AITAI scenario framework
Six plausible AI futures mapped across capability and diffusion.
limited
diffusion
Status quo / stagnation
Tools improve slowly, most institutions don't restructure
Fragmented advantage
Capable AI, but adoption remains concentrated in some nations and sectors
Readiness gap
Transformative AI exists, but global adoption remains low and uneven
embedded
diffusion
Incremental efficiency
Modest tools widely adopted; steady but undramatic gains
Global baseline shift
AI is standard across industries; the competitive floor rises globally
Abundance / rapid transformation
Expert-level AI embedded everywhere; the world has structurally shifted
Patchy / limited diffusion
Status quo / stagnation
Tools improve slowly, most institutions don't restructure
Fragmented advantage
Capable AI, but adoption remains concentrated in some nations and sectors
Readiness gap
Transformative AI exists, but global adoption remains low and uneven
Broad / embedded diffusion
Incremental efficiency
Modest tools widely adopted; steady but undramatic gains
Global baseline shift
AI is standard across industries; the competitive floor rises globally
Abundance / rapid transformation
Expert-level AI embedded everywhere; the world has structurally shifted
Where are we now
April 2026AITAI's assessment of current AI trajectory
Capability is advancing rapidly across benchmarks, coding, and reasoning. But adoption remains shallow and uneven — concentrated in large firms, specific sectors, and a handful of countries. Australia's public investment lags significantly.
The current trajectory points toward:
Supporting evidence
Key signals across both axes
Capability
48.9pp jump on GPQA in one year
Stanford HAI, 2026
SWE-bench: single digits to 70%+
SWE-bench, 2025
Training compute doubling every 6 months
Epoch AI
Inference cost dropped 10x in 24 months
Industry pricing data
Open-weight gap closed from 24 months to 6–12
Stanford HAI, 2026
Diffusion
11% direct US workplace adoption — stalled
Hartley et al., SSRN 2026
OECD firm deployment at 20.2%
OECD, 2025
Only 4% of occupations deeply integrated
Anthropic Economic Index, 2026
Gen AI: 53% adoption in 3 years — fastest GPT ever
Stanford HAI, 2026
Australia: early and uneven
JSA, 2024
A$0.3B government investment vs A$13.8B comparator average
ATSE, 2024
Scenario details
What each future means for Western Australia
1. AI assists with basic research and admin. Most institutions experiment but don't change how they operate. WA has time to prepare.
2. Modest AI tools embedded in firms and governments worldwide. Competitor economies get steady productivity gains. Pressure on WA to keep pace on basics.
3. AI handles complex research and coding tasks. Adoption is concentrated in certain nations and sectors; others lag. A gap opens between early adopters and the rest.
4. AI runs complex projects and produces high-quality analytical work as standard across industries globally. WA faces real competitive pressure to match or lose ground.
5. Frontier AI outperforms humans in research, coding, and creative work, but adoption globally remains low and uneven. The technology is available; the uptake isn't there. WA's response depends on whether it is ahead or behind that adoption curve.
6. AI operates at expert level across most domains and is deeply embedded worldwide. Labour markets, trade, and state capacity are being reshaped. WA must radically redesign how its traditional industries (mining, agriculture, government) operate and capture value.
Policy robustness analysis
Which of WA's five key policy levers hold up across all six scenarios?