There’s a flavor of puzzle in which you try to determine the next number or shape in a sequence. We’re living that now, but for naming the data field. “Predictive analytics.” “Big Data.” “Data science.” “Machine learning.” “AI.” What’s next?
It’s hard to say. These terms all claim to be different, but they are very much the same. They are supersets, subsets, and Venn diagrams with a lot of overlap. (View Highlight)
Note: Fun writing
In finance terms, we’d say that the upswing represents a large and growing delta between the fundamental price (what The New Thing is actually worth) and the observed price (what people are spending on it, which is based on what they think it’s worth). The ensuing crash represents a correction: a sharp, sudden reduction in that delta, as the observed price falls to something closer to the fundamental price. (View Highlight)
Note: Good overview of financial fundamental analysis
This quote from Cem Karsan, founder of Aegea Capital Management, sums it up well. He’s talking about flows of money on Wall St. but the analogy applies just as well to the AI hype bubble:
If you’re on an airplane, and you’re 30,000 feet off the ground, that 30,000 feet off the ground is the valuation gap. That’s where valuations are really high. But if those engines are firing, are you worried up in that plane about the valuations? No! You’re worried about the speed and trajectory of where you’re going, based on the engines. […] But, when all of the sudden, those engines go off, how far off the ground you are is all that matters.
—Cem Karsan, from Corey Hoffstein’s Flirting with Models podcast, S4E1 (2021/05/03), starting 37:30
Right now most of AI’s 30,000-foot altitude is hype. When the hype fades—when changing the name fails to keep the field aloft—that hype dissipates. At that point you’ll have to sell based on what AI can really do, instead of a rosy, blurry picture of what might be possible. (View Highlight)
Note: Vivid analogy of a plane flying and the shifting priorities
