The Tech Trek

Some companies thrive while others quietly lose their edge.For Tanay Kothari, CEO of Wispr Flow, the difference comes down to one idea: people are your responsibility.In this conversation, Tanay shares how that realization changed everything about the way he leads. From early missteps as a young manager to building a company rooted in empathy and accountability, he shows that the strongest cultures are designed with intention, not left to chance.You’ll come away with a practical look at how to build a team that performs at a high level because they feel valued and trusted.Inside the ConversationTanay explains how he built systems that make empathy operational. He spends time understanding each person’s strengths, shapes feedback and growth paths around them, and invests in training people managers who can multiply impact. He also shares why he still keeps a founder’s eye on product quality, customer connection, and hiring as the company grows.Takeaways• Culture doesn’t scale on its own, it must be built with care• Empathy can drive performance without lowering expectations• The three areas Tanay never delegates as a founder• How to recognize when a culture is truly working• What happens when leaders trade control for curiosityTimestamped Highlights00:43 The mission behind Wispr Flow and the future of voice technology01:50 Why treating people as your responsibility changes everything03:39 Building around individual strengths and learning styles06:23 The importance of developing great managers10:35 Small but powerful signals of a thriving culture12:41 The lesson that reshaped Tanay’s approach to leadership15:50 Turning frustration into growth and creating top performers19:30 Interviewing for passion, not just technical skill21:58 The three things a founder should never hand offA line that says it allCulture isn’t a vibe, it’s a decision you make every single day.Call to ActionGreat companies are built by leaders who care as much as they execute. Follow The Tech Trek for conversations that help you grow as both.

Crypto follows patterns—just like every major wave of innovation. In this episode, Brad Holden of Protocol VC breaks down what really drives those cycles, how investors separate substance from hype, and where crypto and AI are beginning to converge.From evaluating early founders to understanding when to double down or step back, Brad shares how top VCs navigate frontier tech markets and what makes a company endure beyond the hype cycle.Key Takeaways• Crypto’s ups and downs follow predictable adoption cycles—and understanding that rhythm matters.• Founders who focus on real problems, not hype, stand out in crowded markets.• AI and blockchain are intersecting through decentralized compute and data transparency.• Great founders show conviction, grit, and self-awareness—qualities investors notice immediately.• The strongest pitches come from founders who lead with their own vision, not what investors want to hear.Timestamped Highlights01:20 — Why crypto moves in repeating cycles and what drives each one03:40 — How blockchain transparency helps investors see real traction06:00 — Evaluating crypto startups: solving problems vs. chasing novelty10:49 — How blockchain complements and verifies AI13:05 — The hidden risk of building around hype15:53 — Why over-customizing your pitch can backfire17:50 — How top VCs view pivots and founder adaptability25:28 — The traits that signal long-term founder successA line worth remembering“Being too early is just another way of being wrong—but betting on the right founder can make up for almost anything.”Call to ActionIf you want to understand where crypto and AI actually intersect—and what real investors look for behind the scenes—follow The Tech Trek on Spotify or Apple Podcasts and join the conversation on LinkedIn.

Edward Khoury, CTO at Jump, joins Amir to unpack what it really means to lean into discomfort as AI transforms engineering. From redefining craftsmanship in the age of AI-generated code to helping teams evolve their skill sets, Edward shares how he’s creating space for experimentation without losing focus on delivery, culture, or shareholder value.This is a conversation about leadership in motion—where the future of engineering isn’t just about writing code faster, but about reshaping how teams learn, build, and think.Key Takeaways• Why leaders must intentionally give engineers time and space to experiment with AI tools• How to balance individual learning with organizational goals and KPIs• The rise of the “product-focused engineer” and what it means for the next generation of builders• Why platform engineering is becoming critical for scaling AI adoption• How embracing discomfort leads to resilience and competitive advantageTimestamped Highlights1:29 — What “leaning into an uncomfortable world” means for engineers today3:40 — Creating space for experimentation while keeping delivery on track6:06 — Balancing freedom to explore with standardization and shared learning8:34 — Navigating the fear that AI will replace engineering roles14:11 — How productivity gains will shift bottlenecks from engineering to product20:31 — Teaching engineers to think like product owners23:45 — Why user adoption will become the next big challenge as development accelerates26:58 — How AI tooling is already shaping hiring plans and org designOne Idea That Stuck“You can’t push everyone through the door—you just have to open it.”Pro TipsEdward suggests pairing engineers with product partners earlier in the process—not after specs are written—to help them understand business context and build stronger product intuition.Call to ActionIf this episode made you think differently about leadership in engineering, share it with a teammate who’s navigating AI adoption. Subscribe to The Tech Trek on Apple Podcasts or Spotify, and follow Amir on LinkedIn for more conversations with the builders shaping the future of tech.

