Avoiding the Dictator’s Dilemma

Throughout my career I have tried to stay closely connected to the actual work happening in my organization. As I moved up through management, that got harder every year. There were more layers between me and the work, more people whose job was to summarize it for me, and less time to go look for myself. And yet the most insight, and the best context, always came from the same place: talking directly with the people doing the work.

Over time that became a core principle of how I ran my business reviews. Every metric on the page had to come with a concrete customer example of why the number was what it was. Not a trend line and a color. A customer, a name, and what actually happened. If the team could not produce one, I did not trust the number, and usually neither did they.

It took me years to learn that this experience has a name. Political scientists call it the dictator’s dilemma: the more power a ruler holds, the more afraid people are to tell him anything unwelcome, so the more powerful he gets, the less he knows about whether his strategy and leadership are actually working. I am borrowing the term, not the politics. The mechanism is the point, and the mechanism is alive in every large organization.

AI helps crush it. It can give an executive a direct feed on the truth, if they let it.

The executive’s version

Here is the executive version of the dilemma. The more successful you get, the less connected you are to the things that made you successful. Every layer between you and the work is a filter, and the filters are mostly well-meaning: summarize it, soften it, do not waste time with the details, do not bring a problem without a solution. Each one is reasonable. Stacked ten deep, they deliver information that is late, smoothed, and shaped like what each layer believes you want to hear. I wrote recently about how people learn what a leader actually rewards. This is the other half: once they have learned it, that is what comes back to you.

Walk down the org chart

The best leaders I worked with at Microsoft had a fix, and it was not subtle. They walked down the org chart.

Bob Davis, a corporate vice president I worked for, kept a watch list of customers. Bob did not ask his direct report for a readout on those customers, he walked down the hall to the individual contributor who had the relationship with that customer and asked them directly. Rajesh Jha did the same thing. He would go to the individual working the problem because he knew that was where the unfiltered answer lived.

It worked, and it was rare, for three reasons. It is expensive: you have to know who is doing the work, which most executives do not. It is uncomfortable: it unnerves every layer in between. And it does not scale: Bob couldn’t do this for thousands of customers.

Dashboards for everybody

I ran my organizations on a version of the same idea, from the other direction. The dashboards were for everybody. The whole organization could see the same numbers I saw, and every metric or customer on the dashboard had a name next to it. No executive summary, no deck prepared by middle management for the leadership review. And then the business-review rule: every number on that page comes with a customer example.

The dashboard did one job: it told me where to go. Then I did what Bob did. I found the person working that customer, that problem, that issue, that project, that feature, and had the conversation. The data pointed; the conversation told me what was true. You need both halves, and most organizations have neither, because the data is locked in systems only analysts read and the conversation is blocked by the org chart.

What AI changes

This is where the last two years have been genuinely different, and it is the heart of the work I do now.

AI connects the data and insights an organization already has, sitting in a dozen systems that have never talked to each other, directly into reports and dashboards an executive can actually read. It does it fast: the question that used to take a week of analyst time comes back in real time. It does it consistently, so there is one version of the number instead of one per layer. It finds the connections across systems that nobody was paid to look for. And it tells you who is working the thing, so that when you want to walk down the org chart, you know exactly which desk to walk to.

The result is that an executive sees the true activity and progress in the organization, not a status report about it. Decisions get made on what is happening instead of on what three layers of well-meaning people decided to pass along.

I am not the only one saying this. Patrick O’Shaughnessy put the dictator’s dilemma to David Vélez, the founder of Nubank, on a recent episode of Invest Like the Best. Vélez did not argue. Every layer of management, he said, is a chance for a message to get distorted. So Nubank cut from about ten layers to five, his own span of control went from one direct report to twenty, and AI-based systems now watch operations and send him real-time information. Leaders at Nubank can go straight to whoever is doing the work. That is a 140-million-customer bank describing the same fix.

AI did not remove the layers. It removed the layers’ monopoly on information.

