There was a time when electricity was the headline.
Newspapers wrote about it as a miracle. Investors chased it. Entrepreneurs built companies around it. Engineers debated AC versus DC as if the future of civilization depended on it. In some sense, it did.
Factories reorganized around electric motors. Cities extended working hours beyond daylight. Entire industries restructured. Electricity was not incremental improvement. It was a new layer of capability.
And then something strange happened.
Electricity disappeared.
Not physically. Economically. Culturally. Strategically.
Today, no serious company claims competitive advantage because it “uses electricity.” No board approves a budget because the firm has successfully “adopted power.” Electricity is assumed. It is infrastructure. It is a platform upon which value is created, not the value itself.
The transformation was real. The conversation faded.
We are watching the same pattern unfold with AI.
The Illusion of the Tool
In the early phases of any general-purpose technology, people mistake it for a tool.
Steam power was a tool. Water wheels were tools. Horses were tools. Electricity looked like a tool. Early adopters plugged it into existing processes. They replaced steam engines with electric motors without redesigning factories. Productivity gains were marginal.
The real shift came later.
Electricity allowed decentralization of power inside factories. Machines no longer needed to be physically arranged around a central shaft. Layouts changed. Workflows changed. Management changed. Entire industries reorganized around the flexibility electric power enabled.
The technology did not simply improve the old model. It made new models possible.
AI is at the same stage many factories were in 1905: swapping steam for electricity without rethinking the factory.
“We use AI for customer support.”
“We integrated AI into our workflow.”
“We automated parts of marketing.”
This is the electric motor bolted onto a steam factory.
The deeper shift is not automation. It is cognition as infrastructure.
From Power to Cognitive Power
Electricity provided physical power everywhere. Cheap, reliable, distributed energy.
AI provides cognitive power everywhere. Cheap, scalable, distributed intelligence.
Not intelligence in the human sense of wisdom or judgment. But pattern recognition. Prediction. Language generation. Optimization. Classification. Simulation.
For most of history, cognitive labor was scarce. Skilled analysis required trained humans. Scaling thinking required hiring.
Now cognition is becoming ambient.
When a capability becomes ambient, its economics change.
Electricity reduced the marginal cost of energy close to zero in relative terms. That enabled refrigeration, computing, telecommunications, and global manufacturing networks.
AI reduces the marginal cost of certain forms of cognition close to zero. Drafting. Summarizing. Translating. Coding. Designing variations. Running scenarios. Generating alternatives.
The question is no longer: “Do we use AI?”
That question will sound as outdated as “Do you use electricity?”
The real question becomes:
What value can you create because cognitive power is ubiquitously available?
The Strategic Misunderstanding
Calling it an “AI strategy” already reveals confusion.
No company ever had a serious “electricity strategy.”
They had manufacturing strategies. Distribution strategies. Cost strategies. Scale strategies. Electricity was embedded inside them.
If your competitive advantage depends on “using AI,” you do not have an advantage. You have access to a commodity.
The strategic work lies elsewhere:
Which constraints disappear because cognition is abundant?
Which bottlenecks remain scarce?
Which business models become viable only when thinking is cheap?
When electricity spread, illumination was not the final value. It enabled nightlife economies, refrigerated supply chains, elevators that allowed skyscrapers, data centers that enabled the internet.
Electricity was not the end product. It was the substrate.
AI is becoming substrate.
The Plug-in Fallacy
Many companies treat AI as a plug.
“We have integrated AI into our CRM.”
“We use AI for forecasting.”
“We deployed AI chatbots.”
This framing assumes AI is a feature layer.
But general-purpose technologies do not stay features. They reshape system architecture.
Electric motors did not just replace steam engines. They dissolved the architectural constraints that steam imposed. Central shafts disappeared. Flexible layouts emerged.
AI dissolves cognitive bottlenecks.
Previously:
Analysis required analysts.
Prototyping required designers.
Coding required developers.
Research required time-consuming synthesis.
Now these tasks can be generated, iterated, and stress-tested instantly.
That changes cycle times. It changes experimentation economics. It changes decision quality. It changes the boundary between exploration and execution.
If cognitive iteration is cheap, then the bottleneck shifts.
The constraint moves from “can we generate options?” to “can we judge them well?”
Judgment becomes the scarce resource.
The Real Scarcity
Electricity did not eliminate scarcity. It shifted it.
Once power was abundant, coordination became critical. Supply chains expanded. Logistics complexity grew. Organizational design mattered more.
Similarly, AI does not eliminate uncertainty. It shifts it.
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When idea generation becomes trivial, clarity becomes scarce. When analysis is abundant, framing becomes decisive. When drafts are instant, taste differentiates.
If everyone can produce competent output, differentiation moves to:
Problem selection
Constraint diagnosis
Strategic coherence
Decision discipline
Abundant cognition amplifies both signal and noise.
Electricity enabled industrial scale. It also enabled pollution at industrial scale.
AI enables intellectual scale. It also enables mediocrity at industrial scale.
The technology is neutral. The architecture around it determines value.
The Platform Pattern
Electricity became invisible because it became foundational.
Factories, appliances, computing, telecommunications, cloud infrastructure — all rely on electricity. Yet none market themselves primarily as “electric.”
AI is moving along the same trajectory.
Today it is visible. It is debated. It is controversial. It is hyped.
