The Tech Trek

What does it take to build and scale AI responsibly inside a major media organization?

In this episode, Amir sits down with Arvind Thinagarajan, formerly Head of Enterprise Data Science & Analytics at Gannett (publisher of USA Today), to unpack how they built a volunteer-driven AI Council that governs, guides, and accelerates AI initiatives across the company.

From prioritization frameworks to cross-functional subcommittees, Arvind shares the inner workings of a model that supports nearly 90 AI use cases — and might just inspire how your org tackles AI at scale.

Key Takeaways

– Gannett’s AI Council is fully volunteer-based and cross-functional, giving every department a voice in how AI gets used.

– New AI ideas bubble up from across the org — the council exists to prioritize and support them, not to own or build them.

– Every pilot starts with a scoped business case, clear success metrics, and a timeline.

– The enterprise rollout phase is intentional — ensuring tech used in pilots aligns with the broader IT stack.

– A separate IT Council works alongside the AI Council to avoid duplicate tools and ensure strategic alignment.

Timestamped Highlights

00:57 – What the AI Council is and how it’s structured

03:54 – How the council started and why it matters in media

06:19 – Subcommittees that cover everything from tooling to compliance

09:40 – Where AI use cases come from (hint: it’s not top-down)

11:39 – Who actually builds the solutions, and how governance plays out

16:55 – 87+ tracked use cases and what happens after a pilot succeeds

Quote of the Episode

“The AI Council exists to make sure there are no silos. We’re here to bring the right skill sets, tool sets, and mindsets together — to solve the right problems, the right way.”

Call to Action

If you’re working on AI in a complex org — or trying to sell into one — this episode will give you the playbook from inside a company doing it right.

Follow the show on your favorite podcast platform, share it with a teammate, and if you liked the episode, leave a quick review. It really helps.

What does it take to build AI systems that are private by design—and ready for tomorrow’s regulations?

In this episode, I’m joined by Rishabh Poddar, CTO and Co-founder of Opaque Systems, to explore how data privacy, compliance, and AI innovation intersect in a rapidly evolving landscape. Rishabh breaks down the impact of emerging privacy laws, the risks with agentic AI systems, and why building cryptographic guarantees into the foundation of your AI stack isn’t optional—it’s essential.

Whether you're deploying AI at scale or just experimenting, this conversation will challenge how you think about trust, governance, and the future of responsible AI.

🔑 Key Takeaways

Data privacy laws are tightening—and conflicting with the growing demand for more diverse data to train AI models.

Agentic AI introduces new risk: non-deterministic systems that act independently demand a fresh approach to guardrails and governance.

Opaque Systems' platform keeps sensitive data encrypted throughout the full AI lifecycle, with cryptographic auditability and verifiable access controls.

The future is about adaptability—not one-size-fits-all compliance, but giving organizations the tools to meet evolving laws.

Enterprise example: ServiceNow reinvented their internal helpdesk with agentic AI while preserving strict internal data boundaries.

⏱ Timestamped Highlights

[00:34] – What is Opaque Systems? The confidential AI platform built from UC Berkeley research.

[01:31] – How privacy laws and AI demands are on a collision course—and why that tension led to Opaque’s creation.

[06:24] – The shift from structured data governance to unstructured, agent-driven challenges.

[10:43] – Why humans breaking trust feels different than AI—and how to build for trust without false assumptions.

[15:30] – Real-world case study: How ServiceNow uses AI + Opaque to safeguard confidential data at scale

[21:18] – You can’t wait for laws to settle—future-proof AI systems must offer customizable compliance tools today.

💬 Quote of the Episode

“Privacy and security need to be baked into the design of a system from the ground up—so you’re protected no matter how the laws evolve.”

🛠 Pro Tips

If you’re designing AI applications for enterprise use, map out where data flows—then assume each step is a risk zone.

Invest early in systems that offer auditable, verifiable data usage—this becomes your insurance as laws change.

Don’t assume your platform team should (or can) see all the data. Siloed access isn't a bug—it’s a feature for privacy.

