BUILDERS

Front Lines Media
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Welcome to BUILDERS — the show about how founders get new technology adopted. Each episode features a founder on the front lines of bringing new tech to market, sharing how they broke into their industry, earned early believers, built credibility, and unlocked real technology adoption. BUILDERS is part of a network of 20 industry-specific shows with a library of 1,200+ founder interviews conducted over the past three years. For the full network, visit FrontLines.io. Brought to you by:  www.FrontLines.io/FounderLedGrowth — Founder-led Growth as a Service. Launch your own podcast that drives thought leadership, demand, and most importantly, revenue.

  1. 6d ago

    "Category education is a long game with uncontrollable accelerants" | Ayal Yogev

    Anjuna Security builds the software stack that makes confidential computing accessible to enterprises — doing for hardware-level data protection what VMware did for virtualization and what CUDA did for GPUs. The company counts three of the top ten global banks among its customers, has expanded into the federal government through defense primes, and recently launched a second product to govern AI agents running inside a trusted computing environment. In a recent episode of BUILDERS, we sat down with Ayal Yogev, CEO of Anjuna Security, to learn how the company sold a category that buyers had never budgeted for, what actually moves the needle when selling to the world's largest banks, and why he calls rationalizing exceptions to your ICP "basically a death warrant." Topics Discussed: ● Why category education is a long game with uncontrollable accelerants — and how the JP Morgan global CEO's open letter to vendors changed buyer conversations overnight ● The two biggest GTM money pits: hiring salespeople before product-market fit and building large conference booths that attract the wrong buyers ● Why a private suite with pre-scheduled meetings adjacent to a conference consistently outperforms booth presence on ROI ● How Anjuna closed three of the top ten banks and what a year-long sales cycle actually looks like from first meeting to signed contract ● Why people movement between banks is a structural referral engine — and how touching 100-plus people inside a single bank compounds over time ● Champion identification in long-cycle enterprise deals: who to find, how deep to go, and what happens when your champion leaves before the deal closes ● Why selling direct to the US federal government was "close to impossible" and what finally worked: defense primes like Booz Allen Hamilton, Lockheed Martin, Leidos, and CACI ● How government grew from roughly zero to 25% of revenue and is tracking toward 50% in the next 12 months ● Why focus is defined by what you say no to — including inbound that's outside your ICP ● The forward-deployed engineer model: why bundling actual engineers into the first enterprise deal changes success rates, churn, and renewals ● Anjuna's second product: a trusted supervisor agent running inside a confidential computing environment to solve the "arctic intern" problem for AI governance

