Now: how do you report this to a board?

The board wants a number that increases to the right. Traffic used to be that number. Clean, measurable, universally understood. Every marketing leader in the world knew how to report traffic. Entire careers were built around growing that number.

It’s becoming meaningless. Not because traffic doesn’t matter, but because the link between traffic and business outcomes is breaking. When an AI system reads your content, synthesizes it, and delivers it to a user who never visits your site, you’ve influenced a decision without generating a pageview. Your content worked. Your dashboard doesn’t know it.1

———

This is a specific version of a bigger problem. The most important work now produces outcomes we can’t measure with existing tools.

We don’t need better metrics. We need metrics that haven’t been invented yet. We’re in the gap between one measurement paradigm and the next.

My team spent over 80 hours producing 11 pieces about AI solutions. By traditional metrics, they were a failure. 101 visits total. By any standard ROI calculation, you’d kill the program. But those pieces are now cited in AI responses across multiple platforms, shaping purchase decisions that will never show up in our analytics.

Try putting that in a slide deck.

———

The temptation is to pick a side. Defend the old metrics because they’re familiar, or abandon them because they’re incomplete. Both are wrong. The old metrics still matter, and they’re increasingly insufficient. You have to hold both.

What you actually need is a measurement system that’s explicitly provisional. Here’s what we know. Here’s what we think we know. Here’s what we’re investing in measuring even though we can’t prove it matters yet.

That last category is what separates leaders building for the next era from leaders optimizing for the last one. It requires asking your organization to trust you. The metrics won’t justify themselves. You have to justify the metrics. That takes conviction that no spreadsheet can provide.

We’re all building for metrics that don’t exist yet. The only question is whether you admit it.2

The real structure of work is: things were one kind of messy, then they became a different kind of messy, and they will continue being messy forever. There is no “after.” There is no point at which you’ve figured it out. There’s just the next mess, which you’re slightly better equipped to handle because of the last one.

I call this the permanent messy middle. Accepting it is one of the most important things you can do as a leader.

———

The messy middle is the space between strategy and success. Between knowing what to do and having done it. Between the promise you made and the delivery of that promise. Most of us permanently operate here. Not at the visionary planning stage or the victorious completion stage. Somewhere in between, covered in the dust of execution.

We don’t talk about this because it’s not a good story. Nobody wants to read a case study that says “we tried a bunch of things, most of them partially worked, we’re still figuring it out, and we probably always will be.” But that’s the honest version of almost every operation I’ve ever seen.

Right now, we’re producing 160 pieces of content a month. From the outside, that looks like a well-oiled machine. From the inside, it’s held together by duct tape and determination.

There’s always a workflow breaking, an editor overloaded, a tool not working as expected, a stakeholder whose requirements just changed, a quality issue that slipped through. Always. The question isn’t whether the machine will have problems. The question is how fast you can fix them.

———

AI will make the messy middle messier, not cleaner. The tools themselves are changing faster than you can build processes around them. You implement a workflow based on one AI capability, and three months later, the capability has changed so much that the workflow needs to be redesigned.

This is permanent. The rate of change in AI tools is not going to slow down to let your operations catch up. You’re going to be redesigning workflows continuously, indefinitely. The organizations that succeed will be the ones that build change fitness. The capacity to absorb continuous disruption without falling apart.

Change fitness isn’t something you learn once. It’s a muscle you build through repeated exercise. Every time you scrap a workflow and build a new one, you get slightly faster at scrapping and building.

Every time you navigate conflicting stakeholder demands, you get slightly better at holding contradictions. Every time a metric becomes meaningless, and you have to invent a new one, you get slightly more comfortable with ambiguity.

———

The messy middle isn’t a phase to endure. It’s where real work lives. The sooner you stop waiting for things to stabilize, for the tools to mature, for the strategy to clarify, for the metrics to settle, the sooner you start operating effectively in the only environment you’re ever actually going to get.

Build boring systems. Adapt every six months. Lead through ambiguity. Find force multipliers. Build for metrics that don’t exist yet.

