Ever since Chinese AI startup DeepSeek launched three years ago, enterprise executives have been and Moonshot’s 2.8-trillion-parameter model , now an independent cybersecurity and risk advisor, thinks they should at least take another look.
“Enterprises should take these models seriously, but neither adopt nor reject them solely because they are Chinese,” he said. “They should be assessed like any other critical technology dependency: jurisdiction, ownership, training and software provenance, licensing, data handling, hosting, security, reliability, and the ability to independently test their behavior. Geopolitical exposure is a legitimate risk factor, but it should be incorporated into technical and supply-chain diligence rather than used as a substitute for it.”
Choose applications with care
He added, “Chinese models may be especially valuable for coding, multilingual processing, high-volume document analysis, research, synthetic-data generation, and privately operated security or forensic workflows, but they should be subject to task-specific testing rather than broad benchmark claims.”
, executive director of FormerGov, focused on Chinese technology concerns when he worked for the US Justice Department as its representative in the US law enforcement Joint Liaison Group (JLG) with China.
“It is way too early for US enterprises to seriously consider these models,” he said. “Until proven otherwise, enterprises should assume that if they use these models, they may be granting China complete access to everything they do through the models, and potentially access to their networks and employees more broadly. At this point, any pros of using such models are strongly outweighed by the potential security, confidentiality, and reliability concerns.”
, enterprise CISO at Aikido Security, added that the very attractive pricing for these Chinese models may be appealing, but suggested that, despite the low cost, they’re ultimately too risky.
“Enterprises should take these models seriously, but not romantically. Parameter count is horsepower measured in a showroom, not braking distance in the rain,” he said. “The real tests are reliability on your data, the cost of a wrong answer, and whether the model behaves predictably under pressure.”
He noted that the benchmarks on the latest open-weights models are impressive, and very close to those of the frontier lab models, which makes the cost ”incredibly seductive, especially when a team does not want to risk their data being used to train those frontier models.”
But the Chinese models can still work in specific circumstances. “The strongest value will be in bounded, reversible and inspectable work: coding inside a sandbox, multilingual translation, document triage, data extraction and other high-volume tasks where outputs can be verified,” he said. “Cheap intelligence is valuable, but only when it is not mistaken for trustworthy judgment.”
Wilkes added that the regulatory issues surrounding Chinese models can be especially problematic. Texas, for example, has , CIO of consulting firm Acceligence, argued that CIOs should seriously consider these models.
“Counterintuitively, the biggest benefit of Kimi and models like it is the lack of guardrails,” Goryunov said. “Think of it as stick shift cars in the era of automatics. If you want ease and comfort, stay with the frontiers because they have cruise control, shift the gears for you and they decide when. If you want performance and control, expand your horizons. But a stick shift assumes you know how to drive one: you bring your own governance, your own evals, your own safety layer. That’s a cost and specialized talent, which is super rare, and for the right organization it’s also the whole point.”
Goryunov’s bottom line: “For internal, high-volume, well-harnessed workloads, [the Chinese models] have moved from ‘watch list’ to ‘rational choice.’”
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