Browse Comments — Clean (de-noised)

Peter Norton is that a bad thing?

The gaping (destructive) hole in the dominant technocracy is the complete lack of biological intelligence. Janine Benyus shared ‘nature manufactures materials under life-friendly conditions (not industrial "heat, beat, and treat") methods. Vast global intelligences and complex structures are created in water, at room temperature, and without toxic waste. This is a failed technology until its development creates "conditions conducive to life". Anything else should be a non-starter.

Ian Read, Ph.D. Unfortunately, the politicians and lobbyists aren't taking the long view. Based on what we see out of them, their focus is strictly short-term and near sighted.

"Nearly a Terawatt" (~900 gigawatts) vs US 2024 Capacity of 1.25 TW. For comparison, average US Power Consumption = 500 GW of 1/2 TW. Yet, interconnection queues are notoriously bloated — historically only 20-30% of queued projects ever get built - ah, we hope. The staggering implication: if even 1/2 of the map gets built and powered primarily by NG, it would roughly double current US power-sector gas consumption. In 2011, MIT published a landmark report, "The Future of Natural Gas," coining NG as a "bridge fuel", MIT co-chair, Henry Jacoby stated: "People speak of gas as a bridge to the future, but there had better be something at the other end of the bridge." Yet, bridges take time - that we ain't got.

Not sure this is a new story. I gave an industry presentation >10 years ago comparing the computational power of the human brain (20W) and a supercomputer doing the exact same thing (10MW). We should not be surprised that AI is going to need vast amounts of power to do simple things.

The biggest AI risk for many organizations may not be the technology itself. It may be adopting AI faster than the organization can operationally, ethically, and culturally absorb it. Every AI workflow sits on top of real infrastructure: energy, data centers, compute, cost, governance, privacy, security, and human decision-making. Yet many companies are still treating AI like a productivity plug-in. That gap matters. When AI starts changing how work is designed, how decisions are made, how employees learn, and how leaders measure performance, it becomes a People Operations issue as much as a technology issue. This is why HR leaders need to understand AI beyond adoption campaigns. We need to understand systems, workflows, governance, workforce capability, and the human consequences of scale. AI may run on infrastructure, but responsible adoption runs on leadership.

the emissions do not seem small for anyone who is choosing to actually pay attention to them.

The Meat Puppets must be satiated . . . . . . . . . . . . Says who?

AI is already transforming the way we work. The real question is no longer just about its power, but its sustainability. The organizations that will lead tomorrow are those that can balance innovation, business performance, and responsible resource management. Executive leadership must now consider energy impact as a strategic component of AI adoption.✨

I live in Utah. Our big sticking point on developing data centers for AI isn't energy per se. It's the lack of water needed to run the power generation and cool the processors.

Sandra Bray Excellent point. The troubling bits are at the systems level, including but not limited to water and minerals needed for GPU production, fossil fuel for construction, energy for treating water chockful of descaling chemicals, lack of purple piping, colocation with municipal water resources, noise pollution, e-waste, and so much more...

Santosh Kurinec And uses a staggering amount of water (UPW) as well...

Dilan Abeya “Today’s energy footprint is probably the smallest AI will ever be.” Respectfully, this is easy to falsify by looking up plans for data center construction over the next few years.

Aaron Hill Electricity and water are inseparably wedded concerns when it comes to data centers. Praying for y’all in the Beehive state.

This is what keeps me up at night. While I love the advancement of Technology, I work in the Energy space for this very reason. How will we feed this machine? Is this sustainable? Should it be even if it is?

Ryan Watters, ELS As I indicated in one of my posts, my opinion is that when (not if) we get to engineering-grade room temperature superconductors then many of our electricity needs will be very much impacted for the better. Which is still some decades away.

Why did they assume that AI will not be able to solve the problem? If we look to math, physics, medicine it’s already solving complex problems humans have never been able to do. I’m betting it can solve the energy and emissions problem too

Ryan Watters, ELS Respectfully, planned data centre construction is exactly the evidence for the claim. If trillions of dollars are being directed towards new generation capacity, transmission infrastructure, data centres, cooling systems and semiconductor supply chains, it’s because future compute demand is expected to be materially larger than today’s. The debate isn’t whether AI demand grows. The debate is whether efficiency gains can outrun demand growth. History suggests the opposite. Every major reduction in the cost of compute has resulted in more compute consumption, not less. Which is why today’s energy footprint is likely the smallest we’ll ever see.

Great work.... Thanks for sharing...

Rory Murphy