In this episode of The Uncertainty Edge, host Sam Sivarajan sits down with Dr. Amy D'Aprix — expert on life transitions, aging, caregiving, and family dynamics, and founder of the Trusted Advisor of Choice program. With a doctorate in social work and over 30 years of experience training thousands of financial advisors across North America, Amy has made it her mission to help advisors bridge the gap between financial planning and the human side of their clients' lives.

They explore the empathy gap between advisors and clients, engaging women clients, and why advisors must shift from retirement planning to longevity planning.

Key Quote "Empathy first, solution second." — Dr. Amy D'Aprix

Key Takeaways

• You don't need to be a therapist — you need to be a connected human being who listens at a deeper level.

• The LIRA framework gives advisors a simple structure to move between life and financial conversations.

• There's a significant empathy gap between what advisors think they communicate and what clients actually feel.

• Retirement is not a single event — shift to longevity planning to ensure life and money go the distance.

Sound Bites

• "Empathy first, solution second."

• "I see you, I hear you, and I understand you — that's all empathy is."

• "The thing AI can never do is say: I'm going to be here with you."

• "Both your life and your money need to go the distance."

• "Keep learning, growing, and adapting — or you're no longer relevant."

Topics Discussed

• 00:54 — Amy's Journey: From Social Work to Financial Services

• 04:20 — You're Not a Therapist — You're a Connected Human Being

• 17:17 — The LIRA Framework: Listen, Empathize, Reassure, Act

• 30:16 — Engaging Women Clients & Building Relationships with Both Partners

• 42:48 — Longevity Planning: Rethinking Retirement for the Long Game

Resources Mentioned

• Learn more about Dr. Amy D'Aprix and her work

• Free caregiving & planning guide — grab the giveaway here

Stay Connected with The Uncertainty Edge

• Subscribe on your favourite podcast platform to never miss an episode.

• Join the conversation on LinkedIn — share your thoughts and connect with other forward-thinking advisors.

• Explore more insights on Sam's website

In February 2024, Klarna CEO Sebastian Siemiatkowski told the world his company’s new AI assistant was doing the work of roughly 700 full-time customer service agents. Resolution times dropped. Customer satisfaction scores held steady. The company said headcount would shrink through natural attrition, from roughly 3,800 employees toward 2,000. Wall Street loved it. Klarna became the poster child for AI-driven efficiency; proof that a “leaner, smarter” company was not just possible but imminent for every knowledge-work employer watching.

Fifteen months later, Klarna walked part of it back. Quality had slipped. Customers wanted a human option, and Klarna began rehiring.

The reversal made headlines as a story about chatbot quality. That’s the small story. The larger story is what the original decision assumed it didn’t need to model. And it’s the same blind spot sitting inside boardrooms making AI staffing decisions right now.

The Layoffs That Don’t Look Like Layoffs

For most of the last three decades, “automation risk” targeted a specific kind of job: repetitive, physical, low-wage. Assembly lines. Call centers. Data entry. The mental model executives carried into every restructuring conversation was that technology displaces the bottom of the org chart.

That model is now wrong, and the people it’s wrong about are not marginal.

They are product managers, Salesforce administrators, senior designers, solutions architects, marketing directors, lawyers, and bankers. People who, eighteen months ago, would have described their jobs as secure; people with mortgages, aging parents, and university-bound kids. In 2026, the distance between “successful professional” and “unexpectedly unemployed” has narrowed further than most leaders are prepared to admit.

This matters because these are not the workers policymakers have spent decades worrying about. They are the ones the system quietly assumed would always be fine; university degrees, white-collar, home-owning, tax-paying. The group least prepared for disruption is now the group most exposed to it, and almost nobody built a contingency plan around that fact.

The Multiplier Nobody Models

Every AI staffing decision I’ve seen modeled treats the layoff as a line-item cost reduction: salary, benefits, severance, done. That math is real, but it’s also incomplete in a specific, predictable way; the same way it was incomplete in every industrial town that lost its anchor employer decades ago.

The people being laid off are also customers, taxpayers, and borrowers. Pull on any one of those threads and the second-order effects show up fast:

• Consumer demand contraction. Many of the companies deploying AI to cut headcount are selling directly to the demographic they’re displacing — software subscriptions, financial products, premium retail, travel. The laid-off employee was often also the customer.

• Credit tightening. Banks read rising layoffs in a sector as rising default risk. Lending standards tighten — for mortgages, small business loans, auto financing — well before default rates actually move, which slows spending further.

• Local service contraction. Massage therapists, independent cafés, boutique gyms, and neighborhood restaurants don’t survive on savings; they survive on discretionary spending from people with steady paychecks. When savings get anxiously jealously preserved instead of used for consumption, these businesses are usually the first to feel it and the last anyone notices.

The Tax Base Nobody’s Budgeted For

There’s a fourth thread, and it’s the one with the worst timing: national tax revenue. Mid-career professional salaries — the product managers, architects, senior analysts — carry disproportionate income tax weight relative to entry-level roles. They’re deep enough into the income curve to be paying real marginal rates, but numerous enough as a group that a wave of mid-tier layoffs shows up in aggregate tax revenues, not just individual hardship statistics.

This is landing at the worst possible moment. Most G7 governments are running large structural deficits, many with multi-year spending commitments already locked in: infrastructure, defense, healthcare, debt servicing on debt itself. Those budgets were built on tax base projections that assumed the professional class kept working, kept earning, kept filing at the same bracket. A government that loses a meaningful slice of its mid-tier income tax base while its spending obligations stay fixed doesn’t get to reduce its deficit; it needs to fund the gap either by more borrowing or by tax increases on a shrinking base of people still earning enough to absorb them. Either path feeds back into the same consumer demand problem the layoffs started.

No CFO models this. It’s not supposed to be their job. But it’s happening because of decisions being made in rooms exactly like theirs.

The Blind Spot in the Boardroom

It is worth reiterating the uncomfortable part. The company doing the cutting is often the same company whose growth depends on the spending power of the people it just cut. Not directly in many cases — but through the same regional economy, the same consumer base, the same credit markets, the same tax-funded infrastructure their business relies on.

Goldman Sachs’s 2023 estimate that generative AI could affect the equivalent of 300 million full-time jobs globally wasn’t a headline about factory floors. It was a headline about the exact roles Klarna targeted first: customer service, administration, mid-level analysis. The IMF’s 2024 assessment that roughly 60% of jobs in advanced economies are exposed to AI, with close to half of those facing negative displacement effects rather than augmentation, tells the same story from a different angle.

Business leaders modeling these decisions are running a narrow P&L — cost per seat, resolution time, headcount ratio — when the more valuable exercise requires a wider one.

• What happens to our own revenue line if this becomes an industry pattern rather than a single company’s edge?

• At what point does “efficiency” become a coordination failure, where every company optimizing individually degrades the demand environment collectively?

E.D.G.E.: Managing Your Own Second- and Third-Order Exposure

Establish

What can you actually control? Your own staffing decisions, your own customer diversification, your own balance sheet resilience. What you can’t control: aggregate layoffs across your sector, competitor decisions, or the speed at which credit tightens in response.

Key question: Have you separated the AI staffing decision itself from the demand environment it might help create?

Diagnose

This is where most leaders stop too early — at the cost side of their own AI decision, never turning the lens back onto their revenue side.

Key question to force the diagnosis: What percentage of our revenue comes from customers whose income is directly exposed to AI-driven displacement — in our industry or adjacent ones? A B2B software company selling seat licenses to mid-sized firms, a regional bank with a mortgage book concentrated in professional-class borrowers, a retailer whose margin depends on discretionary spending — each has a different exposure number, and almost none of them have calculated it.

Go

Purposeful action here isn’t “wait and see”. It’s building the contingency plan before demand actually softens, so the response isn’t improvised under pressure:

• Stress-test revenue against a 10% and 20% contraction in your most AI-exposed customer segment, the same way you’d stress-test for a rate shock.

• Identify which product lines or customer segments are least correlated with mid-tier professional income, and know in advance how quickly you could shift resourcing toward them.

• Review lending, credit, and payment-term policies now, not after delinquency data forces your hand; reactive tightening after the fact tends to overshoot and choke off customers who would have recovered.

Evolve

Build the feedback loop before you need it. Track leading indicators of demand deterioration in your exposed segments — subscription downgrades, order size trends, payment-term requests — rather than waiting for the quarterly revenue number to confirm what’s already happened.

The goal isn’t to predict a recession. It’s to know, faster than your competitors, when your own customer base is starting to feel the effects of decisions being made across the wider economy — including, possibly, your own.

A Personal Note: The Analyst Who Wasn’t There

Thirty years ago, I was the junior investment bank analyst doing grunt work at ridiculous hours. I built valuation models late into the night. I reworked PowerPoint decks for the third time because additional feedback from senior bankers needed to be incorporated. None of it felt like important work in the moment.

But looking back, it was the training ground. Building those models taught me how deals actually got structured. Sitting in on the negotiations I was only there to take notes on taught me how senior bankers read a room, when to push and when to concede. A few years later, I was the one leading those deals; because I’d spent years doing the unglamorous work that builds the judgment senior work requires.

Investment banks are now talking seriously about having AI replace the junior analyst function: the modeling, the deck-building, the number-crunching. The cost logic is sound in isolation. But senior bankers don’t arrive fully formed. They’re built, over years, by doing exactly the work that’s being automated away. If a bank can no longer offer that apprenticeship, it faces a third-order effect nobody’s pricing into today’s efficiency gains. Where exactly does its next generation of rainmakers come from? The answer is the market; through bidding wars for the scarce senior talent every firm is now competing for, having each cut its own pipeline. Today’s junior-analyst savings become tomorrow’s senior-banker premium, and the arithmetic that looked so good in this year’s budget quietly reverses in five.

Your Turn: Model the Second-Order P&L

1. List every role category under consideration for AI substitution — including entry-level roles you think of as “training”, not just cost centers.

2. Estimate what fraction of your own revenue depends on customers whose income sits in the bracket most exposed to AI displacement.

3. Ask finance to model a 90-day and 12-month scenario where 20% of that segment reduces discretionary spending on your products specifically.

4. Ask HR to model where your senior talent comes from in five years if the entry-level rung disappears today.

5. Decide with all four numbers on the table — not just the one that made this quarter’s business case easy.

What will you do the quarter your demand actually drops — and will you have built that plan before you need it, or after?

© The Uncertainty E.D.G.E. | Published every other Tuesday

If this resonated, I’d love to have you as a free subscriber — and forward this to a leader who needs to see around corners.

The Uncertainty E.D.G.E. is for leaders who are accountable for outcomes they can’t fully control — and want a clearer way to think when certainty won’t come.

Essays and conversations on decision-making under pressure, every other Tuesday. Join me at theuncertaintyedge.com.

If the human side of leadership is what draws you, I also write The Good Human Practice — on inner clarity and character for leaders called on to make those hard decisions under pressure.

In this episode of The Uncertainty Edge, host Sam Sivarajan sits down with Matt Granados, CEO of Life Pulse Inc. and productivity coach to Fortune 500 leaders, financial advisors, and professional athletes. Matt challenges the cult of high performance — arguing that most people operate at only 40–60% of their true capacity, not because they lack ability, but because they’re chasing the wrong target. His framework: truth leads to tension, and tension leads to transformation. The result is what he calls optimal performance — high output at a sustainable pace — and it changes everything about how you lead, plan, and live. 

Key Quote

“No one has ever accidentally become fulfilled. Fulfillment takes intentionality.” — Matt Granados

Key Takeaways

•       High performance is overrated. It’s high output at an unsustainable pace compared to others. Optimal performance — high output at a sustainable pace compared to your own ability — is the real goal.

•       Stop solving symptoms. Address root causes and people, like bones properly set, will heal themselves.

•       Operate at 80% capacity — not 100%. That buffer is what lets you surge when needed and recover without burning out.

•       Identity, calling, and assignment are not the same thing. Tie your identity to your role and you’re one job loss away from a crisis. Know who you are first.

•       Plan weekly, act daily. Days are too short, months too long — the week is the right unit of execution.

Sound Bites

•       “You can’t manage time. You can only choose what you do with the time you have.”

•       “Nice avoids conflict. Kindness loves someone enough to keep them in order.”

•       “Excuses end with the problem. Reasons come with a solution.”

•       “When you experience fulfillment in its truest form, nothing else will match it.”

Topics Discussed

00:02 — Introduction: Life Pulse & the Inefficiency Epidemic

08:16 — Symptoms vs. Causes: Why Most Performance Fixes Don’t Stick

13:47 — Truth, Tension & Transformation: The Framework

26:28 — From 40% to 80%: Closing Your Performance Gap

36:13 — Identity vs. Assignment: Who You Are vs. What You Do

Resources Mentioned

Learn more about Matt Granados and Life Pulse Inc.:

lifepulseinc.com

Free resources for Uncertainty Edge listeners:

lifepulseinc.com/TUE

Stay Connected with The Uncertainty E.D.G.E.

•       Join the conversation on LinkedIn — share your thoughts and connect with other forward-thinking leaders.

•       Explore more insights on Sam’s website.

•       Check out Sam’s Two Free Substack newsletters: theuncertaintyedge.com and thegoodhumanpractice.com

On July 9, 2026, Mark Zuckerberg said something that should have made more headlines. Meta, he told Bloomberg, needs all the computing power it can get. And then, in almost the same breath, he said the company was exploring renting some of that computing power out to other companies; because the offers it receives to use its infrastructure are, in his words, “so high that it may make sense” to lease the capacity rather than use it internally.

Reading those two statements together, makes one scratch their head. A company racing to build one of the largest private ‘compute’ portfolios in history—a company whose CEO says it cannot get enough of the stuff—is at the same time shopping the surplus. That is not a contradiction. It is a tell.

Meta is doing what disciplined builders eventually do when the build runs ahead of confirmed internal demand: it goes looking for someone else to absorb the capacity. Nothing is wrong with that instinct. But it reveals the assumption the whole industry stands on—that the demand to justify this build-out is coming, at the prices builders need, and soon.

That assumption is now cracking. And the story of how it cracks is not a technology story. It is a decision-making story; about what you measure, and how you hold the short term against the long.

The Gap Nobody Wants to Name

The largest hyperscalers (Amazon, Google, Meta, Microsoft) are on track to spend somewhere near $700 billion on AI infrastructure in 2026. That is roughly double last year’s figure, deployed in a single twelve-month sprint.

Now set that against what the AI industry actually earns from end users, on the order of $75 billion; roughly a tenth of what is being spent to serve it. It is the technology version of the mantra from the movie The Field of Dreams: if you build it, they will come.

You don’t have to be a bear to find that gap alarming. The bluntest warning came from inside the industry. In a February 2026 interview, Anthropic’s CEO, Dario Amodei, walked through the math for his own company. Given their commitment to ~$1 trillion in compute, if revenue were even modestly short—$800 billion instead of a trillion, or 5x annual growth instead of 10x—then there is, in his words, “no hedge on Earth” that stops the company from going bankrupt. Being wrong by a single year could do it.

The bulls have a fair answer. Building ahead of demand is how you win a platform shift. Nobody laid the fiber after the traffic arrived.

Maybe. But the entire case rests on one critical load-bearing assumption. Every dollar of that $700 billion is a bet that buyers will keep paying premium prices for compute, indefinitely. And in the first half of 2026, the buyers started pushing back, hard.

