I’m a little shocked at how much negativity there is around LLMs among developers. It’s a new tool that requires some learning, and it’s sometimes not so great, but if you’ve used an IDE with real coding assistance built in (eg. VS Code in Edit with Copilot mode - NOT Chat mode, using Claude 3.5), it’s honestly not much worse than a junior dev and 100x faster. And if the code is bad you throw it away and try again 10 seconds later. The amount of speed up I see as a very experienced dev is astronomical. And just like 6 months ago it was awful. How great is it gonna be in a year or two? It doesn’t even have access to running unit tests or reading console errors or IDE hints, and it still generates mostly correct code. Once it gets more deeply embedded it’s just going to improve more and more.
This is the crux. A cool thing has been invented, with real usages. Unfortunately, it's cost hundreds of billions of dollars and it has absolutely zero hope of making the trillions needed to justify that.
Now someone will respond about how it's just a stepping stone, and how the billions are justified by _something completely imaginary, and not invented yet, and maybe not ever_ e.g. agents.
>it's cost hundreds of billions of dollars and it has absolutely zero hope of making the trillions needed to justify that.
The BigTech companies have been flush with liquidity and poured those hundreds of billions into the promising tech, and as result we got a wonderful new technology. There is not much need for those trillions in return - just look at liquidity positions of those companies, they are just fine. If those trillions come in eventually - even better.
>There is not much need for those trillions in return
Whilst you are correct that big tech cos do not need the return to survive, that's not how public markets work at all, and thus not how the incentives for those in charge of the companies work, and so making you actually wrong.
If i were wrong, those companies would be distributing that cash to shareholders instead of chasing any promise of any big chance.
If investment in AI don't pan out (i do think that it will pan out, and those trillions will come) then those companies would just pour even more billions into whatever big thing/promise would come next. Rinse and repeat. Because some of those things do generate tremendous returns, and thus not playing that game is what really constitute true loss of money.
US right now is run by someone whose explicit promises, if actually implemented, have an obvious immedidiate 13-14% reduction in GDP — literally, never mind side effects, I'm not counting any businesses losing confidence in the idea that America is a place to invest, this is just direct impact.
DOGE + deportation by themselves do most of that percentage. The tariffs are a rounding error in comparison, but still bad on the kind of scale that gets normal politicians kicked out.
Nobody has ever been punished for choosing IBM. It’s the same story here. Nobody is going to blame them for following the zeitgeist, but you bet they’d be punished if they didn’t and it doesn’t pan out.
The whole thing is like bitcoin. There’s too many people that benefit from maintaining the collective illusion.
cash on hands GOOG - 100B, AMZN - 80B, FB - 70B, and their core businesses are basically printing money, so they pretty much do have to invest into new things. If somebody sees a multi-billion dollar sink better than AI right now ...
Ideally it was thought to have shortened a very expensive war, and may have prevented the USSR from taking over Europe by leveraging its unquestioned postwar conventional forces advantage.
I don't know why it is so hard to understand. I mean money doesn't really exist without a government[0] and while government plays a role in the market and economy, this role is VERY different than that of a business. A government isn't trying to "make money", is isn't trying to make investors happy, and it certainty can't take existential risks that could make "the company" go bankrupt (or it shouldn't lol).
But I do think (and better understand) there is a failure to understand this at a higher abstraction. One part is simply "money is a proxy." This is an uncontestable fact. But one must ask "proxy for what?" and I think people only accept the naive simple answer. Unfortunately, this "is a proxy" concept is extremely generalization. Everything is an estimation, everything is an approximation, and most things are realistically intractable. We use sibling problems or similar problems to work with that are concrete, but there are always assumptions made and ignoring these can have disastrous consequences. Approximations are good (they're necessary even) but the more advanced a {topic,field,civilization,etc} gets, the more important it is to include higher order terms. Frankly, I don't think humans were built for that (though by some miracle we have the capacity to deal with it).
