This sprint: Stories · Process · Points of View

Nothing attached. Pull one from the vault.

• StoriesPond hockey taught me 5 things about businessWatching kids 8 to adults 40+ play pond hockey: (1) Cycles - seasons of business are inevitable; plan for winter during summer. (2) Creativity - kids try moves on the pond they'd never dare in a structured rink, then master them and bring them back. Where's your pond? (3) Diversity - ages mix, everyone finds their competitive advantage on the fringe. (4) Forward-looking offense - pond games end 36-33; big companies play defense, upstarts just score. (5) One direction - a 5-year-old who played 9 to 5:30 said 'I can't believe how much better I'm getting.' Progress over perfection. Also: the human ATM - if you owned an ATM that gave unlimited money you'd maintain it obsessively. That ATM is YOU.unused
• StoriesLifting with Kobe: the danger of Plan B1995: I was in my early 20s working TV sports in Philly; Kobe Bryant was a 16-year-old lifting weights at St. Joe's with bad form. We became lifting partners. What stuck: his absolute lack of fear and zero talk of Plan B. His future was basketball, period. Getting yelled at on the bench press by a 16-year-old: 'Cmon! You got this! FOCUS!' A room in his parents' house full of unopened scholarship letters; one framed - a handwritten note from Coach K. Lesson I carry as an entrepreneur and coach: vision is seeing it when no one else can, and a back-up plan can be a danger. 'Making the NBA' vs 'being the best ever' are different mindsets. Great for: someone who influenced you, why you believe what you believe.unused
• StoriesA $20 bill and a broken armFocus Insite started with a $20 bill and a broken arm and became a national firm - 2x Inc. 5000, Soaring 76 (Philadelphia Business Journal). 20+ years in entrepreneurship. Early mistakes: tried to do everything myself, hired too fast, ignored red flags, told myself 'I'll coach them up.' You can't coach up the wrong fit. 'Who' by Geoff Smart (heard him at MIT EMP): 'Every bad hire is like playing with a man down.' Then 'Who Not How': stop asking how do I do this, start asking who can do this better than me. You win with WHO.unused
• StoriesI invented my own 100 challenge in 2015Long before this 100-day content challenge, I wrote 'The 100 in 100 Challenge' on LinkedIn (Feb 2015): meet 100 people in 100 days. Track it in your calendar. The first 20 are easy - your close friends. Now convert good friends into great friends. 'You're much better off having 100 meetings than 10,000 followers. Focus on connections, not followers.' Included the Pat Croce story: a fake 'I'll be in Florida anyway' lunch with Harold Katz that brokered Comcast's ownership of the Sixers and Flyers. No tweet gets that deal done. Perfect meta-post for why I'm doing 100 days of content now: same belief, new medium - the human connection is the asset.unused
• ProcessThe 3 PM update ruleEvery active project gets a status update by 3 PM — before end of day, every day, even when the news is 'no change.' Uncertainty is the enemy, not bad news.unused
• ProcessThe verification chainSource → screen → verify → manage → participate → document → deliver. How we decide whether somebody actually belongs in the room, before the client ever meets them. Also the backbone of HumnLayer: documented human provenance for AI workflows.unused
• ProcessHunting vs pulling sampleOur recruiting process when the database won't save you: identify the audience → find where they actually live, work, and gather → targeted outreach → leverage community trust → verify → communicate → deliver. A great recruiter doesn't just have names; a great recruiter knows where to look next.unused
• Points of ViewExperts matter more in high-stakes AIWould you trust healthcare AI if nobody could tell you which clinicians evaluated it? A doctor and somebody pretending to be a doctor aren't two different samples — they're two different realities. The more consequential the AI decision, the more consequential the human behind its evaluation.unused
• Points of ViewAI should serve humansMy biggest belief about AI has nothing to do with technology: AI should serve humans, not the other way around. The future isn't AI versus humans — the winners will combine both. The more powerful AI becomes, the more intentional we need to be about keeping humans in the loop.unused
• Points of ViewHumans are the new data moatEverybody can access similar models. Not everybody can access the right humans. The next AI moat may not be another model — it may be a trusted network of human judgment. Models are becoming commoditized. Verified human expertise isn't.unused
• Points of ViewSynthetic data needs human truthI love AI. I use it constantly. But AI evaluating AI forever is not the end state I want. Synthetic data scales beautifully; human reality is messier — that's exactly why we still need it. AI can simulate a customer, but simulation and lived experience aren't the same thing.unused
• Points of ViewHuman provenance is nextWe track the provenance of datasets. Why aren't we tracking the provenance of human judgment? Model provenance, data provenance — next up: human provenance. Every high-value human signal should leave a trust trail.unused
