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   [20]The New Yorker

   [21]Personal History

A Coder Considers the Waning Days of the Craft

   Coding has always felt to me like an endlessly deep and rich domain.
   Now I find myself wanting to write a eulogy for it.

   By [22]James Somers
   November 13, 2023
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   Artificial intelligence still can’t beat a human when it comes to
   programming. But it’s only a matter of time.Illustration by Dev
   Valladares

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   I have always taken it for granted that, just as my parents made sure
   that I could read and write, I would make sure that my kids could
   program computers. It is among the newer arts but also among the most
   essential, and ever more so by the day, encompassing everything from
   filmmaking to physics. Fluency with code would round out my children’s
   literacy—and keep them employable. But as I write this my wife is
   pregnant with our first child, due in about three weeks. I code
   professionally, but, by the time that child can type, coding as a
   valuable skill might have faded from the world.

   I first began to believe this on a Friday morning this past summer,
   while working on a small hobby project. A few months back, my friend
   Ben and I had resolved to create a Times-style crossword puzzle
   entirely by computer. In 2018, we’d made a Saturday puzzle with the
   help of software and were surprised by how little we contributed—just
   applying our taste here and there. Now we would attempt to build a
   crossword-making program that didn’t require a human touch.

   When we’ve taken on projects like this in the past, they’ve had both a
   hardware component and a software component, with Ben’s strengths
   running toward the former. We once made a neon sign that would glow
   when the subway was approaching the stop near our apartments. Ben bent
   the glass and wired up the transformer’s circuit board. I wrote code to
   process the transit data. Ben has some professional coding experience
   of his own, but it was brief, shallow, and now about twenty years out
   of date; the serious coding was left to me. For the new crossword
   project, though, Ben had introduced a third party. He’d signed up for a
   ChatGPT Plus subscription and was using GPT-4 as a coding assistant.
   [23]More on A.I.

   [24]Sign up for The New Yorker’s weekly Science & Technology
   newsletter.

   Something strange started happening. Ben and I would talk about a bit
   of software we wanted for the project. Then, a shockingly short time
   later, Ben would deliver it himself. At one point, we wanted a command
   that would print a hundred random lines from a dictionary file. I
   thought about the problem for a few minutes, and, when thinking failed,
   tried Googling. I made some false starts using what I could gather, and
   while I did my thing—programming—Ben told GPT-4 what he wanted and got
   code that ran perfectly.

   Fine: commands like those are notoriously fussy, and everybody looks
   them up anyway. It’s not real programming. A few days later, Ben talked
   about how it would be nice to have an iPhone app to rate words from the
   dictionary. But he had no idea what a pain it is to make an iPhone app.
   I’d tried a few times and never got beyond something that half worked.
   I found Apple’s programming environment forbidding. You had to learn
   not just a new language but a new program for editing and running code;
   you had to learn a zoo of “U.I. components” and all the complicated
   ways of stitching them together; and, finally, you had to figure out
   how to package the app. The mountain of new things to learn never
   seemed worth it. The next morning, I woke up to an app in my in-box
   that did exactly what Ben had said he wanted. It worked perfectly, and
   even had a cute design. Ben said that he’d made it in a few hours.
   GPT-4 had done most of the heavy lifting.

   By now, most people have had experiences with A.I. Not everyone has
   been impressed. Ben recently said, “I didn’t start really respecting it
   until I started having it write code for me.” I suspect that
   non-programmers who are skeptical by nature, and who have seen ChatGPT
   turn out wooden prose or bogus facts, are still underestimating what’s
   happening.

   Bodies of knowledge and skills that have traditionally taken lifetimes
   to master are being swallowed at a gulp. Coding has always felt to me
   like an endlessly deep and rich domain. Now I find myself wanting to
   write a eulogy for it. I keep thinking of Lee Sedol. Sedol was one of
   the world’s best Go players, and a national hero in South Korea, but is
   now best known for losing, in 2016, to a computer program called
   AlphaGo. Sedol had walked into the competition believing that he would
   easily defeat the A.I. By the end of the days-long match, he was proud
   of having eked out a single game. As it became clear that he was going
   to lose, Sedol said, in a press conference, “I want to apologize for
   being so powerless.” He retired three years later. Sedol seemed weighed
   down by a question that has started to feel familiar, and urgent: What
   will become of this thing I’ve given so much of my life to?

