Right now the AI LLM PRs we're seeing are just introducing more work for other people, while these so-called builders are looking good with their new dashboards and functionality they're demoing.
But you can't talk to them about the flow of the code. You can't ask them for their thinking as to why certain things are.
It's not built up from the ground with experience from x people taken into account. It's materialized from nothing, with no foundational separation, and barely any abstractions.
No one wants to touch it. The PRs are too large, and the 'authors' of the PRs aren't on call with us.
They get all the glory, but do none of the work.
It's kinda like designing a house and then sending it to an architect and engineer saying: make this work.
You can absolutely do this. It's even right most of the time.
You even have a fair chance of getting a response like that when there isn't anything wrong and the question wasn't rhetorical - which perfectly illustrates the level of the genuine understanding LLMs operate at.
A lot of average people are producing gigantic messes. At least previous to this they were gated by their mediocrity.
I have never seen anywhere in the world people that hates so much the working class as people do in the USA.
In my country the average employee is competent, they do their work and create wealth for the nation.
Again, only in the USA people think that billionaires are the ones creating value. Total non-sense indoctrination.
I find this varies by individual, but the AI taking care of so much boilerplate and rote work of coding, and taking the role of architect, test designer, and reviewer is a lot more productive for me. Check the code may take the same skill, but it's an order of magnitude less work.
Not sure if that's true or if it might be influencing what you're seeing, but it's a thought.
**Lead with the answer when asked how/which/whether.** Name the command/mechanism first; a question seeking understanding isn't a go-ahead to execute. Answer, then offer to act.* EDIT * What's with the downvoting? That's a correct description of what happened. You can't ask an LLM why it did something and expect a coherent response, because there's no thinking chain, and no stored thinking state... At best, you can get a reconstruction of how the context relates to the output (basically a summarization of the context).
> I shouldn’t have said that with confidence
> I got ahead of myself there
> I overstepped, allow me to correct that
It’s wild seeing how often it’s wrong, and I only know it’s wrong because I am an SME or actually reading the sources. Most of my coworkers are not SMEs with what they are asking and do not read the sources.
A huge part of my job now is fixing fuck ups and failures resulting from these slop jockeys who have already moved on to slop up the next task.
There are plenty of valid criticisms or warnings about over-reliance on AI coding, but this is not one of them. Today, I am using a semi-autonomous agentic coding system which has an `interview` functionality built in - when it spits out the PR from the input, if you have questions about the motivation or context for a particular choice, you can start up a clone of the original agent in a sandbox to question it.
Now, you might claim that those responses aren't always reliable, accurate, or consistent, and that claim has a little more weight (though, in my experience, decreasingly so) - but it is _certainly_ not the case that you cannot interview an agent about choices made. I'm literally doing it every day.
I've never worked at a company that didn't have a technical backlog measured in years.
Literally nothing works, all the timers/time counters are different across the pages, constantly commands hardware to do stupid shit, breaks during critical moments/in front of clients.
Eventually mgmt had to institute change freezes for high profile events because the team was breaking too much shit all the time.
The average C suite dipshit doesn't realize that the performance drops off a cliff once your project is more than some fraction of the context window so they will make pretty dashboards all day long but once you need to cover all the edge cases of a real system it all explodes.
AI isn't trained on the type of software style we'll need to create systems using AI, it's trained on how we used to write software. It doesn't reuse code or elegantly structure annoying, it just adds more code until the thing builds and passes some fake tests, even if half of it is functionally dead/unused.
Same with the MS surface(?) tables (not tablets). I saw load of companies buy into the hype and then discard.
The Concorde turned out to be fad (not "useless" - which was your reframing.) Touted as the future of travel, each seat cost about $20,000 of today's dollars, but it turned out even at those high prices people and companies were willing to pay per-passenger, supersonic trans-Atlantic air travel is not economically viable, and was discontinued.
Bold prediction. :)
I think anyone predicting a drop or near-term flattening is not thinking beyond the online bubbles where these tools are discussed. In a local tech meetup a lot of the normal companies are barely coming online with AI tools at their company, and even then with very low limits.
That was clearly a short-term trend that would obviously get fixed. Doesn't say much about AI coding as a business model.
Let me ask you this: is any technology worth so much break-neck adoption without first seeing clear evidence of ROI? No. The adoption is irrational.
