"Soak the Rich" is a Truly Dumb Idea, if a Catchy Slogan

Some of us dislike shallow or “bumper sticker slogan” levels of thinking. Economies and societies are very complicated things and we are very bad at understanding all the cause-and-effect interactions from any single public policy, as well intentioned as we might hope to be. 

Consider the oft-repeated desire to enhance societal fairness by taxation policies that “soak the rich (income or wealth, though income normally gets more attention).” To be fair, some countries do so, even if others have tried and failed to see revenue gains. 

If governments need revenue, why not take more from those who have the most? After all, some might argue, that’s why we have a progressive tax system (the tax rate increases as an individual's or entity's taxable income rises) in the first place. 

One might take the same approach to taxing wealth, though that is even more problematic. Perhaps you have read William Hinton’s Fanshen about the social revolutions attempted in a single Chinese village from 1945 to 1948. They took the “soak the rich” approach to wealth. 

Basically, what they discovered is that expropriating all the tangible wealth of the wealthy did not help. 

Land reform was a success in destroying the old social order and empowering the poor politically, but did not solve the deep economic problems in rural China. 

“Soak the rich” (usually phrased as “pay your fair share”) sounds good to many. But it hasn’t worked where it has been tried, simply because capital is mobile.

High-net-worth individuals have the means to move to lower-tax jurisdictions with relative ease:

• France's 2012 supertax of 75 percent on incomes over €1 million saw a well-publicized wave of departures and was quietly abandoned after just two years having raised far less than projected

• Sweden, which once had some of the world's highest marginal rates and a wealth tax, saw significant capital flight and eventually *cut* taxes substantially, including abolishing its wealth tax entirely in 2007, after concluding the tax was destroying more value than it captured

• The UK's 50percent top rate introduced in 2010 was found by HMRC's own analysis to have raised little net revenue; it was reduced to 45percent in 2013

• Some times a one percent increase causes capital flight. 

Asset restructuring also happens. Wealthy individuals employ armies of accountants and attorneys whose entire professional purpose is legal tax minimization. 

Higher marginal rates also reduce the incentive to take on additional risk, start new ventures, or work additional hours. This effect is debated in magnitude, but virtually no serious economist argues it is zero. 

All of these dynamics are captured in the concept of the “Laffer Curve.” There is some tax rate above which additional increases actually reduce total revenue.

Economists debate fiercely where that peak rate sits, with estimates ranging from roughly 50 percent to 70 percent for top marginal income tax rates. 

But set that all aside. Using the United States as an example, what would be the potential impact if none of the above actually happened?

If the government literally confiscated all the income of the top one percent of filers:

• Any benefit is gained but once

• Confiscating all the wealth of the Forbes 400 would fund the federal government for less than one year, and again, only once

• A two-percent annual wealth tax on fortunes over $50 million might raise $200–300 billion per year, a single-digit portion of the federal deficit, at best

• A 70-percent top marginal income tax rate might raise $50 and $300 billion per year, less than five percent of federal spending.

The fundamental arithmetic problem is that there are not enough “one percent” payers or even “top-10-percent payers” to fund a large modern welfare state.

Wealth taxes have been tried and abandoned by Germany, Sweden, France, Finland, Iceland, and others. Even if no behavioral changes occurred, low single digit rates of revenue increase are about the best we might expect to see from a one-percent wealth tax.

And even Switzerland, with a high payer base and low rates for its wealth tax, only generates about three percent of total tax revenue from that source. 

Confiscatory policies cause behavioral changes by the wealthy, gaming the system in lawful ways.  

But even in a fantasy scenario of zero behavioral response and total compliance, the additional revenue from hyper-progressive taxation on the wealthy would make only a modest dent in the fiscal gaps of large modern governments. 

The numbers simply aren't big enough relative to the scale of government spending. There aren't enough rich people.

source: The Tax Foundation

The point is, simple sound bites, catchy slogans and concise bumper stickers are not a substitute for actual thinking about whether policies actually can work. 

“Soak the rich” income or wealth tax policies fall neatly into those categories. They might make you feel good, but do not work in the real world to the extent you might imagine.

Amara's Law and Generative AI Outcomes: Less than You Expect Now; More than You Anticpate Later

Generative artificial intelligence is as likely to show the impact of Amara's Law as any other new technology, which is to say that initial outcomes will be less than we expect, while long-term impact will be greater than we anticipate.

Amara’s Law suggests that we tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.

Source

Amara’s Law seemingly is the thinking behind the Gartner Hype Cycle, for example, which suggests that initial enthusiasm wants when outcomes do not appear, leading to disillusionment and then a gradual appearance of relevant outcomes later. 

lots of other "rules" about technology adoption also testify to the asymmetrical and non-linear outcomes from new technology.  

“Most people overestimate what they can achieve in a year and underestimate what they can achieve in ten years” is a quote whose provenance is unknown, though some attribute it to Standord computer scientist Roy Amara and some people call it “Gate’s Law.”

The principle is useful for technology market forecasters, as it seems to illustrate other theorems including the S curve of product adoption. The expectation for virtually all technology forecasts is that actual adoption tends to resemble an S curve, with slow adoption at first, then eventually rapid adoption by users and finally market saturation.   

That sigmoid curve describes product life cycles, suggests how business strategy changes depending on where on any single S curve a product happens to be, and has implications for innovation and start-up strategy as well. 

source: Semantic Scholar

Some say S curves explain overall market development, customer adoption, product usage by individual customers, sales productivity, developer productivity and sometimes investor interest. 

