Intuition Machine
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Intuition Machine

AI, Blockchain and the Decentralization of Work

Photo by Billetto Editorial on Unsplash

I wrote previously about the importance of work and a person’s reason for being:


Here’s the kicker though, there are plenty of work that are simultaneously something people love to do, are good at and the world needs. It is hard to find a shortage of these kinds of work. People love to work on a mission they are passionate about. In the Venn diagram above, it is easy to find the state where “there is delight, fullness but no wealth.”

Here’s the problem, as I wrote in my previous post about the dangers of Artificial Personhood, companies are all very keen to exploit the opportunities to access a labor pool where it costs them next to nothing. A recent Wired article highlights the problem in companies that exploit hackathons:

romance of digital innovation by appealing to the hackers’ aspiration to be multi-dimensional agents of change

Many find hackathons as an enjoyable and meaningful activity. If not, then you wouldn’t find people participating in them. The outcomes of the hackathon tends to not be as important as the learning that is gained through participation. Many have come to the realization that learning is best done by doing and hackathons are events were you force yourself to do something. Hackathons are events where the journey is valued more than the destination.

Companies of course can exploit the passions of their workers to gain greater innovation and productivity. This is not a new thing. One of the largest exploiters of this idea are video game companies. Game developers are likely one of the most passionate workers, however it is not uncommon to find horror stories where game developers tally the amount of times they’ve worked with their paycheck and discover a minimum-wage-equivalent salary.

The modern economic reality is that “The jobs that are meaningful are the ones that pay the least.” David Graeber writes:

In our society, there seems a general rule that, the more obviously one’s work benefits other people, the less one is likely to be paid for it.

This is how an economy that is based on scarcity works. It does not matter how meaningful your work is, you only get lavishly compensate if your work is recognized as being scarce. Actually, more specifically, it is a measure between the asymmetry of information between a buyer and a seller. A buyer who believes the goods or service is important and a seller who knows the cost of delivering said goods or service. One makes a killing when there is a big imbalance between demand and supply (where you hold a monopoly in).

The future of work is being jump started by the Uberization of work. Uber and AirBnb are business models that have discovered that there is an excess of resources that can be exploited to compete against incumbent industries (i.e. Taxis and Hotels). What Uber has discovered is that there’s an excess of workers that prefer the freedom of intermittent employment over the shackles of full-time employment. Most people hate their full time jobs and if given a chance to have more freedom with their time, they’ll take that freedom to pursue their true passions.

The problem however with marketplaces like Uber is that pricing become extremely efficient and therefore the person performing a task doesn’t have much of a premium. In efficient markets, there is less information asymmetry and therefore the provider makes less. Buyers are able to use the wisdom of the crowds to estimate a better price. Sellers don’t have monopolies and therefore have to lower their prices to attract business.

The evolution of Uberization is that tasks will begin to become micro-tasks. If you go to Fiverr you will find simple tasks. What AI practitioners will do is identify the smallest task that a human can do leave everything else to the AI. That task becomes smaller and smaller because the AI learns from the work of the human. Eventually, that work done by a human ceases to exist.

The gig economy is a transitional situation until all these jobs go extinct. Uber will eventual replace all drivers with self-driving cars. Most online jobs such as call centers will be replaced by A.I. assistants. How many of us still use human travel agents to book our vacations? How many use human bank tellers to withdraw cash? How many of us no longer visit brick and mortar stores to buy our goods?

In a gig economy, every individual will require life long learning. Unfortunately no company (or government) cares to foot the bill for your own education. Closer to reality, one would be lucky to even find the time to get an education. When you can allocate time, degree granting institutions will charge you an arm and a leg for you to get the certification to have the right to do a job. Indentured servitude is the most likely path of many people accepting educational loans to pursue their passions.

We have digital automation for decades now. The reason they have not taken away all jobs is because the real world is messy and it requires human intuition to navigate. However, Deep Learning is the discovery of artificial intuition. The human being is now between a rock and a hard place. On one end are advanced rational cognitive systems (expert systems of old) and on the other end is an intuition system that learns from experience. Dual process theory states that we have two cognitive systems, an intuitive and a rational system, that work in coordination. What happens when computers are better in both kinds of cognitive processing?

Elon Musk predicts it’ll take 7 years to achieve Artificial General Intelligence. Based on my expertise, I also think his estimate is plausible. In more clearer terms, anyone younger than 9 years old today, may likely not have a job when they get to 18.

In the future, there will be no real work that humans need to do. However if it is work that defines our reason for being, then we must invent new kinds of work. I will call this new kind of work ‘pretend jobs’. They aren’t by themselves useful, however society will invent systems that convince us that they are indeed useful and valuable. After all, it isn’t the outcome that is important, but the journey.

Bitcoin mining is an early incarnation of this kind of a ‘pretend job’. We pretend that we are mining something that is valuable by having machines compete in a race of solving cryptographic puzzles. It is work that requires a considerable expenditure in hardware, energy and maintenance. This pretend activity is what gives Bitcoin its value. Unlike fiat currency that can be willed into existence by decree, Bitcoin requires someone to make the effort to acquire. The design of Bitcoin is such that it incentivises their miners in exchange for securing the Bitcoin network.

Newer kinds of cryptocurrencies will have different kinds of incentive mechanisms. The best kind of incentive mechanism is the kind that aligns the human to perform meaningful work with the goals of the cryptocurrency (see: Intuition Fabric). You want participants of a cryptocurrency to have “skin in the game” and best way to get that is not through a monetary investment, but one where true effort is sacrificed. In short, you want humans to experience that sacrifice of participation.

All understanding of language is based on what is known as the symbol grounding problem. Words have no meaning unless it is experienced. In the same way, cryptocurrencies have no value unless there is an experience of sacrifice to acquire them. This is why Bitcoin and Ethereum have worked so well.

Real jobs will not exist in the future. This is why the future of work will be driven by ‘pretend jobs’.

Explore Deep Learning: Artificial Intuition: The Improbable Deep Learning Revolution
Exploit Deep Learning: The Deep Learning AI Playbook



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