The Conscious Compass: Navigating Life with Awareness and Intention
In the often-complex world of business and daily life, we typically rely on standard measurements to track progress, success, or even risk. However, imagine looking at our activities and interactions through a different, more insightful, and sometimes humorous lens. Andrew Chen, a writer with a knack for unique perspectives, introduces us to a fascinating array of such unconventional metrics, moving beyond the usual business jargon to offer fresh ways of understanding ourselves and our professional environments. His fascination began with a concept called "micromorts," a rather morbid but endlessly interesting way to quantify risk, and expands into a variety of "biz metrics" that shine a light on everything from honesty in startups to the efficiency of decision-making. This essay will explore these intriguing measurements, breaking them down into easy-to-understand terms and revealing how they can provide surprising insights into our personal lives and the world of work.
Understanding Risk: The Micromort
Let's start with the concept that sparked this unique approach: the "micromort". In simple terms, a micromort represents a one in a million chance of death. While it sounds quite grim, its real power lies in its ability to compare and rank the risk associated with almost any activity. Think of it as a universal measuring stick for danger. For instance, the sources mention that activities like mountaineering, BASE jumping, or even just getting old tend to have high micromort values, indicating higher relative risks.
The beauty of the micromort is that it helps us put risks into perspective. We can ask an advanced AI (like an LLM) to generate tables showing the micromorts for a wide array of activities, or even to fine-tune the comparison, such as "tell me the micromorts of road cycling, but only during the daytime in suburban/rural areas". This helps us understand that risk isn't just about the activity itself, but also the specific conditions.
A crucial point about micromorts is the idea of "exposure". If someone cycles for many hours a week, compared to someone who skydives only once, how do their risks compare? This is where the concept of "micromorts per hour" comes into play. We need to account for how long or how often we engage in an activity to get a true sense of its cumulative risk. AI tools are particularly good at helping to calculate and normalize these exposures, providing a clearer picture of personal risk over time. The sources even suggest a "fun prompt" for an AI: "based on what you know about me, what do you think are my highest micromort activities?". This highlights how personalized and insightful this metric can be.
Personal Metrics: Gauging Life's Little Annoyances and Pleasures
Beyond the serious calculation of risk, Andrew Chen introduces several other unconventional metrics that apply more to our personal experiences and interactions. These are often "a nerdy way to complain about people and situations," but they offer a unique window into our daily lives.
Cost per Hour of Pleasure (CPHP): This metric helps us evaluate the true value we get from our spending. A high CPHP means you're paying a lot for a relatively short burst of enjoyment, like going to a concert or a basketball game. While enjoyable, the cost per hour of that specific pleasure is high. On the flip side, a "super nice treadmill that you use 3x/week" would have a low CPHP because its initial cost is spread out over many hours of use, making each hour of enjoyment relatively cheap. The key takeaway here is that anything you use all the time tends to be low CPHP. However, there's a common trap: fooling ourselves into thinking something will be an "everyday forever product" when it will actually "collect dust in your closet". We've all been "very guilty of this".
Complaints per Hour (CPH): This one is straightforward: it measures how often you (or your friends) complain. A high CPH means high negativity. While the goal might be to lower this metric for a more positive outlook, the sources humorously note that sometimes it can be "very enjoyable to complain," making it "effectively infinite zero CPHP" (meaning free pleasure), which "cancels out" some of the negativity.
Phone Pickups per Hour: This metric is a simple indicator of engagement or boredom. If you're with "boring people, or hanging out somewhere boring," the number of times you pick up your phone tends to be "very high". The author shares a personal anecdote of having "many many phone pickups" when writing a book because the process was "intense (and sometimes very boring)," leading him to eventually lock his phone away in a timed safe.
% Conversational Autopilot: This metric describes how much of a conversation is spent on superficial, generic topics. We've all been in those situations, like "group dinners" or "random 1:1 meetings," where conversations drift into "lowest common denominator" subjects: introducing yourself, talking about your job, discussing travel. This is when you put the conversation on "autopilot" because you've "heard and said all the same things already". The author suggests that "great chats often hover around 20-30%" autopilot, meaning there's "enough commonality to be comfortable," but also enough new ground to be memorable. If a conversation is "75%+" autopilot, that's when you might feel like you "want to dine and dash".
These personal metrics, while seemingly lighthearted, offer a fresh way to reflect on the quality of our time and interactions.
Business Metrics: Shining a Light on Efficiency and Honesty
The core promise of Andrew Chen's essay, beyond personal insights, is to introduce new business metrics that reveal truths about how companies operate. These are not your typical financial statements or sales reports; instead, they dig into the underlying dynamics of startup culture and corporate decision-making.
Lies per Second (LPS): This is a bold and critical metric, especially relevant in the fast-paced world of startups. The sources explain that there's a "fine line between pitching and lying". High LPS means that people are "just making up numbers, saying they have customers when it’s just pipeline, removing labels from their graphs, and so on". If you find yourself in a meeting where the LPS is high and "go[ing] even higher as the presentation progresses," it might be "time to end it early". This metric cuts through the hype to reveal genuine transparency.
Meetings per Decision Ratio (MPDR): This metric asks a fundamental question: "How many meetings did it take to make that decision?". This applies to both "big decisions and little decisions". For example, hiring someone can be streamlined with "3-5" interviews and a quick decision, or it can be an "open-ended" process requiring "3 initially, then another 5 then 1 or 2 more, then another 3, and so on". A high MPDR signals that "something is broken". The reasons for this inefficiency can include "no owner to the decision" (meaning no one has the authority to approve), "ill-defined goal or process," or "constantly changing" objectives. In such cases, the situation "needs to be escalated to a decision maker to simplify".
