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The costs are huge! Enterprises are struggling….
Over the last few weeks, I have heard the same thing from multiple people, token costs for Claude, ChatGPT, etc., are expensive and really hard to manage.
From large public companies to hedge funds to small PE shops to large insurance companies, forecasting costs for token spend is not something they can wrap their head around. I have heard from friends at all different types of organizations, who are trying to forecast costs for a new project, or they are pulling their hair out seeing the cost of AI being deployed across the company, and are not sure how some people are spending 10x+ more than others. Why are some teams that aren’t engineering spending so much? What are they getting out of it? Should we just shut people off?
This is obviously an issue. This is CFO’s, heads of revenue, heads of data teams, and senior managers across AI deployment/strategy/budget leaders, all trying to decide what to do. Everyone has to deploy AI. Everyone knows they must incorporate AI to stay relevant. The struggle is how much is too much? How much are we getting out of it? What groups should have all you can eat vs. what groups should be on a fixed price? We all want the buffet, but some of us can’t afford it. Some of us have to eat off the kids’ menu. Some of us can barely afford the side salad. Others need to decide who gets to go wild vs. who should be put on a tight leash.
AI is moving fast. People are spending fast. But I think things are going to change fast in how we spend, what we spend on, and who can spend. We need more analytics, insights, controls, monitoring, and most likely experts. It reminds me of early cloud technology rollouts. The prices were surprising some months when you saw the bill. I remember this clearly when I managed data science teams. Then, as you got better at controlling the spending or spending more wisely, or you hired an expert to optimize your cloud usage, making sure you are also using the right cloud products, you could then dial in your spend and properly forecast.
We will get there soon in AI. But for some, not soon enough!
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