Salary no longer determines prosperity

In the past, it used to be that getting paid a high salary ensured an upper-middle class lifestyle. As recently as the 1990s, a salary roughly in the range of 60K pounds, 80K euros or 100K dollars would ensure access to all the trappings of a comfortable lifestyle: a big house, car, restaurants, etc. This is no longer possible. In some cities, like London, New York, San Francisco or even Munich (Germany being traditionally cheap) those numbers are simply the bar for a normal life. The reasons are well-publicised. Some of them are relatively recent, like food inflation – caused by high natural gas/fertiliser prices, plus a contraction in supply from Ukraine – as well as the pandemic, which caused microchips to skyrocket in price, and also made cars more expensive, both new and secondhand. Gas and electric have shot through the roof, although thankfully they are beginning to come down. However, these are short-term shocks, and relatively minor in the grand scheme of things. For example, food prices increased anywhere from 10 to 35%—hardly massive. Whereas housing costs have effectively doubled, tripled or even quadrupled in some areas, compared to the 70s, 80s, and as recently as the 90s. In addition to this, tuition fees and debt has increased substantially. Because house prices and rents have reached such obscene levels, the path to building wealth has diverged markedly from the conventional path: getting a degree, and going to a big city to work, is more likely to make a young professional poor than rich these days. If you want to live like your parents or grandparents did, i.e. house, kids, etc. you need to think outside the box. May I suggest something along the following: 1. Consider a non-conventional degree. Online-only courses and bootcamps can be vastly cheaper. Also consider emigrating to a country where education costs are more reasonable (this is what I did). And don’t assume you need to study for 5 years to master something; a smart and determined individual can learn a trade or profession in 2 years if they put their mind to it. Learn on the employer’s dime! 2. Don’t be afraid to live with your parents into young adulthood to save money. 3. Finally, and most importantly, get a job which can be done remotely, like software engineering, digital marketing and so on. Do not delude yourself into thinking you can buy in London or San Francisco if you *just* try to get that promotion, because it’s not gonna happen. Housing will be your single biggest financial outgoing. If your job can’t be done remotely, look for work in a medium-sized town in the North of England/Scotland, the Midwest of America, etc.

Would the real data scientists please stand up?

There are a number of myths surrounding the field of data science, the jobs available, and the pay. When I was in my salad years and green with judgement (read: before I started my master’s) I was lead to believe that data scientists are brilliant individuals who go on to earn $10,000 a month right out of school—provided that they can pass the rigorous demands of their educational programme.

The reality is much more nuanced. It is true that there are a small number of graduates who earn that much straight out of school, along with a somewhat larger number of experienced individuals (e.g. Head of Data, Senior Machine Learning Engineer, Data Science Lead). However, there are many things they don’t tell you:

1. Those kinds of salaries are rarely found outside of the US, and since most data scientists have at least a master’s degree, that often means debt—as much as $100,000, for which the Federal interest rate is a criminal 6%;
2. They typically go only to graduates of the top schools;
3. They are typically only for on-site roles in some very expensive cities, e.g. San Francisco, New York and maybe London, where rents are sky-high;
4. They are mostly offered by brand-name tech companies, finance firms, and a few startups; the recruitment process tends to be arduous. It often involves a battery of tests in coding, maths, IQ, and personality tests, in addition to multiple interviews. And by “interview”, think live-coding.
5. By “right out of school”, think PhD. A master’s is just a pre-requisite in many firms.

So clearly, data scientists being paid $100,000+ per year are the minority. If you look at job sites like Glassdoor, you’d think that this is the median salary, but these sites suffer from sampling bias. The actual median salary in the UK is little over 50K GBP. Comparing salaries across countries is difficult because:

1. Exchange rates fluctuate, and purchasing power parity needs to be taken into account;
2. In the US, employers pay 7.65% social security taxes; in European countries, it’s usually higher, e.g. 15% in the UK, 25% Belgium.
3. Rents vary.
4. Student loans vary dramatically.

