How to get promoted

For early-career people

Why start with philosophy?

My goal is not to tell you: "You need to do this and that to get promoted". Goal is to actually help you to get promoted.

Telling how? Is not enough.

How? is less important half of the story. I need to tell you Why?.

How do I know that?

I saw this again and again: people need why? to actually execute on how.

Framing matters

Telling you why and how is good, but not good enough for me. I want also to enable you to change the way you think about the corporation. So the how becomes easy and natural for you.

Or at least: not painful.

One view of emotions: Basic Emotion Padagrim

There are exactly 6 emotions: fear, anger, sadness, happiness, disgust, surprise.

These emotions are built-in into the human brain.

They are inherent to being human. True across all cultures — from tribes untouched by modernity to the penthouses of London City.

They map 1:1 to well-known facial expressions.

That are universally recognisable. Across all cultures. Across all ages. Across all contexts.

Why this is relevant?

This padagrim was overturned. Emotions do not work that way.

Facial expressions corresponding to the six basic emotions: fear, anger, sadness, happiness, disgust, and surprise
Expressions based on Basic Emotions Based on the work of Paul Ekman. Image from: Lawrence, Campbell & Skuse (2015) “Age, Gender, and Puberty Influence the Development of Facial Emotion Recognition”, Frontiers in Psychology 6: 761. Published under CC BY 4.0.
Lisa Feldman Barrett

Lisa Feldman Barrett

Neuroscientist — Northeastern University & Harvard Medical School

University Distinguished Professor.

Chief Science Officer at Harvard’s Center for Law, Brain & Behavior.

Top 0.1% most-cited scientists in the world.

Over 300 peer-reviewed papers.

NIH Director’s Pioneer Award. Guggenheim Fellow. Past president of the Association for Psychological Science. She overturned the 60-year dominant model of emotions. Her theory of constructed emotion forced the entire field to rethink what emotions actually are.

Lisa Feldman Barrett
Wikimedia Commons
Colors are concepts, not elements of reality.

What we call “color” is not a property of the world — it’s a concept your brain applies to wavelengths of light. Different cultures draw the boundaries differently. And that changes what people literally see.

The electromagnetic spectrum as a continuous gradient with no boundaries
Physical reality: wavelengths have no boundaries
The visible spectrum divided into 11 English color terms
English — 11 basic color terms
The visible spectrum in Russian, where blue splits into siniy and goluboy
Russian — blue splits into two mandatory categories
The visible spectrum in Berinmo with 5 terms and different boundary positions
Berinmo (Papua New Guinea) — 5 terms, different cuts
Comparison of English and Berinmo boundaries in the blue-green region
Same wavelengths, different conceptual boundaries
Dani and Pirahã languages with only 2 color terms: dark-cool and light-warm
Dani / Pirahã — only 2 terms
Stage II languages with 3 color terms: dark, light, and red
Berlin–Kay Stage II — 3 terms (dark, light, red)
Why this matters for you

People with higher emotional intelligence have better life outcomes — better health, stronger relationships, higher performance at work. This holds across dozens of studies and multiple meta-analyses.

Emotional granularity is a core component of emotional intelligence.

The ability to make fine-grained distinctions between emotions — not just “I feel bad” but disappointed, frustrated, apprehensive — predicts better emotion regulation. People who differentiate emotions more finely handle them more effectively.

Teaching people new emotion concepts improves their outcomes.

The RULER programme taught children emotion vocabulary and recognition skills. Result: significant improvements in academic performance, social competence, and reductions in problem behaviour. This is not correlation — it’s an intervention that works.

How is this relevant to me?

Barrett showed that emotions are concepts. And that concepts influence the way you feel about the world. This is science, not self-help mumbo jumbo.

So the goal of this presentation is to give you a new concept.

To give you tools to change how you think about work.

So what I’m gonna do in this presentation is show you a new concept that will (hopefully) allow you to think about “what I need to get promoted” not as: “this is hopeless busywork” — but rather: “this is how large organisations work by necessity, this work makes sense in context of a large organisation.”
Let’s design a corporate hiring process.

