Broadcom vs AMD: Which is the Most Promising AI Chip Stock to Bet on After Nvidia?

marsbitPubblicato 2026-06-09Pubblicato ultima volta 2026-06-09

Introduzione

Amidst Nvidia's dominance in the AI chip market, Broadcom and AMD are key contenders for the second-place spot. AMD competes directly in general-purpose GPUs, gaining some traction with clients like Meta but facing significant challenges overcoming Nvidia's entrenched CUDA software ecosystem. In contrast, Broadcom pursues a differentiated strategy by designing custom XPU chips tailored to specific AI workloads for major clients like Anthropic, Google, Meta, and OpenAI. This approach offers efficiency advantages and greater customer stickiness, particularly as AI compute shifts from training to inference. Despite a recent stock sell-off following a Q2 revenue guidance miss, Broadcom's CEO reaffirmed the long-term target of $100 billion in annual AI chip revenue by fiscal 2027. With Q2 AI revenue at $10.8 billion, significant growth potential remains as its custom chip projects ramp up. While Broadcom trades at a higher valuation multiple than AMD, analysts argue this premium is justified given its superior competitive positioning and expected faster growth trajectory, making it the more compelling investment choice following its recent pullback.

Author: Justin Pope

Compiled by: Tide Research

Introduction: Beyond Nvidia, the battle for the runner-up position in AI chips is heating up. AMD has chosen to directly challenge Nvidia's general-purpose GPU path, while Broadcom takes a differentiated approach with custom chips (XPU), securing top clients like Anthropic, Google, Meta, and OpenAI.

Following the latest earnings report, Broadcom's stock price fell sharply, but the CEO still maintains the target of reaching $100 billion in annual AI chip revenue by fiscal year 2027. Motley Fool analyst Justin Pope believes that even though Broadcom is more expensive, this premium is worth paying.

Caption: Source Getty Images

Nvidia still firmly holds the top spot in the AI data center chip market. But the AI pie is simply too large, making the runner-up position equally valuable. According to Statista estimates, the AI chip market will grow to $333 billion by 2030.

This means other companies also have the opportunity to make investors a lot of money. Broadcom (AVGO, down 7.49% on the day) and AMD (down 11.01% on the day) are the two most competitive candidates. Both companies have made progress in the AI chip field, but overall, one is clearly more worthy of holding than the other.

AMD Chose a More Difficult Path

The core question is: How can a smaller company compete for market share with an industry giant?

AMD's strategy is to compete head-on with Nvidia in the field of general-purpose AI chips. To be fair, it has indeed achieved some results. Q1 2026 data center revenue grew 57% year-over-year to $5.8 billion.

AMD benefits from AI hyperscalers' natural desire not to put all their eggs in Nvidia's basket. AMD recently announced it will supply Meta with 6 GW of Instinct GPUs, with the first GW being a custom version.

But AMD is unlikely to truly threaten Nvidia's dominance. Meta and other Nvidia customers are deeply entrenched in Nvidia's CUDA software ecosystem. The moat of CUDA cannot be crossed by hardware specifications alone.

Broadcom's Custom Chip Path is the Winner

To breach Nvidia's moat, one must take a different path. Broadcom has achieved this with its XPU chips.

Unlike AMD, which emphasizes general-purpose AI chips, Broadcom tailors chips to each customer's specific AI workloads. This approach brings efficiency advantages and makes customer relationships stickier. Currently, Broadcom is designing custom chips for companies like Anthropic, Alphabet (Google's parent company), Meta, and OpenAI.

As computing demand shifts from training to inference, efficiency becomes even more critical, further amplifying the advantages of custom chips.

Following the latest Q2 earnings report, Wall Street heavily sold off Broadcom, mainly due to Q3 AI revenue guidance falling short of expectations. But CEO Hock Tan reiterated on the earnings call that the company's long-term expectation of reaching $100 billion in annual AI chip sales by fiscal 2027 remains unchanged. Q2 AI revenue was $10.8 billion, and there is still significant growth potential as custom chip projects ramp up.

