The Tech Trek

• AI Is Reinventing the Car Buying Experience

  In this episode, Amir sits down with Jay Vijayan, Founder and CEO of Tekion, to explore how digital transformation and AI are modernizing the automotive retail industry. They dive deep into the complexities of dealership systems, the supply chain ripple effects of tariffs, and the evolving consumer experience. Jay explains why legacy systems can't meet today’s expectations and how Tekion is building a unified platform that supports everything from purchase to after-sales. They also unpack why delivering a personalized, seamless customer journey may be the key to loyalty in an industry long seen as purely transactional.💡 Key Takeaways:Legacy tech is still rampant: Many dealerships still rely on green-screen legacy systems, which limits innovation and integration.Experience > Price: In low-margin auto sales, long-term value comes from after-sales service — and a standout experience can beat price competition.AI + contextual data = competitive advantage: Fragmented data limits insight. A modern tech stack built on a unified data layer unlocks personalization and operational efficiency.Tariff uncertainty impacts forecasting: The issue isn't just the tariffs themselves — it's the lack of predictability that hampers planning.Customization matters: Great experience is subjective. AI can help dealers tailor the journey to each customer’s preferences.🕒 Timestamped Highlights:00:42 – What Tekion doesJay introduces Tekion’s end-to-end SaaS platform for automotive retail and OEMs.01:41 – State of dealership technologyMany dealerships still use 50-year-old systems. The goal is to modernize the full customer journey, not just the front-end.04:48 – Lessons from Tesla and AppleIt's not about eliminating brick-and-mortar; it’s about giving consumers a seamless experience on their own terms.08:13 – The hidden complexity of auto supply chainsHow global part sourcing and delivery logistics shape the customer experience.12:44 – Post-COVID supply chain improvementsWhat OEMs learned from COVID disruptions and how they’re building more resilient supply chains.15:53 – Data fragmentation and AI limitationsYou can’t power AI effectively without unified, contextualized data across the full customer lifecycle.21:31 – Why experience trumps pricingDealerships make slim margins on sales but higher ones on service. Retention hinges on delivering a great experience.27:58 – What personalization really meansFancy coffee isn’t always the answer. AI can help decode what kind of experience each customer values most.💬 Quote of the Episode:“Experience is something people don’t forget. You may not remember the price, but you always remember how you were treated.” – Jay Vijayan🧠 Career & Business Tips:For operators: Don’t just focus on the sale — optimize the long-term relationship. Invest in service retention and personalized experience.For tech builders: When designing AI-driven tools, infuse business context into your data to make them actionable and useful in real workflows.For founders: A modern stack isn’t just about efficiency — it’s a growth enabler. Start with a centralized platform if you want scale and insight.

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  31:44
• Are Your Apps Ready for AI Agents?

