In the vast hallways of ServiceNow’s annual “Knowledge 25” conference in San Jose, there was no shortage of buzz about the future of artificial intelligence. But this year, the conversation grew notably louder, and at the center of it was the deepening partnership between two of Silicon Valley’s most influential leaders: Jensen Huang of Nvidia and Bill McDermott of ServiceNow.

The companies announced an expanded collaboration, rolling out a new AI reasoning model called “April Memotron,” designed to supercharge enterprise workflows. The draw for both is clear: as generative AI reshapes the business landscape, vendors and customers alike are jostling for platforms that can handle the complexity and scale demanded by modern enterprises.

AI Agents and the Future of Productivity

For Nvidia, the story is no longer just about chips—it’s about profoundly transforming the entire computing stack. “AI has reinvented the entire computing stack, and a gigantic AI…[these] agents are the most complex software that the world has ever built,” said Jensen Huang, CEO of Nvidia, during a CNBC interview at the event. “Remember, this is software that can understand almost any content, any PDF file you give it. It can watch video. It could look at websites, go into your database…It can reason about solving problems. It can think.”

The complexity and intelligence of these AI “agents,” as Huang describes them, is the critical ingredient behind initiatives like ServiceNow’s control tower—the company’s foundational vision for orchestrating data across enterprise silos. McDermott, for his part, credited Nvidia’s platform and innovation for fueling ServiceNow’s own trajectory. “ServiceNow would not be the company that it is without Jensen and without Nvidia,” McDermott said.

Their alliance is rooted in the ambition to make ServiceNow the “enterprise AI operating system,” a term coined by both executives in recent years. By integrating large language models (LLMs) and custom logic into its workflow fabric, ServiceNow aims to consolidate previously fragmented enterprise functions—east to west, across sales, HR, operations, and more; north to south, up and down the technology stack.

A New Era, New Stakes

But the rapid ascent of AI has not come without challenges—some technical, some geopolitical. The U.S. government’s tightening export controls, for example, have struck directly at Nvidia’s earnings potential in one of the largest global markets: China.

Earlier this year, Nvidia was forced to take a write-off of $5.5 billion tied to inventory for the Chinese market, which can no longer be shipped under the latest restrictions. The company’s leadership has consistently emphasized the wider consequences. “China is a very large market. It’s probably going to be a $50 billion AI market in a couple of two, three years,” Huang said on CNBC. “It would be a tremendous loss not to be able to address it as an American company. It’s going to bring back revenues. It’s going to bring back taxes. It’s going to create lots of jobs here in the United States.”

Huang signaled alignment with whatever policies are ultimately set by the U.S. government. “We just have to stay agile, whatever the policies are of the government, whatever is in the best interest of our country, we’ll support and we’ll stay agile and keep moving on.”

For Nvidia, the China constraint is emblematic of a broader complexity: how to drive forward on the AI revolution while negotiating economic, political, and technological headwinds that can shift overnight.

Enterprise AI’s Trillion-Dollar Horizon

Both leaders, however, appear undaunted by the shifting regulatory landscape—even as the “facts on the ground keep changing, the goalposts keep moving,” as Huang put it. The immediate opportunity, they say, is measured in the tens of billions but points to a far larger prize.

“The market for AI is just gigantic. Remember, enterprise AI that we’re going after is brand spanking new,” said Huang. “The [first] billion dollars of enterprise AI…that’s just the beginning. There’s a $1 trillion opportunity out there to revolutionize the way companies are built and the products that companies make.”

For ServiceNow, that opportunity is increasingly tangible. The company has set a target for $1 billion in revenue—attributed directly to agent-driven AI workflows—by the end of 2025. The competitive imperative is clear: those who can unlock value and productivity by harnessing AI at scale stand to redefine not only software, but business itself.

The Next Chapter: From Chips to Platforms

If last year was about the acceleration of generative AI, 2024 appears to be the year industry giants move beyond rhetoric to showcase, and monetize, AI in action. The expanding Nvidia-ServiceNow alliance is one signal flare in a rapidly shifting competitive landscape, as platforms vie not just for customer adoption but for the right to define the next era of enterprise computing.

As large language models leap from consumer applications to the nerve centers of business, the stakes—and the potential rewards—have never been higher. Or, as Jensen Huang observed: “We help the world build new software on top of this new computing platform called GPUs. And so that’s the reason why we have to do it.”

For ServiceNow and Nvidia, that mandate is as much about adaptability in a volatile world as it is about technical prowess. The AI race is just getting started, and both are betting that agility, vision, and relentless engineering will keep them in the vanguard.