The Society of the Statistic: Where Does Science Fit?

One of my favorite films is Dead Poets Society. It’s a beautiful story of the fight for the human experience in a system that treats people as a means to an end. 

It’s also an honest story. Such a fight isn’t easy (the system, after all, is where much of the power rests), and it necessarily comes with real human costs. In DPS, these costs are illustrated by the suicide of Neil Perry, a boy with a talent and love for acting but whose father would have nothing but his becoming a doctor. (As for why Neil’s father has such a simplistic metric of success for his child, it’s related, presumably, to economic status or security. The Perry family is not as wealthy as the other families that send their children to Welton, the prestigious private school where DPS is set.) 

Overall, though, DPS is an optimistic story. In the end, many students have learned to think for themselves and value themselves, and the system has lost some of its sway. This is shown in the final scene, where a majority of the students stand on their desks in homage to Keating, the teacher, and in defiance of Mr. Nolan, the headmaster.

Mr. Nolan, in this story, is the embodiment of the system: serious, austere, unempathetic. And his opening speech to a crowd of students (all of whom are white males, by the way) and parents shows how such a system uses statistics and metrics as its guiding light. It’s the very opening lines of the film:

In her first year, Welton Academy graduated five students. Last year we graduated fifty-one. And more than seventy-five percent of those went on to the Ivy League!

Welton may teach some valuable skills. But ultimately, the school and Mr. Nolan are guided by that Ivy League percentage, that one number. It doesn’t matter if its practices are empowering to the students as individuals or not. This Ivy League percentage is what the parents, the customers of Welton, are looking for.

Keating (played by the late, great Robin Williams), on the other hand, is the embodiment of what it means for people to be an end in themselves, rather than just a means to an end. His guiding light is a meaningful human experience for each student. Though, certainly, what would entail such an experience differs from student to student, and thus Keating’s education is largely an attempt to encourage the students to think for themselves. But he also opens their eyes to the joys of poetry, a human passion largely forgotten by the system (forgotten because, well, there’s not much economic value to poetry). 

One of my favorite scenes of Keating is the following. He’s speaking to his classroom full of students in his usual loud and boisterous manner:

No matter what people tell you, words and ideas can change the world. Now, I see that look in Mr. Pitts’ eye, like 19th century literature has nothing to do with going to business school or medical school. Right? Maybe. 

Mr. Hopkins, you may agree with him, thinking: Yes, we should simply study our Mr. Pritchard and learn our rhyme and metre and go quietly about the business of achieving other ambitions. 

Well, I’ve a little secret for you. Huddle up. Huddle up!

The students gather around Keating, who crouches, and continues in a softer, more intimate voice:

We don’t read and write poetry because it’s cute. We read and write poetry because we are members of the human race. And the human race is filled with passion.

Now medicine, law, business, engineering, these are noble pursuits, and necessary to sustain life. But poetry, beauty, romance, love, these are what we stay alive for.

To quote from Whitman: ‘Oh me! Oh life! of the questions of these recurring, Of the endless trains of the faithless, of cities fill’d with the foolish… What good amid these, O me, O life? Answer. That you here—that life exists and identity, That the powerful play goes on and you may contribute a verse.’

It’s a wonderful scene and a refreshing philosophy. But I do have one thing to add to Keating’s list. I too enjoy poetry, but being the predominantly “left brained” person that I am, I love science even more. And science, at its best and truest, can also be what we stay alive for.

But is there even a need to speak of the value of science? I mean, in the way that Keating speaks of the value of poetry? Science, after all, has much more economic value than poetry, so it’s already valued by our current society, right? 

Well, it would seem so, and it’s often discussed as if it is so, but I’m not so sure. And the reason why stems from the fact that science is not one monolithic entity. There are multiple aspects to it, some of which are valued and some of which are not. For example, Richard Feynman, a true “Keating of Physics,” spoke of these multiple values of science in a speech of his:

The first way science is of value is familiar to everyone. It is that scientific knowledge enables us to do all kinds of things and to make all kinds of things…

Another value of science is the fun called intellectual enjoyment which some people get from reading and learning and thinking about it, and which others get from working in it.

He goes on to describe the intellectual enjoyment aspect further:

The same thrill, the same awe and mystery, come again and again when we look at any problem deeply enough. With more knowledge comes deeper, more wonderful mystery, luring one on to penetrate deeper still. Never concerned that the answer may prove disappointing, but with pleasure and confidence we turn over each new stone to find unimagined strangeness leading on to on to more wonderful questions and mysteries…

Feynman, then, spoke of two values of science in the above quotes. Building on what he said, we can consider three values:

  1. Economic usefulness: the ability to do all kinds of things and make all kinds of things that enable the economy to grow
  2. Individual usefulness: the ability for people to utilize their scientific understanding to do all kinds of things and make all kinds of things (for intrinsic purposes)
  3. Intellectual enjoyment: the experience of fun, awe, mystery we get in the pursuit, discovery, and usage of scientific insight

Looking at it in this way, we can see that what economic forces will tend to incentivize is economic usefulness. They won’t directly incentivize the other two; though, this is not necessarily a problem. If, for example, economic growth could only be obtained by encouraging a significant amount of intellectual enjoyment and the development of a significant amount of individual usefulness, then everyone, scientists and capitalists alike, would be happy. The economy could grow and people could find passion in the pursuit of understanding.

