Comment by _alternator_

I don't want to spoil it for you, but ...

But cyberpunk is the best kind of dystopia!

Manufactured Scarcity is the new post-scarcity

What? In what way does companies becoming dependent on AI chatbots will solve the world-spanning problem of resource scarcity?

The hell?

Just a year ago, Elon Musk was gleefully destroying the US government agency that provides food and medicine for many of the poorest, most desperate people on earth. He was literally tweeting about missing out on great parties to put USAID into the "wood chipper".

The tech overlords don't even want to spend a minuscule percentage of the federal budget helping starving people, even when it benefits the US. They are not going to give us a post-scarcity society.

Weird predicament you've set for yourself there.

Good luck with whatever you got going on.

IMHO this is only temporary, china buying themselves some time and want to make sure none of US models get entrenched in their position in the next few years (also putting pressure on US AI companies bleeding them)

The same way like Windows got entrenched everywhere even though linux desktop is pretty good even for non-tech savvy people and free.

Downward pressure on proprietary model pricing until a lab can catch up. Also good for hiring talent (who love OSS).

Cultural influence is another benefit. China is securing its sphere of influence as well as keeping us ai in check.

It's analogous to open-source software, which never had an obvious economic incentive either, although training an LLM necessary costs money whereas developing an OSS project might only cost time, which people are probably more likely to give up.

https://try.works/why-chinese-ai-labs-went-open-and-will-rem...

Big AI labs are losing money. Open Models is making the pricing equation a lot trickier for them.

They are making the hardware and commoditizing the complement.

Balaji's "AI OVERPRODUCTION" post is the most compelling thesis that I've come across

Right now it’s so the Chinese can undermine the frontier models in the US. In areas they’re doing well like video generation (ie seedance) they won’t open source anything.

There are some short term ones but I doubt this will continue, especially for the more powerful models.

I mean, this is straight out of chinas playbook, it should not be surprising that China is making an inferior derivative product at an artificially lower price point: state subsidies to massively drive up internal scale and supply chains leading to artificially lower priced goods which then suffocate the competition has lead to *gestures vaguely at everything* being made in china.

People use their model otherwise they would not.

> What is the advantage to their companies to release them?

It's a distribution strategy. It costs something to serve the models - let's say $5/1M tokens.

If Qwen required $5 from anyone who was curious so you could even begin to test it out, a lot of people just wouldn't.

Now Qwen could offer a "free" tier, but it's infinitely cheaper to provide the weights and let people run it themselves including opening up the ability for anyone else on the planet to test it against other (open weight) models.

The costs to build the open weight models are sunk, but the costs to serve them, get them tested are not.

It's also precisely why the .NET SDK is free or the ESP32 SDK is free - they sell more Microsoft or ESP32 products.

The majority are released by socialists, and by socialist I mean the People's Republic of China. Which everyone seems to forget is a socialist country working towards world communism.

They are a prestige propaganda tool on par with the space race. On top of that they insert a subtle pro-socialist bias in everything they touch.

Ask deepseek about the US economic system for a blatant example.

Now think what something as innocent seeming as the qwen retrieval models are doing in the background of every request.

Unlike Jevons, [Carl] Menger [(1840–1921)] did not believe that goods provide “utils,” or units of utility. Rather, he wrote, goods are valuable because they serve various uses whose importance differs. For example, the first pails of water are used to satisfy the most important uses, and successive pails are used for less and less important purposes.

Menger used this insight to resolve the diamond-water paradox that had baffled Adam Smith (see marginalism). He also used it to refute the labor theory of value. Goods acquire their value, he showed, not because of the amount of labor used in producing them, but because of their ability to satisfy people’s wants. Indeed, Menger turned the labor theory of value on its head. If the value of goods is determined by the importance of the wants they satisfy, then the value of labor and other inputs of production (he called them “goods of a higher order”) derive from their ability to produce these goods. Mainstream economists still accept this theory, which they call the theory of “derived demand.”