  In this episode of The Tech Trek, Amir sits down with Reed McGinley-Stempel, co-founder and CEO of Stytch, to explore what it means for applications to be agent ready. With the rise of agentic AI—intelligent systems that can take actions on behalf of users—the landscape for SaaS and consumer-facing apps is rapidly evolving.Reed breaks down the core concepts around agent integration, including how apps must prepare to serve not just human users but also AI agents acting on their behalf. They discuss the key challenges companies face: earning user trust, managing consent and privacy, and building in human oversight to minimize costly mistakes.Using real-world examples like coding agents and calendar tools, Reed illustrates how agent adoption succeeds where there's low friction and built-in validation. He also dives into the double standard AI faces, and why even psychologically, humans might need a "human in the loop" long after AI is capable of operating on its own.If you're building applications or thinking about AI integrations, this is a forward-looking conversation you won't want to miss.🧠 Key TakeawaysWhat “Agent Ready” Really Means: Apps must now prepare for a world where both humans and AI agents interact with them—sometimes autonomously.Balancing Trust and Control: Consent, data privacy, and human-in-the-loop confirmations are key to gaining user trust in AI agents.Coding Agents as the First Wave: Software development is a prime use case for agent adoption, thanks to built-in validation workflows and low user friction.Why Mistakes Hit Harder with AI: Users hold AI to a higher standard than humans—especially when the cost of fixing AI mistakes causes more mental fatigue than doing it manually.The Psychological Role of Humans: Even as agents improve, a “human in the loop” may remain necessary just to reassure users, much like early elevator operators.⏱ Timestamped Highlights00:34 – What Stytch does: An API-first identity platform for customer apps.01:24 – What it means to be “agent ready” in 2025.04:29 – The 2 major user concerns: data privacy and efficacy.08:16 – The risk of losing touch with the end user in agent-driven workflows.11:03 – Why coding agents gained early traction: low friction + strong validation.15:58 – Users expect more from AI than junior engineers—sometimes unfairly.20:23 – How agent workflows challenge traditional notions of data consent.24:17 – The future of human-in-the-loop: functional now, psychological later.💬 Notable Quote“Humans hate friction and they hate mistakes. Agents help reduce friction—but only if they don’t make the kind of mistake that breaks trust.” – Reed McGinley-Stempel🔗 Resources MentionedStytch: Identity infrastructure for modern apps🚀 Career Tips (From the Episode)If you're an engineer, expect your role to shift toward problem solving, not boilerplate coding.When working with agents, focus on building validation steps into your workflows—they're key to adoption and trust.Product managers and designers should prioritize consent UX and asynchronous confirmations to balance automation with user control.

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  27:33
• What Is Growth Engineering? Here's How It Really Works

  In this episode, Amir chats with Jason Fellin, Head of Growth Engineering at OnX Maps, to unpack what makes growth engineering unique. Jason shares how his team focuses on speed, experimentation, and measurable business impact rather than long-term architecture. From hiring strategies to cross-functional collaboration with marketing, this conversation offers a tactical look at building and leading a growth engineering org.🧠 Key Takeaways:Validate, Don’t Overbuild: Growth engineering emphasizes testing hypotheses quickly rather than building production-grade features from the start.Non-traditional Skills Matter: Jason looks for candidates with backgrounds in psychology, finance, or even startups—people who bring statistical thinking and business curiosity.Tight Marketing Integration: The growth team plays a critical technical role in enabling marketing through experimentation, CRM tools, and MarTech stack support.Execution Is Kanban, Not Scrum: Speed and flexibility drive the team’s Kanban approach, enabling more fluid iteration on experiments and faster follow-ups on wins.⏱️ Timestamped Highlights:00:00 – Intro to Jason Fellin and OnX Maps’ product ecosystem02:05 – What growth engineering is and why it’s different04:07 – Skill sets that matter on a growth engineering team07:19 – Adapting to short-lived code and failed experiments09:44 – Measuring business impact and tracking team contributions11:46 – Relationship between growth engineering and marketing16:04 – Why the team uses Kanban instead of Scrum19:28 – Advice for engineers who want to move into growth22:58 – How to connect with Jason💬 Quote of the Episode:“We scope to validate, not build… Anything that we build can just be tossed in the wayside of the digital dustbin.” – Jason Fellin💡 Career Tips (from the episode):Cultivate a scientific curiosity—always ask “What would happen if…?”Learn basic statistics—you don’t need deep math, but you should understand how experiment data informs decisions.Focus on business impact—engineers with a product mindset and interest in KPIs thrive in growth roles.Practice scoping for speed—know when to prioritize fast iteration over scalable architecture.