If not, though, the costs are obvious: our scientific society would be like Mr. Nolan.

The Rise of Statistics and the Fall of Science (the Joyful Part)

It’s easy to mistake statistics for science. After all, both are logical and detail-oriented and perhaps performed by the same kinds of logical, detail-oriented people. At the very least, we could imagine that they seem more related to each other than, say, to poetry or music.

But that would be an illusion. If we bring in our separate components of science, economically useful science, individually useful science, and intellectually enjoyable science, I’d claim that, actually, the latter two are more related to poetry and music than they are to statistics. 

Because while poetry and music are certainly more emotionally evocative than science, they do share a most important experiential character with, for example, the intellectually enjoyable science (the science of the idea and of awe and mystery that Feynman spoke of), which is that they’re all holistic and conceptual.

To put it another way: science, like statistics, involves breaking things down, but intellectually enjoyable science involves also putting the things back together. Finding how the things all relate to form a cohesive, meaningful picture.

Statistics/metrics, on the other hand, are one dimensional. They’re broken down, but they’re not meant to come back together, to form a whole. (Or to be more precise, they can be formed together, but only into another single number, a scalar, or as an unstructured collection.) There’s no mechanistic relationships between them because they’re just measurements. Not to disparage measurements of course! Science absolutely depends on measurements! On experimentation and validation. But what I’m speaking of is when statistics transcend their measurement or validator status and become the end itself, the goal that we seek.

This, sadly, happens often. Especially when it comes to economic entities. For example, corporations may aim for a certain growth percentage (e.g. of customers or profit), or investors may aim for a certain ROI. Even for corporations that deal with artistic mediums, such as film studios, metrics tend to be the goal. 

In an ideal world, this could merely be a useful abstraction. The business men and women at the top of an organization could deal with numbers and logistics, while still empowering the inventors or artists below to “do their thing.” But in practice, it doesn’t work this way. The number-fixation trickles down. And the reason, I believe, is that such a system has a fundamental requirement: in order for the top level to guarantee that it can achieve its goal statistic, it needs to be able to convert each component of the lower layer into a statistic as well. Ditto for the layer below, and below. So in any organizational structure, be it a single corporation, or an entire industry, or an entire economy, if the top level goal is a metric, then at some point going down the hierarchy of organization, there needs to be a translation from numbers to people

And such a thing is impossible. You can attempt, futilely, to approximate people and their experiences/concepts with numbers, but you will always lose the holistic human element. That goal statistic at the top will always create an unbridgeable alienation. And, if we look at the history of modern statistics, we see that, actually, alienation is precisely why it was created.

Eugenics, Capitalism, and The Birth of Modern Statistics

Statistics has always had a dirty little secret (one you certainly won’t find in any high school statistics course). The secret is that the “father of modern statistics,” R.A. Fisher, and many other forefathers of statistics, such as Pearson and Galton, were eugenicists, racists. And not just of the “casual,” societal norm kind. Actively so.

Now, I admit, this fact alone doesn’t prove anything about statistics. I mean, as a pure mathematical construct, it is ethically agnostic. Also, it’s easy to imagine that, for example, eugenics and racism were more normalized at the time (late 19th century – early/mid 20th century). And it’s easy too to imagine that Fisher may have just been a kind of “twisted genius” (that the mathematical part of his brain was on full throttle, but he didn’t have a balanced connection to humanity). 

But the relationship between statistics and racism runs deeper. Even the eminent statistician, Andrew Gelman has recently noted this relationship. In a blog post commenting on the article “RA Fisher and the science of hatred” by Richard J. Evans, he says this: 

Fisher’s personal racist attitudes do not make him special, but in retrospect I think it was a mistake for us in statistics for many years to not fully recognize the centrality of racism to statistics, not just a side hustle but the main gig.

It’s clear from Evans’ analysis that, at least for Fisher, eugenics was not merely a “side interest” of his, but crucial to his whole life motivation. Although we can’t be sure how he came to such a worldview, it seems quite possible that his work in statistics and genetics were, to a large extent, motivated to give rational support to his racist beliefs (this assumption coming from the fact that statistics and genetics can be used quite successfully to support such beliefs; of course, in a deluded way that misses the human picture).

But speaking of statistics as a whole, its relationship with racism is a more confusing question. Gelman, for example, has a sense of the deepness of the relationship. We can see, for example, that statistics can be used to support racist views, to speak of averages of different populations and the like. But what exactly is the relationship?

Viewing this through an economic lens, I believe, makes the relationship more clear. 

Consider first the use-cases of statistics/metrics (as goals, rather than measurements):

  1. Processing large quantities of people or things
  2. Organizing people around a common goal (e.g. in lieu of a holistic vision, a common metric is a way to organize people—even large, scattered groups of people—around a common goal)
  3. Removing human/qualitative concerns; reducing them to numerical concerns (e.g. this can be useful for hiding human concerns, because perhaps we don’t want to see them, or numericizing human concerns for the sake of perceived simplification)

Statistics can be used for any of these use-cases, but if you have a “problem” that benefits from all three of these use-cases, then statistics is the perfect tool! 

