SpaceX Has Landed Data Center Deals With Anthropic, Google, and Reflection That Could Be Worth Over $76 Billion Through 2029. Is That Enough to Justify Its Valuation?

SpaceX reportedly signed AI compute leasing deals with Anthropic, Google, and Reflection that could generate over $76 billion through 2029, including $1.25 billion per month from Anthropic and $920 million per month from Google. The article argues the contracts are significant but uncertain because either side can terminate with 90 days' notice and SpaceX's capacity is limited, so the stock case still depends on future Starship and orbital compute execution. The piece is constructive on demand for SpaceX's data-center business but skeptical about the durability and scalability of the opportunity.

Analysis

The market is starting to underwrite a new monetization layer for high-end compute, but the real incremental signal is not the headline revenue size; it is the implied scarcity rent on premium GPU clusters. That is structurally supportive for GOOGL’s AI capex narrative only if it can secure capacity without destroying returns, while NVDA remains the cleaner beneficiary because every expansion path still routes through accelerated hardware demand. The second-order loser is any hyperscaler or private AI lab forced to pay up for short-dated capacity if utilization stays tight into 2027, as that would compress model economics and slow inference-heavy product rollouts.

The key risk is duration mismatch. These contracts look large on paper, but the 90-day termination language means the market should discount them as an option rather than annuity until the next 6-9 months of utilization data confirm persistence. If compute availability normalizes or GPU supply improves faster than expected, pricing power could reset materially by early 2027, turning a valuation support story into a capex overhang for SpaceX-like vertical integrators and for any adjacent private-market data center buildout thesis.

The contrarian view is that investors may be overestimating the importance of the lease revenue and underestimating the strategic value of proving demand density. Even if the contracts break early, the fact that marquee AI buyers are willing to prepay for scarce infrastructure validates continued investment in power, cooling, and networking, which extends the AI supply chain boom beyond chips into electrical gear, interconnects, and datacenter REIT-adjacent names. For public markets, the better expression is not chasing the most speculative platform story, but owning the infrastructure bottlenecks that benefit regardless of which AI model wins.

Key Decisions for Investors

• Go long NVDA into any near-term pullback over the next 2-4 weeks; the trade is cleaner than owning the scarce-capacity narrative directly because hardware demand persists even if lease economics reprice. Risk/reward: favorable if AI capex revisions continue, but trim if gross margin expectations show signs of peaking.
• Add tactically to GOOGL over a 3-6 month horizon on weakness, but only as a relative value long versus other megacap AI spenders. The setup works if external compute access reduces time-to-market for Gemini-related deployments without forcing a step-up in internal capex.
• Pair trade: long NVDA / short a basket of private-AI proxy names and unprofitable data-center aspirants over the next 1-2 quarters. This isolates the beneficiaries of persistent scarcity while shorting the names most vulnerable to a 2027 pricing reset.
• Consider a call spread on a high-quality electrical/grid beneficiary ETF or names tied to data-center power buildout over 6-12 months. The second-order demand is less fragile than compute leasing and should persist even if these specific contracts terminate early.
• Avoid initiating fresh longs in the most speculative private-market AI infrastructure story at current levels; use any strength as an opportunity to fade if the market is pricing 2029 cash flows as if they are already guaranteed.