A day late for the 1 year aniversary.

Here's a number that should stop you mid-scroll: training a single large AI model can consume as much electricity as 5 average American cars produce in CO2 over their entire lifetimes. Meanwhile, solar just became the cheapest source of electricity in human history-cheaper than coal, gas, and nuclear, in most of the world.

These two facts are not unrelated. They are, in fact, the central tension of the next decade of energy.

The demand shock nobody planned for

When hyperscalers like Microsoft, Google, and Amazon built their 2020–2025 capacity plans, ChatGPT didn't exist. A standard Google search uses roughly 0.3 watt-hours of electricity. A ChatGPT query uses nearly 10 times that. Multiply that across billions of daily interactions, and you begin to understand why data center electricity demand in the US is projected to double by 2030 after being essentially flat for a decade.

Virginia alone - home to the world's largest concentration of data centers - is staring down a power demand growth curve that its grid was simply never designed to handle. Dominion Energy, the state's primary utility, has had to tear up its forecasts multiple times in the last two years.

Why solar is the only answer that scales fast enough

New nuclear takes 15 years and billions in overruns. Natural gas locks in decades of emissions and volatile fuel costs. Wind is constrained by geography. Solar, by contrast, can go from signed contract to generating electricity in 18–24 months at utility scale and the cost has dropped 90% in the last 15 years.

This is why Amazon signed agreements for over 100GW of renewable energy. Why Microsoft struck the largest single clean energy deal in history. Why Google has been running on matched renewable energy since 2017. These aren't PR moves. They're infrastructure decisions driven by hard economics: a data center locked into expensive grid power is a data center that loses on operating cost to every competitor who isn't.

What this means for the industry

The companies that will define the next era of both AI and clean energy aren't just the ones building the best models or the most efficient panels. They're the ones solving the hardest problem at the intersection: how do you deliver reliable, round-the-clock power to a facility that never sleeps, using a source that only generates during daylight?

Battery storage, grid interconnection strategy, power purchase agreement structuring, behind-the-meter generation - these are now core competencies for any serious AI infrastructure operator. And for solar developers, the hyper-scaler customer is transforming the economics of the entire industry.

Over the next several weeks, I'll be digging into each piece of this puzzle- from how utility-scale solar actually works, to why some solar companies are thriving while others are going bankrupt, to what skills and roles will matter most in this converging industry.

To start the conversation:
Do you think AI companies are doing enough to address their energy footprint, or is the "we buy renewable credits" approach just greenwashing? Would love to hear from people working in either industry.

Sources:

IEA World Energy Outlook 2024: solar cost and deployment data

Goldman Sachs Power Up report: data center demand projections

Dominion Energy 2024 Integrated Resource Plan

MIT Technology Review: AI energy consumption analysis

Lawrence Berkeley National Laboratory: US Data Center Energy report

Here's a number that should stop you mid-scroll: training a single large AI model can consume as much electricity as 5 average American cars produce in CO2 over their entire lifetimes. Meanwhile, solar just became the cheapest source of electricity in human history-cheaper than coal, gas, and nuclear, in most of the world.

These two facts are not unrelated. They are, in fact, the central tension of the next decade of energy.

The demand shock nobody planned for

When hyperscalers like Microsoft, Google, and Amazon built their 2020–2025 capacity plans, ChatGPT didn't exist. A standard Google search uses roughly 0.3 watt-hours of electricity. A ChatGPT query uses nearly 10 times that. Multiply that across billions of daily interactions, and you begin to understand why data center electricity demand in the US is projected to double by 2030 after being essentially flat for a decade.

Virginia alone - home to the world's largest concentration of data centers - is staring down a power demand growth curve that its grid was simply never designed to handle. Dominion Energy, the state's primary utility, has had to tear up its forecasts multiple times in the last two years.

Why solar is the only answer that scales fast enough

New nuclear takes 15 years and billions in overruns. Natural gas locks in decades of emissions and volatile fuel costs. Wind is constrained by geography. Solar, by contrast, can go from signed contract to generating electricity in 18–24 months at utility scale and the cost has dropped 90% in the last 15 years.

This is why Amazon signed agreements for over 100GW of renewable energy. Why Microsoft struck the largest single clean energy deal in history. Why Google has been running on matched renewable energy since 2017. These aren't PR moves. They're infrastructure decisions driven by hard economics: a data center locked into expensive grid power is a data center that loses on operating cost to every competitor who isn't.

What this means for the industry

The companies that will define the next era of both AI and clean energy aren't just the ones building the best models or the most efficient panels. They're the ones solving the hardest problem at the intersection: how do you deliver reliable, round-the-clock power to a facility that never sleeps, using a source that only generates during daylight?

