Therefore, it’s often a pleasant surprise to find that two things we had previously considered as separate phenomena are actually the same thing. The morning star is the evening star. Lambda calculus and Turing machines. The orbit of planets and the falling of an apple.

I’d like to make the case that motivation and focus are similarly equivalent. To be focused is to be motivated to persist in an activity.

Motivation as Choice

We tend to think of motivation in terms of intensity of desire. Think of a scale from zero to ten, with zero indicating having no interest in doing something and ten as having the maximum possible interest. Motivation, then, measures where we fall on this scale.

But this isn’t really the full picture. What matters with motivation isn’t simply how much we want to do something, but how much we want to do that thing, compared to all our other alternatives.

What’s more, we’re always already doing something. While we often think about the motivation to do some behavior, we’re actually talking about the motivation to stop whatever we were already doing, even if that is just sitting and staring into space, and do something else instead.

From this perspective, it’s pretty clear to me that focus and motivation both refer to the same phenomenon.

Focus is the quality of persisting on a single pursuit. This can be at small timescales, like focusing on a textbook in a quiet library while studying for an exam. But it can also be over much longer timescales, such as choosing to devote your life to painting, rather than music or sculpture or making a lot of money.

What does it mean to be focused in this way? It means that the motivation to stick to the current activity remains consistently higher than the motivation to switch to an alternative.

Lack of Focus as Lack of Motivation

A comment I’ve heard from people who self-report they have difficulty focusing is that they only have difficulty focusing on things that they don’t find motivating. In contrast, they claim, they can maintain a high degree of focus on activities that they personally find interesting.

According to my claim above, this is tautologically true. People, by definition, have a hard time focusing on things that they are not motivated to do. Focus is motivation.

My understanding of the prominent theories of ADHD, for instance, is that it is seen as deficits either in impulse control or in rewards. Put another way, with ADHD, your intentions to stick to consciously-held goals aren’t sufficiently motivating, or the tasks you’re trying to stick to are not sufficiently motivating. The mechanisms here might be different—impulse control is more strongly linked with prefrontal cortical circuitry, whereas rewards are processed in dopaminergic systems—but the effect is ultimately reducing motivation to stay on task.

To Boost Focus, Boost Relative Motivation…

Since focus is motivation, the way to improve focus is to be more motivated. Increase the relative drive you have to do the task you’re supposed to focus on, or, alternatively, reduce the relative drive to do anything else. Both work, but which is more suitable depends on the situation.

Sometimes it’s easier to boost your motivation to do a task. External carrots and sticks can help. But, generally speaking, intrinsic rewards—and costs—matter more. A task may feel important if it has big, long-range incentives for action. A task with a lot of built-in intrinsic rewards feels inherently interesting.

Generally speaking, it’s hard to sustain motivation for tasks you’re supposed to do but aren’t interested in. You can drive effort by holding a clear goal in your mind and committing to it. But effort is exhaustible, and this approach tends to result in decreasing motivation over time.

Capacity for effort is probably at least partly learned through life experiences. Learned industriousness is the idea that regular rewards for effortful action reduce the motivational cost of effort. In contrast, if trying hard typically leads to failure, the result can be learned helplessness instead.

When the activity itself supplies the rewards, our interest level is naturally high, and the driving force of effort is not needed so much. Many people who seem preternaturally able to focus are simply really interested in the things they focus on.

A key to focus, then, is to find ways to do things that are both important and interesting.

…Or Decrease Temptations

Some tasks persistently resist our interest. If you can’t boost your motivation to do something you need to focus on, you can at least reduce the appeal of alternatives.

One way to do this is to raise the costs of temptation. If you put your phone far away from you, it costs a lot of effort to get it to indulge in a distraction. If your phone is in a locked box and the only key is with someone who lives across town, it’s even harder.

Raising the cost of temptation works, but it has two additional challenges.



First, when your interest is low, there are often many possible alternatives that are more interesting, including daydreaming. This means that sustaining focus can feel like playing Whac-A-Mole, where you fight off one distraction only to slip into a different one.

Second, human behavior flows toward rewards. This means we’re loath to create future situations for ourselves that are less-than-optimally rewarding. It’s hard to lock yourself in the library to study, and it’s hard to focus on a dull textbook while you’re there.


Now this latter problem is not unsolvable. We tend to think about future impacts on motivation differently than motivation experienced in the moment. So, in some ways, pre-committing to a course of action you know will be less tempting is actually easier than avoiding those temptations when they occur. You can trick yourself out of akrasia.

But reducing distractions while trying to force yourself to do tasks you have low interest in or experience low rewards from tends not to be a stable solution. You have to cultivate an interest in the thing you’re trying to focus on (because of improved self-efficacy, curiosity, learned industriousness, etc.) or you’ll likely renege on your earlier commitment.

The post Focus is Motivation appeared first on Scott H Young.

But being totally unqualified has never stopped me before, so I’ll try to explain the answer I typically give to this question, which is that I’m skeptical of dramatic proposals to make school considerably more effective or efficient for the average student.

To be clear, that’s not because no improvement is possible. We do know some about things that work that are inconsistently applied: phonics should be taught, cognitive load should be managed, skills should be fully taught and practice should be fun and ample.

But these answers aren’t the kind that satisfy the people who ask me these questions. Instead, having had many of these conversations, I feel like the person asking already “knows” what my response should be:

Isn’t it obvious that school sucks? That we should be teaching critical thinking and problem-solving skills instead of useless facts and theories? That school should be more like real life, with real-world projects and experiments and collaboration? That there should be less of that stuffy work of sitting at a desk and memorizing things?

If you had asked me this question years ago, I probably would have agreed with you. It took reading a lot of research to convince me that this intuitively appealing idea is actually bad. Below, I’d like to explain why.

First, the Evidence

Before I get into the explanation of why these kinds of seemingly-good strategies don’t work, I should begin by pointing out that these ideas are not new. They have been tried, and they have been found wanting.

Entire books have been written pointing out the flaws in many of these strategies. I won’t be able to do the full debate justice here, but, if you’re interested, you can check out Daniel Willingham’s Why Don’t Students Like School? Greg Ashman’s The Power of Explicit Teaching and Direct Instruction or, if you want to learn more about the actual debate between proponents of both sides, try Constructivist Instruction: Success or Failure.

To briefly recap some of the evidence:

• Project Follow Through was one of the largest educational experiments ever conducted. Run in the 1970s, it compared how different teaching methodologies impact student outcomes. Direct Instruction, a method of teaching that has students sit in desks and perform extremely structured drills in unison, performed best.
• Problem-based learning tends to do worse than traditional schooling in medical education. An influential meta-analysis by Albanese and Mitchell, for instance, found that students required more time studying, had worse exam scores and ordered more unnecessary tests compared to traditionally taught students.
• Despite needing to relearn this truth every few decades, the best way to teach kids how to read has been known for centuries: break down the sound-spelling correspondence, and do lots of practice on it before moving up to authentic texts. Approaches based on skipping these drills in favor of “inspiring a love of reading” do worse.
• Practice testing and distributed practice—basically, having regular quizzes spread out over a course—are the studying methods with the best empirical support. Fancy methods like mnemonics and concept maps fare worse.
• General problem solving abilities are neither learned nor taught. While some problem-solving methods have broader applicability than others (such as the scientific process of hypothesis testing), students learn these methods better when they’re explicitly taught rather than simply giving students projects and hoping they’ll reinvent them on their own.

In short, whenever we have high-quality evidence that rigorously compares two teaching methods, the research invariably favors strong, direct instruction plus practice.¹ Or, in other words, the exact stereotype of schooling that so many of the people asking me about school reform despise.

Your Stereotype of School is an Endangered Species

This doesn’t mean education couldn’t be better. My impression upon first encountering the Direct Instruction research was that I had never been taught this way in my entire life.

Clichés are often out of date. I went to grade school in the nineties, when the lofty aims of project-based and discovery learning were the educational orthodoxy. I spent a lot of my school years doing time-consuming projects that had us gluing and coloring and expected us to do our own research.

I’m pessimistic about real reform because the changes needed to make schools more effective are often opposite of what many people intuitively feel. For schools to teach more effectively, they should be more rigorous about carefully defining the knowledge objectives of the class, thoroughly breaking down complex skills into components, and doing lots and lots and lots of practice.

In short, a “better” school probably looks more like the stereotype of an old-fashioned schoolhouse with kids sitting at desks, drilling facts and concepts that are patiently explained by a teacher. To the extent that school becomes more like free play, project-building or acting like a scientist, it will probably be worse.

Why It’s Hard to Improve Schools

Schools face a number of practical constraints that make thorough reform difficult. Students are unmotivated. They range in background knowledge and innate ability. We care about sorting just as much as educating, so schools end up doing both.

But the real reason it’s hard to improve schools is simply that there are fundamental constraints on how the brain learns that prevent radical shortcuts.

The boring truth is that expertise in most subjects is largely a matter of having an enormous library of knowledge and skill. For example, if you want to learn a language, you need to learn a lot of words. Any method that tries to skip over the fact that there are tens of thousands of words to learn is doomed to failure. All skills are like this, it’s simply that the “atoms” of learning are usually less obvious than in languages.