The Assumption That Just Cracked: Demand Is Price-Elastic

Uber gave roughly 5,000 engineers access to agentic coding tools in December 2025. By April, it had burned through its entire 2026 AI budget; a full year’s allocation gone in just four months. It capped spending at $1,500 per tool per month, and its own president admitted the harder truth: rising token spend was getting difficult to tie to measurable product improvement. In June, the Financial Times reported that Amazon, Walmart, Cisco, Meta, and Uber were all curbing internal AI use, capping budgets, or steering staff toward cheaper models.

This exposes what is called the token-cost paradox: more adoption does not automatically equal more revenue at a price anyone can profit from. The industry assumes usage is value. Usage is just usage. When the meter runs and the output can’t be tied to a desired result, sophisticated buyers do exactly what sophisticated buyers always do—they cut spending.

And when they can’t cut, they substitute. When DeepSeek broke through in January 2025—wiping some $600 billion off Nvidia’s value in a single day—it was dismissed as a one-off scare. Eighteen months on, it looks less like a scare and more like a repricing:

• The spread is brutal. Citi, the American bank, puts leading Chinese open-weight models at roughly 18 cents per million tokens against about $4 for the top Western models, a discount of more than 90%. The capability gap that once justified the premium has compressed from more than a year to, by some estimates, about four months.

• Companies are moving, not just benchmarking. In June, the AI startup Lindy shifted 100% of its agent traffic off Anthropic’s Claude models to DeepSeek V4. Founder Flo Crivello said the switch saved millions and improved performance on core use cases. His summary is the whole thesis in seven words: “You don’t need God to write your email.” Airbnb and Cursor’s owner have both disclosed using Chinese open models.

• The aggregate has flipped. On Vercel, DeepSeek’s share of token usage jumped from under 1% to 17% in a single month. On OpenRouter, the share of tokens U.S. companies run on Chinese models has held above 30% every week since February—against just 4.5% in the first half of 2025.

Note what this is not. It is not a collapse in AI usage; usage is exploding. It is a collapse in the assumption that exploding usage flows to premium-priced compute. The bet was never just “demand will grow”. It was “demand will grow and buyers will keep paying premium prices”. The first half is true. The second half is being proven wrong in real time, one routing decision at a time.

The Metric Problem: Counting Capacity Instead of Demand

Look at the numbers the industry celebrates: capex deployed, gigawatts under construction, chips shipped, tokens consumed. Every one of them is a supply-side metric. They tell you how much has been built and how heavily it is being used. Not one of them tells you whether that use clears a profit.

We have seen this before. In the late 1990s, the telecom industry convinced itself that internet traffic was doubling every hundred days; one seductive data-point, extrapolated into a building frenzy. Operators laid fiber across continents and measured progress in route-miles, not in traffic that paid. Much of that fiber was never “lit”, and the companies that spent the most, like Global Crossing, collapsed into some of the largest bankruptcies of the era. The capacity was real. The demand at the price the builders needed was not.

Falling in Love With the Solution: The Iridium Warning

The other error is subtler and more human: you can fall so deeply in love with a technical solution that you never stop to ask whether the market wants it at the price you’ll have to charge. No company illustrates this better than Motorola—because Motorola didn’t just lose money on the bet. It lost its market lead.

The idea was born in 1987, reportedly after the wife of a Motorola engineer, Bary Bertiger, grew frustrated that she couldn’t get a phone signal on a remote Caribbean beach. Bertiger and two colleagues sketched an audacious solution: a ring of satellites in low orbit that would blanket the entire planet, so a single handheld could place a call from anywhere on Earth. The original design called for 77 satellites, and because iridium is the 77th element on the periodic table, the project had its name.

It was breathtaking engineering, and breathtakingly expensive; roughly $5 billion and a full decade from concept to launch. When service went live on November 1, 1998, the ceremonial first call ran from Vice President Al Gore to the chairman of the National Geographic Society. Motorola had built a genuine marvel.

Almost nobody bought it. The handset was a $3,000 brick, the size of a shoe; airtime ran $3 to $7 a minute; and the phone needed a clear view of the sky, so it failed indoors, in cars, and in the very cities where business travelers actually lived. Meanwhile, in the decade it took to build, ordinary cellular had spread everywhere—cheaper, smaller, good enough. Iridium had spent ten years and $5 billion solving a problem the market was quietly solving on its own. A 1996 Gallup study had already flagged the business model as deeply flawed. Motorola launched anyway.

The forecast was 500,000 subscribers in year one. By mid-1999 Iridium had roughly 20,000. On August 13, 1999—nine months after that first call—Iridium filed for bankruptcy, among the largest in U.S. history at the time. The satellites were nearly de-orbited and burned up before a group of investors bought the entire system out of bankruptcy for about $25 million—roughly a penny on the dollar of what it cost to build.

Here is the part that matters most. While Motorola poured capital, engineering talent, and executive attention into its satellite moonshot, it was losing the race it had actually been winning. It had entered the 1990s as the undisputed king of the mobile phone—the iconic StarTAC had more than 30% of the global market—but it clung to profitable analog technology while a Finnish upstart went all-in on digital. In 1998, the very year Iridium launched, Nokia passed Motorola to become the world’s largest handset maker. Motorola never got the lead back. It took a roughly $2.5 billion write-off on Iridium; but the deeper cost was the years and the focus it burned building the wrong future while a rival built the right one.

The E.D.G.E. Framework: This is an ‘Establish’ Failure

Running this case through the E.D.G.E. framework reveals that the failure is largely in the first step itself.

Establish — Four Things the Builders Got Wrong

The Establish Your Foundation step asks four questions: What can you actually control? Are you aligning your goals with your actions? Are you spanning short-term and long-term goals? And what metrics are you tracking? The AI build-out is answering all four badly.

1. Control. Capacity built is controllable. Demand at a profitable price is not—nor is a buyer’s decision to route to a cheaper model, nor a community’s willingness to host your data center. The builders are pouring energy into the one variable they command, and treating the ones they don’t as settled.

2. Align. Watch what the spending is actually aligned to. Not to validated customer demand, but to rivals’ announcements; a land grab in which the trigger to spend is that someone else spent.

3. Span. They are not balancing the short term against the long. Racing to capture a genuine long-term platform shift is defensible. Overriding the near-term signal that demand is price-elastic, in the name of that long-term thesis, is not. Iridium is the cautionary extreme: a demand assumption set in 1987 and never revisited across a ten-year build. Balancing horizons means letting today’s feedback discipline tomorrow’s bet, not silence it.

4. Track. They are tracking the wrong metrics. Capex, gigawatts, chips, tokens: every headline number is a vanity metric dressed as a vital one. They measure the builders’ own enthusiasm. When you measure the thing you can control (spend, gigawatts, usage) instead of the thing that decides whether you succeed (durable demand at a profitable price), you feel intensely productive while walking toward a cliff.

Key question: Are you measuring what you can control, or what actually determines whether you survive?

Diagnose — Separate Signal From Noise

The number everyone cites—usage is exploding—is, for the survival question, noise. The real signal is unit economics: durable demand at a price that clears a healthy margin on a trillion dollars of committed capital. The 20% that drives 80% of this outcome is the price-elasticity of demand, not the adoption curve.

Key question: Is your headline metric a leading indicator of viability, or a lagging indicator of your own enthusiasm?

Go — Purposeful Action, Not Maximal Action

Purposeful is not the same as maximal. Purposeful action builds optionality; staged capex, exit ramps, assets that can be repurposed. Zuckerberg’s instinct to rent the surplus out is, read charitably, a Go move done well: it turns a fixed bet into a fungible one. The next tranche of capital should follow instrumented demand, at a real price.

Key question: What is the smallest commitment that keeps your options open without betting the balance sheet on an unproven demand curve?

Evolve — Build the Feedback Loop

The trap underneath all of this is extrapolation. Projecting limitless growth from a handful of early data-points; that is the narrative fallacy dressed up as a forecast. The antidote is a feedback loop that watches substitution and price sensitivity, not just usage. DeepSeek’s price and Uber’s cap are the feedback. Evolving means letting them inform your strategic response.

Key question: What signal would tell you your demand assumption is wrong—and are you watching for it, or watching the capex counter tick up?

The Discipline

You are almost certainly not deploying $700 billion. But the pattern scales down to any decision on your desk: falling in love with a capability before validating demand, spending because a rival spent, measuring effort instead of outcome, and overriding what this quarter is telling you because the five-year thesis is prettier.

None of this is pessimism about AI. The demand is real and growing. The discipline is narrower than optimism or doubt: refuse to confuse capacity with demand, and refuse to confuse growth with growth at a profitable price.

The builders may yet be proven right that the demand is coming. But the Establish question is the one Iridium never asked in time—and the one the meter at Uber just forced onto every finance team in the country:

At what price? And are you watching what your customers are choosing to do?

© The Uncertainty E.D.G.E. | Published every other Tuesday

If this resonated, I’d love to have you as a free subscriber — and forward this to a leader who needs to see around corners.

The Uncertainty E.D.G.E. is for leaders who are accountable for outcomes they can’t fully control — and want a clearer way to think when certainty won’t come.

Essays and conversations on decision-making under pressure, every other Tuesday. Join me at theuncertaintyedge.com.

If the human side of leadership is what draws you, I also write The Good Human Practice (thegoodhumanpractice.com) — on inner clarity and character for leaders called on to make those hard decisions under pressure.

In this episode of The Uncertainty Edge, host Sam Sivarajan sits down with Jeff Panik, founder of Balance Wealth Partners and a financial advisor with nearly 26 years of experience. A first-generation college graduate who enlisted in the U.S. Army to pay off student loans, Jeff has built his career around filling the financial education gap that leaves so many young adults — and pre-retirees — to figure it out alone. From the dangers of buy now pay later to the hidden complexity of retirement planning, this episode is packed with practical, jargon-free guidance for advisors and clients alike.

Key Quote

“Perfect is the enemy of great — especially when it comes to taking action with your finances.” — Jeff Panik

Key Takeaways

•       Financial literacy is still a crisis — lobbying from credit and lending industries actively works against educating young consumers, leaving them vulnerable before the race even starts.

•       Ask three questions before any purchase: Do I really need this? What will it truly cost me? How long will I be paying for it?

•       Procrastination is the enemy. Taking imperfect action — even saving $25 a month — compounds into financial resilience over time.

•       Retirement planning is a two-year process, not a weekend project. The financial puzzle is unique to every individual; stop copying what your neighbour is doing.

Sound Bites

•       “If someone says something is perfect, you probably want to run from it.”

•       “Financial fog causes people to freeze — and freezing is often the worst decision of all.”

•       “Run your own race. You don’t have their training, their fitness, or their starting line.”

•       “You can have all the money in the world, but if you don’t have your health and your mind, the money doesn’t matter much.”

Topics Discussed

00:02 — Introduction: Jeff’s Journey from Student Debt to Financial Advisor

05:03 — The Financial Literacy Crisis & Why the System Isn’t Helping

10:45 — Buy Now Pay Later, Influencers & the Clickbait Economy

15:47 — Standing at the Starting Line: Critical Early Financial Decisions

27:08 — The Pre-Retirement Puzzle: Planning the Transition Right

37:41 — The Future of Financial Advising & the Great Wealth Transfer

Resources Mentioned

Learn more about Jeff Panik and Balance Wealth Partners:

jeffreypanik.com

Stay Connected with The Uncertainty E.D.G.E.

•       Join the conversation on LinkedIn — share your thoughts and connect with other forward-thinking leaders.

•       Explore more insights on Sam’s website.

•       Check out Sam’s Two Free Substack newsletters: theuncertaintyedge.com and thegoodhumanpractice.com

In 1990, Jerry Sternin arrived in Vietnam with a mandate most leaders would have called impossible. The charity Save the Children had sent him to reduce child malnutrition in the countryside, where roughly two-thirds of children under five were malnourished. The Vietnamese government, weary of well-meaning foreign missions that produced reports and little else, gave him a deadline that doubled as a threat: show measurable results in six months, or your visa will not be renewed.

Sternin knew the academic answer and had scores of studies to support him. Malnutrition was a systemic problem; poor sanitation, contaminated water, poverty, lack of education. He also knew that answer was no use to him. He called it “True But Useless”. No outside expert was going to rebuild Vietnam’s sanitation infrastructure in six months on a shoestring budget.

So he asked a different question. In these same poor villages, facing the same conditions, were there any children who were noticeably healthier than their peers? Surprisingly, there were. And when he studied what their mothers did differently, the answer was almost embarrassingly simple. These mothers fed their children the same daily amount of food but in four small meals instead of two larger meals, fed them actively by hand, and added free, locally available foods (like tiny shrimp and crabs from the rice paddies and sweet potato greens) that most families dismissed as unsuitable for young children.

He hadn’t found a new strategy. He’d found behaviors that already worked; and his job was to get more people to do them, consistently. Within six months, malnutrition in those villages had fallen sharply. Within two years, the great majority of participating children were no longer severely malnourished.

This is the tension at the heart of leadership under uncertainty. Faced with a problem too big to fully solve, most leaders do one of two things. They freeze, waiting for a clarity and certainty that never arrives. Or they reach for the sweeping transformation — the new strategy, the reorganization, the bold initiative — because doing something large feels like control. Sternin did neither. He found the few behaviors that worked and made them stick. Peter Drucker named the principle decades ago: strategy is a commodity; execution is an art.

The Consistency Premium

A 2023 study from McMaster University made the same point in an unlikely setting: the gym. Researchers, including co-lead author Jonathan McLeod, set out to find which training variables mattered most for building muscle: how much you lift, how often, which routine. The striking finding was how little those details mattered relative to a single factor: whether people actually stuck to the program. Get adherence right, and the rest is fine-tuning. Get it wrong, and the perfect plan is worthless.

Leaders instinctively resist this, because it sounds like permission to stop optimizing. It isn’t. It’s a statement about where the leverage lives. The strategy you can sustain beats the superior strategy you abandon; and under uncertainty, sustainability is the harder, rarer quality.

We’ve seen this in markets again and again. The investor who earns a steady 7% annually for twenty years ends up far ahead of the one who earns a brilliant 12% annually for ten years and then stays out of the market, panic-stricken, for the next ten. The edge isn’t in the return. It’s in the staying power.

If consistency is what matters, the real question becomes: how do you get people — clients, teams, yourself — to keep going when the payoff is distant?

Consider debt repayment. The mathematically optimal approach is the “avalanche”: attack the highest-interest balance first, because it costs you the most. It is absolutely correct on a spreadsheet. And it almost always routinely fails in practice.

In 2012, Kellogg researchers David Gal and Blakeley McShane analyzed how 6,000 people actually escaped credit-card debt. The strongest predictor of getting out of debt entirely wasn’t paying down the highest-interest balance; it was closing out the smallest balances first, regardless of interest rate. A 2016 Harvard Business Review study reached a similar conclusion: what keeps people paying is the feeling that they are finishing things, knocking out whole accounts and watching the list shrink.

This is the “snowball” method, and it works because it is mathematically suboptimal. Paying off a small balance barely dents your interest costs. But it delivers something the avalanche can’t: a completed win, early, that proves the plan works and makes you want to keep going. It rewards the right behavior. And as Dave Ramsey likes to say, personal finance is 20% head knowledge and 80% behavior.

The lesson travels far beyond debt. A rational plan people abandon underperforms an imperfect plan people finish. The leader who sequences early, visible wins, even modest ones, buys the one resource every long campaign runs out of first: belief.

