Saturday, April 21, 2018

The Ownership of Production That Matters Most

When we imagine the influence of artificial intelligence on future economic activity, does mass unemployment spring to mind? If so, what about calls to redistribute the revenue proceeds of AI? Alas, that approach would not work as imagined, in part because far more than output and additional revenue are involved, in what we as a society now associate with wealth and prosperity. If deep learning AI can't expand production and output via the traditional, easy to measure methods of yesterday's tradable sector dominance, what might this suggest, re ownership of production means?

As AI becomes more closely associated with non tradable sector activity, the experiential nature of our economic connections comes to the fore. Even though economic value is invariably subject to cultural interpretation, there's been a dramatic shift from our wealth associations as they relate to physical goods, to how we experience the use of our time with others. This helps to explain why students remain willing to assume high human capital investment risks, since work involving the mind is the most sought after product of our lifetimes. Nevertheless, the fact we value these forms of interaction so highly, could explain a growing reluctance to share deep learning with AI. But what might happen, should we elect not to do so?

Since AI could greatly reduce the costs of human capital investment, that changes the essential nature of our "necessary" human capital cost equations. Hence it's time for a broader exploration, of the economic priorities which citizens actually hold. Ultimately, the ability of artificial intelligence to absorb deep learning without extensive costs, could still work in everyone's favor. And even though it would no longer be rational to argue for the labour theory of value as associated with time based product, only recall how the dismissal of the labour theory of value for tradable sector production, brought countless goods within the reach of consumers, and led to the prosperity of our time.

Citizens will ultimately need to distinguish between taking part in the experiential wealth of knowledge, versus the monetary reward of doing so on 20th century terms. Even though deep learning AI might (instead) be used to augment individual professional income in the short run, this approach wouldn't broaden service sector activity or the use of knowledge in society. Instead, opting for shared prosperity is a matter of broader knowledge participation for populations as a whole. Indeed, the differences between earlier technology and the deep learning AI of the present, will have to be acknowledged in terms of the implications they hold for total factor productivity.

Additional output is in fact possible with the deep learning of AI, but it would occur via individual additive processes instead of the multiplication of earlier broadcast means. One sided broadcast communication has its place, but by itself is not capable of preserving a complex and dynamic economy. Fortunately, the "lit torch" of knowledge could extend productive economic complexity to the average citizen, with AI as local repositories for coordination and active application. If we are to assume the peer to peer relationship with AI that makes economic inclusion possible, what matters is ownership of local processes for the production of knowledge. Presently, however, citizens don't yet realize how much their input matters, to make this possibility a reality. Without the input of the average person, 21st century wealth creation processes might not go well. Everyone needs to realize what's at stake.

Should knowledge use providers and governments opt to dismiss the possibilities of AI deep learning, there's a gamble involved. As governments gradually confront rising national debt levels, how will they respond in terms of time based service cutbacks? Whether or not governments default on national debts, or whether they manage to reduce budgets before this happens, the outcome would be essentially the same, re today's extensive costs and subsidies for human capital which can't go on indefinitely. Ultimately, it would be advantageous for all of us to pair with AI, to give knowledge provision and application greater economic sustainability for the long run.

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