We are living through one of those rare moments when technology doesn’t just improve—it transforms. Consumer-ready artificial intelligence arrived in late 2023 with remarkable speed and impact. In such a short time, we’ve seen AI systems pass the bar exam, diagnose rare diseases from medical images, write functional computer code in seconds, translate between languages in real-time with nuance once thought impossible, and generate photorealistic images from simple text descriptions. Students use it to learn, doctors to diagnose, engineers to design, and writers to draft. What once seemed like science fiction has become as mundane as checking email.

But we’ve been here before. The results are impressive, yet extrapolating recent growth into an unbroken trajectory risks serious misallocation of capital and a painful day of reckoning. We believe deeply in the transformative power of AI. Still, we also remember the dotcom era intimately and have studied past technological waves, when the assumption that investments could only go up and growth could only accelerate led investors to overlook an inconvenient truth: the path to Nirvana is rarely smooth. Many of the right bets were made in 1999—on the internet, on e-commerce, on digital transformation—but the timing, winners, valuation, and assumption of frictionless scale proved catastrophically wrong for many start-ups and investors.

This transition to an AI-powered economy is being built at enormous scale through some of the largest capital deployments in modern history. For example, in the first half of 2025, a Harvard study estimates that AI investments accounted for 92% of all U.S. investments, contributing 1.1% to U.S. GDP.

Behind every conversation with ChatGPT, every AI-generated image, every smart assistant response, lies an infrastructure requiring billions of dollars, millions of processors, and—increasingly—gigawatts of electrical power. The sheer magnitude of this investment is exciting to witness. Still, it also demands that we pause and ask a critical question: Will the supply being built at breakneck speed actually meet the insatiable demand investors are pricing in, or are we once again building ahead of the curve?

The Spending Boom

The world’s largest technology firms are investing at a rate once reserved for oil majors, telecommunication companies, and electric utilities. The goal is simple: feed artificial intelligence models that convert electricity into language/knowledge.

The numbers are staggering:

• Microsoft expects $50–55 billion in capital expenditures for fiscal 2025, nearly all directed to AI data centers and Nvidia hardware
• Amazon Web Services plans roughly $60 billion across 2024–2025 to expand its global cloud infrastructure
• Alphabet (Google) lifted its 2025 capex forecast to $48 billion, emphasizing AI accelerators and new data-center construction
• Meta raised its infrastructure guidance to $35–40 billion for 2025 as it retrofits older sites for dense AI racks

Collectively, these tech giants are on pace to exceed $250 billion in spending next year, which is roughly equal to the size of the entire semiconductor industry a decade ago. [Note that these figures have been increased in just the last day.]

These investments have propelled the share prices of companies tied to the AI supply chain—chipmakers, cooling system providers, and grid equipment manufacturers—by triple digits. But the deeper question remains: what is all this capital actually building?

A Quick Guide to the Numbers

To understand where this money is going and what it is empowering, here are a few terms worth knowing:

• Gigawatt (GW): A measure of continuous power equal to one billion watts—enough to light roughly ten million LED bulbs, or power about one million homes
• Token: The smallest chunk of text an AI model processes; think of it as about three-quarters of an English word or one syllable
• Training vs. Inference: Training is the one-time process of teaching a model (think: weeks of intensive computing). Inference is the everyday work—answering your questions, writing text, and analyzing data.

One more note: because we’re dealing with truly massive numbers, we’ll occasionally use scientific notation. To refresh your high school math: 10³ = 1 thousand, 10⁶ = 1 million, 10⁹ = 1 billion, and let’s jump to 10¹⁸, which equals 1 thousand quadrillion. To put the last number into perspective, the U.S. debt of $38 trillion is only 3.8% of a quadrillion.

From Power Plants to Printing Presses

When people picture data centers, they often imagine buildings filled with servers and blinking lights. In reality, they are modern power plants—not generating electricity but consuming it on an industrial scale to turn electrons into knowledge (the industry has moved from calling them data centers to now referring to them as knowledge factories).

