The model was never your hardest problem. Trust was.
Capability leaps and adoption timelines run on different clocks. For enterprise AI, trust and change management remain the bottleneck.
Pillar guide
Human-Centered AIAI strategy and adoption grounded in human needs, organizational reality, and responsible design.
Sam Altman admitted the biggest AI prediction of the decade was wrong, and the reason why says more than the admission itself.
Back in 2023, he expected GPT-4 to blow software businesses wide open almost immediately. It did not happen, and lately he explained why: the economy carries far more inertia than the technology curve does. People keep buying from the same vendors and using the same tools out of habit, not out of any deep AI resistance. Every “AI is replacing X” prediction that has landed late made the same mistake, mistaking a capability leap for an adoption timeline. And thank G-d, otherwise we would go crazy as a beings.
The bottleneck was never the model itself, but it was change management, procurement cycles, and the ordinary human lag between something existing and someone actually trusting it enough to switch.
Altman also described OpenAI’s early coding effort as a near-hopeless bet against a competitor that already had momentum, and it still ended up winning real users. Frontier speed and market adoption run on two different clocks,not matching, and confusing them is exactly where a lot of AI companies are failing right now. And we will enjoy this failures to witness and learn from them.
If you are building for enterprises, the model was never your hardest problem, but trust! Trust was.
Worth pressure-testing which one you are actually optimizing for before you scale.