OpenAI Is in Talks to Raise New Funding at Valuation of $100B or More
OpenAI’s reported bid to raise funding at a $100B+ valuation is prompting debate over whether its current revenues, product-market fit, and governance justify such a price. Commenters weigh the strength of its advantages—brand, talent, data, Microsoft backing, and potential custom chips—against intensifying open‑source and Big Tech competition, unclear moats, and heavy infrastructure costs. Many see echoes of past tech bubbles: some believe generative AI could underpin revenues on the scale of today’s largest platforms, while others doubt that next‑token prediction models can sustain such lofty expectations.
Corporate structure & origins
- Some wonder if the transition from nonprofit to for‑profit (via a capped-profit structure) could have legal or “good faith” implications for early donors.
- Others argue the nonprofit shell is mostly PR; in practice it behaves like a for‑profit, albeit with capped investor returns.
- One thread debates how much an early high‑profile donor actually contributed versus what was pledged, and whether that should imply equity-like claims.
Valuation, hype, and comparisons
- Many see the >$100B valuation as extreme, likening it to WeWork’s peak and other bubble eras (dotcom, crypto, IoT).
- Supporters say this may be the “new PC/internet moment,” so a stratospheric price can still be rational if AI becomes infrastructure‑level.
- Skeptics highlight low current revenues and uncertain profits, suggesting this may be the “pump” phase before an eventual correction.
Moat, competition, and open source
- Several argue AI has little durable moat: models and techniques commoditize quickly; strong open-source models already rival proprietary ones.
- Others counter that OpenAI has advantages: private datasets, scale, talent concentration, early brand dominance (ChatGPT), proprietary research, and lobbying power.
- There’s concern that the company is trying to build a regulatory moat that would disadvantage open‑source models.
Business model and revenue potential
- Discussion over whether a $20/month subscription can scale to hundreds of millions or more users; some see this as plausible, others doubt global consumer willingness/ability to pay.
- Many expect most revenue to be B2B/enterprise (via cloud, productivity suites, and APIs), not direct subscriptions.
- Some users report heavily shifting search and daily tasks to ChatGPT, while others say it’s still a toy for the average person with high churn likely.
Hardware, chips, and resource constraints
- GPU scarcity is a recurring theme; Microsoft is perceived as subsidizing and prioritizing capacity, which itself may be a moat.
- Several expect a big portion of new capital to go into custom chips/fabs and AI-specialized cloud, to escape dependence on expensive third‑party GPUs.
Governance, talent, and market structure
- The recent board crisis leads to worries that key researchers could walk and instantly recreate a competitor, undermining investor security.
- Others say that very mobility and talent concentration are exactly what justify the valuation.
- There’s debate over why such a “next computing platform” is being financed privately instead of via IPO; commenters note private funding avoids short‑term public market pressure and activist investors.
Broader impact, AGI, and personal FOMO
- Opinions split on how far current LLMs are from true AGI and whether next‑token prediction can really yield understanding and reasoning.
- Some think present-day generative tools may be more practically useful than hypothetical AGI.
- A side discussion covers personal regret/FOMO about not starting AI companies, with pushback that it’s still early but that pedigree and networks heavily shape who gets funded.