This AI stock is growing faster than Nvidia and flying under the radar

This AI stock is growing faster than Nvidia and flying under the radar
Devesh Kumar
Apr 28, 2026, 06:36 A.M.

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AVGO (Broadcom)

Buy AVGO. The article shows AI revenue up 106% YoY, strong 2026 AI guidance, and a huge AI backlog scheduled over 18 months. Broadcom isn’t chasing Nvidia’s GPU lane; it’s winning custom AI chips (ASIC/XPUs) plus networking/software for hyperscalers—perfect for inference where cost and power control matter. Forward growth looks faster than Nvidia’s off a smaller base, and the market still screens AVGO at a premium that may not fully price the backlog-to-revenue conversion.

Key Risk: Hyperscalers delay or cancel custom-chip/inference deployments, shrinking backlog conversion and making the 2026 AI ramp miss.

Nvidia (NVDA)

Sell NVDA. The news highlights Broadcom’s “different lane” and faster projected compounding, while Nvidia’s growth is off a much larger base. If AI spend keeps shifting from training to efficient inference, custom silicon and networking suppliers like Broadcom can take share in the datacenter stack, pressuring Nvidia’s relative growth and multiple.

Key Risk: Nvidia’s GPU demand stays dominant for inference (or new workloads keep favoring GPUs), so Broadcom’s lane doesn’t translate into share loss.

  • Broadcom AI revenue surged 106%, while Nvidia posted 65% total growth.
  • Valuation gap remains, but Broadcom’s forward P/E is less consistent.
  • Analysts project faster 2-year growth for Broadcom than Nvidia.

Broadcom stock (NASDAQ: AVGO) is suddenly looking like the AI name Wall Street may have underestimated.

The market value of the company is above US$2 trillion (approx. $2.8 trillion), its stock has hit a record high, and the company is guiding AI revenue sharply higher into 2026.

That makes the comparison with Nvidia especially insightful, as Broadcom is taking a distinct approach instead of going head-to-head in the GPU market.

It is building a different lane in custom AI chips, networking, and software.

For investors, the real question is whether Broadcom is still under-appreciated, or whether the market has already caught up for now, anyway.

Numbers that tell the real story

Broadcom’s latest quarter did not just look good; it looked like a company entering a new phase of scale.

The company said first-quarter AI revenue reached US$8.4 billion (approx. $11.7 billion), up 106% from a year earlier, and it guided second-quarter AI semiconductor revenue to US$10.7 billion (approx. $14.9 billion).

Broadcom also said it has a US$73 billion (approx. $101.8 billion) AI backlog scheduled to ship over the next 18 months.

Nvidia is still growing fast, but it is doing so off a much larger base: fiscal 2026 revenue rose 65% to US$215.9 billion (approx. $301 billion), while data-center revenue climbed 68% to US$193.7 billion (approx. $270 billion).

All growth comparisons below are forward projections, not trailing reported figures.

That distinction matters because analysts expect Broadcom’s growth to compound at a slightly faster pace over the next two years.


Metric
Broadcom Nvidia
AI revenue growth $8.4B in Q1 FY26, up 106% YoY FY26 revenue up 65%; data-center revenue up 68%
Forward P/E Around 41x on analyst-based screens About 24.5x forward earnings
2-year projected growth 147% 124%

The backdrop is still very much an AI spending boom.

The company’s CEO, Hock Tan, said Broadcom has “line of sight” to over US$100 billion (approx. $139.4 billion) in AI chip revenue by 2027.

That makes Broadcom a major player as Deloitte expects the global semiconductor market to reach US$975 billion (approx. $1.4 trillion) in 2026, up 26% from 2025.

Also read: Nvidia is quietly betting 8% of its portfolio on this $10 stock

Why are Broadcom chips different?

The Broadcom story is not really about replacing Nvidia in every job; it is about a different kind of chip.

Nvidia sells general-purpose GPUs that can handle a wide range of AI tasks.

Broadcom builds custom ASICs, or XPUs, that are designed for a specific hyperscaler and a specific workload.

The chips are described as custom processors tailored for a single function, which is exactly why cloud giants like Google and Meta keep turning to them for large-scale AI deployments.

That shift matters because AI spending is moving from training giant models to running them efficiently in the real world.

Inference is where custom silicon can shine, especially when customers want tighter control over cost, power use, and supply.