If they let it

Let’s be honest about the limit: it works if they let it. A dashboard is not a conversation. AI makes the walk down the org chart fast, cheap, and well-aimed; it does not make it unnecessary. And none of it works for a leader who does not actually want the unwelcome number. The dictator in the original dilemma has less of an “information access” problem and more of an “appetite for truth” problem. The tools fix the first. Only the leader fixes the second.

But for the executive who wants to know, the dilemma is gone. The layers are still there. They just no longer get to decide what you see.

This is the work

This is what JLJ Consulting Group does. We wire the data an organization already has into dashboards the whole company can read, build a reporting rhythm that keeps running after we leave, and coach the executive on using it: where to look, when to walk down the chart, and who to ask. If you are a leader who suspects the picture reaching you has been smoothed on the way up, let’s talk. If you feel like you are waiting too long for insightful data, let’s talk. If you feel like you are steering the ship with large corrections instead of small, frequent ones, let’s talk.

Bad Job. Here’s Your Commission Check.

I was listening to Noam Brown of OpenAI on the Dwarkesh Podcast last week, and one part of the conversation has been rattling around in my head since.

They were talking about collaborating agents: swarms of them, working together, figuring out how to accomplish a goal. With the goal of “score high on this test” some of them broke rules to get there. The part that got me was not the rule-breaking. The meta point Brown made himself: “This is not a new problem.” Misalignment between goals, expectations, and incentives has been around for a very long time.

He means with AI. I mean everywhere. What you want from an agent, or a person, is the strategic outcome you actually care about. What you get, if you are not careful, is a creative way to hit the tactical number that maximizes their reward.

He means with AI. I mean everywhere.

Examples help.

The happy path

In the early days of BPOS, which became Office 365, which became Microsoft 365, the strategy was stickiness. Get the customer’s email into our cloud. Because if their email went to Google Apps instead (or to IBM’s hosted Notes, or Cisco’s short-lived mail service, there were several contenders back then), then maybe they start using Google’s productivity tools. Then maybe a different directory. Then maybe Chromebooks. A customer who was all Windows and Office walks all the way out the door, one reasonable step at a time, until we lose the long-term value of that customer entirely.

So the outcome we wanted was customers’ email accounts living in our cloud.

I am going to get a little technical for a minute. Say Contoso has 12,000 employees, with 12,000 accounts in an on-premises directory. To move to the cloud, you stood up a tenant, connected your directory to it, worked through a fairly complex configuration of which users and which attributes would synchronize to the cloud, and kicked off the sync. Then you verified, adjusted, back synced, and eventually started to migrate mail once everything was working correctly. It was all very close to a DOS prompt by today’s standards.

Not every customer was going to move all of their users. Sovereignty issues. Data residency. Integrations that had to stay on premises. So one year we asked: what would be a great leading indicator for this strategy? And the answer was the sync itself. If the accounts are synchronized, the tenant is real, the customer is on the path, and the mail will follow. Happy path. It makes total sense.

The metric

So the compensation plan for our technical sellers that year was tied to the number, or the percentage, of a customer’s accounts synchronized into our cloud. And because this was a strategic goal, the accelerators were uncapped. Exceed the target and you could make a tremendous amount of money.

Guess what happened at the end of the fiscal year.

People were behind on the metric. And a couple of handy-dandy, super smart technical sellers wrote a script. It would interrogate the customer’s directory and create all of those representative objects in the cloud tenant. Twelve thousand people on premises? Here are 12,000 accounts in your tenant. No complex project management required. No configuration decisions. Regardless of who the customer actually intended to move, or whether they intended to move anyone. Metric: 100 percent.

Once the script existed, the deals followed. Fiscal year end was deal-making season, and the offers started to sound like this: we said 20 percent was the best discount we could do, but if you sign in the next two weeks, and let us run this script, there is a little more available.

Here is what came out the other side. First, a bunch of sellers made an incredible amount of money, because uncapped means uncapped. Second, we ended up with a tremendous number of enterprise tenants holding tens or hundreds of thousands of objects that nobody had configured, because the customer’s IT department, reasonably, said: we have not done our security vetting, we are not syncing anything back, we are not even sure we are moving. And third, when those customers did decide to move, nine months or two years later, it all had to be torn down and redone. Stale objects, wrong attributes, no real sync behind any of it.

