The Tech Trek

Elevano

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. What VCs Really Want From AI Startups in 2026

    16H AGO

    What VCs Really Want From AI Startups in 2026

    Susan Liu, Partner at Uncork Capital, joins Amir to break down what actually matters when backing early stage AI companies. From founder market fit to product wedge to the reality of churn, this conversation gets past the hype and into how strong companies separate themselves in a crowded market. If you are building, funding, or evaluating AI startups, this episode gives you a sharper lens on where the market is heading, what Series A investors now expect, and why real ROI is becoming the line between momentum and fallout. What stood out • The best early stage founders usually have earned insight, meaning they have lived the problem before building the solution • In crowded AI markets, the goal is not to be interesting, it is to become one of the few companies that actually wins • AI buyers still care about the same core question, does this drive revenue or cut cost in a measurable way • The Series A bar has moved up fast, and strong growth alone is not enough if retention is weak • Some of today’s biggest AI winners may still face painful churn if they are not truly essential to the customer Timestamped Highlights 00:37 Susan breaks down how Uncork Capital invests at seed and what it takes to get real conviction early 02:00 The three-part framework she uses to evaluate companies, team, market, and product wedge with traction 09:42 Why crowded AI markets are not necessarily a red flag, and how winners still pull away from the pack 17:04 The ROI test every AI startup has to pass if it wants to survive renewals 19:05 Susan’s honest take on 2026, cautious optimism, bigger impact, and a likely wave of churn 24:33 What founders need now to raise a strong Series A in a market where the bar is higher than ever One line that stuck “If you cannot prove one of these two, it is going to be a tough sell. Companies are not going to renew.” Practical takeaways for operators and founders • If your product cannot clearly tie to revenue growth or cost savings, buyers will eventually cut it • Founder credibility matters more when the market gets noisy, especially in AI • A compelling wedge wins attention, but retention is what keeps the story alive • Happy customers who will speak for you can be one of the strongest assets in a fundraise Stay connected If this episode gave you a better lens on AI startups, venture, and what actually drives durable value, follow the show, share it with a founder or operator in your network, and keep up with Amir on LinkedIn for more conversations like this.

    29 min
  2. The Internet Was Built for Humans. AI Is About to Change That.

    1D AGO

    The Internet Was Built for Humans. AI Is About to Change That.

    What happens to e commerce when AI agents start shopping instead of humans? Maju Kuruvilla, Founder and CEO of Spangle, joins the show to unpack a shift most companies are not prepared for. If AI agents become buyers, the entire digital shopping experience must change. Websites today are designed for human psychology, not machines making decisions. In this conversation, Maju explains why context is becoming the most important layer in commerce. From marketing clicks to storefront visits, most companies lose the context that originally inspired a purchase. The future belongs to systems that can capture, carry, and act on that context across every channel. The discussion explores agent driven shopping, the limits of traditional customer data systems, and how AI can reshape both online and physical retail experiences. Key Takeaways • Context matters more than identity. Knowing what someone is trying to do right now is often more valuable than knowing who they are. • Most e commerce experiences reset the customer journey. When someone clicks from an ad to a site, the original inspiration is usually lost. • AI agents will shop differently than humans. They are not influenced by visual design or marketing psychology the same way people are. • Commerce will not become fully agent driven. Instead, brands must design experiences that work for humans, agents, and hybrid interactions. • Physical retail may benefit the most from AI driven context because stores can blend digital signals with real world behavior. Timestamped Highlights 00:00 Why the next generation of e commerce will be built for AI agents, not just human shoppers. 02:08 The hidden problem in online shopping today. Most websites lose the context that brought the customer there. 06:11 Buyer agents and seller agents. How commerce may evolve into AI systems negotiating purchases. 11:38 Why a simple request like “buy a red sweater” is actually a complex problem of interpretation and context. 16:30 How AI could transform physical stores through dynamic recommendations and real time shopping guidance. 22:30 Why collecting endless customer data might be the wrong approach to personalization. 27:59 The future of autonomous shopping and why personal AI agents may eventually handle everyday purchases. A Moment That Sticks “Context is what matters. The fact that I bought a TV before is interesting, but not important. What matters is what I am trying to do right now.” Practical Insight for Builders If you are building AI driven commerce tools, start with the product layer. According to Maju, the foundation is making your product catalog intelligent. AI systems need rich product understanding so they can match intent with inventory. Once the catalog becomes machine readable and context aware, everything else becomes easier to automate. Call to Action If you enjoyed this conversation, follow the show and share this episode with someone working at the intersection of AI, commerce, or product development. New conversations every week with the builders shaping the future of technology.

