Breakthrough AI Operators

Roland Siebelink
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Breakthrough AI Operators is a podcast about how the best startup founders are reinventing how their companies work. Not AI hype. Not vendor pitches. Real operators who've rebuilt significant parts of their business around AI — and can talk honestly about what worked and what didn't. Hosted by Roland Siebelink and Doug Miller, co-founders of Midstage Accelerator (7 unicorns built between them, 100+ leadership teams scaled), each episode features a founder who's achieved a genuine step-change breakthrough in how their company operates. These aren't productivity wins or tool adoption stories — they're companies that are structurally different because of AI. If you're a founder at a 20–300 person company actively figuring out what AI means for your operating model and competitive position, this show gives you real stories from people in the field — not consultants theorizing from the sidelines.

  1. há 4 h

    Your Doorbell Is Now Water Infrastructure | Subeca | Anne Mushow | EP 211

    What does it look like when the person best positioned to disrupt an industry is the one who used to run the process about to become obsolete? One founder managed a 90-person team deploying water meters the slow, decades-old way, watched a hyperscaler quietly ship a piece of infrastructure almost nobody in her industry noticed, and knew instantly her old job wouldn't exist much longer. Anne Mushow is CEO of Subeca, a water-metering company she built after running Xylem's smart-meter deployment team and a stint inside Amazon Web Services, where she watched Amazon Sidewalk launch and recognized what it would mean for the industry she'd just left. Subeca's device clips onto a water meter already in the ground in under a minute and turns it into a connected one, no rip and replace, no dedicated network to build or maintain. The path there took several pivots. Mushow originally imagined utilities and customers sharing usage data directly, until she learned most utilities didn't have granular data to share in the first place. An early version ran on LoRaWAN alone, until Sidewalk's infrastructureless model, running through Ring cameras and Echo devices already sitting in millions of homes, became what she calls the real unlock. Subeca now runs both as a belt-and-suspenders setup. Revenue grew 10x from 2024 to 2025, and the company is on pace for another 10x this year. The team behind that growth is seven technical people. Mushow estimates the same work would have needed 20 to 25 people fifteen years ago, mostly because AI now lets her team iterate on small, deeply tested pieces of code instead of building everything by hand. AI also does a chunk of lead generation, scanning public municipal and HOA meeting records for the exact language that signals a utility is close to a buying decision. Her clearest competitive edge isn't technical, though. Established meter manufacturers have been slow and skeptical about Sidewalk, and Mushow's own history helping launch it from inside Amazon gave Subeca roughly a two-year head start nobody else could easily copy. She's also chosen to stay a point solution on purpose, happily plugging into partners' existing systems as the last-mile connector instead of trying to own the entire stack. Roland flags the headcount comparison as one of the clearest AI-leverage numbers he's heard on the show: a team of seven doing what took 20 to 25 people a decade and a half ago is the kind of benchmark SaaS founders at the $1M to $50M stage should be measuring themselves against, not vague claims of "using AI more." Key Moments: 02:31 — Why Subeca calls itself the data layer AI needs, without calling itself an AI company 04:14 — The three market pressures forcing utilities to finally modernize 06:47 — Seven technical people doing work that would have needed 20 to 25 people fifteen years ago 08:10 — Scanning municipal board minutes with AI to catch a utility right before it's ready to buy 11:36 — The 2016 idea that failed once utilities admitted they didn't even have the data 13:25 — Why Amazon Sidewalk became "the ultimate game changer" over LoRaWAN and cellular 21:55 — The unexpected moat: being the one who helped launch Sidewalk from inside Amazon 23:42 — Choosing to be the "last mile" connector instead of building a walled-garden platform If the AI-leverage math behind your own team isn't as clear as Mushow's seven-person build, Roland works through exactly that kind of benchmarking with SaaS founders inside Midstage. mdstg.ac/drag-erase #SmartWaterMetering #AmazonSidewalk #IoTWaterInfrastructure #FounderGrowth #BreakthroughAIOperators

    Your Doorbell Is Now Water Infrastructure | Subeca | Anne Mushow | EP 211

  2. 18 de ago.