📢 Call to Action

Enjoyed this episode? Follow the show, leave a review, and share it with someone working on AI governance or data privacy. Got questions or thoughts? Reach out to Amir on LinkedIn—he’d love to hear from you.

New episodes drop weekly. Subscribe wherever you listen.

What happens when a data-driven founder leaves Big Tech to tackle a broken healthcare system?

In this episode of The Tech Trek, Amir sits down with Tim Edgar, Co-founder and CTO at Venteur, to unpack how deep personal insight, emotional connection, and data all play a role in identifying real-world problems worth solving. From launching Bing under Satya Nadella to co-founding companies with his sister, Tim shares how his experience across startups and Microsoft shaped his approach to product, purpose, and people.

This one is for builders, operators, and tech professionals who want to do more than just ship features—they want to build something meaningful.

🔑 Key Takeaways:

Startups are fueled by personal conviction: Tim’s best ideas come from emotional moments backed by hard data.

The best founders learn like anthropologists: Talk to people. Watch their reactions. Treat them like masters of their craft.

Complementary co-founders create creative tension: Tim and his sister bring radically different lenses—and that’s their strength.

Big Tech can be an entrepreneurial training ground: Microsoft gave Tim exposure to data scale and mentorship he still draws on today.

Vision + Next Step > Five-Year Plan: Tim shares why obsessing over the next move while keeping an eye on the North Star is his go-to strategy.

⏱️ Timestamped Highlights:

[02:03] – Tim shares the origin story of his first startup—and how a $5 power bill unlocked a much bigger mission.

[03:14] – How growing up with small business owners and private insurance shaped his view of healthcare.

[06:29] – What Tim listens for when validating a problem with customers (hint: it's all about emotion and nuance).

[09:10] – Inside early Microsoft: pitching ideas to Satya Nadella and learning entrepreneurship from within Big Tech.

[13:26] – Building with family: why working with his sister as co-founder has been a superpower, not a liability.

[22:19] – “Vision and next step”: How Tim defines success and stays focused without overplanning the future.

💬 Quote of the Episode:

“Startups are hard. If there’s not some emotional resonance and data that proves it’s worth solving, you won’t have the fuel to keep going.”

🛠️ Resources Mentioned:

Venteur (https://www.venteur.com)

✅ Pro Tips:

When validating an idea, don’t just ask surface-level questions. Let people teach you. Observe their reactions. That’s where the real signals are.

Don’t overlook Big Tech as a training ground. If used intentionally, it can be a launchpad for your founder journey.

🙌 Call to Action:

If you found this episode valuable, share it with a fellow tech leader who’s navigating the leap from ideas to impact.

And don’t forget to subscribe, leave a review.

In this episode of The Tech Trek, Amir sits down with Davy Li, Head of Engineering at Mesa, a startup redefining how homeowners earn rewards on everyday home expenses. Davy shares his personal journey from Big Tech to startup life, unpacks how he’s built Mesa’s engineering team from scratch, and offers a refreshingly candid look at what it means to be an effective leader in a small but growing organization.

From defining cultural values to hiring without a brand name, Davy drops wisdom on leadership, team modeling, and giving engineers the freedom to thrive. If you're building or leading teams in tech—or planning to—this one's packed with insights you can act on today.

🧠 Key Takeaways:

Culture is foundational: Davy built Mesa’s engineering culture before hiring anyone—trust, simplicity, transparency, and bias for action anchor the team.

Give great engineers space: The best devs aren’t waiting on tickets—they’re engaging directly with stakeholders and proposing their own solutions.

Small teams = shared ownership: Davy empowers his team by assigning champions for different technical domains and pushing people outside their lanes.

Startup hiring is scrappy: Without a big brand, Davy had to hustle through referrals and review hundreds of candidates to land A-players.

Upskilling never stops: Whether through reading, podcasts, or trial-by-fire, Davy believes learning is a muscle that needs constant exercise.