Bright Spots: Amplify What Already Works

There’s a second move buried in Sternin’s story, and it’s the one most leaders miss.

When a problem feels overwhelming, the action-biased instinct is to invent or import a solution; bring in the consultants, launch the program, build something new. Sternin did the opposite. He assumed the solution already existed inside the system, being quietly practiced by the people succeeding against the same odds. Find those “bright spots”, understand what they do differently, and champion it across the organization.

This is faster, cheaper, and far more durable than an imported fix, for three reasons:

• It’s already proven in context. The behavior works under your actual constraints; not in a case study from a different industry.

• It’s locally credible. People adopt what their peers do far more readily than what an outsider prescribes.

• It’s executable now. You’re amplifying existing behavior, not waiting on permission, budget, or a system overhaul.

Every organization has bright spots: the sales region quietly beating its targets with the same product and pricing as everyone else, the plant with half the defect rate, the team holding onto people in a high-churn function. The diagnostic question is rarely “What new thing should we do?” It’s “Who is already succeeding here, and what are they doing that we could adopt?”

Sternin captured the deeper truth in a single line: it’s easier to act your way into a new way of thinking than to think your way into a new way of acting.

The E.D.G.E. Framework: From Strategy to Execution

Establish: Control the Inputs, Not the System

Sternin couldn’t control poverty, the political climate, or a six-month clock imposed on him politically. He could control where he looked and which behaviors he reinforced.

• Separate what you can influence — behaviors, sequencing, where attention goes — from what you can’t: the macro environment, the timeline you’ve been handed, the competitor’s next move.

• Stop pouring energy into solving the whole system at once.

Key question: Are you trying to fix the entire system; or amplify the part of it that already works?

Diagnose: Find the 20% That Drives the Outcome

The conventional diagnosis of malnutrition was true but useless. The useful diagnosis was narrow: which specific, repeatable behaviors separated the healthy children from the rest?

• Resist the elegant, comprehensive analysis that produces no action this quarter.

• Identify the few behaviors that, done consistently, move most of the result; the four-meals-a-day equivalent in your own situation.

Key question: What is the smallest set of behaviors that, repeated, would move this outcome the most?

Go: Engineer the First Finishable Win

Purposeful action isn’t the grand launch. It’s the smallest concrete step that can be completed, and seen.

• Sequence for an early, visible win: the individual’s small balance debt paid off, the village cooking group that showed results in two weeks.

• Make it finishable in weeks, not years, so it generates evidence and confidence rather than fatigue.

Key question: What is a win your team could actually complete in the next few weeks?

Evolve: Make Progress Visible So People Don’t Quit

A single win is not enough. Momentum has to be renewed, or people drift back to the old behavior.

• Build a cadence of visible milestones so progress stays felt, not merely claimed.

• Use each win to surface new resources, options, and converts; then point them at the next step.

Key question: How do you make progress visible often enough that people stay in the race?

A Personal Note: Electrical Tape and the Power of Visible Progress

Years ago, I trained in Tae Kwon Do alongside my young daughter. The dojo was full of kids, and like any martial art, it demanded exactly the thing children find hardest: consistency over a long horizon. The obstacle was the belt system. Earning the next belt could take months. An eternity to a seven-year-old, and long enough that motivation quietly leaked away between gradings.

The dojo’s solution was almost comically simple. Electrical tape. Every few weeks, the instructors tested the kids on a small slice of the next level. Pass, and you earned a single stripe of tape on your belt. Three stripes, and the next belt was yours. Nothing about the curriculum changed. What changed was that a distant, abstract goal had been broken into visible, finishable wins; and the kids stayed with it.

I’ve since watched the same dynamic decide whether organizations sustain change. Building three wealth-advisory businesses through more than one market crisis taught me that teams rarely lose faith because the strategy is wrong. They lose faith because the goal is years away and nothing seems to be moving. The leader’s job is to supply the electrical tape: milestones close enough to feel, frequent enough to celebrate, and real enough to mean something.

Your Turn

The next time you face a problem too big to solve outright, resist both the analysis paralysis and the grand launch. Try this instead:

1. Find the bright spot. Who inside your system is already succeeding under the same constraints; and what, specifically, are they doing?

2. Name the 20%. Identify the few behaviors that drive most of the result. Set the rest aside for now.

3. Engineer the first win. Choose one finishable victory you can complete in weeks, not quarters.

4. Add the electrical tape. Set the next two or three milestones now, and decide how each one will be made visible.

The Japanese proverb says it plainly: fall seven times, stand up eight. The leaders who help their people keep standing up don’t do it with a better strategy. They do it by making progress impossible to miss — one finishable win at a time.

© The Uncertainty E.D.G.E. | Published every other Tuesday

If this resonated, I’d love to have you as a free subscriber — and forward this to a leader who needs to see around corners.

The Uncertainty E.D.G.E. is for leaders who are accountable for outcomes they can’t fully control — and want a clearer way to think when certainty won’t come.

Essays and conversations on decision-making under pressure, every other Tuesday. Join me at theuncertaintyedge.com.

If the human side of leadership is what draws you, I also write The Good Human Practice (thegoodhumanpractice.com) — on inner clarity and character for leaders called on to make those hard decisions under pressure.

In this episode of The Uncertainty Edge, host Sam Sivarajan sits down with Radhika Dutt, author of Radical Product Thinking. Radhika challenges Silicon Valley’s most sacred mantras — fail fast, iterate quickly, just start — and offers a sharper, more systematic alternative for leaders building in complex, uncertain environments. From vision debt to puzzle solving, this conversation will change how you think about strategy, execution, and what it really means to lead with clarity.

Key Quote

“You’re voting with your labor for the world you want to create.” — Radhika Dutt

Key Takeaways

•       “Fail fast” works for VCs betting on 10 startups — not for founders who have 2–3 pivots before running out of money or momentum.

•       Vision debt vs. investing in vision: every decision is a trade-off. Make it explicit — or it gets made for you.

•       Goals and targets incentivize teams to prove success, not surface truth. Puzzle solving invites honest reflection.

•       Stay in the problem space longer. Observe first, hypothesize second — don’t jump to solutions before you understand the puzzle.

•       Coachability — genuine curiosity plus willingness to adapt — is the trait that separates leaders who scale from those who stall.

Sound Bites

•       “Treat every pivot like a silver bullet.”

•       “Leaders are always the last to know.”

•       “You’re voting with your labor for the world you want to create.”

•       “You learn not through experience — you learn by reflecting on experience.”

Topics Discussed

00:01 — Introduction: Radical Product Thinking & Challenging Silicon Valley Mantras

04:01 — Who Really Pays the Price of the VC Model

15:59 — Vision Debt vs. Investing in Vision: A Framework for Trade-offs

30:57 — Why OKRs Fail & The Case for Puzzle Solving

43:43 — Coachability, Clarity of Vision & Navigating Uncertainty

Resources Mentioned

Radical Product Thinking by Radhika Dutt — available on Amazon and in bookstores.

Free OHLA Toolkit (Observe, Hypothesize, Learn, Adapt):.

Connect with Radhika on LinkedIn.

Stay Connected with The Uncertainty E.D.G.E.

•       Join the conversation on LinkedIn — share your thoughts and connect with other forward-thinking leaders.

•       Explore more insights on Sam’s website.

•       Check out Sam’s Two Free Substack newsletters: theuncertaintyedge.com and thegoodhumanpractice.com

In February 2011, Nokia’s newly appointed CEO Stephen Elop sent an internal memo to his employees that became one of the most widely read corporate documents of the decade. It was brutally honest.

“There is a burning platform,” he wrote. Nokia was standing on it. The smartphone revolution had already happened. Apple’s iPhone had been out for four years. Android was grabbing a growing market share. And Nokia’s own Symbian software was falling apart. Market share was collapsing. The memo laid out the crisis in clear, stark terms. No spin, no corporate euphemisms, no executive hedging.

It was a masterful piece of diagnosis.

And yet, Nokia still lost. Within two years, the handset division that had once commanded 40% of the global mobile phone market was sold to Microsoft for $7.2 billion, a fraction of what it was worth at its peak. The burning platform memo had correctly identified the fire; and yet, the organization still couldn’t avoid getting burnt.

The question that incident raises is not about Elop’s courage or Nokia’s execution. It is more fundamental than that: how can a leadership team see the problem clearly, name it publicly, and still fail to act effectively on it?

The answer has something to do with rivers.

The Danger of Familiar Water

Heraclitus, the ancient Greek philosopher, observed that you cannot step into the same river twice. The water is always different. The current has shifted. Even you have changed. This was not just mere poetry. It was an accurate description of reality; and one of the most consistently ignored insights in organizational leadership.

In a recent conversation on Trevor Noah’s podcast, geopolitical analyst Ian Bremmer made a related point that stuck with me (and Trevor Noah): before leaders think about strategy, they need to understand the situation as it actually exists today. Not as it existed in their last briefing. Not as it was when they built their mental model of the world. As it is now.

This sounds obvious. But it is rarely practised.

The mistake shows up in three layers, each compounding the one before it:

• We misread the other side — competitors, counterparts, clients, countries; because we are working from intelligence that has aged past its usefulness.

• We misread ourselves — our own capabilities, culture, and risk tolerance have shifted in ways we rarely acknowledge.

• We fail to account for the fact that both are changing simultaneously — which means the gap between our mental model and reality is not static. It widens.

The Elop memo is a near-perfect illustration of all three.

When Mental Models Outlive Reality

Nokia’s crisis was not a surprise. It had been visible for years to anyone willing to look. But looking requires a willingness to question what you think you already know about the competitive landscape. And about yourself.

Nokia’s engineers had actually developed touchscreen prototypes in the early 2000s, years before the iPhone launched. The technology existed inside the building. What didn’t exist was an organizational identity willing to act on it. Nokia saw itself as a hardware company. A phone company. Its mental model of itself, built on two decades of manufacturing dominance, had become more enduring than its ability to see what it needed to become.

The external river had moved. The competitive landscape of 2007 was not the competitive landscape of 1997. But Nokia’s internal model of that landscape was still running on older assumptions: that consumers cared primarily about handset durability, that carrier relationships were the primary competitive moat, that software was secondary to hardware. Those assumptions had been accurate. But they were no longer true.

Elop’s memo identified the external fire. What it couldn’t fully address was the internal one: that Nokia had also changed, in ways its own leadership hadn’t fully processed. The organizational capabilities, culture, and decision-making velocity that had made Nokia dominant were not the capabilities needed to compete in an ecosystem-driven, software-first, app-centric market. Knowing the building was burning did not automatically reveal what tools were available to fight the fire, or whether those tools still worked.

The most dangerous moment for any organization is not when it faces a new competitive threat. It is when its mental model of itself is more powerful than its ability to see what it has actually become.

Nokia is one version of this story. There are others.

The Geopolitical and Corporate Versions

For decades, much of Western foreign and trade policy toward China was calibrated to a specific mental model: China as a developing economy, a low-cost manufacturer, technologically dependent on the West. That model was not wrong. It was simply built on a China that existed in 1995, and it was still driving strategy and policy well into the 2020s.

Bremmer’s point — that leaders must understand the situation as it is, not as they remember it — applies here with full force. The China of today is the world’s dominant manufacturer of electric vehicles, a significant exporter of solar technology, a serious competitor in artificial intelligence and semiconductor development, and increasingly a source of technical knowledge that other countries desperately want access to. That is not the China that many Western policy frameworks were designed to engage.

The contrast is instructive. While some U.S. policy rhetoric remained anchored to older assumptions about Chinese technological dependency, European leaders — pragmatically, and in some cases controversially — were actively signing technology transfer agreements with Chinese EV manufacturers, pursuing green energy partnerships, and treating China as a sophisticated peer from whom real competitive advantage could be gained. Some European countries were crossing the new river; others in the West were still looking for the old one.

This is not a political observation. It is a diagnostic one. When your strategy is calibrated to a counterpart that no longer exists, the gap between your decisions and reality compounds with every quarter. Bremmer’s core insight applies directly to any boardroom, not just any foreign ministry: getting the situational read right is not the preamble to the strategy. It is the strategy’s most critical input. Skip it, and everything downstream is built on sand.

Similarly, Johnson & Johnson faced two defining corporate crises separated by decades. The comparison of these crises is useful; not because the situations were similar, but precisely because they were not.

In 1982, seven people in Chicago died after consuming cyanide-laced Tylenol capsules. CEO James Burke’s response was immediate and unambiguous: a nationwide recall of 31 million bottles, full transparency with regulators and the public, and the development of tamper-evident packaging that became an industry standard. It remains the benchmark case study in crisis leadership.

Decades later, J&J’s subsidiary played a documented role in the opioid epidemic. The company’s response bore no visible resemblance to the Tylenol standard: years of litigation, contested liability, and eventual settlement. And yet inside the organization, the Tylenol playbook remained the reference point. The company’s identity was bound to it.

Judging J&J by the Tylenol crisis standard was not the error. The error was assuming it directly translated.

The J&J that navigated the Tylenol crisis was a different organization operating in a different environment than the J&J that faced the opioid reckoning. The regulatory landscape had changed. The nature of the crisis — slow-building, commercially entangled, diffuse — bore no resemblance to the Tylenol shock event. The competitive and financial pressures on the company had shifted fundamentally. To apply a thirty-year-old playbook to a structurally different situation was to step into a river that looked familiar and find the current running the wrong way. To be clear, this is not to say that Burke’s adherence to J&J’s “Credo”, its mission was wrong, or that such fondational principles were not applicable 30 years later: the Credo establishes a specific hierarchy of responsibility: first to doctors, nurses, patients, and users of products; second to employees; third to communities; and finally to stockholders.

Past success is not a strategy. It is a data point and one that must be re-evaluated against a present that has moved.

The E.D.G.E. Applied: Navigating Fluid Terrain

The challenge Heraclitus identified is not philosophical. It is operational. Here is how the E.D.G.E. framework can help navigate this fluid terrain:

Establish

Before any strategic discussion, map what you actually know versus what you are assuming. The foundational step is not about building confidence; it is about building accuracy. Ask explicitly: What are our three most important assumptions about our competitive environment, our key counterparts, and our own organizational capabilities? When was each last tested with current evidence?

Nokia’s leadership team had data on touchscreen engagement years before the iPhone. That data existed inside the foundation. The failure was in the assumption layer sitting above it; the belief that Nokia’s identity as a hardware company was still the right frame for the market it was competing in.

Diagnose

Separate diagnosis from response, deliberately. Bremmer’s observation is a discipline, not just an insight. Before any high-stakes discussion moves to what should we do, the team must spend real time on what is actually true right now. No solutions in the room until the situational read is complete.

This requires resisting enormous organizational pressure. Action feels productive. Diagnosis feels slow. But a fast response to a misread situation is not speed; it is expensive misdirection.

Go

Act on current reality, not inherited mental models. The Go step is only as good as the diagnosis that precedes it. When J&J’s leadership reached for the Tylenol playbook, they were going fast, but in the wrong direction. When Nokia accelerated hardware production in 2009 and 2010, they were moving decisively — toward a market that was already shrinking.

The question before any significant decision is not are we moving? It is are we moving toward the world as it is, or the world as we remember it?

Evolve

Build the habit of deliberate model updates. This is not scenario planning. It is the more immediate discipline of regularly asking: In what ways has the situation changed since we last assessed this? In what ways have we changed? In what ways has the other party changed?

