Recent episodes of the All-In Podcast have centered on AI infrastructure economics, with guests like Brad Gerstner addressing whether revenue from large language models can cover massive data center capex, gigawatt-scale power constraints, and semiconductor demand. Discussions also covered data center backlash, local economic benefits such as job training and teacher funding from projects like Meta's facilities, and AI doomerism versus practical deployment. The September 18 episode features investigative journalist Nick Shirley examining California infrastructure fraud, high-speed rail inefficiencies, and government waste, aligning with recurring themes of regulatory capture, entitlement fraud, and tech-policy intersections. These verified guest appearances and topic patterns shape trader views on likely discussion points around AI buildout realities and public-sector accountability.
Experimental AI-generated summary referencing Polymarket data. This is not trading advice and plays no role in how this market resolves. · UpdatedAI 50+ times
79%
Hundred / Thousand / Million 10+ times
85%
Anthropic 5+ times
82%
SpaceX 3+ times
45%
IPO
75%
Nvidia
70%
Mark Zuckerberg
50%
Smartphone
50%
86%
Microsoft
71%
-No Qualifying Event-
50%
$262 Vol.
AI 50+ times
79%
Hundred / Thousand / Million 10+ times
85%
Anthropic 5+ times
82%
SpaceX 3+ times
45%
IPO
75%
Nvidia
70%
Mark Zuckerberg
50%
Smartphone
50%
86%
Microsoft
71%
-No Qualifying Event-
50%
This market will resolve to "Yes" if the listed term is mentioned by anyone during the next released episode of the All-In Podcast. Otherwise, the market will resolve to "No".
This market will resolve according to the next episode of the All-In Podcast added to the official YouTube playlist: https://www.youtube.com/playlist?list=PLn5MTSAqaf8peDZQ57QkJBzewJU1aUokl. Specials or other videos posted on the YouTube channel but not included on the All-In Podcast playlist will not be considered.
If no such episode of the All-In Podcast is aired by September 30, 2026, 11:59 PM ET, "-No Qualifying Event-" will resolve to "Yes" and all other brackets will resolve to "No".
The resolution source will be audio of the event.
For full rules, see: https://polymarket-upload.s3.us-east-2.amazonaws.com/market_products/Mentions/Event+Mentions+Contract+DeFi.pdf
Market Opened: Sep 17, 2026, 1:00 PM ET
Resolver
0x65070BE91...This market will resolve to "Yes" if the listed term is mentioned by anyone during the next released episode of the All-In Podcast. Otherwise, the market will resolve to "No".
This market will resolve according to the next episode of the All-In Podcast added to the official YouTube playlist: https://www.youtube.com/playlist?list=PLn5MTSAqaf8peDZQ57QkJBzewJU1aUokl. Specials or other videos posted on the YouTube channel but not included on the All-In Podcast playlist will not be considered.
If no such episode of the All-In Podcast is aired by September 30, 2026, 11:59 PM ET, "-No Qualifying Event-" will resolve to "Yes" and all other brackets will resolve to "No".
The resolution source will be audio of the event.
For full rules, see: https://polymarket-upload.s3.us-east-2.amazonaws.com/market_products/Mentions/Event+Mentions+Contract+DeFi.pdf
Resolver
0x65070BE91...Recent episodes of the All-In Podcast have centered on AI infrastructure economics, with guests like Brad Gerstner addressing whether revenue from large language models can cover massive data center capex, gigawatt-scale power constraints, and semiconductor demand. Discussions also covered data center backlash, local economic benefits such as job training and teacher funding from projects like Meta's facilities, and AI doomerism versus practical deployment. The September 18 episode features investigative journalist Nick Shirley examining California infrastructure fraud, high-speed rail inefficiencies, and government waste, aligning with recurring themes of regulatory capture, entitlement fraud, and tech-policy intersections. These verified guest appearances and topic patterns shape trader views on likely discussion points around AI buildout realities and public-sector accountability.
Experimental AI-generated summary referencing Polymarket data. This is not trading advice and plays no role in how this market resolves. · Updated



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