I myself am saving a small fortune on design and photography and getting better results while doing it.
If this is not all that well I can’t wait until we get to mediocre!
You and anyone else could have avoided spending millions for programmer salaries, had you been allowed to reuse freely any of the many existing proprietary or open-source programs that solved the same or very similar problems.
I would have no problem with everyone being able to reuse any program, without restrictions, but with these AI programming tools the rich are now permitted to ignore copyrights, while the poor remain constrained by them, as before.
The copyright for programs has caused a huge multiplication of the programming effort for many decades, with everyone rewriting again and again similar programs, in order for their employing company to own the "IP". Now LLMs are exposing what would have happened in an alternative timeline.
The LLMs have the additional advantage of fast and easy searching through a huge database of programs, but this advantage would not have been enough for a significant productivity increase over a competent programmer that would have searched the same database by traditional means, to find reusable code.
"Available for use" and "Automatically rewritten to work in your codebase fairly well" is very different, so copyright is probably not the blocker technically
Code is not an asset it's a liability, and code that no one has reviewed is even more of a liability.
However, in the end, execution is all that matters so if you and your cofounder are able to execute successfully with mountains of generated code then it doesn't matter what assets and liabilities you hold in the short term.
The long term is a lot harder to predict in any case.
Code that solves problems and makes you money is by definition an asset. Whether or not the code in question does those things remains to be seen, but code is not strictly a liability or else no one would write it.
This discussion and distinction used to be well known, but I'm happy to help some people become "one of today's lucky 10,000" as quoted from https://xkcd.com/1053/ because it is indeed much more interesting than the alternative approach.
It's not right but it's not wrong either. It at least was a useful way to think about code, and we'll see if that applies in LLM era.
Delta’s airplanes also require a great deal of maintenance, and I’m sure they strive to have no more than are necessary for their objectives. But if you talk to one of Delta’s accountants, they will be happy to disabuse you of the notion that the planes are entered in the books as a liability.
Code isn't a liability b/c it costs money (though it does). Code is a liability like an unsafe / unproven bridge is a liability. It works fine until it doesn't - and at that point you're in trouble. Just b/c you can build lots of bridges now, doesn't mean each new bridge isn't also a risk. But if you gotta get somewhere now, conjuring bridges might be the way to go. Doesn't make each bridge not a liability (risky thing to rely on) or an asset (thing you can sell, use to build value)
If a software company is going bankrupt, it’s very unlikely they will be able to sell code for individual apps and services they may have written for much at all, even if they might be able to sell the whole company for something.
I don't think it's a wrong quote. Code's behavior is the asset, and code's source is the liability. You want to achieve maximum functionality for minimal source code investment.
Just this week, sun-tue. I added a fully functional subscription model to an existing platform, build out a bulk async elasticjs indexing for a huge database and migrated a very large Wordpress website to NextJS. 2.5 days, would have cost me at least a month 2 years ago.
AI is helping me solve all the issues that using AI has caused.
Wordpress has a pretty good export and Markdown is widely supported. If you estimate 1 month of work to get that into NextJS, then maybe the latter is not a suitable choice.
I feel bad for AI opponents mostly because it seems like the drive to be against the thing is stronger than the drive towards fact or even kindness.
My .02c: I am saving months of efforts using AI tools to fix old (PRE-AI, PREHISTORIC!) codebases that have literally zero AI technical debt associated to them.
I'm not going to bother with the charts & stats, you'll just have to trust me and my opinion like humans must do in lots of cases. I have lots of sharp knives in my kitchen, too -- but I don't want to have to go slice my hands on every one to prove to strangers that they are indeed sharp -- you'll just have to take my word.
I'm a non dev and the things I'm building blow me away. I think many of these people criticizing are perhaps more on the execution side and have a legitimate craft they are protecting.
If you're more on the managerial side, and I'd say a trusting manager not a show me your work kind, then you're more likely to be open and results oriented.
I think getting results can be very easy, at first. But I force myself to not just spit out code, because I've been burned so, so, so many times by that.
As software grows, the complexity explodes. It's not linear like the growth of the software itself, it feels exponential. Adding one feature takes 100x the time it should because everything is just squished together and barely working. Poorly designed systems eventually bring velocity to a halt, and you can eventually reach a point where even the most trivial of changes are close to impossible.
That being said, there is value in throwaway code. After all, what is an Excel workbook if not throwaway code? But never let the throwaway become a product, or grow too big. Otherwise, you become a prisoner. That cheeky little Excel workbook can turn into a full-blown backend application sitting on a share drive, and it WILL take you a decade to migrate off of it.
I remember coding BASIC with POKE/PEEK assembly inside it, same with Turbo Pascal with assembly (C/C++ has similar extern abilities). Perhaps you want no more web or UI (TUI?). Once you imagine what you are looking for, you can label it and go from there.
The work was moving the many landing pages & content elements to NextJS, so we can test, iterate and develop faster. While having a more stable system. This was a 10 year old website, with a very large custom WordPress codebase and many plugins.
The content is still in WordPress backend & will be migrated in the second phase.
If AI was good at a certain task then it was a bad task in the first place.
Which is just run of the mill dogmatic thinking.
This would imply companies could delete all their code and do better, which doesn't seem true?
You sound like complete clones of us :-)
We’ve been at it since July and have built what used to take 3-5 people that long.
To the haters: I use TDD and review every line of code, I’m not an animal.
There’s just 2 of us but some days it feels like we command an army.
Why?
Im not even casting shade - I think AI is quite amazing for coding and can increase productivity and quality a lot.
But I'm curious why he's doing this.
It was basically cost prohibitive to change anything significant until Claude became able to do most of the work for us. My cofounder (also CTO of another startup in the interim) found himself with a lot of time on his hands unexpectedly and thought it would be a neat experiment and has been wowed by the results.
Much in the same way people on HN debate when we will have self driving cars while millions of people actually have their Teslas self-driving every day (it reminds me of when I got to bet that Joe Biden would win the election after he already did) those who think AI coding is years away are missing what’s happening now. It’s a powerful force magnifier in the hands of a skilled programmer and it’ll only get better.
But if it can do 90% of the work for you, it is a serious force magnifier.
What Tesla is selling now is the worst of both worlds. You still have to pay attention but it's way more boring so it's really hard to do so. Well until it suddenly decides to ram a barrier at highway speeds.
