Trillions are flowing into AI investment and innovation, driven by ambition and fear of falling behind. But the returns are still more promise than proof.
A global competition is underway: not for territory or weapons, but for algorithms and data. Artificial intelligence has become the new front line of corporate rivalry, driving an unprecedented flow of capital into a technology still struggling to prove its worth.
This year alone, multinational corporations have spent nearly $1.5 trillion on AI initiatives: roughly 5% of all goods and services produced in the US in 2025. The figure is expected to more than double by 2029, according to technology and business consultant Gartner, as companies rush to claim their place in a rapidly changing economic order.
The money isn’t just going into data centers and chips; current spending covers a broad range of applications, from voice recognition and healthcare to finance, law, and robots that clean floors or restock store shelves. These investments are touted as making us wealthier, more productive, and less reliant on repetitive tasks. Just as automobiles replaced horses and word processors replaced typewriters, AI is viewed as the next breakthrough: a technology designed to take over routine, less creative tasks, enabling workers to focus on original ideas that genuinely require human insight.
How distant is this future? Results so far have been limited, and the key question remains: When will these investments start generating significant returns? Experts suggest it will take several years before C-suite leaders adopt the highly flexible strategy needed to realize these rewards.
The debut of ChatGPT three years ago caused a shockwave that triggered the present surge of investment. Some organizations doubled or even tripled their previous spending on machine learning experiments, according to Brook Selassie, vice president of AI and Business Growth Strategies at Gartner. “Unfortunately, in most cases, it didn’t create real value. Humans, change management, and data were not ready, and what was done failed to produce value,” he told Global Finance.
“Companies got very excited about AI, especially generative AI and its ability to create things,” says Selassie. “But not everyone has benefited, because not everyone invested wisely – and with principle. Some got carried away, throwing money at projects that didn’t need AI, or entered the space without sufficient data. Non-AI-ready investments are just like burning cash.”
As the enthusiasm for GenAI has intensified, multiple observers have compared it to another episode of “irrational exuberance,” as famously described by then-Federal Reserve Chairman Alan Greenspan in 1996, when large investments in new internet technology coincided with overly optimistic stock market evaluations. James Bullard, dean of the Mitch Daniels School of Business at Purdue University and former president of the Federal Reserve Bank of St. Louis, has an explanation for the current wave of AI investment in the US.
“I think it really comes down to three big revenue streams that could be challenged by AI,” Bullard says. “The first is search; Google has been the leader there for years, but AI can deliver results that are more precise and more focused on what people mean. That could really shake up Google’s dominance. Then there’s shopping; Amazon is the go-to for online shopping, but AI could do a better job matching customers with exactly what they want and do it faster. That would hit a big part of Amazon’s business.
“And finally, social media, which is all about ads. Meta makes almost all its money from advertising, and AI could disrupt that too by making the connection between users and ads a lot smarter and more direct.”
Fear Is Driving Investing
A mid-October survey by the Conference Board in collaboration with The Business Council, a nonpartisan organization of US business leaders based in Washington, found that fear of missing out is a major reason companies are investing in AI. Among respondents, 43% said they worried about losing competitive advantage to early adopters. More than half (52%) identified the top drivers as cost reduction and operational efficiency.
Executives remain optimistic and eager to deepen their commitment to AI, according to Gartner and other experts. Companies, whether large or small, plan to increase their investments in the coming years because nobody wants to be left behind. While they are aware that there will be winners and losers and that gains will not be evenly distributed across industries, firms, and countries, not increasing investment now and dropping out of the race is seen as a clearly worse option.
“I’ve spoken with maybe 200 large organizations—at the CTO, CEO, and CIO level—and more than 90% of them tell me the same thing: ‘I don’t want to hear about my competitor using AI to create something new, to mitigate a risk, to innovate, or to discover a better diagnostic method, and then read about it in the paper,’” says Selassie.
FOMO is particularly important in the rush to win. “AI is creating winners and losers. In every sector, a relatively small circle of major players shape the competitive landscape,” Selassie notes. “And we don’t expect success to be shared evenly, as if each would claim an equal share of opportunity. Far from it. Advantage will concentrate, and the gains will flow disproportionately to those able to harness AI most effectively.”
Out of nearly 250 multinationals Gartner surveyed, 83% expect to spend more on AI in 2026 than they did in 2025, and even more—89%—plan to spend more on GenAI. That’s a 34% increase for AI overall and a 39% rise for GenAI.
“Silicon Valley tends to be a winner-take-all game,” Bullard warns. “There is always someone who comes out on top and someone who loses. And for the losers, that can mean selling off their data centers or other assets once their revenue streams get taken over by the competition.”
Companies must adopt a strategy that aligns with their overall goals and remains flexible, Selassie advises. The human element must be involved and committed to make the adoption of new technology successful, but, as in numerous cases, cooperation has not been optimal.
