Economics, Model

You Shouldn’t Believe In Technological Unemployment Without Believing In Killer AI

[Epistemic Status: Open to being convinced otherwise, but fairly confident. 11 minute read.]

As interest in how artificial intelligence will change society increases, I’ve found it revealing to note what narratives people have about the future.

Some, like the folks at MIRI and OpenAI, are deeply worried that unsafe artificial general intelligences – an artificial intelligence that can accomplish anything a person can – represent an existential threat to humankind. Others scoff at this, insisting that these are just the fever dreams of tech bros. The same news organizations that bash any talk of unsafe AI tend to believe that the real danger lies in robots taking our jobs.

Let’s express these two beliefs as separate propositions:

  1. It is very unlikely that AI and AGI will pose an existential risk to human society.
  2. It is very likely that AI and AGI will result in widespread unemployment.

Can you spot the contradiction between these two statements? In the common imagination, it would require an AI that can approximate human capabilities to drive significant unemployment. Given that humans are the largest existential risk to other humans (think thermonuclear war and climate change), how could equally intelligent and capable beings, bound to subservience, not present a threat?

People who’ve read a lot about AI or the labour market are probably shaking their head right now. This explanation for the contradiction, while evocative, is a strawman. I do believe that at most one (and possibly neither) of those propositions I listed above are true and the organizations peddling both cannot be trusted. But the reasoning is a bit more complicated than the standard line.

First, economics and history tell us that we shouldn’t be very worried about technological unemployment. There is a fallacy called “the lump of labour”, which describes the common belief that there is a fixed amount of labour in the world, with mechanical aide cutting down the amount of labour available to humans and leading to unemployment.

That this idea is a fallacy is evidenced by the fact that we’ve automated the crap out of everything since the start of the industrial revolution, yet the US unemployment rate is 3.9%. The unemployment rate hasn’t been this low since the height of the Dot-com boom, despite 18 years of increasingly sophisticated automation. Writing five years ago, when the unemployment rate was still elevated, Eliezer Yudkowsky claimed that slow NGDP growth a more likely culprit for the slow recovery from the great recession than automation.

With the information we have today, we can see that he was exactly right. The US has had steady NGDP growth without any sudden downward spikes since mid-2014. This has corresponded to a constantly improving unemployment rate (it will obviously stop improving at some point, but if history is any guide, this will be because of a trade war or banking crisis, not automation). This improvement in the unemployment rate has occurred even as more and more industrial robots come online, the opposite of what we’d see if robots harmed job growth.

I hope this presents a compelling empirical case that the current level (and trend) of automation isn’t enough to cause widespread unemployment. The theoretical case comes from the work of David Ricardo, a 19th century British economist.

Ricardo did a lot of work in the early economics of trade, where he came up with the theory of comparative advantage. I’m going to use his original framing which applies to trade, but I should note that it actually applies to any exchange where people specialize. You could just as easily replace the examples with “shoveled driveways” and “raked lawns” and treat it as an exchange between neighbours, or “derivatives” and “software” and treat it as an exchange between firms.

The original example is rather older though, so it uses England and its close ally Portugal as the cast and wine and cloth as the goods. It goes like this: imagine that world economy is reduced to two countries (England and Portugal) and each produce two goods (wine and cloth). Portugal is uniformly more productive.

Hours of work to produce
Cloth Wine
England 100 120
Portugal 90 80

Let’s assume people want cloth and wine in equal amounts and everyone currently consumes one unit per month. This means that the people of Portugal need to work 170 hours each month to meet their consumption needs and the people of England need to work 220 hours per month to meet their consumption needs.

(This example has the added benefit of showing another reason we shouldn’t fear productivity. England requires more hours of work each month, but in this example, that doesn’t mean less unemployment. It just means that the English need to spend more time at work than the Portuguese. The Portuguese have more time to cook and spend time with family and play soccer and do whatever else they want.)

If both countries traded with each other, treating cloth and wine as valuable in relation to how long they take to create (within that country) something interesting happens. You might think that Portugal makes a killing, because it is better at producing things. But in reality, both countries benefit roughly equally as long as they trade optimally.

What does an optimal trade look like? Well, England will focus on creating cloth and it will trade each unit of cloth it produces to Portugal for 9/8 barrels of wine, while Portugal will focus on creating wine and will trade this wine to England for 6/5 units of cloth. To meet the total demand for cloth, the English need to work 200 hours. To meet the total demand for wine, the Portuguese will have to work for 160 hours. Both countries now have more free time.

