The article argues that the AI boom has physical public costs that should not be quietly shifted onto households and municipalities. Data centers require electricity, transmission, substations, water, land, and cooling infrastructure, while a small group of firms captures much of the upside. The case is not to stop AI investment, but to make developers pay for attributable grid, water, and local infrastructure costs in exchange for faster, clearer approvals.

It also proposes a modest, independently governed sovereign wealth fund funded by AI-linked public revenues or infrastructure-use levies. The fund would diversify public exposure, preserve fiscal capacity, and support underprovided capabilities like public-interest compute, cybersecurity and worker-transition pathways. The broader bargain is to trade a little short-term speed for better cost allocation, stronger workplace safeguards, and a public balance sheet that lets households share in AI-driven wealth.

The article argues that the signaling power of a college degree is weakening as AI reshapes hiring today. Employers want graduates who can ship fast, coordinate AI tools across workflows, and apply human judgment with customers. Degree titles matter less than proof through projects and outcomes, which creates a trap for new grads who need jobs to earn the experience that jobs now demand.

It reframes the college choice for students and families as a cost-benefit problem. College can still buy time, networks, and a portfolio, but tuition and debt make prestige a risky default when nondegree paths can offer stability. The piece argues firms must stop treating graduates as liabilities and build clear training pipelines to create "AI-ready" talent. Even that is incomplete without policy that protects living standards as the degree-to-job pipeline breaks.