If you only have five minutes, this introductory section will get you up to speed on the utility mental model. The utility mental model in one sentence: “utility” in economics roughly equates to the value you derive from experiences and possessions; I use a somewhat broader definition: “utility” refers to how useful something is in a given context.
Four brief examples of the utilitymental model:
No, I don’t want to pay eight dollars for a pretzel and sixty dollars for Wi-Fi! My parents spend way too much time sitting in airports;. Jordan Ellenberg invokes utility and opportunity coststo prove his theorem on this topic – with (napkin) math – in “How Not To Be Wrong.”
Keep your eyes wide open before… and half-shut afterwards. Why is the divorce rate in the U.S. 50%? Research demonstrates that a poor understanding of utilityplays a meaningful role, as I explore in the hyperbolic discounting mental model.
Congratulations! You spent 4 years and $140,000 learning nothing useful. The aforementioned Ellenberg – a professional mathematician, by the way – notes in the introduction to his book that when math teachers tell you “you’ll use this someday,” usually, “it’s a lie.” (I still have never once needed to dilate a polygon and reflect it across a Y-axis. Rotations, at least, are useful in Tetris.) His view is common among successful professionals, many of whom tend to be autodidacts.
For example, PAA house favorite Till Roenneberg – chronobiologist extraordinaire – makes a similar point in “Internal Time” : “the drawback of traditional learning has always been the dissociation between the theory and its application. ‘Why do we have to learn this?’ is probably one of the most frequent and justified questions teachers hear.’”
The difference between the mental models approach and much of formal education is that mental models learners focus on utility: information that can make us more successful professionally, or more content and happy in our personal lives. No forced reading of boring “Pride and Prejudice” here.
We’ll go all the way back into your childhood, if necessary. Why is cognitive behavioral therapy (CBT) a successful approach to treating many challenges, ranging from depression / anxiety to insomnia to “performance” problems, in sharp contrast to murky effectiveness of many other unscientific forms of therapy like psychoanalysis? In addition to its scientific approach, it’s very utility-focused, as Dr. Judith Beck explores in “ Cognitive Behavior Therapy.” CBT is a time-constrained (often no more than 16 weeks) approach that, as Dr. Beck explains, has a:
“strong focus on current problems and on specific situations that are distressing.”
This parsimonious application of the 80/20 principle (which we’ll discuss) is in sharp contrast to the traditional therapy business model, which psychologist Shawn Achor – author of “The Happiness Advantage” – jokingly notes in his famous TED Talk, works like this:
“If you fall below the average, then psychologists get thrilled, because that means you’re either depressed, or you have a disorder, or hopefully both. We’re hoping for both because our business model is if you come into a therapy session with one problem, we want you to leave knowing you have ten, so you’ll keep coming back over and over again!”
If this sounds interesting/applicable in your life, keep reading for unexpected applications and a deeper understanding of how this interacts with other mental models in the latticework. However, if this doesn’t sound like something you need to learn right now, no worries! There’s plenty of other content on Poor Ash’s Almanack that might suit your needs. Instead, consider checking out our learning journeys, our discussion of the inversion, Bayesian reasoning, or sleepmental models, or our reviews of great books like “Other Minds” , The Landscape of History , or “Onward” .
“the models typically aren’t measured on how well they predict practical weather elements. It’s really important if […] you get an inch of rain rather than ten inches of snow. That’s a huge [distinction] for the average consumer, but scientists just aren’t interested in that.” Dr. Bruce Rose, principal scientist at The Weather Channel, via Nate Silver’s “ The Signal and the Noise”
In the age of AI, judgment is more important than ever—and this mental model sharpens that judgment by forcing us to ask “is this actually useful?”. As we’ll explore, various phenomena underlie the tendency of even really smart people to go down rabbit holes that aren’t useful. Understanding utility extends beyond mere economics; it challenges us to consider what truly provides value in different contexts. For instance, Clive Wynne’s Dog is Love presents compelling evidence suggesting that for dogs, the utility derived from social connection with their owner can outweigh even the basic utility of food, particularly after separation. [cite_start]The famous “garage-door study” described in the book showed dogs consistently choosing to greet their returning owner before approaching a readily available food bowl[cite: 251, 253]. [cite_start]This highlights how utility is context-dependent and not always aligned with basic survival needs, touching upon the complex interplay often discussed in the nature vs. nurture mental model regarding innate drives versus learned or socially reinforced behaviors[cite: 169]. Just as we might misjudge a dog’s priorities, we can misjudge the true utility of information, actions, or even AI-generated outputs if we don’t consider the specific context and deeper needs being met.