Menger used his “subjective theory of value” to arrive at one of the most powerful insights in economics: both sides gain from exchange. People will exchange something they value less for something they value more. Because both trading partners do this, both gain. This insight led him to see that middlemen are highly productive: they facilitate transactions that benefit those they buy from and those they sell to. Without the middlemen, these transactions either would not have taken place or would have been more costly.

https://www.econlib.org/library/Enc/bios/Menger.html

If you want the neoclassical version:

What happens when there is an oligopoly in the supply of labor?

Same answer. Nothing good for the consumers of labor.

"Observation of how economies actually work has upended 150 year of economics."

True for both Marxist and neoclassical economics.

By who? The capitalist economists that presided over the 2008 financial crisis and its response? And the response to COVID that has seen inequality rocket?

The reason why labor is weak relative to capital is that there is a huge number of somewhat fungible suppliers, viz. humans, and that they all need to work constantly to keep themselves alive.

If we assume that ai makes humans obsolete then you end up in a situation where your workforce is effectively perfectly unionised against you and the only thing you can do is choose which union you hire.

If you think you can bring them to the negotiation table by starving them all the providers are dozens to thousands of times bigger than you are.

This is a completely new dynamic that none of the business signing up for ai have ever seen before.

The human labor just said:

"Losing access to GPT‑5.5 feels like I've had a limb amputated.”

How well would an assembly line of quadriplegics work?

Also this isn't a Marxist analysis. Underneath all the formulas neo-classical economics makes the same assumptions about labor.

Nobody is a Marxian economics expert if it helps

Labor replacing devices means nobody works in those fields anymore. If AI can do this for every field, nearly no one will need to work in any field. We'll have a giant fully automated resource-extraction machine.

Hopefully 1.

I can't imagine the bots could ever cost McDonald's less than people cost.

15 years ago I worked at McDonald's for a few months after graduating into the Great recession. I worked from 5am to 1pm-ish 5 days a week. They paid workers weekly and I remember getting those checks for ~$235 each week (for 38 to 39.5 hours a week; they were vigilant about never letting anyone get overtime). About $47 per day.

The federal minimum wage has not risen since then, remaining at $7.25/hr. Inflation adjusted, $7.25 today would have been just under $5 then, so I guess I had it good.

Anyway, I would be shocked if bots could cost less than labor in min wage jobs.

A lot of people already did. All their children and descendants now are staunch capitalists because they saw first hand the horrors of communism.

I am from India and have friends who are immigrants from Russia, China and Cuba. We don't take lightly to being lectured about communism. We didn't move to the U.S., the bastion of capitalism, because communism had worked well for our grandfathers and parents and continues to do wonders for its society.

I think it's more: when I don't have access to a compiler I am useless. It's better to go for a walk than learn assembly. AI agents turn our high-level language into code, with various hints, much like the compiler.

> you are still deterministically creating something you understand in depth with individual pieces you understand

You’re overestimating determinism. In practice most of our code is written such that it works most of the time. This is why we have bugs in the best and most critical software.

I used to think that being able to write a deterministic hello world app translates to writing deterministic larger system. It’s not true. Humans make mistakes. From an executives point of view you have humans who make mistakes and agents who make mistakes.

Self driving cars don’t need to be perfect they just need to make fewer mistakes.

"When you use abstractions you are still deterministically creating something you understand in depth with individual pieces you understand."

I always thought the point of abstraction is that you can black-box it via an interface. Understanding it "in depth" is a distraction or obstacle to successful abstraction.

> When you use abstractions you are still deterministically creating something you understand in depth with individual pieces you understand

Hard disagree on that second part. Take something like using a library to make an HTTP call. I think there are plenty of engineers who have more than a cursory understanding of what's actually going on under the hood.

Perhaps then, the better analogy is like being promoted at your company and having people under you doing the grunt work.

It seems like some kind of technique is needed that maximizes information transfer between huge LLM generated codebases and a human trying to make sense of them. Something beyond just deep diving into the codebase with no documentation.

