Afleveringen
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Karl Alomar, Managing Partner at M13 and former COO of DigitalOcean, joins The Tech Trek to share how being an operator changes the way you invest. He explains why M13 was built to be a truly founder-first VC firm—one that acts early, helps proactively, and builds deep relationships rooted in empathy and experience. From spotting great founders to balancing instinct and data, this episode explores how venture capital can drive better outcomes when it focuses on people as much as product.
Key Takeaways
• The most effective VCs act before problems surface, shaping a founder’s path rather than reacting to it.
• Founder–market fit often comes down to whether someone is a specialist with deep expertise or an athlete who can adapt fast.
• Empathy built through years of operating experience creates trust that fuels honest conversations and better decisions.
• Great founders lead with vision—they can inspire, recruit, and align teams behind a clear story of what’s possible.
• Even the best instincts and pattern recognition can’t outplay timing, luck, and market shifts—but reflection and learning can.
Timestamped Highlights
(01:20) How being an operator shaped Karl’s approach to venture capital
(06:48) The three kinds of investors—and why empathy gives operators an edge
(09:54) Creating a safe space where founders can share problems without fear
(14:13) Identifying “athletes” and “specialists” when evaluating founders
(20:33) Pattern matching, instincts, and the role of luck in investing
(23:50) What M13 learns from postmortems on both wins and misses
A Line That Stuck
“To do it the right way, you have to be a proactive investor, not a reactive one.”
Pro Tips
Karl suggests founders build relationships with investors who understand their world and seek out those who can help them see around corners—not just react when things break.
Call to Action
If this episode resonated, follow The Tech Trek on Apple Podcasts or Spotify and connect with Amir Bormand on LinkedIn for more conversations at the intersection of people, impact, and technology.
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In this episode of The Tech Trek, Amir sits down with Michi Kono, CTO of Garner Health, to unpack what it really takes to scale engineering leadership inside a fast growing startup. Michi shares how he balances structure and speed, why formalizing processes too early can slow innovation, and how “the Garner way” blends lessons from big tech with first principles thinking. This is a conversation about leadership maturity, cultural design, and building systems that evolve with your company’s growth.
Key Takeaways
• Leadership scale comes from knowing when to formalize processes, not just how.
• “Six months is never”: waiting on fixes usually means they will never happen.
• Feedback is a gift, and it is on leaders to create the safety for it to flow upward.
• Borrowing from big tech only works when you adapt the principles, not the playbook.
• Engineering leaders should measure success by business outcomes, not just delivery speed.
Timestamped Highlights
01:46 The first signals Michi looked for when stepping into the CTO role
03:49 Turning ad hoc collaboration into structured dependency management
06:36 Why delaying operational fixes is a silent killer for scaling teams
08:38 Building standards only when they solve real, visible problems
12:13 The art of forecasting leadership hiring and team design
14:54 Lessons borrowed from Meta, Stripe, and Capital One, and when not to use them
17:31 Defining “the Garner way” through first principles
20:59 Judging engineering performance through business impact
25:00 Creating true psychological safety for feedback across all levels
A Line That Stuck
“If we can’t execute on the roadmap that lets us actually build a successful business, then I failed as a leader. There are no excuses.”
Pro Tips
When you inherit a growing engineering organization, start by mapping dependencies, not hierarchies. Clarity around how teams interact is more valuable than adding headcount too early.
Call to Action
Enjoyed this episode? Follow The Tech Trek on Apple Podcasts and Spotify, and connect with Amir on LinkedIn for more conversations on scaling teams, leadership, and engineering culture.
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Zijn er afleveringen die ontbreken?
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Vibe coding isn’t just a new buzzword—it’s a complete shift in how engineering teams build, ship, and think. Zach Wills, Director of Engineering at Luxury Presence, joins to share how his team is rewriting the rules of software delivery using AI-assisted workflows. From Greenfield experiments to Brownfield transformations, Zach breaks down the frameworks, lessons, and mindset shifts reshaping what it means to be an engineer.
