I Remain a Skeptic
blog.jsbarretto.com
blog.jsbarretto.com
Apple, Mozillia, and Firefox just released a record number of bug fixes because of AI. So this assertion that Software as not improved in quality is arguably if not provably false. There are 1000s of other examples. I have my own. Personal projects that were stopped because of dependency rot, having 100s of bugs fixed, nearly all dependencies removed, and lots of tests added just by asking. I know tons of others with similar experiences
> I do not feel like I’m falling behind by not using them
Obviously you are correct about "how you feel". But, this argument to me is more like a horse owner claming they don't feel like they're failling behind to trucks that can carry 10x, 100x, 1000x more than their horse. Sure, maybe the horse can go places the truck can't. For most uses the truck is better than the horse.
> The LLM-generated PRs I get are still rubbish.
I can't argue with that. I haven't seed the LLM-generated PRs "you" get. I can say though the LLM-generated bug reports I've seen are 1000x better than human ones. Human bug report "your software breaks, fix it!". LLM bug report "There is a bug in this specfic part of the code for these specific reasons and here's a repo I wrote to demonstrate the bug. Here's instructions on how to run the repo. There's the code to fix the bug. And, here's a test to show the bug is fixed."
Is it perfect? No, sometimes the bug isn't real because the code can only be reached in situtations that can never happen. But still, 7 of 8 times it's a real bug and the bug report are the best I've ever received.
you can go through this list: https://www.firefox.com/en-US/firefox/150.0/releasenotes/ and for those very few issues not still blocked you can read the examples
I myself use them to accelerate programming tasks, so I'm not anywhere near as pessimistic as the author, but the claimed multiples of productivity definitely haven't materialized for me.
A hidden metric here is the number of new bugs created by these fixes. If an LLM creates 10x bugs but create 5x more new bugs, then it is not really an improvement. Because that is 5x more bugs that the user has to observe and report (unless it is a security vulnerability, which the LLMs might detect) before it can be fixed.
If the project has proper test coverage, there should be no significant number of new bugs. This is no different than the possible regressions added by a human-implemented feature.
And LLMs have made implementing a massive number of tests far easier and faster than before the LLM era.
I think you're talking past the author here, who says:
>By not leaning into LLMs I’ve given myself the room to specialise in a smaller set of skills. So far that conscious decision is working out well, and I remain confident about my job security.
"Most uses" might mean boilerplate and simple frontend work that LLMs can do easily because it's formulaic and doesn't require much creativity. OP is simply not doing that type of work.
I don't think anyone sane is really claiming 100x or 1000x speedups anymore. Most people I talk to who use LLMs are closer to 1.5 or 2. Because their job is not mostly boilerplate, there is simply not that much room to be improved.
Over the past two months I’ve built out a very complex web app for a client. Without the aid of AI it would be far smaller, simpler and less capable. And honestly I probably wouldn’t have agreed to take on the job in the first place.
So, better, in other words?
Unfortunately, sometimes nano is not enough.
In the case of this client project, we are only now getting to enough functionality to be generally useful. The complexity that exists in the project is necessary complexity. A lot of coders are simply wrong about complexity, and feel it can always be reduced.
Niklaus Wirth pointed this out over 30 years ago…
https://ieeexplore.ieee.org/document/348001
… and it has grown exponentially worse since.
I think that the software world ought to fear and dread complexity, the same way that the Unix designers feared and dreaded verbosity.
« Note the obsessive use of abbreviations and avoidance of capital letters; this is a system invented by people to whom repetitive stress disorder is what black lung is to miners. Long names get worn down to three-letter nubbins, like stones smoothed by a river. »
https://web.stanford.edu/class/cs81n/command.txt
We ought to be religiously obsessive about avoiding it. In the end, everyone would benefit thereby.
if your job is just writing boilerplate you're probably doing it badly.
if your app is so simple that really all you need to do is sling boilerplate then it might suffice but it's probably still the most unreliable way to develop that software.
if flakiness, bugginess and unreliability are all fine then yea, boilerplate away.
To see an example of this 1000x, all we have to do is look at the flood of bug reports and fixes to browsers and OSes. Does it do 1000x for every project? No, and neither does a pickup truck do 1000x what a horse does. But, even a small pickup truck does more than a horse for most use cases (carrying people or stuff) and similarly, LLMs do more for most use cases.
