For years, the AI conversation has been anchored around a single destination.
AGI.
A moment in time when machines become broadly intelligent and everything changes.
It’s a compelling narrative. It’s also a distracting one.
Because while the industry debates when AGI will arrive, a far more consequential shift is already happening quietly, unevenly, and at a massive scale.
Intelligence is being distributed.
The Obsession With AGI Misses the Real Inflection Point
AGI is a research milestone.
Intelligence distribution is a societal and economic transformation.
Most real-world impact from AI doesn’t come from a single system that can do everything.
It comes from millions of systems doing specific things better than before.
- A junior developer shipping faster
- A small team operating like a large one
- A founder making better decisions with less information
- A business reducing friction across workflows
None of this requires AGI.
It requires intelligence placed exactly where decisions are made.
Intelligence Only Matters Where It Can Be Used
Raw intelligence, sitting in a lab or behind an API, has limited value.
Value is created when intelligence is:
- embedded into workflows
- accessible at the point of action
- aligned with context and intent
- constrained by judgment and rules
In other words, intelligence needs distribution, not just development.
This is why the same model can feel revolutionary in one product and useless in another.
The difference isn’t intelligence. It’s placement.
Distribution Changes the Unit of Power
In the pre-AI world, leverage came from:
- capital
- headcount
- infrastructure
- access to information
In the AI-enabled world, leverage increasingly comes from:
- who can deploy intelligence fastest
- who can integrate it into decisions
- who can compound it across systems
This is why small teams are outperforming large ones. Not because they have better models, but because they have shorter intelligence distribution loops.
Why AGI Is the Wrong Benchmark
AGI frames progress as a finish line.
Distribution frames progress as a slope.
A finish line encourages waiting. A slope encourages building.
While people debate: “Is this AGI?”
Others are asking: “Where should intelligence live next?”
That second question creates momentum.
The Real Divide Isn’t Human vs Machine
The coming divide is not between humans and AI.
It’s between:
- those who can distribute intelligence effectively
- and those who cannot
Organisations that treat AI as a centralised capability will move slowly.
Organisations that treat AI as a distributed layer, across teams, tools, and workflows, will move differently.
They will feel faster, leaner, and more adaptive.
What Intelligence Distribution Actually Looks Like
In practice, intelligence distribution means:
- decision support embedded into tools, not dashboards
- AI assisting during work, not after
- context-aware systems, not generic assistants
- guardrails over autonomy
- augmentation over replacement
This is less dramatic than AGI.
It’s also far more powerful.
What Most People Miss
The most important insight is this:
We don’t need machines that can do everything. We need systems that help humans do the right thing more often.
That is a design and distribution problem, not a research one.
Where This Is Headed
As models continue to improve, the bottleneck will shift:
- not intelligence
- not compute
- not data
But distribution, integration, and trust.
The winners will be those who can place intelligence:
- at the edge of decision-making
- inside real workflows
- in ways people actually use
AGI may arrive someday.
But the future is already being shaped by something more immediate and more practical.
The Real Takeaway
If you’re building with AI today, the most important question isn’t: “How close are we to AGI?”
It’s: “Where should intelligence be distributed next?”
Because progress won’t be defined by a single breakthrough.
It will be defined by how widely and wisely intelligence is applied.
And that shift is already underway.
Top comments (1)
With the development of AGI, we will have better calculation, ideas, processing but the ultimate challenge is the distribution.