China's Z.AI Is Here: Should India Worry About The Next AI Power Shift?

China has unveiled its latest artificial intelligence model, GLM-5.2, developed by Beijing-based startup Z.AI (formerly Zhipu AI). The model is already being compared with some of the world's best AI systems from OpenAI and Anthropic, especially in coding, software engineering and autonomous AI agents. It is also significantly cheaper to run and, unlike many frontier models, is available as an open-weight model that developers can download and customise.

With this, the global AI race has just become a lot more intense. This raises a bigger question for India: Should the country be worried?

Industry experts say the answer isn't panic. It's preparation.

What Exactly Is Z.AI?

Founded in 2019 by researchers from Beijing's Tsinghua University, Z.AI has quickly become one of China's most closely watched AI companies.

Its newest model, GLM-5.2, is built for what the industry calls agentic AI. Unlike traditional chatbots that mainly answer questions, agentic models can independently plan, execute and complete long-running tasks with minimal human intervention.

The model boasts a one-million-token context window, allowing it to process massive codebases and lengthy documents without losing context. It has been designed specifically for coding, debugging, software engineering and enterprise automation. According to a Reuters report, benchmark tests place it close to leading Western AI models in coding and agent capabilities, while offering substantially lower operating costs.

Another major difference is openness.

Because GLM-5.2 is open-weight, developers can modify and fine-tune it for their own applications. That could accelerate innovation -- but it also raises cybersecurity concerns, since malicious actors can potentially remove safety guardrails and adapt the model for offensive purposes.

Should India Be Worried?

Experts believe China narrowing the AI gap should be viewed as a wake-up call rather than a threat.

According to Ashish Tandon, Founder & CEO of Indusface, India's cybersecurity leadership will depend on building a balanced AI ecosystem rather than relying on a single technology source.

"It is always good to have options to build a resilient technology base. The caveat is access. With Chinese models in particular, Indian enterprises will weigh access, availability, and data sovereignty before they commit. Cybersecurity leadership will depend on building a stack that draws on all three: homegrown models, commercial frontier AI, and open source," Tandon said.

His comments come as Chinese AI firms continue making rapid progress despite restrictions on access to advanced American chips. Z.AI's latest models have also been optimized to run on domestic Chinese hardware, highlighting Beijing's push for technological self-reliance.

India's Priorities Are Much Bigger Than One AI Model

While the launch has generated excitement globally, experts say India should avoid focusing solely on the model itself. Instead, the country needs to strengthen every layer of its AI ecosystem.

"India should prioritise all of them, as they feed each other," said Ashish Tandon, referring to AI innovation, cybersecurity talent, regulation and enterprise adoption.

According to him, "Home-grown AI innovation reduces our dependence on commercial frontier models. Cybersecurity talent makes adopting those models safe. Regulation protects sovereignty. And enterprise adoption creates a commercially viable market, which is what funds the innovation to begin with."

That balanced approach could become increasingly important as enterprises begin deploying AI across sensitive sectors including finance, healthcare, manufacturing and critical infrastructure.

Prime Minister Narendra Modi share stage with other world leaders at India AI Summit.

AI Is Becoming A Business Necessity

For businesses, the AI race is already moving beyond chatbot experiments.

Siva Balakrishnan, Founder and CEO of Vserve, says enterprises worldwide are now integrating AI into their core operations. "From our experience working with global enterprises, we're seeing AI move from experimentation to mission-critical business functions," he said.

According to Balakrishnan, organizations are increasingly using AI to improve supply chain resilience, streamline procurement, optimise inventory, enhance customer experience and strengthen decision-making.

However, he cautions that choosing the most powerful AI model is only one part of the equation.

"The success of these initiatives depends less on the AI model and more on enterprise data quality, governance frameworks, and ease of integration into business processes. AI is as good as the operational ecosystem it sits within."

He says many companies struggle not because they lack data, but because their information remains scattered across multiple ERP systems, supplier portals and operational platforms.

The cybersecurity challenge is getting harder The emergence of powerful open AI models also creates a new cybersecurity problem. Modern AI systems can identify software vulnerabilities far faster than human security teams.

Ashish Tandon warns that attackers are already benefiting from these advances.

"The newest AI models find weaknesses such as business logic flaws 100x faster than anyone can fix them. And now Chinese models match Anthropic's Mythos too, so this is not one company's capability. You have to assume attackers already have it."

He added that the challenge is no longer discovering vulnerabilities. "The hard problem now is remediation at machine speed."

According to Tandon, Indusface virtually patched over 48,000 vulnerabilities across Indian applications and APIs last year, protecting them against nearly 2.6 million potential attacks.

Security researchers have similarly warned that open-source frontier AI models can be modified for malicious purposes, potentially making sophisticated cyberattacks more accessible.

What Should Indian Businesses Do Now?

Experts say companies need to rethink how they approach cybersecurity. "Start by assuming weaknesses in your applications will be found and exploited faster than your developers can fix them," Tandon said.

"The practical answer is autonomous virtual patching that shields a vulnerability the moment it is found, instead of waiting weeks or months for a code fix."

His advice is simple. "You cannot predict the next attack. You can close the window it relies on."