The bottom line: The biggest AI earnings week of 2026 produced three simultaneous stories that seem unrelated but are actually one story. Amazon’s cloud business is booming because it hosts the AI. Reddit’s traffic is getting disrupted because AI is replacing search. Leopold Aschenbrenner’s AI hedge fund collapsed because betting on AI infrastructure indirectly — through public equities — is not the same as owning the infrastructure. The companies that win from AI are the ones that run the compute. Everyone else is navigating a disruption.
Amazon: The Direct Beneficiary
Amazon reported second-quarter results that beat Wall Street expectations across the board, according to TechCrunch. Net sales rose 20% and cloud revenue stood out as a particular bright spot. Investors sent Amazon’s stock up nearly 10% in after-hours trading.
The headline number that matters most is capex. Amazon spent $173 billion on property and equipment — a category that covers GPUs, natural gas turbines, and land — for the fiscal year ended June 30, up from $107.65 billion the year before. That is a 61% increase in a single year. Then the company raised its 2026 capex forecast to $220 billion, up from the prior guidance of $200 billion.
This is notable for a specific reason: the conventional wisdom heading into earnings season was that investors wanted tech companies to slow down on data center spending. Amazon did the opposite, raised guidance anyway, and got rewarded. The market is telling you something: at this scale, AI infrastructure is not a cost center, it is a revenue engine.
The numbers support that read. AWS is the division that runs the compute that every AI startup, enterprise AI deployment, and AI researcher depends on. When AI usage goes up, AWS revenue goes up. Amazon does not need to build the best AI model. It needs to run the infrastructure that the best AI models run on. It is doing that at $220 billion per year and growing.
For context on what that infrastructure arms race looks like at the chip and hardware level, see our analysis of the AI infrastructure arms race including AMD Helios, Etched, and Google’s Q1 results.
Reddit: The Collateral Disruption
Reddit reported a strong second quarter on paper. Total revenue of $805 million jumped 61% compared to the year-ago quarter, per TechCrunch. Net income was $253 million, up 183%. Both beat Wall Street’s expectations. The company guided to revenue of $860 million to $870 million next quarter, which also beat expectations.
Reddit’s stock fell over 10% in after-hours trading anyway.
The reason: CEO Steve Huffman’s letter to shareholders. In it, Huffman warned that “search referrals were choppy in the quarter, and traffic was more volatile later in the quarter.” He added context — “the bigger picture is that Reddit remains a destination people choose to visit” — but the market did not focus on the reassurance. It focused on the word “choppy.”
Reddit’s business model depends heavily on advertising revenue, which depends on traffic volume, which depends significantly on organic search referrals. If AI-powered search is answering questions that used to send users to Reddit threads, that traffic does not flow to Reddit. It stays inside the AI’s answer. Reddit does not monetize it. The AI provider — which is likely running on Amazon’s infrastructure — captures the value instead.
This is the disruption pattern that content-heavy platforms face as AI overview and AI chat products absorb search intent that would previously have driven referral traffic. Reddit is large enough and distinctive enough that it will likely adapt — Huffman’s “destination people choose to visit” framing is accurate for certain use cases. But the trajectory of the “choppy” referral trend is the number practitioners in content and media need to watch, not the headline revenue.
Situational Awareness: When the AI Thesis Outpaces the Market
Situational Awareness, the AI-focused hedge fund launched by 25-year-old former OpenAI researcher Leopold Aschenbrenner, sold the majority of its public stock portfolio to Ken Griffin’s Citadel following steep losses over the past month, according to The Wall Street Journal, as reported by TechCrunch.
Aschenbrenner gained prominence for a specific investment thesis: that scaling AI would require a massive build-out in semiconductors, compute, memory, and energy infrastructure. He published essays arguing this case and launched the fund in 2024 with no prior trading experience. The thesis was directionally correct — Amazon’s $220 billion capex number validates the core logic. The fund still holds its Anthropic shares, which are private and presumably still valued at their private round price.
What went wrong is a lesson in the difference between a correct thesis and a profitable trade. The public equities that Aschenbrenner bet on — presumably semiconductor companies, energy infrastructure plays, and AI-adjacent tech stocks — are volatile, react to sentiment, and are already priced by a market that also believes in the AI infrastructure build-out. Buying into a correct thesis after it is consensus is not the same as having the thesis early. The fund’s losses over the past month likely reflect a crowded trade unwinding, not the thesis being wrong.
The private Anthropic position is the interesting part. Private company shares do not mark to market daily. They are valued at the last round price until there is a liquidity event. If Anthropic’s valuation at its next funding round or IPO justifies the current price, that position survives. If the public AI infrastructure trade was crowded, the private one may be as well — it is just not visible yet.
What This Means for You
These three stories, read together, produce a clear framework for thinking about AI value accrual in 2026:
Infrastructure owners win, and the margin is wide. Amazon’s 61% increase in capex is a bet on sustained AI demand, and it is being rewarded. If you are deciding where to invest in AI capabilities for your organization, the track record of infrastructure providers as the durable winners matters. The question is not which AI model will win — it is which providers will run whatever models win. Those providers are building at $200 billion-plus per year.
Traffic-dependent businesses need a plan for AI search disruption. Reddit’s choppy referrals are not unique to Reddit. Any business that depends on organic search traffic from Google or Bing — including content publishers, e-commerce sites, and SaaS companies with SEO-driven acquisition — is exposed to the same dynamic. AI overviews absorb intent that previously converted into clicks. The mitigation is building direct audience relationships (email, app, community) that do not route through search. Our AI content automation playbook covers how to build sustainable content infrastructure that is less dependent on search referral spikes.
AI investment thesis plays are not the same as AI exposure. If you manage a technology budget or an investment portfolio and want exposure to AI’s growth, there is a meaningful difference between owning the companies that run the compute and owning companies whose valuations reflect AI optimism. Amazon’s 20% revenue growth is AI demand showing up in quarterly results. A semiconductor company trading at 40x earnings on AI expectations is a bet on the expectation holding. Know which one you have.
Private AI positions are illiquid by design. Aschenbrenner’s fund still holds its Anthropic shares. That is either the winning part of the portfolio or the last shoe to drop, depending on Anthropic’s path to liquidity. For anyone considering private AI company exposure — through secondary markets, SPVs, or employee equity — the illiquidity premium is real and the timeline to exit is long. Model this honestly before committing.
The Pattern
Amazon built the infrastructure. Reddit runs content on top of infrastructure it does not own, and its traffic is being absorbed by AI tools that run on that same infrastructure. Aschenbrenner bet on the infrastructure build-out through public equities and got squeezed by the trade being crowded.
The hierarchy is consistent: own the compute, or build something sufficiently differentiated that the compute providers cannot replicate your value. Everything in between — traffic-dependent media, commodity AI applications, thematic public market bets — is exposed to the disruption without the upside of running the infrastructure.
For teams building AI-powered products and needing to understand the actual cost structure of running on this infrastructure, our guide to using the Claude API for business, including real token costs, gives you a ground-level view of what the AI infrastructure spend looks like from the customer side.
What to Do Next
If you run a business with significant organic search traffic, pull your referral source breakdown for the past two quarters and look for the pattern Reddit described. If you see it, you have six to twelve months to build direct audience channels before the trend accelerates. Do not wait for it to show up in revenue.
If you are making AI infrastructure investment decisions, the Amazon quarter is your benchmark for what actual AI demand looks like at scale. The companies with the compute, the power, and the cooling capacity are the ones with pricing power. Size your own commitments — to cloud providers, to AI tooling, to infrastructure build-out — against that benchmark.