Wake me up when I can have a beer and watch a movie while it's driving.
By the way, why does your co-founder have to do the rewrite at all?
We have no clue the scope of the rewrite but for anything non-trivial, 2 weeks just isn't going to be possible without AI. To the point of you probably not doing it at all.
I have no idea why they are rewriting the code. That's another matter.
Waiting for it to actually go well to see what else I can do !
Probably the biggest gap would be the ability to ignite, drive, and launch new initiatives. How does an AI agent "lead" an engineering team? That's not something you can code up in an agent runtime. It'd require a whole culture change that I have a hard time seeing in reality. But of course if there comes a point where AI takes all the junior and mid-level coding jobs, then at that point there's no culture to change, so staff/principal jobs would be just as at risk.
That said, the thing it really struggles with is when the best approach is "do nothing". Which, given that a huge chunk of principal level work is in deciding what NOT to do, it may be a while before LLMs can viably take that role. A principal LLM based on current tech would approve every idea that comes across it, and moreover sell each of them as "the exact best thing needed by the organization right now!"
Of course, I could just _tell_ it to rewrite, but that’s different.
As a fractional CTO and in my decade of being co-founder/CTO I saw a lot of people and codebases and most of it is just bad. You need to compare real life codebases and outputs of developers, not what people wished it would be like. And the reality is that most of it sucks and most SWEs are bad at their jobs.
Key thing here. The code was already written, so rewriting it isn't exactly adding a lot of quantifiable value. If millions weren't spent in the first place, there would be no code to rewrite.
Producing a lot of code isn’t proof of anything.
I was expecting a language reference (we all know which one), to get more speed, safety and dare I say it "web scale" (insert meme). :)
Obligatory reference https://www.youtube.com/watch?v=b2F-DItXtZs
This is one of those statements that would horrify any halfway competent engineer. A cowboy coder going in, seeing a bunch of code and going 'I should rewrite this' is one of the biggest liabilities to any stable system.
Right? RIGHT?!
I'm doing the same as your co-founder currently. In a few days, I've rewritten old code that took previous employees months to do. Their implementation sucked and barely worked, the new one is so much better and has tests to prove it.
The "how hard could it be" fallacy claims another!
PM has an idea. PM vibe codes a demo of this idea. PM shows it to the VP. VP gets excited and says "when can we have this." I look at the idea and estimate it'll take two people six months. VP and PM say "what the heck, but AI built the demo in a weekend, you should be able to do this with one engineer in a month." I get one day closer to quitting.
LLMs do the jobs of developers without telling semi-technical arrogant MBA holders “no, you’re dumb”, thereby creating all the same jobs as before but also a butt-ton more juggling expensive cleanup mixed with ego-massaging.
We’re talking a 2-10x improvement in ‘how hard could it be?’ iterations. Consultant candy.
Most people have no clue the craftsmanship, work etc it takes to create a great product. LLMs are not going to change this, in fact they serve as a distraction.
I’m not a SWE so I gain nothing by being bearish on the contributions of LLMs to the real economy ;)
It's a reference to a trope where the VP of Eng or CTO (who was an engineer decades ago) gets it in their head that they want to code again and writes something absolute dogshit terrible because their skills have degraded. Unfortunately they are your boss's boss's boss and can make you deal with it anyways.
I've actually seen it IRL once, to his credit the dude finally realized the engineer smiles were pained grimaces and it got quietly dropped lol.
I trained a small neural net on pics of a cat I had in the 00s (RIP George, you were a good cat).
Mounted a webcam I had gotten for free from somewhere, above the cat door, in the exterior of the house.
If the neural net recognized my cat it switched off an electromagnetic holding the pet door locked. Worked perfectly until I moved out of the rental.
Neural nets are, end of the day, pretty cool. It's the data center business that's the problem. Just more landlords, wannabe oligarchs, claiming ownership over anything they can get the politicians to give them.
If the LLM generating the code introduced a bug, who will be fixing it? The founder that does not know how to code or the LLM that made the mistake first?
For now, it works out for companies - but forward to, say, ten years in the future. There won't be new intermediates or seniors any more to replace the ones that age out or quit the industry entirely in frustration of them not being there for actual creativity but to clean up AI slop, simply because there won't have been a pipeline of trainees and juniors for a decade.
But by the time that plus the demographic collapse shows its effects, the people who currently call the shots will be in pension, having long since made their money. And my generation will be left with collapse everywhere and find ways to somehow keep stuff running.
Hell, it's already bad to get qualified human support these days. Large corporations effectively rule with impunity, with the only recourse consumers have being to either shell out immense sums of money for lawyers and court fees or turning to consumer protection/regulatory authorities that are being gutted as we speak both in money and legal protections, or being swamped with AI slop like "legal assistance" AI hallucinating case law.
There are be plenty of self taught developers who didn't need any "traineeship". That proportion will increase even more with AI/LLMs and the fact that there are no more jobs for youngsters. And actually from looking at the purely toxic comments on this thread, I would say that's a good thing for youngsters to be not be exposed to such "seniors".
Credentialism is dead. "Either ship or shutup" should be the mantra of this age.
Generative AI, as we know it, has only existed ~5-6 years, and it has improved substantially, and is likely to keep improving.
Yes, people have probably been deploying it in spots where it's not quite ready but it's myopic to act like it's "not going all that well" when it's pretty clear that it actually is going pretty well, just that we need to work out the kinks. New technology is always buggy for awhile, and eventually it becomes boring.
Every 2/3 months we're hearing there's a new model that just blows the last one out of the water for coding. Meanwhile, here I am with Opus and Sonnet for $20/mo and it's regularly failing at basic tasks, antigravity getting stuck in loops and burning credits. We're talking "copy basic examples and don't hallucinate APIs" here, not deep complicated system design topics.
It can one shot a web frontend, just like v0 could in 2023. But that's still about all I've seen it work on.
We all know LLMs get stuck. We know they hallucinate. We know they get things wrong. We know they get stuck in loops.
There are two types of people: The first group learns to work within these limits and adapt to using them where they’re helpful while writing the code when they’re not.
The second group gets frustrated every time it doesn’t one-shot their prompt and declares it all a big farce. Meanwhile the rest of us are out here having fun with these tools, however limited they are.