Though, if you only care about two out of three of these use-cases, there are other, more effective tools. For example, if you are running a government, you absolutely need to handle a large population of people and organize them around some common goals. But that doesn’t imply that we need to remove human concerns or convert them to numerical concerns. Earlier we mentioned how an organizational structure based on statistics requires the conversion of each lower layer into statistics as well. But that’s not the only way to organize and to delegate. For example, a government may delegate by geographic region (in a hierarchical fashion). This is great. It does “partition” people, but partitioning doesn’t inherently remove the human element. The human element is only removed when we convert subgroups of people to a mere collection of numbers or properties.

For R.A. Fisher, though, this third use-case, removing human concerns, was crucial. Because his eugenicist goals involved the dehumanization of a specific subpopulation of people. Similarly, our economic system often has a drive for this third use-case as well, as Marx so clearly illustrated.

But, as we said, metrics are not the only way to delegate and organize. And in the case of novel production or innovation (e.g. science, technology, art), as opposed to commodity production, such a forced metric-fixation is counterproductive. Because it disincentivizes innovation in the holistic human realm. As we can see now, for example, we’re quite effective at “innovating” in the realm of financialization (developing new ways of turning big numbers into even bigger numbers), but it’s questionable how much meaningful human innovation we’re doing.

It is worth asking the question, though: is a tool bound by its original purpose? Modern statistics, which revolves around testing the “significance” of numbers, may have been designed explicitly for alienation. But can it be repurposed for nobler causes? 

Well, in some cases, yes. I mean, a chainsaw can be used for ice sculpting in addition to cutting trees. But a tool that is tailored for a specific purpose will naturally lend itself to similar purposes.

So, in the case of statistics, what are these similar purposes? And, more importantly, how did statistics grow from satisfying a relatively small niche of eugenicists to dominating nearly all realms of science and technology as it does today?

Computers and the Early Rise of Statistics

Computers as a tool were not developed for statistics. Or for alienation. As far as I can tell, there’s nothing inherently alienating about a computer. And I, for one, love programming and believe computing as a medium has unlimited potential for creativity, empowerment, and fun. (For example, the book Masters of Doom shows a brilliant example of how people can combine computational mastery, creativity/art, and courage to produce something wholly new and exciting.)

However, computers, being at their core number-crunchers, lend themselves well to the processing of statistics. And thus, economic entities can easily wield computers in order to wield statistics. And even, perhaps, convince the public that what they’re doing is “science.”

To better understand the role of computers in the rise of statistics (and to understand exactly what these quotation marks that I’m putting around “science” are), let’s consider an example. Specifically, of a statistical model that is well-cited within academia: the Five Factor Model of personality (also known as the Big Five or OCEAN). 

Now, the FFM is interesting, so I don’t want to diss it too hard. But it’s important to note the properties of a statistical model like this, contrasted to a more conceptual or mechanistic theory of personality.

First, let’s discuss the history of the FFM. Its development began in 1884 with the “Lexical Hypothesis” of Francis Galton (one of the forefathers of statistics we mentioned earlier). This hypothesis states that any important traits of personality should be encoded in words (e.g. adjectives) of any language. For example, quiet, friendly, gregarious, visionary, creative, resourceful, etc. This hypothesis was a crucial breakthrough for what would become the FFM because it allows us to perform an interesting kind of alchemy: converting people to a set of numbers (in this case, because converting to a fixed set of words is equivalent to converting to a set of numbers).

People began using the lexical hypothesis to develop personality questionnaires throughout the early 1900s. But a big breakthrough came in the 1950s and 1960s with the culmination of computer technology and statistical methods. Specifically, Raymond Cattell worked with Charles Spearman to develop new methods of factor analysis (a statistical method) and to apply that to personality questionnaire data. Cattell originally got 16 factors of personality. But later, with the work of Costa, McCrae, and others throughout the 70s, 80s, and 90s, they began to perfect the methodology and found that 5 factors were more stable and comprehensible.

So what does this model give us as people and as a society? Well, it gives us a way to convert people to a descriptive set of five numbers and traits, with these five traits being those that most efficiently summarize the full population of people (or people-numbers). Knowing what the five traits are is interesting. But the assumption is that such traits are static, unchangeable, so it’s not very actionable from an individual perspective (except perhaps to “know your place” relative to others). From an economic standpoint, though, this population summarization could still be useful, for example, in better allocating labor. 

Although the theory pertains to personality, its key breakthroughs were in (1) data generation (the alchemy of converting people to numbers) and (2) data analysis. In other words, not in the study of people (psychology), as neither involved actually probing into or investigating people in any kind of deep way. 

Now, again, don’t get me wrong. This doesn’t mean that the breakthroughs are not interesting or actual breakthroughs. The study of data and statistics are in themselves interesting and are themselves fields of inquiry. But importantly, these are breakthroughs in applied statistics, not psychology. And applied statistics is moreso a discipline of engineering than it is of science (as Chomsky has stated many times on this same topic).

