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This is what he says: > He disagreed with a recent prediction from Dario Amodei, the CEO of Anthropic, that 90% of code may be written by AI in the next three

This is what he says: > He disagreed with a recent prediction from Dario Amodei, the CEO of Anthropic, that 90% of code may be written by AI in the next three to six months. > "I think the number is going to be more like 20-30% of the code could get written by AI — not 90%" I kind of agree with him. Definitely not in six months :)

jonplackett 2y ago
I can kinda see it doing 90% of the code by volume, but it would be the easy 90%. And the remaining 10% will need to be done by someone who actually knows what they’re doing. Still amazing for time saving but so far I haven’t been able to get even a SwiftUI class written to the standard I’d use it as-is.
- aprilthird2021 2y ago
  I agree with you. The same way compiling code meant that 99% of the code was machine-generated, but you still wrote tons of code. Lots of code is boilerplate or large switch statements (esp in OOP), or other such things. All of which AI code completion makes a breeze. The actual hard parts of code (often the various ways of connecting two disparate systems), AI doesn't really help with unless you tell it what to do and how, so you're still the ultimate important decision-maker in this scenario and can't be "replaced"
- fsloth 2y ago
  Agree, excellent for commodity boilerplate where there is basically just a bunch of 1:1 mappings that need to be filled. Terrible for novelties and problems in a large solution space. The more degrees of freedom there is to fail, the worse it will work.
- wanderingstan 2y ago
  There will also be Jevons Paradox happening: LLMs reduce the “cost” of writing code, so more code gets written. I already find myself building prototypes that I never would have attempted previously; it’s just too easy to have the LLM whip one up. LLMs also make it easier to do “grunt work” coding of writing validation checking and unit tests on non-critical code where previously I wouldn’t have bothered.
Xenoamorphous 2y ago
> I kind of agree with him. Definitely not in six months :) I certainly hope not. But three to six years from now? I’m not so confident.
- techpineapple 2y ago
  Maybe, but I don’t think we’ve definitively answered the question - are LLMs smart. If LLMs get to the point that they can unpack almost any logic that one might need for a function or class that’s amazing, but if it doesn’t understand the why, I think that leads to fundamental limitations in progress. And maybe I’m still not using the right tools, but I’m not finding cursor/claude 3.7 to be close.
  - namaria 2y ago
    I think LLMs are intelligence augmenting. They are a tremendously powerful search tool but you need to know what you are looking for. The idea that they can replace workers is laughable. I am afraid though that the current state of LLMs is too expensive for its use case. So I'm trying not to lean too much on them.
fnord123 2y ago
It depends how one measure it. If you look at the total output if GitHub in a year and try to poorly extrapolate how much code is written in a year - and then Anthropic measures how much code their system spewed out, then sure maybe they emit 90% of the estimated total amount of code. But most people wouldn't define it that way.
- professoretc 2y ago
  Maybe that's why the models are so eager to spit out reams and reams of code, it lets their masters claim a higher percentage (even if most of the code they emit is never used).

jonplackett 2y ago

I can kinda see it doing 90% of the code by volume, but it would be the easy 90%. And the remaining 10% will need to be done by someone who actually knows what they’re doing. Still amazing for time saving but so far I haven’t been able to get even a SwiftUI class written to the standard I’d use it as-is.

aprilthird2021 2y ago
I agree with you. The same way compiling code meant that 99% of the code was machine-generated, but you still wrote tons of code. Lots of code is boilerplate or large switch statements (esp in OOP), or other such things. All of which AI code completion makes a breeze. The actual hard parts of code (often the various ways of connecting two disparate systems), AI doesn't really help with unless you tell it what to do and how, so you're still the ultimate important decision-maker in this scenario and can't be "replaced"
fsloth 2y ago
Agree, excellent for commodity boilerplate where there is basically just a bunch of 1:1 mappings that need to be filled. Terrible for novelties and problems in a large solution space. The more degrees of freedom there is to fail, the worse it will work.
wanderingstan 2y ago
There will also be Jevons Paradox happening: LLMs reduce the “cost” of writing code, so more code gets written. I already find myself building prototypes that I never would have attempted previously; it’s just too easy to have the LLM whip one up. LLMs also make it easier to do “grunt work” coding of writing validation checking and unit tests on non-critical code where previously I wouldn’t have bothered.

Xenoamorphous 2y ago

> I kind of agree with him. Definitely not in six months :) I certainly hope not. But three to six years from now? I’m not so confident.

techpineapple 2y ago
Maybe, but I don’t think we’ve definitively answered the question - are LLMs smart. If LLMs get to the point that they can unpack almost any logic that one might need for a function or class that’s amazing, but if it doesn’t understand the why, I think that leads to fundamental limitations in progress. And maybe I’m still not using the right tools, but I’m not finding cursor/claude 3.7 to be close.
- namaria 2y ago
  I think LLMs are intelligence augmenting. They are a tremendously powerful search tool but you need to know what you are looking for. The idea that they can replace workers is laughable. I am afraid though that the current state of LLMs is too expensive for its use case. So I'm trying not to lean too much on them.

fnord123 2y ago

It depends how one measure it. If you look at the total output if GitHub in a year and try to poorly extrapolate how much code is written in a year - and then Anthropic measures how much code their system spewed out, then sure maybe they emit 90% of the estimated total amount of code. But most people wouldn't define it that way.

professoretc 2y ago
Maybe that's why the models are so eager to spit out reams and reams of code, it lets their masters claim a higher percentage (even if most of the code they emit is never used).