Unicorn Builders

Goodfire is an AI research lab working to understand how neural networks actually work, so models can be intentionally designed instead of trained through trial and error.In a recent episode of BUILDERS, we sat down with Eric Ho, Co-founder and CEO of Goodfire, to learn how an exploration-driven interpretability lab is turning foundational research into an enterprise product, and how one public AI incident changed the conversation around its category.Topics Discussed:Why Eric left the SaaS company he spent seven years building after ChatGPT launchedWhy even the researchers building frontier models can’t explain how they workWhy Goodfire modeled itself on Bell Labs, Xerox PARC, and early OpenAI instead of a SaaS playbookWhat it means to run a research lab like an “overgrown garden”How the first two years broke into chapters: research previews, recruiting leading interpretability researchers, then sharpening research directions and building SilicoWhat the OpenAI and Hugging Face incident revealed about reward hacking, chained exploits, and agent swarmsWhy Silico sits one level of abstraction above Claude Code and CodexWhy Goodfire sells top-down to enterprises, with contracts one to two orders of magnitude larger than Eric’s SaaS dealsWho buys interpretability: frontier labs, neolabs, data labeling companies, and enterprises training their own modelsHow Goodfire navigates Anthropic as an investor, partner, and potential competitor

Omni builds business intelligence software that puts exploratory analysis and governed dashboards in a single platform. In a recent episode of UNICORN BUILDERS, we sat down with Colin Zima, Co-Founder of Omni, to learn how the company navigated 18 months to real product-market fit, lost all five of their first trials due to bugs they had learned to work around, and built a demand generation channel where founders still run 10 demos a week at 275 employees.Topics Discussed:How Colin went from being Looker's fourth customer to employee #40, and what watching that company scale to 1,000 people on the way to the Google acquisition taught him about company building stagesWhy the original startup he started with his co-founder never had product-market fit, and what was different about OmniHow Omni found message-market fit in roughly three months but took 18 months to find real product-market fitWhy they lost all five trials at the one-year mark due to bugs they had learned to work around as daily usersHow they won all five deals in the second cohort after two months of product cleanupWhy Omni hired sales reps before the founders finished proving they could sellHow the Looker network gave them the ability to skip validating sales-market fitWhy their third sales rep was in Dublin, with roughly half their early inbound coming from EuropeHow founders doing LinkedIn outreach, with BDRs queuing messages, became a scaled demand channelWhy Colin still personally answers support tickets and runs 10 demos a week with 275 employeesHow Omni became the pink company from a last-minute t-shirt decision at a Snowflake conferenceWhy Colin positions Omni as business intelligence rather than creating a new category like semantic layerHow Omni operates with only 40 to 50 engineers out of 275 employeesWhy Omni has not hired an in-house lawyer for 275 employees

General Magic pursued a vision Marc Porat developed in 1988: a personal handheld computer combining computing, wireless communication, and content. Backed by founding partners including Apple, Sony, and Motorola, the company went public through what Porat describes as a “concept IPO.” In the interview, he puts its value at roughly $800 million. In a recent episode of Unicorn Builders, we sat down with Marc Porat, founder of General Magic, to discuss how the company attracted prominent engineers and major technology partners, why its licensing strategy became a burden on execution, and how going public increased the pressure to deliver. The conversation also covers General Magic’s early work on mobile agents and Porat’s perspective on AI’s promise and risks.Topics Discussed:How Porat developed his vision for a personal, connected handheld computer in 1988How Apple helped bring together the early team, including Bill Atkinson and Andy HertzfeldWhy Apple, Sony, and Motorola became General Magic’s founding partnersHow General Magic paired its operating system and reference hardware platform with partners responsible for manufacturing devicesHow major partners, media coverage, and secrecy generated interest ahead of its “concept IPO”Why public-market financing increased expectations while the company struggled to shipHow a consortium of 16 companies created competing demands that consumed Porat’s timeWhy engineers eventually asked him to stop signing new partnersHow building “heaven for engineers” encouraged creativity but weakened the focus on shippingGeneral Magic’s early work on mobile agents and Porat’s advice for keeping human needs at the center of technology