And accept that you’ll be doing all of this forever. That’s the job.

Welcome to the messy middle.

Not in those words exactly. But the message is clear. Once you’re in the room, your job isn’t to complain about constraints. It’s about manufacturing success with whatever you have. Conventional wisdom gets conventional results. If you want something better, you have to make unconventional bets and execute them.

I think about this constantly.

———

Satya’s framework boils leadership down to two levers. First, the environment you create for your team: clarity, culture, energy. Second, where you put resources: time, money, people. That’s it. Those are your only moves.

But here’s the part that matters most. He supports bold bets. He even supports failure. What he doesn’t support is intellectual dishonesty. His protocol is four steps: have a plausible theory of how you’ll win. Align every resource to that theory. Monitor whether the theory is holding. And when it stops being plausible, pivot immediately.

The keyword is plausible. Not certain. Not proven. Plausible. You’re allowed to be wrong. You’re not allowed to be vague.

———

A mentor once told me something I didn’t understand until years later. Spend every dollar of your budget. Every single one. I pushed back. Isn’t that wasteful? Isn’t that the opposite of being responsible?

No, he said. Think about it. After you’re fired, what’s the point of that 100k left in your budget?

He wasn’t talking about blowing money. He was talking about conviction. If you believe in your theory of success, fund it completely. Don’t hold back a safety net that only protects your reputation while starving your strategy.

The leader who underspends their budget isn’t being prudent. They’re hedging. They’re keeping one foot out the door. They’re telling their team, through resource allocation, that they don’t fully believe in the plan they just presented.

———

There’s a concept in some African philosophical traditions called Ubuntu. “I am because we are.” The part that connects here is the idea that your word is your currency. You’ll never hurt the 99 people you say no to. But the one person you said yes to and didn’t follow through? That hurts.

This is the same principle Satya is describing, from a different angle. Your word is your resource allocation. When you say “this is our strategy,” you’re making a promise. Not that you’ll be right. That you’ll commit. That every dollar and every hour will point in the direction you said they would.

The worst leaders I’ve worked with weren’t the ones who made bad bets. They were the ones who made no bets at all. Who said yes to everything. Who spread resources so thin across so many “priorities” that nothing got enough investment to succeed. They confused activity with progress. They managed decline and called it a strategy.

———

Jeffrey Snover, translating Nadella’s framework into operational questions, asks something that should keep every leader up at night: Does your actual resource allocation match your stated strategy? If not, you have a dream, not a plan.

That’s the test. Not whether your strategy is brilliant. Whether your calendar, your budget, and your team’s time reflect what you say matters. Most don’t. Most leaders have a strategy deck that says one thing and a resource allocation that says something completely different. And they wonder why nothing changes.

The second question is just as sharp: Do you have the data to detect failure before you run out of time to pivot? If your feedback loop is longer than your runway, failure is inevitable. You won’t see the wall until you’ve already hit it.

Ambiguity doesn’t resolve. Not in this era, probably not ever. The room will always be foggy. You’re always going to be making bets with incomplete information, satisfying stakeholders with conflicting scorecards, and committing to directions you can’t fully validate.

The skill this demands isn’t decisiveness. It’s the ability to hold multiple frames at once and act anyway. To say: I believe in this direction. I could be wrong. We’re committing because waiting for certainty costs more than being wrong.

And then to be intellectually honest about what happens next. Monitor. Measure. Pivot when the theory breaks. But never hedge so completely that nobody knows what you’re doing or why.

Trust isn’t built on being right. It’s built on being consistent. Make the bet. Align the resources. Honor your word. That’s not a temporary condition of uncertain times. That’s the job. Welcome to the room.

None of it lasted. And for a long time, I thought the problem was discipline.

It wasn’t. The problem was the frame. I was trying to engineer productivity by adding. The answer was to engineer it by removing.

———

There’s a line from complexity science that changed how I think about this: “Emergent phenomena are not more than the sum of their parts. They are fewer. Emergence is defined by what is not there. By constraints.”

