The Tech Trek

Chandan Lodha, Co-founder at CoinTracker, joins Amir Bormand to unpack the real shift from big tech to building your own company. From Harvard to Google to Y Combinator, Chandan shares what pushed him to take the leap, how he found the right idea, and what he had to unlearn to lead at startup speed.This conversation is for builders and leaders who want to grow faster, ship faster, and build teams that can actually execute.Key Takeaways• The early career advantage is learning velocity, optimize for environments that stretch you fast• Managing the business is rarely the hardest part, people problems scale with headcount• Big company habits can break you at a startup, especially around distribution, speed, and getting your first users• YC helped most through peer proximity, being surrounded by real users and founders who move quickly• Founder growth is a system, use feedback loops like reviews, 360 input, and personal goal trackingTimestamped Highlights00:00 From Harvard and Google to founder mode, what made him leave the safe path00:35 CoinTracker in plain English, crypto taxes and accounting for individuals and businesses03:32 Leap first, think later, the messy six month search for a real idea05:00 Runway reality, setting a 12 to 18 month window to figure it out06:09 Crypto skepticism to conviction, reading the Bitcoin white paper changed his frame10:05 Leadership lessons at 100 people, why people issues become the main work14:43 Y Combinator benefits, users everywhere and a practical playbook for early company building17:55 Personal growth systems, performance feedback and personal OKRs, plus changing your mind on three issues each year21:04 Becoming a new parent, structure, efficiency, and cutting non essentials23:24 The two skills to build before you leap, building and sellingA line worth keepingManaging the business is easy, managing people is hard.Pro Tips• Set a real runway window, then use it to iterate hard with users every week• Expect to unlearn big company instincts, distribution and speed do not come for free• Build a feedback cadence for yourself, not just your team, reviews and 360 input can surface blind spots• Practice building and selling in small side projects now, those skills compound in any startupCall to ActionIf this episode helped you think differently about leadership and the founder path, follow The Tech Trek on Apple Podcasts or Spotify, and share it with one person who is building or thinking about making the leap.

Joel Dolisy, CTO at WellSky, joins the podcast to reveal why organizational design is the ultimate "operating system" for scaling tech companies. This conversation is a deep dive into how engineering leaders must adapt their strategies when moving between the hyper growth of Venture Capital and the disciplined profitability of Private Equity.Building a high performing team is about much more than just hiring. Joel explains the necessity of maximizing the "multiplier effect" where the collective output far exceeds the sum of individual parts. We explore the pragmatic reality of digital transformation, the "art" of timing disruptive technology adoption like Generative AI, and how to use the Three Horizons framework to keep your core business stable while chasing the next big innovation. Whether you are leading a team of ten or an organization of hundreds, these insights on design principles and leadership context are essential for navigating the complexities of modern software delivery.Core InsightsShifting the perspective of software from a cost center to a core growth enabler is the fundamental requirement for any company aiming to be a true innovator.Private Equity environments require a specialized leadership approach because the "hold period" clock dictates when to prioritize aggressive growth versus EBITDA margin acceleration.Scaling successfully requires a "skeleton" of design principles, such as maintaining team sizes around eight people to ensure optimal communication flow and minimize overhead.The most critical role of a senior leader is providing constant context to the engineering org, ensuring teams understand the "why" behind shifting constraints as the company matures.Timestamped Highlights01:12 Defining the broad remit of a CTO from infrastructure and security to the unusual addition of UX.04:44 Treating your organizational structure as a living operating system that must be upgraded as you grow.10:07 Why innovation must include internal efficiency gains to free up resources for new revenue streams.15:01 Navigating the massive waves of disruption from the internet to mobile and now large language models.23:11 The tactical differences in funding engineering efforts during a five to seven year Private Equity hold period.28:57 Applying Team Topologies to create clear responsibilities across platform, feature, and enablement teams.Words to Lead By"You are trying to optimize what a set of people can do together to create bigger and greater things than the sum of the individual parts there".Expert Tactics for Tech LeadersWhen evaluating new technology like AI, Joel suggests looking at the "adoption curve compression". Unlike the mid nineties when businesses had a decade to figure out the internet, the window to integrate modern disruptors is shrinking. Leaders should use the Three Horizons framework to move dollars from the core business (Horizon 1) to speculative innovation (Horizon 3) without making knee jerk reactions based solely on hype.Join the ConversationIf you found these insights on organizational design helpful, please subscribe to the show on your favorite platform and share this episode with a fellow engineering leader. You can also connect with Joel Dolisy on LinkedIn to keep up with his latest thoughts on healthcare technology and leadership.