My partner and her dad are both economists, and one thing I've learned is that what many people think are "economics questions" are actually "business questions". I think a story from her dad makes this extremely clear. A government agency hired him to look at the cost benefit analysis of some stuff (like building a few hospitals and some other unambiguously beneficial institutions), and when he presented everyone was happy but had a final question "should we build them?" The answer? "That's not the role of an economist." The reason for this is because money can't actually be accurately attributed to these things. You can project monetary costs for construction, staffing, and bills, and you can make projections about how many people this will benefit, how it can reduce burdens elsewhere, and as well as make /some/ projections about potential cost savings. But you can't answer "should you." Because the weight of these values is not something that can be codified with any data. It is an importance determined by the public and more realistically their representatives. Very few times can you give a strong answer to a question like "should we build a new hospital" and essentially in only the extreme cases. I'll give another example. In my town there was an ER that was closed due to budget constraints. This ER was across the street to the local university, which students represent ~15% of the population. The next nearest ER? A 15 minute ambulance ride away and in the next town over. Did the city save money? Yes. Did the sister city's ER become even busier? Also yes. Did people lose access to medicine? Yes. Did people die? Also yes. Have economists put a price on human life? Also yes, but they are very clear that this is not a real life and a very naive assumptions[1]. It is helpful in the same way drawing random squiggles on a board can help a conversation. Any squiggles can really be drawn but the existence of _something_ helps create some point to start from.
[0] okay crypto bros, you're not wrong but low volatility is critical as well as some other aspects. Let's not get off topic
The profit was made by the private sector in supplying goods to the program. Today, private companies do a lot and earn a lot of money from stockpile maintenance.
The Manhattan Project was driven by the U.S. Government, which doesn't need a VC-tier return. The entire business model of VCs is based on the idea that they'll have the occasional 100x return, and if none of the AI companies do that it would destroy the VC model.
Doesn't matter. The Manhattan project was a breakthrough in fundamental science that changed the world. Current generative AI are a solid degree improvement on previous technology that is not remotely as big a leap as the amount of money poured into it assumes it to be.
> I use Cursor sometimes, and VSCode + Continue with llama.cpp, and it's great. That's not worth billions. It's definitely not worth trillions.
That seems like a suspect claim. If you're saying that you, personally, cannot create billions of dollars in value with Cursor & friends that is certainly true - but you are in no position to make a judgement call about where the cap on value creation is for the LLM market is worth based on your personal use cases. LLMs don't just do code completion. We really can't estimate how much potential value is being created without doing some serious data diving and studying of cases.
A better argument would be that the DeepSeek experience suggests these companies have no moat and therefore no way to earn a return on capital. But LLMs are probably going to generate at least trillions of dollars in value because they're on par or ahead of Wikipedia and Google for answering many queries then they also have hundreds of ancillary uses like answering medical questions at weird hours or creative/professional writing.
It's possible to grow an economy by trillions of real value without any actor being able to extract that as a profit or it even showing up in the books as money.
Consider that Wikipedia is much bigger than Encyclopedia Britanica, but because it is given away to everyone for free, it is not counted as E.B.'s max sale price ($2900 in 1989?) times the world's internet connected population (5.6e9?) — $16 trillion.
AI, regardless of value, are priced at the marginal cost to reproduce weights or run inference depending on which you care about.
But I do mean "reproduce" not "invent" — it doesn't matter if DeepSeek's "a few million" was only possible because they benefited from published research, it just matters that they could.
And if the hardware is the bottleneck for inference, that profit goes to the hardware manufacturer, not to the top ten companies who made models.
Where are you getting this from? Outside of o3, every AI provider's API is super cheap, with most productive queries I do coming in under 2c. We have no reason to believe any of them are selling API requests at a loss. I think <2c per query hardly counts as "quite expensive".
The reasoning people have for them selling API requests at a loss is simply their financial statements. Anthropic burned $3B this year. ChatGPT lost $5B. Microsoft has spent $19B on AI and Google has spent close to $50B. Given that revenue for the market leader ChatGPT is $3.7B, it's safe to say that they're losing massive amounts of money.
