A friend of mine leads an engineering org. We go back several years. We talk most months, sometimes over coffee when we’re in the same city, usually just a phone call where we get into what’s actually going on. No agenda, just real talk. I look forward to those conversations. This person has a gear that most people don’t. The kind of leader who gets excited about a messy system problem the way other people get excited about vacation plans.

Last month, something was off. They showed up. Gave updates. Said the right things. But the updates were shorter than usual. Flatter. More “everything’s fine” than anything real. The kind of answers you give when you’re performing normalcy instead of living it. I noticed because I know this person. They don’t do surface-level. And the thing that was missing wasn’t energy or enthusiasm. It was the thing underneath. The quiet drive that used to make them lean into a problem before anyone asked them to. That was gone. Not replaced by frustration. Just... quieter.

I didn’t push it on the call. But afterward, I sat with it. Because I’ve seen that look before. In other people I’ve worked with. In myself.

And when I started paying attention, I saw it everywhere.

It’s not just them

You know how you can tell? Not from surveys. From the way people talk about work now versus two years ago. The engineer who used to send you articles about some new tool they were excited about now just asks if you’ve heard about the latest round of layoffs. The person who used to stay late because they were chasing a problem now stays late because they’re afraid to be seen leaving. The team Slack that used to have side threads about interesting architectural debates now just has standups and status updates. Nobody’s complaining. That’s what makes it hard to spot. They’re not angry, they’re not fighting. They just got quieter. And when you ask how things are going, you get “fine.” A lot of “fine.”

And it’s not just ICs. Every engineering leader I talk to says some version of the same thing. The stress is worse than it’s ever been, and the people they’re supposed to carry are harder to reach.

This isn’t a motivation problem. It’s not about hustle, or mindset, or another team offsite with a keynote and a trust fall. Something is happening to the internal mechanism that makes people want to engage at all.

Every furnace has one

If you’ve ever looked inside a gas furnace, there’s a small flame that never goes out. It doesn’t heat the house. Too small for that. Its only job is to be ready. When the furnace calls for heat, that small flame ignites the main burner. Without it, nothing fires.

That’s a pilot light. (And if you’re an engineer, you already know — we borrowed this exact idea for disaster recovery. Keep the minimum alive so the whole system can fire back up when it needs to. Same principle.)

Drive works the same way. It’s not the big performance — the product launch, the promotion push, the all-hands presentation. It’s the quiet thing underneath that makes you lean in instead of coast. The reason you chase a problem before it’s assigned to you.

Here’s what makes a pilot light interesting: inside the assembly, there’s a small device that sits directly in the flame. It converts the heat into a tiny electrical current. Just enough to hold open the valve that supplies gas. The flame literally sustains itself. No external power needed. As long as it’s burning, the fuel keeps flowing.

But if the flame dips too low, that device cools, the valve closes, and all fuel stops. Not just the pilot light. Everything downstream goes dark.

That’s how drive works. Self-sustaining when it’s lit. And when it dims past a certain point, everything shuts off with it. Energy. Creativity. Ambition. Care. Gone.

What’s blowing on the flame

Three things are dimming the pilot light right now, and none of them are your fault.

The work changed shape. Engineers who used to build for hours straight now spend their days reviewing AI output across half a dozen tasks. The part of work that used to light people up (the building) got quieter. A recent study in Nature found that passively relying on AI reduces your confidence in your own abilities, your sense of ownership over the work, and your feeling that the work means something. And those effects stick around even after you go back to doing things manually.

Effort stopped connecting to meaning. You ship more, but it registers less. You close more tickets, review more PRs, ship more features, move more cards across the board. The link between “I worked hard” and “that mattered” stretched thin. Harvard research on nearly 12,000 diary entries found that nothing fuels motivation more than making progress in meaningful work. When progress stops feeling meaningful, the flame has nothing to burn.

The flame became invisible. Working alongside AI, often alone, often remote. The person who used to notice your energy was off is now a Slack status. When nobody sees the flame, it’s easy to forget it’s there.

You can’t change any of that. The tools shifted. The market moved. The office emptied. But you can adjust. And that’s where the real skill lives.

What you CAN adjust

You can’t control that AI writes a growing share of production code. You can control which problems you personally engage with. Find one thing this week where your judgment (not a model’s) made the difference. The architecture call before something broke. The question nobody else thought to ask. The decision that needed ten years of watching systems fail. That work feeds the flame.

You can’t control that remote work isolated everyone. You can control whether you see people. Not their output. Them. The person whose light is low — your first instinct might be a performance conversation. Sometimes, they just need someone to notice. Be that person for someone this week. And let someone be that for you.

You can’t always control whether the quarterly project lands perfectly, on time, checking every box. You can control whether your team felt progress today. One small win. One moment where someone’s work moved something forward and they knew it. Not a big win. A real one.

You can’t control the pace. You can control whether you gave yourself room to breathe. A pilot light needs oxygen. Smother it and the flame goes out. Sometimes protecting the flame means stepping back. Not quitting. Making room for the fire to find air.

Your flame too

The hardest version of this: your light is low, and you’re the one everyone looks to for heat.

The people supporting the team often carry more stress than the team itself. The hand cupping the flame is shaking.

You adjust the same way. One task. One person. One win. One breath. One day at a time. Your pilot light doesn’t need to be roaring. That small device inside the assembly doesn’t need a bonfire. It needs contact with the flame. As long as there’s heat, the valve stays open. Low is okay. Low is still lit.

And some flames fluctuate because of things that have nothing to do with work. Things you can’t change. Things you adjust around, not through. The flame doesn’t know why it’s being tested. It stays lit anyway.

Hold the button

I talked to my friend again this week. The flame didn’t go out. It got low. What brought it back wasn’t a motivational talk or a new strategy. It was one project that actually needed their judgment. One conversation where someone saw them, not their metrics. One small decision they got to make on their own. One week where progress felt real.

To relight a pilot light, you hold the button down for about thirty seconds. Not long. But you have to hold it. Steady. Deliberate. Until the heat builds back up and the system can sustain itself again.

Drive works the same way. You don’t wait to feel it. You hold the button.

So here is my question to you: what’s one adjustment you can make this week to protect the flame? Yours, or someone else’s.

#Leadership #EngineeringManagement #AI #Motivation #Resilience #TechCareers #FutureOfWork #PilotLight

Somewhere right now, a 2025 CS grad is refreshing their inbox for the 40th time this week. Every listing wants three years of experience with a tool that’s been out for eighteen months. They did everything right. Degree. Side projects. LeetCode until their eyes crossed. They graduated into a market that moved out from under them while they were still studying for it.

Somewhere else, a ten-year veteran just opened a resume they haven’t touched since 2016. They’re staring at job descriptions that feel like they’re written in a language they used to speak. Last year they were the person companies wanted. Now they’re not sure if anyone’s even reading the application.

And somewhere, maybe in the same building where the all-hands just ended, a manager is sitting in their car, engine off, rehearsing confidence for tomorrow’s standup. They’ve already had to deliver the news to people they hired. Now they’re wondering if the next round is them.

Some fights are loud. Layoffs make the news. Markets crash in public. CEOs post restructuring memos with the word “exciting” in the first paragraph while their stock ticks up.

But some fights are quiet. Someone is carrying something into every Zoom call, every interview, every morning. A fight that has nothing to do with a job listing. Something heavier. Something that doesn’t fit in a LinkedIn post. They’re fighting it anyway.

And some fights are silent. Months of applications into a void. Not rejection. Not feedback. Just the inbox and the absence of a reply. The slow, invisible erosion of confidence that nobody tells you is normal.

I see you. All of you.

Fighters fight.

This isn’t in your head

You already feel this. But in case you need someone to say it out loud: only 22% of workers feel confident their job is safe right now. Tech cuts are up 51% over last year, and AI is the named reason in over 12,000 job cut plans so far in 2026. Jack Dorsey cut half his company and told the rest of the industry “most companies are late.”

For new graduates, the picture is worse. 40% of early-career workers have changed or considered changing their career plans because of AI. That’s not a soft market. That’s a generation entering the workforce into a headwind that isn’t their fault.

You’re not imagining it. And you’re not alone in it.

The numbers aren’t the point. You already know the numbers. You’re living them. The point is this: what you’re feeling is proportional to what’s happening. Being scared right now doesn’t mean you’re weak. It means you’re paying attention.

What fighting actually looks like

Fighting isn’t hustle. It’s not “grind harder.” It’s not someone on a stage telling you to wake up at 4 AM and visualize your future. It’s not toxic positivity wearing boxing gloves.

Fighting is a stance. In boxing, your stance is the most honest thing about you. Weight forward, hands up, chin tucked. It’s not a punch. It’s what comes before the punch. It tells the other fighter everything — whether you’re pressing forward or backing up, whether you believe you belong in this ring or you’re just waiting for it to end. You choose your stance before a single punch is thrown.

That’s what fighting looks like right now. Not a knockout. Not a highlight reel. A decision to engage when everything around you says wait, freeze, or scroll LinkedIn for one more hour and call it research.

For the fresh grad, where 39% of workers with less than a year of experience say AI has made it harder to find a job, fighting looks like building something nobody asked for and shipping it anyway. Not because a recruiter told you to. Because the degree and the bootcamp and the portfolio taught you to solve problems, and that mind, the one that breaks things down and figures them out, is yours. The tools changed. That didn’t.

And the same AI that’s changing the game? It helps people just starting out more than anyone else. It’s the best sparring partner you’ve ever had. Nobody can automate the person who looks at a mess and says “I can fix this.” Fighters fight. You didn’t come this far to stop now.

For the veteran, fighting looks like learning the new tool. Not because you wanted to. Not because it’s fair that ten years of expertise doesn’t count the way it used to. But because fighters don’t wait for the game to come back to them. Your ten years aren’t obsolete. Ten years of watching systems break, of pattern-matching across failures, of knowing what questions to ask before the first line of code ships. That’s leverage. That’s the part AI can’t do. Fighters fight. And you’ve been in late rounds before.

For the manager carrying the weight of their team’s future while quietly wondering about their own? Fighting looks like staying in the ring. Not bailing to a “safe” job that doesn’t exist. Rebuilding. The fact that you feel the weight of other people’s careers means you’re exactly the kind of leader who should be in this fight. Even the corner team needs a corner. Fighters fight. Even when the fight is for someone other than yourself.

For the person in the silence. Still applying. Still preparing. Still showing up for interviews that ghost them. The silence is not a verdict. It’s a round. And you’re still in it. Every application is a swing. Every follow-up is a jab. The scorecards in this fight aren’t posted in real-time. You don’t know if you’re behind. You only know you’re tired. Fighters fight. Keep swinging.

Your corner

In boxing, even in the loneliest sport on earth, nobody fights alone. Between rounds there’s a stool, a cutman who works on the damage you can’t pretend away, and a chief second who says one thing. One. Because when you’ve been fighting, your brain can’t hold a paragraph. It can hold a sentence.

If you’re in the ring right now, who’s in your corner? A mentor. A friend who texts back without needing an update. A community that doesn’t judge. Someone who’s been where you are and remembers what it felt like.

And if you know someone who’s fighting, be their corner. Not advice. Not a five-step plan. Just: I see you. Keep going.

Fighters fight

You’re already fighting. I know you are.

The fact that you’re still here — still reading, still applying, still showing up for the standup, still learning the tool you didn’t ask to learn, still getting out of bed on the mornings when nobody would blame you for staying in it. That’s fighting.

Some fights are public. Promotions, job offers, the LinkedIn post about the new role with the heart emoji. Those are the fights people see.

But the fight nobody sees — the 200th application, the interview you prepped for all weekend that ended in silence, the morning after the news, the drive home after the all-hands — that’s where fighters live. Not in the victory. In the stance. In the showing up.

Fighters fight.

That’s you.

Keep going.

#Leadership #CareerAdvice #AI #FightersFight #Resilience #TechCareers #FutureOfWork

The most-repeated phrase on CNBC in the last few weeks is probably “AI is eating software alive.” You’ve heard it. Maybe over coffee, maybe in passing while someone had Bloomberg on in the background. $285 billion disappeared from software stocks in 48 hours back in February. Traders at Jefferies started calling it the “SaaSpocalypse.” Atlassian dropped 35% in a week. The software ETF entered a bear market.

I watched those numbers and thought: that’s a credibility story disguised as a stock story.

Because if you open LinkedIn right now, your feed is telling you the same thing, just on a six-month delay. Scroll back. Six months ago it was full of engineers posting about weekend framework deep-dives. “Spent my Saturday with [shiny new tool]. Staying sharp.” Hundreds of likes. Dozens of “respect” comments. I liked a few of those posts myself.

Fast forward to today. The best AI coding models are solving nearly 80% of real-world software engineering tasks. AI coding tools are pulling in billions in revenue less than a year after launch. An AI agent can scaffold the whole setup those weekend posts were celebrating in about twenty minutes. My point being - that knowledge didn’t just get old - it got replaced.

But buried in the same feed, around the same time, was a quieter post. An engineering director who caught a pipeline design flaw during a review. Saved her team weeks in production. Got maybe 40 likes. Nobody shared it.

Her knowledge is still worth exactly what it was six months ago. Probably more.

I keep thinking about those two posts. Because they represent two completely different kinds of technical knowledge. And the market, in its blunt way, is telling us one of them just went to zero while the other got more valuable. Most of us treat “staying technical” like it’s one thing. It’s not. It’s actually three different things, and they’re moving in opposite directions.

1. The stuff AI already knows

Frameworks, libraries, vendor configs, syntax. This is where most of us pour our “staying sharp” hours. Conference talks, tutorials, weekend side projects, that one Udemy course you bought at 2 AM. I’ve done all of it. It feels productive. Looks great on LinkedIn. And I’m not going to pretend I didn’t enjoy the dopamine of spinning up something new on a Saturday.

That knowledge was already expiring fast. Technical skills lose relevance in about two and a half years now. That was bad enough before AI showed up. AI made it worse. Way worse. Because AI doesn’t just change tools faster. It turns tool knowledge into a commodity the moment you learn it.

The head of Claude Code at Anthropic told Fortune that “pretty much 100%” of their code is now AI-generated. Over 25% of Google’s new code comes from AI. If the companies building these models have already replaced the tool layer internally, what do you think happens to the rest of us? Your team already has a platform engineer AND an AI assistant for Kubernetes. They don’t need you to know the configs.

Maybe we’re all studying for a test that got canceled?

2. The stuff your team can’t see because they’re too close

You’ve probably seen this already, or you will soon. An AI-generated service passes every unit test, looks clean in review, ships without a flag. Two months later it’s the reason another team’s migration is stalled. The coupling was invisible at the PR level. You had to zoom out to see it. And the engineer who prompted it was too close to the code to see the system.

That’s not a one-off. AI now writes over 40% of all production code. Throughput is up across the board. Everyone’s shipping faster. But nobody talks about what CodeRabbit found when they looked at the quality side: AI-generated code carries 1.7x more issues. Incidents per pull request climbed 23.5% last year. Change failure rates rose 30%.

More code. More failures. Same or fewer releases.

So where did the bottleneck go? It moved from writing the code to knowing whether the code should ship. Your team is generating more output than ever. But who’s looking at how it all fits together? Who notices that the service your team just shipped is going to collide with a migration two teams over? That’s not in any unit test. That’s the kind of thing you only catch if you’ve been living in the reviews and the debugging, not the feature work. And right now, most teams are so focused on shipping faster that nobody’s asking whether faster is actually better.

