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Policy robustness across scenarios

Not all policy levers perform equally across all futures. No-regret actions are robust regardless of which scenario unfolds. Conditional levers depend on how AI capability and diffusion actually develop.

Adoption

No regret

1. Status quo / stagnation

Strong

2. Incremental efficiency

Strong

3. Fragmented advantage

Strong

4. Global baseline shift

Strong

5. Readiness gap

Strong

6. Abundance / rapid transformation

Strong

Institutional capability

No regret

1. Status quo / stagnation

Strong

2. Incremental efficiency

Strong

3. Fragmented advantage

Strong

4. Global baseline shift

Strong

5. Readiness gap

Strong

6. Abundance / rapid transformation

Strong

Talent

No regret

1. Status quo / stagnation

Strong

2. Incremental efficiency

Strong

3. Fragmented advantage

Strong

4. Global baseline shift

Strong

5. Readiness gap

Strong

6. Abundance / rapid transformation

Narrow focus

Energy & compute hosting

Conditional

1. Status quo / stagnation

Conditional

2. Incremental efficiency

Conditional

3. Fragmented advantage

Strong

4. Global baseline shift

Very strong

5. Readiness gap

Very strong

6. Abundance / rapid transformation

Very strong

Startups & innovation

Conditional

1. Status quo / stagnation

Conditional

2. Incremental efficiency

Conditional

3. Fragmented advantage

Moderate

4. Global baseline shift

Moderate

5. Readiness gap

Moderate

6. Abundance / rapid transformation

Weak

Policy lever details

Accelerate AI adoption in firms, government, healthcare, and education. Productivity gains materialise when organisations deploy AI into workflows, not when a jurisdiction talks about capability in the abstract.

1. Status quo / stagnation Strong
2. Incremental efficiency Strong
3. Fragmented advantage Strong
4. Global baseline shift Strong
5. Readiness gap Strong
6. Abundance / rapid transformation Strong

Improve regulatory throughput, procurement reform, and management practice. This is the enabling condition for everything else. Without it, approvals slow, procurement stalls, and gains remain trapped in pilots.

1. Status quo / stagnation Strong
2. Incremental efficiency Strong
3. Fragmented advantage Strong
4. Global baseline shift Strong
5. Readiness gap Strong
6. Abundance / rapid transformation Strong

Develop and attract capability needed to implement AI — senior technical talent, applied domain capability, and mid-career workforce retraining. Talent has the longest lead times of any major lever.

1. Status quo / stagnation Strong
2. Incremental efficiency Strong
3. Fragmented advantage Strong
4. Global baseline shift Strong
5. Readiness gap Strong
6. Abundance / rapid transformation Narrow focus

Position WA for energy build-out, transmission readiness, industrial land, and datacentre-enabling approvals. Higher-risk, higher-reward — captures more of the AI value chain through infrastructure if demand lands in WA.

1. Status quo / stagnation Conditional
2. Incremental efficiency Conditional
3. Fragmented advantage Strong
4. Global baseline shift Very strong
5. Readiness gap Very strong
6. Abundance / rapid transformation Very strong

Support translation, founder formation, and deployment-linked innovation. A complement to the main adoption agenda, not the centrepiece. WA's structural constraints in capital and market size mean this is unlikely to be the main route to productivity.

1. Status quo / stagnation Conditional
2. Incremental efficiency Conditional
3. Fragmented advantage Moderate
4. Global baseline shift Moderate
5. Readiness gap Moderate
6. Abundance / rapid transformation Weak