Tomorrow it will be assumed.
Search engines will be AI-native. Software will be AI-native. Customer support will be AI-native. Diagnostics, education, logistics — all AI-native.
At that point, saying “we use AI” will be meaningless.
The strategic question will be:
Given universal cognitive power, what architecture do you build on top of it?
General-Purpose Technologies Change Structures
Economists call electricity a general-purpose technology (GPT). It spreads across sectors. It complements other innovations. It creates new combinations.
AI qualifies.
General-purpose technologies do not create value in isolation. They unlock recombination.
Electricity + refrigeration = global food chains.
Electricity + semiconductors = computing.
Electricity + networks = internet.
AI + sensors = predictive maintenance.
AI + language models = knowledge augmentation.
AI + robotics = adaptive automation.
AI + personal data = hyper-personalization.
The real economic effect emerges in the combinations, not in the base layer.
Which means value accrues to those who redesign systems, not those who merely adopt tools.
The Reorganization Phase
Factories that only replaced steam engines with electric motors saw limited gains.
Factories that redesigned layouts around distributed power saw exponential gains.
We are entering the redesign phase with AI.
Early adoption was feature integration.
Next comes process redesign.
Then comes business model redesign.
When cognitive power is cheap:
Why does onboarding still require weeks?
Why do investment decisions rely on static reports?
Why do product development cycles remain quarterly?
Why are legal reviews sequential?
Why is due diligence manual?
Cheap cognition compresses feedback loops.
The organizations that win will not simply automate tasks. They will restructure workflows around continuous cognitive augmentation.
The Risk of Missing the Shift
There is a paradox with general-purpose technologies.
If you overhype them, you misallocate capital. If you underappreciate them, you miss structural change.
In the late 19th century, some dismissed electricity as overhyped spectacle. Others invested blindly in speculative ventures.
The durable winners were those who understood that electricity required organizational redesign, not just capital expenditure.
The same holds now.
If AI is treated as a PR layer, the impact is superficial.
If AI is treated as infrastructure, architecture decisions follow.
From Efficiency to Possibility
Electricity first improved efficiency. Later it enabled new possibilities.
AI is currently sold as efficiency: faster drafts, automated workflows, cost reduction.
That is phase one.
Phase two is possibility.
What products exist only because cognition is cheap?
Continuous simulation of strategic scenarios
Personalized education at scale
Real-time adaptive pricing and contracts
Self-optimizing supply chains
Autonomous research loops
These are not incremental productivity gains. They are structural shifts.
Electricity did not merely reduce lighting costs. It made night-time economic activity viable.
AI will not merely reduce labor costs. It will make entirely new cognitive-intensive services viable.
The Disappearance Signal
Here is the strange indicator of maturity:
When nobody talks about the technology anymore.
No serious firm markets itself as “electric.”
No serious executive frames their strategy around “using power.”
When AI becomes invisible, its transformation is complete.
The headlines will fade. The debates will quiet. The infrastructure will remain.
The companies that understood early that AI was not a feature but a foundation will have redesigned themselves accordingly.
The others will still be plugging in chatbots.
The Core Question
The critical mistake is to ask:
“Should we use AI?”
That is like asking in 1910:
“Should we use electricity?”
The better question is:
Given that cognitive power is cheap and ubiquitous, what value architecture do we build?
Because when a new layer of capability becomes infrastructure, competitive advantage shifts upward.
Electricity commoditized energy.
AI commoditizes certain forms of cognition.
When a layer commoditizes, value migrates.
To orchestration.
To system design.
To judgment.
To trust.
To brand.
To access.
To integration.
If everyone can generate analysis, who can interpret it well?
If everyone can design variations, who can choose coherently?
If everyone can simulate outcomes, who can decide under uncertainty?
Cheap cognition does not eliminate strategy. It sharpens it.
What This Means for Leadership
Leaders who frame AI as a tool will optimize for incremental gains.
Leaders who frame AI as infrastructure will redesign systems.
This requires uncomfortable questions:
Which roles exist only because cognition was scarce?
Which processes assume slow thinking?
Which approvals exist because analysis was expensive?
Which bottlenecks disappear when iteration is free?
Electricity reduced physical friction. AI reduces cognitive friction.
Friction reduction changes economics.
Lower friction increases volume. Increased volume increases complexity. Complexity increases the need for structure.
The advantage will not lie in access to AI. It will lie in clarity about where to apply it.
The Cognitive Superplug
Electricity installed physical plugs into walls.
AI installs cognitive plugs into workflows.
You can draft, simulate, test, explore, code, summarize, translate, brainstorm — instantly.
But the plug does not define the product.
Electricity in a wall outlet did not determine whether you built a refrigerator, a server farm, or a nightclub.
Similarly, AI in your stack does not determine whether you build better insight, faster learning loops, or just cheaper content.
The plug is neutral.
The architecture is strategic.
Final Thought
Electricity once dominated headlines. Then it vanished into infrastructure and quietly reshaped the world.
AI is following the same trajectory.
The question is not whether you use it.
The question is what you are building on top of it.
When cognition becomes cheap and ubiquitous, value no longer comes from access to thinking power.
It comes from how you design around it.
And eventually, nobody will talk about AI anymore.
They will just talk about the companies that understood what to do once thinking itself became a utility.
