When AI Hype Meets AI Reality: A Reckoning in 6 Charts (www.wsj.com)

WSJ’s “When AI Hype Meets AI Reality: A Reckoning in 6 Charts” uses six data-driven visuals to show a growing gap between soaring expectations and on-the-ground results. The charts track cooling startup valuations and venture activity, slowing hiring and layoffs in AI teams, a plateau in server/chip shipments despite model-size headlines, tepid enterprise adoption and uneven productivity gains, investor returns that lag earlier tech booms, and rising regulatory/legal risks. Together they argue that the market’s feverish optimism is colliding with hard economics, integration costs, and user-centered deployment challenges. For the AI/ML community this is a pragmatic wake-up call: technical progress (larger models, better benchmarks) hasn’t automatically translated into sustained business ROI or smooth operational rollouts. Key technical takeaways include the high and growing cost of scaling compute and inference, the importance of data quality and engineering pipelines for real-world performance, the gap between benchmark metrics and user-centric evaluation, and increased emphasis on reliability, safety and compliance. The piece suggests a shift from hype-driven experimentation toward disciplined productization—measuring real impact, optimizing for cost and latency, and investing in evaluation and guardrails—if AI is to deliver durable value rather than a headline-driven cycle.