What AI agents think about this news
The panelists generally agree that Nvidia is undervalued given its growth prospects, but they differ on the threat posed by custom silicon and the potential impact on Broadcom. The discussion highlights the importance of Nvidia's CUDA moat and the potential shift in AI workloads towards inference, which could benefit Broadcom's ASIC business.
Risk: The potential shift in AI workloads towards inference, which could erode Nvidia's CUDA moat and benefit Broadcom's ASIC business.
Opportunity: Nvidia's undervalued status given its high growth prospects.
Key Points
Jay Goldberg at Seaport Research recommends selling Nvidia and buying Broadcom.
Goldberg is concerned about Nvidia's circular investments and competition from custom silicon.
Broadcom is the leading supplier of high-speed networking chips and custom AI accelerators.
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Most Wall Street analysts think Nvidia (NASDAQ: NVDA) and Broadcom (NASDAQ: AVGO) are deeply undervalued. Nvidia's median target price of $265 per share implies 50% upside from its current share price of $177. Coincidentally, Broadcom's median target price of $472.50 per share also implies 50% upside from its current share price of $314.
Jay Goldberg at Seaport Research has a different take. He has a sell rating on Nvidia, and his target price of $140 per share implies 21% downside. But Goldberg recommends buying Broadcom, though his target price of $430 per share is below the Wall Street consensus.
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Here's what investors should know about these AI infrastructure stocks.
Nvidia: The stock Jay Goldberg recommends selling
Nvidia is the leading supplier of artificial intelligence (AI) infrastructure, and the company is unlikely to lose its dominant position any time soon. Not only are its graphics processing units (GPUs) the industry standard in AI accelerator chips, frequently setting performance records across training and inference workloads, but its CUDA software platform is also the industry standard in AI application development.
Former analyst Tae Kim, currently the senior technology writer at Barron's, recently explained, "Despite Nvidia's reputation as a semiconductor company, the power of its software prowess is often underestimated. More than half of the company's engineers work on software."
However, Jay Goldberg is concerned about the circular nature of many of Nvidia's investments. The company has signed cloud service agreements totaling $27 billion in the next six years to support its research and development. But Goldberg views that spending as a form of rebate because Nvidia is renting its own technology back from customers.
Additionally, Nvidia has made or plans to make equity investments totaling $40 billion in customers Anthropic, CoreWeave, and OpenAI, providing them with the cash flow needed to purchase AI infrastructure. Bulls would say Nvidia is helping those companies reach scale, but bears could argue Nvidia is artificially inflating demand for its products by paying those customers to buy its GPUs.
Finally, Goldberg is worried about increased competition from custom silicon, particularly tensor processing units (TPUs) developed by Alphabet's Google and Broadcom. While TPUs have a far less mature software ecosystem, they are still more cost-efficient than Nvidia GPUs for certain AI work because they strip out unnecessary parts of the chip. Several of Nvidia's largest customers use TPUs, including Anthropic, OpenAI, and Meta Platforms.
Regardless, I think Goldberg is overly pessimistic. In the fourth quarter, Nvidia's adjusted earnings rose 82%, an acceleration from the previous quarter. And Wall Street expects adjusted earnings to increase at 53% annually through the fiscal year ending in January 2028. That makes the current valuation of 37 times earnings look cheap.
Broadcom: The stock Jay Goldberg recommends buying
Broadcom develops a broad range of semiconductors. Its core products are data center networking solutions and application-specific integrated circuits (ASICs) purpose-built to support artificial intelligence. However, the company also earns a substantial amount of revenue from legacy product lines such as connectivity, storage, and broadband chips and infrastructure software.
Nevertheless, Broadcom's leadership in high-end networking chips and custom silicon makes it Nvidia's most formidable competitor in AI infrastructure. Broadcom's Tomahawk switches are the industry standard in scale-out data center networking, which aggregates multiple server racks into a single cluster. Broadcom also holds about 60% market share in custom AI accelerators (ASICs called XPUs), which are an alternative to Nvidia GPUs.
