The Tech Trek

AI is everywhere, but most teams are stuck talking about efficiency and headcount. In this episode, Dave Edelman, executive advisor and best selling author, shares a sharper lens, how to use AI to create real customer value and real growth.We get into the high road vs low road of AI, what personalization should look like now, and why data has to become an enterprise asset, not a bunch of disconnected departmental files.Key Takeaways• Efficiency is table stakes, the real win is using AI to build new experiences that customers actually want• Start with customer friction, find the biggest compromises and frustrations in your category, then design around that• Personalization is no longer limited by content scale in the same way, AI changes the economics of tailoring experiences• You do not always need one giant database, modern tools can pull and connect data across systems in real time• Treat data as an enterprise resource, getting cross functional alignment is often the hardest and most important stepTimestamped Highlights• 00:46 Dave's origin story, from early loyalty programs to Segment of One marketing• 03:33 The high road and low road of AI, growth experiences vs spam at scale• 06:51 Where to start, map the biggest customer frustrations, then build use cases from there• 16:31 The data myth, why you may not need a single mega database to get value from AI• 21:31 Data as a leadership problem, shifting from functional ownership to enterprise ownership• 25:14 Strategy that actually sticks, balancing bottom up automation with top down customer led directionA line worth stealing“Use those efficiencies to invest in growth.”Pro Tips you can apply this week• List the top five customer frustrations in your category, pick one and design an AI powered fix that removes a compromise• Audit your data reality, identify where the same customer facts live in multiple places, then decide what must be unified first• Run a simple test and learn loop, create multiple variations of one experience, measure what works, and keep iterating• Put strategy on the calendar, make room for a recurring discussion that is not just metrics and cost cuttingCall to ActionIf this episode helped you think differently about AI and growth, follow the show, leave a quick rating, and share it with one operator who is building product, data, or customer experience right now.

Amanda Kahlow, CEO and founder of 1Mind, joins Amir to break down what AI changes in modern sales and go to market, and what it does not. If you lead revenue, product, or growth, this is a practical look at where AI creates leverage today, where humans still matter, and how teams actually adopt it without chaos.Amanda shares how “go to market superhumans” can handle everything from early buyer conversations to demos, sales engineering support, and customer success. They also dig into trust, hallucinations, and why the bar for AI feels higher than the bar for people.Key takeaways• Most buyers want answers early, without the pressure that comes with talking to a salesperson• AI can remove friction by turning static content into a two way conversation that helps buyers move faster• The hardest part of adoption is not capability, it is change management and trust inside the team• Humans still shine in relationship and nuance, but AI can outperform on recall, depth, and real time access to the right info• As AI levels the selling experience, product quality matters more, and the best product has a clearer path to winTimestamped highlights00:31 What 1Mind builds, and what “go to market superhumans” actually do across the full buyer journey02:00 The buyer lens, why early conversations matter, and how AI gives control back to the buyer06:14 Why the SDR experience is frustrating for buyers, and where AI can improve both sides09:42 Change management in the real world, why “everyone build an agent” gets messy fast13:04 Why “swivel chair” AI fails, and what real time help should look like in live conversations15:52 Hallucinations and trust, plus the blunt question every leader should ask about human error22:26 Competitive advantage today, and why adoption eventually pushes markets toward “best product wins”A line worth sharing“Do your humans hallucinate, and how often do they do it?”Pro tips you can use this week• Start with low stakes usage, bring AI into calls quietly, then ask it for a summary and what you missed• Build adoption top down, define what good looks like, otherwise you get a pile of similar agents and no clarity• Focus AI on what it does best first, recall, context, and instant answers, then expand into workflow and process laterCall to actionIf this episode sparked ideas for your sales team or your product led funnel, follow the show so you do not miss the next one. Share it with one revenue leader who is trying to modernize their go to market motion, and connect with Amir on LinkedIn for more clips and operator level takes.

