The buzzy generative artificial intelligence space is due for something of a reality check next year, an analyst firm predicted earlier this week, pointing to fading hype around the technology, the rising costs needed to run it, and growing calls for regulation as signs that the technology faces an impending slowdown.
In its annual roundup of top predictions for the future of the technology industry in 2024 and beyond, CCS Insight made several predictions about what lies ahead for AI, a technology that has led to countless headlines surrounding both its promise and pitfalls.
The main forecast CCS Insight has for 2024 is that generative AI “gets a cold shower” as the reality of the cost, risk and complexity involved “replaces the hype” surrounding the technology.
“The bottom line is, right now, everyone’s talking generative AI, Google, Amazon, Qualcomm, Meta,” Ben Wood, chief analyst at CCS Insight, told CNBC on a call ahead of the predictions report’s release.
“We are big advocates for AI, we think that it’s going to have a huge impact on the economy, we think it’s going to have big impacts on society at large, we think it’s great for productivity,” Wood said.
“But the hype around generative AI in 2023 has just been so immense, that we think it’s overhyped, and there’s lots of obstacles that need to get through to bring it to market.”
Generative AI models such as OpenAI’s ChatGPT, Google Bard, Anthropic’s Claude, and Synthesia rely on huge amounts of computing power to run the complex mathematical models that allow them to work out what responses to come up with to address user prompts.
Companies have to acquire high-powered chips to run AI applications. In the case of generative AI, it’s often advanced graphics processing units, or GPUs, designed by U.S. semiconductor giant Nvidia that large companies and small developers alike turn to to run their AI workloads.
Now, more and more companies, including Amazon, Google, Alibaba, Meta, and, reportedly, OpenAI, are designing their own specific AI chips to run those AI programs on.
“Just the cost of deploying and sustaining generative AI is immense,” Wood told CNBC.
“And it’s all very well for these massive companies to be doing it. But for many organizations, many developers, it’s just going to become too expensive.”