When students complain about all the boring facts and skills they had to learn in school, my response is to claim that there isn’t any other type! All skills are simply an accumulation of small bits of facts, procedures and concepts.

Those small bits, in isolation, seem kind of trivial. But quantity has a quality all its own, and with enough well-integrated knowledge the result is expertise that seems almost magical to those who don’t possess it.

This means that improving education comes down to largely two different options:

First, you can increase the efficiency of the system. Efficiency here looks like the kind of factory redesign that increases product throughput—increasing the number of words learned per day, optimizing cognitive load, boosting mnemonic efficiency through spacing and retrieval—without skipping over the fundamental bottleneck in cognition.

Second, you can choose to learn different things. Given the high degree of specificity of most knowledge, the choice of what to learn can have profound consequences. But if choosing a pedagogical method is contentious, curricular choice is even more so! For every “useless” subject that reformers want to discard, there are die-hard advocates arguing that we should be putting in even more.

I believe in both of these things, and I’ve focused much of my writing career on how we can do them better, particularly outside of the typical classroom. But if you want lots of skills, there’s no way around learning a lot of stuff—including a ton of stuff that feels too obscure to be broadly useful.

What About Ed Tech?

Thus far, I’ve mostly been targeting a certain kind of questioner: someone who feels that school was maybe too boring and impractical, and who longs for the possibility that education could be more like play and less like studying.


There are reformers of all stripes, and educational technologists are another side of this debate. These are the people who champion efforts to gamify learning, carefully match teaching to each student’s ability level, develop AI-based tutoring, put an iPad in every child’s hands and so on.

In theory, these ideas are possibly useful. Drills can be boring, so wrapping them with gamification elements that reward progress and engagement might be helpful. Skills can be too hard or too easy, so adjusting difficulty automatically might be helpful. AI-tutoring, too, might help with closing Bloom’s famous 2 sigma problem.

But I’m more skeptical in practice. As Kelsey Piper writes, a lot of ed-tech games have a fairly low density of actual useful learning. I can attest to this: eager to give my son a head start on the phonetic skills involved in reading, I tried a few different iPad games with him. He mostly messed around randomly until he got the reward, largely ignoring the educational content to fixate on the cute cartoon characters.

Gamified learning is a bit like wrapping medicine in candy. Yes, it may help some students swallow some instruction they otherwise find bitter, but in practice it’s easy to pull off the candy, consume it, and throw the medicine away.

Individualized instruction aided by technology does solve some of the problems of differing ability levels. But schools aren’t just solving the cognitive problems of learning, they’re also working on motivational ones. A rigorous, but achievable, standard that applies to everyone may be more sustainable for motivation than an individually-tailored goal for each student.²


Similarly, while I’m hopeful that AI advances will make automated tutoring more useful, it’s still far away from the skill a teacher can provide. As someone who makes use of AI quite a bit in my own learning, I can say that it’s still relatively weak at having a good model of an individual’s skill gaps and conceptual weaknesses. It’s very much at the “better than nothing”—not the “better than teachers”—stage right now.

So, while I’m hopeful that there will be some improvements in technology around the margins, I’m skeptical of anything touted as a radical overhaul in educational process or outcomes.

What About Ultralearning?

Does this line of thinking rule out the methods I describe in Ultralearning? I don’t think so. The big distinction (and it is a big one) between my aims for that book and the aims of educational reformers is that I started with the assumption of a highly motivated learner.

The people I document in that book all began with the starting point that they were willing to work hard, even obsessively, on a project they were deeply motivated to succeed with. In such cases, the classroom structures that facilitate motivation can instead become obstacles: fixed homework assignments, mandatory lectures, exam deadlines. These things keep uninterested students going, but they may hold back the aggressively curious.

For instance, I still believe that full immersion is the best way to learn a language, provided you also augment that with a lot of the studying approaches I describe above, but it’s obviously a high-effort strategy. I’ve spoken to lots of people who have asked me for advice on how to learn a language, but relatively few that took up Vat’s and my actual strategy of avoiding speaking English.

Ultimately, I believe enhanced learning is certainly possible, and a highly motivated person can often do better than the average, and sometimes even the upper end, of what is typically seen in school. But such optimism about the possibility of learning doesn’t so easily transfer to a situation where motivation is much lower.

The post Why I’m Skeptical About Efforts to Revolutionize Schooling appeared first on Scott H Young.


Focusing on increasing the sheer quantity of your reading is generally underrated, especially because reading comprehension and retention are strongly coupled to your background knowledge. You’ll read better if you read more. And so, to a first approximation, if you read more books on a topic, you’ll automatically read better.

I’ll give an example. Recently, I’ve been reading Martha Nussbaum’s, The Fragility of Goodness. The book has an interesting philosophical premise: the good life, indeed even being good, may be contingent on factors outside of our control. She argues the early Greek tragedians understood this, and Western philosophy has spent the last two thousand years trying to deny this possibility.

The book is interesting and fits into some of my broader reading goals of learning about what it means to live a good life, happiness and so forth.



But the book is also challenging to read. This isn’t due to faults in Nussbaum’s prose, but because she assumes the reader brings so much knowledge to the book. She draws lessons from Agamemnon and Antigone assuming the reader is already familiar with these plays. (I was not.)

While unreadable academic writing is practically a cliche, I think this stereotype masks a more fundamental problem: Many topics are deep. To write about them while also repeating every bit of background knowledge informing the conversation would be incredibly tedious. At close to 600 pages, Nussbaum’s book wouldn’t be enhanced if she expanded it to meet popular nonfiction’s assumption of readers having minimal knowledge on the topic. This means to understand The Fragility of Goodness, you need to have already read a lot.

Thus, if you want to read better, start by reading a lot. Quantity and quality reinforce each other, because as you read more, even with only partial comprehension, you build the reference points that allow you to tackle deeper and better books.

The Perils of Path Dependency

That reading better books requires reading more books sounds innocent enough—until you realize that knowledge is not a generic commodity. The more you study within a particular topic, the more adept you get at reading that topic. Indeed, if you study particular arguments, they crawl into your thoughts until you can’t easily separate them from yourself. You become better at seeing one side of a debate, but the other side becomes illegible.

I’ve become sensitive to this problem in my own work. My last book, Get Better at Anything, was originally going to be called Do the Real Thing, and I planned to focus on the research on learning transfer. 

I had encountered some interesting research on the relatively low degree of transfer of learning when researching my first book, Ultralearning. To clarify, the common finding among psychologists is that people who learn a skill tend to get good at it and skills closely related to it. Any benefits of having learned that skill drop sharply as we move to more distant skills, and the benefits learning it brings to completely unrelated skills are hard to distinguish from zero.

I thought this was fascinating and under-discussed. The implication of this, to me, was that we should spend more time doing things closer to what we want to get good at, trying to avoid artificial and substitute activities with vain hopes of transfer.

As I started researching, I got deep into some theories of learning which made almost exactly this argument: Jean Lave’s work on situated learning, Allan Collins and John Seely Brown’s work on cognitive apprenticeship and classic education reformer, John Dewey.

Only after about six months of deep research did I begin to stumble upon the critiques of this viewpoint. Essentially, the critiques offered a theory that neatly explained the findings on transfer, but rejected the takeaway that I was building my initial book around: that doing the real thing is generally best.

In particular, I was persuaded by the work of John Sweller’s cognitive load theory, Siegfried Engelmann’s Direct Instruction, John Anderson’s ACT-R theory. Together, they argue that complex skills are often best learned first in parts; that observation, not pure practice, is essential; and that reverse-engineering expertise is fraught. While direct practice still matters, it is best done in an environment with clear explanations, guided coaching and, quite often, drills that bear little resemblance to the skill you want to improve.

The result was a tricky pivot to write a completely different book from what I had initially set out to write, including a stressful six-month research detour where I forced myself to read a lot more books and papers than I’d care to recall.

The lesson I took from this, and indeed one I seem to have to learn more than once, is that knowledge is path-dependent. The books where you start your reading have an indelible influence on where you end up. It can take heroic levels of reading to undo the effects of a poorly chosen initial book.

This is the dark side of the knowledge dependency I articulated above. Because you retain and understand more when you know more, you end up being better able to understand and assimilate viewpoints that already “fit” with what you know.

When Reading More Makes You Know Less


Some topics, due to lay interest, are notorious for this problem of path-dependency.

Consider nutrition. Of all the topics I discussed in my Foundations project, nutrition spurred the most long-winded rebuttal emails. And this was despite the fact that I was, to the best of my ability, simply parroting the expert consensus. A lot of non-nutritionists, it seems, have heterodox views about diet and have a lot of “knowledge” to back it up.

How can I be so sure I’m the one who’s right? After all, I’m also a non-nutritionist who’s read a lot of books. Perhaps I’m the one with weird beliefs and my correspondents are correct in pointing out my errors.

This is certainly possible, and, based on my theory above, I wouldn’t necessarily know if I was wrong. But, while I cannot claim omniscience, I do know that at least some of my critics were wrong, because they were espousing opposite theories! Indeed, I got a handful of pointed critiques from both plant-based devotees AND carnivore-adjacent low-carbers.

I don’t want to suggest that either of these diets are necessarily unhealthy. From a practical point of view, perhaps it is best that we each simply follow whatever works for us. But a religion stops being merely personal when one begins to proselytize. If you’re not simply arguing “this is what works for me” but “this is what everyone should do”, personal anecdote stops being an effective rebuttal to accumulated scientific research.