Let’s contrast the FFM to a more psychological theory of personality, Carl Jung’s theory of “cognitive functions.” The watered down and bastardized version of this theory, the MBTI, gets a lot of flack (for good reason, since it’s removing the mechanics from a mechanical model to make it more marketable; though, statistically-speaking, the MBTI correlates closely with the FFM). But the original theory, from Jung’s massive tome, Psychological Types, is much more interesting and conceptual. Jung’s theory came from the deep study of people in his personal practice of psychoanalysis, as well as trying to explain differences of mentality among the leaders of the new psychoanalytic movement (e.g. Adler and Freud). For Jung, it started simple, with the concepts of introversion and extraversion (now ubiquitous). But he slowly built up his concepts over time to explain more aspects of how people think (not just categorizing people but explaining their mechanisms of thinking, and observing that the same mechanisms are used interchangeably by different types of people).

Is Jung’s theory “true”? Well, can we even say that Newton’s theory of gravitation is “true”? I mean, given that the “accuracy” of his model was displaced by Einstein’s theory of general relativity? Are the particles of quantum mechanics “real”? What are they exactly?

In other words, it’s difficult to comment on the “truth” of Jung’s theory, but what I can say is that his concepts are, to a good extent, empowering and representative of meaningful aspects of the way people think. Also, there is a beautiful logic to the whole system. It’s largely analogous, in my opinion, to Newton’s theory of gravitation. Newton’s theory is not a statistical theory. He didn’t design it to optimize for predictive accuracy. But his holistic concept of gravity remains as representative and explanatory as ever. And, for example, what Einstein’s theory added to Newton’s picture was not so much better predictive accuracy as it was the wholly new concept of spacetime

Some people claim that non-statistical theories like the ones we discussed are like astrology. But this is a funny analogy considering that astrology is absolutely not a conceptual or mechanistic theory. Astrology claims that things just are a certain way. Why are they that way? I don’t know. They just are. And in this sense of being non-mechanistic, astrology is actually much more similar to statistical models.

I’ve digressed, but coming back to our main points: a good theory should come with predictive accuracy, but predictive accuracy should not be our goal (and predictive accuracy in lieu of a holistic concept is no theory at all, even though it may still have economic usefulness). Also, suffice it to say that even in the early days of computers, computers were already lending themselves to the spread of statistical modeling.

Big Data and Meta-Statistics

Today, we are well beyond the early days of computers. It hasn’t been so many years in the grand scheme of history, but already computers are ubiquitous and have drastically altered the way we live. Among the many effects of this societal shift, one important one is—you guessed it—more widespread statistical modeling, and specifically, more usage of statistics/metrics as goals. The primary things we seek. 

But why exactly has our ubiquity of computers and smartphones led to greater usage of statistics? I can’t speak to the full picture of what has happened, but one crucially important factor is the rise of Big Data, or more precisely, Big User Data.

Over time, we’ve made numerous breakthroughs in the speed and scale of computation, as well as in data storage. And now we have the ability to save and process great swaths of information on how people behave. Furthermore, with the invention of Deep Learning, we have developed powerful methods of what we could call meta-statistics, the ability to take any dataset and create a generic pattern matcher or pattern generator for that data. 

So what do these developments mean? Well, there are many ways that these technologies can be combined for profitable economic purposes. For example, being able to statistically mimic human behavior means that we can automate certain human tasks or functions. (Not to say that the model does it as well as people. Likely not, since it’s mimicry. But if a statistical model can approximate a task well enough for cheaply enough, then it can be used for automation in many cases.) Also, being able to model people’s behavior means being able, to some extent, to direct consumer behavior. A very lucrative skill. 

Not to say that all of our new applications of statistical modeling are manipulative. Certainly not! For example, I love our new voice assistants, which, as far as I can tell, were designed to assist moreso than to automate or control (also, voice assistants are not using statistics at the top level, merely to perform well-defined subtasks, such as converting speech to text and text to speech). Sadly, though, these truly assistive use-cases appear to be less profitable overall in our present economy.

Coming back to the rise of statistics, these profitable use-cases have encouraged corporations to invest in new and better ways of statistical modeling (especially deep learning). And to invest in their development and application across all fields of science, including those fields that are completely unrelated to statistics (i.e. most of them).


I hope it’s clear by now: there is an inherent alienating effect of statistics/metrics. It was part of their design and it continues to lend itself to various forms of imperialism. And this is bad for everyone.

For scientists and technologists, though, there is an additional danger. A more personal danger. When our Keating spirit is destroyed and only Mr. Nolan remains, our very sense of mystery can disappear. I myself have felt this at times, wondering things like have we already discovered most of what there is to discover? Will future generations have anything important left to discover? And I’ve seen others express similar thoughts. 

For example, we may speak of science as a fractal. Or we may speak of low hanging fruit and high hanging fruit, the metaphor of fruit on a tree. These metaphors, however, which are related to finiteness and territory, just come from our identification as an economic agent, our thinking of our own value or success as coming from our contribution to the wider economic system. Capital is finite and territorial, so capitalist science is finite and territorial too. 

