When your AI vendor goes belly up

With hundreds of AI-related companies increasingly crowding the market for enterprise dollars, IT leaders should prepare for the day when at least one of their AI vendors suddenly shutters or gets acquired.

While the AI market overall is showing few signs of slowing down, a shakeout will eventually happen — with potentially significant ramifications for CIOs. IT leaders only need to be reminded of Broadcom’s acquisition of virtualization giant VMware to visualize what could be at stake.

CIOs, of course, are not unfamiliar with the many issues that arise when an IT vendor shuts down or is acquired. However, suddenly losing an AI vendor creates some special challenges, according to industry experts, including data access and privacy concerns and the loss of AI models trained for specific customer needs.

“Assume your AI vendor will get bought or go under, and plan for it now,” says Dave Shanker, cofounder and managing partner at IT consulting firm STS Consulting Group. “Hundreds of these companies launched in three years, and most won’t last. The ones that do will get acquired, and then pricing, terms, and roadmaps can change overnight.”

Set your exit strategy up front

For now, the demand for AI products seems to be moving ahead at full steam. In May, Gartner projected that the worldwide AI market would grow by 47% in 2026, totaling nearly $2.6 trillion.

But many insiders, including Amazon founder and CEO Jeff Bezos and OpenAI CEO Sam Altman, have projected that the AI bubble will eventually burst, with many AI startups buried in the rubble.

One warning sign came in mid-October, when consumer-focused Character.AI filed for bankruptcy, with the startup agreeing to sell some of its assets to Disney for $20 million.

With many vendors likely to go extinct in a few years, it’s incumbent on CIOs to plan ahead for potential market disruptions, Shanker says.

He recommends that IT leaders ensure that their organizations own the data they’re putting into AI models. The organization’s prompts, outputs, fine-tuning data, and anything it has loaded into an AI tool should be exportable in a usable format.

In addition, IT leaders should write the potential exit from an AI vendor into the contract from day one, with notice periods, data return and deletion terms, and the right to leave without a penalty if the vendor is acquired built into the deal.

“Procurement should build this into every contract, AI vendor or not,” he adds. “If they aren’t, that’s a big risk they’re missing.”

IT leaders should also weigh portability against cost, he says. “Sometimes it makes sense to build tightly on one platform, because adding a layer that keeps you portable can double or triple what it costs to run,” he explains. “Redo the analysis at least once a year. Something that’s easy to move today can, after three years of heavy development, leave you stuck.”

AI-specific concerns complicate traditional vendor risk

Rodion Sorokin, CTO of deep-tech venture studio The Capital Index, agrees that CIOs need to prepare for an eventual market shakeup. Sorokin points to information from company shutdown assistance provider SimpleClosure, which noted that AI companies are currently a small and shrinking percentage of the types of closures they handle.

However, the median age at closure is lower for AI companies: 2.30 years versus 3.78 for other companies, according to SimpleClosure. 

“We hypothesize that the 2023-2025 AI cohort is still in its early days, and we’re only beginning to see how it plays out,” SimpleClosure CEO Dori Yona wrote in a blog post,

Even if the early warning signs are few and far between, CIOs should be prepared to quickly switch vendors, Sorokin says. Not only should IT leaders be able to export prompts, outputs, logs, fine-tuning data, and other information in usable formats as STS Consulting’s Shanker advises, but they should also test those processes before they sign a contract with any AI vendor, he recommends.

“Assume every AI vendor will be acquired or shut down within your contract term, and negotiate for that day,” he says.

CIOs should also be aware of specific concerns that pertain to the sudden loss of an AI vendor, Sorokin says. For example, their organization’s data and customizations may live within the vendor’s product, and it’s possible all that gets deleted if the vendor goes under or is acquired, he notes.

In addition, switching AI tools can render AI-aided workflows suddenly inoperable, he says.

“Switching isn’t a migration; it’s a revalidation,” Sorokin says. “A new model behaves differently on the same prompts, so every workflow has to be retested. Without your own evaluation suite, you can’t switch safely.”

CIOs should also know that notice can be measured in days, he adds. In June 2025, Anthropic cut Windsurf’s direct access to its Claude models with less than five days of notice, amid reports of an OpenAI acquisition of the smaller company.

While some observers say there may be safety in going with the big players in the AI space, others suggest startups may offer services that aren’t available from the big boys. In addition, large AI vendors have also cancelled services on short notice, Sorokin notes, with OpenAI shutting down its Sora app in April and the Sora API in September.

“Size helps, but it isn’t continuity,” he says. “Choose portability over logos: an open-weight model you can run yourself as a fallback, and contracts that guarantee export.”

IT leaders exploring options

While signs of a busting bubble are hard to find, many IT leaders are preparing for the future, says Colin Woods, CEO and co-founder of high-performance infrastructure provider FreedomTechSolutions.

A growing number of IT leaders are beginning to reassess the level of dependency they are willing to place on AI platforms and cloud-hosted services, he notes.

“From our perspective, concerns around AI vendor consolidation are already influencing customer decision-making,” Woods explains. “While the rapid growth of the AI market has driven innovation, it has also created a crowded ecosystem of providers, many of whom face uncertain long-term viability.”

In some cases, IT leaders are moving away from hyperscalers for certain AI workloads, with a renewed focus on infrastructure ownership, data sovereignty, and operational control, he says.

“Organizations are increasingly concerned about where their data resides, how it is processed, and what happens if a vendor is acquired, changes its commercial model, or discontinues a service altogether,” he adds. “For many businesses, AI is becoming too strategic a capability to be entirely dependent on an external provider whose roadmap they do not control.”

Some organizations are deploying smaller, dedicated AI environments in regional or edge data centers, Woods says. “These AI nodes allow organizations to run inference and specialized workloads closer to their data, while retaining full governance over models, datasets, and infrastructure,” he adds.