Judging the ROI of an engineer is hard. Adding AI on top of that makes things worse, I think. I've heard AI makes engineers 3X, 5X, 10X and even 100X.
If I told my CEO that I was 4X more effective with AI, I am doubtful he would be willing to spend even 1X my salary on tokens. Even though he would be making out in the end.
At some point the ROI is pretty much vibes, man.
This means that the average engineer is efficient at (say) identifying the first 10 tasks they should do but there are diminishing returns after that? That seems like a weird pattern. Wouldn't it be more likely that certain tasks have a ROI based on how efficient the task is generated?
Like I'm trying to imagine in my head, if you think an engineer is more efficient with the tool, why deny them more tokens. I guess so they think to use them more efficiently?
So, maybe I conclude that I think your conclusion that there must be $1500 per engineer is flawed. And even if it were true, I don't think the benefit would be evenly distributed. I suspect this is a first pass at figuring how to budget them and there will be a second pass.
While it certainly reeks of motivated reasoning, Jensen Huang assertion that an expensive engineer should be using at least their salary in tokens feels more logically sound to me (assuming the average engineer is efficient at using tokens, I have a feeling it's a normal distribution)
At my company we can ask for temporary cap limits if it’s justified, which is fairly common.
Completely agree with that.
Think of people who were very strict with variable names. People who pushed for multiple-levels deep of abstractions for a single API logic that’s not going to be reused. People who believed that coding is craft, rather than just a process to get to the end during work hours. This makes most of these people’s points more-or-less moot.
I was in some of those camps, but I’ve seen coding evolve in the last 15 years. So I understand that these priors need to be updated, as most arguments don’t apply to today’s world.
The more things change, the more they stay the same.
I’m not proponent of AI generating everything without any supervision as of now. But willing to change my mind when it gets better.
Most software engineering jobs are not cutting-edge tech, or research, or solving unsolved problems. Integrations, APIs, figma-to-react pipelines, devops and etc. is what people get hired for. All those can be done much faster in the same-or-better quality by an experienced person with the supplement of AI. It’s hard to imagine any company would go against the grain and slow things down on purpose.
As far as “boring systems are boring”, I can tell you from experience that I work on a pretty boring system, and AI is not all that meaningful in terms of its impact, and it’s not for a lack of trying.
Can it help me create a migration and add an endpoint and such? Sure. But those aren’t the hard problems. They never were.
It’s funny that you think the idea of slowing down is such a bad one, but it is another well-established truth. Slow is smooth, and smooth is fast. This notion of break/fixing your way to prosperity by way of 10,000 ill-conceived PRs is a fool’s game.
Generally we've modified our timelines heavily, systems are working as intended, company is still making money. There are some AI-authored commits that had mistakes that we didn't catch, but I'm sure this could've been an issue even if all were human-authored. I know first-hand multiple other companies who are doing exactly the same thing.
I agree with "slow is smooth, and smooth is fast" for mission critical systems. But super majority of systems are, indeed, not mission critical.
I have watched some projects absolutely explode in LOC added, number of PRs, etc. but I think the more interesting question is: how much of it is directly being done to add customer value, how much of it is churn, etc. you might get some interesting answers.
As so frequently seems to be the case for you and I, we kind of agree but then you drop something that just does not compute for me: "slow is smooth, and smooth is fast" is not specific to "mission critical" systems, it is generally applicable.
As I said in a previous comment, I work on a fairly boring system. Its "criticality" is debatable, but in general we make the same kinds of boring guarantees to our users that even mediocre SaaS products offer: a few 9s of uptime, zero-downtime deploys, etc. AI has made aspects of working on this system easier, but in terms of API surface, how users are using it, how to safely advance its state without breaking existing callers, data migrations across services, and so on, very little has changed.
I have no idea how we can get people motivated to learn these through trial-and-error when AI coding exists though. I remember the days of spending hours on stupid bugs that AI can resolve within a minute. But I recall learning heavily from those experiences. Oh well…
we've got product folks vibing out prototypes (not shippable but clickable) in our main front end in a few minutes to an hour. This would previously have involved 3 people and several weeks, or a ton of figma and documents to fill in the gaps. This saves weeks to months and lets them really experience the items.
