Intro

Frontier AI is colliding with real-world infrastructure. Eiso Kant (Co-CEO & Co-Founder, Poolside) joins the MAD Podcast to unpack Project Horizon— a multi-gigawatt West Texas build—and why frontier labs must own energy, compute, and intelligence to compete. We map token economics, cloud-style margins, and the staged 250 MW rollout using 2.5 MW modular skids.

Then we get operational: the CoreWeave anchor partnership, environmental choices (SCR, renewables + gas + batteries), community impact, and how Poolside plans to bring capacity online quickly without renting away margin—plus the enterprise motion (defense to Fortune 500) powered by forward deployed research engineers.

Finally, we go deep on training. Eiso lays out RL2L (Reinforcement Learning to Learn)— aimed at reverse-engineering the web’s thoughts and actions— why intelligence may commoditize, what that means for agents, and how coding served as a proxy for long-horizon reasoning before expanding to broader knowledge work.

Poolside

Website - https://poolside.ai

X/Twitter - https://x.com/poolsideai

Eiso Kant

LinkedIn - https://www.linkedin.com/in/eisokant/

X/Twitter - https://x.com/eisokant

FIRSTMARK

Website - https://firstmark.com

X/Twitter - https://twitter.com/FirstMarkCap

Matt Turck (Managing Director)

Blog - https://www.mattturck.com

LinkedIn - https://www.linkedin.com/in/turck/

X/Twitter - https://twitter.com/mattturck

(00:00) Cold open – “Intelligence becomes a commodity”

(00:23) Host intro – Project Horizon & RL2L

(01:19) Why Poolside exists amid frontier labs

(04:38) Project Horizon: building one of the largest US data center campuses

(07:20) Why own infra: scale, cost, and avoiding “cosplay”

(10:06) Economics deep dive: $8B for 250 MW, capex/opex, margins

(16:47) CoreWeave partnership: anchor tenant + flexible scaling

(18:24) Hiring the right tail: building a physical infra org

(30:31) RL today → agentic RL and long-horizon tasks

(37:23) RL2L revealed: reverse-engineering the web’s thoughts & actions

(39:32) Continuous learning and the “hot stove” limitation

(43:30) Agents debate: thin wrappers, differentiation, and model collapse

(49:10) “Is AI plateauing?”—chip cycles, scale limits, and new axes

(53:49) Why software was the proxy; expanding to enterprise knowledge work

(55:17) Model status: Malibu → Laguna (small/medium/large)

(57:31) Poolside's Commercial Reality today: defense; Fortune 500; FDRE

(1:02:43) Global team, avoiding the echo chamber

(1:04:34) Next 12–18 months: frontier models + infra scale

(1:05:52) Closing