In this episode I talk with Dr. Jonathan Koomey, the founder of Koomey Analytics and a former researcher & scientist at Lawrence Berkeley National Laboratory. The focus of this episode is on data centers.

We first start with Jon’s interest in the history of science which he studied at Harvard for his undergraduate. We go over Jon’s PhD thesis on why cost-effective energy efficiency measures may not be adopted in new office buildings. Then we get to data centers. Jonathan discusses the difficulties in obtaining the energy usage data for data centers, the tradeoff between innovation and understanding energy use, limitations of AI in research, a phenomenological model named Koomey’s law that Jon developed, GPUs, software to develop more energy efficient AI models, energy efficient vs inefficient data centers, how companies decide where to build data centers & much more. I hope you enjoy!

Koomey Analytics Website: www.koomey.com

PAPERS:

Jon’s PhD Thesis: https://www.researchgate.net/publication/268339858

Estimating Bitcoin Electricity Use: A Beginner’s Guide: https://coincenter.org/estimating-bitcoin-electricity-use-a-beginners-guide

Implications of Historical Trends in the ElectricalEfficiency of Computing: https://www.researchgate.net/publication/224128141

Characteristics of low-carbon data centres:  https://www.nature.com/articles/nclimate1786

Separating fact from fiction in data center electricity forecasts: A guide for regulators: https://gridlab.org/portfolio-item/data-center-load-forecast-report

Electricity Demand Growth & Data Centers: A Guide for the Perplexed: https://bipartisanpolicy.org/report/electricity-demand-growth-and-data-centers/

ARTICLES:

Data center energy use: truth versus myth: https://www2.lbl.gov/Science-Articles/Archive/data-center-energy-myth.html

Koomey’s Law: https://en.wikipedia.org/wiki/Koomey%27s_law

2024 United States Data Center Energy Usage Report: https://escholarship.org/uc/item/32d6m0d1

To better understand AI’s growing energy use, analysts need a data revolution: https://www.cell.com/joule/fulltext/S2542-4351(24)00347-7

BOOKS:

The Design of Everyday Things by Don Norman

Reinventing the Bazaar by John McMillan

Linked: The New Science of Networks by Albert-László Barabási

TEXTBOOK:

Turning Numbers into Knowledge: Mastering the Art of Problem Solving by Jonathan Koomey

VIDEO:

China & AI Supply Chains: DeepSeek, Huawei, Transformers,Robotics, & More with TP Huang: https://www.youtube.com/watch?v=NAXOP5gt_5I&t=689s

CONNECT:

LinkedIn:https://www.linkedin.com/in/adrian-dolinay-frm-96a289106/

GitHub: https://github.com/ad17171717 X:https://twitter.com/DolinayG

Odysee: https://odysee.com/@adriandolinay:0

Medium: https://medium.com/@adriandolinay

PODCAST:

Apple Podcasts:https://podcasts.apple.com/podcast/id1765996824

Audible: https://www.audible.com/pd/B0DC73S9SN

iHeart Radio: https://iheart.com/podcast/202676097/

Spotify: https://open.spotify.com/show/60dPNJbDPaPw7ru8g5btxV

|-Video Chapters-|

0:00 – Intro
1:39 – Jon’s interest in science and energy
2:37 – Energy issues in 2026
7:24 – Jon studying the History of Science at Harvard
10:19 – Jon’s research at UC Berkeley
11:38 – Jon’s PhD Thesis on energy efficiency in new office buildings
19:51 – Rated power vs. actual power draw for hardware
26:32 – Why is energy usage not well measured by tech manufacturers?
28:05 – Critical thinking and the limitations of AI
34:05 – Koomey’s Law
38:59 – CUDA the software behind Nvidia’s GPUs
40:53 – AI software development in China
43:50 – Efficient vs inefficient data centers
49:47 – The shift from On Prem to Cloud
51:39 – How do companies decide where to build data centers?
57:24 – Data centers in the AI age
1:02:19 – Will the US and China dominate in data center compute?
1:05:35 – Does the type of AI algorithm affect energy efficiency of datacenters?
1:08:54 – The issues with assumptions about AI
1:17:38 – Jon’s ideal data center policies
1:24:59 – Book recommendations
1:29:51 – Conclusion