The Tech Trek

Desiree Lee, one of the Business CTOs at Armis, joins Amir to unpack one of the toughest realities in cybersecurity: the biggest risks aren’t always technical, they’re human. From phishing and deepfakes to the way AI is reshaping both attackers’ and defenders’ playbooks, Desiree shares hard-won insights on what companies should actually prioritize. If you’re a tech leader navigating the expanding attack surface, this episode will sharpen how you think about security in the AI era.

Key Takeaways

• Most breaches stem from human behavior, not lack of technology.

• Attackers adopt AI faster because there’s no downside for them—defenders must catch up.

• Fundamentals like patching and asset inventory still make or break resilience.

• AI can reduce noise for security teams by spotting patterns in overwhelming data.

• Small and midsize businesses will benefit from AI-driven tools that lower the barrier to effective security.

Timestamped Highlights

00:34 — How Armis evolved from asset inventory to full security solutions

03:30 — Why security failures are more about psychology than technology

07:32 — The deepfake CFO story and why training alone can’t solve phishing risks

09:18 — Why most enterprises struggle with basics like patching and automation

11:41 — Where AI gives defenders an edge in processing massive data sets

18:33 — Practical ways AI can ease alert fatigue and vulnerability management

21:03 — The overlooked need to label assets by business criticality

A Moment Worth Remembering

“There is no penalty on the attacking side for embracing AI. It’s only good things for them. So they will adopt it quickly.” — Desiree Lee

Resources Mentioned

Armis: armis.com

Desiree Lee on LinkedIn: linkedin.com/in/desireedlee

Pro Tips

Tagging assets with their business criticality is one of the simplest, highest-impact steps companies can take. It turns asset inventories from static lists into real decision-making tools for AI-driven defense.

Call to Action

If you found this episode valuable, share it with a colleague who’s thinking about security and AI. Subscribe on your favorite podcast platform so you never miss future conversations with tech leaders pushing the edge of what’s possible.

Chris Ghanbarzadeh, Senior Director of Engineering at Game Changer, joins the show to unpack the shifting landscape of mobile development. From balancing quality with speed to navigating the rise of AI-assisted engineering, Chris shares how teams can adapt to new tools, methodologies, and user expectations. If you’re building mobile products—or leading teams that do—this conversation will sharpen how you think about stability, velocity, and the future of software development.

Key Takeaways

• Why stability is the true driver of long-term speed in mobile development

• How shifts away from native development are reshaping efficiency and delivery

• The role of AI toolchains in accelerating experimentation and engineering workflows

• Why leaders should avoid top-down mandates when introducing AI and instead create space for experimentation

• How AI may reshape software methodologies, from Scrum to requirements gathering and beyond

Timestamped Highlights

01:45 – The eternal challenge of balancing quality with velocity in mobile engineering

06:42 – Why “stability is speed” and how tech debt slows teams down

09:15 – The move away from native development and how it impacts engineering teams

13:27 – Experimenting with AI toolchains and finding the right balance of autonomy and structure

18:05 – Standardization vs freedom: the future of AI tools in the enterprise

22:23 – Will AI change the very way we build and support software?

A standout moment

“Stability is speed. If you’re constantly chasing velocity without shoring up quality, you end up slower in the long run.”

Resources Mentioned

• Game Changer app – scorekeeping and live streaming for youth sports

Pro Tips

Give your teams space to experiment with AI tools. Celebrate learning—even when experiments fail—so engineers build confidence and marketable skills for the future.

Call to Action

Enjoyed this conversation? Follow the show for more insights from engineering and tech leaders, and share this episode with a colleague who’s navigating mobile development or exploring AI in their workflows.

Moinul Khan, co-founder and CEO of Aurascape, joins the show to unpack what it takes to build a cybersecurity startup in the age of AI. With decades of experience at companies like Zscaler, Palo Alto Networks, and FireEye, Moinul shares why AI demands an entirely new security stack, how agentic AI is changing the game, and why prevention—not dashboards—must be at the heart of real solutions. If you’re a tech leader navigating the future of AI and security, this is a conversation you won’t want to miss.