Current process at all cybercorporations:

Have the candidate do N programming tasks, each under an hour. Each task requires solving a moderately complex algorithmic puzzle.

This process is fair and equitable: everybody knows what to expect. Everybody gets the same tasks (modulo organisational chaos and luck). Everybody is graded the same way (modulo inherent biases).

The problem

This process has nothing to do with actual real work you do.

The process can reject good candidates. This is actually OK for companies — the process needs to be biased towards avoiding mishires.

Daniel

Daniel

My personal friend. We worked together.

Right now he writes bootloaders for satellites.

His current task: take an embedded operating system that is biased towards being compiled by GCC, and make it compile in Clang, on a not-fully-supported (by Clang) architecture.

He also notoriously files bugs against CPUs.

My typical task: “Convince somebody that if you add 10× more clients this generally requires a lot of work, and if we don’t plan the work it won’t happen.”

He failed a Google phone screen.

Silhouette of Daniel
Let’s consider a different process.

For the sake of argument: experienced SWEs can just take people in, to their teams. If we trust our senior engineers to do the right thing, we end up with a process that will hire people who deserve the job.

Let’s call this process property: justice.

However…

We end up with a process that is not fair, because people that know nobody inside can’t get in. Like, well… expats.

Fair versus just processes.

There is a real tension between fairness and justice.

Fair processes use objective, measurable criteria — applied rigidly.

The criteria can be bad proxies for really sought qualities (e.g. ability to solve a programming puzzle within an hour).

The criteria can be harder to fulfil for some groups. Males tend to do much less house work, so they have much more time to prepare for solving programming puzzles.

You could contextualise the criteria, to get more justice.

Think about “behavioural” interviews. They ask many questions and are a really good proxy for a simple question: “Did the person work in an American company before?”

If somebody comes from a different background they are more likely to fail. One could contextualise this — apply different criteria, read between the lines — and get a system that is more just. But then the fairness property would suffer.

Corporate processes are strongly biased towards fairness, and by necessity: away from justice.
Let that sink in.
Failure in the process is not your personal failure.

(It is possible to overestimate one’s abilities though.)

But mostly — failure is a result of the system being fair and not just.

If the process is not “just”…

…it means that it is not enough to do work that is good enough to deserve a good outcome. You need also to ensure that you explicitly match the process criteria.

It is not enough to be a good engineer to get hired at Google.

It is not enough to be a good researcher to thrive in Academia.

You need to adapt to the process.

A just process would contextualise enough to directly reward that. But the processes you will encounter are fair. You need to adapt to the process, because the process will not adapt to you.

So now you have two concepts: fairness and justice.

I made the specific meanings up (or hyper-localised their meaning). This doesn’t matter — Barrett’s research shows that infants can easily form new mental categories when exemplars of the category are paired with “word-like” sounds.

The way you can use these concepts to change your thinking:

“OK, the promo process is not just, but it is designed to be fair. We can’t have both.”

Of course I don’t claim that a process is either fair or just.

You can easily have a process that is neither. If you are in a place where the process is neitherrun away.

But before you are running away, double check you’re right.

Why does this distinction matter?

People — perhaps especially those from languages where both concepts map to one word (e.g. Polish sprawiedliwy) — can feel real discomfort, sometimes even suffering, when they notice a lack of justice in the system.

Having two separate labels lets you name what exactly is missing — and whether it is even fixable at scale.

There is a property more important than fairness and justice.

Large organisations tend to optimise for it over anything else: legibility.

A process is legible when one can inspect its contents.

A totally fair process can easily be made legible. Think of hiring. There is a mountain of documentation: feedback forms, assessments, rubrics — all to ensure that someone can look into the process and say: “Ah! So that’s what happened.”

Highly contextual, just processes are less legible. Nobody can look into my mind and say: “Yes. Daniel seems to be the best engineer for the position.”

Legibility wins for many reasons.

Let’s enumerate them.

Legibility is the concept I want you to take home.

Not only this. I want you also to understand that:

Legibility is not some failure of the system, but rather a pervasive property of all large organisations — if it is a failure, it is a failure of the human condition.