So what are some typical salary bands, responsibilities, and what kind of skills are actually required? If you think that you can get a six figure job just by knowing how to work with sklearn, pandas and matplotlib, you are sadly mistaken. These are simply the basic requirements for an entry-level job. (And not even all the basics, at that.)

In order from highest to lowest:

• $100,000+ per year, or the PPP equivalent in euros and pounds: There are 3 types of people, in my experience.
  • The most “classical” profile is someone with a PhD in machine learning/AI from a top school who has written a few white papers in novel ML algorithms. These people work in really cutting-edge applications.
  • The second kind of person is someone with at least a couple of years of experience who is very proficient at programming, and competent with many technologies: not just Python, but they may also be crack C++, Java or Julia programmers. They know their way around cloud computing, containerisation, distributed computing, and have read a book or two on design patterns. They know SQL and NoSQL. These people productionize ML models. They are basically glorified software engineers.
  • Experienced personnel with deep domain knowledge in medicine, business, finance, or linguistics who also have the technical skills.
• Anywhere from $60K to less than $100K: These are either PhD grads from reputable-but-less-than-top universities (those other than MIT, Oxbridge, Ivy League), or ML engineers with some experience. Fresh PhD grads will start on the low end of the scale.
• From $40K to $60K or PPP equivalent: They know the basics, I guess. Strong Python/R skills and stats. Decent SQL skills. Can wrangle their data, write OOP code, and knows how to setup a Docker container or write a shell script. Fresh grads start on the low end, but salaries increase quickly with a bit of experience.
• Less than $40K: These are typically called data analysts. They might write a Python script or two.
• Unemployed (or not employed as a data scientist): The legions of wannabes from bootcamps, online certificates, and 1 year degrees who write awful code (usually in Jupyter notebooks), don’t understand statistics, don’t know how to query a database, clean data, or understand how to properly evaluate the output of a model.

Some other things to discuss. I hear a lot of people mention domain knowledge; often, these people have a lot of domain knowledge in their field of expertise and are trying to break into data science. I hate to break it to them, but while domain knowledge is valuable, all the domain knowledge in the world won’t help if you don’t the master the basics of programming and statistical analysis. It’s usually the coding part where they struggle.

Is there a shortage of data scientists in the industry? Ten years ago, definitely. Reputable sources still claim that anywhere from 20% to 50% of companies struggle to fill data science roles, but take these numbers with a pinch of salt.

What I can say is that there’s definitely a shortage of talented, experienced data scientists who are willing to work for less than six figures. (The competition for six figure roles is blisteringly intense, with many talented, qualified individuals applying for each role.) The shortage is particularly acute for “full-stack” data scientists who can productionize models and do data engineering. However, there seems to be a big oversupply of bootcamp/online course graduates, as well as degree holders from orthogonal disciplines. The life and social sciences are the worst offenders. A bit of data analysis in SPSS, or rookie Python ability probability won’t net you a job, or a low-paying one at best.

If you are looking to enter the field like I am, my advice is to start by getting a degree in DS, CS or statistics (but make sure it’s a stats course with strong programming requirements). It’s generally easier to become a data scientist by first starting out as a developer or a data engineer (glorified developer). By the way, a position as a data analyst does not prepare you for the reality of data science or software engineering. The stories you hear about data analysts becoming data scientists was from years ago when management didn’t know the difference – or they have the title data scientist but only do analytics work. A data analyst is usually a glorified Excel monkey who maybe knows some Python and SQL. While they might be able to create a simple ML model, they often don't really understand how to interpret the model or tune it, as their statistics knowledge only covers the basics. The learning curve for software engineering is even steeper and such a person can only do their job if they have an army of data engineers and ML engineers working for them.

Although companies like IBM tell us that data is a fast-growing field, the threat of automation should not be discounted. In 10 years, there might not be a market for data scientists, or at least, it won’t employ large numbers of people. The bar could be set even higher as simple, routine tasks become automated.