You Get What You Pay For

With a roster of top-tier AI clients and steady progress toward the $100 billion annual revenue target, Broadcom indeed has a stronger competitive position than AMD. Investors may note that Broadcom's stock is more expensive than AMD's, but this premium is justified.

Caption: AVGO vs AMD Forward Price-to-Sales Ratio Comparison, Source YCharts

Analysts expect Broadcom's growth to be significantly faster than AMD's, and the valuation gap between the two is not that large. Especially after the pullback following the earnings report, Broadcom is more worthy of a buy.

Domande pertinenti

QAccording to the article, what are the main differences between AMD's and Broadcom's strategies in the AI chip market?

AAMD is taking a generalist approach, competing directly with Nvidia in the market for general-purpose AI GPUs. In contrast, Broadcom follows a differentiated, custom chip (XPU) strategy, designing chips tailored to the specific AI workloads of individual clients like Anthropic, Google, Meta, and OpenAI.

QWhat reason does the analyst give for Broadcom's stock being more expensive (having a valuation premium) compared to AMD's?

AThe analyst argues that Broadcom's premium valuation is justified because it holds a superior competitive position with a roster of top-tier AI clients and is projected to grow significantly faster than AMD, as it progresses toward its long-term AI revenue target of $100 billion annually by fiscal 2027.

QWhat was the market reaction and the CEO's response following Broadcom's latest quarterly earnings report?

AFollowing its Q2 earnings report, Broadcom's stock fell sharply due to a Q3 AI revenue forecast that fell short of expectations. However, CEO Hock Tan reaffirmed the company's long-term target of achieving $100 billion in annual AI chip sales by fiscal 2027.

QWhat is identified as a key competitive barrier that makes it difficult for AMD to challenge Nvidia's dominance?

AThe article identifies Nvidia's CUDA software ecosystem as a key competitive barrier or 'moat.' Major clients like Meta are deeply entrenched in this ecosystem, making it very difficult for competitors like AMD to overcome through hardware specifications alone.

QWhy might Broadcom's custom chip (XPU) strategy become even more advantageous in the future, according to the article?

AThe article suggests that as the focus of computing demand shifts from AI training to inference, efficiency becomes more critical. Broadcom's custom chips, which are optimized for specific client workloads, offer efficiency advantages that will likely become even more valuable in this evolving market landscape.

Letture associate

AI Investors' 2026 Anxiety: When Models Devour Everything, What Moat Is Left for Startups?

In 2026, a wave of investor anxiety questions the defensibility of AI startups as models improve, fearing that most companies are just "thin wrappers" destined to be absorbed by foundation models or chipmakers. The author argues against this despair, positing that true moats lie not in benchmark performance but in areas models cannot easily reach. The logic of despair is that if models excel at all measurable tasks, only compute and cutting-edge model weights hold lasting value. However, the essay contends that the most valuable work is inherently "untrainable." Benchmarks measure what can be measured and thus optimized for, but real-world correctness often resides in private, complex systems. Examples include legacy codebases, intricate legal transactions, or hospital workflows. This kind of correctness is proprietary, costly to establish, and cannot be validated quickly—it requires time and trust within an organization. As models commodify visible, measurable tasks from both above (labs absorbing scaffolding) and below (saturation by cheaper models), value shifts to "untrainable ground." This encompasses work where correctness is a private truth, locked behind integration barriers, licenses, liability frameworks, and entrenched user habits. Trust and adoption are slow, human-centric processes that smarter models cannot accelerate. Successful companies defend their position by embedding deeply into client operations, owning the definition of "good" within a specific domain (e.g., Harvey in law, OpenEvidence in medicine), and pricing on outcomes rather than tokens. While labs compete fiercely, they are incentivized to keep the application layer vibrant. The future belongs not to those competing on generic benchmarks but to those navigating unscoreable terrain, doing the "unsexy work" of translation between models and messy human realities. The most cited benchmark scores are thus maps of territory about to become worthless, signaling who will lose the right to define what counts as good.

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AI Investors' 2026 Anxiety: When Models Devour Everything, What Moat Is Left for Startups?

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