Posted by TheEdonian 19 hours ago
Another option is that lower software costs would significantly reduce the cost of whatever non-software product the software supports (manufactured good, electricity, services, telecom etc.) but I don't know in which industry the cost of software is a large portion of the overall product cost.
And there's another thing. A company that makes tractors can't produce food without land. A company that makes metal machining equipment can't make cars without the raw materials. But a software company that makes software that automatically makes software could just produce the result software itself rather than sell the software-making software. If AI ever reaches the point it makes software at a marginal cost that's not much higher than the cost of the AI itself, what would be the incentive of selling that AI?
The way AI makes your processes go faster will have little to do with cutting software development time in itself, but by letting an organization be made with fewer people, which in itself lowers your misalignment issues. A giant company of 200K people will still be about as messy as one today, but you might be able to do a lot more with the same number of people, just like a lone programmer today, without AI, already does quite a bit more than anyone could do by themselves the 80s.
Maybe some of the advantages are that you don't need quite as many developers, or maybe you can use a smaller marketing team, or you don't need to spend that much time answering questions, because an LLM is doing it for you, and it's tracking what it's been asked of it, turning the questions into product research. Either way, the gains come from being able to run leaner, and therefore minimizing organizational misalignment.
The broader issue is the sheer number of businesses that build massively overcomplicated stacks, bought heavily into bandage solutions like AWS lambda, got on dumb tech bandwagons like big data, nosql etc. This is just another one.
I think you can engineer yourself into being leaner, in some businesses AI will help but we’ve had over a decade of “we can just add more complexity” and it just does not work.
I’m a rails guy. People forget for every unicorn there’s 10 9 figure businesses just ticking away on some niche with a VPS, rails and like 4-10 devs.
- shift towards throughput-oriented vs latency-oriented. Can juggle more tasks, but increasingly hard to speed up individual ones.
- strong scaling is tough. Might even see slowdowns for individual tasks, so reliable benefits come from being able to juggle more and eat the per-task inefficiency
- amdahl's law: we can't speed up tasks beyond their longest sequential (human) unit, so our work becomes identifying those bits and working on them. Related: you can buy bandwidth, but you can't buy latency
This is how I felt when I first started seeing people discuss things like AGENTS.md etc.
https://podcasts.apple.com/us/podcast/the-daily/id1200361736...
> "faster typing won't make you faster".....
I understand a Deloitte consultant has specific incentives. But let's first try to answer a baseline question: why do some companies have thousands of software engineers? What do they all do?
And then, a follow-up: what is actually the bottleneck at most companies? What causes "requirements gathering" to take long?
Complexity.
In my experience (medium size businesses, i.e. 200 million to 2 billion annual revenue) we're trying to understand how a complex set of systems and business processes and different businesses (external partners) interact and then trying to morph all of that into a shape that now has capability X layered on top or in the middle.
Here's a concrete example, business X that makes their own products and has retail stores as well as an ecom site wanted to add the ability to put complementary items built by other companies on the website and have them drop shipped from the vendors to the consumers. The final solution involved 21 different interfaces between 4 different systems (ecom system, store system, omni channel system, external drop ship mgmt system) as well as a new internal system to manage this activity. It's takes a significant amount of time to understand and solve for all of the low level details.
You know, typing fast and accurately is kind of important.
The new speed skill that developers now need is speed reading. LLMs just make copious amounts of output (from tests, documentation, diagnostics). They also produce code so quickly that a skill for focusing on weak points is so important.
Also, I have the impression that LLMs bring some gains or benefits for individuals but not relevant enough at the organization level.
Here's a slightly more recent one focused more on comprehension/learning than productivity: https://www.anthropic.com/research/AI-assistance-coding-skil...
Metr attempted to redo that first one to get trends over time, but couldn't recruit enough developers to get reliable results for it.
For a while this is not a problem: I can work with my current mental model. But every generated PR erodes my expertise a little bit. Eventually my mental model won’t fit anymore.
So how much of that model maintenance should I count into my productivity metric? Does that even matter or will the next model be able to reason well enough that my mental model doesn’t matter?