The Tech Trek

Elevano
  • 5.0 (75)
  • TECHNOLOGY
  • UPDATED SEMIWEEKLY

The Tech Trek is a podcast for founders, builders, and operators who are in the arena building world class tech companies. Host Amir Bormand sits down with the people responsible for product, engineering, data, and growth and digs into how they ship, who they hire, and what they do when things break. If you want a clear view into how modern startups really get built, from first line of code to traction and scale, this show takes you inside the work.

  1. The Founder Rules Nobody Tells You

    13H AGO

    The Founder Rules Nobody Tells You

    Healey Cypher, CEO of BoomPop and COO at Atomic, breaks down what separates founders who win from founders who stall. You will hear a clear way to judge whether an idea is truly worth building, plus the trust mechanics that get investors, customers, and teammates to actually follow you. This conversation is a practical map for tech builders who want to pick smarter problems, execute faster, and earn credibility without the founder theater. Key Takeaways Founders matter most, but the idea is still a gate, the same great team can get wildly different outcomes depending on the market and timing VC backed is a specific game, it requires not just big potential, but fast scale, and the incentives are not the same as building a profitable lifestyle business A quick reality check for market size, if you need more than about five to seven percent penetration to hit meaningful revenue, it is usually a brutal path Painkillers beat vitamins, solve an urgent problem people feel right now, or you risk getting cut the moment budgets tighten Trust is built through authenticity, logic, and empathy, if one wobbles, people feel it fast, and progress slows everywhere Timestamped Highlights 00:00:00 Healey’s background, why BoomPop, and what the episode is really about 00:02:00 The post pandemic spend shift and the why now behind modern events and group travel 00:04:30 Founder versus idea, why execution dominates, but the opportunity still decides the ceiling 00:06:40 The VC reality, power law returns, speed, and why some good businesses are still a no for venture 00:09:15 A simple market math test, penetration levels that become a growth wall 00:19:00 Trust as a founder skill, the three ingredients and how to spot when one is missing 00:21:30 Vulnerability as a shortcut to real connection, plus the giver mindset that makes people want you to win A line worth stealing If everyone wants you to win, it is a lot easier to win. Pro Tips for Tech Founders Ask yourself what you naturally look forward to doing, that is often your zone of strength, hire around the tasks you dread Learn the financial basics early, especially cash flow, it is the scoreboard that keeps you alive long enough to win When trust is lagging, check the three levers, are you showing the real you, can people follow your reasoning, do they feel you care about their outcomes What's next: If you build products, lead teams, or are thinking about starting something, follow the show so you do not miss episodes like this. Also connect with me on LinkedIn for short takeaways and clips from each conversation.

