Weekly Learning: Supermoney, Digital Colonialism, and Hard Work

Digital Colonialism

Digital colonialism “is the use of digital technology for political, economic and social domination of another nation or territory.”


…Big Tech is not only global in scope, it is fundamentally colonial in character and dominated by the United States.


…digital colonialism now risks becoming as significant and far-reaching a threat to the Global South as classic colonialism was in previous centuries.

That’s not all:

More broadly, digital colonialism is about entrenching an unequal division of labor, where the dominant powers have used their ownership of digital infrastructure, knowledge, and their control of the means of computation to keep the South in a situation of permanent dependency. This unequal division of labor has evolved. Economically, manufacturing has moved down the hierarchy of value, displaced by an advanced high-tech economy in which the Big Tech firms are firmly in charge.

Digital colonialism:

…has become highly integrated with conventional tools of capitalism and authoritarian governance, from labor exploitation, policy capture, and economic planning to intelligence services, ruling class hegemony, and propaganda.

The result being:

The upshot is, whether you are an individual or a business, if you are using a computer, American companies benefit the most. They own the digital ecosystem.

To summarize:

Instead of sharing knowledge, transferring technology, and providing the building blocks for shared global prosperity on equal terms, the rich countries and their corporations aim to protect their advantage and shake down the South for cheap labor and rent extraction. By monopolizing the core components of the digital ecosystem, pushing their tech in schools and skills training programs, and partnering with corporate and state elites in the South, Big Tech is capturing emerging markets. They will even profit from surveillance services provided to police departments and prisons, all to make a buck.

So what do we do?


In Two economies. Two sets of rules. Tim O’Reilly points out:

Why is Musk so rich? The answer tells us something profound about our economy: he is wealthy because people are betting on him. But unlike a bet in a lottery or at a racetrack, in the vast betting economy of the stock market, people can cash out their winnings before the race has ended.
This is one of the biggest unacknowledged drivers of inequality in America…


In theory, the price of a stock reflects a company’s value as an ongoing source of profit and cash flow. In practice, it is subject to wild booms and busts that are unrelated to the underlying economics of the businesses that shares of stock are meant to represent.

But in reality:

Rich and poor are actually living in two different economies, which operate by different rules. Most ordinary people live in a world where a dollar is a dollar. Most rich people live in a world of what financial pundit Jerry Goodman, writing under the pseudonym Adam Smith, called “supermoney,” where assets have been “financialized” (that is, able to participate in the betting economy) and are valued today as if they were already delivering the decades worth of future earnings that are reflected in their stock price.

The author provides his comment:

…we […] need to put a brake on the betting economy that is creating so much phantom wealth by essentially letting one segment of society borrow from the future while another is stuck in an increasingly impoverished present.
Until we recognize the systemic role that supermoney plays in our economy, we will never make much of a dent in inequality. Simply raising taxes is a bit like sending out firefighters with hoses spraying water while another team is spraying gasoline.

Big problem:

Stock markets have become so central to our imagined view of how the economy is doing that keeping stock prices going up even when companies are overvalued has become a central political talking point. Any government official whose policies cause the stock market to go down is considered to have failed. This leads to poor public policy as well as poor investment decisions by companies and individuals.

Needs big solution:

The tax system could and should become more dynamic rather than more predictable. Imagine if Facebook or Google were to tell us that they couldn’t change their algorithms to address misinformation or spam without upsetting their market and so had to leave abuses in place for decades in the interest of maintaining stability—we’d think they were shirking their duty. So too our policy makers. It’s high time we all recognize the market-shaping role of tax and monetary policy. If we can hold Facebook’s algorithms to account, why can’t we do the same for our government?

How to Work Hard

How to Work Hard proposes that:

There are three ingredients in great work: natural ability, practice, and effort. You can do pretty well with just two, but to do the best work you need all three: you need great natural ability and to have practiced a lot and to be trying very hard.


It’s straightforward to work hard if you have clearly defined, externally imposed goals, as you do in school.

But in real life, the challenge is:

…how to work toward goals that are neither clearly defined nor externally imposed.

