We tend to describe the AI revolution as a revolution of productivity.
AI writes code. AI generates text. AI designs interfaces. AI creates images, music, plans, summaries, strategies, prototypes, pitch decks, and entire product concepts.
The obvious conclusion is that AI makes creation cheaper.
But there is a less obvious consequence: when creation becomes cheap, the world does not automatically become more coherent. It often becomes noisier.
Because the real bottleneck was never only execution.
It was also attention, trust, adoption, coordination, and agreement.
Everyone Has a Silver Bullet Now
In the pre-AI world, building something was expensive.
If you had an idea for a product, you needed time, technical skill, money, infrastructure, and often a team. These constraints acted as filters. Many ideas never reached the prototype stage because the cost of execution was too high.
AI weakens that filter.
Today, one person can generate a landing page, write a product narrative, create a prototype, produce marketing copy, build a rough MVP, and publish a convincing vision in a weekend.
This is exciting. It is also disorienting.
Suddenly, everyone has a project. Everyone has a workflow. Everyone has an agent pipeline. Everyone has a framework. Everyone has a “new way of building software.” Everyone has a nearly finished silver bullet that will supposedly solve a whole class of problems.
The result is not just innovation. It is also saturation.
The world is filling up with ideas that are cheap to express, cheaper to prototype, and still very expensive to understand, evaluate, trust, integrate, and use.
The Problem Is Not Lack of Attention. It Is Capacity.
When people ignore a new idea, it is tempting to describe the problem as a lack of attention.
But “attention” sounds too voluntary, as if people simply refuse to look, refuse to care, or refuse to understand.
A better word is capacity.
A person, a team, a market, or a community has limited capacity for new things. Limited capacity to understand new concepts. Limited capacity to test new tools. Limited capacity to change habits. Limited capacity to trust yet another promise. Limited capacity to reorganize existing workflows around someone else’s vision.
A new idea does not enter an empty space.
It enters a system that is already full.
To adopt something new, people often need to remove, compress, or abandon something that is already there: an existing tool, an existing habit, an existing mental model, an existing workflow, an existing source of trust.
This is why “just look at my project” is rarely a small request.
It asks someone to spend not only time, but cognitive space, emotional energy, professional risk, and sometimes political capital inside a team or organization.
AI increases the supply of new ideas. It does not automatically increase the human capacity to absorb them.
Revenue Becomes the New Reality Filter
In a world where ideas are cheap, having an idea proves very little.
In a world where prototypes are cheap, having an MVP also proves less than it used to.
The stronger filter becomes: is anyone using it, and is anyone paying?
Revenue is not only money. It is evidence.
It shows that someone had a real enough problem, trusted the solution enough, and received enough value to pay for it. Even small revenue can be more meaningful than a polished demo or a beautiful pitch.
A product with ten paying users may be more real than a grand AI platform with a perfect landing page and no adoption.
This changes the credibility ladder.
“Look, I have an idea” is weak. “Look, I built an MVP” is better, but increasingly common. “Look, people use it every week” is stronger. “Look, people pay for it” is stronger still. “Look, it removes work, reduces risk, or saves money in a measurable way” is the beginning of a real business.
AI makes building easier. It does not make usefulness automatic.
The Deeper Shift: From Execution to Selection
The more powerful AI becomes, the deeper this problem gets.
If we imagine a future with AGI-level systems, the cost of execution may fall dramatically across many domains. Writing software, producing content, designing systems, researching options, planning businesses, and generating variations may become almost trivial compared to the past.
But if many things become possible, the hard question changes.
It is no longer only:
“What can we build?”
It becomes:
“What is worth building?”
And even more importantly:
“What are we willing to stop doing so that this can exist?”
The age of scarcity forced direction upon us. Human civilization was built around shortages: food, shelter, safety, labor, knowledge, medicine, transportation, production, capital.
Scarcity was painful, but it also gave structure. It told societies what mattered. It created priorities. It forced cooperation. It made value easier to recognize.
If AI removes or reduces many traditional scarcities, it does not remove the need for direction. It makes direction harder.
An infinite menu is not freedom. It can become paralysis.
When everything can be generated, the scarce resource is no longer only intelligence or execution. It becomes judgment, taste, restraint, and orientation.
The Missing Question: What Do We Want Together?
It is not enough to ask, “What do I want?”
That is only half of the problem.
The harder question is:
“What do we want together?”
This may become one of the central questions of the AGI era.
If every person can generate their own project, their own media, their own assistant, their own ideology, their own virtual world, their own product, their own truth-shaped environment, then society risks splitting into billions of private directions.
Each individual may become more empowered. But shared direction may become weaker.
A civilization does not run on individual optimization alone. It runs on coordination.
Families, teams, companies, cities, scientific communities, open-source ecosystems, and states all depend on some form of shared intent: a fragile agreement about what matters, what should be done, what should be avoided, and how success should be recognized.
In a world of abundant execution, shared intent becomes scarce.
Shared Intent Is Not a Prompt
This matters especially for AI-native software development.
Many teams are now experimenting with AI coding assistants, agents, prompt chains, generated specifications, autonomous pull requests, and automated reviews.
But the central problem is not simply how to generate more code.
The central problem is how to preserve intent.
What exactly are we trying to change? Why does this change matter? What constraints must not be violated? What trade-offs are acceptable? How will we know that the result is correct? Who owns the decision? What should the AI not touch? What does quality mean in this context?
A prompt is not enough.
A prompt is often a temporary expression of desire. Shared intent is stronger. It is a contract between people, tools, systems, and future maintainers.
It defines not only what should be produced, but also how the result should be judged.
This is why specifications, quality gates, review criteria, traceability, tests, architectural constraints, and decision records become more important, not less important, in the age of AI.
The more generation we have, the more we need governance of intent.
From AI Generation to AI Production
This is where the idea of a software factory becomes interesting.
A factory is not just a place where things are produced. It is a controlled system that turns inputs into outputs through repeatable processes, checks, constraints, and feedback loops.
In AI-native development, the “factory” should not simply generate code faster.
That is not enough.
A serious AI software factory should capture intent, orchestrate work, validate outputs, classify errors, measure quality, enforce constraints, and make the production process observable.
It should help answer questions like:
What was the original intent? Which agent or tool changed what? Which assumptions were made? Which checks passed? Which checks failed? Which risks remain? Which human decisions were required? How does this change fit into the existing architecture?
This is not just automation.
It is a capacity-releasing layer.
The value of such a system is not that it adds yet another tool to an already saturated workflow. Its value is that it reduces chaos, compresses review loops, removes repeated explanations, catches low-quality changes earlier, and helps a team preserve shared intent while using AI.
The goal is not to replace human judgment.
The goal is to protect human judgment from being overwhelmed by infinite generation.
The New Scarcity
AI does not eliminate scarcity.
It moves scarcity.
From production to meaning. From execution to selection. From intelligence to judgment. From individual ideas to shared intent.
The future may not be limited by our ability to create things. It may be limited by our ability to agree on what deserves to be created.
That is a very different kind of problem.
And it is not solved by more agents, more prompts, more tools, or more prototypes alone.
It requires new systems for coordination. New rituals of agreement. New ways to express constraints. New methods for validating outputs against intent. New forms of trust between humans and machines, and between humans themselves.
In the age of AGI, the hardest question may not be:
“What can we make?”
The hardest question may be:
“What are we doing together?”
That question is not a technical detail. It may become the central design problem of the next era.