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Posted by TheEdonian 19 hours ago

I don't think AI will make your processes go faster(frederickvanbrabant.com)
551 points | 381 commentspage 5
pron 17 hours ago|
There is another problem. For developers, productivity means "functionality produced per hour of work", but that's not what productivity means for businesses. To them, productivity means "money produced per hour of work", and because AI costs money, it is this number that needs to go up (not quite, as it's more "value" than money, but until the economy adjusts they are similar). Even if we could considerably reduce the time between releases and/or do it with fewer people at scale across the industry, for it to pay off, we'll need to see a corresponding rise in demand for software and/or features.

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?

hibikir 17 hours ago||
Every large corporation is stuck in communication problems and approval processes. They have grown so large as to have minimal alignment between what the company attempts to produce, what makes the company profitable, and what people actually do. Enshittification, The Gervais Principle, Bullshit Jobs. Pick your favorite, flawed way to look at what is going on, it's all blind people touching different parts of the same elephant.

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.

TeriyakiBomb 17 hours ago|
While this is true, it doesn’t stop businesses being overzealous with AI. It’s a compound issue of a decade of ZIRP, grow at all costs and then covid overhiring and AI is suddenly poised as some kind of magical panacea.

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.

lmeyerov 16 hours ago||
It's felt awhile similar to what we see in parallel computing:

- 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

king_geedorah 18 hours ago||
> If you were to give human developers the same amount of feature/scope documentation you would also see your productivity skyrocket.

This is how I felt when I first started seeing people discuss things like AGENTS.md etc.

r2ob 18 hours ago||
Large corporations with orthodox methodologies will take time to extract the best benefits from AI. Small teams, which still remember the original Agile Manifesto, will soar and overtake their competitors.
enoint 17 hours ago|
Speaking about the middle, once I was shown advice from ai that a particular ticket would stall at “frozen middle management” and should be shelved until “coordination” improved. That sounds accurate, but can you imagine what a token-obsessed PM might say?
bicepjai 16 hours ago||
Recent NYT podcast showcased how China and the US are putting time, effort, and money into using AI. I have to say I liked China’s approach of AI percolation into the economy than US approach of walled gardens with cloud.

https://podcasts.apple.com/us/podcast/the-daily/id1200361736...

shay_ker 16 hours ago||
> "requirements were always the bottleneck"

> "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?

RaftPeople 14 hours ago||
> 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.

Etheryte 16 hours ago|||
Isn't the answer to both questions straightforward? Real life is complex and has nearly infinite degrees of freedom. This means it's hard to approximate in software. Over time, real life, your understanding of it and your approximation (the software) all change. Keeping the approximation accurate enough that it's useful takes considerable effort since now you need to understand both the real life and the previously existing approximation of it.
JackSlateur 16 hours ago||
What do they do ? Give power to their management ? "I am responsible for 50 people, I am important". "I managed over 250, I am important, give me money".
slashdave 16 hours ago||
> Every software developer knows that you can’t make projects go faster just by typing faster.

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.

praneetbrar 18 hours ago||
If the underlying workflow is noisy, ambiguous, or overloaded with coordination overhead, faster generation just produces more low-context output to review and reconcile.
q8zd3 15 hours ago|
Honest question: Does anyone know about any quantitative study or analysis on productivity gains using code assistants? Asking for numbers comparing between the "pre AI era" and now.

Also, I have the impression that LLMs bring some gains or benefits for individuals but not relevant enough at the organization level.

Izkata 9 hours ago||
Here's the big one that was being passed around months ago, which nowadays usually gets dismissed out of hand because of when they did it (while ignoring the relative finding): https://metr.org/blog/2025-07-10-early-2025-ai-experienced-o...

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.

adrianN 14 hours ago|||
I believe it is very hard to quantify „productivity“. I’m sure that for suitable definitions you can find gains from coding assistants. Personally I get more code written and more features implemented. Yet I’m very wary of coding assistants because I believe they deal a fatal blow to my ability to understand the system. All LLM generated code is (at best!) code that was written by an intern which I just helped with the design and reviewed (unless productivity expectations cut down my review time and I get LLM assistance for reviews too). My grasp on the inner working of that code is much more tenuous than had I written it myself. I will never become an expert by just reviewing code and prompting.

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?

Supermancho 14 hours ago||
Followup question: Does anyone know about any quantitative study or analysis on productivity without using code assistants? (as a baseline)
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