the immense investment in AI has not translated into adoption at the scale it appears to have. across comparable early periods, AI investment been growing approximately 40%/year, versus 25% for electricity and 16% for IT. however, only 19.8% of U.S. businesses use AI in at least one function, roughly 5 points below both benchmarks. meanwhile the real production deployment gap is even deeper at top companies: recent MIT study shows that only 11% of the S&P500 has "deeply" integrated AI. capability is advancing faster than trust and real implementation. there's one fundamental reason for this: enterprise investment in evaluations is less than 0.1% of where it needs to be. once large enterprises start investing in continuous evaluation loops, they will precisely define what "good" means for their use case. within that context, they will measure the intelligence of any given system. when you measure something, you can improve it and watch performance increase against those measurements. this continuous loop is how enterprises "own their own intelligence." it's irrelevant whether you're building on a closed or open model. in most cases, closed models will perform much better for your use case. what matters is whether you're precisely defining what good means and consistently measuring against it. if you are, then you're owning your own intelligence in a largely model-agnostic way.