Many things in life are difficult to explore:

It can be harder to discover your interests than your talents. There are fewer types of talent than interest, and they start to be judged early in childhood, whereas interest in a topic is a subtle thing that may not mature till your twenties, or even later. The topic may not even exist earlier. Plus there are some powerful sources of error you need to learn to discount. Are you really interested in x, or do you want to work on it because you’ll make a lot of money, or because other people will be impressed with you, or because your parents want you to?

The tricky part is:

Some people figure out what to do as children and just do it, like Mozart. But others, like Newton, turn restlessly from one kind of work to another. Maybe in retrospect we can identify one as their calling — we can wish Newton spent more time on math and physics and less on alchemy and theology — but this is an illusion induced by hindsight bias. There was no voice calling to him that he could have heard.

The implication is that:

…while some people’s lives converge fast, there will be others whose lives never converge. And for these people, figuring out what to work on is not so much a prelude to working hard as an ongoing part of it…

And more challenges along the way:

If you’re working hard but not getting good enough results, you should switch. It sounds simple expressed that way, but in practice it’s very difficult. You shouldn’t give up on the first day just because you work hard and don’t get anywhere. You need to give yourself time to get going. But how much time? And what should you do if work that was going well stops going well? How much time do you give yourself then?
What even counts as good results? That can be really hard to decide.

The answer is bittersweet:

The best test of whether it’s worthwhile to work on something is whether you find it interesting. That may sound like a dangerously subjective measure, but it’s probably the most accurate one you’re going to get.

With a fine print:

For this test to work, though, you have to be honest with yourself.

In conclusion:

Working hard is not just a dial you turn up to 11. It’s a complicated, dynamic system that has to be tuned just right at each point. You have to understand the shape of real work, see clearly what kind you’re best suited for, aim as close to the true core of it as you can, accurately judge at each moment both what you’re capable of and how you’re doing, and put in as many hours each day as you can without harming the quality of the result. This network is too complicated to trick. But if you’re consistently honest and clear-sighted, it will automatically assume an optimal shape, and you’ll be productive in a way few people are.

The Philosophy of AI

The False Philosophy Plaguing AI makes a few interesting points:

…current machine learning models are built on the principle of induction: inferring patterns from specific observations or, more generally, acquiring knowledge from experience. This partially explains the current focus on “big-data” — the more observations, the better the model.


This inductive approach is useful for building tools for specific tasks on well-defined inputs; analyzing satellite imagery, recommending movies, and detecting cancerous cells, for example. But induction is incapable of the general-purpose knowledge creation exemplified by the human mind.

The diagnosis:

Narrowing the focus of scientific inquiry to questions of mechanical problem solving and information processing is to forget that the primary role of science is the search for good explanations. We want to know why, not simply make predictions. Data can corroborate or falsify our theories, but the theories give importance to the data, not vice versa. Theories don’t magically emerge from ever larger datasets. They are creatively conjectured.

Let It Out

Does Venting Your Feelings Actually Help? says:


While letting your negative emotions out may feel good in the moment, science suggests it might make matters worse in the long run.

Anonymous Quotes

7 Famous AI Quotes Explained offer you a few otherwise anonymous quotes you might have heard of but have a hard time remembering.

The Sentiment of UX Design

I helped pioneer UX design. What I see today disturbs me is a recent popular sensation, which has some really good quotes:

Firstly, the essence of UX design doesn’t scale:

Businesses want scaling. And foundational UX work doesn’t scale. It doesn’t lend itself to predictable, repeatable processes and generic cookie-cutter roles. It can’t, because by definition it deals with unknown, slippery, hard-to-define problems that characterize the leading edge of an organically evolving business.

Secondly, traditional production doesn’t afford synthesis:

The same things that make agile a great fit for scaling engineering work—regular sprint tempos; clearly articulated outcomes to be produced; breaking down the complex, unfolding experience of users into concrete elements that can be tied to code—are the very things that make it a terrible fit for foundational UX work. The holism necessary to do foundational UX is antithetical to the assembly-line chunks of user behavior agile requires.

And thirdly, design is inherently political:

Focusing on production-level UX allows organizations to check the “UX” box without having to deal with the messiness that sometimes results when you hire people who are charged with asking questions that have never been asked—questions senior leaders may not know the answers to, or may not want to. The factory floor prefers interchangeable, replaceable parts.


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