   My first enchantment with computers came when I was about six years
   old, in Montreal in the early nineties, playing Mortal Kombat with my
   oldest brother. He told me about some “fatalities”—gruesome, witty ways
   of killing your opponent. Neither of us knew how to inflict them. He
   dialled up an FTP server (where files were stored) in an MS-DOS
   terminal and typed obscure commands. Soon, he had printed out a page of
   codes—instructions for every fatality in the game. We went back to the
   basement and exploded each other’s heads.

   I thought that my brother was a hacker. Like many programmers, I
   dreamed of breaking into and controlling remote systems. The point
   wasn’t to cause mayhem—it was to find hidden places and learn hidden
   things. “My crime is that of curiosity,” goes “The Hacker’s Manifesto,”
   written in 1986 by Loyd Blankenship. My favorite scene from the 1995
   movie “Hackers” is when Dade Murphy, a newcomer, proves himself at an
   underground club. Someone starts pulling a rainbow of computer books
   out of a backpack, and Dade recognizes each one from the cover: the
   green book on international Unix environments; the red one on
   N.S.A.-trusted networks; the one with the pink-shirted guy on I.B.M.
   PCs. Dade puts his expertise to use when he turns on the sprinkler
   system at school, and helps right the ballast of an oil tanker—all by
   tap-tapping away at a keyboard. The lesson was that knowledge is power.

   But how do you actually learn to hack? My family had settled in New
   Jersey by the time I was in fifth grade, and when I was in high school
   I went to the Borders bookstore in the Short Hills mall and bought
   “Beginning Visual C++,” by Ivor Horton. It ran to twelve hundred
   pages—my first grimoire. Like many tutorials, it was easy at first and
   then, suddenly, it wasn’t. Medieval students called the moment at which
   casual learners fail the pons asinorum, or “bridge of asses.” The term
   was inspired by Proposition 5 of Euclid’s Elements I, the first truly
   difficult idea in the book. Those who crossed the bridge would go on to
   master geometry; those who didn’t would remain dabblers. Section 4.3 of
   “Beginning Visual C++,” on “Dynamic Memory Allocation,” was my bridge
   of asses. I did not cross.

   But neither did I drop the subject. I remember the moment things began
   to turn. I was on a long-haul flight, and I’d brought along a boxy
   black laptop and a CD-ROM with the Borland C++ compiler. A compiler
   translates code you write into code that the machine can run; I had
   been struggling for days to get this one to work. By convention, every
   coder’s first program does nothing but generate the words “Hello,
   world.” When I tried to run my version, I just got angry error
   messages. Whenever I fixed one problem, another cropped up. I had read
   the “Harry Potter” books and felt as if I were in possession of a broom
   but had not yet learned the incantation to make it fly. Knowing what
   might be possible if I did, I kept at it with single-minded devotion.
   What I learned was that programming is not really about knowledge or
   skill but simply about patience, or maybe obsession. Programmers are
   people who can endure an endless parade of tedious obstacles. Imagine
   explaining to a simpleton how to assemble furniture over the phone,
   with no pictures, in a language you barely speak. Imagine, too, that
   the only response you ever get is that you’ve suggested an absurdity
   and the whole thing has gone awry. All the sweeter, then, when you
   manage to get something assembled. I have a distinct memory of lying on
   my stomach in the airplane aisle, and then hitting Enter one last time.
   I sat up. The computer, for once, had done what I’d told it to do. The
   words “Hello, world” appeared above my cursor, now in the computer’s
   own voice. It seemed as if an intelligence had woken up and introduced
   itself to me.

