(2024-10-18) The3 Ai Use Cases Gods Interns And Cogs

Drew Breunig: The 3 AI Use Cases: Gods, Interns, and Cogs. We talk about so many things when we talk about AI. The conversation can roam from self-driving cars to dynamic video generation, from conversational chatbots to satellite imagery object detection, and from better search engines to dreamlike imagery generation. You get the point.

After plenty of discussions and tons of exploration, I think we can simplify the world of AI use cases into three simple, distinct buckets:

• Gods: Super-intelligent, artificial entities that do things autonomously. (AGI)
• Interns: Supervised copilots that collaborate with experts, focusing on grunt work. (cyborn)
• Cogs: Functions optimized to perform a single task extremely well, usually as part of a pipeline or interface.

Gods are the human-replacement use cases. The much hyped artificial general intelligences (AGI) that are allegedly just around the corner…

Gods require gigantic models. Their autonomous nature – their defining quality – means a low error tolerance and a broad, general model

Interns are the copilots (and a term I’ve shamelessly borrowed from Simon Willison). Their defining quality is that they are used and supervised by experts. They have a high tolerance for errors because said expert is reviewing their output, and can prevent embarrassing mistakes from going further.

Because they are tools for specific types of experts, Interns are limited to specific domains. They don’t have to be generalists. Their model sizes are large, but not massive.

Today, Interns are delivering the lion’s share of the realized value from AI.

And while Interns are delivering tremendous value, they are secondary to the experts driving them. How do you improve the output of an AI copilot? Simple: find a better expert.

Toys: “But Drew,” you might say, “what about the fun image generator or friendly chatbot I play with occasionally? It doesn’t really fit in the intern bucket.” You’re right. I think these are Toys, a subcategory of Interns defined by their usage by non-experts. Toys have a high tolerance for errors because they’re not being relied on for much beyond entertainment.

Cogs is the last use case bucket. Cogs are comparable to functions. They’re designed to do one task, unsupervised, very well. Cogs have a low tolerance for errors because they run with little expert oversight.

Cogs may be built into data pipelines, performing some enrichment or transformation step, alongside bog-standard functions powered by regex or SQL.

Cogs are, by far, the dominant use case amongst enterprise teams building with AI. As we covered after the DataBricks summit, in most discussions among builders, “AI looks like just another pipeline function.”