Markets as Minds

Man, it’s been hot this week, and hot all over. Heat waves are like petri dishes for observing power systems and electricity markets.

Every evening, just after the Texas sun dips below the horizon, the electricity market does something remarkable. Prices jump, batteries discharge, gas turbines spin up, and lights remain on without any central coordinator directing the show. Nobody centrally commands this ballet. Instead, a dense network of price signals guides generators, storage systems, and consumers in a decentralized yet coordinated response. The economist Friedrich Hayek once described prices as “a system of telecommunications” that relays dispersed information faster than any planner could hope to gather. The modern electricity grid demonstrates the validity of Hayek’s insight.

Electricity regulators, however, are not always trained to see markets as living, learning systems (nor are most people!). They often inherit a mechanical worldview: adjust the dial here, get the output there. But markets resemble ecosystems more than machines; they adapt, self‑correct, and evolve. The recent boom in battery storage investment in Texas provides a vivid, empirical window into that complexity. By tracing how prices, profits, and reliability interact, we can watch an invisible feedback loop guide capital toward society’s needs, and watch it taper off organically when those needs are fulfilled.

A Nervous System Made of Prices

To grasp why battery developers flocked to Texas, picture the grid’s price profile on a typical hot day. My favorite comes from a heat wave in 2024, here looking at August 20.

Abundant afternoon solar energy pushes wholesale prices down to near-zero levels. After sunset, solar generation disappears precisely when air conditioning demand peaks, causing prices to spike dramatically. For battery operators, this daily cycle represents a lucrative arbitrage opportunity: charging cheaply at midday and selling at high prices in the evening.

This daily price spread functions like a neuron transmitting essential signals: abundant midday energy is cheap; evening capacity is scarce. Developers monitoring ERCOT’s real-time price data quickly recognize these patterns, responding in the same way as workers gravitate toward higher wages.

Complex Adaptive Systems on the Grid

These features illustrate how markets are complex adaptive systems. Complex adaptive systems (CAS) share three hallmarks: diverse agents, local (private, known only to the agent) information, and feedback. The electricity market checks all three boxes.

1. Diverse agents. From rooftop‑solar owners to multinational utilities, countless players make independent decisions.
2. Local information. Each agent sees only a sliver of the system—its own costs, its own prices, its own risk tolerance.
3. Feedback. Actions feed back into the market, altering prices and therefore the incentives that guide subsequent actions.

Battery economics in Texas brings these mechanisms into sharp relief. Independent developers, retail energy providers, and virtual power plant aggregators (diverse agents) respond to real-time prices and financing terms (local information) in accordance with their own preferences (private information), and their actions—charging and discharging batteries—influence future price signals (feedback loops). Every act of arbitrage flattens price volatility, reshaping tomorrow’s market conditions and incentives.

The Texas Battery Boom: A Real‑Time Experiment

ERCOT had about 1 GW of grid‑scale storage in 2021. By mid‑2025, the total had grown past 12 GW—an order of magnitude increase in four years. Much of that capacity consists of two‑ and four‑hour lithium‑ion systems whose business model rests on price volatility.

A midsummer day in 2024 tells the story. At 1 p.m., solar output and strong wind drove wholesale prices down to $15/MWh. Batteries began charging en masse. By 7 p.m., as solar faded and residential demand peaked, real‑time prices attempted to breach $500/MWh. Roughly 4 GW of batteries responded, discharging into the tight market and clipping the spike at around $250/MWh. Consumers saved tens of millions; battery owners locked in a healthy margin.

That single afternoon dramatized the feedback at work. The next day’s price spreads shrank. Each successful arbitrage event teaches the market a lesson: evening scarcity is less severe than the previous day implied.

Feedback, Price Volatility, and Market Discipline

Feedback loops in CAS often produce self‑damping behavior. In the Texas grid, every incremental megawatt‑hour of discharge lowers the clearing price a tiny bit. Because arbitrage revenue equals (Price_out – Price_in) × Efficiency, smaller spreads mean lower revenue. The richer the opportunity, the faster it disappears.

Modeling by ERCOT’s Market Monitoring Unit suggests that an additional 10 GW of four‑hour storage would cut average peak‑spread revenue by nearly 40 percent. Developers know this. Their spreadsheets embed assumptions about future spreads, financing costs, and alternative revenue streams (e.g., frequency regulation). When anticipated returns fall below required investment thresholds, new projects slow or stop, naturally steering the market toward equilibrium.

This feedback both protects consumers and disciplines investors. It is no accident that battery deployment surges where volatility is high and pauses once volatility subsides. The system effectively self‑regulates.

Diminishing Returns and the Sweet Spot

The first batteries in Texas enjoyed eye‑popping spreads—sometimes $1,000/MWh or more. Early movers captured windfall profits. Later entrants met a tougher landscape: more competition, narrower spreads, and a need to stack other revenues like ancillary services.

Diminishing returns do not imply that storage ceases to add value; they imply that each additional unit adds less value than the last. The lesson parallels fisheries, urban traffic, and cloud computing: capacity pays most when it is scarce. Once the resource is plentiful, price signals announce that society should redirect capital elsewhere—perhaps to long‑duration storage, demand response, or transmission expansion.

Signals, Not Subsidies

This Texas example underscores why transparent price signals outperform prescriptive subsidies. Suppose Texas had instead offered fixed incentives for storage capacity. Regulators would have faced the impossible task of accurately predicting technology costs, weather variability, and gas prices years in advance. Fixed subsidies persist long after their necessity fades, potentially crowding out more efficient alternatives.

Market-driven price signals, updated every five minutes, dynamically integrate countless variables—cloud cover, generator outages, and even unpredictable demand spikes from events like viral trends. No regulator could match this detailed, rapid responsiveness. Thus, the most effective regulatory approach maintains clear scarcity pricing, avoiding interventions that mute essential price signals and deafen the nervous system.

Lessons for Regulators

1. Let volatility speak. Price spikes are not market failures; they are distress signals that mobilize flexible resources.
2. Reward performance, not technology. Ancillary‑service markets that pay for ramp speed, accuracy, and availability allow batteries, demand response, and hybrid plants to compete on a level field.
3. Preserve granularity. Five‑minute settlement uncovers value streams that hourly averages hide. Granular data shortens the feedback loop.
4. Beware unintended mufflers. Capacity payments tied to static assessments, cost‑based dispatch, or inflexible reserve rules risk dulling the very signals that attract investment.
5. Expect evolution. As arbitrage margins compress, storage owners will pivot. Some will form VPPs aggregating thousands of residential batteries; others will seek hybrid solar‑plus‑storage PPAs. Regulation should accommodate such pivots rather than locking assets into yesterday’s revenue stack.

Trust the Feedback

Texas’s battery storage phenomenon highlights how markets, when permitted to function naturally, behave as complex adaptive systems. Prices guide the flow of information, investments respond dynamically, and each successful arbitrage sets the stage for the next, incrementally improving reliability and affordability.

Policymakers cannot, and should not attempt to, map every detail of this intricate system. Instead, their role is simpler yet crucial: ensure transparent pricing, avoid artificial restrictions, and uphold clear rules of fair market exchange. The grid will handle the choreography. Like any living organism, it just needs room to think, learn, and adapt.

This piece was originally published on Lynne’s Substack, Knowledge Problem. If you enjoyed this post, please consider subscribing here.