These companies are heavily subsidized by investors and their cloud service providers (like Microsoft and Google) in an attempt to gain market share. It might actually work - but this situation, where a product is sold under cost to drum up usage and build market share, with the intent to gain a monopoly and raise prices later on - is sort of the definition of a bubble, and is exactly how the mobile app bubble, the dot-com bubble, and previous AI bubbles have played out.
Not sure if it matters at this point. There will need to be many more rounds of CapEx to realize the promises that have been put forth about these models.
The implication would be that those API requests are being sold at a loss. Amodei wrote in January that Claude 3.5 Sonnet was trained for only a few $10Ms, but Anthropic has been losing billions.
That would be a killer for the current and near future generations of LLM as a business. If they are having to pay many times in compute what they are able to get for the API use (due to open models being near comparable?), then you definitely can't "make up for it in volume".
How? I get that many devs like using them for writing code. Personally I don't, but maybe someday someone will invent a UX for this that I don't despise, and I could be convinced.
So what? That's a tiny market. Where in the landscape of b2b and b2c software do LLMs actually find market fit? Do you have even one example? All the ideas I've heard so far are either science fiction (just wait any day now we'll be able to...) or just garbage (natural language queries instead of SQL). What is this shit for?
Anecdotally, almost every day I’ll overhear conversations at my local coffee shop of non-developers gushing about how much ChatGPT has revolutionized their work: church workers for writing bulletins and sermons, small business owners for writing loan applications or questions about taxes, writers using it for proofreading, etc. And this is small town Colorado.
Not since the advent of Google have I heard people rave so much about the usefulness of a new technology.
These are not the sort of uses we need to make this thing valuable. To be worthwhile it needs to add value to existing products. Can it do that meaningfully well? If not it's nothing more than a curiosity.
To make money though it just needs to have a large or important audience and a means of convincing people to think, want, or do things that people with money will pay to make people think, want or do.
Can you get enough revenue from ads to pay the cost of serving LLM queries? Has anyone demonstrated this is a viable business yet?
A related question: has anyone figured out how to monetize LLM input? When a user issues a Google search query they're donating extremely valuable data to Google that can be used to target relevant ads to that user. Is anyone doing this successfully with LLM prompt text?
I bet Google is utilizing the value of the LLM input prompts with close to the same efficiency they are monetizing search. I that case, there are two questions -- 1) will LLM overtake search? and 2) can anyone beat Google at monetizing these inputs? I think the answer to both is no. Google already has a wide experience lead monetizing queries. And personally, I'd rather have a search engine that does a better job of excluding spam without having to worry whether or not it's making stuff up. Kagi has a better search than any of the LLMs (except for local results like restaurants/maps).
My company uses them for a fuckton of things that were previously too intractable for static logic to work (because humans are involved).
This is mostly in the realm of augmented customer support (e.g. customer says something, and the support agent immediately gets the summarized answer on their screen)
It’s nothing that can’t be done without, but when the whole problem can be simplified to “write a good prompt” a lot of use cases are suddenly within reach.
It’s a question if they’ll keep it around when they realize it doesn’t always quite work, but at least right now MS is making good money off of it.
LLMs are incredible at editing my writing. Every email I write is improved by LLMs. My executive summaries are improved by LLMs. It wont be long until every single office worker is using LLMs as an integral part of their daily stack, people just have to try it and theyll see how useful it is for writing.
Microsoft turned itself into a trillion dollar company off the back of enterprise SAAS products and LLMs are among the most useful.
Various minor thing so far. For example I heard about ChatGPT being evaluated as a tool for providing answers for patients in therapy. ChatGPT answers were evaluated as more empathetic, more human and more aligned with guidelines of therapy than answers given by human therapists.
Providing companionship to lonely people is another potential market.
It's not as good as people at solving problems yet but it's already better than humans at bullshiting them.