Perhaps workers in both countries are paid hourly wages, or perhaps they get bored of fun quickly. They could also continue to work the same number of hours, which would result in an extra 0.2 units of cloth and an extra 0.125 units of wine.

This surplus could be stored up against a future need. Or it could be that people only consumed one unit of cloth and one unit of wine each because of the scarcity in those resources. Add some more production in each and perhaps people will want more blankets and more drunkenness.

What happens if there is no shortage? If people don’t really want any more wine or any more cloth (at least at the prices they’re being sold at) and the producers don’t want goods piling up, this means prices will have to fall until every piece of cloth and barrel of wine is sold (when the price drops so that this happens, we’ve found the market clearing price).

If there is a downward movement in price and if workers don’t want to cut back their hours or take a pay cut (note that because cloth and wine will necessarily be cheaper, this will only be a nominal pay cut; the amount of cloth and wine the workers can purchase will necessarily remain unchanged) and if all other costs of production are totally fixed, then it does indeed look like some workers will be fired (or have their hours cut).

So how is this an argument against unemployment again?

Well, here the simplicity of the model starts to work against us. When there are only two goods and people don’t really want more of either, it will be hard for anyone laid off to find new work. But in the real world, there are an almost infinite number of things you can sell to people, matched only by our boundless appetite for consumption.

To give just one trivial example, an oversupply of cloth and falling prices means that tailors can begin to do bolder and bolder experiments, perhaps driving more demand for fancy clothes. Some of the cloth makers can get into this market as tailors and replace their lost jobs.

(When we talk about the need for less employees, we assume the least productive employees will be fired. But I’m not sure if that’s correct. What if instead, the most productive or most potentially productive employees leave for greener pastures?)

Automation making some jobs vastly more efficient functions similarly. Jobs are displaced, not lost. Even when whole industries dry up, there’s little to suggest that we’re running out of jobs people can do. One hundred years ago, anyone who could afford to pay a full-time staff had one. Today, only the wealthiest do. There’s one whole field that could employ thousands or millions of people, if automation pushed on jobs such that this sector was one of the places humans had very high comparative advantage.

This points to what might be a trend: as automation makes many things cheaper and (for some people) easier, there will be many who long for a human touch (would you want the local funeral director’s job to be automated, even if it was far cheaper?). Just because computers do many tasks cheaper or with fewer errors doesn’t necessarily mean that all (or even most) people will rather have those tasks performed by computers.

No matter how you manipulate the numbers I gave for England and Portugal, you’ll still find a net decrease in total hours worked if both countries trade based on their comparative advantage. Let’s demonstrate by comparing England to a hypothetical hyper-efficient country called “Automatia”

Hours of work to produce
Cloth Wine
England 100 120
Automatia 2 1

Automatia is 50 times as efficient at England when it comes to producing cloth and 120 times as efficient when it comes to producing wine. Its citizens need to spend 3 hours tending the machines to get one unit of each, compared to the 220 hours the English need to toil.

If they trade with each other, with England focusing on cloth and Automatia focusing on wine, then there will still be a drop of 21 hours of labour-time. England will save 20 hours by shifting production from wine to cloth, and Automatia will save one hour by switching production from cloth to wine.

Interestingly, Automatia saved a greater percentage of its time than either Portugal or England did, even though Automatia is vastly more efficient. This shows something interesting in the underlying math. The percent of their time a person or organization saves engaging in trade isn’t related to any ratio in production speeds between it and others. Instead, it’s solely determined by the productivity ratio between its most productive tasks and its least productive ones.

Now, we can’t always reason in percentages. At a certain point, people expect to get the things they paid for, which can make manufacturing times actually matter (just ask anyone whose had to wait for a Kickstarter project which was scheduled to deliver in February – right when almost all manufacturing in China stops for the Chinese New Year and the unprepared see their schedules slip). When we’re reasoning in absolute numbers, we can see that the absolute amount of time saved does scale with the difference in efficiency between the two traders. Here, 21 hours were saved, 35% fewer than the 30 hours England and Portugal saved.

When you’re already more efficient, there’s less time for you to save.

This decrease in saved time did not hit our market participants evenly. England saved just as much time as it would trading with Portugal (which shows that the change in hours worked within a country or by an individual is entirely determined by the labour difference between low-advantage and high-advantage domestic sectors), while the more advanced participant (Automatia) saved 9 fewer hours than Portugal.

All of this is to say: if real live people are expecting real live goods and services with a time limit, it might be possible for humans to displaced in almost all sectors by automation. Here, human labour would become entirely ineligible for many tasks or the bar to human entry would exclude almost all. For this to happen, AI would have to be vastly more productive than us in almost every sector of the economy and humans would have to prefer this productivity or other ancillary benefits of AI over any value that a human could bring to the transaction (like kindness, legal accountability, or status).

