What the developer learns from the experience of AI programming is more down to the attitude of the individual than that.

Here's an example from my perspective.

Recently while developing a way to output a PAL video signal from two digital lines(an endeavour obviously driven by curoiosity more than utility). I learned a great deal about far more than I would have if I had not have used AI. I wasn't blind to what the AI was emitting, It helped me decide upon shifting one output to 0.285v and the other to .715v. Write a program to use pytorch to learn a a few resistors/capacitors to smooth out the signal and a sample of 2-bit data that when emitted though the filters produced a close sine wave for the color burst. AI enabled me to automatically emit Spice code to test the waveform. I had never used spice before, now I know the pains of creating a piecewise linear voltage source in it.

Yesterday I used an AI to make a JavaScript function that takes a Float32Array of voltage level samples and emits a list of timings for all parts of a scanline along with the min and max voltage levels, calculates the position and frequency of the color burst and uses the measured color burst to perform the quadrature decoding to produce a list of YUV values at a lower sample rate.

This should let me verify the simulated waveform so that I can compare the difference between what I intend to emit, and what should be a correct signal. If there turns out to be a discrepancy between what I am emitting and what I think I am emitting, this will come in quite handy.

Perhaps I might learn more if I did all of this myself unaided by an AI, but it would also take much longer, and likely not get done at all. The time I save writing tests, wrangling libraries and software is not stored in the bank. I used that time too, doing other things, learning about those as I did so.