“when you know all the names of that bird in every language, you know nothing about the bird… names don’t constitute knowledge.”
Indeed, the sort of memorization-driven learning of names and dates in school tends to impede, rather than advance, learning. It’s not important what day a specific event in history occurred on, or exactly who was present. It’s important how it happened, and why, and what we can learn from that.
Some historians – like Christopher Browning, author of “Ordinary Men” and John Lewis Gaddis, author of “The Landscape of History” , get this concept.
Many readers are likely aware that the name of this site, “Poor Ash’s Almanack,” is a riff on “ Poor Charlie’s Almanack” , the wonderful book by Charlie Munger that explores the concept of mental models. Munger’s book, in turn, was based on the famous “Poor Richard’s Almanack” published by Benjamin Franklin – see “The Autobiography of Benjamin Franklin” .. Munger much admired Franklin, and I do too. Unfortunately, the main biography I’ve read – Walter Isaacson’s “Benjamin Franklin: An American Life” is pretty terrible and not worth your time to read. In spite of this, a few lines do stand out: what was it that made Franklin special? A focus on utility. Isaacson observes:
“one aspect of Franklin’s genius was the variety of his interests… approached from a very practical rather than theoretical angle. […]
Franklin’s focus was on traits that could help him succeed in this world, instead of ones that would exalt his soul for the hereafter.”
Here’s the way I’d put it:
[bctt tweet=”Ask two questions about everything. A – is this useful? B – does this make me happy? If NO to both, why do it? Much human activity is fundamentally pointless and driven by social proof or status quo bias – not utility.” username=”AskeladdenTX”]
In the interest of intellectual humility, I try not to be overconfident about the scope of my abilities, but I’ll make a rare exception: utility is something I think I do really well. I simply don’t spend time on activities that are non-value-adding. Many of these persist for reasons explored in the culture /status quo biasmental model – simply because everyone else does them and we’ve always done them and we think we need to keep doing them.
I think the concept of utility at its most basic is pretty straightforward, so I won’t belabor it: if you want a more formal introductory discussion, you can’t do better than Ellenberg’s aforementioned “How Not To Be Wrong” or my favorite book of all time, Richard Thaler’s “Misbehaving” .
We’ll move on to three independent discussions; you can read all or only the ones that interest you: First, how does utility interact dose-dependency (a form of nonlinearity)? Second, how does utility interact with product vs. packaging, and why can “supposedly irrelevant factors” lead us to make bad purchase decisions as consumers… or bad financial decisions as investors? Third and finally, how does utility interact with local vs. global optimization and create a challenge in figuring out what we need to be doing and learning?
“One Jelly Donut is a yummy mid-afternoon energy boost. Three Jelly Donuts may induce a tummyache. Twelve Jelly Donuts is fraternity pledge hazing.”
As a value investor, I don’t tend to pay much attention to the Fed or monetary policy, but I’ve always loved that quote from famous investor David Einhorn in his 2012 op-ed on the dose-dependencyof additional quantitative easing.
As I discuss in the nonlinearity model – with an assist from Jordan Ellenberg’s wonderful, previously-referenced “How Not To Be Wrong” – more is not always better (the idea of dose-dependency). Take two extra-strength Tylenol, and your headache or period cramps will go away. Take ten, and there’s a greater-than-zero chance you’ll die of catastrophic, irreversible liver damage.
