Category: Blog

Incident command operates under conditions that expose every weakness in a planning system: time pressure, incomplete information, rapidly changing circumstances, high stakes, and teams that need clear direction without lengthy explanation.

In that environment, a tool that requires re-briefing from scratch every time you open it isn’t a tool. It’s a liability.

The ICS framework — Incident Command System — exists precisely because ad-hoc decision-making under crisis conditions produces inconsistent, dangerous outcomes. It builds structure into chaos. It creates shared mental models across a team that may have never worked together. It gives every person in the chain of command a clear understanding of their role and authority.

AI that supports incident command needs to meet the same standard. It needs to know the incident — the objectives, the current operational period, the resources deployed, the constraints in play. It needs to maintain that context across the entire incident, not reset every session. It needs to support the ICS structure, not operate outside it.

The difference between AI that’s generically helpful and AI that actually supports incident command is the difference between a knowledgeable stranger and a briefed operations chief. One requires you to explain your situation from scratch every time you need a decision supported. The other already knows it.

In a crisis, the time you spend re-briefing your tools is time you’re not spending on the incident. That cost is real. For the operators who work in high-stakes environments, it’s unacceptable.

There’s a reason the operators who are serious about fitness tend to be serious about their business metrics. They’ve already learned the same lesson in a different domain: what you measure changes how you behave.

When you start tracking your food, your training, your sleep — not obsessively, but consistently — you discover things you didn’t know about your own patterns. You thought you were eating well. The data says otherwise. You thought you were training hard. The log reveals you’ve missed four sessions in the last two weeks.

The data doesn’t lie and it doesn’t care about your intentions. It only reflects what you actually did.

Business operates on the same principle. Most operators have a story about how their business is doing that’s more optimistic than the numbers warrant. Not because they’re dishonest — because they’re human. We remember the wins more vividly than the losses. We weight recent positive signals heavily. We find explanations for bad data that protect our thesis.

The practice of tracking — whether it’s your macros or your conversion rates — is fundamentally a practice of confronting reality as it is rather than as you hope it is.

The operators who do this consistently across both domains tend to compound faster. Not because they’re more talented. Because they’re operating on more accurate information about themselves.

Measurement is not about control. It’s about clarity. And clarity, sustained over time, is one of the most powerful competitive advantages available to any operator.

The monthly review is productivity gospel. Block an afternoon at the end of the month, review what you did, assess progress against goals, plan the next month. It’s in every productivity book written in the last twenty years.

It also doesn’t work for most people. Not because the idea is wrong — but because the cadence is wrong.

A month is too long. By the time you sit down to review, the decisions that shaped your month are already in the past and feel fixed. The drift has already happened. You’re doing archaeology, not navigation.

What works better is a daily practice that takes five minutes, not a monthly practice that takes five hours.

Every morning: what must happen today, what should happen if possible, what can wait. Every evening: did what needed to happen actually happen, and if not, why not?

That simple loop, done consistently, catches drift in real time. You notice on day three that your A-priority hasn’t moved, not on day thirty. You adjust while adjustment is still cheap.

The monthly review becomes useful only when it’s reviewing a month of daily decisions — not trying to reconstruct what happened from memory. Use it to look for patterns across your daily logs, not to substitute for the daily practice.

The granularity of your planning practice should match the granularity of your decisions. If you’re making consequential decisions daily, you need daily accountability. Weekly reviews for people making weekly decisions. Monthly reviews for people operating on monthly cycles.

Most operators make daily decisions but review monthly. That gap is where drift lives.

Most operators have advisors. Very few of them get consistent, actionable value from those relationships.

This isn’t because the advisors are bad. It’s a structural problem with how advisory relationships work.

An advisor who meets with you quarterly can only know what you tell them. They’re working with a compressed, curated version of your situation — the highlights you can communicate in a one-hour meeting, filtered through whatever framing you bring to the conversation that day. They don’t have access to the full texture of how you operate, the context behind your decisions, the history of what you’ve tried.