    33 min
  3. How AI Is Modernizing the Equipment Rental Industry

    2D AGO

    How AI Is Modernizing the Equipment Rental Industry

    Most people never think about the technology behind construction equipment rentals. But behind every crane, excavator, and lift is an industry still running on paper, spreadsheets, and manual workflows. In this episode, Andy Feis, CEO and Co-Founder of Renterra, joins Amir to explain how a hundred billion dollar equipment rental market is finally entering the modern software era. The conversation explores how operational software, telematics data, and AI are reshaping one of the most overlooked parts of the industrial economy. Andy shares how rental companies manage fleets of expensive machines, why legacy workflows still dominate the industry, and how platforms like Renterra are bringing cloud software and automation to a sector that has largely been left behind by the tech revolution. This episode also explores the intersection of operational data, AI automation, and real world infrastructure. From fleet optimization to automated maintenance insights, the future of equipment rental may look very different than it does today. Key Takeaways • The equipment rental industry is a massive but overlooked market where over half of construction equipment is rented rather than owned. • Many rental businesses still run critical operations using pen and paper, manual inspections, and outdated spreadsheets. • Operational software is the first step toward modernization, helping companies manage inventory, dispatch, pricing, and maintenance. • Telematics data from machines unlocks powerful insights around maintenance timing, asset valuation, and fleet utilization. • AI will not replace the physical work in industrial sectors, but it can automate low value operational tasks and dramatically improve decision making. Timestamped Highlights 00:00 Introducing the hidden technology opportunity inside the equipment rental industry 02:00 Why many rental companies still rely on paper, binders, and manual equipment checks 06:20 How Andy Feis discovered a massive opportunity inside industrial operations 09:00The low hanging fruit in modernizing equipment rental workflows 11:14 What kind of data heavy machines actually generate and how it can be used 13:03 Where AI actually helps blue collar industries today 20:18 The roadmap for modernizing the industry and what comes next A Moment That Stuck “The industrial sector is an enormous part of the economy, but it has been one of the last places to feel the impact of the broader tech revolution.” Pro Tips If you are building technology for legacy industries, start with operational efficiency before advanced analytics. Modernization works best when it removes friction from existing workflows. Once companies see time savings and operational improvements, they become far more open to deeper data and AI driven insights. Call to Action If you enjoy conversations about technology transforming real world industries, follow the show and share this episode with someone building in construction, logistics, or industrial software.

