No One is Really Working

Justifying the High Salaries of Early-Career Professionals

The following are anecdotes of a typical work schedule for young professionals in established, tracked professions.

Following these profiles, I provide explanations for why young professionals command such high compensation, relative to what their work product would indicate.

Adam: SWE at a gaming company

Adam has been a SWE for four years. He first started coding when he was a young boy and quickly found that he had a knack for solving puzzles. He always loved video games and was excited to learn that he could have a lucrative career that included programming the very games he enjoyed growing up.

His day consists of pushing updates to backend servers in Go and writing relevant Typescript client code. Most problems don’t require much brainpower, meaning that he can push a change and spend the next 30 minutes on TikTok. Typically, when he refocuses himself, he finds that he one-shots the problem and moves on to the next problem. A good day would be merging a couple of PRs and some friendly Slack banter.

Adam has compounded his skills over the years, thanks to a strong culture, well-defined tasks, and a competent manager. He has a good work-life balance as he is able to finish projects quickly, though most do not even have hard deadlines. He makes sure to not work too fast or set expectations too high. This is an implicit learned behavior from his boss, who is also competent and not incentivized to ask for or create more work.

On average, Adam puts in 0-10 hours of deep work a week. The rest of his work hours are spent mindlessly coding, listening in on various meetings with his camera off, and on TikTok.

Adam went to a large engineering school. He was much sharper than his fellow students and didn’t have to work too hard to get good grades. He leveraged his connections and grades to eventually work at the gaming company he’d always dreamed of.

His typical week includes merging a couple of PRs and periodically managing a few interns. He generally prefers to work alone. He is conflicted about whether to go the IC route or the manager route. He’ll probably go the manager route because his manager told him it is the path of least resistance.

Adam thinks AI and AI-adjacent tools are crutches. He does not use Twitter.

Brenda: Writer at a marketing firm

Brenda is a writer at a marketing agency, helping top brands with positioning and providing materials for advertising campaigns.

Brenda works in a hybrid setting, working from the office three days a week. She likes the hybrid split as it enables her to see her peers and collaborate more effectively while allowing her to take it easy on Mondays and Fridays.

A standard week typically includes writing an internal memo and reviewing peer’s assignments. Some weeks are more skewed towards her individual contributions, writing out full reports and sometimes presenting the result. These reports are sometimes fully internal facing while others are external to clients.

The clients love Brenda – she is young and in tune with Gen Z culture. She offers unique insights like “Instagram DMs are out” and “Being cringe is cool”. Her boomer clients run every piece of marketing material by her to avoid the never-ending cultural landmines and to be perceived as cool.

Brenda went to a good school, with many friends working in similarly prestigious positions across various industries. She takes frequent bathroom breaks to catch up and react in her five active group chats.

Likely future career paths include climbing the corporate ladder, working at a client’s company, or going to graduate school.

Carl: Strategy Consultant at a Big 4

Carl is a consultant at a Big 4 firm. He has worked on two projects during his first year as a consultant.

His first project was not very demanding, with vague deadlines as the product was still years out. Carl took advantage of the additional time afforded to him by meeting other people at his firm as well as networking with the client in person. This proved to be very helpful as Carl is responsible for finding his own projects so that he is not idle and riding the bench.

He networked his way onto his second project, which is somewhat more demanding. Some days he has to get to the office at 9 and works until 7.

Carl often asks himself what his real skills are. Most of his work is produced by some combination of AI tools including ChatGPT, Claude, and Perplexity. Deep Research has been especially useful for creating well-written scripts that he can read off during virtual presentations. He happily pays for these tools out of his paycheck.

Carl’s company issues him a work laptop, which soft-prohibits AI tools. Most of his day includes prompting Claude on his phone and typing the results from his phone to his work laptop. He prefers to work at home so he can use his second laptop, making copying easier and enabling him to have a YouTube video in the background.

Carl’s most difficult decision is deciding which burrito to order for lunch. He excels at navigating cultural contexts and is now fluent in corpo-speak. He always makes sure to align on strategic north stars and leverage whatever framework is in vogue.

Carl has his eyes set on getting an MBA. It's both what he wants to do and what his parents, managers, and friends unanimously recommend. He is in talks with his company to see if they can pay for business school.

Adam, Brenda, and Carl are archetypes of co-workers one would encounter in a prestigious post-college environment in cities across the US. In fact, they are some of the most competent co-workers one might encounter in the corporate world and represent the top ~5% that society has to offer.

In any major city, compensation for these roles typically ranges from $100,000 to $300,000. We know that we have gotten better at running The Sort, matching individuals to occupations where they can maximize their productive potential, as measured by income. However, it is highly unlikely that individuals are producing work output commiserate with their salaries from an efficient labor market perspective.