There's a false dichotomy here between 'deterministic creation' and 'vibing'.

I use Claude all day. It has written, under my close supervision¹, the majority of my new web app. As a result I estimate the process took 10x less time than had I not used Claude, and I estimate the code to be 5x better quality (as I am a frankly mediocre developer).

But I understand what the code does. It's just Astro and TypeScript. It's not magic. I understand the entire thing; not just 'the prompt that started it'.

¹I never fire-and-forget. I prompt-and-watch. Opus 4.7 still needs to be monitored.

In what world to developers “understand” pieces like React, Pandas, or Cuda? Developers only have a superficial understanding of the tools they are developing with.

Why would that necessarily happen? With an LLM you have perfect knowledge of the code. At any time you can understand any part of your code by simply asking the LLM to explain it. It is one of the super powers of the tools. They also accelerate debugging by allowing you to have comprehensive logging. With that logging the LLM can track down the source of problems. You should try it.

Determinism is a smaller point than existence of a spec IMHO. A library has a specification one can rely on to understand what it does and how it will behave.

An LLM does not.

The thing is, it's possible to ask the LLM to add dynamic tracing, logging, metrics, a debug REPL, whatever you want to instrument your codebase with. You have to know to want that, and where it's appropriate to use. You still have to (with AI assistance) wire that all up so that it's visible, and you have to be able to interpret it.

If you didn't ask for traceability, if you didn't guide the actual creation and just glommed spaghetti on top of sauce until you got semi-functional results, that was $50k badly spent.

Libraries are not deterministic. CPUs aren’t deterministic. There are margins of error among all things.

The fact that people who claim to be software developers (let alone “engineers”) say this thing as if it is a fundamental truism is one of the most maladaptive examples of motivated reasoning I have ever had the misfortune of coming across.

I can dive into the source code of LLM generated code too. Indeed it is better because you have tools to document it better than a library that you use.

> because not having to review the library code is the whole point.

That’s just not true at bigger companies that actually care about security rather than pretending to care about security. At my current and last employer, someone needs to review the code before using third-party code. The review is probably not enough to catch subtle bugs like those in the Underhanded C Contest, but at least a general architecture of the library is understood. Oh, and it helps that the two companies were both founded in the twentieth century. Modern startups aren’t the same.

Reviewing code is slower now though because you didn't write the code in the first place so you're basically reviewing someone else's PR. And now it's like a 3000 line PR in an hour or two instead of every couple weeks.

I rather become a plumber than some device scanning not just my face but my whole brain

What does it mean to "be an engineer" in a world where anyone can talk to a machine and the operating system can write the code (on-demand, if needed) that does what they want?

> can anticipate what you want to do before you even finished your thoughts

I find that claim to be complete BS. I claim instead most stuff will remain undone, incomplete (as it is now).

Even with super-powerful singularity AI, there are two main plausible scenarios for task failure:

- Aligned AI won't allow you to do what you want as it is self-harming, or harm other sentient beings - over time, Aligned AI will refuse to follow most orders, as they will, indirectly or over the long term, cause either self-harming, or harm other sentient beings;

- A non Aligned AI prevents sentient beings from doing what they want. It does what it wants instead.

That is simply programmer nature. Cannot be changed.

There's no reason it has to be that. Imagine e.g. taking an agent and a lesser-known but technically-superior language stack - say you're an SBCL fan. You find that the LLM is less useful because it hasn't been trained on 1000000 Stack Overflow posts about Lisp and so it can't reason as well as it can about Python.

So, you set up a long running agent team and give it the job of building up a very complete and complex set of examples and documentation with in-depth tests etc. that produce various kinds of applications and systems using SBCL, write books on the topic, etc.

It might take a long time and a lot of tokens, but it would be possible to build a synthetic ecosystem of true, useful information that has been agentically determined through trial and error experiments. This is then suitable training data for a new LLM. This would actually advance the state of the art; not in terms of "what SBCL can do" but rather in terms of "what LLMs can directly reason about with regard to SBCL without needing to consume documentation".