    The Second Time Around: Sandra Trittin on Timing the Grid | Bebop | Sandra Trittin | EP 210

    What happens when you build the right idea a decade too early? Most founders would call that a failure and move on to something else. One founder built the exact same business twice, over ten years apart, and this time the world finally caught up to the idea. Sandra Trittin co-founded a distributed energy management startup back in 2012 and watched the market fail to catch up to the idea. She's doing it again as co-founder of Beebop, the orchestration layer that lets utilities integrate distributed energy resources like solar, storage, and EVs at grid scale. Sandra names three things that had to change before her original thesis could actually work. Homes and businesses now generate and store their own power, so people who used to be pure consumers have become producers too, which scrambles the demand patterns utilities used to plan around. The devices themselves finally became internet-connected and controllable, instead of the 30 or 40 year old hot water tanks she was trying to manage in 2012. And competition increased alongside real customer education, so the pitch no longer has to start from zero. She also tells a story about why none of that replaces field experience: an IT colleague pushed a platform upgrade on a Friday evening in her first startup, and by Saturday morning she had 150 calls from customers with cold showers and furious families, all with her personal phone number. Sandra is candid about the trap of being a repeat founder in the same space. The second time around, she says, you carry pre-assumptions from the first attempt that can blind you as easily as inexperience did the first time. She named two concrete signs she watches for to check whether product-market fit is actually holding: the same competitor names keep coming up across unrelated deals, or sales cycles keep stretching longer across multiple customers at once, both signals that the value proposition has drifted from what the market needs. She's also unusually direct about her own limits, describing herself as a zero-to-one builder rather than a one-to-ten scaler, and structured Beebop's three-person founding team so her CEO holds final say, her CTO owns product and technology, and she owns commercial growth. Roland flags Sandra’s self-assessment as rare among founders at the $1M to $50M stage, most people don't openly say they might be the wrong person for the next phase of growth. The two product-market-fit warning signs she named, repeating competitor names and lengthening sales cycles, are also useful precisely because they're checkable facts rather than a gut feeling. Key Moments: 02:08 — The three inflection points that had to line up before Beebop's 2012 idea finally worked 05:08 — The Friday night software upgrade that left 150 customers with cold showers by Saturday morning 11:46 — The two warning signs Sandra Trittin checks constantly to see if product-market fit is slipping 13:22 — Why founders get "blind" to their own product, and what she watches for on other boards 15:39 — Realizing she's a zero-to-one builder, not a one-to-ten scaler 17:00 — How three co-founders split the CEO, CTO, and commercial roles, and who gets the final word 19:07 — Why mission and core values are "the glue" once you give people freedom over how they work If you've ever wondered whether you're the right person to take your company from one to ten, Roland works through exactly that question with SaaS founders inside Midstage. mdstg.ac/drag-erase #DEROrchestrationPlatform #GridFlexibilitySoftware #VirtualPowerPlant #FounderGrowth #BreakthroughAIOperators

    The Second Time Around: Sandra Trittin on Timing the Grid | Bebop | Sandra Trittin | EP 210

  3. 6 de ago.

    Don't Think AI. Think Business First. | Iveda | David Ly | EP 209

    Picture a city government worried about crime: robberies, car accidents, crooks on the street. An AI vendor walks in pitching license plate recognition and predictive policing. It sounds right. Except the city's more urgent problem might be flooded streets, and no security vendor ever thought to ask about that. One AI CEO built his entire company around asking that question first, which is the opposite of how most AI startups go to market. David Ly founded Iveda in 2003 selling IP camera monitoring as a service, built one of America's first human video monitoring centers with 60 people watching screens around the clock, then shut it down once he realized humans couldn't do the job well and trained machine learning on the footage they'd collected instead. He took the company public on NASDAQ in 2022, not to cash out, but to acquire more. Ly's rule for founders is blunt: don't think about AI first, get to know your business and your real, unflattering problems, and only then ask where AI helps. He walks through a real example: a municipality that Iveda assumed cared about crime prevention actually cared more about flooded streets, so his team built detection for that instead of pitching the license plate features they'd planned to sell. They brought the fix back to that same customer before ever marketing it. He calls most competitors "not wrong, just not accurate," because they pitch a bundle of goods before they've confirmed what's actually broken. He also still flies out to shake a customer's hand personally, even running an AI-native business, because that relationship is what makes a deal stick. The bigger turn in the conversation is personal. A few years ago, Ly's newly hired chief of staff asked him a question that stung: if you're this good and this smart, why are you still running a six or seven million dollar company instead of fifty? That forced him to rebuild Iveda's hiring and culture from the ground up, prioritizing attitude and total alignment across every geography the company operates in over pedigree or credentials, and building a company-wide habit of admitting what you don't understand instead of nodding along in meetings full of acronyms. Roland sees the flooding example as the sharpest version of a mistake he watches SaaS founders at the $1M to $50M stage make constantly: reaching for the AI feature that sounds impressive before confirming what the customer is actually stuck on. The other pattern worth sitting with is how much Ly credits one blunt question from someone close to him for a breakthrough his own instincts couldn't produce. Key Moments: 02:37 — Why "we are a solutions provider" is a cliché, and what actually breaks AI vendors in the field 04:27 — The flooding example: pitching crime prevention when the real problem was something nobody had asked about 07:14 — Why "technology makes anybody look and sound clever," and why that's never enough 09:07 — Still flying out to shake a customer's hand personally, even in an AI-native business 09:49 — Buyer persona versus user persona, and why users just want minimal thinking 13:59 — Getting full market intelligence through a reseller channel network instead of a direct sales team 20:53 — "Why are you still sitting here running a six, seven, ten million dollar company?" 23:01 — Rebuilding hiring around attitude and total alignment instead of degrees and pedigree If the question that changed everything for Ly is one you haven't been asked yet, Roland works through exactly that kind of stuck point with SaaS founders inside Midstage. [mdstg.ac/drag-erase] #AIVideoSurveillance #SmartCityAI #AIVideoAnalytics #FounderGrowth #BreakthroughAIOperators

