The Central Claim

The author argues that people have about five years to build wealth before AI significantly changes the economic position of workers. The author describes this timeline as a theory, not a forecast with a known date. The author states that no one can confirm how long the window will last.

Why Jobs Have Historically Created Wealth

Every job and business exists to solve a problem. A company hires a receptionist to answer its phones, a salesperson to sell its products, and staff to manage finances, edit promotional videos, or design packaging. A person is paid to solve each problem.

For most workers, the main asset is their ability to sell time, knowledge, and skills. Employment has offered a way to move up economically without owning property, a company, or stocks.

The Historical Divide Between Wealth and Labor

For most of human history, economic position was largely fixed at birth:

• Families who owned land and assets generally stayed wealthy.
• People born into poverty often remained poor.
• The owners of assets employed those without them.
• The main way to cross the divide was marriage, and even that carried social stigma.

The author argues that businesses and the free market weakened this barrier. Each major economic shift lowered it further. The author cites social media as the change that finally broke it down.

The Risk Posed by AI

The author argues that AI could reverse this trend. If AI systems perform the work that people are paid to do, fewer workers are needed. A company that once required five employees for a task may need only an AI agent to complete it.

The problems still exist, but fewer people are paid to solve them. The author's reasoning follows this sequence:

1. The value of a worker's time falls as AI takes over tasks.
2. Building wealth through employment becomes harder.
3. Business owners lower costs, increase output, and keep the difference.
4. The gap between owners and workers widens, and the barrier between the two groups could be rebuilt.

Current Evidence

The author cites the following data points.

Claude Usage (Anthropic)

Anthropic, an AI company, examined how Claude is used in practice. According to the author, the company found the following:

The author notes that these figures cover only Claude. They exclude ChatGPT and other AI tools.

Entry-Level Employment (Stanford)

A Stanford study analyzed payroll records covering millions of workers. The study found the following:

• Employment among workers aged 22 to 25 in the jobs most exposed to AI is about 19% below its expected level. The expected level is based on the trend for young workers in less exposed jobs.
• Older, more experienced workers in highly exposed jobs do not show the same gap.
• The study found no evidence of widespread job displacement across the economy.

The author states that entry-level positions, which have traditionally served as the first step on the career ladder, are the first to decline. Experienced workers in the same fields are largely retaining their jobs, but fewer new positions are opening for newcomers.

Labor Share of Business Profits (United States)

The share of business profits paid to workers in the United States has fallen to 52.8%. This is the lowest level since records began in 1947.

The author acknowledges that this decline began before AI became widespread and does not attribute it to AI. The author argues that the trend matters more as businesses produce more output with fewer workers.

Public Response to the Shift

The author observes two common reactions to AI-driven change:

• Some people assume they will be unaffected.
• Others react with panic.

The author considers both responses unproductive.

Historical Precedent

The author links the current shift to earlier technological changes, including computers, the internet, and social media. The author states that three groups emerged during each change:

• People who ignored the change.
• People who feared it.
• People who recognized the change early and adapted.

According to the author, the third group consistently achieved the best results.

The author provides two examples from personal experience:

• Online retail: When the internet became popular, the author opened an online store for a remote-control model business. Most competitors focused on physical shops. Many people considered online shopping a passing trend. The author reports that early adoption gave the business a competitive advantage.
• Social media: Friends in the author's social circle did not see the value of TikTok, which they regarded as a dance application. The author reports becoming the most-followed personal finance creator in the world within a few years of starting to share advice on social media.

The Author's Assessment of AI

The author reports testing a range of AI-based side income methods over recent months. The author states that the results were stronger than expected and that real income opportunities exist.

The author argues that many people will spend the coming years debating whether AI is overhyped, dangerous, or a threat to their jobs. The author contends that people who accept these facts and learn to use AI will be best positioned to benefit.