BUILDERS

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

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

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

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

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 it
• Trains 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 does
• Three questions that reveal whether an AI company will compound or compress as frontier models improve
• Grayswan: a real self-reinforcing red team loop from a Carnegie Mellon AI safety team
• Category creation in the AI era — why announcing on day one is usually wrong

GTM 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

Cognizant is a global technology services company that invests heavily in research-driven thought leadership, publishing studies on AI, cybersecurity, and the future of work that reach audiences from LinkedIn newsletters to Davos.

In a recent episode of BUILDERS, we sat down with Marlowe Newman, Director, Thought Leadership of Cognizant, to learn how B2B technology brands are shifting from the attention economy to the authority economy, and how research-driven content earns buyer trust at both ends of the funnel.

Topics Discussed:

• Why B2B tech is moving from the attention economy to the authority economy
• Why "all press is good press" no longer holds in B2B technology marketing
• How AI removed the friction that once made long-form content a competitive moat
• Why a clear, differentiated, human-produced point of view is now the core thought leadership asset
• How to identify and activate in-house subject matter experts, including those who avoid the stage
• What Marlowe's buyer journey research at Gartner revealed about how software shortlists actually form
• How Cognizant balances societal-level studies like AI and the future workforce with service-line research in cybersecurity, healthcare, and retail
• Why researchers, not CEOs, should front research-driven media pitches

GTM & Technology Adoption Lessons:

• Shift from attention to authority: Marlowe argues the race for attention at any cost is over in B2B. Audiences have grown savvy about opportunistic content, and bad press now costs more than obscurity. Trust and demonstrated expertise are the assets that compound.

• Volume is no longer a moat: The old playbook was to flood the zone and game the Google algorithm with content volume. Marlowe notes the barrier to entry was effort, since nobody wanted to grind out a fifteen hundred-word article, and AI made that barrier vanish. What remains defensible is a differentiated, human-produced point of view backed by proprietary research.

• Thought leadership works both ends of the funnel: At Gartner, Marlowe studied how software buyers form shortlists. Most could not pinpoint where they first heard of a vendor, but they arrived with three to five companies in mind. At the bottom of the funnel, buyers loop back to the same research to justify the purchase to whoever signs the purchase order.

• Time research to land ahead of the market: Quick pulse polls fielded over a couple of weeks produce rich, unique data sets. The planning question is not what buyers face today but what they will face in two months when the report ships. If the topic is current now, it is already too late.

• Match the expert to the format: Willing spokespeople come first. Experts who are not stage-ready can still write bylined articles, and podcasts let people who avoid cameras talk all day in one on one conversations. There is a format for every kind of expert.

• Platform the researchers: Pitching a big study with a CEO quote gives reporters pause because CEOs do not have time to run giant studies. Media want to go a few layers deeper with the people who dug through the data. Letting researchers speak is how a brand transmits authenticity in a market saturated with manufactured versions of it.

• Place bets across a fractured media landscape: A New York Times placement no longer reaches everyone who matters. Societal-level studies earn mainstream coverage, while problem-solution research belongs in trades, LinkedIn, podcasts, and niche media. Both audiences consume different media diets, so both bets are necessary.

// 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

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 firm
• How accountants became the channel behind roughly 95% of leads
• The customer sequence behind Pennylane's growth inflection point
• Using founder-led roadshows and peer referrals in a trust-driven market
• Why Germany offered a four-year window but required a 40% product rebuild
• Giving a local team startup autonomy inside a scaled company
• Building 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.

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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

SafetyWing is building a social safety net for people who live, work, and travel across borders — covering health insurance, income protection, disability, and life insurance that follows you around the world. The company was founded in 2017 and has insured over 400,000 people through its consumer products. Its Trustpilot rating sits at 4.3 stars. Its NPS score is 10x the insurance industry average.

In a recent episode of BUILDERS, we sat down with Joey Flores, Head of Marketing at SafetyWing, to learn how a nine-year-old company built category leadership in global health insurance by rejecting fear-based marketing and betting on an aspirational brand identity.

Topics Discussed:

• How SafetyWing builds trust across a product category where customers hope they never need the product
• Why SafetyWing uses cartoon birds instead of the fear-based visual language that defines traditional insurance
• How "create, don't copy" shapes every marketing and brand decision at SafetyWing
• Why the company launched a US-specific team health plan after years of global-first products
• How SafetyWing is using AI to compress the time between claim submission and resolution
• The ambassador program featuring extreme adventurers and what it reveals about SafetyWing's brand strategy
• How the brand's distinctiveness shortens the customer recognition path in a crowded market

GTM and Technology Adoption Lessons:

• Brand distinctiveness as adoption infrastructure: Joey said the cartoon birds create a shortcut. "the birds are just so damn memorable, like, you don't expect it from an insurance company."

• Aspirational branding in a fear-driven category: Traditional insurance marketing creates urgency through fear of loss. SafetyWing chose the opposite. Joey explained: "We don't think of ourselves as an insurance company. We're building a social safety net that follows people all around the world, literally with the mission of empowering them to go wherever they want, safely, securely, and feel like if something does happen, they are covered."

