The Tech Trek

In this candid conversation, Bryan Mahoney unpacks his journey from CTO to CEO, exploring what it really takes to evolve beyond the engineering org into leading an entire company. We talk about how his curiosity and hands-on technical skills still shape how he leads today, the mental shifts required to manage AI-driven teams, and how leadership demands evolve as a company scales from services to SaaS.Along the way, Bryan reflects on AI’s role in shaping engineering culture, the future of career ladders, and the illusion of control in modern software development. If you’ve ever wondered what it means to be a modern tech CEO—or how AI is transforming the very DNA of how we build software—this one is for you.🔑 Key TakeawaysAccidental CEO: Bryan didn’t set out to become a SaaS CEO—but by leaning into opportunities, he transitioned from a hands-on engineering leader to running a product company.Curiosity Over Playbooks: Rather than relying on frameworks, Bryan values curiosity as the anchor for decision-making and adaptation, especially in fast-evolving areas like AI.Engineering Culture in an AI World: Bryan believes AI won’t eliminate engineers—it will amplify those with experience and curiosity. Promotions may speed up, but depth still matters.Reframing Control in AI Workflows: AI doesn’t remove human control—it shifts it upstream. Engineers become managers of agents, not just code.The Future of Tech Debt: With strong test coverage and agent support, code may become more disposable. The future of engineering may prioritize outcomes over sacred codebases.🕰 Timestamped Highlights00:00 — Intro and Bryan’s path from CTO to CEO01:43 — His early entrepreneurial background and how Cord “accidentally” became a SaaS company03:21 — The new skills Bryan had to learn: go-to-market, packaging, and investor relations06:53 — Why mental models grounded in curiosity matter more than fixed frameworks09:41 — Staying close to the codebase and why technical empathy gives CEOs an edge13:22 — AI and the future of engineering ladders: what’s changing and what still matters18:11 — Could code become disposable? Bryan’s thoughts on AI and tech debt22:37 — The overlooked challenge: AI may help ship fast, but who’s thinking about maintainability?24:01 — Engineers as agent managers: redefining accountability in AI-assisted dev work26:16 — Hollywood glamorized AI, but Bryan reminds us humans are still at the center💬 Quote of the Episode“You're still in control. You're not giving it up—you're shifting it. We all need to be managers, not of humans, but of agents.” — Bryan Mahoney🧠 Career Tips (discussed in episode)Don’t rush the title: Career progression in engineering should be thoughtful. Output may scale faster with AI, but real impact still requires experience and context.Upskill across functions: Moving from CTO to CEO means learning sales, go-to-market, and investor relations. Bryan did it by staying curious and surrounding himself with experts.Stay hands-on if it fuels you: Writing code helped Bryan stay grounded, understand his team better, and maintain technical credibility as a CEO.📚 Resources MentionedGlossier’s Engineering Career Ladder — Bryan open-sourced this while at Glossier. It’s still a great reference for thinking through growth in engineering orgs.Contact Bryan —📧 Email: bryan@cord.co

What does it really take to build company culture from the ground up—especially when you’ve done it more than once? In this episode, Amir sits down with Darren Nix, founder and CEO of Steadily Insurance, to talk about the wins and missteps that come with building startups. Darren shares what’s stayed the same (and what hasn’t) across the four companies he’s founded, and why being deliberate about culture is more important than ever.They get into how founders leave their fingerprints on everything—from hiring to habits—and why your company might accidentally turn into a copy of your employees’ old workplaces if you’re not paying attention. Whether you’re leading a 10-person team or scaling past 100, this episode is packed with hard-earned advice on keeping your culture intentional, honest, and real.🧠 What You’ll LearnWhy the people you hire, fire, and promote say more about your culture than any value statementHow to avoid building a “Frankenstein culture” from your team’s past jobsThe role of founder intuition—and how it can still shape culture even as you scaleWhy candor during hiring isn’t just refreshing—it saves time, trust, and turnoverWhat it means to find your “culture carriers” when you’re no longer in every interview⏱ Highlights by Timestamp00:00 – Intro to Darren & Steadily Insurance01:29 – What’s the goal when building culture from scratch?03:08 – The danger of not defining culture early04:31 – Lessons from 4 startups (and what stuck)07:10 – Founder imprint: how it changes with scale09:45 – Why Darren rejected the “high-turnover sales model”11:47 – Culture as habit: how norms are created (or missed)13:30 – Transparency in hiring: a true story that worked18:12 – Founder mode: when gut instincts outperform data21:31 – “Culture carriers” and how to empower them22:10 – A brilliant metaphor for why startups shouldn’t all look the same💬 Quote to Remember“Trying to do what everybody else does is almost by definition a recipe for an average outcome.” — Darren Nix🎯 Practical Tips (If You’re a Founder or Manager)Be upfront in hiring—describe the real job, not the highlight reel.Define culture through action—who you hire and promote speaks louder than anything else.Look for early signs—small discomforts often signal big problems later.Encourage pattern recognition—intuition is earned, not lucky.

