The article argues that the “AI arms race” framing is colliding with the economics of AI. Governments want scale and interoperability, but also sovereignty: control over data, compute, models, standards and talent. Since the full stack is too costly for most states, sovereignty becomes modular risk management, and energy constraints make compute a strategic bottleneck. Cloud regions still sit under jurisdiction, so access can become a bargaining chip.

Collaboration still pays where externalities cross borders: safety science, benchmarking, incident sharing and interoperable standards. This creates layered coexistence: open coordination at the bottom, control at the frontier. The U.S. pairs safety cooperation with export controls, the EU pools capacity via the AI Act and AI Factories, China enforces tight domestic rules and India bets on sovereignty-through-access and open ecosystems. The takeaway: treat access risk, energy and standards as first-order strategy variables.

The article argues that young people are being set up by outdated social norms that still equate “success” with a prestigious, degree-dependent full-time job. In an AI-disrupted labor market where hiring is weak and searches drain savings, the core issue is not individual effort but a coordination failure: society prepares students for salaried work while the economy supplies fewer stable roles. When expectations lag reality, students can stay stuck chasing shrinking pathways instead of adapting early.

It warns that “grit” and merit narratives can become traps in a market shaped by AI screening, luck, and sudden role closures. The alternative is flexibility and multiple income levers: build a visible personal brand, focus on problems rather than job titles, and stay ready to pivot. For families and schools, the message is to stop treating college and prestige careers as default and to normalize trades, entrepreneurship, and other routes to stability.

The article argues that if AI makes work optional for firms, the state must reconsider what it owes workers. It urges study of Universal Basic Employment (UBE): a legally enforceable standing job offer at a set wage and benefits for anyone willing to work.

Drawing on New Deal relief, public service employment and modern subsidized-job trials, it finds higher incomes and social benefits but uncertain net employment due to crowd-out and fiscal substitution. Because UBE is a wage floor, a high wage could pull workers from low-wage private jobs and raise prices; take-up and costs hinge on financing and wage setting. In an AI economy, the key question is whether public jobs absorb labor private firms no longer demand. The article concludes UBE is neither a cure-all nor impossible and deserves rigorous modeling and large-scale tests alongside UBI and dividends.

The article argues that recent layoffs at profitable firms are being misread as AI-driven job replacement. The real drivers are post-pandemic demand normalization after the 2020–2022 hiring boom and the repricing of capital once rates jumped, which made boards and investors demand visible efficiency. Layoffs became a signal of discipline and margin protection, often paired with AI and data-center commitments.

AI matters mostly as framing and capital-allocation justification. Productivity gains are hard to measure, but headcount cuts show up immediately in revenue-per-employee, so executives cite AI to explain why labor costs must fall now. The cuts also reshuffle power by trimming recruiters, coordinators and middle managers while protecting core engineers and AI specialists, producing leaner, centralized firms. The article concludes this is rebalancing, not collapse, and urges leaders to base decisions on regime shifts and measurable signals, not headlines.

The article argues that weak consumer sentiment is increasingly a jobs story. Unemployment is still low, but hiring is down, applications per opening have surged, and many searches produce no callbacks. People update expectations from signals they can feel, so silence in the job hunt erodes confidence even when top-line labor data looks fine.

It describes a feedback loop: more applicants lead to heavier AI filtering and slower recruiter response, which pushes people to apply even more and feel less capable. That dynamic shows up in survey measures of confidence and helps explain why sentiment is slipping among professional, higher-income households. Mardoqueo concludes that policymakers and employers should track and improve feedback metrics such as hiring rates, response rates and time-to-hire, because these shape spending, saving and voting.

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.