The Tech Trek

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

Tom Drummond, Managing Partner at Heavybit, joins the show to break down what it takes to build and scale AI “picks and shovels” companies for the enterprise. We dive into the realities of selling into one of the hardest markets to reach, why differentiation matters more than ever, and how startups can wedge their way into massive opportunities despite fierce competition.Key Takeaways• Enterprise attention is more competitive than ever—breaking through requires clarity and category creation.• Cold email and traditional outbound are saturated—startups must iterate quickly on channels and messaging.• Landing enterprise deals often starts with developers and end users, not CIOs—grassroots adoption is powerful.• Narrow wedges matter—solve one painful, high-value problem better than anyone else, then expand.• Timing the industry cycle is critical—knowing when markets fragment and when they consolidate can define outcomes.Timestamped Highlights02:03 — Why enterprise attention has never been harder to win04:55 — Differentiation in a sea of lookalike AI infrastructure startups07:34 — Cold email vs content, billboards, and unconventional channels08:35 — The Pareto rule of enterprise revenue and why developer adoption is key11:47 — Competing with big tech incumbents: the power of the narrow wedge21:03 — Where the market is headed: cycles of expansion, contraction, and consolidationA line that stuck“You don't win by being another tool—you win by defining the category everyone else has to fit into.”Call to ActionIf you enjoyed this conversation, share it with a founder or tech leader who's navigating the enterprise market. Make sure to follow the show for more unfiltered conversations with people shaping the future of software and AI.

Jonathan DiVincenzo, co-founder and CEO of Impart Security, joins the show to unpack one of the fastest growing risks in tech today: how AI is reshaping the attack surface. From prompt injections to invisible character exploits hidden inside emojis, JD explains why security leaders can't afford to treat AI as “just another tool.” If you're an engineering or security leader navigating AI adoption, this conversation breaks down what's hype, what's real, and where the biggest blind spots lie.Key Takeaways• Attackers are now using LLMs to outpace traditional defenses, turning old threats like SQL injection into live problems again• The attack surface is “iterating,” with new vectors like emoji-based smuggling exposing unseen vulnerabilities• Frameworks have not caught up. While OWASP has listed LLM threats, practical solutions are still undefined• The biggest divide in AI coding is between senior engineers who can validate outputs and junior developers who may lack that context• Security tools must evolve quickly, but rollout cannot create performance hits or damage business systemsTimestamped Highlights01:44 Why runtime security has always mattered and why APIs were not enough04:00 How attackers use LLMs to regenerate and adapt attacks in real time06:59 Proof of concept vs. security and why both must be treated as first priorities09:14 The rise of “emoji smuggling” and why hidden characters create a Trojan horse effect13:24 Iterating attack surfaces and why patches are no longer enough in the AI era20:29 Is AI really writing production code and what risks does that createA thought worth holding onto“AI is great, but the bad actors can use AI too, and they are.”Call to ActionIf this episode gave you new perspective on AI security, share it with a colleague who needs to hear it. Follow the show for more conversations with the leaders shaping the future of tech.

Daniel Saks, co-founder and CEO of Landbase, joins The Tech Trek to unpack the real meaning of democratizing technology. From agentic AI that works for you—not the other way around—to rethinking workflows and change management, Daniel shares why this shift is bigger than the move from on-prem to cloud. For tech leaders, founders, and operators, this episode reveals how to reclaim time, scale smarter, and prepare for the next wave of AI-native business.Key Takeaways• AI is moving beyond hype—it's becoming the engine that executes real workflows and shifts power from systems to users• Businesses that recapture saved time will unlock significant cost efficiency and growth potential• The gap between idea and implementation is shrinking fast, but durable value will come from solving the hardest problems, not the easiest apps• Change management is now about building AI-native workflows and cross-functional systems, not just adopting tools• Sales and go-to-market leaders can gain an edge by mastering prompting and AI-driven enrichment todayTimestamped Highlights00:56 — Why Landbase built GTM-1 Omni to reimagine go-to-market execution01:40 — From on-prem to cloud to AI-native: the next major leap in democratizing technology04:34 — Why fears about AI replacing jobs miss the bigger story of new roles and industries emerging08:42 — How the pace of product cycles is collapsing and what that means for value creation13:25 — Inside Landbase's “AI Factory” model for automating workflows across functions16:39 — What people actually do with the time they reclaim through AI-driven automation19:23 — How AI is reshaping the role of the salesperson and why adoption speed mattersA line that stood out“You don't have to work for your software anymore—your software works for you.”Call to ActionIf this conversation gave you fresh ideas about how AI is reshaping business, share it with your team and subscribe to The Tech Trek on Apple Podcasts or Spotify. For more insights, follow along on LinkedIn.

Matt McLarty, CTO at Boomi, joins the show to break down what enterprise AI adoption really looks like in 2025. From navigating the hype cycle to identifying practical first steps, Matt shares what separates companies that are seeing value from those stuck in endless pilots. If you're a tech leader wondering how to move beyond experimentation and into measurable outcomes, this episode is your playbook.Key Takeaways• AI adoption is not binary—it's a spectrum, and success depends on linking it to business value, not just “using AI.”• Orientation matters: every company needs an honest assessment of where they are on their digital maturity curve before jumping in.• Small, low-risk bets build the organizational muscle memory required for bigger wins.• The fastest wins often come from augmenting existing automation rather than chasing moonshots.• Companies that succeed treat AI as a tool to solve business problems, not as an end goal.Timestamped Highlights01:38 – Why AI's hype cycle feels like “Mount Everest” compared to cloud and mobile04:50 – Why AI adoption can't be compared to past waves like blockchain or cloud07:36 – The hidden foundation: digital transformation work still matters11:11 – The inversion that changes everything: AI isn't the goal, business outcomes are16:26 – Defining “adoption” as a multi-dimensional spectrum, not a checkbox19:50 – How to recover if your first AI projects fall short28:04 – Building adaptability as a core enterprise competency31:25 – The common traits of companies succeeding with AI right nowA standout moment“AI isn't the end goal—it's just another tool. The real question is, what business problems can we finally solve with it?” – Matt McLartyCall to actionIf this episode gave you a clearer path toward enterprise AI adoption, share it with a colleague and follow the show so you never miss a conversation on where tech leadership is heading.

Vipin Kumar, Head of CUSO IB Data Strategy and Analytics at Deutsche Bank, joins me to unpack one of the toughest problems in financial services: managing data quality in a highly regulated industry. From the outside, it might look like a box-checking exercise. In reality, it's a complex mix of legacy systems, global frameworks, regulatory controls, and the constant push to balance defensive compliance with offensive business value. Vipin makes it real with examples that connect directly to how we all experience data in daily life.Key TakeawaysData quality isn't just about accuracy—timeliness, completeness, and consistency all matter, especially when billions are on the line.Regulations push banks into “defensive” strategies, but there's growing opportunity to apply “offensive” strategies that use data for prediction, analytics, and competitive edge.Measuring effectiveness requires agreement between data producers and consumers, with preventive and detective controls working together.AI and machine learning are starting to automate checks, spot patterns, and even strengthen anti-money laundering defenses.Timestamped Highlights00:45 What data quality means in a regulated industry03:15 The challenges of managing fragmented legacy systems06:40 How producers and consumers measure effectiveness of frameworks09:30 The pizza delivery analogy for making sense of data quality14:20 Why accuracy is harder than timeliness or completeness16:50 The role of AI and machine learning in improving governance19:20 Shifting from defensive compliance to offensive strategy in banking22:40 Regulators testing AI-driven approaches to anti-money launderingMemorable Quote“Producer has preventive controls. Consumer has detective controls. True data quality happens only when both align 100%.” — Vipin KumarCall to ActionIf you enjoyed this conversation, share it with a colleague who thinks about data quality or governance. Don't forget to follow the show on Apple Podcasts or Spotify so you never miss an episode.

Marty Ringlein, co-founder and CEO of Agree.com, joins Amir to unpack why history always repeats itself in technology and what that means for the AI era. From the telephone to the automobile to ChatGPT, the biggest shifts have rarely been things people asked for—they were inventions that reshaped behavior once adopted. Marty explains why skepticism always comes first, how fear fuels resistance, and why optimism is usually rewarded. He also shares how Agree.com is rethinking contracts and payments by automating the painful parts of sales workflows.Key TakeawaysThe most transformative inventions weren't requested—they emerged through evolution and network effects.Human resistance to new tech often comes from energy costs of relearning, not the tech itself.AI isn't eliminating jobs—it's freeing people from low-value work so they can focus on bigger challenges.Every wave of disruption (printing press, cars, internet, mobile, AI) begins with fear, then proves to be a net positive.Timestamped Highlights00:51 — Why Agree.com calls itself “a better DocuSign” and how it integrates signatures, invoicing, and payments02:06 — The history of inventions nobody asked for and why they stuck05:41 — Human pessimism vs optimism when confronting new technologies09:05 — Why fears around AI echo the same debates once had about books, cars, and the cloud13:38 — How automation frees salespeople and engineers to focus on higher-value work18:51 — Are there technologies that have been net negative for society? Marty's take23:21 — Why every generation thinks “this time it's different”Memorable Quote“The biggest things that will change our lives are the ones we don't even know to ask for yet.” — Marty RingleinCall to ActionIf you enjoyed this episode, share it with a colleague who's navigating the AI conversation. Follow The Tech Trek for more conversations that cut through the noise on tech, leadership, and the future of work.

