Valuation analysis updated for Series G (Feb 2026). Anthropic raised $30B at $380B post-money with $14B run-rate revenue, yielding ~27x multiple—now closer to OpenAI's 25x at $500B/$20B. Bull case rests on 88% enterprise retention (vs 76% industry), coding benchmark leadership (80.9% SWE-bench vs GPT-5.2's 74.9%), 500+ million-dollar customers, and dual AWS/Google Cloud partnerships worth tens of billions. Bear case includes severe customer concentration (≈$1.2B or 25%+ from Cursor and GitHub Copilot alone), margin compression (forecast cut from 50% to 40%), and bubble warnings—Sam Altman admits 'AI bubble is ongoing.' Extended scenarios model 1.5-5x growth ($500B-$1.75T) with revised probabilities.
Anthropic Valuation Analysis
Anthropic Valuation Analysis
Valuation analysis updated for Series G (Feb 2026). Anthropic raised $30B at $380B post-money with $14B run-rate revenue, yielding ~27x multiple—now closer to OpenAI's 25x at $500B/$20B. Bull case rests on 88% enterprise retention (vs 76% industry), coding benchmark leadership (80.9% SWE-bench vs GPT-5.2's 74.9%), 500+ million-dollar customers, and dual AWS/Google Cloud partnerships worth tens of billions. Bear case includes severe customer concentration (≈$1.2B or 25%+ from Cursor and GitHub Copilot alone), margin compression (forecast cut from 50% to 40%), and bubble warnings—Sam Altman admits 'AI bubble is ongoing.' Extended scenarios model 1.5-5x growth ($500B-$1.75T) with revised probabilities.
This page covers Anthropic valuation analysis. For company overview, see AnthropicOrganizationAnthropicComprehensive profile of Anthropic, founded in 2021 by seven former OpenAI researchers (Dario and Daniela Amodei, Chris Olah, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish) with early funding.... For IPO timeline, see Anthropic IPOAnalysisAnthropic IPOAnthropic is actively preparing for a potential 2026 IPO with concrete steps like hiring Wilson Sonsini and conducting bank discussions, though timeline uncertainty remains with prediction markets ...Quality: 65/100. For EA capital analysis, see Anthropic (Funder)AnalysisAnthropic (Funder)Comprehensive model of EA-aligned philanthropic capital at Anthropic. At $380B valuation (Series G, Feb 2026, $30B raised): $27-76B risk-adjusted EA capital expected. Total funding raised exceeds $...Quality: 65/100.
Data as of: February 2026. Key figures: Anthropic $380B valuation (Series G), $14B run-rate revenue; OpenAI $500B valuation, $20B ARR.
Quick Assessment
| Metric | Anthropic | OpenAI | Assessment |
|---|---|---|---|
| Valuation | $380B (Series G, Feb 2026) | $500B (targeting $750-830B) | OpenAI 1.3-2.2x larger |
| Revenue (Run Rate) | $14B (Feb 2026) | $20B (Jan 2026) | OpenAI 1.4x higher |
| Revenue Multiple | ≈27x | ≈25x (current), ≈41x (at $830B) | Near parity |
| Gross Margin | 40% (revised down) | 40-50% (70% compute margin) | Similar, both under pressure |
| Enterprise Retention | 88% | Unknown | Anthropic leads industry (76% avg) |
| Path to Breakeven | 2028 | Unknown | Anthropic more transparent |
Overview
AnthropicOrganizationAnthropicComprehensive profile of Anthropic, founded in 2021 by seven former OpenAI researchers (Dario and Daniela Amodei, Chris Olah, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish) with early funding...'s $380 billion valuation (February 2026 Series G) reflects rapid revenue growth from $9B at end of 2025 to $14B run-rate by the time of the funding round. At ≈27x current revenue, Anthropic now trades at a multiple much closer to OpenAIOrganizationOpenAIComprehensive organizational profile of OpenAI documenting evolution from 2015 non-profit to commercial AGI developer, with detailed analysis of governance crisis, safety researcher exodus (75% of ...'s ≈25x (at $500B with $20B ARR)—a significant convergence from the ≈39x multiple at the previous $350B valuation with $9B revenue.
This page provides an investment-grade analysis of bull and bear cases, incorporating data on customer concentration, margin pressure, and competitive dynamics.
Updated thesis: The revenue multiple gap between Anthropic and OpenAI has largely closed (27x vs 25x). The remaining modest premium may be justified by superior enterprise metrics (88% retention, 80% enterprise revenue, 500+ million-dollar customers) and benchmark leadership in coding—or may still reflect overvaluation given customer concentration risk and margin compression.