Nokia’s engineers knew the touchscreen world was coming. That knowledge needed to travel upward into the strategic model; and it needed to arrive before the burning platform memo made denial impossible. Intelligence that arrives after the diagnosis is too late to shape the decision.

The River Check

I have worked with leaders through enough inflection points: organizational crises, market disruptions, and strategic pivots. And I have learned that the ones who struggled most were rarely the ones who lacked intelligence or conviction. They were the ones executing strategies designed for a previous version of their environment, their competitors, and themselves.

The most important shift in those moments was not a new plan. It was the willingness to pause and ask: Is what I think I know still true?

That question, asked honestly and regularly, is the practical version of Heraclitus. Call it the River Check. Before any significant decision, ask your team three questions:

1. In what ways has the external situation changed since we last assessed it?

2. In what ways have our own capabilities and culture changed?

3. In what ways has the counterpart — the competitor, the client, the market — changed?

The answers will not always be comfortable. Nokia’s answers in 2005 would have been deeply uncomfortable. J&J’s answers in the early stages of the opioid crisis would have demanded a harder conversation than anyone wanted to have. But discomfort in the diagnosis is infinitely preferable to catastrophe in the execution.

The river is always moving. The question is whether you are moving with it — or still looking for the crossing you used last time.

© The Uncertainty E.D.G.E. | Published every other Tuesday

If this resonated, I’d love to have you as a free subscriber — and forward this to a leader who needs to see around corners.

The Uncertainty E.D.G.E. is for leaders who are accountable for outcomes they can’t fully control — and want a clearer way to think when certainty won’t come.

Essays and conversations on decision-making under pressure, every other Tuesday. Join me at theuncertaintyedge.com.

If the human side of leadership is what draws you, I also write The Good Human Practice — on inner clarity and character for leaders called on to make those hard decisions under pressure.

In 2002, with the dot-com wreckage still smouldering, Sun Microsystems CEO Scott McNealy was asked to make sense of the days when his own stock had traded at ten times revenue. The Sun share price had run from roughly $5 a share to $64 and back to single digits. He answered with arithmetic, not nostalgia. At ten times sales, he explained, to pay investors back he’d have to hand over every dollar of revenue as dividends for ten straight years: assuming zero cost of goods, zero expenses for 39,000 employees, zero taxes, zero research spending, and flat revenue the whole time. None of which is possible. Then he delivered the line that outlived the era: “What were you thinking?”

McNealy wasn’t defending his shareholders. He was gently mocking them and with the authority of a man who had watched the music stop. The math wasn’t sophisticated. It was the back-of-the-envelope check any careful buyer runs before paying a premium. The scandal was that at the peak, almost no investor ran it.

Here is the part that should concern operators, not just investors: exuberance of that magnitude never stays in the stock price. It leaks into the boardroom — changing what leaders build, what they buy, and how much they pay. The mispricing becomes a strategy.

When Exuberance Writes the Strategy

Every leader lives between hesitation and haste. Exuberance pushes hard toward haste; it makes waiting feel like losing and discipline feel like cowardice. McNealy’s question is a hesitation device disguised as a one-liner: it forces a pause long enough to ask what a price, a deal, or a capital plan actually implies about the future. Most leaders skip that pause, mistake the narrative for the numbers, and discover the difference only when the discount rate forces a recalculation. This issue is about installing the pause before the market installs it for you.

The dot-com bust is remembered as a stock-market event. It was also a graveyard of strategic decisions made by serious people at serious companies.

• AOL–Time Warner, January 2000. Time Warner, a profitable empire of cable, film, and publishing, merged with AOL in a deal valued at about $165 billion, paid largely in AOL’s inflated stock. Within two years the company wrote down roughly $99 billion; real cash flows swapped for a multiple only the narrative could justify.

• The telecom overbuild. WorldCom, Global Crossing, and others borrowed enormously to lay fibre against a forecast that internet traffic would double every hundred days. It didn’t. The capacity sat dark, the debt didn’t, and the bankruptcies followed in 2002; the companies that built on the story didn’t survive to collect.

• WeWork, two decades later. A real-estate company with a software company rating reached a private valuation near $47 billion, fuelled by a backer’s conviction and the founder’s habit of reframing losses as growth. The much-anticipated IPO collapsed in 2019 the moment outside investors ran McNealy’s arithmetic on the prospectus.

Each was a strategic decision. A merger, a build-out, an expansion. And each was priced off a story rather than a payback. Exuberance didn’t just inflate a ticker; it set the agenda for what the company did next.

Why Smart Leaders Question the Math

These weren’t reckless people. They were experienced operators who, inside the narrative, found it harder to do nothing than to act. Several forces conspire:

1. Action bias. Under uncertainty, doing something feels safer than standing still. A bold acquisition signals conviction; restraint looks like you missed the plot.

2. Narrative fallacy. “We are buying the future of media / connectivity / intelligence” is a story that recruits everyone — the board, the press, the analysts. Spreadsheets don’t recruit. Stories do.

3. Social proof and FOMO. When peers are paying the premium and being rewarded for it in the moment, the cost of dissent is immediate and the cost of conformity is deferred. Loss aversion flips: the feared loss becomes “being left behind”, not “overpaying”.

4. Identity attachment. Leaders come to define themselves by the bet. Admitting the price was wrong means admitting they were wrong; so they double down.

McNealy’s point was never that his shareholders were stupid; it was that the supply of buyers willing to skip the arithmetic eventually runs dry, and the people running the companies were riding the same wave.

Here is why this matters today. The arithmetic is back, and its speed of return is its own warning. As of late October 2025, asset manager GMO calculated that companies trading above ten times sales made up more than 30 per cent of U.S. market capitalization. By June 2026, commentary citing Refinitiv data put the figure for the S&P 500 near 51 per cent. At the dot-com peak, roughly 29 companies in the index crossed that line, and it was read as collective insanity. Today, almost 40 companies meet that same criteria. Different decade. Same math — with Shiller’s cyclically adjusted P/E near 40 against a long-run average around 17, and the top 20 names now commanding more than half the index.

For operators, the relevant signal isn’t the multiple; it’s the spillover into strategic decisions across adjacent industries. The dot-com bust was largely confined to internet stocks; this time the premium has bled into the infrastructure beneath the story. Power producers like Vistra and Constellation, recently valued like the sleepy utilities they are, now trade as AI proxies. When a nuclear operator is priced like a semiconductor company, executives in power, real estate, and components start committing capital on the assumption the story holds. And that assumption deserves the McNealy treatment: Big Tech is on track to spend nearly $660 billion on AI this year, consulting company Bain estimates the industry needs roughly $2 trillion in annual revenue by 2030 to justify the build-out, and a widely cited MIT study found 95 per cent of companies have seen no measurable return from generative AI so far.

None of this proves AI doesn’t matter; the closest example may be the telecom and railroad build-outs, where the infrastructure eventually paid off. Bear in mind that the executives and investors who backed the infrastructure build usually lost everything. The operator’s question is whether the cash flows can grow into the prices and capital plans already committed. On Friday, June 5, the Nasdaq fell more than four per cent, its worst day since October, as long-term Treasury yields pushed back above five per cent and about a trillion dollars evaporated in days. The discount rate is the thing that ends these episodes, and it is moving.

The E.D.G.E. Framework: Running McNealy’s Math Before the Market Does

Establish: Separate the wave from the boat

You cannot control the market mania, the multiples, or what your competitors pay. You can control your own capital allocation, your balance-sheet exposure, and whether you let the narrative set your hurdle rate. Most leaders blur this line; they treat “the market expects it” as a fact about their own business.

Key question: Is this decision priced off my cash flows, or off someone else’s story about the future?

Diagnose: Do the arithmetic out loud

Run McNealy’s check on the bet in front of you. If the price implies a payback that requires zero competition, zero margin compression, and flawless execution for a decade, that’s not a forecast; it’s hope masquerading as strategy. Then separate real, paying demand from circular, vendor-financed demand: the 20 per cent that drives the outcome is the implied payback, not the slide that sells the vision.

Key question: If I handed this to a new CEO with no attachment to the story, what payback would they say this price assumes?

Go: Size the bet to survive being wrong

Purposeful action isn’t the same as standing still. Stage the commitment. Prefer reversible moves to irreversible ones. Keep enough balance-sheet slack that a rising cost of capital is an inconvenience, not an obituary. The leaders who survived the telecom build-out weren’t the ones who refused to invest — they were the ones who didn’t bet the company on demand that hadn’t arrived.

Key question: What’s the smallest version of this bet that captures the upside if the story is true; and survives if it isn’t?

Evolve: Decide your behavior in advance

The dot-com investors who blew up didn’t lack information; they lacked a pre-commitment. Decide now how you’ll act if interest rates keeps rising — which projects pause, which exposures you trim, what triggers a recalculation — rather than improvising mid-panic. Build it into governance: a standing review that asks, what reality are we avoiding because the story is still working?

Key question: Have we written down what we will do when the music slows? Or are we planning to improvise?

A Personal Note: The Deal That Didn’t Compute

Years ago, advising on a cross-border acquisition, I sat with a board that had fallen in love with a target. The logic was elegant, the management team charismatic, and a competitor was rumored to be circling; so the price kept climbing in the room before anyone had run a number.

I did something unwelcome. On a single page, I worked out what the offered multiple actually implied: the revenue growth, margins, and years of flawless integration the price was quietly assuming. Laid out plainly, the deal required the target to perform better than it ever had, every year, against a market getting more competitive, not less. It only worked if the story was true and stayed true.

We didn’t walk away; but we did restructure the offer so the upside was real and the downside survivable. We lost the bid. The competitor paid full freight, took the trophy, and spent years writing it down. The discipline wasn’t refusing to act. It was refusing to let the room’s excitement set the hurdle rate.

Ask the McNealy Question

Take the most exciting decision on your desk — the acquisition, the build-out, the expansion everyone agrees you “have to” make.

Step 1: Write the price or commitment at the top of a single page.

Step 2: Ask McNealy’s question: what would this have to return, every year, for the price to make sense? Write the implied payback explicitly.

Step 3: List what the story assumes — demand, competition, margins, timing — and mark which assumptions are facts and which are hopes.

Step 4: Pressure-test the demand. Is it real and paying, or circular, subsidized, or borrowed from a narrative you don’t control?

Step 5: Decide your trigger in advance; the condition under which you pause, restructure, or walk.

Sun’s old slogan was “We put the dot in dot com.” Its former headquarters now belongs to Meta. The buildings outlasted the irrational exuberance; and so does the question every leader should ask before the discount rate asks it for them:

What were you thinking?

© The Uncertainty E.D.G.E. | Published every other Tuesday

If this resonated, I’d love to have you as a free subscriber — and forward this to a leader who needs to see around corners.

The Uncertainty E.D.G.E. is for leaders who are accountable for outcomes they can’t fully control — and want a clearer way to think when certainty won’t come.

Essays and conversations on decision-making under pressure, every other Tuesday. Join me at theuncertaintyedge.com.

If the human side of leadership is what draws you, I also write The Good Human Practice — on inner clarity and character for leaders called on to make those hard decisions under pressure.

In this episode of The Uncertainty E.D.G.E., host Sam Sivarajan sits down with Eric Brotman, CFP, founder and outgoing CEO of BFG Financial Advisors, for a candid conversation on succession planning, internal equity, and what it means to “graduate” from your career rather than retire.

After more than 30 years building a thriving wealth management practice, Eric is executing a carefully planned internal succession—stepping into a chairman and growth role while his team takes over. He shares lessons from three acquisitions, the shift from entrepreneur to sage, and why advisors without a succession plan risk failing their clients, staff, and themselves.

Whether you’re planning your exit, developing future leaders, or guiding clients through transitions, this episode delivers practical, actionable insights.

Key Quote

“Don’t retire. Graduate to the next level.” — Eric Brotman

Key Takeaways

🔹 Start early — Waiting limits your options and increases risk for clients and staff
🔹 Sell equity internally — Builds alignment, accountability, and a stronger culture
🔹 Adopt a “graduation” mindset — Your next chapter is growth, not retreat
🔹 Build a real business — Without systems and succession, there’s nothing to sell
🔹 Shift to wisdom — Your experience becomes your greatest value

Sound Bites

• “Don’t retire. Graduate to the next level.”
• “Equity is never given. It’s always sold or bought.”
• “A lifestyle practice is not a business.”
• “Retirement shouldn’t be the absence of work—it should be the absence of needing to work.”
• “I went from trying to be indispensable to making myself replaceable.”

Topics Discussed

• 00:01 — Eric’s 30-year journey and CEO transition
• 07:12 — Why he chose internal succession over private equity
• 17:29 — Managing risk: key person dependency and M&A lessons
• 27:48 — From intelligence to wisdom
• 43:01 — Redefining retirement

Resources Mentioned

🔗 Eric Brotman Consulting: https://BrotmanConsultingGroup.com
🔗 BFG Financial Advisors: https://BFGFA.com

Stay Connected with The Uncertainty E.D.G.E.

🔗 LinkedIn: Join the conversation and connect with forward-thinking leaders
🔗 Website: Explore more insights from Sam
🔗 Sam's Two Substack Newsletters:
• https://theuncertaintyedge.com
• https://thegoodhumanpractice.com

This article is arriving off my usual second-Tuesday schedule. With SpaceX listing next week — and OpenAI and Anthropic in the coming months — it couldn't wait.

Later this year, investors will be checking their portfolios, looking for familiar blue chips like Apple, Microsoft, Nvidia. These portfolios may look unchanged, but by year-end they could hold significant positions in three large, mostly unprofitable, recently listed companies. Not because their fund manager chose them, but because a market index did.

To much anticipation, and more than a little bit of hype, SpaceX, OpenAI and Anthropic are each targeting initial public offerings (IPOs), collectively valuing these companies at around three trillion dollars. Michael Burry (of Big Short fame) reportedly compared the coming three IPOs to the 2000 dot-com boom, arguing they could raise as much capital as roughly 300 internet and TMT IPOs did in 2000, after adjusting for inflation, and more than the entire U.S. IPO market raised in the decade from 2016 to 2025. These are the priciest IPOs for companies with no current profitability.

But the structural impact on investors is the major story behind these IPOs. Different index providers are simultaneously rewriting the rules to allow new issues to be included in the index at earlier stages and with less profitability. In 2026, three major index providers — S&P Dow Jones, Nasdaq, and FTSE Russell — introduced rule changes that fast-track newly listed companies into the index and waive or reduce profitability screens for megacap IPOs. S&P Dow Jones proposes to halve the IPO seasoning period from 12 months to 6 months and waive profitability requirements for megacap companies; Nasdaq introduced a “Fast Entry” rule effective May 1, 2026, allowing companies in the top 40 of Nasdaq-100 constituents (~$100B+) to enter the index in as little as 15 trading days after IPO; and FTSE Russell implemented a fast-entry rule on May 26, 2026, allowing eligible mega-IPOs to enter Russell US Indexes after just 5 trading days post-listing. This is massively significant. Passive funds now hold over 50% of US equity assets under management, with indexed mutual funds and ETFs accounting for roughly $13–15.4 trillion of US equity passive assets.

SpaceX plans to list in mid-June, targeting a $1.75 trillion valuation and aiming to raise over $75 billion in equity; more than double the capital raised in the ~$30 billion Saudi Aramco IPO, the previous largest raise, in 2019. SpaceX reported almost $5 billion losses on about $19 billion of revenues in 2025. While the Starlink business is profitable, the consolidated SpaceX business is not. Morningstar reportedly values the company at about $780 billion, less than half the target valuation.

