Join the discussion
Write your take first — we'll ask for email only when you're ready to publish.
- Hacker News
- No point in going anywhere for its own sake. When there's a need to go somewhere, we'll try to get there. (But maybe with a different mode of travel.)by shannifin
- This. If AI can do most of the things why do we need any specific place to go? Software engineering is not a jobs program.by simianwords
- I don't think "new libraries and languages, ... won't gain traction". I think that a higher standard is expected for a new library or language to gain traction.
I foresee the entire "technology adoption lifecycle" of innovators, early adopters, early majority, late majority, and laggards will be longer due to the relatively higher expense of adopting a new technology. I don't think it is a bad thing, but it might delay the adoption of promising tech in industries.
Meaningful library/language improvements will have their benefits, regardless if the user is a human or an LLM. Therefore, their adoption will see benefits as well, eventually.
by andful - True intelligence should not have problems adapting to new programming languages. Especially with good TTD and other harnesses, current SOTA models are likely very capable of writing code in esoteric languagesby lukasbm
- They are capable but at a context and token expense handicap. Which means it is pointless and uneconomical.by varjag
- 1. LLMs will enhance the “normcore” - which is fine, and probably slow down churn (which may or may not be good - see JavaScript)
2. The problems are not how fast or how well people develop code. See https://youtu.be/5ybAhgAaEBo?si=C6q-fWXHlEO74kNi where an ex CTO of eBay is proud he doubled 3000 developers time to get out features while totally ripping into eBay’s pathological Management etc
3. The problem has never really been “we cannot write software” - software that is actually in production and working is a tiny sliver of the phase space of software - if it’s in production and working the golden path that software is the top 1% of the 1%
4. The problem is the software that did not get made because the org does not know how to ask for it
That comes from three dimensions
- org pathology (people with the cash cannot agree on best approach)
- software literacy (people with cash don’t know that something is not only possible but common)
- org business model / mission (the guy in video made a deep point - eBay has not grown in real terms since 2008 and is getting its lunch eaten by more targeted competitors. So any CxO who plans to get their bonus based on “growing the revenue” is on a loser. So they fall back to “make a plan, follow the plan, get bonus for hitting your milestones”
This is partly why government departments seem awful and it takes politicians to change what is being measured
So incentives need to be aligned - so change the metrics internally to make them work for the mission.
If you don’t have a mission perhaps that’s where to start
Look at Microsoft - once “a PC on every desktop” was reached they really struggled for a couple of decades.
Perhaps we should just kill all companies once they stop growing …
Edit: One could see that startups are merely experiments in PMF - and once that has been reached, then a small cofe can be left to milk the cash cow while rest of the cash is returned for new experiments (perhaps even using medical drug “endpoint” measures
- Not to be that one guy asking why this is on the front page of HN, but why is this on the front page of HN?by Retr0id
- AI chud bait.by archagon
- Although I don‘t feel the same level of despair as the author, it struck a nerve reading this. I can imagine that many other had a similar reaction.by 3ot
- Tricky to make predictions based on costs or quality when the costlier parts, training and inference, are totally disconnected from reality thanks to VC money and when total cost of ownership over time is radically different.by utopiah
- They aren't totally disconnected from reality. There are plenty of providers who don't have a ton of VC money selling tokens at basically cost price.by IshKebab
- We might not even need libraries after all, since APIs, libraries and abstractions in general exist for humans to grasp complexities. Abstractions have their merits, but they have downsides too, and AI might be a way to solve them. The interesting part for me is where that ends, because it's systems all the way down, and even on a higher level abstractions exist to allow humans to make sense of the world. Services, products, companies, political parties, what if in the future we don't need any of it anymore because the abstraction is obsolete?by groestl
- Where we're going, we won't even need programming languages or libraries at all. AI's will just generate pure binaries full of magic, right? /sby sgt
- Libraries contribute to the humans being able to review things well, so no, they may still have their use cases.
An AI might be able to create everything “from scratch”, sure - but I ain’t sitting there reviewing every bespoke thing it shits out. I want that offloaded to make it maintainable.
by Klonoar - Indeed.
