> McKinsey stated AI could “automate work activities that absorb 60 to 70 percent of employees’ time today” across all occupations, not specifically software development.
I would wager a large portion of that would be both generating and then triaging a certain level of institutional "bullshit communications".
For example, the employee must give daily status updates, and they have a three-item list for the day. They use an LLM to pad out their three-item bullet list into prose that sounds smart and active and impressive.
Then their manager says "TLDR" and uses an LLM to "summarize" it back into almost the original three-item bullet list. Each employee believes they've "saved time" using the tool, but it's a kind of broken-window fallacy [0].
Specification driven design has been around for a long time. Lets see if AI can make it come true. Dr. David Parnas wrore about Parnas Tables https://research.cs.queensu.ca/home/cisc323/2006w/slides/Bil... ; and then there was Eiffel with Design-By-Contract; and the Type System in F-Sharp seems like magic.
Look, maybe AI for professional programming is overhyped, but for the huge number of professionals who need to program as a part of their job, but programming is decidedly NOT their job, but who do not have programmers available to them, LLM's are.monunmental. For these people, these LLMs are huge productivity enhancers. We shouldn't be measuring productivity gain by professional programmers, but by the larger population of professionals who are required to wear many different hats.
Learning coding is easier than ever today, it's no longer the domain of few elite nerds sitting behind desks in large offices. It takes less than two days to learn python from free online resources like Youtube and SoloLearn; surely a small price to pay compared to lifetime of limping with LLM crutches?
The “X% AI coded” metric needs to die. It is completely meaningless.
The nature of development work is changing. A project can be 100% written by AI but guided so closely by humans that the process wasn’t much faster. Alternatively, AI can make a project faster by helping with architecture and other things beyond writing code.
When people quote “70% AI coded”, it implies that 100% is some mythical goal that means AI is writing all code unsupervised. But most production AI code at the moment is still developed in lockstep with humans.
Revenue does exist .. showing demand/value. Also, valuation multiple of 25-70x ARR is in that "captn .. the engine is gonna blow up anything sir". Also, given their current design surface, rapid changes to the "core intelligence layer/substrate" & good usage workflow patterns that are still being discovered ... we are probably creating tech debt faster than value. Specs are great .. but we also discover the [stable/actual/valid] spec as we build (and often at the end of the build).
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[ 3.1 ms ] story [ 31.9 ms ] threadI would wager a large portion of that would be both generating and then triaging a certain level of institutional "bullshit communications".
For example, the employee must give daily status updates, and they have a three-item list for the day. They use an LLM to pad out their three-item bullet list into prose that sounds smart and active and impressive.
Then their manager says "TLDR" and uses an LLM to "summarize" it back into almost the original three-item bullet list. Each employee believes they've "saved time" using the tool, but it's a kind of broken-window fallacy [0].
[0] https://en.wikipedia.org/wiki/Parable_of_the_broken_window
The nature of development work is changing. A project can be 100% written by AI but guided so closely by humans that the process wasn’t much faster. Alternatively, AI can make a project faster by helping with architecture and other things beyond writing code.
When people quote “70% AI coded”, it implies that 100% is some mythical goal that means AI is writing all code unsupervised. But most production AI code at the moment is still developed in lockstep with humans.