Entropy Economics

Last week, Federal Communications Commission Chairman Ajit Pai outlined his plan to auction a huge swath of wireless spectrum known as the C-band. It was a tricky dance because Pai had to find a compromise among satellite firms that currently (under)utilize the spectrum (to beam video and audio content to broadcasters and others) and would like to sell much of it, mobile carriers that would like to buy it for their 5G wireless build-outs, and various political factions who have their own ideas about how to manage the airwaves. 

It looks like Pai’s plan has attracted enough support to auction 280 megahertz (MHz) of spectrum by December 2020. The C-band is attractive because of both its size, which offers lots of capacity, and where it sits on the frequency map, which fills a key strategic role in new 5G network architectures. The additional 280 MHz could nearly double the total existing spectrum now deployed by all major mobile carriers. This particular auction is just one part of a much larger big-bang of spectrum availability.

New research continues to demonstrate that information technologies are even more economically powerful than official data suggest. Over the past five years, I’ve attempted to show that we have underestimated the price declines and performance improvements in smartphones. (See, for example, “The A12 chip: Estimating innovation with iPhone prices,” in which I estimated that it would have cost $28 million to purchase the basic building blocks of an iPhone XS in 1991.) Last summer, the Bureau of Economic Analysis (BEA) partially acknowledged this when it revised its historical price and output figures for mobile phones, cloud computing services, and internal software development.

The BEA’s revision was in part based on excellent work by the Federal Reserve’s David Byrne. Now Byrne and other economists are putting even more analytical meat on the bones. In the process, they are once again dramatically raising estimates of the economic impact of smartphones and internet services.

In newly updated work with Carol Corrado, for example, Byrne seeks to capture missing value from broadband connectivity and content flowing over these networks. His bottom line is that “the innovations boost consumer surplus by nearly $1,700 (2017 dollars) per connected user per year for the full period of this study (1987 to 2017) and contribute more than 1/2 percentage point to US real GDP [gross domestic product] growth during the last ten (2007 to 2017).”

When Neil Armstrong, Buzz Aldrin, and Michael Collins went to the moon 50 years ago this week, they had a large portion of the world’s computing power with them on Columbia and Eagle and behind them in Houston. One NASA engineer estimated that, between 1962 and 1967, the Apollo program had purchased 60 percent of all integrated circuits built in the US.

Today, however, that overwhelming proportion seems paltry in its aggregate power. The two Apollo Guidance Computers (AGC) onboard the spacecraft, for example, each contained 32 kilobits of random-access memory and 72 kilobytes of read-only memory. The AGCs had a primary clock running at 2.048 megahertz, and their 2,048 integrated circuits contained only several tens of thousands of transistors. They also weighed 70 pounds.

By comparison, today’s iPhone XS sports 32 gigabits of dynamic random-access memory, 256 gigabytes of storage, and a processor with 6.9 billion transistors running at 2.49 gigahertz. That’s a million times more memory, several million times more storage, and hundreds of millions times more processing power than the AGCs. All in a package one-hundredth the weight.

See our latest research, in collaboration with the venture firm High Alpha: Startups and the Remaking of the Firm 

On February 5, in San Francisco, Elliott Parker of High Alpha presented this new research at Alloy, a conference focused on corporate innovation. This research takes the famed Innovator’s Dilemma to the next level. It suggests corporate concentration has peaked, argues that firm borders are evaporating, highlights the diminishing returns of traditional R&D and M&A, and shows how big firms might harness the unique strengths of startups to transform and grow.

A few excerpts:

“The dominant business story of the coming decade will be the disaggregation of the firm. After reaching a peak of corporate and industry concentration, the undeniable forces of technology have unleashed a new phase of decentralization . . . Today, thousands of smart people at hundreds of important companies are spending millions of hours and billions of dollars to advance an illusion of innovation. The evidence can be seen in the individual challenges of big firms and the macroeconomic data of a pronounced productivity slowdown in the traditional industries, which make up 70 percent of the economy . . .” 

” . . . Only startups can reimagine and redeploy resources – money, technology, and, most importantly, people – to the radical degrees required. Only startups can learn fast enough, discover new products and markets cleverly enough, incentivize and coordinate talent effectively enough, and deliver real value under constraints jarring enough to achieve breakthrough innovation. That’s great for startups, but where does that leave big firms? In better shape than one might think – but only if the big firms recognize this shift and aggressively exploit it.”

continue reading . . .

On Tuesday, the Royal Swedish Academy awarded the 2018 Nobel Prize in economic sciences to two American economists, William Nordhaus of Yale University and Paul Romer of New York University’s Stern School of Business. Romer is well-known for his work on innovation, and although the committee focused on Nordhaus’ research on climate change, this year’s prize is really all about technology and its central role in economic growth.

Paul Romer, who with William Nordhaus received the 2018 Nobel Prize in Economics, speaks at the New York University (NYU) Stern School of Business in New York City, October 8, 2018 – via REUTERS

Romer’s 1990 paper “Endogenous technological change” is one of the most famous and cited of the past several decades. Until then, the foundational theory of economic growth was Robert Solow’s model. It said growth was the result of varied quantities of capital and labor, which we could control, and a vague factor known as the Residual, which included scientific knowledge and technology. The Residual exposed a big limitation of the Solow model. Capital and labor were supposedly the heart of the model, and yet technology accounted for the vast bulk of growth — something like 85 percent, compared to the relatively small contributions of capital and labor. Furthermore, technology was an “exogenous” factor (outside our control) which didn’t seem to explain the real world. If technology was a free-floating ever-present factor, equally available across the world, why did some nations or regions do far better or worse than others? Read the rest of this entry »