Mark Zuckerberg

Business · Tech leadersPersonMachine-attributedUpdated

As of Oct 23, 2026, Kalshi has settled 41 word markets on Mark Zuckerberg’s appearances since 2025, on 36 different words.

History Wall

1–36 of 36, most settled first
Mark Zuckerberg: every word with settled markets, most settled first
WordSep 17 ’25episodeSep 23 ’26latestLast 2SettledProbability
AI / Artificial IntelligenceSaid: AI / Artificial Intelligence, Sep 17 ’25 episode9 times: AI / Artificial Intelligence, Sep 23 ’26 keynote, counts as said2/2100%2/2100%79%n=2 from settlement history
MetaverseNot said: Metaverse, Sep 17 ’25 episode2 times: Metaverse, Sep 23 ’26 keynote, counts as said1/250%1/250%73%n=2 from settlement history
PrivacyNot said: Privacy, Sep 17 ’25 episode2 times: Privacy, Sep 23 ’26 keynote, counts as said1/250%1/250%73%n=2 from settlement history
LlamaNot said: Llama, Sep 17 ’25 episode0 times: Llama, Sep 23 ’26 keynote0/20%0/20%18%n=2 from settlement history
Open SourceNot said: Open Source, Sep 17 ’25 episode0 times: Open Source, Sep 23 ’26 keynote0/20%0/20%18%n=2 from settlement history
Agent / AgenticNot listed: Agent / Agentic, Sep 17 ’25 episode6 times: Agent / Agentic, Sep 23 ’26 keynote, counts as said1/1100%1/1100%–n=1 too little history for a probability
America / AmericanSaid: America / American, Sep 17 ’25 episodeNot listed: America / American, Sep 23 ’26 keynote1/1100%1/1100%–n=1 too little history for a probability
GlassesSaid: Glasses, Sep 17 ’25 episodeNot listed: Glasses, Sep 23 ’26 keynote1/1100%1/1100%–n=1 too little history for a probability
MuseNot listed: Muse, Sep 17 ’25 episode33 times: Muse, Sep 23 ’26 keynote, counts as said1/1100%1/1100%–n=1 too little history for a probability
OakleyNot listed: Oakley, Sep 17 ’25 episode1 time: Oakley, Sep 23 ’26 keynote, counts as said1/1100%1/1100%–n=1 too little history for a probability
QuestNot listed: Quest, Sep 17 ’25 episode1 time: Quest, Sep 23 ’26 keynote, counts as said1/1100%1/1100%–n=1 too little history for a probability
Ray-BanNot listed: Ray-Ban, Sep 17 ’25 episode5 times: Ray-Ban, Sep 23 ’26 keynote, counts as said1/1100%1/1100%–n=1 too little history for a probability
Superintelligence / Super IntelligenceNot listed: Superintelligence / Super Intelligence, Sep 17 ’25 episode6 times: Superintelligence / Super Intelligence, Sep 23 ’26 keynote, counts as said1/1100%1/1100%–n=1 too little history for a probability
VR / Virtual RealityNot listed: VR / Virtual Reality, Sep 17 ’25 episode6 times: VR / Virtual Reality, Sep 23 ’26 keynote, counts as said1/1100%1/1100%–n=1 too little history for a probability
a16z / AndreessenNot said: a16z / Andreessen, Sep 17 ’25 episodeNot listed: a16z / Andreessen, Sep 23 ’26 keynote0/10%0/10%–n=1 too little history for a probability
Alexander / WangNot said: Alexander / Wang, Sep 17 ’25 episodeNot listed: Alexander / Wang, Sep 23 ’26 keynote0/10%0/10%–n=1 too little history for a probability
AndurilNot said: Anduril, Sep 17 ’25 episodeNot listed: Anduril, Sep 23 ’26 keynote0/10%0/10%–n=1 too little history for a probability
BasedNot said: Based, Sep 17 ’25 episodeNot listed: Based, Sep 23 ’26 keynote0/10%0/10%–n=1 too little history for a probability
ChatGPTNot said: ChatGPT, Sep 17 ’25 episodeNot listed: ChatGPT, Sep 23 ’26 keynote0/10%0/10%–n=1 too little history for a probability
OpenAI / ChatGPTNot listed: OpenAI / ChatGPT, Sep 17 ’25 episode0 times: OpenAI / ChatGPT, Sep 23 ’26 keynote0/10%0/10%–n=1 too little history for a probability
