Key Takeaways

  • Google's core search business proved surprisingly durable in Q1 2026, showing "unequivocally no" erosion from AI chatbots, despite the industry's rush into AI. This confirms that for giants, established revenue streams still anchor investments.
  • Microsoft's Copilot reached 20 million users, a solid milestone, but this adoption is still a fraction of its 450 million paid enterprise seats. The pace of enterprise AI monetization might be slower than the hype suggests.
  • Meta is pouring massive capital expenditure (capex) into CEO Mark Zuckerberg's "AGI pill" vision, causing investor concern even as its core ad business thrives. This highlights the tension between visionary long-term bets and short-term market skepticism.
  • The tech market isn't in a broad dot-com style bubble, according to John Coogan, because the largest companies are generating real earnings. However, Jordi Hays warns that many smaller, private AI ventures are getting “marked up from 20 to 100 to 500 to a billion” with "absolutely no earnings."

The Disagreement: Are We in an AI Bubble?

As Big Tech unveils colossal Q1 2026 capital expenditures, a core tension emerged between John Coogan and Jordi Hays: Is the market headed for a dot-com-style correction, or are today's valuations justified?

Coogan argues against a general bubble, pointing to the solid performance of the tech titans. Google, for instance, showed robust search durability and cloud growth. Microsoft posted strong enterprise AI and Copilot adoption, with 20 million users. Amazon's AWS continued its reacceleration, justifying its sustained capex leadership. Coogan states, “The AI narrative over the last 12 months has basically been like say the biggest number, biggest capex number, biggest deals, just grow grow. Now every uh every hyperscaler reports a big capex number, but each company does have a different story.” He implies these stories, backed by billions in revenue and profit, support their valuations.

However, Hays offers a sharp counterpoint, focusing on the private markets. While the behemoths generate real earnings, a segment of the AI startup world looks eerily familiar to the bubble of the late 90s. Hays challenges, “can you imagine just take one second and imagine that Meta is a private company and their pitch is that we make AI agents for uh selling products. They're at a $200 billion run rate, growing 33% a year. How is it priced?” He then contrasts this with the private market: “But if you look at the rest uh you look at all the companies that have been started in the last like five years especially in the private markets that have been marked up from 20 to 100 to 500 to a billion and beyond. Many of them have absolutely no earnings.”

This is the crux: The titans are spending big because they have the cash flow to do it, and in many cases, they're seeing returns. Smaller, private ventures, however, are riding the same "AI narrative" wave without the underlying financial stability.

Who's Right (and When They're Wrong)

Both Coogan and Hays are right, but they're describing different segments of the market. Coogan accurately describes a public market where massive companies like Google and Microsoft, despite high valuations, are backed by immense, durable revenue streams and profits. Their AI investments are strategic expansions, not Hail Mary passes. The concerns about a widespread, irrational bubble affecting all tech companies are somewhat overstated for these giants.

Hays, on the other hand, zeroes in on a dangerous truth for founders: the private AI market, especially for early-stage companies, shows clear signs of a valuation froth. Investors, driven by fear of missing out on the next big AI play, are marking up companies with little to no revenue or proven business models. This creates an environment where founders might be tempted to prioritize narrative over fundamentals, raising too much capital at unsustainable valuations, without a clear path to profitability.

For a founder, it's critical to understand this distinction. The market's enthusiasm for AI is real, but its discernment between proven revenue and speculative bets varies wildly between public giants and private startups.

What to Do With This

If you're building an AI startup today, stop measuring your company by the capex or valuation multiples of Google or Microsoft. Their strategies, while informative, are detached from the reality of a private venture. Instead, use this Q1 2026 earnings season as a gut check: focus ruthlessly on delivering actual earnings and a clear path to profitability, not just riding the "AI narrative." The private market's current generosity for unproven AI ventures might not last, and those who prioritized revenue and solid unit economics will be the ones who survive the inevitable correction.