If current projections hold, global data-center demand could exceed 100 gigawatts of continuous power by the end of this decade, more than double the industry’s current energy usage. To put that in perspective: that’s roughly the electricity used by 100 million U.S. homes running at once.

About half of that energy, some 50 GW, is expected to go toward AI inference. Inference is what happens every time a model answers a question, writes a paragraph, or generates an image. Training a model may take weeks; inference never stops. It is the daily heartbeat of the AI economy.

So let’s follow that energy and see where it leads.

The Journey from Electricity to Language

This is where we need to use scientific notation, given the size of the numbers. Your eyes may blur, but stick with it!

Starting point: Energy per token
At current efficiency levels, processing each token uses about 0.001 watt-hours of energy. That’s tiny—but remember, this happens billions of times per second across all these data centers.

Step 1: How much total energy?
Fifty gigawatts running continuously for a year gives us a massive pool of energy. The math:

• 50 billion watts × 24 hours × 365 days = about 438 billion kilowatt-hours per year
• After accounting for infrastructure losses, we’re left with roughly 470 trillion watt-hours of usable energy

Step 2: Energy to tokens
Now divide that energy by the cost per token:

• 470 trillion watt-hours ÷ 0.001 watt-hours per token = 470 quadrillion tokens per year, or 470 x10¹⁸ tokens in a year.

If your eyes are glazing over, here’s what that number means in human terms:

In words:
Since each token is about three-quarters of a word, we’re talking about roughly 350 quadrillion words per year. That’s enough to rewrite every book ever published—thousands of times over—every single year.

In conversations:
A typical back-and-forth with ChatGPT uses about 200 tokens (your question plus its answer). At this scale, we’re talking about 2.4 trillion conversations per day—or roughly 1,000 AI interactions for every person on Earth, every single day.

In newsletters:

In writing this newsletter, about 25,000 tokens were used to help investigate the data and edit the resulting newsletter. If the planned infrastructure were limited to newsletters, the capacity could generate 1 million newsletters per second.

In books:
A full-length novel contains about 80,000 words (or roughly 100,000 tokens). These data centers could theoretically generate 4.7 million books per year for every person alive today. That’s around 13 billion new books every single day—more than humanity has written in its entire history, produced daily.

In video:

The examples above result in a staggering amount of output. Video may be the one area that shows how the power assumptions may not be enough. With the assumptions above, the output is equivalent to 400,000 hours of HD video each year. A lot, but not extreme.

A note on uncertainty
The math you’ve just seen is meant to show scale, not predict the future. In the last two years, some LLMs have squeezed ~10× more tokens per watt. We assume that speed won’t continue forever—but we can’t know. New software tricks or memory designs could slash the cost of keeping conversation context; bigger models and longer prompts could raise it. Because we can’t estimate these forces with confidence, we leave them out—while acknowledging they could move the results by multiples, either way.

Investment Perspective

From an investor’s standpoint, this is a paradoxical moment. The capital intensity of AI mirrors that of the early industrial era: enormous upfront costs, multiple large players competing for dominance, and exciting thoughts of the future but with an uncertain long-term payoff. While energy and infrastructure demand are undeniably secular, suppliers’ valuations may already assume an endless boom.

History shows that every transformative technology—from railroads to fiber optics—eventually meets the limits of physics, financing, or both.

At Auour, we see this as a boom with localized bubbles. The global appetite for computation and energy is durable. But the belief that every dollar of capex will compound without friction is not.

The key distinction for investors is this: Are you betting on electricity—the real, structural demand that will persist for decades? Or are you betting on euphoria, which tends to burn brighter and fade faster?

At Auour, we don’t try to predict the exact path of progress—but we do prepare for its uneven rhythm. Our process is built to separate structural shifts from temporary enthusiasm, identifying when optimism turns to overextension and when fear gives rise to opportunity. Regime-based investing helps us navigate cycles like this one—where innovation is real, but valuations and expectations may not move in tandem. In every era of transformation, our aim remains the same: to participate intelligently in growth while protecting against the excesses that too often follow it.

