This is still not an argument for more lines of code. It demonstrates that lines of code are positively correlated with number of features, yes. But that's like saying the number of nails scales with the size of a house. More nails does not create more house.
IANAL, but I feel like if Lyft and Uber aren't careful the courts are going to end up deciding that their drivers are employees instead of independent contractors.
I like wine and almonds. They are valuable commodities with a variety of uses and anyone can enjoy them for a modest price. They can be used and made into many additional valuable things, from sangria to baklava. What can LLMs do for me?
I am glad you like some things. Some people like other things, such as LLMs, or hosted server infrastructure.
Now explain to me why you are allowed to have the things you like which use a lot of water, while other people are not allowed to have the things they like which use a lot of water.
Even unprofitable farms can produce edible food. A datacenter is a machine that uses electricity to make heat and no other physical prouducts. That's a tough justification to make to people who live near one.
I really dont get this. The US is nowhere close to facing a food shortage. If all the desert farms in the west closed beef and a few vegetables would get a tiny bit more expensive. People act like if we didnt do destructive desert farming we'd have to implement rationing.
Food prices rising has been a political hot topic for the last few years. Voters would have your head for any obvious action that made beef get more expensive, especially NIMBY groups and current ranchers.
How many existing data centers do you think you rely on each day? Almonds do not realistically improve my life in nearly the way the internet does, but also who am I to say whether you should be allowed to farm almonds or build a datacenter?
It died before AI came around and today's coding agents are somewhere upwards of twice as competent as whatever the state of the art of automatic coding was in 2020. 8I
A good chunk of that was one-time gains from shifting GPU and memory architectures to better match what LLMs need at scale as well as some algorithmic improvements. Most of the low-hanging architecture optimization has already been harvested. We'll certainly have more algorithmic gains but the consensus is they'll generally be smaller and less frequent.
There's always a chance we'll have some dramatic gains far larger than DeepSeek's optimizations a year ago, but it hasn't happened again yet at even that scale. It would be nice but I certainly wouldn't count on it.
I’m imagining a lot of programmers suddenly being given the impossible task of reporting what worked and what didn’t, and middle management making up some retrospective evaluation with fat PowerPoint decks and meaningless graphs in an effort to present to C-levels some measures of success other than token use.
As the saying goes "figures can't lie, but liars can figure".
If you want to report productivity gains or cost savings from some initiative (increased AI usage or whatever) and need some stats to point to, then you just point to whatever is working, for whatever reason, and attribute the success to the new initiative.
In a company I used to work for, one manager, when pushed to increase machine learning usage (a few years back, before ML became AI), just renamed his product from foo to foo-ML (with ZERO ML usage), and reported how well it is working. He has since been promoted twice.
It’s not clear companies were measuring anything but token usage. What information could leadership have collected to determine what worked, what didn’t, and what needs more data? Other than the balance sheet and revenue, do companies actually have sufficient information to understand the results?
Were they trying to measure other things? Definitely. The COO at Uber, one of the examples in the source article, has talked publicly about how they've searched for (and so far failed to find) a link between micro-level metrics driven by AI and concrete improvements in high level project velocity.
Do these measurements have sufficient information? As much as any, I'd guess. It sounds like you already know that it's pretty hard in general to measure the productive output of software development organizations.
I have no doubt a few companies, like Uber, were measuring other things and had applicable metrics in place before adopting Clod or CoPilot or whatever automation.
I'm speaking in the general sense of companies adopting the latest hype without reflection.
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