Writer and filmmaker Astra Taylor explains fauxtomation as "the process that renders invisible human labour to maintain the illusion that machines and systems are smarter than they are." (Faux means false or fake in French).

Taylor gives an example of standing in line to collect food and the person in front of her expressing amazement at how the app knew his order was done 20 minutes early. The server replies, "I sent you a message."

With the rise in incredible abilities driven by AI chatbots and tools, it's tempting to believe that AI can run anything. But, in many situations, claims of AI magic and sophisticated automation disguise work done by people—often outsourced and poorly paid.

An example of fauxtomation is content moderators, whose job is to review and filter objectionable content from the web when the claim is that sophisticated algorithms and AI are doing the work. Other examples are tagging or searching images, transcribing audio, responding to customer queries, making decisions, or, ironically, training machine learning models. In another twist, ordering on the app or touchscreen at McDonalds or the self-checkout at a supermarket, you, as a customer, become the labour of a server or waiter.

Some have argued that automation polarises work into vast quantities of small, menial tasks feeding the machines on one end and sophisticated, high-skilled work in designing and maintaining the machines at the other.

Amazon has long offered its Mechanical Turk service, which crowdsources assignments with a distributed workforce—they called it artificial, artificial intelligence. The original Mechanical Turk was a hoax from the 1700s where a concealed human chess player fooled onlookers into believing a machine was playing.

As a design and UX professional, I've used the Wizard of Oz prototype technique. Named after the infamous wizard secretly controlling impressive machines from behind a curtain, the idea is to give the appearance of a working software prototype by discreetly controlling what people see based on their actions.

AI promises instant automation, but that's rarely the case. As you work to automate and remove inefficiencies, it's natural that systems should be supported by people.  Human-augmented work may be a more appropriate term. In many domains, especially where safety is at stake, such as in self-driving cars, reviewing legal documents, or automated background checks, oversight by people may remain critical.

Alongside the AI revolution, a quiet revolution nearly as significant for our everyday experience is the tying together of different tools for automation. Tools like Zapier, Google Sheets, Notion, and the growth of APIs allow us to automate workflows across systems, saving massive amounts of time and effort.

While the vision of machines reducing our labour persists, history shows that they often drive increased productivity instead of reducing work. This echoes Jevon's paradox, where fuel efficiency gains usually result in increased fuel usage rather than less.

I saw an excellent talk by machine learning and AI expert James Smith, in which, before you try machine learning ("I hear you have Magic Pixie dust. Can I have some?"), he would ask, "Have you tried 50 IF statements?" These programmed rules will be more maintainable, less complex, and result in responses that are easy to understand.

I learned about fauxtomation from Tom Humberstone's remarkable visual essay What Luddites Can Teach us About Resisting an Automated Future in the MIT Technology Review. Fauxtomation is similar in concept to Potemkin AI, a term from researcher Jathan Sadowski.

You can read Astra Taylor's article, The Automation Charade, or watch her discuss fauxtomation at the AI Now 2018 symposium.

Sketchplanations continues to be done by me, by hand. If you'd like to support me, I'm improving my Patreon, which makes such a difference in helping creators like me keep creating. Thank you.

Here's a fauxtomation sketch by ChatGPT in case you're interested =)

As well as fauxtomation prints, you can get AI department prints

Fauxtomation

It's a phrase I always grew up with: you throw rubbish and trash "away" and take what you don't need to "the dump" or "the tip." However, a comment in a nature documentary made me reevaluate:

"You can't throw anything away. There is no 'away'."

Unless we're throwing a leaf or some grass cuttings back on the lawn, it matters where what we've used ends up. It doesn't go away—it goes somewhere, and eventually, that somewhere will catch up with us.

This is why I think "the garbage dump" is better as "the Recycling centre." And we should try to only buy things that we make good use of. And we should try and repair when we can and, if possible, find another home for what we no longer need rather than "dump" it. And why we try to buy recycled products to give a market for those making them.

