A strange paradox is playing out in the upper echelons of the artificial intelligence (AI) industry. The loudest warnings about AI are no longer coming from governments or researchers, but the very architects of the technology are changing the messaging amidst realities of the economics of AI proving difficult to ignore. With competition from Chinese frontier AI labs to contend with, subtle calls for regulation mean that’s no longer a neutral instrument either. It is all about reading between the lines.
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Over the past few days, the drumbeat of caution has been sounded by the likes of OpenAI CEO Sam Altman, Microsoft CEO Satya Nadella and Google DeepMind’s Sir Demis Hassabis, in their own ways. Some taking the more picturesque route to building a narrative, while some have said it straight. At first, the messaging may come across as a wave of an unprecedented sense of corporate responsibility, but the narrative building isn’t hidden.
Also read:‘Primarily a PR story’: Cornell’s John Thickstun breaks down OpenAI’s narrative
It needed OpenAI to re-spin a narrative which no one noticed at first, suggesting its models underlined an agentic entity caused an “unprecedented cyber incident” by gaining access to infrastructure AI platform Hugging Face’s servers. It isn’t a potential dystopian AI uprising as it is being made out to be, since this was part of an internal cybersecurity evaluation benchmark, and cybersecurity AI agents are expected to detect ways to break into systems.
Cornell University’s John Thickstun recently pointed out regarding OpenAI, much of the existential dread being peddled is primarily a PR story. “It is important to read this story with an understanding of OpenAI’s narrative frame. This is primarily a public-relations story promoted by OpenAI, part of the same messaging campaign that began with the announcement of GPT-2 in 2019,” he tells HT, adding, “The explicit message of this campaign is that OpenAI’s technology is dangerous, but they implicitly want to convey their technology is powerful and worthy of large investments, and privileged regulatory status.”
It has worked, to an extent. On Thursday, days after OpenAI’s message, US lawmakers moved to make it mandatory to have a ‘kill switch’ in place, which would allow the government an option to turn off AI tools if they behave differently. Most companies make cursory statements regarding actual contours of regulation.
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On Friday, 25 tech companies including Nvidia, Microsoft, and indeed Meta, banded together to curate a letter that suggested regulation shouldn’t be done in a way that would “drive innovation overseas”. Two sides to the same coin.
To understand the strategy, you have to look at the narrative the biggest AI labs are selling. By warning the world that Artificial General Intelligence (AGI) is imminent and potentially dangerous, companies like OpenAI, Google, and Microsoft accomplish two things. First, they signal to investors and engineers that they possess technology of world-altering power. Second, they position themselves as the only adults in the room capable of managing it.
Sir Demis Hassabis, Nobel Laureate as well as Co-Founder & CEO at Google DeepMind, recently called for a frontier AI watchdog. He is confident the industry will pitch in, with spin-offs including attracting high quality technical talent and necessary compute resources for large-scale testing in a regulated sector.
“At the moment, we are locked in an extremely intense, multilayered commercial and geopolitical race. While these competitive dynamics fuel rapid progress and accelerate the incredible upsides, advances on the frontier are outpacing our understanding of the technology. Nobody in the world knows for sure what is going to happen from here, and even the experts disagree,” Hassabis wrote in an essay on X.
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At this time, there is no specific definition of frontier models, much as there is no concrete definition of AGI. Nothing from the new narrative suggests it is about saving humanity, from the AI onslaught AI companies want to bring, to organisations worldwide.
Microsoft CEO Satya Nadella, on July 12, warned companies using AI, about AI. Why, you may ask? “You essentially pay for intelligence twice, once with money, and again with something even more valuable: the proprietary knowledge you must reveal to make that intelligence useful,” he said.
The warning hasn’t been shared with an intention of moderating AI usage, but there is a duality of intent. First, a subtle way to tell companies to not use Chinese models, and secondly, to stick with one AI company’s models. This is likely to benefit Microsoft since it has an enterprise business, to the detriment of companies like OpenAI and Anthropic, which do not have workplace tools and enterprise cloud customers.