    24 min
  4. The Future of Earth Intelligence, From Imagery to Answers

    3D AGO

    The Future of Earth Intelligence, From Imagery to Answers

    Luke Fischer, cofounder and CEO of SkyFi, breaks down how earth intelligence is becoming searchable, and why that changes decision making across defense, energy, logistics, and agriculture. You will hear how his path from Army special operations aviation to Head of Flight Ops at Uber shaped SkyFi’s product mindset, plus a practical look at what geospatial imagery and analytics can actually answer today. Key Takeaways • Networks are not nice to have, they are the fastest path to trust, hiring, and deals, especially in government and high stakes markets • SkyFi’s core unlock is access, making it possible to task satellites, pull history, and ask questions of the data, not just look at images • Going commercial first can create a faster iteration loop, then government adoption follows once the product is battle tested • The real product future is answers, not imagery, using natural language queries that return decisions grade insight • Privacy is not only about resolution, it is also about who can buy data, screening, and compliance, because access is the real leverage point Timestamped Highlights 00:47 Earth intelligence in plain English, task satellites, pull decades of history, ask questions like vessel detection or soil moisture 06:32 Why veteran resumes miss the mark, and how to translate leadership without goofy title inflation 10:44 The origin story, a broken buying experience in satellite imagery turns into SkyFi’s wedge 16:42 Selling into government, people game first, acquisition reality, and why patience is a feature 19:46 Use cases you will not expect, livestock behavior, barge counting, palm heights, mineral detection, and more 28:10 Where this is headed, ask a question about the world, get an answer, then move toward proactive intelligence A line worth repeating “Startups are the same thing, you are finding the right people with the right traits to solve these undefined problems in being comfortable with risk.” Practical moves you can steal If you are hiring, screen for comfort with ambiguity, not just pedigree, undefined problems are the job in high growth work If you are selling, build your network before you need it, warm paths beat cold volume every time If you are building product, shorten the feedback loop, commercial iteration can harden the product before slower cycle buyers adopt Call to Action If this episode sparked ideas for how data, defense, or AI driven analytics will reshape markets, follow the show and turn on notifications so you do not miss the next one. Also share it with one operator who makes high stakes decisions and would appreciate a clearer view of what is happening on the ground.

    34 min
  5. Why Research Scientists Are Taking Over AI Startups

    4D AGO

    Why Research Scientists Are Taking Over AI Startups

    Anish Agarwal went from MIT PhD researcher to founding Traversal, an AI company building intelligent site reliability engineering agents for the enterprise. In this episode, he breaks down what it actually takes to lead an AI first company when your entire career was built inside a lab. This is not your typical founder story. Anish never planned to start a company. He was on track to be a professor at Columbia when generative AI hit and rewired his trajectory. Now he is two years into the CEO seat, recruiting top talent away from high paying jobs, and building a product at the intersection of causal machine learning and agentic systems. We get into the mechanics of that transition. How do you go from publishing papers to pitching investors? What does storytelling look like when you are convincing engineers to leave comfortable roles and bet on your vision? And what happens when you start a company without even having an idea? Anish also tackles a question the AI space is wrestling with right now. Is a PhD becoming table stakes for building an AI first company? His answer is more nuanced than you might expect. It is not the degree. It is the training. Reading the landscape, navigating uncertainty, and evaluating models with scientific rigor. Those skills separate builders from everyone else. Key Takeaways The best AI founders are not chasing credentials. They are leveraging research instincts to read where models and architectures are heading, and that foresight creates real competitive edges. Starting a company without an idea is not reckless if you have the right co founders. Anish and his team showed up to a WeWork every day and treated idea exploration like a research problem until the right opportunity clicked. Storytelling is the most underrated leadership skill in technical companies. Whether you are recruiting, raising capital, or explaining your product to nontechnical buyers, packaging complexity into a clear narrative is what moves people. Every decision as a founder is a bet, including the decision to do nothing. Viewing inaction as a strategic choice changes how you prioritize and how fast you move. As AI writes more code, someone has to make sure it works in production. That gap between code generation and reliability is where Traversal lives, and it is only getting wider. Timestamped Highlights (00:36) What Traversal does and why AI powered site reliability engineering is a massive unsolved problem in enterprise software (02:00) The moment generative AI changed everything and why Anish walked away from a career he loved (08:43) How Traversal found its problem without starting with an idea, and the co founder dynamic that made it work (14:29) The real advantage of a PhD in AI and why it has nothing to do with the letters after your name (19:49) Advice for PhDs entering the job market on how to position research experience so hiring managers actually get it (20:29) Two years into the CEO role, what Anish wishes he had known and the skills that matter most for early stage founders Words That Stuck "If AI is writing your code, it has to fix it too. And right now it is only writing the code." Founder Playbook Pick a problem that sustains you for decades. Anish looks for problems that keep getting more complicated because that is where long term value compounds. If the problem has a ceiling, your company does too. Treat recruiting like a core product skill. Painting a compelling picture of the mission is not a nice to have. It is the engine that pulls exceptional talent away from safe, well paying jobs. Think of everything as a series of bets. Fundraising, hiring, product decisions, even waiting. Inaction is a bet too. Once you see it that way, you stop overthinking and start moving with intention. Subscribe to The Tech Trek wherever you listen. If this one hit home, share it with a founder or tech leader navigating their own leap. Follow the show on LinkedIn for more.

