Broadcom CEO Hock Tan is betting that the AI infrastructure boom has enough momentum to withstand growing calls for slower development of frontier models, brushing aside concerns that a moderation in AI progress could weaken demand for the chipmaker’s custom processors and networking equipment.
Tan told CNBC on Monday that Broadcom has no reason to reconsider its long-term AI semiconductor revenue targets, even as investors sharply reassessed the outlook for computing demand following comments from Anthropic CEO Dario Amodei.
Amodei’s weekend essay calling for AI companies to moderate the pace of frontier-model development triggered a broad selloff in AI infrastructure stocks on Monday. The proposal, which was endorsed by OpenAI CEO Sam Altman and Elon Musk, raised fresh questions about whether the enormous investments being made in data centers, accelerators and networking equipment can continue if the development of increasingly powerful models slows.
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Broadcom shares fell 4.8% on Monday, while the iShares Semiconductor ETF dropped 5.6%. Companies supplying other components for data centers also came under pressure as investors considered the possibility that slower frontier-model development could eventually translate into weaker demand for computing infrastructure.
Tan, however, said the market was overestimating that risk.
“No, not in the least,” Tan said on CNBC’s “Mad Money” when host Jim Cramer asked whether the debate over slowing AI development had caused him to reconsider Broadcom’s fiscal 2027 and 2028 forecasts.
“We see the demand for compute infrastructure, for AI development or AI frontier models, and inference for the products that they feed to the world, as continuing to be very strong and, I believe, very durable,” he said.
Broadcom Sees AI Revenue Doubling Again
Tan’s confidence is backed by an unusually large set of revenue expectations for the company’s AI semiconductor business.
During Broadcom’s fiscal 2026 third-quarter earnings call on Sept. 2, he forecast AI semiconductor revenue of $115 billion in fiscal 2027, followed by another doubling to $230 billion in fiscal 2028.
The 2028 forecast was among the strongest elements of Broadcom’s latest earnings report and highlighted the extent to which the company expects AI spending to expand beyond the current generation of data-center infrastructure.
Broadcom’s AI semiconductor business includes custom AI accelerators as well as networking chips used to connect and move data between processors inside large AI systems. That positioning gives the company exposure not only to companies developing frontier models but also to the broader infrastructure required to operate AI applications at scale.
The relationship with Anthropic makes the debate particularly important for Broadcom shareholders. Tan said Anthropic is expected to become Broadcom’s largest custom-chip customer in 2027 and retain that position in 2028.
Google has historically been viewed as Broadcom’s largest custom-chip customer, with the two companies working together on Google’s tensor processing units. Anthropic’s expected rise to the top of Broadcom’s customer base indicates how rapidly AI model developers are becoming major buyers of custom computing infrastructure.
Yet Tan sees a contrast between the demand for training sophisticated models and the much larger potential market created once those models are deployed.
“I don’t know about training, but when you want to productize inference, I see it continuing to be very, very strong,” Tan said.
The view could prove important if AI companies begin to moderate the pace of training frontier models. Training requires enormous bursts of computing power to develop new generations of models, while inference requires computing resources every time those models are used.
As AI moves into consumer products, enterprise software and automated services, inference demand can therefore grow even if the frequency of major frontier-model releases slows.
AI Safety Debate Meets Infrastructure Economics
Amodei’s proposal has introduced a new variable into an AI investment story that has largely been driven by assumptions of ever-increasing model capability and computing requirements.
The Anthropic CEO said that AI companies should take additional time to ensure powerful systems can be properly evaluated and controlled. His three-step plan seeks to moderate the pace of development without “sacrificing commercial advantage or the United States’ lead in AI.”
Tan said he agrees that AI requires restrictions and safeguards, but he does not share the more severe concerns surrounding the technology’s trajectory.
“Like any tool, it’s important to put governances, safeguards on how we use the tool,” Tan said.
But he rejected the idea that AI could simply escape human control.
“It’s not a live animal that will run wild by itself,” Tan said.
His argument puts the infrastructure industry’s position in sharper focus. Broadcom does not need every AI lab to accelerate model development indefinitely to sustain demand. Its business can benefit from the expanding use of AI after models have already been developed, particularly as inference workloads spread across businesses and consumer applications.
Tan also framed the technology in economic rather than existential terms, arguing that generative AI and frontier models will create substantial productivity gains.
“AI, generative AI, the creation of those frontier models … will create huge value,” he said, comparing the technology’s potential impact with the Industrial Revolution that began in England in the 18th century.
“It is still at the end of the day a tool that will make our society, humanity, reach a better level of living,” Tan said.
The immediate market reaction appears as an indication that investors are less willing to assume that AI infrastructure demand will remain insulated from changes in the pace of model development. Broadcom’s exposure to Anthropic makes it particularly sensitive to that question.
Tan’s response, however, is believed to rest on a broader thesis: even if frontier-model training becomes more measured, the commercial deployment of AI could create a separate and potentially larger source of chip demand.