This pops up all over the place. Does more data helps us make better decisions? Yes and no: Nate Silver’s “The Signal and the Noise” does a great job of exploring how the overwhelming amount of data we face can make it similar to pick out the signal; an n-order impact.In Stolen Focus, Johann Hari quotes research Sune Lehmann’s model, which found “one mechanism that will make this happen every time: You just have to flood the system with information.” The more information, the less focus on any one piece. We are “‘drinking from a fire hose” that exceeds our natural capacity. (More information is available in our attention/focus mental model; we will also cover this in more depth in the AI section of this model.)
Turning to medicine, Dr. Jerome Groopman’s “How Doctors Think” touches on this point as well. A main theme is cognition vs. intuition: Groopman’s explores the peril of doctors rushing to snap judgments often overly influenced by cognitive biases like recency or incentives, positing that:
“cogent medical judgments meld first impressions – gestalt – with deliberate analysis.”
Yet there is such a thing as overthinking: if you’re starting at zero, more thought is good, but at some point more may not be better, and may even be worse, For example, Groopman notes that after thirty-eight seconds of reviewing an X-ray,
“many radiologists begin to see things that are not there.”
This type of dose-response tendency shows up everywhere: in “The Up Side of Down,” Megan McArdle explores the counterintuitivedose-dependency of prison as a crime deterrent. If prison is rare and shameful enough that few people you know are in there, a prison sentence is a big deterrent.
Counterintuitively, if it’s so prevalent that half of your community is or has been in the clink, prison seems pretty normal and not-scary: just part of life – and no longer a deterrent.
Marginal utility also shows up in our favorite topic: reading and learning. One of the mistakes I see beginning investors make all the time, for example, is reading too many books about finance and investing. If you’ve read 3-4 books about value investing, reading more tends to be less useful… maybe you’ll pick up one or two more nuggets of information, but the vast majority of the lessons will have been contained in the other books you read.
On the other hand, if you’ve read zero books about psychology, biology, or some other important discipline, then the marginal utility of doing so will be high.
The key takeaway? A book’s value is not absolute – it’s relative. It depends on how much of its material you’re already likely to be familiar with, which is one of the reasons PAA’s book reviews try to help readers figure out which books are likely to have high utility for them at any given time.
Or, in another context: how much is a bottle of water worth? If you’re in the middle of the Sahara Desert and you’re dehydrated, it’s worth its weight in platinum. If you’re in the middle of an Ozarka bottling facility, you wouldn’t trade one for a song.
Marginal utility interacts with personality traits as well: successful investors and entrepreneurs usually have a high “need for cognition,” but more is not always better. If you’re too interested in solving hard problems, you’ll start solving problems for the sake of solving hard problems without worrying about the practical utility – which shows up in contemporary math/science and has been noted by observers like Thomas Kuhn in his famous “The Structure of Scientific Revolutions.” As Buffett/Munger like to say, there’s no need to jump a twenty-foot hurdle if there’s a two-foot one nearby: no points for difficulty.
This idea of “some is good, more is not always better” is so incredibly prevalent across all domains that I could keep cataloging examples for another twelve pages if I wanted to. But I don’t want to do that (and I know you don’t want me to, either!)
So let’s just look at two of the really important take-homes, and you can find the rest of the applications elsewhere on the site.
The Pareto Principle (80/20)
The Pareto Principle shows up in a lot of areas: operations management and so on. While it’s more of a directionally-useful truism than a mathematical law, the idea is that 20% of our actions drive 80% of the results.
Think about it: of all the hundreds of people you’ve ever met and interacted with, probably a small number (outside of your family) have had the biggest impact on your personal development / career / etc. Of all the skills you’ve ever learned, a handful are probably the most important.
Similarly, most of the health benefits of exercise can be derived from 2-3 moderate-intensity exercise sessions per week – you don’t have to run an ultramarathon! Simply making an effort a few times a week to go to the gym, or even just for a walk, can have a big impact on our health.
This is a well-understood phenomenon you can read about elsewhere, so we’ll move on. The takeaway is that you should invest more in actions and relationships that drive real value. Areas like bottlenecks can be particularly profitable. Applying the 80/20 principle to AI usage means focusing automation efforts on tasks where they deliver the highest marginal utility.
