Unused material floats to the top. Use it.
Watching 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.
1995: 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.
Focus 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.
Long 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.
The most important question, even when never said out loud. Content angle: David vs Goliath (out-recruited three bigger competitors combined), a decade of scar tissue, and trust that is checked, not claimed.
Clients want visibility: screened, qualified, scheduled, confirmed, replacements, quota status. Content angle: the 3 PM update rule — great client service is letting someone leave work without wondering if their project is on fire.
Specific job titles, technologies used, diagnoses, purchase behavior, company size, decision-making authority. Content angle: detective-work recruiting — LA Fitness, Roblox library, the impossible three.
No-shows wreck qualitative studies, especially expensive executives, physicians, niche professionals. Content angle: confirmation cadence, community trust, and recruiting the right person in the first place — show rates start at the screener.
Database? LinkedIn? Social? Associations? Purchased lists? Referrals? Direct outreach? Content angle: behind-the-scenes sourcing story — there is no magic panel; identify where the humans actually live, work, and gather.
And specifically: cost per recruit, incentive, project management fee, additional sourcing costs. Content angle: the hidden cost of bad recruits and failed studies — you can save $200 on recruiting and lose $2M on the decision.
Clients want to know how fast you identify problems, adjust sourcing, and rescue the recruit. Content angle: Rescue Recruit stories — the calls nobody wants to receive, 47 of 50 and everybody panicking.
Usually followed by: 'Can you have people ready by next week?' Content angle: speed as a byproduct of knowing where to look (voter study in 41 minutes), plus the honest incidence math behind any timeline.
Clients increasingly worry about fraud, professional respondents, AI-generated answers, and bad data. Content angle: human verification / fraud content — walk the verification chain, trust is checked not claimed.
The first question on every niche, B2B, healthcare, high-income, or hard-to-reach recruit. Content angle: the Impossible Recruit Challenge — answer with the hardest-three story and how hunting beats pulling sample.
The standing invitation: if your vendor is at 47 of 50 and everybody is panicking, that's when it gets interesting around here. DM me or book a call.
REAL NUMBERS (confirmed by Jim, Aug 2026): 10 years in business. Over 2,000 studies/projects completed. $5 million+ paid out in participant incentives. 300,000+ people in the panel and growing. The advantage of 10 years isn't the anniversary. It's the scar tissue. Hooks: 'You don't learn respondent fraud from a PowerPoint. You learn it after 2,000 projects.' / '$5 million in incentives paid teaches you exactly where humans lie and where systems break.'
We filled a voter study in 41 minutes. Speed as a byproduct of knowing where to look.
Samsung recruit: client added three much larger vendors; we out-recruited all three combined. [Pull exact numbers from the project file before using in a post.]
Every 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.
Source → 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.
Our 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.
Don't run six mini-brands. One belief — the human has to be real. Healthcare: the patient has to be real. Cyber: the CISO has to be real. Fortune 1000: the customer has to be real. AI: the evaluator has to be real. Same story, different stakes.
The human is the foundation. The recruit is the research. There is no magic panel. Trust is checked, not claimed. If you can't prove the human, you can't trust the model. AI still needs humans. Before the data, there is a human. Repeat these until people repeat them back.
Reusable post skeleton: 1 What I used to believe → 2 What happened → 3 What surprised me → 4 What I realized → 5 What I believe now → 6 Why it matters to you → 7 Question or invitation. Never open with the lesson — open with the story.
Would 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.
My 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.
Everybody 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.
I 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.
We 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.
A 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.
The 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.
'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.
I'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.
The 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.
Researchers 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.
10 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.
A 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.
The 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.
I'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.
Clients 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.
We 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.
Recruited 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.
Needed 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.
We 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.
A 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.'
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.'