Hacker News Reader

> If you can direct AI and produce more value per $/time spent then a non software engineer directing AI to develop software then you still can produce a lot of value.

Sure, in the same way a skilled photographer can take better photographs on an iPhone than I can. Nevertheless, because any twit can take good enough photos on an iPhone, the need for the specialized skills of an expert photographer are much less than in, say, the 1930s.

That's why AI is dangerous. It's not because it makes human skill useless, it's because it makes non-human skill good enough. When was the last time you hired an expert photographer, or skilled dressmaker, or a master cabinetmaker? Maybe once or twice in your life, because the iPhone camera, fast fashion, and Ikea are good enough.

Software is just language, translating human intent into concrete machine action. That translation being automated doesn't mean, the intent had been made superfluous.

When no human knows what the machine is doing exactly, how do you know your intent is being followed? The seeming sufficiency of superficiality the public "enjoys" is an illusion needing upkeep.

Machines cannot make meaning, nor can they originate intent. They can't keep themselves suspended mid-air, detached from human understanding. You need engineers who understand human and machine, identifying the reasonable among the un-reflected wishes and making it explicit and precise.

> When no human knows what the machine is doing exactly, how do you know your intent is being followed?

No one is saying that no one will know what the computer is doing, but fewer people will have to. We are moving up the abstraction hierarchy, just as we have done since the stone age. In my grandparents' generation, knowing how to fix an internal combustion engine was a common skill among young men; today, it's been abstracted away. In the same way, I don't know how my clothes are manufactured, where my electricity is generated, or whether my CPU's "management engine" is spying on me. I don't need to know the details, because everything "just works." The same thing is happening with AI.

1. The demand for photographs is fixed. The demand for software is infinite.

2. A photo is just what it appears to be. A program's code can't be gleaned from its output. Without checking the code you can't know if it's good enough or not.

3. For a lot of use cases, good enough isn't good enough. A good enough robot in a factory produces unacceptable defect rates, a good enough space rocket explodes way too often.

> The demand for photographs is fixed. The demand for software is infinite.

A highly dubious assertion. If demand for software is infinite, why don't we have infinite software, since we now have the ability to create infinite software?

> Without checking the code you can't know if it's good enough or not.

And even after checking the code, you still won't know if it's good enough, as evidenced by the large number of bugs that get shipped. I think it's clear that AI can do at least a good job checking code as humans can, so my argument stands.

> good enough isn't good enough

It seems like you are playing word games instead of addressing my argument. Can you please explain the point you were trying to make with this argument?

the need for the specialized skills of an expert photographer are much less than in, say, the 1930s

Without even looking it up, I expect that this is patently untrue. I bet way more people make a living as photographers today than in the 1930s.

EDIT: yep, about 4x as many today, and slightly more per capita even.

Not saying your overall point is off, but the fact that you picked a bad example is illustrative of a broader point: it’s very difficult to tell in advance how technology will impact employment.

EDIT: can’t reply so I’ll just add this here:

As I understood it, your entire point was that people don’t have as much need for photographers, dressmakers, and cabinetmakers today, because technology has given us low-skill or no-skill options that are good enough. Is that a fair characterization of your argument?

This is definitely true for some categories (agriculture, or elevator operators), but again, there are more professional photographers working today than in the 1930s. It wouldn’t surprise me if there are more seamstresses / tailors / etc too, more professional carpenters, etc.

Some of this is basic Jevon’s paradox in action, and some of it is just that median American is much wealthier (in real terms) than a century ago, so even though we can buy lots of “good enough”, there’s still a larger market for high-end skilled artisan labor than a century ago.

Not really. The number of photographers doesn't tell you everything about the demand for them. Back then photography was still competing with portraiture as a way for capturing likenesses, both options relatively rare and expensive, but photography certainly cheaper and requiring vastly less skill. It was a very different market than today's where I'd say the work is mostly in weddings.

> If you can direct AI and produce more value per $/time spent then a non software engineer directing AI to develop software then you still can produce a lot of value.

Even if true (which I'm leaning towards that it is), when will companies realize this?

We could be in for a decade long software career recession until the effects of poorly managed and written software trickles down to lost customers and company bankruptcies. Bad decisions in large companies take a very long time to produce clearly visible effects.

> As you develop your skills with AI, you now have the ability to develop your own software products.

I think this is a dead end. We're quickly approaching a state where software is no longer a product that can be sold, but rather a tool used by someone selling a service.

> We could be in for a decade long software career recession until the effects of poorly managed and written software trickles down to lost customers and company bankruptcies. Bad decisions in large companies take a very long time to produce clearly visible effects.

this does not mean software jobs would come back, you'd just end up in the amazon situation where today you use word made by JOXHSWW, then there are too many bugs and you buy another word made by ANSJJGG and so on.

Some B2B software will continue, but B2C can just become a single person fly by night with some models, or you can end up doing it yourself. A couple of years ago a model one-shotting a game of pacman was sci-fi, now it's just another benchmark, maybe the next model benchmarks will be one-shot photoshop or word or whatever. Either way today's C is more interested in doomscrolling tiktok on their phone than to booting up a computer to do some photo editing anyways.

As the article says, "taste" is still important in steering a model, but for how long? and how many people with taste do you really need compared to a few years ago where you needed people with taste AND a lot of people to execute.

It feels AI is just the next step of outsourcing, writing specs in the evening then magically the next morning software appears which might or might not work the way you expected, and the cycle repeated, only today the cycle is in your terminal and takes minutes vs hours/days

If the market is filled with cheap products and a clone of your product can be easily made, then the survival of the business is in positioning, marketing, packaging the product, distribution, sales...

This makes the technical skills required but not sufficient part of the whole business and there are many businesses (like restaurants) that are bad businesses, because many people are willing to open a restaurant as a life-style choice even though most of them are not a good business. This influx of new competitors, make the current restaurant owners profit to go down, because of the increased competition that isn't purely market driven.

In the software world that means "a small product" won't have any moat & pricing power, and your competitors might be wiling to subsidise it for years as a side project, making the whole market suffer from profit erosion (an economic term) leading to zero profit long-term equilibrium.

For example try charging per message (as previously charged by SMS/MMS) in the era of subsidised messenger apps with free calls and messages.

This makes the unique position of SAAS software with 9$/month subscription highly doubtful in the future.

> If the market is filled with cheap products and a clone of your product can be easily made, then the survival of the business is in positioning, marketing, packaging the product, distribution, sales...

Most people stop after having gotten their dopamine hit. It was the case before, it's still the case now with AI.

Try any newly released software, and you'll notice a trove of issues, whether it's UX, UI, features not working properly. Errors, etc. All of the unsexy work that is needed to go from "it works" to "thousands / millions of people can rely on it daily".

Playing the long game has always worked, and will always work.

There are two ways forward for any company (whether big or small):

- Expand the pie: push AI to its limits while keeping your employees, new markets, new offerings

- Keep the same pie, and increase the size of the slices among the shareholders (employees or not)

> the cost of developing software is plummeting

For now, until the frontier models decide to stop losing money and pass the real costs onto the consumers.

DeepSeek is presumably break even or better and costs 20-40x less than frontier models. Its intelligence is lagging the frontier by a lot, but its already plenty good to contribute to certain aspects of software development. There is a lot of opportunity to make model usage more cost effective for software development.