OpenAI is preparing for a September listing at a trillion dollar plus valuation after the latest $850 billion valuation funding round. The company still reported losses of $9 billion in 2025, with an expected loss of $14 billion in 2026 and profitability not expected before 2030.

Anthropic is targeting an October IPO at an estimated trillion-plus dollar valuation. Their revenue is surging with an annualized run rate of $47 billion and their first profitable quarter expected in June 2026.

Investors should bear in mind that these IPO valuations are priced for perfection; everything has to go according to market expectations. The risks are not hypothetical. When SpaceX was valued at $12 billion in 2016, a launchpad explosion that grounded its fleet for four months was a serious but contained setback. Now, at a valuation of $1.75 trillion, a comparable failure would reverberate far beyond the company itself. Blue Origin’s second major launchpad explosion in two months demonstrated as much in May 2026, when the blast erased value across the entire space sector. The same logic applies to OpenAI and Anthropic: they don’t need to fail to disappoint investors, they only need to grow more slowly than the expectations baked into their valuations.

During the dot-com boom, for example, Cisco briefly became the world’s most valuable company, trading at valuations that implied astronomical growth expectations. Cisco then lost most of its value when the bubble burst, and its stock did not trade back above its March 2000 peak until December 2025. Patient investors had to wait 25 years to recover their investment.

This has implications even for passive investors. Index funds have to rebalance their fund investments regularly, reducing existing holdings to replicate the updated index. A retiree’s portfolio, already holding Apple and Microsoft, will quietly get re-weighted toward three unprofitable companies priced at peak valuations. They didn’t choose this. The index chose it for them.

We have seen this before. When Tesla entered the S&P 500 in December 2020, index funds had to buy approximately $80–85 billion worth of Tesla stock in a single rebalancing event — the largest in S&P history, surpassing the prior record of $50.8 billion. Now multiply that by three companies, entering at potentially higher valuations, within a compressed window of months. SpaceX alone at $1.75 trillion would require index funds to absorb a position substantially larger than the Tesla rebalancing.

None of this is an argument against passive investing. It is pointing out that the infrastructure passive investors rely on, the rules governing what goes into an index and when, is being quietly reshaped to accommodate specific companies at specific valuations. And the people who bear the risk of these changes are the same people who were told the whole point of passive investing was that nobody was making subjective and risky decisions for them.

What should a thoughtful investor be watching? Start with your own investment. Most passive investors don’t know the inclusion criteria for the index their retirement is pegged to; and when those criteria change, as they are changing now, the composition of your portfolio changes without your input. One option is to choose equal-weighted versus market-capitalization-weighted ETFs. The latter’s holdings are skewed towards the largest companies in the index; the former isn’t. Second, watch for concentration risk: three mega-IPOs entering major indices within months of each other, at combined valuations of about $3 trillion, will mechanically increase exposure to a handful of unprofitable names, and “diversified” may not mean what it did six months ago. Finally, understand the asymmetry. These stocks are priced for perfection; the upside is already baked into the price, while the downside, whether a technical failure, a revenue miss, or a regulatory shift, is not. And when passive funds cannot sell without deviating from their mandate, any repricing affects everyone in the index, not just the company that stumbled.

The four most expensive words in history are “this time is different.” Investors would do well to remember that these words have proven false every time before.

There is a moment most leaders know well, though few describe it openly.

It’s the moment when you are confronted with a problem so large, so complex, or so unfamiliar that your first instinct is not action but paralysis. You look at the numbers, the market shift, the strategic threat, the organizational dysfunction, and your mind screams: I don’t even know where to begin.

In those moments, intelligence can become a liability. The more variables you see, the more outcomes you imagine. You run the scenarios forward in your head and don’t like where they end. Before you have taken a single step, you are already attached to the outcome—and overwhelmed by it.

I learned this lesson in a very personal way many years ago.

When I was in my second year studying economics, I had to sit for an advanced mathematical economics exam. I remember walking into the exam room reasonably prepared, or so I thought. But when the paper was handed out and I flipped through the questions, something happened. I realized—at least in that first glance—that I couldn’t answer a single question.

Not one.

The questions looked foreign. Complex. Layered. I felt that unmistakable surge of panic. My internal narrative shifted quickly from “I’m prepared” to “I’m going to fail”.

For a few minutes, I did what most people do in that situation: I froze. I stared at the paper and imagined the outcome. I could see the poor grade. I could see the consequences. I could feel the implications.

Then, fortunately, something shifted.

Instead of trying to solve the entire problem I decided to write down what I did know. For each question, I began noting the relevant theory, the background concepts, the likely formulas that might apply. I wrote down definitions. I sketched out relationships between variables. I didn’t yet have the answer, but I had fragments.

Slowly, as I did this, the next step came to me. Then another. A piece of algebra here. A substitution there. An insight about how two variables interacted.

It wasn’t dramatic. It was incremental.

But in that incremental way, I worked through each question. And when the results came back, I had done surprisingly well—far better than I believed possible in those first few panicked minutes.

That exam became a powerful leadership lesson for me. When you don’t know how to solve the whole problem, return to fundamentals. Start with what you know. Clarify what you don’t. Then take the next logical step.

Pretty soon, you will find your way.

The Executive Version of Exam Panic

In business, the equivalent of that exam moment happens more often than we like to admit.

A quarterly miss shakes investor confidence. A competitor launches a disruptive product. A regulatory shift threatens margins. A key team member resigns unexpectedly. Technology changes faster than your systems can handle.

You look at the landscape and think: This is too big.

I was reminded of this recently in a conversation with a senior executive whose company had just missed projections significantly. When I asked what had happened, he didn’t blame the environment. Instead, he said, “We didn’t want to stress our employees or hold them accountable for meeting agreed targets. We thought being employee-first meant avoiding tough conversations.”

He then reflected on something deceptively simple: “Remember BEDMAS from school? The order of operations for solving math problems? You can’t do addition before multiplication and expect the right answer. In business, we tried to focus on the feel-good additions before ensuring the fundamentals were solid. It ended up catching up with us.”

His comment speaks to sequence, yes. But it also speaks to something deeper. When we are overwhelmed, we often reach for what feels more comfortable rather than what is foundational.

In this executive’s case, difficult accountability conversations felt uncomfortable. So, they were deferred. Cultural emphasis replaced operational rigor. But avoiding the first hard step didn’t remove the pressure; it merely postponed it, and in doing so, magnified it.

This is what overwhelm does. It distorts priorities.

Why Overwhelm Leads to Inaction

When leaders feel overwhelmed, three patterns typically emerge.

First, they fixate on the outcome. They imagine the worst-case scenario and allow it to dominate their thinking. Like my initial reaction in that exam hall, they see failure before they see process.

Second, they assume the solution must be equally large. If the problem is enterprise-wide, the solution must be a sweeping transformation. If the market threat is existential, the response must be revolutionary.

Third, they delay starting because they cannot see the full path to the solution.

But complex systems, whether mathematical models or large organizations, rarely yield to sweeping gestures. They respond to disciplined and consistent engagement with fundamentals.

The law of small wins operates here with quiet power. When you break a large, intimidating challenge into smaller, manageable actions, you reduce cognitive load.

You create traction. And perhaps most importantly, you rebuild belief.

You galvanize resources.

You see possibilities.

Progress generates confidence. Confidence fuels further action. Action compounds.

Back to Fundamentals: A Practical Framework

So, what does “going back to fundamentals” look like in practice for executives?

It begins with intellectual honesty.

In that exam hall, my first honest admission was this: I do not currently know how to solve this entire question. My second was more empowering: I do know the underlying principles.

In business, the equivalent might sound like this:

• We cannot fix the entire organization this quarter.

• We cannot outspend our competitors overnight.

• We cannot eliminate uncertainty.

But:

• We can understand our unit economics clearly.

• We can identify which clients, products, or divisions are profitable.

• We can clarify decision rights and accountability.

• We can address one broken process.

• We can have one overdue performance conversation.

The fundamentals are rarely glamorous. They are often operational, sometimes tedious, occasionally uncomfortable. But they are controllable.

This is where the BEDMAS metaphor from grade school remains useful—not as a rigid formula, but as a reminder. In arithmetic, brackets define the structure of the problem. In business, core operations and economic reality define the structure. Exponents multiply impact; in business, that is your competitive advantage. Division and multiplication reflect resource allocation. Addition and subtraction resemble cultural programs, benefits, and initiatives.

If you attempt to add before you stabilize the brackets, the equation fails.

But here is the key: you don’t need to fix the entire bracketed expression at once. You simplify it step by step.

The Power of Writing It Down

One subtle but important aspect of my exam experience was physical engagement. I stopped staring at the problem and started writing.

There is something powerful about moving from mental rumination to tangible action. Writing down what I knew transformed the problem from an abstract threat into a structured challenge.

Executives can apply the same discipline.

When facing a daunting strategic issue, instead of debating endlessly at a high level, write down:

• What do we know for certain?

• What assumptions are we making?

• What data do we lack?

• What are the core variables driving this outcome?

• Which of those variables can we influence directly?

This exercise alone often reduces anxiety. It turns an emotional reaction into analytical engagement.

From there, the next step often becomes visible.

Small Wins as Antidote to Organizational Paralysis

In organizations, overwhelm spreads quickly. If the senior team feels stuck, that feeling cascades downward. Meetings become circular. Initiatives stall. Energy dissipates.

Small wins counteract this.

They are not cosmetic victories or public relations exercises. They are real, measurable improvements in areas that matter. A tightened forecasting process. A clarified pricing model. A renegotiated supplier contract. A focused product redesign. A streamlined approval chain.

Each small win sends a signal: We can move this.

That signal changes behavior.

Teams that believe progress is possible behave differently from teams that believe decline is inevitable. The former experiment, engage, and contribute ideas. The latter protect themselves.

Momentum, once established, becomes self-reinforcing.

Compassion and Accountability Are Not Opposites

One of the more subtle lessons in all of this is that compassion and accountability are not opposing forces. In fact, they depend on each other.

Leaders who avoid difficult conversations in the name of empathy often discover that the long-term result is greater pain—missed targets, restructuring, layoffs, reputational damage.

Conversely, leaders who ground their organizations in clear expectations, disciplined operations, and transparent metrics create a form of stability that reduces anxiety. People understand what is required. They see the connection between effort and outcome. They trust the system.

This is what sustainable leadership looks like: not harshness, not softness, but sequencing. Fundamentals first. Then amplification.

The Question That Changes Everything

If you find yourself—or your organization—overwhelmed by the scale of the challenge ahead, resist the temptation to search for a sweeping solution.

Instead, ask a simpler question:

What do we know?
What is fundamental here?
What is the next logical step?

Not the final answer. Not the five-year vision. The next step.

In that exam hall years ago, I was convinced I was going to fail because I could not see the end. The turning point came when I stopped trying to see the end and focused on the first line of work in front of me.

Leadership is not so different.

The world is complex. Markets are volatile. Organizations are imperfect. You will not always see the full path forward. I was reminded of this basic fact of life years ago when I made my summit attempt on Kilimanjaro. After four days of climbing, we started our summit push at midnight aiming to be on the top for sunrise. In the pitch dark, you could not see the summit; you only saw 5 feet ahead, in the glow from your headlamps. But every step you took, more of the path was revealed. In this way, we made the summit without ever seeing the full path.

The same applies in life and in business. If you focus on fundamentals—if you start with what you know, clarify what you don’t, and take the next disciplined step—progress often reveals itself.

And once progress begins, overwhelm recedes.

Pretty soon, you will find your way.

© The Uncertainty E.D.G.E. | Published every other Tuesday

If this resonated, I’d love to have you as a free subscriber — and forward this to a leader who needs to see around corners.

The Uncertainty E.D.G.E. is for leaders who are accountable for outcomes they can’t fully control — and want a clearer way to think when certainty won’t come.

Essays and conversations on decision-making under pressure, every other Tuesday. Join me at theuncertaintyedge.com.

If the human side of leadership is what draws you, I also write The Good Human Practice — on inner clarity and character for leaders called on to make those hard decisions under pressure.

In this episode of The Uncertainty Edge, host Sam Sivarajan sits down with Herman Chan, co-founder and president of Crimson Financial, an independent wealth management firm built for medical professionals and real estate investors. After 18 years in corporate financial services leadership, Herman made a calculated leap into entrepreneurship — navigating regulatory complexity, client acquisition, and the risk of building a planning-first business in a crowded market.

Herman shares the personal and professional turning points that led to his decision, the frameworks he used to evaluate the risk, how he's measuring early traction, and the strategic partnerships he's building to scale. Whether you're an advisor eyeing independence or a leader weighing a major pivot, this episode delivers sharp, practical insights on moving forward with clarity.

Key Quote

"There is no perfect time. But with the right preparation, you can make any time the right time." — Herman Chan

Key Takeaways

• Big leaps require long runways — Herman spent 12+ months preparing before resigning.

• A planning-first model is a competitive differentiator in a product-saturated market.

• Controlling the controllables — focus on activity and process, not just outcomes.

• Strategic partnerships amplify reach — advising is a team sport, not a solo act.

• Understanding clients' emotional relationship with money is the foundation of lasting trust.
  

Sound Bites

• "Advising is a team sport."

• "Most Canadians think they have a financial plan — they actually have disconnected products."

• "I want to be their financial family doctor — that close and trusted guide."

• "Activity is something I learned a long time ago. Focus on that and the results follow."

• "I'm just getting started — and it's a big blue ocean."
  

Topics Discussed

• 00:02 — Introduction: Herman's leap from corporate leadership to independent advisory

• 02:32 — The decision framework: What finally made the jump feel right

• 09:26 — Planning-first model: Why most Canadians are underserved

• 11:48 — Early metrics: What Herman is tracking to measure traction

• 21:48 — Pre-mortem: Planning for failure scenarios before they happen
  

Resources Mentioned

• Learn more about Herman Chan and Crimson Financial: www.crimsonfi.com

• Financial Planning Association of Canada (FPAC): www.fpac.ca
  

Stay Connected with The Uncertainty Edge

• Subscribe on your favourite podcast platform to never miss an episode.

• Join the conversation on LinkedIn — connect with Sam Sivarajan: https://www.linkedin.com/in/samsivarajan/

• Explore more insights at: https://samsivarajan.com/

There is a kind of professional discipline that leaders have been trained to perform. You walk into the building and become a slightly different person — one who speaks in the language of margins and markets, who sets aside the parent lying awake wondering what world their child will inherit, who sets aside the citizen unsettled by what they read at breakfast. You compartmentalize. You focus. You call it professionalism.

It may be one of the most expensive habits in modern leadership.

The Macro Is Inside the Building

Ask most senior executives what keeps them up at night and they describe a micro world: tariff exposure, talent scarcity, competitive disruption, the next board presentation. These pressures are real. But for most businesses, they are not the primary threat on the horizon. The primary threats are macro — systemic, structural, and already in motion.

We are living through what complexity theorists call a polycrisis: a cluster of interconnected global failures in which the interaction of crises produces harms greater than the sum of their parts. Climate change. Rising inequality. The erosion of the institutional frameworks that underpin commercial life. The standard executive response is to treat these as someone else’s problem to deal with — the domain of governments, regulators, and NGOs, while the real work of running a business gets their focus.

