Caveat, I'm US based, but still hedging by buying hardware and open weight (per-token) vendor. I can no longer trust Big AI nor my government (both parties are trouble). Letting the rich and powerful decide who can access what models is not a future I want. Open weight is the way forward for most people. Frontier will be for niche applications like bio engineering and other niche that are less language oriented and require specialized models.

This was on HN a day or so ago

www.anildash.com/2026/06/23/fight-ai-platform-war/

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recsv-heredoc 14 hours ago | parent [-]

Great article. This is exactly what we're doing from a product perspective.

What if the frontier-minus-6-months assumption does not hold? The US has 5x the AI Capex of China, and 10x the EU. Assuming AI is compute limited (we certainly seem to be given the RAM crisis) - wouldn't it be reasonable to assume frontier models are likely to continue to pull ahead?

	▲	verdverm 13 hours ago \| parent [-]
		I'm not sure capex is the best metric, PPP make's China's money go further. Their techsci funding (subsidizing) produces more per unit. Also consider that Ai needs electricity. China has more than 2x the generation capacity and is building faster than anyone else. Here in the US, we are facing pushback that will slow things down. Then there are the model capability questions. (1) Frontier models are being restricted (2) eventually models will be good enough for most tasks. This later point is why I believe the most of us will eventually have unlimited plans like our mobile data. Switching model/providers is very low effort as well. We already have tools like LiteLLM and GoModel that provide a single endpoint to this capability.