Harnessing Profit in an AI-Driven Era: Human Labor Can't Compete

The captivating power of generative artificial intelligence (GenAI) is hard to dismiss, yet the ultimate goal in the business world remains maximizing profits. Regardless of GenAI's enticing potential, businesses today are still fueled by the fierce ambition to boost earnings.

One might think that the combination of human labor's affordability and our exceptional skills in various fields would secure our competitive edge amid rapid GenAI advancements. After all, humans are impressively efficient analog machines, consuming only around 150 watts of energy per hour—about what one would burn off eating a simple bowl of porridge. This remarkable efficiency translates to a worldwide average wage of roughly $5 an hour, with some areas offering as little as $1 a day for specific jobs.

But the landscape is changing.

In our rapidly digitalizing environment, where the digital has begun to alter our physical realities, traditional models of economics are becoming obsolete. The digital space brings forth a new array of cost considerations for labor:

1. The cost of creating information
2. The expense associated with processing information through computational means
3. The costs related to distributing information

The first phase of global digitalization introduced microchips, which drastically reduced the cost of computational power. The initial general-purpose programmable computer, known as ENIAC, was a game-changer, outperforming its ancestors by an astounding margin of 5,000 times, capable of calculating a rocket's trajectory in just 30 seconds, compared to the 30 painstaking hours required by human beings.

The second phase of digitalization was characterized by the rise of the Internet, which transformed the way we share data over long distances. Before the Internet's inception, transmitting data was prohibitively expensive. However, with the Internet now at our fingertips, sending emails, streaming videos, and utilizing cloud services has become incredibly budget-friendly. By the early 2000s, the cost to transfer a single bit had dropped to about 2 x 10^-10, making the cost of sending 1 kilobyte cheaper than a stamp. Presently, that cost is virtually negligible.

We now find ourselves on the brink of a third phase of digitalization, heralded by the widespread integration of GenAI. While the first phase drove down marginal costs of computation to nearly zero and the second phase did the same for data dissemination, this new stage is set to annihilate the marginal costs tied to the generation of information.

Therefore, even if human brains and bodies remain cost-effective, GenAI is poised to emerge as the more budget-friendly option in our digital information era.

For those who might doubt this outlook, comprehensive analytics are available in the report from venture firm Andreessen Horowitz titled ' The Economic Case for Generative AI and Foundation Models' “.

The article was written with the  Telegram channel ‘s assistance.

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