Are people actually satisfied by that? I personally find "chatting" with an LLM grating and dissatisfying because it often makes very obvious and incongruous errors, and it can't reason. It has no logical abilities at all, really. I think you're really underestimating what a therapist actually does, and what human communication actually is. It's more than word patterns.
I could see this being useful in a "dark pattern" sense, but only if it's incredibly cheap, to increase the cost to the user of engaging with customer support. If you have to argue with the LLM for an hour before being connected to an actual person who can help you, then very few calls will make it to the support staff and you can therefore have a much smaller team. But that only works if you hate your users.
Subjective evaluation of "humanity" and "empathy" in responses is much less important than clinical outcome. I don't think an online chat with a nebulous entity will ever be as beneficial as interactions that can, at least occasionally, be in-person. Especially as the trust of online conversations degrade. Erosion of trust online seems like a major negative consequence of all the generative AI slop (LLM or otherwise).
Clinical outcome of humans doing therapy would be better if for some reason doing therapy worse (less according to taught guidelines) was better. But, sure, we can wait for another research or follow up. It might be true. Therapy has dismal outcomes anyways and the outcomes are mostly independent of which theoretical framework the therapy is done according to. It might be the case that the only value in therapy is human connection that AI fails to simulate. But it seem that for some people it simulates connection pretty well.
> The article is about how the economics of the LLM market is making all tech look bad.
No, it's not. The first half of the article talks about how useless the actual product is, how the only reason we hear about it is because the media loves to talk about it.
LLMs are pretty good at the aspects of coding that I consider to be "the fun part". Using them has made me more productive, but also made my job less fun, because I can't justify spending time using my own brain to do "the fun part" on my employer's dime. And that was something I was particularly good at, which is why I was able to be paid well to do it.
So now my company makes more money, and the work gets done faster, but I can't say I feel appreciative. I'm sure it's great for founders though, for whom doing the work is merely an obstacle to having the finished product. For me, the work is the end goal, because I'm not hired to own the result.
Looks like you haven't used a decent IDE: these things have been standard for decades, locally and with minimal requirements. But wait, now it happens in the Cloud (meh, that's not gonna fly anymore, too last decade)...AND requires massive amounts of power AND cooling, PLUS it's FUBAR about 50/50.
For an incremental improvement...not great, not terrible.
I think LLMs are vastly overhyped and mostly useless, but I use Copilot as glorified autocomplete and like it.
It does what the other poster said: it automates the boring parts of "this db model has eight fields that are mostly what you expect" and it autocompletes them mostly accurately.
You're really comparing an IDE's autocomplete with something that can, at minimum, write out entire functions for you?
You're either completely misremembering what IDEs have been able to do up until 3 years ago, or completely misunderstanding what is available now. Even the very basic "autocomplete" functionality of IDEs is meaningfully better now.
It's kind of analogous to the old taxi drivers who took pride in having a sixth sense knowing which route to take you, vs uber drivers who just blindly follow their navigation
Some of them might have had a really good mental map; but the majority would just take inefficient routes (and charge you some random price that they put into their counter) — plenty of reasons to dislike Uber but having a pre-set price, vetted/rated drivers, and clear routing for a taxi service is a massive plus in my opinion.
Bit of a boomer statement here but maybe this will encourage devs such as yourself to contribute more to open source passion projects that will help dethrone the monopolies. Looking at Valve's investment into Linux via Proton as a great example.
It would be so nice to have a productivity Linux OS that just works on all my devices without tinkering. I want to stop supporting the closed source monopolies, but the alternatives aren't up to par yet. I am extremely hopeful that they will be once mega corps inevitably decay and people tire of the boom-bust cycle.
As technologists, we all want beautifully designed tools, and I'm increasingly seeing that these are only created by passionate and talented people who truly care about tech, unlike megacorps that only care about enriching their board and elite shareholders.
That experience is heavily subsidized and is unprofitable for these companies providing it based on what we know. Even with all of the other developers who are also using the same work flow and espousing how great it is. Even with all of the monthly subscribers at various tiers. It has been unprofitable for several years and continues to be unprofitable and will likely remain unprofitable given current trends.