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.

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.

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.

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

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.

In this episode, Amir sits down with Brian McMahon, CEO and co-founder of Pickle—a fashion rental marketplace aiming to become the Airbnb for everyday items. Brian unpacks how Pickle solved the classic two-sided marketplace dilemma, why hyperlocal supply is their secret weapon, and how AI is powering everything from product tagging to customer support. They also dive into the evolution of Pickle’s fundraising strategy—from getting no investor traction to securing repeat backers. Whether you're building a marketplace, navigating fashion tech, or fundraising in today’s climate, this conversation is packed with insights.🔑 Key TakeawaysTwo-Sided Marketplace Strategy: Pickle launched by creatively seeding inventory from local influencers, solving the chicken-and-egg problem by targeting people who both supply and demand the product.Supply Drives Growth: In marketplaces like Pickle, supply quality and availability are the key levers for growth and retention.Fashion Trends = Opportunity: Pickle thrives by leaning into dynamic, trend-based inventory without owning any products—speed and style come from the community.AI as a Differentiator: From image-based product tagging to automated support for dispute resolution, AI is central to scalability and experience.Fundraising Realism: Brian shares lessons from struggling to raise initially to now securing back-to-back funding rounds with consistent investors.⏱️ Timestamped Highlights00:32 – What is Pickle? A peer-to-peer fashion rental marketplace, like Airbnb for clothes.02:12 – Creative launch strategy: uploading closets from friends and hosting influencer photoshoots.05:06 – Why supply matters most in marketplace momentum.07:45 – How trends impact Pickle’s inventory—and why that’s a strength.09:24 – Search challenges at scale and the different discovery modes for users.12:02 – AI applications: product tagging, onboarding inventory, and handling customer disputes.14:53 – Expansion vision: clothing today, tools, electronics, and party supplies tomorrow16:51 – Fundraising journey: from no traction to repeat backers.20:01 – Advice on blocking out market noise and focusing on building a solid business.21:41 – Connect with Brian: LinkedIn – Brian McMahon💬 Notable Quote"The only thing you can control is the quality of your business… Good businesses will find capital if they’re building something that makes sense." — Brian McMahon📚 Resources MentionedPickle: https://www.rentpickle.comInvestors: Kraft, FirstMark, Burst Capital, FJ Labs💼 Career Tips (from the episode)If you're in a peer-to-peer marketplace startup, be patient. Investors often want to see a full year of retention and repeat behavior before committing.Don’t get distracted by hype events—spend that energy building a product people love