I posted the link. I didn't write it. My name is Liam Proven, thus "lproven", while they are Joshua Barretto, thus "jsbarretto".
I recently experienced some cognitive decline due to chronic health issues. This is unfortunate timing because I am shipping a game with online multiplayer for the first time. So "brain works properly" is kind of a basic job requirement! But you work with what you got...
Some day I have the energy to do programming "manually", but on many days, the difference between "tasks get done" and "I am just staring at the screen reading the same code over and over again" is "ask the LLM to do it."
So I have had to shift my identity from "the guy who does the thing" to "the guy who ensures the thing has been done."
That being said, I do check the diffs carefully. (I tried a more hands-off approach a few months ago, and that was a pretty bad idea.) And I've learned to make the diffs as small as possible: more digestible for me, and less room for the AI to go off the rails.
Nice bonus to small, well defined changes is that small, fast, cheap models can handle them. (Having a great time with Luna lately, even on Low reasoning effort, which I wasn't expecting at all.)
LLMs are really good at making it harder to ignore ignorant people, and you've demonstrated that you're NOT part of the problem.
I think that the industrialization of software development already happened decades ago. The common professional software development methodologies, both waterfall and sprint-based, ARE the industrialization of software development. Isn't modern "agile", sprint-based software development methodology directly influenced by Japanese manufacturing methods? I don't know when software development actually was a guild field, and my personal experience doesn't reach back this far, but based on what I've read at least, it seems to me that even back in the 1990s it was already not a guild field.
In fact the four-year degree you'll get from school is getting increasingly distant from the skills I actually want out of a new grad. It's not impossible to bridge the gap or anything but my transition into the commercial realm in the early 2000s was a cakewalk compared to the sheer number of things I'm asking a new grad to learn as soon as they're settled in at their desk... source control, CI/CD, bug trackers, devops, and that's just the beginning of that list not the end.
Those skills are easily taught. But in every run of the mill project I’ve been on, I’ve been really happy about all the maths and other science stuff I’ve been taught at college. It’s easier to grasp the web DOM, React, and git when you already know about trees and the related algorithms. Easier to learn SQL after opening some books on relational theory (basically the first chapter on database theory) and learning about projections. And definitely easier to learn the nature of distributed systems.
Learning how to use Bash and Ansible is a walk in the park after that.
For similar aesthetic reasons, have only dabbled with code-gen LLMs. Im happy to vibe code css and html but not, well actual code. I'm aware of my own double standard, Im fine with median-reverting banal css and html.
I do worry about some aspects of LLMs :
- google giving AI code snippets so easily, prevents traffic to sites like stack-overflow, where discussion happens
- vibe coding means less people hit the actual hard parts of coding, which result in learning to think as a developer. Thence where the next generation of developers who understand code ?
- code will become a magical, inscrutable black box where no-one understands how it works. DNNs and LLMs have this feature already. At least with LLM discovered math, the proof in lean exists, which is independent at how it was arrived at / searched for.
- if I write a novel, and the DNN AI detects it as AI slop with 68% probability, then what recourse do I have to prove its human written ?
- circular training : subsequent rounds of LLMs trained not on human input, but on LLM slop of the previous generation [ the %ge of which increases ]
- you will own nothing : fortunately it looks like self-hosted open weight LLMS will remain a thing, but the massive spend by hyperscaler companies on Datacenters/GPUs/RAM has driven down supply and driven up prices of CPU, GPU, RAM for enthusiasts / gamers / home AI nerds / garage startups .. which might stifle innovation.
If you actually used LLMs instead of preaching against them from the sidelines with no real experience, you'd know such statements are wrong.
This is the real problem with many of the anti LLM folks - they tell us about LLMs and also tell us they don't use them.
"That thing cannot do the stuff you say it can. And there is no way I will ever try it. But I know for sure I am right."
Which I find weird - the greatest fun for me is making a computer do something awesome. I learned to program because I want that outcome.
I respect if you love the art and craft of hand programming but it's something else entirely to simply deny that LLM's are what they are - which is to say so amazing it is really beyond belief and they are getting more so every year.
If you're a hand programmer forever then fine, just don't make a fool of yourself by saying that everyone is lying about LLMs, to justify your choices.
We've normalized science fiction. Go back 5-10 years and ask anyone whether we'd soon have something like LLMs.
If you step back and try to look at LLMs without emotions and politics, you will be blown away by what this thing can do. We're talking to a machine, folks.