   Most of us never became the kind of hackers depicted in “Hackers.” To
   “hack,” in the parlance of a programmer, is just to tinker—to express
   ingenuity through code. I never formally studied programming; I just
   kept messing around, making computers do helpful or delightful little
   things. In my freshman year of college, I knew that I’d be on the road
   during the third round of the 2006 Masters Tournament, when Tiger Woods
   was moving up the field, and I wanted to know what was happening in
   real time. So I made a program that scraped the leaderboard on
   pgatour.com and sent me a text message anytime he birdied or bogeyed.
   Later, after reading “Ulysses” in an English class, I wrote a program
   that pulled random sentences from the book, counted their syllables,
   and assembled haikus—a more primitive regurgitation of language than
   you’d get from a chatbot these days, but nonetheless capable, I
   thought, of real poetry:

   I’ll flay him alive
   Uncertainly he waited
   Heavy of the past

   I began taking coding seriously. I offered to do programming for a
   friend’s startup. The world of computing, I came to learn, is vast but
   organized almost geologically, as if deposited in layers. From the Web
   browser down to the transistor, each sub-area or system is built atop
   some other, older sub-area or system, the layers dense but legible. The
   more one digs, the more one develops what the race-car driver Jackie
   Stewart called “mechanical sympathy,” a sense for the machine’s
   strengths and limits, of what one could make it do.

   At my friend’s company, I felt my mechanical sympathy developing. In my
   sophomore year, I was watching “Jeopardy!” with a friend when he
   suggested that I make a playable version of the show. I thought about
   it for a few hours before deciding, with much disappointment, that it
   was beyond me. But when the idea came up again, in my junior year, I
   could see a way through it. I now had a better sense of what one could
   do with the machine. I spent the next fourteen hours building the game.
   Within weeks, playing “Jimbo Jeopardy!” had become a regular activity
   among my friends. The experience was profound. I could understand why
   people poured their lives into craft: there is nothing quite like
   watching someone enjoy a thing you’ve made.

   In the midst of all this, I had gone full “Paper Chase” and begun
   ignoring my grades. I worked voraciously, just not on my coursework.
   One night, I took over a half-dozen machines in a basement computer lab
   to run a program in parallel. I laid printouts full of numbers across
   the floor, thinking through a pathfinding algorithm. The cost was that
   I experienced for real that recurring nightmare in which you show up
   for a final exam knowing nothing of the material. (Mine was in Real
   Analysis, in the math department.) In 2009, during the most severe
   financial crisis in decades, I graduated with a 2.9 G.P.A.

   And yet I got my first full-time job easily. I had work experience as a
   programmer; nobody asked about my grades. For the young coder, these
   were boom times. Companies were getting into bidding wars over top
   programmers. Solicitations for experienced programmers were so
   aggressive that they complained about “recruiter spam.” The popularity
   of university computer-science programs was starting to explode. (My
   degree was in economics.) Coding “boot camps” sprang up that could
   credibly claim to turn beginners into high-salaried programmers in less
   than a year. At one of my first job interviews, in my early twenties,
   the C.E.O. asked how much I thought I deserved to get paid. I dared to
   name a number that faintly embarrassed me. He drew up a contract on the
   spot, offering ten per cent more. The skills of a “software engineer”
   were vaunted. At one company where I worked, someone got in trouble for
   using HipChat, a predecessor to Slack, to ask one of my colleagues a
   question. “Never HipChat an engineer directly,” he was told. We were
   too important for that.

   This was an era of near-zero interest rates and extraordinary
   tech-sector growth. Certain norms were established. Companies like
   Google taught the industry that coders were to have free espresso and
   catered hot food, world-class health care and parental leave, on-site
   gyms and bike rooms, a casual dress code, and “twenty-per-cent time,”
   meaning that they could devote one day a week to working on whatever
   they pleased. Their skills were considered so crucial and delicate that
   a kind of superstition developed around the work. For instance, it was
   considered foolish to estimate how long a coding task might take, since
   at any moment the programmer might turn over a rock and discover a
   tangle of bugs. Deadlines were anathema. If the pressure to deliver
   ever got too intense, a coder needed only to speak the word “burnout”
   to buy a few months.