It often is used to describe adoption rates of new services and technologies, including the notion of non-linear change rates and inflection points in the adoption of consumer products and technologies.

In mathematics, the S curve is a sigmoid function. It is the basis for the Gompertz function which can be used to predict new technology adoption and is related to the Bass Model.

Another key observation is that some products or technologies can take decades to reach mass adoption.

It also can take decades before a successful innovation actually reaches commercialization. The next big thing will have first been talked about roughly 30 years ago, says technologist Greg Satell. IBM coined the term machine learning in 1959, for example, and machine learning is only now in use. 

Many times, reaping the full benefits of a major new technology can take 20 to 30 years. Alexander Fleming discovered penicillin in 1928, it didn’t arrive on the market until 1945, nearly 20 years later.

Electricity did not have a measurable impact on the economy until the early 1920s, 40 years after Edison’s plant, it can be argued.

It wasn’t until the late 1990’s, or about 30 years after 1968, that computers had a measurable effect on the US economy, many would note.

source: Wikipedia

The S curve is related to the product life cycle, as well. 

Another key principle is that successive product S curves are the pattern. A firm or an industry has to begin work on the next generation of products while existing products are still near peak levels. 

source: Strategic Thinker

There are other useful predictions one can make when using S curves. Suppliers in new markets often want to know “when” an innovation will “cross the chasm” and be adopted by the mass market. The S curve helps there as well. 

Innovations reach an adoption inflection point at around 10 percent. For those of you familiar with the notion of “crossing the chasm,” the inflection point happens when “early adopters” drive the market. The chasm is crossed at perhaps 15 percent of persons, according to technology theorist Geoffrey Moore.

source 

For most consumer technology products, the chasm gets crossed at about 10 percent household adoption. Professor Geoffrey Moore does not use a household definition, but focuses on individuals. 

source: Medium

And that is why the saying “most people overestimate what they can achieve in a year and underestimate what they can achieve in ten years” is so relevant for technology products. Linear demand is not the pattern. 

One has to assume some form of exponential or non-linear growth. And we tend to underestimate the gestation time required for some innovations, such as machine learning or artificial intelligence. 

Other processes, such as computing power, bandwidth prices or end user bandwidth consumption, are more linear. But the impact of those linear functions also tends to be non-linear. 

Each deployed use case, capability or function creates a greater surface for additional innovations. Futurist Ray Kurzweil called this the law of accelerating returns. Rates of change are not linear because positive feedback loops exist.

source: Ray Kurzweil  

Each innovation leads to further innovations and the cumulative effect is exponential. 

Think about ecosystems and network effects. Each new applied innovation becomes a new participant in an ecosystem. And as the number of participants grows, so do the possible interconnections between the discrete nodes.  

source: Linked Stars Blog 

Think of that as analogous to the way people can use one particular innovation to create another adjacent innovation. When A exists, then B can be created. When A and B exist, then C and D and E and F are possible, as existing things become the basis for creating yet other new things. 

So we often find that progress is slower than we expect, at first. But later, change seems much faster. And that is because non-linear change is the norm for technology products. So is Amara’s Law.

AI Will Bring Less Change Near Term Than We Think

There are good reasons why generative AI will get commercial traction faster than AR, VR or XR: cost, ease of use and scalability. 

Broadly speaking, the cost to create a commercial use case, at scale, is far easier with generative AI. 

Generative AI is software-based, and can be used with virtually any existing application, to add content creation; support or code-writing tasks to any existing app. That means the time to deploy and cost to deploy--while far from insignificant--can rely on existing app use cases and deployed instances. 

Any form of “Metaverse,” AR, VR or XR apps require new specialized hardware, generally are not “mobility enabled” and also require creation of new apps and ecosystems. That takes time and money. 

So generative AI is easier to create and deploy and easier to use. It requires no new hardware; no new behavioral changes; no new applications. It simply adds features to what already exists. 

Since generative AI is essentially a “bolt on” for existing use cases and apps, it can scale quickly. 

Still, some patience will be required, as at-scale commercial use cases will develop more slowly than most expect, even if AI scales faster than XR, VR or AR and metaverse, for example, though interest in metaverse will return eventually.

I learned early in my career making forecasts that it is better to conservative in the early going. Humans nearly always tend to overestimate the near-term impact of any technology and underestimate the long-term impact. 

“We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run” is one way of stating the principle. So is “We always overestimate the change that will occur in the short term and underestimate the change that will occur in the long term.”

Or, “People overestimate what can be done in one year, and underestimate what can be done in ten.” All three statements capture the wisdom of how significant new technologies create change. 

There is a bit of business wisdom that argues we overestimate what can be done near term, but underestimate the long term impact of important technologies or trends. The reason is that so many trends are an S curve or Sigmoid function. 

Complex system learning curves are especially likely to be characterized by the sigmoid function, since complex systems require that many different processes, actions, habits,  infrastructure and incentives be aligned before an innovation can provide clear benefit. 

source: Rocrastination 

Also, keep in mind that perhaps 70 percent of change efforts fail, the Journal of Change Management has estimated. We might then modify our rules of thumb further, along the lines of “even as 70 percent of innovations fail, we will see less change than we expect in one year and more change than we expect in 10 years.” 

At least in part, technological impact increases over time for reasons of diffusion (what percentage of people use the technology regularly) as well as enculturation (it takes time for people and organizations to figure out how to best use a new technology). 

Impact arguably also increases as the ecosystem grows more powerful, allowing many more things to be done with the core technology.