Research on economics, history, and tech @Ryan_Research | Special focus on Ireland
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Just dropped an eBook/PDF of “Money by Vile Means.” Nowhere else will you get:
- What Bitcoin actually is (why it's corrupted)
- Debunked myths of decentralization
- Hidden stablecoin endgame = shadow central banking
If you're tired of the hype, grab it now.
(link below)
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Turns out young people consider data centers “mostly bad” more than old people.
How do Americans feel about data centers? 🤖
The U.S. public has grown more negative toward the possible effects of data centers since the beginning of the year.
Around half of Americans now say data centers are mostly bad for the environment, home energy costs and people’s quality of life nearby.
#DataCenters
ALT Chart comparing views of data centers among different demographic and partisan groups in the U.S.
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Are US Treasuries now a better investment than rental property? protos.com/are-us-treasuries…
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New Irish stats on data center water use correspond to my report’s findings.
“This is equivalent to the total direct water consumption of some 340,600 homes or 18.5 per cent of occupied dwellings in the State, based on 2022 Census figures.”
1/ I wrote everything you ever wanted to know about data centers and their water usage.
Turns out data centers use much more water than tech bros would have you believe.
Read the full essay below or catch the highlights in this thread.
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If you are in the Irish government and thinking through data center policy, my report is a must read
1/ I wrote everything you ever wanted to know about data centers and their water usage.
Turns out data centers use much more water than tech bros would have you believe.
Read the full essay below or catch the highlights in this thread.
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Peter Ryan retweeted
AI's concentration risk: "Top 10% of customers account for 99.5% of model-serving spend and 99% of neocloud spend, leaving the bottom 90% of firms with 0.5% and 1%...The bottom line is that adoption is broadening while the spending base is not, and AI infrastructure will keep depending on a small set of heavy spenders until the tail scales up."
This is certainly evidence of the technology's immaturity—over time the spending base will expand as more companies figure out how to effectively integrate AI to unlock operational value. However, it's also evidence that adoption challenges are far more persistent than the model builders anticipated. I quoted Sam Altman on this in my recent report on "The AI Trade" (sageroadresearch.com/product…): "The economy just has so much inertia. People just keep doing the same things. They keep buying from the same company. They keep using their tools in the same way. I think that’s actually a positive in many ways. It’s going to make this big transition in front of us go smoother and slower. But I think it means we’ve all been too ambitious on timelines."
It's not just about inertia. AI is still plagued by its weaknesses, from hallucination to agentic workflows breaking down midstream. But to the inertia point, AI puts unprecedented transformational demands on enterprises. As I warned in my December report on "GenAI & Productivity" (sageroadresearch.com/product…):
"As much attention was paid to the headline 95% failure estimate by MIT researchers, their explanation for that failure rate was likely a more important long-term consideration in understanding when and how companies will realize productivity gains from genAI. To quote the researchers: “The dominant barrier to crossing the GenAI Divide is not integration or budget, it is organizational design.” McKinsey is delivering a similar message: “Building a business for the agentic age will require a fundamental rewiring of how the business operates, innovates, and protects sources of value creation.” Deloitte is saying much the same: “This is not about adding another tool; it’s about fundamentally rethinking how work gets done from the top down.” It's difficult to look at modern history and identify an enabling technology that demanded the depth and speed of organizational transformation being suggested for genAI today."
For all of AI's capabilities, there is no path to ~$2.5t in annual AI revenue (the estimatdd amount required to offset CAPEX) unless the vast majority of enterprises become relative "heavy spenders" on a manageable timeline. Instead, the tail is elongating slowly while evidence mounts that today's "heavy spenders" are pulling back their spending. According to Ramp data, the top 1% of spenders, the cohort that drives ~80% of OpenAI and Anthropic’s enterprise revenue, cut per-employee spend by nearly 10% in August.
Chart link: apollo.com/wealth/insights-n…
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Interesting
"Megaprojects like data centers not only provide opportunities for employment, but continuous work. Landing a job on a data center construction project is a trades worker’s equivalent of hitting the lottery. The daily routine of going to work at the same location for months or even years is a welcome alternative to the less secure short- and medium-term job hopping that characterizes much of construction."
Mark Erlich on the appeal of the data center boom for building trades unions— and its risks.
phenomenalworld.org/analysis…
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Largest stablecoin provider btw
Bank accounts of a Montana-based payments business working on behalf of Tether and Bitfinex have been seized by federal prosecutors who accuse it of making hundreds of millions of dollars of illegal transfers. ft.trib.al/ccHALdt
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Did you know that Google’s proxies sued journalists for 13 months to prevent their data centers’ water usag being reported. They lost and were forced to reveal their own stats for the first time ever. Turns out they made up 29% of all 2021 city water use. Then in 2025: 40%.
19/ Another case study of localized impacts of data centers on water usage was Google’s data centers in The Dalles, Oregon. In 2021, Google’s The Dalles data centers used 355 million gallons of water or 29 percent of all city water. Local residents felt that “Google’s become a water vampire.” Google planned to build more data centers but wouldn’t reveal its water usage to the public which generated “intense controversy.”
Google’s water consumption records were only revealed after a 13-month legal fight between city attorneys and The Oregonian/OregonLive news group. The city sued the journalists in order to prevent them from publishing Google’s water use information arguing that Google’s water use was a “trade secret.” The city, and by proxy Google, lost.
In a 2022 settlement, the city “agreed to provide public access to 10 years of historical data on Google’s water use and to honor future records requests.” In addition, the city and Google agreed to pay large amounts to related journalist organizations. This lawsuit led to Google disclosing its data center water use for the first time in The Dalles and globally.