So they give you advice that’s reasonable given what they know. Which is, by definition, incomplete.

The best advisors solve this partially through relationship depth — they’ve known you long enough that they can read between the lines, they remember the deals you’ve discussed before, they have a mental model of how you think. But that takes years to build and is expensive to maintain.

The other limitation is social. Advisors have relationships with you. That creates friction around the hardest kind of advice — the kind that challenges your thesis when you’re excited, that tells you what you don’t want to hear, that holds your framework against you when you’re trying to bend it.

Good advisors push through that friction. Most don’t, consistently.

The question isn’t whether to have advisors. It’s whether the advice you’re getting is actually calibrated to you, or calibrated to what’s reasonable for a generic operator in your position. Those produce different recommendations. Only one of them is useful.

When people talk about AI alignment, they usually mean the big philosophical question — how do we make sure advanced AI systems pursue goals that are good for humanity?

There’s a smaller, more immediately practical version of the same problem that almost nobody discusses: how do you align the AI you’re using right now with your specific goals, your specific framework, your specific definition of a good outcome?

Generic AI is aligned with the average user. It’s calibrated to be helpful to the broadest possible audience, which means it’s not precisely calibrated to anyone. For most tasks this is fine. For decisions where your specific situation matters — where your history, your constraints, your red lines are the critical inputs — generic alignment is a liability.

An AI that doesn’t know your investment thesis will give you advice that’s reasonable for a generic investor. An AI that doesn’t know your risk tolerance will calibrate recommendations to a statistical average. An AI that doesn’t know what you’ve tried before will sometimes walk you right back into mistakes you’ve already made.

This isn’t a failure of intelligence. It’s a failure of alignment. The tool is doing exactly what it was built to do — be helpful to everyone. The problem is you’re not everyone.

The operators who get the most from AI are the ones who solve the alignment problem at the individual level — not waiting for the AI companies to solve it at the model level. They encode their framework. They maintain context. They treat alignment not as a technical problem but as a practice.

How aligned is the AI you’re using right now with how you actually operate? That’s worth sitting with.

Everyone talks about the big decisions — the fund raise, the acquisition, the pivot, the hire that changed everything. Those are the ones that get written about, analyzed, turned into case studies.

The decisions that actually shape most operators’ trajectories aren’t the big ones. They’re the small ones made consistently over time.

Which information sources you trust. How much time you spend in meetings versus doing the work. Whether you respond to every message immediately or in batches. How you handle the first sign of underperformance on your team. Whether you re-examine your assumptions when a deal is going well or only when it’s going badly.

None of these feel like high-stakes decisions in the moment. Each one is a small call made under normal conditions. But the pattern of those calls over months and years defines your operating style, your culture, your outcomes.

The problem is that small decisions are almost never examined with the same rigor as big ones. You don’t hold a decision meeting about how you handle your inbox. You don’t bring in advisors to help you think through how you run your Monday morning. You just do what feels right or what you’ve always done.

That’s where drift accumulates. Not in the moments of obvious high stakes — you’re careful there. In the ordinary moments where you’re operating on autopilot.

The discipline that matters most isn’t how you handle the crisis. It’s the quality of attention you bring to the decisions that don’t feel like decisions at all.

Every time you start a new AI session, you’re talking to someone who has never met you.

Most people understand this intellectually. What they underestimate is the operational impact over time.

Consider what you lose when every session resets. You lose the context of every decision you’ve discussed. You lose the framework refinements you’ve made through previous conversations. You lose the red lines you’ve encoded, the patterns you’ve identified, the lessons from past mistakes you’ve worked through with the tool.

Every new session, you start from scratch. You re-explain your situation. The AI catches up as best it can from what you tell it in this session. Then the session ends and everything you built together disappears.

This creates an invisible cost that compounds over time. You’re not just losing convenience — you’re losing the compounding value of a tool that actually knows you. Every session is the first session. The tool never gets better at helping you because it never accumulates knowledge of you.