Emergence is about removing options until the remaining ones have no choice but to combine in interesting ways.

Shakespeare’s best work came out of the tightest boxes. Fourteen lines, strict rhyme, iambic pentameter. The limitations forced creative solutions that unlimited freedom never produces.

The same principle applies to work.

———

When our team went from producing 15 pieces of content a month to 150, the breakthrough was constraining the process. We reduced the number of content formats from twelve to three. We cut the review cycle from five stages to two. We limited each piece to a single core idea with a single call to action.

Output went up. Quality went up. Stress went down. We eliminated the decisions that were eating our energy without producing results, and everything else took care of itself.

Every decision you don’t have to make is fuel for the decisions that matter. Most people exhaust themselves choosing between twelve formats before they’ve written a word. We removed eleven of those choices. The writing got easier because there was less to think about before thinking could begin.

———

Goals work the opposite way. A goal says: here’s something to add to your life. Run a marathon. Launch a product. Hit a revenue number. Goals are aspirational. They point you toward more.

Constraints say: here’s something to remove. No meetings before noon. No more than three active projects. No content piece longer than 1,000 words. Constraints are architectural. They shape the space you operate in.

The difference matters because goals require motivation and constraints require only compliance. You have to want to run a marathon every single morning. You just have to not schedule a meeting before noon. One depends on how you feel. The other doesn’t.

This is why constraints compound, and goals fade. By March, most goals are dead. A well-designed constraint is still working in December because it doesn’t ask anything of you. It just removes an option that was hurting you.

———

The practical application is uncomfortable because it means your job as a leader is to figure out what to eliminate. Which meetings. Which approval steps. Which tools. Which options.

Most organizations are drowning in optionality. They have fourteen communication channels, nine project management tools, and an infinite number of ways to format a slide deck. Every one of those options costs cognitive energy. Every one is a micro-decision that adds up to decision fatigue by 2 pm.

The most productive teams I’ve seen all have one thing in common: a leader who’s willing to kill things. Who looks at a workflow and asks, “What can I remove?” before asking what to add.

Subtraction is harder than addition because it requires judgment. Anyone can add a tool. Knowing which tool to remove requires understanding what actually matters. But that’s the work. Emergence is defined by what is not there. Build your systems the same way.

The entire cast of Friends has the same story. Jennifer Aniston built a real film career after. The other five had varying degrees of success. But all six of them, decades later, are still the Friends cast first and everything else second.

Most people hear this and think it’s limiting. I think it’s liberating.

———

You only need one hit. One project, one role, one company, one idea that lands so well it becomes your identity. The rest of your career can build on it, extend from it, or even contradict it. But that one hit is what opens every door.

Career advice tells you the opposite. Diversify your skills. Build a broad portfolio. Be well-rounded. Don’t put all your eggs in one basket. This sounds reasonable, and it’s mostly wrong.

Well-rounded people are forgettable. The person who’s known for one specific thing gets the call.

———

I’ve been called “the 0 to 1 guy.” I’ve also been called “the scale guy.” Both labels came from the same instinct: find the thing that works, then double and triple down on it until there’s nothing left to squeeze.

That’s how spikes form. You don’t discover your spike by exploring widely. You discover it when someone else names it for you. When you hear “you’re the X guy” enough times, pay attention. That’s the market telling you where your hit lives.

Wes Kao calls this your spiky point of view. A perspective so distinct, so identifiably yours, that it can’t be copied. Most professionals spend their careers building plateaus.

Competent at many things, exceptional at nothing specific enough to be memorable. The spike is the opposite. It’s the one area where you’re so deep, so opinionated that people either love your take or push back hard. Both reactions mean you’ve found it.

Go deep on one thing. Make it yours. Let it become your identity. The breadth comes later, after the hit, when people already know who you are and what you stand for.

———

Finding the hit takes time. Mastery in any field takes 7 to 10 years of real work. You can’t shortcut it, and you can’t fake it. But you can be intentional about where you invest those years.

