Tamay Profile picture
22 Feb, 7 tweets, 2 min read
A recent paper about innovation over the long run reveals a very neat snapshot of the composition of inventions over time. Using data on US patents, it identifies the following key waves:
1840s—70s: Key manufacturing innovations occur (pneumatic process for cheap steel and sewing machine are invented); Transport (improvements in steam-engines. The Bollman bridge, air brake system, cable car are patented); Consumer Goods (board game, toothbrush, picture machine).
1870s-1900s: Electricity and Electronics (Edison patents the electric light, Bell the telephone. Others invent the microphone, computer motion picture, and the radio). In the 1890s Transport innovation peaks (the automobile, airplane, and the submarine are all patented).
1910s-50s: Chemistry (key inventions in plastics: PVC, nylon and bakelite. The broad spectrum antibiotic Tetracyline, and the first oral contraceptive are registered. Rubber, teflon and ethyl gasoline are patented). Chem innovations make up ~50% of all innovations in the 30s.
1950s-70s: the Information Age arrives. Shockley patents the transistor, Bell Labs colleagues the digital computer. Xerox pioneers the interface btwn memory and processor, the first software version mgmt systems, and bitmap graphics. In 1979, Wozniak patents the microcomputer.
1980-2000: A wave of innovations in genetics occurs—recombinant DNA methods, the PCR method for copying DNA segments, heat-stable DNA-replication enzymes. Share of health-related innovations reaches its peak.
1990s onward: the Information Age continues: Koss patents the Excel Function, Bezos patents 1-click buying, Page creates Pagerank. ~80% of top patents now Electronics/IT related. Innovation has hardly ever before been this concentrated in so few sectors.

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More from @tamaybes

22 Nov 20
A few months ago, I wrote an economics dissertation on whether machine learning models are getting harder to find. Here’s a summary of what I found:
Some background. @ChadJonesEcon, @johnvanreenen and others wrote an awesome article that found that ideas are getting harder to find: in semiconductors, agricultural production and medicine, research productivity has been declining steadily.
In my dissertation, I explored to how this story holds up for machine learning. I used a dataset on the top performing ML models on 93 machine learning benchmarks—mostly related to computer vision and NLP—and data on research input derived from data on publications.
Read 12 tweets

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