Simon Lam, VP of Engineering at M1, joins the show to unpack one of the trickiest topics in tech careers: how engineers can build influence without a formal leadership title. Too often, influence is mistaken for charisma or public speaking—but Simon explains why it's really about consistent impact, trust, and understanding how change happens inside teams. If you're an IC who feels stuck at the “senior wall” or a manager wondering how to better evaluate career growth, this conversation delivers clarity and actionable insight.Key Takeaways• Influence isn't charisma—it's the result of consistent impact and trust over time• Engineers can build influence at any stage, from junior to staff, by solving problems and being reliable• Career progression should tie back to impact, not just who speaks the loudest in the room• Change management offers a practical lens for understanding influence in technical settings• Dual career tracks mean engineers don't need to move into people management to keep advancingTimestamped Highlights01:39 Why influence is often misunderstood in engineering careers05:12 Influence vs charisma—and why you don't need to be an extrovert08:47 The virtuous cycle of impact leading to influence13:20 Are companies biased toward rewarding outspoken engineers?17:21 Practical ways ICs can start building impact today22:48 Why you don't need to manage people to have a leadership careerA line worth remembering“Consistent impact is how you build influence.” — Simon LamCall to ActionIf this episode sparked new ways to think about your own career, share it with a teammate who's navigating the same questions. Follow the show for more conversations with leaders shaping the future of engineering.

CJ King, CTO at Torc Robotics, joins the show to talk about the future of autonomous trucking at scale. Instead of asking “can we build one self-driving truck?” Torc is asking, “how do we safely put 10,000 on the road?” From supply chain transformation to regulatory hurdles, CJ breaks down what it really takes to bring production-ready autonomous semis into the market and why the ripple effects will reach far beyond trucking.Key Takeaways• Scaling autonomous vehicles isn't about prototypes—it's about building production-ready systems from the ground up.• Trucks face unique technical challenges, from 1,000-meter perception needs to fully redundant systems that can't rely on cloud compute.• Removing driver limitations could extend operations from 8 hours a day to 20, unlocking major gains in supply chain efficiency.• Regulatory collaboration is critical—success depends on alignment with federal and state agencies, law enforcement, and logistics partners.• Adoption will come in step-functions: once proven safe and reliable, logistics companies are ready to adopt at scale.Timestamped Highlights00:45 – Torc's focus on hub-to-hub autonomous trucking02:03 – Why scaling to thousands of trucks matters more than building one prototype06:48 – The unique technical problems of trucks vs. passenger cars09:25 – How extended operating hours reshape logistics and supply chains14:17 – Working with regulators and law enforcement to ensure safety and compliance17:42 – AV3.0, synthetic data, and billions of miles of training for safer systems22:31 – Building public trust and societal acceptance of autonomous trucking25:21 – Why large-scale adoption will happen in step functions, not tricklesA Line That Stuck With Us“Our bare minimum is to drive as good as a human—our mission is to be safer than one.” – CJ KingCall to ActionIf you enjoyed this episode, share it with someone who cares about the future of tech and logistics. Make sure to follow the show so you never miss conversations that dig into how technology is reshaping our world.

Joseph Krause, co-founder and CEO of Radical AI, joins the show to break down how scientific discovery is being reinvented. From the limitations of the traditional trial-and-error model to the rise of AI-driven self-driving labs, Joseph explains how science is moving from slow, serial processes to a parallel model that unlocks breakthroughs at scale. He also dives into the economics of materials, why big companies can't pivot fast enough, and how the role of scientists is being transformed.Key TakeawaysThe old model of science is serial: slow, linear, and limited by human capacity to read, experiment, and analyze.Negative results—failed experiments—are the true fuel for breakthroughs, but they're rarely captured or shared.Self-driving labs powered by AI create a “materials flywheel,” running 30,000+ experiments a year and learning continuously.Big corporations are trapped by the innovator's dilemma and talent challenges, leaving space for startups to lead.Scientists in the future will focus less on repetitive lab work and more on shaping hypotheses and applying intuition at scale.Timestamped Highlights02:00 How science traditionally works and why it's so slow05:50 Why mistakes and negative results matter more than we admit09:40 The fragmentation of research and why labs don't share data17:15 Inside a self-driving lab and how AI accelerates discovery23:40 Why big material companies can't innovate like startups35:40 The new role of scientists in an AI-powered discovery worldMemorable Line“You don't get a PhD to learn to pipette—you get it to think about how and why the world will change.”Call to ActionIf you enjoyed this conversation, share it with a colleague who geeks out on science and technology. Follow the show on Apple Podcasts or Spotify so you don't miss future episodes exploring where tech is headed next.

John Fiedler, SVP of Engineering and CISO at Ironclad, joins the show to unpack the real challenges of technology leadership. From managing nonstop context switching to measuring success when you're no longer shipping code, John shares hard-earned lessons on how leaders can protect their time, set priorities, and thrive in the chaos. Whether you're moving from IC to manager or scaling as an executive, this conversation offers a candid look at what it truly takes to lead.Key Takeaways• Success in leadership isn't about features shipped—it's about execution, people, and culture.• Context switching is constant, but leaders can design their calendars to minimize the chaos.• Organizational size reshapes the challenge: startups reward speed, enterprises demand process.• Protecting your time isn't optional—leaders who don't own their calendars quickly burn out.• The leap from IC to manager requires starting fresh and mastering a new craft.Timestamped Highlights02:13 The hidden tax of context switching06:53 How John measures success as a leader without code10:45 What really slows executives down inside organizations15:51 How John protects his calendar and finds focus time24:47 The lessons every first-time manager needs to hearA Line That Sticks“If you don't control your calendar, your calendar will control you.”Call to ActionIf this episode resonated, share it with a fellow leader navigating the chaos. Subscribe to The Tech Trek on Apple Podcasts and Spotify for more candid conversations about scaling, leadership, and the future of technology.

Alex Salazar, co-founder and CEO of Arcade.dev, joins the show to unpack the realities of building enterprise agents. Conceptually simple but technically hard, agents are reshaping how companies think about workflow automation, security, and human-in-the-loop design. Alex shares why moving from proof-of-concept to production is so challenging, what playbooks actually work, and how enterprises can avoid wasting time and money as this technology accelerates faster than any previous wave.Key TakeawaysEnterprise agents aren't chatbots—they're workflow systems that can take secure, authorized actions.The real challenge isn't just building demos but getting to production-grade consistency and accuracy.Mid-market companies face the steepest climb: limited budgets, limited ML expertise, but the same competitive pressure.Success starts with finding low-risk, high-impact opportunities and narrowing scope as much as possible.Authorization is the biggest blocker today; delegated OAuth models are key to unlocking real agent functionality.Timestamped Highlights02:02 — Why agents are “just advanced workflow software” but harder to trust than traditional apps04:53 — The gap between glorified chatbots and real enterprise agents that take action09:58 — From cloud mistrust to wire transfers: how comfort with automation evolves14:00 — Chaos at every tier: startups, enterprises, and why the mid-market struggles most26:21 — The playbook: how to pick use cases, narrow scope, and carry pilots all the way to prod34:38 — Breaking down agent authorization and why most RAG systems fail in practice42:09 — Adoption at double speed: what makes this AI wave different from internet and cloudA Thought That Stuck“An agent isn't an agent until it can take action. If all it does is talk, it's just a chatbot.” — Alex SalazarCall to ActionIf this episode gave you a clearer lens on enterprise agents, share it with a colleague who needs to hear it. And don't miss future conversations—follow The Tech Trek on Apple Podcasts, Spotify, or wherever you listen.

Russ d'Sa, founder and CEO of LiveKit, joins the show to unpack the rise of voice AI and what it means for how we interact with technology. From the shift away from static decision trees to dynamic, LLM-powered systems, Russ explains why voice is emerging as one of the most natural interfaces for humans—and one of the most disruptive opportunities for builders. This episode goes beyond surface-level hype to explore real-world use cases, infrastructure shifts, and what's coming next as voice moves from novelty to mainstream.Key Takeaways• Voice AI has moved far beyond Siri and Alexa—LLMs enable open-ended, natural conversations without rigid decision trees.• Two main categories are emerging: open-ended voice experiences (like tutoring and therapy apps) and goal-oriented workflows (like healthcare intake, finance, and customer support).• The biggest barrier isn't just technology, but adoption behavior—older generations default to typing and screens, while younger users and voice-first cultures are accelerating change.• Infrastructure for voice and video AI requires a fundamental shift from stateless web servers to stateful, long-lived conversational systems.• The hardest technical challenge ahead: mastering conversational turn-taking so AI can interact as naturally as a human.Timestamped Highlights01:06 How LiveKit is giving applications the ability to see, hear, and speak04:18 The two main categories of voice AI use cases emerging right now09:53 Why adoption of voice AI depends as much on behavior as on technology14:20 Imagining a 24/7 voice-driven AI that replaces screens and UIs20:30 Why the internet's original infrastructure wasn't built for voice and video AI25:39 The challenge of memory, authentication, and group dynamics in AI conversationsA line worth remembering“If you have a computer that perfectly understands when to speak, when to listen, and adds value in the right moments—why would you ever use anything else?”Call to ActionIf you enjoyed this conversation, share it with a colleague who's curious about where AI is headed. Subscribe on Apple Podcasts or Spotify so you don't miss future episodes diving into the technologies shaping the next decade.

