The rise of industrial software

One thing that has become clearer to me over the years is that reasoning by analogy (like this article does) sounds a lot smarter than it is. If you look from first principles, it's clear that physical goods and software don't share the same properties and thus the analogy falls apart.

Physical goods like clothes or cars have variable costs. The marginal unit always costs > 0, and thus the price to the consumer is always greater than zero. Industrialization lowered this variable cost, while simultaneously increasing production capacity, and thus enabled a new segment of "low cost, high volume" products, but it does not eliminate the variable cost. This variable cost (eg. the cost of a hand made suit) is the "umbrella" under which a low cost variant (factory made clothes) has space to enter the market.

Digital goods have zero marginal cost. Many digital goods do not cost anything at all to the consumer! Or they are as cheap as possible to actively maximize users because their costs are effectively fixed. What is the "low value / low cost" version of Google? or Netflix for that matter? This is non-sensical because there's no space for a low cost entrant to play in when the price is already free.

In digital goods, consumers tend to choose on quality because price is just not that relevant of a dimension. You see this in the market structure of digital goods. They tend to be winner (or few) take all because the best good can serve everyone. That is a direct result of zero marginal cost.

Even if you accept the premise that AI will make software "industrialized" and thus cheaper to produce, it doesn't change the fact that most software is already free or dirt cheap.

The version of this that might make sense is software that is too expensive to make at all because the market size (eg. number of consumers * price they would pay) is less than the cost of the software developer / entrpreneurs time. But by definition those are small markets, and not anything like the huge markets that were enabled by physical good industrialization.

But focusing on production cost is silly. The cost to consumers is what matters. Software is already free or dirt cheap because it can be served at zero marginal cost. There was only a market for cheap industrial clothes because tailor made clothes were expensive. This is not the case in software and that's why this whole industrialization analogy falls apart upon inspection

I think existing skilled programmers are leveraging AI to increase productivity.

I think there are some people with limited, or no, programming experience who are vibe coding small apps out of nothing. But I think this is a tiny fraction of people. As much as the AI might write code, the tools used to do that, plus compile, distribute etc are still very developer focused.

Sure, one day my pastor might be able to download and install some complete environment which allows him to create something.

Maybe it'll design the database for him, plus install and maintain the local database server for him (or integrate with a cloud service.)

Maybe it'll get all the necessary database and program security right.

Maybe it'll integrate well with other systems, from email to text-import and export. Maybe that will all be maintainable as external services change.

Maybe it'll be able to do support when the printing stops working, or it all needs to be moved to a new machine.

Maybe this environment will be stable enough for the years and decades that the program will be used for. Maybe updating or adding to the program along the way won't break existing things.

Maybe it'll work so well it can be distributed to others.

All this without my pastor even needing to understand what a "variable" is.

That day may come. But, as well as it might or might not write code today, we're a long long way from this future. Mass producing software is a lot more than writing code.

Its taken as a fait accompli that LLMs are more productive. But outside some initial scaffolding or code review... I just do not see it.

I am not threatened by LLMs. I would like it if I could code purely in requirements. But every time I get frustrated and just do it myself, because I am faster.

We could have LLM’s capable of doing all that for your pastor right now and it would still take time before these systems can effectively reason through troubleshooting this bespoke software. Right now the effectiveness of LLLM-powered troubleshooting software platforms relies upon the gravity induced by millions of programmers sharing experiences upon more or less the same platforms. Gigabytes to terabytes of text training data on all sorts of things that go bonkers on each platform.

We are now undergoing a Cambrian explosion of bespoke software vibe coded by a non-technical audience, and each one brings with it new sets of failure modes only found in their operational phase. And compared to the current state, effectively zero training data to guide their troubleshooting response.

Non-linearly increasing the surface area of software to debug, and inversely decreasing the training data to apply to that debugging activity will hopefully apply creative pressure upon AI research to come up with more powerful ways to debug all this code. As it stands now, I sure hope someone deep into AI research and praxis sees this and follows up with a comment here that prescribes the AI-assisted troubleshooting approach I’m missing that goes beyond “a more efficient Google and StackOverflow search”.

Also, the current approach is awesome for me to come up to speed on new applications of coding and new platforms I’m not familiar with. But for areas that I’m already fluent in and the areas my stakeholders especially want to see LLM-based amplification, either I’m doing something wrong or we’re just not yet good at troubleshooting legacy code with them. There is some uncanny valley of reasoning I’m unable to bridge so far with the stuff I’m already familiar with.