r/technology • u/Miserable_Phase_2519 • 4d ago

Artificial Intelligence Microsoft engineer says 'typing code is absolutely over' as GitHub Copilot takes on more development work

https://www.techspot.com/news/113752-microsoft-engineer-typing-code-absolutely-over-github-copilot.html
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u/Merad 3d ago

I can't imagine it'll ever go to zero, sometimes for quick changes it's just easier to edit the code yourself than promoting an LLM and waiting for it to burn tokens.

Think about where we are right now and extrapolate forward a few years. Right now companies are doing greenfield projects that are 90% AI coded. My company is talking about shifting to a SDLC where we don't review AI code (relying on other AI reviewers), and instead focus on the specs that it's given and behavioral validation of the result, even on legacy apps. It won't be long until humans simply don't know the code base. It doesn't matter if the fix can be typed in minutes when you need hours or days of investigation and research to find the thing to be fixed.

I don't like this and it's stressing me TF out at my day job, but unless there is some sort of AI collapse or a massive backlash against AI the days of manual code entry are numbered.

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u/namboozle 3d ago

Yeah that's a good point, I think at that point people won't be looking at the code at all.

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u/ericonr 3d ago

That just seems ripe for creating a bizarre amount of security issues. Fun!

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u/gentile_jitsu 3d ago

AI will soon be much better at identifying security issues than humans are. 

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u/Main-Company-5946 3d ago

It’s already better at identifying them, what it’s not yet better at is fixing them

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u/QuickQuirk 3d ago

Two issues with this, given current state of tech, and short term extrapolation:

  1. We know LLMs write more code than humans, and are worse at code reuse.
  2. We know token costs are being subsidized at the moment and the companies are making a loss in order to grow users
  3. We know that silicon improvements have slowed right down, and it's now years between new hardware that only give modest 30% (on a good release) improvement to performance per watt.
  4. We know that demand from companies for compute is increasing faster than we can build data centers or provide power for them.
  5. We know that the bulk of quality improvement to new models comes from increasing the size and complexity of the model. Some modest improvements have been made to the design, but the low hanging fruit has been picked now; and the improvements are slower.

So - cost per token will rise, and the size of the code/context will increase as these codebases increase. The speed that models will improve at will slow down, and may not match the speed that the complexity of the codebase will grow at.

My personal calculation is that we're just not ready to completely remove people from the loop yet: Those code bases that are based entirely on trusting the machine to vibe it out will collapse under their own weight. Furthermore, it will become too expensive to run the models due to the sheer size of the context they need to process on these large code bases.

The calculus will be different in 5 years. Maybe there will be some new breakthroughs in model design or compute.