Hey everyone, welcome back to My Weird Prompts. I am Corn, and today I am feeling a bit overwhelmed by the sheer amount of information we are expected to process every single day. I was looking at my fitness tracker this morning and it told me my readiness score was a sixty-eight, but I felt like a hundred. Then it told me my sleep quality was high, but I woke up with a headache. It made me realize how much we have outsourced our own intuition to these little digital dashboards. We have reached a point where we trust the glowing rectangle on our wrist more than the actual signals our own central nervous system is sending us.
Herman Poppleberry here, and Corn, you are hitting on something huge right out of the gate. We have become a civilization of dashboard watchers. Our friend and housemate Daniel sent us a prompt that really digs into this. He was reflecting on our recent deep dive into impact investing and that famous management mantra: If you can't measure it, you can't manage it. Daniel’s point is that we have taken this idea to such an extreme that the measurement itself has become the distraction. We are measuring everything and, as a result, understanding almost nothing. It is a form of cognitive laziness. We think that if we can just capture enough data points, the truth will somehow emerge from the noise by magic.
It is the ultimate irony of the information age. We have more telemetry, more data points, and more granular visibility into our systems than at any point in human history, yet our ability to actually predict or prevent systemic failures feels like it is at an all-time low. We are drowning in the noise. Daniel mentioned that in his work with networking infrastructure, he sees this all the time. Excessive measurement is not just a neutral byproduct; it is a cost. It degrades the signal-to-noise ratio. It is like trying to listen to a whisper in the middle of a hurricane. The more sensors you add, the more wind noise you get.
That is the thing. It isn't just tech. It is medicine, public policy, and management science. We have this deep-seated psychological bias where we equate more data with more truth. If I have a spreadsheet with ten thousand rows, I feel like I know more than the person who has a spreadsheet with ten rows. But if those ten thousand rows are just capturing jitter and minor fluctuations that do not impact the core mission, then the person with ten rows is actually better informed. They can see the forest, while we are busy counting the individual leaves on a single branch of a single tree. We have mistaken volume for value.
I want to start by framing this as the inverse of Goodhart's Law. Most of our listeners probably know the classic phrasing: When a measure becomes a target, it ceases to be a good measure. But what we are talking about today is slightly different. It is more like: When a measure becomes a mandate, it obscures the reality it was meant to represent. We have moved from observability, which is necessary for keeping systems running, to a form of total surveillance that is just plain distracting. We are so obsessed with the "how much" that we have completely forgotten the "why."
That distinction is crucial. Observability is about having the data you need to answer questions when things go wrong. It is diagnostic. Surveillance, in this context, is the compulsive need to monitor every heartbeat of a system in real-time, even when those heartbeats tell you nothing about the system's long-term health. It is a security blanket for managers and engineers who are afraid of the unknown. It is much easier to build a dashboard with fifty flashing red lights than it is to sit down and think deeply about which three variables actually matter for your business or your health. We are substituting activity for thought.
Let's dive into that technical side first, because it provides a perfect analogy for everything else. In networking and site reliability engineering, there is this concept of cardinality. For those who aren't in the weeds of data science, high-cardinality data is when you have a metric that has a ton of unique values. Think about tracking every single user's unique identification number across every single request they make. If you try to monitor that at scale, you run into what they call a cardinality explosion. Your monitoring system starts consuming more resources than the actual application you are trying to run.
And that is exactly where the signal-to-noise degradation happens. I was reading a study from two thousand twenty-five about alert fatigue in site reliability engineering teams. They found that teams receiving more than fifty alerts per day had a forty percent higher rate of missing critical, system-ending incidents. The more you monitor, the more likely you are to miss the thing that actually kills you. It is like being in a cockpit where every single button is screaming at you because the cabin temperature shifted by half a degree, while the engine is silently falling off the wing. You are so busy silencing the minor alarms that you don't notice the silence of the main engine.
