Why opinion on AI is so divided

This story originally appeared in The Algorithm, our weekly newsletter on AI.

In an industry that doesn’t stand still, Stanford’s AI Index, an annual roundup of key results and trends, is a chance to take a breath. This year’s report is full of striking stats, confirming that the US is leading the race with 5,427 data centers—more than 10 times its closest competitor.

There’s also a stark reminder of hardware vulnerabilities: “A single company, TSMC, fabricates almost every leading AI chip, making the global AI hardware supply chain dependent on one foundry in Taiwan.”

But the main takeaway from the 2026 AI Index is the sheer inconsistency of the field. AI is simultaneously a gold rush and a bubble. For instance, Google DeepMind’s Gemini Deep Think scored a gold medal in the International Math Olympiad but fails to read analog clocks half the time.

This inconsistency leads to a massive perception gap. While 73% of US experts are positive about AI's impact on jobs, only 23% of the public feels the same. This divide stems from the "jagged frontier" of AI capabilities: models are incredibly proficient at technical tasks like coding but struggle with open-ended or everyday tasks.

As researcher Andrej Karpathy noted, power users paying for top-tier models see staggering improvements, while casual users of free versions see a much more mixed bag. The reality is twofold: AI is far better than many realize, yet still surprisingly poor at many things people care about. Anyone betting on the future must account for both.