AI Investment, SACO, BPS, and the Provider/User Divide
1. Discomfort with the “Bubble” and “Circular Investment” Narratives
There is frequent discussion suggesting that current AI investment is
“a bubble” or merely “circular trading.”
However, most of these arguments do not place AGI as the ultimate goal.
They assume that technological progress will continue only incrementally,
which leads to the perception that:
• investment levels look excessive
• ROI does not seem to make sense
Yet, if we consider the possibility that AGI may be humanity’s final invention,
the present moment does not resemble a normal investment cycle.
Rather, it is more natural to view it as:
the final phase just before the goal — a dead heat near the finish line.
2. This Is Not a Chicken Race, but the Final Stage of a Marathon
At first glance, current AI infrastructure investment may appear to be
a “chicken race,” where the first to pull back wins.
In reality, it is closer to:
the decisive moment of whether one can remain in the leading pack of a marathon.
• In the early or middle stages, it is possible to slow down and catch up later
• In the final stage, once pace is lost, returning to the leading group is no longer possible
We are now entering precisely this point of no return.
3. SACO (Smart Always Chicken Out)
SACO
= Smart Always Chicken Out
(an actor that is intelligent, but consistently withdraws at decisive moments)
Typical characteristics of SACO-type organizations:
• their analysis is correct
• their risk assessments are accurate
• however, they refuse to cross the “irreversible line”
• they cannot fully commit to AI infrastructure investment
SACO does not indicate incompetence.
It reflects a form of rationality that was optimal in the previous era.
However, in a phase transition from +AI to AGI:
SACO no longer means “prudence” — it means “elimination.”
4. Are Current AI Investment Levels Really Large?
Most current AI investment is concentrated in the digital layer:
• data centers
• GPUs
• model development
But if we consider a future in which:
• real estate
• cities
• public administration
• infrastructure
are redesigned on a +AI-first premise, then:
today’s AI investment appears small relative to the whole system.
At present, we are effectively paying only for the operating system.
Large-scale investment in the physical world has not yet begun.
5. The Theoretical Optimal Point of AI Investment
In theory, AI infrastructure investment should continue until:
cost reductions achieved by AI ≒ the next round of AI investment.
This is because:
• AI does not suffer quickly diminishing marginal returns
• efficiency gains recursively generate further optimization opportunities
• the moment investment stops, relative speed is lost
This is not a profit-maximization model.
It is an adaptation-speed maximization model.
6. Providers and Users in the BPS Era
In the era of BPS (Blue Planet System), organizations naturally split into
Providers and Users.
Providers
• entities that provide AI infrastructure, data, and optimization structures
• positioned inside BPS
• able to influence rule formation and system evolution
• function as hubs that connect others (Users)
Users
• entities that use a completed BPS
• decision-making is largely externalized
• efficiency gains are possible, but initiative is not
• structurally dependent on Providers
7. The Final Outcome of SACO
Organizations that become SACO typically follow this path:
1. fall out of the leading group
2. become Users within BPS
3. lose relative competitiveness
4. fail to maintain independence
5. are absorbed by Providers
This is often not a bankruptcy scenario,
but rather a rational consolidation driven by market logic.
The irony is that:
AI investments avoided due to SACO
are later executed compulsorily after absorption,
based on external decision-making.