The Tech Trek

Shamoon Siddiqui, CEO and Founder of Human Friendly Robotics, joins The Tech Trek to break down what it really takes to bring robotics into construction. This is not a futuristic thought experiment. It is a grounded conversation about where robots can create value now, why construction has lagged so badly on productivity, and how focused automation could reshape one of the world’s biggest industries.At the center of the discussion is Tyler, a tile laying robot built as a practical entry point into construction automation. Shamoon explains why repeatable workflows matter, where human skill still wins, and how robotics can improve speed, safety, and job site economics without needing to look like a science fiction demo.In this episode• Why construction productivity has moved backward while other industries have surged ahead• Why tiling is the right entry point for construction robotics• How Human Friendly Robotics thinks about deployment, rentals, and product iteration• Where robots can reduce hidden job site injuries tied to repetitive strain• Why the long game is much bigger than tile, with plumbing, electrical, and HVAC in sightTimestamped highlights00:35 Why construction is the right market for robotics right now03:56 The bigger shift from humans moving atoms to machines handling more physical work08:29 Why the business model is built around rentals, not one time equipment sales10:24 The wedge strategy today and the larger vision across licensed trades12:12 The overlooked safety problem of repetitive strain in construction20:44 Why useful robots matter more than robots built for flashy demos“Version one is not going to be as good as version five, but if you continue to rent it from us, we can make sure you get version five when it’s ready.”Practical takeawayThe smartest automation wedge is not the flashiest one. Start with repetitive, measurable work, prove productivity gains in the real world, and expand from there.Follow The Tech Trek for more conversations on robotics, AI, startups, and the technologies changing how real work gets done.#ConstructionTech #Robotics #Automation #ai #FutureOfWork

Mary Elizabeth Poré, Global Vice Chair Client Technology and COO at EY, helps enterprises turn emerging tech into real work. She discusses why culture blocks adoption, why AI is not a magic wand, and how leaders should start with real pain points. Short pilots aren’t enough. Storytelling, affinity groups, and visible leadership drive broader experimentation and adoption.

Michael White, Co founder and CEO of Multiply, joins the show to talk about the path from engineering leadership to the CEO seat, and what it really takes to build in a high trust, high complexity market. If you are thinking about founder readiness, leadership growth, or where AI creates real value in fintech, this episode gets into the parts that matter.Michael shares how early entrepreneurial instincts showed up long before Multiply, what changed as he moved from builder to company leader, and why some of the most important skills in leadership have less to do with code and more to do with communication, conviction, and influence. He also breaks down how Multiply is using AI to improve the mortgage experience without removing the human element people still need in a major financial decision. In this episode:• The mindset shift from engineer to CEO• Why leadership becomes a form of sales• How founder timing can be an advantage, not a delay• Where AI fits in the mortgage process, and where it does not• Why startups can move faster than legacy players in AI adoption Timestamped highlights00:43 What Multiply is building, and why an AI native mortgage company sees a better path to homeownership01:47 The childhood business story that hinted at an entrepreneurial future06:20 What changed in the move from engineering leadership to founder and CEO08:45 Why so much of leadership comes down to influence, alignment, and selling the vision17:19 Why mortgages are such a strong use case for AI, and why the back office is the real opportunity22:39 The startup advantage in AI, speed, focus, and freedom from legacy systems Follow the show for more conversations with founders, operators, and technology leaders building what comes next.

Susan Liu, Partner at Uncork Capital who backs seed-stage AI and vertical software, shares what matters to early investors. She breaks down the team-market-product framework. She explains the product wedge and the ROI test buyers use. She warns that churn is coming for pilots without measurable ROI and outlines how Series A expectations have shifted.

Maju Kuruvilla, Founder and CEO of Spangle, builds commerce infrastructure for an agent-driven future. She explores how AI buyer agents will change shopping, why holding context beats relying on identity, and how product-level intelligence can bridge online and physical retail. The conversation covers agent versus human UX, ambiguous requests like “buy a red sweater,” and privacy-friendly ways to keep context across channels.

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 Highlights00:00 Introducing the hidden technology opportunity inside the equipment rental industry02:00 Why many rental companies still rely on paper, binders, and manual equipment checks06:20 How Andy Feis discovered a massive opportunity inside industrial operations09:00The low hanging fruit in modernizing equipment rental workflows11:14 What kind of data heavy machines actually generate and how it can be used13:03 Where AI actually helps blue collar industries today20:18 The roadmap for modernizing the industry and what comes nextA 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 TipsIf 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 ActionIf 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.

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 pointTimestamped Highlights00:47 Earth intelligence in plain English, task satellites, pull decades of history, ask questions like vessel detection or soil moisture06:32 Why veteran resumes miss the mark, and how to translate leadership without goofy title inflation10:44 The origin story, a broken buying experience in satellite imagery turns into SkyFi’s wedge16:42 Selling into government, people game first, acquisition reality, and why patience is a feature19:46 Use cases you will not expect, livestock behavior, barge counting, palm heights, mineral detection, and more28:10 Where this is headed, ask a question about the world, get an answer, then move toward proactive intelligenceA 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 stealIf you are hiring, screen for comfort with ambiguity, not just pedigree, undefined problems are the job in high growth workIf you are selling, build your network before you need it, warm paths beat cold volume every timeIf you are building product, shorten the feedback loop, commercial iteration can harden the product before slower cycle buyers adoptCall to ActionIf 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.

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 TakeawaysThe 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 foundersWords That Stuck"If AI is writing your code, it has to fix it too. And right now it is only writing the code."Founder PlaybookPick 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.

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 WithGoing 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 inWords 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 TakeawaysRun 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 ShowIf 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.

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 attentionTimestamped highlights00:38 OpenInfer’s mission, intelligence on every physical surface, and why collaboration matters02:07 Electricity as the earlier revolution, intelligence as the next kind of power, and the control problem05:54 Where we really are on the maturity curve, early products are here, mass adoption and safety take time08:31 When the device boundary disappears, it stops being you versus the agent, it becomes one system11:04 Always on inference, and the three forces pushing compute to the edge: cost, reliability, data sovereignty14:40 The Android moment for edge AI, why the operating system layer unlocks developers, apps, and adoptionA line worth replayingThose 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 seamlessCall to actionIf 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.