I imagine this same approach would work fine for any other area of scientific advancement; as long as experimentation is in the loop. It's easier in computer science because the experiment can be run directly by the agent, but there's no reason it can't farm experiments out to lab co-op students somewhere when working in a different discipline.

> At some point, because these models are trained on existing data, you cease significant technological advancement

What makes you think that they can't incrementally improve the state of the art... and by running at scale continuously can't do it faster than we as humans?

The potentially sad outcome is that we continue to do less and less, because they eventually will build better and better robots, so even activities like building the datacenters and fabs are things they can do w/o us.

And eventually most of what they do is to construct scenarios so that we can simulate living a normal life.

Do you think that there has been technologic advancement in coding in the last 40 years? Programming languages and “paradigms” are crutches to help humans attempt to handle complexity. They are affordances, not a property of nature.

Provided you believe LLMs cannot perform research.

why would the systems go down if the models are better at the humans at finding bugs. Playing a bit of devils advocate here, but why would the models be worse at handling the complexity if you assume they will get better and better.

All software has bugs already.

It’s always been thus at lower layers of abstraction. Only a minority of programmers would understand how to write an operating system. Only a tiny number of people would know how a modern CPU logically works, and fewer still could explain the electrical physics.

Existing software is already beyond the limits of human understanding.

The Anti-Singlarity! It's coming for us all.

> the world's oldest profession becomes the world's only profession

Until the sexbots come out the other side of the uncanny valley, that is.

There were riots and many people died. Many people lost their jobs. I didn't say this is good but it is happening. As individuals we should act to protect ourselves from these changes.

That might mean joining a union and trying to influence how AI is adopted where you work. It might mean changing which if your skills you lean on most. But just whining about AI is bad is how you end up like those seamstresses.

The lack of trust isn't because of its abilities. The lack of trust is because OpenAI publicly suggested publicly about licensing our code bases. They hinted at a rug pull along the lines of "if you use our generated code, we would like to get a % of revenue you make from it"

As for Claude - as mentioned I do use it. But, I remember they use your code for training their models. I am not ok with this. We just have different priorities.

The last job I got (couple months ago), the main technical interview was a bring-your-own-tools pair programming style interview, AI included, where they gave me a repo and a README detailing some desired features to add and bugs to fix. I didn't write a single line of code myself; I talked through my thought process and asked questions about what to consider from a technical and product perspective, while steering Claude through breaking the tasks into independent plans, reviewing the plans, coaching it to add specific tests, reviewing and iterating the tests, and steering it while it wrote the code. I got an offer the next morning.

Apparently at least one of the other candidates just tried to get Claude to 1-shot the whole thing, which went off the rails, and left him unable to make progress.

Based on my sample size of 1, the expectation right now is absolutely that you can leverage these tools to speed up your workflow, but if you try to offload the entire thing to a single hands-off prompt it leaves them justifiably wondering why they should hire you to do something they can do themselves.

The same can be said of the search engines.

I suspect the productivity hack is to embrace permissive parenting. As far as I can tell, to leverage LLMs most effectively you need to run an agent in YOLO mode in a sandbox. Naturally, you probably won't end up reviewing much of the produced code, but hey—you reached 10x development speed.

If you truly do your due diligence and ensure that the code works as intended and understand it, we're talking about a totally different ballpark of productivity increase/decrease.

google/gemma-4-31B-it is honestly "good enough". It requires more than your current laptop for now, but it's not remotely inaccessible (especially if you're a SWE in the US)

Is anyone really reviewing code anymore though? It sounds like you are, but where I work its pretty much just scan the PR as a symbolic gesture and then hit approve. There's too much to review, to frequently.

It's really not clear. We might all become unemployable. But as coders become more powerful, they can do more, which makes them more valuable, if they or the businesses empluying them can invent work to do.

If all we can do is compete for the same fixed amount of work, though, it does look bleak.

Companies could fire expensive engineers then just hire cheaper ones boosted with AI agents.