Well run large companies often waste a lot, in order to (1) hedge risks of being left behind, (2) ensure they have options in the future in possible growth or new efficiency areas, and (3) to start on long learning curves for skills and capabilities that appear likely to be a baseline necessity in the long run. Bonfires of money. Predictably. Because all three of those concerns require highly speculative action to pr…
This reminds me of the exploration-exploration trade-off in reinforcement learning: you want to maximise your long term profits but, since your knowledge is incomplete, you must acquire new knowledge, which companies do by trying stuff. Prematurely dismissing GenAI could mean missing out on new efficiencies, which take time to be identified.