> The whole discourse around LLMs is so utterly exhausting. If I say I don't like them for almost any reason, I'm a luddite. If I complain about their shortcomings, I'm just using it wrong. If I try and use it the "right" way and it still gets extremely basic things wrong, then my expectations are too high.
As I’ve said, I use LLMs, and I use tools that are assisted by LLMs. They help. But they don’t work anywhere near as reliably as people talk about them working. And that hasn’t changed in the 18 months since I first promoted v0 to make me a website.
Using LLMs has made it fun for me to make software again.
If you hired a human, it will cost you thousands a week. Humans will also fail at basic tasks, get stuck in useless loops, and you still have to pay them for all that time.
For that matter, even if I'm not hiring anyone, I will still get stuck on projects and burn through the finite number of hours I have on this planet trying to figure stuff out and being wrong for a lot of it.
It's not perfect yet, but these coding models, in my mind, have gotten pretty good if you're specific about the requirements, and even if it misfires fairly often, they can still be useful, even if they're not perfect.
I've made this analogy before, but to me they're like really eager-to-please interns; not necessarily perfect, and there's even a fairly high risk you'll have to redo a lot of their work, but they can still be useful.
> salesman can convince executives that it does
I tend to think that reality will temper this trend as the results develop. Replacing 10 engineers with one engineer using Cursor will result in a vast velocity hit. Replacing 5 engineers with 5 "agents" assigned to autonomously implement features will result in a mess eventually. (With current technology -- I have no idea what even 2027 AI will do). At that point those unemployed engineers will find their phones ringing off the hook to come and clean up the mess.
Not that unlike what happens in many situations where they fire teams and offshore the whole thing to a team of average developers 180 degrees of longitude away who don't have any domain knowledge of the business or connections to the stakeholders. The pendulum swings back in the other direction.
Maybe I shouldn't have used the word "replaced", as I don't really think it's actually going to "replace" people long term. I think it's likely to just lead to higher output as these get better and better .
Anecdotally I’ve seen no difference in model changes in the last year, but going from LLM to Claude code (where we told the LLMs they can use tools on our machines) was a game changer. The improvement there was the agent loop and the support for tools.
In 2023 I asked v0.dev to one shot me a website for a business I was working on and it did it in about 3 minutes. I feel like we’re still stuck there with the models.
Now it's reversed. More often than not its method is better than mine (e.g. leveraging a better function/library than I would have).
In general, it's writing idiomatic mode much more often. It's been many months since I had to correct it and tell it to be idiomatic.
Of course, sample size of one, so if you haven't gotten those results then fair enough, but I've at least observed it getting a lot better.
It is a helper, a partner, it is still not ready go the last mile
If you hired a staff engineer to sit next to me, and I just had him/her write 100% of the code and never tried to understand it, that would be an unwise decision on my part and I'd have little room to complain about the times he made mistakes.
> The whole discourse around LLMs is so utterly exhausting. If I say I don't like them for almost any reason, I'm a luddite. If I complain about their shortcomings, I'm just using it wrong. If I try and use it the "right" way and it still gets extremely basic things wrong, then my expectations are too high.
I’m perfectly happy to write code, to use these tools. I do use them, and sometimes they work (well). Other times they have catastrophic failures. But apparently it’s my failure for not understanding the tool or expecting too much of the tool, while others are screaming from the rooftops about how this new model changes everything (which happens every 3 months at this point)
That experience gave me a deep appreciation for how incredible LLMs are and the amazing software they can power—but it also completely demystified them. So by all means, let’s use them. But let’s also understand there are no miracles here. Go back to Shannon’s papers from the ’60s, and you'll understand that what seems to you like "emerging behaviors" are quite explainable from an information theory background. Learn how these models are built. Keep up with the latests research papers. If you do, you’ll recognize their limitations before those limitations catch you by surprise.
There is no silver bullet. And if you think you’ve found one, you’re in for a world of pain. Worse still, you’ll never realize the full potential of these tools, because you won’t understand their constraints, their limits, or their pitfalls.
See my previous comment (quoted below).
> If I complain about their shortcomings, I'm just using it wrong. If I try and use it the "right" way and it still gets extremely basic things wrong, then my expectations are too high.
Regarding "there are no miracles here"
Here are a few comments from this thread alone,
- https://news.ycombinator.com/item?id=46609559 - https://news.ycombinator.com/item?id=46610260 - https://news.ycombinator.com/item?id=46609800 - https://news.ycombinator.com/item?id=46611708
Here's a few from some older threads: - https://news.ycombinator.com/item?id=46519851 - https://news.ycombinator.com/item?id=46485304
There is a very vocal group who are telling us that there _is_ a silver bullet.
If your metric is an LLM that can copy/paste without alterations, and never hallucinate APIs, then yeah, you'll always be disappointed with them.
The rest of us learn how to be productive with them despite these problems.
I struggle to take comments like this seriously - yes, it is very reasonable to expect these magical tools to copy and paste something without alterations. How on earth is that an unreasonable ask?
The whole discourse around LLMs is so utterly exhausting. If I say I don't like them for almost any reason, I'm a luddite. If I complain about their shortcomings, I'm just using it wrong. If I try and use it the "right" way and it still gets extremely basic things wrong, then my expectations are too high.
What, precisely, are they good for?
I agree that there's definitely some overhype to them right now. At least for the stuff I've done they have gotten considerably better though, to a point where the code it generates is often usable, if sub-optimal.
For example, about three years ago, I was trying to get ChatGPT to write me a C program to do a fairly basic ZeroMQ program. It generated something that looked correct, but it would crash pretty much immediately, because it kept trying to use a pointer after free.
I tried the same thing again with Codex about a week ago, and it worked out of the box, and I was even able to get it to do more stuff.
The gist is this: Programmers view computers as deterministic. They can't tolerate a tool that behaves differently from run to run. They have a very binary view of the world: If it can't satisfy this "basic" requirement, it's crap.
Programmers have made their career (and possibly life) being experts at solving problems that greatly benefit from determinism. A problem that doesn't - well either that needs to be solved by sophisticated machine learning, or by a human. They're trained on essentially ignoring those problems - it's not their expertise.
And so they get really thrown off when people use computers in a nondeterministic way to solve a deterministic problem.