Key Takeaways
Why vibe coding feels less like automation and more like a new management skill for engineers
The real differences between Greenfield and Brownfield AI-assisted projects—and how to avoid the biggest traps
How “trusting the autonomous loop” became a core principle for speed and quality
The cultural shift that happens when developers stop typing every line of code
Why teams that embrace AI early will outpace their competition, not replace their people
Timestamped Highlights
02:20 — The moment vibe coding clicked and how it compressed days of work into hours
06:45 — Testing AI in a five-year-old codebase with tens of thousands of commits
10:45 — Engineers are becoming more like managers of autonomous agents
14:40 — The hidden emotional impact of giving up “manual” coding
17:30 — Inside Zach’s eight-rule framework for productive AI workflows
25:25 — Why SDLC as we know it is breaking apart—and what replaces it
30:00 — Why fearing AI misses the point entirely
Memorable Line
“If AI can do something I was doing yesterday, I never want to do that thing again. My value comes from what only I can do.”
Pro Tip
Start small but think organizationally. Train your engineers to lead AI, not just use it. The biggest unlock isn’t speed—it’s mindset.
Call to Action
If this conversation sparked new ideas about how your team could work smarter, follow The Tech Trek wherever you listen and connect with Amir on LinkedIn for more behind-the-scenes insights.
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From a farm in Adelaide to the front lines of AI-powered personalization.
Tullie Murrell, CEO and co-founder of Shaped, shares how he went from researcher to founder and built a platform helping businesses deliver the kind of intelligent recommendations once reserved for big tech.
We explore the mindset shifts, technical leaps, and founder lessons that shaped his path—from Meta’s AI labs to democratizing personalization for everyone else.
Key Takeaways
• The best founders know when to trade technical depth for go-to-market mastery. Tullie learned that 70% of startup success lives outside the codebase.
• Real personalization is no longer just for Meta, Amazon, or TikTok—new model architectures are closing the gap for everyone.
• Flexibility early in your career opens unexpected doors. Choosing Meta over Google gave Tullie room to explore and evolve.
• AI research isn’t just about papers—it’s about transforming how people experience products and decisions in real time.
• The future of personalization sits at the intersection of generation and intent—content created and adapted for each individual moment.
Timestamped Highlights
00:35 — What Shaped does and how it’s redefining AI-driven recommendations
03:00 — From a farm in Australia to computer science and a path to Silicon Valley
07:30 — Why joining Meta offered more freedom than Google
13:25 — The insight that sparked Shaped: how Meta’s personalization drove massive engagement
19:00 — Leaving Big Tech, embracing discomfort, and starting over as a founder
22:45 — The moment he realized go-to-market mattered more than code
29:00 — How new AI breakthroughs are rewriting what’s possible in personalization
33:55 — Real-time generation meets personalization: where we’re headed next
A standout moment
“Most founders think success is 70% product and 30% go-to-market. I learned it’s the other way around.”
Pro Tip
If you’re a technical founder, study go-to-market strategy as hard as you studied your first programming language. It’s the difference between a great product and a great company.
Call to Action
If you enjoyed this episode, share it with a founder or engineer exploring their next leap. Subscribe to The Tech Trek on Apple Podcasts or Spotify, and follow Amir on LinkedIn for more conversations at the edge of tech, leadership, and innovation.
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Jason Eubanks, Co-Founder and CEO of Aurasell, shares the path that led him from a small town in rural Ohio to building one of the most ambitious AI-driven CRM platforms on the market. His journey reveals how limited opportunity can spark relentless ambition and how early lessons in persistence shaped the mindset of a founder willing to take on giants.
Key Takeaways
• A clear purpose often starts from simple beginnings that demand creativity and discipline.
• The hardest experiences can build the confidence to face uncertainty without fear.
• Great products are born when you question accepted norms and rebuild from first principles.
• Growth happens when you move before comfort arrives.
• Progress depends on focusing on the next meaningful step rather than the entire mountain ahead.
Timestamped Highlights
[01:49] Growing up in a small Ohio town where college was rare
[05:58] Discovering technology after realizing civil engineering wasn’t the right fit
[11:17] Researching careers in a library and choosing a future in tech and sales
[17:16] Early family struggles that shaped resilience and perspective
[22:57] Building Aurasell to challenge entrenched enterprise software
[26:57] The lesson every ambitious professional needs to hear about taking risks early
A Line That Stuck
“I’ve already seen what it’s like to lose everything. So when you’ve been there, the idea of taking a big risk doesn’t feel so scary anymore.”