    24 min
  6. From Exit to Starting Over: What Nobody Tells You About Building Again

    FEB 27

    From Exit to Starting Over: What Nobody Tells You About Building Again

    Harry Gestetner built a creator economy platform in college, sold it, and walked away. Then he did the one thing nobody expected. He jumped back in and started building hardware. In this episode, the founder and CEO of Orion (a sleep tech company making smart mattress covers) sits down to talk about what really happens after an exit, why most founders can't stay away from building, and what changes when you go from software to physical products. Harry shares what surprised him about the acquisition process, how he thinks about evaluating new startup ideas, and why he believes hardware is "life on hard mode." He also gets into the mental side of founding, from managing stress to staying sharp when everything feels uncertain. What You'll Walk Away With Going through an exit sounds like the finish line, but Harry explains why it's actually a reset. You trade ownership and freedom for financial security, and at some point, most founders start craving the creative control they gave up. Not every idea deserves your time. Harry talks about running new concepts through a "disqualification period" where you actively try to poke holes before committing. The ones that survive that process are worth going all in on. Hardware changes the game. Software lets you pivot fast. Hardware gives you 18 month product cycles, inventory headaches, and supply chain complexity. Conviction has to be higher before you start. The best startup ideas come from problems you and your friends actually have. If enough people share that problem, you've got a market. Knowledge compounds across startups. Harry compares the founder journey to an elastic band. Once you've been stretched, you never go back to your original form. Every challenge you survive makes the next one more manageable. Timestamped Highlights [00:34] What Orion actually does and how it makes six hours of sleep feel like ten [03:01] The emotional arc of an exit that nobody talks about, from relief to restlessness [05:34] How Harry evaluates startup ideas and why he uses a disqualification process [09:30] Why building hardware is "life on hard mode" and what made him take it on anyway [10:39] The elastic band theory of founder growth and why learning compounds over time [15:49] His advice for early career founders: pick one thing and go all in Words That Stuck "As a founder, you're sort of like an elastic band. The more you get stretched, you never go back to the original form." Tactical Takeaways Run every new idea through a disqualification period. Actively look for reasons it won't work before you commit. The ideas that survive that scrutiny are the ones worth building. Build around problems you personally experience. If your friends share the same frustration, there's a good chance others do too. That's your market signal. If you're going to start something, go all in. Stop hedging across multiple projects. Pick one idea and dedicate yourself to it completely until it works. Keep Up With The Show If this episode hit home, share it with a founder or someone thinking about taking the leap. Subscribe wherever you listen so you never miss an episode. And connect with us on LinkedIn for more conversations like this one.

