📊 Full opportunity report: The runway.How enterprise-revenuelock becomes the load-bearing valuation argument. on ThorstenMeyerAI.com — validation score, market gap, and execution plan.
TL;DR
OpenAI and Anthropic are both preparing large IPOs, emphasizing enterprise revenue as the key to their high valuations. The success of this strategy depends on whether enterprise lock can justify the multiples amid profitability concerns.
OpenAI and Anthropic are each preparing to go public in 2026 with valuations exceeding $900 billion, relying heavily on enterprise revenue as the core justification for their high multiples, despite ongoing concerns about profitability and margins.
Both companies are experiencing rapid revenue growth: OpenAI is generating approximately $25 billion annually, with over 40% coming from enterprise clients, while Anthropic crossed a $30 billion annualized revenue rate by April 2026, with about 80% from enterprise customers. Despite this, both face significant losses—OpenAI is projected to lose around $14 billion in 2026, and Anthropic’s gross margins are around 40%, with internal forecasts aiming for 77% by 2028.
Their valuations are based on multiples of 25 to 40 times revenue, which are higher than typical public software companies. Industry watchers, including Goldman Sachs and JPMorgan, note that these multiples are driven by the belief that enterprise lock will sustain durable, expanding revenue streams, thus supporting the high valuation despite the lack of profitability.
Both labs are emphasizing enterprise revenue as the load-bearing element of their valuation argument, with Anthropic focusing on a clear, margin-focused enterprise-first story, and OpenAI combining consumer scale with enterprise acceleration. The core question is whether the margins and revenue durability will materialize enough to justify the multiples, or if the high valuations are built on an unproven disruption thesis.
The runway.
How enterprise-revenue
lock becomes the load-
bearing valuation
argument.
a multiple no incumbent commands
OpenAI racing 40% → parity
forecast the valuation requires
not cash-flow positive before ~2030
$1T target ÷ ~$25B
run-rate revenue
>$900B reported ÷
~$30B run rate
OpenAI gross margin ·
95% of users are free
- ~80% enterprise revenue from the start
- Claude Code >$2.5B, 54% of the coding-tool segment
- ~40% margin today, 77% forecast by 2028
- Ad-free · PBC + Long-Term Benefit Trust
- Risk: a single-product (Claude Code) concentration
- 900M weekly users · enterprise 40% → parity
- Subscriptions + API + ads pilot + government
- Deployment Company >$4B + Tomoro acqui-hire
- The brand name for AI · broadest distribution
- Drag: consumer margin it is racing to offset
compute-burdened
by 2028 ·
inference cost
must fall
the valuation requires it
The runway is the time between the compute bill and the margin that pays it. The IPO is the refueling. And the enterprise lock is the bet that the disruption the agents are causing will, before the runway ends, become an annuity durable enough to justify the largest valuations ever assigned to companies that have never turned a profit.Thorsten Meyer · The Runway · Enterprise Reorg 04
Why Enterprise Lock Is Central to Valuation
The reliance on enterprise revenue as the primary valuation lever indicates a shift in how AI companies are being valued publicly. It reflects a belief that contracted, embedded, and expanding enterprise relationships can generate predictable, durable revenue streams that justify high multiples, even if profitability remains distant. This approach underscores the importance of enterprise lock as a strategic and financial concept, potentially setting a precedent for future AI and software IPOs. The success or failure of this strategy will influence how markets value disruptive AI firms and how they approach growth, margins, and profitability in this emerging sector.
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The Growing Role of Enterprise Revenue in AI Valuations
Over the past three years, AI labs like OpenAI and Anthropic have shifted focus from consumer-facing models to enterprise applications, driven by the need for more predictable revenue streams. OpenAI’s GPT models are now embedded in enterprise workflows, and Anthropic’s focus on enterprise clients has led to significant revenue growth, with many clients spending over $1 million annually. Despite this, both companies are still operating at a loss, with high compute costs and uncertain margins. The upcoming IPOs are seen as a test of whether enterprise lock can sustain the high valuation multiples that are based on revenue growth and disruption potential, rather than profitability.
“The core of these IPOs is the enterprise revenue lock, which is being used to justify valuations that traditional public markets might find hard to accept based solely on current profitability.”
— Thorsten Meyer
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Uncertainties Surrounding Margins and Revenue Durability
It remains unclear whether the margins necessary to sustain high valuations will materialize at scale, or if the high compute costs and competitive pressures will erode profitability before revenue streams become truly durable. The upcoming IPO filings and audited financial disclosures will be critical in testing whether the enterprise lock thesis holds under scrutiny, especially as investors demand more concrete proof of sustainable margins.
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Next Steps in IPO and Market Validation
The companies are expected to file their S-1 registrations in late 2026, which will include audited financials and detailed disclosures about margins, revenue composition, and cost structures. The market will closely scrutinize whether the enterprise revenue can support the high valuation multiples, and if the companies can demonstrate a clear path to profitability. Investor reactions and analyst assessments will significantly influence the success of these IPOs and set a precedent for future AI valuations.
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Key Questions
Why are OpenAI and Anthropic focusing on enterprise revenue for their IPOs?
They believe that enterprise revenue, being contracted and embedded within workflows, offers a more predictable and durable income stream, justifying higher valuation multiples compared to consumer usage models with thin margins.
What are the main risks associated with their reliance on enterprise lock?
The primary risks include whether margins will materialize as projected, if enterprise relationships will remain durable amid competitive pressures, and whether high compute costs will erode profitability before revenue streams stabilize.
How will the upcoming IPO filings test the enterprise valuation thesis?
The filings will include audited financials, revealing margins, costs, and revenue composition, providing concrete data to assess whether enterprise lock can sustain the high valuation multiples in the long term.
What does this mean for the future of AI company valuations?
If the enterprise lock thesis proves valid, it could establish a new standard for valuing AI firms based on contracted, embedded revenue streams rather than immediate profitability, influencing the entire industry’s approach to growth and investment.
Source: ThorstenMeyerAI.com