For everyone else, the world, and its solutions, are mostly non-deterministic. When they solve a problem, or when they pay people to solve a problem, the guarantees are much lower. They don't expect perfection every time.
When a normal human asks a programmer to make a change, they understand that communication is lossy, and even if it isn't, programmers make mistakes.
Using a tool like an LLM is like any other tool. Or like asking any other human to do something.
For programmers, it's a cardinal sin if the tool is unpredictable. So they dismiss it. For everyone else, it's just another tool. They embrace it.
[1] This, of course, is changing as they become better at coding.
I use LLMs, I code with a mix of antigravity and Claude code depending on the task, but I feel like I’m living in a different reality when the code I get out of these tools _regularly just doesn’t work, at all_. And to the parents point, I’m doing something wrong for noticing that?
> And to the parents point, I’m doing something wrong for noticing that?
There's nothing wrong pointing out your experience. What the OP was implying was he expects them to be able to copy/paste reliably almost 100% of the time, and not hallucinate. I was merely pointing out that he'll never get that with LLMs, and that their inability to do so isn't a barrier to getting productive use out of them.
> he'll never get that with LLMs, and that their inability to do so isn't a barrier to getting productive use out of them.
This is _exactly_ what the comment thread we're in said - and I agree with him. > The whole discourse around LLMs is so utterly exhausting. If I say I don't like them for almost any reason, I'm a luddite. If I complain about their shortcomings, I'm just using it wrong. If I try and use it the "right" way and it still gets extremely basic things wrong, then my expectations are too high.
> If it were terrible, you wouldn't use them, right? Isn't the fact that you continue to use AI coding tools a sign that you find them a net positive? Or is it being imposed on you?
You're putting words in my mouth here - I'm not saying that they're terrible, I'm saying they're way, way, way overhyped, their abilities are overblown, (look at this post and the replies of people saying they're writing 90% of code with claude and using AI tools to review it), but when we challenge that, we're wrong.
Ah, no. This is wildly off the mark, but I think a lot of people don't understand what SWEs actually do.
We don't get paid to write code. We get paid to solve problems. We're knowledge workers like lawyers or doctors or other engineers, meaning we're the ones making the judgement calls and making the technical decisions.
In my current job, I tell my boss what I'm going to be working on, not the other way around. That's not always true, but it's mostly true for most SWEs.
The flip side of that is I'm also held responsible. If I write ass code and deploy it to prod, it's my ass that's gonna get paged for it. If I take prod down and cause a major incident, the blame comes to me. It's not hard to come up with scenarios where your bad choices end up costing the company enormous sums of money. Millions of dollars for large companies. Fines.
So no, it has nothing to do with non-determinism lol. We deal with that all the time. (Machine learning is decades old, after all.)
It's evaluating things, weighing the benefits against the risks and failure modes, and making a judgement call that it's ass.
scamming people
I haven't heard that at all. I hear about models that come out and are a bit better. And other people saying they suck.
>Meanwhile, here I am with Opus and Sonnet for $20/mo and it's regularly failing at basic tasks, antigravity getting stuck in loops and burning credits.
Is it bringing you any value? I find it speeds things up a LOT.
Gemini now often produces output that looks significantly better than what I could produce manually, and I'm an expert for web, although my expertise is more in tooling and package management.
Probably less than that, practically speaking. ChatGPT's initial release date was November 2022. It's closer to 3 years, in terms of any significant amount of people using them.
I'm not trying to be pedantic, but how did you arrive at 'keep improving' as a conclusion? Nobody is really sure how this stuff actually works. That's why AI safety was such a big deal a few years ago.
I will acknowledge that I don't have any evidence of this claim, so maybe the word "likely" was unwise, as that suggests probability. Feel free to replace "is "likely to" with "it feels like it will".
I think the big problem is that the pace of improvement was UNBELIEVABLE for about 4 years, and it appears to have plateaued to almost nothing.
ChatGPT has barely improved in, what, 6 months or so.
They are driving costs down incredibly, which is not nothing.
But, here's the thing, they're not cutting costs because they have to. Google has deep enough pockets.
They're cutting costs because - at least with the current known paradigm - the cost is not worth it to make material improvements.
So unless there's a paradigm shift, we're not seeing MASSIVE improvements in output like we did in the previous years.
You could see costs go down to 1/100th over 3 years, seriously.
But they need to make money, so it's possible non of that will be passed on.
I have no idea what this is called, but it feels like a lot of people assume that progress will continue at a linear pace for forever for things, when I think that generally progress is closer to a "staircase" shape. A new invention or discovery will lead to a lot of really cool new inventions and discoveries in a very short period of time, eventually people will exhaust the low-to-middle-hanging fruit, and progress kind of levels out.
I suspect it will be the same way with AI; I don't now if we've reached the top of our current plateau, but if not I think we're getting fairly close.
I'll interpret it as meaning 1800s to 1900s to 2000s. I'd argue that we haven't yet seen the same step change as 1800s to 1900s this century because we're only just beginning the ramp up on the new technology that will drive progress this century similar to how in 1926 they were still ramping up on the use of electricity and internal combustion engines.
Let's take electricity as the primary example though since it's the one you mentioned and it's probably more similar to our current situation with AI. The similarities include the need for central generating stations to supply raw power to end users as well as the need for products designed to make use of that power and provide some utility to the consumer. Efficiency of generation is also a primary concern for both as it's a major cost driver. Both of those required significant investment and effort to solve in the early days of electrification.
We're now solving similar problems with AI, instead of power plants we're building datacenters, instead of lightbulbs and washing machines we're developing chat bot integrations and agents, instead of improving dynamos we're improving GPUs and TPUs. I fully expect we'll follow a similar curve for deployment as we find new uses, improve existing ones and integrate this new power source into an increasing number of domains.
We do have one major advantage though, we've already built The Grid for distribution which saves a massive amount of effort.
This article is a good read on the permeation of electricity through the economy
https://www.construction-physics.com/p/the-birth-of-the-grid
Alphabet / Google doesn’t have that issue. OAI and other money losing firms do.
I made a joke once after the first time I watched one of those Apple announcement shows in 2018, where I said "it's kind of sad, because there won't be any problems for us to solve because the iPhone XS Max is going to solve all of them".