Stop chasing shiny objects and start driving real business outcomes. Marathon Health CTO Venkat Chittoor joins the show to explain why AI is the ultimate enabler for digital transformation but only when it is anchored by a rock solid business strategy. Essential Insights for Tech LeadersAI is not a standalone strategy. It is a powerful tool to accelerate a pre-existing business North Star. Success in digital transformation follows a specific maturity curve. Start with personal productivity, move to replacing mundane tasks, and eventually aim for cognitive automation. Governance must come before experimentation. Establishing guardrails for data privacy is critical before launching any AI pilot. Measure value through tangible efficiency gains. In healthcare, this means reducing administrative burden or "pajama time" so providers can focus on patient care. Don't let marketing speak fool you. Always validate vendor claims against your specific industry use cases. Timestamped Highlights00:50 Defining advanced primary care and the mission of Marathon Health 02:44 Why AI strategy is useless without a defined business strategy 05:01 The three steps of AI adoption from productivity to cognition 12:14 How to define success metrics for a pilot versus a scaled V1 solution 16:40 Real world ROI including call deflections and charting efficiency 21:43 Advice for leaders on data quality and avoiding vendor traps A Perspective to CarryAI is actually enabling [efficiency], but without a solid business strategy, AI strategy is not useful. Tactical Advice for the FieldWhen launching an AI initiative, focus heavily on the underlying data quality. Ensure your team accounts for data recency, accuracy, and potential biases, as these factors determine whether an experiment succeeds or fails. Start small with pilots to build muscle memory before attempting to scale complex systems. Join the ConversationIf you found these insights helpful, subscribe to the podcast for more deep dives into the tech landscape. You can also connect with Venkat Chittoor on LinkedIn to follow his work in healthcare innovation.

Stop treating data governance as a "data cop" function and start using it as a high ROI offensive weapon. In this episode, Peter Kapur, Head of Data Governance and Data Quality at CarMax, breaks down how to move beyond defensive compliance to drive profitability, customer experience, and better data science outcomes.Critical Insights for LeadersShift from defense to offense Data defense covers the mandatory regulatory and legal requirements like privacy and cybersecurity. Data offense involves everything else that hits your bottom line, such as investing in data quality to save or make money.Prioritize problems over frameworks Avoid bringing rigid policies and "data geek" terminology to business leaders. Instead, spend time listening to their specific data struggles and apply governance capabilities as solutions to those problems.Data quality makes governance tangible Without high quality data, governance is just a collection of abstract policies. Improving data quality empowers data scientists to produce better models and gives analytics teams the ability to discover and trust their data.Key Moments in the Conversation02:41 Defining the clear line between defensive regulation and offensive growth 06:03 Why data quality and data governance must sit together to be effective 11:00 Shifting from "data school" to "business school" to communicate value 13:12 Quantifying the ROI of data governance through customer wins and time savings 18:35 Actionable advice for starting an offensive strategy from scratch Wisdom from the Episode"If we meet the laws, we meet the regulations, we meet the legal, how do we leverage our data? It is a mindset shift versus, let me lock my data down, no one use it." Tactical Advice for ImplementationEnsure adoption through personalization Design tools and processes that are personalized to specific roles so they feel like a natural part of the workflow rather than a burden.Focus on the eye of the consumer Treat every person in the organization as a "data citizen" and remember that data quality is ultimately defined by the needs of the people consuming it.Join the ConversationSubscribe to the podcast on your favorite platform to catch every episode. Follow us on LinkedIn to stay updated on the latest trends in data leadership.

Afrooz Ansaripour, Director of Data Science at Walmart, joins the show to explain how global leaders are shifting from simple historical tracking to predicting psychological triggers and customer intent. This episode explores the evolution of customer intelligence and how Generative AI is turning massive data sets into personalized, value driven experiences. Listeners will learn how to balance hyper personalization with foundational privacy to build lasting consumer trust.Key InsightsPredict intent rather than just reporting past transactions to understand why a customer is with the brand.Use Generative AI as an explainability layer to transform complex data platforms from black boxes into conversational tools.Prioritize customer trust as a critical part of the user experience rather than just a legal requirement.Integrate digital and physical signals to create a 360 degree view that reveals insights which would otherwise be invisible.Focus on rapid technology adoption and curiosity as the primary drivers of success in modern AI teams.Timestamped Highlights01:51 Identifying the challenges and opportunities when managing millions of real time signals.06:43 Strategies for showing genuine value to the customer without making them feel like just a part of a sale.09:51 How LLMs are fundamentally changing the way data teams interpret unstructured feedback and behavioral patterns.14:42 Managing privacy and ethical data practices while building personalized conversational AI.19:14 Stitching together the online and offline journey to create a seamless customer experience.22:52 The necessary evolution of data science skills toward storytelling and execution bias.A Powerful Thought"Personalization should never come at the expense of customer trust." Tactical StepsCombat the garbage in garbage out problem by refining cleaning processes to handle modern AI requirements.Build an interactive layer or chatbot on top of data products to make insights instantly accessible and automated.Translate technical insights into real world decisions to ensure customers actually benefit from data models.Next StepsSubscribe to the show for more insights into the future of tech. Share this episode with a peer who is currently navigating the complexities of customer data.

