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Anthropic’s pending IPO could change the market’s competitive dynamics, intensify the financial spotlight and influence AI model pricing.
The expected November debut will be a watershed event for AI and the broader technology sector. Anthropic could surpass SpaceX as the largest IPO ever, if the $2 trillion valuation reported in various media outlets comes to pass. SpaceX went public in June at a valuation of $1.77 trillion. Anthropic would likely mark the largest pure-play AI IPO. SpaceX’s offerings include reusable launch vehicles, connectivity services and a social media platform — in addition to AI.
Speculation around Anthropic’s IPO comes at a time when AI’s ability to translate massive investment into meaningful returns comes under increased scrutiny. Concerns about the technology’s safety have also grown.
While Anthropic seems willing to launch in this environment, one of its top rivals, OpenAI, will wait it out for now. Sam Altman, CEO at OpenAI, told Fortune that the company won’t proceed with an IPO this year, citing “everything happening with safety.”
Dean Lyulkin, a capital markets expert and CEO at Cardiff, a small business lender, said investors are likely to read the IPO timing in two ways: one that flatters OpenAI and one that doesn’t.
“The flattering version has precedent in payments,” he said. “Mastercard went public ahead of Visa and helped establish a valuation for payment networks. Visa came later into a market that already got its economics.”
The first version points to the benefits of having a competitor warm up and educate the market. The other version focuses on the willingness to undergo heightened financial scrutiny.
“OpenAI may simply not have enough confidence in its own story and economics in relative terms,” Lyulkin said. “An IPO would give investors much greater visibility into compute costs, margins, capital requirements and customer growth. When your closest competitor is apparently prepared to subject itself to that scrutiny and you aren’t, it’s reasonable for investors to ask serious questions.”
That scrutiny has already begun. According to a leaked prospectus, Anthropic had 2025 revenue of $4.6 billion, but a net loss of $42 billion and an operating loss of $8.06 billion.
Jacob Krell, senior director, secure AI solutions and cybersecurity, at Suzu Labs, a cybersecurity and AI firm, cited other data points from the prospectus: $7.33 billion in compute and infrastructure consumption in 2025 — more than half of operating expenses — and $518 billion in future commitments for cloud, computing and infrastructure. He said those figures describe a company that’s scaling its cost base before showing the capability to produce enough margin to pay for the systems behind them.
That Anthropic’s projected valuation surpasses that of SpaceX suggests the market’s emphasis on AI models, Krell said, noting SpaceX’s vertically integrated data centers, its Starship program, Starlink and X social media applications.
“I guess Wall Street is kind of predicting that the models are going to be worth so much more than the actual infrastructure,” Krell said. “But that’s not really how it’s panning out, because so much of this spend is going to the infrastructure. There is a lot to model development, but it’s dwarfed by the actual infrastructure spend.”
Damon Small, board member at Xcape, a cybersecurity and managed services provider, said he sees the AI investment climate tracking with the 1990s dot-com bubble. The enthusiasm and investment surrounding the dot-com era produced a wave of companies whose business models fell short of investor expectations, he said. Many failed. But the technology proved valuable and ultimately transformed the economy, he added.
Small believes AI will follow a similar trajectory. AI providers confront the same challenge as the dot-coms: how to parlay the potential of a world-changing technology into sustainable, profitable businesses, he noted.
“If you have net losses that are an order of magnitude greater than your revenue, when are the investors going to get that money back?” Small asked. “I think that’s put us in a bit of a precarious situation.”
Spotlighting the frontier model providers’ financial performance will eventually affect enterprise AI customers. The pressure to meet investor expectations is likely to alter providers’ pricing, product lineups and other elements of business strategy, industry executives said.
Model providers must deal with pressure from a couple of angles. Their supply-side costs could increase as they push to purchase the same infrastructure components. At the same time, they will need to optimize shareholder value, Krell noted.
Those dual pincers will “have them try to extract as much as they can from the customers,” Krell said. “We could potentially see substantial price increases.”
Krell pointed to one particular concern: AI startups that base their entire business model around the frontier AI supply chain.
“They’re basically wrapping the frontier AI calls in their software,” he said. “If their [frontier AI companies] prices rise, they’ll have to pass their prices to consumers.”
Chris Ebert, senior vice president of go-to-market at SCG, a company that advises businesses on AI use and cost-control strategies, questioned the prospect of price hikes.
“I wouldn’t assume a public listing automatically means higher prices,” he said. “Competition among AI providers is strong, and unit costs have generally come down.”
Ebert said the bigger issue for most companies is the total cost of AI as usage grows, rather than the price of an individual API call.
Small said he would advise AI companies to keep their entry price points as low as possible and offer an expanded suite of products or services that would demand premium pricing. Model vendors already offer pro-level tiers, with OpenAI launching a $500 per month Pro 500 plan in September.
Newly public AI companies, however, might simultaneously employ multiple approaches — price increases, a broader array of product tiers and model options that encourage cost-effective consumption. Vladimir Lukić, global leader for Boston Consulting Group’s Tech and Digital Advantage practice, said AI providers will experiment with such measures and determine which gives them the greatest yield.
In one tactic, AI providers already offer native services that help customers choose different models based on the task to be performed — summarization versus deep research, for instance. Increasingly sophisticated users can evaluate token prices across various prompt types and make cost-conscious decisions.
“The LLM providers are now starting to learn and say, it’s in my interest for you to do the effective things and be prudent on the cost side,” Lukić said. “Because if I do it well, the consumption overall will go up.”
Ebert also cited the importance of matching the model to the task.
“The question companies should be asking isn’t which model is the cheapest, but which approach delivers the best business outcome at an acceptable total cost,” he said.
The prospect of bellwether AI IPOs signals a new financial landscape that will shape model economics and potentially trigger a vendor shakeout. Against that backdrop, what can enterprise leaders do to prepare as they scale AI agents and pursue other strategic deployments?
Lukić said businesses must attend to the essentials. In the case of agentic AI, the context layer is a key point of control. That’s where an enterprise’s business rules reside to harden the models or direct the agents, he noted.
“Keep focusing on that,” Lukić said, noting the context layer transcends the specific frontier model in use.
Indeed, BCG’s Applied AI Index 2026, published in September, said companies should maintain their “enterprise cortex” — which includes business rules and operating context — under their exclusive control, emphasizing flexibility across models and providers.
Lukić also highlighted the importance of managing data, managing the linkage between core systems and storage systems, and differentiating between systems of record and analytics layers. Upskilling the workforce to build, manage and evolve agents is also a key step, he said.
“Those are no-regrets moves that, regardless of which of the LLMs is around two or three years from now, you will need to do in any scenario,” Lukić said.
John Moore is a freelance writer who has covered business and technology topics for 40 years. He focuses on enterprise IT strategy, AI adoption, data management and partner ecosystems.
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