In the last few years, the hype around artificial intelligence has become stratospheric. Riding a wave of venture capital, tech leaders promised us AI would revolutionise work, boost productivity and lead to incredible new breakthroughs. OpenAI, the creator of ChatGPT, set a new record when it attained US$110 billion in investments several months ago – and its CEO, Sam Altman, recently claimed Australia could become a “data capital of the world.”

Sky-high promises have been accompanied by sky-high investment in data centres, the sprawling server farms that power the training, execution, and maintenance of these models. A monstrous new hyperscale facility proposed for Sydney’s west – 1 gigawatt across 52 hectares – would rank among the world’s biggest. It will join 162 existing centres and 90 in the works across Australia, which is projected to be the world’s third largest data centre market by the early 2030s.

But if AI backers are all in, public sentiment is far more mixed. A new study ranked Australia equal lowest on the scale of global AI sentiment, with 81% supporting stronger rules for how organisations use AI and 68% worried about losing control over decisions made by AI on their behalf.

Grassroots movements against AI are growing. Last month, a “Stop the Slop” event challenging the Sydney data centre was relocated to a larger venue due to high interest. It joins other campaigns like StopAI and PauseAI that aim to slow down data centre development, ask how AI is impacting jobs and the environment, and consider more equitable and sustainable alternatives.

And in the last few months, videos have begun surfacing of students at commencement ceremonies booing speakers like former Google chief executive Eric Schmidt, who speak in rapturous tones about “standing on the edge of technological transformation” and how AI will touch “every profession”, “every classroom”, and “every relationship”.

Faith in these monumental claims – and the monumentally expensive infrastructure they rely on – is slipping.

What is the AI business model?

AI’s financial costs are astronomical. As tech critic Ed Zitron has shown over and over again, the major players are burning billions to keep models running, while lucrative profits remain tantalisingly out of reach. Some enterprises now spend more on rapidly rising token costs, the per-use cost of a model, than human workers. Even by cynical economic standards, the numbers don’t add up.

What exactly is the AI business model? Where is the killer app that will deliver genuine value and see millions of individuals or thousands of corporates pay costly subscription fees? “We have no idea how we may one day generate revenue,” admitted OpenAI CEO Sam Altman in 2019, “once we build a generally intelligent system, we can ask it to figure out a way to generate an investment return.” While the landscape has certainly shifted since then, use cases and revenue remain murky.

Hard evidence of AI’s contribution – rather than the vacuous claims of pitch decks and industry keynotes – remains largely elusive.

A recent survey of 6,000 senior business executives across the United States, United Kingdom, Germany and Australia found positive perceptions but a disappointing reality: around 90% of firms said AI has had no impact on employment or productivity over the past three years. Another study, from MIT last year, found that 95% of generative AI pilots failed to deliver tangible financial value to the organisation, so were abandoned.

If the upsides are unclear, the negatives are increasingly apparent. Politically, generative AI provides the perfect weapon to “flood the zone” with misleading or outright false content, muddying the informational waters and amplifying........

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