riskable

Gemini thinks it's peony...

Note: Gemini is usually very good at identifying plants but some plants are notoriously difficult for anyone to pin down except horticulturalists (e.g. experts). For example, Pothos vs. Heartleaf Philodendron or more dangerous, Queen Anne's Lace vs Poison Hemlock. AI also has trouble identifying grasses (in general) because many can look the same from a picture. Even close up! Yet a human (in person) would be like, "these two grasses are nothing alike!"

It's all about pattern matching. If two leaves from two different plants have the same appearance and growth pattern, AI can easily get it wrong. In general, though, it usually does a really good job.

I've been researching this a bit... I've come to the conclusion that there is no AI bubble. In fact, we're only just getting started down this road. Unless there's some massive 100x efficiency breakthrough in training AI and inference, the entire world is going to be building seemingly endless AI data centers (and the normal compute kind, e.g. for stuff like AWS, Google/YouTube, Meta, banks) for at least a decade. Probably a little longer (12-15 years before demand levels out).

Everyone thinks that "AI data center" means ChatGPT, Claude, Gemini, etc but there's 10,000x more demand for AI than those services. Think: Pharmaceutical companies trying to find proteins, scientists (and big agriculture!) trying to model the weather, and other businesses trying to automate stuff. Not just software; robots and things like conveyor belts.

Another example: Ever use one of those self-checkouts that's mostly just a camera pointing down, where you place the stuff you're purchasing? That uses AI too.

Having said that, there is a great big bubble in AI: OpenAI, specifically. That will definitely pop one day. And hopefully, the DRAM bullshit will go along with it.