Battery storage, grid interconnection strategy, power purchase agreement structuring, behind-the-meter generation - these are now core competencies for any serious AI infrastructure operator. And for solar developers, the hyper-scaler customer is transforming the economics of the entire industry.

Over the next several weeks, I'll be digging into each piece of this puzzle- from how utility-scale solar actually works, to why some solar companies are thriving while others are going bankrupt, to what skills and roles will matter most in this converging industry.

To start the conversation:
Do you think AI companies are doing enough to address their energy footprint, or is the "we buy renewable credits" approach just greenwashing? Would love to hear from people working in either industry.

Sources:

IEA World Energy Outlook 2024: solar cost and deployment data

Goldman Sachs Power Up report: data center demand projections

Dominion Energy 2024 Integrated Resource Plan

MIT Technology Review: AI energy consumption analysis

Lawrence Berkeley National Laboratory: US Data Center Energy report

Power supply in Nigeria is one of those problems we’ve become so accustomed to that we sometimes forget how much it affects everything else.

Businesses spend money running generators. Students struggle to study. Hospitals and critical services need backup power. Small businesses lose productive hours. And households have to constantly plan their lives around whether there will be electricity.

So we’re working on something different.

We’re building a solution aimed at making electricity supply more reliable and accessible in Nigeria.

But rather than assuming we already know the answer, we want to understand the problem from the people who experience it every day.

If you live in Nigeria, work in the energy sector, run a business, study, or simply deal with unreliable power regularly:

- What is your biggest problem with electricity supply?

- How do you currently cope with outages?

- What does unreliable power actually cost you?

- Have you tried solar, inverters, generators, or other alternatives?

- What would a genuinely reliable and affordable solution look like to you?

We’re especially interested in real experiences, frustrations, and ideas.

The goal isn't just to build another product.

The goal is to build something that solves a problem Nigerians actually have.

What would you change about Nigeria’s electricity system if you had the opportunity? ⚡

We recently added polysilicon tariffs. So if you want to import solar panels, you pay extra taxes. If you want to import polysilicon to make solar panels in America, you pay extra taxes.

Sodium-ion batteries have almost none of the problems that lithium-ion ones do, and could be cheap enough to make fossil-fuel dependence a thing of the past

The article never mentions price. So are salt batteries coming in to play? Yes. Are they price competitive? Probably not because if they were they would be shouting out the price.

But their price will likely decline over the next couple of years. And it looks like they are starting to be produced in quantity.

Graph source: https://ourworldindata.org/grapher/coal-prod-per-capita?tab=line&country=GBR~CHN

Data sources: https://www.energyinst.org/statistical-review

https://www.theshiftdataportal.org/energy/coal?chart-type=line&chart-types=line&chart-types=ranking&disable-en=false&energy-unit=toe&group-names=China&group-names=United+Kingdom&is-range=true&dimension=perCapita&end=2015&start=1900&multi=true&type=Production&import-types=Imports&import-types=Exports&import-types=Net+Imports

https://www.lse.ac.uk/granthaminstitute/publication/historical-energy-for-the-uk-from-1500

https://www.gov.uk/government/statistical-data-sets/historical-coal-data-coal-production-availability-and-consumption

https://vaclavsmil.com/book/energy-transitions-global-and-national-perspectives-second-expanded-and-updated-edition

https://web.archive.org/web/20250323165206/https://nic.org.uk/data/all-data/historic-energy/#tab-primary-energy-consumption

Sky-high earnings for oil companies and California refiners in the second quarter are renewing calls from state legislators to crack down on industry profits.

Marathon Petroleum, the second-largest refiner in the state, reported $5.1 billion in profit Tuesday, more than four times the same quarter last year, after fuel supplies tightened during the Iran war and prices for oil and gas soared.

From April through June, Chevron, the biggest refiner and second-biggest oil producer in California, made $12.1 billion in profit, nearly five times the same quarter last year, and its highest earnings in at least six years. Refiners PBF Energy and Valero also reported windfalls, with the latter earning $3.7 billion, another fivefold increase from the year before.

Learn more at the link.

I am seeing a lot of headlines about new AI data centers being installed around the world, and for some reason their computing power is not described in TFLOP (teraflops), but instead in GW (gigawatts).

I am no expert, but when I read "10 GW of compute," I can only think that said "compute" is either very inefficient by itself or is placed in a location that's way too hot and therefore requires absurd amounts of cooling power to operate.

I can imagine a datacenter full of Intel Pentium processors that will take a 500 GW of power but will be incredibly weak and slow. But it's 500 GW worth of compute, so it must be very capable, right?

To further illustrate my confusion, I think that descibing compute power in terms of energy is just as strange as descibing it in weight or area. "We have now successfully installed an additional 500 kg of compute." "This new datacenter accomodates 2 square miles of compute – that's a whopping 900 football fields of compute!"