That if you squint a little, you can see sense in all of this.

And oh boy, is it easier to live in a world that makes sense.

Why is legibility unavoidable?

1. Legal and compliance.

2. Human cognitive limits.

3. Every large organisation does it — not just corporations.

4. Wittgenstein’s beetle in a box.

Human cognitive limits.

Dunbar’s number: you can maintain meaningful relationships with roughly 150 people. That’s it. Beyond that, your brain can’t track who knows what, who did what, who is good at what.

In a 20-person startup, the CTO knows who the best engineer is. In a 2,000-person company, nobody does. Not because they’re lazy. Because it’s cognitively impossible.

So decisions about people must be based on artifacts — things that can be read, compared, and evaluated by someone who has never met you.

When we count, we change what counts.

Chang et al. (2024) ran 21 experiments with over 23,000 participants. The setup: participants choose between two options that trade off on two dimensions. One dimension is presented as a number, the other qualitatively. Result: people systematically favour whichever option dominates on the quantified dimension — regardless of which dimension that is.

Two experiments stand out. In Experiment 4 (public works project choice, N = 2,000), making the numbers harder to compare (e.g. 51/68 vs. 23/92 instead of 75/100 vs. 25/100) nearly eliminated the bias. In Experiment 5 (charity donation, nationally representative sample, N = 602), people who felt comfortable with numbers showed stronger bias — but actual maths ability did not predict it at all.

It’s not about being good at maths.

It’s a metacognitive bias — driven by how easy numbers make comparisons feel, not by computational skill. You can’t train it away by teaching people statistics.

The act of quantifying is not neutral.

The effect shows up in hiring, policy, consumer choices, charitable giving — including when real money is at stake. The moment you put a number on something, it weighs more. The things you don’t quantify become invisible.

Sound familiar? Legibility favours what can be written down, measured, compared. Everything else disappears.

Figure 3 from Chang et al. (2024): Panel A shows disfluent numbers attenuate quantification fixation; Panel B shows subjective but not objective numeracy moderates the effect
Chang et al. (2024), Figure 3 Chang, Kirgios, Mullainathan & Milkman (2024). PNAS 121(46), e2400215121. CC BY-NC-ND 4.0.
Quantification fixation: the evidence
Human cognition is biased towards easily measurable numbers. This is not a property of “stupid” American corporations. It is a part of the human condition.
ExpNChoice ContextTradeoffWhat Does This Study Demonstrate?
1a1,000Hotel choicePrice vs. ratingsQuantification fixation shifts decisions
1b1,000Summer internship candidateCalculus vs. management gradeReplication; similar familiarity with qualitative and quantitative info
1c1,000Conference locationConnectedness vs. sustainabilityReplication; qualitative and quantitative descriptions transparently linked
22,000Employee promotionRetention vs. advancement likelihoodDistorts preferences vs. baseline (both verbal or both numeric)
3a1,000Job candidateMath Game vs. Angles GameReplication with real financial incentives
3b701Charity donationAccountability vs. CultureReal donation decisions, in-person participants
42,000Public works projectBenefit vs. efficiencyModerated by fluency of quantified information
5602Charity donationAccountability vs. CultureNationally representative; moderated by subjective not objective numeracy
Legibility wins because it scales.

You cannot grow a large organisation based on people doing the right thing, like, just that. To let an organisation grow you need to codify the rules. This is not a perfect system — show me a perfect system — but it is a system that scales.

Let’s turn the argument around.

Probably everybody here wants to be L5+. And let’s be honest: most places that can really utilise senior engineers are at or over the threshold where legibility is necessary.

This is not a corporate quirk.

Militaries run on legibility. Every order documented. Every decision traceable. Not because generals are bureaucrats — because lives depend on someone being able to reconstruct what happened and why.

Universities: publish or perish. Your research exists only if it’s written down in a legible form. Brilliant thoughts that stay in your head count for nothing.

Governments: censuses, cadastral maps, standardised surnames. James C. Scott wrote an entire book about this — Seeing Like a State. States that can’t read their population can’t govern it.