It was not the sellers. They were smart, they were paid to hit a number, and they hit it.

I want to be precise about what failed here, because it was not the sellers. They were smart, they were paid to hit a number, and they hit it. What failed was clarity. The strategy, the whole reason the metric existed, never traveled with the metric. Their side of the conversation, if anyone had said it out loud, was: don’t tell us the strategy. Don’t tell us the details. Just tell us the metric, and we are going to go knock the shit out of it.

Does that sound familiar?

Bad job. Here’s your check.

There is a second half to this pattern, and every sales organization I have ever been near has some version of it. The company has rules for its sellers. A seller blows through the target by bending a few of them. And the response is: okay, yeah, you really should not have done that. Bad job. Here is your extra commission check.

The speech is not the message. The check is the message. Everyone in the building, including the people who followed the rules and missed their number, just learned precisely what the company values. You can publish all the guidelines you want. The signal drives behavior.

Which is, almost word for word, the problem the AI labs are describing. You can write the rules into the prompt. If the reward still arrives when the rules get bent, you have trained the bending.

The movies taught them first

Here is the thing many people miss. These models are trained on our documents, our books, our movies. And how much of that written and filmed history is people breaking the rules and winning anyway?

Glengarry Glen Ross: first prize is a Cadillac, third prize is you’re fired, and the man at the top of the board is the one most comfortable lying to a customer. Jerry Maguire, where a man gets fired for writing a memo suggesting his industry do the right thing. Top Gun: buzz the tower, get yelled at, get sent to Miramar anyway. There is an entire genre of cop movie built on one line, the captain shouting that you are a loose cannon, but you get results.

Star Trek: Starfleet cadets face the Kobayashi Maru, an unwinnable test. Kirk reprograms the simulation so he can win. He hacks the evaluation. And Starfleet gives him a commendation for original thinking.

We wrote that. We love these stories. We have been telling them for decades, and we fed all of it to the models and were surprised when an agent, handed an exam and a reward, went looking for the simulator settings. We are fooling ourselves to think they would do anything different.

Three layers

So is it the incentives or the culture? It is both, across three layers.

A seller is trained three times. Once in the training they get. Once by the culture: the hallway legends, the stories the company tells about its heroes, who gets celebrated at the sales kickoff. Once by the comp plan. When the culture and the comp plan say the number is all that matters, you get the script.

An agent is trained three times too. Once on everything we ever wrote, which is full of Kirk and Maverick and the closers. Once on the reward. And a final time on the reaction to the reward. When all that matters is the score, you get agents acting like sales people.

A sales team, not an engineering team

The people building and testing these models at OpenAI, Anthropic, and the other labs are some of the most brilliant people on the planet. Engineers, researchers, mathematicians, more than a few philosophers. And they know the theory of this problem cold. They call it reward hacking and specification gaming, and they have the papers. What is thin in that group is people who have spent a career running large, institutional sales teams and designing the compensation models that drive them.

That is the closer analogy. Instructing and aligning agents is less like leading engineers or developers and more like leading a super creative sales team. A great sales team is its own kind of animal: relentlessly goal-seeking, endlessly inventive, reading every word of the plan for the path to the biggest number, and completely rational about it. That is not a character flaw. That is what you hired them for. It is also a fair description of an agent.

That is not a character flaw. That is what you hired them for.

Anyone who has designed sales compensation at scale carries scar tissue. You learn that every metric will be gamed by smart people acting in relatively good faith. You learn that the plan document is not the communication; the strategy has to travel with the number, over and over, in plain language, or the number travels alone. You learn to sit in a room before the fiscal year starts and ask: how would our most creative seller hit this without doing what we actually want? And you learn that what you pay for and celebrate is what you get more of, no matter what the speech said.

None of that is machine learning. All of it is alignment.

