A codec that uses AI isn't necessarily going to be using it to synthesize content. It could use it for things like improving rate-distortion optimizations and early-skip heuristics.
Modern video codecs are complex beasts. It's not as simple as "take a macroblock and find some motion vectors that minimize residual below a certain threshold, else code as intra-block". They have hundreds of mutually-exclusive techniques to compress a certain area of the frame. Determining which technique will require the smallest residual is done with fast early-skip heuristics that often make the wrong decision. The official manual for x265, the H.265/HEVC encoder (https://x265.readthedocs.io/en/master/cli.html), has literally hundreds of options, almost all about tuning these myriad of heuristics for your particular input.
AI can be used to enhance things like early-skip heuristics. "This block looks like it'll benefit from a really-detailed motion search in this particular area" or "we'll save bits if we bypass the DCT step and quantize the block directly" or "this frame should definitely be a B-frame". Encoders already use heuristics to do this (brute forcing all possible decisions to find which is optimal is too slow), but they don't always make the best decision. An AI could be used to improve that.
Now when I say AI, I'm not talking about massive, multi-billion weight monstrosities that synthesize nonsense, but extremely simple neural networks with a few thousand weights. The popular Opus codec uses a simple NN to estimate whether or not a frame of audio is speech or music, and uses that determination to decide whether to use their speech-optimized algorithm (SILK) or their music-optimized algorithm (CELT) to encode that particular frame. It's a short read but a very good one: https://jmvalin.ca/opus/opus-1.3/
This could be extended to video encoders without being used for interpolation where they would be liable to synthesize things that aren't there, DLSS-style.
Modern video codecs are complex beasts. It's not as simple as "take a macroblock and find some motion vectors that minimize residual below a certain threshold, else code as intra-block". They have hundreds of mutually-exclusive techniques to compress a certain area of the frame. Determining which technique will require the smallest residual is done with fast early-skip heuristics that often make the wrong decision. The official manual for x265, the H.265/HEVC encoder (https://x265.readthedocs.io/en/master/cli.html), has literally hundreds of options, almost all about tuning these myriad of heuristics for your particular input.
AI can be used to enhance things like early-skip heuristics. "This block looks like it'll benefit from a really-detailed motion search in this particular area" or "we'll save bits if we bypass the DCT step and quantize the block directly" or "this frame should definitely be a B-frame". Encoders already use heuristics to do this (brute forcing all possible decisions to find which is optimal is too slow), but they don't always make the best decision. An AI could be used to improve that.
Now when I say AI, I'm not talking about massive, multi-billion weight monstrosities that synthesize nonsense, but extremely simple neural networks with a few thousand weights. The popular Opus codec uses a simple NN to estimate whether or not a frame of audio is speech or music, and uses that determination to decide whether to use their speech-optimized algorithm (SILK) or their music-optimized algorithm (CELT) to encode that particular frame. It's a short read but a very good one: https://jmvalin.ca/opus/opus-1.3/
This could be extended to video encoders without being used for interpolation where they would be liable to synthesize things that aren't there, DLSS-style.