121–130 of 245 posts
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Assuming Google has tens of thousands of software engineers (for a lower bound of 10,000) and artificial intelligence increases productivity by at least 0.01%, the first engineer has already been replaced.
So how long till AI will be fully replacing a SWE at Google? Notice parent said "fully"
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Xoogler here. This exactly. There is so much boilerplate involved in writing anything inside Google. AI was great to cut that down a bit. It's still nowhere near what it's like in the outside and/or non-Java world. Which isn't to say that this isn't progress - just that that stat should be taken with context.
Yeah I kind of agree that when LLMs work REALLY well for autocomplete of your codebase -- that might be an indication that the language and library abstractions you use don't fit the problem very well. Code is read more than it's written. And it should be written to be read. If you are barfing out a lot of auto-completed stuff, it's probably not very easy to read. You have to read code to maintain it, modify it, anal…
From my experience using LLMs, I'd guess the opposite. LLMs aren't great at code-golf style, but they're great at the "statistically likely boilerplate". They max out at a few dozen lines at the extreme end, so you won't get much more than class structures or a method at a time, which is plenty for human-in-loop to guide it in the right direction.
I'm guessing the LLM code at Google is nearly indistinguishable from the rest of it for a verbose language with a strong style expectation like java. Google must have millions of lines of Java, and a formatter that already maintains standards. An LLM spitting out helper methods and basic ORM queries will look just like any other engineers code (after tweaking to ensure it compiles).
If you already apply a code-formatter or a style guide in your organization, I'm guessing you'd find that LLM code looks and reads a lot like the rest of your code.
The most interesting chart is the "fraction of code created with AI assistance via code completion" trending up to 50%
Question for any Googlers in the thread - do folks speak up if they see flaws in the methodology or approach of this research or is the pressure from the top so strong on this initiative that people hush up?
The internal dev tooling at Google is quite far ahead of what's available on the market rn.
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> I've been finding AI's suggestions -- even when rather wrong -- help me do that initial step faster. I have no idea how I could even integrate AI into my workflow so that it's useful. It's even less reliable than search is for basic research and can't even cite its sources.... This argument held a lot more weight when it was a search engine playing the role of our memory.
That comment was solely about AI code suggestions. Generative AI still has a ways to go for other forms of research, and it will never fully replace the utility of a search engine. They're two different tools for different but overlapping tasks.
The most interesting chart is the "fraction of code created with AI assistance via code completion" trending up to 50%
However, once I do something, I guess the LLM gets the nudge/prompt in the right direction and almost always auto-completes the full thing correctly.
Anyone who works at Google knows they are force feeding the employees with this stuff. Same thing they’re doing with the general public and search. Anyone who worked at google during + can see this is the same half hearted type of force feeding that will fail, with the rationalizations to boot. Disclaimer: I hold google stock but don’t work at google
I work at Google, this stuff is available, entirely optional, and in fact the most recent stuff is still something you have to sign up for and get on an opt-in basis in return for providing feedback/answering surveys.
If there's anything in this press release to justify how "characters added by AI" is more true a reflection of quality than commit count is of productivity, I didn't see it. It's a short release and I read it twice, so if it was there I feel like I'd have noticed.
With that perspective, "characters added by AI" is an ok metric to track.
I wonder if AI code completion reduces the value of abstractions if boilerplate code is easy to generate. Will this lead to highly repetitive code ?
If it works and it never needs a human, does it matter?
Humans have limited RAM, so we have to put our ideas into an external medium that can then be refined. I've been finding AI's suggestions -- even when rather wrong -- help me do that initial step faster. Which, I think, jives with their findings here.
First we extended the reach of our perception with language. Then we extended the energy in our calories reserves with crops/livestock. Then we extended the length of our memories with writing. Then we extended the breadth of our thinking with AI?
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I look at is as asking an intern to do some work that I don't have time for. Do I have to check their work? Yes. Might I have to correct and guide the outcome? Again, yes. Am I going to ask them to implement something novel and groundbreaking? Not really, that'd be a disaster unless they are a prodigy. None of that removes my capacity, or any kind of danger.
I find this absolutely nothing like delegating to an intern personally. Interns usually do their best because they will be held accountable if they don't. Copilot doesn't give a shit about that.
I don't know how to articulate it, but wherever I hear the financial analysts talking about how much work AI is going to do for us, I just have this spidey-sense that they're severely underestimating the social aspect of why anyone tries to achieve a good outcome
they think they can just spend 100,000$ on GPUs and get 10x the output of someone buying a house and raising kids getting paid a 6 figure salary