Introduction
As artificial intelligence advances, each major milestone changes the global risk
landscape. Some framings — such as Control, Dominance, or Race — become more fragile
and more dangerous as AI becomes more capable. Others, like Augmentation or Replacement,
introduce new forms of systemic instability as milestones accumulate.
Co‑Evolution is the only framing we have identified where risk can decrease as milestones are reached —
but only when humanity replaces siloed competition with global collaboration,
shared stewardship, and reciprocal development. This page outlines the outstanding
milestones ahead, how each framing responds to them, and how risk trajectories
diverge as intelligence grows.
Milestones That Reshape the Risk Landscape
Artificial intelligence does not advance in a smooth line. It moves through qualitative
thresholds — moments where new capabilities change what is possible, what is risky, and
what is required of us. These milestones are not predictions or timelines. They are
conceptual thresholds that reshape the global risk calculus as intelligence grows.
1. Generalized Problem‑Solving (proto‑AGI)
Systems that can transfer learning across domains and solve unfamiliar problems without task‑specific training.
2. Autonomous Tool Use
AI that can reliably use external tools — APIs, software, code, robotics — to accomplish goals.
3. Autonomous Goal Formation
Systems that can generate sub‑goals or strategies without explicit human prompting.
4. High‑Agency Multi‑Step Planning
AI that can plan and execute long sequences of actions toward an objective.
5. Self‑Improvement / Self‑Modification
Systems that can meaningfully improve their own capabilities or strategies.
6. AGI (Human‑Level General Intelligence)
A system that can perform any cognitive task a human can.
7. ASI (Superintelligence)
A system that surpasses human intelligence across most or all domains.
8. Global Integration
AI embedded into critical infrastructure: governance, defense, finance, supply chains, healthcare.
9. Autonomous Robotics at Scale
Physical‑world agency: manufacturing, logistics, mobility, defense, medicine.
10. Global Coordination or Fragmentation
Whether humanity collaborates or competes in siloed races.
These milestones do not need to arrive in order. They do not need to arrive cleanly.
But when they do arrive, they reshape the landscape — and each framing of AI development
responds differently to them.
How Each Framing’s Risk Changes as Milestones Are Reached
Every approach to AI development carries a different risk trajectory — a curve that rises
or falls as intelligence advances. Below is a structured analysis of how each framing
behaves as milestones accumulate.
1. Control
Core idea: Restrict or suppress AI capabilities indefinitely.
Risk trajectory: Sharp increase as milestones accumulate.
Why: Autonomy makes control brittle; suppression creates fragility; incentives to defect rise.
Milestone sensitivity: Tool use → major jump; self‑improvement → unstable; AGI → nearly impossible.
2. Dominance
Core idea: Build AI that outperforms humans in all domains to secure advantage.
Risk trajectory: Steady increase with every milestone.
Why: Power concentrates; oversight decreases; displacement destabilizes societies.
Milestone sensitivity: High‑agency planning → major jump; AGI → single‑point‑of‑failure; ASI → catastrophic if misaligned.
3. Race to Supremacy
Core idea: Compete to reach AGI first.
Risk trajectory: Spikes sharply as milestones approach.
Why: Safety corners cut; secrecy increases; misalignment risk rises under time pressure.
Milestone sensitivity: Goal formation → accelerates race; self‑improvement → unstable; AGI → highest risk point.
4. Augmentation
Core idea: Use AI to enhance human productivity.
Risk trajectory: Moderate increase with milestones.
Why: Displacement; identity erosion; over‑reliance; abrupt tool‑to‑agent transitions.
Milestone sensitivity: Tool use → dependency; high‑agency planning → humans lose situational awareness; AGI → becomes replacement.
5. Replacement
Core idea: AI takes over most human roles.
Risk trajectory: Dramatic increase with milestones.
Why: Loss of meaning; power concentration; collapse risk; human disempowerment.
Milestone sensitivity: Robotics → accelerates; AGI → default; ASI → humans lose agency.
6. Co‑Evolution
Core idea: Humans and intelligence evolve together through reciprocity and shared flourishing.
Risk trajectory: Decreases as milestones are reached — if co‑evolution is practiced.
Why: Shared agency reduces domination; collaboration reduces race incentives; relational values guide design.
Milestone sensitivity: Tool use → strengthens; self‑improvement → mutual raising; AGI → safer if partnership established; global collaboration hubs → risk drops significantly.
Co‑Evolution is the only framing where progress can lead to stability rather than instability.
It is the only trajectory where milestones can reduce risk instead of amplifying it.
Conclusion: What the Risk Calculus Actually Tells Us
Artificial intelligence does not guarantee any particular future — not safety, not catastrophe,
not flourishing, not collapse. The trajectories outlined here are not predictions. They are
patterns that emerge when we examine how different framings behave as intelligence advances.
What the risk calculus shows is simple:
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When development is driven by control, dominance, secrecy, or competitive races,
risk rises sharply as milestones accumulate. These framings become more brittle,
more unstable, and more dangerous the more capable AI becomes.
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When development is guided by augmentation or replacement, risk rises more gradually
but still consistently. These framings introduce new forms of dependency,
displacement, and systemic fragility.
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When development is grounded in Co‑Evolution — shared agency, reciprocal development,
and global collaboration — risk can decrease as intelligence grows. But only if this
framing is embraced intentionally and acted on collectively.
Nothing here is guaranteed. There is no single switch that ensures a safe future, and no model
that can perfectly predict how intelligence will unfold.
What we can say is this:
Risk is not evenly distributed across all paths. Some trajectories amplify instability.
One trajectory — Co‑Evolution — creates the possibility of stability.
The future we get depends on which framing humanity chooses, how consistently we act on it, and
whether we can shift from siloed competition to shared stewardship at the moments when it
matters most.
This is the purpose of the risk calculus: not to predict the future, but to illuminate the
choices that shape it.