[atleastoptimal]

Remember this is the worst AI will ever be from here on out. Models are only going to get better, faster, cheaper, more accessible and more easily deployable.

I think people need to realize that just because an AI model fails at one point, or some certain architecture has common failure modes, that billions of dollars are poured into correcting those failures and improving in every economically viable domain. Two years ago AI video looked like a garbled 140p nightmare, now it's higher quality video than all but professional production studios could make.

AI agents don't get tired. They don't need to sleep. They don't require sick days, parental leave, or PTO. They don't file lawsuits, they don't share company secrets, they don't disparage, deliberately sandbag to get extra free time, whine, burn out or go AWOL. The best AI model/employee is infinitely replicatable, and can share its knowledge with other agents perfectly and clone itself arbitrarily many times, and it doesn't have a clash of egos working with copies of itself, it just optimizes and is refit to accomplish whatever task its given.

All this means is that gradually the relative advantage of humans in any economically viable domain will predictably trend towards zero. We have to figure out now what that will mean for general human welfare, freedom and happiness, because barring extremely restrictive measures on AI development or voluntary cessation by all AI companies, AGI will arrive.

[choeger]

Yet, AI agents don't replace software engineers.

Imagine a software company without a single software engineer. What kind of software would it produce? How would a product manager or some other stakeholder work with "AI agents"? How do the humans decide that the agent is finished with the job?

Software engineering changes with the tools. Programming via text editors will be less important, that much is clear. But "AI" is a tool. A compressed database of all languages, essentially. You can use that tool to become more efficient, in some cases wastly more efficient, but you still need to be a software engineer.

Given that understanding, consider another question: When has a company you worked for ever said "that's enough software, the backlog is empty. We're done for the quarter with software development?"

[atleastoptimal]

AI agents are replacing junior software engineers now at big companies, or at least lowering the number they are hiring.

Currently AI failure modes (consistency over long context lengths, multi-modal consistency, hallucinations) make it untenable as a "full-replacement" software engineer, but effective as a short-term task agent overseen by an engineer who can review code and quickly determine what's good and what's bad. This allows a 5x engineer to become a 7x engineer, 10x become a 13x, etc. which allows the same amount of work to be done with fewer coders, effectively replacing the least productive engineers in aggregate.

However, as those failure modes becomes less and less frequent, we will gradually see "replacement". It will come in the form of senior engineers using AI tools noting that a PR of a certain complexity is coded correctly 99% of the time by a given AI model, so they will start assigning longer, more complex tasks to it and stop overseeing the smaller ones. The length of tasks it can reliably complete get longer and longer, until all a suite of agents needs is a spec, API endpoints and the ability to serve testing deployments to PM's, and it begins doing first only what a small, poorly run team could accomplish, but month after month gets better and better until companies start offloading entire teams to AI models and simply require a higher-up team to check and reconfigure them once and a while and budget manage token use.

This process will continue as long as AI models grow more capable, less hallucinatory over long-context horizons, and agentic/scaffolding systems become more robust and effectively designed to mitigate and deal with the issues affecting the AI models that do exist. It won't be easy or straightforward, but the economic potential gains are so enormous that it makes sense that billions are being poured into any AI agent startup that can snatch a few IOI medalists and a coworking space in SF.

[choeger]

You're very optimistic in the potential of these tools. I tend to agree, but I think that they will find their master in formal systems. If productivity raises as you're predicting, the world won't accept 99,9% correct software anymore. There will be demand for 100% correctness.

Regarding the potential economic gains, they're exactly the salary of software engineers. That's a decent amount but not massive.

Compare this to civil engineering, architecture, and craftsmen. None have been replaced because machines let amateurs do something resembling their job.

[shaky-carrousel]

Oh no, no, this isn't the worst AI will ever be. Way worse LLMs are yet to come once the cost cutting efforts begin.

[atleastoptimal]

I mean it in that this is the worst the "best currently existing AI model" will ever be

[shaky-carrousel]

Yes, I know. But the economic reality is that people won't have access to the best existing model, but the most profitable one.

[xigoi]

Assuming that AI models don’t disappear, this is a tautology. Without that assumption, you can’t be sure.

[stevage]

> AGI will arrive.

This does not follow. Your argument, set in the 1950s, would be that cars keep getting faster, therefore they will reach light speed.

[atleastoptimal]

That analogy only makes sense if current AI capabilities : AGI :: 1950s car speed : light speed.

The speed equivalent of AGI is way below light speed, in that the requirements for silicon to replicate the synaptic complexity of the human brain is far below the maximum compute human civilization can achieve as allowable by physics.

The more important question is whether the progress we've seen in AI is putting us on reliable track to hit AGI in the near future. My opinion is that we are, and not just because Demis, Sam, Elon and Dario say so, though they have very good reasons for believing so (yes, besides mere hype and speculation.)

[reducesuffering]

Exactly. The inability of people to extrapolate towards the future and foresee second-order effects is astounding. We've seen this in climate change and we've just seen this in COVID. The ones with foresight are warning about the massive upheaval coming. It's time for people to shake away their preconceived notions, look at the situation with fresh eyes, and deeply think about what the technology diff from 5 years ago to today, means for 5 years from now.

[xienze]

> Exactly. The inability of people to extrapolate towards the future and foresee second-order effects is astounding.

On a related note, many people also assume that just because something has been trending exponential that it will _continue_ to do so...

[atleastoptimal]

Things on an exponential trend tend to continue unless they hit a fundamental limit that leads to an inflection point and then a sloped off S-curve.

Moore's law continued on an exponential for decades. The fundamental limit in terms of transistor density are the laws of physics (uncertainty principle will eventually be a problem), but so far so many paradigms in compute improvement have emerged (especially in GPUs and AI-specific compute) that it has become super-exponential in some respects.

So the question is whether there is a fundamental barrier that AI will hit. The main issues people bring up are a lack of high quality human-generated data, fall-off in value per compute spent, and limits to autoregressive models. However it seems that pretraining has been the only paradigm beginning to show diminished returns but test-time compute and RL are still on the exponential curve.

[_puk]

Haven't they already started to regress?

I'm bullish on specific areas improving (I'm sure you could selectively train an LLM on the latest Angular version to replace the majority of front-end devs given enough time and money, it's a limited problem space and a strongly opinionated framework after all), but for the most part enshittification is already starting to happen with the general models.

Nowadays even ChatGPT doesn't bother to even refer to the original question posed after a few responses, so you're left summarising a conversation and starting a new context to get anywhere.

So, yeah, I think we're very much into finding the equilibrium now. Cost vs scale. Exponential improvements won't be in the general LLMs.

Happy to be wrong on this one..

[atleastoptimal]

Are you using the free version of ChatGPT, or just 4o?

Whatever model is cheap to provide inference for free is irrelevant when it comes to discussing SOTA AI capabilities and their impact. The state of the art has been reliably improving markedly over the past 3 years. o3, Claude opus 4, gemini-2.5 all surpass their predecessors in every benchmark and indicate that improvement isn't slowing down.

If GPT-5 comes out and it's somehow worse then I'll concede to your point, but so far the claim that the latest models are getting worse is mere speculation and makes no sense given that most labs are already aware of the potential for data contamination and such and have taken measures to ensure high data quality for the models they're spending hundreds of millions to train.