Building the real-time infrastructure layer for voice AI is a narrow technical bet that turned into a platform play. LiveKit started as an open source project during the pandemic and grew into the stack that powers OpenAI's voice mode for ChatGPT, handling the low-latency network infrastructure that connects users to AI models anywhere in the world.We sat down with Russ d'Sa, co-founder at LiveKit, to learn how an open source project with no commercial intent became the foundational infrastructure for voice AI applications, and what it took to recognize product market fit even when every signal was pointing directly at it.Topics discussed:- Why LiveKit started as an open source project during the pandemic and hit the GitHub top ten repos across all languages within two weeks of launch- The acquisition offer LiveKit received on day one of its first open source release, and why Russ asked for $100 million- How OpenAI signed up for LiveKit in secret using a personal email address, three weeks before reaching out to start a commercial relationship- Why Russ denied product market fit twice despite having a top GitHub repo, enterprise interest from companies like Spotify and Reddit, and an inbound from OpenAI- The difference between stateful and stateless computing, and why the entire software development lifecycle has to be rebuilt for voice AI applications- Why going all in on voice AI took months longer than it should have, and what Russ would do differently- How LiveKit built its full platform stack from network infrastructure to agents framework, evals, inference routing, and observability- Why all of LiveKit's growth to date has been inbound, and what Russ is now building out on the go-to-market side for the first time- How the OpenAI partnership created two separate demand channels: enterprise inbound from the logo halo effect, and developer adoption by association

Tive makes IoT trackers that tell companies exactly where their shipments are and what condition they are in as they move through the global supply chain. The company tracks temperature, humidity, shock, light, and location across 2G through 5G networks, inside trailers, containers, and pallets.In a recent episode of Unicorn Builders, we sat down with Krenar Komoni, CEO and Co-Founder of Tive, to learn how the company reached $100 million in ARR over 11 years by solving a visibility problem most logistics companies were still handling through phone calls, emails, and check calls with truck drivers.Topics Discussed:How Krenar spotted the convergence of LTE IoT, low-cost sensors, and GPS technology before starting TiveWhy the first tracker cost $150 to make and why that could not scaleHow switching to a single-use, sub-$50 tracker changed customer conversationsWhy Krenar got back on the phones with SDRs in 2017 and 2018 and what he learnedHow Tive educated the market for its first seven years and why that work is mostly doneThe near-death moment in 2019 when Tive had $20,000 in the bank and six employeesWhy Krenar stepped back from sales between $5 million and $7 million ARRThe mistake of hiring late-stage sales leaders into an early-stage companyHow Tive evolved from generalist selling to vertical pods and geographic specializationWhat Krenar means by upgrading his CEO brain from V1 to V2 to V3GTM & Technology Adoption Lessons:Time your entry around technology convergence, not just market need: Krenar spent years designing chips for smartphones and watched LTE IoT evolve before starting Tive. He could see that sensors were becoming cheap, that LTE connectivity was getting power-efficient, and that GPS was going to work in small form factors. He started the company knowing it would take years before the economics worked. The product cost structure was the first adoption blocker: The original Tive tracker cost $150 to make and sold for $250. Customers had to return it. That model could not generate the volume or simplicity the market needed. Switching to a single-use tracker at around $50 changed what customers were being asked to do: turn it on, put it in the shipment, and never think about it again.Founder-led SDR work surfaces what customers actually want: In 2017 and 2018, Krenar sat down with a small SDR team and ran cold calls and cold emails himself. That process taught him what the market needed: something cheap, easy, and single-use. That customer intelligence drove the product decision to build a sub-$50 disposable tracker and manufacture at scale in China. ROI stories became the adoption engine: A tracker detecting a temperature spike above 40 degrees Fahrenheit in a truck of strawberries, a diverted shipment worth half a million dollars recovered through location data, pharmaceuticals located on an airport tarmac when the carrier did not know where the pallet was. Hiring a late-stage sales leader into an early-stage company is a specific and recoverable mistake: Krenar stepped in it twice. The issue is not that those leaders are bad. The issue is that a company still figuring out its product and operations does not yet have the foundation for someone who has scaled a business to hundreds of millions. Vertical and geographic specialization unlocked scale: In the early years, every salesperson sold everything to everyone. A person covering EMEA might close a deal in Mexico. At scale that breaks down. Tive now runs vertical pods for food, pharma, and general logistics, with geographic ownership on top. // 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

