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General-purpose AI assistant for individuals and teams.
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Artificial Intelligence · Foundation Models
OpenAI
Develops general-purpose AI models, products, and developer infrastructure.
- Research status
- Research coverage
- Headquarters
- San Francisco, CA
- Coverage
- 4 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.
OpenAI API
Developer platform for models, tools and agents.
Codex
AI software-engineering agent and coding platform.
Sora
Generative video product.
01 / THE BUSINESS
How OpenAI creates value.
Subscriptions and usage-based access connect consumer, developer and enterprise customers to AI models and tools. The central economic question is how paid demand develops relative to compute, research and distribution costs.
SectorAI
Founder coverage2 profiles
Research checkedSep 18, 2026
Listing & ownership updateOpenAI announced a confidential draft S-1 submission on June 8, 2026. A confidential submission is not a completed IPO, an approved offering or an established trading date.Original announcement
02 / PEOPLE BEHIND THE COMPANY
Meet the builders.
Co-founder
Sam Altman
Co-founded OpenAI and previously led Y Combinator. His interviews connect AI research, product distribution, infrastructure spending and the organizational choices involved in scaling an AI company.
Explore founderCo-founder
Greg Brockman
Greg Brockman is a co-founder of OpenAI. The company’s founding and leadership material provides context for its work in foundation models.
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
New investment announcement
Disclosed amount$110BPre-money valuation$730BNamed participants: SoftBank · NVIDIA · Amazon
Announced investment commitments; the company said additional investors could join as the round progressed.
Read the announcement
04 / IN THEIR OWN WORDS
Watch. Listen. Form your view.
Stripe Sessions · 2026-05-19
Sam Altman in conversation with Patrick Collison
A founder conversation about AI and the businesses being built around it.
Watch on YouTube ↗OpenAI
OpenAI — 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
- Model capability, enterprise adoption, compute economics, and platform distribution.
- A stronger product position becomes economically meaningful when customers renew, expand and pay enough to cover delivery costs.
Pressure-test the thesis
- Capital intensity.
- Competition and commoditization.
- Governance and regulatory uncertainty.
What to watch next
01
Enterprise and developer adoption — what changed in the latest original disclosure?
02
Compute capacity and unit economics — what changed in the latest original disclosure?
03
Product and model release cadence — 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.
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.
Model capability, enterprise adoption, compute economics, and platform distribution.
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 and developer adoptionCompute capacity and unit economicsProduct and model release cadenceRisks to underwrite
Capital intensityCompetition and commoditizationGovernance and regulatory uncertainty 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 OpenAI
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.