To that end, we have made some adjustments to our portfolios, moving tactical cash down to 10% and reducing our exposure to the largest U.S. companies. We have been discussing the eventual need to broaden our exposure to international markets and the sectors of the economy that can benefit from advances in AI. This week, we took a step in that direction.

“The market always does what is least convenient for the most participants at that time.” – David King, portfolio manager at Columbia Threadneedle

Investing in financial markets brings on many uncertainties. Valuations, a critical element to our investment positions, are built on expectations of the future potential profits. We spend a large part of the day reflecting on the resulting expectations built into global markets, looking for those areas where the gap between expectation and reality move to extreme levels.

Prior to listing them, we thought it important to review the various stages of bubbles as the gap between expectations and reality can drive markets there. Probably the most important item to remember; bubbles spend more time expanding than they do deflating. The cynic may be right in the end but those on the positive side can typically look right longer. Be careful as it can breed over-confidence. So, be warned, in the end, bubbles pop.

Bubbles can impact many aspects of life but those impacting the financial markets grab our attention. We found the graph from Credit Suisse (shown below) interesting. Looking back across many historical asset peaks, today’s global equity markets appear tame. One should not take this as a reason to be bullish, yet it does seem at odds with those signaling imminent doom.

Mind the Gap: Bitcoin

According to the Financial Times “The Japanese exchange at the heart of bitcoin’s recent surge has said its investors are fueling the cryptocurrency’s feeding frenzy as they buy in with leverage up to 15 times their cash deposit”.

We cannot recommend anyone look to Bitcoin as an investment.  We realize that we are on the older side and may not ‘get it’ (the only defense we have heard from proponents), but we would like to think that our relatively deep understanding of technology, company business models, and asset valuation methodologies have built up our understanding of the potential of it and its crypto-currency offspring (tether, ether, etc).

A few facts…

• Up until the last few months, Bitcoin trading was dominated in Japan (30%), Korea (21%), and China (30%+).  (bitcoinity.org)
• The jumps in Bitcoin appeared to be aligned with when China restricted assets from crossing their borders.  Bitcoin offers a method of circumventing country borders, perfect for those wanting out or those wanting to hide acts that may be less-than-legal.
• There is no inherent value that lies beneath Bitcoin that we can see.  It has been proven hackable (not scarce as proposed).  It can take days to complete a trade (low conversion liquidity). And has no defense against quantum computers as that technology matures (expected over next 2-3 years) making it very easy to steal. Article discussing this.
• All currencies that we can think of convey some inherent value.  Typically, the tax revenues of the citizens within a country’s borders can support its currency. Bitcoin does not.  In the case of Bitcoin, there is no claim on some current or future asset.  No tax revenues collected.  No basic materials that can be mined.  The perceived value is in what you think you can sell it for to someone else.  That is the greater fool theory.  The only way we know of not becoming the greater fool is to not play the game.

We would suggest reading this article as an indication of the lack of transparency and risk involved with Bitcoin and its brethren.

As it pertains to block chain, it is a technology that many can build around. Opportunities exist to utilize it for new products and services. However, we suggest leaving that to the venture capitalists at this point. Just our two bitcents…

Mind the Gap: China

China’s banking sector has become the largest in the world, with assets surpassing $34 trillion. At that level, China’s banking sector assets are three times the size of China’s economic output, at $11.2 trillion. Within this article, Eswar Prasad, former China head of the International Monetary Fund states, “The massive size of China’s banking system is less a cause for celebration than a sign of an economy overly dependent on bank-financed investment, beset by inefficient resource allocation, and subject to enormous credit risks.” We would add the mismatch of assets (long-term and illiquid) and liabilities (shorter term and with a high expectation of instant liquidity).