The US economy is pretty much a big vibes-based Ponzi scheme now, so I don't think we can single-out AI, I think we have to blame the fact that the CEOs running these things face no negative consequences for lying or embellishing and they do get rewarded for it because it will often bump the stock price.
Is Tesla really worth more than every other car company combined in any kind of objective sense? I don't think so, I think people really like it when Elon lies to them about stuff that will come out "next year", and they feel no need to punish him economically.
I'd rather characterize it as extremes of Greater Fool Theory.
The technology is neat, the people selling it are ghouls.
I know it's good tech for some stuff, just not for everything. It's the same with previous bubbles. VR is really great for some things but we were never going to work with a headset on 8 hours a day. Bitcoin is pretty cool but we were never going to do our shopping list on Blockchain. I'm just so sick of hypes.
But I do think it's good tech, just like I enjoy VR daily I do have my local LLM servers (I'm pretty anti cloud so I avoid it unless I really need the power)
It's not really about the societal impacts for me, at least not yet, it's just not good enough for that yet. I do worry about that longer-term but not with the current generation of AI. At my work we've done extensive benchmarking (especially among enthusiastic early adopters) and while it can save a couple hours a week we're nowhere near the point where it can displace FTEs.
It's a business, but it won't be the thing the first movers thought it was.
I work commercializing AI in some very specific use cases where it extremely valuable. Where people are being lead astray is layering generalizations: general use cases (copilots) deployed across general populations and generally not doing very well. But that's PMF stuff, not a failure of the underlying tech.
On the pro-AI side, people are conflating technology success with product success. Look at crypto -- the technology supports decentralization, anonymity, and use as a currency; but in the marketplace it is centralized, subject to KYC, and used for speculation instead of transactions. The potential of the tech does not always align with the way the world decides to use it.
On the other side of the aisle, people are conflating the problematic socio-economics of AI with the state of the technology. I think you're correct to call it a failure of PMF, and that's a problem worth writing articles about. It just shouldn't be so hard to talk about the success of the technology and its failure in the marketplace in the same breath.
I haven’t followed this author but the few times he’s come up his writings have been exactly this.
Then Gemini got good (around 2.5?), like I-turned-my-head good. I started to use it every week-ish, not to write code. But more like a tool (as you would a calculator).
More recently Opus 4.5 was released and now I'm using it every day to assist in code. It is regularly helping me take tasks that would have taken 6-12 hours down to 15-30 minutes with some minor prompting and hand holding.
I've not yet reached the point where I feel letting is loose and do the entire PR for me. But it's getting there.
I think that's the key. Healthy skepticism is always appropriate. It's the outright cynicism that gets me. "AI will never be able to [...]", when I've been sitting here at work doing 2/3rds of those supposedly impossible things. Flawlessly? No, of course not! But I don't do those things flawlessly on the first pass, either.
Skepticism is good. I have no time or patience for cynics who dismiss the whole technology as impossible.
This is not a lofty goal. It's what we always expect from a competent human regardless of the number of passes it takes them. This is not what we get from LLMs in the same amount time it takes a human to do the work unassisted. If it's impossible then there is no amount of time that would ever get this result from this type of AI. This matters because it means the human is forced to still be in the loop, not saving time, and forced to work harder than just not using it.
I don't mean "flawless" in the sense that there cannot be improvements. I mean that the result should be what was expected for all possible inputs, and when inspected for bugs there are reasonable and subtle technical misunderstandings at the root of them (true bugs that are possibly undocumented or undefined behavior) and not a mess of additional linguistic ones or misuse. This is the stronger definition of what people mean by "hallucination", and it is absolutely not fixed and there has been no progress made on it either. No amount of prompting or prayer can work around it.
This game of AI whack-a-mole really is a waste of time in so many cases. I would not bet on statistical models being anything more than what they are.
It sounds like what you are seeing lines up with his predictions. Each model generation is able to take on a little more of the responsibilities of a software engineer, but it’s not as if we suddenly don’t need the engineer anymore.
For math it just did its first "almost independent" Erdos problem. In a couple months it'll probably do another, then maybe one each month for a while, then one morning we'll wake up and find whoom it solved 20 overnight and is spitting them out by the hour.
For software it's been "curiosity ... curiosity ... curiosity ... occasionally useful assistant ... slightly more capable assistant" up to now, and it'll probably continue like that for a while. The inflection point will be when OpenAI/Anthropic/Google releases an e2e platform meant to be driven primarily by the product team, with engineering just being co-drivers. It probably starts out buggy and needing a lot of hand-holding (and grumbling) from engineering, but slowly but surely becomes more independently capable. Then at some point, product will become more confident in that platform than their own engineering team, and begin pushing out features based on that alone. Once that process starts (probably first at OpenAI/Anthropic/Google themselves, but spreading like wildfire across the industry), then it's just a matter of time until leadership declares that all feature development goes through that platform, and retains only as many engineers as is required to support the platform itself.
In the academic world, and math in particular, I'm not sure. In a way, you could say it doesn't change anything because proofs already "exist" long before we discover them, so AI just streamlines that discovery. Many mathematicians say that asking the right questions is more important than finding the answers. In which case, maybe math turns into something more akin to philosophy or even creative writing, and equivalently follows the direction that we set for AI in those fields. Which is, perhaps less than one would think: while AI can write a novel and it could even be pretty good, part of the value of a novel is the implicit bond between the author and the audience. "Meaning" has less value coming from a machine. And so maybe math continues that way, computers solving the problems but humans determining the meaning.
Or maybe it all turns to shit and the sheer ubiquity of "masterpieces" of STEM/art everything renders all human endeavor pointless. Then the only thing that's left worth doing is for the greedy, the narcissists, and the power hungry to take the world back to the middle ages where knowledge and search for meaning take a back seat to tribalism and war mongering until the datacenters power needs destroy the planet.
I'm hoping for something more like the former, but, it's anybody's guess.
I guess cynics will yap about capitalism and how this supposedly benefits only the rich. That seems very unimaginative to me.
Does it? How exactly is the common Joe going to benefit from this world where the robots are doing the job he was doing before, as well as everyone else's job (aka, no more jobs for anyone)? Where exactly is the money going to come from to make sure Joe can still buy food? Why on earth would the people in power (aka the psychotic CxOs) care to expend any resources for Joe, once they control the robots that can do everything Joe could? What mechanisms exist for everyone here to prosper, rather than a select few who already own more wealth and power than the majority of the planet combined?
