And what do you feel is the role of universities? Certainly not just to learn the language right? I'm going through a computer engineering degree and sometimes I feel completely lost with an urge to give up on everything, even though I am still interested in technology.
A lot of engineers and programmers did not go to school.
Theyre explicitly saying that most software will no longer be artisianal - a great literary novel - and instead become industrialized - mass produced paperback garbage books. But also saying that good software, like literature, will continue to exist.
As such, the article's point fails right at the start when it tries to make the point that software production is not already industrial. It is. But if you look at actual industrial design processes, their equivalent of "writing the code" is relatively small. Quality assurance, compliance to various legal requirements, balancing different requirements for the product at hand, having endless meetings with customer representatives to figure out requirements in the first place, those are where most of the time goes and those are exactly the places where LLMs are not very good. So the part that is already fast will get faster and the slow part will stay slow. That is not a recipe for revolutionary progress.
Your point that most software uses the same browsers, databases, tooling and internal libraries is a weakness, a sameness that can be exploited by current AI, to push that automation capability much further. Hell, why even bother with any of the generated code and infrastructure being "human readable" anymore? (Of course, all kinds of reasons that is bad, but just watch that "innovation" get a marketing push and take off. Which would only mean we'd need viewing software to make whatever was generated readable - as if anyone would read to understand hundreds/millions of generated complex anything.)
> You get the exact same browser, database server and (whatsapp/signal/telegram/whatever) messenger client as basically everyone else.
Maybe I'm exaggerating here but I've heard things pretty close in "chrome vs Firefox" and "signal vs ..." threads. People are really passionate about tiny details. Or at least they think that's that they're passionate about.
Unfortunately I think what they don't realize is that passion often hinders that revolutionary progress you speak of. It just creates entrenched players and monopolies in domains where it should be near trivial to move (browsers are definitely trivial to jump ship)
I think this is understating the cost of jumping. Basically zero users care about the "technological" elements of their browser (e.g. the render engine, JS engine, video codecs) so long as it offers feature equivalence, but they do care a lot about comparatively "minor" UX elements (e.g. password manager, profile sync, cross-platform consistency, etc) which probably actually dominate their user interaction with the browser itself and thus understandably prove remarkably sticky ("minor" here is in terms of implementation complexity versus the rest of a browser).
Perhaps a good analogy is the spreadsheet. It was a complete shift in the way that humans interacted with numbers. From accounting to engineering to home budgets - there are few people who haven't used a spreadsheet to "program" the computer at some point.
It's a fantastic tool, but has limits. It's also fair to say people use (abuse) spreadsheets far beyond those limits. It's a fantastic tool for accounting, but real accounting systems exist for a reason.
Similarly AI will allow lots more people to "program" their computer. But making the programing task go away just exposes limitations in other parts of the "development" process.
To your analogy I don't think AI does mass-produced paperbacks. I think it is the equivalent of writing a novel for yourself. People don't sell spreadsheets, they use them. AI will allow people to write programs for themselves, just like digital cameras turned us all into photographers. But when we need it "done right" we'll still turn to people with honed skills.
It's the article's analogy, not mine.
And, are you really saying that people aren't regularly mass-vibing terrible software that others use...? That seems to be a primary use case...
Though, yes, I'm sure it'll become more common for many people to vibe their own software - even if just tiny, temporary, fit-for-purpose things.
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.
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.
Missing the point. The barrier to make software has lowered substantially. This not makes mediocre devs less mediocre and for a lot of businesses out there being slightly less mediocre is all they need most of the time. Needing decent devs 20-40% of the time is already a big win in terms of expenses. Making small quick mediocre software that later on you need a decent dev for a couple of months to clean as opposed to pay and keep that dev for several years to make the software from scratch.
Yes, it is not very efficient, but neither are those Cobol apps in old banks. It's always about it being just good enough that it works not beautifully crafted software that never breaks. The market can stay alive longer than you can keep a high salary job as a very experienced dev when you are competing against 100 other similarly experienced devs for your job.
Unlike clothing, software always scaled. So, it's a bit wrongheaded to assume that the new economics would be more like the economics of clothing after mass production. An "artisanal" dress still only fits one person. "Artisanal" software has always served anywhere between zero people and millions.
LLMs are not the spinning jenny. They are not an industrial revolution, even if the stock market valuations assume that they are.
The most radical development in software tools I think, would be more tools for non-professional programmers to program small tools that put their skills on wheels. I did a lot of biz dev around something that encompassed "low code/no code" but a revolution there involves smoothing out 5-10 obstacles with a definite Ashby character that if you fool yourself that you can get away with ignoring the last 2 required requirements you get just another Wix that people will laugh at. For now, AI coding doesn't have that much to offer the non-professional programmer because a person without insight into the structure of programs, project management and a sense of what quality means will go in circles at best.