It can be hard work. It's easier and more fun to bring new, shiny things into my life than to find homes for older, less shiny things. But there is no "away." The effort will pay us back in the long run.

(I don't mean to get preachy about it, it's just the comment has stuck with me from the moment I heard it)

There is no "away"

The Overton window is a simple device to consider the full spectrum of policy ideas and identify the range of politically acceptable ones.

Attitudes generally change slowly, as does policy to support them—with exceptions. Ideas that seemed radical or unthinkable at one time may gradually become plausible and popular. As this happens, the Overton Window shifts or expands.

Consider how attitudes have changed towards slavery, segregation, women's rights, animal welfare or gay marriage. Or consider policies made alongside the development of driverless cars or supporting sustainable energy. At one time, prohibition—banning the production and sale of alcohol—was popular enough to become policy in the US, though it would be unlikely to be proposed now.

Generally, the public is unlikely to elect politicians if they stand on policies too far outside the Overton window. And elected politicians will have difficulty enacting policies outside the window.

Named after Joseph Overton of the Mackinac Center for Public Policy, the original model was a cardboard window that could slide over paper, and looking through the window, you could see policy options in one direction towards more government control and in the other towards less government control. He called it the window of political possibilities. The Centre named it after him after his death. He made the slider vertical to avoid associations with the political left or right.

The Overton Window

Most things increase in demand as they get cheaper — as the price of electric cars comes down we can expect more people to buy more of them. Veblen goods are the opposite. They thrive on exclusivity. Veblen goods are more desirable exactly because they are more expensive. If everyone could buy a Rolex, the vintage whisky, or that supercar, then they wouldn't seem so super anymore. Many luxury items are veblen goods.

The I Am Rich iPhone app briefly sold for $999.99 and displayed only a glowing red gem and a mantra about being rich. Genius/madness.

Veblen goods are named after Thorstein Veblen, coiner of the concept of conspicuous consumption.

Veblen goods

Fungible goods are mutually interchangeable in that, in principle, you could swap in any unit for any other unit of the same type or kind, and it would be equivalent.

Money is a classic fungible good. A $10 bill is equivalent to any other $10 bill, so if you were to borrow $10 from someone, they wouldn't care or expect to get the same $10 bill back.

Non-fungible goods are unique and not mutually interchangeable. Each house, even cookie-cutter houses, is in a unique location with different neighbours, quirks, and histories.

A print of the Mona Lisa on your wall is not the same as having the original from the Louvre. Swapping people between work teams, even people with similar experience, will still result in different outcomes.

Distinguishing Fungible from Non-Fungible Goods

A nice thought experiment to distinguish fungible and non-fungible goods is to ask if, if you were to borrow one, it would make a difference if you returned a different one.

For example, if you were to borrow my car, you could return it to me with a different tank of fuel (a fungible good), and it wouldn't make any difference to me. But if you were to return a different used car (a non-fungible good), that would be rather odd.

If you've heard of NFTs (Non-Fungible Tokens) and would like to understand their flaws and promise better, I recommend the NFT Freakonomics episode of the 3-part series What can Blockchain do for you?

Related Ideas to Fungible and Non-fungible Goods

Fungible - non-fungible

A swan in Europe was a white swan. So, naturally, swans were thought of as only white by Europeans until news came from travelers to Australia in the late 1600s where black swans are in fact relatively common. Black swan events can turn a worldview upside down. They are unprecedented, unexpected, rare, and with a significant impact.

The infrequency of black swan events means prior data about them is too little to reliably guess the likelihood of one, and you may not even think to anticipate the event anyway. Except after the fact, when all of a sudden many people will claim to have predicted it — black swan events are prone to be rationalised in hindsight, believing that all the signs were there beforehand even if they weren't.