Pro Tips
Seek situations that stretch you. Every challenge adds another layer of experience that will serve you later.
Call to Action
If this story pushed you to think differently about risk and growth, follow the show for more founder conversations that reveal what it takes to build something lasting in tech.
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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 Conversation
Tanay 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 Highlights
00:43 The mission behind Wispr Flow and the future of voice technology01:50 Why treating people as your responsibility changes everything
03:39 Building around individual strengths and learning styles
06:23 The importance of developing great managers
10:35 Small but powerful signals of a thriving culture
12:41 The lesson that reshaped Tanay’s approach to leadership
15:50 Turning frustration into growth and creating top performers1
9:30 Interviewing for passion, not just technical skill
21:58 The three things a founder should never hand off
A line that says it all
Culture isn’t a vibe, it’s a decision you make every single day.Call to Action
Great 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 Highlights
01:20 — Why crypto moves in repeating cycles and what drives each one
03:40 — How blockchain transparency helps investors see real traction
06:00 — Evaluating crypto startups: solving problems vs. chasing novelty
10:49 — How blockchain complements and verifies AI
13:05 — The hidden risk of building around hype
15:53 — Why over-customizing your pitch can backfire
17:50 — How top VCs view pivots and founder adaptability
25:28 — The traits that signal long-term founder success
A 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 Action
If 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.
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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 advantage
Timestamped Highlights
1:29 — What “leaning into an uncomfortable world” means for engineers today
3:40 — Creating space for experimentation while keeping delivery on track
6:06 — Balancing freedom to explore with standardization and shared learning
8:34 — Navigating the fear that AI will replace engineering roles
14:11 — How productivity gains will shift bottlenecks from engineering to product
20:31 — Teaching engineers to think like product owners
23:45 — Why user adoption will become the next big challenge as development accelerates
26:58 — How AI tooling is already shaping hiring plans and org design
One Idea That Stuck
“You can’t push everyone through the door—you just have to open it.”
Pro Tips
Edward 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 Action
If 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.
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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 Tips
If 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 Action
If 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.
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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 Action
If 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.
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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 industry
Timestamped Highlights
00:36 – The origin story: building an e-commerce engine for liquor stores
04:00 – When niche focus becomes a gateway to full-scale growth
06:49 – 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 consumers
12:16 – Empowering micro-brands through digital visibility and data
16:42 – How narrowing your scope can actually open new opportunities
19:48 – Lessons from scaling in a regulated market
22:49 – Yosi’s advice for founders navigating complex industries
Standout 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 Tips
For 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 Action
If 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.
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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 faster
Memorable Moment
“It’s much better to be a few degrees off from optimal and moving fast than stuck in indecision for two weeks.” — David Petrou
Pro Tips
When 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 Action
If 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.
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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 Highlights
00:45 — The hidden overlap between building and inheriting a team
03:25 — Why self-awareness and low ego are critical when replacing a leader
06:51 — How “building” can lead to blind spots if you hire for similarity
11:38 — Finding alignment between company values and your leadership style
15:25 — How to read the room and earn feedback in your first 90 days
21:47 — What to look for when interviewing for a role where you’ll inherit a team
A 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 Action
If 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.
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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 families
Timestamped Highlights
01:26 — Jarah’s early jobs in retail and fast food and how they shaped her perspective
06:56 — Why frontline workers are less likely to be displaced by AI than software engineers
11:23 — Building against the grain: focusing on people instead of replacement tech
13:31 — Why robotics companies still hire frontline workers alongside automation
17:47 — Launching the American Labor Utilization Rate to track real work happening now
21:44 — Three pillars of WorkWhile’s mission: earning, upskilling, and financial access
25:17 — How word of mouth drives organic growth among workers and families
Memorable Line
“Even the companies building the future of automation still need people—and they’ve been our customers since day one.”
Call to Action
If 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.
