Why going second could be OpenAI's advantage over Anthropic

Anthropic is expected to file its S-1 as soon as next week, kicking off a run of upcoming AI IPO filings that may include OpenAI afterward. The discussion highlights a key investor focus on compute obligations—potentially more important than near-term revenue growth—when evaluating AI listing economics. It also suggests the company that files second could ultimately see the better outcome, depending on positioning and disclosures.

Analysis

The first public filing in this cohort is less about valuation optics and more about forcing the market to price AI as a capital-intensity story, not a pure software growth story. If the disclosure shows compute commitments, model-training obligations, or customer concentration that resemble quasi-fixed liabilities, investors will likely re-rate the whole frontier-model stack on free-cash-flow durability rather than top-line CAGR. That is structurally bullish for infrastructure owners and vendors with pricing power, and structurally bearish for high-multiple AI application names whose economics depend on cheap model access.

The immediate winners are the picks-and-shovels layer: NVDA, AVGO, MSFT, and AMZN should benefit if the filing confirms that demand is durable and pre-committed. The less obvious second-order effect is on power, networking, and server supply chains, where every incremental training dollar tends to expand into rack, cooling, and interconnect spend; that helps SMH more than software ETFs like IGV. Over 1-3 months, the market may rotate toward the infrastructure trade as public-market investors benchmark the new listing against private-market narratives and demand proof that AI revenues can outrun compute burn.

The main risk is that the first filing is unexpectedly clean: improving unit economics, limited contractual commitments, and credible path to margin expansion would blunt the bearish read-through and lift the entire AI complex. The contrarian view is that filing second could actually be better positioned if the first one educates investors, but only if it avoids inheriting a higher disclosure bar. What would falsify the thesis is evidence that compute obligations are already declining as a percent of revenue or that roadshow demand supports a much richer multiple despite heavy capex intensity; in that case the trade is not the capex bear, it is the consensus underestimating the addressable market.

Key Decisions for Investors

• Go long SMH / short IGV for 1-3 months into the filing window: thesis is that the market will reward infrastructure economics over AI software multiples as disclosure forces a reset on capital intensity.
• Buy NVDA or AVGO on any post-filing weakness, with a 3-6 month horizon: if compute commitments are large, hyperscaler and accelerator demand remains the cleanest monetization path.
• Add MSFT vs. a basket of AI application names on valuation pullbacks: cloud owners can externalize compute risk and keep pricing power, while app-layer names face multiple compression if unit economics disappoint.
• Set an alert for any S-1 disclosure showing compute obligations > revenue growth by a wide margin or persistent negative gross margin after training costs; that would be the trigger to fade the broader AI beta.
• If the first filing comes in clean, flip the trade: cover infra overweights and consider a tactical long in the second filer, since it may benefit from better investor education and tighter terms.