It's 2026, and as new AI tools seem to emerge every week while unemployment ticks up, some may ask: are we headed toward a Universal Basic Income scheme?

As more and more tasks become automated, from data analytics to summarizing reports and beyond, almost every person I've spoken to lives with a lingering fear that AI could replace their job. Without a job, a person must find an alternative way to pay their living expenses.

Enter the idea of Universal Basic Income (UBI). Under a UBI arrangement, each individual receives a minimum fixed payment, supposedly allowing them to live without earning an income from a job.

If you have been reading this blog for a bit now, you know we have been skeptical of the “AI Bubble.” Our skepticism, or at least my own, has mostly centered around the economic implementation lagging the hype. We spent the better part of 2025 watching companies buy massive amounts of GPU compute to build smarter chatbots, yet aggregate productivity statistics barely budged. (Yes, we have some data now that shows the effects of AI on productivity but not nearly as much as you would think).

While the market was distracted by the “Brain” trade (LLMs, data centers, and NVIDIA chips), you may have missed the momentum building in the “Body” trade.

Most large companies have formal policies, enterprise licenses, internal copilots, or approved tool stacks. In many sectors, AI is already embedded in day-to-day work. If adoption alone were the constraint, we should already see it in the productivity data.

And yet, the aggregate numbers remain underwhelming.

This tension is often framed as disappointment or hype fatigue. I think it is better understood as a timing and measurement problem. In this post, we will suggest a different, slightly more uncomfortable explanation to the fears of an AI bubble.

With the pace of technological change, prevalence of job loss, and worsening socioeconomic inequality, individuals face more mental health challenges than ever. In conjunction with the AI boom, the market for AI in mental health is large and growing. On a global scale, it was estimated at $1.13 billion in 2023 and is projected to reach $5.08 billion by 2030.

Access to high-quality life advice or therapy matters now more than ever. Many entrepreneurs have spotted an opportunity: creating AI for mental health. Daniela Andrade is one such individual. Daniela graduated from Harvard in 2025 and is Head of Growth at Resolution, a startup that serves primarily young women by providing them with an “AI guardian angel” called Fabio to help them navigate toxic relationships.

I’ve been in the holiday mood since right after Halloween. If you’re like me, holidays also mean thinking about the possibilities of the future. So, after watching Hocus Pocus and switching to Polar Express, I found myself doom-scrolling through economic forecasts.

The IMF’s latest World Economic Outlook paints a sober picture. Global growth is expected to slow to just 3.1% in 2026, with the outlook described as “dim prospects” amid persistent downside risks. In plain English: the economic pause many of us felt this year isn’t ending anytime soon.

Michael Burry has a knack for walking into the party right when the music is loudest and asking where the fire exits are.

In 2005, that meant shorting the U.S. housing market while everyone else was busy securitizing granite countertops. Today, it means betting against the AI trade while the rest of us are delighting in auto-drafted emails and AI-polished pitch decks. On paper, he is “short AI.” In practice, his long game is quieter and more elemental: water rights, water-rich farmland, water utilities.

He is betting on scarcity against a story that pretends resources are infinite.

To many people, the economy represents a vast mystery of supply chains, tariffs, and uncertainty. To most professionals, making everyday business decisions regarding pricing, budgeting, or forecasting demand for a product appears an intractable problem. The study of economics attempts to put some structure on these moving pieces.

With the rise in economic uncertainty spurred by recent societal developments, such as AI, it’s worth asking whether AI itself can provide expert-level economic decision-making for individuals and organizations to sort through the noise.

Last week, I attended a gathering of economists and data scientists from major tech companies, all focused on how technology is reshaping business and work. Practitioners swapped insights on everything from labor market trends to AI experiments. The AI revolution promises to upend how we work and this is happening against the current backdrop of the “pause economy” with hiring and investment in a cautious lull. The discussions ranged from the cooling tech job market to cutting-edge methods in causal inference and AI measurement, and deeper questions about whether AI is a substitute for human work or a “bicycle for the mind.”

It began with headlines.

“Amazon cuts 14,000 corporate jobs as spending on artificial intelligence accelerates.”

“Salesforce CEO: ‘I need less heads.’”

“Meta axes 600 roles amid AI expansion.”

Each announcement laced with brutal irony, revealing companies not in distress but actually flourishing through the very mechanisms that deemed their hires expendable. Fair is fair, though, right? Nothing illegal happened, and technically this is an expectation of the marketplace. So, one can only wish these families all the best… right?

Then came the tag: “AI and robots will replace all jobs,” making work essentially “optional, like growing your own vegetables.”

In my last article, I discussed how companies and policymakers can step in to help in an AI-driven labor market. What about educational institutions? While students themselves can develop skills to prepare, educational institutions can play a role in guiding them on which skills to develop.

A college education implicitly promises increased access to career opportunities and higher earnings. But students often don’t know what they don’t know when it comes to preparing for careers. Upon entering college, they usually can’t name the particular skills they’ll need to succeed as, say, a data scientist. Instead, they place their trust in key college courses to provide relevant material.

Last week, I spent several days in Philadelphia for the Annual Meeting of the National Association for Business Economics (NABE). The event brings together economists from central banks, think tanks, corporations, and universities to assess the state of the global economy. The theme this year was Global Economy in Transition: Finding Opportunity Amid Disruption. Sessions explored the pressures shaping global business and policy: geopolitical instability, shifting trade regimes, monetary divergence, and the disruptive force of technology. There were also discussions of long-term structural issues such as climate risk, labor markets, and the balance of economic power.

As Kent and I highlighted two weeks ago, conventional recession indicators suggest a healthy economy, though recent trends in the labor market for college graduates paint a different picture. We pointed to some appalling anecdotes and statistics, including the case of the Computer Science major who submitted 5,762 job applications, only to hear back from none.

This reality begs the question of whether job creation in the age of AI will ever speed up as individuals, companies, governments, and educational institutions adapt. If so, when?