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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 Highlights
02:03 — Why enterprise attention has never been harder to win
04:55 — Differentiation in a sea of lookalike AI infrastructure startups
07:34 — Cold email vs content, billboards, and unconventional channels
08:35 — The Pareto rule of enterprise revenue and why developer adoption is key
11:47 — Competing with big tech incumbents: the power of the narrow wedge
21:03 — Where the market is headed: cycles of expansion, contraction, and consolidation
A 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 Action
If 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.
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Jonathan DiVincenzo, co-founder and CEO of Impart Security, joins the show to unpack one of the fastest growing risks in tech today: how AI is reshaping the attack surface. From prompt injections to invisible character exploits hidden inside emojis, JD explains why security leaders can’t afford to treat AI as “just another tool.” If you’re an engineering or security leader navigating AI adoption, this conversation breaks down what’s hype, what’s real, and where the biggest blind spots lie.
Key Takeaways
• Attackers are now using LLMs to outpace traditional defenses, turning old threats like SQL injection into live problems again
• The attack surface is “iterating,” with new vectors like emoji-based smuggling exposing unseen vulnerabilities
• Frameworks have not caught up. While OWASP has listed LLM threats, practical solutions are still undefined
• The biggest divide in AI coding is between senior engineers who can validate outputs and junior developers who may lack that context
• Security tools must evolve quickly, but rollout cannot create performance hits or damage business systems
Timestamped Highlights
01:44 Why runtime security has always mattered and why APIs were not enough
04:00 How attackers use LLMs to regenerate and adapt attacks in real time
06:59 Proof of concept vs. security and why both must be treated as first priorities
09:14 The rise of “emoji smuggling” and why hidden characters create a Trojan horse effect
13:24 Iterating attack surfaces and why patches are no longer enough in the AI era
20:29 Is AI really writing production code and what risks does that create
A thought worth holding onto
“AI is great, but the bad actors can use AI too, and they are.”
Call to Action
If this episode gave you new perspective on AI security, share it with a colleague who needs to hear it. Follow the show for more conversations with the leaders shaping the future of tech.
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Daniel Saks, co-founder and CEO of Landbase, joins The Tech Trek to unpack the real meaning of democratizing technology. From agentic AI that works for you—not the other way around—to rethinking workflows and change management, Daniel shares why this shift is bigger than the move from on-prem to cloud. For tech leaders, founders, and operators, this episode reveals how to reclaim time, scale smarter, and prepare for the next wave of AI-native business.
Key Takeaways
• AI is moving beyond hype—it’s becoming the engine that executes real workflows and shifts power from systems to users
• Businesses that recapture saved time will unlock significant cost efficiency and growth potential
• The gap between idea and implementation is shrinking fast, but durable value will come from solving the hardest problems, not the easiest apps
• Change management is now about building AI-native workflows and cross-functional systems, not just adopting tools
• Sales and go-to-market leaders can gain an edge by mastering prompting and AI-driven enrichment today
Timestamped Highlights
00:56 — Why Landbase built GTM-1 Omni to reimagine go-to-market execution
01:40 — From on-prem to cloud to AI-native: the next major leap in democratizing technology
04:34 — Why fears about AI replacing jobs miss the bigger story of new roles and industries emerging
08:42 — How the pace of product cycles is collapsing and what that means for value creation
13:25 — Inside Landbase’s “AI Factory” model for automating workflows across functions
16:39 — What people actually do with the time they reclaim through AI-driven automation
19:23 — How AI is reshaping the role of the salesperson and why adoption speed matters
A line that stood out
“You don’t have to work for your software anymore—your software works for you.”
Call to Action
If this conversation gave you fresh ideas about how AI is reshaping business, share it with your team and subscribe to The Tech Trek on Apple Podcasts or Spotify. For more insights, follow along on LinkedIn.
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Matt McLarty, CTO at Boomi, joins the show to break down what enterprise AI adoption really looks like in 2025. From navigating the hype cycle to identifying practical first steps, Matt shares what separates companies that are seeing value from those stuck in endless pilots. If you’re a tech leader wondering how to move beyond experimentation and into measurable outcomes, this episode is your playbook.
Key Takeaways
• AI adoption is not binary—it’s a spectrum, and success depends on linking it to business value, not just “using AI.”
• Orientation matters: every company needs an honest assessment of where they are on their digital maturity curve before jumping in.