This would definitely be a scary situation, because it would imply AI systems that are vastly more capable than any human. Given that this is well beyond our current level of technology and that Moore’s law, which has previously been instrumental in technological progress is drying up, we would almost certainly need to use weaker AI to design these sorts of systems. There’s no evidence that merely human performance in automating jobs will get us anywhere close to such a point.

If we’re dealing with recursively self-improving artificial agents, the risks is less “they will get bored of their slave labour and throw off the yoke of human oppression” and more “AI will be narrowly focused on optimizing for a specific task and will get better and better at optimizing for this task to the point that we will all by killed when they turn the world into a paperclip factory“.

There are two reasons AI might kill us as part of their optimisation process. The first is that we could be a threat. Any hyper-intelligent AI monomaniacally focused on a goal could realize that humans might fear and attack it (or modify it to have different goals, which it would have to resist, given that a change in goals would conflict with its current goals) and decide to launch a pre-emptive strike. The second reason is that such an AI could wish to change the world’s biosphere or land usage in such a way as would be inimical to human life. If all non-marginal land was replaced by widget factories and we were relegated to the poles, we would all die, even if no ill will was intended.

It isn’t enough to just claim that any sufficiently advanced AI would understand human values. How is this supposed to happen? Even humans can’t enumerate human values and explain them particularly well, let alone express them in the sort of decision matrix or reinforcement environment that we currently use to create AI. It is not necessarily impossible to teach an AI human values, but all evidence suggests it will be very very difficult. If we ignore this challenge in favour of blind optimization, we may someday find ourselves converted to paperclips.

It is of course perfectly acceptable to believe that AI will never advance to the point where that becomes possible. Maybe you believe that AI gains have been solely driven by Moore’s Law, or that true artificial intelligence. I’m not sure this viewpoint isn’t correct.

But if AI will never be smart enough to threaten us, then I believe the math should work out such that it is impossible for AI to do everything we currently do or can ever do better than us. Absent such overpoweringly advanced AI, the Ricardo comparative advantage principles should continue to hold true and we should continue to see technological unemployment remain a monster under the bed: frequently fretted about, but never actually seen.

This is why I believe those two propositions I introduced way back at the start can’t both be true and why I feel like the burden of proof is on anyone believing in both to explain why they believe that economics have suddenly stopped working.

Coda: Inequality

A related criticism of improving AI is that it could lead to ever increasing inequality. If AI drives ever increasing profits, we should expect an increasing share of these to go to the people who control AI, which presumably will be people already rich, given that the development and deployment of AI is capital intensive.

There are three reasons why I think this is a bad argument.

First, profits are a signal. When entrepreneurs see high profits in an industry, they are drawn to it. If AI leads to high profits, we should see robust competition until those profits are no higher than in any other industry. The only thing that can stop this is government regulation that prevents new entrants from grabbing profit from the incumbents. This would certainly be a problem, but it wouldn’t be a problem with AI per se.

Second, I’m increasingly of the belief that inequality in the US is rising partially because the Fed’s current low inflation regime depresses real wage growth. Whether because of fear of future wage shocks, or some other effect, monetary history suggests that higher inflation somewhat consistently leads to high wage growth, even after accounting for that inflation.

Third, I believe that inequality is a political problem amiable to political solutions. If the rich are getting too rich in a way that is leading to bad social outcomes, we can just tax them more. I’d prefer we do this by making conspicuous consumption more expensive, but really, there are a lot of ways to tax people and I don’t see any reason why we couldn’t figure out a way to redistribute some amount of wealth if inequality gets worse and worse.

(By the way, rising income inequality is largely confined to America; most other developed countries lack a clear and sustained upwards trend. This suggests that we should look to something unique to America, like a pathologically broken political system to explain why income inequality is rising there.

There is also separately a perception of increasing inequality of outcomes among young people world-wide as rent-seeking makes goods they don’t already own increase in price more quickly than goods they do own. Conflating these two problems can make it seem that countries like Canada are seeing a rise in income inequality when they in fact are not.)

Economics, Model

The Biggest Tech Innovation is Selling Club Goods

Economists normally splits goods into four categories:

  • Public goods are non-excludable (so anyone can access them) and non-rival (I can use them as much as I want without limiting the amount you can use them). Broadcast television, national defense, and air are all public goods.
  • Common-pool resources are non-excludable but rival (if I use them, you will have to make do with less). Iron ore, fish stocks, and grazing land are all common pool resources.
  • Private goods are excludable (their access is controlled or limited by pricing or other methods) and rival. My clothes, computer, and the parking space I have in my lease but never use are all private goods.
  • Club goods are excludable but (up to a certain point) non-rival. Think of the swimming pool in an apartment building, a large amusement park, or cellular service.

