeople have always tried to talk to computers in a natural way. The difference now is that the systems finally understand us well enough to meet us where we already are.Pro Tips• Look for AI that reduces busy work across tools rather than chasing broad capability.• Prioritize context rich interactions in your product planning. It will define user expectations for years to come.• Treat multi person workflows as the next major opportunity. Most teams still rely on manual coordination.Call to actionIf this episode helped you think differently about where AI is actually useful, follow the show and share it with someone who is building product in this space. And join me on LinkedIn for weekly insights on tech, people, and impact.

Soham Mazumdar, CEO and co founder of Wisdom AI, joins the show to talk about what it really takes to build again after major exits at Facebook and Rubrik. We get into the mindset shift required for a new startup, the danger of relying too much on past playbooks, and how to stay grounded when expectations rise.If you want a real look at repeat founder decision making, this is the conversation to listen to.Key Takeaways• The biggest advantage of being a repeat founder is the ability to attract talent and early believers, but it does not replace the need for fresh thinking.• Pattern matching can help with people decisions but can block you everywhere else if you assume the past will repeat.• Feedback can steer you or mislead you. The real work is separating patterns from outliers and understanding the motivation behind what someone says.• Every new company pulls you back to zero. Past success does not win customers or validate your idea.• Early career operators who want to build should leap sooner than later. Even a failed startup can shape a long career.Timestamped Highlights02:01 How building tactile and Rubrik shaped his approach to Wisdom AI04:07 What actually drives someone to found a company after a giant exit05:52 Why repeat founders must fight the urge to reuse old playbooks10:06 How to course correct when your first instincts are wrong13:38 The danger of reacting too fast or too slow to customer feedback18:02 How expectations shift once you have a track record24:42 Why Wisdom AI connected with his earliest experiences at Google25:28 The advice he wishes someone had given him before startup number oneA standout line“The world pulls you down to the ground fast. Whatever you think you are, a new company reminds you that none of it matters unless you execute.”Practical advice from the conversation• Do not treat feedback as instructions. Treat it as signal to study. Look for repeated patterns, not one loud voice.• Approach every new company with a clean mind. If your old patterns do not match the new environment, abandon them quickly.• Think of your career as a long arc. Early risks create unexpected doors later.Closing noteIf the episode gave you something to think about, follow the show and share it with someone who wants a real look at the founder journey. You can also join the community on LinkedIn for more insights and upcoming episodes.

In this episode of The Tech Trek, Amir sits down with Sadi Khan, Co-Founder and CEO of Aven, to unpack how technology can make capital fairer for everyone. Sadi explains how Aven is tackling one of the world's biggest inefficiencies—the trillion-dollar burden of consumer credit card debt—and why the solution lies in reducing the cost of capital through innovation. This is a deep dive into building products that require not just engineering skill, but endurance, conviction, and a long-term mindset.Key Takeaways• Aven's mission is to cut credit card interest payments in half by rethinking how consumers access and use home equity.• True innovation often comes from solving inefficiency, not chasing market trends.• Complex problems create strong moats when founders are willing to grind through technical and regulatory barriers.• Founders should pick problems worth spending a decade on—pivot less, persist more.• Product success depends on identifying your “axis” and going all-in on being the best at that one thing.Timestamped Highlights00:40 — How Aven's hybrid credit card + HELOC model is lowering the cost of borrowing for homeowners04:10 — The moment Sadi realized the cost of capital was a massive, overlooked problem12:34 — Why most lenders haven't solved this yet and how Aven's approach differs19:33 — Building what others couldn't: how persistence and engineering precision led to breakthroughs23:36 — Choosing execution risk over market risk and what it takes to stay with a problem long enough to solve it37:47 — Why picking the right “axis” is how great companies build an unshakable moatMemorable Line“The only problems worth working on are the ones worth working on for a very long time.”Call to ActionIf you enjoyed this episode, follow The Tech Trek for more conversations at the intersection of people, impact, and technology. Subscribe on your favorite platform and share it with someone building bold ideas.

Svetlana Zavelskaya, Head of Software Engineering for Data Platform and Infrastructure at Quanata, joins the show to unpack what it really takes to make the “impossible” possible in tech. From re-architecting a startup codebase to scaling innovation inside an insurance giant, she shares how her team turns complex R&D challenges into production-ready systems. This conversation dives deep into engineering discipline, AI tool adoption, and why the next wave of insurance innovation is powered by data and software.Key Takeaways• Real innovation often means balancing speed with long-term architecture decisions• AI coding tools are valuable for exploration but need governance and clear security guardrails• POCs fail when expectations aren't aligned, not because the tech doesn't work• Insurance tech is evolving fast through telematics and context-based data models• Well-structured, well-documented code is still the foundation for scalable innovationTimestamped Highlights00:33 How telematics is changing the economics of insurance and rewarding better drivers03:59 Cars as software platforms and what that means for data privacy and innovation06:02 The growing pains of re-architecting an organically built startup codebase08:38 Evaluating new AI tools and maintaining data security across teams11:08 Why most AI POCs never make it to production16:29 How Quanata's R&D work feeds into State Farm's larger technology initiatives20:40 Safe-driving challenges, behavioral change, and saving lives with dataA Thought That Stuck“If we can prevent just 1 percent of drivers in the world from using their phone behind the wheel, imagine how many lives we can save.”Pro Tips• Before starting a POC, define if it's an experiment or a potential product foundation• Let engineers explore new tools but build frameworks to govern how data and results are handledCall to ActionIf you enjoy exploring how data, AI, and engineering innovation come together to solve real-world problems, follow The Tech Trek on Apple Podcasts or Spotify and share this episode with a colleague who builds at the edge of what's possible.

