The rapid rise of artificial intelligence (AI) has sparked a heated debate among experts, with some warning that the hype surrounding the technology may be overshadowing genuine scientific advancements.
DeepMind Co-Founder Demis Hassabis recently drew parallels between the current AI frenzy and the cryptocurrency boom, raising concerns about the potential impact on the field’s progress.
The debate over whether AI is overpromised has significant implications for the commercial landscape as businesses rush to capitalize on the technology’s potential. Observers say striking a balance between enthusiasm and realism will be crucial for the healthy growth of AI-driven commerce.
“While generative AI is powerful, it is still only one segment of AI,” Muddu Sudhakar, co-founder and CEO of the generative AI payments platform Aisera, told PYMNTS. “AI encompasses a variety of categories. But with so much attention on generative AI, it means that these areas get neglected and crowded out. It could also limit research, which could mean less innovation.”
Interest in AI is growing. According to the PYMNTS Intelligence report “Consumer Interest in Artificial Intelligence,” the average consumer uses around five AI technologies weekly, including web browsing, navigation apps, and online recommendations. Nearly two-thirds of Americans are interested in AI assistants for tasks like booking travel, with AI enhancing the personalization of in-car experiences. These intelligent systems, leveraging generative AI, tailor recommendations to users’ behaviors and preferences far beyond simple list-based suggestions.
Hassabis expressed concerns to the Financial Times regarding the surge of investment in generative AI startups and products, likening the frenzy to other speculative bubbles. “The billions of dollars being poured into generative AI startups and products brings with it a whole attendant bunch of hype and maybe some grifting and some other things that you see in other hyped-up areas, like crypto or whatever,” he said.
Some experts say the hype surrounding AI has reached a fever pitch, with grandiose promises and astronomical investments obscuring the reality of the technology’s current capabilities.
One of the main issues with AI hype is that it creates unrealistic expectations among the public and investors. When companies make bold claims about their AI-powered products or services, they often fail to deliver on those promises, leading to disappointment and erosion of trust.
“Most people in the AI space have good intentions and don’t want to mislead consumers or users,” Zohar Bronfman, co-founder and CEO of Pecan AI, told PYMNTS. “I don’t doubt that they’re working hard to deliver the best AI products they can. What’s been ignored, though, is that generative AI so far just hasn’t provided significant business value. It’s fascinating and powerful, but so far, most business users have come up empty-handed when they try to use it to really drive business impact.”
Sudhakar pointed out the excessive investment in large language models (LLMs), suggesting it may overshadow other vital areas of AI research. This focus risks limiting innovation and neglecting emerging technologies that could offer more significant advancements or solutions to pressing challenges in the field.
“How many of these do we need?” he said. “How can you really tell which one is better? It’s not clear. This is why I think just a handful of state-of-the-art models will ultimately prevail. That being said, there will be many SLMs [small language models] that address lots of edge cases, but even in this area, many will fade away.”
Sudhakar raised a looming issue in AI: the dwindling supply of data necessary to train LLMs. This scarcity, he warned, could become a significant bottleneck in the development and advancement of these models, potentially hindering progress in AI research and applications.
“One alternative is to use synthetic data,” he added. “This is an emerging area and could use much more focus.”
Sudhakar also highlighted the importance of shifting focus toward what will eventually succeed the current transformer models in AI. Based on a deep learning architecture, transformer models have revolutionized how machines understand and generate human-like text by enabling them to process words about all the other words in a sentence rather than one at a time.
He added, “This is a powerful model, but it has limitations, such as with hallucinations, which are based on the underlying probabilities.”
While generative AI gets all the attention, the real workhorses of AI, machine learning techniques for prediction and optimization, aren’t hyped nearly enough, Bronfman said.
“Tested and proven machine learning methods can quickly take business data and extract a great deal of value,” he added. “They may not seem as shiny and new as generative AI, but they definitely shine when they’re integrated into business systems the right way. These recognized methods deserve more attention and investment so businesses can achieve the transformative benefits of AI.”
Some commenters say that the best use of AI might not be for commerce. Ilia Badeev, head of data science at Trevolution Group, told PYMNTS that the significance of employing AI for nonprofit and scientific endeavors receives inadequate attention.
“I would like to see more hype around AI researchers,” he added. “Imagine a ScientistGPT that possesses information from all currently existing textbooks and scientific studies and can use it to advance theoretical and practical science.”
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