Ironically, from a purely intellectual perspective, a lot of people who have gone deep into the rabbit hole here actually know less than they would if they hadn’t read anything at all. Meaning, if they simply had absorbed the vague popular sentiments about eating healthy (which, to be clear, are also biased), they probably would be closer to the orthodox viewpoint than after reading a ton of books.

While I may sound like a know-it-all scold here, I’m very much including myself in this critique. If you are interested in learning and knowledge, you should always be somewhat wary of the possibility that you’ll invest a lot of time and effort and end up with views that diverge even more strongly from our best estimate of “truth” than if you hadn’t begun reading in the first place.

I’ve previously made the case for believing expert orthodoxy, but my argument here doesn’t hinge upon it. It’s certainly possible to reject the standard position in a field after having carefully deliberated on all the evidence. But the conclusion you arrive at shouldn’t depend on which book you read first.

My Heuristics for Reading Better

So how can you read better books?

As already mentioned, many of the best books aren’t written for beginners. They depend on the reader already having considerable background knowledge for proper understanding. If you haven’t learned arithmetic and algebra, calculus is going to feel confusing and painful.

This means reading great classic books on your own, without a companion course or tutor, is sometimes a struggle. You simply haven’t acquired enough knowledge to fully appreciate them yet.

That doesn’t mean you can’t try. But it may take multiple re-readings and many detours to other books to squeeze all the juice out.

What counts as “better” is relative to your knowledge base. Just as the “best” ski slope isn’t necessarily the steepest, but the one that matches your abilities, the best book for you may not be the same one an extremely well-read person would reach for.

And yet “better” isn’t purely a matter of personal preference. Some books provide excellent starting points into a long and satisfying journey into a world of ideas. Other books offer a detour that will cause you to get lost in the brambles.

Some strategies that have worked for me to help sort these two apart are:

1. Start with textbooks. I’ve praised the virtues of textbooks before, and I’ll say it again. Most people who want to actually end up with knowledge (and aren’t just looking for entertainment) would do better to read a textbook rather than a general-audience nonfiction book. If textbooks are too expensive or boring for you, more popular general survey books are also usually good. For instance, most “For Dummies” books are a smarter read than most of the glossy bestsellers covering the same topics.
2. Use ChatGPT and Wikipedia. AI certainly has its weaknesses. Sycophancy is rampant, so if you ask leading questions intended to “prove” your hunch, you’ll too often get it. But, if you’re genuinely curious about whether a given point of view is standard within a field, AI doesn’t do a terrible job. (It’s a lot better than, say, Quora.) Wikipedia is also really good here too, but, because it is organized by encyclopedia topics rather than specific questions, it can take more work to find an answer.³
3. Read in projects. Don’t get one book, get ten. If you have a bunch of books on a topic, then as long as you read them relatively close together in time, you’re more likely to average out their arguments than if you read only one book and allow it to take up too much real estate in your head.
4. Look for critics. Some risk of path-dependency can be prevented by actively seeking out rebuttals to stress-test your viewpoints. This, too, can be AI-accelerated since “find me the author/papers that best argue against XYZ” is a fairly simple AI request that used to require a lot of searching.

But the methods we use are downstream of our motivations. The way to read better books is, fundamentally, to be deeply curious about what other people think, curious enough to want to spend a lot of your time working to understand them. If you’re motivated by something other than curiosity—a desire to prove a point, to be part of a “team”, or to simply be entertained—you’ll probably get what you’re looking for, it just may not be knowledge.

The post How to Read Better Books appeared first on Scott H Young.

But I’m struck by a weird asymmetry in science fiction: it’s way easier to imagine dystopias than utopias. Most worlds of science fiction are markedly worse than ours in pretty catastrophic ways. Utopian science fiction barely exists.⁴

Some of that asymmetry may be simply that stories require conflict, and a dystopian society naturally generates conflict that makes for an interesting story. A novel where everyone’s happy and leads contented, fulfilling lives is, well, kinda boring.

Recently, I’ve been thinking the reason for this negative skew may be different: perhaps human beings just aren’t designed to view any world as utopian. We’re both problem-centered in our focus, making us ignore the things that are going well, and our drives and needs are frequently misaligned so, even when we get what we want, we don’t always get what we need.

Why are We So Rich, Yet Everyone Feels Poor?

I’ve spent a lot of time thinking about Scott Alexander’s essay on the vibecession. 

To summarize briefly: Americans think the economy sucks. Yet, by almost all measures, it is doing fantastically well. Alexander tries to diagnose why economists and everyday people disagree to such a large extent.

The knee-jerk reaction is that the economists are just wrong. But Alexander goes to great lengths to explain why this probably isn’t true. In most ways economists can measure, people aren’t doing too badly: unemployment is down, inflation is under control, and, for Americans at least, they’re much, much richer than almost everywhere else in the world, both in terms of the very top and also for most of the middle.

I know it’s fashionable in certain circles to dismiss economists entirely as being an apologia for laissez-faire capitalism and right-wing ideologues. But I think this criticism mistakes a certain subset of economists (who are currently dwindling in number) for the field as a whole. There are plenty of left-leaning economists, and the professional incentives hardly align with suppressing pessimism about the state of the economy for everyday people.

So, it seems likely to me that if there was a really good case for why the economy is actually terrible and the vibecession were justified, economists would endorse it.

My personal guess is that pessimism about the economy can probably be explained by an overall increasing negativity in news media. When people are asked about their personal situation, they tend to respond more positively, but when asked about “the economy”, the question expands to other people, and respondents are more likely to rely on what they hear from news reports.

News has gotten a lot more negative. But that simply bumps the question up further: why is the news so pessimistic? Why does our society feel increasingly dystopian?

You Get What You Want, But You Can’t Always Get What You Need

Media pessimism is probably best explained by consumer preference. People are more likely to read about bad news.


This doesn’t mean people want bad things to be happening in the world. It’s simply that if everything is good, we don’t need to hear about it. Our evolved preference is to seek out information when there are potential threats.

This makes an asymmetry in news production that’s similar to the one we see in science fiction. Feel-good stories are the minority. When they do appear, they’re often kind of boring. Feel-bad or feel-scared stories dominate the headlines because these drive reader interest.

We have always wanted to hear bad news, but the vibecession is recent. The reason we feel particularly pessimistic about the world today is that media has become increasingly optimized to give us what we want. The hypercompetitive, algorithm-driven media environment is simply a much more desirable product, from the perspective of consumer preferences, than the more boring newspaper era.

This isn’t limited to news media pessimism. Our society has gotten much better at servicing nearly all of our most basic drives:

• Hunger. Food today is more abundant, convenient, palatable and cheaper than ever before. Nearly all of our diet-related health problems come from eating too much, not from nutritional deficiencies or toxic food additives.
• Entertainment. Infinite feeds of short-form video, algorithmically tailored to our personal interests. Our dwindling ability to read books and think deeply is directly related to the always-on faucet of easy entertainment.
• Romance. Swipe right, swipe left. An infinite sea of potential mates at our fingertips, allowing us either to indulge forever in short-term hookups or scrutinize endlessly to find the perfect partner, who, of course, doesn’t actually exist.
• Physical ease. Our physical environment requires increasingly less effort. The result is that we don’t move enough and are sicker as a result.

It’s easy to blame “capitalism” or big corporations or some other nefarious force for all of this. After all, if the big tech companies didn’t insist on making phones so addictive, maybe we would read more books. And if industrial food producers didn’t make such ultra-processed junk, we wouldn’t have so many health problems.

But the companies are all locked into the same bind. If Meta doesn’t make the most addictive social media website, they’ll be outcompeted in the attentional marketplace by ByteDance or YouTube or some new start-up that will give people what they desire.



The real fault isn’t in the companies, but in ourselves.

Victims of Our Own Success

As we’ve become richer and more technologically advanced, we’ve become better at delivering the things people want—but what we want isn’t always what we need.

It’s hard to describe our current moment as a utopia with a straight face. Indeed, I have a lot of fears and misgivings about the direction society is taking. I’m worried about democracy, war, warming, divisiveness and the possibility that the robots will eventually kill us all.

But if we define a utopia as a society that gives people an abundance of the things they want, then, at least compared to nearly all actually-existing societies, we’re living in it! 

True, there are still problems that might be solved in some glorified Star Trek future that don’t exist today: teleportation, robot butlers, cures for aging, and world peace. But if we avoid speculative futures, it’s pretty clear that we’re closer to the utopia of human desires than we’ve ever been in the past.

Yet, if we look at human flourishing, the kinds of things we need to be psychologically fulfilled, the picture doesn’t look so good. Rates of depression and anxiety have skyrocketed. People are anxious, fearful, inattentive and unhappy.

One story you can tell about this trend is that technology is to blame. The rise of smartphones, social media and easy entertainment have glued us to screens rather than real friends, hobbies and time for reflection. If so, the problem would be not that we live in a world without those things (friends, hobbies and time for reflection still exist, after all), but that our world has too much the things people desire, and they crowd out the things we actually need.

Stoicism, Social Control and Ozempic

If this diagnosis is correct, what can we do about it? I see only three options, all of which have serious drawbacks.

