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 global AI surge doesnt just fall from the sky. In Australia, industries are already going through structural shifts, and not every part of the economy moves at the same pace. By 2035, some areas are expected to expand quite a lot, while others seem to stall, or even head toward outright decline.
Healthcare is on top for job growth; Agriculture is the only industry that is projected to shrink.
Looking at 19 Australian industries, the 10-year employment forecasts show a clear divide. Healthcare and Social Assistance should pick up more than 540,000 jobs by 2035, largely because the population is getting older. At the same time Agriculture is the only sector expected to lose workers. Sectors such as Retail Trade and Administrative Services , which have historically been more exposed to automation are showing weak growth. That pattern hints that AI displacement might already be nudging how employers plan their workforce.
Industry-level data kind of shows where jobs are picking up. Still, figuring out who is actually most at risk means looking deeper inside those industries at the people doing the work their earnings too, and, their gender, kind of side by side.
Low-paid, female-dominated occupation groups seem to be the most vulnerable to AI displacement.
These two charts map Australia’s eight big occupation groups across two key dimensions: average median weekly earnings, and female workforce share. Community and Personal Service workers are the most female heavy group, and they are also sitting in the lower wage range. Clerical and Administrative workers which is a group of historically linked with automation exposure are similarly female dominated and also relatively low-paid. On the other hand, Managers and Professionals make substantially more and have a lower immediate displacement risk. The overall picture is pretty clear: AI risk doesn’t get spread evenly across the board, and gender inequality ends up shaping 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