Compare that to a human advisor you’ve worked with for five years. They know your history. They remember the deals you passed on and why. They understand your framework well enough to apply it without you re-explaining it. They get better at helping you because they know more about you.

That’s the standard AI should be held to for high-stakes work — not ‘is the output reasonable?’ but ‘does this tool know me well enough to give me advice I couldn’t get from a stranger?’

Most tools fail that test by design. The question is whether that’s a limitation you’re willing to accept.

Most planning apps are very good at storing information. You put your tasks in, you check them off, you feel organized. What they’re not good at is connecting your daily execution to your actual goals.

This is a design problem, not a usage problem. Most tools treat tasks and goals as separate categories. You manage them separately, you review them separately, and the connection between them is something you have to maintain manually in your own head.

The result is a common phenomenon: people who are very busy but not making progress on what actually matters. The urgent consumes the important. The daily list fills up with things that feel productive but don’t compound.

A planning system that works differently keeps your goals visible every single day — not as a separate tab you review monthly, but present in your daily view, connected to your daily decisions. The question ‘what should I do today?’ becomes inseparable from ‘what am I actually trying to build?’

When that connection is visible daily, the priority queue changes. The A-tasks start looking different. The things that feel urgent but don’t serve your goals become easier to deprioritize.

The other thing a connected planning system does is surface drift early. If you haven’t made progress on a goal in two weeks, you want to know that on day fifteen — not when you do your quarterly review and wonder where the time went.

Planning isn’t about managing tasks. It’s about staying aligned between what you’re doing and what you’re trying to build. The system should make that alignment visible, not something you have to maintain manually.

There’s a pattern I’ve noticed across the operators I’ve worked with over the years: the ones who are serious about their physical training tend to be serious about their decision-making frameworks. Not always. But often enough to be worth examining.

The connection isn’t about willpower or character. It’s about systems thinking.

Someone who has built a sustainable training practice has already solved a hard problem: how to maintain consistent execution on something that doesn’t have immediate, obvious rewards. You don’t feel dramatically different after one workout. The results are long-term and compounding. The practice has to survive days when you don’t feel like it, weeks when life gets in the way, months when progress isn’t visible.

That’s exactly the same problem as maintaining a decision-making framework under pressure.

When you’re in the middle of an exciting opportunity, your framework feels like an obstacle. The red lines feel arbitrary. The process feels slow. The discipline that makes the framework useful is the same discipline that makes you finish the workout when you don’t feel like it — not because of how it feels today, but because of what it builds over time.

The operators who are serious about both tend to understand something that others don’t: consistency is a force multiplier. Not intensity. Not talent. Not even intelligence. The thing that compounds most reliably is showing up with the same framework, day after day, making the same quality of decision regardless of how you feel.

Your body and your business respond to the same inputs. Discipline in one tends to show up in the other. This isn’t motivation content — it’s pattern recognition from watching a lot of operators over a long time.

Some operators keep getting better for decades. They make more precise decisions at 50 than they did at 35. Their judgment compounds. Their edge sharpens.

Others plateau early. They get competent, they stay competent, and they stop growing. Not because they stop working hard — but because they stop updating their framework.

The difference, as far as I can tell, comes down to one thing: whether they built a system for learning from their own decisions.

Most operators have no such system. They make a call, it plays out, they move on. If it worked, they feel validated. If it didn’t, they feel bad for a while and then move on. In neither case do they systematically extract what the outcome tells them about their framework.

The operators who compound do something different. They track their decisions — not obsessively, but enough to create a record. They note what they believed at the time of the decision, what they expected to happen, and what actually happened. Then they look for the pattern in the gap.

That gap is the most valuable data available to any operator. It tells you specifically where your judgment is miscalibrated. Not in the abstract — for you, in your domain, with your particular blind spots.

No outside advisor can generate that data. No AI can generate it without access to your history. It can only come from a disciplined practice of reviewing your own decisions against your own stated framework.

The operators who compound are the ones who treat their own track record as a source of intelligence, not just a record of what happened.