3. Scar tissue

Data pipelines will always be underestimated. Rewrites always take 3x longer than anyone promises. Distributed systems always have consistency trade-offs. These truths don’t change with the framework of the year. They don’t show up in a tutorial. You earn them by watching things break for a decade.

They’re exactly what AI gets wrong. AI generates code that works in isolation. But it doesn’t know what happened the last time someone tried this architecture at scale. The schema migration that locked the table for 20 minutes. The abstraction that looked clean in staging but collapsed under real load. The model can’t see those. You can. Because you were there when it broke.

That’s the competence your team actually cares about. Goodall’s research across thousands of U.S. and British workers found that a boss’s technical competence is the single strongest predictor of job satisfaction. Their judgment, not their output. Jeff Dean’s credibility at Google wasn’t built by knowing their current codebase. It was built over 25 years of watching what works and what breaks at planetary scale. That kind of knowledge doesn’t expire. It compounds.

This is the only layer getting more valuable. Everything else is getting cheaper.

Now connect those dots

AI gutted the first layer and made the third worth more at the same time. “I can write that” stopped being impressive. “I know why that will break at scale” is the scarce thing now. The data across hundreds of thousands of engineers backs this up: top AI adopters ship 2x the throughput, but most teams aren’t even measuring whether that throughput is producing better outcomes.

Boards went from “are you experimenting with AI?” to “show me results.” The engineering manager who can judge AI output before it ships, who knows what the model’s blind spots will cost in production — that’s what credibility looks like in 2026. Not “I learned the new framework.” That’s already automated.

Come back to the feed

Go look at your LinkedIn feed again. The posts with the most engagement are still about tools. Meanwhile, the engineering director who saved her team three weeks by catching a design flaw? She’s not posting about it. Nobody writes “I convinced my team NOT to build something today” and gets 500 likes. But that’s the work that compounds. And the market is starting to agree.

Years ago, when I moved from engineering into leadership, the hardest thing wasn’t learning to manage. It was letting go of the first layer. I’d built my identity on being the person who knew the stack, who could jump in and write the fix. Letting go of that felt like losing something. It took me a while to realize I wasn’t losing credibility. I was shifting where it lived. The moments my teams actually trusted me most had nothing to do with code I wrote. It was the architecture call I made before a system buckled. The migration risk I caught because I’d watched a similar one fail five years earlier at a different company. Pattern recognition, not syntax.

That transition used to take years. You’d grow into it gradually as you moved from IC to manager to director. AI just compressed it. Today, a first-time engineering manager is watching their team generate production code with an AI agent on day one. The first layer isn’t something they’ll slowly outgrow. It’s already gone. The question that used to be “will I stay technical enough?” is now “which layer of technical am I building?” And if you’re still answering that question with frameworks and tutorials, AI already answered it for you.

So here is my question to you: your last 10 “staying technical” hours — how many went to stuff AI can already do for your team, and how many went to the judgment it never will?

#Leadership #EngineeringManagement #AI #SoftwareEngineering #TechnicalLeadership

Robert Kiyosaki sold 44 million copies of Rich Dad Poor Dad. Most people remember the book for “your house is not an asset” and the argument that followed. His company went bankrupt. His seminars got investigated. I’m not here to defend any of that.

But in the sequel, Cashflow Quadrant, he did something I still think about. He drew a cross on a page. Labeled four boxes. E, S, B, I. Employee. Self-Employed. Business Owner. Investor. And that one drawing explained more about why people get stuck in their careers than most business books manage in 300 pages.

Four boxes. Two lines. A framework millions of people can reproduce from memory.

That drawing changed the way I thought about money. Then it changed the way I thought about my career. And eventually, it changed the way I lead teams. I did somewhat followed Kiyosaki’s financial advice. But, the key is, the act of drawing, of forcing a messy problem into a shape I could see, became the way I understood things.

The best leaders I’ve worked with do this. They pick up a marker and draw the situation before they try to solve it. Quadrants, axes, arrows on a whiteboard. A messy sketch that makes a complex problem simple enough to act on.

The leaders who struggle? They talk. They write decks. They schedule another meeting. But they never pick up the marker.

Why Drawing Works

Now let’s not call it intuition. Dan Roam built an entire thesis around it: any problem can be made clearer with a picture. Bezos sketched Amazon’s flywheel on a napkin in 2001. A loop so simple that every employee could redraw it from memory. It became the strategic anchor for a trillion-dollar company. Collins drew three overlapping circles and called it the Hedgehog Concept. Took companies an average of four years to figure out what goes in those circles, but once they could draw it, alignment followed.

There’s a reason for this. Kahneman and Klein agreed that expert intuition works in environments with regular patterns and adequate practice. Frameworks give you the patterns. Drawing gives you the practice. Lowy and Hood cataloged 56 of these frameworks in a book literally called The Power of the 2x2 Matrix. Four boxes is about the limit of what humans process at a glance. More than that and you’re back to a spreadsheet.

Kiyosaki’s quadrant is one of those 56. And for me, it was the first.

Where It Started

I picked up Rich Dad Poor Dad the way everyone does. Someone recommended it, I read it in a weekend. But the sequel is where things clicked. Kiyosaki drew that cross and suddenly the way I was thinking about cash flow, about my career trajectory, snapped into a shape I could see. Left side of the quadrant. Trading time for money. And seeing it drawn made it impossible to unsee.

I started drawing that quadrant myself. On napkins. In notebooks. The act of drawing forced me to be honest about where I was. Then I started drawing other things. Business model sketches, revenue structures, the cash flow cycle for a side project. Messy, quick, on the back of whatever was nearby. But every time I drew it, the problem got simpler.

Drawing became the way I thought. Not the way I presented — the way I understood.

When I became an engineering manager, the habit came with me. I didn’t consciously decide to “apply visual frameworks to leadership.” I just kept reaching for the marker. A hard conversation? Draw it. A strategy decision with too many variables? Two axes, four boxes, start placing things. The frameworks changed but the instinct was the same one Kiyosaki’s book had wired into me: if you can’t draw the situation, you don’t understand it yet.

Draw the Opportunity

SWOT has been drawn on whiteboards since the 1960s. Four boxes, two minutes, and suddenly the conversation shifts from “what should we do?” to “given these strengths and this threat, what’s our move?” The Ansoff Matrix does the same thing for growth: existing vs. new products across existing vs. new markets. You look at it and immediately see that your growth plan lives in the “new product, new market” box. The highest-risk quadrant. And nobody realized that until someone drew it.

Consulting understood this decades ago. BCG’s Growth-Share Matrix turned portfolio allocation into four animals: stars, cash cows, question marks, dogs. Eighty percent of what a strategy consultant delivers to a client is a drawing with boxes. The drawing forces the conversation that 47 slides couldn’t.

Now - here’s the skill: can you walk into a room where three VPs have been debating a product bet for six weeks, draw a simple impact/effort matrix on the whiteboard, place the options on it, and watch the room align in 15 minutes? The drawing didn’t solve the problem. It made the problem visible. And visible problems get solved. Invisible ones get another meeting.

Draw the Team

The drawings for strategy are well-known. The drawings for people are just as powerful, and most managers don’t use them.

Kim Scott drew Radical Candor as a 2x2: care personally on one axis, challenge directly on the other. That one drawing explains why your nicest manager might be your worst. Ruinous empathy, where you like someone too much to tell them the truth. The Skill/Will matrix gives you a coaching prescription for each direct report in seconds. High skill, low will? They’re not underperforming. They’re bored. That’s a completely different intervention than a performance plan. A 30-second drawing, a completely different outcome.

And then there’s the personal diagnostic. This is where Kiyosaki’s quadrant came back to me. Not for cash flow this time. For management.

Years ago, early in my management career, I was in a skip-level. My manager’s manager asked a question that still sits with me: “How does your team operate?” Simple question. I started answering. Five minutes in, I noticed something. Every sentence started with “I.” I review the PRs. I make the architecture calls. I handle escalations. I onboard new hires. I wasn’t describing a team’s operating model. I was describing me.

That afternoon I went to a whiteboard and drew Kiyosaki’s cross. The same one I’d drawn years ago for cash flow. E, S, B, I. And I landed exactly where I didn’t want to be. S. The self-employed quadrant. The one where Kiyosaki says “the self-employed person doesn’t own a business, the business owns them.” My team couldn’t function without me in the room. I wasn’t leading a system. I was the system. And the day I called in sick, three things stalled because the context lived in my head and nowhere else.

Gerber has a name for this. Working IN the business instead of ON it. Jade Rubick put it sharper: “Competence becomes dependency. Dependency becomes centralization. Centralization becomes a bottleneck.” That was me. The quadrant I’d first drawn about money was now telling me something about my management. I’d bought myself a job.

Can you sketch how your team actually works — how work comes in, how decisions get made, how someone new ramps up — in 60 seconds on a whiteboard? If you can draw it, a system exists outside your head. If the whiteboard stays empty, you ARE the system.

Draw the Trend

The third thing great leaders draw isn’t a framework. It’s a line.

A trend line. Across sprints. Across quarters. Across the morale data you’ve been collecting in 1:1s that you haven’t connected yet.

Most managers react to individual data points. “We had a rough sprint.” Leaders who draw? They sketch the last six months and see the pattern: we’ve been declining since September, and the floor is about to give way.

Traders do this every day. Support levels: the baseline performance a team doesn’t drop below. Resistance levels: the ceiling they keep hitting. False breakouts: the post-offsite energy that fades in two weeks. You don’t need a Bloomberg terminal. You need a whiteboard and the willingness to draw the line across six months of data instead of reacting to the last two weeks.

Klein’s research explains why this works. He studied fire commanders, military leaders, ICU nurses. They don’t compare options analytically. They recognize patterns from experience and act. The trend line is how you build that pattern library. You can’t recognize a pattern you’ve never drawn.

I’ve always loved running operational WBRs for exactly this reason. Weekly business reviews where you put the numbers on the wall and stare at them together. Not to celebrate or panic. To see the shape. Week over week, the line tells you things that no single data point can. A metric that’s been quietly sliding for eight weeks doesn’t show up in a status update. It shows up when you draw the line.

The Full Circle

Every framework in this post is a drawing. Four boxes. Two axes. A line across data points. None of them require artistic talent. They require the willingness to pick up a marker, look at a messy situation, and reduce it to something a room full of people can see at the same time.

I started drawing because a personal finance book told me to put myself in a quadrant. That habit followed me into management, into strategy, into the way I read my teams. The frameworks changed. The instinct didn’t.

Think about the last time you were stuck on a hard decision. A team problem, a strategy bet, a resource trade-off. Did you talk about it? Write about it? Schedule a meeting about it?

Or did you draw it?

So here is my question to you: when was the last time you picked up a marker — and what did the drawing show you that the conversation couldn’t?

#Leadership #EngineeringManagement #RichDadPoorDad #Strategy #Management

Last Friday, Jayson Tatum walked onto an NBA court for the first time in 298 days. Standing ovation. 15 points. 12 rebounds. 7 assists in 27 minutes.

But here’s what stuck with me: the Celtics were 42-21, second in the East as of this writing. Their best player hadn’t touched a court in ten months, and the team didn’t blink. Not because they replaced him. Because his coach built a system where Tatum’s presence, on the bench, in film sessions, in the locker room, kept shaping the team even when his body couldn’t play.

If you manage an engineering team, that’s your job too. And most of us haven’t figured it out yet.

As a die-hard Lakers fan, it hurts me to keep writing about other teams’ stars. But I’ve watched this pattern my whole life. Kobe on the bench with a torn achilles. Byron Scott said three words: “I wanted him around.” Phil Jackson won 11 rings not by being the smartest X’s-and-O’s coach, but by building a system where everybody touched the ball, everybody was empowered.

That’s coaching. The coach doesn’t play. The coach builds the system that makes the players around them better.

You’re the Coach

Gallup studied 27 million employees over 20 years. One finding towered over everything else: managers account for 70% of the variance in team engagement. Seventy percent. Not the mission statement. Not the tech stack. You.

The problem is right there in the same data. Manager engagement dropped to 27% globally. Workloads are up 51%. The instinct when you’re drowning is to do more yourself, not less. You review every PR. You answer every Slack thread. You sit in every meeting because “context.”

Liz Wiseman calls this the accidental diminisher. Diminishers get less than half their team’s capability. Not because they’re bad. Because they’re doing too much. She documented a case where a manager assigned a talented engineer routine tasks and solved their hard problems for them. The engineer ended up using 20-25% of their actual talent. Twenty-five percent.

You’ve got senior people on your team. The question isn’t whether they’re talented. It’s whether you’ve built a system where their presence — not their PRs — multiplies everyone around them.

Three ways to build that system.

1. Put Them in the Film Room

Mazzulla brought injured Tatum into the coaches’ film sessions and gave him a seat on the bench. Tatum said he spent time “seeing the game from their perspective.” His body couldn’t play. His basketball brain never stopped working.

Your team has film rooms. The CoEs. The postmortem. The design review. The experiment retro. But there’s a difference between your senior engineer attending these and your senior engineer running them. When they’re attending, they’re a participant. When they’re running them, their judgment shapes how the entire team thinks about the problem.

It’s your Staff Engineer leading the CoE after the outage, asking the questions that turn a 2 AM incident into a systemic fix. It’s your senior Data Scientist running the experiment review before the team wastes two weeks on a test with a confounding variable baked in. Not because you delegated a task. Because you put the person with the deepest judgment at the center of the room.

Make the film session exist. Recurring, not reactive. And name the person who runs it.

2. Let Them Set the Energy

When Curry broke his wrist in 2019, he became the Warriors’ self-appointed hype man. The Mavericks racked up $200K in fines for bench energy so contagious the NBA tried to legislate it. But the example that sticks with me is LeBron. Even in a quiet game, when a young Laker hits a three, LeBron is the first one up. The towel. The chest bump. He doesn’t celebrate his own plays anymore. He celebrates theirs. And the room changes.

This isn’t soft. The research is clear: team cohesion directly predicts performance. And that cohesion doesn’t come from the manager. It comes from the senior IC who writes the Slack post celebrating a junior’s first production deploy, even though their own project got deprioritized. The one who presents the team’s work at the leadership review, not for credit, but for awareness.

Stop hoarding the cultural work. Give your senior people the weekly wins slot, the team demo, the shoutout channel. Let them carry the temperature.

3. Make Their Presence the Standard

I keep coming back to those three words. “I wanted him around.” Kobe on the bench didn’t score a point. But his presence on the sideline changed how every Laker prepared. Patty Mills said Tim Duncan’s presence “makes you put your alerts up and your ears stand up.”

You know this feeling. The senior engineer whose name on a code review makes everyone refactor before they submit. Nobody told them to. The bar just moved. The data architect whose presence in the pipeline review means people check their contracts before anyone asks. The standard is implicit, not enforced.

Don’t let your senior people fade into the background because they’re “not shipping this sprint.” Their presence IS the shipping — of standards, of culture, of the bar.

Put their name on the review rotation. Make them the decision owner for a domain. Make their presence structural, not optional.

The Modern Diminisher

Now flip it. The manager who reviews every PR themselves, answers every architecture question before the senior IC sees it, sits in every data review because “context.” Their most talented people rubber-stamp reviews and write code two levels below their capability.

There’s a new version of this trap. AI reviews code now. AI flags architecture issues. So why put senior engineers in the film room at all?

