Wiener’s Cybernetics Lessons for AI in Utilities

Norbert Wiener published The Human Use of Human Beings in 1950—before SCADA was mainstream, before smart meters, and decades before “AI agents” became a boardroom phrase. Yet his core obsession still maps perfectly onto the problems utilities are wrestling with in December 2025: how to build feedback-driven automation without turning people into afterthoughts.

Paul Jones’s recent poem marking the book’s 75-year shadow lands on a line that utilities should take seriously: “Between humans and machines, feedback loops of love and grace.” That’s not sentimentality. It’s a design requirement. Energy systems are the biggest real-world cyber-physical machines we operate, and they only stay reliable when feedback loops are accurate, fast, and—crucially—aligned with human intent.

This post sits in our AI in Robotics & Automation series because modern grids increasingly behave like autonomous fleets: distributed sensors, automated controllers, and machine decision-making at the edge. The question isn’t whether you’ll automate. It’s whether you’ll automate in a way that improves reliability, safety, and trust.

Cybernetics is the original playbook for grid AI

Cybernetics is the science of control and communication in animals and machines. In utility terms: measurements come in, decisions go out, and the loop repeats. The grid is nothing but loops—frequency control, voltage regulation, protection coordination, market dispatch, asset maintenance cycles.

Wiener’s big warning wasn’t “don’t automate.” It was don’t confuse control with understanding. If your feedback is wrong, delayed, or gamed, your automation becomes brittle.

Why this matters more in 2025 than it did five years ago

Utility automation used to be localized and slow. Now it’s faster, more coupled, and more complex:

• Inverter-dominated generation changes grid dynamics (less inertia, faster transients).
• DERs create millions of endpoints rather than hundreds of large assets.
• Extreme weather events stress networks in ways planners didn’t historically model.
• AI models increasingly influence dispatch, outage response, and maintenance prioritization.

The reality? You can’t “set and forget” grid AI. A cybernetic system lives or dies by continuous feedback, continuous validation, and careful boundaries.

“Every web conceals its spider”: the hidden control problem in utility AI

Jones’s poem slips in a line that feels tailor-made for modern machine learning: “Every web conceals its spider.” In utilities, the “spider” is usually one of three things:

1. An objective function you didn’t make explicit (cost minimized at the expense of resilience).
2. A proxy metric standing in for the real world (SAIDI improved by deferring hard-to-serve customers).
3. A vendor model nobody can fully interrogate (black-box dispatch optimization with unclear constraints).

Utilities are right to demand explainability, but I’m going to take a stance: explainability alone isn’t enough.

What you need is governable control:

• Clear operating envelopes (what the system is allowed to do)
• Clear escalation paths (when the system must ask for help)
• Clear audit trails (what data drove what decision)
• Clear performance guarantees (how it behaves under drift and stress)

That’s cybernetics applied: control, feedback, accountability.

Practical example: grid optimization vs. human operations

AI-driven grid optimization often promises peak shaving, congestion relief, or loss reduction. All good.

But if your model is rewarded for solving congestion at any cost, it may:

• Over-cycle batteries (shortening life)
• Increase switching operations (wearing assets)
• Create operator surprise (loss of trust)

A Wieneresque approach forces a discipline: define “good control” as a multi-objective problem—cost, safety, asset health, customer impact, and regulatory compliance.

AI in energy behaves like robotics—because it is robotics

Robotics isn’t just arms in factories. Robotics is sense → decide → act in the physical world. The modern grid is doing that at scale.

When utilities deploy AI at the edge—substations, feeders, inverters, BESS controllers—they’re effectively building a distributed robotic system.

Where robotics-style AI shows up in utilities

• Automated switching and FLISR (fault location, isolation, and service restoration)
• Volt/VAR optimization using predictive load and solar forecasts
• Autonomous DER orchestration to manage backfeed and local congestion
• Grid-connected battery control for frequency response and ramp management
• Mobile robotics for inspection (drones, crawlers, and computer vision at transmission scale)

If you’ve worked in robotics & automation, you already know the hard part: the world is messier than the training data.