Sumit Arora, VP of Advanced Technology at Ascend Learning, joins the show to unpack the real challenges of turning AI prototypes into production-ready systems. From managing non-deterministic outputs to rethinking the relationship between engineering and product, Sumit shares hard-earned lessons on what it actually takes to build AI that works at scale. If you're navigating how to move beyond experiments and deliver AI products that stick, this episode will give you a clear look at the path forward.Key Takeaways• Scaling AI is not about building smarter prototypes—it's about mastering distributed systems, security, and availability.• The best AI teams combine deep systems engineering with practical product sense.• Traditional software requirements processes won't work for AI. Co-creation between product and engineering is essential.• Innovation pods—small, cross-functional teams—can accelerate experimentation without killing momentum.• Success at scale comes from modular, reusable AI systems that can plug into multiple contexts.Timestamped Highlights02:14 — Why building a working AI demo is easy, but scaling it into a reliable product is hard04:49 — Lessons from the big data revolution and how AI is moving even faster08:41 — The skill sets AI teams really need and why distributed systems expertise trumps pure ML13:13 — Designing user experiences for AI and why response times redefine UX expectations17:00 — The evolving relationship between product and engineering in the AI era23:10 — How innovation pods help organizations experiment without stalling production teams26:47 — Why modular, self-contained AI systems are the key to scaling across an enterpriseA Line That Stuck“You can't requirement doc your way to AI success. Product and engineering have to co-create and move fast.”Call to ActionIf you found this conversation useful, share it with a colleague, subscribe to the show, and leave a quick rating—it helps us bring more tech leaders and practitioners to the table.

Sean McCarthy, co-founder and CEO of BackOps, shares how a career in sales prepared him to build an AI-driven logistics company from the ground up. In this episode, Sean reveals how observing real-world pain points at Amazon inspired BackOps' mission and why coming from a non-technical background can actually be a founder's advantage. This is a conversation about scaling, selling, and leading with insight — perfect for anyone thinking about making the leap from operator to founder.Key TakeawaysWhy non-technical founders are uniquely positioned to solve operational problems with AIThe mindset shift required to go from running sales to running an entire companyHow to validate an idea before leaving a stable, well-paying jobWhat it really takes to hand off sales when it's been your superpowerPricing insights that help ensure you're building a scalable businessTimestamped Highlights01:45 Sean's Amazon journey and what time spent in warehouses taught him about customer pain points04:14 The moment he saw the same issues plaguing both small and nine-figure sellers — and spotted an opportunity07:37 How becoming a CEO forced him to rewire his focus beyond sales and build internal infrastructure12:18 Why having a technical co-founder was non-negotiable — and how AI tooling is changing that equation15:18 The tough decision to leave Amazon and how he measured risk versus regret17:59 Learning to let go and trust others with the sales process while still staying close to customersMemorable Moment“Talk to the people that would actually buy your product. Measure the pain point. If it's a one or two out of ten, it's probably not worth building. If it's a nine or ten, and they'll pay for it, now you have something.”Pro TipsValidate early and price with intention. Don't just ask if someone would use your product — ask exactly what they'd pay for it. Those conversations can save months of wasted build time.Call to ActionIf this episode resonated, share it with a friend who's considering the leap into entrepreneurship. Follow the show for more conversations with founders, operators, and tech leaders building the next generation of companies.

Dmitri Sedov, Chief Data and Analytics Officer at Allvue, joins to explore why transformation isn't a destination but an ongoing journey. He shares how financial services are navigating the current wave of AI, what it means to balance short-term expectations with long-term strategy, and why open, modular ecosystems are critical to staying competitive. This conversation goes beyond buzzwords to uncover how leaders can embrace change without getting lost in it.Key Takeaways• Transformation isn't about a single endpoint—it's about staying curious, iterative, and open to multiple paths.• AI is accelerating faster than previous technology cycles, but its value lies in solving real customer problems, not chasing hype.• Companies that lagged in past waves of innovation may now have an opportunity to leapfrog forward.• The smartest moves aren't “rip and replace” but incremental improvements that build on existing engines.• Open, interoperable, and modular approaches reduce risk and keep technology flexible for the future.Timestamped Highlights01:26 – Why digital transformation is less about checking boxes and more about a mindset shift09:47 – The balancing act of managing short-term ROI while exploring long-term AI potential15:25 – A 24-hour hackathon prototype that changed how Dmitri thinks about multi-agent AI22:34 – Why interoperability and modularity matter more than big monolithic solutions27:43 – How to plan roadmaps when technology outpaces predictability31:43 – The future of “human in the loop” and what workforce transformation might look likeA line that stuck“Transformation is never about reaching one destination. The more you fixate on a single path, the more likely you'll be left behind.”Call to ActionIf this episode gave you new perspective on AI and transformation, share it with a colleague who's wrestling with the same challenges. And don't forget to follow the show so you're ready for the next conversation on building smarter, more resilient companies.

What if the key to real work-life balance isn't about escaping work, but transforming it into your outlet for curiosity and passion? In this episode, Hassaan Raza, co-founder and CEO of Tavus, explores how technology is removing friction in our daily lives and why teaching machines to be more human could unlock new levels of creativity, accessibility, and balance. From redefining what “work you love” actually means to the role AI will play in democratizing opportunity, this conversation challenges assumptions and paints a picture of a future where tech becomes a true partner to people.Key TakeawaysWork-life balance may not come from hobbies outside work, but from making work itself a fulfilling outlet.Technology's real value lies in removing friction and making tools more accessible to everyone, not just the highly technical.AI and human-computing advances could act as equalizers, giving people with different learning styles or backgrounds the same opportunities.Hollywood dystopias aside, machines can be designed to replace bad tools, not people—and that creates empowerment, not fear.The future of tech is less about replacement and more about enhancing human creativity, productivity, and quality of life.Timestamped Highlights00:33 — Hassaan explains Tavus' mission to teach machines how to be human.04:05 — Why some engineers care more about solving puzzles than the outcomes.07:08 — How passion-driven work blurs the line between career and hobby.12:12 — The obsession with removing friction and making machines easier to use.16:37 — Addressing fears about AI taking jobs and reframing the conversation.20:57 — How AI can open doors for non-traditional learners and democratize education.A Line That Stands Out“If you find work that you really love and are passionate about, that can be your outlet—you don't need balance because the work itself becomes your balance.”Call to ActionIf this episode gave you a fresh perspective on work, technology, and balance, share it with someone who's wrestling with the same questions. And don't forget to follow the show so you never miss the next conversation.

Dane Atkinson, CEO and founder of Odeko, joins the show to unpack the reality of evolving as a founder. He shares why the first idea you start with rarely survives, how to know when it's time to pivot, and why anchoring on a mission instead of a product keeps you in the game. This conversation dives into frameworks for making hard calls, the messy middle of startup life, and what it really takes to endure as a multi-time founder.Key Takeaways• Your first idea probably won't be the one that works—focus on the customer and the mission, not the concept.• Pivoting is brutal but necessary; small experiments can create the proof you need to shift direction.• Founders who learn from failure are more likely to succeed in their second or third ventures.• Having a North Star rooted in mission makes the day-to-day grind and tough decisions bearable.• The best outcomes come when investors give founders space to experiment and even fail.Timestamped Highlights00:43 – Why Odeko's mission is to help small coffee shops compete with giants01:44 – The flawed brilliance of Odeko's first AI-driven product and the hard pivot that followed05:28 – The painful trap of chasing product-market fit and the danger of sticking too long10:24 – Building proof for a pivot and the difference between charisma-driven sales and true demand14:04 – Why most successful founders are “multi-run players” and what VCs often miss about failure17:02 – How staying mission-driven keeps founders motivated through setbacksA line worth remembering“You can change the product, you can change the delivery, but if you have a North Star that matters, you'll always know how to steer the company back on track.”Founder TipTest new directions quietly alongside your current model. Early prototypes not only prove viability but also help you win over skeptical teammates, boards, and investors.Call to ActionIf this episode gave you something to think about, share it with a fellow founder or operator who's in the middle of their own evolution. And don't forget to follow The Tech Trek so you never miss the next conversation on scaling, leadership, and building companies that last.

What does a day in the life of a startup CTO really look like? Mo El Mahallawy, CTO and co-founder of Shepherd, shares the unfiltered journey from being engineer number one to leading a 50-person Series A company. He opens up about the hardest phases of building, what shifts as your company scales, and how he manages energy, priorities, and mental health along the way. This episode is a practical playbook for any founder, CTO, or tech leader navigating growth.Key Takeaways• The early days as a startup CTO are often the hardest—you're coding, recruiting, managing, and wearing every hat at once.• Growth means trading code for vision: shifting from building features to setting direction and enabling your team.• Time management is only half the battle—energy management and knowing when you do your best work is equally critical.• Planning is a mental health strategy: when you control your roadmap, you avoid the burnout of constant reaction mode.• Taking big swings matters more than just paying down tech debt—bets move the business forward.Timestamped Highlights02:41 – The origins of Shepherd and why Mo left Airbnb to build in insurance tech.07:28 – What makes the early-stage CTO role one of the toughest in startups.10:44 – Mo's brutally honest description of those days: “like chewing glass.”16:45 – How the CTO role evolves post-Series A and the challenges of stepping out of code.20:44 – Why energy balance beats pure time management.26:42 – Mo's take on mental health and how planning became his best defense against fatigue.A line worth remembering“Your life is going to suck, and then it's going to be great—but your job is to make this work.”Pro Tips• Use your strongest energy hours for high-leverage work, not busywork.• Build your network early at iconic companies—you'll rely on those relationships later.• Don't shy away from big bets; they create momentum that tech debt never will.Call to ActionIf you found Mo's story valuable, share this episode with someone thinking about becoming a founder or CTO. And don't forget to follow the show on your favorite podcast app so you never miss the next set of scaling playbooks.