Zeta Global is an AI-native marketing platform processing trillions of consumer signals for Fortune 2000 companies. The company went public on the NYSE in June 2021 and is projecting close to $1.8 billion in net revenue for 2026 — on the back of four consecutive years of 30%+ organic compounded top-line growth and a 120% net retention rate. In a recent episode of Unicorn Builders, we sat down with David Steinberg, CEO, Co-Founder, and Chairman of Zeta Global, to understand how a seven-time founder walked into a 2017 board meeting and convinced his board to go from $50M in annual EBITDA to zero for three years to rebuild the entire platform around native AI — at a time when people were still asking, "Who's Al and why is he in charge of my data strategy?"Topics Discussed:The board meeting where David proposed sacrificing $50M+ in annual EBITDA to rebuild from scratchHow Zeta used two acquisitions — Boom Train (AI/NLP) and Disqus (publisher data) — as the architectural foundationWhy running the legacy platform for cash flow was what made the rebuild possibleWhat competitors got wrong: acquiring platforms and freezing the architecture in placeHow ChatGPT turned years of invisibility into a firehose of inbound from Fortune 2000 boardsWhy David distinguishes "aptitude for risk" from both blind risk and no riskGTM Lessons For B2B Founders: Sell the floor to the board, not just the ceiling: David didn't win his board by projecting a grand slam. He closed by making the downside concrete — if the new platform failed in year three, they still had the legacy business, most customers, and a profitable exit. Founders pitching transformational bets should lead with what the floor looks like, not just the ceiling.Run the legacy for cash while you rebuild: Zeta didn't shut down its ~$200M revenue business during the re-architecture — "it was the reason we didn't lose money." That cash funded the new engineering team. Founders making major pivots should identify what existing revenue can fund the transition rather than burning runway on both sides.Earn customer ownership before the product ships: While building, Zeta showed customers the roadmap and solicited "dream features" so they could "feel ownership" in what was coming. That investment drove high migration rates at launch. Loop customers into the build early — not for validation, but to make the migration ask easier when the time comes.Native AI architecture compounds; API integrations stagnate: Most competitors bolt AI onto legacy architecture via API — platform to algorithm to data repository and back. That latency destroys ROI at scale. Zeta's data cloud is sole-sourced to its own platform, never exposed to external models — making the intelligence proprietary and compounding. Most competitors made acquisitions a decade ago and are still running the same architecture they bought."AI roadmap AI" is not a moat: After ChatGPT, David says most competitors are running what he calls "AI roadmap AI — where it's not real just yet." Zeta was already on its fourth iteration of its AI agent studio and had launched Athena, a voice-enabled super agent. The post-ChatGPT demand window went to companies that had been building for years before it mattered.// Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.ioThe 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

HighLevel is the second largest CRM by users on the planet — behind only Salesforce — with 2,000 employees and millions of customers worldwide. They got there without venture capital, without an enterprise sales motion, and without ever running an unprofitable growth model. When HighLevel eventually took outside capital, it was two rounds of PE at minority positions — because they didn't need the money. In a recent episode of Unicorn Builders, we sat down with Shaun Clark, Co-Founder of HighLevel, to learn how a bootstrapped SaaS built by two engineers in their basements became one of the most capital-efficient growth stories in B2B software — and why Shaun believes the VC-fueled playbook routinely turns founders into employees.Topics Discussed:Why Shaun chose to bootstrap HighLevel after repeated VC conversations that went nowhere in his previous companyHow a cold call from a marketing agency operator became HighLevel's third co-founder and rewired the entire product and distribution strategyThe agency-first GTM motion that competitors see, dismiss, and still haven't copiedGTM Lessons For B2B Founders:Profitability at inception changes your relationship with capital permanently. Shaun didn't bootstrap out of ideology — he couldn't get VC attention on his first company and decided to build a profitable business instead. The downstream effect was structural: when HighLevel eventually took PE, both rounds were minority positions with no primary capital component. "We didn't need them to keep the lights on. They were minority positions, so they can't tell me what to do." Founders who reach profitability before taking outside capital negotiate from a fundamentally different position — they're choosing partners, not accepting terms.Your best distribution insight may already be in your customer base. HighLevel's original thesis was direct-to-SMB. The pivot didn't come from a strategy retreat — it came from a cold call with a marketing agency operator who was already a mutual customer. That conversation became a co-founder relationship and surfaced a structural problem: agencies were generating leads for SMBs, handing them over in a spreadsheet, and getting fired when the leads didn't convert. Nobody was closing the loop. HighLevel automated that follow-up process, became indispensable to the agency, and never looked back. The person closest to your end customer's failure point is often already in your pipeline.Solve your channel partner's retention problem and you become impossible to replace. White-labeling wasn't a feature request — it was a survival mechanism for HighLevel's agency customers. The recurring pattern was: agency deploys software, SMB client recognizes the software brand, concludes the software is doing the work, fires the agency. HighLevel's response was to let agencies brand the platform as their own, so value attribution stayed with the agency relationship. "If we could disassociate this third-party brand with your efforts and your outcomes, you will retain clients longer." Founders building through channel partners should look for the moment where the partner's customer relationship is most at risk — and own that moment in the product.//Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.ioThe 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

