Comment by hintymad
7 hours ago
It looks vibe coding, or at AI coding in general, has been challenging a few empirical laws:
- Brooks' No Silver Bullet: no single technology or management technique will yield a 10-fold productivity improvement in software development within a decade. If we write a spec that details everything we want, we would write soemthing as specific as code. Currently people seem to believe that a lot of the fundamentals are well covered by existing code, so a vague lines of "build me XXX with YYY" can lead to amazing results because AI successfully transfers the world-class expertise of some engineers to generate code for such prompt, so most of the complex turns to be accidental, and we only need much fewer engineers to handle essential complexities.
- Kernighan's Law, which says debugging is twice as hard as writing the code in the first place. Now people are increasingly believing that AI can debug way faster than human (most likely because other smart people have done similar debugging already). And in the worst case, just ask AI to rewrite the code.
- Dijkstra on the foolishness of programming in natural language. Something along the line of which a system described in natural language becomes exponentially harder to manage as its size increases, whereas a system described in formal symbols grows linearly in complexity relative to its rules. Similar to above, people believe that the messiness of natural language is not a problem as long as we give detailed enough instructions to AI, while letting AI fills in the gaps with statistical "common sense", or expertise thereof.
- Lehman’s Law, which states that a system's complexity increases as it evolves, unless work is done to maintain or reduce it. Similar to above, people start to believe otherwise.
- And remotely Coase's Law, which argues that firms exist because the transaction costs of using the open market are often higher than the costs of directing that same work internally through a hierarchy. People start to believe that the cost of managing and aligning agents is so low that one-person companies that handle large number of transactions will appear.
Also, ultimately Jevons Paradox, as people worry that the advances in AI will strip out so much demand that the market will slash more jobs than it will generate. I think this is the ultimate worry of many software engineers. Luddites were rediculed, but they were really skilled craftsmen who spent years mastering the art of using those giant 18-pound shears. They were the staff engineers of the 19th-century textile world. Mastering those 18-pound shears wasn't just a job but an identity, a social status, and a decade-long investment in specialized skills. Yeah, Jevons Paradox may bring new jobs eventually, but it may not reduce the blood and tears of the ordinary people.
Intereting times.
> Kernighan's Law, which says debugging is twice as hard as writing the code in the first place. Now people are increasingly believing that AI can debug way faster than human (most likely because other smart people have done similar debugging already). And in the worst case, just ask AI to rewrite the code.
I thought you were gonna go the opposite direction with this. Debugging is now 100x as hard as writing the code in the first place.
> Lehman’s Law, which states that as a system's complexity increases as it evolves, unless work is done to maintain or reduce it. Similar to above, people start to believe otherwise.
Gotta disagree with this too. I find a lot of work has to be done to be able to continue vibing, because complexity increases beyond LLM capabilities rapidly otherwise.
> I thought you were gonna go the opposite direction with this. Debugging is now 100x as hard as writing the code in the first place.
100x harder if a human were to debug AI-generated code. I was merely citing other people's beliefs: AI can largely, if not completely, take care of debugging. And "better", rewrite the code altogether. I don't see how that could be a better approach, but that might just be me.
I still run into plenty of situations where the LLM-agent wrote the code really inexpensively, but is totally unable to debug it, and you can sink tons of time trying to get it to do so before giving up with nothing to show for it, and trying to figure it out yourself.
2 replies →
Assuming that AI challenges all that is in my perception a bit simple.
> Brooks' No Silver Bullet
Just because a person can create code or "results" much faster now, it doesn't say anything about productivity. Don't mistake dev productivity for economic productivity.
> Kernighan's Law, which says debugging is twice as hard as writing the code
Debugging is such a vague term in these matters. An AI may be decent to figure out their error they introduced into their code after it runs its own tests. But a production bug, i.e. reported from a user, can be very hard for AIs due to their utter lack of context.
> Dijkstra on the foolishness of programming in natural language. > ... > Lehman’s Law, which states that as a system's complexity increases as it evolves, unless work is done to maintain or reduce it.
No clue what the argument is here, "people believe otherwise" isn't.
> Also, ultimately Jevons Paradox
Actually relevant tech people confirm the paradox in the long run. Companies slash jobs now because they tend consolidate in chaotic times.
Interesting, though I disagree on basically all points...
> No Silver Bullet
As an industry, we do not know how to measure productivity. AI coding also does not increase reliability with how things are going. Same with simplicity, it's the opposite; we're adding obscene complexity, in the name of shipping features (the latter of which is not productivity).
In some areas I can see how AI doubles "productivity" (whatever that means!), but I do not see a 10x on the horizon.
> Kernighan's Law
Still holds! AI is amazing at debugging, but the vast majority of existing code is still human-written; so it'll have an easy time doing so, as indeed AI can be "twice as smart" as those human authors (in reality it's more like "twice as persistent/patient/knowledgeable/good at tool use/...").
Debugging fully AI-generated code with the same AI will fall into the same trap, subject to this law.
(As an aside, I do wonder how things will go once we're out of "use AI to understand human-generated content", to "use AI to understand AI-generated content"; it will probably work worse)
> just ask AI to rewrite the code
This is a terrible idea, unless perhaps there is an existing, exhaustive test harness. I'm sure people will go for this option, but I am convinced it will usually be the wrong approach (as it is today).
> Dijkstra on the foolishness of programming in natural language
So why are we not seeing repos of just natural language? Just raw prompt Markdown files? To generate computer code on-the-fly, perhaps even in any programming language we desire? And for the sake of it, assume LLMs could regenerate everything instantly at will.
For two reasons. The prompts would either need to raise to a level of precision as to be indistinguishable from a formal specification. And indeed, because complexity does become "exponentially harder"; inaccuracies inherent to human languages would compound. We need to persist results in formal languages still. It remains the ultimate arbiter. We're now just (much) better at generating large amounts of it.
> Lehman’s Law
This reminds me of a recent article [0]. Let AI run loose without genuine effort to curtail complexity and (with current tools and models) the project will need to be thrown out before long. It is a self-defeating strategy.
I think of this as the Peter principle applied to AI: it will happily keep generating more and more output, until it's "promoted" past its competence. At which point an LLM + tooling can no longer make sense of its own prior outputs. Advancements such as longer context windows just inflate the numbers (more understanding, but also more generating, ...).
The question is, will the market care? If software today goes wrong in 3% of cases, and with wide-spread AI use it'll be, say, 7%, will people care? Or will we just keep chugging along, happy with all the new, more featureful, but more faulty software? After all, we know about the Peter principle, but it's unavoidable and we're just happy to keep on.
> Jevons Paradox
My understanding is the exact opposite. We might well see a further proliferation of information technologies, into remaining sectors which have not yet been (economically) accessible.
0: https://lalitm.com/post/building-syntaqlite-ai/
> The question is, will the market care? If software today goes wrong in 3% of cases, and with wide-spread Al use it'll be, say, 7%, will people care? Or will we just keep chugging along, happy with all the new, more featureful, but more faulty software?
This is THE question. I honestly think the majority will gladly take an imperfect app over waiting for a perfect app or perhaps having no app at all. Some devs might be able to stand out with a polished app taking the traditional approach but it takes a lot longer to achieve that and by that point the market may be different, which is a risk
> And in the worst case, just ask AI to rewrite the code.
"And in the worst case just pay for it twice."
That leads to a dead end.