The Tech Trek

The Tech Trek brings together founders, investors, and tech leaders to share how they build teams, scale products, and drive growth. Hosted by Amir Bormand, the show delivers candid conversations with CEOs, VCs, and engineering leaders on hiring, leadership, and navigating the challenges of building technology companies. We explore early hiring strategies, product and engineering challenges, funding insights, and the leadership lessons that separate good teams from great ones.

Will AI Really Take Frontline Jobs?

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.

10-03
28:55

How Startups Break Into the Enterprise AI Market

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.

10-02
24:59

How Attackers Are Using AI to Outpace Defenses

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.

10-01
27:42

AI Is Rewriting Workflows Faster Than We Can Adapt

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.

09-30
24:21

Enterprise AI Adoption in 2025: What Actually Works

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.

09-29
34:54

Why Data Quality Is So Hard to Get Right

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.

09-26
25:42

History Always Repeats in Tech

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.

09-25
26:15

How Engineers Can Build Influence Without a Leadership Title

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.

09-24
26:58

The Future of Autonomous Trucking

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.

09-23
28:15

AI Labs Are Reinventing Science Forever

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.

09-22
40:20

The Hardest Part of Tech Leadership

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 Highlights02:13 The hidden tax of context switching06:53 How John measures success as a leader without code10:45 What really slows executives down inside organizations15:51 How John protects his calendar and finds focus time24:47 The lessons every first-time manager needs to hearA Line That Sticks“If you don’t control your calendar, your calendar will control you.”Call to ActionIf this episode resonated, share it with a fellow leader navigating the chaos. Subscribe to The Tech Trek on Apple Podcasts and Spotify for more candid conversations about scaling, leadership, and the future of technology.

09-19
30:12

From POC to Production: Enterprise Agents Explained

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 TakeawaysEnterprise 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 Highlights02:02 — Why agents are “just advanced workflow software” but harder to trust than traditional apps04:53 — The gap between glorified chatbots and real enterprise agents that take action09:58 — From cloud mistrust to wire transfers: how comfort with automation evolves14:00 — Chaos at every tier: startups, enterprises, and why the mid-market struggles most26:21 — The playbook: how to pick use cases, narrow scope, and carry pilots all the way to prod34:38 — Breaking down agent authorization and why most RAG systems fail in practice42:09 — Adoption at double speed: what makes this AI wave different from internet and cloudA 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 SalazarCall to ActionIf this episode gave you a clearer lens on enterprise agents, share it with a colleague who needs to hear it. And don’t miss future conversations—follow The Tech Trek on Apple Podcasts, Spotify, or wherever you listen.

09-18
50:13

The Future of Voice AI

Russ d’Sa, founder and CEO of LiveKit, joins the show to unpack the rise of voice AI and what it means for how we interact with technology. From the shift away from static decision trees to dynamic, LLM-powered systems, Russ explains why voice is emerging as one of the most natural interfaces for humans—and one of the most disruptive opportunities for builders. This episode goes beyond surface-level hype to explore real-world use cases, infrastructure shifts, and what’s coming next as voice moves from novelty to mainstream.Key Takeaways• Voice AI has moved far beyond Siri and Alexa—LLMs enable open-ended, natural conversations without rigid decision trees.• Two main categories are emerging: open-ended voice experiences (like tutoring and therapy apps) and goal-oriented workflows (like healthcare intake, finance, and customer support).• The biggest barrier isn’t just technology, but adoption behavior—older generations default to typing and screens, while younger users and voice-first cultures are accelerating change.• Infrastructure for voice and video AI requires a fundamental shift from stateless web servers to stateful, long-lived conversational systems.• The hardest technical challenge ahead: mastering conversational turn-taking so AI can interact as naturally as a human.Timestamped Highlights01:06 How LiveKit is giving applications the ability to see, hear, and speak04:18 The two main categories of voice AI use cases emerging right now09:53 Why adoption of voice AI depends as much on behavior as on technology14:20 Imagining a 24/7 voice-driven AI that replaces screens and UIs20:30 Why the internet’s original infrastructure wasn’t built for voice and video AI25:39 The challenge of memory, authentication, and group dynamics in AI conversationsA line worth remembering“If you have a computer that perfectly understands when to speak, when to listen, and adds value in the right moments—why would you ever use anything else?”Call to ActionIf you enjoyed this conversation, share it with a colleague who’s curious about where AI is headed. Subscribe on Apple Podcasts or Spotify so you don’t miss future episodes diving into the technologies shaping the next decade.

