As investment in artificial intelligence continues to surpass unthinkable levels, skepticism continues to grow. Many are concerned the world is marching towards a dangerous cliff and some expect the AI bubble to burst soon, if not later.
Whatever happens, embedded in the last few years of AI hype is an important lesson for companies that are considering new and emerging technologies: It’s not necessarily that companies should be more cautious or conservative about investing in new solutions; rather, it’s that they need to be more strategic.
Is Gartner’s Hype Cycle Hyping a Bust?
According to Gartner, worldwide spending on AI is forecast to reach $2.5 trillion in 2026, a significant 44 percent increase over the previous year. To contextualize this amount of spending, the Wall Street Journal reported that the AI financing of just Meta, Amazon, Microsoft and Google will be one of the largest capital investments in United States history, bigger than the Apollo program that put humanity’s first astronauts on the moon, eclipsing the decades long development of the consequential interstate highway system and greater than the railroad construction of the 19th century that revolutionized westward expansion.
Even with whopping quantities of money flowing, many point to Gartner’s hype cycle as proof that AI is over hyped. But the final stage of the famed model, following the “Peak of Inflated Expectations” and the “Trough of Disillusionment,” is often overlooked: The so-called “Plateau of Productivity” is full of sustained and practical benefits.
For companies that are worried about an AI bust, the question isn’t how to avoid it, but rather how to prepare for it by hastening the journey to the promised land that is the “Plateau of Productivity.” Because value from AI will largely be found in projects with standardized processes using familiar AI techniques, Gartner in its most recent Hype Cycle update recommended that AI leaders look at composite AI techniques.
“GenAI still has potential to be a transformational technology with profound business impacts on content discovery, creation, authenticity and regulation, automation of human work, and customer and employee experiences,” Gartner noted.
“Still, GenAI faces challenges, including ethical and societal concerns, limited security best practices and nefarious uses like deep fakes and disinformation.”
Challenges of Implementing AI
Clearly, AI projects are rife with challenges. While many of those projects will bear fruit, companies must be patient waiting for their harvest.
“GenAI requires a higher tolerance for indirect, future financial investment criteria versus immediate return on investment,” Gartner reported. “Historically, many CFOs have not been comfortable with investing today for indirect value in the future. This reluctance can skew investment allocation to tactical versus strategic outcomes.”
Along with patience, success requires realism, according to Debanjan Saha, CIO of DataRobot. While GenAI models will continue to improve, he predicts that they eventually will hit a ceiling with regard to capabilities. After all, most of the written text that’s available has already been used to train models, which leaves audio and video as the next frontier.
“When you lift a cup from a table … nobody writes it down,” Saha told The Forecast. “But there are a lot of videos which show how we go and grab the cup and pick it up from the table. Those are the kind of data sources which are now going to make these models more and more intelligent. [But] I do think there is a limit to which you can feed them information and make them better.”
Is AI in a Bubble?
A growing chorus of analysts and outlets argue that AI’s explosive growth is not only unsustainable, but is merely a fragile bubble on the verge of total implosion.
While the AI market continues to boom, more and more cracks are emerging across the entire industry. Most recently, OpenAI shuttered Sora, one of its flagship products, thereby cancelling a billion dollar deal with Disney in the process.
What are the real risks in the AI market?
To a person skimming news headlines, it might appear as if investment in AI technology is continuously and quite impressively mounting, but with a discerning eye a troubling pattern begins to emerge. This massive economic boom, that is currently propping up much of the U.S. stock market, may be in large part due to circular deals, where all of the main players are each other’s biggest investors and customers.
For example, chip giant and stock market superstar Nvidia has pledged a massive $30 billion to OpenAI, one of its leading customers. In addition, Microsoft’s $13 billion investment in OpenAI was curiously reciprocated as OpenAI became a premier user of its cloud services. Similarly, tech giants Google and Amazon each individually poured billions into Anthropic, creator of ChatGPT competitor Claude, only for Anthropic to announce its use of Amazon Web Services and Google chips.
That is just the tip of the iceberg for this fragile and circular house of cards. As the same money goes back and forth between a few major players, perhaps AI is a much larger industry on paper than actually in practice.
As the major companies become increasingly symbiotic, the industry becomes dangerously vulnerable to failure, especially as this gamble has spilled over into infrastructure, where massive bets on AI are being played out in the construction of tons of new data centers and the power plants needed to support them. If the AI bubble bursts, retirement accounts and financial institutions may be in peril.