But speaking of humanist science, a much more natural metaphor is the one Newton gave centuries ago, towards the end of his life:

I do not know what I may appear to the world, but to myself I seem to have been only like a boy playing on the sea-shore, and diverting myself in now and then finding a smoother pebble or a prettier shell than ordinary, whilst the great ocean of truth lay all undiscovered before me.

We’ve found many more smooth pebbles and pretty shells since Newton’s time, but the ocean remains. And that’s because unlike capitalist science, real human science, the science that we stay alive for, is unbounded.

AI Research: the Corporate Narrative and the Economic Reality

In his widely-circulated essay, The Bitter Lesson, the computer scientist Rich Sutton argued in favor of AI methods that leverage massive amounts of data and compute power, as opposed to human understanding. If you haven’t read it before, I encourage you to do so (it’s short, well-written, and important, representing a linchpin in the corporate narrative around machine learning). But I’ll go ahead and give some excerpts which form the gist of his argument:

The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin.

In computer chess, the methods that defeated the world champion, Kasparov, in 1997, were based on massive, deep search. At the time, this was looked upon with dismay by the majority of computer-chess researchers who had pursued methods that leveraged human understanding of the special structure of chess. When a simpler, search-based approach with special hardware and software proved vastly more effective, these human-knowledge-based chess researchers were not good losers…

A similar pattern of research progress was seen in computer Go… Enormous initial efforts went into avoiding search by taking advantage of human knowledge, or of the special features of the game, but all those efforts proved irrelevant, or worse, once search was applied effectively at scale.

In speech recognition… again, the statistical methods won out over the human-knowledge-based methods… as in the games, researchers always tried to make systems that worked the way the researchers thought their own minds worked—they tried to put that knowledge in their systems—but it proved ultimately counterproductive, and a colossal waste of researcher’s time, when, through Moore’s law, massive computation became available and a means was found to put it to good use.

This is a big lesson. As a field, we still have not thoroughly learned it, as we are continuing to make the same kind of mistakes.

We want AI agents that can discover like we can, not which contain what we have discovered. Building in our discoveries only makes it harder to see how the discovering process can be done.

Now before getting to the many problems with Sutton’s argument, I have a caveat to add, and I think it’s best to add it up front: I am one of the “sore losers” that he mentions. I, like many others, colossally wasted my time making a Go AI that utilized understanding. It felt like fun, like a learning experience, simultaneously exploring the mysteries of Go and programming. But now, I realize it was just a sad squandering of calories and minutes. If only I had devoted myself to amassing a farm of 3,000 TPUs and making a few friends at the local power grid station. If only I had followed the money and cast my pesky curiosity aside, I could’ve—just maybe—gotten to AlphaGo first! Then I wouldn’t be sitting here, in this puddle of envy, writing today. But alas, here I am.

In all seriousness, though, my first criticism of Sutton’s piece is this: it’s too “winnerist.” Everything is framed in terms of winners and losers. A zero sum game. But I question whether that is an accurate and productive view of science, technology, and even of games like Chess and Go.

Let’s start with the games. I’m more familiar with Go, so I’ll speak on that. Although Sutton cites the 70 year history of AI research, Go has existed for thousands of years. And it’s always been valued as an artistic and even spiritual pursuit in addition to its competitive aspect. For example, in ancient China, it was considered one of the four arts along with calligraphy, painting, and instrument-playing. And many Go professionals today feel the same way. The display of AlphaGo marketed Go as an AI challenge, a thing to be won—nay, conquered—rather than a thing to be explored and relished in (and it’s clear the benefits to DeepMind and Google of marketing it in such a way). But historically, that has not been the game’s brand within the Go world. Nor was that even its brand in the Go AI world (which consisted largely of Go nerds who happened to also be programming nerds).

For players, these games are about competition, yes, but also about learning, beauty, and mystery. For corporations, they’re just a means to an end of making a public display of their technology (one completely disconnected, by the way, from the actual profit-making uses of said technology). And for AI researchers, well, that’s up to them to decide. If they’re also players, they will likely value Go or Chess for Go or Chess’s sake. But if not, it makes sense to pursue what is most rewarded in the research community. 

But we don’t have to look far to see that what is rewarded in the ML research community is largely tied to corporate interests. After all, many eminent researchers (such as Sutton himself) are employed by the likes of Google (including DeepMind), Facebook (a.k.a. Meta), Microsoft, OpenAI, etc. Of the three giants of deep learning—Hinton, Bengio, and Lecun—only Bengio is “purely academic.” And even in the academic world, funding/grants largely come from corporations, or are indirectly tied to corporate interests. 

This brings me to a conjecture. It’s related both to winnerism and what has been “most effective” in being rewarded by the ML/AI research community. The conjecture is that both are reflections not of an ideal scientific attitude, merely of the unfettered capitalist society that we find ourselves in.

Are we, as Sutton says, attempting to discover our own “discovery process”? In the hopes of, I don’t know, creating <INSERT FAVORITE SCI-FI AI CHARACTER>? Or are we perhaps discovering new profit processes for the corporations that have the most data and compute power? 

