Part I · Chapter 3

Why 'career' is not a salary number

Two engineers

Priya and Marcus both earn $95,000 a year. They are the same age and have the same years of experience. Their lives are not alike at all.

Priya works at a consumer app company. She is on-call one week in three, which means she must stay reachable and fix problems at night. Forty other companies in her city hire for exactly the same skills, so she competes with thousands of people every time she looks for work. Her strongest framework came out three years ago, and in another two it will be a weak line on her CV. To stay employable she studies most weekends, and nobody pays her for those.

Marcus works on software for medical devices. His work must meet a safety standard called IEC 62304, and a regulator checks it. Learning that standard took him two years, and very few people have done it. When his company advertises a job, they wait months to fill it. His weekend study is close to zero, because the standard changes slowly. His knowledge grows more valuable each year instead of less.

Same salary. Opposite careers.

Compare the two on pay alone and they are identical. On every other measure of how a life actually goes, they are not close. This book is that comparison, done properly, 46 times.

What is wrong with normal career advice

Most career advice optimises one number, which is pay, and treats everything else as a feeling. Is the work interesting? Is the team nice? Will this last? People answer those with opinions, because nobody has organised them into anything you can measure.

That is backwards. Pay is the easiest thing to find out and one of the least useful things to compare. A salary tells you what a job pays this year. It tells you nothing about whether you can enter the field, whether you can stay in it, or whether it will exist in ten years. So this book measures ten things instead of one.

The ten dimensions

Every profile in Part VI scores a career on these ten dimensions. Here is what each one means.

Each one is scored 1 to 5, and 5 is always the good end for you. Five of these ten name a bad thing, so there the 5 means less of it. A 5 on AI exposure means the work is hard to automate.

1. Regional availability. Where the jobs actually are. Some careers exist in one country. Some exist everywhere.

2. Remote viability. Can you do this work from somewhere else? Some work cannot leave the building, for reasons of law, secrecy or safety.

3. Entry pathway. How people actually get in. Not the route the adverts describe. The route real people took.

4. Pay by region. What the work pays, in each place it exists, from the best sources available. One score cannot hold that spread, and Chapter 12 shows the same work paying very differently by country. So this number sorts careers against each other. It does not predict what you will earn.

5. Competition density. How many people compete for each opening. High pay with high competition can be worse than lower pay with none.

6. Cost to enter. What it costs you to reach your first job. Money, but also time and energy.

7. Cost to maintain and progress. What it costs every year after that, to stay employable and to move up.

8. AI exposure. How much of this work can a machine now do, and how fast is that changing.

9. Other disruption. Everything else that could remove the work. Offshoring, regulation, company mergers, a market disappearing.

10. What employers actually screen for. What decides who receives an offer. Often not what the job advert says.

Where these ten dimensions are weakest

Two of them are barely published anywhere. Two more are built entirely from people who succeeded. Both weaknesses are worth knowing before you use the scores.

The two nobody publishes

Look again at dimensions 6 and 7. Cost to enter, and cost to maintain.

You can find pay data for almost any job in ten minutes. You cannot find these. No government measures them. No survey asks about them. They appear in no salary report anywhere, and they are the two that decide whether you last.

Priya and Marcus earn the same. Priya pays for her career every weekend, forever. Marcus paid two hard years at the start and very little since. Over ten years, that difference is enormous. It never appears in a salary comparison.

When you look at a career, ask both questions separately. What does it cost me to get in? What does it cost me every year after that? A low entry cost with a high maintenance cost is a trap, and it is the most common trap in computing.

The two built only from survivors

Now a different warning, about dimensions 3 and 10.

Entry pathway says how people get into a role. What employers screen for says what decides who receives an offer.

Ask where both of those come from. Job adverts, and the accounts of people who got hired.

Notice who is missing. Nobody anywhere measures the people who took the same route and did not arrive. They sent the applications, did the course, made the sideways move, and are not in any dataset, because nothing collects them.

This book names that problem when it belongs to somebody else. Chapter 7 says of a bootcamp’s own outcome figures that they cannot tell you anything about the people who paid, studied and never got in.

The same objection lands on these two dimensions, and it is fair. They describe routes that worked for people who are now inside. They cannot tell you the odds.

So read them as direction, not probability. “People reach security work from a service desk” is well evidenced. “You will reach security work from a service desk” is not, and this book never has the data to say it.

Chapter 30 maps these routes in detail and repeats the warning there, because that chapter is entirely built from arrivals. Appendix B records it as an open gap. It is arguably the most important thing this book does not know.

The asymmetry rule

Here is the most important idea in this book. Read it twice.

A career is only as good as its worst dimension for your situation.

The dimensions do not average out. One bad score can make the other nine irrelevant.

An example. A defence software job in the United States might pay very well, face almost no competition, and be nearly safe from AI. Nine dimensions look excellent. But the job needs a security clearance, and a clearance needs citizenship. If you are not a citizen, the score on that one dimension is zero.

Not low. Zero. And zero multiplied by every other strength is still zero.

The same job is superb for one reader and worthless to another. The job did not change. The reader did.

This is why the book cannot simply rank careers from best to worst. A ranking would have to pretend all readers are the same person. Instead, Chapter 17 shows you how to score these careers against your own limits.

Your binding constraint

The dimension that scores zero for you has a name in this book. It is your binding constraint.

Find it first. Not last.

Most people research careers in the wrong order. They read about pay. They grow excited. They spend six months learning. Only then do they meet the thing that rules them out. The clearance. The degree. The visa. The city they cannot move to.

Turn it around. Start with the thing most likely to eliminate options, and filter on that before you look at anything else. If you cannot leave your country, remove every region-locked career on day one. What remains is smaller, and every option in it is real.

A short list of possible careers beats a long list of impossible ones.

What to do with this chapter

You now have the framework. Chapter 21 shows you how to read a profile that uses it.

The rest of this Part teaches you to check any claim about a job market, including the claims in this book. Part II describes what happened to computing work by 2026. Part IV helps you choose, and Part VI applies these ten dimensions to 46 careers.

One thing to carry with you from here. When somebody tells you a career is good, your first question is not “how much does it pay?” It is “good for whom, and on which dimension?”