This Sunday morning, my son played two games I wrote 23 and 24 years ago for a device now long gone:
(This is a "Telenor Smartphone", an ISDN-based beast of a device from the early 2000s)
To be precise, what he played was an HTML5 port of a Snake game and a PacMan clone, running in Chrome on my Mac. Claude Code, with Opus 5 as the underlying model, got both right on the first attempt. When he found PacMan a bit too hard, we added a "cheat mode" where the ghosts are 20% slower and you can eat them for twice as long. Then it was just right, and he had good fun. Which is my first and most optimistic point: AI can make all of us builders, and being more efficient, we can build and tinker much more than before.
As a side track, I also asked Claude to review the old code, expecting to be told I was a genius for having pulled this off on the side while studying. Not so much. When a review opens with "what is most impressive is not the code..." and goes on about the level of reuse we would expect today (it did bring up Fowler; in my defense, what we expected in terms of reuse in 2001 was not quite what we expect now), you know it is not giving you an A+. I did get credit for some architectural choices and for being clear about tradeoffs in the comments and whatnot. I'll take it.
Back from the 25-year-old vintage code. In my role as product manager at Artifik, where I still code even if slightly less than I would like, I estimate AI has made me factors, not percentages, more productive at writing code. We also find AI very useful for testing, for design work and prototypes in particular, and for general office work, but we have a way to go before we see quite the same improvement there. Which brings me to my second point, in the form of a question: Will AI deliver the same level of improvement for all work as it already has for coding? My current best guess is that it very likely will. Going beyond work that manifests itself digitally will likely only happen in the longer term, even if Optimus and others already do impressive limited demos.
This is a slide I often use when I present about our use of AI externally:
(Please go and see "Annie Get Your Gun" on Broadway rather than suing me, image courtesy of Spotify album from the musical. Fun fact: In Norwegian "AI" sounds more like I, so it works even better)
A concrete signal of that spreading beyond code: Cloudflare has just open sourced Cloudflare OS, the AI productivity environment they use internally. The interesting part is what they call gadgets. When you make a slide deck there, you are not connecting to a slide deck service somewhere in the cloud. The system creates a private instance of the slide deck software just for you, and if it is missing something you need, you ask your agent to add it. A gadget sits somewhere between a document and an application, rewritten by the user with AI as they go, and it blurs the line between apps and data. Worth a pause, because blurring that same line is exactly what SaaS did to the dominant design before it, when your data stopped being a file on your machine and became something living inside someone else's application. SaaS may be about to get the treatment it gave the desktop.
Their contribution policy may be the most interesting thing in the repo, and it is where this stops being about Cloudflare. They are not accepting external code contributions, and the reason they give is worth quoting in full:
"AI has made writing code easy. The hard part, today, is not writing the code, but reviewing it, making sure quality stays high, and keeping the product coherent. In that light, unfortunately, external code contributions are 'donating' the easy part of the job, while creating more of the hard work."
My main point of this post is about tech debt, and not the conventional kind. I am quick to dismiss the "when you use AI you create a lot of code no-one owns or fully understands" worry. This happens with human team members as well. It just happens faster when you are more effective. And getting to know unknown code is something AI does insanely well. In fact you can argue that it does more or less exactly that every time you use it, however perfect your harness is. (Look out for "I had to build my own harness or model router" as the same risk marker as the people who wanted to home-cook their own template engine or dependency injection framework a decade ago, by the way.)
My worry is about what I will call Generational Tech Debt. The problem is not that nobody owns the code, it is that right now review, taste and coherence do not scale the way producing code does. Which manifests itself in Cloudflare not wanting external contributions. I should also admit to being guilty of producing quite a bit of that work myself, loving to "get things done" with AI - but I will not side track into definitions of done just here.
What I want to talk about instead is this generational tech debt: The competence, and even the taste, for coding and architecture that never gets built in the first place. I don't mean only that the people who knew a particular piece have left. I mean that fewer and fewer people learn coding deeply, and therefore never build the muscle memory that later shows up as sound architectural choices, and as not making the same mistake twice.
It could be that in the long run we will simply see other competencies mattering more, and that the next generation of software professionals has a different profile. That is a real possibility, not a strawman. But there is some combination of skill, taste and creativity that I think takes longer to develop than AI can currently shortcut, and right now I do see the danger.
Readers of the poker series here will have seen this film before. Solvers arrived, and a generation of players got sharply better and sharply more correct. What did not obviously come along was the ability to look at a specific bad opponent and work out how to take his money, because a solver assumes the guy across the table is playing as well as you are. It also cannot tell you which spot to solve, or what your bet sizes should be, and it hands you an answer per node, while the real work is compressing thousands of those into something you can use at the table without a laptop.
I think coding is running the same experiment, with one difference that should worry us more, not less. In poker there was a ground truth. You could go home, run the spot, and find out you were wrong. There is no solver for "should this be a separate service", and no printout telling you your abstraction was bad. The bill arrives three years later, disguised as something else, usually as reduced velocity.
Which leaves a tension I have not resolved. My first point was that AI makes all of us builders. But at this moment in time, AI coding is still at its best in the hands of people who were already very good at coding and building manually. Both of those look true to me. The uncomfortable question is: Where should the next cohort of already-very-good software people come from, if the ladder they climbed is the part being automated. This is the more fundamental tech debt - a generational one - I wanted to highlight as a topic for discussion, with potential impact not only on individual companies, but on society and progress.
This might of course change. For casual users, AI already mostly catches your intent correctly, where 6-12 months ago you had to get the prompt just right. I suspect much the same will happen to coding. It could be that when humans look back, what amazes them will not be "you can instruct computers in your own language to do things from scratch", but the perhaps relatively brief period of give or take 70 years where we actually had to have specialists communicate with computers in their language, rather than use ours.
So what do I want my son to do to be prepared for this world, besides playing the games I coded a quarter of a century ago? I don't have a clean answer, which is a little uncomfortable in a post that spends this long on a danger. The cliché is still the best I have: Enjoy what you do, be very willing to adapt, and love learning. I know the feeling of being in the steepest part of the steepest S-curve is partly a feature of getting older, but heck, things are moving faster than ever. At least I do enjoy the ride. The title was not intended as a pun on what I owe my son in advice or tech training. But as an installment on that debt, I try to supplement Mark Rober (former NASA engineer, of thinking-like-an-engineer YouTube and Netflix fame, and one of my son's favorites) as best I can.