The author spends a good amount of bytes telling us that they don't want to hear this argument even though they expect it.
Perhaps, and the externalities often unaccounted for or hand-waved away.
Even the US Government is getting involved in subsidizing these companies and all of the infrastructure and resources needed to keep it expanding. We can look forward to even more methane power plants, more drilling, more fracking, more noisy data-centres sucking up fresh water from local reserves and increased damage to the environment that will come out of the pocket books of... ?
Update: And for what? "Deep Research"? Apparently it's not that great or world-changing for the costs involved. It seems that the author is tired of the yearly promise that everything is just a year or two away as long as we keep shovelling more money and resources into the furnace.
Inference isn’t that expensive. A single junior dev costs orders of magnitude more than the amount of inference I use. Companies in growth mode don’t have to make money, it’s a land grab right now. But the expense is largely in the R and D. You can build a rig to run full models for 10-20k right? That’s only a month or two of a junior dev’s time, and after that it’s just electricity. And you could have dozens of devs using the same rig as long as they could timeshare. I don’t see where the economics wouldn’t work, it’s just there’s no use in investing in the hardware until we know where AI is going.
Yeah, you can build a rig to run full models for 10-20k... That's a big reason OpenAI might not make it. The whole article is about LLMs not being a viable business.
If the old metric is right, that it is ten times harder to debug code than to write it, having something that writes buggy code 100x faster than you can understand it is a problem.
Especially given that you can ask an LLM to optimise code and on multiple runs it can not tell if it's is improving or degenerating.
Not totally. But you might be surprised at the things you can do. Cursor has some template-like files where you can basically teach the AI “when we do X, do it this way.” Or you can change the global prompt to add the things it should keep in mind when working with you.
If you actually take the time to tell it “hey, don’t do it this way,” it can definitely do it differently the next time.
On top of that, is anyone training models on their own codebase, and noting to AI which patterns are best practice and which aren’t?
There are a ton of ways to make it better than the baseline copilot experience
> I’m a little shocked at how much negativity there is around LLMs among developers.
While the timeline is unclear; it seems likely that LLMs will obsolete precisely the skills that developers use to earn their income. I imagine a lot of them feel rather threatened by the rapid rate of progress.
Pointing out that it is already operating at junior dev quality and rapidly improving is unlikely to quiet the discontent.
I use LLMs in coding. There are Junior Devs in my team.
If you think LLMs operate at "junior dev" capacity you either don't work with junior devs and is just bullshitting your way around here, or you just pick pretty awful junior devs.
LLMs are alright. An okay productivity tool, although its inconsistencies many times nullify productivity gains - By design they often spit out wrong results that look and sound very plausible. A productivity blackhole. Its mistakes are sometimes hard to spot, but pervasive.
Beyond that, if your think that all a dev does is spit out code, and since LLMs can spit out code it can replace devs in some imaginary timeline, you are sorely mistaken. The least part of my work is actually spitting out code, although it is the part I enjoy the most.
I honestly feel way nore threatened by economic downturns and the looming threat of recession. The only way LLMs threaten me is by being a wasteful technology that may precipitate a downturn in tech companies, causing more layoffs, etc nd so forth.
The value of developers is not the code they output. It's the mental models they develop of the problem domain and the systems they build. LLMs can output code without developing the mental models.
Code is liability. The knowledge inside developers' heads is the corresponding asset. If you just produce code without the mental models being developed and refined, you're just increasing liability without the counterpart increase in assets.
If you define "junior" based mostly on age, then LLM's aren't yet at the level of a good "junior".
If you base it on ability, then an LLM can be be more useful to a good developer than 1 or more less competent "junior" team members (regardless of their age).
Not because it can do all the things like any "junior" can (like make coffee), but because the things it can do on top of what a "junior" can do, more than makes up for it.