The first is prudence. Cultivate virtue. Resist vice. Delete social media from your phone. Buy only veggies and whole grains for your pantry. Don’t drink, gamble or own a television.

I’m sympathetic to this path. And, given that this is the only factor that we can, as individuals, realistically change, it’s usually the one I spend more time advocating. It requires recognizing that there has always been a tug-of-war in the human soul between the steeds of passion and reason, but in today’s world the passions are being pulled by a rocket ship.

But, if “have more willpower” was a universal solution, we’d have adopted it by now. Losing weight by eating fewer calories than you burn has always worked. But sustaining weight loss through willpower alone has astonishingly low success rates.

I don’t think this failure of willpower means we should stop trying. But if we’re in an escalating arms race with technology increasingly able to satisfy our most basic drives, that effect isn’t going to be balanced with increasing willpower by all but the saintly few.

The second option is regulation. Tax sodas. Ban smartphones from school. Force social media platforms to moderate content and change their algorithms.

This path has the advantage of not relying on the limits of human willpower. If ultra-processed foods become illegal, we won’t need to use self-discipline to avoid eating them.

But I’m also skeptical that social control can ever provide the full solution in a democratic society.

In Julia Belluz and Kevin Hall’s book Food Intelligence, the authors go to great lengths to describe how the hyperpalatable and over-processed food environment is making us fat and sick. But, when they get to the section of what to do about this, it becomes clear that the solutions that would actually work would be draconian.

Banning soda, junk food, fast food and anything ultra-processed would work, but outside of a few health-obsessed foodies, I doubt it could reach a critical mass of acceptance in society. If prohibition didn’t work for alcohol, it’s hard to imagine a scenario where it would work for donuts.

The kinds of social control we’re more likely to get are those where only a minority is impacted. Smartphone bans in school are popular because kids don’t get a vote. Smartphone bans for grown ups, in contrast, feel like a violation of civil rights.

Similarly, my country, Canada, is considering banning smoking permanently for those born after a certain year. I understand the impulse. But it’s only popular because most people don’t smoke at all. Alcohol also causes many social harms, but I see no similar proposals for banning booze, because teetotalers are in the minority.

Ultimately, I have a hard time understanding the people who scoff at the idea of individuals applying willpower and habit changes to avoid vice, yet seem enthusiastic about solving the problem with government regulation and control. The same people who don’t have enough willpower to avoid overeating and obsessive phone usage are, supposedly, the same people who will vote for a policy to prevent them from accessing the objects of their desires.⁵

So, if willpower is weak and social regulation requires collective willpower, what does that leave us with?

The third option seems to be: use technology to modify our brains to change what we actually want.

Until recently, this would have been the premise of a (probably dystopian) science fiction novel. But with the arrival of GLP-1 agonists, it’s the world we’re now living in. 

GLP-1 drugs work not by speeding up your metabolism or preventing you from accumulating fat but by changing how much you want to eat in the first place. Users experience reduced “food noise” and cravings for junk food. Grocery stores are apparently even going to need to increase the amount of produce they stock, as a new group of shoppers suddenly feel the urge to buy kale and broccoli.

The effects don’t seem to be entirely limited to eating behaviors either, with some early reports suggesting the medications may help with other kinds of addictive behaviors such as drug abuse or alcoholism.

GLP-1 drugs seem to work well with few major side-effects, but they’re hardly the only example. ADHD cases have exploded, in part due to loosening diagnostic criteria. But also because inattentiveness is a spectrum and paying attention is harder than it used to be. Medications may make it easier for people to focus and ignore distractions. Unfortunately, the side-effects of these drugs are probably worse than GLP-1s, but it’s possible to imagine a new focus wonder drug of the future that has fewer side-effects and more widespread adoption.

I find it hard to get enthused about a future where we create an abundance of human vices, and cure that abundance by creating drugs that make us desire them less. It sounds, well, dystopian. But I suspect that this will end up being the path humanity follows, if only because it is easiest.

The post What if We’re Already Living in Utopia? appeared first on Scott H Young.

The book went on to become a surprise bestseller, with over 200,000 copies sold and dozens of translated editions. To this day, the bulk of new reader emails I get are from people who discovered me through Ultralearning.

A question I get asked a lot is how the book would change if it were published today. In 2019, the conversation about AI was still a whisper. Now, it’s deafening.

Today, I’d like to walk through Ultralearning and look at what’s changed, what hasn’t, and what I think the future holds for learning and education.

What Hasn’t Changed

The basic message of Ultralearning, I believe, still holds up pretty well:

Technology is widening the gulf between the haves and have-nots of human capital. Learning in school is insufficient. To achieve, we need to continually add to our skills and knowledge, and doing so efficiently is imperative given our information-saturated environment.

AI has only accelerated those trends.

While some early reports suggested AI might be an equalizer, helping mediocre programmers and writers produce at a higher level, I think those early takes now seem naive. If anything, the fruitful branches of the skill tree for becoming a professional programmer have only gotten higher—with tasks that were previously for junior devs now wholly within the grasp of automated agents.

Some prognosticators suggest that the culmination of this process will be the devaluing of all human skills. Why bother learning anything at all if AI will soon do it better than you?



I’m skeptical of this as a final outcome. I tend to think there will continue to be humans doing human jobs far into the future, if only because certain kinds of work are inherently humanistic. But the medium-term outcome seems to clearly back the urgent need for humans to learn deeper and more robust skills to compete.

AI has not fundamentally changed the effort involved in learning. Ultralearning was written from a particular vantage point: a person eager to learn and willing to do the hard work required. These people have always been a minority, and AI cannot change the intrinsic effort required.

So, as a proportion of the population, I don’t expect an explosion in impressive autodidacts any more than we saw with the arrival of the Internet. The world’s knowledge is already at our fingertips, but most people will still prefer to watch funny videos instead. AI certainly isn’t changing that.

But, at a tactical level, AI has created new possibilities (and pitfalls) that didn’t exist when I wrote Ultralearning. So let’s look at some of those, following the nine principles of the book.

Principle #1: Meta-learning

This is probably the chapter most in need of a rewrite. Self-education has always stumbled on the bootstrapping-problem of knowledge: how do you organize an effective learning project when you lack the knowledge to organize it?

My solution in the book was to encourage people to do research: figure out how a skill works, talk to experts and map out what you need to learn before you start.

AI has dramatically reduced the cost of doing this kind of research, and not only for academic subjects. Even obscure practical skills can now be broken down into discrete subtopics, practice activities, lists of facts, concepts and more.



My go-to approach to tackling a new topic area these days is to fire up ChatGPT and get it to start with a Deep Research on the topic, beginning with some of my major questions. The resulting document isn’t usually on par with genuine experts, but I very quickly narrow in on what sorts of directions I need to take to fill in my research.

Similarly, if you’re learning a less academic skill set, using AI can surface the current best practices and give you the basic building blocks for a learning project.

I very rarely stay totally within AI responses for meta-learning. It’s always good to get to the ground truth of some genuine expert or teacher’s curriculum. Finding those teachers and experts and the organizing paradigms that lead to them is much easier now with AI.

Principle #2: Focus

AI hasn’t changed this principle. Learning anything requires time. Even when you do projects efficiently, they’re still an enormous amount of work. If you can’t put the time in, you can’t get the results.

Learning also requires attention. If you can’t devote large chunks of undistracted time to a project, you’ll fail to build deep skills and understanding.



The attentional ecosystem has only gotten worse since Ultralearning was published. When I was doing projects in my early twenties, the major distractions were Reddit threads and the occasional Facebook post. Now, an endless treadmill of short-form video content on our phones means we can play the attentional slot machine all day without pause.


Currently, I see AI-generated content as less appealing than human-generated content, so I don’t see it making the problem of addictive social media much worse. Perhaps in a few years AI-generated feeds will be more enticing than human-created content, and I’ll need to revise this point.

Principle #3: Directness

Practice the skill you want to get good at. Do the real thing and avoid substitutes.

AI probably makes this harder. Because AI is so compelling, there’s a temptation to do AI-mediated practice rather than engaging in the hard, scary, and sometimes uncomfortable, real-world skill that directness suggests.

Take language learning, for instance. In Ultralearning, I was highly skeptical of the gamified drills offered by apps like Duolingo. To me, they simply omit so much of the actual skill of conversing in another language that you could play these games for years and still feel uncomfortable ordering food at a restaurant.

Since then, I’ve heard people claim that they’re using AI to learn languages, writing—and even social skills(!!).

Of course, one could easily imagine someone who is having real conversations, publishing essays and attending social events simply using AI to shore up some weak points. But, more often, I worry that people are using the verisimilitude that AI creates to try to avoid doing the real thing entirely.

Principle #4: Drill

The counterpart to directness is drill: breaking down a complex skill into smaller parts, focusing on those smaller parts either in isolation or with greater focus to make selective improvement. These drills can include conjugation exercises for Spanish, practicing layups for basketball, making value studies for painting, and more.

Here AI presents a whole range of new opportunities through AI-generated practice problems, flashcards, worksheets or feedback.

For instance, one of the major difficulties in my language learning projects had been how much weight to put on vocabulary study through flashcards. On the one hand, an efficient spaced-repetition system, backed by some careful mnemonics, can make it much faster to acquire a few thousand words of basic vocabulary. On the other hand, flashcards can lead to brittle knowledge that is difficult to generalize to real conversations.