It is the boy who cried wolf, but at a nanosecond scale. And we see this in how we monitor performance, too. For a long time, the standard was monitoring uptime. Is the site up? Yes or no? That is a binary, simple metric. But then we got sophisticated. We started looking at latency percentiles. We look at the fiftyth percentile, the ninety-fifth, the ninety-ninth. And while that is useful for finding edge cases, it creates this trap where you can spend months of engineering time and millions of dollars trying to shave five milliseconds off your ninety-ninth percentile latency, while ignoring the fact that your actual user experience is terrible because the interface is confusing or the product itself doesn't solve a problem.
You're hitting the nail on the head. You are optimizing the metric, not the outcome. Often, those high-percentile latencies are just outliers caused by things you cannot control, like a stray cosmic ray or a weird routing hiccup in a data center halfway across the world. But because we can measure it, we feel we must manage it. We become obsessed with the tail of the distribution and lose sight of the median experience. This is what Daniel was talking about with the signal-to-noise ratio. The more granular you get, the more you are measuring noise. You are literally trying to optimize for chaos.
This carries over perfectly into medicine. Daniel made a vital observation about patient access to granular data. We are seeing this now with continuous glucose monitors. These were originally designed for people with type one diabetes, where it is a literal life-saver. But now, healthy people are wearing them and watching their blood sugar spike after eating a piece of fruit. They see a graph moving in real-time and they panic. They think they are becoming insulin resistant, when in reality, their body is just doing exactly what a healthy body is supposed to do. It is responding to glucose.
It is the medicalization of everyday life. We have created what doctors call the worried well. When you give someone a constant stream of high-frequency data without the medical training to interpret it, you are not empowering them; you are giving them a new hobby called hypochondria. A single blood panel taken once a year is a snapshot that provides a useful baseline. A continuous stream of data points is a movie, and most people do not realize that the movie is supposed to have ups and downs. If you react to every frame of the movie, you are going to make some very bad health decisions. You might stop eating fruit because of a temporary spike, even though fruit is objectively good for you.
It is the same issue we talked about in the networking context. We are mistaking fluctuations for trends. In a complex system, whether it is a human body or a global network, there is always going to be variance. Stability is not a flat line; it is a dynamic equilibrium. But dashboards love flat lines. Dashboards make us think that any deviation from the mean is a problem to be solved. We have lost the ability to tolerate normal variance. We want everything to be perfectly predictable, which is another way of saying we want everything to be dead.
And that brings us to the management side of things. This is where the measurement trap really starts to rot organizations from the inside out. We have shifted from outcome-based metrics to activity-based metrics. Because it is hard to measure whether a manager is actually a good leader or whether a creative team is actually being innovative, we measure things that are easy to count. How many emails did they send? How many hours were they logged into the system? How many tickets did they close? This is the McNamara Fallacy, named after Robert McNamara during the Vietnam War. He tried to run the war based on body counts and kill ratios, ignoring the qualitative reality that they were losing the hearts and minds of the people.
It is the ultimate expression of the bureaucratic mind. If you can't measure the quality of the work, you measure the volume of the artifacts. We see this in the public sector all the time. Think about education policy. We wanted to improve schools, so we decided to measure them. But you can't easily measure whether a child is becoming a critical thinker or a virtuous citizen. So, we measured standardized test scores. And what happened? Teachers stopped teaching the subjects and started teaching the test. The metric became the target, and the actual goal of education was buried under a mountain of data. We ended up with students who are great at filling in bubbles but can't write a coherent essay.
It is a tragedy. And it leads to a total loss of institutional knowledge. When you rely entirely on metrics, you stop trusting the judgment of the people on the ground. You stop trusting the teacher who knows that a specific student is struggling because of a home situation, because the dashboard says that student's math score dropped by five points. The dashboard is treated as the objective truth, and the human observation is treated as subjective bias. But as we know, the dashboard is just a collection of biases that have been codified into software. It is a digital filter that removes the most important context.
