“The safest way to build a powerful system may be to make sure that nobody controls the whole system.”
Artificial Intelligence is usually discussed as a race between giants. Who has the largest model? Who has the most GPUs? Who has the biggest data centre? Who will build the first truly autonomous AI?
These are important questions. But perhaps we are asking the wrong question.
What if the greatest benefit of AI does not come from making a handful of corporations enormously powerful?
What if it comes from making millions of ordinary people economically powerful?
That would produce a very different AI revolution.
The concentration problem
Imagine a world in which five or six companies control most of the world’s advanced AI. They own the models, computing infrastructure and interfaces. Millions of businesses and individuals depend upon them.
At first, this looks extremely efficient. A few companies can invest billions in research and build infrastructure that no small company could afford.
But efficiency and resilience are not the same thing.
A centralized system can be extremely efficient while also being extremely fragile. If most of the world’s AI capability passes through a few gateways, those gateways become points of failure.
A failure at one level can therefore spread far beyond the original company.
- A technical failure can affect millions of users.
- A cyberattack can have enormous consequences.
- A pricing change can alter the economics of entire industries.
- A regulatory decision can affect everyone downstream.
There is also a deeper problem.
Power tends to follow infrastructure.
The more people depend upon a system, the more important the people controlling that system become.
One giant AI or millions of experiments?
Now imagine a different future.
AI becomes cheap enough that a small business can use capabilities that previously required a large corporation. A two-person company can conduct sophisticated research. A teacher can create an educational platform. A programmer can build software without a large development team.
A designer can compete with an agency.
A small manufacturer can use AI for logistics, forecasting and quality control.
A scientist can explore thousands of possibilities before applying for a grant.
The important change is not that the big corporation becomes 20% more efficient.
The important change is that many more people become capable of doing things that were previously uneconomic.
That is decentralisation.
And it may be one of AI’s most important social effects.
The small entrepreneur has a different incentive
There is a subtle economic reason why small businesses may become particularly important to the AI revolution.
A small entrepreneur has very little to protect. If AI makes marketing ten times cheaper, the entrepreneur celebrates. If a new AI model makes an existing business model obsolete, the entrepreneur can simply change direction.
There is no enormous legacy organisation to defend.
A large corporation has a different problem.
It may have thousands of employees, established products, distribution networks, long-term contracts and billions of dollars invested in existing infrastructure.
A new technology can therefore be both an opportunity and a threat.
The company may have to ask:
“Will this new technology destroy a profitable business that we already own?”
That is the problem of cannibalisation.
Innovation can threaten the innovator
A company does not necessarily maximise the speed at which its own products become obsolete.
If an existing product produces excellent profits, deliberately destroying that revenue stream can be a difficult corporate decision.
A small competitor does not have the same dilemma.
It can adopt the disruptive technology immediately.
This creates a fascinating possibility with AI:
The largest AI companies may build the technology, but the greatest economic disruption may come from the smallest users of that technology.
A teenager building an application from a bedroom. A teacher creating a specialised educational service. A scientist working independently. A small manufacturer entering a market previously dominated by large companies.
The technology may be centralised at the infrastructure layer while becoming highly decentralised at the application layer.
That could be a powerful combination.
AI as an economic equaliser
For centuries, capital determined what an individual could accomplish.
If you wanted to manufacture something, you needed a factory. If you wanted to publish at scale, you needed a printing press and distribution. If you wanted sophisticated financial research, you needed analysts.
AI changes some of these equations.
It does not eliminate capital.
It does something subtler.
It reduces the minimum scale required to attempt something.
Suppose the cost of starting a new experiment falls dramatically.
More people can try.
If the cost falls again, even more people can participate.
If the marginal cost of experimentation approaches zero, experimentation becomes almost universal.
Most experiments will fail.
That is not necessarily a problem.
In fact, it may be the point.
Millions of failures are not necessarily waste
Centralised systems often try to identify the best solution before deploying it.
Decentralised systems can allow thousands of solutions to compete.
Nine hundred and ninety-nine may fail.
One may succeed spectacularly.
The cost of discovering that one successful idea is the combined cost of all the failures.
AI could make those failures dramatically cheaper.
That creates a positive feedback loop:
- More participants
- More experiments
- More competition
- More discoveries
- More successful businesses
- More participants
This is very different from asking one large organisation to predict the future correctly.
The future is difficult to predict.
It may be much easier to let millions of people try different futures.