Money and Happiness
More money only drives more happiness to a point. The reasons for this are obvious but unintuitive. Say you’re broke and can only afford an old beater car that doesn’t have working A/C, doesn’t have power steering, and threatens to break down or not start every few weeks. Obviously, this is a high-stresssituation and your life would be much improved if you owned a reliable, economical used car.
And new cars have nice features! It probably still drives a lot of incremental utility for most people to move up from a 10-year-old used Toyota to, say, a new (or near-new) one – perhaps one with a bigger engine and a better sound system, too.
Is there a difference between that and a performance/luxury car? Yes – but those kinds of cars cost 2-3x the new Toyota, and probably don’t bring you 2-3x the utility.
And by the time you’re talking about a Koenigsegg or a Bugatti Veyron, well, it’s difficult to plausibly argue that you’re a hundred times better off with one of those.
Graphically, that looks like this: see how the curve flattens out pretty quickly?
Of course, if you want to spend a lot of money on your car and you have the budget, go for it – if it’ll bring you more happiness than the other potential uses of the money it costs (the opportunity cost). I have a friend who owns a BMW M3 competition and his primary passion in life is taking it to the track.
Few people explicitly consider marginal utility and opportunity costs when they make decisions, but it’s well-validated by research that we’re better off when we do: it’s generally found, for example, that there’s a positive correlation between happiness and income up to the high five figures, but it levels off after that.
(At the extreme, the marginal utility of having $10 billion vs. $1 billion is… not very much. I guess you could buy more sports teams? But there’s little you can practically do, as an individual, with $10 billion that you cannot do with $1 billion.)
Those studies are merely an average and many of us would like higher levels of income – which is totally fine. I’d argue that many of us, in fact, need higher incomes than that given Americans’ propensity to undersave (see Sunstein and Thaler’s Nudge , for example) – but the point remains that rather than blindly assuming “more is better,” we should be focusing on what really drives value for each of us and pursuing the highest marginal-utility options.
So is pursuing money and money alone the right life path? Probably not. What is? We’ll get into that in the third section on local vs. global optimization.
Application / impact: more isn’t always better. Stop eating when you’re full, solve the problems that have the biggest impacts, and don’t assume that spending more money will make you happier.
ANDY: Well Aunt Bee, a bargain’s not always a bargain. When you buy sugar in a sack that’s too big to handle –
AUNT BEE: Now don’t bring up that freezer again. ANDY: I wasn’t going to. But since you did… it’s just been sitting out there fillin’ up the back porch. AUNT BEE: But you forget what I paid for it. I saved almost a hundred dollars! ANDY: Only thing that’s ever been kept in that freezer’s a dead mouse, and he climbed in there to get warm! Hundred dollar mouse trap, you got to grab a bargain like that!
Clearly, the human desire to find a bargain is not confined to our generation… The Andy Griffith Show had a whole episode in Season Four, Bargain Day, devoted to the perils of focusing more on the discount to list price (contrast bias) than the utility of what you’re actually getting.
I view this as an example of the product vs. packagingmental model – i.e., the fact that even though we only get to eat/use/enjoy the product, we can be disproportionately influenced by the packaging (which will be thrown away anyway).
Why are you reading this silly page when you could be laughing your head off at The Andy Griffith Show? Pictured: all the dogs of Mayberry eagerly following 150 lbs of beef on a wagon. (The physics of how 9 – 10yo Opie is managing to pull that much weight remain unclear).
In the context of consumer behavior, this phenomenon is obviously pervasive: we’ve all bought something on sale that we never ended up using. (I used to be a dedicated clearance-rack shopper, even if it meant purchasing ill-fitting clothes I ended up not wearing.)
It even shows up in investing: one of the great intellectual contributions of the aforementioned Charlie Munger to his business partner Warren Buffett at Berkshire Hathaway was the realization that sometimes it’s better to pay a higher price for a better asset than it is to buy a mediocre one “on sale” – as Aunt Bee discovers when her antiquated freezer conks out with a whole 150-pound side of beef in it – an amount she bought, by the way, to get a bulk-purchase discount. (Hilarity ensues.)
