OpenAI vs Anthropic: The IPO Race, Valuations, and What It Means for AI in 2026

Anthropic vs OpenAI valuations: big private market claims and why an IPO matters

A Reddit post is making bold claims about Anthropic overtaking OpenAI in private market valuation and both firms gearing up for IPOs. It also argues that the market values AI software very differently to physical retail giants like Walmart. Here’s what’s being claimed, what to watch for, and why it matters for developers and businesses in the UK.

Source: This Is why they are doing IPO (Reddit)

Key claims from the Reddit post

“Anthropic Dethrones OpenAI in Private Markets… valuation hit $965 billion… eclipsing OpenAI’s… $852 billion.”

“The Walmart Disconnect… generates nearly 15 times the annual revenue of Anthropic, yet it is valued roughly the same or slightly lower.”

“Both Anthropic and OpenAI have confidentially filed for IPOs.”

These are extraordinary statements. They may reflect sentiment in secondary markets or rumour, but they are not independently verified at the time of writing. Public reporting to date has pointed to much lower figures for both companies. Treat these numbers as speculative until any IPO filing (S-1) is published.

Private vs public markets: why the numbers can look wild

Private valuations are often set in funding rounds with preference terms (e.g., liquidation preferences) that don’t translate to ordinary share value. In frothy markets, secondary trades can create headline numbers that don’t survive contact with public market scrutiny. An IPO typically compresses that optimism into a price range supported by audited revenue, costs, and forward guidance.

If the IPO race is real, what the S-1 will need to show

An S-1 (the US IPO prospectus) lays out audited financials and risk factors. If Anthropic and OpenAI do file, here’s what matters:

• Revenue mix and growth – how much is true enterprise software (subscriptions, seat licences) versus consumption-based API?
• Gross margin – revenue minus cost of goods sold, especially GPU time, inference serving, and support. This shows how much headroom there is to fund R&D.
• Compute commitments – long-term contracts with cloud providers and their impact on cash burn and margins.
• Customer concentration – reliance on a small set of large enterprise buyers increases risk.
• Unit economics – costs per 1,000 tokens or per task and how they trend with model efficiency improvements.
• Safety, data, and compliance – disclosures around training data sources, user data handling, and regulatory exposure.

Topic	Claim in the Reddit post	What to verify in an S-1
Valuation	Anthropic at ~$965B, OpenAI at ~$852B	IPO price range, fully diluted share count, and valuation at listing
Enterprise traction	Growth “heavily driven” by tools like Claude Code	Revenue by product, enterprise retention, net dollar expansion
Profitability	Not discussed	Gross margin trend, operating losses, cash burn and runway
Infrastructure costs	Implied “massive” capital burn	Cloud/GPU commitments, capex, and energy costs

“Software multiples” vs Walmart: the valuation disconnect explained

The post highlights that Walmart’s huge revenue base doesn’t automatically equate to a higher valuation than an AI startup. That’s about margins, growth, and scalability.

• Software is prized for high gross margins and low marginal cost at scale, so investors pay for growth and operating leverage.
• Retail has lower margins, high working capital needs, and physical logistics that don’t scale with the same economics.
• However, foundation model providers face heavy infrastructure costs. Serving large models is not classic software-as-a-service – it can behave more like a capital-intensive utility until efficiency catches up.

The open question is whether AI leaders can push unit costs down faster than enterprise demand pushes usage up. An IPO will force clarity.

What this means for UK developers and buyers

Pricing, contracts, and vendor lock-in

If IPOs are on the horizon, both companies will be incentivised to show improving margins and predictable revenue. Expect potential price changes, more tiered plans, stricter rate limits, enterprise minimums, and push towards annual commitments. For UK teams, bake in exit strategies: abstraction layers, multi-model routing, and clear data portability.

Data protection and compliance

UK organisations remain bound by UK GDPR. Look for disclosures about:

• How customer prompts and outputs are processed, stored, and used for training or fine-tuning.
• Regional data residency options and on-by-default privacy controls.
• Model behaviour controls (content filters, audit logs) for regulated sectors.

Guidance: see the ICO’s resources on AI and data protection.

Availability and infrastructure risk in the UK

Compute supply, energy availability, and cooling constraints all affect uptime and cost. Expect more discussion of efficiency, including energy and water usage, as part of risk disclosures and ESG reporting. For a primer on why cooling water matters and what “sustainable” actually means in data centres, read my analysis: AI, waste water, and the truth about data centre cooling.

Balanced view: what to be excited about, and what to be cautious of

Potential upsides

• Enterprise-grade tooling like AI coding assistants can materially shift developer productivity and cycle times.
• Public market discipline could improve transparency on reliability, model updates, and long-term support.
• More competition may mean better safety tooling, observability, and SLAs that large UK buyers can rely on.

Trade-offs and risks

• Model quality is still variable by task; hallucinations and bias remain active risks that require testing and guardrails.
• Consumption-based pricing can escalate quickly; forecast usage and cap spend with budgets and alerts.
• A post-IPO focus on margins might slow open research and push more features behind enterprise gates.

How to read the next headlines sensibly

Take valuation headlines with a pinch of salt until the paperwork lands. What matters for teams on the ground are fundamentals: model capability for your use-case, latency and cost at your scale, compliance posture, and the vendor’s operational maturity.

If and when S-1 filings appear, skip the hype and go straight to the numbers and risk factors. They’ll tell you whether today’s AI leaders look more like classic software platforms – or capital-heavy utilities racing to become one.