Beating the AI Doom Cycle

**Nathaniel Whittemore** (0:00)
Today on the AI Daily Brief, we're discussing the AI Doom Cycle and how we can move out of doom desperation into a place of enlightened excitement.
The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI.
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Final note, today's episode is one extended episode instead of a split between headlines and main. Turned out all the headlines fit in the context of the main, but I'm sure we'll be back with our normal format tomorrow. Welcome back to the AI Daily Brief. As I was preparing the show, I noticed that a lot of the stories that people have been discussing over the weekend and the ones that I wanted to cover had in a strange way a relationship to each other that was worth exploring. Not because they were directly related, but because they were all part of something that I've been thinking about for a while, which as of this episode, I am calling the AI Doom Cycle.
Today, I want to explore what the Doom Cycle is, why different types of people in different contexts fall in different parts of it, and how I think we can get to the far side of it, which I believe is the healthiest place from which to actually engage with the big questions around AI, whether it's policy or something else.
Now, you guys probably recognize the inspiration for this, which is, of course, Gardner's famous Technology Hype Cycle Chart. The idea of the Hype Cycle Chart is that new technologies tend to, in their argument, follow a pattern as they diffuse throughout society. It kicks off with an innovation trigger, the sparks of that new thing that becomes available, surges up to what they call the peak of inflated expectations. This is the very top of the Hype Cycle when everyone's excited about a thing. It often comes with big capital injections in the form of venture capital and investment. It's the part of the curve where a big chunk of people are convinced that this new thing is the new thing that's going to change everything. The peak of inflated expectations doesn't last all that long, and from there, you crash back down into the trough of disillusionment. This is the period during which expectations have bottomed, in many cases, not because the technology is bad, but because it hasn't lived up to its inflated expectations. A lot of times, this is, in and of itself, a vector of time, where it's not even a reassessment of what the technology can do, but a recognition that the timeline for it doing those things is a lot longer than previously thought. Now, not every technology climbs out of the trough of disillusionment. Some stay mired there for a very long time. Metaverse, I'm looking at you.
But for those that do, the next stage is what Gardner called the slope of enlightenment. This is where, freed from those inflated expectations, people actually start to figure out the right ways to use that technology, the things that it's actually good for, the intersection with people's lives or workflows that make it valuable, if not as earth shattering as maybe people once thought. That leads eventually to the plateau of productivity where the technology is actually diffused into the world and is a valuable part of whatever environment it's supposed to be around, even if it never reaches the glorious heights of expectations that it once had. Now, a lot of ink has been spilled on this chart, including much research that suggests that most technologies don't actually follow it exactly, but I think the reason it has resonance is that it is intuitively reflective of our individual relationships with technology in many cases. And I think one of the most revealing things that is true in almost all cases is that even when we realize early on that a thing is going to be extremely impactful in the world, we just tend to wildly underestimate how long it's going to take to make that impact. The AI Doom Cycle in that in this case, I'm describing the emotional and cognitive states of people and their relationship with AI more than the technology itself.