• Small, low-risk bets build the organizational muscle memory required for bigger wins.
• The fastest wins often come from augmenting existing automation rather than chasing moonshots.
• Companies that succeed treat AI as a tool to solve business problems, not as an end goal.
Timestamped Highlights
01:38 – Why AI’s hype cycle feels like “Mount Everest” compared to cloud and mobile
04:50 – Why AI adoption can’t be compared to past waves like blockchain or cloud
07:36 – The hidden foundation: digital transformation work still matters
11:11 – The inversion that changes everything: AI isn’t the goal, business outcomes are
16:26 – Defining “adoption” as a multi-dimensional spectrum, not a checkbox
19:50 – How to recover if your first AI projects fall short
28:04 – Building adaptability as a core enterprise competency
31:25 – The common traits of companies succeeding with AI right now
A standout moment
“AI isn’t the end goal—it’s just another tool. The real question is, what business problems can we finally solve with it?” – Matt McLarty
Call to action
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Vipin Kumar, Head of CUSO IB Data Strategy and Analytics at Deutsche Bank, joins me to unpack one of the toughest problems in financial services: managing data quality in a highly regulated industry. From the outside, it might look like a box-checking exercise. In reality, it’s a complex mix of legacy systems, global frameworks, regulatory controls, and the constant push to balance defensive compliance with offensive business value. Vipin makes it real with examples that connect directly to how we all experience data in daily life.
Key Takeaways
Data quality isn’t just about accuracy—timeliness, completeness, and consistency all matter, especially when billions are on the line.
Regulations push banks into “defensive” strategies, but there’s growing opportunity to apply “offensive” strategies that use data for prediction, analytics, and competitive edge.
Measuring effectiveness requires agreement between data producers and consumers, with preventive and detective controls working together.
AI and machine learning are starting to automate checks, spot patterns, and even strengthen anti-money laundering defenses.
Timestamped Highlights
00:45 What data quality means in a regulated industry
03:15 The challenges of managing fragmented legacy systems
06:40 How producers and consumers measure effectiveness of frameworks
09:30 The pizza delivery analogy for making sense of data quality
14:20 Why accuracy is harder than timeliness or completeness
16:50 The role of AI and machine learning in improving governance
19:20 Shifting from defensive compliance to offensive strategy in banking
22:40 Regulators testing AI-driven approaches to anti-money laundering
Memorable Quote
“Producer has preventive controls. Consumer has detective controls. True data quality happens only when both align 100%.” — Vipin Kumar
Call to Action
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Marty Ringlein, co-founder and CEO of Agree.com, joins Amir to unpack why history always repeats itself in technology and what that means for the AI era. From the telephone to the automobile to ChatGPT, the biggest shifts have rarely been things people asked for—they were inventions that reshaped behavior once adopted. Marty explains why skepticism always comes first, how fear fuels resistance, and why optimism is usually rewarded. He also shares how Agree.com is rethinking contracts and payments by automating the painful parts of sales workflows.
Key Takeaways
The most transformative inventions weren’t requested—they emerged through evolution and network effects.
Human resistance to new tech often comes from energy costs of relearning, not the tech itself.
AI isn’t eliminating jobs—it’s freeing people from low-value work so they can focus on bigger challenges.
Every wave of disruption (printing press, cars, internet, mobile, AI) begins with fear, then proves to be a net positive.
Timestamped Highlights
00:51 — Why Agree.com calls itself “a better DocuSign” and how it integrates signatures, invoicing, and payments
02:06 — The history of inventions nobody asked for and why they stuck
05:41 — Human pessimism vs optimism when confronting new technologies
09:05 — Why fears around AI echo the same debates once had about books, cars, and the cloud
13:38 — How automation frees salespeople and engineers to focus on higher-value work
18:51 — Are there technologies that have been net negative for society? Marty’s take
23:21 — Why every generation thinks “this time it’s different”
Memorable Quote
“The biggest things that will change our lives are the ones we don’t even know to ask for yet.” — Marty Ringlein
Call to Action
If you enjoyed this episode, share it with a colleague who’s navigating the AI conversation. Follow The Tech Trek for more conversations that cut through the noise on tech, leadership, and the future of work.
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