Because AI made the judgment gap worse. Production success rates hit a five-year low this year. Teams are writing more code and failing more often. The bottleneck isn’t generating code anymore. It’s knowing whether the code should ship. That’s judgment. AI doesn’t understand your system’s unwritten rules. It doesn’t feel the blast radius. That’s what your senior engineers provide. And it’s the manager’s job to protect their time for exactly this work. Not bury them in low-leverage tasks.

Here’s what nobody talks about: AI creates an ownership gap. Engineers are starting to say “the AI wrote that” in postmortems. Not my code. Not my problem. That’s where the energy, the presence, the human fabric of a team matters more than it ever has. AI amplifies whatever foundation you have. If your senior people aren’t in the room, AI amplifies mediocrity at machine speed.

Where the Scoreboard Changes

I see this in my managers. I see it in my teams.

The leader who reviews every PR because it feels productive. The one who answers every architecture question in Slack because it’s faster. I’ve been that person. The scoreboard said “what did you build?” and we kept playing that game long after the job changed.

The hardest part isn’t the framework. It’s the identity shift. You built your career on being the person with the answers. Now the job is building the system where other people find them. That shift happens at every level. IC to senior IC. Manager to director. And it never gets easier, because the instinct to do it yourself never fully goes away.

There’s a crude metric that floats around engineering circles. PRs written versus PRs reviewed. People have strong opinions about it, and the numbers alone don’t tell you much. But the direction they point? The more senior you go, the more your impact shows up in other people’s work. Not yours.

I won’t pretend the organizations have caught up. Most promotion frameworks still reward output. Most performance reviews don’t have a line for “ran the postmortem that prevented the next outage.” That’s a real gap. But the best leaders I’ve worked with figured out how to make influence visible. And how to fight for their people when the system didn’t.

That’s what coaching is. You stop keeping score for yourself. And you make sure the score counts for the people doing the work.

The Question

Think about the most senior people on your team right now.

Are they running the film room? Setting the energy? Raising the standard?

Or are you trying to play every position while they sit on the bench?

So here is my question to you: are you coaching, or are you playing?

#Leadership #EngineeringManagement #NBA #Multipliers #CultureAndTeams

You’ve heard of the haves and the have-nots. It used to be about money.

The top 10% of American households own 67% of the wealth. The bottom 50% own 2.5%. We call it the K-shaped economy. Two populations, same starting point, opposite trajectories. Wealth compounds. Capital makes capital. And the gap accelerates.

Now imagine that same K-shape. But instead of money, it’s what you know. How you work. Whether you’ve figured out how to think alongside AI or whether you’re still doing everything the way you did three years ago.

That K is already here. And most people are on it without realizing which arm they’re on.

The New Haves and Have-Nots

OpenAI’s latest enterprise data tells a story most leaders haven’t internalized yet. Workers at the 95th percentile of AI adoption send six times as many messages as the median employee. Same company. Same tools. Same license.

For coding tasks, it’s 17x. For data analysis, 16x.

This isn’t a gap between companies. It’s a gap between the people sitting next to each other. And it compounds the exact same way.

The question I get asked more than any other: “How do I get started with AI?” From team members. From people I mentor. From new hires who feel behind on day one. What they’re really asking: is the gap already too wide to close?

It hasn’t. But the math is about to get a lot harder. Because this gap compounds. In both directions.

The AI Haves

I remember the exact moment it clicked for me.

I was working on a strategy one-pager. The kind I used to spend a full afternoon writing, staring at a blank document, deleting half of it, starting over. This time I opened an AI tool, spoke my rough thinking out loud, and had structured text back in three minutes. I pasted it into Claude, asked it to challenge my reasoning. Spoke my response to the pushback. Three rounds. Fifteen minutes. A draft that would have taken four hours.

That was week one. Saved some time. Felt like a nice trick.

But here’s what happened over the next few months. I started building prompts for the work that repeated. Meeting prep. First-pass research. Competitive analysis. Over 80% of AI users report saving at least an hour every day. I was saving more than that.

The real shift wasn’t the time savings. It was what we did with the time.

By month three, I stopped using AI to produce and started using it to think. Pressure-testing a product strategy I wasn’t sure about. Exploring edge cases I’d normally skip. Prepping for the hard conversation I’d been putting off. The time savings became capability savings. I wasn’t just faster. I was attempting things I wouldn’t have tried before.

By month six, the question changed entirely. It stopped being “should I use AI for this?” and became “how does AI change what’s possible here?”

75% of leaders and managers are already at this stage. The AI haves didn’t begin with more talent. They began with a first experiment. And compound interest did the rest.

The AI Have-Nots

The other arm of the K doesn’t look like failure. That’s what makes it dangerous. It looks like Tuesday.

You come in. You do your work. Same quality you’ve always delivered. But the person across the hall finished the same deliverable in a third of the time and spent the rest of their morning prototyping something new.

That’s the time trap. Without AI, a task that takes you three hours takes a colleague 45 minutes. You’re spending your entire day on execution. They’re already on the next problem. And the next one. And the next one.

Then comes the learning trap. Because your day is full, you never learn the tools. And because you never learn the tools, you miss what the AI haves discover every week. You’re not behind by one capability. You’re behind by a compounding series of capabilities you don’t even know exist.

I’ve seen this in my own teams. Good people. Same tools. But the ones who didn’t engage didn’t realize the baseline had moved until the gap was visible to everyone in the room. Not a talent problem. A compounding problem.

40% of American workers are completely disengaged from AI. No usage, no interest. 45% of non-users believe AI can’t help their work. One in five hasn’t even heard it’s being used at work.

The confidence trap might be the worst one. The longer you wait, the more intimidating it feels. Starting today feels harder than starting a year ago, even though the tools are easier to use.

Compound erosion doesn’t send a warning. It’s silent. You don’t feel the gap forming. You just wake up one morning and realize you can’t see across it anymore.

If You’re Just Starting Your Career — This Part Is for You

If you just graduated, or you’re in your first or second job, or you’re still figuring out what your field even looks like — there’s a version of the K that hits different.

The numbers are real:

• Entry-level jobs are down 13% in AI-exposed occupations (Stanford)

• UK tech graduate roles dropped 46% in a single year

• 66% of enterprises are reducing junior hiring because of AI

• 38% of engineering leaders say AI has reduced direct mentoring of juniors

The grunt work that used to build expertise — code reviews, financial models, research summaries — is the first work AI replaces. The career ladder isn’t harder to climb. The bottom rungs are disappearing.

Here’s the deeper problem: without domain expertise, you can’t give AI meaningful direction. Without a point of view on what “good” looks like, AI is just a fancy autocomplete. You get output. You can’t evaluate it.

But here’s what the research actually says. And it surprised me.

AI doesn’t help experts the most. It helps beginners the most.

• Brynjolfsson’s study: 35% productivity improvement for novice workers. Almost nothing for experienced ones.

• The HBS/BCG “Jagged Frontier” study: below-average performers improved 43% with AI. Top performers? Only 17%.

• Junior developers build skills 2-3x faster when they use AI as a tutor, not a code generator.

Your disadvantage — no point of view yet — is actually your advantage. No bad habits to unlearn. No “but I’ve always done it this way.” AI can compress the journey from “I don’t know this field” to “I have a point of view” from years to months.

Only if you use it to build judgment, not just generate output. Here’s how:

• Ask why, not just what. Don’t accept AI’s answer. Ask it to explain its reasoning. Ask for the counterargument. The act of evaluating AI’s thinking builds yours.

• Catch the mistakes. The moment you spot where AI got it wrong, you’re building domain expertise. Every correction sharpens your point of view.

• Reinvest the time. When AI handles the grunt work in 20 minutes instead of three hours, don’t fill those hours with more grunt work. Study the domain. Learn why things work, not just what they produce.

That first real experiment, the one where you stop watching from the sideline and actually engage — is the first deposit in your compound interest account.

The Question Behind the Question

We spent the last three years debating whether AI would take jobs. The better question was always which K it would put you on.

So here is my question to you: where are you on the K right now?

Are you compounding? Or are you standing still while the baseline moves under your feet?

And if you’re just starting your career: are you using AI to generate output? Or are you using it to build a point of view?

Because the K doesn’t close on its own. It only compounds.

#Leadership #AI #FutureOfWork #KnowledgeEconomy #CareerGrowth

My daughter’s favorite place in the world is a convenience store.

Not a theme park. Not a beach. A 7-Eleven in Tokyo.

Japan is her favorite country, so we end up going a lot. Sometimes twice a year. And every single trip, the first thing she asks when we land isn’t “where’s the hotel?” It’s “can we go to 7-Eleven?”

Fresh onigiri she picks out herself. Egg sandwiches that belong in a café. Little strawberry desserts she somehow always finds. She treats it like a destination.

She has never once asked to go to a 7-Eleven at home.

Same logo. Same name. Completely different place. And honestly? She’s not wrong. A five-year-old figured out something most business leaders miss.

Here’s the thing: 7-Eleven is an American brand. Started in Dallas, Texas, 1927. A company called Southland Ice selling groceries next to ice blocks. Japan licensed the brand in 1974, and a manager named Toshifumi Suzuki rebuilt the entire concept from scratch. Got so good at it that when the American parent went bankrupt, the Japanese licensee bought them.

The student bought the teacher.

And now the US is spending billions trying to copy Japan back. 1,300 new food-focused stores by 2030, Japanese-style onigiri and egg sandwiches in American aisles. Revamped stores are already showing 45% higher sales.

The Pattern Nobody Talks About

7-Eleven Japan runs a 27% operating margin. The US runs 3.5%. Same brand.

But this isn’t just a 7-Eleven story. Apple copied Xerox PARC’s graphical interface. Samsung studied the iPhone with a 132-page internal document that became court evidence. Toyota studied Ford’s assembly line and built a manufacturing system Ford itself couldn’t replicate. Tencent copied ICQ in 1999. Everyone called them China’s biggest copycat. Then they built WeChat, so original that Facebook and Instagram started copying them.

This pattern has been hiding in plain sight for decades. The companies that copy spend 60-75% less than the ones that invent. Almost half of market pioneers fail. And the fast followers? They capture three times the market share of the companies that went first.

Copying isn’t a shortcut. It’s a strategy. But only if you don’t stop there.

I’ve been studying this in business, in tech, and honestly in my own career. It always follows three stages. The companies that copied and failed? They stopped at stage one.

1. Copy to Learn (Not Copy to Ship)

Suzuki didn’t copy America’s hot dogs and Slurpees. He copied the format. The idea that a small store could serve a neighborhood’s daily needs. Then he rebuilt everything else.

That distinction is everything.

Tencent didn’t clone ICQ’s interface. They studied what messaging could become in a country where people used internet cafés, not desktops. Samsung didn’t replicate the iPhone. They studied what a smartphone needed to feel like, then went somewhere Apple refused to go: bigger screens, open ecosystem. Spotify didn’t copy Napster. They studied what Napster proved and built the legal version.

There’s a name for this: the “Fast Second”. You don’t win by being first. You win by learning faster.

This isn’t just a business pattern. Herminia Ibarra’s research in HBR found that new leaders do the exact same thing. They learn by imitating mentors. Copying their communication style, their meeting cadence, their decision-making approach. Feeling like a fake during this phase isn’t a red flag. It’s a sign you’re growing.

We all read Netflix’s culture deck. That’s the curriculum. Not the diploma.

2. Develop Your Own Point of View (POV)

This is the stage most people skip. And it’s the only one that matters.

Suzuki’s constraints became his advantages. Dense Japanese cities? He clustered 50-60 stores in tight pockets so one delivery truck could hit them all and shelves could rotate three times a day. A culture that demands quality from a corner store? He built unannounced inspections and daily reports. A market obsessed with freshness? He ate his own store’s bento every single day and gave feedback.

The format was American. The point of view was entirely Japanese.

I’ve done the lazy version of this. I once joined a new team and installed the exact operating rhythm from my previous role. Same standups, same sprint cadence, same review format. Looked right on paper. But the old team had years of shared trust and a culture of candor. The new team experienced the same rituals as micromanagement.

I was copying without a point of view.

It took me months to stop and ask: what would tight feedback and high standards look like for THIS team, at THIS stage? That question changed everything.

Your constraints aren’t reasons you can’t copy Google’s playbook. They’re the reason you can build something Google can’t.

3. You Know You’ve Made It When They Copy You Back

7-Eleven Japan bought the American parent. Now the US is trying to become Japan. Their new CEO said it plainly: “Long-term success breeds complacency. This is our opportunity to reinvent.”

Tencent: “biggest copycat” in 2010. By 2020, Western platforms were copying WeChat’s super-app model. Samsung: lost a billion-dollar lawsuit to Apple for copying the iPhone. A year later, the Wall Street Journal asked: “Has Apple Lost Its Cool to Samsung?” Toyota: studied Ford. Created TPS. Now Ford studies Toyota.

Hawaii tells the story at a smaller scale. Japan bought the Hawaii 7-Eleven operation in 1989. They didn’t copy Tokyo. They installed the principles and rebuilt for local context. Spam musubi since 1994. Different products in every neighborhood. Now the US mainland is studying Hawaii as the model.

And then there’s Famima!! A Japanese convenience store chain that opened in LA in 2004. Copied the output perfectly. Japanese food, clean stores, great design. Never developed a point of view about what a konbini should be in America. All stores closed by 2015.

The food was right. The thinking was missing.

The Real Question

The best companies didn’t start with a breakthrough idea. They started by studying someone else’s. The difference between the copycats that failed and the ones that built empires is always the same three stages.

So here is my question to you:

What are you copying right now? From another team, another company, another leader you admire?

Are you still at stage one, importing the playbook? Or have you started developing a point of view about what those principles would look like in your world, with your constraints, for your people?

Because copying is how the best companies start. The question is whether you have the courage to make it your own.

#Leadership #Strategy #Innovation #Management #SevenEleven #FutureOfWork

My wife caught me in the kitchen last Tuesday, pacing around the island, talking to no one. Hands gesturing at the air. Voice rising. Mid-sentence about an org design I’d been stuck on for weeks.

She stared at me for a good ten seconds. “Are you arguing with yourself again?”

I was. And in fifteen minutes of talking to myself, I’d cracked a problem I’d been staring at in a Word document for three hours.

About a year ago, I wrote my very first post in this series. It was called “Write to Think“ The argument was simple -

In the AI era, writing isn’t about communication, it’s about thinking. Writing forces clarity. It’s a gym for your brain.

I still believe every word of it. But I was incomplete.

Over the past year, something shifted in how I work. I stopped writing as my first step. I started talking. Not into a meeting. Not to a colleague. To myself — and increasingly, to an AI that listens, pushes back, and helps me iterate at a speed my keyboard never could.

And no, this isn’t about dictation. Apple has had that since 2011. Google since 2010. Those tools transcribe your words. What I’m describing is different: a thinking cycle that runs faster when you speak it than when you type it. The AI tools that matured this past year made that gap impossible to ignore.

Speaking Forces Thinking

Here’s what nobody tells you about typing: you self-edit before the thought fully forms. You stare at a blinking cursor. (No, not that Cursor, though I love that one too.) You type half a sentence. Delete it. Retype. The thought never fully arrives because it keeps getting filtered through the bottleneck of your fingers.

When you speak, you can’t do that. You have to push the idea forward, start to finish, in real time. Speaking demands that you organize, commit, and articulate — out loud, in the moment.

And then you catch yourself mid-sentence: “Actually, no, what I really mean is...” You reframe. You self-correct. You discover what you actually think by hearing yourself say it.

That self-correction loop is the entire point.

