Just 4 "Magnificent Seven" Stocks Are Still More Valuable Than SpaceX. Could SpaceX Be a Better Buy Than All of Them?

The article argues Alphabet is the best all-around buy among the Magnificent Seven alternatives, citing a $160.2 billion trailing 12-month net income base, a 26.3 forward P/E, and growing AI momentum. It highlights Nvidia as a beneficiary of SpaceX’s planned orbital AI data centers and notes Google reportedly pays $920 million per month for SpaceX compute capacity over three years. Microsoft and Apple are framed as more mature, defensive tech names, while SpaceX’s explosive post-IPO run is presented as exciting but still speculative relative to the incumbents.

Analysis

The market is implicitly pricing a new capex cycle for AI infrastructure, but the more important read-through is that compute is shifting from a software-margin story to a power, packaging, and logistics story. If orbital data centers become even partially real, the first beneficiaries are not the “platform” names but the picks-and-shovels around advanced networking, high-reliability GPUs, and eventually launch/servicing supply chains. That favors NVDA over the rest because the harder the operating environment, the more buyers will pay for the most failure-resistant silicon and rack-level systems.

GOOGL is the quiet compounder here: it has the strongest combination of balance sheet, internal AI capability, and optionality from being both a customer and indirect owner in the ecosystem. The second-order effect is that Alphabet can hedge multiple outcomes — if space compute stalls, it still monetizes AI demand on Earth; if it scales, it owns a partial claim on the infrastructure layer. That makes the market likely underappreciate the value of its “embedded venture capital” exposure versus the headline multiple.

MSFT looks like the most vulnerable to narrative compression over the next 3-6 months because investors are increasingly demanding evidence that enterprise AI spend is incremental rather than cannibalizing existing software budgets. The danger is not an outright decline in fundamentals, but multiple compression if Azure growth fails to reaccelerate fast enough to offset concerns around Copilot attach and software seat pressure. AAPL remains the lowest-beta beneficiary: it may not win the AI arms race, but its capital-return machine and installed base make it a relative safe harbor if the market starts punishing long-duration capex stories.

The contrarian point: the article’s enthusiasm for orbital compute may be directionally right but timing is likely too early. The execution risk from launch cadence, in-orbit maintenance, radiation hardening, and unit economics means the real beneficiaries may see financial impact well before the end-state thesis is proven. In practice, that argues for owning the suppliers now and fading the most economically exposed legacy software narratives only on weakness, not chasing the headline on speculative infrastructure.

Key Decisions for Investors

• Long NVDA vs short MSFT for 3-6 months: buy the highest-quality AI infrastructure enabler while fading the risk of multiple compression in enterprise software; target a 15-20% relative spread if AI capex remains the dominant market theme.
• Overweight GOOGL on pullbacks over the next 1-2 months: best risk/reward among megacap AI names because it has direct upside from compute demand and downside protection from diversification; add if forward P/E stays near the mid-20s.
• Sell covered calls on AAPL into strength for the next quarter: use it as a capital-preservation vehicle rather than a growth engine; upside is likely capped unless a credible AI product catalyst emerges.
• Avoid initiating fresh long MSFT positions until Azure and Copilot KPIs show reacceleration: the stock can work, but the setup is now a prove-it story, and the risk/reward is less attractive than GOOGL or NVDA.
• For more aggressive accounts, consider a small basket long of AI infrastructure suppliers versus a short of software incumbents over 6-12 months: the market is likely to reward tangible compute monetization over abstract AI promises.