  2. 6d ago

    When adoption means nobody knows your product exists | Mario Di Dio

    Helium is building a decentralized wireless network and the software layer that runs on top of it, aggregating community-deployed Wi-Fi access points into a single network that carriers, MVNOs, and large indoor venues can use to serve their own subscribers. In a recent episode of BUILDERS, we sat down with Mario Di Dio, CEO of Helium, to learn how the company grew a network of forty five thousand access points serving one to two million people a day by selling offload capacity to carriers instead of building its own towers. Topics Discussed: ● Why telco buyers are structurally risk averse and what that does to a startup's sales motion ● How Mario first encountered the Helium model in 2020 while at CableLabs, and what made it click ● The FreedomFi acquisition and how Mario came in to lead the mobile network effort ● Why Helium sold the consumer brand it had built specifically to create demand on its own network ● The reorg, the layoffs, and what a single point of focus did to the organization ● Why running enterprise B2B and consumer under one brand made brand voice nearly impossible ● How the AT&T and Telefonica Movistar partnerships solved the demand side without a consumer product ● The AWS analogy Helium uses to move carriers from CapEx-heavy tower builds to an OPEX connectivity model ● Why invisibility is both the proof of adoption and the monetization problem in telco ● What Mario watches for as the catalysts that accelerate carrier adoption of the offload model GTM & Technology Adoption Lessons: Kill the demand-generation channel once partners own demand. Helium built a consumer phone brand to create demand on the network it was deploying. Once carrier partnerships landed, the demand problem belonged to someone else's subscriber base. A single brand cannot carry two buying motions. Helium was selling to enterprise telco and to consumers at the same time, under one name. Mario described the cost directly: "What kind of brand voice do you have when you're at the same time you're selling to an enterprise B2B as well as to a consumer brand, right? So you have to pick one." Risk aversion in telco is a pricing signal, not an obstacle to argue with. Mario's read on why telco buyers move slowly: "the penalty for a small error is gigantic and nobody's giving you credit when stuff works." Reframe the buyer's cost structure, not the product. Helium's pitch to carriers is an economic one. "now we are switching the model a little bit like AWS did for cloud from a model where the carrier owns everything, very CapEx intensive." Surgical coverage beats blanket coverage as a category argument. Mario points to comments from Starlink's president on an earnings call about hanging small cells off satellite backhaul as validation of Helium's approach. His position: "the old model of blanketing coverage of connectivity, it's dead. Invisibility is what adoption looks like, and what makes it hard to charge for. "The more successful is the technology that you produce, the less visible it is to the end customer." Helium's own evidence: "the majority of that people don't even know that they are using Helium." Refocusing is an org decision before it is a strategy decision. On his first three months as CEO: "one of the hardest things that you have to do is to refocus the organization, which means also reorg, which means let some people off, some friends that you work with." Compress time to thesis with two levers. Mario's answer on what a CEO actually controls when the market timing is uncertain: "One is nudge the industry, the other one is ship. Just ship. Continue to ship product and continue to keep everybody focused on meeting those timelines." // Sponsors: Front Lines, Silicon Valley's leading Podcast Production Studio. We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. Mention you are a listener and get a 10% discount. www.FrontLines.io/Podcast-as-a-Service

    When adoption means nobody knows your product exists | Mario Di Dio

  3. Sep 29

    How the AI training data market restructured around 10 frontier labs | Olga Megorskaya

    Toloka is an AI data platform that produces training data, evaluations, and human expert contributions to help AI labs and enterprises build higher quality models. In a recent episode of Front Lines, we sat down with Olga Megorskaya, Founder and CEO of Toloka, to learn how the company has navigated every major phase of AI development and what she actually believes about the future of human expertise in AI systems. Topics Discussed: - How Toloka has evolved from basic crowdsourcing to RLHF to RL gyms across a decade of AI development - Why the shift from foundational models to post-training and fine-tuning is driving new demand for human data - How the types of human expertise Toloka requires have shifted as AI capabilities grew - Why managing human efforts at scale is, in Olga view, a purely technological problem - The customer concentration risk that came with the era of foundational models - Why enterprise AI adoption is Toloka main bet for diversifying beyond frontier labs - Why generating AI output is now easy and why verifying it remains the hard problem - How physical AI and robotics is creating a new demand curve for human data - Whether expert recruitment has gotten harder or easier as AI data work has become more respected - The statistical methods from 1979 that Toloka quality management systems are still built on

  4. Sep 29

    Why AR has 1 million analysts and almost no software that works | Caitlin Leksana

    Fazeshift is building AI-native accounts receivable automation for mid-market and enterprise companies. In a recent episode of Front Lines, we sat down with Caitlin Leksana, Co-Founder and CEO of Fazeshift, to learn how she discovered one of enterprise software most overlooked problems and why AI is the first technology capable of actually solving it. Topics Discussed: - How Caitlin discovered the accounts receivable problem while building a different company - Why accounts payable has been solved but accounts receivable remains a manual, analyst-heavy operation - The swivel chair problem that defines daily life for AR analysts - Why there are roughly 1 million AR analysts in the United States, about the same as the number of public school teachers - Why AI ability to handle variable, company-specific workflows is what finally makes AR automation possible - How Fazeshift starts at the data layer before applying any AI - What creating a market looks like in practice: no budgets, no timelines, no competing vendors - How Caitlin ran founder-led sales for a year before hiring a founding account executive - Why Fazeshift SDR team now drives 80% of top-of-funnel pipeline - Why Caitlin looks at Salesforce as a brand model, not a product comparison

