AI model efficiency is a red herring. The real performance drain lies in neglected post-processing and data synchronisation.
FO Take · Score 78
Organisations obsess over model speed, missing the obvious. The core AI often delivers, but bloated post-token processes and poor data handling grind progress to a halt. This isn't about smarter algorithms; it's about dismantling the convoluted, inefficient workflows built around them. Stop polishing the engine when the wheels are square. This systemic myopia cripples innovation.
The strongest counter
Focusing solely on post-processing ignores fundamental model inefficiencies. A faster model reduces the overall workload, making subsequent steps less critical. Optimising both is essential, not one over the other.
