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TL;DR: the expectation that a business can be "saved" by bolting AI onto an existing organization is unrealistic. Detail: My experience is that commercially vi

by YouWhy 3y ago

TL;DR: the expectation that a business can be "saved" by bolting AI onto an existing organization is unrealistic. Detail: My experience is that commercially viable AI requires a leadership that gets AI and can execute very careful tech/product development facing end-to-end business problems, obsess over data quality, pivot and and manage risks in a way that's subtler than shutting the whole thing down. None of these are possible when attempting to transform a pre-existing business. Consider AI and classic Google Search: in the best case scenario, AI will cannibalize on the search. In the worst case scenario, it will generate no lift. The middle ground is elusive at best. What does seem to work across pre-existing businesses is when AI is used to provide embellishments/optional upsells. But of course that's not the dramatic transformation that a lot of people not in the scene seem to be expecting.

zoogeny 3y ago
This analysis matches my current experience. I'm at a small startup and I've been trying my best to push them to invest in AI features. The company is willing to invest very trivial amounts of time/effort in AI embellishments but they are extremely slow to move on any kind of transformational technologies. I had a call with the CEO recently and he was saying that he wasn't interested in foundational AI technologies. I don't mean foundational models (as if a small B2B startup could even consider such a thing). I don't even mean medium/large scale fine-tuning work. I mean, he wasn't interested in investigating broadly applicable AI capabilities which could apply to multiple product features. I think of it like "dipping the toe into AI" strategies. You get a couple of days to try out some prompting and then spend the vast majority of the time bolting the LLM output onto an existing feature. What I believe we need are a few big companies to emerge where the new LLM stuff is at the very core of the business so it can show startups how it should be done. I can't even blame this company for being hesitant to invest - there is no proven track record to judge potential success against. At least old-school SaaS feature development work has some basis for projecting revenue. The margin for error on deep LLM features is completely unknown.

by YouWhy 3y ago

zoogeny 3y ago

This analysis matches my current experience. I'm at a small startup and I've been trying my best to push them to invest in AI features. The company is willing to invest very trivial amounts of time/effort in AI embellishments but they are extremely slow to move on any kind of transformational technologies. I had a call with the CEO recently and he was saying that he wasn't interested in foundational AI technologies. I don't mean foundational models (as if a small B2B startup could even consider such a thing). I don't even mean medium/large scale fine-tuning work. I mean, he wasn't interested in investigating broadly applicable AI capabilities which could apply to multiple product features. I think of it like "dipping the toe into AI" strategies. You get a couple of days to try out some prompting and then spend the vast majority of the time bolting the LLM output onto an existing feature. What I believe we need are a few big companies to emerge where the new LLM stuff is at the very core of the business so it can show startups how it should be done. I can't even blame this company for being hesitant to invest - there is no proven track record to judge potential success against. At least old-school SaaS feature development work has some basis for projecting revenue. The margin for error on deep LLM features is completely unknown.