    Don't Think AI. Think Business First. | Iveda | David Ly | EP 209

  4. 29 de jul.

    Product Ahead, Then GTM Ahead | SpotDraft | Shashank Bijapur | EP 208

    What do you do when the exact data your AI needs to get smart is legally required to stay private forever? A confidentiality agreement doesn't show up in a public training set, by definition it can't. One founder discovered that gap years before generative AI existed, and had to build an entire operating platform just to earn the right to the data his own product needed. Shashank Bijapur is co-founder and CEO of SpotDraft, an AI-native contract lifecycle management platform he built with his co-founder Madhav after leaving a law career at White & Case. SpotDraft has processed more than a million contracts, raised a $54 million Series B, and recently became the first CLM to run contract analysis entirely on-device. The idea started on a New Year's Eve in 2017, when Bijapur was running due diligence reports instead of celebrating, read a headline about Elon Musk building self-driving cars, and couldn't square that cars were driving themselves while he was still copy-pasting contract language by hand. He and Madhav's first hypothesis was straightforward: train a model on legal contracts and automate redlining. It fell apart fast, because confidentiality agreements are confidential. There was no real training data to learn from, only invented data that made accuracy a coin flip. So they took two steps back and built the actual infrastructure lawyers were missing, workflows, a repository, a real system of record, which is what eventually became the CLM. AI was never bolted on afterward. It's the reason the company exists. That same instinct shaped a more recent bet. Industries like pharma and defense kept raising the same objection: where does our data go once it touches an LLM? SpotDraft partnered with Qualcomm to run contract analysis entirely on-device, in airplane mode, with nothing leaving the machine, making it the first CLM to offer that option. Bijapur also described becoming his own company's bottleneck around year five, answering every question himself until his team stopped trying to decide anything without him, a loop his COO broke by introducing OKRs so people knew exactly what they could decide without asking. Roland has seen the founder-as-bottleneck pattern often with SaaS leaders at the $1M to $50M stage, and what he found most useful here is how directly Bijapur named the fix: it's not about hiring smarter people, it's about explicitly telling them what they're allowed to decide without you. The other pattern worth sitting with is SpotDraft spending three full years in pure product mode with no go-to-market function at all, on the belief that great technology speaks for itself. It doesn't, and Bijapur said as much. Key Moments 01:49 — The origin story: a recovering lawyer, a due diligence report, and a New Year's Eve at White & Case 02:18 — Reading about Elon Musk's self-driving cars while still copy-pasting contract language by hand 04:05 — Why training an AI model on legal contracts almost couldn't work: confidential means confidential 10:42 — "After Shakespeare, what's the most difficult language you can read? That damn contract" 12:27 — The Qualcomm partnership, and why on-device AI mattered for pharma and defense clients 18:16 — Realizing he had become his own company's bottleneck, and what broke the loop 20:22 — Getting product and go-to-market to finally run in sync instead of chasing each other — If you recognize the moment where your own team stopped deciding anything without checking with you first, that's exactly the kind of stuck point Roland works through with SaaS founders inside Midstage. mdstg.ac/drag-erase #AIContractManagement #LegalAI #CLMPlatform #FounderGrowth #BreakthroughAIOperators

    Product Ahead, Then GTM Ahead | SpotDraft | Shashank Bijapur | EP 208

  5. 14 de jul.

