The AI Alibi:
When "Efficiency" Is Just Redundancy With Better PR

Let us begin with Teresa Lim. For 23 years, Teresa has been one of the most recognisable voices in Australian advertising — the reassuring tone behind everything from baby formula to Test cricket. She is, by any reasonable measure, a professional who has built something irreplaceable over two decades. And she is now, with justifiable alarm, watching companies reach for a 15-second clip of her voice and a machine that will work for a fraction of the price and never ask for a coffee break.

Teresa's situation is real, and it deserves to be taken seriously. But it is also, somewhat inconveniently for the narrative, quite different from what is actually driving the wave of corporate redundancies currently sweeping through Australian technology companies. Because here is the thing about the 1,000-odd Australian jobs cut in recent weeks by WiseTech, Block, Atlassian, and Telstra: most of them, if the analysts are to be believed, were not taken by AI at all.

They were taken by falling share prices, over-hiring during the post-pandemic boom, investor impatience, and the old-fashioned corporate instinct to reach for a headcount reduction whenever the quarterly numbers look uncomfortable. AI, in these cases, is less a cause than a costume — the explanation that sounds visionary rather than reactive, that makes a redundancy program sound like a transformation strategy.

Source: Guardian Australia, March 2026

The Art of AI-Washing Your Layoffs

Neal Woolrich, a human resources adviser at Gartner, has a term for this: he calls it using AI as "cover." His economic modelling found that only 1% of recent job cuts resulted from AI productivity gains. The other 99% had other explanations — financial pressure, over-hiring, shifting market conditions — that companies were understandably reluctant to cite when "AI transformation" was available as an alternative.

It is, if you think about it, a rather elegant piece of corporate communications. "We are letting you go because our share price halved and we hired too many people during the boom" is an accurate statement that reflects badly on management decisions. "We are evolving our workforce for the AI era" is a statement that sounds bold, forward-thinking, and vaguely inevitable — as though the robots have arrived and there is simply nothing to be done. One of these statements generates sympathy for the company. The other does not.

— Neal Woolrich, human resources adviser, Gartner

Block — the company that owns Afterpay and is chaired by Twitter co-founder Jack Dorsey — cut 4,000 workers worldwide, including around 700 Australians. Its share price had fallen 35% since October. Retrenched employees questioned whether AI could actually do their jobs. WiseTech's headcount had doubled following an acquisition; its share price had halved. Atlassian's stock had fallen by half in just two months. These are not the circumstances of companies drowning in AI-driven surplus productivity. These are the circumstances of companies with financial problems looking for solutions that are easier to sell to the market than an admission that they hired too many people when money was cheap.

Meanwhile, In the Real Economy

While the technology sector has been busy staging its AI drama, the broader Australian economy has been behaving rather less cinematically. Call centres — which everyone assumed would be the first institution to swap its headsets for chatbots — are, according to Randstad, still steadily hiring humans. Almost one in three Australian businesses are using AI for advanced tasks, per Reserve Bank research, but this has not yet translated into the wholesale hiring slowdown that the more excitable forecasters predicted.

Lochlan Halloway of Morningstar puts it with useful precision: his firm has increased AI use for data gathering previously handled by entry-level analysts. "But that doesn't mean we're cutting our junior headcount." This is a distinction worth dwelling on. Efficiency gains and headcount reductions are not the same thing. A business can become more productive per person without necessarily needing fewer people — particularly if it is also, in the process, finding more things worth doing.

The Junior Problem Is Real — and It's Complicated

There is, however, one area where the concern is genuinely warranted, and it would be dishonest to wave it away. Junior and entry-level roles are being squeezed — particularly in consulting, marketing, and finance — as companies reach for AI tools to handle short-term work that would previously have gone to a graduate or an intern. Camilla Clarke of Give a Grad a Go observes this directly: smaller firms, especially in consulting and marketing, are hiring fewer juniors and using AI for project work that previously served as the entry point for young professionals.

Finance graduates are, reports the CEO of job platform Striver, pivoting away from analytics — where AI is increasingly capable — and toward human-facing roles: financial advice, relationship management, the kind of work where the client specifically wants a person on the other end of the conversation.

This is not a trivial problem. The junior role is not merely a way of getting cheap labour to do basic work. It is the mechanism by which the next generation of senior professionals learns their craft. If companies hollow out the bottom of the pyramid, they will eventually discover — usually at considerable inconvenience — that they have also hollowed out the pipeline to the top.

Source: Randstad Workmonitor, Reserve Bank of Australia, 2026

The Better Way to Do the Sums

Here is an argument that does not appear to have featured prominently in any of the analyst briefings accompanying the recent round of redundancies. If AI genuinely makes your people dramatically more productive — and in many cases it does — then you have acquired something rather more valuable than a justification for cutting staff. You have acquired the ability to do more than you could before. To build more, serve more, grow more, attempt more.

A software team that can now complete in a day what previously took six months has not merely reduced its cost per feature. It has also massively expanded its capacity to build features. The question is not how many engineers you can let go — it is how many things you can now build that were previously impossible because you ran out of time and people. The first answer saves money this quarter. The second answer builds a company that is difficult to compete with in five years.

The companies currently announcing redundancies and calling it transformation are making a choice — one that may well look rational in a quarterly earnings report and considerably less so in a longer-term audit of what was lost. WiseTech, it is worth noting, announced its cuts while eleven of its largest customers had fewer than 20% of their expected users live on its platform. Major implementations were mid-rollout. The experienced engineers who understood those implementations are now, apparently, surplus to requirements.

What This Actually Means If You Run a Business

The large technology companies making headlines are operating under specific pressures — institutional shareholders, public market expectations, the need to demonstrate to investors that they have a plan for a world in which AI is ubiquitous. These pressures produce decisions that make sense in their specific context and are largely irrelevant to the experience of running a business of five, twenty, or fifty people.

For the small and medium business owner, the relevant question is not "how many staff can I replace with AI?" It is "what could my existing team do if they had two extra hours a day?" The answer to the first question is probably some, possibly none, and almost certainly fewer than you think. The answer to the second question is, in almost every business we have seen, rather remarkable.

The person who no longer spends ninety minutes a day on first drafts, data entry, and follow-up emails does not disappear. They start doing the things that were always on the list and never reached: the client they meant to call, the proposal they meant to customise, the problem they meant to think carefully about. These are not small things. They are frequently the things that most directly determine whether the business grows or stagnates.

The Reasonable Conclusion

The current wave of AI-attributed job cuts is, in significant part, a story about companies with financial problems using a convenient narrative. That does not mean AI is not transformative — it is, genuinely, and the transformation is still early. But the transformation does not have to flow in the direction of reduced headcounts. It can equally well flow in the direction of expanded ambition.

Teresa Lim's anxiety is real and deserves legislative attention. The junior talent pipeline problem is real and deserves serious thought from business leaders who have not yet noticed they are sawing off the branch they grew from. And the broader workforce anxiety — one in three Australians, per Randstad — is real and deserves something better than corporate press releases dressed as transformation strategies.

What it does not deserve is to be used as cover for decisions that had nothing to do with AI in the first place. The machines are not, on current evidence, nearly capable enough to be blamed for everything that is being attributed to them. That particular alibi is not yet earned.

When it is, we will tell you.