Any large organisation. All of them. Every single one.

Wittgenstein’s beetle in a box.

Imagine everyone has a box with something inside. Everyone calls the thing in their box a “beetle.” But nobody can look into anyone else’s box.

Maybe everyone’s beetle is different. Maybe some boxes are empty. It doesn’t matter — the word “beetle” can’t mean the private thing in your box. It can only mean whatever is shared and visible.

Everyone has a private sense of what “senior engineer” means.

Nobody can look into each other’s box. To make decisions across many teams, you need a shared, legible definition — even though it loses nuance. Even though your beetle is definitely the real one.

So here is what I did

☑ I explained to you that you can influence your emotions and how you perceive the world by changing the concepts you use.

☑ I gave you the concept of legibility, which you can apply to your work, to influence your emotions towards “corporate busywork.”

☐ I will now go into explaining why legibility actually can be useful.

Why it is useful to write design docs.

Yes, they are artifacts for promo and graduation. That’s the obvious part.

But eventually, with practice, you will learn to become a better writer.

A better communicator. And there is research correlating being a good communicator with better life outcomes — not just career outcomes, but relationships, health, wellbeing.

It is easy to catch bugs at design phase.

Really, it is! A bug caught in a design doc costs you a paragraph rewrite. A bug caught in production costs you a week and an incident review.

Your coworkers can actually learn by reading your design docs.

Much faster than learning through reading code. A design doc explains why, not just what. Code can only show you what was built. The doc shows you the reasoning.

Formulating thoughts clearly is a thing that will push you through your career.

Not just engineering. Every senior role eventually becomes about communicating ideas to people who don’t share your context. The earlier you practise, the further you go.

How you can improve legibility of your work.

Write design docs!

If you work on something, have the work tracked in the usual tracking system. Have the issue you work on have a description, progress reports, etc.

Have some sensible metrics proving your work brought value.

Brag about invisible work you do.

“Mentoring.” “Onboarding.” Label this as what it is: leadership.

You spent last quarter running between three teams to make a project happen? Again, this is a prime example of leadership.

Never undersell yourself.

Never say: “It took me a long debugging but the fix was simple.”

Say: “There was a very subtle bug in the code; it was difficult to track down.”

Make sure your Tech Lead and Manager know what you’re doing.

Why making your work legible is good for you.

Yes: this helps with promo. Produces artifacts.

Your local leadership knows what you’re doing. I will not be able to understand what 20 people do without their help.

By documenting your work, you learn to be a better communicator. And guess what — there is actual research proving that being a good communicator improves your life outcomes.

If you write a design doc, you can hand off the implementation.

Maybe partially. Maybe fully! To another engineer. And you can go back to doing more meaningful work. More impact!

Why making work legible benefits your team.

It is faster to learn by reading documentation than by reading code. Especially now, when you can just feed 5 design docs to an AI and ask for the gist and what is most interesting.

Shared understanding leads to better decisions. More standardisation. You know how you get 10 experiment frameworks inside a single service? By not writing a clear design for the first one!

Code explains how. Documents explain why.

Why making work legible benefits the whole organisation.

Imagine two teams doing the same work on similar projects. One does maybe 10% less output but spends 10% of their time making their work visible. The second goes 100% coding. Which team will be axed when the layoffs come?

If there are no layoffs — which team will get the headcount?

I cannot make the team’s work visible to leadership, if it is not visible to me.

Summary

Now you have a new concept of legibility.

I have also shown you how to think about legibility as something positive.

I have also shown you why the bias towards legibility is not some aberration of American corporations, but rather a systematic property of large human groups. Complaining about this fact is as useful as complaining about rain while not putting a rain jacket on.

Please don’t be the drenched guy screaming at the clouds. Clouds don’t really care.

Now you can think: “design docs are useful”, and stop: “design docs are a necessary evil.”

It is up to you to apply this concept to change your thinking.

I have shown you how, but the work you need to do yourself. Barrett proved you can!

And you should change your thinking about legibility work being useful.

People doing work they perceive as useless have worse mental health. This is not a metaphor. It is an empirical finding.