   From the beginning, I had the sense that there was something
   wrongheaded in all this. Was what we did really so precious? How long
   could the boom last? In my teens, I had done a little Web design, and,
   at the time, that work had been in demand and highly esteemed. You
   could earn thousands of dollars for a project that took a weekend. But
   along came tools like Squarespace, which allowed pizzeria owners and
   freelance artists to make their own Web sites just by clicking around.
   For professional coders, a tranche of high-paying, relatively
   low-effort work disappeared.
   [25]“I should have known he has absolutely no morals—Ive seen how he
   loads a dishwasher.”
   “I should have known he has absolutely no morals—I’ve seen how he loads
   a dishwasher.”
   Cartoon by Hartley Lin
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   The response from the programmer community to these developments was
   just, Yeah, you have to keep levelling up your skills. Learn difficult,
   obscure things. Software engineers, as a species, love automation.
   Inevitably, the best of them build tools that make other kinds of work
   obsolete. This very instinct explained why we were so well taken care
   of: code had immense leverage. One piece of software could affect the
   work of millions of people. Naturally, this sometimes displaced
   programmers themselves. We were to think of these advances as a tide
   coming in, nipping at our bare feet. So long as we kept learning we
   would stay dry. Sound advice—until there’s a tsunami.

   When we were first allowed to use A.I. chatbots at work, for
   programming assistance, I studiously avoided them. I expected that my
   colleagues would, too. But soon I started seeing the telltale colors of
   an A.I. chat session—the zebra pattern of call-and-response—on
   programmers’ screens as I walked to my desk. A common refrain was that
   these tools made you more productive; in some cases, they helped you
   solve problems ten times faster.

   I wasn’t sure I wanted that. I enjoy the act of programming and I like
   to feel useful. The tools I’m familiar with, like the text editor I use
   to format and to browse code, serve both ends. They enhance my practice
   of the craft—and, though they allow me to deliver work faster, I still
   feel that I deserve the credit. But A.I., as it was being described,
   seemed different. It provided a lot of help. I worried that it would
   rob me of both the joy of working on puzzles and the satisfaction of
   being the one who solved them. I could be infinitely productive, and
   all I’d have to show for it would be the products themselves.

   The actual work product of most programmers is rarely exciting. In
   fact, it tends to be almost comically humdrum. A few months ago, I came
   home from the office and told my wife about what a great day I’d had
   wrestling a particularly fun problem. I was working on a program that
   generated a table, and someone had wanted to add a header that spanned
   more than one column—something that the custom layout engine we’d
   written didn’t support. The work was urgent: these tables were being
   used in important documents, wanted by important people. So I
   sequestered myself in a room for the better part of the afternoon.
   There were lots of lovely sub-problems: How should I allow users of the
   layout engine to convey that they want a column-spanning header? What
   should their code look like? And there were fiddly details that, if
   ignored, would cause bugs. For instance, what if one of the columns
   that the header was supposed to span got dropped because it didn’t have
   any data? I knew it was a good day because I had to pull out pen and
   pad—I was drawing out possible scenarios, checking and double-checking
   my logic.

   But taking a bird’s-eye view of what happened that day? A table got a
   new header. It’s hard to imagine anything more mundane. For me, the
   pleasure was entirely in the process, not the product. And what would
   become of the process if it required nothing more than a three-minute
   ChatGPT session? Yes, our jobs as programmers involve many things
   besides literally writing code, such as coaching junior hires and
   designing systems at a high level. But coding has always been the root
   of it. Throughout my career, I have been interviewed and selected
   precisely for my ability to solve fiddly little programming puzzles.
   Suddenly, this ability was less important.

   I had gathered as much from Ben, who kept telling me about the
   spectacular successes he’d been having with GPT-4. It turned out that
   it was not only good at the fiddly stuff but also had the qualities of
   a senior engineer: from a deep well of knowledge, it could suggest ways
   of approaching a problem. For one project, Ben had wired a small
   speaker and a red L.E.D. light bulb into the frame of a portrait of
   King Charles, the light standing in for the gem in his crown; the idea
   was that when you entered a message on an accompanying Web site the
   speaker would play a tune and the light would flash out the message in
   Morse code. (This was a gift for an eccentric British expat.)
   Programming the device to fetch new messages eluded Ben; it seemed to
   require specialized knowledge not just of the microcontroller he was
   using but of Firebase, the back-end server technology that stored the
   messages. Ben asked me for advice, and I mumbled a few possibilities;
   in truth, I wasn’t sure that what he wanted would be possible. Then he
   asked GPT-4. It told Ben that Firebase had a capability that would make
   the project much simpler. Here it was—and here was some code to use
   that would be compatible with the microcontroller.