A major cause of my ambivalence with flashcards is that the paradigm assumes each word is an atomic fact. But what we are actually learning when we learn new words is not merely a definition or translation. Instead, we’re also learning contextual associations for how that word typically appears in spoken or written language. It’s how we know the difference between the words small and petite, or big and grand. These associations have to be learned implicitly, and can’t simply be memorized as part of the definition.

Now, with AI, we can generate flashcards that always place the to-be-learned word in a novel sentence, giving us the needed repetition alongside the variation required for learning contextual cues. This, to me, is a major upgrade over the flashcard paradigm.

Conjugations are another area that is difficult to learn without premade practice questions. The issue is that what needs to be learned isn’t a fixed association (e.g., agua -> water) or a verbalized rule (e.g., “change -ar to -o for first-person present tense”) but rather a procedural mapping that needs to take a variable input and give a variable output.

To learn procedures like this effectively, we need flashcards that vary the input/output relationship to show all permutations of the pattern. The problem is that this used to be hard to do before AI. Now, of course, we can use AI to generate infinite variations of the same basic practice problems, which solves the material gap that exists for a lot of skills.

Principle #5: Retrieval

Memory is strengthened more by recall than by review. If you want to learn something by heart, you need to practice remembering it, not just looking at it.

I’ve seen a lot of claims that AI can be helpful with this aspect of learning. For instance, AI tools can generate quizzes based on the books you’re reading allowing you to deepen your knowledge of the content.

I tend to be a bit skeptical about the utility here. Not because quizzes or practice questions are bad (they certainly aren’t), but a lot of the value in retrieval comes from selecting what knowledge you ought to retrieve.

For instance, a naive way to do retrieval practice is simply to quiz yourself on every factual claim made in a text or book. But rarely is the main goal of learning a complete verbatim memory of every factual claim in a book. Instead, we typically want to be able to restate the main ideas and understand the key points and concepts.

Sometimes we may have more idiosyncratic goals, like remembering the authors of key studies for future research or knowing the dates to put historical events inside a chronological context. But memorizing every single fact in a text is almost never a good use of limited studying time.

This is not an idle concern. The world of knowledge is infinite. The effort needed to memorize every fact from one text is effort that cannot be spent on other texts. I’d much rather remember the gist of ten books—their big, important ideas—than know every bit of trivia contained in just one of them.

Practice problems and quizzes designed by a teacher avoid this problem because the teacher has in mind clear educational goals. When they ask a question on a test, it is because they think it is important to know that fact or idea. But if we give an AI a random text without this pedagogical context, the chance that it’s going to narrow in on what is important is much lower—not because of insufficiently capable AI, but because it doesn’t have a useful goal. If you asked a human to generate a quiz from a random text absent any pedagogical goals, they’d also make a bad quiz.

Retrieval, of course, doesn’t need quizzes to work. Free recall, the paradigm where you simply try to remember as much as you can from a source, works remarkably well and definitely doesn’t require AI. So does writing essays about topics you’re learning, which may soon become a lost art. These are low-tech tools that work amazingly well for retrieving knowledge.

Principle #6: Feedback

Feedback is essential for learning. But we often get sparse or incomplete feedback in our learning efforts, which slows down progress.

In symbolic domains, where the skill is primarily mediated through tokens and text, I think currently-existing AI can do a ton to enhance feedback. If I’m trying to improve as a writer, I can get AI to critique my use of research, word choice and storytelling. If I’m trying to improve as a programmer, I can be shown more efficient design patterns or algorithms for solving the same task.


A while back, I recorded some promotional videos in Mandarin for a translation of my book. I wrote the script myself, but then I asked AI to offer suggestions, and it fixed some places where I wasn’t speaking very idiomatically. Before AI, I would have had to pay someone for that advice.

In non-symbolic domains, where AI still underperforms human beings, the value of AI feedback is a lot more limited. I can’t easily use AI to give me feedback on art, skiing or interviewing ability at the moment, so human feedback remains essential.

AI also can’t replace the need for direct feedback from the environment. Entrepreneurs need data about product-market fit. Comedians need to know whether their jokes are funny. Writers like me need to know what their audience already thinks and believes. That kind of feedback is essential to the skill, and AI can’t offer a substitute.

The more dangerous cases are areas where AI could give good feedback, in theory, but it’s been trained not to because people often don’t like getting true feedback. Sycophancy is rampant. For a lot of us, hearing nice things about our ideas and skills is more desirable than hearing the truth.

Principle #7: Retention

I’ve always had mixed feelings about mnemonics. They can be incredibly powerful. The right chaining of visual associations or spatial memories can make indelible links between hard-to-associate facts. But they also take a while to learn and can be time-consuming to apply.

AI has the potential to make mnemonics more valuable. My friend and language-learning inspiration, Benny Lewis, for instance, told me that he’s been using AI these days to help him generate “sounds like” associations for the keyword mnemonic.



For those unfamiliar with the method, the basic idea is to take a foreign language word and create a phonetic clue by mapping it to a similar sounding word or phrase in English (or another language you know well) and then visually mapping that to a highly memorable picture.

For instance, if you’re trying to remember the French word chavirer -> to capsize, you can make a phonetic clue of “shave an ear,” then you have a mental picture of an oversized ear sitting in a canoe, shaving its beard while the canoe flips over. Visualize that mentally once or twice and the association sticks, whereas it may take dozens of repetitions for the direct association to take root.

The keyword method works, but it hasn’t always performed well in lab experiments. The reason is that it often takes too much time and training to get right. Modern LLMs are well-suited to the kind of wordplay tasks required to generate these sorts of images.

Spacing is another area where I expect AI to be some help, particularly the newer agentic AI paradigm. A major hiccup in applying spacing in learning is that it is a logistical nightmare to keep track of all the things you’ve learned and ensure some measure of regular re-exposure. Spaced repetition software does this for flashcards, but, as already discussed, those have fairly narrow applications.

However, I can easily imagine a future where an AI agent helps you manage your workload by resurfacing questions and ideas from material you’ve recently studied. With some guidance, you may even solve some of the retrieval problems mentioned earlier by getting it to quiz you on the major ideas.

Principle #8: Intuition

Understanding is central to learning. But the process of gaining understanding is still somewhat mysterious and poorly understood.

While I’m generally in favor of a knowledge-in-pieces model of conceptual learning, where understandings are built bit by bit through many exposures, it’s also clear that a well-chosen analogy, metaphor or explanation can suddenly make the entire idea “click.”

In Ultralearning, I shared the Feynman Technique my somewhat-apocryphal method of self-explanations that I made heavy use of during the MIT Challenge. The basic method is simple:

1. Write down the concept or idea you want to explain.
2. Write out an explanation as if you were teaching it to someone else.
3. Whenever you get stuck, go back to your study material and notes and re-read until you understand.

The technique works, but it is often frustrated by #3. If you don’t understand, even after reading the notes more deeply, you may waste a lot of time trying to find a better explanation.



Similarly, the method can backfire when conceptual confusion is glossed over rather than dug into—you may maneuver around your own ignorance rather than confronting it. This is why the method benefits from specificity: if you’re having difficulty solving a problem, make the topic of your teaching that exact problem, not the concept it tests in general terms.

AI has massive power to resolve both of these problems. For starters, while I find AI explanations are still somewhat inferior to good teachers, the gap is closing, and well-posed questions can generally get accurate answers. Using AI as a Socratic tutor is one of the ways it can help build understanding.

Second, AIs can ask pointed follow-up questions to reveal gaps in knowledge you don’t even know you are missing. I now frequently upload portions of essays I write where I explain some bit of science or history and ask the AI what I’m getting wrong. Often it nitpicks, but there are definitely occasions where I have a basic misconception.

The pitfall, of course, is that an on-demand system that can explain anything can also make it easy to skip steps #1 and #2 of the Feynman Technique. It’s very easy to ask AI to generate the explanation, skim through it and convince yourself you could have generated it on your own.



The risk of using AI to learn is that not learning at all is always the lowest effort strategy, and most models are designed to allow you to do exactly that. Without guardrails, the default is to skip over the mental work needed to build intuition, even if the technology can, in theory, assist in constructing a deeper understanding.

Principle #9: Experimentation

Experimentation, the process of trying out different things and figuring out what works, both within the skill you’re trying to master and in the process of learning itself, is a recurring theme in Ultralearning.

The new AI tools offer an acceleration of these possibilities. Not only because many new possible methods for learning now exist, such as on-demand Socratic tutoring, procedurally-generated practice problems, knowledge management, mnemonics generation and more, but also because many of the seemingly-useful applications are really pitfalls in disguise.

If I had to go back and redo any of the challenges I wrote about in Ultralearning, the possibilities for learning them would have changed dramatically. The MIT Challenge could have used AI to fill in material gaps, given me extra practice problems and gotten me unstuck when my self-explanations only led to confusion. The Year Without English could have had auto-generated flashcards, grammar explanations and corrective feedback on conversation recordings. I could have vibecoded software that could automatically give me detailed corrective feedback on the accuracy of my portrait drawings.

What wouldn’t have changed is the mental effort involved in learning skills, nor the joy and struggle in actually learning them. Despite the momentous technological changes we’re experiencing, I am still convinced that both the value and strain in learning new things will be an enduring constant.

