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World Congress 2026 Europe • Jul 9, 2026 • Session details

The Sustainability Race: AI's Promises, Pitfalls and Potential

Alexander Gerfer , Gülnaz Öneş , Wolfgang Oels , Awi Lifshitz

Could AI's massive power consumption outweigh its climate benefits? Discover how tech leaders are leveraging edge computing and system thinking to make artificial intelligence truly sustainable.

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#1 about 4 min

Corporate initiatives for renewable energy and sustainable technology

Energy and technology companies outline their goals for reaching net zero and funding climate action.

#2 about 4 min

Assessing the energy demands versus sustainability benefits of computation

The massive electricity requirements of machine learning are weighed against its potential to accelerate the energy transition.

#3 about 3 min

Reinvesting technology profits into global forestry and climate action

Search engine revenue can be redirected to finance large-scale environmental projects like reforestation and renewable energy.

#4 about 5 min

Minimizing data center power loss through efficient hardware design

Optimizing component cooling and power conversion minimizes massive energy losses within machine learning operations.

#5 about 4 min

Addressing the hidden resource costs of expanding data centers

Scaling computational hardware drives severe consumption of water and rare earth materials that edge computing can help mitigate.

#6 about 3 min

Evaluating computation as an all-purpose tool with lasting emissions

While raw material and water consumption can eventually be offset, the massive carbon output of central data centers creates permanent atmospheric damage.

#7 about 4 min

Applying systems thinking to enterprise sustainability goals and deployments

Organizations monitor the environmental footprint of major technological deployments to ensure alignment with corporate net zero objectives.

#8 about 2 min

Preventing unnecessary energy consumption with optional generative features

Allowing users to opt out of machine learning functionality prevents forced energy expenditure and supports efficient computing models.

#9 about 3 min

Implementing direct current grids and waste heat recovery systems

Adopting direct current architectures and redirecting server heat to vertical farming significantly minimizes overall data center energy waste.

#10 about 5 min

Establishing cross-chain accountability for sustainable machine learning usage

Engineers, corporate leaders, and individual users must collaboratively regulate their consumption limits and advocate for efficient global energy policies.

Matching moments

1:25 min

Addressing the sustainability and power consumption of AI

Christian Heilmann Christian Heilmann · WWC Europe 2026

2:43 min

Distinguishing between sustainability by software and in software

Hendrik Lösch Hendrik Lösch · WWC 2024

1:39 min

Balancing human-centric AI collaboration with environmental sustainability practices

Madalena Costa Madalena Costa · WWC 2024

4:03 min

Managing massive power consumption scaling in AI data centers

Stephan Gillich Stephan Gillich +3 · WWC 2024

4:24 min

Balancing heavy compute demands with environmental sustainability goals

Mike Butcher Mike Butcher +3 · WWC 2024

3:57 min

Strategies for accelerating innovation and maximizing AI value

Stephan Gillich Stephan Gillich · WWC 2024

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