   Afraid to use GPT-4 myself—and feeling somewhat unclean about the
   prospect of paying OpenAI twenty dollars a month for it—I nonetheless
   started probing its capabilities, via Ben. We’d sit down to work on our
   crossword project, and I’d say, “Why don’t you try prompting it this
   way?” He’d offer me the keyboard. “No, you drive,” I’d say. Together,
   we developed a sense of what the A.I. could do. Ben, who had more
   experience with it than I did, seemed able to get more out of it in a
   stroke. As he later put it, his own neural network had begun to align
   with GPT-4’s. I would have said that he had achieved mechanical
   sympathy. Once, in a feat I found particularly astonishing, he had the
   A.I. build him a Snake game, like the one on old Nokia phones. But
   then, after a brief exchange with GPT-4, he got it to modify the game
   so that when you lost it would show you how far you strayed from the
   most efficient route. It took the bot about ten seconds to achieve
   this. It was a task that, frankly, I was not sure I could do myself.

   In chess, which for decades now has been dominated by A.I., a player’s
   only hope is pairing up with a bot. Such half-human, half-A.I. teams,
   known as centaurs, might still be able to beat the best humans and the
   best A.I. engines working alone. Programming has not yet gone the way
   of chess. But the centaurs have arrived. GPT-4 on its own is, for the
   moment, a worse programmer than I am. Ben is much worse. But Ben plus
   GPT-4 is a dangerous thing.

   It wasn’t long before I caved. I was making a little search tool at
   work and wanted to highlight the parts of the user’s query that matched
   the results. But I was splitting up the query by words in a way that
   made things much more complicated. I found myself short on patience. I
   started thinking about GPT-4. Perhaps instead of spending an afternoon
   programming I could spend some time “prompting,” or having a
   conversation with an A.I.

   In a 1978 essay titled “On the Foolishness of ‘Natural Language
   Programming,’ ” the computer scientist Edsger W. Dijkstra argued that
   if you were to instruct computers not in a specialized language like
   C++ or Python but in your native tongue you’d be rejecting the very
   precision that made computers useful. Formal programming languages, he
   wrote, are “an amazingly effective tool for ruling out all sorts of
   nonsense that, when we use our native tongues, are almost impossible to
   avoid.” Dijkstra’s argument became a truism in programming circles.
   When the essay made the rounds on Reddit in 2014, a top commenter
   wrote, “I’m not sure which of the following is scariest. Just how
   trivially obvious this idea is” or the fact that “many still do not
   know it.”

   When I first used GPT-4, I could see what Dijkstra was talking about.
   You can’t just say to the A.I., “Solve my problem.” That day may come,
   but for now it is more like an instrument you must learn to play. You
   have to specify what you want carefully, as though talking to a
   beginner. In the search-highlighting problem, I found myself asking
   GPT-4 to do too much at once, watching it fail, and then starting over.
   Each time, my prompts became less ambitious. By the end of the
   conversation, I wasn’t talking about search or highlighting; I had
   broken the problem into specific, abstract, unambiguous sub-problems
   that, together, would give me what I wanted.

   Having found the A.I.’s level, I felt almost instantly that my working
   life had been transformed. Everywhere I looked I could see GPT-4-size
   holes; I understood, finally, why the screens around the office were
   always filled with chat sessions—and how Ben had become so productive.
   I opened myself up to trying it more often.

   I returned to the crossword project. Our puzzle generator printed its
   output in an ugly text format, with lines like
   "s""c""a""r""*""k""u""n""i""s""*" "a""r""e""a". I wanted to turn output
   like that into a pretty Web page that allowed me to explore the words
   in the grid, showing scoring information at a glance. But I knew the
   task would be tricky: each letter had to be tagged with the words it
   belonged to, both the across and the down. This was a detailed problem,
   one that could easily consume the better part of an evening. With the
   baby on the way, I was short on free evenings. So I began a
   conversation with GPT-4. Some back-and-forth was required; at one
   point, I had to read a few lines of code myself to understand what it
   was doing. But I did little of the kind of thinking I once believed to
   be constitutive of coding. I didn’t think about numbers, patterns, or
   loops; I didn’t use my mind to simulate the activity of the computer.
   As another coder, Geoffrey Litt, wrote after a similar experience, “I
   never engaged my detailed programmer brain.” So what did I do?