>> If you think LLMs operate at "junior dev" capacity you either don't work with junior devs and is just bullshitting your way around here, or you just pick pretty awful junior devs.
I’ve hired lots of junior devs, some of them very capable. I’ve been in this industry for more than 15 years. LLMs operate at junior dev capacity, that’s pretty clear to me at this moment.
Yep. There are people who love programming, it's the best part of the work anyhow! And then there are people who come and tell that whatever you do doesn't matter and they are more content on getting a new app by writing a prompt and deploying possibly buggy code. Two different crowds of people.
I'm in a middle. I enjoy Zed and its predictions, I utilize R1 to help me to reason. I do _not_ ever want to stop programming. And I see so often whenever somebody less experienced than me shows me look how Cursor did this with three prompts, can we merge? And the solution is just wrong and doesn't solve the hard issues.
For me the biggest issues are the people who want to see the craft of programming gone. But I do enjoy the tooling.
> it seems likely that LLMs will obsolete precisely the skills that developers use to earn their income
I’m not particularly worried. I think it’s obvious that software engineering is definitely an “intelligence complete” problem. Any system that can do software engineering can solve any problem that requires intelligence. So, either my job is safe or I get to live through the fall of almost all white collar disciplines. There’s not a huge middle ground.
Although perhaps this is just the programmer stereotype of thinking that if someone can code, they can do anything.
> Any system that can do software engineering can solve any problem that requires intelligence. So, either my job is safe or I get to live through the fall of almost all white collar disciplines. There's not a huge middle ground.
How about the middle ground where a human using AI replaces you?
I was thinking the same but it's not really what the post is about. They talk about there are use cases for LLMs and devs can be benefiting.
What it goes into is how over hyped and over valued these companies are. They've blown through $5bn of compute each in a year and their revenue is abysmal. Microsoft won't report on ai separately, probably because it's abysmal.
I'm positive on LLMs for coding. But I think I have to agree with their assessment. Coding seems like the best area for these tools and what we see now is great. It's probably even worth $10b to the IT industry maybe eventually. But they're not paying for it yet, clearly. And I also think it's just not going to have huge significance outside our industry. The people I rub shoulders with outside of work have not mentioned or asked about it once, which is not necessarily meaningful but it does reveal the limits of hype too.
I think the usefulness is just very domain specific. If you're writing some types of boilerplate or often-tutorialized code it can spit out something very reasonable. Other types of code, like say in game dev, it stumbles around and never produces anything usable.
But like you said, in a few more years we'll see! It does feel like there's some missing pieces yet to be figured out to truly "reason" and generalize.
> If you're writing some types of boilerplate or often-tutorialized code it can spit out something very reasonable. Other types of code, like say in game dev, it stumbles around and never produces anything usable.
This makes me think of a quirk I discovered recently which is that ChatGPT simply won't generate a picture of a 'full glass of wine'. It generates pictures with all sorts of crazy waves/splashes in the glass but the glass is always half full no matter how you prompt it.
I'm not enough of an expert to make any deductions from this, but I think it hints at what the limitations of the currently models are.
For easy things, LLM assist has sped things up a lot for me.
For medium complexity things, I can get them done quickly without manual coding if I have a clear understanding in mind of what the implementation should look like. I supply the requirements, design and strategy and it's fairly easy to "keep things on the rails". The "write a PRD first" hack (https://www.aiagentshub.net/blog/how-to-10x-your-development...) works pretty well. Agent with YOLO mode and terminal access rips, particularly if you have good tests.
For tasks where I know the spec of the feature but don't clearly understand how I would design / implement the feature myself, it's hit-and-miss. Mostly miss for me.
I also haven't had much success with niche libraries, have to stick to the most popular library/tool/framework choices or it will quickly get stuck.
Whereas I've been disabling AI assist features because I find them actively disruptive to the development process. When it ghost pops up text suggesting what I should do, it's sometimes right...but it breaks flow. It forces me to read and parse apparently correct code, and decide if it is correct or it's just a mirage which is valid but not actually what I'm doing at all.