09-17
30:04

From Prototype to Production

Sumit Arora, VP of Advanced Technology at Ascend Learning, joins the show to unpack the real challenges of turning AI prototypes into production-ready systems. From managing non-deterministic outputs to rethinking the relationship between engineering and product, Sumit shares hard-earned lessons on what it actually takes to build AI that works at scale. If you’re navigating how to move beyond experiments and deliver AI products that stick, this episode will give you a clear look at the path forward.Key Takeaways• Scaling AI is not about building smarter prototypes—it’s about mastering distributed systems, security, and availability.• The best AI teams combine deep systems engineering with practical product sense.• Traditional software requirements processes won’t work for AI. Co-creation between product and engineering is essential.• Innovation pods—small, cross-functional teams—can accelerate experimentation without killing momentum.• Success at scale comes from modular, reusable AI systems that can plug into multiple contexts.Timestamped Highlights02:14 — Why building a working AI demo is easy, but scaling it into a reliable product is hard04:49 — Lessons from the big data revolution and how AI is moving even faster08:41 — The skill sets AI teams really need and why distributed systems expertise trumps pure ML13:13 — Designing user experiences for AI and why response times redefine UX expectations17:00 — The evolving relationship between product and engineering in the AI era23:10 — How innovation pods help organizations experiment without stalling production teams26:47 — Why modular, self-contained AI systems are the key to scaling across an enterpriseA Line That Stuck“You can’t requirement doc your way to AI success. Product and engineering have to co-create and move fast.”Call to ActionIf you found this conversation useful, share it with a colleague, subscribe to the show, and leave a quick rating—it helps us bring more tech leaders and practitioners to the table.

09-16
30:54

From Sales Leader to Startup CEO

Sean McCarthy, co-founder and CEO of BackOps, shares how a career in sales prepared him to build an AI-driven logistics company from the ground up. In this episode, Sean reveals how observing real-world pain points at Amazon inspired BackOps’ mission and why coming from a non-technical background can actually be a founder’s advantage. This is a conversation about scaling, selling, and leading with insight — perfect for anyone thinking about making the leap from operator to founder.Key TakeawaysWhy non-technical founders are uniquely positioned to solve operational problems with AIThe mindset shift required to go from running sales to running an entire companyHow to validate an idea before leaving a stable, well-paying jobWhat it really takes to hand off sales when it’s been your superpowerPricing insights that help ensure you’re building a scalable businessTimestamped Highlights01:45 Sean’s Amazon journey and what time spent in warehouses taught him about customer pain points04:14 The moment he saw the same issues plaguing both small and nine-figure sellers — and spotted an opportunity07:37 How becoming a CEO forced him to rewire his focus beyond sales and build internal infrastructure12:18 Why having a technical co-founder was non-negotiable — and how AI tooling is changing that equation15:18 The tough decision to leave Amazon and how he measured risk versus regret17:59 Learning to let go and trust others with the sales process while still staying close to customersMemorable Moment“Talk to the people that would actually buy your product. Measure the pain point. If it’s a one or two out of ten, it’s probably not worth building. If it’s a nine or ten, and they’ll pay for it, now you have something.”Pro TipsValidate early and price with intention. Don’t just ask if someone would use your product — ask exactly what they’d pay for it. Those conversations can save months of wasted build time.Call to ActionIf this episode resonated, share it with a friend who’s considering the leap into entrepreneurship. Follow the show for more conversations with founders, operators, and tech leaders building the next generation of companies.