   Perhaps what pushed Lee Sedol to retire from the game of Go was the
   sense that the game had been forever cheapened. When I got into
   programming, it was because computers felt like a form of magic. The
   machine gave you powers but required you to study its arcane secrets—to
   learn a spell language. This took a particular cast of mind. I felt
   selected. I devoted myself to tedium, to careful thinking, and to the
   accumulation of obscure knowledge. Then, one day, it became possible to
   achieve many of the same ends without the thinking and without the
   knowledge. Looked at in a certain light, this can make quite a lot of
   one’s working life seem like a waste of time.

   But whenever I think about Sedol I think about chess. After machines
   conquered that game, some thirty years ago, the fear was that there
   would be no reason to play it anymore. Yet chess has never been more
   popular—A.I. has enlivened the game. A friend of mine picked it up
   recently. At all hours, he has access to an A.I. coach that can feed
   him chess problems just at the edge of his ability and can tell him,
   after he’s lost a game, exactly where he went wrong. Meanwhile, at the
   highest levels, grandmasters study moves the computer proposes as if
   reading tablets from the gods. Learning chess has never been easier;
   studying its deepest secrets has never been more exciting.

   Computing is not yet overcome. GPT-4 is impressive, but a layperson
   can’t wield it the way a programmer can. I still feel secure in my
   profession. In fact, I feel somewhat more secure than before. As
   software gets easier to make, it’ll proliferate; programmers will be
   tasked with its design, its configuration, and its maintenance. And
   though I’ve always found the fiddly parts of programming the most
   calming, and the most essential, I’m not especially good at them. I’ve
   failed many classic coding interview tests of the kind you find at Big
   Tech companies. The thing I’m relatively good at is knowing what’s
   worth building, what users like, how to communicate both technically
   and humanely. A friend of mine has called this A.I. moment “the revenge
   of the so-so programmer.” As coding per se begins to matter less, maybe
   softer skills will shine.

   That still leaves open the matter of what to teach my unborn child. I
   suspect that, as my child comes of age, we will think of “the
   programmer” the way we now look back on “the computer,” when that
   phrase referred to a person who did calculations by hand. Programming
   by typing C++ or Python yourself might eventually seem as ridiculous as
   issuing instructions in binary onto a punch card. Dijkstra would be
   appalled, but getting computers to do precisely what you want might
   become a matter of asking politely.

   So maybe the thing to teach isn’t a skill but a spirit. I sometimes
   think of what I might have been doing had I been born in a different
   time. The coders of the agrarian days probably futzed with waterwheels
   and crop varietals; in the Newtonian era, they might have been obsessed
   with glass, and dyes, and timekeeping. I was reading an oral history of
   neural networks recently, and it struck me how many of the people
   interviewed—people born in and around the nineteen-thirties—had played
   with radios when they were little. Maybe the next cohort will spend
   their late nights in the guts of the A.I.s their parents once regarded
   as black boxes. I shouldn’t worry that the era of coding is winding
   down. Hacking is forever. ♦

   Published in the print edition of the [26]November 20, 2023, issue,
   with the headline “Begin End.”

More Science and Technology

     * Can we [27]stop runaway A.I.?
     * Saving the climate will depend on blue-collar workers. Can we train
       enough of them [28]before time runs out?
     * There are ways of controlling A.I.—but first we [29]need to stop
       mythologizing it.
     * A security camera [30]for the entire planet.
     * What’s the point of [31]reading writing by humans?
     * A heat shield for [32]the most important ice on Earth.
     * The climate solutions [33]we can’t live without.

   [34]Sign up for our daily newsletter to receive the best stories from
   The New Yorker.
   [35]James Somers is a writer and a programmer based in New York.

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