AI/DS Column

AI speed is a policy choice, not a universal race Rushing adoption can deepen inequality and strain education systems Measured AI adoption builds lasting capacity and stability In 2024, the United States saw a substantial amou

Enterprise AI competition is decided inside procurement systems, not public ad campaigns The real battle is over who controls enterprise AI orchestration and workflow integration Governance, interoperability, and institutional trust now matter more than model branding

German firms adopted generative AI fast, but productivity gains are flattening The next phase is converting adoption into durable agentic AI productivity Education and policy must shift from tools to systems, governance, and measurement

The third AI stack is a political ambition, not an industrial reality China’s open-source push wins users, not hardware supremacy Europe and Korea must focus on interoperability and skills, not full-stack rivalry Between Augu

Digital truth can no longer be judged by human sight or sound alone Institutions must certify reality, not just detect fakes after harm occurs Education systems now play a central role in rebuilding trust in evidence In

Federal AI adoption depends on tools and training, not elite titles DOGE proved rapid automation can work but exposed skill gaps Lasting reform requires institutionalized AI, not rollback Getting AI into

HAIP faces pressure as national AI governance models increasingly diverge China’s state-led approach changes the incentives for voluntary global standards Without economic rewards, HAIP risks losing durability in strategic competition

HAIP increases transparency but does not yet change behavior Voluntary reporting without incentives becomes symbolic Real impact requires linking HAIP to audits and procurement By June 2025, the OECD repo

Advanced economies push AI policy because productivity gains are visible and immediate Poorer countries lag as low returns and weak capacity dampen urgency Education policy can still slow the widening AI divide Since the em

China’s AI edge is increasingly driven by faster, cheaper access to power and land U.S.

AI adoption in Europe is still limited, with most firms using AI only as a supporting tool The gap between AI hype and real workplace use reflects risk, skills gaps, and institutional limits Policy and education must focus on practical capacity, not promises of rapid transformation

Health data monetization is failing because patients do not trust technology firms with sensitive medical records Turning health data into a commodity ignores consent, governance, and healthcare’s real economics Without strong safeguards and public oversight, most health data projects will keep breaking down

AI in education needs compute; cooling drives water, power, and trust costs Require verified standards for data center water cooling, power, and heat reuse Site compute in low-water regions and reuse heat to scale AI responsibly Operatin

Orbital data centers could ease power and cooling limits on Earth The costs and climate trade-offs are still unclear Education should set rules now before orbit becomes a new dependency Global data centers consumed roughly 415 TW

SB 53 is AI safety policy that also shapes U.S.

AI video streaming is mainstream; tools are easier, directing still matters Without rights, provenance, and QC, slop scales and trust falls Train hybrids and set standards to gain speed without losing story Back in D

Humanoid robot limitations endure: touch, control, and power fail in the wild Hype beats reality; only narrow, structured tasks work Fund core research—tactile, compliant actuation, power—and use proven task robots We don't

48-hour takedowns for non-consensual deepfakes Narrow guardrails curb abuse, not innovation Schools/platforms: simple, fast reporting workflows Deepfake abuse is a vast and growing problem.

AI tools exclude people through missing data and bugs Count “no-decision” cases and use less-exclusionary methods with human review Set exclusion budgets, fix data flows, and publish exclusion rates A quiet fact sets

AI hiring discrimination comes from human design choices, not neutral machines “Autonomous” systems let organizations hide responsibility while deepening bias Education institutions must demand audited, accountable AI hiring tools that protect fair opportunity