What does it take to build a startup in a space where the ground shifts every 90 days? Rahul Sonwalkar, founder and CEO of Julius AI, joins the show to share how he navigates product market fit, resource constraints, and constant model evolution while scaling an AI-first company. Instead of relying on long-term roadmaps, Rahul runs Julius with rapid feedback loops, deep user focus, and a mindset that every team member contributes to AI development. This conversation is a playbook for founders and operators facing uncertainty and speed in equal measure.Key Takeaways• Why Rahul believes rigid long-term roadmaps can hold founders back in AI and fast-moving markets• How limited resources force sharper prioritization, and how to decide what makes the cut• The two-way product market fit Julius found by serving both data teams and business stakeholders• Why direct user conversations and dogfooding are non-negotiable for early-stage companies• A glimpse into the future of BI as AI agents that monitor and surface key metrics automaticallyTimestamped Highlights00:43 – Julius AI's origin story and how Rahul validated product market fit05:08 – The surprising way both data teams and business users bring Julius into organizations09:24 – Why Rahul avoids long-term roadmaps and favors month-to-month iteration13:55 – The role of feedback loops and customer support in shaping product direction17:27 – How to prioritize when you only have resources for two out of ten critical features19:39 – Rahul's vision for how BI and analytics will evolve with AI agentsA Standout Line“Overnight, things you thought were impossible for the next 12 months can suddenly become possible. That's why you can't have a rigid roadmap when building with AI.”Founder's LessonUse your own product daily. When the entire team feels the same friction as your users, the right priorities become obvious.Call to ActionIf you're a founder, operator, or investor looking for practical lessons on building in uncertain terrain, follow the show for more conversations like this. And if Rahul's approach resonated, connect with him on LinkedIn or explore Julius AI.

Matt Lynch, CTO and co-founder of Sage, joins the show to share how his team is using modern technology to transform elderly care. From rethinking outdated nurse call systems to capturing real-time data that improves both patient safety and caregiver retention, Matt breaks down the technical and human challenges of modernizing this critical industry. This is a story of mission-driven innovation, scale, and the surprising role data plays in quality of life.Key Takeaways• The aging population is outpacing caregiver availability, creating an urgent need for tech-driven solutions.• Most facilities still rely on fragmented or low-quality data, making it difficult to improve care.• Real-time documentation and streamlined workflows give caregivers credit for their work and reduce burnout.• New technology can deliver results at a fraction of the cost of outdated systems.• Adoption succeeds when tech is simple, resident-driven, and seamlessly fits into caregiver routines.Timestamped Highlights00:34 — Why Sage was founded: the gaps in elderly care tech that inspired three co-founders04:36 — The looming scale problem: a 40% surge in the aging population with flat caregiver growth06:56 — How poor data practices erode care quality—and what synchronous documentation changes12:05 — Why old systems cost 10x more and how modern tools flip the economics18:06 — Giving caregivers proof of their work and reducing turnover with better data21:54 — Lessons in building: why focusing on workflow software beat hardware reinventionA line worth remembering“Caregivers finally get credit for the work they're doing—and that completely changes how families and facilities work together.”Pro TipStart simple. In elderly care tech, the real breakthrough wasn't reinventing hardware but streamlining the caregiver workflow to make adoption natural.Stay ConnectedIf this episode resonated, share it with a friend in tech or healthcare. Don't forget to follow the show on Apple Podcasts and Spotify so you never miss new conversations with the builders shaping our future.

What does it really mean to build in stealth—and when does it help or hurt? In this episode, Amir sits down with Yoni Michael, co-founder of Typedef, an AI infrastructure startup that recently came out of stealth. Yoni shares why his team chose to stay under the radar early on, how they balanced secrecy with customer discovery, and the lessons they learned about finding product-market fit in a noisy AI landscape. If you're a founder or tech leader navigating early-stage strategy, this conversation offers practical insights you can apply right away.Key Takeaways• Stealth mode isn't all or nothing—there's a spectrum between total secrecy and open visibility• Execution and speed of iteration matter more than protecting “the idea”• Customer discovery should start before you even write code• Messaging is never final—test, refine, and keep adjusting as you learn from design partners• Investors expect shifts at the seed stage, but keeping them in the loop builds trustTimestamped Highlights00:38 — Why Typedef chose to launch in stealth and what they're building in AI infrastructure04:31 — The double-edged sword of operating in a crowded AI market09:22 — How Yoni approaches customer discovery without giving away too much13:55 — Shaping messaging and narrative before coming out of stealth19:49 — Managing investor expectations when your product vision evolves25:21 — How to connect with Yoni for advice and community in AI infraA line that stuck with us“Your competitive edge isn't the idea—it's the ability to execute and course correct fast enough to hit your runway.”Resources mentioned• Typedef: typedef.ai• FENEC open-source framework: [GitHub link from Typedef site]• Yoni Michael on LinkedIn: linkedin.com/in/yonimichaelPro TipsYoni advises founders to test messaging as early as possible—whether through decks, demo sandboxes, or LinkedIn posts. The feedback loop is as valuable as product feedback.Stay connectedIf this episode gave you something to think about, share it with a founder or tech leader who'd benefit. And don't forget to follow The Tech Trek on Apple Podcasts, Spotify, or YouTube so you never miss future conversations.

Simone Kalmakis, VP of Engineering at Viam, joins the show to share what it takes to lead teams that bridge hardware and software in the robotics space. With a background in software and machine learning, she now manages engineers with expertise ranging from code to mechanical systems. Simone talks about how she fosters collaboration across disciplines, adapts her leadership style, and keeps her teams aligned while building cutting-edge robotics solutions. If you want to understand what it's really like to lead at the intersection of AI, software, and hardware, this conversation is for you.Key Takeaways• Why managing hardware and software together requires humility, curiosity, and alignment• How integrating testing across both domains changes the development process• The role of “20% projects” in helping engineers learn and innovate faster• Why passion and purpose matter more than hours worked when it comes to work-life balance• Why strong engineers without PhDs can still excel in robotics and AITimestamped Highlights01:05 — How Viam bridges the gap between physical devices and AI04:04 — The challenges of testing hardware versus software, and why small changes have big consequences10:57 — How hobbyist projects and 20% time accelerated Simone's growth as a leader in robotics14:09 — What one-on-ones look like when leading both software and mechanical engineers16:14 — Attracting and retaining top robotics and AI talent in a competitive market22:54 — How passion and vision drive sustainable work-life balance in high-pressure environmentsA Line That Stuck With Us“Even the slightest change in hardware can have huge ramifications, and it's humbling to lead people whose expertise far outweighs your own.”Call to ActionIf you enjoyed this episode, share it with a colleague who's curious about robotics or leadership at the hardware-software frontier. And don't forget to follow the show so you never miss insights from top tech leaders.

What does it really mean to be data-driven? Mark Gergess, VP of Data and BI at DoubleVerify, joins the show to unpack how data teams can go beyond dashboards to drive meaningful business action. From building an internal consulting lens to evaluating the latest AI tools, Mark shares how his team translates complex data flows into measurable revenue impact. If you've ever wrestled with the gap between insights and outcomes, this conversation will hit home.Key Takeaways• Being data-driven is about driving action, not just reporting numbers• Stakeholders don't care about your data problems—they care about business outcomes• The biggest challenge with AI adoption isn't the model, it's the use cases• Efficiency gains from AI should shift focus from ETL tasks to solving real business problems• Data culture health is measured by how naturally teams rely on data day-to-dayTimestamped Highlights01:17 How DoubleVerify helps advertisers build safer, more effective digital campaigns04:55 Why the definition of “data-driven” still varies and why it matters09:25 Measuring whether data efforts are moving the needle on revenue13:15 How to separate hype from value when evaluating AI and GenAI tools17:10 Lessons from the data science boom and why companies must go “all in” with AI25:31 Can AI act as your junior analyst? Where efficiency gains really show up27:01 How freeing up time changes the structure of data teams and boosts business impactA thought worth holding onto“It's not about dashboards. It's not about reporting. It's about doing something with the information.”Pro TipsMark recommends treating AI as a “junior analyst”—let it handle quick, lower-priority questions so your team can focus on bigger business challenges.Call to ActionEnjoyed the conversation? Share this episode with a colleague who talks about being “data-driven.” Subscribe on your favorite podcast platform and connect with me on LinkedIn for more insights from leaders shaping the future of data and technology.

Teddy Solomon, co-founder and CEO of Fizz, joins the show to share his journey from Stanford dropout to leading one of the fastest-growing Gen Z social platforms. In this conversation, Teddy breaks down what it's like to learn leadership on the fly, the misconceptions around Gen Z founders, and how he's scaling a product used by 95% of students at some of the top universities in the country. His story is a rare look at what it takes to build, adapt, and lead with composure at a young age.Key Takeaways• How Teddy turned a pandemic side project into the leading college social platform in the U.S.• Why staying “high with the lows and low with the highs” is his guiding principle for leadership and culture• The role of mentorship and humility in navigating early CEO decisions• How to earn respect and lead teams older and more experienced than you• Why real-world interaction still matters in an era dominated by digital connectionsTimestamped Highlights[01:00] Teddy explains how Fizz became the go-to social app for U.S. college campuses[02:45] The bold decision to drop out of Stanford after meeting a key mentor and investor[09:15] Why bringing in an external CEO was the best move for the company's survival[12:30] Lessons on leadership: performing your best in the worst moments[15:59] The reality of leading teams as a young founder[18:45] Challenging Gen Z stereotypes through action and connectionMemorable Line“You need to stay high with the lows and low with the highs. If you can perform at your best when things are at their worst, you give your company the best chance to survive.”Pro TipsFocus on results and culture. Age, background, and pedigree matter far less than the ability to create a strong team environment and deliver outcomes.Call to ActionIf you enjoyed this episode, share it with someone who's passionate about leadership and startups. Follow the show for more conversations with founders shaping the future of tech, and connect with us on LinkedIn for behind-the-scenes insights.