ChinaNot said: China, Sep 17 ’25 episodeNot listed: China, Sep 23 ’26 keynote0/10%0/10%–n=1 too little history for a probability
Elon / MuskNot said: Elon / Musk, Sep 17 ’25 episodeNot listed: Elon / Musk, Sep 23 ’26 keynote0/10%0/10%–n=1 too little history for a probability
GeminiNot said: Gemini, Sep 17 ’25 episodeNot listed: Gemini, Sep 23 ’26 keynote0/10%0/10%–n=1 too little history for a probability
HorizonNot listed: Horizon, Sep 17 ’25 episode0 times: Horizon, Sep 23 ’26 keynote0/10%0/10%–n=1 too little history for a probability
Palmer / LuckeyNot said: Palmer / Luckey, Sep 17 ’25 episodeNot listed: Palmer / Luckey, Sep 23 ’26 keynote0/10%0/10%–n=1 too little history for a probability
NFTNot said: NFT, Sep 17 ’25 episodeNot listed: NFT, Sep 23 ’26 keynote0/10%0/10%–n=1 too little history for a probability
NuclearNot said: Nuclear, Sep 17 ’25 episodeNot listed: Nuclear, Sep 23 ’26 keynote0/10%0/10%–n=1 too little history for a probability
OculusNot said: Oculus, Sep 17 ’25 episodeNot listed: Oculus, Sep 23 ’26 keynote0/10%0/10%–n=1 too little history for a probability
PradaNot listed: Prada, Sep 17 ’25 episode0 times: Prada, Sep 23 ’26 keynote0/10%0/10%–n=1 too little history for a probability
Smoke / SmokerNot said: Smoke / Smoker, Sep 17 ’25 episodeNot listed: Smoke / Smoker, Sep 23 ’26 keynote0/10%0/10%–n=1 too little history for a probability
TalentNot said: Talent, Sep 17 ’25 episodeNot listed: Talent, Sep 23 ’26 keynote0/10%0/10%–n=1 too little history for a probability
ThreadsNot said: Threads, Sep 17 ’25 episodeNot listed: Threads, Sep 23 ’26 keynote0/10%0/10%–n=1 too little history for a probability
TikTokNot said: TikTok, Sep 17 ’25 episodeNot listed: TikTok, Sep 23 ’26 keynote0/10%0/10%–n=1 too little history for a probability
TrumpNot said: Trump, Sep 17 ’25 episodeNot listed: Trump, Sep 23 ’26 keynote0/10%0/10%–n=1 too little history for a probability
UFCNot said: UFC, Sep 17 ’25 episodeNot listed: UFC, Sep 23 ’26 keynote0/10%0/10%–n=1 too little history for a probability
WinklevossNot said: Winklevoss, Sep 17 ’25 episodeNot listed: Winklevoss, Sep 23 ’26 keynote0/10%0/10%–n=1 too little history for a probability
  • Y: settled yes (a number: our count)
  • N: settled no
  • not listed
  • probability next time, with n
  • Select a word or a cell for its quotes
What the columns mean
Last N
of the appearances shown, how many counted as said
Settled
every settled market of the word: said / listed
Probability
the calibrated probability for the next listing; n is the settled observations behind it when it was computed, '+tx' adds the transcripts, 'new' means too little history

Appearances and settled record

latest 2

Our transcript counts matched how 15 of 15 settled words resolved (100.0%). How we check.

Recent appearances and how their listed words settled
DateAppearanceTextWordsSettledOur count agreed
Sep 23, 2026KeynoteWhat will Mark Zuckerberg say during Meta Connect keynote?Captions · machine-attributed1610 yes · 6 no15/15 (100%)
Sep 17, 2025EpisodeWhat will Zuck say during the TBPN Podcast?no transcript263 yes · 23 no–

Citeparrot.markets, “Mark Zuckerberg: mention market word history”, as of Oct 23, 2026, https://parrot.markets/business/mark-zuckerberg (CC BY 4.0)

Agents: this page as TSV at /api/v1/entity/mark-zuckerberg · words: /api/v1/history?entity=mark-zuckerberg · columnar JSON in #parrot-data · llms.txt · MCP