Turns out, psychologists have known this for nearly a century. Vygotsky showed in the 1930s that talking to yourself isn’t a quirk. It’s how humans develop higher-order thinking. He called it “private speech“, and decades of research have confirmed it improves planning, problem-solving, and self-regulation, well into adulthood.

There’s even a name for it: the “self-explanation effect“. People who verbalize their reasoning show up to 20% improvement on complex tasks compared to those who just re-read the material.

We’ve always known this works. We just spent forty years typing instead.

So the quality of thinking improves when you speak. But that’s only half the story. The other half is speed.

The Conversation Loop — A Faster Flywheel

In “Write to Think“ my thinking cycle was: brain dump -> shape your thoughts -> spar with AI -> make it actionable. Good cycle. But every turn of that flywheel required typing. And typing runs at 40-50 words per minute for most people.

Speaking? About 200 words per minute — roughly 4x faster.

That’s not just a 4x improvement in input speed. It’s a 4x improvement in iteration.

Let’s do the math:

• The write-to-think loop: think -> type your question (slow) -> wait for AI -> read -> think -> type your follow-up (slow again). Each full cycle: 8-10 minutes.

• The talk-to-think loop: speak your question -> read AI response -> speak your reaction right back -> response again. Each full cycle: 2 minutes.

Same flywheel from “Write to Think” But now the cycle runs 4-5x faster. More iterations in less time. Better thinking, compressed.

Here’s what that actually looks like. Last week I needed to prepare a strategy brief. Normally I’d open a Word doc, stare at it, type for two hours, delete half of it, try again.

Instead, I put on my airpods and opened Wispr Flow (and no, this is not a sponsored post. Nobody’s paying me to say this. It just genuinely changed how I work). I paced my office and spoke my rough thinking for about three minutes. Clean, formatted text appeared on screen. No “ums,” no filler, no cleanup needed.

I pasted that into Claude. “Challenge this reasoning.” Read the pushback. Spoke my response to the pushback. Another round. Three rounds total, fifteen minutes. I had a structured first draft that would have taken an entire afternoon of typing and staring.

Brain dump -> shape -> spar. The cycle I described a year ago. Just running at a completely different speed.

Now, you might be thinking: ChatGPT has a voice mode. So does Gemini. Why not just talk to them directly?

I tried. You start explaining your thinking, pause to collect a thought, and the AI jumps in. It mistakes your pause for a finished sentence. You restart. It happens again. The turn-taking is still clunky enough to break the exact thinking flow you’re trying to protect.

But there’s a bigger reason I don’t use them. Those voice modes lock you into one app. I don’t want to do all my thinking inside ChatGPT.

I want to speak into whatever I’m already working in. Cursor has voice input -- I use it when I’m coding (or better yet, org design, as I discovered lately). Notion when I’m planning. Claude when I’m sparring. Slack when I’m replying to someone. A system-wide dictation layer means I speak, clean text shows up wherever my cursor is, and I pick the best tool for the job.

That’s the real shift. Not “use this one AI’s voice feature.” It’s making your entire workflow voice-native.

Why Now? From Transcription to Comprehension

We’ve had voice dictation for fifteen years. But every tool until recently did one thing: transcribe. Every “um,” every false start, every “so basically” -- faithfully captured and dumped on your screen. You’d spend twenty minutes cleaning it up. The tool created more work, not less.

What showed up this past year is something different: comprehension.

Tools like Wispr Flow, and others emerging in this space, don’t just hear your words. They run LLMs under the hood to understand what you mean. They know what app you’re in and adapt the tone. They read the context around your cursor. You say “so the main thing is we need to, like, rethink how we onboard people from scratch” and it outputs: “We need to fundamentally rethink our onboarding approach.”

The filler is gone. The intent stays.

Old dictation gave you a transcript you had to fix. AI-powered voice gives you a clean first draft you can build on. That’s the difference between a tool that slows you down and one that accelerates the entire talk-to-think cycle.

And it’s getting faster. The speech models powering these tools improved dramatically in the past year. Real-time processing. Better accuracy across accents and technical vocabulary. The gap between what you say and what shows up on screen is now nearly zero.

Which means the bottleneck was never your brain. It was never even your voice. It was the technology between the two. And that bottleneck just disappeared.

One Year Later

A year ago, I wrote “Write to Think” and told you to write more in the AI era. I still believe that. Writing is still the gym. It still forces clarity. It still makes your thinking sharper.

But if I’m honest about what actually changed my productivity this past year, it wasn’t writing more. It was talking more. Speaking rough thinking out loud, letting AI turn it into structured text, then sparring with that text at the speed of conversation instead of the speed of typing.

The “Write to Think” flywheel didn’t break. It just got a motor.

So here is my question to you: when was the last time you solved a hard problem by typing about it, and when was the last time you solved one by simply talking it through?

#Leadership #Productivity #AI #VoiceAI #WriteToThink #TalkToThink #FutureOfWork

You don’t have to be a member of the Academy to appreciate World War Z. You don’t even have to like zombie movies. But you do need to know the concept that movie burned into the corporate subconscious: The “Tenth Man Rule”.

It’s based on real Israeli intelligence protocols (or at least, the Hollywood version of them). The rule is simple: If nine people in a room agree on a strategy, the tenth person has a mandatory duty to disagree.

Even if the dissent seems crazy. Even if it seems unlikely. The Tenth Man has to assume the other nine are wrong to prevent the kind of groupthink that gets you eaten by zombies.

Now, as an engineering and data leader, I build products, and I generally don’t have to worry about zombies. Well, except for that one legacy codebase we’re all afraid to touch, but that’s a different horror story.

But I do have product launches. I have market shifts. And mostly, I have blind spots.

And here is the honest, slightly terrifying truth I don’t usually say in All-Hands meetings:

When 100% of you nod at my strategy, I don’t feel supported. I feel unprotected.

The Nightmare of the Polite Meeting

In the past years, the scariest meetings I sat in weren’t the ones with shouting matches (and I love heated debates, it shows we care, but that’s a story for a different time). They were the ones with silence.

You know the ones. I present a roadmap that is clearly aggressive, maybe a little delusional. I look around the table.

Nod. Nod. Nod. “Looks good.” “We can do it.”

I walk out of those meetings sweating.

Why? Because, in these type of meetings, it shows that I live on top of the Iceberg of Ignorance.

That’s a real study, by the way. Sidney Yoshida ran it. The data says that senior leaders only see about 4% of the actual problems on the ground.

Granted, my doubts on the 4% number. And I see dashboards, WBRs beyond just the strategy decks and the P&Ls.

That said, let’s assume - you live in the 96%. You see the line of code that produces technical debt, the API latency that could cripple a tier-1 service, the client and customers grumbling, and the fact that the “simple migration” I just pitched is actually going to require rewriting the entire backend.

If you stay silent to be polite, or to be a “good soldier,” you aren’t validating my genius. You are letting me steer the ship into an iceberg I literally cannot see because I’m standing on the bridge looking at the clouds.

Be My Sonar, Not My Echo

I don’t need an echo chamber. I can get an echo chamber by talking to myself in the shower.

What I need is Sonar.

An echo just repeats what I said: “Course looks good, Captain.” (This feels nice for my ego, briefly, until we sink).

Sonar pings back the hard truth: “Obstruction ahead. Turn hard starboard or we die.” (This creates friction. It’s annoying. It saves the ship).

So, how do you be the Sonar without getting fired for being “negative”? You have to learn the art of the Strategic No.

Here are three ways to tell me I’m wrong that will actually make me love you.

1. The Time Traveler (The Pre-Mortem)

I usually suffer from “optimism bias.” I love my plan. It’s my baby.

Don’t tell me “This is risky.” I’ll get defensive.

Instead, be a time traveler. Say this:

“Let’s assume it’s six months from now and this launch failed. What broke?”

Now we aren’t arguing about my idea. We are solving a hypothetical puzzle. You shift me from “Defending My Ego” to “Fixing the Future.”

2. The Price Tag

I honestly don’t know the true cost of what I’m asking half the time. I just know I want it fast.

Don’t tell me we can’t do it. Tell me the price. And tell me regardless how many times I said I hated the word LOEs.

“We can hit that aggressive deadline, but the ‘price’ is skipping the data validation layer. If the source schema changes, the entire pipeline crashes. Are you willing to buy that risk?”

See what you did there? You didn’t block me. You became my financial advisor. You gave me the agency to decide if the speed is worth the cost. (Spoiler: It usually isn’t) (And our Product leaders are usually doing way better than Engineering/Science leaders on this - oh the empathy).

3. The Recommendation

Don’t just drop a problem on my desk. Bring a shovel so we can dig our way out.

If you’re going to shoot down my plan, you need to have a Point of View on where to go next.

“The timeline is tight. I recommend we cut Feature Z to ensure stability. Here is the draft plan.”

That proves you aren’t just a ticket-taker. You’re an owner.

The “Safe Word”

Now, there is a catch.

Amazon and Netflix made “Disagree and Commit“ famous. It’s a great corporate slogan, but it can feel a little cold.

Here is how I translate it: The Audit vs. The March.

Before the decision is made, we are in the Audit Phase. I need you to tear it apart. Be the Tenth Man. If you stay silent here, you are failing the team.

But once the decision is made? We switch to the March Phase. We stop auditing. We start rowing.

The most valuable thing you can say to a leader is this:

“I’ve flagged the risks, I’ve told you the cost, but now that you’ve made the call, I’m going to make this work.”

That isn’t submission. That is professional agility.

Silence is Suspicious

Here is the bottom line.

When I hire smart people, I’m not paying for their compliance. I’m paying for their friction.

If we are in a room and we all agree, then most of us are redundant.

So tomorrow, look around your next meeting. If it feels too comfortable, if the nods are too automatic... that is your cue.

Be the Tenth Man. Be the Sonar.

I don’t need you to cheer me on. I need you to check the instruments.

And BTW, be ready for my follow-up question too - “what do you think?“

Last week, I confessed to being “lazy” with my notes. I argued that the ROI of organizing files is zero, and that we should treat our Second Brains like data lakes - dumping everything in and using AI to retrieve it later.

This week, I’m doubling down. I’m going to tell you how to be “lazy” with AI empowered automation.

Okay, here’s where the idea came from -

This might not surprise you - most of us hold a rigid belief about leadership: My primary duty is to empower my team to be the force multipliers. If I am heads-down optimizing a Spark join, fine-tuning hyperparameters, or debugging a race condition, I am not looking at the horizon. I am not clearing obstacles for my crew. I am not leading.

As for me, I stepped away from code contribution for a long time - ever since I became a people manager - a loong time ago. It is a principle I was taught early in my career, and one I now pass down to every leader I mentor: You must be the Captain of the Ship.

Now the theory is - the Captain’s role is not to be the best sailor on the rigging or the fastest mechanic in the engine room. The Captain’s role is to stand on the bridge, scan the horizon for storms, and set the bearing. If I am heads-down fixing a piston (or code), no one is steering the vessel. The moment I touch the codebase, I stop being an enabler for my team and start being a bottleneck.

That was the rule. Until recently, when I found a surprising way to do this better - coding. And more precisely, Vibe Coding.

The fact is that with Generative AI, the economics of software creation had fundamentally inverted. I realized that in addition to building proprietary data, I also needed to build my own tools - hyper-personalized, bespoke tools - to navigate the complexity of modern leadership.

I call this practice “Vibe Coding Burner Code for your Leadership” And the output isn’t DTC software. It is something much more powerful for a leader.

The Economics of Burner Code vs Production Assets

Now, for my fellow non-tech leaders, please don’t get scared away by “coding.” It’s actually quite simple. Let me explain.

To understand why you should be building software, you have to realize that “Code” is not a monolith. There are two distinct species, and mixing them up is why most leaders hesitate to start.

• Type 1 is the Asset. This is what your engineering team builds. It is designed for customers, which means it lives under the tyranny of the “Usual Suspects”: five-9s reliability, massive scalability, and perfect security. It is expensive because it has to be. It demands rigorous testing, peer review, and maintenance. Its goal is Engineering Excellence.

• Type 2 is the Utility. This is what I build. I call it Burner Code because it is designed for speed. The bar here is dramatically lower: “Does it run?” It lives on my laptop, not a server. It doesn’t need to scale to a million users; it just needs to work for one. And the cost isn’t measured in sprints or budget cycles - it’s measured in minutes of “vibing” with an AI. The goal isn’t excellence; it is simply high ROI.

This is the mental shift.

In the engineering world, “Burner Code” sounds reckless. In the executive world, it is a superpower. It’s like a “Burner Phone” - you use it for a specific mission, and when the mission is done, you toss it. You have zero attachment and zero maintenance debt.

If I spend 4 hours debugging a complex script that saves me 5 minutes of work, that is terrible ROI. I should have just done the work manually.

But if I spend 15 minutes prompting an AI to write a messy, ugly script that saves me 10 hours of manual data entry? That is Infinite ROI.

I don’t care if the code is “spaghetti.” I don’t care if it lacks error handling. If it breaks, I don’t fix it - I throw it away and generate a new one.

Here are two examples of how “Burner Code” has force-multiplied my leadership in ways that off-the-shelf software never could.

Story 1 - The Mail Scanner

I had a ghost in my machine. specifically, a folder on my Mac that held 25 years of scanned mail, documents, and insurance policies.

Because I used to be a librarian, they were meticulously organized by date. But because I am human, I could never find anything. If I wanted to find “that one insurance policy from 2014,” I was at the mercy of Spotlight search and my own hazy memory of how I named a file a decade ago. It was dead data. I wanted it alive inside my AI Second Brain (Notion).

The problem was the friction. To fix this consuming Sunday fun meant one of two painful choices:

1. The Manual Slog: Spend my next 10 Sundays manually uploading and tagging PDFs.

2. The Software Hunt: Scour the App Store for the “perfect” document management app. I’d spend hours reading reviews, pay a $15 monthly subscription for a slick tool that promises to “organize my life,” and eventually realize it forces me into their rigid structure. I’d be stuck manually tagging files in a proprietary system that doesn’t talk to the rest of my brain.

So, I tried the Burner Code way.

I didn’t write a spec. I didn’t open a Jira ticket. I opened Cursor and typed a prompt as casual as a text message:

> “Hey, I have a local folder of PDFs. Write a script to OCR them, extract the text, and shove them into Notion. Use the file creation date as the property date.”

The AI generated the code. I ran it. It crashed immediately (Notion API rate limits).

In my past life as an engineer, I would have sighed and spent the afternoon reading API documentation to implement exponential backoff.

As a Vibe Coder, I just typed: ‘It crashed. Make it sleep for 1 second between uploads.’

It fixed the code. I hit run again.

Twenty minutes later, while I was sipping my coffee, that ugly, 50-line script chewed through two decades of bureaucratic debris. It indexed my entire financial and legal history into my AI.

Buying a “perfect” software would have cost me a monthly subscription and forced me into a generic workflow that didn’t fit my needs. My Burner Code cost $0, saved me months of manual labor, and was hyper-personalized to exactly how I wanted my archive to work.

Story 2 - Escaping Spreadsheet Paralysis

I was paralyzed by a file named FY26_Org_Model_v4_FINAL.xlsx.

As leaders, we live in these models. We don’t just run them to execute changes; we run them to think. We analyze our teams regularly - not always to take action, but simply to know the terrain. We need to visualize the structure, simulate the “what-ifs,” and play with different configurations to find the one that unlocks the most impact.

But the moment you try to simulate that change, the file becomes a drag. You are trying to answer a simple strategic question - “What happens if we move Data Science closer to the vertically optimized business teams?” - but the tool fights you.

