Hal Varian

Supported by Greenhaven Road Capital, finding value off the beaten path.

Hal Varian is the author of two economics textbooks and co-author of Information Rules: A Strategic Guide to the Network Economy. Varian spoke at the ESMT business school in Berlin in 2018.

Varian’s talk is titled Artificial Intelligence Economics and Industrial Organization. AI EIO, “kind of like old MacDonald had a farm.”

Varian represents the techno-economy as a pyramid with three layers. A base layer of data, a next layer up of information and the next layer of up knowledge. It’s only at the knowledge peak where we get action.

“There’s a lot of debate about the data economy, the information economy, the knowledge economy, but they’re all part of the same process, and no part can stand on its own.”

A series of recent breakthroughs is part of the reason the AI EIO conversation has entered the public sphere. “Many techniques have emerged that just a few years ago we thought were impossible.”

Data is our sand at the beach, models our buckets, and then it’s up to us. “These days the scarcest factor by far,” said Varian, “is expertise.” Rory Sutherland agrees, “One of my great worries about big data is that there are far more big data sets than there are mathematicians competent enough to handle them.”

Think of data as a raw material, like sand or oil – with one big difference. “Some people say ‘data is the new oil’. My response is, they have one thing in common. To be useful they have to be refined. A barrel of oil isn’t worth much but turn it into gasoline, kerosene, or hydrocarbons and it’s worth something. It’s the same thing with data.”

But data is non-rival. Two organizations can, and maybe should, have the same data. “It’s a mistake to talk about data ownership because it’s too narrow a concept.” Instead, maybe data is something to license.

Good organizations have people that argue well. Good economics have organizations that compete. Cordial clashes are fractal. “Usually pluralism is important because it allows a diversity of viewpoints and in many cases allows for a significant improvement in terms of unitizing that data.”

Varian emphasized the abundance of services for data. He also said that Google Home started as Goog-411. The call in service, “learned how to recognize voices, learned how to recognize accents, learned all these different things in this very limited domain of directory information. That was enough to get started and now I think we have one of the best voice recognition systems in the world.”

Maybe a better analogy is that data is like a river, we are the settlers, and models are the mill. It took a lot of effort to discover the data/river, and we had to be willing to venture out and harness it. Now we need to figure out how to use it well. “The challenge is finding the expertise to extract the value from the data.”

The goal isn’t perfection, just improvements. Robots won’t take our jobs but they can make our jobs better. “We think it has to be people plus machines to really be effective.” How well someone works with machines is a key part of Tyler Cowen’s book Average is Over.

The good news is that everyone is kind of figuring things out together. Cowen wrote that computers play chess moves no human understands. Varian said that computers find indicators of health (and sex) in the eye that no human understood – at least for a couple of weeks. The doctors did figure it out, and that’s kind of what we’re all doing on the banks of this river.

Varian has other good talks on YouTube, if you want more check out Bots and Tots.