People ask: “Are you dunking on neolabs?” Most neolabs will fail. That’s normal. But I believe more AI breakthroughs are coming. I’m skeptical of those following the same playbook as OpenAI and Anthropic. Raising $1B and spending 50% on GPUs doesn’t make you a $10B company. I’m rooting for the contrarians. Challenge scaling laws. Challenge transformers. Challenge how models learn. Challenge the next-token prediction paradigm. My bigger concern is that brilliant researchers don’t necessarily make great founders. In my experience, researchers often struggle to turn their research into great products / sustainable businesses.

It is very important for the entire ai startup ecosystem for key inference vendors to be 1) capable of getting capacity that they can freely allocate, 2) secure, 3) reliable, 4) always driving cost and perf let’s not forget how much of an enabler cpu-era cloud was for innovation! no capex! outsource much of your infra ops (and more every year)! every small-mid customer “owning, building and running their own landed power, cooling qnd chips” completely ignores the lessons of economic history. we had colo 20y ago…public cloud won in an age where closed frontier labs weigh every (limited) GPU served by api vs. internal training and 1st party product use, inference abundance beyond them is a prerequisite for a rich startup ecosystem