Australia's first national report on the impact of artificial intelligence on the labor market, prepared by the Department of Employment and Workplace Relations, found no evidence of mass layoffs directly linked to AI adoption. However, the study recorded a significant slowdown in employment growth in professions most vulnerable to automation through generative models.
The key finding of the report: from November 2022 to February 2026, employment in the fifth of professions with the highest exposure to generative AI grew by only 5.6%. For comparison, in the group of professions least affected by the technology, this figure was 9.5%. The difference of 3.9 percentage points is an early and moderate signal, not evidence of catastrophic job losses.
The department's model shows that for a profession with an AI exposure level one standard deviation above the mean, employment by February 2026 was approximately 2% lower than would have been expected if pre-ChatGPT trends had continued. Importantly, the result is sensitive to methodology: the negative relationship between AI exposure and employment growth ceases to be statistically significant when using alternative measures or excluding the COVID-19 period from the analysis.
Who is at risk?
The most vulnerable turned out to be routine cognitive professions: office workers, data entry operators, accountants, advertising and marketing specialists, as well as programmers. Professions requiring physical labor and personal care are least susceptible to automation: caregivers, electricians, truck drivers, carpenters, and plumbers.
The report also revealed a gender and educational gap. In the most vulnerable professions, men make up only 43.7% of workers, whereas in the least vulnerable group, they account for 69.5%. The share of workers with a bachelor's degree or higher in the high-exposure group reaches 43.7%, compared to 14.9% in the low-exposure group. This means that women and highly educated specialists are at increased risk.
It's not that straightforward
The picture is not simply one of displacing "white-collar" workers. Some professions with high AI exposure continue to grow actively. For example, employment in the "Software and Applications Developers" category in Australia reached 199,000 people by February 2026 — 25% higher than the November 2022 level. Additionally, the authors found no deterioration in the situation for graduates: the labor market for them remains stable, and the share of those working in positions requiring a degree has even increased. This distinguishes Australia from the United States, where young professionals are considered the first risk group.
No mass upheaval, but signals exist
In February 2026, Australia's unemployment rate was 4.2% — lower than any value in the decade before COVID-19. The employment-to-population ratio reached 64%, above the pre-pandemic level. The authors did not observe an accelerated redistribution of workers between professions. However, in the group of highly exposed professions, employment, hours worked, and the number of vacancies grew more slowly, while the unemployment rate increased more sharply. Nevertheless, the current weakness may reflect not only the impact of AI but also previous structural trends, post-pandemic market restructuring, and macroeconomic factors.
The report cites Anthropic CEO Dario Amodei, who predicts that AI could eliminate up to half of entry-level "white-collar" jobs and raise unemployment to 10–20% within one to five years. However, Australian data so far does not confirm such radical scenarios.
My analysis: The study shows that the labor market is adapting to AI more slowly than many feared. The slowdown in employment growth in vulnerable professions is a reality, but it has not yet escalated into mass layoffs. The key challenge for investors and analysts is to separate the effect of AI from other factors, such as the post-COVID economic restructuring. The report confirms my long-standing hypothesis: AI is changing the employment structure, but it is doing so gradually, not through an instantaneous shock. For the crypto industry, this means that automation in areas such as DeFi and smart contract auditing will occur evolutionarily, not revolutionarily.