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The AI revolution will require a massive build-out of new infrastructure.
That means more chips, more data centers, more electrical equipment… and far more power. As businesses continue to grasp AI’s potential, we continue to see more and more announcements with facilities now measured in gigawatts (GWs).
OpenAI alone has announced partnerships with the semiconductor world representing nearly 30GW of new data center demand, and most believe we’re still in the early innings of the build-out. The question is, where will all of that power come from?
We see three options:
Build new generation. That's already happening. But given the explosion of demand, if you want a new gas turbine, you won’t see it until after 2029. New nuclear will take even longer, extending into the 2030s. And renewables, while faster to install, are not able to provide the primary power required for data centers given their intermittent nature.
Contract with a regulated utility. Regulated utilities have predetermined Return on Equity they can earn. Generally, working with a regulated utility means longer interconnection studies, potential political pushback, and strict regulatory guidelines that dictate who can connect and when.
Partner with an independent power producer (IPP). Over the past 12 months, many hyperscalers have turned to IPPs, which own generation assets, set their own electricity prices, and can often move faster than regulated utilities. We are seeing hyperscalers willing to pay 3-4x the regulated or market rate for expedited access to these large energy resources.
The universe of IPPs is not very large, and we are seeing in early agreements that many are choosing to go nuclear. Nuclear generation is baseload energy (24/7), clean, and produces a lot of power in one location. Amazon announced a transaction with Talen Energy in March 2024; Microsoft partnered with Constellation Energy in September 2024 to bring the Crane Clean Energy Center (formerly Three Mile Island) back online; and as recently as this October, NextEra Energy unveiled plans to restart its Duane Arnold nuclear plant in conjunction with Google.
For more on this C:\Take, watch Max:

When private multiples are lofty, the right question isn't "Is the price high?" but "Is the growth durable?"
The chart above shows adoption and usage growth for two code-first models:
Claude Code (Anthropic) —> 30x growth in NPM downloads over the past five months
Codex (OpenAI) —> 2x total installs a month after GPT-5 launch
The steep usage curves indicate strong product-market fit for AI coding assistants and agents. By driving 20–40% in developer productivity, these tools justify premium pricing and accelerate seat expansion. The growth reflects compounding improvements in model capability and productization — reasoning, longer task length, agentic workflows — that are already translating into willingness to pay.
If revenue compounds at high rates, today’s 20–40x forward revenue multiples could compress quickly. For example, sustained 70–100%+ growth with improving gross margins can drive a seemingly expensive entry multiple into the single digits within just a few years.
So when we’re asked whether private markets are too expensive, we focus instead on growth and durability. Through that lens, select opportunities with sustained adoption curves can be attractive, even at elevated initial valuations.
For more on this C:\Take, watch Lucas:

Major hyperscalers like Alphabet and Meta boosted their capex forecasts alongside their latest earnings reports, once again surpassing Wall Street’s already lofty expectations.
For much of the year, sell-side analysts have been playing catch-up, repeatedly raising their AI infrastructure spending projections with each new company update. Following this latest round of upward revisions, consensus capex forecasts for 2026 now stand at ~$518 billion, up 29% year-over-year - and an astonishing 65% above what analysts had expected at the beginning of the year.
These new spending plans underscore not only the sustained momentum behind the AI buildout, but also how much investors and analysts may still be underestimating the true scale and staying power of the overall AI investment cycle.

Many believe AI stocks are trading in a valuation bubble reminiscent of the dot-com era. We see it differently.
The chart above shows the valuation gap between TMT stocks (blue line) — home to many of today’s supposedly “inflated” AI names — and non-TMT stocks (green line) over the past 30 years. The spread has clearly widened over the past decade, but it’s nowhere near the extremes seen in the late 1990’s.
We took the analysis a step further by comparing the valuations of leading tech giants across both periods. What we found is that today’s 20-30x multiples look remarkably tame next to the 70x-plus frenzy of the pre-bubble era.
This exercise helps frame our current thinking — less “let’s wait for the pullback because things look too pricey” and more “can you believe we need to pay a modest premium in order to participate in what may be the next industrial revolution?”

Stablecoins have already evolved into an increasingly important part of global finance, with monthly transaction volumes now exceeding $1 trillion, or +160% y/y.
Philippe discussed the transformative potential of stablecoins with Stripe Co-Founder and CEO Patrick Collison at a recent company event. In addition to his assertion that stablecoins “should absolutely revolutionize the financial system,” Philippe made three bold predictions:
Stablecoins will replace checking accounts: The high yields offered by stablecoins could render checking accounts obsolete, effectively rebuilding the modern banking system on-chain.
Stablecoins will become the world’s offshore bank: Citizens of countries with unstable economies — from Argentina to Turkey — will have access to safe, dollar-based savings outside of their local systems.
Stablecoins will reinforce the dollar’s dominance: When the choice is between holding your wealth in a U.S. dollar-pegged asset vs anything else, the answer is obvious.
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