That is such a powerful point. We act as if data is neutral, but the decision of what to measure is a deeply political and subjective act. In episode one thousand three hundred twenty-four, we talked about the dark side of impact investing, and this fits perfectly there. The whole environmental, social, and governance, or E-S-G, movement is essentially one big measurement trap. We are trying to boil down the incredibly complex moral and environmental impact of a global corporation into a single numerical score. It is an attempt to quantify the unquantifiable.
E-S-G scores are the height of absurdity. You have these massive ratings agencies that give a company a high score for diversity while that same company is polluting a river in a developing nation. But because the diversity metric is easy to count and the river pollution is harder to quantify in a standardized way, the score looks great. These arbitrary scores often have zero correlation with actual environmental impact or long-term financial performance. But because pension funds and institutional investors need a number to put in their reports to show they are being responsible, they treat these scores as gospel. It is a performance of virtue, not actual virtue.
It is a checkbox culture. It is the philanthropy paradox we discussed back in episode one thousand three hundred forty-one. When you demand a clear return on investment for every dollar of kindness, you end up only funding the things that are easy to measure, not the things that are actually effective. You might fund a program that gives out ten thousand pairs of shoes because you can count the shoes, but you won't fund a program that works on systemic legal reform in a corrupt country because you can't put a chart in your annual report showing the progress of justice on a month-to-month basis. We are starving the most important work because it doesn't fit into a cell in a spreadsheet.
Precisely. Measurement is a form of control, and we have become addicted to the illusion of control that data provides. We see this in public policy as well. Governments love key performance indicators because they provide a veneer of accountability. But in the social sciences, there is something called Campbell's Law, which is very similar to Goodhart's Law. It says that the more any quantitative social indicator is used for social decision-making, the more subject it will be to corruption pressures and the more apt it will be to distort and obstruct the social processes it is intended to monitor.
We saw this in the United Kingdom with the National Health Service waiting list targets. The government set a target that no one should wait more than four hours in the emergency room. So, what did the hospitals do? They started holding people in ambulances outside the hospital so they wouldn't technically be admitted to the emergency room until they were sure they could see them within four hours. The metric was met, the dashboard was green, but the actual patient experience was worse because they were stuck in an ambulance in the parking lot instead of a bed in the hallway. It is the Cobra Effect—where the solution to a problem makes the problem worse.
It is wild. And we think we are being data-driven! I actually prefer the term data-informed. Being data-driven means you have taken your hands off the steering wheel and let a spreadsheet drive the car. Being data-informed means you are looking at the dashboard, but you are also looking out the windshield and using your own judgment to decide where to go. If your G-P-S tells you to drive into a lake, a data-driven person drives into the lake. A data-informed person sees the water and hits the brakes.
I love that distinction. Let's talk about the practical limits here. Why is the measurement trap so much more dangerous today than it was thirty years ago? I think it is simply because the cost of data collection has dropped to near zero. It used to be expensive to measure things. You had to hire people to do surveys, manually record data, and pay for storage. Now, storage is cheap, sensors are everywhere, and we can automate the telemetry of almost everything. We are measuring things just because we can, not because we should.
Right. The friction is gone. When there is no friction to collecting data, we just collect it all. It is the digital equivalent of hoarding. We think we might need this data later, so let's just log it. But then we realize that we don't have the human bandwidth to actually analyze it. So we build an A-I to analyze it. And then the A-I finds correlations that aren't actually there, or it starts optimizing for a metric that doesn't matter, and we find ourselves even further away from reality. We have created a feedback loop of noise.
It reminds me of the maps in that Jorge Luis Borges story, where the cartographers build a map so detailed that it is the same size as the empire itself. At that point, the map is useless. You might as well just look at the empire. Our dashboards have become so granular that they are starting to approach the complexity of the systems they are supposed to simplify. If your dashboard has a thousand metrics, it is no longer a summary; it is just a second, slightly more confusing version of reality. You have traded the complexity of the world for the complexity of the interface.