Diversity is a form of intelligence
Different people see different problems.
A large corporation tends to have a particular organisational culture. Its employees share incentives, databases, management structures and strategic assumptions.
Millions of independent users do not.
One person may use AI to solve a problem in agriculture. Another may apply it to education. Another to medicine, manufacturing, entertainment or finance.
Another may discover an application nobody else considered important.
This diversity creates something that resembles an evolutionary system.
- Variation produces experiments.
- Competition selects successful ideas.
- Failure eliminates weak ideas.
- Adaptation produces better solutions.
AI therefore becomes more powerful not merely because one model becomes smarter, but because millions of humans discover new ways to use intelligence.
The China question
China provides an interesting economic case study because its development has produced an enormous ecosystem of private businesses, entrepreneurs and specialised smaller enterprises alongside very large corporations.
The lesson is not that one country’s model is perfect.
The interesting point is that economic diversity can itself become an industrial asset.
A country with millions of businesses has millions of potential experiments.
Some will fail.
Some will remain small.
Some will become regional leaders.
A few may become global companies.
That is how economic evolution works.
AI could accelerate that process dramatically.
Instead of asking only which country has the most powerful AI company, perhaps we should ask:
Which economy allows the largest number of people to use AI productively?
That may prove to be the more important measure.
Decentralisation also improves resilience
Consider two networks.
In the first, everything depends upon one central hub. If the hub fails, the entire network suffers.
In the second, thousands of independent nodes communicate with one another. Some nodes can fail without bringing down the entire system.
This is basic network resilience.
The same principle applies to economies.
A diversified economy has redundancy.
If one company disappears, another can replace part of its function. If one technology fails, another may emerge. If one business model becomes obsolete, thousands of others can continue experimenting.
Decentralisation therefore isn’t only about competition.
It is also about survival.
Less “Sarah Connor” economics
This is where the science-fiction analogy becomes surprisingly useful.
The classic AI nightmare imagines one overwhelmingly powerful artificial intelligence becoming a centralised system that humanity can no longer control.
Everyone then has the same problem:
Stop it.
But why construct the economic equivalent of that scenario in the first place?
Why should there be one dominant intelligence infrastructure on which everyone depends?
A more distributed ecosystem creates another possibility.
Instead of one AI becoming the centre of civilisation, we could have millions of AI applications, agents, companies and systems interacting with one another.
They compete.
They cooperate.
They check one another.
They fail independently.
They evolve independently.
That is much less cinematic.
It may also be much safer.
Don’t build one Skynet. Build an ecosystem.
Decentralisation does not mean eliminating big companies
This distinction is important.
The argument is not that large AI companies are bad.
Building frontier AI may require enormous capital, specialised chips, research teams and data centres.
Large organisations can do things that individuals cannot.
The question is what happens after the capability exists.
If the frontier model remains available only to giant corporations, AI could reinforce existing economic concentration.
If increasingly powerful intelligence becomes cheap and widely accessible, the same technology could weaken that concentration.
The ideal system may therefore contain both:
- Large infrastructure providers at the bottom.
- Millions of independent users and businesses at the top.
That resembles the internet itself.
A relatively small number of companies operate enormous infrastructure, while billions of people use that infrastructure to create things the infrastructure owners never imagined.
The real AI productivity test
AI is being sold primarily as a productivity technology.
That creates a simple economic test:
Does the value created by AI substantially exceed the cost of AI plus the human time required to use it?
If someone spends $100 on AI, the AI eventually needs to create substantially more than $100 of value.
That value can come from:
- Saving time
- Preventing expensive mistakes
- Creating new revenue
- Replacing repetitive work
- Making expertise affordable
- Allowing a person to operate a larger business
But there is a limit.
The average consumer has only so much time and money.
If AI consumes enormous amounts of human attention while producing relatively little additional value, the economics become questionable.
More tokens are not necessarily more productivity.
More conversation is not necessarily more productivity.
A beautifully written explanation of what could be done is not the same as actually doing it.
Ultimately, output has to matter.
The time-value ceiling
This may become one of the most important constraints on the AI industry.
Human attention has economic value.
If an AI assistant saves me one hour but requires two hours of supervision, editing and correction, it has not necessarily increased my productivity.
It may simply have moved the work around.
The best AI therefore isn’t necessarily the one that generates the most tokens.
It is the one that produces the most valuable completed work per unit of human attention.
That is especially important for entrepreneurs.
