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Artificial intelligence may appear weightless, but its backbone is built on vast, energy-hungry data centers. In this episode, host Kokou Agbo-Bloua explores how these facilities—from corporate server farms to hyperscale sites—have become the brain of the AI boom. Kokou dissects the dual demands of AI: training massive models and running inference, and how these processes are fundamentally reshaping global energy and water consumption while fuelling a trillion-dollar investment race.

Later, Sikander Rashid, Global Head of AI Infrastructure at Brookfield Asset Management, joins to discuss how investors are navigating soaring demand for computational power amid the global race towards Artificial General Intelligence (AGI). He shares his insights on how balancing carbon mitigation with capacity expansion could reshape global capital flows and addresses the age-old question: are we in an AI boom or a bubble?

Tune in now to uncover the hidden infrastructure behind AI—and what it means for the future of technology, finance, and the planet.

Credits

Presenter & Writer: Kokou Agbo-Bloua

Producers & Editors: Jovaney Ashman, Jennifer Krumm, Louis Trouslard

Sound Director: La Vilaine, Pierre-Emmanuel Lurton.

Music: Cézame Music Agency

Graphic Design: Cédric Cazaly

Whilst the following podcast discusses the financial markets, it does not recommend any particular investment decision. If you are unsure of the merits of any investment decision, please seek professional advice.


Hosted by Ausha. See ausha.co/privacy-policy for more information.

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Chapters

1. Introduction to Data Centers and AI (00:01:51)

2. What is a Data Center? (00:02:06)

3. Types of Data Centers (00:03:40)

4. Training vs. Inference in AI (00:05:20)

5. Energy Consumption of Data Centers (00:07:18)

6. Water Usage and Environmental Impact (00:09:36)

7. Data Center Locations and Clustering (00:12:08)

8. Future of Data Centers and AI (00:13:55)

9. Interview with Sikandar Rashid on AI Infrastructure (00:17:44)

10. Sustainability in AI Investments (00:27:51)

11. Conclusion and Future Outlook (00:31:46)

54 episodes