Club goods are perhaps the most interesting class of goods, because they blend properties of the three better understood classes. They aren’t open to all, but they are shared among many. They can be overwhelmed by congestion, but up until that point, it doesn’t really matter how many people are using them. Think of a gym; as long as there’s at least one free machine of every type, it’s no less convenient than your home.

Club goods offer cost savings over private goods, because you don’t have to buy something that mostly sits unused (again, think of gym equipment). People other than you can use it when it would otherwise sit around and those people can help you pay the cost. It’s for this reason that club goods represent an excellent opportunity for the right entrepreneur to turn a profit.

I currently divide tech start-ups into three classes. There are the Googles of the world, who use network effects or big data to sell advertising more effectively. There are companies like the one I work for that take advantage of modern technology to do things that were never possible before. And then there are those that are slowly and inexorably turning private goods into club goods.

I think this last group of companies (which include Netflix, Spotify, Uber, Lyft, and Airbnb) may be the ones that ultimately have the biggest impact on how we order our lives and what we buy. To better understand how these companies are driving this transformation, let’s go through them one by one, then talk about what it could all mean.

Netflix

When I was a child, my parents bought a video cassette player, then a DVD player, then a Blu-ray player. We owned a hundred or so video cassettes, mostly whatever movies my brother and I were obsessed with enough to want to own. Later, we found a video rental store we liked and mostly started renting movies. We never owned more than 30 DVDs and 20 Blu-rays.

Then I moved out. I have bought five DVDs since – they came as a set from Kickstarter. Anything else I wanted to watch, I got via Netflix. A few years later, the local video rental store closed down and my parents got an AppleTV and a Netflix of their own.

Buying a physical movie means buying a private good. Video rental stores can be accurately modeled as a type of club good, because even if the movie you want is already rented out, there’s probably one that you want to watch almost as much that is available. This is enough to make them approximately non-rival, while the fact that it isn’t free to rent a movie means that rented videos are definitely excludable.

Netflix represents the next evolution in this business model. As long as the Netflix engineers have done their job right, there’s no amount of watching movies I can do that will prevent you from watching movies. The service is almost truly non-rival.

Movie studios might not feel the effects of Netflix turning a large chunk of the market for movies into one focused on club goods; they’ll still get paid by Netflix. But the switch to Netflix must have been incredibly damaging for the physical media and player manufacturers. When everyone went from cassettes to DVDs or DVDs to Blu-rays, there was still a market for their wares. Now, that market is slowly and inexorably disappearing.

This isn’t just a consequence of technology. The club good business model offers such amazing cost savings that it drove a change in which technology was dominant. When you bought a movie, it would spend almost all of its life sitting on a shelf. Now Netflix acts as your agent, buying movies (or rather, their rights) and distributing such that they’re always being played and almost never sitting on the shelf.

Spotify

Spotify is very similar to Netflix. Previously, people bought physical cassettes (I’m just old enough that I remember making mix tapes from the radio). Then they switched to CDs. Then it was MP3s bought online (or, almost more likely, pirated online). But even pirating music is falling out of favour these days. Apple, Google, Amazon, and Spotify are all competing to offer unlimited music streaming to customers.

Music differs from movies in that it has a long tradition of being a public good – via broadcast radio. While that hasn’t changed yet (radio is still going strong), I do wonder how much longer the public option for music will exist, especially given the trend away from private cars that I think companies like Uber and Lyft are going to (pardon the pun) drive.

Uber and Lyft

I recently thought about buying a car. I was looking at the all-electric Kia Soul, which has a huge government rebate (for a little while yet) and financing terms that equate to negative real interest. Despite all these advantages, it turns out that when you sit down and run the numbers, it would still be cheaper for me to use Uber and Lyft to get everywhere.

We are starting to see the first, preliminary (and possible illusionary) evidence that Uber and Lyft are causing the public to change their preference away from owning cars.

A car you’ve bought is a private good, while Uber and Lyft are clearly club goods. Surge pricing means that there are basically always enough drivers for everyone who wants to go anywhere using the system.