The post I Wrote Ultralearning. This is What I’d Change Because of AI appeared first on Scott H Young.

This observation isn’t new. I’m certainly not the first to notice this problem, and this is far from the first time I’ve written about it.


But I thought I’d try to categorize, in general, what holds us back from doing things we want to do, but can’t seem to find the motivation to stick with. This doesn’t result in any quick fixes, but it does help us diagnose the problems we face and what the solutions might look like.

Motivation Problem #1: The Work is Too Unpleasant

Many of the things we have a hard time feeling motivated to do involve immediate pain or drudgery for potentially long-term or abstract benefits. Research suggests that our weighting of the present moment over the future self is a major cause of procrastination.

Sometimes this unpleasantness is unavoidable. If you need to go to the dentist to get a root canal, the next couple of hours aren’t going to be much fun—but doing nothing will only make things worse. Impulsiveness, or the propensity to weight the present higher than the future, is a major factor in such problems of willpower.

In other cases, the unpleasantness is at least somewhat malleable. For instance, a major factor I credit for my exercising more regularly is the fact that exercise becomes less aversive the more you do it. When you’re in great shape, exercising is fun (or at least less painful), so getting in shape forms a positive feedback loop.

In other cases, modifying the task itself can make doing it less unpleasant. You can change the environment, combine it with other rewards, make it easier, more interesting, turn it into a game or pair it with other activities you enjoy.

Motivation Problem #2: The Work is Too Scary

Fear and anxiety can undermine motivation when we anticipate pain, even if that pain is less likely or less severe than we imagine.


It’s probably best to distinguish anxiety from unpleasantness by the kind of belief we have about the task. Something might be genuinely unpleasant, but its benefits still outweigh the costs. There, the problem is impulse control and trying to tilt the balance in favor of our more prudential self.

In the anxiety case, the problem isn’t that the actions we need to take are necessarily unpleasant, but that we exaggerate how bad they’ll feel or the likelihood of a really bad experience. Here, the problem is courage, and we can solve that by exposing ourselves to the fear to undermine our irrational beliefs.

Since the problem here is that our beliefs don’t match reality, exposing ourselves to the anxiety-provoking situations and confronting the discrepancy between our expectation and reality is a powerful method for overcoming fears and anxieties. The fact that fears are often driven by unconscious threat-detection circuitry lends credence to the idea that we can’t generally talk ourselves out of these anxieties; instead, we require direct experience to combat them.

Motivation Problem #3: We Don’t Know How

Knowledge is often dismissed as a poor excuse for not taking action. After all, if we were really motivated to solve a problem, wouldn’t “learn how to solve the problem” be step number one?

But lacking skill and knowledge can hold us back. Part of it is that ignorance changes our calculus of the effort required. If I’m not sure how investing works, I might hold off on saving money because I imagine a long project of learning about investing as a necessary prerequisite before doing anything with my money.

The bigger way know-how impacts our motivation, however, is when a lack of knowledge prevents us from even thinking about a kind of problem, let alone how to solve it. Knowledge often creates both the awareness of an opportunity as well as the means for reaching it.

For instance, knowing an inventor is a major factor in becoming an inventor yourself. No doubt this is partly because having contacts helps you succeed with your creations. But another big part is that knowing an inventor not only gives you knowledge about how to succeed in that career path, but even the idea that you could become an inventor in the first place.

Motivation Problem #4: We Don’t Believe We Can Do It

Our sense of self-efficacy is a major driver of our behavior. If we don’t feel like putting in effort will yield results, we’ll quite rationally hold back.

Yet self-efficacy is built through mastery experiences. So if we hold back, we can’t learn. If we can’t learn, we can’t build confidence. If we can’t build confidence, we’ll have low self-efficacy and remain stuck.

Still, I think low self-efficacy drives a lot of motivational challenges. The trick is figuring out how to get out of the loop of being stuck in our beliefs. In general, I’ve found two approaches that have worked for me.


The first approach is to apply a lot more focus and attention than the problem deserves. Basically, convince yourself that you’ll succeed through overwhelming force, and put in way more effort than the problem seems to merit. Then, when you start to experience some traction, it’s easier to shift into a more stable pattern of motivation and effort more commensurate with the value of the goal.


This works, but it has the Catch-22 problem of being difficult to apply when self-efficacy is really low. We need to believe that we can succeed with unreasonable effort, even if modest effort hasn’t been sufficient in the past.

The second approach is to start building from very small successes. Pick goals that seem super easy and doable, succeed at those and build up your confidence. Think of this as a behaviorally-motivated positive spiral: you try to do something that you can definitely do, you do it, and then you try something slightly harder.

This also works, but it has the limit that small successes don’t always build much self-efficacy for really hard problems.

Motivation Problem #5: We Don’t Value the Outcome Enough

Values are tricky. On the one hand, if we don’t value something, it’s rational not to be motivated to pursue it. In this case, we don’t value something enough because our beliefs about the benefits and costs don’t weigh things highly enough for us to focus on it. Failure to be motivated is simply a difference in priorities.

But a rational lack of motivation can still be ignorant.


For instance, if you’re a heavy smoker, but don’t think smoking is all that bad for you, or you think it’s only going to cause problems when you’re too old to make life worth living anyways—so why not have a cigarette?—then lacking the motivation to quit isn’t exactly irrational, but it is ignorant.



I’m not sure there’s a solution here, other than to say it helps to read broadly and stay informed about many topics. Motivated reasoning can lead us to discount information sources that present inconvenient truths, but if we’re sufficiently curious in the broad sense, we’ll probably stabilize on a responsible level of beliefs about the true costs and benefits of certain courses of action.

Motivational Problem #6: We Have an Incentive to Fail

Perverse incentives are a mainstay of pathological psychology stories. Consider a person who “fears success”, so they self-sabotage. Or a person who likes to be taken care of, so they don’t take care of themselves.

I tend to think these stories are exaggerated, and that the Freudian impulse that all behavior is really a repressed motive is not nearly as common as people may think.

However, perverse incentives can explain a lot of problems that we may be quick to blame on “motivation.” For instance, if you work a job where the only reward for working hard and getting your work done quickly is to be assigned greater tasks and responsibilities, then it’s unsurprising that the natural tendency is to throttle your effort. Or, if saving a lot of money means spendthrift friends and relatives needle you for a loan, it’s unsurprising if you aren’t motivated to save some of your cash for a rainy day.

Motivation Problem #7: It’s Simply Not a Priority

In isolation, of course we should exercise. And maintain our relationships. And keep a clean house. And eat healthy. And journal. And meditate. And focus on our hobbies. And also our careers. And community service.

In short, many problems of motivation are not “failures” of willpower. They’re simply rational decisions to allocate our limited time, attention and effort in places that matter more.

Time, of course, is fixed. We can only ever spend 24 hours in a day. Thus, anything that has a fixed time commitment must necessarily crowd out other opportunities for that same slice of time.

Energy and attention are more malleable. But they’re still limited. This is one reason why habits can be effective. If a behavior requires the same time, but less attention or effort, it may be easier to “afford” compared to a new behavior which requires both time AND effort.

Still, in a lot of cases, our self-described motivational failings aren’t failings at all. They’re simply a rational prioritization of something more important.

What Do These Causes Have in Common?

The common denominator of all these causes is that they’re breakdowns of an otherwise-rational system that weighs costs and benefits in making motivational choices.

 We’re:

• Overweighting the present compared to the future. (Cause #1)
• Exaggerating the expected costs and downside. (Cause #2)
• Underrating our long-term ability to succeed. (Cause #4)
• Underrating the long-term value of success. (Cause #5)
• Faced with hidden costs. (Cause #6)


Only #3, which has to do with our knowledge of the problem itself, and thus the precondition to be motivated in the first place, properly lies outside of this kind of calculus. And, of course, #7 isn’t a problem at all, rather it’s a rational consequence of our having finite time, energy and attention.

None of these mean that problems of motivation are easy to solve. But they do suggest some diagnostic starting points if you feel you ought to have more motivation than you do towards a particular course of action. Tilt the equation in your favor, and you can motivate yourself to do anything you choose.

The post How to Motivate Yourself to Do Anything appeared first on Scott H Young.

Yet, the evidence is pretty clear that most people don’t exercise. Nearly half claim not even to meet the standard guidelines, and there’s good evidence that most who claim to meet the guidelines actually do not.

I say this not to scold, but to sympathize. Prior to a few years ago, I was also someone who probably thought I was getting enough exercise, but I actually wasn’t. I was busy, and I wasn’t motivated. Exercise was always on my “should-do” list, but not always achieved in practice.

Today, I feel like exercising nearly every day is relatively easy. Not automatic, I never find myself jogging spontaneously, but easy in the sense that I don’t find it harder to stick to in my schedule than other daily chores like cooking meals or cleaning up the kitchen.

And, perhaps predictably, the benefits of that shift were both substantial and as-advertised. I do have more energy, less stress and better sleep. I even lost some weight.

Given all that, I’d like to share some of what I think made the shift possible for me. This isn’t to preach or indulge in self-satisfaction, but simply to try to understand what factors might have held me back in the past on the off chance they’re holding you back too.