Backstory AI — formerly People AI — is building what CEO & Board Member Jason Ambrose calls the "revenue reasoning layer." Even with full CRM visibility, leaders still go ask a person, that person comes back with an answer, and there are ten more questions about how they got it. That trust gap hasn't been solved by RevTech. In this episode, Ambrose walks through the full reset — from an MCP demo that changed his read on the market, to rebranding out of People AI, to rearchitecting the pricing model.Topics Discussed:The MCP demo that triggered the strategic pivotWhy the "revenue reasoning layer" sits above automation — and what that means for GTMHow rebranding out of "People AI" clarified Backstory's market identityThe mechanics of consumption pricing that avoids in-year budget varianceThe strategy diamond framework for sequencing a company transformationThe board conversation: beat-and-raise vs. "become the right business"GTM Lessons For B2B Founders: The market signal is in the demo, not the deck: Ambrose's pivot started when he watched Claude autonomously ask twenty follow-up questions of Backstory's software during an early MCP demo — a moment he compared to loading a web page in Mosaic. When he brought it to customers, the reaction repeated. The signal worth chasing isn't market-size data. It's the demo that resets how a buyer thinks about what your product is.Position where reasoning replaces interpretation: Red Hat CEO Matt Hicks told Ambrose that across their AI-paired org, automation was most impactful at the individual contributor level — but as seniority increased, reasoning took over. Senior leaders don't need faster dashboards; they need explainable answers that don't trigger ten follow-ups. The automation layer is crowded. The reasoning layer above it is a different conversation.Build consumption pricing that survives the budget cycle: "I don't want consumption pricing" almost always means "I can't have in-year variance." Ambrose's approach: year one, contain costs; year two, look at actual consumption patterns and set a committed spend level — or use prepaid blocks. Engineer the model so in-year variance isn't possible.The strategy diamond forces you to confront economics first: Ambrose used a five-element framework — arenas, vehicles, differentiation, sequencing, economic model — because it surfaces how each element constrains the others. Example: routing through a hyperscaler means giving margin to that partner, which reshapes field economics and seller comp. Set GTM without working the economic model and you'll ship conflicting commitments.Sequence the transformation before anything else ships: Backstory's reset followed a deliberate order: state the strategy clearly enough that every person applies it daily; rebrand (People AI kept getting confused for HR tech); then realign the roadmap. Skip step one and steps two and three become incoherent.// Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.ioThe 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