That is the signal-to-noise ratio in a nutshell. We need to rediscover the art of restraint. We need to realize that every metric we add has a cognitive cost. It is another thing to monitor, another thing to worry about, and another thing that can be gamed. I think we need to start talking about metric pruning. Just like you prune a tree to make it healthier, we should be regularly auditing our dashboards and deleting any metric that doesn't lead to a specific, pre-defined action. If you aren't going to change your behavior based on a number, why are you looking at it?
That is a helpful practical takeaway. If a metric doesn't trigger a decision, why are you looking at it? If your server latency goes from twenty milliseconds to twenty-five milliseconds, and you don't do anything about it until it hits fifty, then you don't need to see the twenty-five. You only need to see the alert when it hits fifty. We need to move from continuous monitoring to exception-based monitoring. We need to trust the system to run itself until it actually breaks, rather than hovering over it like a helicopter parent.
And we need to prioritize leading indicators over lagging indicators. A lot of what we measure is the equivalent of looking in the rearview mirror while driving. Revenue is a lagging indicator. It tells you how you did last month. It doesn't tell you how you are going to do next month. A leading indicator might be something like customer sentiment or the velocity of your product development. Those are harder to measure, they are more qualitative, but they are much more valuable for decision-making. They give you a chance to change course before you hit the wall.
It is about moving from quantitative metrics to qualitative insights. This is something we talked about in episode eight hundred sixty-seven when we were looking at the democracy dashboard. You can measure how many people vote, but that doesn't tell you if the democracy is healthy. You have to look at the quality of the discourse, the strength of the institutions, and the level of trust in the system. Those things don't fit neatly on a bar chart, but they are what actually matter. We have to stop being afraid of things that can't be counted.
It requires a certain level of courage to say, I am not going to measure this because I don't think a number can capture it. In a corporate or government setting, that is a scary thing to say. It is much safer to provide a bad number than no number at all. A number provides cover. It provides plausible deniability. If things go wrong, you can say, look, the metrics were all green! It wasn't my fault. But if you rely on your judgment and things go wrong, you have to take responsibility. Measurement has become a tool for risk evasion.
That is the heart of the issue. Measurement has become a way to outsource responsibility to an algorithm or a dashboard. If we want to escape the measurement trap, we have to be willing to reclaim our own agency. We have to be willing to say, I know the data says X, but my experience and my intuition tell me Y, and we are going with Y. We need to put the human back in the loop. We need to stop acting like we are just the biological components of a data-processing machine.
And that brings us back to the human-in-the-loop filter. We are seeing a lot of talk about A-I-driven observability, where the A-I is supposed to filter out the noise for us. But I worry that this is just going to create a new version of the trap. If the A-I is filtering the data, we are even further away from reality. We are looking at a summary of a summary. We are trusting the A-I's bias instead of our own. We need humans who are deeply embedded in the systems they are managing, who understand the nuances that a sensor can never capture.
I think about the old-fashioned mechanic who could listen to an engine and tell you exactly what was wrong just by the sound. He wasn't looking at a computer screen; he was using his senses and his years of experience to interpret a complex acoustic signal. He had a high-bandwidth connection to the reality of the machine. We have lost a lot of that tacit knowledge in our obsession with explicit, quantifiable data. We have traded wisdom for information.
We have. And we see the consequences in everything from the supply chain crisis to the way we handle public health. We are so focused on the data points that we miss the systemic shifts. We are looking at the trees and completely ignoring the fact that the climate of the entire forest is changing. We are measuring the speed of the car while it is driving off a cliff.
So, how do we fix it? We've talked about metric pruning and leading indicators. What else can our listeners do in their own lives and careers to avoid the measurement trap? How do we stop being slaves to the dashboard?
