GoKawiilBroadcom CEO Hock Tan on Monday brushed off concerns about what a potential slowdown in frontier AI model development could mean for the chipmaker's business, telling CNBC that the company stands by its long-term revenue targets.
Tan's comments came as investors were rattled by Anthropic CEO Dario Amodei's weekend essay advocating for a moderation in the pace of model development. Amodei's message, which received support from OpenAI CEO Sam Altman and Elon Musk, prompted investors in AI infrastructure providers to reconsider their expectations for compute demand during Monday's session.
Shares of Broadcom — whose custom-chip business counts Anthropic as one of its most important customers — fell 4.8%. A broad basket of chip stocks, the iShares Semiconductor ETF, fell 5.6%. The stocks of companies selling other data-center components were also hit hard.
"No, not in the least," Tan said on "Mad Money," when Jim Cramer asked whether anything in the AI slowdown debate has caused him to reconsider Broadcom's fiscal 2027 and 2028 AI semiconductor 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."
On Broadcom's fiscal 2026 third-quarter earnings call on Sept. 2, Tan forecast AI semiconductor revenue of $115 billion in fiscal 2027, before doubling again to $230 billion in fiscal 2028. The better-than-expected target for 2028 was one of the bright spots in Broadcom's earnings report. Broadcom's AI revenue consists of both custom AI accelerators and networking chips used in AI systems.
Tan also said Anthropic is on track to become Broadcom's largest custom chip customer in 2027 and sustain that designation in 2028 — illustrating why Broadcom investors are so sensitive to Amodei's commentary. Google has historically been considered Broadcom's biggest custom customer, co-designing Google's tensor processing units.
In Monday's CNBC interview, Tan was particularly optimistic about demand for inference, the day-to-day usage of AI models after they've been trained.
"I don't know about training, but when you want to productize inference, I see it continuing to be very, very strong," Tan said.
Amodei's weekend essay intensified the debate over whether the AI industry is moving too quickly in developing powerful models. Amodei proposed a three-step plan to slow the pace of development without "sacrificing commercial advantage or the United States' lead in AI."
Tan said he agrees with Amodei about the need for some restrictions on AI, but he suggested he was less alarmed about where the technology is headed.
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