Conversely I'm shocked the negativity hasn't graduated to naked hostility among developers. A group that tends to pride itself on clarity of thought entranced by bullshit generators? A group that tends to pride itself on correctness of work cheerfully adding tools to their workflow that provably fuck up in unpredictable ways and that have to be monitored constantly for just such behavior? Why not hire a few junior devs instead if that's your jam, at least you can train a human towards competence.
> I’m a little shocked at how much negativity there is around LLMs among developers
There's a Quentin Tarantino quote where he says there are 2 kinds of film critics. There are those who love movies and there are those that love the movies they love.
A lot of developers really seem to love the technology they love.
These people are where most of the negativity is coming from. And my guess is that the people who are encouraged by LLMs and not negative (mostly) aren't taking time out of their days to write long blog posts or argue about it online.
I use Copilot for autocompleting the boring boilerplate. I like it. I also think LLMs are mostly useless.
It's not the technology, it's the stupid overhype. It really feels like all the HODL bitcoin cultists have finally gotten over their lost apes and found a new technogod.
So many people in these threads are convinced it's about to gain sentience. That's not going to happen. You get the people outing themselves by saying "it does my job better than me!"
If you say something honest and direct like "their output is mediocre and unreliable" or "the RNG text generator is not capable of thinking, you're just Clever Hans-ing yourself" or "if it does your job better than you, that says more about you than about it", you get people clutching their pearls like a Stanford parent whose kid got a D.
arXiv has turned into a paper mill of AI startups uploading marketing hype cosplaying as "research".
We’re already here. Check out Cline or Roocode. The technology is incredible.
I wrote a custom MCP to grab tickets from my Kanban board, Roo will pull down the tickets and start implementing them. I then have another agent that QAs, and either kicks the ticket back, or moves the ticket to human review.
I’m doing this on a real world micro SaaS. It has about 50 paying customers, and I’m the sole developer. I did a complete rewrite and the AI was able to complete about 90% of the project. I estimate I can get about a week’s worth of coding done in a day with this setup. I haven’t even scratched the surface of optimizing this workflow.
I’m also just one guy working nights and weekends, I’m sure there are many startups solving this same problem. It’s amazing to be shipping features this quick, but as a developer I’m terrified of what this is going to do to our careers.
As a developer I'm seeing less hate than apathy from my colleagues, but rather the hatred I do see is from people trying to push LLM's ON developers.
So it's from middle management levels riding the hype train, and possibly trying to save money and getting bonuses for it at the expense of other people.
Just like when offshoring was in its same point on Gartner's hype curve.
"Everyone has a model, but no one has a business".
I disagree. Tools which need to be babysat, the way LLMs do, slow you down rather than speed you up. It's like having to mentor a junior team member, except a human will eventually learn and you can just let him work. LLMs are incapable of learning, so you can't ever leave the phase where they are a drain.
So, in essence, it's now incrementally better than a templating script (except when worse), but Have Faith, it will be Better Soon. TBH, that's the same song that's been on repeat since the Dartmouth Workshop. In 1956. Jam yesterday and jam tomorrow, never any jam today.
I don't really get the hate; work is boring, if some tool can make it happen faster and with less effort I'm all for it. I don't hear the line cooks at McDonalds complaining that they have to use a semi-automated grill that beeps at them instead of an open fire.
Honestyl. It has its drawbacks but I am usually at 50x with few different agents running side by side. What we need is better GPU competition with tons of ram.
Doesn't that just scream "bad design" at you? Shouldn't we be aiming for agents that require less GPU? And agents that are good enough that we don't have to shop around for "competing prices" on answers?
Writing the code isn't the hard part, and wrangling the computer is the part of the work I enjoy most. My problem with AI bros is that they explicitly want to automate all the shit people like to do, such that we can all finally be free to work service jobs.
I feel like you missed the first third of this article that was quite clear they are not saying there are no uses cases. They are saying there doesn't seem to be an economic model that makes sense.
