
Apparently, even super AI will be challenged when recommending against corporate profits ...
Q: How is AI going to help reverse climate change when fossil fuel investment/profits encourage its continued use?
(
Perplexity) "AI can help reverse climate change by optimizing energy use, improving renewable energy systems,
and aiding environmental monitoring—but at the same time, fossil fuel investments and profits drive AI’s use to further
exploit oil, gas, and coal, undermining climate progress unless strong policy interventions are enacted.
AI’s positive climate impacts include:
Decarbonizing industry: AI platforms can track and reduce industrial emissions, optimize electricity usage,
and improve efficiency, potentially cutting emissions by up to 10–30% in some sectors.
Accelerating the deployment of renewables: AI helps integrate variable energy sources like wind and solar
into power grids by forecasting supply/demand and improving grid management.
Environmental monitoring: AI processes satellite and sensor data to detect deforestation, monitor ice melt,
predict extreme weather, and inform climate adaptation strategies.
However,
major AI and tech companies are also accelerating fossil fuel extraction:
AI is used by oil and gas companies to, for example, 'predict the next oil field' or monitor offshore installations,
making fossil fuel extraction faster and cheaper.
Big tech investment in fossil fuel projects, and lobbying against climate legislation, support the continued,
profitable exploitation of hydrocarbons.
As a 'general purpose technology,' AI enhances both clean and dirty technologies; it can just as easily
boost oil/gas profits as it can support renewables.
Key insight: The direction AI pushes climate outcomes depends on societal priorities and policy choices.
Without regulation, AI will continue to be used by the fossil fuel sector as long as investment returns
are attractive and political will to restrict such uses is lacking.
Policy and governance actions proposed by experts include:
Regulating or banning the use of AI for new fossil fuel exploration and extraction.
Redirecting public funding and research incentives toward AI applications in clean energy,
efficiency, and climate adaptation.
Increasing transparency about tech industry partnerships with fossil fuel companies.
Limitations:
AI is not a magic solution — its climate benefits hinge on robust policies that shift investment from fossil fuels to renewables.
Current trends show tech companies profiting from both climate solutions and fossil fuel growth, with only a modest tilt
toward clean technologies so far.
In summary: AI can accelerate climate action or fossil fuel extraction, depending on incentives and regulation. To align AI with goals,
policy intervention to restrict its use in fossil fuel industries and promote its role in low-carbon innovation is essential."
Q: Doesn't AI's rapidly increasing need for energy encourage continued fossil fuel use?
(
P) "AI’s rapidly increasing demand for electricity is substantially raising power consumption by data centers, a trend driven
especially by large-scale AI training and inference workloads. This surge puts significant pressure on energy infrastructure and,
if grids remain heavily dependent on fossil fuels, can indeed encourage continued or increased fossil fuel use to meet growing demand.
Key points from current data and projections:
Data center electricity demand is booming due to AI. By 2030, global power demand from data centers (with AI as the main driver)
is expected to more than double, with worldwide annual consumption reaching 945 TWh—exceeding the current electricity usage
of entire countries like Japan.
In the US, data center power demand is expected to more than double from 2024 to 2035,
with AI data centers alone projected to account for up to 8.6% of total US electricity demand by 2035.
AI-driven data centers could soon use as much electricity annually as 22% of all US households.
The speed and scale of this demand growth threaten to outpace both efficiency gains in AI computing
and the rollout of new renewable energy sources.
If new data centers are powered using existing grid mixes, additional demand will be met with whatever
energy sources are available — including fossil fuels, which still play a major role in most grids.
Why this trend encourages fossil fuel use:
Grid dependence on fossil fuels: Where grids are still primarily powered by coal, gas, or oil, any new demand — such as that
created by AI — tends to be met with an increase in fossil-based generation, especially during periods of peak usage or grid strain.
Pace of renewable deployment: While tech companies advocate for clean energy, most grids cannot transition fast enough
to match both existing and new AI-driven power demand, making fossil fuels the fallback option.
Physical and regulatory limits: Expanding renewable capacity and grid infrastructure is often slowed by permitting, supply chain,
and investment barriers, causing new high-energy users like data centers to rely on the current energy mix.
Countervailing factors (and their limits):
Efficiency improvements in AI hardware and software could slow the pace of increase, but so far, booming AI adoption
is forecast to outstrip these gains through at least 2030.
Some leading tech companies purchase renewable energy credits or build dedicated clean power, but the overall effect
on national and global energy systems is still heavily diluted by fossil fuel dependence.
Conclusion:
Unless the grid’s power mix shifts rapidly toward renewables, AI’s increasing appetite for energy will lead
directly to higher fossil fuel consumption in most regions, which risks offsetting AI’s potential benefits for decarbonization.
The net climate impact of AI therefore depends on accelerated clean energy deployment, strong efficiency mandates,
and regulatory action to ensure new (and existing) data centers run on low-carbon power."
Rod