The idea of black swan events is from Nassim Nicholas Taleb. He writes:

"One single observation can invalidate a general statement derived from millennia of confirmatory sightings of millions of white swans. All you need is one single black bird."

Examples Taleb gives include the September 11 terrorist attack, the 2008 financial crisis, World War I, or the rise of the Internet.

More Taleb sketches: on mistakes, the firehouse effect, and the Lucretius problem. Also, hindsight bias.

Black swan events

Physics envy refers to the wish that a field of study would turn out to be as precise, as predictable, as repeatable, and as amenable to modeling and theory that replicates practice as physics is.

As it turns out, most fields that involve people — such as economics and the social sciences — are rarely so tidy and orderly and neatly mathematically modeled. They are instead usually messy, uncertain, and filled with context, nuance, history and confounding factors such that making sense of them in broadly applicable ways is rather difficult. And while we do our best to quantify and identify effects, or cleanly isolate our cognitive biases or our quirks of behavioural psychology, rarely are they able to live up to the purest standards of physics. It's enough to turn one green with envy.

It reminded me of the best laid plans of our Saturday sports coaches turning for their substitutes to execute their plans, only to find the players climbing trees in the adjacent field.

Physics Envy

The Gini coefficient, or Gini index, is a common way to gauge income or wealth inequality within a population.

The coefficient gives a single number showing how equal a distribution is. For example, in a population where everyone earns the same amount the Gini coefficient would be 0 — total equality — and in a population where one person earned all of the population's money, the Gini coefficient would be 1 (sometimes expressed as 100%) — maximum inequality. In practice all countries' Gini coefficients are somewhere in between, at around 0.25 to 0.5 after taxes.

Like any simple measure the Gini has its difficulties, like being easily misinterpreted when compared, but is still widely used. The BBC has a short piece on its history and an alternative measure called the Palma ratio.

Of course, we have some high profile zillionaires at the moment. You might like this old sketch of wealth inequality in America.

The Gini coefficient

I've been there. I'm holding some packaging that kind of looks like it ought to be recyclable but isn't one of the standard products that are asked for. I could put it in the bin and then it'll head straight to landfill, or I could put it in the recycling and then it has a chance of being recycled, right? So it often seems better to recycle it in the hope that it might be recovered rather than consign it to certain landfill. This is known as wishcycling.

Sadly, from all that I've read about wishcycling it's not the best approach. When we put in non-recyclables into a recycling bin it contaminates the high quality recyclable materials and several things can happen:

A lot of dry recycling is still manually sorted in Materials Recovery Facilities (MRFs). When the quality of the input goes down it can require more people to sort it. It's not a pretty job if it also contains items that are contaminated with food waste

Non-recyclable items that processing machines aren't designed for can damage them and mean costly maintenance

Items like batteries that make it into regular recycling can start fires in paper bales which is dangerous and expensive

Non-recyclables mixed in can mean a lower quality end product to use to turn into new products. This can reduce what it can be used for and make it harder to sell

Paying more pickers and sorters and servicing machines costs money, and selling lower quality products brings less in, both of which reduce the profitability and potential viability of recycling operations

So the advice I have learned to take on board is:

Counterintuitively, if we want to recycle more, it seems at the moment we have to recycle less.

And even better is to reduce and reuse where possible in the first place.

Wishcycling

How small changes in customer demand can produce considerable disruption upstream in the supply chain. For instance, a sudden surge in the buying of toilet paper may cause retailers to put in larger orders to make sure they can keep up with demand. Manufacturers, in turn, may put in even larger orders for raw materials to make sure they don’t get caught out. In the meantime, customers had bought all the toilet paper they ever needed and demand suddenly dropped causing retailers to cancel all their orders, manufacturers to halt production and suppliers to be left with big stockpiles of unwanted raw materials.

I learned about the bullwhip effect from Tomas Tunguz where he considers economic effects of the coronavirus. He explains it with a brewery example in his article on proxy metrics for startups.

