This story kind a links global AI investment trends to Australia’s workforce, so it shows a wider risk picture. The investment numbers highlight how quickly capital is moving into artificial intelligence, and the employment outlooks suggest the real pressure already building on Australian workers to adjust. Now these datasets don’t really prove that every single job loss happens directly because of AI investment. Instead, they help explain why the size of AI funding is something you can’t ignore for workforce planning: technological disruption is speeding up, while a lot of Australian roles are still not really ready.
It starts with the investment cue, where global private AI funding has surged pretty fast since 2013.
The United States has pulled far ahead in the global AI investment race.
Private investment in artificial intelligence has grown from billions into hundreds of billions of dollars over the last decade. The United States leads this wave, landing at $241 billion in 2025 alone, which far outstrips China and Europe. Grasping the scale of this funding is key context for what comes next. Because this money is not just sitting there-it’s building the technologies that will reshape how Australians work.
Investment scale tells one part of the story , but the way that money actually moves around gives you another dimension-like, who’s pushing AI growth and by what mechanisms.
Mergers and acquisitions end up dominating how AI gets funded globally.
Not all AI investment is the same though. Big corporations are swallowing AI startups via mergers and acquisitions at a really fast pace, while private venture capital is backing the next set of AI tools. Put together, these two funding streams suggest AI development is not going quiet it’s more like consolidating, then accelerating. For workers in AI-exposed industries the implications are pretty direct : established firms are quickly picking up AI capabilities, which can cut demand for some jobs, while boosting demand for other ones.
This chart zooms in from the global AI investment story to the ground level reality for Australian workers. While capital pours into artificial intelligence, the effects are not landing evenly across the economy. Some industries are expanding fast, while others are stalling and the gap between them is widening.
Healthcare is on top for job growth; Agriculture is the only industry projected to shrink.
Each industry now shows two bars side by side of the lighter bar is the 5-year outlook to 2030, and the longer red bar is the full 10-year projection to 2035. This lets you see not just where industries are headed, but how quickly they are getting there. Healthcare and Social Assistance is the set to add more than 540,000 jobs by 2035, driven largely by an ageing population and rising demand for care. Professional, Scientific and Technical Services follows closely, reflecting growing demand for specialised knowledge work. At the other end, Agriculture is the only sector expected to lose workers in the short term, dipping slightly to 2030 before recovering by 2035. Sectors like Retail Trade and Administrative and Support Services which have historically been more exposed to routine task automation are showing weak growth across both time horizons. That pattern hints that AI displacement may already be nudging how employers plan their future workforce needs. Industries where both bars are roughly equal in length are growing steadily. Where the red bar barely stretches beyond the light blue one, growth is expected to slow in the second half of the decade, a warning sign that structural pressures are building.
Industry-level data shows where jobs are picking up. But figuring out who is actually most at risk means looking deeper at the people doing the work, their earnings, the size of their workforce, and their gender, all at once.
Low-paid, female-dominated occupation groups face the greatest AI displacement risk.
This chart maps all eight of Australia’s major occupation groups across three dimensions simultaneously. The length of each lollipop stem shows average median weekly earnings, so the further right, the higher the pay. The colour of each dot reveals female workforce share, moving from pale yellow for male-dominated groups through to deep red for the most female-dominated ones. The size of each dot reflects total workers employed in that group, so larger dots represent bigger slices of the Australian workforce. Reading all three together tells a striking story. Community and Personal Service workers sit at the lower end of the pay scale, carry a deep red dot indicating a majority female workforce, and employ a substantial number of Australians. Clerical and Administrative workers follow a very similar pattern. Both groups have historically been linked to routine task automation, and both now face growing exposure to AI-driven displacement. Managers and Professionals sit at the opposite end higher pay, smaller female share, and lower immediate displacement risk. The overall picture is clear: AI risk is not spread evenly, and gender inequality shapes who carries the heaviest load.
So the last question is the big one, for both workers and policymakers, like where are the jobs actually ending up. The employment projections out to 2035 show a pretty clear split between the the occupation groups. Some are likely to rise a lot, mainly because there’s ongoing demand that AI can’t easily copy think of care, specialized know-how, and complex judgement. Others are stuck with weak growth, which basically signals that automation is already reshaping what people want from those roles, right now.
Professionals will add more than 845,000 jobs by 2035 - while Labourers and Sales will see the weakest expansion.
In this chart, every Australian occupation group is ranked by its projected job growth from 2025 to 2035. The colour on each bar indicates the 10-year percentage growth rate, so you can spot which groups are growing fastest compared to their current scale. If you hover over any bar, you can compare the 2025 baseline with the 2035 projection, plus the overall job change and the growth rate too.
Claude (Anthropic, 2025) was used during this assignment for code review and debugging, fixing minor technical errors in chart rendering and warning messages, and checking formatting against the assignment rubric.
All visualisations, data sourcing, story concept, narrative framing, and design decisions were the author’s own work completed in R using RStudio.
Anthropic. (2025). Claude (claude-sonnet-4-6) [Large language model]. https://www.anthropic.com
Jobs and Skills Australia. (2024). Occupation profiles data — February 2024 [Dataset]. Australian Government. https://www.jobsandskills.gov.au/data/occupation-and-industry-profiles
Jobs and Skills Australia. (2025). Employment projections — May 2025 to May 2035 [Dataset]. Australian Government. https://www.jobsandskills.gov.au/data/employment-projections
Maslej, N., Fattorini, L., Perrault, R., Gil, Y., Parli, V., Kariuki, N., … Wald, R. (2025). The AI index 2025 annual report. AI Index Steering Committee, Stanford University. https://hai.stanford.edu/ai-index/2025-ai-index-report
Our World in Data. (2026). Corporate investment in artificial intelligence by type [Dataset]. Based on Quid via Stanford AI Index Report (2026). https://ourworldindata.org/grapher/corporate-investment-in-artificial-intelligence-by-type
Our World in Data. (2026). Private investment in artificial intelligence by region [Dataset]. Based on Quid via Stanford AI Index Report (2026). https://ourworldindata.org/grapher/private-investment-in-artificial-intelligence
Anthropic. (2025). Claude (claude-sonnet-4-6) [Large language model]. https://www.anthropic.com