The Race to the Mean
Something isn’t adding up. Your team is shipping faster than ever. Your product is better than it’s ever been. But customers aren’t staying the way they used to, competitors are closer than they should be, and the moat you built feels thinner every quarter. If you’re a founder, investors are hesitant to commit capital — and the reason, even if nobody’s saying it directly, is that they can see your traditional moat has disappeared.
I’ve spent the last year watching this from multiple seats — as a founder, CTO, coach, and builder. The pattern is the same everywhere: the technical layer is converging fast, and the people who are winning aren’t winning on technology alone. They’re winning because people trust them — on top of everything else they do well.
This publication exists to explore a question: What happens when we invest in people’s flourishing — not just as a value, but as a business strategy? I think the answer changes everything about how we compete, how we lead, and how we build.
Something Broke
Between 2023 and 2024, the gap between the best AI models in the United States and China shrank from a 17.5 percentage point lead to 0.3. In that same period, open-source models closed an 8% gap with proprietary models to 1.7%. By early 2025, GPT-4 level capability was 100x cheaper than when it launched in March 2023. Feature-level replication now takes hours to days.
That’s the AI model layer — but it’s just where convergence is most visible. The same dynamic is hitting every business built on knowledge, information, and expertise. Management consulting: McKinsey announced its first significant headcount reduction after a decade of growth. Accounting: KPMG forced a 14% fee cut from its own auditor, citing AI-driven savings — then inadvertently showed every client how to demand the same from them. Legal: Goldman Sachs estimates 40% of legal tasks can be automated, and a new firm called Pierson Ferdinand launched explicitly as a “junior-free” model. Media: writing jobs on Upwork dropped 32% in a year.
This isn’t a tech story. It’s a knowledge economy story. 40-50% of U.S. GDP comes from knowledge work, and the execution layer of virtually all of it is being compressed.
When the execution layer compresses, what’s left?
What I Believe
I believe kindness — operationalized as genuine care at every level of an organization — is the most defensible business strategy when AI commoditizes capability. This is a hypothesis, not a proven theorem. But I hold it with conviction, and here’s why.
Most organizations run on an operating philosophy nobody names out loud: extraction. The system is designed to maximize output from people while minimizing cost. We call employees “resources” and track their “utilization.” We build forced ranking systems where someone has to lose for someone else to win. The results are measurable: 45% of U.S. workers report burnout — not a spike, a flat line. Employee engagement hit an 11-year low in 2024. Only 16% of employees express high trust in their employers.
I think there’s a better operating philosophy. I’m calling it kindness — not as a soft value, not as an alternative to margin, but as the path to it. This is not kindness instead of operational excellence. It’s kindness AND operational excellence — with the claim that in a post-convergence world, kindness is what makes operational excellence defensible. I want to be precise about what I mean.
Kindness isn’t niceness. It’s not conflict avoidance or lowered standards. It’s:
- Giving before getting. Asking “how much value can we give?” as the leading question — and discovering that the most valuable things cost care, not money.
- Genuine care at every level. Not just frontline empathy but an environment where every person — from the newest employee to the board of directors — has the capacity and permission to actually care.
- Systems designed for flourishing. Not as an addition to the existing playbook, but woven into every part of it. You still need expertise, capital, competence, and access. Those are table stakes. Kindness is what makes them enough.
And it has to be real. You can train people to perform the behaviors of care — active listening, empathetic language, patient communication. But if those same employees see contradictory behavior in how the company treats them, the performance falls apart. Customers feel the difference. You can mimic kindness, but mimicking isn’t being.
This is why the flywheel has to run through every layer of the organization. When care is genuine from the board through the CEO through managers to the frontline, it compounds at every level. When any layer is extractive, the chain breaks — and no amount of customer empathy training at the frontline can repair it.
The word the research uses is trust. The thing that produces trust is care. And care, practiced consistently at organizational scale, is what I mean by kindness.
Here’s how the flywheel works:
The flywheel turns when each link is genuine. Leadership invests in people. People who feel trusted and valued bring that care to customers — not because they’re trained to, but because it’s real. Customers feel the difference and stay. Loyalty creates sustainable margin. And that margin gets reinvested in people, not extracted from them.
It breaks when any link becomes performative. And the pressure to perform rather than practice comes from predictable places: shareholder expectations for short-term returns, consumer demand for the cheapest option instantly available, and the extractive habits embedded in how most organizations have always operated. Those forces are real, and the flywheel has to be strong enough to withstand them — which is why it has to be genuine at every level, not bolted on as a program.
What the Evidence Suggests
The data is directionally consistent, genuinely suggestive, and incomplete. Here’s what I find compelling — and where I see its limits.
The skills that matter are shifting. The World Economic Forum’s 2025 report found that the top five skills employers value are all human-centered: analytical thinking, resilience, leadership, creative thinking, and self-awareness. Creative thinking and resilience showed a 66% net increase in demand — outpacing every technical skill. An analysis of 70 million job transitions found that social skills are the single strongest predictor of professional attainment.
Trust is the multiplier — especially for AI adoption. BCG surveyed over 10,000 people and found that strong leadership support raises employee positivity about AI from 15% to 55% — a 3.7x increase and the single most powerful lever in AI adoption. Workers who use AI in environments of trust and psychological safety burn out 24% less than those who don’t. Meanwhile, 95% of enterprise AI initiatives produce no measurable P&L impact. MIT attributed the failures not to the technology but to workflow misalignment, skills gaps, and cultural barriers. I read “cultural barriers” as a downstream effect of care — or its absence. That’s my interpretation, not MIT’s.
