The fundamental frustration most engineers have with AI coding is that they are used to the act of _writing_ code being expensive, and the accumulation of _understanding_ happening for free during the former. AI makes the code free, but the understanding part is just as expensive as it always was (although, maybe the 'research' technique can help here).
But let's assume you're much better than average at understanding code by reviewing it -- you have another frustrating experience to get through with AI. Pre-AI, let's say 4 days of the week are spend writing new code, while 1 day is spent fixing unforseen issues (perhaps incorrect assumption) that came up after production integration or showing things to real users. Post-AI, someone might be able to write those 4 days worth of code in 1 day, but making decisions about unexpected issues after integration doesn't get compressed -- that still takes 1 day.
So post-AI, your time switches almost entirely from the fun, creative act of writing code to the more frustrating experience of figuring out what's wrong with a lot of code that is almost correct. But you're way ahead -- you've tested your assumptions much faster, but unfortunately that means nearly all of your time will now be spent in a state of feeling dumb and trying to figure out why your assumptions are wrong. If your assumptions were right, you'd just move forward without noticing.
I think you are reading too far into this. The title of the technical paper is “ Video generation models as world simulators”.
This is “just” a transformer that takes in a sequence of noisy image (video frame) tokens + prompt, and produces a sequence of less noisy video tokens. Repeat until noise gone.
The point they’re making, which is totally valid, is that in order for such a model to produce videos with realistic physics, the underlying model is forced to learn a model of physics (a “world simulation”).
But let's assume you're much better than average at understanding code by reviewing it -- you have another frustrating experience to get through with AI. Pre-AI, let's say 4 days of the week are spend writing new code, while 1 day is spent fixing unforseen issues (perhaps incorrect assumption) that came up after production integration or showing things to real users. Post-AI, someone might be able to write those 4 days worth of code in 1 day, but making decisions about unexpected issues after integration doesn't get compressed -- that still takes 1 day.
So post-AI, your time switches almost entirely from the fun, creative act of writing code to the more frustrating experience of figuring out what's wrong with a lot of code that is almost correct. But you're way ahead -- you've tested your assumptions much faster, but unfortunately that means nearly all of your time will now be spent in a state of feeling dumb and trying to figure out why your assumptions are wrong. If your assumptions were right, you'd just move forward without noticing.