The VC expects to lose most of the time and hit it out of the park once in a while, for a large net gain.
For companies trying to automate or increase productivity with AI, there are unlikely to be any massively profitable winners that will make up for the failures, so too many failures is going to hurt.
Obvious examples are things like AI-assisted language translation for books if you are a publisher, implementation of cancer screening technologies if you are a healthcare provider, AI-assisted drug discovery if you are a pharma firm, AI-assisted discovery if you are a legal provider, AI trading algo's if you are a hedge fund...
All of these could result in some pretty profitable winners. It's also the sort of thing that can result in 'losers' if you call it wrong - e.g. if there is a 20% chance of success and I don't invest and my competitors do, what happens if I am wrong? You don't want to be the Kodak of your industry. As an example - if I am the worlds biggest provider of call centres, do I really want to bet big that my industry will never be automated, or do I want to start investing now to prepare for that as a possibility?
I don't think your Kodak example works very well here - they were a classic business school case of not realizing what business they were really in, but most uses of AI (whether one means LLMs, or something else like most of your examples) are going to be automation/productivity enhancement, not "pivotal" changes.