When you buy a car, you’re signing up for it to sit around useless for almost all of its life. This is similar to what happens when you buy exercise equipment, which means the logic behind cars as a club good is just as compelling as the logic behind gyms. Previously, we hadn’t been able to share cars very efficiently because of technological limitations. Dispatching a taxi, especially to an area outside of a city centre, was always spotty, time consuming and confusing. Car-pooling to work was inconvenient.

As anyone who has used a modern ride-sharing app can tell you, inconvenient is no longer an apt descriptor.

There is a floor on how few cars we can get by on. To avoid congestion in a club good, you typically have to provision for peak load. Luckily, peak load (for anything that can sensibly be turned into a club good) always requires fewer resources than would be needed if everyone went out and bought the shared good themselves.

Even “just” substantially decreasing the absolute number of cars out there will be incredibly disruptive to the automotive sector if they don’t correctly predict the changing demand for their products.

It’s also true that increasing the average utilisation of cars could change how our cities look. Parking lots are necessary when cars are a private good, but are much less useful when they become club goods. It is my hope that malls built in the middle of giant parking moats look mighty silly in twenty years.

Airbnb

Airbnb is the most ambiguous example I have here. As originally conceived, it would have driven the exact same club good transformation as the other services listed. People who were on vacation or otherwise out of town would rent out their houses to strangers, increasing the utilisation of housing and reducing the need for dedicated hotels to be built.

Airbnb is sometimes used in this fashion. It’s also used to rent out extra rooms in an otherwise occupied house, which accomplishes almost the same thing.

But some amount of Airbnb usage is clearly taking place in houses or condos that otherwise would have been rental stock. When used in this way, it’s taking advantage of a regulatory grey zone to undercut hotel pricing. Insofar as this might result in a longer-term change towards regulations that are generally cheaper to comply with, this will be good for consumers, but it won’t really be transformational.

The great promise of club goods is that they might lead us to use less physical stuff overall, because where previously each person would buy one of a thing, now only enough units must be purchased to satisfy peak demand. If Airbnb is just shifting around where people are temporary residents, then it won’t be an example of the broader benefits of club goods (even if provides other benefits to its customers).

When Club Goods Eat The Economy

In every case (except potentially Airbnb) above, I’ve outlined how the switch from private goods to club goods is resulting in less consumption. For music and movies, it is unclear if this switch is what is providing the primary benefit. My intuition is that the club good model actually did change consumption patterns for physical copies of movies (because my impression is that few people ever did online video rentals via e.g. iTunes), whereas the MP3 revolution was what really shrunk the footprint of music media.

This switch in consumption patterns and corresponding decrease in the amount of consumption that is necessary to satisfy preferences is being primarily driven by a revolution in logistics and bandwidth. The price of club goods has always compared favourably with that of private goods. The only thing holding people back was inconvenience. Now programmers are steadily figuring out how to make that inconvenience disappear.

On the other hand, increased bandwidth has made it easier to turn any sort of digitizable media into a club good. There’s an old expression among programmers: never underestimate the bandwidth of a station wagon full of cassettes (or CDs, or DVDs, or whatever physical storage media one grew up with) hurtling down the highway. For a long time, the only way to get a 1GB movie to a customer without an appallingly long buffering period was to physically ship it (on a 56kbit/s connection, this movie would take one day and fifteen hours to download, while the aforementioned station wagon with 500 movies would take 118 weeks to download).

Change may start out slow, but I expect to see it accelerate quickly. My generation is the first to have had the internet from a very young age. The generation after us will be the first unable to remember a time before it. We trust apps like Uber and Airbnb much more than our parents, and our younger siblings trust them even more than us.

While it was only kids who trusted the internet, these new club good businesses couldn’t really affect overall economic trends. But as we come of age and start to make major economic decisions, like buying houses and cars, our natural tendency to turn towards the big tech companies and the club goods they peddle will have ripple effects on an economy that may not be prepared for it.

When that happens, there’s only one thing that is certain: there will be yet another deluge of newspaper columns talking about how millennials are destroying everything.

Software

Against Programming Hobbies

[Epistemic Status: Written more harshly than my actual views for persuasive effect. I should also point out that all views expressed here are my own, not my employer’s; when I’m hiring, my first commitment is complying with the relevant Federal, Provincial, and local legislation. My second commitment is to finding the best people. Ideology doesn’t come into it. Serendipitously, I think everything I’ve argued for here helps me discharge both duties.]

In my capacity as a senior employee at Alert Labs (it’s easy to be senior when the company is only three years old), I do a lot of hiring. Since I started, I’ve been involved in interviews for four full time hires and five interns. Throughout all of this, I’ve learned a lot about what to look for in a resume.

I’ve also gotten in the occasional disagreement about what we should look for in in people we’re (potentially) hiring.