Desiree Lee, one of the Business CTOs at Armis, joins Amir to unpack one of the toughest realities in cybersecurity: the biggest risks aren't always technical, they're human. From phishing and deepfakes to the way AI is reshaping both attackers' and defenders' playbooks, Desiree shares hard-won insights on what companies should actually prioritize. If you're a tech leader navigating the expanding attack surface, this episode will sharpen how you think about security in the AI era.Key Takeaways• Most breaches stem from human behavior, not lack of technology.• Attackers adopt AI faster because there's no downside for them—defenders must catch up.• Fundamentals like patching and asset inventory still make or break resilience.• AI can reduce noise for security teams by spotting patterns in overwhelming data.• Small and midsize businesses will benefit from AI-driven tools that lower the barrier to effective security.Timestamped Highlights00:34 — How Armis evolved from asset inventory to full security solutions03:30 — Why security failures are more about psychology than technology07:32 — The deepfake CFO story and why training alone can't solve phishing risks09:18 — Why most enterprises struggle with basics like patching and automation11:41 — Where AI gives defenders an edge in processing massive data sets18:33 — Practical ways AI can ease alert fatigue and vulnerability management21:03 — The overlooked need to label assets by business criticalityA Moment Worth Remembering“There is no penalty on the attacking side for embracing AI. It's only good things for them. So they will adopt it quickly.” — Desiree LeeResources MentionedArmis: armis.comDesiree Lee on LinkedIn: linkedin.com/in/desireedleePro TipsTagging assets with their business criticality is one of the simplest, highest-impact steps companies can take. It turns asset inventories from static lists into real decision-making tools for AI-driven defense.Call to ActionIf you found this episode valuable, share it with a colleague who's thinking about security and AI. Subscribe on your favorite podcast platform so you never miss future conversations with tech leaders pushing the edge of what's possible.

Chris Ghanbarzadeh, Senior Director of Engineering at Game Changer, joins the show to unpack the shifting landscape of mobile development. From balancing quality with speed to navigating the rise of AI-assisted engineering, Chris shares how teams can adapt to new tools, methodologies, and user expectations. If you're building mobile products—or leading teams that do—this conversation will sharpen how you think about stability, velocity, and the future of software development.Key Takeaways• Why stability is the true driver of long-term speed in mobile development• How shifts away from native development are reshaping efficiency and delivery• The role of AI toolchains in accelerating experimentation and engineering workflows• Why leaders should avoid top-down mandates when introducing AI and instead create space for experimentation• How AI may reshape software methodologies, from Scrum to requirements gathering and beyondTimestamped Highlights01:45 – The eternal challenge of balancing quality with velocity in mobile engineering06:42 – Why “stability is speed” and how tech debt slows teams down09:15 – The move away from native development and how it impacts engineering teams13:27 – Experimenting with AI toolchains and finding the right balance of autonomy and structure18:05 – Standardization vs freedom: the future of AI tools in the enterprise22:23 – Will AI change the very way we build and support software?A standout moment“Stability is speed. If you're constantly chasing velocity without shoring up quality, you end up slower in the long run.”Resources Mentioned• Game Changer app – scorekeeping and live streaming for youth sportsPro TipsGive your teams space to experiment with AI tools. Celebrate learning—even when experiments fail—so engineers build confidence and marketable skills for the future.Call to ActionEnjoyed this conversation? Follow the show for more insights from engineering and tech leaders, and share this episode with a colleague who's navigating mobile development or exploring AI in their workflows.

Moinul Khan, co-founder and CEO of Aurascape, joins the show to unpack what it takes to build a cybersecurity startup in the age of AI. With decades of experience at companies like Zscaler, Palo Alto Networks, and FireEye, Moinul shares why AI demands an entirely new security stack, how agentic AI is changing the game, and why prevention—not dashboards—must be at the heart of real solutions. If you're a tech leader navigating the future of AI and security, this is a conversation you won't want to miss.Key Takeaways• Traditional security stacks can't keep up with dynamic, evolving AI tools• Prevention-focused solutions matter more than dashboards or API visibility• Agentic AI is both an opportunity and a security challenge that startups must address• CISOs are rethinking consolidation and becoming more open to best-of-breed solutions in AI security• Building with a long-term prevention mindset creates stronger, more resilient startupsTimestamped Highlights00:37 — Aurascape's mission to deliver an all-encompassing AI security solution02:27 — The “aha” moment: why legacy firewalls and proxies can't secure AI08:23 — How Aurascape's vision has evolved from public AI tools to securing private and third-party applications13:17 — Agentic AI, MCP protocols, and why startups need to secure the next wave of AI agents16:44 — Best-of-breed vs consolidation: where the security market is really heading20:37 — Advice for founders: why prevention-first is the only real path to solving security problemsA standout moment“If you try to patch what you have built in the last 20 years, you will fail. If you want to secure AI, you have to build your entire stack from the ground up.” — Moinul KhanResources MentionedAurascape.aiPro TipDon't build for a quick exit. Focus on prevention, even if it's the harder road—it's what truly solves customer problems in cybersecurity.Call to ActionIf you enjoyed this episode, share it with someone exploring AI security. Subscribe or follow the show for more conversations with the builders shaping the future of tech.

Cat Miller, CTPO of Talkiatry, shares her unconventional path from coding to leadership, including a detour into acting before rising into executive roles. She talks candidly about the realities of building a tech career you actually want, navigating transitions from engineering to management, and what it takes to succeed at the VP and C-level. This conversation is packed with lessons for anyone in tech who's asking themselves, “What's next for me?”Key Takeaways• Career paths in tech don't have to be linear. Detours can provide perspective that makes you a stronger leader.• Early dissatisfaction with day-to-day coding doesn't mean you don't belong in technology—it often evolves into broader roles.• Doing your current job really well is often the fastest way to position yourself for growth when opportunity knocks.• Building a network of peers and mentors is essential when stepping into senior leadership.• Self-awareness and documenting your wins helps you stay grounded and measure progress at the executive level.Timestamped Highlights01:29 — Why coding wasn't fulfilling long-term and how Cat thought about her next move04:54 — Leaving a stable job to explore acting, and how planning made the risk manageable10:35 — Returning to tech and finding the “perfect fit” role that shifted her career trajectory16:35 — Rethinking work-life balance and how mission-driven work changes the equation19:17 — Lessons from moving from VP to C-suite and the role luck and preparation both play24:40 — Why building a strong peer network is critical once you reach the CTO level29:14 — Tracking wins and staying accountable for your own performance as a leaderMemorable Line“It just feels really gross to be bad at your job. So why wouldn't you always do your best to be good at it?”Pro TipsWrite down your team and personal wins each quarter. It helps you see progress that isn't always obvious in the day-to-day grind.Stay ConnectedIf this episode resonated with you, share it with someone who's navigating their own tech career path. Follow the show for more conversations with leaders who've carved their own way forward.

What does it take to reimagine how hardware products are built in a world moving at the speed of AI? Michael Corr, founder and CEO of Duro, shares how he turned two decades of experience in engineering and manufacturing into a modern platform that helps hardware teams move faster and smarter. From journaling early product ideas to navigating the relentless pace of innovation, Michael reveals what it really means to be a founder when the path is anything but straight.Key takeaways• Why traditional hardware manufacturing processes create hidden risks—and how software can solve them• The journaling habit that helped shape Duro's first product features• How to balance investor demands with long-term product vision• The danger of chasing every shiny object as a CEO and how to filter noise for your team• Why adaptability matters more than rigid 5-year plans in today's tech landscapeTimestamped highlights00:36 — How Duro is reinventing product lifecycle management for hardware teams05:39 — “If I were king for a day…” the origin story of Duro06:52 — The role of note-taking and journaling in building a company from scratch09:31 — Staying true to a mission while adapting to market and investor pressures14:54 — The trap of chasing every customer request and how to avoid burning out your team19:03 — Why looking beyond 18 months is mostly speculation in a fast-changing industryMemorable insight“All we can really focus on is the next 12 to 18 months—everything beyond that is just speculation.”Resources mentionedDuro website: getduro.comPro tipWhen you're leading a fast-moving company, not every customer request deserves a green light. The best founders know when to say no, even to a big check, to protect long-term focus.If you enjoyed this episode, follow the show for more conversations with tech leaders shaping the future of software, hardware, and everything in between.

