By: Editorial Team, StoneX Media
With OpenAI having filed confidentially with the Securities and Exchange Commission and SpaceX preparing to enter public markets, the financial opacity that has surrounded frontier AI companies is about to dissolve. For years, Wall Street has operated on venture capital whisper numbers and estimated run rates, placing Anthropic's annual recurring revenue somewhere in the $47 to $50 billion range and OpenAI's at roughly $33 to $35 billion, with no audited disclosure to confirm either figure. When these companies cross from private to public, they will face a level of scrutiny that every major software company knows well, including quarterly reporting, gross margin analysis, and the profitability benchmarks that separate genuinely exceptional technology businesses from those that are simply expensive. The real question is not the size of the AI valuation premium, but the ability of the firms commanding it to survive the discipline of public markets.
Yi Fu Lee covers AI, cybersecurity, infrastructure, and enterprise software equities as a Managing Director and Senior Equity Research Analyst at Benchmark StoneX, holding CFA, CPA, CMT, and CAIA designations alongside experience valuing businesses across public markets, private equity, and venture capital. That cross-market background gives him a direct view of the distance between how frontier AI firms present their financials to private investors and what quarterly reporting discipline will actually reveal once they list.
The transition from private to public markets means more than a liquidity event for early investors. It forces a company into a new relationship with information, one where quarterly results, margin disclosures, and revenue composition become matters of public record rather than selective briefings to favored capital allocators. Lee is direct about what that shift means. "You get that extra level of scrutiny from the Wall Street community. There's nothing to hide. There's no more second guessing, and you're more accountable. You have to be accountable for those quarterly reports." Until that accountability arrives, even the most closely watched figures in AI remain educated guesses sourced from investors who hold private data rather than published disclosures. Lee acknowledges that the current ARR estimates for both Anthropic and OpenAI are, in his words, best guesses informed by the venture capital community, and that the real picture will only emerge once these companies are subject to the same reporting standards as Microsoft, CrowdStrike, or Snowflake. For the broader capital markets, he argues that transition will be a net positive, giving investors the data they need to make longer-term, better-informed decisions.
The Rule of 40 is a durable benchmark in software investing, combining a company's revenue growth rate with its free cash flow margin to produce a single figure that separates sustainable growth businesses from those burning capital without a credible path to profitability. Lee applies it directly. "A top-tier software firm, it's not unreasonable to have 85 to 90% gross profit margin. I always talk about the rule of 40, which is your revenue growth plus your free cash flow margin. And 40 plus is probably a very good standard to be in." Against that benchmark, OpenAI's current position tells a different story. "Right now, the numbers from OpenAI show that for every $1 that they earn they burn $2.20. You're not getting there." He draws on direct experience here. When Lee took CrowdStrike public in 2019, early cash burn was accepted as part of the growth story, but investor patience was finite. "In the beginning, the first year or 18 months you can burn cash, but eventually investors will demand that this will not happen forever. They flip positively and get profitable." The implication for OpenAI is clear. The AI premium currently embedded in private valuations will need substantiation, and quarterly reporting will be the mechanism that either justifies or erodes it.
Top tier 1 SaaS/software firms and hyperscalers are playing nicely in the sandbox as partners now but Lee envisions this new IPO class of frontier AI labs will likely compete with them in the future as they gain scale. He draws a clear line between firms with structural defensibility and those whose core functions are vulnerable to replication by frontier models. "If you're in a business of simple automation, data entry software, or call center, that could be replicated by the likes of the frontier model, then I would probably have to rethink whether this is a viable business model to continue in the next 5 or 10 years." The risk is sharpest in categories defined by workflow rather than deep technical moat. In his view, CRM platforms carry particular exposure. "Specifically, if you're a CRM system, a customer relationship management system, where there could be chances where I could replicate what you do, that's when I get a little bit worried." By contrast, he sees companies like CrowdStrike in cybersecurity and Datadog in infrastructure observability as well positioned, their moats built on complexity and mission-criticality rather than process efficiency. The arrival of publicly listed AI companies will sharpen the market's ability to draw precisely this distinction, applying the same scrutiny to the disruptors that it will now apply to those being disrupted.
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--- Written by Gus Farrow, Senior Manager, StoneX TV
--- Expert: Yi Fu Lee, Managing Director, Senior Equity Research Analyst, Benchmark StoneX
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