Overview
- Stanford’s human-centered AI assessment projects a shift to rigorous evaluation in 2026, prioritizing measurable impact, transparency, privacy and explainability in sensitive fields such as health.
- Reporting highlights compute and data-center bottlenecks that demand multibillion-dollar builds, with capital spending currently outpacing revenues from AI applications and prompting investment rethinks.
- Geopolitical divergence across the EU, US and China is expected to raise compliance costs as some multinationals operate separate AI systems, while more countries pursue local models to bolster data sovereignty.
- Deeper AI integration expands the attack surface, with emerging threats like instruction injection and model poisoning requiring updated cybersecurity approaches.
- Labor-market studies foresee net job growth by 2030—about 170 million roles created and 92 million displaced—underscoring the need for continuous reskilling and human strengths such as creativity and emotional intelligence.