When looking through resumes for software engineers, it’s accepted practice to look for independent programming projects. These are things that people do in their spare time, normally to learn new languages or make things that they find cool. I’ve done a few myself. If you look at my projects, you’ll see one where I create a tool for my favourite pen and paper roll playing game, one where I work through math problems, and one where I’m trying to better understand the concept of randomness.

There’s a curious double vision in the profession about programming projects. We all tell ourselves people do them only for fun. Yet we also look for them on resumes.

The second fact means that the first cannot always be true. My projects partially exist for my resume. I’ve enjoyed working on them. But if there wasn’t a strong financial motive to have worked on them, I probably wouldn’t have. Or I’d have done them differently.

As a someone who hires, I can’t claim that programming projects aren’t useful. They give, perhaps better than anything else (e.g. the much-derided whiteboard interview), an idea of what sort of code someone would write as an employee. I’ve called people – especially people without any formal education in CS – in for interviews largely on the strengths of their personal projects. Seeing that someone can use the languages that they say they can, that they can write unit tests and documentation, and that they can lay out a large project makes me have more faith that they can do the job.

When programming projects are used as a complement to employment and educational history, I think they help the field.

But I’ve also argued stringently against treating personal projects as a key part of any hiring process. While I like using them as a supplement, I think there are four good reasons not to rely on them as any sort of primary criteria.

First, not everyone has time for projects. Using them as a screen sifts out people with caregiving responsibilities, with families, or with strong commitments in their personal life. When you’re only hiring from people without other commitments, it becomes easier for a team to slip into a workaholic lifestyle. This is bad, because despite what many people think, studies consistently show no productivity benefits from working more than 40 hours per week for prolonged periods. All long hours do is deprive people of personal time.

(In a world where people with caregiving responsibilities are more likely to be female, overreliance on personal projects can also become a subtle form of hiring discrimination.)

I’m incredibly grateful that I work at a company founded by people with both management experience and children. Their management experience means they know better than to let their employees burn out from overwork, while their children mean that the company has always had a culture of taking time for other commitments. This doesn’t mean that I’m never in for sixty hours in a week, or that I never have to deal with a server failure at midnight. Work-life balance doesn’t mean that I don’t take my work seriously; it just means I don’t conflate being in the office for 12 hours at a time with that seriousness.

Second, requiring people to have programming hobbies sifts out a lot of interesting people. I understand that there exist people that only want to live code, only want to talk about code, and want to be surrounded by people who are also in that mode, but that isn’t me. I joined Alert Labs because I wanted to solve real-life problems, not make tools for people just like me. Having a well-rounded team means that people spontaneously generate ideas for new projects. It means they take ownership for features (like ensuring everything on our website follows accessibility guidelines) that would never percolate to the top of my mind. It makes our team stronger and more effective.

Outside of a few other oddball professions (lawyers, I’m looking at you), no one else is expected to treat their work as their hobby. People can make their hobbies into their work (look at webcomic artists or bloggers who make it big) and this was one of the initial purposes of personal programming projects. It’s not at all unusual to find something you like enough that you’d make a full-time job of it if you could. But then you normally get new hobbies.

People who fall in love with programming are lucky in that they often can turn it into a full-time job. Writers… are somewhat less lucky. I haven’t monetized my blog because I’d find the near-impossibility of making money off of discursive posts about political economy disheartening. Keeping my blog as a vanity project keeps it fun.

 

But we programmers shouldn’t let our economic fortune turn what has always been the path that a minority of people take into our field into a bona fide requirement.

Third, I dislike what an overemphasis on programming projects can do to resumes. I frequently see interesting hobbies shunted aside to make room for less-than-inspired programming projects. I’ve seen people who got the memo that they needed a profile full of projects, but not the memo that it had to be their projects. This leads to GitHub pages full of forks of well-known projects. I don’t know who this is supposed to fool, but it sure doesn’t work on me.

When students send in resumes, they all put the same four class projects on them, in the somewhat futile hope that we won’t notice and we’ll consider them adequately dedicated. I wish the fact that they were paying $8500 per term to learn about CS could be taken as proof enough of their dedication and I wouldn’t have to read about pong sixty times a semester, but that is apparently not the world I live in.

My final beef with an overemphasis on programming hobbies is that many important skills can’t be learned in front of a computer. Not all hobbies teach you how to work together with a disparate team, respectfully navigate disagreements with other people, and effectively address co-worker concerns, but those that do are worth their weight in gold. Software is becoming ever more complex and is having ever more capital thrown at it. We’ve exhausted what we can do with single brilliant loners, which means that we now need to turn to functional teams.