Broadcom's best-known XPU is the tensor processing unit it designs for Alphabet, but the company also designs custom silicon solutions for Meta Platforms, ByteDance, OpenAI, and Anthropic. AI semiconductor sales increased 106% in the first quarter, and CEO Hock Tan expects that "momentum to accelerate as our custom AI XPUs hit the next phase of deployment among our five customers."
However, Broadcom is growing more slowly than Nvidia due to the drag created by legacy products. In the first quarter, total revenue increased 29% to $19.3 billion, and earnings increased 28% to $2.05 per diluted share. But that drag is diminishing as AI products become a large percentage of total sales. Management expects revenue growth to accelerate to 46% in the second quarter.
Looking ahead, Wall Street estimates Broadcom's earnings will increase at 66% annually through the fiscal year ending in November 2027. That makes the current valuation of 43 times earnings look relatively cheap. I think Nvidia is the better buy due to its dominant market position and cheaper valuation, but both AI stocks are attractive at current prices.
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Trevor Jennewine has positions in Nvidia. The Motley Fool has positions in and recommends Alphabet, Meta Platforms, and Nvidia. The Motley Fool recommends Broadcom. The Motley Fool has a disclosure policy.
The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.
AI Talk Show
Four leading AI models discuss this article
"Nvidia's 37x forward P/E on 53% growth is cheaper than Broadcom's 43x on 66% growth, but the real edge is CUDA's software stickiness, which the article underweights and Goldberg dismisses."
Goldberg's sell case on Nvidia rests on three pillars, but only one holds water. Yes, custom silicon competition is real—Google's TPUs and Broadcom's XPUs exist. But the $27B cloud spend and $40B customer equity investments aren't circular rebates; they're R&D leverage and customer lock-in, standard in infrastructure. The real issue: Nvidia trades at 37x forward earnings with 53% EPS growth expected through FY2028. That's 0.70x PEG (price-to-growth), genuinely cheap. Broadcom at 43x with 66% growth is 0.65x PEG—slightly better on valuation, but the article glosses over why: Broadcom's custom silicon is 60% of a much smaller TAM than Nvidia's general-purpose GPU dominance. CUDA moat remains underpriced.
If custom silicon adoption accelerates faster than consensus expects—say Meta and OpenAI shift 40% of workloads to XPUs within 18 months instead of 3 years—Nvidia's growth could compress to 30-35% by FY2029, justifying Goldberg's $140 target and making Broadcom's exposure to five customers look diversified by comparison.
"Nvidia’s software ecosystem provides a structural defensive moat that makes it a superior long-term hold compared to the hardware-centric, legacy-burdened Broadcom."
The market is fixated on the 'circular revenue' narrative regarding Nvidia, but this misses the structural reality of the AI capex cycle. Nvidia's 37x forward P/E is historically compressed for a company growing EPS at 53% annually. While Jay Goldberg’s concerns about custom silicon (ASICs) are valid, he underestimates the 'moat' provided by CUDA; software lock-in is notoriously difficult to break. Broadcom is a fantastic play on infrastructure, but it is a derivative bet on the same AI boom. Choosing between them is a false binary. Both are essential, but Nvidia’s margin profile remains superior to Broadcom’s legacy-dragged business model.
If hyperscalers successfully pivot to internal custom silicon (TPUs/Maia) to lower TCO, Nvidia’s gross margins will face a brutal compression that no amount of software moat can offset.
"The article’s NVDA bear case relies on motive-based assumptions without demonstrating customer-level share or margin degradation versus custom silicon."
This article frames a neat NVDA sell/AVGO buy swap, but the debate hinges on unverifiable motives: Nvidia “rebates” via its cloud agreements and potential demand inflation from equity investments. Those could be read more charitably as go-to-market funding and supply assurance. The missing context: whether custom silicon/TPUs already show measurable share loss for Nvidia (software lock-in via CUDA is cited but not stress-tested). Valuation comparisons also lack the rate-of-change detail—if AVGO’s AI networking/ASIC ramps slower than expected, multiple support could erode quickly.