    20 Years to Build the Product People Cry Over | Mixbook | Andrew Laffoon | EP 207

    Andrew Laffoon is co-founder and CEO of Mixbook, a photo book platform he has led for 20 years—from hobbyist niche to mainstream storytelling product. In this episode, Andrew explains how Mixbook built Story Mode (an AI-powered photo book creator powered by OpenAI, Claude, and Google, launched 8 days before this recording), what stalled growth after the first $25M, and why the exec team is never the answer when the bottleneck is the founder. Topics covered: - Why 98% of people couldn't make a meaningful photo book before Story Mode—and what that says about storytelling as a market - The exec-team-as-mentors trap: why hiring smart people can't fix a founder who hasn't grown into the role yet - Flywheel thinking: how to identify the bottleneck, measure it, and compound growth on a schedule - "The first job of a CEO is to define reality": what this means for a company at 130 people - Sam Altman's persuasion warning—and what a founder deploying AI should actually be worried about --- Mixbook is offering listeners 50% off their first order with code BREAKTHROUGH. Worth trying if you want to see what Story Mode does with your own photos before deciding whether AI-written narration actually holds up. https://bit.ly/4fvDI64 If you recognize the wall Laffoon described, growth that's stalled for reasons you can't quite name, Roland works through exactly that kind of stuck point with SaaS founders inside Midstage. mdstg.ac/drag-erase #AIPhotoBook #StoryDrivenAI #PersonalizedPhotoGifts #FounderGrowth #BreakthroughAIOperators

    20 Years to Build the Product People Cry Over | Mixbook | Andrew Laffoon | EP 207

  6. 7 de jul.

    300M Calls, 70 People: How Vertical AI Beats Horizontal Every Time | Flip | Brian Schiff | EP 206

    Would you turn away a paying customer to protect a product that isn't ready for them? Last year, a major telecom company came to a fast-growing AI startup wanting to buy in. The founder said no, not because the money was bad, but because his product hadn't earned the right to serve that industry yet. Brian Schiff is co-founder and CEO of Flip, the vertical voice AI platform that grew out of a failed idea about college ride-hailing. Schiff and his co-founder Sam met at Cornell and built a ride-hailing app for taxi companies in upstate New York, where ridesharing itself was still technically banned. The app worked fine, but the phones at these taxi companies never stopped ringing. That was 2018, right as Alexa and Google Home had their moment, and it pushed the pair toward what Schiff calls a product pivot rather than a market pivot: they kept selling to the same customers, just sold them something new. Cornell's eLab program had drilled one lesson into them, be concrete about the problem and who you're solving it for, and that discipline shaped everything after. It also shaped Flip's contrarian bets. Most AI customer service companies build the technology first and go looking for somewhere to apply it, what Schiff calls technology outward instead of customer inward. Flip built industry by industry instead, transportation first, then retail, then healthcare, turning down the telecom client because an untrained agent would have been, in his words, the dumbest agent you've ever talked to. Flip also priced itself on usage from day one, charging only for resolved calls, a model investors compared to payment processors, which get valued at a fraction of typical SaaS multiples. Schiff built it anyway because it removed the buyer's risk. Roland has coached plenty of SaaS founders who feel the pull to say yes the moment a bigger check shows up, and Schiff's telecom refusal is the clearest version of that discipline he's heard on the show. What stands out isn't the refusal itself, it's that Flip proved out transportation completely before touching retail, and retail before touching healthcare. That sequencing is the exact pattern Roland wishes more founders at the $5M to $15M stage would follow instead of chasing logo diversity to look bigger than they are. Key Moments: 02:00 — The "ten years to overnight success" story, and why it started in taxi dispatch instead of AI 05:40 — A market pivot versus a product pivot, and why Flip chose the harder one 07:42 — Technology outward versus customer inward: the split Schiff says defines the AI customer service race 11:05 — The usage-based pricing bet investors warned would cap Flip's valuation 18:23 — Turning away a telecom client because the agent "would not be able to do anything" for them 24:19 — Building a client reference list as long as the client list itself 27:53 — "Everything in this company is written in pencil, not pen" If turning down a revenue-adjacent client to stay disciplined about who you actually serve is a decision you're wrestling with, Roland works through exactly that tradeoff with SaaS leadership teams inside Midstage's advisory work. mdstg.ac/drag-erase #VerticalAI #VoiceAI #AICustomerService #GoToMarket #BreakthroughAIOperators

    300M Calls, 70 People: How Vertical AI Beats Horizontal Every Time | Flip | Brian Schiff | EP 206

  7. 30 de jun.

