The Tech Trek

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.

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 systemsTimestamped Highlights01:44 Why runtime security has always mattered and why APIs were not enough04:00 How attackers use LLMs to regenerate and adapt attacks in real time06:59 Proof of concept vs. security and why both must be treated as first priorities09:14 The rise of “emoji smuggling” and why hidden characters create a Trojan horse effect13:24 Iterating attack surfaces and why patches are no longer enough in the AI era20:29 Is AI really writing production code and what risks does that createA thought worth holding onto“AI is great, but the bad actors can use AI too, and they are.”Call to ActionIf 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.

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 todayTimestamped Highlights00:56 — Why Landbase built GTM-1 Omni to reimagine go-to-market execution01:40 — From on-prem to cloud to AI-native: the next major leap in democratizing technology04:34 — Why fears about AI replacing jobs miss the bigger story of new roles and industries emerging08:42 — How the pace of product cycles is collapsing and what that means for value creation13:25 — Inside Landbase’s “AI Factory” model for automating workflows across functions16:39 — What people actually do with the time they reclaim through AI-driven automation19:23 — How AI is reshaping the role of the salesperson and why adoption speed mattersA line that stood out“You don’t have to work for your software anymore—your software works for you.”Call to ActionIf 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.

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 Highlights01:38 – Why AI’s hype cycle feels like “Mount Everest” compared to cloud and mobile04:50 – Why AI adoption can’t be compared to past waves like blockchain or cloud07:36 – The hidden foundation: digital transformation work still matters11:11 – The inversion that changes everything: AI isn’t the goal, business outcomes are16:26 – Defining “adoption” as a multi-dimensional spectrum, not a checkbox19:50 – How to recover if your first AI projects fall short28:04 – Building adaptability as a core enterprise competency31:25 – The common traits of companies succeeding with AI right nowA 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 McLartyCall to actionIf this episode gave you a clearer path toward enterprise AI adoption, share it with a colleague and follow the show so you never miss a conversation on where tech leadership is heading.

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 TakeawaysData 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 Highlights00:45 What data quality means in a regulated industry03:15 The challenges of managing fragmented legacy systems06:40 How producers and consumers measure effectiveness of frameworks09:30 The pizza delivery analogy for making sense of data quality14:20 Why accuracy is harder than timeliness or completeness16:50 The role of AI and machine learning in improving governance19:20 Shifting from defensive compliance to offensive strategy in banking22:40 Regulators testing AI-driven approaches to anti-money launderingMemorable Quote“Producer has preventive controls. Consumer has detective controls. True data quality happens only when both align 100%.” — Vipin KumarCall to ActionIf you enjoyed this conversation, share it with a colleague who thinks about data quality or governance. Don’t forget to follow the show on Apple Podcasts or Spotify so you never miss an episode.

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 TakeawaysThe 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 Highlights00:51 — Why Agree.com calls itself “a better DocuSign” and how it integrates signatures, invoicing, and payments02:06 — The history of inventions nobody asked for and why they stuck05:41 — Human pessimism vs optimism when confronting new technologies09:05 — Why fears around AI echo the same debates once had about books, cars, and the cloud13:38 — How automation frees salespeople and engineers to focus on higher-value work18:51 — Are there technologies that have been net negative for society? Marty’s take23:21 — 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 RingleinCall to ActionIf 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.

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 advancingTimestamped Highlights01:39 Why influence is often misunderstood in engineering careers05:12 Influence vs charisma—and why you don’t need to be an extrovert08:47 The virtuous cycle of impact leading to influence13:20 Are companies biased toward rewarding outspoken engineers?17:21 Practical ways ICs can start building impact today22:48 Why you don’t need to manage people to have a leadership careerA line worth remembering“Consistent impact is how you build influence.” — Simon LamCall to ActionIf 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 Highlights00:45 – Torc’s focus on hub-to-hub autonomous trucking02:03 – Why scaling to thousands of trucks matters more than building one prototype06:48 – The unique technical problems of trucks vs. passenger cars09:25 – How extended operating hours reshape logistics and supply chains14:17 – Working with regulators and law enforcement to ensure safety and compliance17:42 – AV3.0, synthetic data, and billions of miles of training for safer systems22:31 – Building public trust and societal acceptance of autonomous trucking25:21 – Why large-scale adoption will happen in step functions, not tricklesA 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 KingCall to ActionIf 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 TakeawaysThe 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 Highlights02:00 How science traditionally works and why it’s so slow05:50 Why mistakes and negative results matter more than we admit09:40 The fragmentation of research and why labs don’t share data17:15 Inside a self-driving lab and how AI accelerates discovery23:40 Why big material companies can’t innovate like startups35:40 The new role of scientists in an AI-powered discovery worldMemorable 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 ActionIf you enjoyed this conversation, share it with a colleague who geeks out on science and technology. Follow the show on Apple Podcasts or Spotify so you don’t miss future episodes exploring where tech is headed next.