Sean Neville, co-founder and CEO of Catena Labs (and co-founder of Circle), joins the show to explore the rise of AI economics and what it means for the future of payments, trust, and financial systems. From stable coins powering machine-to-machine transactions to identity layers for AI actors, Sean unpacks the building blocks that could reshape how value flows online. This episode is for anyone curious about the intersection of AI, crypto, and the next era of digital finance.Key Takeaways• AI actors are evolving into full economic participants, capable of executing payments and workflows.• Stablecoins provide a more efficient and borderless payment rail compared to legacy systems, especially for AI-driven transactions.• The biggest hurdle isn't technology—it's trust, identity, and accountability in agent-to-agent interactions.• B2B use cases are likely to adopt AI-powered payments faster than consumer markets due to inefficiencies in existing flows.• AI to human payouts and human to AI pay-ins will likely arrive before true AI-to-AI payment systems go mainstream.Timestamped Highlights00:33 — What Catena Labs is building: a regulated AI-native financial institution03:16 — Why the internet is becoming agent native and what that means for AI economics05:35 — The trust hurdle: how AI can move from 60% reliable to 99.9% through tuning and workflows08:38 — Why legacy payment rails aren't built for AI actors and how stablecoins change the game11:59 — The missing piece: agentic identity and why it matters for accountability15:48 — Could AI actors one day open bank accounts? Building toward semi-autonomous financial participation19:06 — Why B2B transactions will likely see AI payments before consumers do24:26 — Stablecoins vs. crypto: why digital dollars are the foundation for AI-native paymentsMemorable Line“If I can't trust a chatbot to get a chocolate cake recipe right, how can I trust it with my money? Yet at the same time, this is the worst it will ever be—it's only getting more capable at an unprecedented pace.” – Sean NevilleResources MentionedCatena Labs – catenalabs.comSean on X – @PSNevilleCall to ActionIf this conversation got you thinking about the future of AI and finance, share it with a colleague who's curious about the space. Don't forget to follow the show so you don't miss the next episode.

Sara Wyman, founder and CEO of Stackpack, joins me to share her journey from investment banking and a Wharton MBA to launching a company that's redefining how finance and operations teams manage vendors. From surviving the Bear Stearns collapse to scaling Etsy and Affirm through IPOs, Sara's career has been built on spotting patterns and acting with conviction. In this episode, she breaks down how she validated her idea with 75 CFOs before writing a line of code, why timing and conviction matter more than a perfect resume, and what it really takes to leave the safety of corporate life to build something of your own.Key Takeaways• Why solving a problem you've lived through yourself is the best foundation for a startup• How interviewing potential customers before building can double as both research and sales• Why founders should outsource what they're not great at instead of spinning wheels• The hidden advantage of years of work experience when stepping into a founder role• Why pace setting—not just hiring—is one of the founder's most critical responsibilitiesTimestamped Highlights00:39 — What Stackpack does and how it helps finance teams gain full visibility into spend and contracts02:10 — Lessons from investment banking, the Lululemon IPO, and the realization she wanted to be the CEO, not the banker04:30 — Spotting the problem of vendor chaos and validating it through 75+ CFO conversations07:12 — The leap from corporate security to founder risk and why timing mattered more than age12:47 — A different founder path: starting with customers and funding before building the team17:15 — Why the stereotype of the 24-year-old coder isn't the reality of most successful exits19:45 — Hard-earned lessons: outsource what you don't excel at and embrace the founder role as a pace setterA Standout Moment“If you're not awesome at something, outsource it or find the person that is. You don't get bonus points for struggling through work that isn't your strength.”Pro TipTalk to customers before you build. Sara's early interviews not only validated her idea but converted into her first paying design partners.Call to ActionIf Sara's journey resonated with you, share this episode with someone considering the founder path. Don't forget to follow the show on your favorite platform so you never miss stories like this one.

Troy Astorino, co-founder and CTO at PicnicHealth, joins Amir to unpack one of healthcare's most stubborn problems: fragmented medical records. Troy shares how Picnic Health is using AI to unify patient data, cut through friction, and improve both individual care and clinical research. This conversation dives into the technical, regulatory, and human sides of healthcare data—and why accuracy matters more than ever.Key Takeaways• Why interoperability in healthcare has failed despite billions invested• How AI transforms messy, inconsistent records into unified patient data• The critical role of low-friction design in patient adoption• Balancing accuracy, human oversight, and scalability in medical AI• What recent FDA guidance signals about the future of AI in healthcareTimestamped Highlights00:40 — How Picnic Health helps patients and researchers get all their records in one place05:16 — Why data portability across EMRs is still broken despite decades of effort09:40 — Friction as the biggest barrier to patient adoption (and why it matters for outcomes)10:42 — Inside Picnic's AI pipeline: from raw documents to unified patient profiles17:18 — Tackling accuracy: expert-level thresholds, guardrails, and continuous auditing24:47 — Why AI is judged against perfection while humans get a pass on errors29:45 — The FDA's evolving approach to regulating AI in healthcareA thought that stands out:“Having systems that don't just work in theory but actually work in practice—because they're low friction—is critical for real usage in healthcare.”Resources Mentioned• Picnic Health: https://picnichealth.com• FDA Draft Guidance on AI in Healthcare (2024)• HL7 standards overview (for context on interoperability)Pro Tips for Tech LeadersThink about adoption the way Picnic Health does: remove friction first. Even the most sophisticated AI solution fails if the user experience creates barriers. Start with the end user, not the system.Call to ActionIf you found this conversation valuable, share it with someone working in health tech or data science. Subscribe to The Tech Trek on Apple Podcasts and Spotify so you never miss new insights on where tech and leadership intersect.

Pritesh Patel, Director of AI at Fisher Phillips, joins The Tech Trek to unpack how AI is reshaping knowledge-based businesses and what that means for industries like law, consulting, and beyond. From shifting revenue models to practical adoption challenges, Pritesh shares how firms can embrace AI early, stay competitive, and unlock new opportunities. This episode is a roadmap for leaders who want to move from incremental efficiency to real transformation.Key Takeaways• AI is disrupting the traditional “revenue per person” model, pushing knowledge firms toward more outcome-driven approaches• Early adoption matters: experimenting now gives companies a competitive edge rather than playing catch-up later• Success in AI transformation starts with deeply understanding business outcomes, not just implementing new tools• Human expertise will remain essential, but AI will free professionals to focus on higher-level, creative problem-solving• Iteration speed is a critical advantage: nimble firms can innovate faster than larger, slower-moving competitorsTimestamped Highlights01:32 – Defining knowledge-based businesses and why AI is changing the game04:33 – How old business models are being disrupted by automation and new expectations08:55 – Translating technical expertise into outcomes that resonate with non-technical stakeholders14:23 – A framework for identifying high-impact opportunities before choosing a technology solution16:34 – Building an innovation engine through fast prototyping and iteration21:16 – The role of trust, validation, and regulation in the future of AI-powered knowledge workQuote of the Episode“You don't want to be in a situation where you're adapting late because of competition. If you start early, you can shape the future of your industry instead of reacting to it.” — Pritesh PatelPro Tips• Focus first on business outcomes, not technology. Identify the most impactful functions, then explore how AI can enhance them• Use prototyping to spark ideas and build momentum. A working demo creates buy-in faster than presentationsCall to ActionIf this conversation sparked ideas about how AI could reshape your business, share the episode with a colleague who would benefit. Subscribe to The Tech Trek for more conversations with leaders driving the future of technology, and connect with us on LinkedIn to continue the discussion.

Udhay Durai, Executive Director of Data Platform and Engineering at Evolus, joins the show to unpack his journey from consulting to leading enterprise data teams. He shares how the high-pressure, quick-delivery mindset from consulting can be a secret weapon in a corporate setting, and what changes when you shift from delivering outputs to owning long-term outcomes. From navigating different types of pressure to building sustainable systems that scale, Udhay offers candid insights for anyone considering a similar transition.Key Takeaways• The consulting mindset of speed and adaptability can be a major advantage in enterprise roles when paired with long-term thinking• Pressure exists in both consulting and full-time roles, but the nature of that pressure—and how you manage it—differs greatly• Consultants focus on outputs, while enterprise leaders are measured on outcomes that stand the test of time• Generalist experience across domains can complement deep subject matter experts in a corporate team• Bringing incremental change and a “flywheel” approach from consulting can accelerate enterprise delivery without sacrificing reliabilityTimestamped Highlights01:34 — Why quick wins and stakeholder empathy are essential in consulting03:28 — How the pressure changes when you own the platform instead of just delivering a project05:32 — Outputs vs outcomes and why the shift matters in enterprise leadership09:48 — Turning generalist consulting experience into an asset in a full-time role11:43 — The biggest mindset and skill gaps to address when making the switch13:42 — Adapting consulting habits for long-term success in product companiesQuote of the Episode“Pressure is there in both consulting and enterprise. The difference is in consulting you deliver outputs—enterprise leaders deliver outcomes.”Resources MentionedUdhay Durai on LinkedIn — https://www.linkedin.com/in/udhay-duraiCall to ActionIf this episode gave you new perspective on career transitions, share it with a colleague or friend who's considering a similar move. Follow the show for more real-world tech leadership conversations.

