AI has cut entry-level hiring 14% while total employment holds
An effect concentrated in one part of the workforce can be invisible in a national average and severe for the people inside it.
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News
What happened in AI, and what it means. Model releases, company moves, funding and regulation.
US chip export controls reversed twice in five months
The test is now where a company is headquartered, not where the chips are delivered. Nvidia forecasts zero China data-centre revenue.
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The right to be forgotten meets a model that cannot forget
European law gives you the right to have your personal data deleted. A trained model has no mechanism for forgetting anything.
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Ten governments can now test frontier AI, but none can block a launch
AI safety institutes evaluate models on access the labs grant voluntarily. The clearest signal of where they are heading is what they have been renamed.
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33 states now regulate election deepfakes, with five different rules
Thirty-three states now regulate AI-generated political content. Two had their laws struck down, and there is still no federal baseline.
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45 states filed 1,561 AI bills, and Congress still has not acted
45 states introduced 1,561 AI bills by March 2026. One hiring tool can face five different definitions of algorithmic discrimination.
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AI and copyright: what the rulings actually turn on
Training on books has survived as fair use. Anthropic still paid $1.5 billion, because how the data was obtained is judged separately from how it was used.
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The EU AI Act explained: what it requires and who it reaches
Risk tiers, not technology tiers. The same model can sit in two different categories depending on the decision it participates in.
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Every software company says it does AI now. Four questions that check
Repositioning and real change look identical in a press release. Pricing, delivery and attributed revenue separate them.
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What an AI valuation actually means, and what it hides
A $190B valuation is the price of a 2.6% slice extrapolated across everything that did not trade. Run-rate, preferences and compute commitments are all invisible in it.
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How AI companies make money, and which model is failing
Five business models with wildly different economics. Selling compute works, selling implementation works, and selling tokens is squeezed from both sides.
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Who keeps the money in the AI stack: chips, models or apps
Silicon, infrastructure, models and applications have completely different economics. Margin concentrates where substitution is hardest, which is not where the attention is.
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