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Artificial Intelligence · Foundation Models
Anthropic
Builds Claude AI models with a focus on reliable, steerable systems.
- Research status
- Research coverage
- Headquarters
- San Francisco, CA
- Coverage
- 3 product lines mapped
- Last reviewed
- September 2026
Product map
What the company builds
Products and operating platforms help connect company-level news to the revenue pools, customers, competitors, and supply chains it may affect.
Claude Code
Agentic coding tool for software-development workflows.
Claude API
Developer access to Anthropic models and tools.
01 / THE BUSINESS
How Anthropic creates value.
Claude is sold through consumer subscriptions, enterprise products and APIs, including cloud distribution partners. Adoption matters alongside inference costs, customer retention and the capital required to develop new models.
SectorAI
Founder coverage2 profiles
Research checkedSep 18, 2026
02 / PEOPLE BEHIND THE COMPANY
Meet the builders.
Co-founder
Dario Amodei
Dario Amodei is a co-founder of Anthropic. An AI researcher whose work helped shape the company’s emphasis on model capability, reliability and safety.
Explore founderCo-founder
Daniela Amodei
Daniela Amodei is a co-founder of Anthropic. Helped build the organization and operating approach around Anthropic’s research and commercial work.
Explore founderSelected founders; historical founding roles are distinct from current management positions.
03 / CAPITAL & OWNERSHIP
Follow the financing.
Selected disclosed events, newest first. Announcement values describe that transaction and date; they are not current share prices. Debt, equity and secondary transactions are identified separately.
- Equity
Series F
Disclosed amount$13BPost-money valuation$183BNamed participants: ICONIQ · Fidelity Management & Research · Lightspeed Venture Partners · Coatue · GIC
Historical round included for its primary-source detail; this record is not a claim that no subsequent financing occurred.
Read the announcement
Explore the named investors.
Names refer to the cited transactions. They do not establish current ownership, ownership percentages, or endorsement of this website.
04 / IN THEIR OWN WORDS
Watch. Listen. Form your view.
Dwarkesh Podcast · 2026-02-13
Dario Amodei — scaling, compute and AI economics
Discusses model progress, compute investment, industry economics and regulation.
Watch on YouTube ↗Anthropic
Anthropic — official video channel
Product demonstrations, company presentations and original updates, linked from the company’s own website.
Visit original material ↗Interviews reflect the speaker’s perspective at publication. Company statements are not independent verification.
05 / EDITORIAL ANALYSIS
The opportunity. The open questions.
Our interpretation of the business model and cited sources, not a valuation or a recommendation.
The case to investigate
- Enterprise traction, safety-led differentiation, and cloud distribution partnerships.
- A stronger product position becomes economically meaningful when customers renew, expand and pay enough to cover delivery costs.
Pressure-test the thesis
- Compute concentration.
- Pricing pressure.
- Fast-moving model competition.
What to watch next
01
Enterprise usage — what changed in the latest original disclosure?
02
Model performance and cost — what changed in the latest original disclosure?
03
Cloud-channel expansion — what changed in the latest original disclosure?
YOUR RESEARCH WORKSPACE
Keep the questions that matter.
Use this checklist to record what you have investigated. Your notes stay in this browser.
06 / RESEARCH TRAIL
Go straight to the source.
Company overviewanthropic.comCompany newsroomanthropic.comFounding & leadership backgroundanthropic.comSeries Fanthropic.com · Sep 2, 2025
Research checked Sep 18, 2026. Selected public disclosures, not a complete capitalization table or securities-filing search. Missing information is left unconfirmed. No investment availability or IPO date is implied.
Enterprise traction, safety-led differentiation, and cloud distribution partnerships.
Private-company information is incomplete, episodic, and difficult to verify. Treat every data point as a starting point for diligence—not a substitute for company-approved materials, legal review, or investment analysis.
Add to my private currentSignals to monitor
Enterprise usageModel performance and costCloud-channel expansionRisks to underwrite
Compute concentrationPricing pressureFast-moving model competition Underwriting map
What an investor should actually diligence
Separate recurring software revenue from usage-based inference, services, consumer subscriptions, licensing, and strategic financing. Model revenue quality after compute and distribution costs.
Buyer map
01
Developers and technical teams
02
Enterprise business units
03
Regulated and security-sensitive organizations
04
Consumers and prosumers
Economics to request
01
Revenue and annualized run-rate by product
02
Gross margin after inference and hosting
03
Net revenue retention and cohort expansion
04
Compute commitments, utilization, and vendor concentration
05
Sales efficiency and support burden
Moat tests
01
Model quality persists after price normalization
02
Proprietary data or workflow context improves retention
03
Distribution is owned rather than rented
04
Switching costs survive model commoditization
Milestone map
01
Major model or product release
02
Enterprise deployment expansion
03
Material distribution partnership
04
Improving inference efficiency
Comparable lensesFoundation-model platformsCloud AI servicesVertical AI applicationsDeveloper infrastructure
Underwriting framework
How to diligence Anthropic
A company-specific starting point based on the operating realities of artificial intelligence. Replace assumptions with verified company materials, customer evidence, legal documents, and expert work.
Annualized recurring revenue versus contracted value
Inference gross margin after compute
Net revenue retention by customer cohort
Research and compute spend per dollar of growth
Production workloads rather than pilots
Usage depth across teams and workflows
Model performance on customer-specific tasks
Renewals without unsustainable discounting
Foundation-model providers
Cloud AI platforms
Application-layer AI companies
Open-source and in-house substitution cost
Is differentiation in models, data, distribution, or workflow?
How quickly does capability commoditize?
Who controls the customer and the compute bill?
What safety or regulatory event could slow adoption?
No securities are offered here.
SMYC does not represent that shares are available, transferable, appropriately priced, or suitable for any person. Private securities are illiquid, speculative, subject to transfer restrictions, and may result in total loss.