The Economic Reality

Either way, it stands to reason that if we want to judge what is “most effective” as a technology, we should look beyond a small set of famed AI challenges and to its predominant economic use cases. What are these funders actually using machine learning for? Forget the marketing, the CEO-on-a-stage stuff. Where are the dollars and cents actually coming from? 

Well, from what I’ve seen in the industry, the most profitable use-cases appear to fall into two main categories. Unfortunately, neither involve creating my favorite sci-fi AI character. Instead, they involve (1) automation and (2) directing consumer behavior. 

Let’s start with the first: automation. This one should not surprise any historian of business, as the automation of labor is as old as capitalism itself. But what is perhaps unique about software, and especially machine learning, is that we’re no longer limited to the automation of physical labor. We can automate mental labor as well. (Hence the assault on human understanding, which has now become a competitor.)

One example we can see is in the automation of doctors, or certain functions of doctors. I saw an example of this at PathAI, a Boston-based healthcare company. But examples are now widespread in the healthcare industry (and even Google and Amazon have moved into the healthcare industry). The way PathAI used deep learning was actually a principled, humanistic way to use deep learning. Specifically, it was used to segment pathology images, which are huge gigapixel images that no human can fully segment (though doctors do have effective sampling procedures). Once segmented, the images were converted to “human-interpretable features” which could be used by doctors and scientists to aid in their search for new treatments. It did really empower doctors, scientists, and programmers.

But it’s not all peaches and cream. Many applications are not empowering, and there is a lot of manipulative media bias. Like with Go, winnerist marketing has overtaken healthcare. For example, there are many widely publicized studies that compare (in a highly controlled environment) the prediction accuracy of doctors to an ML model in classifying cell types from pathology images. The implication is that if the model performs better, we should prefer having the model to look at our pathology images. Such a limited metric in such a highly controlled environment, though, does not prove much. And more importantly, you will not see corporations trying to quantify and publicize the many things that doctors can do that the machines can’t do. Nor trying to improve the skills of doctors. No. If anything, they want humans to look like bad machines so their actual machines will shine in comparison.

Now, I hope it’s clear from this example that technology is not the problem. I’m not arguing to go back to the golden ages of bloodletting and tobacco smoke enemas. What I am arguing, or simply highlighting, is the fact that corporations do not optimize for empowerment. They optimize for growth, and marketing in any and every way that aids that growth.

Which brings me to our second category: directing consumer behavior. Two clear examples of this are in recommender systems (for media products) and advertisement. Both of which are the lifeblood of ML juggernauts like Google, Facebook, and TikTok.

The idea is that with enough consumer data and smart algorithms, we can build accurate models of real consumers. Then, using those models, we can to some extent direct consumer behavior. To sell more shit. (Sorry, I’m still annoyed by my purchase, through Facebook, of a Casper pillow many years ago.) 

I mean, it’s nothing new to say: these social media products are like cigarettes. Yada yada. How can we make tech that’s not cigarettes? Somehow, though, we brush the issue under the rug most of the time. We convince ourselves that ML advancements are going towards machine translation to communicate with displaced refugees. But honestly, we should be more bothered, especially about products targeted towards young people.

My last tenure at Google (working in recommender systems) was a bit disturbing actually. I guess I was just sheltered in the past. But this experience was striking because I worked there, left for about three years, then came back to the same team. And I could see just how much had changed—for the worse—in Google’s frenzied attempt at neverending growth. 

For one, my team had shifted far away from its old, understanding-centric approaches and towards the black box optimization of primarily engagement and growth-related metrics. This helped with scale, but it alienated us programmers from the product and users, which makes it much harder to develop a product that’s actually useful

Even worse, Google is very worried now about the fact that young people are not using Search as much. And they are trying to “solve” this problem through some very stupid and unethical means. (I mean, if being unethical is your only option and you’re honest about it, then, you know, it is what it is; but in this case the organization was being stupidly and dishonestly unethical, a truly remarkable set of traits.)

The Downsides of Solutions Without Understanding

Anyways, coming back to Sutton’s examples—Chess, Go, and speech recognition—we can see that they’re all constrained domains with well-defined measures of “success.” Winning in the case of Chess and Go, and word prediction accuracy in the case of speech recognition. I did argue that these are not true measures of success from a human perspective. But, to some extent, it works for these examples, the domains that make for good AI challenges.

Most products that are meaningful to people, however, are not as constrained as Chess, Go, and speech recognition. They can’t be boiled down to a single metric or set of metrics that we can optimize towards (even if we were to use solely user-centric and not company-centric metrics). And the attempt to do so as a primary strategy leads to products with less craftsmanship and less humanism. Or worse. 

For one, relying on the model to do the “understanding,” rather than ourselves, disincentivizes us from developing new understanding. Of course, we’re free to pursue understanding in our own time, but it’s less funded and therefore economically disincentivized. We put all our eggs into the model basket and the only path forward is more data, more compute power, and incremental improvements to model capacity. This hinders innovation in any domain that’s not machine learning itself. And, in fact, the corporate media that bolsters such approaches hinders innovation even more because would-be innovators get the impression that this is just how things are done, when in fact, it may merely be in the best interests of those with the most data and compute power. 