In this episode, Amir sits down with Aaron Painter, CEO of Nametag, to explore how deepfakes and generative AI are reshaping identity security in the workplace. They discuss real-world attacks, such as the MGM breach, and how enterprises are responding with new technologies—from cryptographic identity verification to re-verification protocols. Aaron shares what companies are doing right, where they're vulnerable, and the role of identity in the future of enterprise security.🧠 Key Takeaways:Deepfakes aren’t sci-fi anymore: Attacks like the MGM breach show how synthetic audio and video are already being used to bypass security.The weakest link is recovery, not authentication: Help desk processes, especially for locked-out employees, are prime targets for exploitation.Identity is now a real-time problem: Enterprises must move beyond one-time verifications and adopt re-verification strategies throughout the employee lifecycle.Security meets cryptography: Combining AI, biometrics, and cryptographic tools gives defenders an edge over increasingly sophisticated attackers.🕒 Timestamped Highlights:00:32 – What Nametag does: High-assurance identity verification for sensitive enterprise scenarios.02:53 – Breakdown of the MGM attack and how help desk impersonation led to ransomware.05:42 – Arms race: Deepfake detection vs. deepfake creation—and why cryptography matters more.08:03 – How awareness of this attack vector is spreading among security professionals.10:35 – Why global hiring and remote work increase exposure to identity fraud.12:07 – The maturity of enterprise adoption and where large organizations are in their security journey.14:54 – One major insight: Identity is not a moment—it’s a continuous process.18:40 – New use cases: re-verification during suspicious behavior, locked USB ports, or privileged access.21:35 – The growing complexity of the CISO’s job and why identity is central to security strategy.23:26 – Aaron’s resource recommendation: follow him and Nametag on LinkedIn for up-to-date insights.💬 Quote Highlight:“Identity is a real-time question. It’s not about verifying once—it’s about knowing who someone is at every critical moment.” – Aaron Painter🛠️ Resources Mentioned:Nametag: https://www.getnametag.comFollow Aaron on LinkedIn for ongoing insights and updates about deepfake threats and enterprise identity protection.📈 Career Tips (from the episode):If you work in IT, InfoSec, or People Ops: push for better identity verification tools, especially during onboarding and help desk recovery.Security isn’t just a tech problem—it’s a workflow problem. Recognizing the human and process weaknesses can be just as important as your tools.Enterprises that treat identity as part of every access point—not just login—are better equipped for a world where AI is both tool and threat.#techleaders #startup #founder #softwaredevelopment #tech #careergrowth #techleadership  #cto #engineering #softwareengineering #careeradvice #startups #ai #artificialintelligence #leadership #agenticai #agentic #security #cybersecurity #cyber 

In this episode, Amir speaks with Anna Patterson, founder of Ceramic AI, about what it truly means to lead an AI-first company. They unpack the differences between engineering and AI leadership, the chaos and creativity of early-stage research, how Ceramic AI is betting on emerging talent, and why managing AI roadmaps is an exercise in uncertainty and invention. Anna also shares perspectives from her experience at Google and how search engine wars inform today’s AI landscape.💡 Key Takeaways:AI Leadership = Research LeadershipManaging AI projects is less like traditional engineering and more like guiding research — with unknowns, pivots, and breakthroughs.Invention and Market Fit Are Separate RisksStartups must solve both: the technical challenge and the business case. Success in one doesn't guarantee the other.Competing with Giants Means Betting on TalentCeramic AI doesn’t try to match OpenAI or Anthropic on salaries. Instead, they hire promising but overlooked researchers and invest in their growth.Motivation is Self-DrivenPeople with deep academic or research backgrounds bring strong self-motivation — a must-have trait in early-stage, high-risk AI environments.Vertical AI and Pointed Models Are the FutureRather than aiming to compete broadly, building specialized models for specific workflows could be the path for emerging players.⏱️ Timestamped Highlights:00:38 – Ceramic AI’s efficient training stack for long-context models01:29 – Why AI leadership mirrors research more than engineering03:37 – Managing a roadmap when invention and success are uncertain05:34 – Staying competitive when Big Tech might absorb your feature07:10 – Prepping new hires for startup chaos08:49 – How Ceramic AI hires promising talent that others overlook10:37 – Breakdown of the AI infrastructure pipeline: from pretraining to inference12:57 – Lessons from search engine wars and how they might reflect AI’sn evolutio14:42 – The messy near-future of models: distillation, specialization, and competition16:08 – Keeping morale and creativity high with flexibility, fun, and sleep18:22 – Balancing coding and leadership as a technical founder19:31 – How Anna envisions her evolving role at Ceramic AI🛠️ Mentioned Resources:Contact Anna: anna@ceramic.ai🎯 Career Tips (discussed):Bet on Early Talent: If you're early in your career and not yet established, smaller companies might be more willing to take a chance on your potential than large labs.Be Startup-Ready: Know what you’re getting into. Embrace ambiguity, multiple directions, and creative chaos — especially in AI startups.Stay Curious and Motivated: A research mindset — driven by deep curiosity and self-direction — is essential in a domain where there’s no guaranteed outcome.💬 Quote:“One thing about researchers… there's a deep self-motivation. Nobody is dying for you to graduate. You have to want it — deeply.” – Anna Patterson