    26 min
  2. Modernizing Healthcare Without the Buzzwords

    1D AGO

    Modernizing Healthcare Without the Buzzwords

    Ty Wang, cofounder and CEO of Angle Health, breaks down what it means to give back through public service, then shows how that same mindset drives his mission to modernize healthcare for small and midsize businesses. We get into why legacy health plans feel opaque and painful, what an AI native health plan actually changes behind the scenes, and how better data and workflows can create real cost stability for employers. Ty shares his path from a federal scholarship and national service work to Palantir, and why he chose one of the most regulated, least glamorous industries to build in. If you have ever wondered why healthcare feels impossible to navigate, or why renewals can blindside a company, this conversation will give you a clear mental model of the problem and a practical view of what modernization looks like when it actually ships. Key Takeaways Healthcare feels broken because the infrastructure is fragmented, data is siloed, and even basic questions become hard to answer across inconsistent systems Modernizing healthcare is not just about a new app, it is about rebuilding the operational core so workflows, claims, underwriting, and member experience can run on integrated data Small and midsize businesses are hit hardest by cost volatility because they lack transparency, predictability, and negotiating leverage, yet health insurance is often a top line item after payroll A strong approach to regulated markets is collaborative, treat regulators as partners in consumer protection, not obstacles to work around Mission and impact can be a recruiting advantage, especially when the technical problems are genuinely hard and the outcomes touch real people fast Timestamped Highlights 00:40 What Angle Health is, and what AI native means in a real health plan 02:05 The scholarship path that pulled Ty into public service and set his trajectory 04:06 The personal story behind the mission, the American dream, and why access matters 09:38 Why healthcare infrastructure is so complex, and how siloed systems create bad experiences 11:33 Why SMBs get squeezed, and how manual administration blocks customization at scale 13:20 The real pain point for employers, cost volatility and zero predictability before renewal 16:55 Why the tech can expand beyond SMBs, but why the SMB market is already massive 19:51 Lessons from building in a regulated industry, and why credibility and funding matter 22:26 Hiring for high agency, mission driven talent in a world full of AI companies A line that sticks “Unless you are lucky enough to work for a big company, these modern healthcare services are still largely inaccessible to the vast majority of Americans.” Pro Tips for tech operators and builders If you are modernizing a legacy industry, start with the infrastructure layer, fix the data model, integrate the systems, then automate workflows In regulated markets, build relationships early, show how your product improves consumer outcomes, and make compliance a design constraint, not a bolt on When selling into SMBs, predictability beats perfection, give customers a clear breakdown of what drives costs and what they can control What's next: If this episode helped you see healthcare and legacy modernization more clearly, follow the show on Apple Podcasts or Spotify and subscribe so you do not miss the next conversation. Also, share it with one operator or builder who is trying to modernize a messy industry.

    26 min
  3. The Hidden Fintech Behind the Compute Boom

    2D AGO

    The Hidden Fintech Behind the Compute Boom

    Gabe Ravacci, CTO and co-founder at Internet Backyard, breaks down what the “computer economy” really looks like when you zoom in on data centers, billing, invoicing, and the financial plumbing nobody wants to touch. He shares how a rejected YC application, a finance stint, and a handful of hard lessons pushed him from hardware curiosity to building fintech infrastructure for compute. If you care about where compute is headed, or you are early in your career and trying to find your path without overplanning it, this one will land. Key Takeaways • Startups often happen “by accident” when your competence meets the right problem at the right time • Compute accessibility is not only a chip problem, it is also a finance and operations problem • Rejection can be data, not a verdict, treat it as feedback to sharpen the craft • A real online presence is less about networking and more about being genuinely useful in public • Time blocking and single task focus beats grinding when you are juggling school, work, and a startup Timestamped Highlights 00:28 What Internet Backyard is building, fintech infrastructure for data center financial operations 01:37 The first startup attempt, cheaper compute via FPGA based prototyping, and why investors passed 04:48 The pivot, from hardware tools to a finance informed view of compute and transparency gaps 06:55 How Gabe reframed YC rejection, process over outcome, “a tree of failures” that builds skill 08:29 Building a digital brand on X, what he posted, how he learned in public, and why it worked 13:36 The real balancing act, dropping classes, finishing the degree well, and strict time blocking 20:00 Books that shaped his thinking, Siddhartha, The Art of Learning, Finite and Infinite Games A line worth keeping “The process is really more important than any outcome.” Pro Tips for builders • Treat learning like a skill, ask better questions before you chase better answers • Make focus a system, set blocks, mute distractions, and do one thing at a time • Share what you are learning in public, not to perform, but to be useful and find signal Call to Action If this episode sparked an idea, follow or subscribe so you do not miss the next one. Also check out Amir’s newsletter for more conversations at the intersection of people, impact, and technology.

