Artificial intelligence is entering a new phase in which its impact is no longer confined to laboratories, technology companies or experimental projects. AI is increasingly becoming part of everyday business, scientific research and professional work, raising the possibility that the technology could fundamentally change how economies operate.
The phrase “Move 37” has become a powerful symbol of that transformation. It refers to the famous move made by Google DeepMind’s AlphaGo during its 2016 match against Go champion Lee Sedol. The move appeared unconventional to human experts, but ultimately proved highly effective.
At the time, Move 37 demonstrated something remarkable about machine intelligence: an AI system could discover strategies that were difficult for humans to imagine, rather than simply copying the decisions of human experts.
A decade later, that idea is being applied far beyond board games.
AI systems are increasingly capable of generating software, analysing scientific data, producing images and video, assisting researchers, handling customer interactions and supporting complex professional tasks. The technology is also moving rapidly into areas that once appeared resistant to automation.
The change is particularly visible in the workplace. Businesses are deploying AI tools to summarise documents, analyse information, write computer code and automate repetitive tasks. Some companies are beginning to redesign entire workflows around AI rather than simply adding AI tools to existing processes.
This shift could have major economic consequences.
The earlier waves of digital technology primarily helped people communicate, store information and access services more efficiently. Modern AI systems are increasingly capable of performing portions of knowledge work themselves.
That distinction has attracted the attention of researchers and technology leaders, who argue that increasingly capable AI could accelerate scientific discovery and technological development.
Google DeepMind chief Demis Hassabis has been among the prominent figures arguing that AI could become a powerful tool for solving difficult scientific problems. Systems developed by the company have already demonstrated capabilities in areas such as protein structure prediction and scientific research.
The rapid development of generative AI has also transformed the technology industry. Companies are investing enormous sums in computing infrastructure, data centres and advanced models as they compete to build increasingly capable systems.
The pace of progress has created both excitement and uncertainty.
Supporters believe AI could increase productivity, improve healthcare, accelerate research and help solve problems that have challenged scientists for decades. Businesses see the technology as a potential source of major efficiency gains.
Critics and researchers, meanwhile, warn that rapid adoption could create significant risks. These include job displacement, misinformation, cybersecurity threats, privacy concerns and the possibility that powerful AI systems could become difficult to control.
Another important question is whether the economic benefits of AI will be distributed broadly. If a small number of technology companies control the most powerful models and computing infrastructure, the gains from the AI revolution could become concentrated among a relatively small group of firms.
The technology is also becoming increasingly accessible. AI tools that were once available only to major research organisations can now be used by individuals, small businesses and developers around the world.
That widespread availability is helping accelerate experimentation. New applications are emerging across education, finance, medicine, manufacturing, entertainment and scientific research.
The significance of Move 37, therefore, extends beyond the historic Go match. It represents a moment when a machine demonstrated a form of problem-solving that surprised some of the world’s strongest human players.
Today’s AI revolution is producing similar moments across many industries.
The difference is that these breakthroughs are no longer happening in a single laboratory or on a single game board. They are appearing simultaneously across the global economy.
AI may not transform everything overnight, but the speed and breadth of its adoption suggest that the transition has already begun. The central question is no longer whether artificial intelligence will change the way people work and live, but how profound that change will ultimately become.