What if the best people on your investing team are still in college? Peter Harris, Partner at University Growth Fund, breaks down how they run a roughly 100 million dollar venture fund with 50 to 60 students doing real diligence, real founder calls, and real deal work.You will hear how their student led model stays disciplined with checks and balances, why repeat games matter in venture and in business, and how this approach creates a flywheel that helps founders, investors, and the next generation of operators win together.Key Takeaways• Student led does not mean unstructured, the process is built around clear stages, data room access, investment memos, student votes, and an advisory style investment committee, with final fiduciary responsibility held by the partners• Real autonomy is the unlock, when interns are trusted with meaningful work, the best ones level up fast and start leading teams, not just supporting them• The goal is win win win outcomes, founders get capital plus a high effort support network, investors get disciplined underwriting, students get experience that compounds into career leverage• Repeat games beat short term incentives, the alumni network becomes a long term advantage, bringing the fund into high quality opportunities years later• Mistakes are inevitable, the difference is containment and systems, avoiding errors big enough to break trust, then building process improvements so they do not repeatTimestamped Highlights00:32 A 100 million dollar fund powered by 50 to 60 students, and what empowered really means01:43 The decision path, from founder screen to student memo to student vote to the advisory investment committee06:44 Why most venture internships underdeliver, and how longer tenures change outcomes10:37 Repeat games and the trust flywheel, how former students now pull the fund into top tier deals13:55 What happens when something goes wrong, damage control, learning loops, and confidentiality as a core discipline24:39 The bigger vision, expanding beyond venture into additional asset classes to create more student opportunitiesA line worth stealingIf you give people real autonomy, they'll surprise you with what they do.Pro Tips• If you are building an internship program, start by deciding what real ownership means, then build guardrails around it, not the other way around• Treat trust like an asset, design your process so every stakeholder wants to work with you againCall to ActionIf you enjoyed this one, follow The Tech Trek and share it with a founder, operator, or student who cares about building real advantage through talent and process.

Yulun Wang, executive chairman and co founder at Sovato Health, joins Amir Bormand to unpack the next wave after telemedicine, procedural care at a distance. If you have ever wondered what it would take for a top surgeon to operate without being in the same room, this conversation gets practical fast, from the real bottlenecks inside operating rooms to the health system changes required to make remote robotics mainstream.Key takeaways• Better care can actually cost less when the right expertise reaches the right patient at the right time• Telemedicine is already normalized, which sets the stage for faster adoption of remote procedures once infrastructure and workflows catch up• Surgical robots already have two sides, the surgeon console and the patient side, today connected by a short cable, the leap is making that connection work reliably across hundreds or thousands of miles• Volume drives proficiency, the outcomes gap between high volume specialists and low volume settings is one of the biggest reasons access matters• Operating rooms spend more than half their time on steps around surgery, which creates room to dramatically increase surgeon throughput when workflows are redesignedTimestamped highlights• 00:42 What Sovato Health is building, bringing procedural expertise to patients without requiring travel• 02:10 The early days of surgical robotics and the transatlantic gallbladder surgery on September 7, 2001• 05:30 The counterintuitive idea, higher quality care can reduce total cost in healthcare• 10:27 What actually changes for patients, local hospitals stay the destination, expertise becomes the thing that travels• 14:57 Why repetition matters, the first question patients ask is still the right one• 17:53 Inside the operating room schedule, where time is really spent and why productivity can jumpA line that sticks“Healthcare is different, higher quality, if done right, costs less.”Practical angles you can steal• If you are building in regulated industries, adoption is rarely about the tech alone, it is about trust, workflows, and incentives• If you sell into health systems, position the value around system level outcomes, access, quality, and margin improvement, not just novelty• If you are designing new workflows, look for the hidden capacity, the biggest gains often sit outside the core taskCall to actionIf you want more conversations like this at the intersection of tech, systems, and real world impact, follow The Tech Trek on Apple Podcasts and Spotify.

Moiz Kohari, VP of Enterprise AI and Data Intelligence at DDN, breaks down what it actually takes to get AI into production and keep it there. If your org is stuck in pilot mode, this conversation will help you spot the real blockers, from trust and hallucinations to data architecture and GPU bottlenecks.Key takeaways• GenAI success in the enterprise is less about the demo and more about trust, accuracy, and knowing when the system should say “I don't know.”• “Operationalizing” usually fails at the handoff, when humans stay permanently in the loop and the business never captures the full benefit.• Data architecture is the multiplier. If your data is siloed, slow, or hard to access safely, your AI roadmap stalls, no matter how good your models are.• GPU spend is only worth it if your pipelines can feed the GPUs fast enough. A lot of teams are IO bound, so utilization stays low and budgets get burned.• The real win is better decisions, faster. Moving from end of day batch thinking to intraday intelligence can change risk, margin, and response time in major ways.Timestamped highlights00:35 What DDN does, and why data velocity matters when GPUs are the pricey line item02:12 AI vs GenAI in the enterprise, and why “taking the human out” is where value shows up08:43 Hallucinations, trust, and why “always answering” creates real production risk12:00 What teams do with the speed gains, and why faster delivery shifts you toward harder problems12:58 From hours to minutes, how GPU acceleration changes intraday risk and decision making in finance20:16 Data architecture choices, POSIX vs object storage, and why your IO layer can make or break AI readinessA line worth stealing“Speed is great, but trust is the frontier. If your system can't admit what it doesn't know, production is where the project stops.”Pro tips you can apply this week• Pick one workflow where the output can be checked quickly, then design the path from pilot to production up front, including who approves what and how exceptions get handled.• Audit your bottleneck before you buy more compute. If your GPUs are waiting on data, fix storage, networking, and pipeline throughput first.• Build “confidence behavior” into the system. Decide when it should answer, when it should cite, and when it should escalate to a human.Call to actionIf you got value from this one, follow the show and turn on notifications so you do not miss the next episode.