The Bullwhip Effect

This simple model for the progression of ideas is sometimes called a dialectic. It has many philosophical roots, but regardless, it’s a nice way to think of how ideas and perhaps society can progress. Something is created or put forward. It has some benefits but gives rise to negative effects, and somehow, we figure out a resolution as progress. That new solution then becomes the basis for the next step.

In engineering, I learned that we progress by resolving contradictions. As a very simple example, it’s nice to have a walking stick. But a full-length walking stick is difficult to transport. You want it to be long when you’re walking and short when you’re travelling with it. So, we invent retractable ones. But retractable ones break more easily, so we invent…to be determined.

You also see a similar idea in stories. See the story spine.

It’s sometimes erroneously called Hegel’s dialectic, perhaps partly because Hegel’s work was sometimes so difficult to grasp, and this is nicely simple.

Thesis, antithesis, synthesis

The law of diminishing returns: at some point, doing more of the same stops paying off like it used to.

You might think that adding more of a good thing would make things better. And, yes, that’s often true, up to a point. However, the law of diminishing returns suggests that as you add more of a single factor, you are likely to yield progressively lower benefits.

It’s like the old adage, “Too many cooks spoil the broth.” Another cook or two might help, but beyond that, they’ll probably start getting in each other’s way. Aside from cooking broth, you might see the law play out in spending on advertisements, quality improvements in production or your wealth.

And we should also never forget that more is different.

The sketchplanation is an homage to perhaps my favourite comedy sketch with an early-career Simon Pegg in the series Big Train: The Cake Factory (video). Well worth two and a half minutes.

I updated this sketch for the release of Big Ideas Little Pictures. Here's the original

Law of diminishing returns

The story goes something like this. Back in colonial India the top Brit in charge decided there were too many cobras around Delhi. To reduce the population they put in place a cash reward, or bounty, for anyone who brought in a dead cobra. The intention was clear. Legend has it that people did bring in the cobras reliably because some enterprising souls had started breeding cobras for the very purpose of getting the bounty. When the authorities realised this they scrapped the scheme, the cobra farms closed and the bred cobras were released into the wild significantly increasing the cobra population by a few orders of magnitude. Hence, the cobra effect: when a well-intentioned measure can have the opposite effect to that desired.

It’s not clear how true this story is, but the cobra effect has been demonstrated many times. The Freakonomics podcast on the cobra effect discusses some excellent examples (with references), including:

A bounty on rats in French colonial Hanoi — where the bounty was earned by bringing in a rat tail — which went the same way as the cobra story

Removing wild pigs at Fort Benning, Georgia, at $40 a pigs tail that didn’t make a dent. It could be the hunters would bait the pigs with all sorts of tempting scraps. Many pigs learned to avoid the hunters and instead bred like crazy while well-fed on scraps.

The capital of Colombia, Bogotá, tried to reduce traffic congestion by restricting each car from driving one day during the week. The result: people started buying more than 1 car and did more driving on weekends.

Publication of proposed areas of habitat to be protected for endangered species meant land developers responded by building in those areas before the land was protected.

People are smart. Incentives work—in the sense that you tend to get what you incentivise. So be careful what you are incentivising. As Charlie Munger said: "Show me the incentive, and I'll show you the outcome."

The cobra effect

I grew up with the idea that there are developed countries and developing countries, the “rich” world and the “poor” world. I didn’t actively consider this teaching. It’s just always been there. And it’s wrong.

In his excellent book Factfulness, Hans Rosling shows that wealthier countries tend to have smaller families and a low child mortality rate. In contrast, poorer countries are more likely to have larger families and a high child mortality rate.

Plotting the spread of countries in the 1960s gives a reasonable approximation of a cluster of “developed” countries with small families where most children survive and a cluster of “developing” countries with larger families where more children die. But that was in the ’60s.

The world has changed a lot since then, and that model of dividing the world into two buckets no longer fits the data. Most people in the world are somewhere in the middle.