I do believe that creative jobs are immune from automation, and certainly, there are no AI models that can write stories, so I still plan on being a writer in the long-term. We’ll have to wait and see whether my creative abilities end up being more lucrative than my technical ones.

Degrees, Money and the Future

For a long time, it was politically incorrect to protest the huge increase in university/college attendance across developed nations in Europe and North America. University attendance went from approximately 14% in the 1970s to over 40% today. Tony Blair famously wanted 50% of youngsters to attend university. A degree was lauded as a springboard to socio-economic mobility, and graduates were supposed to boost GDP growth and tax revenues by taking up high-skilled jobs.

But the reality is turning the dream into a nightmare. The sad truth is that the number of graduates exceeds the number of graduate jobs, and many graduates—particularly from some of the humanities and social sciences—end up under-employed. Furthermore, the burden of college debt acts like a vice on the economy. It doesn’t matter whether the debt is private debt (like in the US) or public debt (like in most European countries). Most obviously, debt harms consumption. It has knock-on social effects on fertility and demographics; young people burdened with debt start a family later (or don’t start it at all), and struggle to afford a house. Moreover, from a macroeconomic perspective, the loans are dangerous, since a lot of them are bad—that is to say, some graduates will repay the full amount, or repay the interest, or repay within the expected maturity period.

The hard truth, that young people, parents, and policymakers don’t want to hear, is that high-skilled, university-level jobs make up at best 20% of the available jobs. I am also highly skeptical that this will increase in the future. The percentage may increase, but only because the total number of jobs will decrease as more jobs are destroyed by automation (high-skilled jobs are much more resistant to the effect of automation). The jobs that require a degree are highly technical and/or vocational: medicine, pharma, nursing, dentistry; programming; engineering; data science; and so on.

Automation, AI and the post-scarcity economy are things that have been discussed elsewhere in more detail than I will go into here. The long and short of it is: we are heading towards are a post-scarcity economy. We are not there yet, and the process will take time. Also, let me be clear that a post-scarcity economy does not mean the end of scarcity; some goods and services will remain scarce, but the majority will be available in abundance. To give you an idea: food, energy and consumer goods will be abundant. Heck, they are already pretty abundant right now. Have you ever seen a shortage of nails? Nails, like many other goods, can be mass-produced for a very low price, and they require almost no human input in their creation. Other things will remain more scarce, especially things that require a lot of high-skill human labour.

That’s the key, the salient point of this little essay: labour. Historically, we have regarded labour, work, as something valuable and worthy. An entire religion, Protestantism, revolves around the value of work. Work means steady payment; a livelihood. But labour is inherently tied to scarcity. Humans have to labour because things are scarce: food, shelter, medicine and so on. So what happens when all the work is done by robots and software? Let’s assume that everything could be done by robots or computers, just for the purposes of this thought experiment.

Ideally, we would live in a utopia; humanity would never want for anything. In reality, the barrier lying between us and this utopian vision is capitalism. We admire capitalism because it has worked well for the last two centuries or so—with government regulation and the managing of natural monopolies, of course. Adam Smith was more or less right. He was misunderstood by the braying free market neoliberals; Adam Smith never argued that the “invisible hand” would make markets self-regulating in general (only if a very specific set of criteria were fulfilled). A mixed market+command economy is the way to go. This is something that France, the UK, Scandinavia and Germany understood, but which the US and Soviet Union did not.

Unfortunately, I think the economic model of the past century may no longer function in this post-scarcity future, because it is still predicated on the idea of renumeration for labour. To put it bluntly: in the future, many, perhaps most, of the population will be unemployed. They will be unemployed for no other reason than that they will have nothing to do.