    20 min
  7. Edge AI Is Shifting From Chat To Action

    FEB 26

    Edge AI Is Shifting From Chat To Action

    Behnam Bastani, CEO and cofounder of OpenInfer, breaks down why the last two years of AI feel explosive, and why the next wave is not chat, it is action at the edge. We get into always on inference, what actually forces compute to move closer to the data, and the missing layer that makes edge AI scale: the Android like infrastructure that lets devices collaborate instead of living in silos. Key takeaways • The hype spike is real, but the runway is decades, it took compute, sensors, and communication protocols maturing over generations to unlock this moment • AI is shifting from conversational to actionable, which means continuous, always on inference becomes the norm • Edge wins when cost, reliability, and data sovereignty matter, cloud and edge will coexist, but the workload placement changes • The biggest bottleneck is not just silicon, it is the infrastructure layer that makes building and deploying across devices easy, plus a shared fabric so devices can cooperate • Adoption is as much a human story as a technical one, this shift lands faster and broader than previous tech transitions, so anxiety is predictable and needs real attention Timestamped highlights 00:38 OpenInfer’s mission, intelligence on every physical surface, and why collaboration matters 02:07 Electricity as the earlier revolution, intelligence as the next kind of power, and the control problem 05:54 Where we really are on the maturity curve, early products are here, mass adoption and safety take time 08:31 When the device boundary disappears, it stops being you versus the agent, it becomes one system 11:04 Always on inference, and the three forces pushing compute to the edge: cost, reliability, data sovereignty 14:40 The Android moment for edge AI, why the operating system layer unlocks developers, apps, and adoption A line worth replaying Those are going to be the three pillars that really enforces that edge and cloud are going to live together. Pro tips for builders • If your product needs real time decisions, design for intermittent networks from day one, reliability is not optional • Treat data sovereignty as a product feature, not a compliance afterthought, it is becoming the moat • Push for interoperability early, the fabric that lets devices share the right data is what makes edge feel seamless Call to action If this episode helped you rethink where AI should run and what it takes to ship it in the real world, follow the show and share it with one builder who is working on edge, robotics, devices, or applied AI.

    27 min
  8. How to Build a Data Team From Scratch (And Get Leadership to Invest)

    FEB 25

    How to Build a Data Team From Scratch (And Get Leadership to Invest)

    Building data capability from zero is not a tooling problem, it is a trust and prioritization problem. In this episode, Laura Guerin, Head of Data and Data Science at Bevi, breaks down how she goes from blank slate to real business impact, without getting trapped in endless plumbing or endless meetings. Laura shares how she runs an early listening tour, prototypes value before asking for bigger investment, and decides when to hire scrappy generalists versus specialists. We also get practical on AI, where it helps, where it is unnecessary, and why quality data and a clean semantic layer still decide whether anything works. Key takeaways • Start with business priorities, then map data work to the actions and outcomes leaders actually care about • Prototype the end deliverable fast, even if the backend is duct tape at first, then scale after stakeholders see value • Use cases first for AI, most problems do not need AI, but the right problems can see real acceleration • Early teams win with adaptable generalists who can wear multiple hats across data, analytics, and data science • Trust is a shared responsibility, build reliability, then create a culture where users flag weirdness quickly Timestamped highlights 00:44 Bevy explained, smart bottle less dispensers and why the business context matters for data priorities 02:01 The listening tour playbook, exec alignment, stakeholder map, and using AI to synthesize themes into a SWOT 04:00 The MVP reality, manual prototypes to prove value, then the conversation about scalable pipelines 06:33 AI without the hype, use cases, when AI is not needed, and two examples with clear business impact 09:22 Hiring from zero, why generalists first, the data analytics data science spectrum, and the personality traits that matter 14:21 Self service reimagined, Slack as the interface, semantic layer and permissions, and how to keep a single source of truth 20:19 Keeping trust when things break, checks and balances plus a shared responsibility model 22:39 Making innovation real, baking it into expectations so the team has time to learn and test new approaches A line worth stealing Data on its own is not typically a priority. It is more about the action or the impact that comes out of the data. Pro tips • Run a structured listening tour early, capture themes, then pick two or three priorities you can deliver quickly • Show the business an MVP output first, then use that proof to justify the unglamorous backend work • Treat AI like any other tool, define the problem, validate the use case, then confirm the data quality inputs Call to action If you are building analytics, data products, or AI inside a growing company, follow the show and subscribe so you do not miss the next operator level conversation. Share this episode with one leader who is asking for data outcomes but has not funded the foundation yet.

    25 min
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5
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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
    638
  • Rating
    Clean
  • Copyright
    © Elevano
  • Show Website
    The Tech Trek