Category
Systems & Infrastructure
Publish Date
20 December 2025
For most of the last decade, progress in AI followed a simple rule:
Bigger models win.
More data. More parameters. More compute.
Each leap felt inevitable, almost gravitational.
That era is ending — not because models stopped improving, but because scale alone stopped being the bottleneck.
The new constraint isn’t intelligence.
It’s control.
When Intelligence Becomes Abundant
Today, access to powerful models is no longer scarce. The same underlying intelligence can be rented, swapped, fine-tuned, or replaced with surprising ease. Breakthroughs arrive faster, but their half-life shrinks just as quickly.
This creates a strange paradox:
Capability increases
Differentiation decreases
When everyone has access to “smart enough,” intelligence stops being the advantage. The advantage shifts to how that intelligence is organised, directed, and restrained.
This is where the old scaling law quietly breaks.
The New Scaling Law Isn’t About Training
The next generation of AI value doesn’t scale at training-time.
It scales at run-time.
What matters now is not how large the model is, but how well the system:
routes tasks between tools and models
maintains memory across interactions
evaluates its own outputs continuously
enforces constraints without human babysitting
optimises cost, latency, and risk in real time
In short: orchestration.
This is not a semantic shift. It’s an architectural one.
Orchestration Is the New Moat
Orchestration sounds abstract until you see what happens without it.
Without orchestration:
agents drift
costs spike
behaviour becomes unpredictable
reliability degrades over time
With orchestration:
smaller models outperform larger ones
systems improve through feedback, not guesswork
intelligence becomes repeatable, not fragile
This is why the future winners won’t be defined by which model they chose — but by the systems they built around the models.
Models will keep changing.
Systems will compound.
Why This Is Harder Than It Looks
Building orchestration layers is harder than scaling a model, because there’s no single lever to pull.
It requires:
systems engineering discipline
product restraint
a willingness to design for edge cases rather than demos
comfort with invisible work that only shows its value over time
It also requires letting go of a comforting illusion — that intelligence alone is enough.
It isn’t.
HEBB’s View
At HEBB, we design for the assumption that models will improve, commoditise, and be replaced.
Our focus is not on owning intelligence, but on extracting dependable outcomes from it.
That means:
model-agnostic architectures
orchestration layers that learn from use
systems that grow more reliable, not more chaotic, as they scale
We don’t optimise for peak performance in isolation.
We optimise for consistency over time.
From Demos to Infrastructure
The first wave of AI rewarded those who could show what was possible.
The next wave will reward those who can make it boring — predictable, repeatable, and trusted enough to disappear into infrastructure.
This is where real scale begins.
The new scaling law isn’t about who builds the biggest brain.
It’s about who builds the system that lets intelligence operate safely, cheaply, and continuously in the real world.
That’s the layer we’re building toward.