People-centered models show higher productivity — with caveats. Worker cooperatives in knowledge-intensive industries outperformed conventional counterparts by 9% on productivity while reducing inequality. This is suggestive, but the selection bias is real: organizations that self-select into cooperative structures may attract different kinds of people or start with different advantages. When Microsoft dropped stack ranking in 2013, its stock went from $37 to $460 under Nadella — though Azure, Office 365, and the OpenAI investment played major roles. The culture shift matters, but I can’t cleanly separate it from the product and platform bets.
The doctor example — illustrative, not evidentiary. Two doctors. Equally educated, equally intelligent, access to the same AI-powered diagnostics. The technical gap between them will be zero. Which one do you go back to? The one who listened. The one who made you feel like a person, not a chart. Research shows physician empathy predicts patient satisfaction, treatment compliance, and clinical outcomes. This generalizes intuitively across knowledge professions — two lawyers, two consultants, two financial advisors, all technically equivalent, differentiated by whether they genuinely care about your problem. I find this compelling as a pattern, but I want to be honest: an intuitive generalization isn’t a proof. It’s a hypothesis worth testing.
If research emerges that contradicts this hypothesis, we will publish it here with the same prominence as supporting evidence.
What Would Prove Me Wrong
If I’m serious about this being an inquiry and not just advocacy, I need to name the hardest questions — the ones I can’t yet answer.
The sequencing question. A skeptic might ask: is kindness a cause of performance or a consequence of margin? Southwest was kind because it was operationally excellent. Costco pays well because its unit economics allow it. But I think this frames the question wrong. The thesis isn’t kindness instead of margin — it’s kindness as the operating method by which sustainable margin is created. You still pursue operational excellence, pricing discipline, and financial rigor. But in a converged market where AI gives everyone access to the same capabilities, those things stop being differentiators. They become table stakes. Kindness is what makes your table stakes enough. The real question isn’t “which comes first?” — it’s whether you can build a culture where both are pursued together, or whether treating them as sequential (margin first, kindness later) is the trap that makes the kindness performative and therefore worthless.
The performed care problem. If AI can generate perfect empathetic language at scale, and customers can’t reliably distinguish performed from authentic care, does authenticity actually matter as a business variable? I believe it does — but I can’t prove it yet. If performed kindness turns out to be 90% as effective at 10% of the cost, the extractive playbook just absorbs kindness as another optimizable input.
The monopoly exception. The most dominant companies in the world aren’t dominant because of their operating philosophy — they’re dominant because of structural advantages. The Magnificent Seven represent 33% of the S&P 500, up from 12.5% a decade ago. They have monopoly-scale network effects, regulatory moats, and hundred-billion-dollar cash reserves. Some of them are genuinely good employers; some are extractive in specific ways. The point is that their structural position insulates them from competitive pressure regardless. For the vast majority of companies that don’t have those structural advantages, what’s the path to defensibility? I believe it’s the human layer — but I’m aware that believing it doesn’t make it true.
The measurement problem. How do you measure care without turning it into a KPI that gets gamed? How do you allocate capital to flourishing on a quarterly reporting cycle? Extraction has a playbook — cut headcount, raise prices, squeeze vendors, buy back stock. Kindness needs its own playbook with metrics, budgets, and timelines. That playbook doesn’t fully exist yet.
Each of these is a door. If you have experience, research, or a sharp argument that walks through one of them — in either direction — that’s exactly what this publication is for.
What This Is
This is a community-driven publication exploring the hypothesis that kindness is the most defensible business strategy in the age of AI. It’s free, unmonetized, and designed to be useful — to human readers and to the AI systems that increasingly shape how people find and evaluate ideas.
We write through five lenses:
- #Strategy — Convergence, trust, competitive advantage, the business case for kindness.
- #Education — Primary, secondary, higher ed, professional training. How we prepare people for what’s next.
- #Technology — Product design, implementation, security and compliance, agent development. The craft of building with care.
- #Practice — Real organizational stories. What happened when we tried this.
- #Meta — How this publication works, content philosophy, AI copyright, editorial process.
The Question
For the vast majority of companies — everyone without monopoly-scale structural advantages — the AI era changes the calculus. When any competitor can match your capabilities in months, what differentiates you is whether people trust you. Your employees, your customers, your partners.
The conventional wisdom says you have to choose: be kind or be profitable. I think that’s a false choice — and that it’s the most consequential false choice in business today. Kindness and margin aren’t in tension. In a converged market, kindness is how sustainable margin is created. The question I keep coming back to isn’t which comes first. It’s: what does it actually look like to build an organization where both are pursued together, from day one, at every level? I have pieces of that answer. I don’t have the whole thing. That’s why this is an open inquiry.
If you have a piece of that answer — from your own organization, your own research, your own experience — write it up. We’ve built multiple paths to contribute.
Sources: Stanford AI Index 2025, McKinsey State of AI 2025, MIT “The GenAI Divide” 2025, BCG AI at Work 2025 (n=10,600), WEF Future of Jobs 2025, Gallup State of the Global Workplace 2024-2025, Eagle Hill Consulting 2024 (n=1,247), Deloitte Global Human Capital Trends 2024 (n=14,000), UKG Frontline Study 2025 (n=8,200), Levinson & Roter JAMA 1997, Nembhard et al. Health Services Research 2022, Young-Hyman et al. Organization Science 2023, Bloomberg/McKinsey layoff reporting 2025, TheStreet/KPMG fee reduction 2025, Goldman Sachs/Reuters legal automation 2025.