The Tech Trek

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.