Pick the area where your curiosity runs deepest, and the competition is thinnest. Go deeper than anyone around you is willing to go. And when you hear someone call you “the X guy,” stop diversifying and start squeezing.

Month 7 through 12, the early chaos settles into patterns. The metrics look promising. People notice.

Month 14, the ground shifts.

———

In management, they call this second-year syndrome. A team has a breakout first season, and the second year, everything falls apart. The environment adapted. Opponents studied the playbook. The element of surprise is gone.

The same thing happens in projects and companies. Year one, you’re the underdog. Year two, you’re the incumbent. Your own success sets the expectations. And scrappy execution, founder energy, and sheer novelty were never supposed to scale into year two.

———

The systems you built for month 6 are breaking at the month 14 volume. The team that ran on excitement is grinding through repetition. Stakeholders want results they can put in a board deck.

You look at the metrics and can’t tell if you’re consolidating before the next jump or slowly dying. From where you’re sitting, both look identical.

Here’s the part nobody says out loud: quitting at month 14 feels rational. You have real data now, and it’s underwhelming. The original excitement wore off months ago. Every alternative looks better because you haven’t lived through its month 14 yet.

The people who kill good projects at this stage are making reasonable decisions with incomplete data. They’re reading the dashboard correctly. The dashboard is just lying to them.

———

I’ve watched good projects die here, and bad projects survive. The difference between persistence and denial only becomes visible in hindsight. You can’t tell which one you’re in while you’re in it.

What carries you through month 14 is the systems you built during the good months. They keep producing while you figure out whether the strategy still makes sense. Motivation died around month 10. Systems don’t need motivation. They just run.

Having been through it before helps too. The first time, you think something is broken. The second time, you recognize the feeling. Third time, you build for it from day one.

Most case studies skip from “we launched” to “we scaled.” Fourteen months of confusion compressed into “after some initial challenges.” I’d trust the ones that describe the trough more than the ones that pretend it didn’t happen.

This wasn’t always true. My father did the same thing for 40 years, and the core skills barely changed. But the rate at which professional skills expire has been accelerating for decades, and AI just hit the fast-forward button.

I think the actual half-life of a professional skill in 2026 is about six months. Not the foundational stuff — critical thinking, communication, judgment. Those compound. I mean the specific tactical skills: the tools you use, the workflows you follow, the techniques that make you efficient. Every six months, a meaningful chunk of that becomes obsolete.¹

———

The natural response to this is anxiety. A lot of people are anxious. But there’s a more useful response: stop identifying with your skills and start identifying with your ability to acquire new ones.

There are two types of professionals emerging right now. The first type defines themselves by what they know: “I’m a React developer,” “I’m a content strategist,” “I’m a data analyst.” The second type defines themselves by how fast they can learn: “I figure things out.” The first type is increasingly fragile. The second is antifragile.1

I watched this happen in real time with content creators. The ones who survived weren’t the ones who rebranded on LinkedIn or reshared hot takes about the future of content. They were the ones who quietly changed what they did all day. They learned the full pipeline — ideation to distribution.

They picked up a new tool in a week while everyone else was still debating whether to adopt it. They broke their workflows and rebuilt them, over and over, without waiting for permission or a playbook.

The shift is subtle but profound. Writers ask, “Is this good?” Content engineers ask, “Does this work?” Both questions matter. Only one scales.

———

The companies that understand this are investing differently. Instead of training people on specific AI tools, which will themselves be obsolete in six months, they’re building what some call “change fitness.” Not the capacity to adapt to any specific change, but to change itself.

This sounds abstract. It’s actually concrete. Change fitness means: can you spend 7 hours a week learning a new tool while maintaining your current output? Can you abandon a workflow you spent months perfecting because a better one emerged? Can you be a beginner again at something, repeatedly, without it destroying your confidence?

Most people can’t. Not because they lack intelligence, but because being a beginner is uncomfortable, and humans are wired to avoid discomfort. The six-month shelf life means you have to be comfortable being uncomfortable, permanently.

That’s the real skill. And unlike everything else, it doesn’t expire.

This is completely wrong.