That’s why Wiener’s insistence on feedback integrity matters. Your system needs to detect when it’s wrong—not just when it’s uncertain.

Responsible AI for utilities: “freedom’s always a contingency”

Another poem line hits a nerve: “Control, yes, but rare freedom to some degree—freedom’s always a contingency.” In grid AI, “freedom” looks like autonomy: letting systems take actions without human approval.

Autonomy is earned. Utilities should treat it as a staged capability, not a purchase order.

A pragmatic autonomy ladder for utility AI systems

1. Advisory: Model recommends actions; humans execute.
2. Guardrailed automation: Model executes within tight constraints; humans review exceptions.
3. Conditional autonomy: Model runs most of the time; hands off under known risk states.
4. High autonomy: Model executes broadly; humans manage policy and rare edge cases.

Most utilities should aim for level 2 or 3 in the next 12–24 months for high-impact use cases. Level 4 is possible, but only after you’ve proven operational safety, data quality, and governance.

“Feedback loops of love and grace” translates to operator experience

If operators don’t trust the system, they’ll route around it. If they route around it, it won’t learn from reality. And then it fails during the one event you bought it for.

In practice, respectful human-machine design means:

• Explain actions in operational language, not ML language
• Show constraint satisfaction (“within thermal limit,” “within switching budget”)
• Make overrides easy and non-punitive (overrides are signal, not sabotage)
• Log decisions with enough context to learn later

This is how you build systems that people accept under stress.

Three high-value AI use cases utilities should prioritize in 2026

Utilities get pitched dozens of AI initiatives. If you want lead-worthy, budget-defensible projects, prioritize use cases that tie directly to reliability metrics, asset risk, and renewable integration.

1) Predictive maintenance that includes operational context

The best predictive maintenance models don’t just predict failure—they recommend the lowest-risk intervention window.

Combine:

• Condition monitoring (partial discharge, vibration, thermal)
• Work history and outage constraints
• Loading forecasts and weather risk

Then output: “Replace within 21 days; highest-risk window is next heatwave; recommended work window is Tuesday 02:00–06:00 with switching plan A.”

That’s automation that respects the grid as a living system.

2) AI-assisted switching with constraint-aware safety

Switching is where “automation” becomes physical reality. A good approach is:

• Train on historical switching, but verify with a power-flow/contingency engine
• Enforce hard constraints (clearances, lockout/tagout status, protection settings)
• Require operator sign-off when uncertainty is high or topology is unusual

This is robotics thinking: perception plus model-based control.

3) Renewable integration through short-horizon forecasting + control

Solar and wind variability isn’t new. What’s new is how quickly the grid can change when renewables dominate.

The practical win is pairing:

• 5–60 minute probabilistic forecasts
• Automated setpoint control for BESS/DER
• Feeder-level constraints and transformer thermal models

The outcome is fewer violations, fewer curtailments, and smoother operator workload.

“We are old enough to be friends”: what a utility-grade AI program looks like

Utilities don’t need AI theater. They need AI programs that behave like engineering.

Here’s a field-tested checklist I’ve found works when you’re trying to move from pilots to production:

1. Define control objectives explicitly (include reliability and asset health, not just cost).
2. Treat data quality as a reliability function (bad telemetry is a grid risk).
3. Keep a model-based backstop (physics + rules) when ML is uncertain.
4. Build monitoring like you mean it (drift, confidence, latency, sensor health).
5. Operationalize governance (who owns what, how changes are approved, how incidents are handled).
6. Design for humans under stress (storms, cyber incidents, abnormal topologies).

That’s how you earn the right to more autonomy.

Where to go next

Wiener’s work—echoed through Jones’s poem—pushes a simple message: automation is a relationship, not a replacement. The grid doesn’t need AI that acts clever in a demo. It needs AI that behaves predictably at 3 a.m. during a feeder fault in freezing rain.

If you’re mapping your 2026 roadmap for AI in energy and utilities, start by auditing your feedback loops: what you measure, what you optimize, what you hide, and what you assume people will tolerate. That’s where reliability, resilience, and trust are won.

Where are your automation loops strongest today—and which loop would hurt the most if it failed at the worst possible time?