Then they hand it off to someone who knows all that stuff who is also using AI and the impl also gets done faster.
The PMs are either moving infinitely faster, or at least 30x faster and not blocked constantly by others.
basically you're not comparing people who don't know much (tech) with those who do, you're comparing them before and after access to AI.
I setup k3s, and tons of what would be otherwise unnecessarily complicated stuff on my laptop for my side projects with additional home servers, smart house stuff. Otherwise k8s and things like that would have been daunting to learn and in theory and without constant professional exposure, etc...
Microservices in Go, Rust, which I didn't have any previous experience with, games in C and other languages. Didn't know anything about low level memory management before. Was just mainly TypeScript person. Just constantly building random fun stuff.
The question is, how quickly does a junior with no experience builds intuition without trial and error.
Often that started with the macro recorder. Then you worked out what that "recorded" code/sludge did, removed the crud you didn't need or want, improved the logic and so on. I bought books to understand it better. Now you can ask a (different) LLM "what is this? why is it used? How would I?" etc which is probably a faster learning curve than books, newsgroups and old school personal home pages with good info.
I would have been quite surprised when I first used a VBA macro in anger just how far I would go down the rabbit hole. C, asm, verilog, Linux were no part of what I originally signed up for!
Some people will specialise in the equivalent of recording macros and go no further. And this will be fine for code that gets it done but doesn't matter too much in the other dimensions (security, reliability, usefulness without the authors' support, etc.) Much like VBA utilities inside companies that were useful way back when. Other people will want what they produce to be better, even good, and they will learn about floating point [1] and all the rest, much as I did. Probably learn pretty fast too. [2]
[1] https://docs.oracle.com/cd/E19957-01/806-3568/ncg_goldberg.h...
[2] Working out how to write an excel vba webserver and using it to collect and and collate summary data from various divisions into reports was seedy as hell, solved the actual business problem (given ridiculous but intractable constraints) and isn't something you can record. We all have stories from a misspent youth that we're simultaneously ashamed and yet somehow proud of.
No, but you do need to know the answer to respond to that 3AM page about prod being down.
everyone making comparisons to the dotcom bubble seems misguided. this is clearly computing 2.0 imo
I have my concerns with current inference pricing in that there's a non-zero possibility for a rug pull in the future for the subscription plans for organizations and individuals that can still use them. For now, its only companies larger than ~150 users that need to pay per token, but what if that wasn't the case? Not every company can afford over $1k/month/employee to give them access to AI tooling, further making it harder to compete against the behemoths. If we get to a point where an individual can no longer pay $100/month for nearly unlimited usage and instead must pay per token, that's going to be a problem.
Personal computing eventually became an equalizer (until we started centralizing on mainframes again, aka the cloud) because it got cheap. My hope is that inference also gets just as, if not cheaper.
I have high hopes for local AI and open weight models and we will continue the ethos of local, personal computing and not needing to offload everything to OpenAI/Anthropic/Google, etc. to get work done once the hardware and hardware availability catch up.
All companies who make this transition will be more or less at the mercy of model providers.
Most other workers are served fine by $20-30 worth of tokens on a budget model. You don't need Opus to help support write emails.
I'm optimistic that the demand for AI accessibility will drive programmatic interfaces in places where companies were previously reluctant to.
The general thrust that everything would be online was correct, it was just that the market mistimed and misallocated of capital by a decade or more. There was massive spending on infrastructure capacity that we wouldn't end up needing until the 2010s. There were hype driven valuations completely disconnected from business fundamentals just because a company was an 'internet' company. Things were going from cutting edge to obsolete in less than a year. There were breathless promises that this was business 2.0! Of course, none of that sounds remotely like what is going on today...
I'm optimistic about AI, but I also don't think that it is going to change everything as fast as promised.
Most directly, human labour. Labour is always a problem for capital. At a certain level of AI competence, businesses don't need to pay humans to complete the work they need doing in order to operate. I don't think anyone would dispute AI competence isn't growing steadily.
You update it for them every 3/4 years (if they're lucky).
It probably makes a bit more sense to compare it to existing software subscriptions like Office, or the old-school 'per-seat' licenses per user for software.
Probably not worth it risking your job for a 200$/month good, but at 5K, I'm sure some folks will be tempted. Especially if companies do stupid things like token usage leaderboards.
