We study the macroeconomic implications of narratives, defined as beliefs about the economy that spread contagiously. In an otherwise standard business-cycle model, narratives generate persistent and belief-driven fluctuations. Sufficiently contagious narratives can "go viral," generating hysteresis in the model's unique equilibrium. Empirically, we use natural-language-processing methods to measure firms' narratives. Consistent with the theory, narratives spread contagiously and firms expand after adopting optimistic narratives, even though these narratives have no predictive power for future firm fundamentals. Quantitatively, narratives explain 32% and 18% of the output reductions over the early 2000s recession and Great Recession, respectively, and 19% of output variance.

For all our advancements, we’re still a storytelling species subject to animal spirits. Interpreting the shadows cast by the communal fire and sense making through story.

Narratives matter.

Glamming up a robot risks overpromising what the robot as a product can actually do.  That risks disappointing customers. And disappointed customers are not going to be an advocate for your product/robot, nor be repeat buyers.

Replace “robot” with whatever you sell.

Marketing and selling is a series of promises made to a potential customer. The product better keep—or better yet, exceed—those promises.

The spectacle should be ancillary to the product—an attention magnet. The spectacle shouldn’t be the promise.

This post combines two things I like: Dracula and AI.

as a rule of thumb, in most companies if one type of information “belongs” to one C-level executive and another type to another, the chances of the company building an AI that takes advantage of both is quite low.

If you’re at the cutting edge or attempting to get there, if you’re doing new things in new ways, you aren’t fighting a war, you’re dealing with a vampire. What’s worse, it’s the beginning of Dracula and you have no idea what they look like.

Any new AI that’s more than an iterative improvement of a previous one will need not new algorithms but new questions, not bigger data but stranger combinations of data sets.

There’s always a vampire waiting to suck the life out of your business.

Convergent thinking helps with normal problems.
Divergent thinking helps with vampires.

🧛‍♂️

AI will turbocharge contextual capabilities, and the most important piece of content these days is impressions.

Enter Integral Ad Science:

The tool, called Quality Attention for Publishers, uses machine learning to gauge how much attention readers might give webpages.

The next wave of measurement?

In recent years, attention metrics, which aim to estimate the quality of content adjacent to ad inventory, have become all the rage as advertisers and publishers continue to shift away from more traditional metrics like viewability.

Attentive impressions > impressions