The concerns don’t stop there. Many of these celebrated AI companies aren’t even profitable. Both OpenAI and Anthropic are facing multi-billion dollar losses annually and are not expected to break even in the near term. Industry wide, 95% of generative AI pilots are failing according to a MIT report.
"These models are being hyped up, and we're investing more than we should," Daron Acemoglu, a Nobel Prize winning economist at MIT told NPR.
What should companies do to protect themselves?
As nervousness about AI continues to mount, organizations that want to win and protect themselves must focus on practical, high-ROI AI use cases.
“You’re going to be investing in relatively small compute clusters and looking for very specific use cases that matter to you,” Rajiv Ramaswami, president and CEO of Nutanix, told CNBC. “Some of the use cases that generate positive returns very clearly so far have been around customer service and all aspects of customer service, around coding and software development, around document summarization and analysis. These kinds of use cases are pervasive.”
A common error, for instance, is using AI for use cases that require human intervention. Consider Coca-Cola’s “Create Real Magic” campaign, where the company invited customers and artists to use AI tools like GPT-4 and DALL-E to create digital art for the campaign. In short order, Coca-Cola received criticism for its AI-made holiday ad, a remake of the classic 1995 “Holidays Are Coming” ad.
“Critics called the ad ‘unnatural,’ ‘soulless’ and ‘lacking emotional depth,’ claiming it did not show the sincerity and warmth of the original,” noted Gilbert, who said the ad illustrates “the difficulties of using AI in creative areas where human input is essential.”
The key is to improve proven or existing systems rather than leaning into the hype of the technology’s supposed infinite potential.
Customer support automation and deflection is the clear leader for companies wanting quick wins, according to Dev Nag, CEO of AI startup QueryPal. He said it’s the perfect use case because companies can quickly implement and also clearly measure results. Many businesses see both cost savings and improved customer satisfaction within just a few weeks.
“Modern AI can handle a significant portion of routine inquiries while actually providing better service than traditional automated systems, and it gives existing agents more time to focus on strategic accounts,” Nag said.
Predictive maintenance is another promising use case that can quickly generate value. Instead of waiting for machines and equipment to malfunction, organizations use AI to monitor their performance in real time and predict problems before they occur. Businesses can then plan for maintenance or switch to another machine, preventing disruptive and costly downtime.
“Look for processes that involve lots of routine document processing, analysis or decision making,” Nag said. “These are areas where AI can augment human capabilities effectively without requiring massive organizational changes, and they often have the best existing data for training. The key is starting with well-defined, bounded problems where success can be clearly measured, current spend is well understood and historical data is plentiful.”
Getting the Most Value from AI
So, is AI a bubble waiting to burst, or a juggernaut that will continue to get bigger and stronger over time?
“I think we're all getting AI fatigue,” Steve McDowell, chief analyst at NAND Research said in a video interview with The Forecast.
“With a lot of these technology transitions, whether it's the internet, whether it's the smartphone, whether it's the PC, going way back, there's a brief burst of time where we're very excited about the technology pieces, but the value of technology comes in how I use it. So have we hit a wall on AI? No, but I think it's time to change the conversation. Let's stop talking about GPUs. We'll stop talking about who the providers are, and let's start talking about what are we going to do with it and how's it going to change my business.”
He said as organizations continue making investments in AI, they will increasingly focus on aspects that create real business value. To do that, organizations will need to get clear about how they will measure success.
McDowell said IT practitioners will lean on software to manage across different infrastructures. Innovation in this area is causing a lot of disruption and it’s changing “the way that we think about infrastructure,” said McDowell.
“I like GPT-in-a-Box that the Nutanix is delivering because that's a software set of capabilities,” he said. “I can deploy that at the edge or I can roll that out in the cloud, if that makes sense.
Enterprises also need to develop internal capabilities, whether that's training existing staff or hiring new talent, according to Nag.
“You need people who understand both the technology and your business domain,” Nag said. “The organizations that are succeeding with AI right now are the ones taking a thoughtful, measured approach focused on specific business outcomes rather than chasing the latest buzzwords or trying to transform everything overnight.”
Whether artificial intelligence technology continues its meteoric march or collapses suddenly like a dramatically popped bubble, companies can strategically prepare themselves for whatever the future holds.
This is an updated version of the original article published on January 9, 2025.
Jennifer Goforth Gregory has written for Microsoft, Adobe, IBM, Google, Salesforce, Verizon and AT&T. In her spare time, she rescues more than 130 homeless dachshunds each year and finds them new forever homes.
Ken Kaplan and Jason Lopez contributed to this story.
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