Nor is the behavior limited to consumers: even business purchasers, eager to get bulk discounts, can forget to be mindful of then-order impacts on inventory management and other aspects of the business. While dated, Eli Goldratt’s classic business novel “The Goal” provides a stellar example of this sort of behavior, and also drives home the 80/20 principle discussed in the previous section.
Yet despite its existence, economics as a profession historically failed to recognize its existence as part of the utility model. PAA house favorite Richard Thaler did, though, and he explains this concept to great impact in “Misbehaving” – my favorite book of all time.
On pages 59 – 63, Thaler notes that he eventually decided that standard utility theory only explained “acquisition utility” – the surplus between utility and price paid. Consumers also have “transaction utility” – the quality of the “deal” relative to the “reference price.
The interesting experiment here is that people’s enjoyment of a cold beverage on a beach, given a certain price, is dependent upon where they were told the beer was bought, and what they think it should have sold for at the type of establishment (bodega vs. fancy hotel). Of course, our focus on transaction utility can be bad both in making us buy things we shouldn’t, as well as in making us not buy things we should. Thaler examines how too much focus on transaction utility can
“prevent our consuming special experiences that will provide a lifetime of happy memories.”
Thaler advises (rationally) to behave like an econ, not a humanand focus solely on value paid vs. utility gained. “Misbehaving” later touches on the idea of narrow framing, and here’s a quick example: a lot of people make purchases based on contrast bias and narrow framing. Just as transactional utility can skew human judgment, automation bias can lead us to overestimate the utility of AI outputs simply because they were machine-generated.
It’s hard to overcome the powerful force of transactional utility. This is something to be mindful of if you’re running a business. For retailers, doing away with transaction utility is a massively bad business strategy… as J.C. Penney found out when it tried to do away with sales and move to everyday low pricing. Bargain-addicted customers went bonkers.
As with all mental models, the human phenomenon of “transaction utility” is neither universally good nor bad: it simply is, and we have to acknowledge, accept, and respond to it. Thaler, in the book’s interlude, actually provides a practical example of how businesses can utilize this phenomenon: they can provide consumers with more transaction utility at low or no cost without necessarily giving them more acquisition utility.
Thaler overviews, for example, how he encouraged a local ski resort to create additional “transaction utility” by offering “ten packs” of tickets (in advance) for the price of six full tickets – this helped to bring in cash notwithstanding how much or little it snowed (reducing risk in the business).
Few people actually skied 10x/year, so the resort didn’t even really give up much in the way of economics (and although Thaler didn’t specify, variable costs were probably pretty low even if people did come). Yet it helped the resort financially, and made consumers happier. “Misbehaving” delves deeper into this topic, as well as the aforementioned marginal utility and plenty of other interesting topics, so go check it out.
Application / impact: as a consumer, focus on the utility of the item being purchased relative to what you’re paying for it, not the quality of the deal. As an investor or business manager, however, be mindful that consumers dofocus quite strongly on transaction utility, so improving the perceived quality of the deal – even if the actual utility does not change whatsoever – can make consumers happier, and you more successful.
Utility: The AI Angle
With AI impacting our work and life, focusing on utility becomes paramount for effective decision-making. AI tools can generate vast amounts of information and automate numerous tasks, but can also generate hallucinated outputs that look great but aren’t really, and can help companies ratchet up the war for our attention. This new reality forces a critical question: what is the utility of a human doing a specific task?
The answer lies in dividing labor based on cognitive strengths. As we explore in more depth in the focus vs attention mental model:
AI’s Utility: Massive Parallel Processing (The “Grunt Work”) AI excels at tasks analogous to massive parallel processing. It can sift through terabytes of data, analyze millions of variables, summarize 100 documents, or generate 50 variations of a first draft in seconds. These are tasks that, for a human, are repetitive, slow, and low-utility. There is simply no marginal utility in a human performing this “grunt work” anymore. This is the 80% of effort that can be automated, freeing human capacity. (See the focus/attention mental model as well as Tae Kim’s The Nvidia Way).
Human Utility: The Strategic Single Thread (The “Judgment Work”) Humans provide the strategic “single thread” of consciousness, judgment, and intent. Our utility isn’t in processing the data; it’s in deciding what data matters and what to do with it. This is the 20% of effort that drives 80% of the results. Human focus should be redirected to these high-utility tasks that AI cannot perform.