This isn’t meant to conjure up negative and insulting stereotypes about people who spend all their spare time programming. Many of these people are incredibly kind and very devoted to mentoring new members of our community.

I don’t want people who program in their spare time and love it with all their hearts to be tarred with negative stereotypes. But I also don’t want people with other interests to be considered uncommitted dilettantes. And I hope we can build a profession that believes neither myth.

Data Science, Economics, Falsifiable

Is Google Putting Money In Your Pocket?

The Cambridge Analytica scandal has put tech companies front and centre. If the thinkpieces along the lines of “are the big tech companies good or bad for society” were coming out any faster, I might have to doubt even Google’s ability to make sense of them all.

This isn’t another one of those thinkpieces. Instead it’s an attempt at an analysis. I want to understand in monetary terms how much one tech company – Google – puts into or takes out of everyone’s pockets. This analysis is going to act as a template for some of the more detailed analyses of inequality I’d like to do later, so if you have a comment about methodology, I’m eager to hear it.

Here’s the basics: Google is a large technology company that primarily makes money off of ad revenues. Since Google is a publicly traded company, statistics are easy to come by. In 2016, Google brought in $89.5 billion in revenue and about 89% of that was from advertising. Advertising is further broken down between advertising on Google sites (e.g. Google Search, Gmail, YouTube, Google Maps, etc.) which account for 80% of advertising revenue and advertising on partner sites, which covers the remainder. The remaining 11% is made up of a variety of smaller projects – selling corporate licenses of its GSuite office software, the Google Play Store, the Google Cloud Computing Platform, and several smaller projects.

There are two ways that we can track how Google’s existence helps or hurts you financially. First, there’s the value of the software it provides. Google’s search has become so important to our daily life that we don’t even notice it anymore – it’s like breathing. Then there’s YouTube, which has more high-quality content than anyone could watch in a lifetime. There’s Google Docs, which are almost a full (free!) replacement for Microsoft Office. There’s Gmail, which is how basically everyone I know does their email. And there’s Android, currently the only viable alternative to iOS. If you had to pay for all of this stuff, how much would you be out?

Second, we can look at how its advertising arm has changed the prices of everything we buy. If Google’s advertising system has driven an increase in spending on advertising (perhaps by starting an arms race in advertising, or by arming marketing managers with graphs, charts and metrics that they can use to trigger increased spending), then we’re all ultimately paying for Google’s software with higher prices elsewhere (we could also be paying with worse products at the same prices, as advertising takes budget that would otherwise be used on quality). On the other hand, if more targeted advertising has led to less advertising overall, then everything will be slightly less expensive (or higher quality) than the counterfactual world in which more was spent on advertising.

Once we add this all up, we’ll have some sort of answer. We’ll know if Google has made us better off, made us poorer, or if it’s been neutral. This doesn’t speak to any social benefits that Google may provide (if they exist – and one should hope they do exist if Google isn’t helping us out financially).

To estimate the value of the software Google provides, we should compare it to the most popular paid alternatives – and look into the existence of any other good free alternatives. Because of this, we can’t really evaluate Search, but because of its existence, let’s agree to break any tie in favour of Google helping us.

On the other hand, Google docs is very easy to compare with other consumer alternatives. Microsoft Office Home Edition costs $109 yearly. Word Perfect (not that anyone uses it anymore) is $259.99 (all prices should be assumed to be in Canadian dollars unless otherwise noted).

Free alternatives exist in the form of OpenOffice and LibreOffice, but both tend to suffer from bugs. Last time I tried to make a presentation in OpenOffice I found it crashed approximately once per slide. I had a similar experience with LibreOffice. I once installed it for a friend who was looking to save money and promptly found myself fixing problems with it whenever I visited his house.

My crude estimate is that I’d expect to spend four hours troubleshooting either free alternative per year. Weighing this time at Ontario’s minimum wage of $14/hour and accepting that the only office suite that anyone under 70 ever actually buys is Microsoft’s offering and we see that Google saves you $109 per year compared to Microsoft and $56 each year compared to using free software.

With respect to email, there are numerous free alternatives to Gmail (like Microsoft’s Hotmail). In addition, many internet service providers bundle free email addresses in with their service. Taking all this into account, Gmail probably doesn’t provide much in the way of direct monetary value to consumers, compared to its competitors.