    24 min
  4. Data Fabric Meets AI, The Trust Layer Most Teams Skip

    3D AGO

    Data Fabric Meets AI, The Trust Layer Most Teams Skip

    Data leaders are being asked to ship real AI outcomes while the foundations are still messy. In this conversation, Dave Shuman, Chief Data Officer at Precisely, breaks down what actually determines whether AI adoption sticks, from hiring “comb shaped” talent to building trusted data products that make AI outputs believable and usable. If you are building in data, AI, or analytics, this episode is a practical map for what needs to be true before AI can move from demos to dependable, repeatable impact. Key Takeaways Comb shaped talent beats narrow specialization, AI work rewards people who can span multiple skills and collaborate well Adoption is a trust problem, and trust starts with data integrity, lineage, context, and a semantic layer that business users can understand Open source drives the innovation, commercialization makes it safe and usable at enterprise scale, especially around security and support Data must be fit for purpose, start every AI project by asking what data it needs, who curates it, and what the known warts are Humans are still the last mile, small workflow choices can make adoption jump, even when the model is already accurate Timestamped Highlights 00:56 The shift from T shaped to comb shaped talent, what modern AI teams actually need to look like 05:36 Hiring for team fit over “world class” niche skills, and when to bring in trusted partners for depth 07:37 How open source sparks the ideas, and why enterprises still need hardened, supported versions to scale 11:31 Where AI adoption is today, why summarization is only the beginning, and what unlocks “AI 2.0” 13:39 The trust stack for AI, clean integrated data, lineage, context, catalog, semantic layer, then agents 19:26 A real adoption lesson from machine learning, and why the human experience decides if the system wins A line worth stealing “You do not just take generative AI and throw it at your chaos of data and expect it to make magic out of it.” Pro Tips for data and AI leaders Hire and build teams like Tetris, fill skill voids across the group instead of chasing one perfect profile Use partners for the sharp edges, but require knowledge transfer so your team levels up every engagement Make adoption easier by designing for human behavior, sometimes the smallest workflow tweak beats more accuracy Build governed data products in a catalog, then validate AI outputs side by side with dashboards to earn trust fast Call to Action If this helped you think more clearly about AI adoption, talent, and data foundations, follow the show and turn on notifications so you do not miss the next episode. Also, share it with one data or engineering leader who is trying to get AI out of pilots and into real workflows.

    29 min
  5. Cloud Costs vs AI Workloads, The Storage Decisions That Decide Scale

    4D AGO

    Cloud Costs vs AI Workloads, The Storage Decisions That Decide Scale

    Cloud bills are climbing, AI pipelines are exploding, and storage is quietly becoming the bottleneck nobody wants to own. Ugur Tigli, CTO at MinIO, breaks down what actually changes when AI workloads hit your infrastructure, and how teams can keep performance high without letting costs spiral. In this conversation, we get practical about object storage, S3 as the modern standard, what open source really means for security and speed, and why “cloud” is more of an operating model than a place. Key takeaways • AI multiplies data, not just compute, training and inference create more checkpoints, more versions, more storage pressure • Object storage and S3 are simplifying the persistence layer, even as the layers above it get more complex • Open source can improve security feedback loops because the community surfaces regressions fast, the real risk is running unsupported, outdated versions • Public cloud costs are often less about storage and more about variable charges like egress, many teams move data on prem to regain predictability • The bar for infrastructure teams is rising, Kubernetes, modern storage, and AI workflow literacy are becoming table stakes Timestamped highlights 00:00 Why cloud and AI workloads force a fresh look at storage, operating models, and cost control 00:00 What MinIO is, and why high performance object storage sits at the center of modern data platforms 01:23 Why MinIO chose open source, and how they balance freedom with commercial reality 04:08 Open source and security, why faster feedback beats the closed source perception, plus the real risk factor 09:44 Cloud cost realities, egress, replication, and why “fixed costs” drive many teams back inside their own walls 15:04 The persistence layer is getting simpler, S3 becomes the standard, while the upper stack gets messier 18:00 Skills gap, why teams need DevOps plus AIOps thinking to run modern storage at scale 20:22 What happens to AI costs next, competition, software ecosystem maturity, and why data growth still wins A line worth keeping “Cloud is not a destination for us, it’s more of an operating model.” Pro tips for builders and tech leaders • If your AI initiative is still a pilot, track egress and data movement early, that is where “surprise” costs tend to show up • Standardize around containerized deployment where possible, it reduces the gap between public and private environments, but plan for integration friction like identity and key management • Treat storage as a performance system, not a procurement line item, the right persistence layer can unblock training, inference, and downstream pipelines What's next: If you’re building with AI, running data platforms, or trying to get your cloud costs under control, follow the show and subscribe so you do not miss upcoming episodes. Share this one with a teammate who owns infrastructure, data, or platform engineering.