The primary utility of human focus in the age of AI lies in strategic single-thread judgment, not competing with the AI’s processing power. While the AI crunches the numbers and generates the variations, the human must aggressively guard the “single thread” of intention and critical thought. This begins with defining the global objective – the “why.” An AI can locally optimize a function or a paragraph, but only a human can provide the ultimate purpose, strategy, and desired outcome for the work, ensuring we aren’t generating impressive results for the wrong goal.
With the cognitive load of grunt work removed, humans must dedicate their attention to high-utility tasks like discernment and synthesis. We must serve as the ultimate arbiter of utility by evaluating AI output, connecting disparate concepts, and filtering the signal from the noise. This active judgment is the only effective defense against the “product vs. packaging“ trap, where an AI’s fluent, confident delivery (the packaging) hides a lack of substantive value or outright hallucination (a zero-utility product). Furthermore, our utility is magnified by asking the right questions, as the value of an AI’s answer is capped by the novelty and strategic criticality of the human’s prompt. Ultimately, only humans can provide the necessary context and empathy, which are vital for translating strategic output into action and true wisdom.
In practice, this framework means we should automate the “parallel” by delegating all repetitive, data-heavy tasks to AI. We should use AI for the “what” but dedicate our most precious resource human cognition to the “why” and “what’s next,” rigorously verifying the accuracy and substantive value of the machine’s output. The highest utility role for a human, therefore, is to direct the parallel processor (AI) and critically judge its output, not to compete with it on speed or volume.
“when you are in the thick of things, you can get confused by small stuff. I knew when I was eighty that I would never, for example, think about why I walked away from my 1994 Wall Street bonus right in the middle of the year at the worst possible time.
That kind of thing just isn’t something you worry about when you’re eighty years old. At the same time, I knew that I might sincerely regret not having participated in this thing called the Internet that I thought was going to be a revolutionizing event. When I thought about it that way… it was incredibly easy to make the decision.”
But so did Howard Schultz’s very similar decision to leave his lucrative job to fiddle around with a theretofore unknown espresso concept in Seattle called “Starbucks” or something like that (I forget; the name is kinda hard to remember.)
Ben Franklin – the king of practicality – made a number of thought-provoking observations on the topic of the utility of scientific exploration. Again from Walter Isaacson’s “Benjamin Franklin: An American Life” – these are about the only useful parts!):
“what is the use of a newborn baby? … “It does not seem to me a good reason to decline prosecuting a new experiment which apparently increases the power of man over matter until we can see to what use that power may be applied.
When we have learned to manage it, we may hope some time or other to find uses for it, as men have done for magnetism or electricity, of which the first experiments were mere matters of amusement.”
Talking about balloons, for example, Franklin saw no immediate purpose but believed investigating them might:
“pave the way to some discoveries in natural philosophy of which at present we have no conception… important consequences that no one can foresee.”
In a more contemporary setting, a similar view is expressed by physicist Geoffrey West – former president of the Santa Fe institute – in his wonderful book “Scale” :
“many of us recognize [… dismissive rhetoric] aimed at scholarly or academic research [… that is] out of touch with the ‘real world.’ Well, no doubt, much of it is. But much of it isn’t, and more to the point, it is very often difficult to perceive in the moment the potential impact of some piece of seemingly arcane research. Much of our entire technologically driven society and extraordinary quality of life that many of us are privileged to enjoy is the result of such research.”
Discrete vs. Recurring Payoffs
Here’s another way to frame utility: focusing on the lifespan of the utility of something you’re working on. Certain forms of knowledge have a short expiration date: for example, a weather forecast or traffic report on any given day in Dallas will help you know what clothes to wear and what time to leave to make it to the football game, but neither of them will help you figure out if you want to live in Dallas.
On the other hand, long-term, base rate type data on traffic and weather conditions in Dallas will be more instructive in helping you determine if it’s the sort of place you want to live.