Rebecca Henderson argues that this response is not just ethically inadequate, it is strategically incoherent. Henderson is not some community advocate railing from outside against the system — she is the John and Natty McArthur University Professor at Harvard, holding a joint appointment across General Management and Strategy at Harvard Business School, with twenty-one years previously at MIT’s Sloan School. A mechanical engineer by training, with a doctorate in business economics, and a former management consultant to legacy companies in transformation, her book Reimagining Capitalism in a World on Fire was named a Financial Times Best Business Book of the Year. When she makes the business case for engaging with systemic risk, she is speaking from capitalism’s intellectual centre, not its periphery.

Her argument is clear: companies have spent decades externalizing their costs onto shared systems — the atmosphere, public health infrastructure, the labour market — and those systems are now degrading in ways that are eliminating the conditions on which future profit depends. You cannot extract indefinitely from a limited and shared resource you depend on. The macro is not outside the building. It is the very foundation the building sits on.

The Cost of Climate in Black & White

Abstract warnings about long-term risk tend to dissipate in the presence of quarterly targets. So let’s be specific.

The Potsdam Institute for Climate Impact Research, in a study published in Nature in 2024, estimated that climate change will cost the global economy approximately $38 trillion per year by mid-century — roughly 17 percent of projected global GDP — even if every emissions commitment already made is honored. Harvard economists Adrien Bilal and Diego Känzig found that every additional 1°C of global warming reduces world GDP by more than 20 percent in the long run — equivalent to the Great Depression experienced permanently rather than temporarily. Swiss Re, whose entire business depends on accurate risk modeling, estimated losses of approximately $23 trillion in annual GDP by 2050 under current trajectories.

For decades, the standard rebuttal was reassuring: technology will solve it. Efficiency gains, renewable energy, carbon capture — the ingenuity of the market would find a path. That argument gets harder and harder to sustain. Global data centers consumed approximately 415 terawatt-hours of electricity in 2024 — equivalent to the annual demand of Pakistan — and that figure is projected to more than double by 2030. A typical AI data centre uses as much electricity as 100,000 households. Google’s 2023 greenhouse gas emissions were 48 percent higher than in 2019, driven primarily by data centre expansion.

The hardware itself compounds the problem. Manufacturing the GPUs on which AI runs requires extraction of rare earth minerals — cobalt, lithium, nickel — in processes that are energy-intensive, frequently toxic, and conducted largely in communities with the least political power to resist.

The technology that was supposed to solve our climate problem is running on the same energy infrastructure it was supposed to replace.

Inequality: The Market You Are Gradually Shrinking

The second thread of the polycrisis shows up on both sides of the ledger simultaneously — in the workforce and in the demand picture.

On the workforce side, financial insecurity does not stay at home when employees arrive at work. Research by Sendhil Mullainathan and Eldar Shafir found that financial stress imposes a persistent cognitive burden equivalent to losing a full night’s sleep. Workers who cannot afford healthcare, who carry the burden of economic precariousness, do not perform as well, engage as deeply, or stay as long. A business that minimizes wage costs while maximizing the financial anxiety of its frontline workforce pays for that decision in turnover, productivity, and engagement — in ways that never appear on the P&L.

On the demand side, extreme inequality contracts the market. This is the calculation Henry Ford made in 1914 when he paid his assembly workers wages of five dollars a day, double the going rate — not out of charity, but because he wanted his workers to afford the cars they were building. In January 2015, Mark Bertolini made a structurally identical decision at Aetna, raising the company’s minimum wage from $12 to $16 an hour for approximately 5,700 employees. When he examined data on his frontline staff, he found many were enrolled in Medicaid and relying on food stamps. “Here we are a Fortune 50 company,” he told CNBC, “and we’re about to put these people into poverty.” He estimated the $26 million yearly cost could generate approximately $120 million annually in reduced turnover and retraining costs. During his tenure, Aetna’s stock moved from under $30 to over $200 a share.

The numbers tell the story plainly. The top 10 percent of earners now account for just over 49 percent of US consumer spending, up from 43 percent in 2020. Everyone else — roughly three-quarters of the population — faces stagnant wages, depleted savings, and costs for housing, food, and energy that have risen faster than income for the better part of the past five years. The K-shaped recovery since Covid has not corrected. It has calcified.

For most businesses, this is not background noise. It is a structural contraction of the addressable market. Unless your business model is explicitly designed to serve the ultra-wealthy — Bugatti supercars, Hermès handbags, private aviation — the shrinking of the middle is shrinking your customer base. And even the luxury end offers no safe harbor. The global luxury consumer base fell from about 400 million in 2022 to about 330 million in 2025, a decline of roughly 70 million people, with the contraction concentrated among aspirational buyers. The business leader who reads rising inequality as a social problem rather than a commercial one is misreading their own financial statements.

Here too, technology has not been the equalizer it was promised to be. Venture capital leaders donated more than $283 million during the 2024 US election cycle — three times the 2020 figure — directed largely toward deregulation, tax preferences, and antitrust protection. Tech companies are lobbying to block state-level AI regulation, secure access to public data, and obtain tax breaks unavailable to ordinary businesses. The technology revolution has concentrated unprecedented wealth in an extraordinarily small number of hands, and those hands are actively working the political system to keep it that way.

The Invisible Infrastructure You Are Standing On

What makes it rational to sign a contract and expect it to be enforced is not the virtue of the counterparty — it is a legal system with the independence to compel performance. What makes it rational to invest abroad is not goodwill — it is property rights, arbitration frameworks, and legal predictability that rest on precedent rather than the preferences of whoever holds power. The World Justice Project’s Rule of Law Index shows a global pattern of institutional deterioration for the eighth consecutive year, with 68 percent of countries declining in 2025. Atlantic Council research finds the rule of law to be the single strongest correlate of economic growth across 164 countries over thirty years of data.

And once again, technology is not a bystander. It is an active participant in the erosion. The mergers that have consolidated media, communications, and platform power have been waved through by regulatory bodies whose independence has been progressively weakened — in some cases, by the very companies seeking approval. The TikTok divestment saga illustrated how the line between regulatory oversight and political negotiation has become nearly invisible. Tech companies have mounted extensive lobbying campaigns aimed at shaping or weakening enforcement of the EU’s Digital Services Act, while critics argue that large platforms increasingly deploy antitrust rhetoric strategically to preserve dominant market positions rather than expand competition.

A company that treats institutional erosion as someone else’s concern is quietly deteriorating the very ground it stands on. When institutions weaken, the costs show up in risk premiums, shortened planning horizons, and the growing advantage of buying political access over building competitive merit.

The Divided Self — And What It Costs

None of this explains why capable, intelligent leaders continue to treat these systemic risks as external to their strategic responsibility. The data is not ambiguous. The business case, as Henderson has documented extensively, is strong and there to be made.

What is missing is permission.

For nearly half a century, the dominant ideology of corporate life has been supplied by a single essay. In September 1970, Milton Friedman published a piece in the New York Times Magazine under the title “The Social Responsibility of Business Is to Increase Its Profits”. His argument was unambiguous: a corporate executive is an agent of the shareholders who employ them, and any deviation from the goal of maximizing their returns — spending on social causes, reducing pollution beyond legal requirements, paying workers more than the market demands — is a form of theft. The executive who acts on broader social obligations, Friedman wrote, is “in effect imposing taxes” on shareholders without their consent, undermining the foundations of a free society. Bringing personal values into professional decisions was not, in his framing, an act of integrity. It was an act of subversion.

The essay gave generations of business school graduates moral cover for a narrowed self. If the only legitimate goal was shareholder value, then the parent, the citizen, and the person with a conscience about the future were not just irrelevant to business decisions — they were actively dangerous. To act on personal values was to betray your fiduciary duty. The result was exactly the professional persona the doctrine was designed to produce: strategic, analytical, competitive — and with the rest of the person’s self left at the door.

What is less often noted is how selective that reading of Friedman actually was. Even he acknowledged that profit maximization must occur within “the rules of the game, which is to say, engage[s] in open and free competition without deception or fraud”. He was not, by his own account, licensing the externalization of costs onto the public, or the political capture of the regulatory systems that set the rules. The Friedman doctrine that corporate culture absorbed was a misreading even of Friedman. And it was a truncation that conveniently served those who stood to benefit most from it.

The philosopher Jean-Paul Sartre had a name for what Friedman’s doctrine produced. He called it bad faith — the condition of playing a role so completely that you deny having any other self. His illustration was the waiter who performs being a waiter with such precision that he ceases, in his own mind, to be a person who also happens to wait tables. The leader who says “my only obligation is shareholder value” and uses that to soothe their conscience, calm their parental anxiety, dismiss their citizen’s unease, is doing the same thing. It is not rigor. It is self-deception with a legal citation attached.

The parent who lies awake worrying about the world their child will inherit and the executive who decides on a share buyback to increase the stock price or price climate risk into capital allocation are the same person. When they are permitted to be the same person at the strategy table, better decisions follow.

The Permission That Was Always There

There is a point in Henderson’s argument that directly answers the fiduciary objection most commonly deployed against this kind of thinking.

When a publicly traded company receives a hostile takeover bid, its board frequently rejects the offer — even at a substantial premium to current share price — often because they believe the offer undervalues the long-term prospects of the company. This action is protected by the business judgment rule, which presumes that directors acting in good faith to protect the company’s long-term interests are not liable for outcomes that later prove unfortunate.

If a board can lawfully reject a premium acquisition to protect long-term value, on what logical basis can that same board dismiss the material risks posed by climate exposure, labor instability, or institutional erosion? These are financial risks — recognized as such by insurers, institutional investors, and central banks. The fiduciary argument against engaging with them has always been a narrow reading of what fiduciary duty actually requires. The permission is not missing from the law. It is missing from the culture.

What This Asks

The business rationale for engaging seriously with the polycrisis is the same rationale that informed Ford’s and Bertolini’s wage decisions and every board that has sacrificed short-term returns to protect long-term value. You cannot sustainably profit from a system you are helping, even inadvertently, to destabilize.

But it asks something more personal: that leaders stop maintaining the fiction that their professional and human selves do not know about each other. The deepest uncertainty leaders face today is systemic — and it cannot be navigated by a curated half-person. It requires the whole: someone who brings their full intelligence and full conscience to the decisions that matter most.

The leader who checks their whole self at the door is not being rigorous. They are being diminished. And the organizations they lead are diminished with them.

© The Uncertainty E.D.G.E. | Published every other Tuesday

If this resonated, I’d love to have you as a free subscriber — and forward this to a leader who needs to see around corners.

The Uncertainty E.D.G.E. is for leaders who are accountable for outcomes they can’t fully control — and want a clearer way to think when certainty won’t come.

Essays and conversations on decision-making under pressure, every other Tuesday. Join me at theuncertaintyedge.com.

If the human side of leadership is what draws you, I also write The Good Human Practice — on inner clarity and character for leaders called on to make those hard decisions under pressure.

In this episode of The Uncertainty Edge, host Sam Sivarajan sits down with George Pillari — healthcare executive, crisis management specialist, and author of The Seven Deadly Stupidities — for a candid conversation on leading through crisis.

George has spent decades navigating high-stakes turnarounds, managing billion-dollar stakeholder negotiations, and dissecting business failures through his blog, The Voice Behind the Cautionary. He shares hard-won lessons on transparency, the patterns behind high-profile failures, managing with incomplete information, and why job security is a myth every professional must confront.

Key Quote

“There is no job security. Zero.” — George Pillari

Key Takeaways

• Transparency is the #1 crisis tool — open communication with all stakeholders defuses tension faster than anything else.

• Lenders are partners, not adversaries. Treating them as allies changes the entire dynamic of a turnaround.

• Great crisis leaders listen more than they speak. They prompt questions, hear from everyone, and resist the urge to dictate.

• Job security is a myth. Spend an hour a week exploring the market — not to find a new job, but to stay sharp and aware.

• Second-order thinking is essential. Before acting, ask: what’s the worst that can happen, and can I live with it?

Sound Bites

• “There is no job security. Zero.”

• “You have to stay current.”

• “AI is going to change things.”

• “Incomplete information equals risk.”

• “You’ve got to make friends with the ‘bad guys’ — because there shouldn’t be any bad guys.”

Topics Discussed

• 00:00 — Introduction to Crisis Management and Leadership

• 01:33 — George Pillari’s Journey: From Healthcare Economics to Crisis Management

• 07:59 — The Importance of Transparency in High-Stakes Negotiations

• 26:47 — Navigating Accountability: Profiles of Greed and High-Profile Failures

• 39:18 — Job Security in an Uncertain World — and What to Do About It

Resources Mentioned

George Pillari’s Resources:

• Blog: thecautionary.com

• Email: gp@stupid.blog

• Book: The Seven Deadly Stupidities by George Pillari

• Referenced: The Secret Race by Tyler Hamilton

Stay Connected with The Uncertainty Edge

• Subscribe on your favourite podcast platform to never miss an episode.

• Join the conversation on LinkedIn — share your thoughts and connect with other forward-thinking leaders: linkedin.com/in/samsivarajan

• Explore more insights on Sam’s website: samsivarajan.com

In August 1949, in a remote canyon in Montana, called Mann Gulch, a team of firefighters led by Wagner Dodge faced imminent death. The wildfire they had parachuted in to battle was fanned by the hot, dry winds and quickly cut off their only escape route. His team panicked and desperately tried to outrace the approaching flames. This was an exercise in futility leading to most of them perishing in the blaze. It was, at its core, an attempt to solve the problem—put out the blaze, outrun the flames.

Dodge, on the other hand, quickly realized the problem was not solvable with the traditional means. He couldn’t outrun the flames, he couldn’t put out the fire. Instead, he did something radically different. He stopped running.

He set fire to the patch of grass in front of him and threw himself down into the ashes. The approaching wildfire scorched past him, leaving him untouched, because the small fire he had deliberately set removed any fuel for the wildfire. This escape fire, as it is known today, worked because it sought to tame the environment, not correct the problem.

That lesson has many applications today, but not many leaders have properly internalized it yet. For example, in April 2026 on Tesla’s quarterly earnings call, Elon Musk admitted that Hardware 3, the core operating system of Tesla cars, which had been touted as the foundation for autonomous self-driving (ASD) cars, wasn’t up to the task. For ten years, Musk had been proselytizing that ASD was just around the corner. Four million consumers took that message to heart and shelled out millions of dollars for Tesla automobiles. Only to have the rug pulled out from under them on that earnings call. What solace did Musk offer? He said that Hardware 4—the next iteration—could be the beginning of an answer and offered loyal Tesla owners a discount on the purchase of a car with Hardware 4. Shockingly, Tesla share prices went up 4 per cent on the news.

This is not about the irrationality of the stock market or consumers. It is about the challenges facing leaders—all leaders—when we assume that our problems can be solved with an algorithmic approach.

Risk is Not Uncertainty

This is where the distinction between risk and uncertainty is critical. Most people use the terms interchangeably. But they mean very different things. Economist Frank Knight, in 1921, was the first person to provide a useful distinction. Risk is when you don’t know the outcome, but you know the full distribution of outcomes. Think poker, blackjack, even chess. You might not know how the next hand will play out or what move your opponent will make, but the range of outcomes is fixed and the probability of each outcome can be accurately estimated. Uncertainty, on the other hand, is when you don’t know the outcomes and you can’t identify, let alone calculate, the odds. A useful analogy is to think of the roulette wheel. Risk is betting on where the roulette wheel stops. You don’t know where it stops, but you know everywhere it could stop and you can calculate the probabilities of each stop. Uncertainty is a player flipping the table over in the middle of a spin.