This fact is easy to miss by ML/AI researchers because they are pursuing understanding (of how to make the best models). But it can do a grave disservice to people researching other domains if (1) ML is applied in an end-to-end fashion that doesn’t rely on understanding and (2) it’s heavily marketed and publicized in a winnerist way. This flush of marketing and capital shifts the culture of those domains to more of a sport (one that consists of making stronger and stronger programs, like a billionaire BattleBots), rather than a scientific pursuit.

Secondly, as with the quote “one death is a tragedy, a million deaths is a statistic,” the overuse of metrics can actually be a way for corporations to hide the harm they may be doing to users (from their own employees). This is clear with recommender systems and media products for example. It’s much easier to look at a metric that measures youth engagement than to look at some possibly unsavory content that real young people are consuming. If we attempt to solve a user problem, then validate it with metrics, that’s just good science. But when metrics become our “northstar,” then the metrics are necessarily separating us from users.

It’s important to note, by the way, that none of this relies on the people running our corporations being “bad people” (though they may be). It’s just the nature of investor-controlled companies where the investors are focused on ROI. (I mean, speaking of how metrics can separate us from users, consider the effect of the ROI metric, especially when investments are spread across many companies.) When push comes to shove and growth expectations are not met, whatever standards you have are subject to removal. I certainly saw this at Google, which at least from my limited perspective, was more focused on utility in the past. The pressure trickles down the chain of command. Misleading narratives are formed. Information is withheld. The people who don’t like what they see leave. And what you’re left with is an unfettered economic growth machine. The only thing preventing this development is resistance/activism from the inside (employees) and resistance/activism from the outside (which can lead to new regulations).

This is the real “bitter lesson.” As a field, we have most certainly not learned it yet, simply because the corporations that benefit the most from ML control the narrative. But there is, I believe, a “better lesson” too. 

The Better Lesson

Luckily, none of these downsides I’ve mentioned have to do with the technology itself. Machine learning and deep learning are incredibly useful and have beautiful theories behind them. And programming in general is an elegant and creative craft, clearly with great transformative potential.

Technology, though, ML included, always exists for a purpose. It always has to be targeted towards a goal. As Richard Feynman put it in his speech on The Value of Science, science and technology provide the keys to both the gates of heaven and the gates of hell, but not clear instructions on which gate is which. So it’s up to us, not as scientists and technologists but as human beings, to figure that out. And to spread the word when we do.

The Death of Mystery is an Illusion

There is an existential worry I’ve had. I don’t think of it often, but it creeps up from time to time and never fully goes away. It appears in many different forms, such as the following:

  • Have we already discovered the fundamentals of science? Is there much less left to discover than has already been discovered?
  • Given that we’ve already globalized the world, is our sense of awe or mystery about the world permanently gone or diminished?
  • In the near future, will scientists or technologists even be able to make new breakthroughs? Will everything worth discovering already be discovered? Will they be left, at best, to merely explore highly specialized niches?

I call this existential worry the death of mystery. In general, I don’t worry much about the first two bullets above, but I tend to feel the third bullet more: even if mystery is not dead yet, it often feels to me that it is dying.

But today, I had an epiphany that clarified this fear of mine and gave me strong reason to believe that the fear is much more of an illusion than I originally thought.

I got this epiphany while reading The Myth of Artificial Intelligence by Erik J. Larson. To give some context, the thesis of the book is that the vision of AI—portrayed, for example, by Ray Kurzweil, Elon Musk, and Nick Bostrom—as a form of superintelligence that is imminent given our current technology, is in fact a myth and a myth that is often pushed with ulterior motives, such as to grow the AI bubble through fear-mongering, or more generally to profit from fear-mongering in some way. In reality, Larson claims, how to create a superintelligence is a complete scientific unknown. Our current approaches to AI, while useful computational methods, in no way indicate that we are heading towards anything resembling a so-called superintelligence. Based on my experience in the field, I must say that I completely agree with Larson.

But let’s return to the subject of this post before we stray too far… This is the passage that triggered my epiphany:

Mythology about AI is bad, then, because it covers up a scientific mystery in endless talk of ongoing progress. The myth props up belief in inevitable success, but genuine respect for science should bring us back to the drawing board. This brings us to the second subject of these pages: the cultural consequences of the myth. Pursuing the myth is not a good way to follow “the smart money,” or even a neutral stance. It is bad for science, and it is bad for us. Why? One reason is that we are unlikely to get innovation if we ignore a core mystery rather than face up to it. A healthy culture for innovation emphasizes exploring unknowns, not hyping extensions of existing methods—especially when these methods have been shown to be inadequate to take us much further. Mythology about inevitable success in AI tends to extinguish the very culture of innovation necessary for real progress—with or without human-level AI. The myth also encourages resignation to the creep of machine-land, where genuine invention is sidelined in favor of futuristic talk advocating current approaches, often from entrenched interests.

My god, what an insightful passage… We can see that this myth about AI is a specific case of the death of mystery. And in this case, it’s not a real death at all! It’s a hoax. A faked death. Entrenched interests are pushing this hoax because they benefit from it. And it’s easy to see how they could benefit: for one, positive speculation about technology means higher valuations of technology companies.