You move a row. A VLOOKUP breaks. You fix the formula. The pivot table for the “Budget” doesn’t refresh. You email Finance to validate the headcount costs. They take 4 days to reply. By the time you get the answer, you’ve forgotten the hypothesis.

The tool was dictating the speed of my thought. This rigid workflow forced me to choose between accuracy (slow) and agility (messy).

So, I tried the Burner Code way.

I realized I didn’t need a spreadsheet; I needed a simulator.

I fed the raw CSV exports into Cursor and asked for the analysis that actually matters to a leader:

> “Ingest these CSVs. Build me an interactive dashboard to track my core health metrics: Span of Control, Location Strategy, Reporting Layers, and FTE/Contractor ratio. Now, make it playable. Let me drag this entire Functional Team to a different VP and instantly see the impact on our ‘Big Rock’ capacity vs. ‘Fast Lane’ throughput. Also, add a toggle: What happens to the budget if I swap the next 5 Data Science hires for Machine Learning Engineers?”

The Result:

Now I didn’t spend the afternoon debugging Airflow DAGs, wrestling with Pandas dataframes, or waiting for a Spark job to spill to disk. I bypassed the entire ETL grind. Thirty minutes later, I had a functioning “Org Sandbox” running in my browser - rendering insights with zero latency.

It wasn’t enterprise software. It didn’t have a login screen. But it allowed me to play “God Mode” with my organization. I dragged cards. I tested hiring mixes. I ran 50 scenarios over lunch in the time it usually takes to debug one Excel formula.

The spreadsheet is the default tool for this, but it is a poor substitute for imagination. The limitation of a spreadsheet isn’t its features; it’s that it cannot read my mind. It forces me to translate my fluid strategic thoughts into rigid static rows. No generic tool, no matter how advanced, can perfectly predict how I want to simulate a specific trade-off or visualize a hiring risk. Some people call what I built “hyper-personalization.” I agree. But ultimately, it works because it is a direct brain dump. It fits the shape of my brain because it came directly from it.

The Death of Im Not Technical

We used to have a valid excuse. We could say, “I can’t solve this because I don’t know how to code.”

That excuse is gone.

The barrier to software creation has dropped from “Years of Study” to “Minutes of English.”

This implies a terrifying new reality for leaders: If you are stuck manually updating a spreadsheet or waiting for your Data Analytics team to build a dashboard, it is no longer a technical limitation. It is a choice.

You are the only person who knows exactly how your brain works. You are the only one qualified to build the tools that extend it.

So stop waiting for a tool that reads your mind. Build the tool that is your mind.

Use it. Drain the value. Throw it away.

Don’t build for the Ages. Build for the Afternoon.

#VibeCoding #Leadership #Productivity #AI #ExecutiveOps #FutureOfWork

What is your favorite thing to do on a Sunday night?

For me, it should be watching the Lakers game. LeBron is likely doing something defying the laws of physics in his 23rd season.

But for years, I wasn’t doing that.

Okay, actually, that’s a lie. I was watching it. But I wasn’t present. I was watching it while stressing over my absolute least favorite thing.

I was staring at a folder in Evernote called _INBOX_PENDING.

If you know GTD, you know this folder. It’s the digital purgatory where you dumped every PDF, every screenshot, and every “important” article from the last week, promising yourself you’d file them “later.”

Well, later is here. And it sucks.

For the last 20+ years, I have been telling myself a massive lie about productivity. The lie is that being “organized” means having a perfect taxonomy. We are told that if we just find the right hierarchy - if we just create the perfect system of nested folders - we will finally feel in control. (for some of us from the Disney mafia, this was very much true with the legacy GoPub systems, I just picked up this context for my personal life.)

So we spend our Sundays acting like librarians. We tag. We color-code. We move files from “Downloads” to “Projects > 2026 > Q1 > Research”.

But let’s stop lying to ourselves.

It feels like work because it’s tedious. It feels like value because it’s orderly. But in reality, we are just rearranging deck chairs on the Titanic. We are spending more time maintaining our “Second Brain” than we are actually using it to think.

Now till AI became a thing, I started to realize that I wasn’t building a knowledge base. I was digging a Digital Graveyard.

Because once a file went into that perfectly organized folder structure... I never opened it again.

The Death of the Green Elephant

I didn’t come to this realization willingly. I was forced into it.

For 15-ish years, Evernote was my external hard drive. I was a power user. I had thousands of notes, meticulously tagged. I bought the different type of scanners, and digital pencils.

Then, the product changed. It got slow. The features bloated. The “Green Elephant” in the room wasn’t remembering anything anymore; it was just taking up space. And honestly it was probably the most downloaded 1-star app in Apple AppStore for a long time.

But the real breaking point wasn’t the app. It was my own workflow with AI as a new tool.

I found myself doing something ridiculous. I would open an old note in Evernote, hit Cmd+C, switch tabs to ChatGPT, paste it in, and type: “Read this and tell me if I missed anything important”.

Stop and look at that workflow.

I was manually acting as the data pipe between my storage (Evernote) and my compute (LLM). I was the friction.

Evernote didn’t grow, that doesn’t mean I shouldn’t!

It hit me like a ton of bricks: Why isn’t the compute living inside the storage?

Why am I paying rent for a storage unit that makes me do all the heavy lifting?

The “Dumpster” Architecture

That was the moment I stopped acting like a Librarian and started acting like a Data Engineer.

I moved everything to Notion. (Note: I don’t work for them, I just use them. Use whatever works for you).

(And I’m no expert with Notion either - here’s a good YouTube video if you’re interested)

But I didn’t move there to build a pretty dashboard with emojis and inspirational quotes. I moved there to build a Data Lake.

If you’re in data engineering, you know the shift. We stopped building rigid “Data Warehouses” years ago because the schema maintenance was a nightmare. We moved to “Data Lakehouse“ - just dump the raw data in one big pile and structure it later when you query it.

So I applied that to my life. I call it the “Junk Drawer” Protocol.

• I stopped tagging.

• I stopped filing.

• I stopped worrying about “where” things go.

I created one massive database called “The Archive” for all legacy Evernote notes. (The migration sync process was excruciatingly painful, and rate limited after 100 notes.) And I just dump everything there. Plus some simple main pages for home and work.

The friction of capture dropped to zero. But the magic of retrieval went through the roof.

The “Auto-Index” Ritual

Now, when I save a messy meeting notes or a rambling voice memo, I don’t format it.

I just hit a button that asks the AI to do two specific things inside the note:

1. Connect the Dots: I ask it to “extract the context and connect it to previous notes on this topic.” It reads the note and links it to the 3 previous related notes about that project. It builds the breadcrumbs for me.

2. The “Search Beacon”: I ask it to generate a Key Takeaways section at the top. This isn’t just for me to read; this summary becomes the high-quality source text for future semantic searches.

I give it a 30-second glance to make sure it didn’t hallucinate. Then I close the tab.

I spend 30 seconds reading instead of 30 minutes filing.

The “Nod”

Now, for my fellow data scientists and engineers reading this - I know what you’re thinking.

“You just described RAG.”

Yes. Exactly.

I just built a personal Retrieval-Augmented Generation pipeline for my life. My messy notes are the proprietary vector embeddings; my question is the prompt.

Instead of searching for keywords (which fails if I search for “budget” but wrote “cost reduction”), I search for concepts.

I ask my Second Brain:

“When is my next doctor appointment? Any specific questions I should last based on my lab results?”

The Notion AI performs a semantic search, retrieves the relevant chunks from five different messy documents, and synthesizes an answer: “Here’s the details for your next appointment. I’d suggest to get the panel blood work done 2 days prior to it, I set a reminder for you in the Home Notes along with the list of questions to ask.”

It finds the truth, even if the keywords don’t match. It gives the LLM “long-term memory” consisting of my own life.

The ROI of Laziness

My Sundays are mine again.

I watch the Lakers game (and yes, they look good in the Sunday whites). My notes stay messy. My “Inbox” is technically overflowing but archived, but my mind is clear.

We have to stop treating “organization” as a virtue. It’s a mechanic. And in the age of AI, it’s a mechanic that should be automated.

Be honest with yourself. When was the last time you actually went back into that perfectly organized ‘Resources’ folder you spent hours curating?

Never.

Because if it takes work to file it, it’s a chore. If it takes work to find it, it’s a graveyard.

I stopped trying to be a Librarian and started building the data assets that only matter to me. My note-taking changed from ‘Just-in-Case’ filing to ‘Just-in-Time’ retrieval.

So, here is my question to you:

How much of your “Second Brain” is actually just a digital graveyard?

(Next Friday, I’ll share how I use this same “lazy” mindset to automate my boring admin work using ‘trash code’ - without writing a single line of Python myself.)

#Leadership #Productivity #AI #Notion #SecondBrain #DataEngineering #FutureOfWork

If you live on the West Coast, you know the sight.

It’s 11:00 AM in Burbank near where the Studios are. The drive-thru line is already 15 cars deep, snaking around the building and spilling out into the street to block the right lane of traffic.

You walk inside. The air hits you - toasted buns, grilled onions, and the frantic energy of the lunch rush.

To the customer, it looks like a synchronized dance. The crisp white uniforms. The paper hats. The “Animal Style” shouts. It feels nostalgic. It feels simple.

But step behind the counter, and the illusion of simplicity vanishes.

This is not a retro diner; it is more of a biological battlefield, as my AI research friend tells me. The deep fryers drift in temperature. The potatoes change chemical composition by the hour. And the entire system relies on “servers” who are essentially teenagers thinking about prom.

The Store Manager stands at the “Final Window” and spots a Double-Double. The lettuce is crisp, the bun is toasted, but the cheese fold is off by a fraction of an inch. It is edible. It is warm. It is 95% perfect.

The Manager doesn’t fix it. They trash it. They stop the line.

In 2018, it was revealed that the average In-N-Out store manager earned $160,000+ - triple the industry average. Why?

Because in a chaotic system, the person who refuses to ship “mostly okay” is the only asset that matters. The market pays a massive premium for the only person in the building who cares more about consistency than throughput.

And the reality I’ve been thinking about lately: We are all In-N-Out managers now.

The Shift

Why does this resonate so deeply? Because for the last twenty years, we didn’t have to worry about the “cheese fold.” We built with “steel.”

Traditional software engineering is Applied Physics. It is deterministic. You write a function. It executes. It does not get tired. It does not “hallucinate.” We grew accustomed to being Architects - designing structures that stood still.

But Generative AI has forced us into the kitchen. We have traded our steel for “biology.”

An LLM isn’t a calculator, it feels more like a biological system. Like that 19-year-old line cook, it has “moods” (temperature). It gets “confused” (drift). It “lies” to save face (hallucination).

The mistake I see a lot of best leaders making right now is trying to manage this biological chaos using an architect’s blueprint. We are trying to code our way out of a problem that can only be operated.

The friction comes from a simple mismatch: we are acting like Architects, but the job now demands a Shift Manager. We need to trade the safety of the blueprint for the heat of the kitchen and learn the sweaty, unglamorous art of Operational Rigor.

Here are three learnings from In-N-Out managers that we can apply directly to our AI strategy.

1. The “No-Avocado” Rule

In-N-Out managers face constant pressure to add items. Customers beg for avocado. They want bacon. They want chicken. It would be profitable. Yet, the answer is always No.

Why? Because avocado oxidizes. It turns brown in minutes. By introducing one new ingredient, you introduce a variable that decays faster than the meat. You create a “weak link” in the supply chain that threatens the consistency of the entire system.

In the AI world, we are currently making the opposite mistake. We are obsessed with volume, not value. We launch 100 different “menu items” - a Legal Bot, a Marketing Agent, a Coding Assistant - hoping one of them tastes good.

The result isn’t business impact; it’s a menu of shiny AI powered apps no one uses. We create a bloated portfolio of tools that look impressive on a roadmap but deliver zero sustainable business impact. We are scaling complexity before we have established value.

Earn the Expansion. Think twice about impact before you expanding the portfolio. Focus on the “Cheeseburger” - that single, boring use case that solves a real business problem with 100% reliability. You don’t earn the right to add the second item until the first one is actually feeding the business.

2. The “Sugar” Test

The potato is a liar.

It looks consistent on the outside, but its chemistry changes with the weather. A potato that looks perfect might have high sugar content. If you fry it, it burns.

So, the Manager performs a “Fry Test” every morning. They don’t trust the supplier’s rating. They slice a batch, fry it, and see how it reacts in their oil, at their temperature. They verify the ingredient works for their specific standard before serving it.

We are drowning in options. We have proprietary models, open-source weights, and home-grown fine-tunes. We look at public leaderboards and benchmarks to decide which one to use.

But public leaderboards are like supplier ratings - they tell you how the potato performs on average, not how it performs in your kitchen. No one knows your use cases better than you. A model that aces a generic benchmark might completely fail on your proprietary data.

Run Your Own Fry Test. Build your own internal evaluation set based on your specific use cases. Pre-test every model - whether it’s GPT-5 or a local Llama - against your data. Does it burn? Is it consistent? You can’t pick the right “potato” for the job if you haven’t tasted it first.

3. The Expensive Trash Can

The Manager and the Cook are natural adversaries. The line Cook’s incentive is speed. The Manager’s incentive is perfection.

The most critical tool in the store is the Trash Can.

To an outsider, the trash can looks like failure. It looks like lost money. Why throw away a burger that is 95% perfect? But to the Manager, the trash can is an investment. The cost of the wasted ingredients is the “tuition” they pay to ensure the customer never sees a mistake.

Now when building with AI, we are allergic to the trash can. We treat “discarded compute” as inefficiency. We want every token we pay for to reach the user. This is wrong. In a probabilistic system, “waste” is the only way to guarantee quality. You might need to generate three draft emails to find the one that is safe to send.

Budget for the Trash Can. Forget zero shot a perfect app. Build systems that generate, critique, and discard in the background. If you aren’t paying for “wasted” tokens - if your trash can is empty - your quality standards are just a hallucination.

The Apron Over the Hoodie

We spend so much time looking for the “10x Engineer” or the “10x Researcher”—the geniuses who invent the recipes. But the In-N-Out story teaches us that the real value isn’t in the recipe. It’s in the discipline.

The next era of AI won’t be defined by who has the smartest model. It will be defined by who has the most disciplined kitchen.

So, here is my question to you:

Look at your current AI projects. Are you acting like a Chef trying to invent a new dish, or a Manager trying to stop a bad burger from leaving the window?

I’d love to hear your thoughts.

#Leadership #AI #OperationalExcellence #Management #InNOut #FutureOfWork #Strategy

I was flying back home from the holidays last week, and the energy in the cabin was frantic.

You know the vibe. Every overhead bin is stuffed to capacity. The flight attendants look exhausted. We had finally pushed back from the gate, and I was looking at the Flighty app for the potential early arrival to beat the crowd at the immigration line. I’ve flown million+ miles; I have the system down to a science.

Then, the plane stopped. The engines spooled down.

The Captain’s voice crackled over the intercom: “Folks, we’ve got a maintenance indication on the panel. We’re going to have to return to the gate to have a mechanic take a look”.

The collective groan was visceral.

My internal monologue kicked in immediately. Even with all my experience, I wasn’t thinking about safety. I was thinking about competence - you know - the CoE culture you have fostered and celebrated.

“Why now?”

“The plane sat at the gate for an hour. Why didn’t they catch this during the turn?”

“This is a failure of planning.”

