Yeah, that's a great question. I should probably write about it properly, if only to structure my own thoughts.
The main thing I gained was the wisdom that comes from the pain of doing it wrong.
The most painful lesson was finding out I was more attached to the idea that I had a validated product, than to actually having a validated product. (And treating marketing as an afterthought... lmao)
Also if I had realized how long it was going to take, I would have taken a very different approach. I accidentally took my R&D mindset into production, which... might have worked, if I were immortal and retired!
In short, aim to ship 10x sooner, because everything takes 10x longer than you think. (Reality has 100x more detail than it ought to.)
On the bright side, I also learned to put one damn foot in front of the other, persist despite the horror, and finish the damn thing.
> reducing the skill required to operate AI agents
For accomplishing the same task, yes.
But given these tools straight out of science fiction, why on earth would you be stuck doing the same things? There's no point spending human thought over something an agent has just automated yesterday.
Think bigger, take on more ambitious projects that are perpetually at the limits of what you and AI can accomplish.
> one where software engineers can focus on data structures, software architecture and algorithms.
I see this a lot and I'm not sure why people don't think AI will be able to do this too. The self-play training that got them writing code can be used for this too.
Given enough context for a business problem, sure.
But LLMs are not in a condition to judge how you should pick the technical solution to a business problem with several stakeholders, risks, and so on.
With the speed AI moves, a lot of technical decisions become reversible. And while engineering often makes decisions that could lie elsewhere in the business, outside of engineering, I could imagine those decisions moving elsewhere in a fully AI world.
Do you have examples of things that would be hard to train for? One that could be compensated for with changes elsewhere in the business process?
Yes, if Alex from BizDev is a scheming moron who consistently lies about the priority of features, it’s hard to keep an LLM on the loop about it when transcribing meetings and feature requests. If your boss is gonna be unavailable for a month and that means that a junior devs garbage PRs will be getting merged because the second in command is much laxer then you need to be aware for that and so on.
What I mean is that these things decant into technical decisions and even with all the AI in the world running a DB schema migration does not become any more trivial.
In the long term, I think you're correct. In the medium term, AI still won't know your business-specific workflows and data relationships, and humans are needed to define those things and let the AI build the scaffolding around it.
There is a sort of conceit here that tech is everything. There doesn't need to be "no jobs", it could just (or mostly) be the tech sector that is impacted and jobs are displaced. In the history of concentrated sector losses, the US public hasn't been kind to those displaced. The world marches on, no matter how important we think we are.
You are right, my answer was meant for parent's "“AI agent, here’s our assets, make money”" and "If every year 50% of the job disappears, how long until there is no human-ai team, just ai.". I mean it in a broader sense than just IT workers. The world will be fine without us if that's where it all stops.
If at some point most enterprises run as "agent, make me money" without any humans, then money is a dead concept.
Why does that matter? b2b sector doesn't make any sense if there is no "clients" at some end point. There is no money in the end of the chain if everybody is jobless. And if 99% of the population don't have magic numbers, the magic numbers concept just doesn't make sense.
The whole modern society and capitalism exist on assumption of meritocracy: an entrepreneur takes the risks and provides the organization and gets the reward. If there is some perfect machine that always brings profits, there is no risk. Everybody can make such "organization" saying "/goal make me money". The current model also assumes that if you learn and work hard enough, then you get a success of some level. That assumption of the meritocracy also breaks.
If there is no organizational work, nor risks, nor meritocracy, nor "fair reward for hard work" the whole model fades. There are might be different rational choices. One is probably to liquidate the owners of the companies; nationalize the model's output and distribute the profits, for example. Still many questions about everyday life for completely jobless society will arrive then. It might be something else, but people generally do what they see fair, and they also want to eat something.
Ownership is a fragile idea, Russian Empire shows that pretty clearly. It exists only when most of the society agrees that the ownership is fair or when you have enough force to maintain it against those who don't, which is not the case when the majority is on the other side.
You are packing so many assumptions into here about how the world works that are just wrong, I have no way to really respond.
Your strongest point is “will people stand for a world with no jobs, where only a percentage of the population owns most things?” And the answer is who knows, people stand for a massive amount of inequality right now, and technology for suppressing the masses is getting much better every year. It’s a big unknown imo, and probably contingent on too many things to predict at all.
Yeah, some people are is saying "Oh coding is solved, but we still need people to guide the architecture."
But coding was "solved" by self-play against "small" problems. Is there any reason training couldn't be scaled up to even longer horizon tasks like evolving an architecture in a growing business? The increasing load from a growing business, both in terms of usage and product accretion, can be entirely simulated in a data-center, and therefore trained against.
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