Some OpenAI investors are expressing skepticism about the company’s valuation and strategy as Anthropic’s revenue accelerates, according to reporting by TechCrunch citing the Financial Times. The debate centers on what financial metrics imply for AI platform economics: OpenAI is reorienting around enterprise customers while investors weigh whether Anthropic’s scale—particularly in coding tools—changes the expected risk and return profile of each company’s next phase.
Valuations under scrutiny as Anthropic’s revenue climbs
TechCrunch reports that some OpenAI investors are skeptical as the company “scrambles to reorient itself around enterprise customers and fend off Anthropic,” per the Financial Times. In parallel, Anthropic’s operating momentum is quantified in the same account: its annualized revenue rose from $9 billion at the end of 2025 to $30 billion by the end of March. The source attributes the jump “largely” to demand for Anthropic’s coding tools.
The valuation comparison is central to investor concern. One investor who has backed both companies told the Financial Times that “justifying OpenAI’s recent round required assuming an IPO valuation of $1.2 trillion or more.” Against that benchmark, Anthropic’s “current $380 billion valuation” is framed as a “relative bargain.” The implication for technology investors is that the market’s pricing of AI capability and adoption may be shifting toward specific use cases—here, coding—rather than broad “general model” expectations.
Secondary-market signals: demand for Anthropic shares versus OpenAI discount
Beyond headline valuations, TechCrunch notes that “the secondary market tells a similar story.” Specifically, it reports that “demand for Anthropic shares has grown nearly insatiable while OpenAI shares are trading at a discount.” While the source does not provide exact share-price figures, the direction is clear: trading behavior appears to reward Anthropic’s revenue trajectory and its positioning in developer-facing tools.
From a technology standpoint, this matters because coding tools represent a measurable workflow integration point. When a model product is tied to software development tasks, revenue growth can reflect recurring demand from teams that need to generate, review, or refactor code. The source’s linkage—Anthropic’s revenue growth “driven largely” by coding tools—suggests that investors are watching not only model performance, but also monetization pathways that map to developer productivity.
OpenAI’s enterprise pivot and investor confidence
OpenAI CFO Sarah Friar is cited by TechCrunch as pushing back on the skepticism. She points to OpenAI’s fundraising as evidence of investor confidence: the company’s $122 billion raise is described as the “largest private fundraising in history.” The underlying technological question is how OpenAI will translate model capability into enterprise adoption at scale—an area the source says OpenAI is actively working on as part of a broader reorientation.
However, the investor critique described by TechCrunch focuses on the assumptions required to justify OpenAI’s capital raise. If the justification hinges on an IPO valuation of $1.2 trillion or more, then the company’s enterprise strategy may be evaluated against a high bar: enterprise deployments would need to generate sufficient revenue growth to support that valuation trajectory. The source does not state whether OpenAI has published specific enterprise metrics; it only characterizes the strategic direction and the investor valuation debate.
In other words, the technology competition here is also a capital-markets competition. Investors appear to be asking whether enterprise-focused distribution and product packaging can keep pace with developer-tool momentum—particularly when Anthropic’s coding tools are associated with rapid revenue acceleration.
Coding tools as a demand driver
TechCrunch’s summary repeatedly returns to coding tools as the key demand driver behind Anthropic’s revenue increase. That detail is important for understanding how AI product categories can affect company-level expectations. Coding tools tend to sit closer to the software lifecycle—where teams can test outputs, measure time saved, and integrate assistance into existing developer workflows. The source does not provide performance benchmarks, pricing, or customer counts, but it does indicate that the revenue curve is tied to demand for that product category.
At the same time, the source frames OpenAI’s challenge as “reorient[ing] itself around enterprise customers.” Enterprise adoption can involve different technical and commercial requirements, such as integration into internal systems and procurement cycles. The source does not detail those technical requirements, so any conclusions about implementation complexity would be speculative. Still, the juxtaposition suggests observers may watch whether coding-tool growth continues to outpace enterprise monetization—or whether OpenAI’s enterprise pivot narrows the gap.
TechCrunch also includes a historical reference tied to valuation dynamics. It notes that “Altman has been here before,” referencing his tenure leading Y Combinator, when “aggressive valuation inflation left some portfolio companies financially stranded” while others proved worth every penny and then some. The source uses this as context for why investor expectations around valuation can become a binding constraint, especially when capital is raised at a scale that requires optimistic future outcomes.
Investor positioning: market dynamics and competitive outlook
One of the most specific quotes in the TechCrunch summary comes from Roy Luo, an Iconiq Capital partner. Luo is described as having invested over $1 billion in Anthropic while holding a smaller stake in OpenAI. He told the Financial Times that “There’s room for both, but there is fundamentally a number one and a number two dynamic, and the number one will win disproportionately,” adding, “We picked.”
Luo’s comments, as presented in the source, frame the competition as winner-takes-more dynamics rather than a market where both firms expand evenly. While the source does not define which metric determines “number one,” it places emphasis on valuation justification, secondary-market demand, and Anthropic’s coding-tool driven revenue growth. Observers may therefore interpret the “number one” as the company that most effectively converts AI capabilities into sustained, monetizable adoption—whether through developer tooling, enterprise deployments, or both.
Source: TechCrunch