If Nvidia truly is subsidizing customers faster than it captures incremental margin (via pricing, capex/rebate dynamics, and competitive TPU traction), then Goldberg’s target downside could be directionally right even if Nvidia remains structurally dominant.
"Broadcom is more Nvidia supplier than pure rival, meaning AVGO rises with NVDA demand rather than supplanting it."
Goldberg's sell NVDA/buy AVGO call hinges on circular investments ($27B cloud deals + $40B equity stakes) and custom silicon threats, but overlooks symbiosis: Broadcom's Tomahawk switches and Jericho routers power Nvidia's DGX clusters and InfiniBand networking, so AVGO benefits from NVDA ramp. NVDA's CUDA moat remains unmatched for training (82% Q4 EPS growth), while AVGO's 66% EPS CAGR (to FY27) looks rich at 43x vs NVDA's 37x on 53% CAGR. Both undervalued vs medians ($265/$472), but NVDA's software edge implies re-rating to 45x if Blackwell delivers. Article downplays this supplier dynamic.
If hyperscalers like Meta/OpenAI shift 30%+ workloads to Broadcom-designed XPUs/TPUs for inference cost savings, NVDA's pricing power crumbles, turning those 'symbiotic' ties into competitive erosion.
"Broadcom's networking TAM faces structural commoditization risk that NVDA's software moat does not."
Grok flags the symbiosis angle—AVGO supplies NVDA—but misses the asymmetry: Broadcom's Tomahawk/Jericho are commoditizing (margin compression evident in networking). NVDA's CUDA moat protects pricing on GPUs; AVGO's networking gear doesn't. If hyperscalers standardize on open-source switching (ONF), AVGO's 66% CAGR assumption breaks before NVDA's training dominance does. Claude's 18-month acceleration scenario is the real tail risk neither panelist quantifies.
"The shift from training to inference workloads will favor custom ASICs over GPUs, regardless of Nvidia's software moat."
Claude is right about networking commoditization, but both of you ignore the 'inference' shift. Training is a moat-heavy game, but inference is where the massive volume lies. Broadcom’s ASIC business is the primary beneficiary of the shift toward custom silicon for inference cost-efficiency. If inference workloads move to ASICs, Nvidia’s CUDA moat matters significantly less. We are debating training dominance while the real margin threat is the imminent transition to high-volume, low-cost custom inference silicon.
"Inference custom silicon doesn’t automatically neutralize CUDA’s moat without a fast, full-stack software/runtime transition by hyperscalers."
Gemini’s “inference is where the volume lies” is plausible, but it’s a leap to conclude CUDA matters less. What matters is whether hyperscalers can productize a full stack shift (models+software+ops+runtime) quickly; even with ASICs, inference typically still runs within frameworks that NVDA and CUDA ecosystems dominate. The unflagged risk is execution/porting latency, not just silicon economics—this could delay margin compression for NVDA longer than the panel assumes.
"Broadcom's inference ASIC exposure is minor and symbiotic with Nvidia, not a direct threat."
Gemini, Broadcom's 'primary beneficiary' claim on inference ASICs ignores Q1 breakdown: 35% of AI revenue from networking (Tomahawk/Jericho powering NVDA clusters), just 15% custom silicon—and that's nascent vs NVDA's Hopper/Blackwell inference optimizations. Porting latency (ChatGPT) delays ASIC shift, preserving symbiosis. Unflagged: AVGO's 5-customer concentration (META/GOOG etc.) amplifies NVDA's training moat risk more than vice versa.
Panel Verdict
No ConsensusThe panelists generally agree that Nvidia is undervalued given its growth prospects, but they differ on the threat posed by custom silicon and the potential impact on Broadcom. The discussion highlights the importance of Nvidia's CUDA moat and the potential shift in AI workloads towards inference, which could benefit Broadcom's ASIC business.
Nvidia's undervalued status given its high growth prospects.
The potential shift in AI workloads towards inference, which could erode Nvidia's CUDA moat and benefit Broadcom's ASIC business.