I looked across the aisle. Sitting across was an off-duty pilot, deadheading back to his home base. He didn’t look annoyed. He didn’t check his watch. He just closed his eyes and leaned back.

“Doesn’t this drive you crazy?” I asked him, motioning to the jetway connecting back to the plane. “We were ten feet from freedom.”

He opened one eye and smiled.

“Not really,” he said. “A delay at the gate is inconvenient. A problem in the air is tragic. If we’re turning back, it means the system is working. It means someone - probably a junior mechanic or a first officer - saw something and wasn’t afraid to stop the show.”

He paused, then added ...

“I’d rather be on the ground wishing I was in the air, than in the air wishing I was on the ground.”

That conversation shifted my entire perspective.

Now, I, for one, look at a gate delay and see a broken process. But if you look closer, you realize that a gate delay is not a failure of planning. It is a spectacular success of culture.

It is a $100M+ machine stopped by a $20 decision - proof that the culture values Stop Work Authority more than the schedule.

The Last Minute Pivot

Most leaders view “last-minute” issues as a symptom of fragility. We ask, “Why didn’t we catch this upstream?”

But in complex, high-stakes environments - whether it’s launching a rocket, deploying code, or flying a jet - the “last minute” isn’t an arbitrary point where luck runs out. It is a functional boundary.

In aviation, the aircraft sits dormant at the gate until the final moments before pushback. It’s only then that the systems are fully energized, pressurized, and handed over to the human operators. That transition creates stress.

The contrarian view is this: “Last minute” catches are the only proof that your safety mechanisms are actually working.

If your team never pulls the emergency brake right before a launch, you don’t have efficiency. You have a ticking bomb.

The Red Wine Save

That conversation sparked an obsession. I wanted to see if this “culture of stopping” was just a nice theory or if it actually played out in the messy reality of the tarmac. So, I did my homework. I started digging through pilot forums and incident reports to see what “pulling the cord” actually looks like.

That is where I found this thread on r/aviation, where a pilot shared a story that perfectly illustrates the cost - and value - of vigilance.

He was performing a pre-flight walkaround in sub-zero temperatures (-20°F). He’s tired, he wants to go home, and the pressure to leave on time is immense. But he notices a long, thick stripe of frozen red liquid across the bottom of the fuselage.

In aviation, red fluid usually means one thing: Skydrol. Hydraulic fluid. The lifeblood of the flight control system.

“Well, [shoot], this looks like the mother of all hydraulic leaks,” he thought.

He makes the call. He delays the flight. Mechanics swarm the plane. They tear off panels, bleed lines, and refill reservoirs to troubleshoot the leak. The passengers are furious. The delay clocks in at over two hours.

The verdict?

It wasn’t hydraulic fluid. It was Merlot.

A flight attendant on the previous leg had poured a leftover bottle of red wine down a galley sink, which discharged overboard via a drain mast. The extreme cold had frozen it to the fuselage instantly.

Now, would it play out this way everywhere? This pilot would be reprimanded. “You delayed a flight for wine? You cost us thousands of dollars for nothing?”

But in a High Reliability Organization (HRO), this was a “Good Catch.”

The delay was the price of verification. The pilot was embodying what organizational theorists Karl Weick and Kathleen Sutcliffe call a “Preoccupation with Failure.” In their HRO framework, this principle demands that you treat any weak signal as a symptom of a catastrophic problem until proven otherwise.

The pilot prioritized investigating the anomaly over guessing the outcome. He didn’t assume the best-case scenario (it’s just a spill); he respected the system enough to assume the worst-case scenario.

He stopped the line because he didn’t know the answer.

And he can.

The Toyota Andon Cord

This brings us back to the most famous concept in manufacturing history. You likely know the story. It’s the Andon Cord.

In the Toyota Production System, the Andon Cord is a physical rope that hangs above the assembly line. If any worker - from the newest hire to the senior manager - sees a defect, they pull the cord.

The entire line stops.

It is expensive. It is disruptive. It is annoying.

But Toyota realized that fixing a defect on the line costs $1. Fixing it after the car is sold costs $1,000 (and your reputation).

In aviation, the “cord” is the logbook entry. When a pilot or mechanic writes up a fault at the gate, they are pulling the cord. They are stopping the massive machinery of the airline schedule because they see something that doesn’t look right.

The “Gate Return” is simply the Andon Cord in action.

For the fellow engineers reading this, you know this concept by a different name: “Shifting Left“. In engineering, we obsess over moving testing earlier in the development lifecycle because fixing a bug in production is 100x more expensive than fixing it in code review. A gate return is the ultimate physical manifestation of Shifting Left. It is the discipline to catch the failure while you are still on the ground (development), rather than dealing with it at 30,000 feet (production).

Leadership Takeaway

Here is the uncomfortable reality I see in many teams. We claim to want “Agile” and “Quality,” but we punish the people who pull the cord.

We create cultures where raising a “last-minute” issue is seen as being “not a team player.” So, people stay silent. They ship the code with the bug. They sign the contract with the bad terms. They launch the product with the flaw.

They normalize the deviance because they are afraid of the delay.

But if you can’t stop the line, you don’t have a process. You just have momentum.

The Andon Cord is the difference between driving the machine and just holding onto the wheel.

Do your teams have the authority - and the psychological safety that Amy Edmondson champions - to halt a product launch or a deal closing 10 minutes before the deadline if they see red fluid on the fuselage?

If they don’t, you aren’t “agile” - you’re just fragile. You are flying on luck, not on process.

So, here is the question:

Who on your team has the power to pull the Andon Cord? And when was the last time you thanked them for doing it?

I’d love to hear your thoughts.

#Leadership #AviationSafety #HighReliabilityOrganizations #Culture #StopWorkAuthority #Management #AndonCord #Resilience

Imagine I offer you a deal right now.

• Option A: I hand you a suitcase with $10,000 cash. It covers your mortgage for a few months. It’s safe. No strings attached.

• Option B: We flip a coin. Heads, you win $100,000. That’s a down payment. That’s seed capital. Tails, you get nothing.

Which one do you take?

If you calculate the “Expected Value“ Option B is worth $50,000. It is mathematically 5x better than Option A.

But if you are “managing risk” in the traditional corporate sense, you take the suitcase (Option A). It’s safe. It’s certain. You won’t look foolish for going home with zero.

But the best leaders I know? The ones actually moving the needle?

They take the flip. And if it lands on Tails... they just find another coin.

They do this because they understand the single most important truth about high-stakes decision-making: You are not playing Chess. You are playing Poker.

The reason so many brilliant leaders are currently frozen in analysis paralysis is that they are trying to play Chess on a Poker table.

The Certainty Trap - The Decay Rate of Information

In Chess, you have perfect information. You can see every piece on the board. There is a theoretically “correct” move for every configuration. If you stare at the board long enough, you can solve it.

In Business (and Poker), you have imperfect information. You can’t see the market’s hand. You can’t see the competitor’s hidden cards. You don’t know if the regulator is bluffing.

The anxiety you feel - that knot in your stomach when you look at your Q4 roadmap - isn’t because you aren’t smart enough. It’s because you are waiting for the fog to clear. You are waiting for 100% certainty.

But here is the uncomfortable reality I tell my teams: If you wait for 100% of the information, you are no longer making a decision. You are making a calculation.

And there is a hidden tax to this calculation: The Decay Rate of Information.

Every day you wait for perfect data, the data you do have becomes 1% less true. The competitor launches. The candidate takes another offer. The customer sentiment shifts.

By the time you finally feel “safe,” you arrive at a decision that is factually correct for a reality that no longer exists.

A imperfect decision made with speed often beats a perfect decision made with latency.

Bet Sizing - The Amazon Door Strategy

So, if we accept that we can’t wait for perfect info, how do we bet without going broke?

We need a system for Bet Sizing.

This is where Jeff Bezos’s “One-Way vs. Two-Way Door“ concept stops being a cliché and starts being a risk management tool.

Most leaders suffer from paralysis because they treat every decision like an “All-In” moment. They treat a $5,000 marketing experiment with the same gravity as a $50M acquisition. They fold winning hands because they are scared to bet blind.

To fix this, you have to label your bets properly:

• The Two-Way Door - These are your low-stakes, reversible bets. If you walk through and don’t like the room, you simply turn around and walk out. The strategy here isn’t perfection; it’s speed. The value of the bet isn’t just the outcome, but the information you gain from the flop. Launching a feature flag to 5% of users isn’t a marriage; it’s a date. If it goes bad, you pay the check and leave. In this zone, the “Undo” button is your single best risk management tool.

• The One-Way Door - These are the irreversible decisions where you can’t go back without paying a massive tuition. Selling the company, firing a co-founder, or migrating your entire data stack to a new cloud provider - these are the moments that require deep diligence. Here, you slow down. You gather more information. You deliberate. This is where you actually spend your precious “governance tokens” ensuring you have the conviction to push all your chips into the middle of the table.

The mistake isn’t making the wrong bet. The mistake is treating a Two-Way Door like a One-Way Door. Speed is a quality of the decision.

Playing for Position Not a Single Hand

Now, let’s look one step further. This is where the amateurs get separated from the pros.

Amateur players play the cards in their hand. Pro players play for Position.

In leadership, we tend to look at decisions in silos. “Is this a good deal?” “Is this a good job offer?” “Is this a profitable client?”

But real strategic leverage comes from Second-Order Thinking. You have to ask: “Does this decision buy me the asset I need for the next move?”

An agency leader take on a client that was a nightmare on paper. The margins were razor-thin. The demands were high. The P&L looked terrible. A novice leader would have folded instantly to “protect the bottom line.”

But this leader wasn’t playing the P&L of that specific deal. He was playing for position. He knew that having that specific logo on the website validated the agency for the Enterprise tier. He was willing to “lose” $50k on the hand to buy a seat at the table where $1M deals were happening. And he was right - that case study generated $2M in new pipeline within six months.

Now - here’s another example. This logic also applies perfectly to your own career trajectory. I often see leaders agonizing over the choice between “Golden Handcuffs” - a safe, high-paying role at a tech giant - and a chaotic, lower-paying role at a high-growth startup.

Let’s be honest: walking away from RSU grants feels like setting money on fire.

To the siloed thinker, the startup looks like a bad bet. “Why take a 30% pay cut to work harder with less stability? That’s folding a winning hand.”

But the strategic thinker isn’t looking at the salary; they are looking at the Asset Accumulation. They realize that the “safe” job is actually a plateau. They choose the messy startup role because it is the only place they can buy the specific asset they are missing: the ability to build from zero to one.

They are consciously trading short-term liquidity for long-term Career Capital. They know that the “loss” they take on their paycheck today is the tuition they pay to become a Founder or CTO three years from now. They aren’t folding; they are buying in.

Does this bet give you the chips you need to sit at the high-stakes table next year?

The Poker Mindset vs Resulting

I’ll leave you with this concept from Annie Duke‘s Resulting. It is the dangerous tendency to judge the quality of a decision solely by its outcome.

In poker, you can play a hand perfectly - calculate the odds, read the table, make the right bet - and still lose to a lucky river card. That doesn’t mean you made a mistake. It just means the 10% chance event happened.

If you judge your leadership solely on outcomes, you will drive yourself insane. You will become risk-averse. You will pick Option A every single time because you are terrified of the 50% chance of zero.

But if you judge your leadership on the quality of your process - on your ability to distinguish a One-Way Door from a Two-Way Door, and your ability to play for the next hand - you build something resilient.

The goal isn’t to be right every time. The goal is to keep playing until the odds tip in your favor.

So, here is my question to you:

Look at the biggest decision stalling your team right now. Are you waiting for 100% certainty on a Two-Way Door?

Stop waiting for the chessboard to clear. The coin is in the air. Call it.

I’d love to hear your thoughts.

#Leadership #DecisionMaking #Strategy #Management #CareerGrowth #RiskManagement #PokerVsChess

I didn’t run every day for four years to get fit.

I am not a fast runner. I don’t run marathons, and I’m certainly not chasing a podium. By most athletic standards, I am aggressively average.

I ran to prove a point: The New Year’s Resolution is a liar.

For 1,500 consecutive days, I laced up my shoes. It didn’t matter if I had a 5am early morning meeting, a red-eye flight to Tokyo, or a keynote presentation. The run happened first.

People ask me why I kept going after the first year. They assume it was about discipline or health. It wasn’t. It was an engineering stress test. I wanted to see if I could build an anchor point that ignored the calendar entirely.

The “Fresh Start” Trap

Yep, it’s 2026 now. We are approaching the most dangerous day of the year for high performers.

Strava calls it “Quitter’s Day“.

It falls on the second Tuesday of January. It is the statistical cliff where the “New Year, New Me” dopamine wears off, and approximately 80% of resolutions quietly die.

We are conditioned to believe that January 1st is a magical threshold. We treat the calendar like a reset button, assuming that a change in date will trigger a change in character. We believe that the version of us that existed on December 31st, 2025 - tired, inconsistent, perhaps over-indexed on holiday sugar - will be replaced by a disciplined machine the moment the ball drops for 2026.

The data says otherwise.

For a high performer, a “clean slate” isn’t an asset. It’s a liability. It erases the operational data you need to iterate.

The problem isn’t your willpower. It’s your reliance on a “Fresh Start” in the first place.

I ran 1,500 days to prove that you don’t need a New Year. You just need an Anchor.

New Year Resolution vs. Anchor Habits

Yes, I understand the appeal, 100%.

We love the “New Year’s Resolution” because it feels like a pardon. It reduces the cognitive load of our past failures. It offers a cheap dopamine hit of hope without the immediate cost of action. It allows us to dream about the outcome (the six-pack, the promotion, the finished book) without confronting the friction required to get there.

We do this because hope is an emotional resource.

But performance is an engineering problem. And you cannot build a skyscraper on an emotional foundation.

If you want to see the structural failure of emotion-based planning, just wait six weeks.

Visit any Equinox or commercial gym around February 14th. The sea of new faces from January has evaporated. You won’t see the “New Year, New Me” crowd. You will see dust motes dancing in the light shafts over unused treadmills. The motivation burned off. The structure wasn’t there to catch the load.

To survive “Quitter’s Day” in 2026, you have to stop relying on the volatile fuel of motivation and start building with the binary physics of Anchor Habits.

The Physics of Stability

Now, my theory here is - we need to stop talking about “goals” and start talking about system stability.

In civil engineering, an anchor bolt is a heavy-duty fastener used to connect structural and non-structural elements to concrete. Its job is to transfer tension and shear forces from the machine to the foundation. Without it, the machine vibrates itself apart.

In your life, stress is the shear force. The resolution is just paint - it looks good, but it bears no load. To stabilize the system, you need a protocol that transfers the stress into a foundation of discipline.

My Own 1500-Day Story

For me, this concept wasn’t just theory; it was my operational reality.

I didn’t treat running as a “fitness goal” or a hobby. I treated it as a structural necessity - my personal anchor bolt.

I ran every single day for over four years. I closed my Apple Watch rings for 1,500+ consecutive days. It was a perfect, gamified record of consistency.

And then, the glitch happened.

I was restoring my settings to a new iPhone, and a data migration error wiped the streak. In a single digital hiccup, 1,500 days of “proof” vanished. The counter reset to zero.

(I admit, I was mad and stared at the phone in disbelief for ten minutes - I might have even restored the phone five times hoping it was a fixable bug - but the number remained zero).

A “Resolution” mentality would have been devastated. If I were running for the badge, for the dopamine of the streak, or for the “Fresh Start,” I would have quit. The gamification was gone.