Similarly, the general death of mystery is not a real death either. It’s often a media fabrication, or simply the result of the current cultural attitude, which is constantly changing. I wonder if people in past generations—even hundreds of years ago—ever felt that mystery was dead; I suspect some did.

The simple fact is that if you fixate on what has already been discovered and consider it the end-all-be-all, you will feel that mystery is dead, and if you fixate on the obvious mysteries in front of your face, you will feel that mystery is everywhere. It’s as simple as that. So let’s stop reading or listening to “thought leaders,” and let’s focus on those obvious mysteries in front of our faces.

Lessons from Reading 10,000 AngelList Applications

One of the first people I interviewed off of AngelList was a man from Lagos. He had created a website that displayed Manchester United scores, and it was beautiful. The layout was clean. The colors were bright. It even had elegant animations when you clicked or hovered. Also, by checking the Github commit log, I could see (with some degree of certainty) that he was the one that had actually made the website. I thought to myself, “this guy must be a talented designer and love Manchester United.”

Three days later, though, I saw someone else (this time from the US) who had also created a Manchester United website. In fact, they had created the exact same Manchester United website. Right down to the pixel.

How could this have happened?

Well, it’s possible that they both copied the code, or that one of them copied the code from the other. But I doubt it. After all, their Github commit logs differed and demonstrated their separate processes for building the website. No, I think what they did was something much worse: they each spent a dozen hours building the exact same thing when they could’ve just as easily spent a dozen hours building something new.

Clark: …I will have a degree, and you’ll be serving my kids fries at a drive-thru on our way to a skiing trip.

Will: [smiles] Yeah, maybe. But at least I won’t be unoriginal.


The beauty of software is that you can create anything. Software may often be limited to the virtual world of “bits” rather than “atoms”, but within that world, the sky is the limit. Games, art, scripts to automate chores and tasks, money-making schemes, AI. It can all be created with code. And yet, 4 out of 5 portfolios I see are made up entirely of copied templates.

The first thing I look for now is the intrinsic motivation to build things that you actually want to build. I’d much rather see an applicant create something ugly but novel than something beautiful but replicated. I’d much rather see a video game than a covid tracker, even if the covid tracker was not based on a template.

I saw a surprising stat recently: in the 2020-2021 school year at MIT, out of all declared majors, 43% were computer science or had “computer science” somewhere in the name. Almost half. For comparison, physics made up 5%, architecture 1%, and philosophy a measly 0.3%.

In the talent war for atoms vs bits, it seems that bits is winning, at least at MIT. Heck, even their School of Humanities has a computer science major.

At other technical universities, the percentage is smaller. But that may be largely due to rules in place to hold back the flood of wannabe CS students. For example, at Berkeley, even if you’re admitted as a computer science student, get a few too many Bs in your freshman year (God forbid a C) and you’re out.

Given the rapid rise in computer science students and software job applicants around the world, a lot of truly talented and motivated young people have told me that they worry that the field will become too saturated in 10 years and they’ll be left without a job. Will it? My take is that once you “break in,” say by getting a job in big tech or at a prestigious startup, you’re in for good, but that it will become harder and harder to break in.

That’s how, for example, universities work, and everything else in the world works. So the same should be true for software engineering jobs (and already is true for that matter).

That said, I do believe those truly talented and motivated individuals will continue to be able to break in. Being concerned about saturation, though, is a smart worry. Because you don’t want let yourself become part of the saturation. Don’t just hop on the machine learning or crypto hype train because there’s hype. Build what you want to build, and learn what you want to learn.

The number one most important thing for any application—not just software job applications—is standing out. And more importantly, getting inside the mind of your reviewer to know what will stand out.

When I interviewed at Google several years ago on the Pittsburgh campus, I had only one “behavioral interview” (the other four were programming challenges without much chit-chat). The interviewer was with a thin, soft-spoken guy with a goatee, and he came in to the conference room holding my resume. He said, “I looked at your resume and there’s one thing that stands out: you played football. Have you learned anything from playing football that you think will help you in a software engineering role?”

Ironically, just a few days prior, I debated whether to remove the “varsity football” line from my resume. I had only played for one season and had not actually played in the games. Also, I knew that joining the football team had helped me get into the CS program at CMU, and I didn’t want interviewers to suspect that I wasn’t as smart as the other students. Thankfully, though, I ignored those worries and left the line in. And now I realize that my worries were completely stupid!

I mean, Google gets a million smart applicants from top programs every year. As a former Google interviewer, I can say that if I saw “Stanford” or “MIT” on your resume, yes I’d notice it, but I would never say “wow” based on that detail alone; I had seen too many other resumes from the same schools. Same goes for seeing a 3.9 GPA (in fact, if I see too high of a GPA, like a 4.0, I tend to make slightly negative assumptions, but that’s a topic for another post). Quality trumps quantity almost always.

Of course, what stands out completely depends on the mindset of the interviewer and differs drastically from application to application. Applying to one undergrad institution requires something different from applying to another undergrad institution, same for MBA programs, PhD programs, jobs in software, jobs in consulting, you name it. But standing out and having the telepathy to know what will stand out is key.