New leaders face a choice fast. Do you adapt to the organization you inherit, or reshape it around the way you lead?In this conversation, Amir sits down with Gian Perrone, engineering leader at Nav, to unpack how org design really works in the first 30 to 120 days, and how to drive change without spiking anxiety or losing trust.You will hear how Gian treats leadership as triage, why “listen and learn” is rarely passive, and what separates a thoughtful reorg from one that feels chaotic.Key takeawaysLeaders almost always arrive with hypotheses, the real work is testing them without rushing to force a playbookA reorg is not automatically bad, perception turns negative when the why is unclear and people feel unsafeOver communicating helps, but thinking out loud too often can create noise, a structured comms plan keeps change steadyA simple way to spot a collaborative culture is to disagree in the interview and see how they respondManagers are the front line in change, set clear expectations so teams hear a consistent story about what is changing and whyTimestamped highlights00:01 What Nav does, and the real question behind org design for new leaders01:59 Why “first 90 days” is usually triage, not passive observation04:14 The reorg stopwatch, and why structure reflects your worldview08:36 How to communicate change without destabilizing teams12:54 A practical interview move to test whether a company truly collaborates17:03 The manager layer, how Gian sets expectations so change lands wellA line worth repeating“If you arrive and something is on fire, you are going to fix it.”A few practical moves worth stealingWhen you are new, write down your hypotheses early, then use real signals to confirm or kill themFloat a change as a real idea first, gather feedback, then come back with details before you finalizeCreate a simple comms map of who hears what, when, and from whom, then follow itBe matter of fact about changes, teams often mirror the tone you setCall to actionIf this episode helped you think more clearly about leadership and org design, follow the show and share it with one operator who is navigating change right now.

Nir Soudry, Head of R&D at 7AI, breaks down how teams can move from early experimentation to real production work fast, without shipping chaos. If you are building AI features or agent workflows, this conversation is a practical look at speed, safety, and what it actually takes to earn customer trust.Nir shares how 7AI ships in tight loops with a real customer in mind, why pushing decisions closer to the engineers removes bottlenecks, and how guardrails and evaluation keep fast releases from turning into security risks. You will also hear a grounded take on human plus AI collaboration, and why “just hook up an LLM” falls apart at scale.Key takeaways• Speed starts with focus, pick one customer and ship something usable in two or three weeks, then iterate every couple of weeks based on real feedback• If you want velocity, remove the meeting chain, get engineers in the room with customers and push decisions downstream• Agent workflows are not automatically testable, you need scoped blast radius, strong input and output guardrails, and an evaluation plan that matches real production complexity• “LLM as a judge” helps, but it is not magic, you still need humans reviewing, labeling, and tuning, especially once you have multi step workflows• In security, trust is earned through side by side proof, run a real pilot against human outcomes, measure accuracy and thoroughness, then improve with tight feedback loopsTimestamped highlights00:28 What 7AI is building, security alert fatigue, and why minutes matter02:03 A fast shipping cadence, one customer, quick prototypes, rapid iterations03:51 The velocity playbook, engineers plus sales in the same meetings, fewer bottlenecks08:08 Shipping agents safely, blast radius, guardrails, and why testing is still hard14:37 Human plus AI in practice, how ideas become working agents with review and monitoring18:04 Why early AI adoption works for some customers, and how pilots build confidence24:12 The startup reality, faster execution, traction, and why hiring still mattersA line worth sharing“When it's wrong, click a button, and next time it will be better.”Pro tips you can steal• Run a two to four week pilot with one real customer and ship weekly, the goal is learning speed, not perfect coverage• Put engineers directly in customer conversations, keep leadership focused on unblocking, not gatekeeping• Treat every agent like a product surface, define strict inputs and outputs, sanitize both, and limit what it can affect• Build evaluation around real workflows, not single prompts, and combine automated checks with human review• Add feedback buttons everywhere, route feedback to both model improvement and the team that tunes production behaviorCall to actionIf you want more conversations like this on building real tech that ships, follow and subscribe to The Tech Trek.