09-15
36:51

AI Is a Journey, Not a Destination

Dmitri Sedov, Chief Data and Analytics Officer at Allvue, joins to explore why transformation isn’t a destination but an ongoing journey. He shares how financial services are navigating the current wave of AI, what it means to balance short-term expectations with long-term strategy, and why open, modular ecosystems are critical to staying competitive. This conversation goes beyond buzzwords to uncover how leaders can embrace change without getting lost in it.Key Takeaways• Transformation isn’t about a single endpoint—it’s about staying curious, iterative, and open to multiple paths.• AI is accelerating faster than previous technology cycles, but its value lies in solving real customer problems, not chasing hype.• Companies that lagged in past waves of innovation may now have an opportunity to leapfrog forward.• The smartest moves aren’t “rip and replace” but incremental improvements that build on existing engines.• Open, interoperable, and modular approaches reduce risk and keep technology flexible for the future.Timestamped Highlights01:26 – Why digital transformation is less about checking boxes and more about a mindset shift09:47 – The balancing act of managing short-term ROI while exploring long-term AI potential15:25 – A 24-hour hackathon prototype that changed how Dmitri thinks about multi-agent AI22:34 – Why interoperability and modularity matter more than big monolithic solutions27:43 – How to plan roadmaps when technology outpaces predictability31:43 – The future of “human in the loop” and what workforce transformation might look likeA line that stuck“Transformation is never about reaching one destination. The more you fixate on a single path, the more likely you’ll be left behind.”Call to ActionIf this episode gave you new perspective on AI and transformation, share it with a colleague who’s wrestling with the same challenges. And don’t forget to follow the show so you’re ready for the next conversation on building smarter, more resilient companies.

09-12
36:02

Can Tech Make Work Feel Less Like Work?

What if the key to real work-life balance isn’t about escaping work, but transforming it into your outlet for curiosity and passion? In this episode, Hassaan Raza, co-founder and CEO of Tavus, explores how technology is removing friction in our daily lives and why teaching machines to be more human could unlock new levels of creativity, accessibility, and balance. From redefining what “work you love” actually means to the role AI will play in democratizing opportunity, this conversation challenges assumptions and paints a picture of a future where tech becomes a true partner to people.Key TakeawaysWork-life balance may not come from hobbies outside work, but from making work itself a fulfilling outlet.Technology’s real value lies in removing friction and making tools more accessible to everyone, not just the highly technical.AI and human-computing advances could act as equalizers, giving people with different learning styles or backgrounds the same opportunities.Hollywood dystopias aside, machines can be designed to replace bad tools, not people—and that creates empowerment, not fear.The future of tech is less about replacement and more about enhancing human creativity, productivity, and quality of life.Timestamped Highlights00:33 — Hassaan explains Tavus’ mission to teach machines how to be human.04:05 — Why some engineers care more about solving puzzles than the outcomes.07:08 — How passion-driven work blurs the line between career and hobby.12:12 — The obsession with removing friction and making machines easier to use.16:37 — Addressing fears about AI taking jobs and reframing the conversation.20:57 — How AI can open doors for non-traditional learners and democratize education.A Line That Stands Out“If you find work that you really love and are passionate about, that can be your outlet—you don’t need balance because the work itself becomes your balance.”Call to ActionIf this episode gave you a fresh perspective on work, technology, and balance, share it with someone who’s wrestling with the same questions. And don’t forget to follow the show so you never miss the next conversation.

09-11
25:15

Evolving as a Founder

Dane Atkinson, CEO and founder of Odeko, joins the show to unpack the reality of evolving as a founder. He shares why the first idea you start with rarely survives, how to know when it’s time to pivot, and why anchoring on a mission instead of a product keeps you in the game. This conversation dives into frameworks for making hard calls, the messy middle of startup life, and what it really takes to endure as a multi-time founder.Key Takeaways• Your first idea probably won’t be the one that works—focus on the customer and the mission, not the concept.• Pivoting is brutal but necessary; small experiments can create the proof you need to shift direction.• Founders who learn from failure are more likely to succeed in their second or third ventures.• Having a North Star rooted in mission makes the day-to-day grind and tough decisions bearable.• The best outcomes come when investors give founders space to experiment and even fail.Timestamped Highlights00:43 – Why Odeko’s mission is to help small coffee shops compete with giants01:44 – The flawed brilliance of Odeko’s first AI-driven product and the hard pivot that followed05:28 – The painful trap of chasing product-market fit and the danger of sticking too long10:24 – Building proof for a pivot and the difference between charisma-driven sales and true demand14:04 – Why most successful founders are “multi-run players” and what VCs often miss about failure17:02 – How staying mission-driven keeps founders motivated through setbacksA line worth remembering“You can change the product, you can change the delivery, but if you have a North Star that matters, you’ll always know how to steer the company back on track.”Founder TipTest new directions quietly alongside your current model. Early prototypes not only prove viability but also help you win over skeptical teammates, boards, and investors.Call to ActionIf this episode gave you something to think about, share it with a fellow founder or operator who’s in the middle of their own evolution. And don’t forget to follow The Tech Trek so you never miss the next conversation on scaling, leadership, and building companies that last.

