What 'went wrong' with GenAI in business and the enterprise? Princeton professor warns of 'unsolved challenges'
'The AI-in-your-face approach by Microsoft and Google has led to features that are occasionally useful and more often annoying.'
Artificial general intelligence is almost here and everything is about to change for humanity. Mass unemployment is on the immediate horizon and LLMs will soon replace everyone from copywriters to business analysts.
Those are some of the many hyperbolic claims about AI that have failed to come to pass since ChatGPT rocketed Generative AI into the public consciousness.
Now Arvind Narayanan, Director of the Princeton Center for Information Technology Policy, has warned that something "went wrong with generative AI from a business perspective."
He said that adoption is likely to be much more sedate than promised, meaning the benefits of GenAI will not be felt in businesses anytime soon.
In a post on X, he wrote: "There are unsolved research challenges when it comes to making LLM-based AI assistants that actually work. Enterprise adoption will likely be even slower.
"The barriers include integrating it into existing products and workflows and training people to use it productively while avoiding its pitfalls. We should expect this to happen on a timescale of a decade rather than a year."
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Narayanan said that people "found a thousand unexpected uses" for ChatGPT when it launched, which "got AI developers overexcited."
"They completely misunderstood the market, underestimating the huge gap between proofs of concept and reliable products," he wrote. "This misunderstanding led to two opposing but equally flawed approaches to commercializing LLMs."
OpenAI and Anthropic "focused on building models and not worrying about products" taking six months to release a ChatGPT iOS app and eight for an Android.
Google and Microsoft then "shoved AI into everything in a panicked race, without thinking about which products would actually benefit from AI and how they should be integrated."
"Both groups of companies forgot the 'make something people want' mantra," he continued. "The generality of LLMs allowed developers to fool themselves into thinking that they were exempt from the need to find a product-market fit, as if prompting is a replacement for carefully designed products or features."
Into this void stepped bad actors, who "disproportionately tended" to be early adopters" because they "are more invested in figuring out how to adapt new technologies for their purposes, whereas everyday users want easy-to-use products."
"This has contributed to a poor public perception of the technology," Narayanan added. "Meanwhile the AI-in-your-face approach by Microsoft and Google has led to features that are occasionally useful and more often annoying. It also led to many self-owns due to inadequate testing like Microsoft's early Sydney chatbot and Google's Gemini image generator. This has also caused a backlash."
Narayanan also said that change was taking place and continued: "OpenAI and Anthropic seem to be transitioning from research labs focused on a speculative future to something resembling regular product companies. If you take all the human-interest elements out of the OpenAI boardroom drama, it was fundamentally about the company's shift from creating gods to building products.
"Google and Microsoft are slower to learn, but my guess is that Apple will force them to change. Last year it was seen as a laggard on AI, but it seems obvious in retrospect that the slow and thoughtful approach that Apple showcased at WWDC is more likely to resonate with users."
Narayanan became the fourth director of the Princeton Center for Information Technology Policy last year, prompting Dean Andrea Goldsmith to describe his work as “highly influential in areas such as artificial intelligence, blockchain technologies and social media, cutting through hype and technical detail to reveal how to best harness these technologies for good, and the public policies that can help facilitate that goal.
