The bubble lies in larger LLMs, says Cognizant AI chief Babak Hodjat

Even if the LLM funding bubble bursts, enterprise AI adoption-driven by agentic systems and real use cases-will remain intact, says Cognizant's chief AI officer

There is an artificial intelligence (AI) bubble when it comes to building large language models (LLMs) with eye-popping funding, but enterprise adoption of AI will survive and remain unscathed even if it were to burst in the next two years, Cognizant’s chief AI officer said.

 

Computer scientist Babak Hodjat, widely known as the co-inventor of natural language technology that contributed to the development of Apple’s virtual assistant Siri, believes there is a misconception among builders who think they are going to build super intelligence or artificial generative intelligence — and that whoever builds it first will dominate the world. 

“It’s like a sci-fi,” he said in an interaction with Business Standard. 

Concerns over an AI bubble, and whether it resembles the dotcom bubble at the start of the century, have gained traction over the last few months as investors wonder whether the billions of dollars spent by Google, OpenAI, and Meta, among others, to build even larger LLMs are justified, and what the timelines for returns on such gargantuan investments are.

 

“You look at OpenAI, Anthropic or Google and then you look at all the industry that feeds into it because these large language models are ever larger. They need much, much more data. They need very strong data centres to be able to train as well as for inference,” said Hodjat.

 

That bubble, however, will not impact the adoption of agentification processes in enterprises, as investments in those areas of AI enablement have been within reasonable limits. “Even if the bubble were to burst to some extent next year or the year after, enterprise adoption of AI wouldn’t shrink because of that,” he added.

 

The reason, Hodjat believes, is that multi-agentic systems will be needed by enterprises across various functional areas such as coding, HR, marketing and supply chain.

 

“We are transitioning over to the expectation, now being grounded in how I can best use AI to agentify my enterprise. I think we’re making that transition. I’m hoping that 2026 will be the year in which we’ll see more and more,” he said.

 

And yet, enterprise adoption has been well below expectations as organisations still try to figure out their AI strategy, investments and proportionate returns within a reasonable span of time. Despite widespread experimentation and investment, AI has struggled to translate early adoption into meaningful, scalable business value, exposing a widening gap between enthusiasm and impact.

 

The McKinsey Global Survey on the state of AI shows that nearly 90 per cent of enterprises globally are using AI in some part of their organisation. Yet, when asked how many have fully scaled those use cases, the number drops to just 7 per cent.

 

Findings from the MIT Project NANDA report, The GenAI Divide: State of AI in Business 2025, reinforce this disconnect. Despite $30–40 billion in enterprise investment in generative AI, nearly 95 per cent of organisations are seeing zero return.

 

“People are coming to this recognition that AI enablement requires engineering. It requires proper design. It requires tail layering and customising, and you can’t just sprinkle your organisation with copilots and be done,” Hodjat said.