Nikhil Gupta, founder and CTO of Vapi, joins Amir to talk about how voice AI is reshaping the way we connect with businesses. From customer support to healthcare, Nikhil explains how voice agents can bring back the human side of digital interactions. This is a look at where real conversation meets real technology and what happens when machines start to understand us like people do.Key Takeaways• Voice AI creates genuine, human-like engagement instead of the usual scripted support.• The next wave of AI will personalize relationships at scale while protecting privacy.• Full duplex voice models will make conversations flow naturally and feel real.• Businesses will use voice agents to understand customers, not just respond to them.• Our phones and screens may evolve as voice becomes the primary interface.Timestamped Highlights01:08 — What Vapi does and how it reached 400,000 developers02:15 — Why voice AI is one of the few areas showing clear ROI06:09 — How AI can make customer relationships human again11:18 — Building trust and privacy into voice-based systems16:48 — Blending text, voice, and context into a single experience19:05 — Rethinking our devices as voice replaces the screenA moment that stands out“Every person should feel like they can just text their hospital, and it knows exactly who they are, what they need, and when to help.” — Nikhil GuptaPro TipStart small. Use voice AI where conversation improves experience or clarity. It's not about automation; it's about creating connection.Call to ActionShare this episode with someone exploring AI in their business and follow The Tech Trek for more stories about people, impact, and technology.

Alex Daniels, Founder and CTO at Predoc, joins the show to share how he is building a mission driven healthtech company that is changing how medical data is accessed and used. He opens up about the personal story that inspired Predoc, how he keeps culture authentic while scaling, and what zero turnover really looks like in a startup. From hiring philosophies to equity design to managing context switching, Alex brings a deeply human view of leadership in engineering.Key Takeaways• Building culture starts with personal connection. Founders who share their why help every new hire connect to mission and meaning.• The best hiring filters are values and networks, not just tech stack alignment.• Predoc's culture formula of high agency, urgency, meritocracy, and transparency keeps turnover at zero.• Equity is not just compensation. It is shared ownership and long term motivation.• Flat structures and super ICs can scale effectively when leaders stay close to the work.Timestamped Highlights[01:30] How a personal loss and a lifelong heart condition inspired Predoc's mission to fix healthcare data[05:20] Inside Predoc's culture formula and why it has helped them retain every hire for three years[09:40] Why core values stay constant but merit evolves as the company grows[13:00] Rethinking equity and risk for early startup employees[15:10] How Predoc combats AI assisted interview cheating and keeps hiring authentic[23:45] Building a flat team structure where directors are still super ICs[30:00] Alex's approach to managing context switching and mental decompressionMemorable Line“We cared about what he cared about and why would he care about what we care about if I don't care about him?”Call to ActionIf you enjoyed this conversation, follow The Tech Trek for more candid talks with founders and tech leaders shaping the future of engineering and culture. Subscribe on Spotify or Apple Podcasts and join the discussion on LinkedIn.

Jordan Tigani, CEO and cofounder of MotherDuck, knows what world class infrastructure looks like. He spent years building Google BigQuery before taking those lessons into the startup world. In this episode, he breaks down why building infrastructure products is fundamentally different from typical SaaS and why founders who don't understand that difference are in for a painful surprise.What You'll LearnThere are no shortcuts in infrastructure. You can't just wire together existing open source components and call it a product. Real infrastructure requires contributing meaningfully to the state of the art, and that takes time, money, and deeper technical investment than most founders expect.Starting with startups, not enterprises, is often the smarter play. Early stage infrastructure companies should target other startups first because they're more comfortable with bleeding edge tech, have lower security barriers, and won't force you to spend three engineers building custom auth instead of your actual product.Scaling down is the new scaling up. Jordan saw pressure at SingleStore to make databases smaller and more efficient, not just bigger. That insight led to MotherDuck, which is built on DuckDB—a database that can run in a car, scale to massive cloud instances, and challenge the coordination overhead of legacy distributed systems.Bottoms up engineering cultures win in infrastructure. At BigQuery, engineers close to customer problems could ship fast and independently. Jordan's recreating that at MotherDuck by removing layers between engineers and customers, because creative problem solving requires understanding business constraints, not just technical ones.Convincing people you can scale is half the battle. The best proof is customers who look like your next target and can vouch for you. Next best is real data and benchmarks. If you don't have those yet, lean on implementation support and help prospects test at scale themselves. Early on, sometimes all you have is your word.Timestamped Highlights[01:22] Why infrastructure takes longer to build than typical SaaS products and why there's no shallow way to do it[06:57] The MVP dilemma: finding product market fit when enterprises demand reliability from day one[11:44] Lessons from BigQuery and SingleStore—what to carry over from big tech and what to leave behind[21:21] The gap in the market that led to MotherDuck: why distributed databases don't scale down and why that matters now[26:10] Redefining scale: why 100 users on one giant instance isn't necessarily better than 100 auto scaling individual instances[29:08] The hierarchy of proof: from customer testimonials to benchmarks to trust me, it'll workA Line to Remember“If you really want to build an infrastructure product, you can't just string existing components together. You actually have to contribute meaningfully to improving the state of the art.”Stay ConnectedIf this breakdown of infrastructure startups resonated with you, subscribe so you don't miss future episodes. And if you're building in this space or thinking about it, connect with Jordan on LinkedIn. He's committed to paying forward the help he got as a founder.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.