Given how difficult it can be to determine exactly what lines of inquiry will prove profitable (for the reasons discussed above), one easy filter that’s probabilistically likely to improve our likelihood of encountering useful information is to heavily focus on information with a longer lifespan of utility.
Johann Hari’s Stolen Focus powerfully argues that much modern information consumption, particularly via social media, provides very low, short-lived utility while actively degrading our capacity for deep thought. He contrasts this with the high, recurring utility derived from activities like reading books, which build empathy and understanding over time. Prioritizing information sources and activities based on their long-term utility, as suggested by the focus/attention mental model, can be a powerful strategy for personal growth and effective decision-making.
As Hari puts it:
“What is th[e] message [of Twitter?] First, you shouldn’t focus on any one thing for long. The world can and should be understood in short, simple statements of 280 characters. Second: the world should be interpreted and confidently understood very quickly. Third: what matters most is whether people immediately agree with and applaud your short, simple, speedy statements. A successful statement is one that lots of people immediately applaud; an unsuccessful statement is one that people immediately ignore or condemn. When you tweet, before you say anything else, you are saying that at some level you agree with these three premises. You are putting on goggles and seeing the world through them.”
“I realized one of the key reasons why social media makes me feel so out of joint with the world, and with myself. I think all of these ideas – the messages implicit in these mediums – are wrong…. the world is complex. To reflect that honestly, you usually need to focus on one thing for a significant amount of time, and you need space to speak at length. Very few things worth saying can be explained in 280 characters.”
Shawn Achor presents a stunningly insightful analysis of this topic in “Before Happiness” , specifically from pages 156 – 158. I won’t spoil the discussion, but if you want to visualize it, take a look at the following for my interpretation of the “time” angle:
Generally speaking, lifespan and length correlate with utility. Snapchats – designed to self-destruct in a matter of seconds – are the epitome of no-utility, completely-useless information with no lifespan that we should all strive to eliminate from our lives (TikTok is another great example).
Similarly, short-cycle news, unless it has a direct impact on your life/business, is here today and forgotten tomorrow. These are discrete / one-time payoffs: you get whatever utility out of them you get, but there’s no more coming, ever. On the other hand, “evergreen” skills or concepts – things like empathy, or an understanding of trait adaptivity – are very permanent in nature. The value of knowing a specific coding language may fade over time if that language is no longer used; similarly, insights about competitive dynamics in a specific industry may no longer be useful if circumstances in that industry change dramatically.
Conversely, chances are that being able to understand and empathize with human beings will never not be a useful skill, nor will understanding how the world selects for and against traits. These, by nature, are recurring payoffs: once you learn them, you get to reap the rewards over, and over, and over again, in an almost guaranteed manner. Cal Newport touches on this topic in “Deep Work” as well, incidentally, although I prefer Achor’s version.
This shows up in business models, too: many of the most powerful businesses are those in which you don’t have to start from scratch again every January 1. Companies with “recurring revenue” – whether software licenses, or membership fees – generally tend to do pretty well because they have a high return on investment in creating a new customer if they stick around for a while.
Too much focus on (long-term productive capacity) is like a person who runs four hours a day, bragging about the extra ten years of life it creates, unaware he’s spending them running. – Stephen Covey”
PAA readers please note: I strongly encourage eating your vegetables. Decades of research demonstrate a correlation between higher vegetable consumption and lower all-cause mortality that is likely causal. Anyone who knows me is likely familiar with my near-obsession with Swiss chard, brussels sprouts, and sweet potatoes. Turnips are pretty much the only vegetable I haven’t been able to make friends with. Turnips just suck. Also, smoking is bad for obvious reasons, and alcohol disrupts your REM sleep.
Application / impact: be mindful that focusing too much on either short-term or long-term utility can have detriments on the other; it’s important to balance the two and not be overconfident about our ability to know exactly what’s going to be useful and what won’t be. At the same time, throwing up our hands and not trying at all is unlikely to get us anywhere, and careful thought generally yields results.
“the drawback of traditional learning has always been the dissociation between the theory and its application.
ANDY: Well Aunt Bee, a bargain’s not always a bargain. When you buy sugar in a sack that’s too big to handle –