One thing is to practice what I call strategic ignorance. You don't need to check your stock portfolio every day. You don't need to check your sleep score every morning. You don't need to look at your website's real-time traffic every hour. By checking these things less frequently, you are naturally filtering out the high-frequency noise and allowing the long-term trends to emerge. It is like looking at a painting. If you stand two inches away, all you see are brushstrokes. You have to step back to see the image. Strategic ignorance is the act of stepping back.
That is a great point. I think we also need to be much more skeptical of any metric that claims to represent a complex human or social reality. When someone shows you a single number for employee engagement or social impact, you should immediately ask: What was left out? What was the cost of collecting this number? And who stands to benefit from this number being high? We need to become more literate in the limitations of data, not just the power of it.
Always follow the incentives. If a metric is being used to determine bonuses or funding, it is almost certainly being gamed. This is why I think we need to move toward a more holistic, narrative-based approach to evaluation. Instead of just looking at the numbers, let's look at the stories. Let's look at the case studies. Let's talk to the people who are actually being affected by the system. A story can capture a systemic failure that a metric will never see.
It is about re-humanizing our data. We have spent the last twenty years trying to turn humans into data points. Maybe the next twenty years should be about turning data points back into humans. We need to remember that the purpose of measurement is to serve human goals, not the other way around. We are the masters, the data is the servant. We seem to have forgotten that.
I couldn't agree more. And this applies to our political lives as well. We are constantly bombarded with polls and economic indicators that are supposed to tell us how the country is doing. But those numbers often feel completely disconnected from the reality of people's lives. We are told the economy is great because the gross domestic product, or G-D-P, is up, but people are struggling to pay for groceries. We are told the country is more divided than ever because of social media sentiment analysis, but then we talk to our neighbors and realize we have a lot in common. The metrics are creating a false reality.
It is the same trap. The metrics are capturing the noise of the extremes, while the quiet reality of the middle is completely ignored. We need to stop letting the dashboards define our reality. We need to look out the window. We need to talk to each other. We need to trust our own eyes more than we trust a bar chart on a news site.
You're spot on. We have become so obsessed with the representation of reality that we have forgotten reality itself. It is like the paradox of the lighthouse. If the light is too bright and the fog is too thick, you just end up seeing a wall of white. You lose all depth perception. You need the shadows to see the shapes. You need the gaps in the data to understand the structure.
And we have definitely lost the shadows. We are like people who bought a telescope so powerful they can only see the dust on the lens. We want everything to be perfectly illuminated, perfectly quantified. But life happens in the nuances, in the things that can't be put into a cell in a spreadsheet. It happens in the silence between the alerts.
Wisdom is knowing what to ignore. That is a hard lesson to learn in a world that is constantly screaming for our attention, but it is perhaps the most important skill for the twenty-first century. We need to be the masters of our tools, not their servants. We need to know when to look at the dashboard and when to turn it off.
Well said, Herman. This has been a compelling discussion, and I think it is one that our listeners will be thinking about the next time they look at a dashboard. Before we go, I want to remind everyone that if you are enjoying these deep dives, we would really appreciate it if you could leave us a review on your podcast app. It genuinely helps other people find the show and join the conversation.
It really does. And if you want to find more episodes, including our archive of over one thousand three hundred discussions on everything from battery chemistry to geopolitics, head over to myweirdprompts dot com. You can find our R-S-S feed there, and if you are on Telegram, just search for My Weird Prompts to get notified every time a new episode drops.
We also have a contact form on the website if you want to send us a prompt like the one Daniel sent us today. We love hearing from you and exploring the topics that are on your mind.
Definitely. Thanks to Daniel for sparking this conversation. It is always good to be reminded that sometimes, the best thing you can do for your business, your health, or your sanity is to just turn off the dashboard for a while and breathe.
Amen to that. Thanks for listening to My Weird Prompts. I'm Corn.
And I'm Herman Poppleberry.
Take care, everyone.
Goodbye.