But the Anchor Habit revealed the truth immediately. The digital badge disappeared, but the biological adaptation remained. The resting heart rate didn’t reset. The VO2 max didn’t reset. The mental calluses I built running in the rain didn’t dissolve.

The Resolution mentality collapsed. The Anchor Habit held.

This is the difference. A Resolution is fragile; it breaks when the conditions aren’t perfect. An Anchor is anti-fragile; it holds the ship regardless of the storm.

The Lagging Indicator

Most resolutions fail because they target the wrong metric. They focus on the output rather than the input.

Take the most common resolution: “I want to lose 20 pounds.”

This is a structural error. You cannot “do” a weight loss. You can only “do” a behavior. Weight is a Lagging Indicator - it is the delayed result of previous actions. When you focus on the scale, you are trying to drive the car by staring at the rearview mirror.

Whether you are pouring concrete or writing code, the physics are the same.

In engineering, we monitor Leading Indicators - latency, error rates, and saturation - to predict the output. If you optimize the leading indicators, the system stability takes care of itself.

Most people treat health like a subtraction problem: “I just need to eat less.”

It fails because it fights biology. Deprivation triggers a starvation response, your energy tanks, and the “February Crash” becomes inevitable. You are trying to drive the car by staring at the rearview mirror.

The Anchor ignores the scale and focuses entirely on the Leading Indicator - the code you run every day.

Don’t set a goal to “lose 10 pounds.” That’s a wish, not a plan.

Create a plan: “Eat 30g of protein within 30 minutes of waking up.”

This is an executable command. It stabilizes your system and kills cravings at the source. If you get the inputs right, the output takes care of itself.

Anchor Stacking

The Resolution is the ribbon cutting ceremony. The Anchor is the concrete pouring.

The mistake most people make is trying to build the entire skyscraper on Day 1. They commit to a 5am run, a cold plunge, and a meditation hour all at once. That is a recipe for system collapse.

Let’s call it - Anchor Stacking.

You start with one binary action that you actually want to do (or know you can do), and then bolt a tiny habit onto it.

You identify the Anchor (the thing that happens no matter what), and you use it as the trigger for the new habit.

• The Anchor: “I run at 5am.” (This is the non-negotiable).

• The Stack: “Immediately after I take off my running shoes, I do 10 pushups.”

You have already paid the “activation energy.” You are awake. You are in gear. The friction is zero.

Start with the run. Then add the pushups. Then add the protein.

You don’t need to be a Navy SEAL tomorrow. You just need to show up.

The victory isn’t the marathon. It’s the fact that you laced up your shoes when it was dark outside.

That is the win. Everything else is just mileage.

#Leadership #PersonalDevelopment #SystemsThinking #HighPerformance #Habits

We have been conditioned to believe that “sweat equity” is the only equity.

If you aren’t grinding, you aren’t growing. We treat exhaustion as a badge of honor and silence as a defect. We fill every gap in our calendar with a “sync,” a “touchbase,” or a “quick huddle,” convinced that activity is the proxy for value.

But the economic reality of 2025 contradicts this.

Most of what we call “hard work” today is just anxiety disguised as productivity. It is motion, not progress. It is the frantic treading of water in a sea of low-value tasks.

I’m going to argue that your most productive hour last week wasn’t in a boardroom. It was probably when you were staring at the ceiling, or five miles into a run, thinking about absolutely nothing.

We need to talk about the strategic return on doing nothing.

The Efficiency Paradox

I get it. I really do.

When you are building a career, volume matters. You need reps. You need to prove to the market (and yourself) that you can handle the load. We clear our inboxes because it gives us a dopamine hit of “completion.” We attend back-to-back meetings because visibility feels like job security.

(That said, honestly, it also feels safer to be busy than to be alone with your own thoughts. Silence is terrifying when you don’t know what it might say.)

We operate this way because it worked in a linear economy. If you moved more widgets than the guy next to you, you won.

But we have hit the Efficiency Paradox.

According to 2025 Microsoft telemetry, the average executive is now interrupted every two minutes. We have fragmented our attention spans to the point of structural failure. We have optimized our schedules so perfectly that we have engineered out the very thing we are paid to produce: Judgment.

In an AI world, execution is cheap. AI can answer emails. AI can write reports. AI can “grind” 24/7 without coffee or complaints. If your value proposition is “working hard,” you are competing with zero-marginal-cost software.

You lose that trade every time.

The asset is no longer execution; the asset is insight. And the data shows that insight refuses to enter a busy room.

The Neurobiology of Doing Nothing

To understand why stillness is a competitive advantage, we have to strip away the wellness fluff and look at the hardware.

1. The Lazy Brain is the Innovation Engine

Look, I’m not a neuroscientist. I’m an operator. And my job isn’t just to drive revenue; it’s to protect my team from burning out their engines. Here is how I explain it to my leaders when I see them grinding themselves into dust.

Think of your team’s focused brain - the part that answers emails and crunches numbers - as the “Day Shift.” They are logical, linear, and fast. They are the ones who keep the lights on. But if you force them to work a double shift every single day, the brain don’t just get tired - they get stupid.

When you allow your team to stop focusing - when you encourage them to stare out the window or zone out - you are letting the Day Shift go home. That is the only time the “Night Shift” can clock in.

Scientists call this the Default Mode Network, but as a leader, I view it as the “innovation crew.” This crew wanders the empty office of the brain, finding connections between files that the Day Shift was too busy to notice.

A 2025 study by Shofty et al. confirmed what every experienced manager knows: if you never let the Day Shift leave, the Night Shift never shows up. You get efficiency, sure. But you lose the breakthrough. And eventually, you lose the team.

2. The Runner’s High

This explains why your best ideas rarely happen at your desk.

Runners and endurance athletes know this phenomenon well. There is a reason your strategy deck finally “clicked” at Mile 5, when your legs were heavy and your lungs were burning. It’s a mechanism called Transient Hypofrontality.

Running is metabolically expensive. To save energy for motor function, the brain temporarily down-regulates the Prefrontal Cortex. The Prefrontal Cortex is your inner CEO - it’s the voice of logic, planning, and crippling self-doubt.

When you run hard enough, you effectively take the CEO offline.

With the “Boss” out of the building, the rigid filters disappear. Weird ideas are allowed to mingle. The constraints of “that will never work” fade away, and pure association takes over. You aren’t running to get fit. You are running to induce a state of temporary cognitive madness where innovation becomes possible.

(If you run with a podcast at 2x speed, you are cheating yourself. You are keeping the Day Shift on the clock. Unplug. Let the boredom hurt a little.)

3. The Sheldon Cooper Busboy Method

You don’t need a lab coat to see this in action. You just need to watch The Big Bang Theory.

In the episode “The Einstein Approximation,” genius physicist Sheldon Cooper hits a cognitive wall. He is trying to figure out why electrons behave as if they have no mass when moving through graphene. He tries everything the “Hustle Culture” playbook suggests: skipping sleep, staring at whiteboards, even playing in a ball pit.

Nothing works. The “Day Shift” is burned out.

He realizes his conscious focus is the bottleneck. To fix it, he takes a menial job as a busboy at the Cheesecake Factory. He needs a task that is physically repetitive but mentally undemanding - wiping tables, stacking plates - to occupy his hands so his “back-burner brain” (the DMN) is free to run simulations.

The breakthrough happens in a cinematic moment of clumsiness. While clearing a table, he drops a tray of dishes. The shattering sound - or perhaps the scattered pattern of the mess - triggers the epiphany. He realizes the electrons move in a wave pattern. He shouts “I’ve done it!” and walks out on the job immediately.

He didn’t solve the Nobel-level physics problem by thinking harder. He solved it by doing the dishes.

Sometimes, the most strategic thing you can do for your Q1 strategy is to perform a menial task that lets your subconscious take the wheel.

A Simple Holiday Thought

To be honest, this is just what is going through my mind today.

We spend 11 months chasing speed. We optimize, we hack, we sprint. And maybe, just maybe, this weird liminal space between Christmas and New Year’s is the only time we are actually allowed to catch up on stillness.

I’d love to know what is on your mind today. Are you already plotting your 2026 takeover, or are you happily doing absolutely nothing? (There is no wrong answer!)

However you choose to spend these last few days of the year, I hope you find some peace. You’ve earned the reset.

Wishing you the happiest of holidays and an amazing 2026.

#Leadership #Productivity #MentalHealth #Neuroscience #StrategicLaziness

Everyone wants to be Apple.

Everyone wants a “Walled Garden” - locking users in so they never leave. It’s the gold standard. It’s safe. It’s profitable.

But the most impressive business victory I’ve watched in the last decade didn’t come from building a higher wall. It came from tearing the walls down completely.

I admit it. I’m a bit of a gear-head. If you ask me why I’d pick the Sony a9 III with its global shutter over the higher-resolution a1 II just to kill rolling shutter and sync flash at 1/80,000th of a second, I can talk your ear off all day long.

But you don’t need to be a photographer to understand this lesson.

This is the story of the Sony Alpha, and it’s a masterclass for any leader trying to disrupt an incumbent.

Part I - The Fortress of 2010

Rewind to 2010. The camera world wasn’t just a market; it was a fortress.

Two giants, Canon and Nikon, controlled nearly 75% of the professional market. Turn on any NBA game or the Olympics, and what did you see on the sidelines? A sea of white Canon L-lenses.

They didn’t just have market share; they had a moat made of glass.

This was the era of the “Lens Tax”. If you were a pro (or dream to be one), you were married to your system. Switching brands meant selling $20,000 worth of glass for pennies on the dollar. It was financial suicide.

And if you tried to buy a cheaper third-party lens from Sigma or Tamron? Good luck. Back then, those companies had to reverse-engineer the autofocus algorithms. The result was hunting focus, missed shots, and a general feeling that you were buying “second-class” gear.

The message from the giants was clear: “If you want professional performance, you pay our tax. There is no other way”.

Sony had amazing sensors (they made the engines for everyone else!), but they had zero lens ecosystem. They couldn’t out-Canon Canon. If they tried to build a walled garden from scratch, they would have starved to death before they laid the first brick.

Part II - The “Anti-Walled Garden” Playbook

So, they did the exact opposite. They looked at the “Walled Garden” playbook and threw it in the trash.

On February 8, 2011, Sony made a decision that looked insane to the industry giants: They released the basic specifications of their “E-mount” for free.

While everyone else was suing reverse-engineers or charging massive licensing fees, Sony practically handed the blueprints to Sigma and Tamron and said, “Come build in our backyard. We won’t sue you; we’ll help you”.

It was the ultimate “Trojan Horse”!

Playbook Move #1: Weaponizing the Competition

Suddenly, the math changed.

While Canon and Nikon shooters were stuck waiting for expensive proprietary glass, the Sony ecosystem exploded.

Sony didn’t just invite “cheap” lenses; they invited innovation. Sigma launched their legendary “Art” series - lenses that were optically sharper than Canon’s for half the price - and they worked natively on Sony.

I watched friends switch systems not because they liked the Sony menus (let’s be honest, nobody liked the menus back then), but because the ecosystem was simply smarter.

• Scenario A: Buy a Canon body + 1 expensive first-party lens.

• Scenario B: Buy a Sony body + 3 incredible Sigma Art lenses for the same price.

Sony realized they weren’t selling cameras; they were selling a platform. They let others make the profit on the glass, so they could win the war for the sensor.

Playbook Move #2: Tearing Down the Internal Wall (The “One Mount” Magic)

This is the strategic genius that often gets missed. Sony decided that one mount would rule them all.

Look at the chaos of their competitors. Canon had EF for DSLRs, EF-S for crop sensors, EF-M for their failed mirrorless experiment, and eventually RF. A confused mess of adapters and incompatibility.

Sony said: “Whether you buy a $500 vlog camera or a $6,000 cinema beast, it’s the same E-mount”.

This removed the internal friction of upgrading. A beginner could buy a pro lens and grow into it. A pro could buy a compact APS-C camera as a “B-cam” and use their master lenses on it instantly.

They didn’t just respect the user’s investment; they respected their intelligence.

Part III - Now - The Overtake

Critics laughed at first. “Why give away your IP?” “First-party lens sales are where the profit margins are!”

But the strategy worked.

By 2018/2019, the “impossible” happened. The electronics company overtook the optical giants. In the full-frame mirrorless market, they held the #1 spot for years before the giants finally woke up.

Sony realized something the others missed: In a digital age, the Network Effect is more valuable than the Moat.

• On one hand - the walled garden of the competitors - beautiful, manicured, but lonely and expensive.

• Now on the other hand - Sony’s “Public Park” - chaotic, diverse, crowded, and thriving.

The Takeaway: Stop Trying to Be Apple

We are conditioned to believe that “Walled Gardens” are the only way to win. We confuse control with strategy.

But Sony proved that if you are the challenger, a wall doesn’t protect you. It isolates you.

The brilliance of the Alpha strategy wasn’t just “openness”. It was leverage.

• They didn’t try to beat Canon’s 50 years of lens manufacturing.

• They weaponized the rest of the industry to do it for them.

Sometimes the best way to build a moat isn’t to dig it yourself. It’s to let everyone else bring their own shovels.

So, here is the hard question:

Look at your current strategy. Are you building a platform that invites an army of allies? Or are you just building a lonely fortress?

I’d love to hear your thoughts.

#BusinessStrategy #OneMount #SonyAlpha #OpenInnovation #Marketing #Photography #UnderdogStory #Leadership

I was looking at the box scores for the Thunder recently, and Shai Gilgeous-Alexander’s averaging over 30 points per game, he’s leading the league in steals, and he’s shooting with an efficiency that defies modern usage rates.

Now, don’t get me wrong - I am a die-hard Lakers fan. It physically hurts me to write this. But after watching the Thunder absolutely destroy us a few weeks ago, I had to put my pride aside and respect what I was seeing. It wasn’t just a win; it was a clinic.

And if you dig deeper into the play-by-play data of those blowouts, you find the most impressive stat of all:

In their biggest wins, SGA barely touches the floor in the 4th quarter.

He isn’t injured. He isn’t in foul trouble. He’s sitting on the bench, towel around his shoulders, watching the rookies close out the game against my Lakers.

Why?

Because his dominance isn’t about hero ball in the final seconds. It’s about systemic control in the first three quarters. He disrupts the defense so thoroughly, creates so many easy looks for his teammates, and builds such an insurmountable lead that by the time “crunch time” arrives, the game is already won.

He doesn’t need to hit the buzzer-beater because he ensured the buzzer wouldn’t matter.

Now, flip the camera to our world.

We have a completely backward view of what “Principal” level leadership looks like in Engineering, Product Manager and Data Science roles.

We tend to glorify the “Hero Principal.” You know the archetype: The system is crashing at 2 AM, the deadline is looming, and the Principal Engineer swoops in, writes 2,000 lines of complex code, fixes the bug, and saves the launch. We applaud them. We give them bonuses. We say, “Thank god we have them”.

But I’m going to tell you the uncomfortable truth I’ve learned from watching truly elite organizations:

If your Principal Engineer has to save the day in the 4th quarter, they have failed.

True Principal leadership - whether you are an engineer, a data scientist, or an architect - isn’t about heroics. It’s about obsolescence. It’s about playing the game so well in the early stages that the 4th quarter becomes a victory lap for the team, not a rescue mission for the leader.

I’ve been thinking deeply about this transition. Moving from “Senior” to “Principal” is not just a promotion; it’s a paradigm shift. It’s moving from being the best player on the court to changing the geometry of the game itself.