Current Valuation Context
Revenue Multiple Comparison
| Company | Valuation | Revenue (Run Rate) | Multiple | Data Source |
|---|---|---|---|---|
| Anthropic | $380B (Series G, Feb 2026) | $14B (Feb 2026) | ≈27x | Anthropic |
| Anthropic (prev.) | $350B (Nov 2025) | $9B (end 2025) | ≈39x | Bloomberg |
| OpenAI | $500B | $20B (Jan 2026) | ≈25x | i10x |
| OpenAI (proposed) | $750-830B | $20B | 37-41x | TechCrunch |
Key insight: Anthropic's revenue growth from $9B to $14B compressed its revenue multiple from ≈39x to ≈27x, bringing it much closer to OpenAI's ≈25x. The valuation itself only increased 8.6% ($350B → $380B) while revenue grew 56%. If OpenAI closes its $100B round at $830B, OpenAI would trade at ≈41x—significantly above Anthropic's current multiple.
Revenue Growth Trajectories
| Company | 2024 | 2025 | Current Run Rate | 2026 (Guidance) | 2027 (Projected) |
|---|---|---|---|---|---|
| Anthropic | $1B | $9B | $14B (Feb 2026) | $20-26B | $34.5B |
| OpenAI | $6B | $20B | $20B (Jan 2026) | $46B (2.3x) | $92B (2x) |
Both companies are growing extraordinarily fast. OpenAI projects reaching $100B revenue by 2028. Epoch AI
Valuation Progression
| Date | Round | Valuation | Revenue (ARR) | Multiple |
|---|---|---|---|---|
| May 2021 | Series A | $550M | ≈$0 | — |
| April 2022 | Series B | $4B | ≈$10M | 400x |
| March 2025 | Series E | $61.5B | ≈$1B | 62x |
| Sept 2025 | Series F | $183B | ≈$5B | 37x |
| Nov 2025 | Microsoft/Nvidia | $350B | ≈$9B | 39x |
| Feb 2026 | Series G | $380B | ≈$14B | 27x |
Multiple compression from 400x to 27x reflects a maturing business with rapidly growing revenue, not declining prospects.
Bull Case: Arguments for Higher Valuation
1. Enterprise Metrics Excellence
Anthropic's enterprise fundamentals outperform industry benchmarks:
| Metric | Anthropic | Industry Average | Advantage |
|---|---|---|---|
| Enterprise retention | 88% | 76% | +12 percentage points |
| Revenue from enterprise | 80% | Varies | High-quality revenue |
| Multi-year commitments | Growing | Uncommon | Better forecasting |
| Large accounts (>$100K) | 7x YoY growth | — | Strong expansion |
The 88% retention rate suggests genuine product-market fit and switching costs. Enterprise contracts include SLA guarantees, compliance certifications (HIPAA, SOC 2 Type II, ISO 27001), and custom data retention policies that create lock-in.
2. Coding Benchmark Leadership
Claude leads the most commercially valuable benchmark category—software development:
| Benchmark | Claude Opus 4.5 | GPT-5.2 | Gemini 3 Pro | Leader |
|---|---|---|---|---|
| SWE-bench Verified | 80.9% | 74.9% | 76.8% | Claude |
| Terminal-bench 2.0 | 59.3% | — | — | Claude |
| Prompt injection resistance | 4.7% success | 21.9% | 12.5% | Claude |
| AIME 2025 (math) | — | 100% | — | GPT-5.2 |
| GPQA Diamond (science) | — | — | 91.9% | Gemini |
Source: LM Council, Vellum
Coding is arguably the highest-value AI application today. Claude's leadership in SWE-bench and security (lowest prompt injection rate) directly supports enterprise adoption. However, no single model dominates all categories—GPT-5.2 leads reasoning, Gemini leads multimodal.
3. Dual Cloud Infrastructure Partnerships
Anthropic has secured massive infrastructure commitments from both major cloud providers:
Amazon Web Services:
- $8B total investment from Amazon
- 1 million+ Trainium2 chips committed
- $11B dedicated data center in Indiana
- Projected $1.28B → $3B → $5.6B AWS revenue (2025 → 2026 → 2027)
Google Cloud:
- "Tens of billions" TPU deal announced October 2025
- Expands beyond AWS dependency
- Access to both Trainium and TPU architectures
This dual-cloud strategy reduces infrastructure risk and provides leverage in chip negotiations.