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

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

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

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

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

What does it take to lead analytics at a truly global scale? In this episode, Amir sits down with Anant Veeravalli, Global Chief Data and Analytics Officer at Media Brands (part of IPG), to unpack how he built and scaled a Center of Excellence (COE) that spans regions, brands, and disciplines. Anant shares the real-world challenges of aligning thousands of data professionals under one strategic vision—and why analytics is far more than just reporting.If you're leading data, analytics, or transformation work inside a large enterprise, this one is packed with battle-tested insight on structure, talent, AI adoption, and the real work of enabling data to drive business value.Key TakeawaysA COE must be built around client outcomes, organizational excellence, and scalable innovation—not just reporting structureTrue analytics value comes from harmonizing data, tools, and talent while making insights accessible and actionableA skills-first approach helps align talent to opportunity, enabling flexibility and specialization at scaleAI isn't just a buzzword—it's already reshaping content, audience segmentation, modeling, and competitive intelligenceCommunication during change is everything. Transparency, context, and repetition are essential to alignment and trustTimestamped Highlights03:00 — Why Media Brands needed a global Center of Excellence for analytics05:30 — How they approached organizational change and stakeholder education08:50 — Mapping the COE into four key capabilities: growth, audience analytics, data science, and data engineering11:50 — Delivering flexible analytics support across diverse clients and geographies15:45 — How AI is driving faster insights, better segmentation, and creative automation21:30 — The #1 thing Anant would do differently if starting over: communicate the why more consistently and directlyQuote of the Episode“We overly underestimate the value of transparent communication. If people don't understand why the change matters to them, they'll never be aligned.”Pro TipsInvest early in an internal asset library to avoid duplicated effort and unlock speedWhen hiring, prioritize specialization over generalization—then connect specialists across a shared frameworkDon't just train on AI tools. Raise the entire organization's AI literacyCall to ActionEnjoyed this episode? Follow The Tech Trek for more conversations with leaders building the future of tech, data, and innovation. Share this episode with someone navigating data transformation—or connect with Anant Veeravalli on LinkedIn to keep the conversation going.

What if your phone didn't need to hold your data at all? In this episode of The Tech Trek, Amir sits down with Jared Shepard, CEO of Hypori, to explore how virtualization at the edge is transforming security, mobility, and data ownership. Jared breaks down Hypori's secure virtual mobile OS, originally built for the Department of Defense, and how it's now entering the enterprise and consumer spaces. From eliminating mobile device management to protecting sensitive data from AI exposure, this conversation is a wake-up call for any tech leader thinking about security at the edge.Key Takeaways:Hypori's virtual mobile OS allows users to access enterprise data securely without storing it on their device.Virtualization collapses the attack surface by removing the edge device as a security risk.U.S. enterprises prioritize convenience and security, while Europe pushes privacy due to GDPR—Hypori bridges both.AI will soon enhance Hypori's platform through predictive resource allocation and network optimization.The military's extreme security standards helped Hypori harden its platform far beyond typical commercial use cases.Timestamped Highlights:01:30 — What Hypori is and how it turns any device into a secure, data-less terminal05:30 — Real-world BYOD use cases, from consultants to GDPR-compliant European enterprises11:20 — How virtualization changes the AI risk equation and protects enterprise data from agentic threats15:50 — Why cybersecurity should stop blaming users and start simplifying their responsibilities18:45 — How virtualization shrinks the attack surface and simplifies network defense22:59 — What it's like building for the Department of Defense and how that shaped Hypori's productQuote of the Episode:“Maybe it doesn't have to be a company's fight versus your fight for whose data belongs on your phone. What if we could just take that problem away?”Resources Mentioned:Hypori: www.hypori.comCall to Action:If this episode got you rethinking your mobile security strategy, share it with your team or your CIO. Subscribe to The Tech Trek for more conversations at the intersection of leadership, innovation, and real-world security.

Feross Aboukhadijeh, founder and CEO of Socket, joins The Tech Trek to pull back the curtain on software supply chain security, why legacy tools are failing, and what it really takes to build trust into modern development. Feross explains how Socket is tackling vulnerabilities most vendors can't even detect and shares why they made a rare early-stage acquisition—and how it's reshaping their roadmap.Whether you're an engineering leader, security pro, or founder eyeing M&A moves, this episode offers sharp insights into product strategy, AI implications, and the real work behind the scenes.Key Takeaways:Socket proactively secures the software supply chain by detecting malicious code injections and not just known vulnerabilitiesLegacy tools rely on outdated databases and can't keep up with real-time threats or malicious actorsThe explosion of AI-generated code is expanding the attack surface and introducing new vectors like “slop squatting”Socket's acquisition of Kawana was driven by tight product fit, culture alignment, and shared technical DNA—not just business rationaleReachability analysis reduced Socket's security alert noise by 80 percent, boosting signal and developer trustTimestamped Highlights:01:00 — What Socket actually does and why open source dependency risk is a blind spot for most companies06:40 — Why most tools in this space haven't solved the real security problem—and how Socket is different11:50 — AI's unexpected impact on software security and the rise of hallucinated packages16:30 — Behind Socket's acquisition of Kawana and how academic research drove product synergy22:58 — How integrating the acquisition is evolving Socket's roadmap and deepening its technical edge25:00 — What Feross learned from the legal side of M&A and how his past experience at Yahoo helped shape this oneQuote of the Episode:“We care way more about first-party code than third-party code, even though it all runs in one app. That has to change.”Resources Mentioned:Socket: https://socket.devCall to Action:Enjoyed the episode? Follow The Tech Trek to catch conversations with the builders shaping the future. And if you're deep in security or scaling a dev team, check out socket.dev or reach out to Feross directly—he's happy to share lessons learned.

What does it take to deliver innovation at just the right moment? In this episode of The Tech Trek, Amir sits down with Eric Hoffert, CTO at Kargo and former video leader at Apple and Spotify, to unpack the art and science of innovation timing. From building QuickTime at Apple to launching video at Spotify a decade before the market caught up, Eric shares stories that blend conviction, timing, and deep tech insight. This episode is a must-listen for anyone thinking about where AI, video, and advertising are headed—and how to lead through the chaos.Key Takeaways:Innovation is a blend of vision, timing, and execution—being first doesn't matter if the world isn't ready.AI is shifting us from an attention economy to an intention economy, transforming how video content and advertising are personalized.The best tech products often emerge from the intersection of diverse disciplines, creative conviction, and platform thinking.Timing mistakes are common—even industry giants miss the mark by years—but conviction keeps the momentum alive.Future video experiences will be radically personal, possibly generated in real time based on your preferences.Timestamped Highlights:00:58 — What Kargo does and why art + technology is their core advantage02:04 — The behind-the-scenes story of inventing QuickTime at Apple12:50 — Why Spotify's video ambitions in 2011 were 15 years ahead of their time17:33 — Can advertising become seamless and actually helpful? The AI-powered opportunity22:23 — Scene-level targeting and privacy-preserving personalization in video26:49 — Eric's 3 keys to innovating at the right time: see around corners, surf the wave, move fastQuote of the Episode:“We're shifting from an attention economy to an intention economy—where you're in the driver's seat of what you watch, and how it's monetized.”Call to Action:If this conversation got you thinking about where tech is headed, share it with a fellow builder or product leader. Follow the show for more deep dives into the minds shaping tomorrow's tech—and drop a comment to let us know what resonated most.

What if your infrastructure could predict demand before it happens? In this episode, Nilo Rahmani, CEO and co-founder of Thoras AI, breaks down how predictive scaling is transforming the Kubernetes landscape. With over a decade of experience in site reliability engineering, Nilo shares why the observability market is slower to adopt AI—and why that might finally be changing. If you're navigating the pressures of DevOps or building AI tools for technical teams, this conversation is a must-listen.Key TakeawaysAI adoption in reliability engineering isn't about replacing humans—it's about reducing fire drills and enabling better decision-making.Predictive scaling using ML can dramatically cut cloud costs and reduce latency—without compromising reliability.DevOps teams remain cautious with AI due to the high stakes of downtime and the need for human-in-the-loop decision-making.The best tools won't just optimize infrastructure—they'll increase engineer confidence and operational readiness.Nilo's founder journey started with a thesis and became unstoppable once she “couldn't unsee the better way.”Timestamped Highlights[01:02] What Thoras AI actually does—and how it tackles the double challenge of utilization and cost[03:12] Why reliability engineering is a high-stakes, thankless job and how AI can change that[08:54] Can AI fully handle outages at 2 a.m.? Why human-in-the-loop still matters[13:22] The low-hanging fruit: where ML delivers value fast in infrastructure planning[17:56] Increasing confidence, not replacing engineers—rethinking developer experience with AI[24:38] Nilo's founder story: from SRE to CEO, driven by a problem too obvious to ignoreQuote of the Episode“I couldn't unsee that there's a better way. Using machine learning to make decisions in reliability engineering is the obvious next step.”Resources MentionedThoras AI: thoras.aiConnect with Nilo on LinkedIn: linkedin.com/in/nilo-devopsCall to ActionEnjoyed this episode? Share it with someone who lives on-call or is building for DevOps teams. Subscribe on your favorite platform and leave a review—it helps more tech professionals discover the show.

What happens when UX design collides with generative AI? In this episode of The Tech Trek, Amir sits down with Mickey Alon, CEO and co-founder of Eucera, to explore how AI-first design is redefining SaaS product experiences. Mickey shares his vision for conversational UX, why menus are becoming obsolete, and how intelligent agents will soon become the most valuable “team member” in your product. If you build, lead, or design in tech—this one will get you thinking differently.Key Takeaways• Traditional UI can't keep up with modern feature sets—AI-first UX unlocks faster access to value• Conversational interfaces offer personalization and productivity that static workflows can't match• User expectations are evolving rapidly thanks to tools like ChatGPT—SaaS must catch up• AI-first design challenges product teams to rethink roadmaps, roles, and even user trust• Future UX will be hybrid: visual, prompt-driven, and increasingly agenticTimestamped Highlights03:12 — Why traditional menus break as SaaS features grow04:45 — The gap between AI-powered hype and true AI-first product experiences08:25 — How AI can personalize UX based on user skill level and intent17:50 — The need for audit trails and observability in AI-driven workflows21:30 — Will UX roles shrink or expand in the age of AI-first design?25:20 — What happens when every product is just an agent? Where do you differentiate?Quote of the Episode“The companies that will deliver AI-first experiences will outperform—because you're deploying the best person in the company, which is the agent, to assist any number of users in real time.”Call to ActionIf this episode made you rethink the future of product design, share it with a teammate or PM who needs to hear it. Subscribe to The Tech Trek for more smart conversations at the edge of tech, product, and leadership. And connect with Mickey Alon on LinkedIn if you want to dive deeper into AI-first UX.

