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 Swisher:

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.

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.

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?”

We see today’s AI-led rally as durable and supported by fundamentals, rather than a bubble.

One key indicator is the extraordinary pace of AI adoption, with ChatGPT reaching mass penetration years faster than the internet or PCs. In the video below, Jaimin expands on this dynamic and outlines several additional drivers to support our broader thesis.