Understand the economics of AI before it reshapes everything around you!

If you've found yourself frustrated — genuinely frustrated — by the quality of public conversation about AI and the economy, you're in the right place.

Not because the frustration isn't warranted. It absolutely is. The mainstream coverage swings between two poles: breathless optimism about abundance and productivity, and panicked headlines about mass unemployment. What almost never makes it into either story is the structural economic analysis that would actually tell us something. The labor share data. The job-creation math behind data center investment. The historical record of what technological disruptions do to bargaining power and wealth concentration when left unaddressed.

Mr Elon Musk warns: Only 3 years left before the end of the "work for salary" era!

Elon Musk broke his silence with a bombshell statement about the future of the job market and employment: The remaining time to earn money through "human effort" won't exceed 3 years at most!

After this period, most salaried jobs will be automated, and the social contract that has lasted for the past 200 years will end: "We borrow your time for 40 years and we'll give you a salary"!

Key highlights from the warning and the anticipated historic shift: Collapse of the value of "human time": Salary is paid for lending/selling your time... When the machine becomes faster, cheaper, and better at utilizing this time, the value of wage labor will suddenly collapse!

The biggest wealth transfer in history: The current gap between those who have embraced the shift and started moving, and those waiting for its announcement in the news bulletins, is the difference between survival/richness, and ruin.

What to do during these critical three years? Transform your time and effort immediately into assets that machines cannot take from you, the most prominent of which are:

Real tangible assets (land, forest, and energy). Infrastructure and computing hardware. Strong network relationships (Human Network). The ability to produce food and basic resources.

In the period between 2035 and 2036, the lesson won't be "how much do you earn monthly?" but "what do you actually own?" The shift from the concept of "wage employee" to "asset owner" is no longer a side option, but a matter of survival faster than anyone imagines!

PwC’s 2026 Global AI Jobs Barometer analyzed over one billion job advertisements in 27 countries and territories. The average wage premium for workers with AI skills reached 62%, up from 57% a year earlier. PwC’s release names machine learning as an example of an AI skill. The release also says AI is increasing the emphasis on human skills such as judgment, creativity and leadership.

EY US announced the rewards on August 31. The $100 million goes to professionals who develop future-focused skills, advance the firm’s culture, drive innovation and deliver exceptional client service. The program has three award categories. Collaboration and leadership appear in the first, along with learning and experimentation. Technology appears in the second, along with innovation, disruption and growth. CBS News describes the rewards as bonuses for workers who show human skills such as leadership, judgment, business acumen, collaboration and an ability to adapt.

I built The AI Economy Lab because I believe rigorous economic analysis shouldn't be locked behind graduate seminars or 80-page working papers. The concepts aren't that complicated — capital-biased technological change, Ricardo's crowding-out insight, the mechanics of hyperscaler capex — but they require careful introduction and honest engagement with what the data actually shows. Some of those numbers are alarming. I'll tell you when they are, and why, and what the range of serious interpretations looks like.

The course is structured to build your analytical toolkit from the ground up: we start with first principles on capital and labor, work through the crowding-out and job-creation evidence, examine the inequality mechanisms in detail, and then apply all of it to the policy debates that matter — UBI for Learning, windfall taxes, labor market reform. We close with the skills to do this analysis yourself, on new announcements and new data, long after the course ends.

I don't think you need to be talked at. I think you need the theory cum praxis frameworks, the evidence, and an intellectually honest guide willing to say: here's what we know, here's what's genuinely uncertain, and here's how to tell the difference. That's what I've tried to build here.

Come ready to read carefully, think hard, and update your priors when the evidence calls for it. That's the only prerequisite that matters.