And more helpful than dismantling a worldview is providing a new model to replace it: a model with four income levels instead of two. The numbers reflect most people’s reality, with most of us somewhere in the middle.

Related Ideas to A world of Four Income Levels

For more, check out the Factfulness book, or Gapminder. And you could do much worse than watch Hans’ entertaining TED talk: the best stats you’ve ever seen.

A world of 4 income levels

Jevon's Paradox is the counterintuitive idea that improvements in fuel efficiency tend to increase, rather than decrease, overall fuel use.

In 1865, William Stanley Jevons, a British economist, observed that despite significant improvements in coal efficiency in steam engines, total coal consumption actually rose. Rather than saving fuel, efficiency made coal-powered energy more economical, driving increased industrial use. This observation became known as Jevon's Paradox.

Though first identified towards the end of the Industrial Revolution, Jevon's Paradox is still highly relevant today, particularly in discussions around energy efficiency, sustainability, and technology.

Fuel-efficient cars – When vehicles become more fuel-efficient, the cost per mile driven decreases. As a result, people tend to drive more, offsetting the efficiency gains.

AI and computing power – Advances in AI and computing efficiency reduce the cost of processing power, leading to increased overall usage of AI models and cloud computing.

Battery technology – More efficient batteries lead to the proliferation of battery-powered devices rather than reducing energy consumption.

Water-saving devices – Low-flow showerheads and toilets may save water per use, but in some cases, they lead to longer showers or more frequent flushing.

Lighting — Though LED lights are vastly more efficient than their predecessors, we are continually finding new ways to use our low-wattage lights that we had never dreamed of before.

Household appliances – In her book More Work for Mother, Ruth Cowan addresses the ironies of household appliances, such as washing machines and vacuum cleaners, and how improvements in efficiency often led to more work through more frequent washing, not less.

This tendency is sometimes referred to as the rebound effect, where changes in behaviour partially or fully cancel out expected energy or resource savings.

With the rise of AI and automation, Jevon's Paradox is playing out again in new ways:

Computational efficiency – Though AI models are becoming more efficient—DeepSeek a case in point—the demand for AI-generated content, large language models, and deep learning applications is growing rapidly.

Automated processes – Instead of reducing overall workloads, automation often leads to more tasks being completed rather than fewer tasks needing to be done.

Cloud computing – More efficient data centres have not reduced overall energy use because the demand for cloud services and machine learning applications continues to skyrocket.

Understanding Jevon's Paradox can help in designing better policies and strategies to ensure efficiency gains lead to real reductions in resource consumption. Some approaches include:

Carbon pricing and regulation – Ensuring that energy efficiency is paired with policies that discourage excessive consumption. (see Goodhart's Law)

Shifting behaviors – Encouraging individuals and businesses to actively reduce energy use, rather than simply taking advantage of efficiency improvements.

Technology with constraints – Implementing smart grid systems, time-based energy pricing, and consumption limits to prevent unchecked increases in demand.

Jevon's Paradox reminds us that efficiency alone is not a guaranteed solution for reducing resource consumption. Instead, it often requires complementary policies, incentives, and behavior changes to make efficiency gains truly effective.

The Automation Paradox – The better the machines get, the more we struggle when they fail

Wishcycling — Hoping something can be recycled

Save some landfill with reusable diapers/nappies

Jevon’s Paradox

There don’t seem to perfect definitions for either recessions or depressions. A recession is commonly defined as two successive declining quarters of GDP, but it’s not gospel. And a depression is a kind of prolonged recession for 3–4 years. So I liked Harry Truman’s clarifying explanation — US president from 1945–1953:

A recession is when your neighbour loses his job.  A depression is when you lose yours.  —Harry Truman 

Recession vs depression

This is surely at least part of the reason that “software is eating the world.”

But I’m sure coffee will stick around for a bit too.

Economics sketches