Why not ask ChatGPT?
> A huge reason VCs and tech tycoons put billions into funding LLMs was so they could undermine coders and depress wages
is just pure speculation, totally unsupported, and almost certainly untrue, and makes very little sense given the way LLMs and ChatGPT in particular came about. Every time I read something from Anil Dash it seems like it's this absolutely braindead sort of "analysis".
> Vibe coding might limit us to making simpler apps instead of the radical innovation we need to challenge Big Tech
is also pure speculation and doesn't make sense. In fact, enabling people to create small and simple apps could well indeed challenge and weaken dependence on big tech.
I stopped reading and closed the page.
What AI usage has underlined is that we are forever bound by our ability to communicate precisely what we want the AI to do for us. Even if LLMs are perfect, if we give it squishy instructions we get squishy results. If we give it a well-crafted objective and appropriate context and all the rest, it can respond just about perfectly. Then again, that is a lot of what programming has always been about in the first place - translate human goals into actionable code. Only the interface and abstraction level has changed.
I don't know how much "VCs and tech tycoons" want to undermine coders specifically, but they see a huge opportunity to make money by making things much more efficiently (and thus cheaper) than they can be made now. The way to they plan to do that is to reduce the cost of labor. Which means either automating away jobs or making jobs much less specialized so that you don't need a highly-paid craftsman.
Think about Henry Ford setting up an assembly line where a worker sits at the same location and performs the same action all day, every day. You don't need a highly-skilled, highly-paid person with leverage and power to do that job.
Was there anything original in it? I'd like to ask this article, what was your knowledge cut-off date?
I think this is one of the reasons we don't see huge productivity gains. Most F500 companies have pretty proprietary gnarly codebases which are going to be out-of-distribution. Context-engineering helps but you still don't get near the performance you get with in-distribution. It's probably not unsolvable but it's a pretty big problem ATM.
https://khromov.github.io/svelte-bench/benchmark-results-mer...
I found that LLMs sometimes get confused by Lit because they don’t understand the limitations of the shadow DOM. So they’ll do something like throw an event and try to catch it from a parent and treat it normally, not realising that the shadow DOM screws that all up, or they assume global / reset CSS will apply globally when you actually need to reapply it to every single component.
What I find interesting is all the platforms like Lovable etc. seem to be choosing Supabase, and LLMs are pretty terrible with that – constantly getting RLS wrong etc.
Sounds to me like that there is simply more React code to train the model on.
I'm reminded of an old quote by Dijkstra about Fortran [1]: "In the good old days physicists repeated each other's experiments, just to be sure. Today they stick to FORTRAN, so that they can share each other's programs, bugs included."
I've encountered that same problem in some older scientific codes (both C and Fortran). After a while, the bugs somewhat become features because people just don't know to question them anymore. To me, this is why it is important to understand the code thoroughly enough to question what is going on (regardless of who or what wrote it).
[1] https://www.cs.utexas.edu/~EWD/transcriptions/EWD04xx/EWD498...
I don't understand this argument. I mean the same applies for books. All books teach you what has come before. Nobody says "You can't make anything truly radical with books". Radical things are built by people after reading those books. Why can't people build radical things after learning or after being assisted by LLMs?
> I don't understand this argument. I mean the same applies for books. All books teach you what has come before. Nobody says "You can't make anything truly radical with books". Radical things are built by people after reading those books.
Books share concepts expressed by people understanding those concepts (or purporting to do so) in a manner which is relatable to the reader. This is achievable due to a largely shared common lived experience as both parties are humans.
In short, people reason, learn, remember, and can relate with each other.
> Why can't people build radical things after learning ...
They absolutely can and often do.
> ... or after being assisted by LLMs?
Therein lies the problem. LLMs are not assistants.
They are statistical token (text) document generators. That's it.
For those cases, I think LLM-assisted coding has the ability to drastically change the usual formula and help bring into being projects that people would previously only daydream about, hoping that the world aligns with their vision one day and they can magically spin up a team to work on it.
Coding agents are fast becoming at least a part of that team. If you're idea is in a domain where they've had a lot of high-quality training code, they can already do a pretty amazing job of getting a project off the ground. If you're a programmer with at least some domain knowledge and can guide the design and push the agent past tough spots, you can keep the project going when the LLMs get bogged down.
I think at the very least, we're going to see some incredibly impressive prototypes, if not complete implementations, of OSes, programming languages, hypermedia systems, protocols, etc. because one passionate programmer threw a lot of LLM time them.
Basically lots of people are going to be able to build their own TempleOS now. Some of those might end up being impactful.
Aperocky•1h ago
Eventually we will gravitate back to square one, and business people are not going to be writing COBOL or VISUAL BASIC or the long list of eventual languages (yes this now include natural ones, like English) that claim to be so easy that a manager would write it. And Googling/Prompting remain a skill that surprisingly few has truly mastered.
Of course all the venture capital believe that soon we'll be at AGI, but like the internet bubble of 2001 we can awkwardly stay at this stage for quite a long time.