AI mega hubs
2 GW + data hubs will emerge in every global region
North America
In the US, traditional hubs like Northern Virginia will scale to 8.5 GW by 2030, Dallas (2.8 GW), and Phoenix (2.7 GW)
will also see massive absorption.
Europe
London (2.7 GW), Frankfurt (2.68 GW), and Paris (2 GW) are forecast to lead demand by 2030. Many new hubs are
growing fast in Europe in particular, driven to some extent by data sovereignty regulations as well as development and
latency factors. Also look out for steep growth in Oslo, Barcelona, Madrid, Zaragoza, Lisbon, Berlin, and Dusseldorf.
Asia Pacific
In Asia Pacific (excluding China, where growth is expected to be as or more rapid), Tokyo (2.8 GW), Sydney (2.4 GW),
and Johor (2.2 GW) will have the largest capacities by 2030. New APAC AI hubs will also grow fast in Busan, Kyushu,
Auckland, and Perth.
The cost of artificial intelligence
will drop exponentially
While demand for high-performance (and very
expensive) GPUs like the H100 is unprecedented,
and there will continue to be fluctuations in the cost
of different training models, the averaged cost of
artificial intelligence itself is declining rapidly.
The cost of the cheapest Large Language Model
(LLM) with a minimum Massive Multitask Language
Understanding (MMLU) score of 83 has decreased
by approximately 10x every year from 2022 (GPT-3)
to 2024 (Llama 3.2 3b). This reduction in the cost of
AI training and inference does not, however, lead to
reduced consumption. Instead, like improvements in
automobile fuel efficiency that led to more car sales
and larger cars, the declining price of intelligence
is expected to fuel an exponential increase in the
demand curve for AI.
The scale of investment is accelerating this cost-
per-unit benefit. Hyperscalers are making enormous
investments in GPUs and infrastructure because, in
the race to deliver services and achieve Artificial
General Intelligence (AGI) they understand that the
risk of under-investing is dramatically greater than
the risk of over-investing.
The resulting explosion in demand will yield
substantial returns, with increasing efficiency
driving rather than limiting overall utilization and
innovation. This counter-intuitive phenomenon is
the Jevons Paradox.