    26 min
  6. AI Is Changing Art Faster Than You Think.

    FEB 6

    AI Is Changing Art Faster Than You Think.

    This is an early conversation I am bringing back because it feels even more relevant now, the intersection of AI and art is turning into a real cultural shift. I sit down with Marnie Benney, independent curator at the intersection of contemporary art and technology, and co-founder of AIartists.org, a major community for artists working with AI. We talk about what AI art actually is beyond the headlines, where authorship gets messy, and why artists might be the best people to pressure test the societal impact of machine learning. Key takeaways • AI in art is not a single thing, it is a spectrum of choices, dataset, process, medium, and intent • The most interesting work treats AI as a collaborator, not a shortcut, a back and forth that reshapes the artist’s decisions • Authorship is still unsettled, some artists see AI as a tool like an instrument, others treat it as a creative partner • The fear that AI replaces creativity misses the point, artists can use the machine’s unexpected output to expand human expression • Access matters, compute, tooling, and collaboration between artists and technologists will shape who gets to experiment at the frontier Timestamped highlights 00:04:00 Curating science, climate, and public engagement, the path into tech driven exhibitions 00:07:41 What AI art can mean in practice, datasets, iteration loops, and choosing an output medium 00:10:48 Who gets credit, tool versus collaborator, and the art world’s evolving rules 00:13:51 Fear, job displacement, and a healthier frame, human plus machine as a creative partnership 00:22:57 The new skill stack, what artists need to learn, and where collaboration beats handoffs 00:29:28 The pushback from traditional art circles, philosophy and intention versus novelty 00:37:17 Inside the New York exhibition, collaboration between human and machine, visuals, sculpture, and sound 00:48:16 The magic of the unknown, why the output can surprise even the artist A line that stuck “Artists are largely showing a mirror to society of what this technology is, for the positive and the negative.” Pro tips for builders and operators • Treat creative communities as an early signal, artists surface second order effects before markets do • If you are building AI products, study authorship debates, they map directly to credit, accountability, and trust • Collaboration beats delegation, when domain experts and technologists iterate together, the work gets sharper fast Call to action If this episode hits for you, follow the show so you do not miss the next drop. And if you are building in data, AI, or modern tech teams, follow me on LinkedIn for more conversations that connect technology to real world impact.

    51 min
  7. AI in the Enterprise, Why Pilots Fail and What Actually Scales

    FEB 5

    AI in the Enterprise, Why Pilots Fail and What Actually Scales

    Most teams are approaching AI from the wrong direction, either chasing the tech with no clear problem or spinning up endless pilots that never earn their keep. In this episode, Amir Bormand sits down with Steve Wunker, Managing Director at New Markets Advisors and co author of AI and the Octopus Organization, to break down what actually works in enterprise AI. You will hear why the real challenge is organizational, not technical, how IT and business have to co own the outcome, and what it takes to keep AI systems valuable over time. If you are trying to move beyond experimentation and into real impact, this conversation gives you a practical blueprint. Key takeaways • Pick a handful of high impact problems, not hundreds of small pilots, focus is what creates measurable ROI • Treat AI as a workflow and change program, not a tool you bolt onto an existing process • IT has to evolve from order taker to strategic partner, including stronger AI ops and ongoing evaluation • Start with the destination, redefine the value proposition first, then redesign the operating model around it • Ongoing ownership matters, AI is not a one and done delivery, it needs stewardship to stay useful Timestamped highlights 00:39 What New Markets Advisors actually does, innovation with a capital I, plus AI in value props and operations 01:54 The two common mistakes, pushing AI everywhere and launching hundreds of disconnected pilots 04:19 Why IT cannot just take orders anymore, plus why AI ops is not the same as DevOps 07:56 Why the octopus is the perfect model for an AI age organization, distributed intelligence and rapid coordination 11:08 The HelloFresh example, redesign the destination first, then let everything cascade from that 17:37 The line you will remember, AI is an ongoing commitment, not a project you ship and forget 20:50 A cautionary pattern from the dotcom era, avoid swinging from timid pilots to extreme headcount mandates A line worth keeping You cannot date your AI system, you need to get married to it. Pro tips for leaders building real AI outcomes • Define success metrics before you build, then measure pre and post, otherwise you are guessing • Redesign the process, do not just swap one step for a model, aim for fewer steps, not faster steps • Assign long term ownership, budget for maintenance, evaluation, and model oversight from day one Call to action If this episode helped you rethink how to drive AI results, follow the show and subscribe so you do not miss the next conversation. Share it with a leader who is stuck in pilot mode and wants a path to production.