Key Takeaways

• Traditional security stacks can’t keep up with dynamic, evolving AI tools

• Prevention-focused solutions matter more than dashboards or API visibility

• Agentic AI is both an opportunity and a security challenge that startups must address

• CISOs are rethinking consolidation and becoming more open to best-of-breed solutions in AI security

• Building with a long-term prevention mindset creates stronger, more resilient startups

Timestamped Highlights

00:37 — Aurascape’s mission to deliver an all-encompassing AI security solution

02:27 — The “aha” moment: why legacy firewalls and proxies can’t secure AI

08:23 — How Aurascape’s vision has evolved from public AI tools to securing private and third-party applications

13:17 — Agentic AI, MCP protocols, and why startups need to secure the next wave of AI agents

16:44 — Best-of-breed vs consolidation: where the security market is really heading

20:37 — Advice for founders: why prevention-first is the only real path to solving security problems

A standout moment

“If you try to patch what you have built in the last 20 years, you will fail. If you want to secure AI, you have to build your entire stack from the ground up.” — Moinul Khan

Resources Mentioned

Aurascape.ai

Pro Tip

Don’t build for a quick exit. Focus on prevention, even if it’s the harder road—it’s what truly solves customer problems in cybersecurity.

Call to Action

If you enjoyed this episode, share it with someone exploring AI security. Subscribe or follow the show for more conversations with the builders shaping the future of tech.

Cat Miller, CTPO of Talkiatry, shares her unconventional path from coding to leadership, including a detour into acting before rising into executive roles. She talks candidly about the realities of building a tech career you actually want, navigating transitions from engineering to management, and what it takes to succeed at the VP and C-level. This conversation is packed with lessons for anyone in tech who’s asking themselves, “What’s next for me?”

Key Takeaways

• Career paths in tech don’t have to be linear. Detours can provide perspective that makes you a stronger leader.

• Early dissatisfaction with day-to-day coding doesn’t mean you don’t belong in technology—it often evolves into broader roles.

• Doing your current job really well is often the fastest way to position yourself for growth when opportunity knocks.

• Building a network of peers and mentors is essential when stepping into senior leadership.

• Self-awareness and documenting your wins helps you stay grounded and measure progress at the executive level.

Timestamped Highlights

01:29 — Why coding wasn’t fulfilling long-term and how Cat thought about her next move

04:54 — Leaving a stable job to explore acting, and how planning made the risk manageable

10:35 — Returning to tech and finding the “perfect fit” role that shifted her career trajectory

16:35 — Rethinking work-life balance and how mission-driven work changes the equation

19:17 — Lessons from moving from VP to C-suite and the role luck and preparation both play

24:40 — Why building a strong peer network is critical once you reach the CTO level

29:14 — Tracking wins and staying accountable for your own performance as a leader

Memorable Line

“It just feels really gross to be bad at your job. So why wouldn’t you always do your best to be good at it?”

Pro Tips

Write down your team and personal wins each quarter. It helps you see progress that isn’t always obvious in the day-to-day grind.

Stay Connected

If this episode resonated with you, share it with someone who’s navigating their own tech career path. Follow the show for more conversations with leaders who’ve carved their own way forward.

What does it take to reimagine how hardware products are built in a world moving at the speed of AI? Michael Corr, founder and CEO of Duro, shares how he turned two decades of experience in engineering and manufacturing into a modern platform that helps hardware teams move faster and smarter. From journaling early product ideas to navigating the relentless pace of innovation, Michael reveals what it really means to be a founder when the path is anything but straight.

Key takeaways

• Why traditional hardware manufacturing processes create hidden risks—and how software can solve them

• The journaling habit that helped shape Duro’s first product features

• How to balance investor demands with long-term product vision

• The danger of chasing every shiny object as a CEO and how to filter noise for your team

• Why adaptability matters more than rigid 5-year plans in today’s tech landscape

Timestamped highlights

00:36 — How Duro is reinventing product lifecycle management for hardware teams

05:39 — “If I were king for a day…” the origin story of Duro

06:52 — The role of note-taking and journaling in building a company from scratch

09:31 — Staying true to a mission while adapting to market and investor pressures

14:54 — The trap of chasing every customer request and how to avoid burning out your team

19:03 — Why looking beyond 18 months is mostly speculation in a fast-changing industry

Memorable insight

“All we can really focus on is the next 12 to 18 months—everything beyond that is just speculation.”

Resources mentioned

Duro website: getduro.com

Pro tip

When you’re leading a fast-moving company, not every customer request deserves a green light. The best founders know when to say no, even to a big check, to protect long-term focus.

If you enjoyed this episode, follow the show for more conversations with tech leaders shaping the future of software, hardware, and everything in between.

Sean Neville, co-founder and CEO of Catena Labs (and co-founder of Circle), joins the show to explore the rise of AI economics and what it means for the future of payments, trust, and financial systems. From stable coins powering machine-to-machine transactions to identity layers for AI actors, Sean unpacks the building blocks that could reshape how value flows online. This episode is for anyone curious about the intersection of AI, crypto, and the next era of digital finance.

