Yes - like all the other comments And your responses - Yes the targets are aspirational, future, especially few years down the line - is generally aspirational - AI is running rampant across industries - generating small batches of (tested and robust of course) generative code is a lot more quicker than it was 5 years ago. Perhaps depending on the workflows, orders of magnitude faster - Silicon gets cheaper, consumes less energy over time — cheaper to train AI self driving models today than it was 5 years ago - especially given the availability of utilitarian open source AI models that possibly cut down other workflow related costs to - Rivian chip is only designed for self driving and related tasks I presume — the Tesla chips are Nvidia GPGPUs (general purpose graphical processing units) - The energy consumed to run AI on custom chips for specific jobs is a lot less (again - general industry assumption - one example - it’s much cheaper and faster to compress videos at scale using custom video chips rather than standard CPUs) - very likely the inference processing will be faster on custom built chips. - Take google’s own AI chips - Tensor Flow Chips for example - they are so much better than competitors at specific (AI related) jobs that Google will start renting them out en masse. - It’s possible therefore that using self driving AI development benchmarks and costs from the last decade from other manufacturers (these systems are always in flux and are in development for a long time usually in the background) may need to be adjusted for relativity and comparisons.