  5. Sep 29

    How Wing VC separates tracks from trains to predict which AI companies will endure | Jake Flomenberg

    Wing Venture Capital Partner Jake Flomenberg opens every IC meeting at Wing with a question that didn't used to matter: if a frontier model ships something meaningfully better, does this company still have reason to exist? In this episode of BUILDERS, he walks through the trains vs. tracks framework, the three diagnostic tests he runs on every AI company, and what a real flywheel looks like versus a pitch deck concept. Topics Discussed: Why "enduring differentiability" is now Wing's central IC question — and why early ARR no longer answers itTrains vs. tracks: which you're building determines your entire sales motion and pricing model"Blast radius" as a diagnostic to stress-test your own switching cost before a buyer doesThree questions that reveal whether an AI company will compound or compress as frontier models improveGrayswan: a real self-reinforcing red team loop from a Carnegie Mellon AI safety teamCategory creation in the AI era — why announcing on day one is usually wrongGTM Lessons For B2B Founders: Know whether you're building a track or a train — then sell accordingly. Trains sit on existing infrastructure: easy to adopt, easy to remove. Tracks require full re-architecture to replace because workflows and accountability structures get built around them. Jake's diagnostic: what's the blast radius of removing your product? Affects a couple of people's workflows — train. Changes how the whole org makes decisions — track. The mistake isn't building either — it's misrepresenting which you are. AI agent infrastructure sold as "just sign up and try it" understates the gravity of the decision. An AI writing tool locked into a multi-year contract overclaims stickiness that isn't there.Ask whether frontier models are fuel or a threat — before you fundraise. Jake calls it the most underappreciated question in AI: does a better base model make you stronger or more replaceable? Durable companies treat frontier models as fuel — their assets and value appreciate as base models improve. When a new model ships, is your differentiation compressing or compounding? If compressing, rethink before a better-informed investor asks.The flywheel test: answer all three specifically, or don't claim you have one. (1) How does your product get better as it operates? (2) How quickly? (3) How does the customer experience that improvement? Vague answers mean aspirational, not operational. The Grayswan contrast: 15,000+ red teamers generate attack trajectories that train a custom attacker LLM called Shade, which stress-tests a runtime firewall called Signal until nothing gets through. Each layer compounds. That's what operational looks like.Race to be last, not first. The companies that matter in ten years are racing not to be replaced — which means building, through close design partner work, a story that solves the problem today and five years out, not whack-a-moling whatever surfaces this quarter. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

    How Wing VC separates tracks from trains to predict which AI companies will endure | Jake Flomenberg

  6. Sep 4

    Founder-led roadshows: how Pennylane built trust inside local accounting communities across Germany and France | Tobias Janiesch

    Pennylane spent its first two years operating an accounting firm to experience the workflows it intended to fix. It then sold the profitable business, removed the channel conflict, and went all-in on software. After three years of deliberate customer development, growth accelerated to more than one million SMEs on the platform. In this episode of BUILDERS, Tobias Janiesch, Managing Director - Germany at Pennylane, breaks down the accountant-led distribution model, the trust-building behind its growth, and why entering Germany required both a product rebuild and a startup-style local team. Topics Discussed: Why Pennylane operated—and then sold—its own accounting firmHow accountants became the channel behind roughly 95% of leadsThe customer sequence behind Pennylane's growth inflection pointUsing founder-led roadshows and peer referrals in a trust-driven marketWhy Germany offered a four-year window but required a 40% product rebuildGiving a local team startup autonomy inside a scaled companyBuilding embedded AI on a unified financial data layer GTM Lessons for B2B Founders: Make the channel successful before asking it to distribute: Pennylane saves accountants 30–40% of their time and gives them a workspace their clients use. The accountants then invite the SMEs; approximately 95% of leads originate through them. The channel works because the product improves the partner's own economics.Earn the right to move upmarket: Pennylane first made 20 digital-first firms successful, expanded to the next 50, and used each cohort to uncover edge cases. Only then did it pursue larger firms. The first flagship customer helped turn three years of steady growth into rapid acceleration.Enter conservative markets through the progressive edge: Pennylane started with accountants already running digital firms. Their recommendations in WhatsApp, Facebook, and professional communities transferred trust to more cautious peers. Identify the insiders whose credibility can bridge the adoption gap.Localize the product and the operating model: Germany could reuse roughly 60% of the French product; 40% required rebuilding around local rules. Pennylane added German accounting expertise early and let the local entity operate like a startup, using lightweight tools before institutionalizing systems such as Salesforce.Turn trust into operating metrics: The founders avoided promising features they could not ship and reviewed commitments against delivery each year. In Germany, the team measures real activation through monthly VAT submissions and expects no early churn. Track recurring workflow adoption—not logins or account creation.// Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership.www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe.www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here:https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

