26 February 2024
Forbes
Op-eds
In the whirlwind of technological advances, two
revolutions stand poised to reshape our world: the rise of Artificial
Intelligence (AI) and the urgent shift to clean energy.
These
concurrent shifts promise to drive economic growth through productivity, employment,
and investment. In terms of investment volumes looking ahead until 2030, the
energy transition will probably be larger by a factor of ten. Over that period
and beyond, these shifts will intersect in ways that could amplify their
benefits—or challenges. Both will happen in similar geographies, notably China,
North America, the European Union and India. They will also access similar
pools of global capital.
AI
will be an enabler for cleaner energy deployment
The
energy transition needs to be managed in a way that it does not impose high
costs on the consumers, while ensuring reliable energy supplies and AI will act
as an enabler to achieve both. At ReNew, leveraging AI has not only improved
our electricity output by up to 1.5% from existing solar and wind installations
but also streamlined maintenance, demonstrating AI's potential to enhance
efficiency and reduce costs.
Big
data, and innovation in analytics, enable us to measure inputs from satellites,
sensors and weather monitoring stations to predict solar radiation and wind
speed, accurately forecasting the supply of renewable energy generation. On the
side of the equation, AI is accumulating terabytes of historic consumer data to
forecast consumer demand for electricity. Balancing supply and demand is
critical in preventing supply disruptions and blackouts.
Globally,
almost $3 trillion worth investment is being allocated between now and 2030 to
lay the wires and infrastructure to transport clean energy from points of
generation to the consumers. Several companies are already leveraging AI for
strategic decision making in terms of planning which type of grid is suitable
to which location, all the way down to the size of the wires. With several of
these wires running thousands of miles, it is difficult to inspect and maintain
them. New machine learning software predicts anomalies in wiring and failures
of transformers, saving time and money. While actual numbers are larger, even
5% savings on capital expenditure for installation and replacement, will result
in reduced expenditure of $150 billion in the next 7 years.
However,
AI is an enabler for more efficient and sustained fossil fuel driven activities
too
Like
the internet, AI is a tool that is useful for everyone, including the fossil
fuel sector. It is an equalizer. Companies like BP, Shell, Exxon are already
using AI to lower the cost of extracting oil and gas. Autonomous vehicles,
based largely on AI, are increasing in numbers - most of which run on gasoline.
By making travel cheaper and more convenient, autonomy could increase the
number of vehicle miles travelled. If media reports are to be believed, General
Motors Co.’s Cruise and Alphabet Inc.’s Waymo are likely to begin offering
self-driving taxis in San Francisco shortly. The merger between electrification
and autonomy of vehicles is likely to take some time, based on improvements in
batteries, sensors, and computation capabilities.
AI
will also be a huge energy guzzler
Training
AI models (meaning setting up AI models to spot patterns in datasets) and
delivering inferences (meaning numbers, text, videos, imagery based on the
patterns) require huge amounts of computing power and data storage. The energy
demands of AI, particularly for data centers, are soaring, potentially
rivalling the consumption of entire countries like Brazil, South Korea or
Germany. According to the IEA, data centre energy usage stood at around 460
terawatt hours in 2022.
To be
candid, I am cautiously optimistic about deploying Artificial Intelligence.
Drawing lessons from the energy transition journey, there are three areas where
we must collectively pay particular attention, to ensure that AI makes a strong
positive contribution to humanity.
Diversification
of solution providers: A
single US based firm holds around 80% of the high-end AI chip market. The few
big tech firms hold most of the computational capabilities, datasets and
servers that will enable AI to even function. Much like energy transition,
capabilities are concentrated in one or two nations, posing risks of
disruptions and trade controls. A wider set of nations, including from the
global south, need far more active domestic policies to develop their own AI
technologies, solutions, and business models.
Governance
to ensure reliability, accountability, and dispute resolution: As the use of AI grows, there will be ever more capturing of data. This
makes us prone to errors (due to use of poor-quality data), cyber-attacks and
data-theft. These will need appropriate legal provisions, that ensure access by
clients to datasets used by service providers and allocation of
responsibilities for safety and privacy of the data.
Making
it low carbon, before we are locked-in: AI
being a sunrise sector, presents an opportunity for being lower carbon right
from its early stages. We have the technological solutions to do so and a
number of the top 10 companies globally that run data centres have adopted bold
targets for achieving net zero emissions. Accountability towards meeting these
targets will need to be ensured over the next few years. Large investors, that
have many of these companies in their portfolios, currently seem to be focused
on understanding and minimizing the social risks of AI. There must also be a
focus on the environmental implications. Equally, the biggest clients must
start accounting for emissions due to AI services received by them in their
Scope 3 emissions, and take steps to reduce them significantly.
As we
stand at the crossroads of these technological revolutions, our choices today
will determine whether AI becomes a pillar for a sustainable future or a missed
opportunity. Embracing these lessons with caution and optimism is not just
advisable; it's imperative.
Contributor
CEO of ReNew, India’s leading
decarbonization solutions company.
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