Behavior, Then Beliefs

Interestingly, if I had to weigh their relative importance, I think changes to my beliefs made exercise stick more than changes to my habits did.

But even if mindset matters more than habits, if I look at my own case, the behavior changes came first.

I started out my recent exercise habits with a fairly rigid early-morning exercise routine, though I have largely defaulted back to afternoon or evening workouts, as was most convenient in the past. The difference now is that I actually do my workouts pretty much every day rather than just a couple times a week (at best).

One of my biggest attitude shifts was simply changing the belief of how much exercise I thought I ought to be getting. I exercise more now, in large part, because I believe I should be exercising a lot more. But I doubt I could have adopted that belief when I was getting far less exercise—it just would have made me feel guilty, and I would have rationalized it away.

For any change to stick, I think you need to change your mindset. But the mindset changes you need are generally only possible once you already have seen yourself succeed behaviorally.

Behavioral Strategies to Get Started Exercising

The basic recipe I followed for exercising regularly was simple:

1. Make exercising regularly a committed priority for at least a month (preferably 2 to 3 months).
2. Aim to get some exercise every day (not just X days per week).
3. Start easier than seems necessary, and ramp up the intensity slowly.

The first part is simple: you can stick to almost any behavior if you focus on it. Prioritizing exercise for a month is probably the minimum, but making exercise a priority for the next three months is better if you’ve struggled with it in the past.

Contrary to a lot of popular wisdom, simply repeating a behavior for a month (or three) doesn’t make it automatic—but it does make it feel more normal.

The rationale for daily exercise is simply that inconsistent schedules lead to constant decision making. Every day, you are asking: should I exercise today? If the answer is always “yes” then there’s nothing to think about. Whereas if you give yourself the opportunity to skip days without a good reason, you’ll end up skipping workouts much more often.

Daily exercise forces problem solving. If you have to fit exercise in every day, it forces you to be a lot more creative about scheduling in a busy life. Maybe you can’t always do an hour-long workout at the gym, but you can fit in twenty minutes.

Finally, the slow ramp up is critical. Any change in how much or what you do to exercise is likely to leave you feeling sore or physically tired. If you really overdo it, your efforts can result in injury. By keeping early workouts well below what you feel capable of, you can prevent those physical symptoms from becoming obstacles to making the behavior stick.

Part of the reason exercise can be a hard habit to create is that exercising feels awful when you’re out of shape. This is doubly true for more intense exercise. A lot of people know they should exercise, but starting with too much intensity means they find the whole thing so unpleasant that they can’t stick with it.

Ultimately, getting in better shape tends to make the act of exercise more intrinsically rewarding. It feels good to run when your chest isn’t pounding during a slow jog. It feels good to lift heavy weights and not have agonizing muscle pain. Some of getting over a dislike of exercise seems to be a matter of overcoming that initial change to your fitness levels.

Mindset Shifts That Mattered for Me

As mentioned previously, I think the habit-changing protocol is fairly straightforward, but that’s not what leads to long-term stickiness. My own habits have changed multiple times since I started exercising every day, and I suspect they’ll turn over again in the years to come. Life is simply too chaotic to expect the exact same routine to work perpetually.

Belief changes, in contrast, can be more durable. Here were some of the biggest mindset shifts that have helped me stick with exercise:

1. Exercise is about health and mind, not about looking good. You should want to exercise (a lot) even if it never results in losing a single pound of fat or looking any better in a bathing suit. Physical appearance is a poor motivator, and emphasizing it crowds out the actually-good reasons to stay fit.
2. Cardio matters; lifting weights is not enough. This may be a gendered thing, but, like a lot of guys, I used to focus on lifting weights and treated cardio as an afterthought. Strength training matters, but for different reasons. If you want to get the full benefits of exercise, you need to get your heart pumping.
3. The goal is to plateau, not progress indefinitely. I used to get motivated when I’d see new results. The problem was that mindset led to feeling demotivated when I was plateauing. Now I try to view exercise more like bathing or tidying, it’s not an aim to ever-higher perfection, but a maintenance activity that is successful even if it just keeps you at the same place.
4. Focus on the minimum, and the average will follow. Another trap I would fall into was an obsession with workout quality. I’d make overly complicated regimens to follow and fail to stick with them. However, when I focus on hitting a minimum target, the overall quality of my workouts tends to improve with fairly little added effort.
5. It is both desirable and achievable to exercise a lot more. Perhaps the biggest mindset shift was simply the amount of exercise in my routine I considered sufficient. As mentioned previously, I used to be a fairly regular 3-4x per week gym goer, with the caveat that 3-4x per week often ended up being 1-2x, or even zero during busy or stressful weeks. Shifting to the expectation that I should exercise every day, and often for more than thirty minutes, has made exercise a consistent part of my schedule. At the same time, I don’t think this belief could have changed at all had I not first established the habits that made this new baseline feel achievable.

What worked for me may not work for you. Indeed, it’s totally plausible to come away from reading this essay with a reinforced belief of why this wouldn’t work for you; you’re probably quite different from me, and you are dealing with different challenges and obstacles.

But I do think that, however you make it work, exercise is one of the highest value-added habits most of us have available. So finding a way to motivate yourself to do so regularly is one of the better investments you can make.

The post How to Motivate Yourself to Exercise Regularly appeared first on Scott H Young.

This blog followed the formative years of my life. I wrote my first post when I was 17, still a senior in high school. When I started, I was closer in age to my children than to my current age.

The internet today is unrecognizable compared to the place I started writing two decades ago. In 2006, YouTube was less than a year old, Facebook was still limited to college students, Netflix sent DVDs in the mail, and Instagram, Twitter and TikTok didn’t exist yet.

The internet was a smaller, weirder place back then. Sharing writing online was a hobby for nerds like me who could set up their own website—normal people read magazines and newspapers. Today, nearly everything people watch and read is online.

The personalities that were attracted to blogging were also different. The mostly text-based media meant early blogging afforded quasi-anonymity. Despite using my real name, I didn’t tell many people I knew in real life I was blogging. Writing online allowed me to connect with people over shared interests, even when those interests didn’t always overlap with my real-life peers’.

Today, the idea of going into writing online as way of expressing yourself quasi-anonymously feels quaint. With the dominance of video and viral media, achieving celebrity is seen as the end goal rather than an unwanted side-effect of trying to share your perspective.

This blog feels like a bit of an anachronism. Here I am, still writing blog posts on a personal website like it’s 2006. I’m lucky that I got to build an audience during those early days, and that I can continue to write here despite my idiosyncrasies. It’s hard to say if I had been born twenty years later if I would have had such an opportunity.

On Changing My Mind

Precocity cuts both ways. My early arrival to the internet likely made my current career possible. But it also meant that I made a lot of early mistakes I probably wouldn’t have made had I began today.

Re-reading old essays sometimes makes me cringe. Watching old videos even more so.

 At some point, my essays stopped sucking by my own standards (admittedly, a low bar), and I no longer feel embarrassed by my writing. But, given that I started writing as a teenager, all my old writing (and dorm-room videos) are still there for all to see.

Intellectual consistency is another victim of starting so early. I’ve flip-flopped on many ideas. Sometimes more than once. So much so that it’s become a bit of a joke among my team members that my most popular essays begin with “I was wrong about __.”

These varying perspectives, taken over twenty years, mean I sometimes end up getting in disagreements with myself or with readers preferring a stance I took years ago. While I’d like to think my writing and ideas have gotten better, I’ve also changed as a person. The perspective at 17 and at 37 necessarily differs in ways that can’t simply be accounted for as accumulating wisdom.

I expect twenty years from now my perspective will be quite different again. Assuming, of course, I’m still lucky enough to spend so much of my time writing.

On Giving Advice

Writing online, of course, is a pretty good gig if you can get it.

 I have, for two decades, had my dream job. Being able to make stuff, share it online, and earn enough to survive was my dream at 17. It still is, although by now I’ve gotten so used to it that it’s easy to take for granted.

I have a lot of sympathy for people who email me asking how they might be able to do what I do. But I’m not certain I’m well situated to give good advice.

The metamorphosis of the internet means the path I took to get here literally doesn’t exist anymore. Back then, a nobody with a personal website could rank for top search engine terms, webcam footage could go viral on YouTube, and the hot thing for gaining subscribers was something called a “blog carnival.” (Seriously.)

If anyone reading this eventually does find success creating things online, it will certainly be through a very different route than the one I took.

While I can’t really give any help to someone who wants to replicate my job, I can hopefully offer some suggestions for keeping it. After twenty years, I think my only advice is to write the kind of stuff you like to read. That way, you’ll know you’ll reach an audience of at least one person.

The post Reflections on Writing for 20 Years appeared first on Scott H Young.

On Monday, I’ll be opening registration for my new course, Everyday Energy. The course is a three-month program designed to help you build practices into your life that promote greater vitality—and productivity.

In case you missed it, I’ve written a brief essay series discussing our human energy crisis, the biological roots of our exhaustion and the need to return to natural rhythms of effort and rest. Today, I’m going to examine the meanings we ascribe to work, and why our society’s dominant paradigm so often leads to burnout.

Burnout is not just being tired. If it was, a good night’s sleep would be enough to fix our energy. Instead, burnout is what happens when exhaustion becomes entrenched.