• Points of ViewStop training AI on mystery humansA question AI companies should ask more often: who exactly taught your model that? We're spending billions training AI and sometimes can't answer who the humans behind the feedback were. Anonymous human feedback may eventually look as crazy as anonymous financial transactions.unused
• Points of ViewCheap sample is expensiveThe lowest bid can become the most expensive vendor on the project. Cheap recruiting stops being cheap the second you redo the interview. A fake CISO at $400 isn't a $400 problem — it wastes incentives, moderator time, stakeholder trust, and the roadmap decision built on it.unused
• Points of ViewTrust is checked, not claimed'Verified' is becoming one of the most abused words in research and AI. Checking a box isn't verification. More respondents aren't the answer — more trustworthy respondents are.unused
• Points of ViewHumans aren't rows in a spreadsheetI've never met a spreadsheet row. I've met patients, executives, parents, doctors, people with actual stories. Data becomes dangerous when we forget there was supposed to be a human behind it.unused
• Points of ViewDefensible decisionsThe question isn't 'Did we complete 50 interviews?' It's 'Would you defend those 50 people in front of the board?' Imagine your CEO asking: 'How do we know these people were real?' If the decision is going upstairs, the provenance of the humans should go with it.unused
• Points of ViewThe recruit IS the researchResearchers obsess over methodology and treat recruiting like logistics. Backwards. The research doesn't begin when the moderator asks Question 1 — it begins when you decide who gets in the room. The recruit isn't admin. It's the foundation.unused
• Points of ViewThere is no magic panel10 years in: there is no magical database with everybody in it. People change jobs, move, leave industries. Hard-to-reach people aren't sitting in a panel waiting to be contacted. We have 300,000+ people in our own panel and growing — and we STILL go hunting when the study calls for it, because a panel isn't a recruiting strategy. It's one fishing hole.unused
• Points of ViewBad people, bad data, bad decisionsA beautiful research report can still be built on a rotten foundation. Your methodology can be flawless and your conclusion still wrong. Before you question the model, survey, or discussion guide — question the humans.unused
• Points of ViewWrong human → wrong signal → wrong insight → wrong decisionThe most expensive respondent isn't the one with the highest incentive — it's the one who never belonged in the study. You can save $200 on recruiting and lose $2 million on the decision it produces. Verification isn't overhead. It's risk management.unused
• StoriesThe people who fooled usI've learned more about respondent quality from the people who fooled us than from the people who didn't. Ten years teaches you where humans lie, where systems break, and where you need another checkpoint. Tell the mistake, then the checkpoint it created.unused
• StoriesWhere the 3 PM update rule came fromClients have families, deadlines, and internal pressure. Information before end of day means someone leaves work without wondering whether their project is on fire. Silence from a vendor creates stress faster than bad news does. That rule has almost nothing to do with recruiting.unused
• StoriesThe rescue recruitWe built a company by getting phone calls nobody wanted to receive: vendor at 47 of 50, everybody panicking. The last three seats can be harder than the first 47 combined.unused
• StoriesThe Roblox library studyRecruited children (with parents) to a San Francisco library to observe real Roblox behavior. Some studies you can't do from a panel — you go to where the humans actually are.unused
• StoriesThe LA Fitness recruitNeeded people inside a tiny radius in Los Angeles. A panel wasn't going to save us. Partnered with local gym managers, ethnic newsletters, and community groups that already had trust. What do LA Fitness managers have to do with market research? In our world, quite a lot.unused
• StoriesGoogle Sheets and an attitudeWe didn't start with a beautiful tech stack. Google Sheets, free tools, referrals, social media. Bootstrapping's brutal advantage: if we didn't produce, we didn't get paid. We couldn't outspend the big companies, so we had to outthink them.unused
• StoriesDavid vs Goliath: the Samsung recruitA client brought three much larger competitors onto the same recruit as us. Little Focus Insite produced more recruits than all three combined. Lesson: size isn't the advantage — knowing where to look is. [Confirm exact counts from project records before posting numbers.] Hooks: 'Three giants versus little Focus Insite.' / 'Sometimes size isn't the advantage.'unused
• StoriesThe hardest three (47 → 3 → 50)Signature founder story. For years companies didn't call us for the first 47 people — they called for the three nobody else could find. One day I asked: if we can find the hardest three, why aren't we recruiting all fifty? That question built Focus Insite. The next question: why can't we provide the verified human layer behind AI? Hooks: 'The three hardest people in a 50-person study changed my entire business.' / 'They'd recruit 47. We'd get called to find the impossible three.'unused