4. Talent Moat
Anthropic has assembled exceptional AI research talent:
Founding Team (7 ex-OpenAI researchers):
- Dario AmodeiPersonDario AmodeiComprehensive biographical profile of Anthropic CEO Dario Amodei documenting his 'race to the top' philosophy, 10-25% catastrophic risk estimate, 2026-2030 AGI timeline, and Constitutional AI appro...Quality: 41/100 (CEO) - Former VP Research at OpenAI
- Daniela AmodeiPersonDaniela AmodeiBiographical overview of Anthropic's President covering her operational role in leading $7.3B fundraising and enterprise partnerships while advocating for safety-first AI business models. Largely d...Quality: 21/100 (President) - Former VP Operations at OpenAI
- Chris OlahPersonChris OlahBiographical overview of Chris Olah's career trajectory from Google Brain to co-founding Anthropic, focusing on his pioneering work in mechanistic interpretability including feature visualization, ...Quality: 27/100 - Interpretability pioneer
- Tom Brown - Lead author of GPT-3
- Jared Kaplan - Scaling laws pioneer
Key Acquisitions:
- Jan LeikePersonJan LeikeComprehensive biography of Jan Leike covering his career from DeepMind through OpenAI's Superalignment team to current role as Head of Alignment at Anthropic, emphasizing his pioneering work on RLH...Quality: 27/100 (2024) - Former OpenAI Superalignment co-lead
- John Schulman (2024) - OpenAI co-founder, invented PPO algorithm
- Holden KarnofskyPersonHolden KarnofskyHolden Karnofsky directed $300M+ in AI safety funding through Open Philanthropy, growing the field from ~20 to 400+ FTE researchers and developing influential frameworks like the 'Most Important Ce...Quality: 40/100 (2025) - Coefficient Giving co-founder
Team Scale:
- Interpretability team: 40-60 researchers (largest globally)
- Safety researchers: 200-330 (20-30% of technical staff)
5. Open Source Threat Declining
The competitive threat from open-source models has diminished:
| Metric | 2024 | 2025 | Trend |
|---|---|---|---|
| Open source enterprise share | 19% | 11% | Declining |
| Llama enterprise production | Higher | 9% | Declining |
| Anthropic/OpenAI/Google share | — | 88% | Consolidating |
Source: Menlo Ventures
Llama 4's launch "underwhelmed in real-world settings." The performance gap between open and proprietary models widened throughout 2024-2025, reducing the threat of commoditization.
Bear Case: Arguments Against Higher Valuation
1. Severe Customer Concentration Risk
This is the most significant undisclosed risk. Anthropic's revenue is highly concentrated:
| Customer | Estimated Revenue | Share of Total |
|---|---|---|
| Cursor | ≈$600M | ≈13% |
| GitHub Copilot | ≈$600M | ≈13% |
| Combined | ≈$1.2B | ≈25%+ |
Source: VentureBeat
Nearly a quarter of Anthropic's revenue comes from just two coding tool customers. If either relationship ends or shifts to a competitor, revenue would drop significantly. This concentration in AI-assisted coding also means Anthropic is vulnerable to any disruption in that specific market.
2. Margin Pressure and Compression
Anthropic recently cut its gross margin forecast:
| Metric | Original Forecast | Revised Forecast | Change |
|---|---|---|---|
| 2025 Gross Margin | 50% | 40% | -10 points |
| Cause | — | Rising inference costs | Structural |
Source: The Information, WebProNews
AI inference costs scale with usage. Unlike traditional software with near-zero marginal costs, every AI query burns compute. As revenue grows, so do costs—potentially faster than efficiency gains can offset.
For comparison, OpenAI claims 70% "compute margin" but overall gross margins are 40-50% after partner revenue shares and free-tier subsidies. SaaStr
3. AI Valuation Bubble Warnings
Multiple credible sources warn of bubble conditions:
| Source | Warning | Date |
|---|---|---|
| Sam Altman (OpenAI CEO) | "AI bubble is ongoing" | 2025 |
| Jamie Dimon (JPMorgan) | "Higher chance of meaningful drop" than markets reflect | 2025 |
| DeepSeek launch | Nvidia dropped 17% in one day | Jan 2025 |
| Market concentration | 30% of S&P 500 in 5 companies—"greatest in half a century" | Late 2025 |
Source: Wikipedia, Oliver Wyman
When the CEO of OpenAI acknowledges a bubble, valuations across the sector deserve skepticism.
4. Competitive Benchmark Parity
While Claude leads coding, it does not dominate across categories:
| Category | Leader | Claude's Position |
|---|---|---|
| Coding | Claude | #1 |
| Mathematical reasoning | GPT-5.2 | Behind |
| Scientific knowledge | Gemini 3 Pro | Behind |
| Multimodal/context | Gemini (1M tokens) | Smaller context |
Source: Fello AI
The market appears to be evolving toward model routing—using different models for different tasks—rather than winner-take-all. This limits any single company's ability to capture the entire market.
