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Google CEO says more than a quarter of the company's new code is created by AI

businessinsider.com

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Re: Google CEO says more than a quarter of the company's new code is created by AI

#13

Post by dyauspitr »

kev009 wrote: post #11

I would hope a CEO, especially a technical one, would have enough sense to couple that statement to some useful business metric, because in isolation it might be announcement of public humiliation.

Or a statement of pride that the intelligence they created is capable of lofty tasks.

Re: Google CEO says more than a quarter of the company's new code is created by AI

#18

Post by dmix »

kev009 wrote: post #11

I would hope a CEO, especially a technical one, would have enough sense to couple that statement to some useful business metric, because in isolation it might be announcement of public humiliation.

The elitism of programmers who think the boilerplate code they write for 25% of the job, that's already been written before by 1000 other people before, is in fact a valuable use of company time to write by hand again.

IMO it's only really an issue if a competent human wasn't involved in the process, basically a person who could have written it if needed, then they do the work connecting it to the useful stuff, and have appropriate QA/testing in place...the latter often taking far more effort than the actual writing-the-code time itself, even when a human does it.

Re: Google CEO says more than a quarter of the company's new code is created by AI

#19

Post by jeffparsons »

Scene_Cast2 wrote: post #6
ausbah wrote: post #3

i would be may more impressed if LLMs could do code compression. more code == more things that can break, and when llms can generate boatloads of it with a click you can imagine what might happen

This actually sparked an idea for me. Could code complexity be measured as cumulative entropy as measured by running LLM token predictions on a codebase? Notably, verbose boilerplate would be pretty low entropy, and straightforward code should be decently low as well.

Not quite, I think. Some kinds of redundancy are good, and some are bad. Good redundancy tends to reduce mistakes rather than introduce them. E.g. there's lots of redundancy in natural languages, and it helps resolve ambiguity and fill in blanks or corruption if you didn't hear something properly. Similarly, a lot of "entropy" in code could be reduced by shortening names, deleting types, etc., but all those things were helping to clarify intent to other humans, thereby reducing mistakes. But some is copy+paste of rules that should be enforce in one place. Teaching a computer to understand the difference is... hard.

Although, if we were to ignore all this for a second, you could also make similar estimates with, e.g., gzip: the higher the compression ratio attained, the more "verbose"/"fluffy" the code is.

Fun tangent: there are a lot of researchers who believe that compression and intelligence are equivalent or at least very tightly linked.

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