Qantas shifts 850 jobs to India amid AI automation claims

Qantas has confirmed plans to outsource 850 jobs to India, justifying the decision by claiming that the roles will eventually be replaced by artificial intelligence. The announcement has drawn scrutiny from economic analysts, who argue that the airline is using the promise of future automation to obscure a conventional offshoring strategy.

The move comes amid a broader debate regarding the impact of AI on the Australian labour market. While several major companies, including NAB, Canva, and Atlassian, have recently announced staff reductions, the role of AI in these decisions remains contested. John Quiggin, a professor at the University of Queensland’s school of economics, noted that the Qantas decision appears consistent with the airline’s long-standing strategy of reducing local labour costs.

Regarding the rationale provided by Qantas, Quiggin questioned the logic of sacking existing staff to set up new operations overseas if the roles are destined for automation. He suggested that the airline’s invocation of AI serves to complicate the narrative surrounding what is essentially a transfer of work to a lower-cost jurisdiction. He noted that if the jobs are truly destined for automation, the process of relocating them to a new operation overseas seems redundant.

Other major Australian employers have also faced questions about their hiring and firing practices in the age of AI. NAB, which has announced mass redundancies in most years since 2010, explicitly denied that AI played a role in its recent job loss announcements. Analysts observe that for banks like NAB, such restructuring is often a standard business practice rather than a response to technological shifts. Despite the recurring nature of these sackings, the bank's total employment numbers have remained largely unchanged over the years, as staff turnover and retirements balance out the reductions.

The broader economic outlook for AI remains uncertain. Despite significant investment in infrastructure, such as data centres, and a boom in stock market valuations for AI-linked firms, there is little evidence of widespread productivity growth. Quiggin compared the current climate to the 1990s, noting that the primary impact of the technology may be felt in financial markets rather than through a displacement of the workforce. He referenced the 1987 observation by Nobel laureate Robert Solow, who noted that the computer age was visible everywhere except in productivity statistics.

The landscape for technology firms is also shifting. Canva faces potential disruption due to the ease with which AI can replicate graphic design tasks, allowing users to generate content in seconds. Conversely, companies such as Atlassian may be more resilient, as their software offerings focus on complex project management and organisation-wide data, which are harder to replicate through piecemeal AI use.

The future of the AI sector itself remains speculative. Industry leaders such as OpenAI, Anthropic, Meta, and Alphabet are facing competition from cheap open-weight models developed by firms like DeepSeek in China and Mistral in France. These competitors are operating with a fraction of the investment, raising the possibility that large language models could eventually become a standard utility rather than a source of profit. If this occurs, Quiggin suggests that the most significant impact will be an apocalypse in the stock market rather than in the jobs market.