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Stanford University

stanford.edu
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Overall score
49.2Poor
—
Rank
#404
Prev: #0
media avg
60.0
82 peers
Agent runs / week
1
Across 1 providers

Our mission of discovery and learning is energized by a spirit of optimism and possibility that dates to our founding.

Markets
US
Locales
en-us
Opportunity cost · media

Stanford University could be leaving ~$5,181,600 in subscription & ad revenue on the table every year

Stunt Double’s 2027 agent-traffic model projects 34% of content discovery and subscription sessions in the media sector will be initiated or completed by AI agents[1][2]. Stanford University currently scores 49.2 on the Stunt Double Index[3], 1.0 points below the media peer average, with a 51-point gap to the ideal agent experience (100). The loss figure below applies that gap to the projected agent-driven slice of a typical annual subscription & ad revenuebaseline for this sector — it is a directional estimate, not a measured conversion rate.

Gap to leader
50.8 pts
Below media avg
1.0 pts
Modelled revenue at risk
$5,181,600

Estimate assumes a $30M combined subscription and ad-revenue baseline. Claim your domain to replace this placeholder with your reported subscription & ad revenue.

Sources
  1. [1]Gartner (2025) — Intelligent Agents in AI Really Can Work Alone. Here’s How.
  2. [2]Adobe Analytics (2025) — Generative AI traffic to U.S. retail sites (weekly trend)
  3. [3]Stunt Double (2026) — Stunt Double Index methodology & scoring rubric

Category breakdown

Brand awarenessCan agents recognise you exist?
80
DiscoveryWill they pick you?
50
Information retrievalCan they read your site?
75
Market rankingWhere do you sit in the lineup?
35
AccuracyDo they tell the truth about you?
65
Task completionCan an agent complete a task on behalf of a user?
30
Delegated accessDo you let agents in, safely?
12
Contact & communicationCan an agent reach a human?
35

By agent provider

By agent provider
Session quality, 30-day rolling, N ≥ 10 per provider
Claude
Anthropic
0.0
ChatGPT Agent
OpenAI
100.0
Gemini
Google
0.0
Perplexity
Perplexity
Copilot
Microsoft
Browserbase Operator
Browserbase

Where agents get stuck

Public summary · full session replay available to verified owners
mediumBrand awareness
No Organization JSON-LD. Agents can’t tell who owns the site at a glance.
mediumDiscovery
No sitemap.xml. Agents can’t enumerate indexable pages.
lowDiscovery
No llms.txt. You miss the emerging standard for giving agents a curated map of the site.
mediumInformation retrieval
No JSON-LD structured data. Agents must infer entities from markup.
mediumMarket ranking
No Product/Service schema on the homepage. Agents can’t easily compare offerings.
lowMarket ranking
No review or aggregateRating schema. Agents have no signal for ranking vs peers.
mediumAccuracy
No freshness headers. Agents can’t tell when content was updated.
mediumAccuracy
No JSON-LD at all. Every factual claim must be inferred from prose.
mediumTask completion
No visible primary call-to-action in the server-rendered HTML. Agents have nothing concrete to click on behalf of a user.
highTask completion
No reachable entry point for delegated tasks — nothing at `/pricing`, `/signup`, `/cart`, `/book`, or similar responds without auth.
mediumTask completion
No mention of delegated auth primitives (passkey, OAuth, MCP). Agents must drive a full browser session.
highDelegated access
No MCP manifest. Agents can’t auto-discover tools or delegated capabilities.
highDelegated access
No public API docs detected. Agents have no scoped way in; they must drive a browser.
lowDelegated access
robots.txt doesn’t mention any agent user-agents. The policy for agents is implicit.
mediumContact & communication
No contact email visible in server-rendered HTML. Agents can’t fall back to email.
lowContact & communication
No help, support, or FAQ hub linked. Agents can’t self-serve an answer before reaching out.