On 22 June 2026, IEEE Spectrum marked seventy years since the birth of artificial intelligence—a gathering of researchers at Dartmouth College in 1956 where the term "artificial intelligence" was first coined by John McCarthy and his colleagues. That same week, Stanford University released new data on how much the world is actually using AI today, and the numbers tell a surprising story: the technology that most people associate with American innovation is spreading far faster in other countries.

The Paper That Changed Everything

When IEEE Spectrum selected the ten most important AI research papers from the last twenty-five years, one stood out: "Attention Is All You Need," published in 2017 by a team led by Ashish Vaswani. This paper introduced a new way for computers to learn—called the transformer architecture—that became the foundation for every major AI chatbot and language model you interact with today. ChatGPT, Claude, Gemini—all of them rely on this 2017 idea. A single paper from summer 2017 reshaped an entire industry in less than five years. That rarely happens in technology.

The other papers on IEEE's list tell a longer story: how scientists moved from teaching computers to follow rigid rules to building systems that learn from vast amounts of data, similar to how you learn by observing patterns in the world around you. But the transformer paper is the one that made large AI systems practical and powerful enough to deploy at scale.

The Adoption Gap Nobody Expected

Here is where the story gets interesting. Stanford's new data shows that Singapore leads the world in AI adoption at 61%, followed by the UAE at 54%. The United States ranks 24th globally, with only 28.3% of organisations using AI. For a country that invented the technology and builds most of the world's AI systems, that is a surprisingly low number.

What Stanford's report calls a "readiness gap"—the distance between how capable AI has become and how prepared organisations actually are to use it wisely. Companies have been rushing to buy AI tools, but many have not set up the internal safeguards, oversight processes, and clear responsibility structures needed to deploy them responsibly.

Why is the US adoption rate so low despite American leadership in AI research. Singapore and the UAE are smaller, more centralised countries with aggressive national plans and the government machinery to push AI adoption quickly across their economies. The United States is larger and more decentralised—millions of individual companies make their own decisions about technology adoption based on their own risk tolerance and technical readiness. That diversity slows things down. It is not a failure of ambition, but a structural fact of how a large, fragmented economy operates.

One caveat: the adoption numbers reported by Stanford are based on self-reported data and averaged across entire countries, which can hide real differences between, say, Silicon Valley tech companies and rural hospitals or mid-sized manufacturers. The numbers are meaningful, but they are not the whole picture.

What This Moment Means

Seventy years ago, the Dartmouth researchers asked whether a machine could ever be made intelligent. Today, the question has shifted. AI is now capable enough and powerful enough to deploy at scale. The real question is: who is using it, how prepared are they to use it responsibly, and where are the benefits and risks going to show up first.

The field has delivered far more on its technical promises than skeptics in the 1950s and 60s imagined. The challenge for the next decade will not be building smarter AI. It will be closing the gap between what these systems can do and what the organisations running them are actually equipped to manage.