B2B pricing is still way harder than it should be, even in 2026. In this conversation, Tina Kung, Founder and CTO at Nue.ai, breaks down why quote to revenue can take weeks, and how a flexible pricing engine can turn it into something closer to one click.You will hear how fast changing pricing models, AI driven products, and new selling motions are forcing revenue teams to rethink the entire system, not just one tool in the stack.Key takeaways• B2B quoting is basically a shopping cart, but the real complexity is cross team workflow, accounting controls, and downstream revenue rules.• Fragmented systems break the moment pricing changes, and in fast markets that can mean you only get one real pricing change per year.• AI companies often evolve from simple subscriptions to usage, services, and even physical goods, which creates billing chaos without a unified backbone.• Commit based models can make revenue more predictable while staying flexible for customers, but only if you can track entitlement, burn down, overspend, and approvals cleanly.• The most useful AI in revenue ops is not just insight, it is action, meaning it can generate the right transaction safely inside a system of record.Timestamped highlights00:43 What Nue.ai actually does, one platform for billing, usage, and revenue ops with intelligence on top02:43 Why a one minute checkout in B2C turns into weeks or months in B2B05:28 The real reason quote to revenue stays broken, fragmentation and brittle integrations08:03 How AI era pricing evolves, subscriptions to consumption, services, and physical goods12:51 Why Tina designed for flexibility from day one, and what 70 plus customer calls revealed19:42 Transactional intelligence, AI that can create the quote, route approvals, and move revenue work forwardA line worth keeping“It should be as easy as one click.”Practical moves you can steal• Map every pricing change to the downstream work it triggers, quoting, billing, revenue recognition, and approvals, then measure how many handoffs exist today.• If you sell both self serve and enterprise, design for multiple selling motions early, because the same objects can have totally different context and risk.• Treat pricing as a product surface, if your systems make changes slow, you are giving up speed in the market.Call to actionIf you want more conversations like this on how modern tech companies actually operate, follow the show on Apple Podcasts or Spotify, and connect with me on LinkedIn for clips and episode takeaways.

Tim Bucher, CEO and cofounder of Agtonomy, joins Amir to break down what physical AI looks like when it leaves the lab and shows up on the farm. Tim shares how his sixth generation farming roots and a lucky intro computer science class led to a career that included Microsoft, Apple, and Dell, then back into agriculture with a mission that hits the real world fast.This conversation is about building tech that earns its keep, delivers clear ROI, and improves quality of life for the people who keep the food supply moving.Key takeaways• Deep domain experience is a real advantage, especially in ag tech, you cannot fake the last mile of operations• The win is ROI first, but quality of life is right behind it, less stress, more time, and fewer dangerous moments on the job• Agtonomy focuses on autonomy software inside existing equipment ecosystems, not building tractors from scratch, because service networks and financing matter• One operator can run multiple vehicles, shifting the role from tractor driver to tech enabled fleet operator• Hiring can change when the work changes, some farms started attracting younger candidates by posting roles like ag tech operatorTimestamped highlights00:42 What Agtonomy does, physical AI for off road equipment like tractors01:45 Tim's origin story, sixth generation farming roots and the class that changed his path03:59 Lessons from Bill Gates, Steve Jobs, and Michael Dell, and how Tim filtered the mantras into his own leadership05:53 The moment everything shifted, labor pressure, regulations, and the prototype built to save his own farm09:17 The blunt advice for ag tech founders, if you do not have a farmer on the team, fix that11:54 ROI in plain terms, one person operating a fleet from a phone or tablet14:29 Why Agtonomy partners with equipment manufacturers instead of building new vehicles, dealers, parts, service, and financing are the backbone17:39 The overlooked benefit, quality of life, reduced stress, and a more resilient food supply chain20:18 How farms started hiring differently, “ag tech operator” roles and even “video game experience” as a signalA line that stuck with me“This is not just for Trattori farms. This is for the whole world. Let's go save the world.”Pro tips you can actually use• If you are building in a physical industry, hire a real operator early, not just advisors, get someone who lives the workflow• Write job posts that match the modern workflow, if the work is screen based, label it that way and recruit for it• Design onboarding around familiar tools, if your UI feels like a phone app, training time can collapseCall to actionIf you got value from this one, follow the show and share it with a builder who cares about real world impact. For more conversations like this, subscribe and connect with Amir on LinkedIn.