As of 2026, OregonLive reported “Google’s data centers gulped down nearly 550 million gallons of water in The Dalles in 2025. That’s nearly 40% of all the water consumed in the entire city.” Additionally, Google refuses to reveal its forecasted water consumption at its The Dalles data centers despite the prior settlement. OregonLive noted the city’s 2024 water master plan suggests that “most or all” of the city’s doubling in business water consumption by 2034 will come from Google’s data centers, while residential water consumption will remain relatively flat. For additional context, this water allocation debate was compounded by The Dalles’ persistent droughts.
ryanresearch.substack.com/p/…
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Another example of the apples-to-oranges error was comparing data center water use exclusively to agriculture, in general, or crops like almonds, in particular. Total industrial water withdrawals make up only 12.5 percent of total irrigation water withdrawals. Total industrial water consumption make up only 2.3 percent of total irrigation water consumption. Irrigation for agriculture is huge because agriculture is a huge component of national and international trade. Thus, comparing any non-agricultural use to this large component would downplay the severity of the non-agricultural component.
Further, Effort News CEO Brian Chau alluded to almond farm water use to downplay that of data centers. The US composes 77 percent of all global almond production. The second largest almond producer is the EU with 10 percent. For comparison, the US only composes 6 percent of global wheat production. It is silly to use such disproportionately large categories to compare against data centers because any industrial activity would also look small by comparison. When Chau’s point is taken to its logical conclusion, it would suggest any industrial activity has no water use constraints because they would be similarly small in size relative to almond farming water use. This is obviously a logical fallacy.
What’s worse than cherry-picking the facts?
Making them up entirely.
@brianchau57 explains why he thinks AI-powered journalism could move us away from the “I made it the f--- up” problem.
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Water consumption by data centers is not a myth.
It’s much larger than tech bros claim. Further, Nvidia reported an erroneous water use estimate.
You can read all this in my report below.
Jensen Huaan: The data-center industry failed to engage communities early enough.
"water consumption, that's a myth now. these data centers use less water evaporative. They don't use evaporation anymore. It uses less water than a swimming pool evaporating all year long."
newer data centers recycle their cooling water, use much less evaporation. He says operators can also add power generation, make the grid stronger, lower electricity costs, and create the economic demand needed for solar, hydro, fission and fusion projects.
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From "CBS Sunday Morning" YouTube channel, (full video link in comment)
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Peter Ryan retweeted
My latest in the FT: Not even SpaceX can be bothered with the space industry anymore 🤡🚀
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Safeguards are being ripped up
Rumor mill is saying the SEC is gearing up to revamp KYC requirements so users only need to KYC once to access tokenized securities onchain.
Our sources are saying this would enable composability across different onchain venues & tokenization platforms to let users clear more volume by splitting orders across venues using a zk based identity system.
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One novel insight I found in this was that the trend to co-locate data centers with their power plants driven by a goal to reduce electricity burdens on the local area will more concentrate indirect water usage from the power plants than the previously more diffused third party supply.
1/ I wrote everything you ever wanted to know about data centers and their water usage.
Turns out data centers use much more water than tech bros would have you believe.
Read the full essay below or catch the highlights in this thread.
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The first chart does not have a y-axis. It’s likely also dominated by 2 firms (Anthropic and OpenAI). The 4th chart does not cite a source but looks to grab a Progress Britain chart that incorrectly understated China’s GWs making the whole chart dubious. Overall, there is no acknowledgement of bubble dynamics concentrated in America despite many factors below suggesting this as a possibility.
This viral Stripe chart shouldn't surprise anyone.
It's EU policy coming to its conclusion.
This is what we get when we celebrate publishing regulatory pdfs and closing critical energy sources.
We should get used to charts like this.
So when I speak Europeans campaigning (aka glueing-their-hand-to-something) for all this degrowth wankery, I'm always puzzled as to why exactly. The degrowth is already here—it's just not evenly distributed
Here's 9 facts
1. Productivity growth numbers in the US vastly outpaces EU equivalents. (e.g. 2022→2025 shows a 7x difference)
2. The tech sector alone explains accounts for ⅔ of the EU–US productivity gap since 2000, according to ECB.
3. In 2024 + 2025, 5 AI heavy companies—Nvidia, Meta, Microsoft, Alphabet and Amazon—accounted for 40% of the S&P 500’s total return.
4. Competing in AI requires 3 essential ingredients: capable models, advanced chips, and affordable electricity. So naturally the EU is working against this.
5. Electricity: EU prices for energy-intensive industries are ~2x US levels and 50% above China’s, according to the IEA. Why is this?
6. European governments left themselves dangerously dependent imported gas, while also pulling batshit moves like closing nuclear plants. EU carbon pricing added insult to injury.
7. Models: The EU AI Act adds compliance requirements, training-data + copyright policies, and plenty of risk-management obligations. As with all things EU they'll never explain fully how to be compliant until you're in court. The result: is minimal model development in EU and very delayed AI product+feature roll out from big (easy-to-fine) companies.
8. Chips: Europe’s semiconductor ambitions are undermined by insufficient investment, fragmented funding, slow delivery, and of course high operating costs. Net effect is they're on course to miss their 2030 goals by >40%
9. Putting it all together, we’re making it harder and more expensive to resource, build and deploy AI in Europe, and risking another decade of falling behind.
The result will be even slower growth relative to the rest of the world.
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The reason data centers use so much water is because they use so much electricity. Water is used at their electricity supplying power plants.
Compared to an average factory, data centers use 17-32x more electricity. For large to hyperscale data centers, it’s 80-827x.
1/ I wrote everything you ever wanted to know about data centers and their water usage.
Turns out data centers use much more water than tech bros would have you believe.
Read the full essay below or catch the highlights in this thread.
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