I just can't believe that LLM haters really like computers or technology, because we haven't seen anything this interesting in decades.
And? I didn't sign up to talk to a machine.
Has it actually made your daily life better? All I see as a result of LLMs is endless slop everywhere, plateauing in the skill levels of coworkers, and badly informed people in management positions pushing the humans to meet impossible deadlines because "otherwise we'll replace you with a cheaper worker and an LLM".
I am an LLM optimist, but I'll be honest and say it hasn't made my life better. My optimism relies on the models plateauing, and people finally rebelling against the widespread slop to realize that LLMs are just another (very useful!) tool, but don't meaningfully replace people in any capacity.
It also added functionality to my terminal that I didn't have time to add, and added functionality to my window manager I wanted but didn't have time to add, both of which improve my general quality of life - I spent 12+ hours in front of my machine a day, only part of which is work, so ergonomics matters.
Those are just a couple of examples.
Don't leave us hanging, let us know what was this software for?
Yup. As someone who thinks computing has been in decline since Mac OS X 10.4, LLMs are the first major tech trend that I've been impressed by in 20 years.
LLMs are incredibly impressive. It just doesn't follow from that that they're capable of all the things proponents claim they are.
> I just can't believe that LLM haters really like computers or technology
In a lot of ways the reverse is true. The LLM coding ecosystem seems designed by people who are unaware or actively despise the fact that they have access to a computer, and almost the entire point of LLMs is to make interacting with computers less like interacting with computers and more like interacting with people.
That is the thing I am mad about. We are getting bastardized versions of the science fictions of our childhood.
I fantasized about instant communicators across worlds, and we get mobile phones that work by planting a gazillion antennas across the globe. And people hail them as futuristic and say things like this.
I fantasied about human like robots and positronic brains, and we get a regurgitation of past humanity, in text, ensuring a future of total intellectual and artistic winter.
I fantasized a future with perfect health, but we get a million doctors and hospitals and a normalised dependency on medicines and an existence that is unthinkable without health insurance!
I fantasized about antigravity flying cars, and we get drones.
What ever it is, these things are blocking the path to the science fiction of my childhood.
So that is why I am not excited about these things. Not because I don't like computers and technology, but because I REALLY like them.
I have. Which is how I know that such statements are correct.
Unlike the author, I'm an LLM optimist; but this take doesn't make sense to me. The thing you show cannot be "hey look at this LLM-based workflow, that's so much better than last year".
Where is the big browser, operating system, or other piece of work that has been able to quickly compete with the existing entries in the market due to LLMs?
The only one that I can think of off the top of my head is the rewrite of Bun in Rust. I think the fact that Claude Code runs on millions of devices on top of that rewrite is the sort of impressive demonstration that would prove that LLMs have lasting, systemic effects -- but it is telling that the only one I can think of was made by the extremely well funded lab that also happens to build the LLM.
It should still be taken with a grain of salt IMO
Why would it "quickly compete"? What if AI gives you a 100x leverage but not a 100000x leverage?
With the 100000x leverage, you could spin up a full browser and compete, but not with the 100x. With 100x, you could use fewer people or do it in less time, but the amount of effort that goes into browsers still doesn't make it "an afternoon for one person".
I think it's undeniable that an excavator is ridiculously more powerful than a human at digging holes. Let's say it's 100x more efficient.
But if you had 1000 people work on a big hole every day for 5 years, the excavator wouldn't manage to do the same in a day, but it would allow you to do the same with 10 people and 10 excavators in 5 years, or with 50 people and 50 excavators in one year.
I don't think it scales linearly the more complexity grows, but "it hasn't been done, therefore it isn't possible" seems wrong. Maybe the big labs could build a browser, but they'd much rather spend the attention of 50 engineers on something else.
- people who preach against PV solar... while people I actually know broke even 10 or more years ago and have been happily getting "free power" for a lot of their needs
- people who preach against electric cars... while people I actually know are driving them with no "range anxiety" or "replaced main batteries", but their non-engineer spouse/kids are driving the cars fine too.
- people who preach that tesla self driving isn't viable/etc... while lots of people are using tesla to drive themselves around for > 90% of their driving
What about the people who do have a lot of LLM experience and agree with OP?
> That thing cannot do the stuff you say it can. And there is no way I will ever try it. But I know for sure I am right.