    Founder-led roadshows: how Pennylane built trust inside local accounting communities across Germany and France | Tobias Janiesch

  7. Aug 24

    Finding the buyer who was already on the hook | Ria Shah

    Handl Health is building an AI platform that aggregates and analyzes publicly available healthcare pricing data, helping brokers and benefits consultants design, evaluate, and personalize health plans for self-insured employers. In a recent episode of BUILDERS, we sat down with Ria Shah, Co-founder & Chief Product Officer of Handl Health, to learn how the company turned newly mandated price transparency data into a plan design platform that brokers and benefits consultants rely on. Topics Discussed: How price transparency legislation created a now-or-never moment to build on terabytes of newly published contracted ratesWhy neither co-founder was technical, and how Ria learned Python and PySpark to ingest 300 billion row machine readable filesHow an NIH grant and a free consumer cost estimator revealed the wrong initial customerWhy Handl Health moved past direct-to-employer sales to the broker and benefits consultant channelWhere brokers see value first: prospecting new business with carrier comparisons and personalizing renewals with claim-level cost projectionsThe three-year education arc from explaining what an MRF is to a market where everyone needs a price transparency partnerHow Handl Health escaped the checkbox compliance perception by layering actuarial modeling and steerage on top of public dataWhy white glove service remains core even as the company productizes a services-heavy workflow GTM & Technology Adoption Lessons: Follow the burden until you find the buyer who owns it: Handl Health started consumer-facing with a free cost estimator, then realized that reaching the masses meant going through self-insured employers, who hold majority market share in how most Americans access healthcare. But employers are overburdened and understaffed, so the company went to the brokers and benefits consultants those employers already trust.Sell to the channel that is already on the hook: Brokers must give sound recommendations to employer clients about which network to rent and which point solutions to buy, while working with disparate data and no ROI visibility on vendors. When they started using the platform, Ria said their reaction was "wait, we can do this in minutes." Adoption is fastest where accountability and pain already sit together.Market education runs in stages, then flips: Ria described years one to three as skepticism that the regulations would even hold and that the data was useful. The company went from explaining what an MRF was, to convincing the market that MRFs contain useful data, to a market where everyone needs a price transparency partner and the only question is which one. Early adopter champions carried the company through the skeptical years.Regulation creates data, not a category: Early on, products like Handl Health's were perceived as checkbox compliance costs. The escape was the layer above the public data: actuarial modeling, cost projections, and steerage assumptions that help employers bring down costs and help members shop for higher value, lower cost care.Align the commercial model with channel growth: Ria said the commercial model came down to aligning incentives. As long as the platform helps brokers grow their book of business and differentiate in the market, they keep coming back and find new ways to partner.Productize the workflow, keep the concierge: Handl Health is productizing a highly services business. AI tooling automates much of the analytics, but when brokers run reports or stratify networks, the team wraps their arms around those partners to interpret results together. Ria does not expect that to go away, because the white glove layer is what makes the channel stick. // Sponsors: Front Lines -- Silicon Valley's leading Podcast Production Studio. We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. Mention you are a listener and get a 10% discount. www.FrontLines.io/Podcast-as-a-Service