I believe that we will need a Universal Basic Income at some point in the not-too-distant future. Moreover, I think a successful economic model of the future will still have capitalism (i.e. capital, companies, competition etc.) but in a far more diminished way. Capitalism will be limited to areas of rapid innovation, scarcity, and high differentiation. Everything else will be administrated by organs of the state, be it local, regional or national governments. Money will still exist, so this will be socialism, not communism. The state already administers 40–50% of the economy measured by GDP, so this should not be a big pill to swallow (newsflash, Americans). Why will it be adminstered by the state, you ask?

This leads onto the next point. Capitalism is unstable and destructive. It creates a small number of winners and a large number of losers. (Sorry, right-wing Americans.) In the future, this tendency will be exaggerated until society will fray apart. An industrial reserve army of the unemployed—an army of losers—will be created. There are only three possible scenarios. One, capitalism tries to maintain itself through coercion. Two, socialism prevails. Or three, massive social unrest results in anarchy, and the post-scarcity economy is destroyed, bringing us back to the status quo ante… by which I mean something before 1770: the medieval world. This scenario seems more likely in third world countries that are politically unstable.

Let’s go back to square one, and the original point of this essay. More university degrees will not lead to better pay or employment. There are powerful economic and technological forces at play that are leading us to a world with high unemployment. Instead of creating more debt and broken dreams, policymakers need to focus on managing the transition to a post-scarcity economy based on UBI, the provision of basic goods, and reduced inequality.

What does this look like, in practical terms? Well, it won’t be a world of perfect equality. I don’t believe such a thing is possible or even desirable—and I say this as a staunch socialist. Natural inequality is the reality of the world we live in. Some humans are smarter than others, or more talented, or hardworking; they should be rewarded. So the economic situation will look like this. Everyone will be guaranteed a basic income of, say, 2000 euro a month. They can earn extra by participating in the labour market when possible. Some people will earn high salaries, like 3K, 4K, 5K a month because they do something difficult (and they will pay taxes on their income). And there will be a few millionaires or billionaires, but fewer than there are today. The state’s revenues will shift from being predominantly taxation funded to being funded more by the sale of goods, e.g. food and housing. This is because there won’t be enough taxpayers to fund public goods like healthcare and (obviously) UBI itself otherwise.

Money is hard to understand for the layperson. Money is not scarce; it can be created at will. But scarce things do have a higher price. This is why creating more money causes inflation: there is more money, but the number of goods does not increase.

How can the state fund UBI? It’s not through taxation: you can tax UBI, but since that money comes from the government, there is no mathematical way to fund UBI with itself. Rather, the state can simply print money and ensure that the supply of goods (which it controls) matches up. The state will also tax corporations that make large amounts of money through the sale of goods and services. This system will ensure that basic goods are provided, but also that people have disposable income to spend on more whimsical things—art, fine dining, holidays, whatever—at their discretion. Remember that this will not be a truly post-scarcity society, just a “mostly” post-scarcity one. Provided that the money supply is managed sensibly, this system will work very well.

Now that I think about it, this system could already be partially implemented in the world we live in today, since we are already approaching post-scarcity in some ways. But to accomplish this, we have to have political awareness. We cannot allow our politicians to further the interests of the rich, and pull the wool over our eyes. People are not unemployed because they are lazy, or because they don’t have a degree, or because they don’t know how to code. Only 1% of the population knows how to code, and demand is at most 2 or 3%. People are unemployed because of technological growth.

Some people will not like what I am saying about degrees. Humanities people in particular think that humanities degrees are being devalued in favour of STEM. But actually my argument has nothing to do with funding one or the other. It is true that humanities degrees do not pay as well as (most) STEM degrees, so that is a good argument to avoid going into debt for one. But I would very much be in favour of subsiding degrees so that the best humanities students can study for free. The real problem—which I want you, the reader, to understand—is when we make 1 in 2 youngsters get a degree just for the sake of it. Believe me, all those psychology students (psychology is one of the most popular majors) don’t really want to be doing a degree; they just want to party. Them getting a degree, however, prejudices bright students who are genuinely interested in learning. It strains financial resources and devalues the worth of a degree.

Thanks for reading this far. If you have some thoughts to share, please comment below.