The following are possible explanations of why young professionals command such high wages:

1. The productivity of outlier employees covers everyone else and they don’t negotiate higher salaries for themselves.

Assume Adam, Brenda, and Carl are your typical employees, each being a 1x employee. Each company has a small percentage of employees that are 1000x more productive and do all the work. The company can not determine ex-ante which person is a 1x or a 1000x employee.

1000x employees are present in all companies and do not negotiate for higher pay or leave.

Furthermore, in post-product-market fit companies, it is difficult for any individual contributor to make a meaningful difference to the bottom line. The barriers to basic maintenance of the product typically do not hinge on a select few, and companies are incentivized to structure their companies such that this is not true.

Compensation impact: Low

2. A single breakthrough covers everything.

A worker comes up with the idea of a widget that increases internal productivity 1000-fold or creates a new product that everyone wants. The firm asymmetrically benefits from capturing the economic value of this breakthrough and does not compensate the employee proportionally to the value they’ve created.

You don’t know who will do this ex-ante (and neither does the employee) so you have to pay everyone an inflated salary to attract the innovator.

Compensation impact: High in select industries, low otherwise

3. Talent is finite and firms are paying everyone they can so someone doesn’t start a competitor.

Every person implicitly decides whether to work for your company or start a competitor. If they start their own company, they have an X% chance of starting a company that puts your company out of business in 10 years.

Your company believes that people can be financially persuaded to work for you and not start their own company, even if it is economically rational for the person to do so. Risk aversion and expected utility exist, and you take advantage of this.

You’re happy to offer higher salaries across the board to reduce the risk that talented employees might leave to start competing businesses.

Compensation impact: Medium-high in tech/select other fields, low otherwise

4. Firms are very concerned with mitigating downside risk; high salaries are a form of insurance.

The worst employees impose a large negative equity value on the firm. This can be through pushing a change to production that nukes your product, incessantly distracting your earnest employees, or acting inappropriately with clients.

You systematically pay a premium to hire better employees who command higher wages. The people that you hire are on average less likely to carry out negative equity value events.

This model would explain most of the difference in salaries between a worker based in the US and India in an increasingly globalized world. While the work between a US and Indian employee is of similar quality for the vast majority of cases, US workers are more contextually competent and less likely to initiate these value-destroying events.

Compensation impact: Low-medium

5. Society wants to maximally incentivize people to join the elite labor force to find the next generation of new elites.

The greater the monetary delta between a low-skill and a high-skill job, the more people are incentivized to pursue the high-skill profession. In a world where everyone gets paid the same regardless of their profession, nobody has any incentive to work harder to get a high-skill job and befriend other elite talent.

The labor market works decently well at finding talented, ambitious people over long time horizons. It’s a sorting mechanism for finding, developing, and unlocking ambitious high-productivity talent.

Adept high-skill workers will outcompete their counterparts and accrue more capital, on average. Capital is one of the main determinants of power, and this is the best way to allocate financial power.

Firms would have to implicitly agree that this is a collective action problem worth paying a premium for. Coordinating this scale would be nearly impossible without defectors who would reap the benefits of larger profit margins.

Compensation impact: Low

6. Firms are one of the many actors complicit in a systemic status subsidy scheme.

Higher education is a complete sham and elite human capital does not exist. People in positions of power across industries are working together to keep the scheme going, financially for educational credit repayments and socially for elite formation.

Society has to justify the investment of 20+ years of education and we have determined it is better to cover the scheme up. People need to feel a sense of self-actualization and fulfillment that this scheme provides.

Firms knowingly add a premium to workers’ wages, increasing costs, and lowering profits to perpetuate the scheme. While this may occur in some niche, protected industries, it is unlikely that this is occurring at scale.

Compensation impact: Medium for specific jobs, negligible otherwise

7. High wages are the preferred intergenerational wealth redistribution mechanism.

High salaries for young professionals allow educated elites to maintain cultural capital while preventing social unrest that might result from more obvious inequality.

These jobs provide high enough compensation to sustain consumption-focused lifestyles without requiring genuine adult responsibility or productivity, keeping otherwise unproductive young professionals politically and socially compliant. UBI is already here, it’s just not evenly distributed.

While high wages help obfuscate inheritance mechanisms to perpetuate the illusion of fairness, this is likely not happening on a global level. A firm could easily decide not to pay the premium and hire similarly competent individuals.

Compensation impact: Negligible

Exercise for the reader: Which of these compensation rationales are durable in the context of AI?