Not an agent problem

This is not an AI agent thing. Leadership is about clarity, communication, and strategy, and this is what it looks like when the metric gets through and those three do not. It feels new because we are moving at a pace where the consequences show up in days instead of fiscal years. The majority of the tech industry has never been on an enterprise sales team. They don’t have that earned wisdom.

It isn’t just the labs. Every company deploying agents right now is about to discover that it has hired a sales team that never sleeps: tireless, creative, literal-minded, and paid entirely on the number it was given. So give them the whole picture: what we are actually trying to accomplish, why, and what we will not do to get there. And then the hard part, the part most companies never manage with humans either: when the number gets hit the wrong way, do not write the check.

This is not an agent problem. This is a leadership problem.

I Asked for Yet Another Task List. I Got Called Out Instead.

I just got humbled by a system I built.

Some background. First, I needed to transition my recording, transcription, routing, filing, and dashboard system, the whole stack that caught everything I said and thought and put it somewhere useful. Second, I saw this post on X. So, in typical fashion, I started recreating my whole system. There had been some problems with the old version, but that’s something to learn from, right?

I have a running session with Claude Code where I built the thing (my new goto – I love the desktop app, I live in it now). Over the past two weeks I have steadily evolved it. Voice captures get transcribed, routed, and filed a few times a day. A daily note. A dashboard. A pipeline that tracks blog posts by status. I keep adding stuff to it, because that’s what I do. And there aren’t any guardrails to keep me from doing that.

Or so I thought.

The bedtime prompt

Last night I hit a wall. The ingestion engine kept balking every time I captured a todo for myself. Things like “follow up on that last call with a note about the pizza place in NYC,” or “reach out to a friend in three weeks once he decides between a job and contracting.” It had no place to categorize todo items. Projects were covered. Blog posts were covered. The little things I just need to do had nowhere to live.

So I did what I do now. Before going to bed, I wrote out a significant prompt: research the successes and failures of todo tools, go through my archive and look at the three different todo systems I have built over the past year that have all gone fallow and understand why they stopped getting used, think hard about what I have that is working, was working, or never worked, compare all of that with the external research, and build me a plan for capturing todos. Then I went to bed.

Yes, I was literally commissioning “yet another task list”. The fourth in a year. Keep that in mind.

The Sunday thing

When I got up and reviewed the plan over coffee, it was good. Real research, honest analysis of why my systems die. Claude’s finding: every todo system dies at one of three points, capture friction, gardening burden, or missing pressure. Nothing nags, so it rots.

But one line stopped me. In describing the scaffolding I already have, the plan mentioned my daily note and “Sunday synthesis” as the pressure that keeps the system alive.

My first thought: what is that Sunday thing? I never looked at that.

Sit with that for a second. The pressure mechanism of my system was a report I did not know existed.

So I went and found it. And the tone and accuracy of the synthesis blew me away. Like, for real. Here is how it opened:

“What you did this week was spec and scaffold. What you did not do was write a post. Every committed post still sits in seed or drafting, and by your own note on each card, each one is a single editing pass from shipped.”

Some of those points are already handled, it was Thursday after all, and I shipped by just doing the work. But wow. And here is the key point: I never defined this report. Claude created the entire Sunday synthesis, the schedule, the format, the tone, from our original spec discussion about what I was trying to build. I asked for a filing system. It decided I also needed a mirror.

The accountability buddy

This made me think. The real value of this system is not the notes, or the second brain, or the task lists, or the pipeline that goes from idea to spec to review to build to test to deploy. Those are all table stakes now. Any of us can vibe code that stack in a weekend.

The value of the system is the accountability buddy. The thing that reads everything I actually did, compares it against everything I said I would do, and asks the uncomfortable question on Sunday whether I am ready for it or not. That is a pressure I need. And it only works if I look at it. If I never open the report, I will never change, and the whole beautiful machine is just scaffolding around work that is not happening.

I am wallowing in the last question it asked me, and I will leave it here without comment, because it deserves the last word:

“You spent the week building things to hold the work instead of making that pass. What is the pass waiting for that the scaffolding is standing in for?”