Perhaps we will go from:
framework X -> avoid writing boiler plate
to
AI -> avoid writing boiler plate
And AI may generate the more explicit boiler plate more reliably directly, than the higher level framrwork with leaky abstractions.
by DrScientist - What about readability? Are people going to give up on reading and understanding the code? Maybe the abstractions aren't only necessary for writing the code?by tmnvix
- The libraries are there for avoiding repetition. It is useful on its own without other benefits like implicit/explicit boundaries, readability by humans etc. If all lego(base unit software) pieces were unique shaped it might fit your requirements more efficiently but the cost of building those unique shaped legos explodes with the size of the lego piece required.
LLMs compress the known shapes well and fit them to solve for a problem but they are still not good enough to build from scratch a large new lego piece which fits a full problem perfectly. And such a large lego piece might not be the most efficient solution either and might be difficult to prove so.
by sumitkumar - They also help AI to manage complexity, in just the same way.by HPsquared
- Libraries do a LOT more than that. Abstractions also make LLMs more efficient, and lets some problems be handed off to much cheaper algorithms. They also mean that the way data is handled can be standardized which means you don't have to design an entire protocol from scratch every time you want to send data somewhere.
Every advantage libraries have given us still exists in the world of AI.
by Turskarama - > Business will accept 99.99 at fraction of cost of 99.999.
In my experience, there are far too many 9s in that sentence. But the sentiment absolutely holds.
by beej71 - > you might find the quality subpar, but in terms of cost ratio, it is commercially good enough. Business will accept 99.99 at fraction of cost of 99.999.
What is this based on?
- I mean, can't you just look around? Also from a business standpoint it just makes sense. Diminishing returns on product quality has been known for a long time. Look at Displays for instance. A set number of dead pixels is seen as okay, not because it is okay, but because the cost increase to have no dead pixels is extremely high.by zipy124
- Now that there are many models on par for most day to day coding tasks, I think this will prove to be a complete fallacy. If anything, since LLMs, my tolerance for bad software has gone down.
Example- I'm no longer a Claude subscriber, but in the past it was the only game in town. Not any more: I'm simply done dealing with the countless issues in their client. With Pi, I'm no longer sitting there like an idiot slamming the enter key because ask_user_questions is broken again and I have to force quit the application (just to mention one of the countless issues). Hoping my org switches away from Claude too...
by borzi - It is based on Anthropic Uptimes. If anthropic wanted 99.999 they will have to acquire 10 times more GPUs/infra to match the traditional free resources of 99.999 services.by sumitkumar
- Nothing, this is pure speculation. Nobody knows how this is going to pan out - definitely not this guy.
It might turn out AI does all programming in future. But it also might turn out that it's incapable of long term maintenance. We don't know yet.
by IshKebab - Personally, based on over a decade of professional software engineering experience at all sort of organisations, from small startups, Government, big tech, financial services of all scale.
In terms of literature, you can look into "enshiftication" as a starting point into research around cost savings and value extraction in business.
Also, about 7 years ago, I had some ideas about how AI will pan out, and here is the releveant excerpt from it:
> [S]oftware development industry, the very workhorse of automation and tools of efficiency is eating itself away. The outward image may look very different with the software development becoming an integral part of more and more industries, yet, the number of developers required for a project of given complexity is reducing at unimaginable rate.
https://gist.github.com/omeid/9a180f9acccc8409d6c5f3e7fa1a77...
by omeid2 - > Software Engineering as science will be largely dedicated to AI development
Which will require fewer people.
Simple software engineers who work on CRUDs and are not PhD-s and stuff will go away. Most of software is like this. The few percent who work on kernels, AI models, etc. will still have work. The rest won't, or rather much less people will be needed to simply use AI to do that work.
by dmortin - Software engineering has been getting more efficient for decades and over that time there’s been a massive increase in the number of software engineers.
The Mythical Man-Month describes teams of ten engineers operating before the Internet. Today (pre-LLMs) that’s probably 1-2 people.
by alecbz - But right now AI assisted development isn't really making you more productive by an order of magnitude. It's easily a game of whack-a-mole right now. I estimate that an excellent programmer equipped with Claude Code only sees a 30-40% improvement in speed right now
What you speak of may happen, but I doubt it will happen in the next 10 years.
by sgt - Sometimes I feel the same but then I sit in a refinement meeting and have to handhold the stakeholders through the process of finding out what they actually want, what they don't want, which trade offs they find acceptable and which they don't. And while AI is infinitely patient, my feeling is that being good at this is a super individual people skill and I know that more often than not the people I am talking to are kind of happy they are talking to a person instead of a machine. However when it comes to actually implementing the stuff, AI reliance is now necessary with the timelines given.by Escapado