Nobody is saying it, but the advent of artificial intelligence and machine learning may be springboarding the increased intensity of the competition for nuclear fusion – which many are now saying is far less than “30 years away.” The burning question among the few who are fully aware of the stakes in this race is “Who will get there first?” coupled with “Does first matter more than best?”
The greatest obstacle to fusion supplying the world with limitless electricity is learning how to maintain a balance between magnetic confinement and the severe heat (100 million degrees Celsius). Fusion produces cleaner energy than fission (its only byproducts are helium and other greenhouse gases – not radiation), and its fuels – deuterium and tritium – can easily be sourced from seawater and lithium. Scientists today are trying multiple ways to skin this cat.
Serious consideration of nuclear fusion began in the 1930s with the discovery of tritium by a research team led by experimental physicist Ernest Rutherford, who had earlier collaborated with Niels Bohr in the discovery of the neutron. In 1938, University of Michigan scientist Arthur Ruhlig proposed that deuterium-tritium fusion occurs with a very high probability when the two are brought into close proximity – but then World War II put fusion research into the freezer.
The most celebrated event in the revival of fusion research came at the Geneva Superpower Summit in November 1985, when General Secretary Mikhail Gorbachev proposed a collaborative international project to develop fusion energy for peaceful purposes. A year later the European Union (as Euratom), the U.S., Japan, and the Soviet Union agreed to jointly pursue the design for a large international fusion facility they called ITER (the way).
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