> it’s honestly not much worse than a junior dev and 100x faster. And if the code is bad you throw it away and try again 10 seconds later. The amount of speed up I see as a very experienced dev is astronomical
Personally, I find that waiting for the code to generate, then reviewing the code carefully, then deciding if I need to rewrite it to be more painful, more error prone, and much slower than writing the code correctly.
Especially since this AI junior never learn from it’s mistakes.
I think it speaks to different approaches to how individuals write code.
> How great is it gonna be in a year or two?
I would bet that it’s about the same (not great code, generally), but the tools fail to generate responses less often and likely would have more context.
Hopefully they become fast enough to run offline or at least feel more instantaneous.
Yeah I'm surprised by all the negativity as well. I'm listening to the post right now (using xtts-v2 finetuned on a voice I like lol). Sounds like these companies are overvalued / over hyped. Maybe they are / some of these companies go the way of myspace, but LLMs are incredibly useful for me.
I'm able to do a so much more using LLMs (Mistral-Large, Qwen2.5 and R1 locally, Claude via API) than without them.
Personally, I've found DeepSeek R1 to be a profoundly good model for thinking through problems across fields.
I had a complex finance situation that I was struggling with, both from a mathematical/taxation perspective and a personal psychological finance hangup. I spent a few good hours talking to it through everything and had a mental breakthrough. To get the same kind of insight, I would have to pay a financial advisor AND a psychologist for several hours.
That all of this was free while someone calls it a "con" seems completely wrong
(I got my CFA cousin to look over the numbers and he agreed with R1's advice, fwiw)
Yeah, I've had similar experiences. I still hesitate if it's a field I don't know too well of course (never trust an LLM), but R1 has been able to solve things I've been stuck on. And watching it's <think></think> process has been insightful. Only issue is that it ties up all my GPUs while I run it.
Hopefully Mistral can copy their technique and give us a 123b reasoning model.
It's just status anxiety. Mid engineers go on and on claiming theres literally no value from LLMs even possible in principle while top tier people are using them as force multipliers.
Who? How? This is not what I've seen where I work. There's a bunch of hubbub and generalized excitement, and lots of talk about what could be done, or what might be done, but not very much actual doing. I must just be a clueless "mid".
Guido van Rossum - "I use it every day. My biggest adjustment with using Copilot was that instead of writing code, my posture shifted to reviewing code."
https://www.youtube.com/watch?v=-DVyjdw4t9I
These kinds of responses are my favorite dark pattern rhetorical device, because you can assert literally _anything_ in this format and almost nobody will refute you, because the cost of refuting bullshit is 100x the cost of producing it.
Anyways, here goes.
1. Guido uses Copilot like I do - as a StackOverflow replacement to write the dumb boilerplate code. A much less flattering quote is "It doesn't save me much thinking, but [it helps] because I'm a poor typist". Also it's literally a minute or two of a three hour podcast.
2. A lot of code is autogenerated lol. Again, it's all the boring boilerplate stuff.
3. The cofounder of OpenAI is a biased source lol
4. He's an AI researcher, of course he runs that stuff.
5. Again, similar to Guido. He's using it for the boilerplate. Nothing wrong with enjoying using it as a toy, as he is here. But he's not doing serious work with it.
There's no virtue in hyping this stuff like a HODL bitcoin cultist.
I believe your examples are - unironically - misleading.
1- he states that the generated code is most likely wrong. He is appreciative of it though because he is a very poor typer so he doesn't have to do that part as much
2- so that's not supporting your argument that the 'top' devs are using it. Besides it doesn't say how it's counted, nor how much time is spent reviewing and correcting it
3- actually okay. But is he using it for production code? Doesn't say
4- he definitely doesn't talk about coding, only brainstorming and writing text.
5- your best one. Still, the use case here is side projects not production
You might still be right, I definitely do not compare myself to these people, but trying to glue some sources together makes a poor argument.
And the subject on hand is more that just using LLMs, it's the role of LLMs in the dev work environment