I think believing in this post-scarcity utopian fairy tale is a lot less imaginative and grounded than the opposite scenario, one where the common man gets crushed ruthlessly.
We don't even have to step into any kind of fantasy world to see this is the path we're heading down, in our current timeline as we speak, CEOs are foaming at the mouth to replace as many people as they can with AI. This entire massive AI/LLM bubble we find ourselves in is predicated on the idea that companies can finally get rid of their biggest cost centers, their human workers and their pesky desires like breaks and vacations and worker's rights. And yet, there's still somehow people out there that will readily lap up the bullshit notion that this tech is going to somehow be used as a force of good? That I find completely baffling.
Many cynics seem to believe rich people are demons with zero consideration for their fellow humans.
Rich and powerful persons are still people just like you, and they have an interest in keeping the general population happy. Not to mention that we have democratic mechanisms that give power to the masses.
We will obviously transition to a system where most of us can live a comfortable life without working a full time job, and it's going to be great.
Can people get their words straight before typing?
I'm not sure how much of that is because Google Search has worsened versus LLMs having improved, but it's still a substantial shift in my day-to-day life.
Something like finding the most appropriate sensor ICs to use for a particular use case requires so much less effort than it used to. I might have spent an entire day digging through data sheets before, and now I'll find what I need in a few minutes. It feels at least as revolutionary as when search replaced manually paging through web directories.
> In 2029, AI will not be able to watch a movie and tell you accurately what is going on (what I called the comprehension challenge in The New Yorker, in 2014). Who are the characters? What are their conflicts and motivations? etc.
> In 2029, AI will not be able to read a novel and reliably answer questions about plot, character, conflicts, motivations, etc. Key will be going beyond the literal text, as Davis and I explain in Rebooting AI.
> In 2029, AI will not be able to work as a competent cook in an arbitrary kitchen (extending Steve Wozniak’s cup of coffee benchmark).
> In 2029, AI will not be able to reliably construct bug-free code of more than 10,000 lines from natural language specification or by interactions with a non-expert user. [Gluing together code from existing libraries doesn’t count.]
> In 2029, AI will not be able to take arbitrary proofs from the mathematical literature written in natural language and convert them into a symbolic form suitable for symbolic verification.
Many of these have already been achieved, and it's only early 2026.
[1]https://garymarcus.substack.com/p/dear-elon-musk-here-are-fi...
My understanding of the current scorecard is that he's still technically correct, though I agree with you there is velocity heading towards some of these things being proven wrong by 2029.
For example, in the recent thread about LLMs and solving an Erdos problem I remember reading in the comments that it was confirmed there were multiple LLMs involved as well as an expert mathematician who was deciding what context to shuttle between them and helping formulate things.
Similarly, I've not yet heard of any non-expert Software Engineers creating 10,000+ lines of non-glue code that is bug-free. Even expert Engineers at Cloud Flare failed to create a bug-free OAuth library with Claude at the helm because some things are just extremely difficult to create without bugs even with experts in the loop.
The second claim about novels seems obviously achieved to me. I just pasted a random obscure novel from project gutenberg into a file and asked claude questions about the characters, and then asked about the motivations of a random side-character. It gave a good answer, I'd recommend trying it yourself.
Like, it behaves as if any answer is better than no answer.
At most of these comprehension tasks, AI is already superhuman (in part because Gary picked scaled tasks that humans are surprisingly bad at).
In some instances you'll get results that are shockingly good (and in no time), in others you'll have a grueling experience going in circles over fundamental reasoning, where you'd probably fire any person on the spot for having that kind of a discussion chain.
And there's no learning between sessions or subject area mastery - results on the same topic can vary within same session (with relevant context included).
So if something is superhuman and subhuman a large percentage of time but there's no good way of telling which you'll get or how - the result isn't the average if you're trying to use the tool.
Using Gemini Notebooks I've found it passable at summarizing chapters, listing characters, major theme and so on, and it can do this in English.
"What is the symbolism of the Black Stone?"
> In the sources, the Black Stone (referred to as the Pierre-Noire) serves as the central religious icon of the Syrian Sun cult brought to Rome by the Emperor Elagabalus. Its symbolism is multifaceted, ranging from fertility and virility to a mystical pursuit of universal unity.
> It represents the perennity of the Sun, which fertilizes the world, causing "germs to rise" and spreading them through the atmosphere. It is viewed as the "definitive form" of divinity, intended to absorb and replace the "transitory forms" of all other Roman, Greek, Egyptian, and Persian gods, including the Christian "Kreistos". > Virility and Phallic Symbolism > > The sources explicitly characterize the Black Stone as a gigantic sacred phallus. It is described as: • An icon of virility and the "organ of generation" • A "unisexual icon" that materializes the generative force of nature • A representation of "virility in activity," which is why it is often paraded and elevated in an "orgasmic" or "colossal adoration"
> The Androgyne and Universal Unity - Beyond simple fertility, the philosopher Atillius explains a more complex, "mad" metaphysical project associated with the stone. It symbolizes "Life One" (Vie Une) and the return to a unisexual state of perfection. • The Androgyne: Atillius believes that by pursuing "the male sex by the male sex," the cult "inutilizes" the female sex to eventually create the Androgyne—a self-sufficient being containing both sexes • Unity: The stone signifies the fusion of all generative forces into a single Unity, reversing the "separation of the sexes" which is viewed as a state of unhappiness and impotence. • Marriage of Moon and Sun: The ritual marriage of the goddess Astaroth (representing the Moon and the female principle) to the Black Stone (representing the Sun and the male principle) symbolizes the merging of the Orient and Occident into this unified life principle > > Destruction of the Symbol - The Black Stone ultimately becomes a symbol of Oriental pollution and decadence to the Roman populace. During the final rebellion against Elagabalus, the stone is torn from its temple on the Palatine, defiled with filth, and broken into pieces to ensure that its "signification of Life" would never again dominate Rome.
This is all accurate to the book, even teasing out a couple themes that were only subconsciously present to me.
The NotebookLM version gives citations with links to the original text to support all these assertions, which largely are coherent with that purpose.
The input is raw images of a book scan! Imperfect as it is it still blows my mind. Not that long ago any kind of semantic search or analysis was a very hard AI problem.
