Tech companies are investing billions in developing and deploying generative AI. That money needs to be recouped. Recent reports and analysis show that it’s not easy.

According to an anonymous source from the Wall Street Journal, Microsoft lost more than $20 per user per month on generative AI code Github Copilot in the first few months of the year. Some users reportedly cost as much as $80 per month. Microsoft charges $10 per user per month.

It loses money because the AI model that generates the code is expensive to run. Github Copilot is popular with developers and currently has about 1.5 million users who constantly trigger the model to write more code.

Geoffrey Hinton warned AI was likely to become smarter than humans and might start writing its own code.

Today’s most advanced AI models have many flaws, but decades from now, they will be recognized as the first true examples of artificial general intelligence.

When a group of researchers asked an AI to design a robot that could walk, it created a “small, squishy and misshapen” thing that walks by spasming when filled with air.

The researchers — affiliated with Northwestern University, MIT, and the University of Vermont — published their findings in an article for the Proceedings of the National Academy of Sciences on October 3.

“We told the AI that we wanted a robot that could walk across land. Then we simply pressed a button and presto!” Sam Kriegman, an assistant professor at Northwestern University and the lead researcher behind the study, wrote in a separate blog post.

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“It’s never really the goal of anybody to write papers — it’s to do science,” says Michael Eisen, a computational biologist at the University of California, Berkeley, who is also editor-in-chief of the journal eLife. He predicts that generative AI tools could even fundamentally transform the nature of the scientific paper.

But the spectre of inaccuracies and falsehoods threatens this vision. LLMs are merely engines for generating stylistically plausible output that fits the patterns of their inputs, rather than for producing accurate information. Publishers worry that a rise in their use might lead to greater numbers of poor-quality or error-strewn manuscripts — and possibly a flood of AI-assisted fakes.

“Anything disruptive like this can be quite worrying,” says Laura Feetham, who oversees peer review for IOP Publishing in Bristol, UK, which publishes physical-sciences journals.