• Trust has to be rebuilt with each new product: SafetyWing's 4.3 Trustpilot score and 10x industry NPS are earned on existing products. But when the company launched income protection, Joey noted: "when we, when we roll out new products, for example, there's a bit of like doing that again, right? Like, okay, I trust you with my health. But like, you know, our newest product has income protection.

• Create, don't copy as a design constraint: When redesigning the homepage, the team came in with a standard three-plan pricing layout. A founder pushed back. Joey said: "every landing page I've ever been on has three plans next to each other. And the goal is always that you pick. The consumer will pick the middle plan... I was like, this is just like, I'm so tired of seeing this.

• Ambassador programs as brand proof: Sponsoring Ria G., who is running 20,000 km from China to Portugal, completing her 66th consecutive ultra marathon at the time of recording, is not a traditional brand awareness play. It is a live demonstration of what SafetyWing makes possible.

• AI for claims process improvement: SafetyWing deployed AI to flag missing claim documents at the point of submission rather than days later after human review. Joey described the practical impact: "it doesn't just shorten it by the two days it took a human to review it, it may shorten it by the two weeks that they would have waited to sit down again and do it."

• US expansion as a distinct product bet: The global team health plan has been running for years. The US plan launched at the start of 2026 because US healthcare operates in a different regulatory and pricing environment.

// 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

Labrynth is an outcome-based AI regulatory intelligence platform that helps regulated entities accelerate permitting, compliance, and licensing workflows. Stuart Lacey, CEO and Founder, built the Red Tape Index as a free, unpaywalled public resource measuring regulatory burden across U.S. jurisdictions and expanding globally. He used it to create inbound pipeline, earn mainstream media coverage, and demonstrate the company's core thesis: give the market something useful before asking it to give you anything back.

In a recent episode of BUILDERS, we sat down with Stuart Lacey, CEO and Founder of Labrynth, to learn how his team generated tens of millions of impressions, a direct call from the mayor of Washington D.C., and coverage from Bloomberg to Fox News with no paywall and no sales outreach, and why deterministic AI is the only model that works in regulated compliance environments.

Topics Discussed:

• Why Stuart built the Red Tape Index as a free, public data resource with no paywall
• How the index generated tens of millions of impressions and mainstream media coverage
• The story of Mayor Bowser calling Labrynth directly after D.C. ranked 508th
• What it costs to build and launch your own industry index (approximately $50K, approximately 8 weeks)
• How Labrynth reduced LA County permitting from 188 days to 20 using AI
• Why probabilistic AI fails in regulated environments and how hermeneutical agents fix it
• How Labrynth structures outcome-based pricing and when clients push back to per-transaction
• The three differentiators behind Labrynth: outcome alignment, self-improving backend, high trust

GTM and Technology Adoption Lessons:

• Give value publicly before asking for anything in return: Stuart built the Red Tape Index with no paywall and free data access for anyone. The Data Center Readiness Index launch generated 10 million impressions in under a week, 18,000 full print media replays, and a 14.75% click-through rate.

• Use data to create gravity: The Red Tape Index works because it turns anecdotal regulatory pain into measurable, comparable data. That data becomes a conversation starter, a competitive lever for cities, and a trust signal for enterprise buyers. Mayors call. Media covers it.

• Index economics anyone can replicate: Building an index costs approximately $50K and takes about 8 weeks, including data scraping, actuarial science methodology, build, go-to-market, publishing, support, and follow-on. Stuart's point: "I think if you're a mid sized to large size company, you're probably blowing that money on party and marketing right now with no direct return on it.

• Deterministic AI is the only viable AI for regulated environments: Most companies deploy probabilistic AI in compliance and regulatory contexts, which by definition only "probably" gets the right answer. Labrynth uses hermeneutical agents, which Stuart describes as "role context specific deterministic agents that can actually define whether or not something is actually correct and then measure the difference between right and wrong and then help you get it right."

• Outcome-based pricing creates trust but sometimes clients ask to exit it: Labrynth prices against client outcomes, not seat licenses. Stuart noted that in about half of cases, after seeing what the platform delivers, the client's finance team comes back and asks to revert to regular pricing.

• Output-based AI pricing is a trap: Stuart specifically pushed back on the output-based pricing model gaining traction in AI services. "I would put a cautionary note there to anyone that's kind of thinking that through Brett, output based might mean that volume matters.

// 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

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 rates
• Why neither co-founder was technical, and how Ria learned Python and PySpark to ingest 300 billion row machine readable files
• How an NIH grant and a free consumer cost estimator revealed the wrong initial customer
• Why Handl Health moved past direct-to-employer sales to the broker and benefits consultant channel
• Where brokers see value first: prospecting new business with carrier comparisons and personalizing renewals with claim-level cost projections
• The three-year education arc from explaining what an MRF is to a market where everyone needs a price transparency partner
• How Handl Health escaped the checkbox compliance perception by layering actuarial modeling and steerage on top of public data
• Why 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.
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• 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