It’s an increasingly common experience among data scientists - you’re minding your own business, getting your job done, and your boss’s boss walks in to your office or drops in to your Slack DMs. “Do we have AI in the product? What’s your plan for making our product AI powered?” (That’s if you don’t just get ambushed with this question in a big meeting in front of a bunch of executives.

The Economics of Generative AI What’s the business model for generative AI, given what we know today about the technology and the market? OpenAl has built one of the fastest-growing businesses in history. It may also be one of the costliest to run. The ChatGPT maker could lose as much as $5 billion this year, according to an analysis by The Information, based on previously undisclosed internal financial data and people involved in the business.

What would you say it is you do here? Now that many of us are returning to the office and getting back into the swing after a winter break, I have been thinking a bit about the relationship between machine learning functions and the rest of the business. I have been getting settled in my new role at DataGrail since November, and it has reminded me how much it matters for machine learning roles to know what the business is actually doing and what they need.

Businesses are jumping on a bandwagon of creating something, anything that they can launch as a “Generative AI” feature or product. What’s driving this, and why is it a problem? The AI Hype Cycle: In a Race to Somewhere? I was recently catching up on back issues of Money Stuff, Matt Levine’s indispensable newsletter/blog at Bloomberg, and there was an interesting piece about how AI stock picking algorithms don’t actually favor AI stocks (and also they don’t perform all that well on the picks they do make).