Is your AI training cluster thirsty? Let's talk water.
A practical look at AI cooling water demand, where the risk concentrates, and how teams can mitigate it.
Big difference for energy infrastructure investing.
By the time TechCrunch reports a regulatory change, the market has already priced it in. By the time the WSJ writes about grid capacity issues, smart money has already repositioned.
We built an intelligence agent that pulls from prediction markets instead.
A prediction market isn't one analyst's opinion. It's hundreds or thousands of traders putting money on their beliefs. Wrong beliefs get punished. Right beliefs get rewarded.
Insiders, analysts, and experts trade on prediction markets. When they start moving on a position, it often precedes public news by weeks.
News says "Ohio might block data center permits." A prediction market says "65% chance Ohio blocks new data center permit by Q2 2025." The second is actionable.
News is published once. Prediction markets update every second as new information arrives. You get real-time probability adjustments.
In late 2024, Polymarket showed rising odds of Ohio regulatory action against datacenters weeks before the Public Utilities Commission announced their decision. Traders who were watching the market could reposition before the headline hit.
By the time news outlets reported "Ohio Rules Against Tech Companies on Grid Costs," the information was already priced in.
Our prediction market intelligence agent:
We're not replacing news. We're adding a probability layer on top.
For anyone making energy infrastructure bets: if you're only reading news, you're already behind.
GreenCIO's Intelligence Feed includes real-time prediction market signals alongside traditional news. Request a demo to see it in action.
A practical look at AI cooling water demand, where the risk concentrates, and how teams can mitigate it.
Why we moved from traditional SaaS patterns to a multi-agent operating model for infrastructure intelligence.
How code-first skills and tighter context routing drove major cost reductions without quality loss.
Why grid-visibility tooling may become the limiting factor for AI data center expansion.
What the EU AI Act means for AI energy reporting, compliance timelines, and exposure management.
How structured disagreement between specialist agents produced better portfolio decisions.
Why LCOE remains a core metric for comparing technologies and underwriting long-horizon energy risk.
How carbon-aware workload scheduling reduces both emissions and compute cost volatility.
Inside our ingestion pipeline for extracting, scoring, and publishing infrastructure signals automatically.
A portfolio-level briefing on grid constraints, power costs, and capital-allocation implications.
Who is funding hyperscale buildout, where structures are changing, and what risk shifts to lenders.
A practical playbook for lowering AI energy intensity without sacrificing delivery speed.