    24 min
  8. AI Is Rewriting Manufacturing Quality, Here’s What Changes

    FEB 4

    AI Is Rewriting Manufacturing Quality, Here’s What Changes

    Manufacturing is getting faster, messier, and more expensive when quality slips. Daniel First, Founder and CEO at Axion, joins Amir to break down how AI is changing the way manufacturers detect issues in the field, trace root causes across messy data, and shorten the time from “customers are hurting” to “we fixed it.” Episode Summary Daniel First, Founder and CEO at Axion, explains why modern manufacturing is living in the bottom of the quality curve longer than ever, and how AI can help companies spot issues early, investigate faster, and actually close the loop before warranty costs and customer trust spiral. If you work anywhere near hardware, infrastructure, or complex systems, this is a sharp look at what “AI first” means when real products fail in the real world. You will hear why quality is becoming a competitive weapon, how unstructured signals hide the truth, and what changes when AI agents start doing the detection, investigation, and coordination work humans have been drowning in. What you will take away Quality is not just a defect problem, it is a speed and trust problem, especially when product cycles keep compressing. AI creates leverage by pulling together signals across the full product life cycle, not by sprinkling a chatbot on one system. The fastest teams win by finding issues earlier, scoping impact correctly, and fixing what matters before customers notice the pattern. A clear ROI often lives in warranty cost avoidance and downtime reduction, not just “efficiency” metrics. “AI first” gets real when strategy becomes operational, and contradictions in how teams prioritize issues get exposed. Timestamped highlights 00:00 Why manufacturing is a different kind of problem, and why speed is harder than it looks 01:10 What Axion does, and how it detects, investigates, and resolves customer impacting issues 05:10 The new reality, faster product cycles mean living in the bottom of the quality curve 10:05 Why it can take hundreds of days to truly solve an issue, and where the time disappears 16:20 How to evaluate AI vendors in manufacturing, specialization, integrations, and cross system workflows 22:40 The shift coming to quality teams, from reading data all day to making higher level decisions 28:10 What “AI first” looks like in practice, and how AI exposes misalignment across teams A line worth repeating “Humans are not that great at investigating tens of millions of unstructured data points, but AI can detect, scope, root cause, and confirm the fix.” Pro tips you can apply When evaluating an AI solution, ask three questions up front: how specialized the AI must be, whether you need a full workflow solution or just an API, and whether the use case spans multiple systems and teams. Treat early detection as a first class objective, the longer the accumulation phase, the more cost and customer damage you silently absorb. Align issue prioritization to strategy, not just frequency, cost, or the loudest internal voice. Follow: If this episode helped you think differently about quality, speed, and AI in the real world, follow the show on Apple Podcasts or Spotify so you do not miss the next one. If you want more conversations like this, subscribe to the newsletter and connect with Amir on LinkedIn.

    25 min
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5
out of 5
75 Ratings

About

The Tech Trek is a podcast for founders, builders, and operators who are in the arena building world class tech companies. Host Amir Bormand sits down with the people responsible for product, engineering, data, and growth and digs into how they ship, who they hire, and what they do when things break. If you want a clear view into how modern startups really get built, from first line of code to traction and scale, this show takes you inside the work.

Information

  • Creator
    Elevano
  • Years Active
    2020 - 2026
  • Episodes
    623
  • Rating
    Clean
  • Copyright
    © Elevano
  • Show Website
    The Tech Trek