    From 50 to 5,000 Customers in a Month: How Pictory Clawed Its Way to Product-Market Fit | Pictory | Vikram Chalana | EP 205

    In 2019, before ChatGPT, before Sora, before most people had heard the word "foundation model" used in a business context, one founder made a call that looked strange at the time: he decided not to train his own AI models. Not because he couldn't. Because he believed the models would arrive — and that the real value would be in the workflow built on top of them. Vikram Chalana took his first company, Winshuttle, from zero to 350 employees and 2,000 customers across 66 countries before selling it to Symphony Technology Group in 2018. He's now co-founder and CEO of Pictory, an AI video creation platform that has brought 20,000 organisations away from traditional video production with a team of 60 and total funding of $4.7M, in a market where direct competitors have raised hundreds of millions. The primary thread of this conversation is capital efficiency as deliberate strategy — not constraint. Vikram describes the founding decision to compose best-in-class AI infrastructure, integrating ElevenLabs, Veo, HeyGen, and others rather than building proprietary models, as the single biggest factor in Pictory's margins and speed. The workflow layer is the moat. The model underneath it is swappable. That logic, which felt contrarian in 2019, has played out in ways that are now visible in the market: Pictory is profitable; some of its better-funded competitors are still explaining what they spent the money on. The second thread is how product-market fit actually arrives — and what it looks like when it does. In summer 2021, Pictory went from 50 paying customers to 5,000 in a single month, after running an AppSumo campaign that found a completely different customer segment than the enterprise marketing teams the company had been chasing. The insight Vikram draws from that: product-market fit is a combination of product and market, and the same product on a different market is a different company. Before that AppSumo experiment, Pictory was struggling. The product has not changed, the customer has. Roland notes that Vikram's approach to the AI model question is a sharper version of what he sees in the most capital-efficient SaaS companies he works with — founders who identified one specific structural bet early and stayed disciplined about it, not because they had certainty, but because the logic of the bet remained sound as new information arrived. The founders who raised heavily to build proprietary AI infrastructure are now carrying that investment on their balance sheets. The founders who stayed composable are building on top of the same underlying models, at lower cost, with more flexibility. Vikram's willingness to name that dynamic directly is unusual — and useful. Key Moments: 00:00 — From 50 to 5,000 customers in a month: what product-market fit actually feels like from inside it 02:46 — Why Pictory decided not to train its own AI models in 2019, and what that bet looked like before anyone knew it would pay off 08:57 — The current AI model stack: how Pictory integrates Claude, OpenAI, Google Veo, ElevenLabs, HeyGen, and others, and what swappability actually buys you 12:11 — Why workflow stickiness matters more than model quality: the front-end moat Pictory is betting on 14:05 — The AppSumo experiment: how switching markets, not products, took Pictory from struggle to 5,000 customers in four weeks 18:14 — CEO vs. CTO: how Vikram draws the line between the two roles, and what the Winshuttle transition taught him about giving up control 22:24 — Working on the business, not in it: what the phrase actually means, and why knowing it doesn't make it any easier to do 27:06 — The Pictory CREDO (Curiosity, Respect, Expeditiousness, Data, Openness), and what happens when a core value gets tested by a layoff or a firing 22:46 — Why the person who got you to $1M might not be the right person to get you to $10M, and why that's not a failure on their part 24:43 — Why good products sometimes die because the founder got bored: Vikram's discipline around not rebuilding what already works 29:47 — Vikram's one piece of advice for newer founders: stop being so risk averse, just jump in — Pictory offers a free trial at pictory.ai — worth visiting if you're a founder or marketer who wants to see what AI video production actually looks like without a production budget. If you're navigating the AI stack question — whether to build, buy, or compose — and want to pressure-test the logic of your bet against what Midstage has seen across dozens of software companies at the $1M–$50M stage, that's a conversation worth having. mdstg.ac/drag-erase #AIVideoCreation #TextToVideoAI #AIVideoForMarketing #AIVideoGeneration #BreakthroughAIOperators

    From 50 to 5,000 Customers in a Month: How Pictory Clawed Its Way to Product-Market Fit | Pictory | Vikram Chalana | EP 205

  8. 23 de jun.

