The Tech Trek

Michael Marmo, founder and chief executive of CurbWaste, joins The Tech Trek to share how he went from catching fastballs in Europe to building software that runs the daily work of waste haulers. We walk through the very human side of leaving a sports identity, starting at the bottom in a family waste business, and finally asking a simple question about founding a company. Why not meIf you are sitting inside an industry and quietly seeing the gaps that no product seems to solve, this conversation is a playbook in how to turn that insider view into a real business, even if you do not come from a traditional tech background.Key takeaways• Identity can change, but the work habits that made you good at sports or any craft can transfer directly into building a company, especially persistence, dealing with failure, and showing up every day• You do not have to love a specific activity forever, you can follow the deeper thread underneath it, like merit, teamwork, and visible impact, and find those same traits in a very different industry• Deep time inside an industry lets you see painful, repeatable problems, and that is often a better seed for a product business than starting with a clever idea and pivoting until something sticks• A clear why for the product and a clear why you are the person to build it are not nice to have, they are what convince customers, hires, and investors to follow you when things get hard• Great founders do not pretend to be good at everything, they are honest about what they do not know, learn just enough to make good calls in product, engineering, and go to market, and then surround themselves with people who fill the gapsTimestamped highlights00:32 Michael explains what CurbWaste does and how it runs a hauler business from first customer contact through billing01:21 From college baseball and pro teams in Europe to the first job in media and tech sales, and the identity shock that came with that change06:27 What it really felt like when the game ended, why mens leagues did not scratch the itch, and how that led to a quiet reset in the working world09:11 Starting at the bottom in a family recycling center, discovering a love for the waste industry, and why it felt like a merit based team environment15:24 Walking the floor at Waste Expo, not finding the software he needed, deciding to fund and build his own tools, and seeing other haulers facing the same problems19:40 The moment hearing the Yelp founder speak turned into a personal question, why not me, and how that idea of trying anyway shapes the way he thinks about founding todayA line that stayed with me“At the end of the day he tried. He had an idea and he acted on it and pursued it. That really resonated. I was like, why not me”Practical notes for future founders• Before you write any code or quit your job, write down why this problem matters, why it matters now, and why you are willing to keep going when it stops being fun• If your first answer to why is only about money, keep digging until you find something that still feels true on a hard day, because you will have a lot of those• Use your current role as a live lab, list the moments that feel broken, expensive, or slow, and ask which of those could actually support a business if you solved them well• Be direct with yourself about weak spots, whether that is product, tech, or selling, then build a basic understanding and lean on people who are strong where you are notCall to actionIf you enjoy stories that get inside how real founders make the leap from operator to builder, follow The Tech Trek in your favorite podcast app and share this episode with someone who is quietly thinking about starting something of their own.

Jason Ash, Chief of Data at Symetra, joins the show to unpack how a mid sized insurer is rebuilding its data stack and culture so business and technology actually pull in the same direction. He shares how his team brings actuaries, product leaders, and engineers into one data platform, and why opening that platform to non technical contributors has been a turning point. If you work in a regulated industry and are trying to move faster with data, this conversation gives you a very practical view of what it takes.Key takeaways• Business and tech only work when they share context and trustJason has sat in both seats, first as an actuary and now as a data and engineering leader. That dual background helps him translate between risk, regulation, and modern data practices, and it shapes how he frames projects around shared business outcomes rather than tools.• Put data leaders inside business line leadership, not on the outsideSeveral of Jason’s managers sit on the leadership teams for Symetra’s life, retirement, and group benefits divisions. They hear priorities and constraints at the same time as product and distribution leaders, which lets them frame data as a value add for new products instead of a back office cost.• Treat the warehouse as a shared product and measure contributors, not just tablesSymetra’s dbt based warehouse started with about five contributors. Over three years they grew that to more than sixty, and half of those people sit outside the core data team. Business users learn to contribute SQL, documentation, and domain knowledge directly into the repo, which spreads ownership and reduces bottlenecks.• Shift stakeholders away from big bang launches to steady deliveryJason pushes his teams to think like software engineers. Rather than promising a perfect data product on a single date, they deliver an early slice of data, have partners use it right away, collect feedback, and improve every month. That builds trust and avoids the usual disappointment that comes with one big release.• Use maturity as a guide for where to investEarly on, his group picked a few strong champions who were willing to accept slower delivery in exchange for building real infrastructure. Now that the platform and practices are in place, the focus is on scale, reuse, and getting more people to build on the same foundation, including as AI capabilities start to reshape the work.Timestamped highlights00:53 Jason explains what Symetra actually does and how their product mix makes data work more complex than the company size might suggest02:19 From actuary to Chief of Data, and what sitting on both sides of the fence taught him about business and technology expectations08:08 Why mixing data engineers, data scientists, actuaries, and analysts on the same problems leads to stronger solutions than any single discipline alone13:44 How embedding data leaders into each business division’s leadership group changed when and how data enters product discussions16:38 The dbt story at Symetra, and how more than sixty people across the company now contribute directly to the shared data warehouse26:22 Moving away from big bang data launches and setting expectations around early value, continuous feedback, and ongoing quality improvements32:06 The tension between safety and speed as AI advances, and what Jason worries about most for established insurers that move too slowlyPractical moves you can steal• Put data leaders on business line leadership teams so they hear priorities and constraints in real time, not after the roadmap is set• Track how many unique people contribute to your data warehouse and make that a visible success metric across the companyStay connectedIf this episode helped you think differently about data leadership in regulated industries, share it with a colleague who owns product, data, or actuarial work.

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.