If someone claims to be able to fly by strapping bird wing shaped pieces of plywood with feathers glued on to their arms, do you need to personally jump off a tower with them to say they don't work, or can you look at the results of others attempting it and draw conclusions based on that? LLM proponents are making claims about their capabilities which can relatively easily be checked without using LLMs yourself. To pick an example where LLM proponents are correct, anyone who says LLMs can't generate syntactically valid code can be proven wrong fairly easily by producing an example of syntactically valid, LLM generated code.
Further, if you read the rest of the paragraph you responded to, it's clear that this claim is about the state of the industry as a whole. "Software has not improved in quality, got faster, become cheaper to produce (when you exclude the mountain of poor-quality demoware that no reputable organisation would touch with a barge pole), or become more capable." Whether this is true or not is something that can be evaluated without ever having prompted yourself, or even arguably without being a developer at all.
(BTW, I am the OP: I posted this, but I didn't write the article.)
No, the real problem here is that you do not understand the anti-LLM arguments.
I won't touch LLMs for anything except translation. But my anti-LLM stance is nothing whatsoever to do with whether they work or not (and that is even without the extreme torturing of the word "work" that the advocates do.)
My objection is based on the terrible crimes committed in building them (from theft), training them (at catastrophic ecological cost), in running them (ditto), in the effects on their individual users (cumulative brain damage), on their corporate users (economic destruction), and so on.
If they work is incidental. The believers like what LLMs do. For me, by my definitions, LLMs do not work. They extrude content that I never ever want to see, or read, or here, or want to execute. But for the botlickers, that is enough: they extrude a product. The botlickers want product.
I don't care. I don't want the product, but more to the point, I want the machines making the product not to exist... and if the price of that is all the people using the machines to never work again, and all the companies using them to go bankrupt, I would be absolutely delighted.
I don’t really agree that AI can’t make development faster, though. What the author describes as the negative AI outcome is blind vibecoding. There are many other ways to use it, and even the basic “enhanced autocomplete” is a net benefit, especially since that functionality is dirt cheap or free.
We have been programmed to believe that MBA wet dreams are inevitable because "$x hundreds of billions invested can't be wrong" but they very often are.
https://getdx.com/blog/ai-productivity-gains-are-10-percent-...
Although the botlickers will find this hard to believe, nothing has happened this year to make we skeptics -- like me, and like the author of this piece, who is for clarity not me -- change our minds.
Extraordinary claims require extraordinary evidence.
Linus and Greg K-H suddenly deciding they like it is not evidence.
> Linus and Greg K-H suddenly deciding they like it is not evidence.
You don't seem particularly interested in changing your mind
I want evidence to consider changing. Why should I? Because someone somewhere found a tool useful? Because they are big names? That's argument from authority. I don't buy it.
I need compelling reasons. I have yet to see any. All I see is a lot of people jumping on bandwagons.
Recently I got it to help me explore and compare curve smoothing algorithms and it helped me to test my own formulations that I hadn't seen elsewhere, and converged on computing segments of:
||T(s)-T(t)||^2. / |s-t|^3, where s and t are arc length indices, and we sum over multiple scales of offsets and where T is the (unit) tangent of the curve.
Just because that doesn't involve you means its not useful? Its ability to give highly bespoke scripts makes it far more useful to me than hiring an opinionated software developer!!!!!
You have to think about the big picture. Otherwise, the picture goes out.
1. LLMs are profoundly, inexcusably unethical.
2. The kind of software and tools one can build with LLMs might be better off not built at all.
These sorts of thoughts seem to blow people's minds. Well, tough. Get used to having a bigger mind.
But I spent a lot of time in the second half of this week dealing with friction with a team that is very annoyed that I'm moving fast and using an agile methodology so I can't tell them the exact, precise REST calls that I'm going to have for them in six months designed to a tee and signed off in triplicate before they start development against it. Manifesting that increase in code production as real value to the business is going to take more from me than just spewing the code out more quickly.
AI isn't creating this problem. I would have had this problem anyhow even if I were writing all the code by hand again. I know, because I've been there before. But the increased velocity is manifesting in increased organizational stress and not just increased velocity.