One needs to ask if the low rate environment driven by the US and Europe allowed China’s unstable asset build-up. As monetary accommodation is removed in the US and Europe, it will be interesting to see the impact on China.

Mind the Gap: Interest Rates

We all have been living the last decade in an environment of downward pressure on interest rates. (Some may argue that it has been going on for well over 30+ years and the last decade is the icing on the cake!) As depicted in the chart below, the central bank’s around the world are taking their foot off the pedal with the US slightly tapping the brakes.

We have been discussing the disconnect we continue to see between actual economic readings and interest rates.

Ample evidence suggests as central banks move away from accommodative stances, interest rates around the world can see a material move up. Who can absorb those increases is in question. We continue to think the US and the corporations within it are more likely to sustain growth in a rising rate environment given the large cash levels and healthy profit levels. China and the weaker European companies are our prime concern.

Mind the Gap: Municipal Debt

Outside of death, the only certain thing is taxes. It is this claim on taxes that gives a currency value, funds common services, and allows municipalities of all sorts to take on debt at low rates.

Though the historical default rate of municipal debt is only 0.1%, the trend as shown above is not in the right direction. Jefferson County, AL in 2008, Detroit in 2013, Puerto Rico in 2017. Chicago is in trouble, and Connecticut also. There are ample examples of poor fiscal governance across all levels of government. Unlike the federal government where they can follow you (and collect from you) no matter where you live, states and municipalities lack that luxury.

To add salt to the wound, municipal pension funding is lackluster at best. Current unfunded obligations are $2 trillion. Not only are they underfunded, they have been progressively taking on more risk. As stated in this Bloomberg article, “Federal Reserve data show that in 1952, the average public pension had 96 percent of its portfolio invested in bonds and cash equivalents. Assets matched future liabilities. But a loosening of state laws in the 1980s opened the door to riskier investments. In 1992, fixed income and cash had fallen to an average of 47 percent of holdings. By 2016, these safe investments had declined to 27 percent.”

And if you were hoping to sleep well tonight because you have municipal bonds in your portfolio, you should note that while 55% of municipal bonds had insurance in 2008, only 8% had such insurance in 2016 according to the NY Times.

Conclusion

The global equity markets have experienced a nice year so far. We see reasons to maintain a fully-invested stance. However, there are a plethora of reasons to warrant cynicism and caution. We feel that our algorithms are designed appropriately given the concerns we raised. Time will tell.

Current Model Positioning

Our current stance is to be fully invested yet defensively positioned. Our momentum signals, typically a strong signal for the bulk of a cycle, continue to be positive across most global markets and sectors. Our credit market signals are flirting with a cautionary zone though the credit markets continue to be liquid and stable. Valuation is high though not at extreme levels relative to history. Lastly, our interaction signals, measuring the propensity for a localized disturbance to become a global issue, are stable and constructive. The combination of these signals brings us to a fully invested stance yet defensively positioned.

IMPORTANT DISCLOSURES

This report is for informational purposes only and does not constitute a solicitation or an offer to buy or sell any securities mentioned herein. This material has been prepared or is distributed solely for informational purposes only and is not a solicitation or an offer to buy any security or instrument or to participate in any trading strategy. All of the recommendations and assumptions included in this presentation are based upon current market conditions as of the date of this presentation and are subject to change. Past performance is no guarantee of future results. All investments involve risk including the loss of principal.
 
All material presented is compiled from sources believed to be reliable, but accuracy cannot be guaranteed. The information contained in this report has been obtained from sources believed to be reliable, Auour Investments LLC makes no representation as to its accuracy or completeness, except with respect to the Disclosure Section of the report. Any opinions expressed herein reflect our judgment as of the date of the materials and are subject to change without notice. The securities discussed in this report may not be suitable for all investors and are not intended as recommendations of particular securities, financial instruments or strategies to particular clients. Investors must make their own investment decisions based on their financial situations and investment objectives.