Is it more than 30% likely that in the short term (say 5 years), Google isn't wrong? If you applied massive scale to the AI algorithms of 1997, you would get better performance, but would your result be economically useful? Is it possible we're in a similar situation today where the real-world applications of AI are already good-enough and additional performance is less useful than the money spent on extra compute? (self-driving cars is perhaps the closest example: clearly it would be economically valuable, but what if the compute to train it would cost 20 billion US dollars? Your competitors will catch up eventually, could you make enough profit in the interim to pay for that compute?)
Is it more than 30% likely that in the short term (say 5 years), Google isn't wrong? If you applied massive scale to the AI algorithms of 1997, you would get better performance, but would your result be economically useful? Is it possible we're in a similar situation today where the real-world applications of AI are already good-enough and additional performance is less useful than the money spent on extra compute? (self-driving cars is perhaps the closest example: clearly it would be economically valuable, but what if the compute to train it would cost 20 billion US dollars? Your competitors will catch up eventually, could you make enough profit in the interim to pay for that compute?)