    Finding the buyer who was already on the hook | Ria Shah

  8. Aug 24

    AI visibility is not SEO 2.0 | Imri Marcus

    Brandlight is building an AI visibility platform that helps enterprise brands, most of them Fortune 500 companies, monitor and influence how they show up in AI engines across AI search, AI ads, and agentic commerce. In a recent episode of BUILDERS, we sat down with Imri Marcus, CEO & Co-Founder of Brandlight, to learn how the company won Fortune 500 customers from day zero and turned AI visibility into a CMO priority instead of an SEO project. Topics Discussed: Why calling AI visibility "SEO 2.0" is the industry's most prevalent misconceptionWho owns AI visibility inside enterprise marketing departments, and why Brandlight always starts from the CMOHow Brandlight answers skeptics who claim AI engine results cannot be influencedThe advisory board of Fortune 50 CMOs and agency CEOs that opened enterprise doors from day zeroWhy enterprise AI visibility programs require orchestrating close to a hundred stakeholdersHow the education burden in first CMO conversations disappeared between late 2024 and todayWhat happens to SEO, brand, legacy search, and Google as search shifts into AI enginesReddit's real role in an AI visibility strategy GTM & Technology Adoption Lessons: Refuse the frame that shrinks your category: Imri said the most prevalent misconception is that AI visibility is SEO 2.0. Brandlight positioned it as a completely new marketing channel where traffic just happened to be the first KPI that got hit. The framing decides who owns the problem and how big the budget conversation can be.Start the sale where the orchestration lives: The SEO team is involved in every account Brandlight works with, but Imri called a single-department home the wrong place for a big enterprise. Brandlight always tried to start from the CMO and preach a holistic orchestrated approach, because PR, content, partnerships, and even legal teams need to get involved.Build the door-opening system before the market exists: Brandlight went after the Fortune 500 from day zero and built an advisory board of over a dozen advisors, people who are or were Fortune 50 CMOs and CEOs of some of the biggest agencies. The advisors made the initial connections, and the first iterations of the platform were seen by Fortune 50 CMOs.Treat early education as pipeline, not lost deals: At the end of 2024, the first 15 minutes of every CMO conversation was education. Not all of those CMOs purchased in 2024 or 2025, but it made sense to all of them, and Imri said the vast majority became customers later on.Answer skepticism with proof at the largest possible scale: Against the claim that AI answers cannot be influenced, Brandlight presented on the ANA's big stage with its customer Kimberly-Clark: over 12 months, the program took 12 out of 12 brands to lead their categories.Anchor urgency in behavior data, not predictions: Imri said that when Brandlight started, less than 1% of search happened in AI engines, and now it is over 50%. Brandlight analyzes billions of data points daily and can show traditionally successful marketing organizations losing share of voice while smaller companies overtake them.Position the new channel as additive, not substitutive: Imri argued SEO foundations still matter and brand still matters. AI visibility is an addition, not a substitution, which lowers the perceived risk of adopting it. // Sponsors: Front Lines -- Silicon Valley's leading Podcast Production Studio. We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. Mention you are a listener and get a 10% discount. www.FrontLines.io/Podcast-as-a-Service

    AI visibility is not SEO 2.0 | Imri Marcus
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About

Welcome to BUILDERS — the show about how founders get new technology adopted. Each episode features a founder on the front lines of bringing new tech to market, sharing how they broke into their industry, earned early believers, built credibility, and unlocked real technology adoption. BUILDERS is part of a network of 20 industry-specific shows with a library of 1,200+ founder interviews conducted over the past three years. For the full network, visit FrontLines.io. Brought to you by:  www.FrontLines.io/FounderLedGrowth — Founder-led Growth as a Service. Launch your own podcast that drives thought leadership, demand, and most importantly, revenue.

Information

  • Creator
    Front Lines Media
  • Years Active
    2025 - 2026
  • Episodes
    948
  • Rating
    Explicit
  • Copyright
    © Front Lines Media
  • Show Website
    BUILDERS