In his book How to Be Smart in a Smart World, the behavioral scientist Gerd Gigerenzer provides another useful characterization for this problem. He categorizes tame problems (with fixed rules, bounded possibility space—such as chess, GPS route mapping) versus wild problems (open-ended, unscripted, emergent—like driving through a crowded city, leading an organization through a crisis or managing a portfolio through a pandemic).

The error we keep making is that we treat wild problems as tame problems. That we use the same approach, the same models, the same solutions to solve both sets of problems. That was literally the challenge at Mann Gulch. Fighting wildfires can be a tame problem—indeed Dodge’s team had been able to use their playbook successfully in previous situations. But tame problems can quickly become wild when the circumstances change—as they did at Mann Gulch. A long, hot, dry summer, gusting winds, a valley that had only one entry and exit point. This is where trying to solve wild problems using a tame problem playbook can quite literally become deadly.

The Tram and The Car

So, what does risk and uncertainty (or tame vs wild problems) have to do with Elon Musk, Tesla or ASD? Everything.

Autonomous self-driving is a wild problem and Musk is trying to solve it like a tame problem. Driving in a busy city is a wild problem, not a tame problem. Think about the unexpected factors you have to navigate in your daily commute. A child suddenly darting in front of your car chasing a ball. A construction worker waving you through a red light. A car suddenly slamming its brakes in front of you. You have to decide whether to swerve into the next lane (possibly hitting someone else) or onto the sidewalk (possibly hitting a pedestrian) or keep going straight (definitely slamming into the car ahead). These are split second decisions where the roulette wheel has been overturned and you need to decide what to do.

Trying to solve these situations as a tame problem means that you invest in significant compute resources (faster, larger AI models) that can incorporate an increasing number of scenarios. The problem is that the permutation and combination of those scenarios is endless. And the more interactions there are (cars, pedestrians, traffic lights, construction, etc.), the more scenarios arise. No AI algorithm can be programmed for every possible scenario—because programming the AI beforehand requires us to imagine the scenario beforehand.

Gigerenzer’s broader point is that for some wild problems, the better solution is often to change the environment rather than rely only on more sophisticated prediction or control. He uses the rise of the motor car as an illustration: as cars entered public life, safety improved not by making drivers or vehicles infinitely smarter, but by redesigning the surroundings through road rules, lane markings, traffic signals, speed limits, and other infrastructure that made the system more predictable.

In a similar vein, Gigerenzer argues that the benefits of autonomous transport may come less from building an ever-smarter super-algorithm but more from taming the environment. One example is driverless rail or tram systems. Because they run on fixed tracks, with controlled crossings, clear right-of-way rules, and limited interaction with unpredictable traffic or pedestrians, the problem becomes much more structured and therefore easier for algorithms to handle reliably.

A great example is Waymo. Waymo’s genius was in recognizing that the goal was never to solve driving—it was to solve enough of driving to make deployment viable and safe. By pre-mapping every street in its operational zones in three-dimensional detail, by geofencing its vehicles within locales it genuinely understands, Waymo tamed its environment rather than trying to tame a wild problem. Not fully tame—the unexpected still happens, and that is precisely the point. But tame enough that, when the unexpected does appear, it stands out sharply against the known, stable model of what should be there, and the system can respond to it rather than be blindsided by it. Gigerenzer calls this ecological rationality—intelligence fitted to its environment, rather than intelligence pretending to float free of it. Waymo built an entire company on that principle. The fifteen million completed commercial rides are the proof of concept.

And yet, we are still buying the promise of that universal algorithmic solution. It is just over the horizon. That is why Tesla consumers and shareholders still keep buying, even though “just over the horizon” has been the mantra for ten years.

The Algorithm Keeps Getting Promised

The allure of the magical algorithm is enticing and, sadly, has bedevilled many industries for many years. Long-Term Capital Management, the hedge fund set up by Nobel Laureates, had to be bailed out in 1997 by the Fed. Their models said $50 million was the worst loss a single day could bring. Reality delivered $4.6 billion in losses in under four months—an outcome so far outside the model’s probability distribution that, as historian Niall Ferguson noted, it should not have happened in the entire lifetime of the universe. This was not a fat tail. It was proof that the tail and the model inhabited different realities entirely.

Similarly, in the aftermath of the 2008 financial crisis, Goldman Sachs’ CFO David Viniar famously remarked that they had witnessed “25 standard deviation moves, several days in a row.” Such events were statistically supposed to occur only once every 100,000 years; yet they happened repeatedly in a matter of days, delivering a humbling lesson about the limits of control.

Aviation offers a sharp parallel. Boeing’s MCAS system—software designed to automatically correct the 737 MAX’s handling characteristics—was modelled and stress-tested against known flight parameters. It was never designed for what actually happened: a faulty sensor triggering it repeatedly, overpowering pilots who had never been told the system existed. Lion Air Flight 610. Ethiopian Airlines Flight 302. 346 people dead. The model and the reality never inhabited the same world.

In 2006, six volunteers enrolled in a clinical trial for a new drug called TGN1412. The preclinical trials—which met every regulatory requirement—showed it was safe. Within 90 minutes of their first dose, all six were in intensive care with multiple-organ failures. The dose administered was 500 times smaller than the level found safe in animal studies. The models hadn’t failed through negligence. They simply could not see what they could not see: that the human immune system would respond in a way no animal study could ever have predicted. The wild problem had worn the costume of a tame one.

This is not to argue that the progress in large-language and other AI models are not genuine achievements. They are powerful tools. And they are well-suited to solve tame problems, like sales emails and chatbot interactions for standard customer service enquiries. The challenge is that these same tools are being touted as the answer to wild problems. And that’s not just going to hurt investors and consumers in the pocketbook, it is going to leave us wholly dependent and wholly flummoxed when we come face to face with wild problems. Exactly what happened to Wagner Dodge’s team at Mann Gulch.

What This Means for How You Lead

So, what does this mean for you as a leader? I see four direct, practical implications:

1. Be honest about which problem you are actually facing. Many organizations automatically treat all problems as tame problems. They create more detailed financial models, they brainstorm scenarios, they create more fancy PowerPoint decks to brief executives. These actions, while soothing in the moment, ignore the real underlying problem until it is too late.

2. When the problem is wild, look to tame the environment before you try to solve the problem. Can you constrain the decision space? Can you run a limited pilot before rolling out firm-wide? Create clearer rules of engagement that reduce the number of genuinely novel situations your employees face? Simplifying the operating environment is not the same as simplifying your thinking—it is often the most sophisticated move available.

3. Resist the algorithm when the environment is wild. This is not an argument against gathering more data or more analytical rigour. It is an argument for knowing where those tools genuinely help and where they create false confidence. A navigation system is enormously useful for route planning. It is not a substitute for judgment when road conditions are nothing like the map. You don’t drive faster in the middle of a blizzard because your GPS has mapped out the route.

4. Recognize that simple heuristics are not a failure of sophistication. Gigerenzer’s deeper point is that simple rules of thumb—ecological heuristics—often outperform complex optimization in genuinely uncertain environments. Not because they are smarter, but because they are more robust. They make fewer assumptions about a future they cannot know. David VanBenschoten ran the General Mills pension fund for 14 years without ever finishing higher than 27th percentile in annual returns—and ended up in the 4th percentile overall, outperforming 96% of his peers. His edge wasn’t brilliance or innovation; it was using simple rules for the disciplined avoidance of catastrophic loss while everyone else chased outliers.

The E.D.G.E. Applied

The difference between a good framework and a useful one is specificity. Here is how my E.D.G.E. framework applies to the only problem that actually matters: knowing what kind of problem you are dealing with before you try to solve it.

Establish. Draw the smallest honest circle around what you actually control—your process, your principles, your decision criteria. Not the market, not the competitor, not the technology curve. Tesla’s error wasn’t ambition. It was selling promises that lived outside that circle as though they were inside it.

Diagnose. Before reaching for a model, name the problem type. Risk—where historical data is a reasonable guide, or uncertainty—where the tail events are genuinely novel and no model built on the past can see them coming. LTCM, Boeing, TGN1412, Tesla. Different industries, same original sin: someone called a wild problem tame, and built a business on the misdiagnosis.

Go. In wild environments, move in small, reversible steps. Waymo maps a city, validates it, deploys commercially, then expands. VanBenschoten refused to lose badly and let compounding do the rest. The question isn’t how do I solve everything? It’s what is the bounded version of this problem I can actually deliver on? Find your track. Run on it.

Evolve. Wild problems don’t yield to a single solution—they yield to progressive understanding built through real engagement. Every Waymo ride is information. Be the organization that learns from what actually happens, not the one that defends what the model predicted.

The tram is still running. On its rails, on schedule, delivering real value within constraints it has never pretended don’t exist. The self-driving car is still working out what to do about the mattress on the highway.

The world doesn’t always reward the boldest promise. It rewards whoever figures out how to operate effectively inside the actual constraints of the actual problem. That is not a smaller ambition. That is the E.D.G.E.

© The Uncertainty E.D.G.E. | Published every other Tuesday

If this resonated, I’d love to have you as a free subscriber — and forward this to a leader who needs to see around corners.

The Uncertainty E.D.G.E. is for leaders who are accountable for outcomes they can’t fully control — and want a clearer way to think when certainty won’t come.

Essays and conversations on decision-making under pressure, every other Tuesday. Join me at theuncertaintyedge.com.

If the human side of leadership is what draws you, I also write The Good Human Practice — on inner clarity and character for leaders called on to make those hard decisions under pressure.

sustainability, ESG, corporate responsibility, financial capital, systems thinking, impact investing, consumer behavior, corporate governance, life-affirming economy, weasel speak

Summary

In this conversation, Lorraine Smith, a sustainability strategist, discusses the complexities and challenges of corporate sustainability and ESG practices. She emphasizes the need for meaningful change rather than mere compliance and highlights the role of consumers and investors in perpetuating unsustainable systems. Lorraine advocates for a rethinking of capitalism to align with sustainability goals and shares insights on identifying successful practices in the field. The discussion also touches on the importance of navigating uncertainty and building a values-aligned practice in the face of systemic challenges.

Takeaways

Our efforts to improve the system often reinforce it.

Corporate sustainability work is often self-perpetuating.

We need to focus on what works rather than what doesn't.

Weasel speak in corporate language obscures real progress.

Consumers and investors are complicit in unsustainable systems.

Sustainability should be integrated into capitalism, not seen as separate.

Identifying bright spots can lead to replicable success in sustainability.

Trusting one's gut is crucial in navigating uncertainty.

Clarity in what you want to change is essential for making progress.

Uncertainty is a natural part of life and decision-making.

Titles

Rethinking Corporate Sustainability

The Weasel Speak of ESG Disclosures

sound bites

"Can I see what's changed?"

"Sustainability is part of capitalism."

"Trust my gut."

Chapters

00:00 Introduction to Sustainability and ESG Challenges

02:55 The Evolution of Corporate Responsibility

07:23 Understanding Weasel Speak in Corporate Disclosures

20:16 Consumer and Investor Complicity in Unsustainable Systems

27:16 Rethinking Capitalism and Sustainability

30:06 Understanding Life's Principles

33:27 The Interconnectedness of Systems

36:37 Bright Spots in Sustainability

38:11 The Purpose of Systems

41:17 Aligning Purpose with Action

44:08 Ownership and Community Engagement

46:54 The Reckoning of Corporate Purpose

49:38 Rethinking Financial Capital

51:00 Navigating Uncertainty in Values

55:11 Trusting Your Gut

57:03 20251231 Podcast Video Intermission.mp4

57:13 20251231 Podcast Video Outro.mp4

Resources Mentioned

• www.blorrainesmith.com

Stay Connected with The Uncertainty EDGE

• Subscribe on your favorite podcast platform.

• Join the conversation on LinkedIn.

• Explore Sam's website.

Free Resources

• The Uncertainty E.D.G.E. newsletter — Strategies for navigating financial uncertainty.

• The Good Human Practice newsletter — Insights on leadership, resilience, and client relationships.
  

Late in 2007, Reed Hastings sat in Netflix’s headquarters, watching his DVD-by-mail business print money hand-over-fist. The company was profitable, growing, and dominating a market where Blockbuster was struggling. From the outside, everything looked perfect. But Hastings saw something others missed: the end of physical media was coming, and it was coming fast.

Against investor skepticism and the strong performance of their core business, Hastings launched Netflix Streaming with just 1,000 titles. Four years later, he tried an aggressive split, creating Qwikster as a separate DVD business. The customer backlash was immediate and brutal. Stock prices plummeted. Hastings reversed the decision but maintained his long-term commitment to streaming.

By 2019, Netflix had transformed completely: DVD‑by‑mail subscribers in the U.S. had dropped from a peak of tens of millions earlier in the decade to about 2.15 million, while global paying streaming subscribers had grown to roughly 167 million. The company that once risked disrupting its own DVD business became the streaming giant that disrupted everyone else.

The behavioral lesson here is profound: we diagnose disruption best when we’re not under pressure. Most leaders wait until crisis forces clarity—but by then, options are limited and costs are high. Netflix succeeded not because they predicted perfectly or executed flawlessly (Qwikster proved they didn’t), but because they diagnosed an uncomfortable truth while their DVD business was still thriving.

The Present Bias Trap

Humans—and organizations—suffer from present bias. We overweight current success and underweight future threats. When profits are strong, the whisper “everything is fine” drowns out the scream “change is coming”.

Netflix resisted this trap. They didn’t confuse “profitable today” with “sustainable tomorrow”. While competitors optimized their current models, Netflix diagnosed the future even when present performance suggested no urgency.

The Sunk Cost Fallacy

Companies cling to infrastructure they’ve built—DVD distribution centres, logistics systems, customer relationships—even when future demands require different capabilities. These sunk costs create gravitational pull toward the past.

Blockbuster couldn’t let go of their physical stores. Borders outsourced online to Amazon. Kodak invented digital photography but couldn’t let go of film. Blackberry couldn’t let go of enterprise customers or physical keyboards. Netflix recognized that their DVD infrastructure, while profitable, was becoming a liability.

The Confirmation Bias Echo Chamber

Success creates echo chambers. Leaders surround themselves with data that validates the current model, miss signals of disruption, and hire people who think the same way. The more successful you are, the more insulated you become from uncomfortable truths.

Netflix maintained diagnostic discipline even during their most profitable years. They asked “What would disrupt us?” when everything seemed fine. This is the leadership equivalent of preventive medicine—diagnose problems before they become crises.

The Status Quo Mentality

“If it ain’t broke, don’t fix it” is the enemy of innovation. This mentality prevents diagnostic honesty when business is performing well. Why disrupt what’s working?

Hastings famously said in 2005, “We named the company Netflix, not DVD-by-Mail”. This simple statement revealed his diagnostic clarity: they were always about entertainment delivery, not physical media. The method was secondary to the mission.

Loss Aversion Paralysis

The fear of cannibalizing profitable businesses prevents honest diagnosis about what the future demands. Netflix had to accept that streaming would eventually kill their DVD business—a loss they could prevent by embracing it first.

Most companies try to protect what they have instead of building what’s next. They optimize for today at the expense of tomorrow.

Leadership in the Diagnostic Age

The way to avoid this trap is to optimize diagnosis to be prepared for an uncertain tomorrow:

• Diagnose during strength, not weakness. The capacity to see objectively—to diagnose what’s coming even when it contradicts what’s working—separates leaders who adapt from those who optimize themselves into obsolescence. As the old saying goes, you don’t fix a leaky roof in the middle of a thunderstorm.

