Comments by "Mikko Rantalainen" (@MikkoRantalainen) on "New AI Learned to Design Computer Chips: The View of a Chip Designer" video.

@rrmackay I think we're already at the point where no single human can understand the whole CPU package when we already have 100+ billion transistors. This is because such transistor counts can be understood only at a level of "sea of transistors" even though the CPU design is "done by humans" nowadays. In reality, humans only create the overall architecture and then rest is genererated by computer program. If I understood this video correctly, this was about replacing that fully(?) deterministic computer program with a version that run AI algorithms instead. If the AI only does the optimization it will not touch the logic of the chip so it's still equally "easy" to understand to other modern designs. When we get to the area where AI starts to drop steps from the actual algorithms (logic), then human understanding will suffer a lot more. For an example of this, see AlphaTensor and how it improved 4x4 matrix multiplication from 49 operations to 47 operations. Previous state-of-art algorithm called Strassen's algorithm was from year 1969! Now imagine something much more complex than 4x4 matrix multiplication and trying to verify that the variant with reduced steps is still correct.
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If AI "only" does layout optimization and the actual computational features are designed by humans, AI designed parts are not going to be harder to understand. Consider it like a chess board: AI can compute more accurately than humans why given board position is the best option but it doesn't prevent humans from understanding the position as-is. The design space the AI works in this context is combination of all allowed designs and the task is to find the optimal one. And when the possible space has complexity 10^90000, there can never be exhaustive search. To accomplish full exhaustive search of even 10^40 possible states has theoretical minimum required energy a bit more than the total energy generated by the Sun during the whole lifetime of the Sun. As a result, the current algorithms to approximate the optimal solution in search space of 10^90000 options is going to be a pretty crude approximation, no matter how you do it. It appears that AI can already do much better approximation than any human, especially if you give both the same time to accomplish the task. A well made AI designed layout would be one where no human can improve it even though the layout can be fully understood.
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@peppernickelly AI system can also be cloned whenever it shows promise. Once we have even close to human level intelligence in AI, we can try teaching multiple copies of the AI for a year and then take one or two best behaving systems as the basis for next year. Considering how much promise a superhuman level AI would have, it should be easy business decision to allocate e.g. 50 highly trained pedagogy wizards and a couple of psychologists per AI to maximize the probablitity of great learning results in a year. Even with 10 paraller projects like that you would need "only" 500–1000 humans to do the work. Facebook/Meta is already paying salary for about 80000 software developers so a project with only 1000 people would be small risk for them. And note that the baseline AI doesn't need to be at the level of an average human. If it can execute at the level of a somewhat mentally disabled person, it's already good enough for a project like this. Imagine how much a human with mental disabilities could be helped if you could throw 1000 highly trained professionals to help that single human to achieve their best. For a single human, that's too expensive in practice. For a possible AGI system, that's just an investment decision!
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9:00 "AI can help us in the process of chip design but we'll always need human engineers" I think "always" is too bold a prediction. When we invent AGI and it has acquired enough skills, it can do better work than any human engineer. In short term, such AGI will be more expensive to use than human work but in long run AGI will take over all design jobs. I'd currently estimate that this happens between years 2030 and 2050. And the society at large is failing to understand that we should be making changes to social structures already because the change will be so huge that it will be hard to implement.
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@lil_ToT-XFZ1 I think majority of the people are conservative. They think that if something has been acceptable for previous 50 years, it must be acceptable for next 50 years, too, so there's no hurry. However, the actual timeframe for the need of major change in society thanks to AI getting into general use will be closer to 5 years instead of 50 and people are not ready for this.
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