Detailed Analysis
Anthropic's continued dominance in enterprise AI markets has generated significant investor interest, but the company's private status has left retail investors unable to participate directly in its growth. A retail investor analysis circulating on Reddit's r/Anthropic community proposes a workaround: constructing a "proxy barbell" trade using two public companies — Zoom (ZM) and SK Telecom (SKM) — that hold confirmed equity stakes in Anthropic and, crucially, carry those stakes as a meaningful percentage of their own market capitalizations. The author's central analytical lens is Anthropic stake value as a percentage of the holding company's market cap, a metric designed to isolate capital efficiency rather than raw dollar exposure.
The analysis systematically filters out otherwise obvious candidates. Alphabet and Amazon, both confirmed Anthropic investors with stakes in excess of $100 billion in absolute terms, represent only approximately 3-4% of their respective multi-trillion dollar market caps, rendering any Anthropic revaluation essentially invisible at the stock price level. Salesforce offers marginally better exposure at roughly 6% of market cap but is dismissed as too entangled with broader SaaS sentiment to serve as a clean proxy. What remains are Zoom and SK Telecom. Zoom's stake — made when Anthropic was valued at just $4.1 billion — now constitutes an estimated 15-30% of Zoom's contracted market cap, creating what the author describes as high-reflexivity tracking stock behavior. SK Telecom's Anthropic exposure represents approximately 20% of its market cap, and the company trades at a significant valuation discount despite a forward P/E near 13x and 89% year-over-year growth in AI data center revenue.
The "barbell" construction is deliberate in its risk architecture. Zoom represents the high-beta, momentum-driven leg: a pure software play whose compressed valuation amplifies any upside from an Anthropic IPO or valuation markup. SK Telecom represents the defensive leg: a cash-flowing, dividend-paying telecommunications company with structural AI infrastructure catalysts and what the author characterizes as a "Korea Discount" — the well-documented tendency for Korean equities to trade at lower multiples than comparable Western peers due to governance perceptions, conglomerate structures, and foreign investor hesitancy. The author discloses a 50-50 personal allocation across both positions, adding practical weight to the theoretical framework.
The broader context of this trade idea reflects a growing phenomenon in AI investing: as frontier AI companies remain private longer and at increasingly stratospheric valuations, retail investors have begun constructing indirect exposure vehicles through strategic corporate shareholders. This mirrors patterns seen during the pre-IPO phases of companies like SpaceX, where investors bought into Alphabet or other public holders as proxy vehicles. In Anthropic's case, the dynamic is particularly pronounced because Anthropic has raised capital across multiple rounds from a diverse set of strategic partners specifically to build out cloud infrastructure relationships with AWS and Google Cloud, making the shareholder register unusually distributed across public companies of varying sizes. The ZM/SKM thesis exploits the inefficiency created by that distribution — the same stake that is nearly invisible on Amazon's balance sheet represents a potentially company-defining asset on Zoom's.
Whether the trade performs as theorized depends heavily on timing and Anthropic's own trajectory. The argument assumes Anthropic continues to capture enterprise market share, that a liquidity event or third-party revaluation occurs within a relevant investment horizon, and that markets will efficiently transmit that revaluation into ZM and SKM stock prices. Each assumption carries non-trivial risk. Zoom faces secular competitive pressure from Microsoft and Google in its core collaboration business, and SKM's AI data center growth, while impressive, remains a small portion of total revenues. Nevertheless, the analytical framework itself — screening for stake-to-market-cap efficiency rather than absolute dollar exposure — represents a disciplined and replicable methodology for navigating the increasingly common challenge of accessing private AI upside through public markets.
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