The research into the causes of burnout is complex and fascinating. To put it simply, burnout happens when:

• First, we’re overworked. Demands exceed our ability to cope or recover. Fatigue results.
• This leads to feeling a lack of control and an inability to keep up, which results in our feeling less competent. We feel helpless and impotent.
• Finally, our feelings of exhaustion and helplessness give way to increased cynicism about the work itself. Work is not simply too much and too hard, it feels ultimately meaningless.

These three components of burnout—exhaustion, cynicism and inefficacy—explain why going on vacation doesn’t fix burnout. We may be able to fix momentary fatigue, but by the time burnout has set in, our beliefs about the work itself and our ability to cope with it have soured.



Fixing burnout isn’t easy. The hardened feelings of cynicism and inefficacy need to be replaced with renewed feelings of competence and meaningfulness of our striving. Sometimes that can be fixed within an existing role, but often it requires finding a new workplace, job or even career path.

The persistence of burnout explains why energy management is so important. We want to stop the vicious cycle of exhaustion before it settles into hard-to-adjust beliefs of inefficacy or cynicism.

Avoiding burnout isn’t the only reason to care about energy management. There’s a state opposite to burnout that’s worth cultivating: flourishing. Just as burnout can become entrenched, flourishing can create enduring resilience and offer a wellspring of energy.

Flourishing: The Opposite of Burnout

If burnout is caused by a combination of fatigue without recovery, feelings of inefficacy, and increased cynicism about the meaning of work, flourishing is the opposite. When you’re flourishing at work, you:

1. Have a healthy balance between effort and recovery.
2. Feel confident about your abilities.
3. Feel stable and secure about the meaning of your work.

When these three conditions are met, the effect is resilience. You have more capacity to dig deep in moments of crisis, and you can take on much bigger efforts without feeling overwhelmed.



Flourishing also neatly illustrates that the opposite of burnout isn’t being relaxed, sprawled out on a beach somewhere. While fixing the state of physical exhaustion is often a priority when we’re overwhelmed, the fantasy of fleeing from all work—whether that’s counting the days until retirement, buying lottery tickets or escaping in video games—is a means of coping, not thriving.

Flourishing isn’t a compromise. It’s not a protective stance to prevent us becoming burned out, but one that embraces challenges and strivings in a way that brings more vitality to our lives.

A Flattening of Meaning

I’ve previously explored the biological roots of fatigue and how our culture of work helps to promote burnout. When we undermine our sleep, diet and exercise, fail to manage stress and work outside of natural rhythms, we create conditions ripe for exhaustion to creep in.

But our philosophy of work exacerbates these problems. Our culture sends us conflicting messages on the meaning we’re supposed to derive from work.

On the one hand, we’re supposed to follow our passions, find our voice and have an impact. Work, in this light, isn’t simply things that need to be done, rather it becomes a transcendent mission. That’s a noble vision, but it often conflicts with the mundane reality of our actual jobs. By requiring meaning in our work to reach some ecstatic ideal, we often pass over the actually existing meaning in our everyday tasks.

On the other hand, we’re taught to maximize economic value, to see work as a means to an end—namely leisure. In this view, work is the price you pay to do the things that really make life valuable.

In an interesting essay, the philosopher Agnes Callard writes about teaching a class on Aristotle’s views on work and leisure. She comments that what the students are doing now, pursuing the life of the mind, was true leisure in Aristotle’s sense. But, for most of the students in her class, attendance is more like “work”—a thing they need to do to get good jobs and live in society.

Both of these visions make it harder for us to find genuine meaning in our work. The transcendent calling ignores the mundane and leaves us longing for a kind of work that doesn’t exist. The transactionalist attitude negates the possibility of meaning in work, with everybody working for the weekend.

What’s needed is a philosophy of work that grounds work in a stable appreciation of its value. Ultimately, the value of our work, and the meanings that will sustain a life of flourishing, are not limited to a few rare professions. It is something available to us all, provided we are able to see it.

If you’ve enjoyed this lesson series and would like to go deeper, I’m opening registration for Everyday Energy on Monday. The course will dive deeper into the biology, psychology and philosophy of energy, and help you cultivate enduring practices for a flourishing life. I hope to see you there!

The post Lesson Four: The Opposite of Burnout appeared first on Scott H Young.

Registration for my new course, Everyday Energy opens next week. In anticipation, I’ve written an essay series covering the central philosophy of the course. If you’re just joining us, check out the first essay on why we’re living through a human energy crisis, and an essay on the biological roots of our exhaustion, which we cover in more depth in the first month of the course.

Nature works in rhythms. Machines work non-stop. Many of our psychological difficulties with work—procrastination, burnout, strain and exhaustion—reflect a misguided attempt to use the logic of machines instead of the logic of nature in guiding human effort.

It wasn’t always this way. Early human existence was rarely easy, but it did follow the logic of nature’s rhythms.

Anthropological research into hunter-gatherer communities, often used as a stand-in for our Paleolithic ancestors, shows that they work much harder than we do, physically. The Hadza in southern Africa, for instance, engage in a little more than two hours of moderate-to-vigorous physical activity per day. That’s roughly 4x the standard recommendation for health, and as much as 14x the amount we typically get in Western countries.



But while this lifestyle is laborious, it isn’t unceasing. Other researchers, looking at a !Kung tribe found that they only “worked” an average of two and a half days per week. Even the most industrious member studied, who went hunting over half of the days researchers recorded, put in a little less than 32 hours per week.

The invention of farming, and our exit from the proverbial Eden of our Paleolithic ancestors, left us relatively poorer, with worse diets, shortened stature and new diseases.

But despite the poorer material conditions, we still worked within natural rhythms. Days began at dawn and ended at sundown, often with a rest during midday. Effort was seasonal, with intense periods during harvest and lighter efforts in winter. In as much as a third of the year, work was restricted due to feast days, festivals and religious observations.

The Invention of Clock Time

This changed with the invention of clock time. Before clocks, our understanding of time was intrinsically tied to nature’s rhythms. Even the length of an hour could vary, depending on the amount of daylight in each season.

With clocks came a new understanding of duration. Instead of flexible rhythms, time now had a fixed, unvarying duration, untethered from the natural world. With clock time came new possibilities to regulate labor and demand more machine-like adherence to a schedule.

Clock time’s dominance became complete during the Industrial Revolution. Workers put in 12–16 hour days, with few breaks and no vacations. In the course of a year, a medieval peasant might have worked 1200 to 1800 hours. In contrast, an early factory worker might have put in over 3000 hours.

Today, few of us have the same grueling schedule as an early Industrial-era factory worker. But while we may have gained perks in the form of better pay, free coffee and comfortable chairs, we have only become increasingly alienated from natural rhythms of work and rest.

Smartphones and email mean that work doesn’t end when we leave the office. Work projects and meetings spill into evenings and weekends. Deadlines and performance reviews leave us wary of taking too many days of vacation.

A Return to Rhythm

The way we work is unhealthy and unnatural. By replacing our prior rhythms with clock time, we’ve severed the traditional cycles of work and recovery. As a result, we feel squeezed between procrastination and frenzy, exhaustion and apathy.

The solution is a return to rhythms. Not simply working less (although, for many of us, it would be an improvement) but switching from an unceasing pace to a work routine characterized by periods of effort followed by recovery.

Unfortunately, any would-be reformer of our current system runs into two problems.

The first is that, despite its unsuitability as a model for human work, the machine-logic of unceasing effort is embedded in our economy. While I think we would be healthier and happier if we had working rhythms tied to nature, as our ancestors did, I don’t long for a return to the days before antibiotics, indoor plumbing and refrigerators.

Is our machine-like approach to work simply a necessity to maintain our modern standard of living?

I believe not. There is considerable cultural variation in our approach to work, from the extreme workaholism of white-collar Japan, to the leisurely lunch hours taken in France. Despite this, it’s the French, not the Japanese, who have higher labor productivity.

This suggests to me that the way we work is a product of culture, rather than a natural result of some path to ever-increasing optimization. Office workers in Japan put in heroic hours because it is expected of them, not because it maximizes useful work. We do what is “normal”, even if what is “normal” is deeply unnatural.

This leads us to our second problem: if the ways we work are part of our culture, how can an individual buck the trend? How can we have healthy cycles of work and rest when surrounded by a culture that operates on the machine metaphor?

This is a tricky problem, but not an insurmountable one. It goes without saying that some compromise is necessary. We always flow, in part, to the rhythms dictated by the broader society. Depending on our position, that may be a gently nudging tide or a rushing rapid.

Yet we’re not entirely at the mercy of our environment. Compared to our factory-working forebears, most knowledge workers have considerable autonomy in many dimensions of our work. We can create healthier rhythms, even as the broader work culture persists in its ceaseless flow.

In my upcoming course, Everyday Energy, we will spend a month working on exactly this problem: how do you carve out natural cycles of rest and recovery within the constraints imposed by your job? We may not be able to return to the past, but we can return to a rhythm of life that is humane and sustainable.

In the next essay, I’ll talk about how the meaning of work has flattened, and the problem of burnout and disillusion that this entails. After that, I’ll open registration for the full three-month course, Everyday Energy. I hope you’ll join me!

The post Lesson Three: Rhythmic Productivity—A More Humane Way to Work appeared first on Scott H Young.