Shahryar Qadri, CTO of OneImaging, joins me to unpack a hard truth about healthcare tech: the goal is not to remove humans, it is to give them more room to be human.We talk about where cost “optimization” actually helps patients, why radiology is a perfect fit for AI but still held back by data access, and how better workflows can improve trust, speed, and outcomes without losing the human touch.OneImaging sits in the radiology benefits space, helping members book imaging in a national network with more transparency and a high touch booking experience, while helping employers cut imaging costs significantly.Key takeaways• The “human touch” in healthcare is not going away, the better play is using tech to increase capacity so caregivers can spend more time being caregivers• Cost optimization is not always about paying less for expertise, it is often about wasting less human time, improving trust, and removing friction around services• Healthcare still runs on outdated plumbing in places you would not expect, including fax based workflows that slow everything down• Radiology is one of the best real world use cases for AI, but the bigger blocker is getting access to imaging data in usable form, not model capability• Your health data is already “there”, but it is not working for you yet. The next wave is tools that scan your longitudinal record and surface what to ask your doctor about, so you can be a stronger advocate for your own careTimestamped highlights• 00:36 What OneImaging actually does, and why “transparent imaging” is more than a pricing story• 02:00 Why healthcare stays personal, and how tech should increase capacity instead of replacing care• 03:36 The real definition of cost optimization, commodity versus service, and where trust matters• 07:01 The surprising reality of imaging ops, why it still feels like 1998, and what gets digitized next• 17:19 AI in radiology is real, but the data access and interoperability gap is the bottleneck• 24:21 Your CDs are full of value, the problem is we do almost nothing with that data todayA line worth replaying“These LLM models are the worst that they'll ever be today. They're only going to get better and better and better.”Call to actionIf this episode sparked a new way of thinking about healthcare tech, follow The Tech Trek on your podcast app, share it with a friend in product or engineering, and connect with me on LinkedIn for more conversations like this.

Swarupa Mahambrey, Vice President of Software Engineering at The College Board, breaks down what tech debt really looks like in a mission critical environment, and how an engineering mindset can prevent it from quietly choking delivery. She shares a practical operating model for paying down debt without stopping the roadmap, and the cultural habits that make it stick.You will hear how College Board carved out durable space for engineering excellence, how they use testing and automation to protect reliability at scale, and how to make the trade offs between features, simplicity, and user experience without slowing the team to a crawl.Key Takeaways• Tech debt behaves like financial debt, delay the payment and the interest compounds until even simple changes become painful• A permanent allocation of capacity can work, dedicating 20 percent of every sprint to tech debt can reduce support load and improve delivery• Shipping more features can slow you down, simplifying workflows and validating with real usage can increase velocity and reduce tickets• Resilience is not about avoiding every failure, it is about designing for graceful degradation so spikes and outages become small blips instead of crises• Automation is not “extra,” it is part of the definition of done, including unit tests as acceptance criteria and clear code coverage expectationsTimestamped Highlights• 00:00 Why tech debt is a mindset problem, not just a backlog problem• 01:00 Tech debt explained with a real example, what happens when a proof of concept becomes production• 03:45 The feature trap, how “powerful” workflows can overwhelm users and explode maintenance costs• 11:03 Engineering Tuesday, one day a week to strengthen foundations, not ship features• 14:39 Stability vs resilience, designing systems that bend instead of shatter• 20:06 Testing and automation at scale, unit tests as a requirement and code coverage guardrailsA line worth keeping“If we don't intentionally carve out space for engineering excellence, the urgent will always crowd out the important.”Practical moves you can steal• Protect a fixed slice of capacity for tech debt, make it part of the operating model, not a one time cleanup• Treat automation as acceptance criteria, no test, no merge, no release• Use pilots and targeted releases to learn early, then iterate based on metrics and real user behavior• Design for graceful degradation with retries, fallback paths, and clear failure visibilityCall to actionIf this episode helped you think differently about tech debt and engineering culture, follow The Tech Trek, leave a quick rating, and share it with one engineer who is fighting fires right now.