Here is an english analysis of the text that easily showed up in an internet search:
https://www.cantab.net/users/leonardo/Downloads/Varian%20Sym...
This source includes analysis of "the Black Stone."
LLMs haven't solved any of the 2029 predictions as they were posited. But I expect some will be reached by 2029. The AI hype acts like all this is easy. Not by 2029 doesn't mean impossible or even most of the way there.
4 is close, the interface needs some work to allow nontechnical people use it. (claude code)
I'm still trying to find humans that do this reliably too.
To add on, 5.2 seems to be kind of lazy when reading text in images by default. Feeding it an image it may give the first word or so. But coming back with a prompt 'read all the text in the image' makes it do a better job.
With one in particular that I tested I thought it was hallucinating some of the words, but there was a picture in the picture with small words it saw I missed the first time.
I think a lot of AI capabilities are kind of munged to end users because they limit how much GPU is used.
1) Is it actually watching a movie frame by frame or just searching about it and then giving you the answer?
2) Again can it handle very long novels, context windows are limited and it can easily miss something. Where is the proof for this?
4 is probably solved
4) This is more on predictor because this is easy to game. you can create some gibberish code with LLM today that is 10k lines long without issues. Even a non-technical user can do
If a movie or novel is famous the training data is already full of commentary and interpretations of them.
If its something not in the training data, well I don't know many movies or books that use only motives that no other piece of content before them used, so interpreting based on what is similar in the training data still produces good results.
EDIT: With 1 I meant using a transcript of the Audio Description of the movie. If he really meant watch a movie I'd say thats even sillier because well of course we could get another Agent to first generate the Audio Description, which definitely is possible currently.
Sure, another model might have gotten it right, but I think the prediction was made less in the sense of "this will happen at least once" and more of "this will not be an uncommon capability".
When the quality is this low (or variable depending on model) I'm not too sure I'd qualify it as a larger issue than mere context size.
Can AI actually do this? This looks like a nice benchmark for complex language processing, since a complete novel takes up a whole lot of context (consider War and Peace or The Count of Monte Cristo). Of course the movie variety is even more challenging since it involves especially complex multi-modal input. You could easily extend it to making sense of a whole TV series.
Consider also that they can generate summaries and tackle the novel piecemeal, just like a human would.
Re: movies. Get YouTube premium and ask YouTube to summarize a 2hr video for you.
> Re: movies. Get YouTube premium and ask YouTube to summarize a 2hr video for you.
This is different from watching a movie. Can it tell what suit actor was wearing? Can it tell what the actor's face looked like? Summarising and watching are too different things.
https://github.com/JUNJIE99/MLVU
https://huggingface.co/datasets/OpenGVLab/MVBench
Ovis and Qwen3-VL are examples of models that can work with multiple frames from a video at once to produce both visual and temporal understanding
Which is a relatively trivial task for a current LLM.
You're referring to casual reading, but writers and people who have an interest and motivation to read deeply review, analyze, and summarize books under lenses and reflect on them; for technique as much as themes, messages, how well they capture a milieu, etc. So that's quite a bit more than "no human"!
Yes, you just break the book down by chapters or whatever conveniently fits in the context window to produce summaries such that all of the chapter summaries can fit in one context window.
You could also do something with a multi-pass strategy where you come up with a collection of ideas on the first pass and then look back with search to refine and prove/disprove them.
Of course for novels which existed before the time of training an LLM will already contain trained information about so having it "read" classic works like The Count of Monte Cristo and answer questions about it would be a bit of an unfair pass of the test because models will be expected to have been trained on large volumes of existing text analysis on that book.
>reliably answer questions about plot, character, conflicts, motivations
LLMs can already do this automatically with my code in a sizable project (you know what I mean), it seems pretty simple to get them to do it with a book.
I've done that a few month ago and in fact doing just this will miss cross-chapter informations (say something is said in chapter 1, that doesn't appears to be important but reveals itself crucial later on, like "Chekhov's gun").
Maybe doing that iteratively several time would solve the problem, I run out of time and didn't try but the straightforward workflow you're describing doesn't work so I think it's fair to say this challenge isn't solve. (It works better with non-fiction though, because the prose is usually drier and straight to the point).
I think the arbitrary proofs from mathematical literature is probably the most solved one. Research into IMO problems, and Lean formalization work have been pretty successful.
Then, probably reading a novel and answering questions is the next most successful.
Reliably constructing 10k bug free lines is probably the least successful. AI tends to produce more bugs than human programmers and I have yet to meet a programmer who can reliably produce less than 1 bug per 10k lines.
You imperatively need to try Claude Code, because it absolutely does that.
I'm quite sure people who made those (now laughable) predictions will tell you none of these has been achieved, because AI isn't doing this "reliably" or "bug-free."
Defending your predictions is like running an insurance company. You always win.
The keyword being "reliably" and what your threshold is for that. And what "bug free" means. Groups of expert humans struggle to write 10k lines of "bug free" code in the absolutist sense of perfection, even code with formal proofs can have "bugs" if you consider the specification not matching the actual needs of reality.
All but the robotics one are demonstrable in 2026 at least.
Just earlier today I asked it to give me a summary of a show I was watching until a particular episode in a particular season without spoiling the rest of it and it did a great job.
If Bill Gates made a predication about computing, no matter what the predication says, you can bet that 640K memory quote would be mentioned in the comment section (even he didn't actually say that).
I appreciate good critique but this is not it
The goalposts keep getting pushed further and further every month. How many math and coding Olympiads and other benchmarks will LLMs need to dominate before people will actually admit that in some domains it's really quite good.
Sure, if you're a Nobel prize winner or PhD then LLMs aren't as good as you yet, but for 99% of the people in the world, LLMs are better than you at Math, Science, Coding, and every language probably except your native language, and it's probably better at you at that too...
COULD I do this stuff before? Sure. But I wouldn’t have. Life gets in the way. Now, the bar is low so why not build stuff? Some of it ships, some of it is just experimentation. It’s all building.
Trying to quantify that shift is impossible. It’s not a multiplier to productivity you measure by commits. It’s a builder mind shift.
Correction. The genAI has built it.