A small business owner doesn’t need an AI that can discuss business strategy for eight hours.
They need one that can:
- Finish the report.
- Build the website.
- Analyse the numbers.
- Find the customers.
- Write the proposal.
- Prepare the presentation.
- Identify the problem they hadn’t noticed.
AI becomes economically transformative when it moves from conversation to execution.
What happens if AI succeeds?
Imagine millions of people suddenly acquire something resembling a small professional staff.
Not human employees.
AI capabilities.
A student has a tutor.
A freelancer has an analyst.
A small business has a marketing department.
A researcher has a research assistant.
A creator has an editor.
A programmer has a development team.
An entrepreneur has access to expertise that previously required substantial capital.
The result could be a dramatic expansion in the number of economically viable people.
That is perhaps a more interesting definition of the AI revolution than simply saying:
“Machines become intelligent.”
Machines becoming intelligent is a technological achievement.
Humans becoming more capable is the economic achievement.
The danger of winning too much
There is a paradox here.
The AI industry could become so successful at concentrating intelligence that it accidentally reduces the diversity required for long-term innovation.
If a handful of companies control the models, infrastructure, distribution and data, everyone else becomes dependent on them.
That may produce enormous short-term efficiency.
But eventually the system may become:
- Less experimental
- Less diverse
- Less competitive
- More politically sensitive
- Potentially more fragile
The irony would be extraordinary.
Humanity creates artificial intelligence to expand its possibilities—and then concentrates that intelligence so heavily that it reduces the number of people allowed to experiment with those possibilities.
The better objective
Perhaps the goal should therefore not be:
Build the most powerful AI company.
Nor even:
Build the most powerful AI.
A better objective might be:
Build the most powerful ecosystem of human beings using AI.
That changes the success metric.
We would measure:
- How many small businesses became viable?
- How many new entrepreneurs emerged?
- How many scientific experiments became affordable?
- How many people gained access to expertise?
- How many new products were created?
- How much human time was liberated?
- How broadly did economic opportunity spread?
These are harder questions.
They are also more meaningful.
From AI empire to AI ecosystem
The future of AI does not have to resemble a pyramid.
It could resemble a forest.
There can be enormous trees.
But there are also millions of smaller plants, fungi, insects and organisms interacting with one another.
No single organism contains the entire system.
The diversity is the strength.
The same principle could apply to AI.
A few enormous companies may provide the foundational infrastructure.
Millions of smaller companies may turn that infrastructure into products.
Billions of people may use those products in ways nobody predicted.
Some will fail.
Some will become successful.
Some will create entirely new industries.
That unpredictability isn’t a bug.
It may be the mechanism through which civilisation discovers its next stage.
The real choice
We therefore face two very different visions.
Model 1 — Concentrated AI
- A few companies control frontier intelligence.
- Everyone else becomes dependent on them.
- Productivity gains flow disproportionately upward.
- Failure at the centre can affect enormous populations.
- Existing economic structures become harder to challenge.
Model 2 — Distributed AI
- A few companies may provide infrastructure.
- Millions of businesses use it independently.
- New entrants can challenge incumbents.
- Failures remain more localised.
- Thousands of experiments compete simultaneously.
- Economic power becomes more widely distributed.
The first may be more efficient.
The second may be more resilient.
The first concentrates power.
The second distributes opportunity.
Build an ecosystem, not an empire
The safest AI future may not be the one where intelligence is weakest.
It may be the one where intelligence is distributed widely enough that no single failure can cripple everyone.
The most productive AI economy may not be the one with the largest corporation.
It may be the one with the greatest number of economically viable experiments.
And the most successful AI system may ultimately be judged not by how many tokens it generates, but by how much human capability it unlocks.
There is an old tendency in technological history to mistake scale for strength.
Sometimes scale is strength.
Sometimes it is simply a larger point of failure.
AI gives us a rare opportunity to choose differently.
We can build systems that make people more dependent on a few centres of power.
Or we can build systems that give millions of people the ability to create, compete and experiment independently.
Perhaps the most important question about the AI revolution is therefore not:
“How powerful will AI become?”
It is:
“How widely will that power be distributed?”
Because a billion people with a little more intelligence may ultimately be more powerful—and considerably safer—than one institution with almost all of it.
“The future of AI should not be an empire of intelligence. It should be an ecosystem of intelligence—powerful enough to transform civilisation, but distributed enough that civilisation remains in control.”