Software is still eating the world, and AI is speeding up the clock. In this episode, Amir talks with Tariq Shaukat, co CEO at Sonar, about what it really takes for non tech companies to build like software companies, without breaking trust, security, or quality. Tariq shares how leaders can treat AI like a serious capability, not a shiny add on, and why clean code, governance, and smart pricing models are becoming board level topics. Key Takeaways• “Every company is a software company” does not mean selling SaaS, it means software is now core to differentiation, even in legacy industries. • The hardest shift is not tools, it is mindset: moving from slow, capital style planning to fast iteration, test, learn, and ship. • AI works best when leaders stay educated and involved, outsourcing the whole strategy is a real risk. • “Trust but verify” needs to be a default posture, especially for code generation, security, and compliance. • Pricing will keep moving toward value aligned consumption models, not simple per seat formulas. Timestamped Highlights• 00:56 What Sonar does, and why clean code is really about security, reliability, and maintainability • 05:36 The Tesla lesson: mechanics commoditize, software becomes the experience people buy • 09:11 Culture plus education: why software capability cannot live in one silo • 14:21 Cutting through AI hype with program discipline and a “trust but verify” mindset • 18:23 Boards, governance, and setting an “acceptable use” policy for AI before something goes wrong • 25:18 How software pricing changes in an AI world, and why Sonar prices by lines of code analyzed A line worth saving:“Define acceptable risk as opposed to no risk.” Pro Tips you can steal• Write down what you want AI to achieve, the steps to get there, and the metric you will use to verify outcomes. • For code generation, scan and review before shipping, treat AI output like a draft, not a final answer.• Set clear rules for what is allowed with AI inside the company, then iterate as you learn. Call to ActionIf you want more conversations like this on software leadership, AI governance, and building real impact, follow The Tech Trek and subscribe on your favorite podcast app. If someone on your team is wrestling with AI rollout or developer productivity, share this episode with them.

Gregg Altschul, Vice President of Technology at FanDuel, shares a clear and practical look at how leaders can create real alignment across personal, team, and company goals. He explains why transparency drives trust, how to build a path for growth at every level, and why the best managers help people pursue their long term North Star while still delivering for the business. This is a thoughtful and modern blueprint for tech leadership and team development.Key TakeawaysTeams move faster when the company goal is translated into a simple set of objectives that every level can understand and act on.Transparency is the anchor for healthy goal setting and creates the space for honest conversations about career direction.Managers should encourage long term North Star thinking since it keeps people growing even after short term milestones are reached.Succession planning should be an active part of how teams operate so progress never depends on a single person.People can stay committed to their work even if they have long term plans outside the company, and supporting those plans often improves retention.Timestamped Highlights02:19 How top level business goals get distilled into specific team and personal goals that engineers can act on.04:57 The role of transparency in helping teams understand the why behind each objective.07:34 Helping ICs tie personal development to broader company needs while still honoring their ambitions.09:28 Creating a safe environment for honest career conversations in a world of hybrid and remote work.15:14 Why knowing a person's long term plans makes succession planning easier for everyone.17:45 How Gregg works with his own manager on growth even when the title ladder narrows at the VP level.A standout idea from Gregg“As long as you have a North Star you will grow. Whether you ever reach the exact role you picture is not really the point. The point is growth.”Call to actionIf this conversation helped you rethink how goals work inside your team, share it with a colleague who will appreciate it. Follow the show so you never miss new episodes and connect with me on LinkedIn for more conversations with leaders shaping the future of engineering and data.