I haven't got any skin on either side here, but doesn't the fact the genAI can build it imply that what you are doing is heavily trodden ground, that there will be less and less need for developers like you, and will gradually lead to many developers (like you) being cut out of the market entirely.
For personal stuff it's wonderful. For work, it seems like a double edged sword that will eventually cut the devs that use it (and those that don't). Even if the business owners aren't completely daft and keep a (vastly diminished) workforce of dev/AI consultants on board, that could easily exclude you or me.
It's going well if all the jobs it eradicates can be replaced with just as many jobs (they can't), or the powers that be catch on and realise there isn't that many jobs left for humans to do and institute some form of basic income system (they won't).
Even as I use it, and I use it everyday, I can't really assess its true impact. Am I more productive or less overall? I'm not too sure. Do I do higher quality work or lower quality work overall? I'm not too sure.
All I know, it's pretty cool, and using it is super easy. I probably use it too much, in a way, that it actually slows things down sometimes, when I use it for trivial things for example.
At least when it comes to productivity/quality I feel we don't really know yet.
But there are definite cool use-cases for it, I mean, I can edit photos/videos in ways I simply could not before, or generate a logo for a birthday party, I couldn't do that before. I can make a tune that I like, even if it's not the best song in the world, but it can have the lyrics I want. I can have it extract whatever from a PDF. I can have it tell me what to watch out for in a gigantic lease agreement I would not have bothered reading otherwise.
I can have it fix my tests, or write my tests, not sure if it saves me time, but I hate doing that, so it definitely makes it more fun and I can kind of just watch videos at the same time, what I couldn't before. Coding quality of life improvements are there too, I want to generate a sample JSON out of a JSONSchema, and so on. If I want, I can write the a method using English prompts instead of the code itself, might not truly be faster or not, not sure, but sometimes it's less mentally taxing, depending on my mood, it can be more fun or less fun, etc.
All those are pretty awesome wins and a sign that for sure those things will remain and I will happily pay for them. So maybe it depends on what you expected.
The irony of a five sentence article making giant claims isn't lost on me. Don't get me wrong: I'm amenable to the idea; but, y'know, my kids wrote longer essays in 4th grade.
Right around then, we can send a bunch of reconnaissance teams out to the abandoned Japanese islands to rescue them from the war that’s been over for 10 years - hopefully they can rejoin society, merge back with reality and get on with their lives
Yeah you could ask ChatGPT or Claude to write code, but it wasn't really there.
It needs a while to adopt the model AND the UI. As in software are the first one because we are both makers and users.
1) https://en.wikipedia.org/wiki/Gartner_hype_cycle
or
2) "First they ignore you, then they laugh at you, then they fight you, then you win."
or maybe originally:
"First they ignore you. Then they ridicule you. And then they attack you and want to burn you. And then they build monuments to you"
So I'm not really sure how to parse your statement.
Even then, given the deep impact of LLMs and how many people are using them already, it's a stretch to say LLMs will have no effect on the development of AGI.
I think it's pretty obvious that AGI requires something more than LLMs, but I think it's equally obvious LLMs will have been involved in its development somewhere, even if just a stepping stone. So, a "precursor".
Second of all, GenAI is going well or not depending on how we frame it.
In terms of saving time, money and effort when coding, writing, analysing, researching, etc. It’s extremely successful.
In terms of leading us to AGI… GenAI alone won’t reach that. Current ROI is plateauing, and we need to start investing more somewhere else.
...and yet we still see these articles claiming LLMs are dying/overhyped/major issues/whatever.
Cool man, I'll just be over here building my AI based business with AI and solving real problems in the very real manufacturing sector.
But can they write grammatically correct statements?
> Trying to orient our economy and geopolitical policy around such shoddy technology — particularly on the unproven hopes that it will dramatically improve– is a mistake.
The screenshots are screenshots of real articles. The sentence is shorter than a typical prompt.
gpt-oss isn't bad, but even models you cannot run are worth getting since you may be able to run them in the future.
I'm hedging against models being so nerfed they are useless. (This is unlikely, but drives are cheap and data is expensive.)
And yes, I do understand the code and what is happening and did have to make a couple of adjustments manually.
I don't know that reducing coding work justifies the current valuations, but I wouldn't say it's "not going all that well".
As said in the article, a conservative estimate is that Gen AI can currently do 2.5% of all jobs in the entire economy. A technology that is really only a couple of years old. This is supposed to be _disappointing_? That’s millions of jobs _today_, in a totally nascent form.
I mean I understand skepticism, I’m not exactly in love with AI myself, but the world has literally been transformed.
Seems like black and white thinking to me. I had it make suggestions for 10 triage issues for my team today and agreed with all of its routings. That’s certainly better than 6 months ago.
I hate generative AI, but its inarguable what we have now would have been considered pure magic 5 years ago.
You're not losing your job unless you work on trivial codebases. There's a very clear pattern what those are: startups, greenfield, games, junk apps, mindless busywork that probably has an existing better tool on github, etc. Basically anything that doesn't have any concrete business requirements or legal liability.
This isn't to say those codebases will always be trivial, but good luck cleaning that up or facing the reality of having to rewrite it properly. At least you have AI to help with boilerplate. Maybe you'll learn to read docs along the way.
The people claiming to be significantly more productive are either novice programmers or optimistic for unexplained reasons they're still trying to figure out. When they want to let us know, most people still won't care because it's not even the good kind of unreasonable that brings innovation.
The only real value in modern LLMs is that natural language processing is a lot better than it used to be.
Are we done now?
I just used ChatGPT to diagnose a very serious but ultimately not-dangerous health situation last week and it was perfect. It literally guided me perfectly without making me panic and helped me understand what was going on.
We use ChatGPT at work to do things that we have literally laid people off for, because we don't need them anymore. This included fixing bugs at a level that is at least E5/senior software engineer. Sometimes it does something really bad but it definitely saves times and helps avoid adding headcount.
Generative AI is years beyond what I would have expected even 1 year ago. This guy doesn't know what he's talking about, he's just picking and choosing one-off articles that make it seem like it's supporting his points.
The same goes for code as well.
I’ve explored Claude code/antigravity/etc, found them mostly useless, tried a more interactive approach with copilot/local models/ tried less interactive “agents”/etc. it’s largely all slop.
My coworkers who claim they’re shipping at warp speed using generative AI are almost categorically our worst developers by a mile.