I think the thinking in the article is completely backwards about the economics. I mean, the point of software is you can write it once and the cost to deploy a billion units is trivial in comparison. Sure, AI slop can put the "crap" in "app" but if you have any sense you don't go cruising the app store for trash but find out about best-of-breed products or products that are the thin edge of a long wedge (like the McDonald's app which is valuable because it has all the stores baacking it)
The difference with software is that software is design all the way down. It only needs to be written once, similar to how a mass-produced item needs only be designed once. The copying that corresponds to mass production is the deployment and execution of the software, not the writing of it.
The user experience will be less constrained as the self arrangement of pixels improves and users do not run into designer constraints, usually due to lack of granularity some button widget or layout framework is capable of.
"Artisanal" software engineers probably never were their own self selected identity.
Have been writing code since the late 80s, when Windows and commercial Unix were too expansive and we all wrote shoddy but functional kernels. Who does that now? Most gigs these days are glue code to fetch/cache deps and template concrete config values for frameworks. Artisanal SaaS configuration is not artisanal software engineering.
And because software engineers were their own worst enemy the last decade; living big as they ate others jobs and industries; hate for the industry has gone mainstream. Something politicians have to react to. Non-SWEs don't want to pay middle men to use their property. GenAI can get them to that place.
As an art teacher once said; making things for money is not the practice of a craft. It's just capitalism. Anyone building SaaS apps through contemporary methods is a Subway sandwich artist, not the old timey well rounded farmer, hunter, who also bakes bread.
The article is very clearly not saying anything like that. It's saying the greatest barrier to making throwaway comments on Russian social media is not speaking Russian.
Roughly the entire article is about LLMs making it much cheaper to make low quality software. It's not about masterpieces.
And I think it's generally true of all forms of generative AI, what these things excel at the most is producing things that weren't valuable enough to produce before. Throwaway scripts for some task you'd just have done manually before is a really positive example that probably many here are familiar with.
But making stuff that wasn't worth making before isn't necessarily good! In some cases it is, but it really sucks if we have garbage blog posts and readmes and PRs flooding our communication channels because it's suddenly cheaper to produce than whatever minimal value someone gets out of hoisting it on us.
I've worked for a lot of people involved in the process happily request their software get turned into spaghetti. Often because some business process "can't" be changed, but mostly because decision makers do not know / understand what they're asking in a larger scheme of things.
A good engineer can help mitigate that, but only so much. So you end up with industrial sludge to some extent anyway if people in the process are not thoughtful.
As Bryan Cantrill commented (quoting Jeff Bonwick, co-creator of ZFS): code is both information about the machine and the machine:
* https://www.youtube.com/watch?v=vHPa5-BWd4w&t=4m37s
Whereas an architect creates blueprints which is information, that gets constructed into a building/physical object, and a design engineer also creates documents that are information that get turned into machine(s), when a developer writes code they are generating information that acts like a machine.
Software has a duality of being both.
How does one code and not create a machine? Produce a general architecture in UML?
What software developers produce is not a machine by itself. It's at most a blueprint for a machine that can be actualized by combining it with specific hardware. But this is getting a bit too philosophical and off track: LLMs can help produce source code for a specific program faster, but they are not very good at determining whether a specific program should be built at all.
"The thing that is remarkable about it is that it has this property of being information—that we made it up—but it is also machine, and it has these engineered properties. And this is where software is unlikely anything we have ever done, and we're still grappling on that that means. What does it mean to have information that functions as machine? It's got this duality: you can see it as both."
It's not about software and hardware needing each other, but rather about the strange 'nature' of software.
He has made the point before:
> We suffer -- tremendously -- from a bias from traditional engineering that writing code is like digging a ditch: that it is a mundane activity best left to day labor -- and certainly beneath the Gentleman Engineer. This belief is profoundly wrong because software is not like a dam or a superhighway or a power plant: in software, the blueprints _are_ the thing; the abstraction _is_ the machine.
* https://bcantrill.dtrace.org/2007/07/28/on-the-beauty-in-bea...
(Perhaps @bcantrill will notice this and comment.)
> If the hardware is not present, then the software will be just bytes on a storage device.
And what do you mean by "hardware" and what is meant by 'running software'? If you see a bunch of C or Python or assembly code, and you read through it, is it 'running' in your brain? Do you need 'real' CPUs or can you run software on stuff that is not made of silicon but the carbon between your ears?
Instead of people downloading / purchasing the same bits for a particular piece of software which is cookie cutter like a two-piece from Men's Suite Warehouse, we can ask LLM for custom bit of code: everyone getting a garment from Savile Row.
I think the author's analogies are on point.
Design engineers can leave little details out and let contractors figure out the details. Software has no such luxury.
Software has design, edge case finding, and actually constructing the process.
Design is only 1/3 of the process in construction.
What software developers actually do is closer to the role of an architect in construction or a design engineer in manufacturing. They design new blueprints for the compilers to churn out. Like any design job, this needs some actual taste and insight into the particular circumstances. That has always been the difficult part of commercial software production and LLMs generally don't help with that.
It's like thinking the greatest barrier to producing the next great Russian literary novel is not speaking Russian. That is merely the first and easiest barrier, but after learning the language you are still no Tolstoy.