AI is perhaps even helping solve it to some degree, though far from totally. I have written before about how people eventually learned not to play the "oh well we can't do this until we have documentation" card on me [1]. This week they played the "well, I see you have docs but they aren't in our precise format". Guess what AI can do in about 15 minutes really well? You may recall the term "style transfer" getting tossed about a lot 3-4 years ago, and it is still something AI is extremely good at, and "take these docs in this format and convert them to that format" is just a style-transfer problem. AI really does chew at the "oh but we need docs" old-school card... and they can't even complain about the quality of the AI docs because in order to do that, they'd have to actually read them, and that is not the point of the "but we need docs" card, you see....
You can think of it as an impedance mismatch, or as a translation problem. But no matter how you think about it, it's real, and it's a problem - especially if upper management lives on the non-agile side of the fence.
edit: I get it, it's not a popular opinion.
But am I wrong?
If the problem of hallucination in AI has been solved, maybe I missed it?
But they're right much more often than they used to be. If you haven't tried a near-Frontier model lately (say, Claude Opus or ChatGPT Sol) you probably need to update your priors for just how often they can be right.
AI is being used in contexts that involve life and death. Being right more often is not good enough in that context. If we are going to put AI in drones and have them autonomously acquire and fire on enemies, perhaps the fact that all AI hallucinates at all makes it morally and ethically unusable for purposes in which the stakes are very high, such as life and death.
This is what scares me about it. And also what I was able to accomplish air-gapped with a Ryzen 7 and RTX 3060 12GB scared me.
This is not the kind of thing we should all have to speculate about, someone should have solid answers. Perhaps the fact that some, and maybe most, AI models are black boxes even to those that trained them, should have been the first red flag and that was years ago.
This is really well said. I had not put my finger on it before. Maybe this is a next move in the saga to avoid labor unions in engineering.
Especially if you replace people with robots that don't need days off and have the people train their replacements.
I’m in the final prep for ship phase of a project that started February. It’s a bottom-to-top rewrite of a project that’s been shipping for two years, and took two years to write, initially.
I’m deliberately doing it all with the $20/month ChatGPT sub. After it ships, I’ll move to the $100/month sub; but I want to be able to say that the entire project was done on a low-tier LLM subscription.
That might be because of some lag, but another explanation might be that the tools are really effective at increasing someone's perception of their productivity while net productivity gains measured over long time windows are low or zero. Another explanation might be that the incompetent/negligent users are causing enough harm to nearly neutralize the gains brought by proficient / effective users. I don't know what the explanation is, but there is a mystery in the divergence between users' perceptions of their own productivity, and the observed productivity gains in the economy.
e.g. one explanation might be that time spent prompting is time spent not improving, or learning, so it would not be surprising then that the productivity gains might be similar in scale to the productivity lost from learning / improvement that never happens.
another explanation might be that prompting just delays work that will have to be done later when fixing errors. When encountering an error that the LLM doesn't fix on the first attempt the human-in-the-loop doesn't understand the problem very well: they will either pull the slot machine for a while until they hit lucky, or grit their teeth and sit down to understand what the code is doing in the excruciating and boring detail required to find and solve bugs, which is exactly the work that they had offloaded onto the LLM earlier.
Do coding agents help me with highly technical work where it’s some deep thought and discussion with coworkers and eventually I change five lines of code? Not really.
But I can debug a weird kernel panic or optimizer bug from a standing start in an hour when it would have taken me all day without these tools.
As long as you make sure you genuinely understand everything that comes out of it, and your domain has enough coverage in the LLM training data/context, its an incredible tool.
- Everyone and their mother using an LLM is slowly and steadily destroying their brain cells and abilities in the process while creating a learned dependence
- Give it about 5 yrs, mark my words. The studies ll come out saying "95% programmers cannot write a hello world without an LLM now"
- Now someone smart ll hop in on my comment and bring calculators into the argument and I have answer waiting for them that those do (not able to put a direct link here for some reason)
Doesn’t really support the rest of the argument. If you’re a skeptic and think it’s all hype, there’s nothing to worry about.
The worst scenario isn't actually that machines are intelligent and you lose your job, it's that they're stupid and you lose your job because in our quantitative glutton culture qualitative judgement is entirely gone to begin with
Yes. But.
Industry can miss its revenue targets, Oracle can go bankrupt (looking more and more likely) OpenAI, Anthropic and SpaceX might find "true value" at about 10% of current valuations (that feels right to me), all that can happen with the associated economic chaos... and still out of it comes world shaping new technology
Both things can be true
Like... have you used these tools at all?
You don't have to go full vibe coding to see the clear value they provide and its order of magnitude leap over the tools we had before...