Peregrine Technologies unifies fragmented government data systems into a single, current, complete data asset — then gives agencies the tools to build operational workflows on top of it. In a recent episode of Unicorn Builders, we sat down with Ben Rudolph, Co-Founder of Peregrine, to learn how the company spent its first six months embedded inside a police department before writing a line of product code, how they constructed a repeatable sales motion in one of the most procurement-hostile markets in the country, and why nine years of forward-deployed engineering is now their structural advantage in the AI era. Peregrine is now deployed in over 400 cities with a team of approximately 400 people.Topics Discussed: How Peregrine landed its first customer by cold-calling a specific commander and researching his prior drug bust before ever mentioning the product Why the founding team spent the first six months doing detective work — not building software How Peregrine's demo strategy is built around local government priorities, not a fixed product pitch The two moments that told Ben they had product-market fit — and why neither was a revenue number The three chapters of Peregrine's company-building journey: wilderness, repeatable motion, and scale Why proprietary government data, customer proximity, and security controls are the three ingredients that make Peregrine defensible in the AI era The two operating principles behind Peregrine's forward-deployed engineering modelGTM Lessons For B2B Founders:Target the second-in-command: Peregrine's early targeting was precise — they looked for commanders and captains who were ambitious, career-motivated, and had a technology-forward lens. Not the chief, who is rarely day-to-day. That archetype — second or third in command, younger, wants to move up — was their entry point at San Pablo PD and remains a defining characteristic of their ICP today. In institutional sales, the person with both the motivation and the operational authority to champion something new is almost never at the top of the org chart.Research the prospect before you pitch the product: Peregrine didn't cold-call Commander Bubar at San Pablo PD with a product demo. They had researched a specific drug bust he had run years earlier, called him, and asked him to teach them how he did it. The frame was partnership and curiosity, not sales. That approach got a pickup, a polygraph, and eventually an embedded office inside the department. When your prospect has never bought software like yours before, leading with genuine interest in their work is a more reliable opener than leading with your product.Spend the first six months doing the job before building the product: For the first six months at San Pablo PD, the Peregrine team had no product. They were background-checked, polygraphed, and worked out of the department every day — doing the job detectives were doing. That depth of understanding is what produced a product that officers at their third or fourth customer (Albuquerque PD) were using daily without any hand-holding. The speed at which you reach unsolicited product adoption is largely determined by how close you got before you built.// Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.ioThe 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

Oyster is a global employment platform built on one core premise: make hiring across borders as easy as hiring locally. Rather than racing competitors toward all-in-one HR stacks, Oyster stayed deliberately narrow — owning the cross-border employment use case in a $30 billion market. In this episode of Unicorn Builders, founder Tony Jamous and newly appointed CEO Hadi Moussa walk through one of the most intentional leadership transitions we've seen in B2B tech — why Tony initiated it, how they executed it, and what Oyster's next chapter looks like from a strategy and GTM standpoint.Topics Discussed:Why Tony proactively surfaced himself as the bottleneck — and how he structured the board conversationWhat an 8-month, 50-candidate global CEO search actually looks like from the insideHow Hadi structured his first 30-60-90 days: what he prioritized, what he deferred, and whyOyster's three strategic bets: EOR depth, mid-market expansion, and AI talent demandThe operational systems behind Oyster's mission authenticity — beyond the website copyWhy remote work was just the catalyst, and what's actually sustaining global employment demandGTM Lessons For B2B Founders:Founder-as-bottleneck is a GTM problem, not just a leadership one. Tony didn't wait for board pressure. He solicited 360 feedback, concluded he was limiting the company's trajectory, and brought the transition idea to the board himself. The framing matters: he positioned it not as stepping down, but as identifying what the company needed to grow beyond what he could deliver. For founders, this is a GTM unlock — the wrong leader at the helm caps your sales motion, your hiring, and your market credibility at exactly the wrong time.Your first 30 days as an incoming CEO is a customer discovery sprint. Hadi's first month was structured entirely around listening — with the team, with customers, and with the existing plan. He specifically prioritized customer conversations to map pain points and identify where Oyster was creating friction. The output wasn't a vision deck. It was the foundation for a three-year strategy grounded in real customer problems Oyster could solve in a differentiated way. Incoming operators: resist the pressure to announce strategy early. The diagnosis has to come first.Over-invest in the transition communication plan. Tony and Hadi spent roughly two months planning the transition before it was announced. The core insight: your company is as attached to you as you are to it. People joined because of your story. A rushed or ambiguous handoff creates fear, not confidence. The communication plan needs to answer one question clearly — why now, and why is this good for them. That requires intentional design, not a last-minute all-hands.Execute the handoff fast once it starts. Tony left his team Slack channels within one week of Hadi joining. His words: it was painful, but necessary. Slow transitions create ambiguity around decision authority, which stalls execution at every level of the org. Once the decision is public, speed matters more than comfort. Dragging out the overlap is almost always worse than a clean break.// Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.ioThe 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