Key Takeaways

• AI actors are evolving into full economic participants, capable of executing payments and workflows.

• Stablecoins provide a more efficient and borderless payment rail compared to legacy systems, especially for AI-driven transactions.

• The biggest hurdle isn’t technology—it’s trust, identity, and accountability in agent-to-agent interactions.

• B2B use cases are likely to adopt AI-powered payments faster than consumer markets due to inefficiencies in existing flows.

• AI to human payouts and human to AI pay-ins will likely arrive before true AI-to-AI payment systems go mainstream.

Timestamped Highlights

00:33 — What Catena Labs is building: a regulated AI-native financial institution

03:16 — Why the internet is becoming agent native and what that means for AI economics

05:35 — The trust hurdle: how AI can move from 60% reliable to 99.9% through tuning and workflows

08:38 — Why legacy payment rails aren’t built for AI actors and how stablecoins change the game

11:59 — The missing piece: agentic identity and why it matters for accountability

15:48 — Could AI actors one day open bank accounts? Building toward semi-autonomous financial participation

19:06 — Why B2B transactions will likely see AI payments before consumers do

24:26 — Stablecoins vs. crypto: why digital dollars are the foundation for AI-native payments

Memorable Line

“If I can’t trust a chatbot to get a chocolate cake recipe right, how can I trust it with my money? Yet at the same time, this is the worst it will ever be—it’s only getting more capable at an unprecedented pace.” – Sean Neville

Resources Mentioned

Catena Labs – catenalabs.com

Sean on X – @PSNeville

Call to Action

If this conversation got you thinking about the future of AI and finance, share it with a colleague who’s curious about the space. Don’t forget to follow the show so you don’t miss the next episode.

Sara Wyman, founder and CEO of Stackpack, joins me to share her journey from investment banking and a Wharton MBA to launching a company that’s redefining how finance and operations teams manage vendors. From surviving the Bear Stearns collapse to scaling Etsy and Affirm through IPOs, Sara’s career has been built on spotting patterns and acting with conviction. In this episode, she breaks down how she validated her idea with 75 CFOs before writing a line of code, why timing and conviction matter more than a perfect resume, and what it really takes to leave the safety of corporate life to build something of your own.

Key Takeaways

• Why solving a problem you’ve lived through yourself is the best foundation for a startup

• How interviewing potential customers before building can double as both research and sales

• Why founders should outsource what they’re not great at instead of spinning wheels

• The hidden advantage of years of work experience when stepping into a founder role

• Why pace setting—not just hiring—is one of the founder’s most critical responsibilities

Timestamped Highlights

00:39 — What Stackpack does and how it helps finance teams gain full visibility into spend and contracts

02:10 — Lessons from investment banking, the Lululemon IPO, and the realization she wanted to be the CEO, not the banker

04:30 — Spotting the problem of vendor chaos and validating it through 75+ CFO conversations

07:12 — The leap from corporate security to founder risk and why timing mattered more than age

12:47 — A different founder path: starting with customers and funding before building the team

17:15 — Why the stereotype of the 24-year-old coder isn’t the reality of most successful exits

19:45 — Hard-earned lessons: outsource what you don’t excel at and embrace the founder role as a pace setter

A Standout Moment

“If you’re not awesome at something, outsource it or find the person that is. You don’t get bonus points for struggling through work that isn’t your strength.”

Pro Tip

Talk to customers before you build. Sara’s early interviews not only validated her idea but converted into her first paying design partners.

Call to Action

If Sara’s journey resonated with you, share this episode with someone considering the founder path. Don’t forget to follow the show on your favorite platform so you never miss stories like this one.

Troy Astorino, co-founder and CTO at PicnicHealth, joins Amir to unpack one of healthcare’s most stubborn problems: fragmented medical records. Troy shares how Picnic Health is using AI to unify patient data, cut through friction, and improve both individual care and clinical research. This conversation dives into the technical, regulatory, and human sides of healthcare data—and why accuracy matters more than ever.

Key Takeaways

• Why interoperability in healthcare has failed despite billions invested

• How AI transforms messy, inconsistent records into unified patient data

• The critical role of low-friction design in patient adoption

• Balancing accuracy, human oversight, and scalability in medical AI

• What recent FDA guidance signals about the future of AI in healthcare

Timestamped Highlights

00:40 — How Picnic Health helps patients and researchers get all their records in one place

05:16 — Why data portability across EMRs is still broken despite decades of effort

09:40 — Friction as the biggest barrier to patient adoption (and why it matters for outcomes)

10:42 — Inside Picnic’s AI pipeline: from raw documents to unified patient profiles

17:18 — Tackling accuracy: expert-level thresholds, guardrails, and continuous auditing

24:47 — Why AI is judged against perfection while humans get a pass on errors

29:45 — The FDA’s evolving approach to regulating AI in healthcare

A thought that stands out:

“Having systems that don’t just work in theory but actually work in practice—because they’re low friction—is critical for real usage in healthcare.”