I see this shift happening across three distinct dimensions.

Dimension 1 - They Lead by Example

In his book Staff Engineer, Will Larson talks about the “Solver” archetype. But there’s a nuance here that often gets missed.

A lot of leaders stop “doing” when they get promoted. They retreat to PowerPoint and architecture diagrams. They become “Ivory Tower” architects who suggest impossible designs.

The best Principal Engineers, Product Managers and Data Scientists do the opposite. They get more hands-on, but selectively. They don’t take the easy tickets; they take the hardest, messiest problem, and they solve it with such terrifying elegance that it raises the bar for everyone else.

They lead by example.

• From Business to Architecture: They don’t just ask for requirements; they understand the P&L better than the PM, and they design systems that solve the business problem, not just the technical one.

• From Empathy to Excellence: They show what “customer empathy” looks like in code. They show what “clean documentation” looks like by actually writing it.

They are the “Gold Standard.” When a Junior Engineer asks, “How good does this need to be?” they don’t point to a wiki page. They point to the Principal’s last PR.

They prove it’s possible. They break the 4-minute mile so the rest of the team knows they can run it too.

Dimension 2 - They Lead by Creating the Environment

Here is a rule of thumb I use: A Senior Engineer solves a problem. A Principal Engineer solves a class of problems.

If a Principal Data Scientist is manually fixing a data pipeline error for the third time, they are failing.

The core of this dimension is creating the environment and the process - what Tanya Reilly brilliantly calls “Glue Work” in The Staff Engineer’s Path - but elevating it to system design.

Think about it. It is incredibly hard for a team to “do everything right” while trying to grow and ship fast. If the process is heavy, or the tools are broken, your team burns out.

The Principal leader realizes that their output isn’t code; their output is leverage.

• They don’t just fix the bug; they build the linter that prevents the bug.

• They don’t just run the model; they build the MLOps platform that lets any analyst run the model.

• They delegate not by dumping tasks, but by building guardrails that make it safe for others to fail.

They lead by creating an environment where the “right thing” is also the “easiest thing”. They are the ones clearing the brush so the team can sprint.

Dimension 3 - Force Multiplier that Makes Others Better

This is the most important dimension, and it brings us back to SGA.

SGA is leading the league in steals, but look at his assists. Look at the open looks his teammates get. When he drives, the defense collapses, and suddenly everyone else on the floor has space to operate.

In the leadership world, Liz Wiseman calls this the difference between a “Diminisher” and a “Multiplier“.

• The Diminisher is the genius in the room who makes everyone else feel small. They have to be the one to solve the hard problem to validate their own ego.

• The Multiplier is the genius who makes everyone else smarter.

In the Principal World (PE/PPM/PDS), this is the ultimate metric - making people around them better!

Most of their responsibilities are actually about transferring their superpower to the team.

• It’s sitting with a Senior Engineer and refactoring code together, not to fix it, but to teach them how to see the patterns.

• It’s working long hours with a Staff Data Scientists, not to take over their ADD (architecture design doc), but cut down the feedback loop and let them learn and refine the design.

• It’s letting the team present the win to the stakeholders while they sit in the back.

• It’s absorbing the chaos and ambiguity so the team can focus on execution.

The “SGA Effect” in engineering is when a Principal Engineer can go on vacation for two weeks, and the team’s velocity doesn’t drop. That means they have successfully transferred their context, their standards, and their confidence to the group.

The Question to You

We need to stop rewarding the arsonist firefighters - the leaders who let projects burn until the 4th quarter so they can be the hero.

The true hero is the one sitting calmly on the bench in the 4th quarter, cheering on a team that is winning by 20 points because of the foundation they built in the first three.

So, here is my question to you:

Look at your current “star players” (or look in the mirror). Are they playing for the buzzer-beater, or are they building a team that doesn’t need one?

#Leadership #PrincipalEngineer #DataScience #StaffPlus #TeamCulture #Management #Multipliers #NBA #SGA #OKCThunder #GoLakers

You know this, deeply - it’s that special kind of dread that settles in at 8:58 AM.

You’ve done the “right” thing. You’ve blocked the time on your calendar. You’ve labeled it “Office Hours - Open Door”. You’ve invited the entire organization - maybe 300, 500, or even 1,000+ people. You log in two minutes early, adjusting your camera, ready to be the accessible, transparent leader the books tell you to be.

And then... silence.

You stare at a grid of black boxes. Maybe one brave soul uncomfortably unmutes to ask about the new cafeteria vendor. But mostly, it’s just you and the crickets. You sit there for 15 minutes, sipping coffee, until you awkwardly end the call early, telling yourself, “Well, I guess the team is happy. If they had big issues, they’d ask”.

I used to tell myself that lie, too.

I sat in those empty Zoom rooms, mistaking silence for contentment. But after a few too many of these “ghost town” sessions, I realized the uncomfortable truth.

Silence isn’t a sign of clarity. It’s usually a sign of fear, confusion, or disconnection.

I realized I wasn’t running an “Open Door”; I was running an empty waiting room. And in a remote-first, high-velocity world, a waiting room is a failure of leadership.

So, I decided to burn the format down. I stopped hosting “Office Hours” and started hosting a “Podcast.”

The “Ivory Tower”

We tend to think of the “Ivory Tower” as an ego problem - leaders who refuse to come down to the front lines. But I’ve found that most leaders want to connect. The problem isn’t intent; it’s physics.

There is a concept I call Information Decay.

Research and experience both show that strategic context degrades by roughly 50% every time it passes through a layer of management. By the time your crystal-clear vision statement moves from the Board Room to the VP, to the Director, to the Manager, and finally to the Junior Engineer, it has become a game of “Telephone”.

The nuance is stripped away. The “why” is lost. The engineer doesn’t hear the strategy; they hear a ticket number.

This creates a massive disconnect. The people who need the context the most - the ones building the product - are the furthest from it. And yet, we expect them to come to us and ask the perfect clarifying question in a room full of 500 people?

Now, realistically - who wants to be the junior employee who raises their hand in front of the VP and says, “I don’t understand the strategy”? No one. That’s not an opportunity; that’s a risk.

Expecting the team to drive the agenda is a failure of empathy. It places the burden of bravery on them.

I realized it was my job to lift that burden. I needed to stop waiting for questions and start broadcasting the signal.

From “Availability” to “The Broadcast”

The pivot was simple but profound. I stopped asking, “Does anyone have questions?” and started saying, “Here is what you need/want to know”.

I began treating this weekly slot not as a meeting, but as a live production. I adopted the rigor of a content creator - the best of Warner Bros. Discovery.

1. Consistency is Trust

Just like your favorite YouTuber or Podcaster, I show up at the same time, on the same channel, every single week. Rain or shine. Crisis or calm. (Granted, some times it gets really challenging to run a show at 30,000 feet)

This builds a “muscle” of reliability. In times of uncertainty, the simple fact that the team knows I will be there on Friday mornings creates a psychological anchor. The reliability is the message.

2. The “Opt-In” Paradox

This is critical: Attendance is strictly optional. No roll call. No guilt trips.

There’s a twist here. If I have to mandate you to be there, my content isn’t good enough. The goal is to make the signal so high-value, so honest, and so clarifying that missing the meeting feels like a competitive disadvantage. I want people to tune in because they’re afraid of missing the truth, not because they’re afraid of missing a check-in.

3. The “Must-Watch” Factor

Now, here’s the controversial part: I don’t record these sessions.

By not recording it, we create urgency - a “you had to be there” moment. This shifts the dynamic from corporate obligation to live podcast.

But here’s the real unlock: It activates the network. When the meeting ends, attendees become broadcasters. They return to their teams saying, “Did you hear what was said about the AI/ML Committee?” This bypasses “Telephone Game” distortion because the primary signal was strong, and it empowers the middle layer to own the message.

The Facts, The Assumptions, and “The Read”

Now, you can’t just ramble for 30 minutes. That’s a hostage situation, not a broadcast.

Especially in times of high anxiety - company separation, merger rumors, reorgs, market shifts, AI disruption - the team craves stability. But as leaders, we often can’t promise stability. The world is too volatile.

What we can offer is clarity.

I structure every single podcast around a specific 3-Bucket Framework. This allows me to be radically transparent without being reckless.

Bucket 1: The Facts

“Here is what we know for sure”. This is the raw news. The revenue numbers. The signed contracts. The departure of a leader. No spin. No sugarcoating. Just the baseline reality that we all share.

Bucket 2: The Assumptions

“Here is what we don’t know, but here is the assumptions that I’m making”. This is the vulnerable part. This is where I admit that I don’t have a crystal ball. I might say, “We don’t know if this AI trend will stick, but we are assuming it will, so we are shifting priorities”. This separates the signal from the guess. It invites the team to understand the risk profile of our strategy.

Bucket 3: The Read

“Here is how I personally see it, and how I read it”. This is the subjective interpretation. It’s where I humanize the data. “I read this market shift as a massive opportunity for us, not a threat, because of X, Y, and Z”.

This framework does something powerful. It doesn’t just give the team orders; it teaches them how I think. It democratizes context. It allows a senior engineer to make a micro-decision on Thursday afternoon that aligns with my strategy, simply because they understand my logic stream from Friday morning.

Now The Interaction - Engineering Psychological Safety

But what about the engagement? How do we solve the “Silent Room”?

I realized that people aren’t afraid of the answers; they are afraid of the asking. They are afraid of being seen as the one asking the tough question.

So, I adopted a “Long time listener, First time caller” approach. (*cough*, *cough* Creamer)

I stopped taking live questions only. Instead, I collect them all week via anonymous tools (MS/Google Forms). This removes the fear of attribution.

And I read each one of them out loud before I get to the 3 buckets.

The moment a leader reads the “quiet part” out loud and answers it honestly - using the Facts/Assumptions/Read framework - the fear in the room evaporates. You prove, instantly, that this channel is a safe space for truth. You prove that you respect them enough to treat them like adults.

The Challenge for You

Leadership isn’t just about making decisions. It is about making sure those decisions are understood, internalized, and trusted by the people who have to execute them.

If you are a leader sitting in silence, waiting for your team to ask you what’s going on, you are failing them. You are letting the vacuum of information be filled with anxiety and rumors.

So, here is my question to you:

Look at your calendar for next week. Are you running an empty waiting room, or are you hosting a show your team actually wants to tune into?

Turn on the mic. Broadcast the signal. The silence is louder than you think.

#Leadership #Communication #RemoteWork #Management #Transparency #TeamCulture #Strategy #InternalComms

It’s the Friday after Thanksgiving.

If you’re reading this, the Slack notifications have finally slowed down. The urgent emails have stopped. The “Black Friday” rush - whether you’re in 2026 planning or just managing the Q4 sprint - is momentarily paused.

I’m sitting here, not looking at roadmaps, burn-down charts, or OKRs. I’m thinking about the amazing people who made this year possible.

And as I reflect on the teams that have truly moved the needle, I’ve noticed a persistent glitch in how we talk about high performance.

For years, the standard leadership dogma has been that the holy grail of high-performing teams is “Skin in the Game“. You know the theory: align incentives, give equity, tie bonuses to outcomes, and share the risk. The logic is economically sound - if they lose when we lose, they’ll work harder. If they win when we win, they’ll stay longer.

It’s a transaction.

And for a long time, the industry accepted this as the ceiling of motivation.

But look closer at the projects that actually defied gravity. The ones where the team pulled off the impossible not because they had to, but because they refused to let each other fail.

You’ll realize that “Skin in the Game” is incomplete.

Skin in the game buys you alignment. It buys you compliance. It keeps people from leaving when the stock price dips.

But it doesn’t buy the thing that actually matters in a crisis.

What you really want is Heart in the Game.

Skin in the game is an economic contract. Heart in the game is a psychological covenant. It’s the difference between an employee who stays late to hit a bonus, and one who stays late because they don’t want to let their team down.

And the bridge between the two isn’t more equity. It’s radical, specific appreciation.

The Appreciation Gap

Here is the uncomfortable truth I’ve observed across organizations: Most leaders feel grateful, but they fail to transmit it.

We assume the paycheck communicates value. We assume the quarterly bonus communicates respect. We assume that because we know how important the team is, they must know it too.

But they don’t.

There is a massive signal loss between our intent and their impact. This is the “Appreciation Gap”. To close it, we have to stop treating gratitude like a generic soft skill and start treating it like a high-fidelity signal.

Turning Up the Signal

Most appreciation is just noise. “Great job team” is filler. It’s the leadership equivalent of a generic “Happy Birthday” on a Facebook wall. It doesn’t register.

To get Heart in the Game, you have to change the frequency.

1. It starts with the signal-to-noise ratio.

Lazy appreciation is broad; real appreciation is high-definition. If you tell someone “Thanks for the hard work on the release,” they hear noise. But if you say, “I saw how you refactored that legacy pipeline for the features used in this model to reduce latency by 20ms - that was a craftsman’s choice”, they hear a signal. They know you are paying attention. And attention is the highest form of currency a leader has.

Now to do that - you must know and you must truly care!

2. It moves from the code to the character.

We usually default to high-fiving the launch or the sales number - the outcome. But outcomes often involve luck. Character is a choice. When you praise the resilience someone showed during an outage, or the diplomacy they used in a heated meeting, you aren’t just rewarding a result; you are reinforcing an identity. Telling someone “You are the kind of person who stays calm in a storm and lead the team through uncertainties” is infinitely more sticky than “Good job fixing the bug”.

3. And finally, the ultimate form of thanks isn’t words at all. It’s trust.

The highest compliment you can pay a high performer isn’t a gift card; it’s autonomy. It’s looking at them and saying, “I appreciate you so much that I am going to get out of your way”. When you hand over the keys to a critical project and trust their judgment over your own, that is the moment “Skin in the Game” transforms into “Heart in the Game.”

The Friday Protocol

Since it’s a quiet Friday, let’s not just read about this. Let’s do the work.

We often wait for performance reviews to give feedback. That’s too late. The half-life of gratitude is short.

Here is a simple protocol for today:

1. The “Unseen Work” Scan: Look for the “glue” - the person documenting the API that everyone hates writing, the senior engineer mentoring the intern when they could be coding, the PM de-escalating the heated Slack thread. Find the work that usually gets zero glory.

2. The “No-Ask” Note: Send a DM or a text. Right now. But here is the rule: It must be purely gratitude. No “by the way, can you check this?” attached. No “Happy Thanksgiving, are we ready for Monday?” A “naked” thank you.

3. The Reverse: Acknowledge that as a leader, you are standing on their shoulders, not the other way around.

To My Teams: The Heart Behind the Hustle

This isn’t just theory for me. As I sit here today, I am overwhelmed with gratitude for the teams I have the privilege to lead, and those I’ve worked with in the past.

To the engineers who stayed on the call when the incident dragged into the early morning hours, not because you were told to, but because you wouldn’t leave a teammate to debug alone.

To the data scientists who spent weeks wrestling with messy datasets of 100s of millions users and refusing to torture the data until it confessed, just to ensure our decisions were based on truth, not hope.

To the product managers who shielded the team from chaos so the builders could build.

To the quiet leaders who spoke the hard truth when it would have been easier to stay silent.

I see you. I appreciate the craft you bring to the code, but I appreciate the character you bring to the culture even more. You have taught me that while “skin in the game” builds companies, “heart in the game” builds legacies.

Thank you for your trust, your resilience, and your heart.

#Leadership #Thanksgiving #Gratitude #TeamCulture #HeartInTheGame #Management #HumanLeadership