5. OpenAI's Scale Advantage
OpenAI has significant advantages that may widen:
| Metric | OpenAI | Anthropic | Gap |
|---|---|---|---|
| Weekly active users | 800M | Unknown | Massive |
| Revenue | $20B | $14B | 1.4x |
| Total raised | — | $67B+ | — |
| Valuation (proposed) | $750-830B | $380B | 2.0-2.2x |
Source: TechCrunch
If OpenAI raises $100B at $830B, it will have significantly more capital to invest in compute, talent, and product development.
Revised Valuation Scenarios
Given corrected data, here are updated probability-weighted scenarios:
| Scenario | Valuation | Multiple vs Current | Probability | Key Drivers |
|---|---|---|---|---|
| Bear | $175-250B | 0.5-0.7x | 15-20% | Bubble correction, customer churn |
| Base | $380B | 1x | 40-50% | Status quo, margin pressure offsets growth |
| Moderate Bull | $500-700B | 1.3-1.8x | 20-30% | Diversified customers, sustained growth |
| Strong Bull | $1-1.75T | 2.6-4.6x | 5-10% | Market leader, AGI progress |
Key change from previous analysis: With the Series G at $380B and $14B revenue (≈27x multiple), Anthropic's valuation premium over OpenAI has largely disappeared. The revenue growth story is now the primary justification rather than a premium multiple.
Unit Economics Deep Dive
Gross Margin Comparison
| Company | Compute Margin | Overall Gross Margin | Trend |
|---|---|---|---|
| Anthropic | Unknown | 40% (revised) | Declining |
| OpenAI | 70% | 40-50% | Improving |
| Mature SaaS | N/A | 70-80% | Stable |
AI companies operate with structurally lower margins than traditional SaaS due to inference costs. This may improve with efficiency gains, but the timeline is uncertain.
Path to Profitability
| Milestone | Anthropic | OpenAI |
|---|---|---|
| Stop burning cash | 2027 | Unknown |
| Breakeven | 2028 | "Years away" |
| Positive FCF | 2027 (projected $17B by 2028) | Unknown |
Source: Deep Research Global
Anthropic projects faster path to profitability, which partially justifies its premium multiple.
Implications for Stakeholders
For Investors
| Scenario | Return | Risk Assessment |
|---|---|---|
| Bear (-50%) | -50% | Customer concentration, bubble burst |
| Base (0%) | 0% | Current pricing is fair at $380B |
| Moderate Bull (+30-85%) | +30-85% | Growth execution, multiple expansion |
| Strong Bull (+160%+) | +160%+ | Market dominance, requires exceptional execution |
The risk/reward profile has improved since Anthropic's revenue multiple compressed from ≈39x to ≈27x. The downside risk from multiple compression is reduced, though sector-wide corrections remain a risk.
For EA-Aligned Capital
See Anthropic (Funder)AnalysisAnthropic (Funder)Comprehensive model of EA-aligned philanthropic capital at Anthropic. At $380B valuation (Series G, Feb 2026, $30B raised): $27-76B risk-adjusted EA capital expected. Total funding raised exceeds $...Quality: 65/100 for detailed philanthropic capital analysis:
| Valuation | Risk-Adjusted EA Capital |
|---|---|
| $175B (bear) | $12-35B |
| $380B (current) | $27-76B |
| $700B (moderate bull) | $50-140B |
| $1T+ (strong bull) | $70-200B+ |
For the AI Safety Field
Anthropic's trajectory matters for the field regardless of exact valuation:
- Talent attraction: Even at current valuations, Anthropic attracts top safety researchers
- Model legitimacy: Demonstrates "safety lab" can compete commercially
- Research funding: Higher valuations fund more safety research
- Industry influence: Success encourages competitors to adopt safety practices
Key Uncertainties
| Uncertainty | If Resolves Positive | If Resolves Negative |
|---|---|---|
| Customer concentration | Diversifies, reduces risk | Major customer churns |
| Margin trajectory | Efficiency gains, 50%+ margins | Continues declining |
| Benchmark leadership | Maintains/extends coding lead | Loses to GPT/Gemini |
| Bubble dynamics | Soft landing | Sharp correction |
| OpenAI execution | OpenAI stumbles | OpenAI pulls ahead |
Methodology Notes
This analysis uses:
- February 2026 revenue data where available (Anthropic Series G announcement)
- Multiple independent sources for each claim
- Explicit acknowledgment of prior errors
- Risk-weighted scenario probabilities
Limitations:
- Private company financials are estimates
- Customer concentration data is from single source
- Margin data may be self-reported
- Competitive benchmark data varies by source