Ken Ringdahl, CTO at Emburse, joins The Tech Trek to share what it really looks like to grow from engineer to CTO without losing your love for building. He talks about staying close to the code while leading a three hundred person org, how he learned the business side on the job instead of through an MBA, and why curiosity is still his strongest tool. If you are an engineer who cares about leadership, AI, and long term impact, this one will hit close to home. the-tech-trek_copy-of-ken-ringd…Key takeawaysThe best engineering leaders stay technical for as long as they can, then pick their spots to lean in where the business needs them most.You can learn the business side on the job by raising your hand for cross functional work and building real relationships with sales, finance, and product leaders.Curiosity is a career advantage, both in technology and in leadership, because the quality of your questions shapes the quality of your decisions.A practical AI strategy comes from listening to customers, partners, and internal experts, then translating that into focused product bets instead of chasing shiny tools.Do not rush into management just for the title, a deep foundation as an engineer will make every future leadership decision stronger.Timestamped highlights00:38 Ken explains what Emburse does and how modern spend management lives at the intersection of software, data, and finance. the-tech-trek_copy-of-ken-ringd…01:30 How he balances being an engineer at heart with the reality of leading many teams and products as CTO.03:41 Ken reflects on missing his coding days, what he still tinkers with, and why he chose the bridge role between tech and business.08:32 Learning leadership without an MBA, creating your own opportunities, and attaching yourself to people you can learn from across the company.14:58 How he stays smart on AI through office hours, internal experts, cloud partners, customers, and investor networks.21:22 His biggest advice for engineers who want to move into leadership and why he actually went back to a more hands on role before moving up again.One line that stayed with me“Even if you want to be a leader, do not rush it. Do not go so fast that you do not get that foundation.” the-tech-trek_copy-of-ken-ringd…Practical moves for your own careerStay technical as long as you can, then choose a few focus areas such as architecture, AI strategy, or cloud patterns where you can still go deep.Use curiosity as your main tool, ask simple but sharp questions of finance, sales, and customers so you see how technology really creates value.Look for chances to run cross functional projects early in your career so that by the time you step into leadership, you already understand how the wider business works.Treat partners, customers, and internal experts as an extended brain trust, especially when you are trying to shape an AI and platform strategy.Listen and stay connectedIf this episode helped you think differently about your own path from engineer to leader, follow The Tech Trek, leave a rating on your favorite podcast app, and share it with one person on your team. To keep the conversation going, connect with Ken on LinkedIn and find me there as well for more stories from leaders who are building real impact with technology.

Karan Talati, cofounder and CEO at First Resonance, joins me to unpack what modern manufacturing really looks like inside factories that build rockets, drones, reactors, and other complex hardware. We dig into why only a small slice of factories run on real systems today, what a true factory operating system unlocks, and how that connects directly to national security and the AI boom.If you care about where all of this new compute, energy, and defense hardware will actually come from, this conversation gives you a clear view of the stack, the gaps, and the opportunity. Key takeaways• Only a small fraction of factories in the United States use a manufacturing execution system, which leaves a huge gap between legacy on prem tools, paper processes, and generic workflow apps that were never built for hardware work• Cloud infrastructure and open interfaces now make it possible to deploy a purpose built factory operating system at a cost and speed that works for both fast moving startups and long standing suppliers• Reindustrialization does not mean bringing every product back onshore, it means being deliberate about the layers of manufacturing that matter most for national security, chips, optics, and other high value components• The real foundation for modern manufacturing is talent, there is a major chance to re skill people into highly technical, well paid roles in aerospace, semiconductors, energy, and more• AI and agent style workflows will sit across design, manufacturing, and field operations so that hardware teams can close feedback loops, shorten timelines, and make better decisions with the data they already generateTimestamped highlights[00:40] Karan explains what First Resonance does and why he calls it a factory operating system for complex industries like aerospace, defense, energy, and autonomy[01:55] How we ended up with only about fifteen percent of factories running on an MES, and why most hardware work still lives on paper, spreadsheets, and ad hoc tools[06:49] A clear walkthrough of how offshoring looked like a rational path for decades, and why it created hidden risk across chips, optics, and other critical components[11:46] Which parts of manufacturing should come back onshore, why you do not want everything local, and how workforce strategy fits into the new industrial map[16:35] What a horizontal stack across design, factory systems, test, and field data can look like, and how AI agents can keep teams in sync across that stack[23:02] The real timelines of hardware in the age of AI, why software is speeding up physical development, and why examples like SpaceX and TSMC matter for the next decadeA line that stayed with me“Hardware and software are not separate worlds, they are one system that is now converging faster than most people realize.”Practical moves for tech leaders• Map your current manufacturing and hardware workflows, even if you are at a software first company, find the paper, spreadsheets, and disconnected tools that support anything physical you ship• Look for one or two places where a factory operating system or modern MES could remove handoffs, for example design changes that take weeks to reach the line or test data that never feeds back into engineering• Treat manufacturing careers as part of your talent strategy, help your teams see these roles as high skill and high impact, not as a side trackCall to actionIf this episode gave you a clearer view of how hardware, AI, and national security tie together, share it with one other person who should be thinking about the factory side of their roadmap. Follow and subscribe to The Tech Trek so you never miss deep dives like this, and connect with me on LinkedIn if you want more conversations at the edge of data, engineering, and real world impact.

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.

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.