In an informal survey of 300+ Cerebral Valley AI Conference attendees asking which $1B+ valuation startup to short, most voted Perplexity, followed by OpenAI
- An audience at a top AI conference in San Francisco was asked what startup they would short. — Perplexity, an AI search …
Context & Ripple Effects
Perplexity’s valuation narrative has accelerated from its earlier AI-answer-engine positioning to successive reported financing discussions: a proposed round that could value it at $18B followed by a reported $20B post-money fundraising target. The conference result is a narrow but notable sentiment check against that rapid repricing.
The poll is not a market price or a diligence finding. Its relevance is that it identifies where a technically informed audience sees the greatest disconnect risk among highly valued AI companies, with OpenAI also attracting skepticism.
First-order effects
- Perplexity takes the most immediate reputational hit: the result gives investors and prospective employees a visible shorthand for concerns about whether its valuation can be supported.
- OpenAI’s second-place result similarly signals that even the category’s leading private AI companies face heightened expectations; the survey itself does not change either company’s operations or valuation.
Second-order effects
- Future financing conversations for Perplexity may receive more scrutiny around the conversion of search usage into durable revenue, following its earlier reported rapid growth in queries and fundraising valuation.
- AI-search rivals and large platform incumbents can use investor skepticism toward standalone answer engines to press their distribution advantages, while investors may demand clearer evidence of defensibility before backing comparable companies.
Third-order effects
- If this skepticism broadens beyond a conference audience, private AI markets may increasingly separate companies with demonstrable distribution and monetization from those priced primarily on strategic narrative and model access.
- The pattern points to a more disciplined phase of AI valuation-setting, in which high-cost inference businesses face closer examination of where value accrues; the poll alone is insufficient to establish that shift.
The trend: AI investors are moving from rewarding broad generative-AI exposure toward testing whether application-layer companies can defend valuations against distribution, monetization, and compute economics.