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  24:00
• Her Journey: Sales Leader to Cybersecurity CEO

  In this episode, Amir sits down with Brooke Motta, CEO and co-founder of RAD Security, to unpack her career pivot from sales leadership to becoming a founder in the cybersecurity space. Brooke shares how her go-to-market background shaped her approach to building RAD, the challenge of stepping into technical leadership, how she’s managing growth through hiring, and what’s ahead for security and AI. Whether you're a technical founder or commercial operator, this one’s packed with practical insight.💡 Key Takeaways:Sales Skills Scale: Brooke explains how her early career at Rapid7 taught her to build pipeline from scratch—skills that directly translated to startup leadership.Learning to Lead Technically: She shares how non-technical founders can learn quickly by knowing how they learn, and surrounding themselves with customers and engineers.Go-To-Market Meets CEO: Juggling the CRO and CEO hats requires recognizing when to zoom out, empower others, and avoid falling back into old comfort zones.Security Needs Speed: RAD was born to solve the tension between engineering velocity and security friction.AI for Security Efficiency: RAD’s new AI agentic layer is helping CISOs dramatically cut down GRC and risk reporting times.⏱️ Timestamped Highlights:00:37 – What RAD Security does: a CADR platform with an AI layer for better query and integration.01:28 – Brooke’s sales journey at Rapid7 and how that shaped her operator mindset.04:06 – CEO vs. sales mindset: learning when to stay in your lane and when to manage across functions.06:09 – Becoming more technical by learning through founders, engineers, and users.07:47 – Brooke’s early vision to lead, and why startup DNA suits her better than corporate environments.09:19 – Building a "can-do" culture and why intangibles matter when hiring.10:39 – Transitioning from doing the selling to hiring and enabling a sales team.13:27 – The founding insight: helping security enable engineering speed, not block it.15:31 – RAD's "do more with less" efficiency campaign for CISOs.📣 Featured Quote:“You need to make sure as the leader of your company that you understand the market, your buyers, how your product works—and how people actually use it.” — Brooke Motta

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  20:58
• Forge Your Own Leadership Path

  In this episode, Richard Girges, CTO at MNTN, breaks down the appeal and risk of emulating high-profile leaders like Elon Musk or Steve Jobs. From startup life to scaling teams, Richard shares how leaders can avoid the missteps of mimicry and instead cultivate their unique "mode of genius." You’ll learn how intuition, failure, and self-awareness play a vital role in effective leadership—and why copying the “death stare” won’t make you a visionary.🔑 Key TakeawaysEmulating leaders can be a shortcut—but often a dangerous one. Traits that are easy to imitate (like quirks) may not reflect the true drivers of success.Leadership styles must align with your personal values and stage of growth. What works at an early-stage startup can break things at scale.Finding your “mode of genius” means identifying what energizes you and where you're naturally skilled or deeply motivated to improve.Failure is inevitable—and essential. The best leaders lean into it, learning through feedback loops and rapid testing.Developing a decision-making framework (like minimal viable tests) helps bypass analysis paralysis.⏱️ Timestamped Highlights[00:01:00] What MNTN does: reinventing TV advertising with data-driven performance[00:03:00] The dangers of misapplying advice from famous founders[00:06:00] Why we gravitate toward copying successful traits—and why that’s risky[00:08:00] Emulating Elon Musk? It might work—if you’re still early stage[00:10:00] What “mode of genius” means—and how Richard found his[00:13:00] How to decide what leadership traits are worth adopting[00:15:00] Failure as a feature, not a bug, in startup leadership[00:17:00] The power of intuition and decision velocity[00:18:00] MVP-style frameworks to reduce decision fatigue[00:20:00] Why execution beats overthinking in fast-moving spaces like AI💬 Quote Worth Sharing“If you’re not failing, then you’re probably not even running a startup.” — Richard Girges🧰 Mentioned ResourcesY Combinator's advice: “Do things that don’t scale”Rand Fishkin’s book (likely “Lost and Founder”): Influential in Richard’s leadership values💼 Career Advice (from the episode)Don’t blindly adopt leadership styles—look for alignment with your own values.Learn through failure. Let intuition guide you and refine it through repetition.Early in your career, test different leadership behaviors and refine based on what resonates—not just what’s trendy.Adopt a fast-feedback loop: test small, learn fast, iterate often.

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