09-10
25:22

A Day in the Life of a Startup CTO

What does a day in the life of a startup CTO really look like? Mo El Mahallawy, CTO and co-founder of Shepherd, shares the unfiltered journey from being engineer number one to leading a 50-person Series A company. He opens up about the hardest phases of building, what shifts as your company scales, and how he manages energy, priorities, and mental health along the way. This episode is a practical playbook for any founder, CTO, or tech leader navigating growth.Key Takeaways• The early days as a startup CTO are often the hardest—you're coding, recruiting, managing, and wearing every hat at once.• Growth means trading code for vision: shifting from building features to setting direction and enabling your team.• Time management is only half the battle—energy management and knowing when you do your best work is equally critical.• Planning is a mental health strategy: when you control your roadmap, you avoid the burnout of constant reaction mode.• Taking big swings matters more than just paying down tech debt—bets move the business forward.Timestamped Highlights02:41 – The origins of Shepherd and why Mo left Airbnb to build in insurance tech.07:28 – What makes the early-stage CTO role one of the toughest in startups.10:44 – Mo’s brutally honest description of those days: “like chewing glass.”16:45 – How the CTO role evolves post-Series A and the challenges of stepping out of code.20:44 – Why energy balance beats pure time management.26:42 – Mo’s take on mental health and how planning became his best defense against fatigue.A line worth remembering“Your life is going to suck, and then it’s going to be great—but your job is to make this work.”Pro Tips• Use your strongest energy hours for high-leverage work, not busywork.• Build your network early at iconic companies—you’ll rely on those relationships later.• Don’t shy away from big bets; they create momentum that tech debt never will.Call to ActionIf you found Mo’s story valuable, share this episode with someone thinking about becoming a founder or CTO. And don’t forget to follow the show on your favorite podcast app so you never miss the next set of scaling playbooks.

09-09
32:02

Scaling Without a Roadmap in Fast-Moving Markets

What does it take to build a startup in a space where the ground shifts every 90 days? Rahul Sonwalkar, founder and CEO of Julius AI, joins the show to share how he navigates product market fit, resource constraints, and constant model evolution while scaling an AI-first company. Instead of relying on long-term roadmaps, Rahul runs Julius with rapid feedback loops, deep user focus, and a mindset that every team member contributes to AI development. This conversation is a playbook for founders and operators facing uncertainty and speed in equal measure.Key Takeaways• Why Rahul believes rigid long-term roadmaps can hold founders back in AI and fast-moving markets• How limited resources force sharper prioritization, and how to decide what makes the cut• The two-way product market fit Julius found by serving both data teams and business stakeholders• Why direct user conversations and dogfooding are non-negotiable for early-stage companies• A glimpse into the future of BI as AI agents that monitor and surface key metrics automaticallyTimestamped Highlights00:43 – Julius AI’s origin story and how Rahul validated product market fit05:08 – The surprising way both data teams and business users bring Julius into organizations09:24 – Why Rahul avoids long-term roadmaps and favors month-to-month iteration13:55 – The role of feedback loops and customer support in shaping product direction17:27 – How to prioritize when you only have resources for two out of ten critical features19:39 – Rahul’s vision for how BI and analytics will evolve with AI agentsA Standout Line“Overnight, things you thought were impossible for the next 12 months can suddenly become possible. That’s why you can’t have a rigid roadmap when building with AI.”Founder’s LessonUse your own product daily. When the entire team feels the same friction as your users, the right priorities become obvious.Call to ActionIf you’re a founder, operator, or investor looking for practical lessons on building in uncertain terrain, follow the show for more conversations like this. And if Rahul’s approach resonated, connect with him on LinkedIn or explore Julius AI.

09-08
24:57

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