    Unicorn in the Making: The Playbook for Scaling from $1M to $100M | Midstage Accelerator | Payandeh Ekrami | EP 204

    Most founders believe their team isn't aligned because the team isn't capable enough. Almost none of them have considered that the team can't align around a direction that lives entirely inside the founder's head. That distinction — between a team development problem and a founder bottleneck problem — is the one that determines whether a scaling company breaks through or breaks down. Payandeh Ekrami is a seasoned enterprise technology sales executive with senior leadership roles at Snowflake, Anaplan, and Samsung, specialising in helping high-growth companies build and scale revenue organisations. In this episode, she turns the mic around on Roland — interviewing him about the patterns he's observed across three unicorn journeys and what those patterns mean for any founder navigating the mid-stage transition today. The conversation surfaces two things that rarely appear together in the same discussion. The first is Roland's diagnostic of why founder-led companies stall — not at the market or product level, but at the operating level, when the habits that built the business start blocking the next stage of growth. The second is the more personal thread Payandeh draws out: Roland's underlying conviction that every person can find a context where they genuinely excel, and that one of the most important things any leader can do — including a founder — is help the people around them find that fit rather than forcing them into shapes that don't suit them. Roland names several specific tools and moments that illustrate how Midstage Accelerator does its work. The first workshop exercise — asking every leadership team member to write down independently what they believe they're responsible for — has made teams cry from the frustration of seeing how misaligned they actually were, with a head of engineering optimising for zero outages while the CEO expected ten monthly product launches. The practice of asking a founder CEO to sit quietly and speak last in workshop discussions reliably reveals which team members have been contributing original thinking and which have been echoing the founder back. And the question Roland poses before every engagement — do you work for yourself, or do you work for the company? — functions as a direct filter for whether the founder is building a business or building a legacy. Roland notes that the founders who reach the mid-stage with the strongest track records are often the hardest to work with at first — precisely because their instinct to be the smartest, most decisive person in the room is what got them there. The move from managing by assignment to managing by area, from controlling every detail to setting outcomes and trusting people to own their domains, is not a knowledge problem. It is a psychological one. What changes the equation, in his experience, is not convincing founders to delegate — it is lighting a bigger fire for what becomes possible when they do. Key Moments: 00:00 — Roland's opening claim: why seeing the movie three times is the core value he brings to founders 02:16 — What makes the mid-stage different from early-stage startup and from running an established company 05:50 — Why Roland calls himself an accelerator, not a coach — and what the distinction actually means 08:24 — The workshop instruction Roland gives every new founder CEO: sit down, be quiet, speak last 10:02 — What that silence reveals about who's actually thinking on the team versus who's been echoing the founder 12:52 — The alignment exercise that has made leadership teams cry: write down what you think you're responsible for 14:19 — The one question Roland asks before signing any engagement — and why the answer is a green flag or a red flag 16:00 — The Orange Communications "today and tomorrow" model: how to keep innovation alive without burning down operations 20:00 — The flywheel framework: four functions, one simple picture, and why it makes delegation feel possible 25:04 — What Roland looks for before taking on a client: ambition, coachability, and pride in the craft of leadership If you're a founder CEO navigating the transition from doing everything yourself to leading through a team, and you want a diagnostic on where the real bottlenecks are, reach out to Midstage Institute to start the conversation. mdstg.ac/drag-erase #StartupScaling #UnicornFounder #FounderCEOCoaching #MidStageStartup #BreakthroughAIOperators

    Unicorn in the Making: The Playbook for Scaling from $1M to $100M | Midstage Accelerator | Payandeh Ekrami | EP 204
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Sobre

Breakthrough AI Operators is a podcast about how the best startup founders are reinventing how their companies work. Not AI hype. Not vendor pitches. Real operators who've rebuilt significant parts of their business around AI — and can talk honestly about what worked and what didn't. Hosted by Roland Siebelink and Doug Miller, co-founders of Midstage Accelerator (7 unicorns built between them, 100+ leadership teams scaled), each episode features a founder who's achieved a genuine step-change breakthrough in how their company operates. These aren't productivity wins or tool adoption stories — they're companies that are structurally different because of AI. If you're a founder at a 20–300 person company actively figuring out what AI means for your operating model and competitive position, this show gives you real stories from people in the field — not consultants theorizing from the sidelines.

Informações

  • Criado por
    Roland Siebelink
  • Anos de atividade
    2025 - 2026
  • Episódios
    35
  • Classificação
    Explícito
  • Copyright
    © 2026 Roland Siebelink
  • Site do podcast
    Breakthrough AI Operators