Rick Doten, cybersecurity startup advisor and AI researcher, joins the show to unpack how AI-assisted development is reshaping software—and what it means for security. From startups rushing to ship faster code to the unseen risks of “vibe coding,” Rick explains how engineering teams can balance innovation with secure, resilient design.If your dev team is using AI tools to boost velocity, this conversation might change how you think about your SDLC, code review, and even your threat model.Key Takeaways• AI-assisted coding speeds up output but can multiply security risks if context isn’t baked in.• Startups often trade speed for security early on—and that can be expensive to unwind later.• Traditional fundamentals like OWASP and BSIMM still apply, even as architectures evolve with agents and MCP.• AI creates a widening gap between companies that can secure their models and those that can’t.• “Vibe coding”—non-devs using AI to build—introduces a new wave of shadow code leaders must prepare for.Timestamped Highlights[02:09] The real range of how startups are using AI-assisted tools—and why security is often an afterthought.[05:12] Why AI-generated code is not just another form of third-party code.[09:40] The hidden risk: code volume grows faster than your ability to secure it.[15:51] How AI is widening the gap between resource-rich enterprises and everyone else.[18:25] The new fragility of systems—where architecture and resilience start to break.[22:07] Rethinking SDLC: integrating AI tools without losing security fundamentals.[25:29] “Vibe coding” and what happens when non-engineers start shipping code.Memorable Insight“AI isn’t lazy like humans—it doesn’t just fix one thing. It rewrites everything. That’s why every line has to be re-scrutinized.”Pro TipsIf your startup doesn’t have a dedicated security function yet, start with the basics: integrate OWASP checks into your CI/CD, use non-human accounts correctly, and automate code review gates early. Don’t wait until production to harden your systems.Call to ActionIf this episode sparked ideas for your dev or security team, share it with someone who’s experimenting with AI-assisted tools. Follow The Tech Trek for more conversations at the intersection of engineering, AI, and leadership.

What happens when a telehealth CTO takes AI beyond code generation and into the heart of the software development lifecycle?Matt Buckleman, Co-founder and CTO of Hone Health, joins to share how his team uses AI not just to accelerate development, but to rethink workflows—from documentation and traceability to sentiment analysis across teams. This episode dives deep into how he’s blending engineering fundamentals with modern AI agents to create a smarter, more adaptive SDLC.Key Takeaways• Why AI’s biggest near-term value isn’t in code generation—it’s in improving process and communication.• How Hone Health evolved its SDLC from three engineers on Slack to a 30+ person organization using agent-based automation.• The hidden advantage of consistent naming conventions and traceability when applying AI to production systems.• How AI can automate the “soft” but essential parts of software delivery, like documentation, requirements gathering, and developer sentiment tracking.• What it takes to create feedback loops that make AI genuinely useful inside technical workflows.Timestamped Highlights[02:09] Flexible, anti-dogmatic SDLC: why strict process frameworks can slow learning.[09:00] When more engineers doesn’t equal more output—the hidden cost of coordination.[13:00] AI for experts vs. juniors: why prompting mirrors domain mastery.[18:38] Offloading the unglamorous work: how LLMs now handle code comments, documentation, and swagger generation.[23:50] Shared ownership and experimentation: how Hone’s engineering team pilots new AI tools.[28:40] Turning meeting transcripts into smarter requirements: how agents refine specs automatically.[32:00] Using sentiment analysis to spot risk and burnout across engineering projects.Memorable Line“LLMs are great at patterns in text—and that makes them better than people at understanding what’s really happening inside your workflow.”Call to ActionIf you enjoyed this conversation, follow The Tech Trek on Spotify or Apple Podcasts for more real-world discussions at the intersection of AI, engineering, and leadership. Share this episode with a teammate rethinking their own SDLC.

