AI Angst

Summary

My input stream is full of it: Fear and loathing and cheerleading and prognosticating on what generative AI means and whether it’s Good or Bad and what we should be doing. All the channels: Blogs and peer-reviewed papers and social-media posts and business-news stories. So there’s lots of AI angst out there, but this is mine. I think the following is a bit unique because it focuses on cost, working backward from there. As for the genAI tech itself, I guess I’m a moderate; there is a there there, it’s not all slop. But first… The rent is too damn high · I promise I’ll talk about genAI applications but let’s start with money. Lots of money, big numbers! For example, venture-cap startup money pouring into AI, which as of now apparently adds up to $306 billion. And that’s just startups; Among the giants, Google alone apparently plans $75B in capital expenditure on AI infrastructure, and they represent maybe a quarter at most of cloud capex. You think those are big numbers? McKinsey offers The cost of compute: A $7 trillion race to scale data centers. Obviously, lots of people are wondering when and where the revenue will be to pay for it all. There’s one thing we know for sure: The pro-genAI voices are fueled by hundreds of billions of dollars worth of fear and desire; fear that it’ll never pay off and desire for a piece of the money. Can you begin to imagine the pressure for revenue that investors and executives and middle managers are under? Here’s an example of the kind of debate that ensues. “MCP” is Model Context Protocol, used for communicating between LLM software and other systems and services. I have no opinion as to its quality or utility. I suggest that when you’re getting a pitch for genAI technology, you should have that greed and fear in the back of your mind. Or maybe at the front. And that’s just the money · For some reason, I don’t hear much any more about the environmental cost of genAI, the gigatons of carbon pouring out of the system, imperilling my ...