🕒 Timestamped Highlights:

(00:40) What Mesa does and how their homeowner rewards work

(02:09) Adjusting from Big Tech to startup: different challenges, different muscles

(03:40) The four cultural values that define Mesa’s engineering org

(06:57) How being closer to the business shifts the way you lead engineering

(09:09) What Davy prioritized when modeling the team from scratch

(11:14) Creating space for engineers to solve real business problems

(13:14) Borrowing from Cash App and Spotify: PR auto-approvals, DRIs, and ownership

(16:50) How Davy closes skill gaps as a manager—and why trying new things matters

(18:31) Promoting psychological safety and cross-functional growth on the team

(21:14) Why he's more hands-on with code now than ever before

(22:47) Startup hiring realities: no brand, no inbound, just grit

(24:55) Best way to reach Davy if you want to connect

💬 Quote to Remember:

“The fastest way to a good idea is to have a lot of ideas—and the fastest way to grow is to just try things.”

🧩 Leadership Tips (from the episode):

• Create psychological safety: Failure should be normalized. That’s how teams experiment and grow.
• Start with culture, not code: Even in the earliest hiring stages, define how you want your team to operate.
• Push responsibility downward: Assign DRIs and let engineers own the full scope of a project—not just the tickets.
• Hire for slope, not just skills: A-players attract A-players, but only if you're willing to dig deep and sell the mission.

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 Takeaways

Accidental 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 Highlights

00:00 — Intro and Bryan’s path from CTO to CEO

01:43 — His early entrepreneurial background and how Cord “accidentally” became a SaaS company

03:21 — The new skills Bryan had to learn: go-to-market, packaging, and investor relations

06:53 — Why mental models grounded in curiosity matter more than fixed frameworks

09:41 — Staying close to the codebase and why technical empathy gives CEOs an edge

13:22 — AI and the future of engineering ladders: what’s changing and what still matters

18:11 — Could code become disposable? Bryan’s thoughts on AI and tech debt

22:37 — 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 work

26:16 — 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 Mentioned

Glossier’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: [email protected]

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 Learn

Why the people you hire, fire, and promote say more about your culture than any value statement

How to avoid building a “Frankenstein culture” from your team’s past jobs

The role of founder intuition—and how it can still shape culture even as you scale

Why candor during hiring isn’t just refreshing—it saves time, trust, and turnover

What it means to find your “culture carriers” when you’re no longer in every interview

⏱ Highlights by Timestamp

00:00 – Intro to Darren & Steadily Insurance

01:29 – What’s the goal when building culture from scratch?

03:08 – The danger of not defining culture early

04:31 – Lessons from 4 startups (and what stuck)

07:10 – Founder imprint: how it changes with scale

09:45 – 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 worked

18:12 – Founder mode: when gut instincts outperform data

21:31 – “Culture carriers” and how to empower them

22:10 – 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 does

Jay introduces Tekion’s end-to-end SaaS platform for automotive retail and OEMs.

01:41 – State of dealership technology

Many 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 Apple

It'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 chains

How global part sourcing and delivery logistics shape the customer experience.

12:44 – Post-COVID supply chain improvements

What OEMs learned from COVID disruptions and how they’re building more resilient supply chains.

15:53 – Data fragmentation and AI limitations

You can’t power AI effectively without unified, contextualized data across the full customer lifecycle.

21:31 – Why experience trumps pricing

Dealerships make slim margins on sales but higher ones on service. Retention hinges on delivering a great experience.

27:58 – What personalization really means

Fancy 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 Takeaways

What “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 Highlights

00:34 – 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 Mentioned

Stytch: 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 ecosystem

02:05 – What growth engineering is and why it’s different

04:07 – Skill sets that matter on a growth engineering team

07:19 – Adapting to short-lived code and failed experiments

09:44 – Measuring business impact and tracking team contributions

11:46 – Relationship between growth engineering and marketing

16:04 – Why the team uses Kanban instead of Scrum

19:28 – Advice for engineers who want to move into growth

22:58 – 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