• Separate current performance from future viability. These are distinct questions. A business can be highly profitable today and completely unsustainable tomorrow. Leaders must learn to hold both truths simultaneously.

• Accept diagnostic mistakes as learning. Qwikster failed, but Netflix learned about transition pace without abandoning their core diagnosis. Good diagnosis doesn’t mean perfect execution—it means staying committed to the truth even when execution falters.

• Build diagnostic discipline into governance. Create regular strategic reviews that ask “What would disrupt us?” and “What reality are we avoiding?” Make diagnosis structural, not episodic.

Financial advisors often reinforce client biases (”your portfolio is fine”) rather than forcing honest diagnosis of risks, gaps, or needed changes. The greatest service you can provide is helping clients see what they’re avoiding.

For leaders, this means creating organizational cultures where uncomfortable truths can surface. When teams fear delivering bad news, silence becomes toxic. The most effective leaders cultivate psychological safety where data can challenge assumptions, where dissent is encouraged, and where early warning signals aren’t buried under optimism pressure.

Create metrics that force diagnosis. Track leading indicators of disruption (technology costs, bandwidth availability, customer behavior shifts) not just lagging indicators of current success. What you measure is what you pay attention to.

The Diagnosis Difference

Netflix succeeded because they diagnosed an uncomfortable truth while their DVD business was still thriving: physical media was dying, and streaming would replace it. Most companies fail this diagnostic test because success creates blind spots.

Current profits whisper “everything is fine” while future disruption approaches silently. The capacity to see objectively—to diagnose what’s coming even when it contradicts what’s working—separates leaders who adapt from those who optimize themselves into obsolescence.

Great diagnosis doesn’t guarantee perfect execution, but without it, execution becomes irrelevant. In a world of constant change, the leaders who win are those who can diagnose the future while enjoying the present.

This is the Uncertainty E.D.G.E.—not having perfect information but using the information you have with clarity and courage. Like Netflix diagnosing disruption while thriving, we must learn to see what’s coming even when everything seems fine.

📖 Want to build diagnostic discipline into your leadership? My book The Uncertainty E.D.G.E. explores frameworks for seeing clearly when success creates blind spots. [Get your copy]

Here’s what one reviewer had to say:

Every risk professional knows: risk and uncertainty aren’t the same thing. This book provides the tangible, actionable framework to help master that difference. Through diverse real-world examples—both successes and failures—you’ll see exactly how to navigate uncertainty. Whether you’re managing risk or dealing with uncertainty, these tools and lessons will elevate your decision-making.

Mark Hughes | Former Group Chief Risk Officer, RBC | Board Director and Chair of the Risk Committee, UBS Group AG

🎙️ Go deeper: Earlier on The Uncertainty E.D.G.E. Podcast, I spoke with Philip Setter, who built a six-figure digital insurance business through content marketing. His videos started terribly—bad webcam quality, blank wall, no microphone—but he didn’t wait for perfect conditions. He started. And six months later, when traction came, he doubled down. Philip reveals three uncomfortable truths: you learn by doing, not researching; your niche isn’t a cage—it’s a spotlight; and intent creates authenticity. For professionals facing decisions where waiting feels as risky as acting: This episode shows what judgment looks like when certainty is impossible. [Listen to the full conversation with Philip Sutter]

© The Uncertainty E.D.G.E.™ | Published every other Tuesday

If this sharpened how you're thinking about a decision you're currently facing, forward it to someone carrying the same weight — and subscribe if you aren't already.

The Uncertainty E.D.G.E.™ is a biweekly newsletter on navigating consequential decisions when certainty isn't available.

Grounded in behavioral science and three decades of operating experience. No prediction. No false confidence. Just clearer judgment.

There is a quieter counterpart to this work. If you're also asking what it means to lead with purpose and character — not just clarity — I reflect on that separately in The Good Human Practice: thegoodhumanpractice.com

Takeaways

• Max emphasizes the importance of curiosity and asking questions.

• He believes that what doesn't kill you makes you stronger.

• Max advises that hope is not a strategy in business.

• Finding the right franchise is an art that requires personal insight.

• He encourages exploring all options when considering a franchise.

• Max shares that he gets bored if he follows the conventional path.

• He highlights the importance of having a plan and following it.

Sound bites

• "Hope is not a strategy."

• "I always want to be challenged."

• "Have a plan and follow your plan."

Chapters

00:00 Introduction to Uncertainty and Leadership

02:35 Max Emma's Journey: From Setbacks to Success

06:28 Navigating Bankruptcy and Starting Over

12:41 Innovation vs. Risk in Business

18:29 Helping Corporate Refugees Transition to Entrepreneurship

24:47 The Art of Finding the Right Franchise

30:26 The Shift from Micromanagement to Delegation

34:42 Future Trends in Franchising and Business Ownership

Resources Mentioned:

Website: franchisewithmax.com (franchise brokerage, BooXkeeping.com franchising, and bookkeeping services)

Stay Connected with The Uncertainty EDGE

• Subscribe on your favorite podcast platform.

• Join the conversation on LinkedIn.

• Explore Sam's website.
  

Free Resources

• The Uncertainty E.D.G.E. newsletter — Strategies for navigating financial uncertainty.

• • The Good Human Practice newsletter — Insights on leadership, resilience, and client relationships.

Dan Heath opens his book Upstream with a parable.

Two friends are walking along a river when they spot a child drowning. They dive in, pull the child to shore, and begin CPR. Before they can catch their breath, another child floats past. They drag her out too. Then another child appears. And another.

As one friend keeps wading in, pulling children from the current, the other turns and starts walking briskly upstream.

“Where are you going?” the first friend shouts.

“I’m going to find out who’s throwing kids in the river.”

It’s a joke, of a kind. But it is also one of the most precise descriptions of leadership failure available — the tendency to become so expert at managing consequences that we never pause to question the conditions producing them. We get very good at the rescue. We never think to address the problem at the source. And part of the reason we fail to do that is because we don’t think about what comes after the action we take. This is what is called second-order thinking — not just “if I do x, y happens,” but deeper; if I do x, y happens, but then z might follow, in which case a might occur, and so forth.

Now apply this to the world as it stands in April 2026.

The Strait of Hormuz — twenty-one miles wide at its narrowest, carrying roughly 20% of the world’s petroleum supply every day — effectively closed in early March. Most headlines told the same story: oil prices spiking, gasoline expensive. Brent crude past $100 a barrel. Californians paying over $5 at the pump.

That was the obvious story. The visible child in the water.

What nobody was talking about loudly enough was the cascades already forming downstream. The fertilizer that couldn’t move. The crops that wouldn’t grow. The grocery prices that will rise six months later. The families who would eventually brave hazardous crossings — not because they chose to leave, but because staying had become impossible. The governments that might not survive the winter.

Most conscientious leaders wade in to rescue after the fact. But the best ones, the stewards for the next generation, are already thinking downstream and acting beforehand.

There Is a Name for This

The Hormuz crisis is not just an energy crisis, a food crisis, or a geopolitical crisis. It is a problem blindness crisis — the term Heath uses for our collective failure to see what’s coming even when the signals are entirely visible.

Heath identifies three forces that conspire to keep leaders from thinking both upstream and downstream.

• Problem blindness: we simply don’t see the problem forming.

• A lack of ownership: nobody’s job description requires them to trace the connection between a closed shipping lane and a food riot eighteen months later.

• Tunnelling: when leaders are managing an immediate crisis, the scarcity of time and attention actively crowds out the bigger, slower-moving consequences that will eventually prove more damaging than the original shock.

The Hormuz cascade is a masterclass in all three.

How One Chokepoint Becomes a World Crisis

The energy shock arrives first — visible within hours. Brent crude sharply higher, US gasoline rising into the $3.20–$4.00 range, jet fuel spiking first in Asia. This is the wave that’s making the front page.

Then the agriculture story surfaces — and most people miss it. The Strait is not just an oil route. Roughly one-third of all globally traded fertilizer travels through it, including as much as two-thirds of worldwide seaborne urea. Within three weeks of the closure, urea prices had risen more than 28%. At the New Orleans import hub, urea jumped from $516 to $683 per metric ton in a single week. Fifty-four US agricultural groups wrote to President Trump as planting season began: “The closure of the Strait of Hormuz sent fuel and fertilizer prices skyrocketing.” The question is no longer whether input costs would rise. It is whether farmers would simply plant less.

Crop yields follow fertilizer — not immediately, but inevitably. When fertilizer becomes unaffordable, farmers ration or leave acreage fallow. The result, months later, is lower output of corn, wheat, soybeans, and rice. Brazil imports nearly half its fertilizer through the Strait. African nations reliant on imported grain face the sharpest impacts. The numbers don’t lie: even a modest yield reduction triggers meaningful food inflation. The OECD-FAO estimates that a single year of synthetic-fertilizer disruption could push the global food price index up by 6% by 2028. For low-income countries, that shock isn’t theoretical — food inflation has already peaked at 30%, and double-digit price spikes have become the norm during supply disruptions.

Food inflation destabilizes economies and governments. Higher energy and food costs erode purchasing power faster than almost any other force. Stock markets price in the direct effects quickly — shares of energy companies up, airlines and consumer staples hammered — but the more dangerous effect builds slowly. Higher cost of living compresses corporate earnings; rising unemployment weakens demand further; central banks face the stagflation dilemma of raising rates into a slowing economy. The feedback loop tightens.

Then comes the wave that will define geopolitics: displacement and migration. When people cannot feed their families, when institutions cannot protect them, when staying is more dangerous than leaving — they move. They move toward stability, which in the current landscape means Europe and North America. The countries most exposed to fertilizer shortages are precisely those with the least fiscal resilience and the most existing political fragility. Some countries will not survive the combination of shocks in their current form. And when they don’t, their citizens will arrive at borders already the subject of fierce political debate.

The migration debate will intensify. The political pressure will escalate. And the extraordinary irony will be this: the very political actors who are angriest about rising migration will, in many cases, be the same ones who supported or failed to question the actions that made it inevitable.

The Libya Trap: Debating Downstream While Creating the Problem Upstream

This is not a hypothetical pattern. We have watched it play out with painful clarity.

The bombing campaigns in Libya, Syria, and Iraq — each justified on its own first-order terms — shattered the institutional frameworks that had contained global migration patterns. Libya’s collapse following the 2011 NATO intervention eliminated the state structures that served as a de facto migration buffer between sub-Saharan Africa and Europe. Syria’s civil war, inflamed by external intervention from multiple directions, produced the largest refugee crisis Europe had seen since World War II. The 2003 Iraq invasion generated millions of displaced people and catalyzed the regional instability that continues to produce refugee flows today.

In each case, the first-order logic was debated — sometimes vigorously, often not. But the downstream consequences were rarely modelled with anything approaching the rigor they deserved. What happens to the state when you remove the regime? Who fills the vacuum? Where do the populations go? Why would we expect these populations to act differently than we would under the same circumstances?

And then, when the consequences arrived, political discourse did not return upstream to question the decisions that produced them. It stayed firmly, almost aggressively, downstream. Border policy. Deportation numbers. Migration caps. Integration failures. The full weight of democratic debate was brought to bear on managing the consequences of decisions that were never adequately challenged when they were being made. Worse, previous decisions and their consequences were never factored in when the next similar decision was faced.

Heath calls the downstream zone the “zone of response” — and observes that while it is always optional to work upstream, it is never optional to respond to the problem once it arrives. The boats appear. The camps fill. The political crisis erupts. And everyone is suddenly very busy, none of them asking: why, exactly, are we here?

This is not a case for or against any particular intervention. It is a case for second-order thinking — for the insistence that any consequential decision be accompanied by a disciplined analysis of what is likely to follow, several steps out. And for the intellectual honesty to acknowledge when a downstream problem is, in material part, a consequence of upstream choices.

The Boardroom Version of the Same Mistake

Corporate history tells the same story, wearing different clothes.

Boeing and the 737 MAX. The logic of upgrading the existing 737 platform rather than developing a clean-sheet aircraft was commercially rational. The downstream consequences: the MCAS flight control system installed to compensate for aerodynamic changes, without adequate pilot training requirements, two fatal crashes, 346 deaths, the longest grounding of a commercial aircraft in aviation history, and over $20 billion in estimated losses. The cost-cutting decision that seemed rational in isolation looked very different when the full cascade was traced.

Kodak’s Digital Inversion. Kodak invented the digital camera in 1975 and suppressed it to protect the film business — financially logical by the metrics of the day. The downstream consequence was a decade-long window during which competitors built digital competency while Kodak defended a declining margin. The consequence after that: bankruptcy in 2012, despite having invented the technology that disrupted it. Protecting the present can often destroy the future.

In each case, the decision made sense given first-order analysis. The catastrophe lived in the cascade. And in each case, after the consequences arrived, the discourse focused on managing them rather than on the upstream decisions that had made them inevitable.

How to Think in Cascades

The Hormuz crisis is being experienced, in most boardrooms, as an energy story. It is, in reality, a cascade story — one playing out across fertilizer markets, crop yields, food inflation, sovereign debt, political fragility, and eventually mass displacement, arriving at borders as a migration crisis that will be debated with passion and confusion, as though it emerged from nowhere.

Dan Heath would recognize the pattern immediately. Leaders expertly managing downstream consequences of upstream decisions never adequately challenge and analyze them. The same dynamic produced the migration pressures that followed interventions in Libya and Iraq. The same pattern brought down Kodak, trapped Boeing, and made the Treaty of Versailles a blueprint for the next war instead of the end of the last one. The first-order logic was coherent each time. The catastrophe lived in the cascade.

When facing any significant decision, work through three questions — and resist the temptation to stop at the first answer.

Ask “And then what?” — at least twice. The first answer is usually visible and already priced in. The oil price spike was obvious within hours. The migration event is eighteen months away. Both are part of the same causal chain.

Ask “Who else moves?” Every decision changes the incentive structure for other actors — governments, competitors, populations, adversaries, even your team. Model their response, not just your own action. Wells Fargo’s sales targets didn’t just pressure staff; they quietly rewrote what “success” meant on the front line. Leadership created the conditions, then stopped watching. By the time the fraudulent accounts surfaced, millions of customers had been affected and the reputational damage was irreversible.

Ask “What makes this fragile?” Downstream consequences rarely create new vulnerabilities — they expose existing ones invisible under normal conditions. The Hormuz closure didn’t create agricultural fragility in Brazil or political fragility in the Sahel. It revealed it. The best time to see the fragility is before the stress test arrives.

And perhaps most importantly: when the debate will be loudest downstream, ask questions loudest upstream. The noise of the crisis is not a signal that the crisis is the right level of analysis. It is usually a signal that the real question has not yet been asked.

For leaders and advisors, this is the edge. It is not managing the downstream faster or better than everyone else — while laudable, anyone can wade in and pull children from the river. The distinctive value is being the person willing to walk upstream and ask who is doing the throwing, before the next child appears.

“The measure of a leader is not how well they manage the crisis everyone sees. It’s how much of it they saw coming — and how far upstream and downstream they were willing to look.”

The Uncertainty E.D.G.E.™ publishes every two weeks — frameworks for people responsible for consequential decisions, when certainty isn’t coming.

If this was useful, forward it to someone carrying a decision they haven’t named yet.

The outer work of leadership — how we decide — has a counterpart. If you’re also asking what it’s all for, I explore that separately in my free Substack newsletter: thegoodhumanpractice.com

To explore working together: samsivarajan.com