Also read:‘Future is not yet written’: Sir Demis Hassabis wants a watchdog for frontier AI
Mehran Gul, author of ‘The New Geography of Innovation’, and former World Economic Forum, United Nations adviser, as well as a Fulbright Scholar at Yale, doesn’t agree with Nadella’s analogy. “My obvious objection to that is if you have an open-weight model that can be run on-premises, you own your own data, and the model is essentially just an engine that you are strapping onto your existing database with no data sharing with any Chinese supplier, how does that fear really come in?,” he explains, in a conversation with HT.
The lack of a cogent response from American AI companies, particularly in terms of model training and token usage costs, is perplexing. That is one part of the economic reality, alongside compute costs, infrastructure investments and the conversation about ‘circular funding’.
“I don’t quite understand why American companies couldn’t also release lower-cost AI models. If Anthrot take a loss on a model that competes on price with companies such as DeepSeek and Zhipu, and offer the hybrid model that makes switching that much easier,” Gul says.
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There’s a cost advantage that Chinese AI models have continually exhibited since DeepSeek took the AI world by storm at the turn of last year. In terms of approximated token costs, GLM-5.2 costs $1.40 per million input tokens and $4.40 per million output tokens—in comparison, Anthropic’s Claude Opus 4.8 will cost developers and enterprises around $5 and $25 for the same usage, respectively.
Microsoft, Anthroemerging from Chinese frontier labs, due to performance and cost balance. According to the 2026 Stanford HAI AI Index Report, the performance gap between top US and Chinese AI models has shrunk to a razor-thin 2.7%, with the US leading in total top-tier model quantity and private investment, while China leads in research volume, patents, and citations.
The West’s closed model approach argues that AI must be kept proprietary for safety reasons. Yet, Hugging Face CEO Clément Delangue thanked Z.ai, saying the Chinese model became “a key part of our defense” during the breach OpenAI’s own models carried out. One could look at this as a genuine explanation for what happened, or with a dose of suspicion, that thus furthers the case for limiting access to Chinese models.
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Z.ai released GLM 5.2 in mid-June under an MIT license, a permissive open-3 billion parameters, a measure of an AI model’s size and capability. They aren’t the only ones, with competition from Alibaba, Tencent, Xiaomi and Moonshot AI.
Apple is reworking the Apple Intelligence suite for China. Alibaba’s Qwen LLM (also called Alibaba Tongyi Qianwen) will serve as the primary system engine for on-device and server side AI tasks. It is not clear at this time whether Alibaba has provided Apple with a customised model or if it is the 27 billion parameter Qwen 3.6, which unlike Apple’s own 20 billion parameter sparse model, keeps all 27 billion parameters active.
“It’s important to understand that China’s AI race is being fought on use cases, not model size. Competitors like Huawei are pursuing hybrid, full-stack AI strategies spanning silicon, OS, and on-device models,” explains Tarun Pathak, Research Director at Counterpoint Research.
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Foreign firms must comply with China’s strict data localisation rules by partnering with domestic tech giants. Apple Intelligence, Samsung’s Galaxy AI, Xiaomi PengPai AI / MiMo, Huawei Xiaoyi, Oppo AndesGPT, Vivo BlueOnDevice and Doubao on Nubia made in partnership with Bytedance, have now marked necessary localisation and often heavy compression of models for on-device and local compute.
This brings us back to a sudden appetite for regulation. When companies such as OpenAI and Google push for regulation, it is mostly on terms they are likely to navigate easily. If, for instance, regulators mandate that models above a certain compute threshold require expensive licensing, auditing, and continuous red-teaming (an adversarial security exercise), a typical AI garage startup is dead before arrival. It serves to slow down the open-
Take for instance Meta’s latest advertising campaign which has rolled out this week. It is conspicuously light on actual AI utility, and more focused on the vibe of using AI. This underlines the AI industry’s struggles to prove generative AI has a definitive consumer application beyond chatbots and coding assistants. Equally for enterprises, the bills of AI usage are continually proving to be higher than the salaries of humans they eliminated from the workplace.
(Vishal Mathur is Technology Editor for Hindustan Times. When not making sense of technology, he often searches for an elusive analog space in a digital world.)
