Convergence 01 — The Age of AI Six connected investigations. Each begins with current evidence, explores what could plausibly emerge over the next 3–5 years, and ends by asking the question the next investigation answers. Start at 001, or read the full Convergence overview →
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The Age of AI Is Here. What Happens Next?
Experts increasingly describe AI as a general-purpose technology — the rare kind that reorganizes work, capital, and infrastructure. Measurable changes have already begun. This investigation follows the evidence and asks what could plausibly emerge over the next 3–5 years.
Raises: “If AI changes organizations, what happens to the workforce?”
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Are AI Layoffs a Strategic Mistake?
Companies are citing AI in layoff announcements while the productivity evidence is still forming. What leaves the building when experienced people do — and what do the historical parallels say about cutting ahead of the payoff?
Raises: “If companies keep hiring fewer people, how does hiring itself change?”
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Why AI Is Changing How Companies Hire
Résumés written by AI, screened by AI, ranked by AI. When both sides of hiring automate, what happens to the signal a résumé was supposed to carry — and what replaces the credential?
Raises: “If hiring changes, what kinds of work emerge?”
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What New Jobs Could Emerge in the Age of AI?
Every general-purpose technology ended some kinds of work and created others. This investigation looks for the early evidence: which roles are evolving, which skills are gaining value, and where genuinely new occupations are appearing.
Raises: “If AI creates new industries, what infrastructure supports them?”
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Why AI Is Driving the Biggest Data Center Buildout in History
GPUs, fiber, cooling, water, construction crews, and an unprecedented wave of capital. This is where AI stops being software and becomes physical infrastructure.
Raises: “What powers all of this?”
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Can Our Energy Infrastructure Keep Up?
Data centers are asking the grid for power on a scale utilities haven’t planned for in decades. Nuclear restarts, natural gas, renewables, transmission — the last constraint is electricity.
Raises: “The Convergence closes — and the weekly research cycle begins.”
Convergence 02 — Work, Infrastructure, and Institutional Memory
Four investigations demonstrating the publication's breadth beyond AI and labor — institutional knowledge, hiring markets, infrastructure ownership, and a flagship synthesis connecting them to demographic transition. Read the full Convergence overview →
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The Great Knowledge Drain
A federal system nearly rewritten on an unrealistic timeline. Nuclear, aviation, and mainframe workforces all approaching retirement at once. And the entry-level roles that used to train replacements are shrinking too.
Raises: “How are organizations trying to hire around that gap — and why does it feel so broken?”
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Why Hiring Feels Broken
Employers say they can’t find talent. Workers say they can’t get a response. The sharpest finding: 85% of employers claim skills-based hiring; fewer than 1 in 700 hires actually reflect it.
Raises: “The same organizations struggling to hire are making massive AI infrastructure bets — who captures that value?”
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Who Will Run Tomorrow’s AI Infrastructure?
One EUV maker. One dominant fabricator. One dominant chip designer. Governments buying equity instead of just regulating. A new “neocloud” tier already being tested by its own former customers.
Raises: “What happens when infrastructure control isn’t accountable to the people who depend on it?”
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The Great Transitions
Why does the world feel like it’s changing all at once? The evidence points to demographic transition — aging, shrinking workforces — as a real, documented driver compounding with everything else this convergence found.
Raises: “The convergence closes — the weekly research cycle continues.”
Published investigations
Standalone investigations published ahead of the Convergence — their evidence feeds the episodes above.
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Why Is AI Restarting Nuclear Reactors?
The most famous failed reactor in America is being revived to power software. Intelligence turns out to have a body — with an electricity bill, a construction schedule, and a two-century-old paradox attached.
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Where Did the Entry-Level Job Go?
Payroll records from millions of workers show the youngest losing ground in AI-exposed jobs while older colleagues hold steady. The jobs we learn on are the jobs machines now do best — this time, the machine learns on our rung.
New to the format? See how an exploration is structured →