OpenAI and Anthropic Revenue Breakdown
OpenAI’s reported $3.6B in annual revenue—mostly from $20/month ChatGPT subscriptions—and sky‑high $150B+ valuation are prompting questions about sustainability, given multibillion‑dollar annual losses and heavy R&D and compute costs. Commenters debate whether OpenAI has a durable moat through brand, UX and ecosystem, or whether large language models will become a low‑margin commodity where switching providers is trivial and open models erode pricing power. Many also highlight strategic risks around dependence on Microsoft’s infrastructure, uncertain future monetization paths (ads, enterprise, APIs), and the possibility that investors are effectively funding a high‑stakes race to AGI with unclear payoff.
Valuation, Revenue & Losses
- OpenAI reportedly has ~$3.6–3.7B revenue (mostly $20/mo subscriptions) but is expected to lose ~$5B+ this year; some say real loss with stock comp could be $8–10B.
- Several commenters note the original “P/E ~43” claim is incorrect because earnings are negative; actual P/E is undefined/negative.
- Some compare price-to-sales (~40x+) to big tech (e.g., Amazon ~3x), calling the valuation “peanuts vs. price”; others say high losses are typical for hypergrowth.
Business Model, Unit Economics & Churn
- Roughly 75% of revenue is estimated from ChatGPT subscriptions, ~25% from API/enterprise (including Microsoft-related usage).
- Debate on whether subscriptions or API are more profitable; one estimate claims API has ~50% gross margins and that unlimited $20 plans are loss-making for heavy users.
- Several note high free usage (≈180M users) vs. ~11M paying users and significant churn after 1–3 months.
- Some expect eventual “closing of the hand”: worse free offering and/or higher prices to force upgrades.
Moat, Competition & Commoditization
- Strong brand (“ChatGPT” as generic LLM term) and UX seen as key advantages; others argue models are commodity and switching providers is trivial (e.g., via Bedrock).
- Concerns that open-source and rival models (Anthropic, Google, Meta, Chinese labs, Pika, etc.) erode proprietary moats.
- Debate over whether this ends as a commodity, low-margin “airline/WeWork” situation vs. a dominant, ad-funded or subscription giant akin to Google Search.
Microsoft Relationship & Infrastructure Risk
- Discussion of complex IP/profit-sharing deal; some worry Microsoft is racing to replace OpenAI and that OpenAI would struggle to fund its own $100B+ datacenter buildout.
- Others think OpenAI’s valuation, brand, and access to capital alleviate this risk.
Adoption, Use Cases & Labor Impacts
- Many pay for AI tools and find them indispensable, especially for coding; others find free versions or local models “good enough” and refuse subscriptions.
- Split views on long-term impact on developers: from “just another productivity tool” to serious threat to junior roles and high salaries.
Investment Angles
- Suggestions to invest indirectly via GPUs, datacenters, power generation, or “AI shovel sellers,” rather than frontier model labs themselves.