As AI costs rise, there’s little evidence of major utility in game development | Opinion | GamesIndustry.biz
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As AI costs rise, there’s little evidence of major utility in game development | Opinion
The era of heavily subsidised AI compute is ending – but the experiences of developers who have worked with it suggest limited productivity gains that will make higher costs hard to justify.
Image credit: Bernd Dittrich / Unsplash
Opinion
by Rob Fahey<br>Contributing Editor
Published on June 19, 2026
It’s been almost impossible to avoid the conversation about Generative AI in the games industry over the past couple of years. No matter which side you fall on personally – from starry-eyed evangelists, via the cautiously interested and the ethically concerned, all the way to the hard-bitten harbingers of dystopia – the discussion itself has never been off the agenda.
Much of the discussion, however, has involved more heat than light – for the simple reason that nobody really knew to what extent AI would actually be useful in game development. Lots of people made sweeping predictions – in line with their own opinions about the technology, for the most part – but there’s not been much in the way of actual hard data arising from genuine experiences of trying to incorporate AI tools into creative workflows.
Some of the most basic facts about AI’s potential and its role in the industry, in fact, have been incredibly vague. In what areas, and by how much, might it boost productivity? What tasks can and can’t it do? How much additional work is required to supervise and verify its outputs? Even more fundamental; how much will it cost?
Productivity gains from using AI tools are real – but they’re inconsistent, highly task specific, and extremely reliant on human supervision
In the past couple of months, it feels like some answers have started to emerge – not just for the games industry, but for every sector that has dipped its toes in these waters. Companies have been working with various types of AI tools for a while now; some have integrated it more deeply into their workflows than others, but almost every company in a tech-related sector has at least dabbled. Consequently, the body of actual experience and data around what AI can accomplish is growing rapidly – and with it has come a much-needed injection of reality to the debate.
The growing consensus seems to be that productivity gains from using AI tools are real enough – but they’re inconsistent, highly task specific, and extremely reliant on careful, skilled human supervision. There’s an undercurrent of cautious optimism about the productivity-boosting potential for certain tools, in certain areas – but little sign that the dream of massive wins at little or no cost might materialise.
Cost, in fact, is the second factor that’s changing the conversation materially. Less than a year ago, developers who voiced concerns over the actual utility of AI often struggled to get a fair hearing from senior management. The evangelising salespeople who had sold them on the dream of AI had also furnished them with ready-baked dismissive counterarguments; concerned developers were just Luddites (a moniker readily embraced by those who actually know the history of that unjustly maligned group) trying to ringfence their job security, or simply failing to understand that the breakneck progress of the technology would sweep away their arguments about quality and reliability.
The AI bills, both metaphorical and literal, have started to come due
Today, there’s a far more receptive ear for those concerns in a lot of boardrooms, and it’s largely because the AI bills – both metaphorical and literal – have started to come due. The past few months have seen an industry-wide shift towards token-based billing instead of flat rates, accompanied by a move away from the subsidised introductory rates many corporate clients had been enjoying. Microsoft’s Copilot services were one of the first major dominoes to fall, but the trend is more or less universal. The actual costs of AI are increasingly being passed through to customers rather than being soaked up by tens of billions of dollars of private funding, and executives who last year wanted AI integrated into every facet of their company’s business are belatedly starting to wonder about the long-term costs involved.
Those changes, of course, hit hardest of all for use cases that burn huge numbers of tokens – use cases like generating complex assets, or working with extremely large codebases, scenarios which are rapidly becoming far more expensive as realistic, unsubsidised pricing takes hold. As those bills mount up, AI needs to have a business case that’s based on more than hype, vibes, and all-consuming FOMO;...