Resources Mentioned

• Picnic Health: https://picnichealth.com

• FDA Draft Guidance on AI in Healthcare (2024)

• HL7 standards overview (for context on interoperability)

Pro Tips for Tech Leaders

Think about adoption the way Picnic Health does: remove friction first. Even the most sophisticated AI solution fails if the user experience creates barriers. Start with the end user, not the system.

Call to Action

If you found this conversation valuable, share it with someone working in health tech or data science. Subscribe to The Tech Trek on Apple Podcasts and Spotify so you never miss new insights on where tech and leadership intersect.

Pritesh Patel, Director of AI at Fisher Phillips, joins The Tech Trek to unpack how AI is reshaping knowledge-based businesses and what that means for industries like law, consulting, and beyond. From shifting revenue models to practical adoption challenges, Pritesh shares how firms can embrace AI early, stay competitive, and unlock new opportunities. This episode is a roadmap for leaders who want to move from incremental efficiency to real transformation.

Key Takeaways

• AI is disrupting the traditional “revenue per person” model, pushing knowledge firms toward more outcome-driven approaches

• Early adoption matters: experimenting now gives companies a competitive edge rather than playing catch-up later

• Success in AI transformation starts with deeply understanding business outcomes, not just implementing new tools

• Human expertise will remain essential, but AI will free professionals to focus on higher-level, creative problem-solving

• Iteration speed is a critical advantage: nimble firms can innovate faster than larger, slower-moving competitors

Timestamped Highlights

01:32 – Defining knowledge-based businesses and why AI is changing the game

04:33 – How old business models are being disrupted by automation and new expectations

08:55 – Translating technical expertise into outcomes that resonate with non-technical stakeholders

14:23 – A framework for identifying high-impact opportunities before choosing a technology solution

16:34 – Building an innovation engine through fast prototyping and iteration

21:16 – The role of trust, validation, and regulation in the future of AI-powered knowledge work

Quote of the Episode

“You don’t want to be in a situation where you’re adapting late because of competition. If you start early, you can shape the future of your industry instead of reacting to it.” — Pritesh Patel

Pro Tips

• Focus first on business outcomes, not technology. Identify the most impactful functions, then explore how AI can enhance them

• Use prototyping to spark ideas and build momentum. A working demo creates buy-in faster than presentations

Call to Action

If this conversation sparked ideas about how AI could reshape your business, share the episode with a colleague who would benefit. Subscribe to The Tech Trek for more conversations with leaders driving the future of technology, and connect with us on LinkedIn to continue the discussion.

Udhay Durai, Executive Director of Data Platform and Engineering at Evolus, joins the show to unpack his journey from consulting to leading enterprise data teams. He shares how the high-pressure, quick-delivery mindset from consulting can be a secret weapon in a corporate setting, and what changes when you shift from delivering outputs to owning long-term outcomes. From navigating different types of pressure to building sustainable systems that scale, Udhay offers candid insights for anyone considering a similar transition.

Key Takeaways

• The consulting mindset of speed and adaptability can be a major advantage in enterprise roles when paired with long-term thinking

• Pressure exists in both consulting and full-time roles, but the nature of that pressure—and how you manage it—differs greatly

• Consultants focus on outputs, while enterprise leaders are measured on outcomes that stand the test of time

• Generalist experience across domains can complement deep subject matter experts in a corporate team

• Bringing incremental change and a “flywheel” approach from consulting can accelerate enterprise delivery without sacrificing reliability

Timestamped Highlights

01:34 — Why quick wins and stakeholder empathy are essential in consulting

03:28 — How the pressure changes when you own the platform instead of just delivering a project

05:32 — Outputs vs outcomes and why the shift matters in enterprise leadership

09:48 — Turning generalist consulting experience into an asset in a full-time role

11:43 — The biggest mindset and skill gaps to address when making the switch

13:42 — Adapting consulting habits for long-term success in product companies

Quote of the Episode

“Pressure is there in both consulting and enterprise. The difference is in consulting you deliver outputs—enterprise leaders deliver outcomes.”

Resources Mentioned

Udhay Durai on LinkedIn — https://www.linkedin.com/in/udhay-durai

Call to Action

If this episode gave you new perspective on career transitions, share it with a colleague or friend who’s considering a similar move. Follow the show for more real-world tech leadership conversations.