“Firms got very excited about AI. But not all have benefited, because not everyone invested wisely,”
Brook Selassie, vice president of AI and Business Growth Strategies at Gartner
Proof Will Be In The Profits
“The question is: Why won’t we fail again? What will be different this time? The answer, at its core, is that our strategy must be vigilant,” says Selassie. “There’s an overwhelming amount of new technology emerging, and it demands deep organizational change. That’s why AI investments, first, must stay tightly connected to the company’s strategic intent. Second, we must upgrade the human element. And third, we must keep the strategy agile, because it’s becoming obsolete faster than ever. It’s really the combination of those three—especially the last one—that will make the difference.”
AI adoption remains far from complete, says Dana Peterson, chief economist and leader of the Economy, Strategy & Finance Center at The Conference Board, and the jury is still out on whether there will be a return on investment. “Over the past two years, firms have been focused on figuring out how to use AI without exposing their intellectual property,” she notes. “Since public AI tools claim ownership of user input, companies have been investing in secure, internal AI environments where employees can work safely. This effort continues to ramp up as businesses seek to expand AI use without risking their IP.”
In most cases, they have reported marginal productivity gains and some time savings for their employees. A few large companies, for example, have begun asking their managers to use AI when writing staff appraisal reviews, saving some time but not truly innovating products or processes. Similarly, basic coding is often generated by AI.
Nevertheless, adoption is proceeding apace.
Stefano Puntoni, professor of marketing at The Wharton School and co-director of Wharton Human-AI Research (WHAIR), is among the authors of the third annual WHAIR report, published last month. The survey, which included over 800 enterprise decision-makers across the US, indicates that GenAI has transitioned from pilot projects to mainstream corporate use, with 82% of leaders utilizing it weekly and nearly half using it daily.
“From our report, it seems that people, overall, view the technology as useful,” Puntoni says. “Our results contrast somewhat with the more skeptical and negative conversations that have been circulating about returns on GenAI investments. They suggest that, at least for large companies and more senior leaders, the tone—the vibe—is positive.”
Nearly three-quarters of leaders reported using structured ROI tracking to measure the profitability of investments, according to the survey, with 75% of these indicating positive return on their investment in GenAI.
On average, over 80% of enterprise leaders expect AI investments to generate returns within 2 to 3 years. Already, 11% report reallocating budgets from legacy programs to AI-proven initiatives. While much of today’s adoption focuses on productivity-driven use cases, the study also reveals the emerging wave: almost one-third of AI technology budgets (31%) are now allocated to internal R&D projects.
“As leaders across functional areas continue to increase investment in GenAI,” says Puntoni, “the overwhelming feedback is they are not only looking to use AI to boost employee productivity, which has become table stakes, but to integrate it effectively and responsibly into workflows to drive measurable ROI.”
The War Of Titans
Much of today’s AI boom is propelled by US tech giants aiming to protect their revenue streams as the technology transforms how the world works, shops, and communicates. According to Gartner, Amazon, Google, Meta, and Microsoft alone are expected to spend $364 billion on AI-related capital investment this year. New entrants, including Chinese companies and emerging AI cloud providers, are adding to a broader group of participants, but the big four remain dominant.
“My take is that OpenAI and Microsoft are threatening three huge revenue streams controlled by what are basically quasi-monopolies,” Bullard says. “These are mega-cap companies, and in response, all three have poured massive investments into AI to fend off competition. So now you’ve got four mega caps in the race, all building AI capabilities to either defend their turf or go after someone else’s. There are other players, of course, but they’re mostly sideshows compared to these four.”
In October, after a restructuring, Microsoft acquired a 27% stake in OpenAI’s for-profit arm, OpenAI Group PBC, which was valued at approximately $135 billion at the time.
In mid-October, OpenAI announced a partnership with Walmart. In 2026, customers will be able to shop within ChatGPT using an instant checkout feature that allows them to chat and buy simultaneously without leaving the interface. OpenAI has not yet said when it will release this feature.
Given the tech giants’ massive resources, the fight could last for years. But no one expects AI investment to either cease soon or lead to significant productivity gains in the near term. When modern computers were introduced, it took years for their benefits to appear in economic data. This was seen as a paradox due to the gap between the adoption of information technology and the national GDP measures.
Nobel Laureate Robert Solow famously quipped, “You can see the computer age everywhere but in productivity statistics.” Likewise, economists expect the impact of AI investment will only be felt in the future. Most experts and surveys indicate that respondents generally expect returns within two years, if not longer.
Something of a consensus is forming. It’s not the productivity gains so far that will change the game, but the things we haven’t seen yet.
“It’s clear that the investments that have been announced, and even those that have been made, are enormous,” says Puntoni. “What kind of payoffs should companies be looking for to justify such massive investments? It’s hard for me to say whether they’re justified or not, but one thing I can say is that, in my opinion, for these investments to be justified, they need to have an impact that goes beyond cost savings and productivity gains. GenAI should be used to create new customer experiences, new products, new industries, and new jobs. It should generate top-line growth. It can’t just be about cost optimization.”