Data and AI are everywhere right now, but most teams are still guessing where to start. In this episode, Cameran Hetrick, VP of Data and Insights at BetterUp, breaks down what actually works when you move from AI hype to real business impact. You will hear a practical way to choose AI and analytics projects, how to spot low risk wins, and why clean, governed data still decides what is possible. Cameran also shares a simple mindset shift, stop copying broken workflows, and start rethinking the outcome you are trying to create.Key Takeaways• AI is a catchall term right now, the best early wins usually come from “assist” use cases that boost speed and quality, not full replacement• Start with low context, low complexity work, then earn your way into higher context projects as data quality and governance mature• Pick use cases with an impact versus effort lens, quick wins create proof, buy in, and budget for bigger bets• Stakeholders often ask for a data point or feature, but the real value comes from digging into the goal, and redesigning the workflow• Data teams cannot stop at insights, adoption matters, if the next team cannot act on the output, the project stallsTimestamped Highlights00:40 BetterUp's mission, building a human transformation platform for peak performance01:57 AI as a “catchall,” where expectations are realistic, and where they are not05:19 A useful way to think about AI work, context versus complexity, and why “intern level” framing helps07:33 How to choose projects with an impact and level of effort calculator, and why trust in data is everything10:33 The hard part, translating stakeholder requests into real outcomes, and reimagining workflows instead of automating bad ones13:47 Systems thinking across handoffs, plus why teams need deeper business fluency, including P and L basics16:59 The last mile problem, if the next stakeholder cannot act, the value never lands20:27 The bottom line, AI does not change the fundamentals, it accelerates themA Line Worth Saving“AI is like an intern, it still needs direction from somebody who understands the mechanics of the business.” Practical Moves You Can Use• Run every idea through two quick questions, what business impact do we expect, and what level of effort will it take• Look for a win you can explain in one minute, then use it to fund the harder work• When someone asks for a metric or feature, ask why twice, then validate the workflow, then redesign the outcome• Invest in governed data early, untrusted outputs kill adoption fastCall to ActionIf this episode helped you think more clearly about AI in the real world, follow the show, leave a quick review, and share it with one operator who is trying to move from experiments to impact. You can also follow Amir on LinkedIn for more clips and practical notes from each episode.

Yogi Goel, cofounder and CEO of Maxima AI, breaks down how he hires outlier talent, people who think like future founders and thrive when the plan changes fast. We get practical on what to look for beyond pedigree, how to assess it without relying on easy resume signals, and how culture scales when your team doubles.Yogi also shares what Maxima AI is building, an agentic platform for enterprise accounting that automates day to day operations and month end work, and why the best teams win by pairing speed with real ownership.Key takeaways• Outlier candidates often look “non standard” on paper, the signal is founder mentality, fast thinking, grit, and a point to prove• Hiring gets easier when it is always on, keep a living bench of great people long before you have a headcount• Use long form conversations to assess how someone thinks, not just what they have done, ask for their life story and listen for the choices they highlight• Train the specifics, but set a baseline for domain aptitude, then coach the narrow parts once the fundamentals are there• Culture scales through leaders and through what you reward and penalize, not through posters and slogansTimestamped highlights00:39 What Maxima AI does and the real value of agentic accounting01:38 Defining an outlier candidate as a future founder, and why school matters less than you think07:34 The conveyor belt approach to recruiting, building an inventory of great people before you need them11:35 Where to draw the line on training, test for general aptitude, coach the specifics14:20 How diverse teams disagree productively, bring evidence, run small bets, then double down or pivot18:25 Scaling culture with values driven leaders, and the simple rule of reward versus penaltyA line worth keeping“Culture is two things, what you reward and what you penalize.”Pro tips you can steal• Keep a short list of the best people you have ever met for each function, update it constantly• Ask candidates for their journey from day zero, then pay attention to what they choose to emphasize• When the team disagrees, grab quick evidence, customer texts, small pulse checks, then place a small bet that will not kill the company• Expect great people to want autonomy and scope, manage like a mentor, not a hovercraftCall to actionIf this episode helped you rethink hiring, share it with a founder or engineering leader who is building a team right now. Follow the show for more conversations on people, impact, and technology, and connect with Yogi Goel on LinkedIn by searching his name and Maxima AI.

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.