Economic Impact of AI

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Updated November 2, 2022

You’re reading an excerpt of Making Things Think: How AI and Deep Learning Power the Products We Use, by Giuliano Giacaglia. Purchase the book to support the author and the ad-free Holloway reading experience. You get instant digital access, plus future updates.

We wanted flying cars, instead we got 140 characters.Peter Thiel*

Jennifer woke up early on Monday morning. Before going to work, she received a personalized message distilling all information that she needed to know for the day. She walked out of her house and hailed an autonomous car that was waiting for her. As her car rode from her home to her office, Jennifer’s AI assistant briefed her about her day and helped her make some decisions. She arrived at her office in just under ten minutes, going through an underground tunnel.

That’s a future that seems far off, but it might be closer than we think. Deep learning might make most of these predictions reality. It is starting to change the economy and might have a significant economic impact. ARK Invest, an investment firm based in New York, predicts that in 20 years, deep learning will create a $17 trillion market opportunity.* That is bigger than the economic impact that the internet had.

Even though these predictions are far off, deep learning is already having an impact on the world. It is already revolutionizing some fields in artificial intelligence. In the past seven years, machine learning models for vision and language have been completely overtaken by deep learning models. These new models outperform any other “old” artificial intelligence techniques. And every few months, a bigger and newer model outperforms state-of-the-art results.*

In recent years, due to the rapid progress in natural language processing and understanding, the AI community has had to develop new and harder tests for AI capabilities. Models are getting better so fast that researchers have to come up with new benchmarks almost every year.*

We are starting to see deep learning slowly affect our lives. The technology is being added to most major software packages to help people be more productive. Gmail’s Smart Complete is one of them. It helps people write emails faster by auto-completing sentences. Google is adding similar features to other products. With Android 10, Smart Reply was embedded into the operating system.

Other companies are also looking to improve their software with deep learning. Recently, Microsoft featured the work that OpenAI is doing with its language models. It demonstrated that it could automate* some of the work that software engineers do.

These features seem to have a small impact right now, but their effect on our lives will accelerate, and they will have a bigger impact than most predict.

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From self-driving car systems to music recommendation engines, traditional software is slowly being replaced by trained neural networks. That, in turn, increases productivity for software engineers.

Deep learning is not only increasing productivity for software engineers and white-collar workers; other markets are also being disrupted. Transportation will see an increasing influence of artificial intelligence. Currently, there are around 3.5M truckers working in the United States.* With self-driving cars and trucks, most of these jobs will be replaced by computers.

Jobs being replaced doesn’t mean that the economy will implode. With automation, productivity in some areas increases, which frees capital to other areas of the economy. Other sectors of the economy have been growing steadily. For example, consumer spending as a percentage of GDP in food services and recreation services has been growing since the 60s.*

Figure: Spending on leisure and hospitality as percentage of the total economy.*

In a 2017 interview, Marc Andreessen, a famous investor in Silicon Valley, explained that there are two kinds of sectors of the economy: the fast-change sectors and the slow-change sectors.

The fast-change sectors include retail, transportation, and media. They are sectors in which technology has had an enormous impact. There is a massive change in those sectors, and there are massive productivity improvements, which cause gigantic churn in jobs. And at the same time, prices have fallen rapidly.

The other sectors, the slow-change sectors, include healthcare, education, construction, elder care, childcare, and government. In those sectors, the opposite is happening: there is a price crisis. The prices for products and services in these areas are rising fast. The Financial Times* showed that 88% of all the price inflation since 1990 is attributed to healthcare, construction, and education.

Marc Andreessen also stated that the worries of unemployment and job displacement come from the lump of labor fallacy.*

The lump of labor fallacy is the recurrent panic that happens every twenty-five to fifty years over whether the job market pool is fixed, meaning that an influx of workers, such as younger people, immigrants, or machines, will take all the jobs, driving out other workers. This effect never actually happens.

A good example of this fallacy happened with cars. When the automobile went mainstream 100 years ago, the same panic happened that may occur in the future with self-driving cars. At the time, people worried that all jobs for people whose livelihood depended on taking care of horses—everybody running stables, all the blacksmiths—were going to disappear.

But in reality, more jobs were created with the creation of cars. Manufacturing jobs in auto plants became a large sector of the economy. Car companies became such a huge employer that the US government had to bail out these companies in 2008 to keep all their employees working.

Not only that, but there were jobs created to pave streets for cars. A lot of concepts were built from what the creation of cars allowed. The idea of restaurants, motels, hotels, conferences, movie theaters, apartment complexes, office complexes, and suburbs were all expanded after the creation of cars.

The number of jobs created by the second, third, and fourth order effects of the creation of cars was one hundred times the number that disappeared. Marc Andreessen argues that with the creation of new technology, the efficiency of that market goes up, liberating capital that can be invested in other areas.

Others that are more concerned about the lack of innovation than the economic effects of innovation. In a few presentations, Peter Thiel argued that he is far more worried about the lack of good technologies than the danger of evil in technology applications or their consequences.

Peter Thiel argues that there hasn’t been much innovation in past years. For example, he argues that the nuclear industry has been dead for decades, while other promises like cleantech just became toxic words for losing money badly.

If technology has had such an impact on society, then the price of goods would have gone down. But Peter argues that, for example, the price of commodities has not gone down as technology expanded.

In fact, there was a famous bet between two economists, Simon and Ehrlich,* in the 80s. Simon said that the price of commodities would go down in the next decade, while Ehrlich said that it would go up. Simon was right in the 80s, meaning that commodity prices went down in that decade.

But if you look at the next decades, from 1993 to 2003, and 2003 to 2013, commodity prices have gone up, which would show that technology has not had as significant an effect on the economy as some people have predicted.

Peter Thiel stated that most innovation has happened only in the world of bits, and not the world of atoms, and that computers alone can’t do everything. He argued that people are free to do things in the world of bits, and not free to do stuff in the world of things.

But we might start to see the effects in the world of atoms. Battery prices have been falling for years, following Wright’s Law.** Batteries cost around $1,000/kWh in 2010 and have since fallen to around $100/kWh. Solar panel prices have followed the same curve. The cost to decode the human genome has fallen faster than Moore’s Law.* The world of atoms might be at the tipping point of disruption.

Artificial General Intelligence

Detective Del Spooner: Human beings have dreams. Even dogs have dreams, but not you, you are just a machine. An imitation of life. Can a robot write a symphony? Can a robot turn a … canvas into a beautiful masterpiece?

Robot Sonny: Can you?

I, Robot (2004)

Using the past as an indicator of the future, this final chapter addresses how artificial intelligence systems might evolve into artificial general intelligence. It explains the difference between knowing that versus knowing how. And given that the brain is a good indicator of how AI systems evolve, we know that for the animal kingdom there is a high correlation of intelligence to the number of pallial and cortical neurons. The same has been true for deep learning. The higher the number of neurons, the more performant a multilayer neural network is. While artificial neural networks still have a few orders of magnitude less neurons than the human brain, we are marching toward that milestone. Finally, we’ll talk about the Singularity, a point where artificial intelligence might be hard to control.

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