Artificial intelligence poses an unprecedented threat to energy security in the global North, which could seriously undermine decarbonisation targets, put enormous pressure on the grid and lead to energy market volatility, which will inevitably spill over into the economy as a whole. After decades of stable energy demand, the energy required for data centres is now soaring and is expected to continue to grow at an alarming rate.
The energy challenge of artificial intelligence
By 2030, AI is projected to account for 3.5% of global electricity consumption and 9% of U.S. power generation (a huge growth rate compared to the country’s current growth rate of about 3.5%). According to Rystad energy’s projections, electric vehicles and AI will add 290 terawatt-hours of electricity demand to the U.S. grid by the end of this decade. That would put their collective electricity use at roughly the same level as the entire country of Turkey, the world’s 18th largest economy.
Jason Shaw, chairman of the Georgia Public Service Commission, the U.S. electricity regulator, told the Washington Post earlier this year, ‘When you look at the numbers, it’s staggering. It makes you scratch your head and wonder how we got into this situation. How did the projections get so far off? This presents us with an unprecedented challenge.’

Energy efficiency benefits of quantum computing
In the face of this rapidly growing problem, leaders in the public and private sectors are scrambling to come up with new ways to meet the tech industry’s new unmet energy needs without seriously compromising energy security or climate outcomes.Rystad analyst Surya Hendry said, ‘This growth is a race against time to expand power generation without overwhelming the power system to expand generation without overwhelming it.’
Slowing the growth of AI, perhaps the most logical solution to this dilemma, seems completely out of the question. In the U.S., the technology has received rare and strong bipartisan support, as maintaining leadership in the emerging industry is seen as a key strategy for national security, the economy, cybersecurity, and tech industry governance. Can’t put the genie back in the bottle.
There is no doubt that with huge energy growth on the horizon, the scale of the problem of powering AI in the near future is so great that the solution relies more on future technological approaches than on existing technologies. tech giants such as Bill Gates and Sam Altman have called for increased investment in fusion research as a potential way of unlocking vast amounts of clean energy. Others are not only working on ways to efficiently produce cleaner energy, but also on ways to make AI consume less.
One potential solution to the latter approach could be found through quantum computing. While ordinary computers operate on binary, with 1s and 0s acting as switches, quantum computing operates on quantum bits, which can be turned on and off at the same time, like flipping a coin heads or tails before it lands. This simultaneous switching on and off is called superposition, and it could revolutionise computing as we know it.

In some cases, quantum computers may be 100 times more energy efficient than standard supercomputers. This could have huge implications for artificial intelligence, for which quantum computing may be particularly well suited.
Peter Chapman, president and CEO of quantum computing company IonQ, was recently quoted in Forbes as saying, ‘For the things that quantum computing excels at, like AI processing, no GPU can compete with us. These workloads will eventually flow to quantum computing, and current technology simply can’t compete with that.’ He added: ‘Quantum computing – our next-generation chip – to simulate what it’s doing, you’d need 2.5 billion GPUs that run on two standard wall sockets.’ Chapman said his company could have a prototype of such a chip ready in as little as six to nine months.
Diverse needs for solutions
While the scalable use of quantum computing would be a huge step in the right direction for the tech industry, the country and the world, it should not be seen as a panacea. Will Thompson of Barclays, who recently co-authored a study on AI power consumption, says that solving the AI energy puzzle ‘first requires a way to expand and modernise the grid infrastructure, combining renewable energy with utility-scale storage, utilising the nuclear energy we have, and expanding new carbon-free forms of energy. This would include geothermal, advanced nuclear small modular reactors (SMRs) and fusion technologies.’
In addition, since quantum computing has a long way to go before it becomes a commercial reality, a broad approach to improving clean energy and energy efficiency is a top priority.