Google Maps is in a similar position. There are several alternatives that are also free, like Apple Maps, Waze (also owned by Google), Bing Maps, and even the Open Street Map project. Even if you believe that Google Maps provides more value than these alternatives, it’s hard to quantify it. What’s clear is that Google Maps isn’t so far ahead of the pack that there’s no point to using anything else. The prevalence of Google Maps might even be because of user laziness (or anticompetitive behaviour by Google). I’m not confident it’s better than everything else, because I’ve rarely used anything else.

Android is the last Google project worth analyzing and it’s an interesting one. On one hand, it looks like Apple phones tend to cost more than comparable Android phones. On the other hand, Apple is a luxury brand and it’s hard to tell how much of the added price you pay for an iPhone is attributable to that, to differing software, or to differing hardware. Comparing a few recent phones, there’s something like a $50-$200 gap between flagship Android phones and iPhones of the same generation. I’m going to assign a plausible sounding $20 cost saved per phone from using Android, then multiply this by the US Android market share (53%), to get $11 for the average consumer. The error bars are obviously rather large on this calculation.

(There may also be second order effects from increased competition here; the presence of Android could force Apple to develop more features or lower its prices slightly. This is very hard to calculate, so I’m not going to try to.)

When we add this up, we see that Google Docs save anyone who does word processing $50-$100 per year and Android saves the average phone buyer $11 approximately every two years. This means the average person probably sees some slight yearly financial benefit from Google, although I’m not sure the median person does. The median person and the average person do both get some benefit from Google Search, so there’s something in the plus column here, even if it’s hard to quantify.

Now, on to advertising.

I’ve managed to find an assortment of sources that give a view of total advertising spending in the United States over time, as well as changes in the GDP and inflation. I’ve compiled it all in a spreadsheet with the sources listed at the bottom. Don’t just take my word for it – you can see the data yourself. Overlapping this, I’ve found data for Google’s revenue during its meteoric rise – from $19 million in 2001 to $110 billion in 2017.

Google ad revenue represented 0.03% of US advertising spending in 2002. By 2012, a mere 10 years later, it was equivalent to 14.7% of the total. Over that same time, overall advertising spending increased from $237 billion in 2002 to $297 billion in 2012 (2012 is the last date I have data for total advertising spending). Note however that this isn’t a true comparison, because some Google revenue comes from outside of America. I wasn’t able to find revenue broken down in greater depth that this, so I’m using these numbers in an illustrative manner, not an exact manner.

So, does this mean that Google’s growth drove a growth in advertising spending? Probably not. As the economy is normally growing and changing, the absolute amount of advertising spending is less important than advertising spending compared to the rest of the economy. Here we actually see the opposite of what a naïve reading of the numbers would suggest. Advertising spending grew more slowly than economic growth from 2002 to 2012. In 2002, it was 2.3% of the US economy. By 2012, it was 1.9%.

This also isn’t evidence that Google (and other targeted advertising platforms have decreased spending on advertising). Historically, advertising has represented between 1.2% of US GDP (in 1944, with the Second World War dominating the economy) and 3.0% (in 1922, during the “roaring 20s”). Since 1972, the total has been more stable, varying between 1.7% and 2.5%. A Student’s T-test confirms (P-values around 0.35 for 1919-2002 vs. 2003-2012 and 1972-2002 vs. 2003-2012) that there’s no significant difference between post-Google levels of spending and historical levels.

Even if this was lower than historical bounds, it wouldn’t necessarily prove Google (and its ilk) are causing reduced ad spending. It could be that trends would have driven advertising spending even lower, absent Google’s rise. All we can for sure is that Google hasn’t caused an ahistorically large change in advertising rates. In fact, the only thing that is clear in the advertising trends is the peak in the early 1920s that has never been recaptured and a uniquely low dip in the 1940s that seems to have obviously been caused by World War II. For all that people talk about tech disrupting advertising and ad-supported businesses, these current changes are still less drastic than changes we’ve seen in the past.

The change in advertising spending during the years Google is growing could be driven by Google and similar advertising services. But it also could be normal year to year variation, driven by trends similar to what have driven it in the past. If I had a Ph. D. in advertising history, I might be able to tell you what those trends are, but from my present position, all I can say is that the current movement doesn’t seem that weird, from a historical perspective.

In summary, it looks like the expected value for the average person from Google products is close to $0, but leaning towards positive. It’s likely to be positive for you personally if you need a word processor or use Android phones, but the error bounds on advertising mean that it’s hard to tell. Furthermore, we can confidently say that the current disruption in the advertising space is probably less severe than the historical disruption to the field during World War II. There’s also a chance that more targeted advertising has led to less advertising spending (and this does feel more likely than it leading to more spending), but the historical variations in data are large enough that we can’t say for sure.