You need all four quadrants: risk capital, industry, academics, and that elusive X-factor. The hard work of AI/ML theory, such as issues around generalizability and ethics, is still done around whiteboards and academic conferences.
A more useful metric may be the proportion of proprietary versus open discovery. I don't know if I can point to a single example where researchers have not rushed to put their latest breakthroughs on OpenReview or Arxiv. Even knowledge of a technique, without the underlying models or data, is enough to influence the field.
Academic free inquiry and intellectual curiosity, looks very different than product-focused solutions-oriented corp R&D. A good working example looks something like Google AI's lab in Palmer Square, right on the Princeton campus. Researchers can still teach and enjoy an academic schedule. I think it was Eric Weinstein who said something to the effect that if you were a johnny come lately to the AI party, your best bet would just be to buy the entire Math Department at IAS! In practice, its probably easier to purchase Greenland ;)
A more useful metric may be the proportion of proprietary versus open discovery. I don't know if I can point to a single example where researchers have not rushed to put their latest breakthroughs on OpenReview or Arxiv. Even knowledge of a technique, without the underlying models or data, is enough to influence the field.
Academic free inquiry and intellectual curiosity, looks very different than product-focused solutions-oriented corp R&D. A good working example looks something like Google AI's lab in Palmer Square, right on the Princeton campus. Researchers can still teach and enjoy an academic schedule. I think it was Eric Weinstein who said something to the effect that if you were a johnny come lately to the AI party, your best bet would just be to buy the entire Math Department at IAS! In practice, its probably easier to purchase Greenland ;)