How do we ship code faster without sacrificing quality or accountability? Greg Foster, co-founder and CTO at Graphite, joins the show to unpack how AI is reshaping code reviews, developer workflows, and the very definition of software engineering. From AI-assisted reviews to the challenge of maintaining context in a world of auto-generated code, Greg shares hard-won insights from the front lines of dev tools innovation. If you care about shipping fast, staying secure, and evolving your engineering org for what's next — this one's for you.Key Takeaways• Code review is becoming more about collaboration and less about bug catching• AI-generated code introduces a new challenge: how engineers maintain context without writing the code themselves• Developer experience is shifting toward orchestration, not just authorship — prompting, reviewing, shipping, and owning• Stack-based workflows are essential for speed, safety, and parallel progress in an AI-assisted world• Even with AI in the loop, human accountability — especially for security and architecture — remains criticalTimestamped Highlights2:10 – Why Graphite calls itself “code review for the age of AI”4:50 – What code review really means today (hint: it's not just about bugs)8:40 – The hidden cost of losing context when you're not writing the code12:05 – How the developer experience is evolving with AI-generated code16:10 – Is tech debt still a problem if code becomes disposable?21:00 – Inner vs. outer loops of development — and why the bottleneck is shifting26:10 – Why we hold AI to a higher standard than human engineersQuote of the Episode“We used to get context for free — just by writing the code. But in a world of AI code gen, we're going to need new ways to absorb and maintain that context.” – Greg FosterResources MentionedGraphite: https://graphite.devGreg on LinkedIn: https://www.linkedin.com/in/gregmfosterEmail Greg: greg@graphite.devPro TipsStack your PRs to keep shipping fast and safely. Whether it's AI or human writing the code, small, parallelized changes are easier to review, test, and deploy — especially when you're operating at high velocity.Call to ActionEnjoyed this episode? Share it with a fellow engineer, follow the show, and leave a review on Apple Podcasts or Spotify. For more insights like this, connect with us on LinkedIn or subscribe to our newsletter.

What does it take to build startups that last and come back for more? In this episode, Amir sits down with Russ Fradin, serial founder, longtime investor, and now CEO of Larridn. With nearly 30 years of experience and billions raised across multiple ventures, Russ shares what he's learned about founding companies, hiring the right people, navigating pivots, and representing other people's money with integrity. This isn't a highlight reel. It's a grounded, real-world look at what actually makes a great founder.Key Takeaways• Great founders haven't changed. The barriers to entry have• The best ideas evolve constantly. Early-stage success is about the team• Founding with the right people creates longevity and joy in the journey• Angel investors are betting on judgment, not just ideas• Fulfillment comes from building with people you respect and admireTimestamped Highlights00:53 Why Russ and his co-founder launched Larridn to reimagine productivity in the age of AI03:48 Lessons from 29 years of company building, from pre-Netscape to today05:36 How the startup world has changed and what hasn't since the 90s12:06 What makes the journey worthwhile even when startups fail14:56 How Russ chose the right co-founders and why it still matters most17:52 Knowing which idea to chase and when to pivot with purpose21:24 What representing other people's money really means to him as a founder and angel investorQuote of the Episode“There's just nothing better you can do with your time than go to work every day trying to build something amazing with amazing people.”Pro TipsWhen choosing your next venture, ask: where do I have unfair advantage? It's not just about solving a big problem. It's about solving the one you're uniquely qualified to tackle.Call to ActionEnjoyed this episode? Share it with a founder or investor in your circle. Subscribe to The Tech Trek for more conversations with leaders who've done the work and are still doing it. Follow Amir on LinkedIn for more insights and episode drops.

What do founders get wrong when trying to build a startup? Jeff Gibson, CTO and co-founder at Kintsugi, joins the show to break down how he approaches building around real business problems—not flashy features. Drawing from pre-IPO roles at Atlassian and his journey scaling Kintsugi, Jeff shares why understanding cash flow, revenue mechanics, and operational bottlenecks is critical for building something that lasts. Whether you're a startup founder or tech leader, this one's full of sharp insights on building with purpose.Key Takeaways • Solving “boring” problems can be wildly valuable—if you understand where the money flows • Great businesses start with a clear grasp of what companies actually value, not just what users say they want • Pre-IPO cleanup reveals hidden complexity in compliance, revenue recognition, and internal tooling • Pivoting without a strong North Star leads to wasted cycles; solve for the cause, not just symptoms • Not every successful business needs to be venture scale—but it does need to be viable and focusedTimestamped Highlights 01:17 — What Kintsugi actually does, and why indirect tax is a massive hidden challenge 03:49 — The “pre-IPO cleanup” playbook and how it shaped Jeff's understanding of business systems 06:52 — Why chasing product-market fit is risky if you don't deeply understand the business problem 09:44 — Talking to 100 customers before writing a single line of code 12:57 — The opportunity in low-innovation, high-value spaces (think CRMs, billing, compliance) 16:44 — Niche wins: why a $10M business in a focused segment can be more valuable than chasing unicorn statusQuote of the Episode “You don't want to find a boring problem that's commoditized. You want a boring problem that's valuable.”Resources Mentioned • Kintsugi: https://www.kintsugi.comCall to Action If you found Jeff's insights helpful, follow The Tech Trek for more conversations with builders and leaders shaping the future of tech. Share this episode with a founder friend, and don't forget to subscribe wherever you listen. Want to keep the conversation going? Connect with Jeff on LinkedIn.

Ashok Srinivas, SVP of Engineering at Aledade, joins The Tech Trek to break down what it really means to have impact as an engineering leader. With experience at Microsoft, Snapchat, Indeed, Dropbox, and now Aledade, Ashok brings clarity on how to assess your value, earn trust, and align technical strategy to business outcomes. Whether you're leading at a scrappy startup or an enterprise giant, this conversation offers a grounded and practical lens on leading with purpose, adjusting your playbook, and knowing when to pivot.Key Takeaways• Your first 90 days as a leader should be about listening, learning the culture, and earning trust• Technical strategy only matters if it maps to business value—long-term bets need short-term execution• Engineering leadership changes based on company stage: wartime vs peacetime, scale vs speed• Culture and resilience matter more than expertise—especially in remote, high-change environments• Great leaders don't just bring the right tools—they know when to use them, and when to stay curiousTimestamped Highlights00:36 — What Aledade does and why healthcare impact is personal02:14 — From chip design to engineering leadership: Ashok's career journey04:09 — Matching your leadership style to company stage and market dynamics06:39 — Why trust-building matters more than early change-making10:24 — How Ashok evaluates engineering impact across people, product, and execution13:13 — The thrill of learning new business models—and why he keeps switching industries16:41 — Aligning OKRs with team performance while still shipping hands-on21:51 — The most underrated skill in engineering orgs: resilience in the face of ambiguityQuote of the Episode“Strategies change all the time. If your team isn't aligned through culture, they won't be ready to pivot—and that's what really holds you back.” — Ashok SrinivasResources Mentioned• Radical Candor by Kim ScottCall to ActionEnjoyed the episode? Share it with an engineering leader you respect. Then subscribe to The Tech Trek so you never miss conversations like this—real insights from people building the future.

Vijaye Raji, CEO and founder of Statsig, left two decades of success at Microsoft and Facebook to start from scratch—at age 41. In this episode of The Tech Trek, we unpack the mindset, planning, and trade-offs that come with becoming a first-time founder later in life. If you've ever wondered what it really takes to leave the safety of big tech to chase a startup dream, this one's for you.What You'll Learn• Why Vijaye treated the decision to become a founder separately from the idea for Statsig• How he de-risked the leap by financially preparing his family for the journey• The emotional rollercoaster of being a solo founder—and how he stays grounded• The biggest blind spots coming from big tech to startup life (hello, sales and SOX compliance)• How he thinks about pivoting, product strategy, and avoiding the “limping-along” trapTimestamps to Catch02:03 – Why he walked away from Meta and Microsoft04:32 – The real difference between “wanting to start a company” and knowing what to build06:17 – How he set a 10-year plan—and avoided the dangerous middle zone11:54 – What he didn't know until he had to do it himself: sales, marketing, compliance15:28 – How he structured support at home to take the leap without a co-founder21:40 – Tactical advice for future founders to build toward entrepreneurship intentionallyQuote of the Episode“Startup is not an individual affair—it's a family affair. It affects people around you in subtle ways, and some not so subtle.”Resources Mentioned• Statsig: https://www.statsig.com• Connect with Vijaye on LinkedIn: https://www.linkedin.com/in/vijayePro Tip from VijayeIf you're planning to start a company in the next five years, structure your career today to pick up the missing skills: sales, marketing, financials, hiring, and firing. Be intentional about it.Enjoyed the episode?Follow The Tech Trek for more real conversations with startup builders, tech leaders, and product thinkers. Like, subscribe, and share this episode with someone who's thinking about taking the leap. And if you've got thoughts or feedback—drop a comment or connect on LinkedIn.