Time to First Excuse (TFE): This metric measures how quickly an excuse appears when a poor result is presented. If a team has a "bad month" or customers "hate [a] new product," you can often "count the minutes before a poor result is presented, along with an excuse for why it happened". Common excuses include "Seasonality" or "Our marketing was poor". A key observation here is that "It’s never the team’s fault, by the way". A low TFE often leads to a "low TNE" (Time to Next Excuse), meaning excuses keep coming quickly. When the TFE "approaches zero," it's a strong sign that "the team probably needs to get replaced/rebooted".
Numbers vs Text Ratio: This metric tracks the evolution of information presentation in a startup. In the "very very beginning," when an idea is just forming, it's "all story, and all text/narrative" – a low ratio because there are "no numbers because there’s no revenue, no customers, and no product". However, "Fast forward a few years," especially by a "Series B" funding round, a presentation "should start to see numbers everywhere". You'd want a deck "chock full of retention curves, financial metrics, etc., etc.," indicating a very high "numbers vs text ratio". If this ratio remains too low even in later stages (e.g., a startup that has "raised $50M show up and just present a deck that’s all numbers"), then the "Meetings Per Decision metric is going to skyrocket" or "no one will want to invest". This metric highlights the importance of data-driven progress as a company matures.
PowerPoints per Launch (PPPL): This metric highlights the bureaucratic hurdles within organizations. In many startups, the culture is to "learn by shipping" – they "launch features, see how people react, then launch more features". This is considered "zero powerpoints per launch". However, as startups grow into larger companies, there are "more coordination costs," and new initiatives can become stalled by "death by PowerPoint". To pitch a new idea, you're asked to "make a powerpoint," then "roadshow it," make more PowerPoints for "market research," "competitor analysis," and "KPIs". This can lead to "consensus-building hell". The only way to significantly reduce PPPL is to "involve senior executives who want to push it forward".
Dollar per IQ Point: This is a cheeky but insightful metric for hiring. It essentially measures how much you're paying for a certain level of intelligence. For example, hiring a "top 1% Stanford CS student" might mean you're "spending moderate dollars per IQ point". But you might get an "even cheaper Dollar per IQ Point if you hire a top 1% student from a state school" because they might have high IQ points but won't "demand the king’s ransom". The humorous extreme is that you'd hire the "most expensive Dollar per IQ Points in the world" by hiring a "bottom 10% Stanford student". This metric encourages thinking about value and efficiency in talent acquisition.
Decision to Rumination Ratio: This metric examines the balance between thinking about a decision and actually making it. Sometimes, people spend "weeks" thinking through something, while other times a decision is made "in a snap". What's "weirdly" interesting is that the amount of time spent isn't "correlated at all" with the importance of the decision. For example, someone might spend "months to pick out a new car" but then "jump on a new opportunistic job with only a week of thought". Ideally, we'd want this ratio to be normalized: "a lot of time for big decisions, and only a little time for small decisions". However, the sources note that "we all suck at this". Important life decisions like "who you marry, what city you live in, and your career" often don't get the "months or years full-time" of thought, expert consultation, or help they deserve. Instead, people "just kinda do what feels right and sometimes it works and sometimes it doesn’t," leading to "shotgun weddings, moving to a new city on a whim, ragequitting a job, impulsive breakups, getting a tattoo while on vacation, impulse buying a pet, etc., etc.". This metric highlights the common human tendency to misallocate our mental energy in decision-making.
Beyond the List: Other Amusing Metrics
The discussion around these concepts also brought up a few other humorous, niche metrics:
Time To Buzzword Bingo (TTBB): This is defined as the "Time required in a Board meeting to complete your Buzzword Bingo card". It's a playful jab at meetings filled with corporate jargon.
Buzzwords Per Minute (BPM): A related metric, "especially relevant in pitch meetings," measuring the frequency of buzzword usage. Examples of "single word-score buzzword examples" include "Platform Play," "Hockey Stick," and any sentence with the word "Hacking". "Double word-score examples" are phrases like "AGI-ready Architecture," "Neural-Symbolic AI," and casually throwing "RAG" into a conversation.
TTP: While the P is left to "intuit," in the context of video games, this refers to "How long does it take a player to create genitalia in a game?". A record was cited as "under 5 mins in Spore," indicating how quickly players can get to specific content creation. This highlights how even very specific, unusual activities can be measured for speed and efficiency.
The Future of Measurement
These metrics are currently "fun/conceptual ideas," but Andrew Chen envisions a future where they could become standard. With the rise of advanced technologies like AI note-taking apps that track meetings, it might one day be possible to automatically measure many of these ratios. Imagine having "alerts automatically fire when the Lies Per Second metric exceeds a certain threshold," especially as AI tools become capable of "automatically fact check[ing] every sentence on a video conference". This futuristic vision suggests a world where transparency and efficiency are not just ideals but quantifiable, real-time indicators.
In conclusion, Andrew Chen's collection of unconventional metrics offers a fresh and insightful way to analyze both our personal lives and the dynamics of business. From the serious calculation of risk with "micromorts" to the telling signs of inefficiency in "Meetings per Decision Ratio" and the stark realities of "Lies per Second," these measurements provide a unique lens through which to observe human behavior and organizational effectiveness. While some are designed to be humorous or "nerdy," they all serve to provoke thought and offer a deeper understanding beyond superficial observations. As technology advances, it's not far-fetched to imagine a future where these imaginative metrics become practical tools, helping us to make better decisions, foster more honest environments, and ultimately lead more fulfilling lives, both personally and professionally.