Thanks to Claude, as a hobby, I've built and shipped 4 iOS apps in the last month. None of them are world-changing, but that's 4 in the last month thanks to Claude and zero in the 15 years before it. I'm clearly getting my subscription's worth out of Claude and squeezing more juice out of my own devices. It's a fun hobby! (And I'm sure if I used Android I could do more and customize my own device even more!)
This is what skeptics are missing. The strongest answer to what AI has delivered to society so far is the new, fulfilling hobby of vibecoding. Their disparagement is like being skeptical of the routine, regular investment into iterative improvements in smartphone cameras purely on the basis that you can't pinpoint a specific lump of business process value. The vibecoding is the point. That's what people want and what they are willing to pay for.
People like OP have frivolously wasted large chunks of their life writing and maintaining open source code that no valuable businesses really care about, just because they have this dumb hobby of typing out code and thinking about logic. (I'm saying this tongue in cheek.) They, of all people, should be to understand the unlock that AI is for a large swath of humanity.
There are things that LLMs remain uniquely suited for, while the echo chambers go for everything.
There's no requirement to use new technology. Maybe what you do, or how you do it is just fine, and that's OK.
Skepticism today can remind me of someone who went into a cave for 3-4 years with a dated take of LLMs on day 1 and not remember software improves, and LLMs are software.
There are no shortage of uniquely working solutions with LLMs if sone seeks to find them, and also the self-validating echo chamber that says that it doesn't if that's desired. Which parts of software development, or how software development may better happen differently with the involvement of AI remains to be seen.
What exactly is this referring to? My thinking here is "code is a hypothesis", and you don't know if it's solid or not until it contacts the real world. (And ideally tests, and maybe a proof or two.) And what we're seeing lately is that the hypotheses often get disproven decades later (longstanding kernel bugs etc.)
The comments here gush about how they can write more code, but they don't say anything about the quality of the software being built and if they have improved on some points with it (cheaper maintenance, faster reaction to feedback, less bugs overall). Very much like a musician clamoring how he can write a lot more music sheets, but there's no concert in sight to listen to his music.
(Except modders. Modders will be sad.)
The thing about complex systems is that they are not predictable. Right now the answer is whatever you think is right, and a lot of scenarios seem plausible.
It is a fun intellectual moment trying to make sense, but maybe only time will tell how the dynamics work out.
My apps are also not super complicated.
You have to stop thinking like an employee or a worker bee, and start thinking like a boss.
Guess what, the work you give your boss isn't 100% gold either. And sometimes you are preoccupied, or tired, so what you deliver isn't the best of all possible deliverables. Sometimes you hallucinate, i.e. you are dead wrong when you think you are dead right.
This is the job of a boss: take fallible employees and turn what they produce into something actually valuable. The boss lets the grunts do the grunt work, and then they combine the output together, and do the due diligence, etc etc.
It's an entirely new skillset. But learning the new skillset isn't the real blocker--the real blocker is that you've got to stop thinking like a grunt and start thinking like an executive.
WHAT is it that you really want to do? Frame that question as clearly and as concretely as you can. Then farm out thee work to human or AI grunts, and be prepared to do your due diligence on the result.
Why not replace all mid-execs with LLMs trained on every MBA textbook and linked-in blog post ? why stop at grunt coders, graphic designers and phone marketeers ?
Can we reduce the role of CEO to the following prompt : "make money, by any legal means"
I don’t think it can be denied that the models do show an ability to find security vulnerabilities that may have otherwise been missed
Never mind the more subtle issues.
"Bots found lots of issues and we fixed those issues" does not mean anyone was exploiting those issues. They may have remained buried forever.
The result is not "it's now more secure". The result is at best "it is theoretically more secure."
This is not controversial. In the least.
It's ironic the author links to Naur's paper because I use LLMs to dig through code faster, question my assumptions, review my drafts, or generate drafts for my review. It's not because I am bad at it, I used to be very proud of being able to do this better than others. Anyway, this does not lead to more "lines of code" as the author suggests and instead it can lead to higher quality code.
FWIW, I also don't believe you can let LLMs make critical judgement by somehow stringing together more LLMs. And I am also not making a claim on whether the $1.5T investment is proportional to its benefits. But to say "industry has nothing to show for it", or to bring up strawmen like "LLM-generated PRs" and "lines of code", makes me question whether the author has tried studying the applications of LLMs.