Honestly, a lot of the problems people have with programming that they use AI to solve can be solved with better language design and dev tools.
For example, I like LLMs because they take care of a lot of the boilerplate I have to write.
But I only have to write that boilerplate because it's part of the language design. Advances in syntax and programming systems can yield similar speedups in programming ability. I've seen a 100x boost in productivity that came down to switching to a DSL versus C++.
Maybe we need more DSLs, better programming systems, better debugging tools, and we don't really need LLMs the way LLM makers are telling us? LLMs only seem so great because our computer architecture, languages and dev tooling and hardware are stuck in the past.
Instead of being happy with the Von Neumann architecture, we should be exploring highly parallel computer architectures.
Instead of being happy with imperative languages, we should be investing heavily in studying other programming systems and new paradigms.
Instead of being happy coding in a 1D text buffer, we should be investing more in completely imaginative ways of building programs in AR, VR, 3D, 2D.
LLMs are going to play a part here, but I think really they are a band-aid to a larger problem, which is that we've climbed too high in one particular direction (von-neuman/imperative/text) and we are at a local maxima. We've been there since 2019 maybe.
There are many other promising peaks to climb, avenues of research that were discovered in the 60s/70s/80s/90s have been left to atrophy the past 30 years as the people who were investigating those paths refocused or are now gone.
I think all these billions invested in AI are going to vaporize, and maybe then investors will focus back on the fundamentals.
LLMs are like the antenna at the top of the Empire State Building. Yes, you can keep going up if you climb up there, but it's unstable and eventually there really is a hard limit.
If we want to go higher that that, we need to build a wider and deeper foundation first.
For example, I like LLMs because they take care of a lot of the boilerplate I have to write.
But I only have to write that boilerplate because it's part of the language design. Advances in syntax and programming systems can yield similar speedups in programming ability. I've seen a 100x boost in productivity that came down to switching to a DSL versus C++.
Maybe we need more DSLs, better programming systems, better debugging tools, and we don't really need LLMs the way LLM makers are telling us? LLMs only seem so great because our computer architecture, languages and dev tooling and hardware are stuck in the past.
Instead of being happy with the Von Neumann architecture, we should be exploring highly parallel computer architectures.
Instead of being happy with imperative languages, we should be investing heavily in studying other programming systems and new paradigms.
Instead of being happy coding in a 1D text buffer, we should be investing more in completely imaginative ways of building programs in AR, VR, 3D, 2D.
LLMs are going to play a part here, but I think really they are a band-aid to a larger problem, which is that we've climbed too high in one particular direction (von-neuman/imperative/text) and we are at a local maxima. We've been there since 2019 maybe.
There are many other promising peaks to climb, avenues of research that were discovered in the 60s/70s/80s/90s have been left to atrophy the past 30 years as the people who were investigating those paths refocused or are now gone.
I think all these billions invested in AI are going to vaporize, and maybe then investors will focus back on the fundamentals.
LLMs are like the antenna at the top of the Empire State Building. Yes, you can keep going up if you climb up there, but it's unstable and eventually there really is a hard limit.
If we want to go higher that that, we need to build a wider and deeper foundation first.