This is a continuation of sharing my ideas, identical to the pop-corn brain ideas for the taking
While most of us are chasing incremental improvements to internal processes with AI, or baking the next incremental step in personalization (myself included), there are opportunities with AI that are truly massive. Energy is one of them.
The problem
The electrical grid faces an impossible balancing act: electricity must be produced and consumed in perfect balance every single second, or the entire system crashes. This was manageable when predictable coal/gas plants served predictable demand. Now the grid has millions of unpredictable solar panels and wind turbines trying to serve increasingly variable demand from EVs and AI data centers.
Grid operators using 1970s-era tools are drowning in complexity. A region with just 1,000 power assets can produce nearly a billion failure scenarios from only three equipment outages. Meanwhile, 70% of power transformers are 25+ years old and failing just as AI data centers will consume 20% of U.S. electricity by 2030 (up from 2.5% in 2022). Human operators making decisions in minutes can’t handle systems that need millisecond responses to prevent cascading blackouts.
The analogy
This is like air traffic control trying to manage millions of unpredictable aircraft (renewables + EVs) with 1970s radar systems (current grid management) while air traffic quadruples (AI + electrification) and airports age out (infrastructure replacement). AI can transform the unsexy but critical infrastructure that powers modern civilization. The grid is becoming impossibly complex for humans to manage, just as AI becomes capable enough to handle the complexity.
The solution
AI Grid Platform: One System, Three Core Capabilities
AI routing engine
- Processes all grid data in real time, evaluates millions of possible scenarios, and makes sub-second routing decisions to keep supply and demand balanced.
- Provides automated emergency response and creates “synthetic inertia” by coordinating distributed resources during grid stress.
Predictive Infrastructure Intelligence
- Analyzes sensor data to forecast equipment failures 6–12 months ahead.
- Optimizes replacement and maintenance schedules, aligning them with demand patterns to reduce downtime and costs.
Autonomous Grid Orchestration
- Coordinates renewables, storage, EVs, and demand response in real time.
- Acts as an “AI CFO for the grid,” maximizing ROI for every infrastructure dollar through scenario modeling, regulatory compliance automation, and peer-to-peer energy trading.
These core pillars are not fantasy but very real and tangible. The Argonne National Laboratory has shown a PoC of solving the critical SCUC problems at 12x normal speed, and National Grid is investing $100M in AI solutions – this is very much a field moving forward outside the normal hype bubble.
Market size
- TAM (Global Grid Modernization): $158+ billion annually, growing to $400+ billion by 2030
- US Private Utilities: $1.1 trillion planned investment (2025-2029), serving 72% of customers
- EU Grid Investment: €584 billion modernization plan + €170 billion digitalization mandate
- Combined Addressable Market: $1.8+ trillion over next decade
Challenges
- Regulatory approval: AI systems must meet strict reliability standards; need gradual deployment starting with advisory roles before autonomous control
- Legacy system integration: Must overlay existing infrastructure through APIs and interfaces rather than wholesale replacement
- Utility adoption speed: Conservative industry culture, though regulatory mandates are forcing modernization
- Data access and standardization: Need comprehensive sensor networks and standardized data formats across utilities
- Competition from tech giants: Google, Microsoft, Amazon moving into grid AI space with massive resources
- Cybersecurity requirements: Grid AI systems become critical infrastructure targets requiring robust security
My notes
Unlike consumer AI apps, utilities must buy this technology due to regulatory mandates and physical necessity. The market size eliminates commercial risk – even partial success in a multi-trillion dollar essential industry generates massive returns. The space can provide defensive moats through regulatory relationships and infrastructure integration, recurring revenue from decades-long utility relationships, network effects improve performance with scale, first-mover advantages last for decades in regulated industries.
Success requires building comprehensive platforms rather than point solutions. Argonne National Laboratory proved core algorithms work (12x speed improvement), but left the commercial integration opportunity wide open. The winners will combine technical capability with deep utility industry relationships and regulatory expertise. This is a game that is won not only be technical brilliance but trust. connections, and political lobbying.
Grid infrastructure moves slowly, but regulatory pressure and AI capability are converging now. The companies that establish platforms today will own the energy routing layer that powers the AI economy of tomorrow. This is akin to building the next NVIDIA. Think in second order derivatives. As energy demand surges, you can try to solve the production problem or the distribution problem. Even if the production is solved (nuclear, fusion, renewable + storage) the distribution problem remains. Also the distribution problem is solvable within the normal time horizon of venture capital goldilocks time of 10 years.