The hard part is building systems that execute at scale without you. And by “scale” I mean something specific: can you go from producing 5 things to producing 50 things without the quality falling off a cliff and without you personally touching every single one?

Last year, our team went from 5 videos a month to 50. Same team, same budget, roughly the same number of hours. The strategy didn’t change. What changed was the system underneath it — the workflows, templates, review cycles, and escalation paths. The operational plumbing that never makes it into a strategy deck but determines whether the strategy actually works.1

———

Nobody gets promoted for building a great checklist. Nobody gives a conference talk about their editorial calendar. But the difference between 45 pieces and 50 pieces in a month isn’t a strategy problem. It’s a system problem. And the difference between those two numbers is the difference between “we got close” and “the system is predictable.”

Predictability is what lets you take risks. When you know your baseline is solid — that 50 pieces will ship on time, that quality is maintained, that the pipeline doesn’t collapse if one person gets sick — then you can experiment. You can try a new format, test a new channel, or invest in a weird idea. The machine keeps running while you explore.

Without predictability, every day is firefighting. And firefighting feels productive. You’re busy, you’re solving problems, you’re clearly needed. But you’re not building anything. You’re just preventing collapse.

———

The irony of AI entering the workplace is that it makes systems thinking more important, not less. Everyone’s excited about AI automating individual tasks. But automation without systems just produces faster chaos.

You can use AI to write ten blog posts in the time it used to take to write one. But if you don’t have a system for editing, fact-checking, optimizing, publishing, and measuring those ten posts, you’ve just created ten problems where you used to have one.

Strategy is a story you tell about the future. Systems are what make it happen.

The leaders who will thrive in the AI era aren’t the ones with the best strategies. They’re the ones who can tell you exactly what happens to a piece of content between the moment it’s ideated and the moment it’s published. Step by step. Every time. Fifty times in a row without it breaking.

They don’t spend their time on the things AI can automate — scheduling, reporting, performance tracking. Those are the tasks they do at 9 pm after a day spent doing the thing no one wants to talk about: absorbing ambiguity.

A CEO sets a direction. An individual contributor needs a task. Somebody has to convert one into the other. That somebody is a middle manager, and the conversion process is almost entirely illegible.

It involves understanding that when the CEO says “we need to move faster,” she means something different than the VP of Engineering does when he says the same thing. It means knowing which of your team members needs explicit direction and which needs room. It means having five definitions of success and knowing all of them are right.

———

I’ve watched this play out in content operations. You have a CEO who wants AI visibility proof. A CMO who wants messaging proof. A board that wants traffic growth. A team that wants to know if their work matters. Same company, same quarter, completely different scorecards.

No AI agent is going to sit in a room with those four stakeholders and navigate those competing truths. Not because the technology isn’t good enough. Because the job isn’t information processing. It’s meaning-making.

What will actually happen is something more interesting than mass elimination. The middle management layer will split into two types. The first type — those who primarily route information and enforce process — will indeed be compressed. AI is already better at this. It tracks tasks, surfaces blockers, and generates status updates. If your primary value is being a human router, you should be worried.

The second type — the ones who translate, interpret, and absorb contradiction — will become more important, not less2. Because as organizations adopt AI tools, the gap between “what the tool can do” and “what the organization needs done” will actually widen.

Somebody has to bridge that gap. Somebody has to figure out that the AI-generated analysis is technically correct but organizationally wrong, because it doesn’t account for the political dynamics of the last board meeting.

———

The real risk isn’t that companies will eliminate too many middle managers. It’s that they’ll eliminate the wrong ones. They’ll keep the process enforcers because their work is visible and measurable, and cut the ambiguity absorbers because their work is invisible and unmeasurable.

And then they’ll spend two years wondering why their AI transformation isn’t working, why strategy isn’t translating to execution, and why teams feel lost despite having better tools than ever.

The messy middle of an organization is called that for a reason. It’s where all the mess lives. You can automate a lot of things, but you can’t automate the willingness to sit in confusion until it turns into clarity. That’s a human function. It might be the most human function there is.