Yosi Dediashvili-Drossos, Co-Founder and CTO of City Hive, joins Amir to unpack how a hyper-focused approach helped transform a niche idea into the dominant e-commerce platform for the liquor industry. From bootstrapping into a complex, highly regulated space to giving small brands a voice, Yosi shares how City Hive built the connective tissue across the entire alcohol supply chain—bridging brands, distributors, and local retailers through data, trust, and mission-driven execution.Key Takeaways• Why narrowing your focus often creates more growth than going broad• How City Hive turned regulatory complexity into a competitive advantage• The power of connecting all layers of an industry—brands, distributors, and retailers—through one platform• Why small, single-SKU brands now have a real chance to compete• What founders need to know before tackling a regulated industryTimestamped Highlights00:36 – The origin story: building an e-commerce engine for liquor stores04:00 – When niche focus becomes a gateway to full-scale growth06:49 – Why the liquor supply chain is one of the most fragmented in the U.S.10:22 – The uphill battle for small brands trying to reach consumers12:16 – Empowering micro-brands through digital visibility and data16:42 – How narrowing your scope can actually open new opportunities19:48 – Lessons from scaling in a regulated market22:49 – Yosi’s advice for founders navigating complex industriesStandout Moment“You can’t solve everything at once. Focus on the next real problem that’s in front of you—if you do that well, you’ll eventually build something that can solve the bigger picture.”Pro TipsFor founders entering regulated markets: Don’t start by trying to fix the system. Start by understanding one piece of it deeply enough that you can actually move it forward.Call to ActionIf you enjoyed this episode, follow The Tech Trek for more conversations with founders building technology that powers real-world industries. Share this episode with someone tackling a complex market—there’s a lot they’ll take away.

What happens when a 17-year Google veteran starts over with a 10-person AI startup? David Petrou, founder and CEO of Continua AI, joins Amir to unpack what it really takes to go from Big Tech stability to startup chaos. They dive into what to keep, what to unlearn, and how to build a high-performing team when everyone has to wear ten hats.From career ladders to “vibe coding,” David shares a candid look at the tradeoffs, mindset shifts, and hard lessons behind scaling something new in AI.Key Takeaways• Career ladders are a luxury—startups win by hiring for adaptability and shared ownership, not rigid progression.• Moving from Big Tech to startup means trading resources for speed—and rediscovering why building things is fun again.• Productivity at small teams thrives on decisive action and ruthless prioritization, not endless debate.• AI is transforming software development—but human experience still defines whether the tools actually deliver.• The best retention strategy in a startup: keep the work interesting and the problems worth solving.Timestamped Highlights[00:48] How Continua AI brings “social AI” into group chats[05:35] Why hiring for collaboration beats hiring for raw talent[08:51] The real gap between Big Tech engineers and startup engineers[11:19] What David had to unlearn after 17 years at Google[18:58] How limited resources force sharper technical decision-making[22:32] Productivity at early-stage startups—making faster decisions and moving forward[26:41] “Vibe coding,” AI-assisted development, and why experienced engineers adapt fasterMemorable Moment“It’s much better to be a few degrees off from optimal and moving fast than stuck in indecision for two weeks.” — David PetrouPro TipsWhen hiring for an early-stage startup, focus less on titles or ladders and more on whether the person thrives without structure. The ability to figure things out independently is the best predictor of success.Call to ActionIf this episode gave you a fresh take on startup leadership, share it with someone thinking about making the leap from Big Tech to founder life. Follow The Tech Trek for weekly insights from leaders shaping the future of tech and AI.

When you step into a new leadership role, do you prefer to build a team from the ground up—or inherit one that already exists?Ashwin Baskaran, VP of Engineering at Mercury, joins the show to unpack what really changes between these two scenarios—and what stays the same. From managing team dynamics to molding culture and earning trust in the first 90 days, Ashwin shares practical frameworks every engineering leader can apply.Key Takeaways• Building and inheriting share more similarities than most leaders realize—the principles of empathy, awareness, and low ego are universal.• When inheriting a team, awareness is your first superpower. Learn the organization before making moves.• Building from scratch gives freedom, but also more ways to make mistakes if you over-index on hiring people who think like you.• The best leaders telegraph intent early and seek alignment through action, not reassurance.• Feedback should be about context and priorities, not personal validation—it builds credibility and trust faster.Timestamped Highlights00:45 — The hidden overlap between building and inheriting a team03:25 — Why self-awareness and low ego are critical when replacing a leader06:51 — How “building” can lead to blind spots if you hire for similarity11:38 — Finding alignment between company values and your leadership style15:25 — How to read the room and earn feedback in your first 90 days21:47 — What to look for when interviewing for a role where you’ll inherit a teamA Line That Stuck“You want to find a problem that the team and company care about—and solve it in a way that feels aligned with their values.”Call to ActionIf this conversation helps you think differently about leadership transitions, share it with someone who’s stepping into a new role. Subscribe to The Tech Trek for more conversations that bridge technical leadership with real-world growth.

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.