I was mostly disputing the claim about data advantages, which often gets thrown around willy nilly to favor big corporations, because it fundamentally goes against the nature of where AI is heading.
I am not disputing that having more financial resources would help anyone hire talent and build awesome infrastructure, and certainly the latest way of training AlphaGo Zero was aided by earlier experiments that relied on labeled data and extensive computational effort. However, by no means do I think big corps have a lock on these kinds of advancements. There will always be great people who would rather go the startup route, and both algorithmic and hardware advancements are drastically reducing the operational cost of training AI systems. Thus, when it comes to AI, I think very small teams will be able to get very far with the right approach.
AlphaGo Zero is the product of incremental improvements made by the same group of computer Go experts that built the original system. _They_ learned from each experiment and incorporated that knowledge in the next version. It was not obvious _a priori_ that the AlphaGo Zero architecture or training method would succeed; if it had been, then the AlphaGo team would not have built the earlier versions. And while it runs on "only" 4 TPUs for inference, those TPUs are each about 30x as powerful as the computers that the original AlphaGo ran on, so it's more like a reduction from ~180 GPUs to 120 equivalent GPUs.