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

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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

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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

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Simon Lam, VP of Engineering at M1, joins the show to unpack one of the trickiest topics in tech careers: how engineers can build influence without a formal leadership title. Too often, influence is mistaken for charisma or public speaking—but Simon explains why it’s really about consistent impact, trust, and understanding how change happens inside teams. If you’re an IC who feels stuck at the “senior wall” or a manager wondering how to better evaluate career growth, this conversation delivers clarity and actionable insight.

Key Takeaways

• Influence isn’t charisma—it’s the result of consistent impact and trust over time

• Engineers can build influence at any stage, from junior to staff, by solving problems and being reliable

• Career progression should tie back to impact, not just who speaks the loudest in the room

• Change management offers a practical lens for understanding influence in technical settings

• Dual career tracks mean engineers don’t need to move into people management to keep advancing

Timestamped Highlights

01:39 Why influence is often misunderstood in engineering careers

05:12 Influence vs charisma—and why you don’t need to be an extrovert

08:47 The virtuous cycle of impact leading to influence

13:20 Are companies biased toward rewarding outspoken engineers?

17:21 Practical ways ICs can start building impact today

22:48 Why you don’t need to manage people to have a leadership career

A line worth remembering

“Consistent impact is how you build influence.” — Simon Lam

Call to Action

If this episode sparked new ways to think about your own career, share it with a teammate who’s navigating the same questions. Follow the show for more conversations with leaders shaping the future of engineering.

CJ King, CTO at Torc Robotics, joins the show to talk about the future of autonomous trucking at scale. Instead of asking “can we build one self-driving truck?” Torc is asking, “how do we safely put 10,000 on the road?” From supply chain transformation to regulatory hurdles, CJ breaks down what it really takes to bring production-ready autonomous semis into the market and why the ripple effects will reach far beyond trucking.

Key Takeaways

• Scaling autonomous vehicles isn’t about prototypes—it’s about building production-ready systems from the ground up.

• Trucks face unique technical challenges, from 1,000-meter perception needs to fully redundant systems that can’t rely on cloud compute.

• Removing driver limitations could extend operations from 8 hours a day to 20, unlocking major gains in supply chain efficiency.

• Regulatory collaboration is critical—success depends on alignment with federal and state agencies, law enforcement, and logistics partners.

• Adoption will come in step-functions: once proven safe and reliable, logistics companies are ready to adopt at scale.

Timestamped Highlights

00:45 – Torc’s focus on hub-to-hub autonomous trucking

02:03 – Why scaling to thousands of trucks matters more than building one prototype

06:48 – The unique technical problems of trucks vs. passenger cars

09:25 – How extended operating hours reshape logistics and supply chains

14:17 – Working with regulators and law enforcement to ensure safety and compliance

17:42 – AV3.0, synthetic data, and billions of miles of training for safer systems

22:31 – Building public trust and societal acceptance of autonomous trucking

25:21 – Why large-scale adoption will happen in step functions, not trickles

A Line That Stuck With Us

“Our bare minimum is to drive as good as a human—our mission is to be safer than one.” – CJ King

Call to Action

If you enjoyed this episode, share it with someone who cares about the future of tech and logistics. Make sure to follow the show so you never miss conversations that dig into how technology is reshaping our world.

Joseph Krause, co-founder and CEO of Radical AI, joins the show to break down how scientific discovery is being reinvented. From the limitations of the traditional trial-and-error model to the rise of AI-driven self-driving labs, Joseph explains how science is moving from slow, serial processes to a parallel model that unlocks breakthroughs at scale. He also dives into the economics of materials, why big companies can’t pivot fast enough, and how the role of scientists is being transformed.

Key Takeaways

The old model of science is serial: slow, linear, and limited by human capacity to read, experiment, and analyze.

Negative results—failed experiments—are the true fuel for breakthroughs, but they’re rarely captured or shared.

Self-driving labs powered by AI create a “materials flywheel,” running 30,000+ experiments a year and learning continuously.

Big corporations are trapped by the innovator’s dilemma and talent challenges, leaving space for startups to lead.

Scientists in the future will focus less on repetitive lab work and more on shaping hypotheses and applying intuition at scale.

Timestamped Highlights

02:00 How science traditionally works and why it’s so slow

05:50 Why mistakes and negative results matter more than we admit

09:40 The fragmentation of research and why labs don’t share data

17:15 Inside a self-driving lab and how AI accelerates discovery

23:40 Why big material companies can’t innovate like startups

35:40 The new role of scientists in an AI-powered discovery world

Memorable Line

“You don’t get a PhD to learn to pipette—you get it to think about how and why the world will change.”

Call to Action

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John Fiedler, SVP of Engineering and CISO at Ironclad, joins the show to unpack the real challenges of technology leadership. From managing nonstop context switching to measuring success when you’re no longer shipping code, John shares hard-earned lessons on how leaders can protect their time, set priorities, and thrive in the chaos. Whether you’re moving from IC to manager or scaling as an executive, this conversation offers a candid look at what it truly takes to lead.

Key Takeaways

• Success in leadership isn’t about features shipped—it’s about execution, people, and culture.

• Context switching is constant, but leaders can design their calendars to minimize the chaos.

• Organizational size reshapes the challenge: startups reward speed, enterprises demand process.

• Protecting your time isn’t optional—leaders who don’t own their calendars quickly burn out.

• The leap from IC to manager requires starting fresh and mastering a new craft.

Timestamped Highlights

02:13 The hidden tax of context switching

06:53 How John measures success as a leader without code

10:45 What really slows executives down inside organizations

15:51 How John protects his calendar and finds focus time

24:47 The lessons every first-time manager needs to hear

A Line That Sticks

“If you don’t control your calendar, your calendar will control you.”

Call to Action

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Alex Salazar, co-founder and CEO of Arcade.dev, joins the show to unpack the realities of building enterprise agents. Conceptually simple but technically hard, agents are reshaping how companies think about workflow automation, security, and human-in-the-loop design. Alex shares why moving from proof-of-concept to production is so challenging, what playbooks actually work, and how enterprises can avoid wasting time and money as this technology accelerates faster than any previous wave.

Key Takeaways

Enterprise agents aren’t chatbots—they’re workflow systems that can take secure, authorized actions.

The real challenge isn’t just building demos but getting to production-grade consistency and accuracy.

Mid-market companies face the steepest climb: limited budgets, limited ML expertise, but the same competitive pressure.

Success starts with finding low-risk, high-impact opportunities and narrowing scope as much as possible.

Authorization is the biggest blocker today; delegated OAuth models are key to unlocking real agent functionality.

Timestamped Highlights

02:02 — Why agents are “just advanced workflow software” but harder to trust than traditional apps

04:53 — The gap between glorified chatbots and real enterprise agents that take action

09:58 — From cloud mistrust to wire transfers: how comfort with automation evolves

14:00 — Chaos at every tier: startups, enterprises, and why the mid-market struggles most

26:21 — The playbook: how to pick use cases, narrow scope, and carry pilots all the way to prod

34:38 — Breaking down agent authorization and why most RAG systems fail in practice

42:09 — Adoption at double speed: what makes this AI wave different from internet and cloud

A Thought That Stuck

“An agent isn’t an agent until it can take action. If all it does is talk, it’s just a chatbot.” — Alex Salazar

Call to Action

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