NFTs? My company had nothing to do with blockchain but I ended up working on NFT integration regardless.
Because there's not a single piece of evidence that this has improved the quality of the delivered software, or for that matter even the speed of features any of these companies produce, in fact if anything the opposite.
The point of software development, the hint is in the name, is to develop software, not consume tokens. If Uber was now full of 10x engineers the stock price of Uber would be up, not down on a yearly basis. Hilariously enough the only company whose stock price is up appears to be Antrophic
i.e. I am able to write about 1k lines of code of "acceptable" quality per week. Which means in 1 year, there will be about 5Ok LoC. I am pretty sure, that I would have to spent like 60-80% of time to maintain 1st year code and the rest to make new features in the second year so I would have to hire more people and spent time to onboard them to maintain velocity. All of that are rough estimates, probably overoptimistic and way worse in 3rd year. Good luck doing such estimates with code agents. Even worse if you already have huge amounts of legacy code.
As for why they got accepted so quickly 1) the industry's long running desperation to deskill computer programming 2) the addictive psychology baked into LLMs "That's an elegant solution! Shall I ... ?"
So there's a huge number of HN posters claiming that the price of tokens will go UP over time rather than down (that's how Moore's Law works, right???) or that code bases that AI contributes to will spontaneously combust, or something.
I mean, Github Copilot's pricing just went up considerably, so I guess they were right?
In the long term, tokens will fall in price. Obviously. (If "tokens" continues to be the unit)
In the short to medium term, for the IPOs to succeed, people have to start actually paying for what they are using, so the price will go up, and is going up, quite a lot. Once their value is set they will slowly fall from that point (or some point maybe halfway, depending on how much the market is willing to continue to subsidise).
I am an AI cynic, but I am now an informed cynic; I am learning agentic tools so I know where they are useful and I know my enemy.
I think the "fad" here is cloud-based, metered AI being a dominant work mode.
Nothing, so far, has suggested to me that any other outcome is likely than edge- to local-scale, on-device, on-laptop, on-prem models getting good enough to the point where people use them by default and use the cloud models only when they need the extra oomph.
I cannot believe that there is anything other than an enormous incentive for companies like Uber to find local, small model and on-premises solutions to their problems, not least while pricing is so changeable and people are getting nasty surprises.
Betting on OpenAI and Anthropic being around over the long term in the form that they are now, that feels like valley hopium. Utility monopolies essentially always derive from physical/geograpical limitations, don't they?
While I hope local AI continues to exist, I'm skeptical that it will take over, for the same reason running your own servers hasn't taken over. It's just hard, and involves spending huge sums of money up front.
It's also not really clear how much tokens are being subsidized. The discussion reminds me of Uber. For years people on HN claimed that Uber was going to collapse once they ran out of VC money. Then... that never happened, and everyone just moved on to discussing other things.
Now, that doesn't mean running your own LLM will be easy, but this will mean it's a lot more likely that there will be at least regional LLMs, in my opinion. I.e. there will be Google, whichever (if any) is left standing of OpenAI or Anthropic, and then there will be Chinese hosted LLMs, probably Indian hosted LLMs, European hosted LLMs, plus LLMs hosted on managed services (i.e. Bedrock). For sure I see large banks on the like being able to host the best OSS or even licensed LLMs on their own cloud infrastructure accounts (i.e. at AWS, Azure, etc).
And that's on top of the LLMs running on owned server infrastructure plus actual local, on device LLMs.
If you look at what Uber is spending per developer per month, they clearly have some headroom to consider whether more-local, unmetered AI tools on device, on premises, in private cloud, can be cost-effectively used to cut down how much money they are pouring into Anthropic and OpenAI. Not least because a bit of centralised effort might lead them to distilled models that are better for their purposes. Some of that budget could go into simply putting a bit more capacity on a developer's desk.
Can they do it now for everything? Obviously not. But IMO there is no reason at all for planning and scaffolding tasks to be done with cloud models, and there are many reasons why it might be better to do document processing without leaving the premises.
The incentives are there on the technical, operations and particularly on the business levels, and the relative disruption of the switch really small, considering that all the tooling can use different models for different tasks already. They must at least be investigating the possibility; it's irresponsible not to.
Not impossible, not unlikely, probably 50-50.