• Google announced that it will begin selling TPUs to select third-party data center operators, marking the company’s formal entrance into the merchant AI accelerator market where Nvidia dominates
• The share of AI inference workloads is increasing; the shift toward inference is making the economics of custom silicon increasingly difficult for hyperscalers to ignore, and Nvidia may be facing a Rubin delay—three converging factors opening the door for TPUs
• The large coherent shared memory of TPU pods is a key feature that Google is banking on to differentiate from Nvidia systems

Google blew the doors off with its latest earnings report—cloud growth rapidly accelerated, margins expanded, and backlog soared 400% YoY to $462 billion. However, the quarter’s most pivotal development wasn’t in the financials, rather it came from a strategic announcement.

In April, Google announced it would begin selling its TPUs to select third-party data center operators, which is something the market has anticipated for nearly a decade. The TPU-versus-Nvidia-GPU debate has long fueled both bulls and bears; yet it may finally carry real stakes. Google’s announcement is far from a coincidence—it is driven by several converging factors that make now the right moment to move.

As hyperscalers look to monetize their models, AI workloads are expanding from training to inference. This changes the focus away from accumulating expensive compute to a very different goal, which is lowering cost per token in order to scale inference economically.

In a previous article covering Google’s TPUv7, we stated: “[…] custom silicon’s cost advantages and ability to drive lower inference serving costs at scale creates a strong value proposition for Big Tech.” Building on this, Nvidia may be facing a Rubin delay, which opens a window of opportunity for Google to make the case for diversification beyond a single vendor for AI accelerators.

Below, we look at how Google’s entrance into the merchant AI accelerator market sits at the center of three converging trends – and how the newly released TPU v8 generation positions custom silicon to meet the moment, giving Google a fighting chance against Nvidia.

The Shift from AI Training to Inference: Why It Opens a Window of Opportunity for Google

To understand why the market is opening up for more players, we should first discuss why inference is becoming the dominant AI workload—and what this means for Nvidia.

Training frontier models is a discrete, multi-month event with a clear beginning and end. By contrast, inference is the revenue-generating phase, and thus, runs continuously with no ending point. Both training and inference workloads will continue to grow as labs build better models and monetize them. However, the always-on nature of inference will result in inference being the higher volume workload over time.

According to industry analysts, inference could take the larger share as soon as 2027.  McKinsey estimates that in 2026, 31.2 GWs of data center demand will be allocated to training, and 31.2 GWs will be allocated to inference—an even 50/50 split. However, by 2027, inference becomes the larger share. By 2030, inference accounts for 93.3 GWs of demand, compared to training’s 62.2 GWs—or a 60/40 split.

Google TPU v8 Explained: 8i vs 8t and the Inference Advantage

At Cloud Next in late April, Google unveiled its latest TPU v8 in two configurations—the training-optimized 8t and the inference-optimized 8i. Notably, the Ironwood TPU v7 was the first TPU optimized for inference, but v8 marks the first time that the architecture has been split for two distinct purposes. As Google looks to capitalize on inference becoming the primary AI workload, splitting the v8 into two separate chips allows it to target this part of the market more effectively.

TPU v8 Architecture: Why Google Split Training and Inference

With the 8i, Google is positioning itself to beat out Nvidia on one key aspect – coherent shared memory, a key anchor in improving inference efficiency.

While the 8i’s pod size only scales 4.5X over Ironwood’s 256-TPU pod to 1,152 TPUs per pod, pod-level HBM capacity increases by 7X to 331.8 TB versus 49.2 TB with Ironwood. Yet the key here is that this HBM capacity is coherent across the pod, across all 1,152 chips.

This is arguably the most critical point to understand surrounding Google’s architectural advantage with the 8i, that this 331.8 TB of memory is shared across the entire pod over Google’s inter-chip interconnect (ICI). ICI is similar to Nvidia’s NVLink—with both allowing for the fastest chip-to-chip memory access within a pod. Compare this to Nvidia’s NVL72, where true memory coherency only extends at rack-scale across 72 GPUs and just 20.7TB of HBM. Scaling out to 1,152 of Nvidia’s GPUs would span 16 racks, yet memory does not become a unified pool shared across the entire cluster.

By keeping the maximum amount of memory in a shared domain with the TPU 8i, large frontier models with long context windows can run with minimal latency.

How TPU v8i Lowers Cost Per Token: SRAM and Boardfly

Several other key decisions reinforce the 8i’s inference capabilities—pursuant to the ultimate goal of increasing inference efficiency by reducing latency, helping reduce cost per token as inference and agentic AI expand. These include boosting SRAM capacity per chip, and introducing a new networking topology, dubbed Boardfly.

SRAM capacity is where Google is driving latency improvements at the chip level, increasing on-chip SRAM by 3X to 384MB for the 8i. SRAM is the fastest memory available to a chip, and the larger pool allows more of the chip’s working memory, or KV cache, to stay on the fastest tier possible. In doing so, latency falls as the KV cache does not have to be retrieved from slower HBM. With 1,152 chips, the pod’s total SRAM capacity is 432 GB.

Google’s new Boardfly topology is its second lever in reducing latency. With Boardfly, Google connects ‘building blocks’ of four TPUs into boards, consisting of eight building blocks, that are then fully linked together as one pod. This is achieved via direct optical long-haul links, flattening the topology and reducing networking hops for any chip-to-chip communication from 16 hops to just seven. Google says this reduction in hops drives a “50% improvement in latency for communication-intensive workloads.”

As stated, the result of these improvements is lowering the 8i’s cost per token. In line with this, Google notes that TPU 8i delivers up to an 80% performance-per-dollar improvement over the Ironwood TPU, particularly at low-latency targets for large MoE models. The 8i’s deployment would compound the already significant serving cost reductions Google achieved in 2025. Last quarter, Google’s CEO stated there was a 78% reduction in Gemini serving unit costs in 2025.

As chips spend less time sitting idle, Google—or any other TPU operator—can process more tokens at the same price. This strikes at the core of inference economics—minimizing the cost per token.

Deploying agentic AI within enterprises dramatically increases the need for memory in comparison to chatbots. Agents can act autonomously, performing complex multi-step tasks, drawing from organization-specific workflows, policies, and data—all of which require increased memory. Overall, Nvidia notes that agentic systems consume up to 15X more tokens than traditional AI applications. As token consumption vastly increases, lowering cost per token is critical to scaling agentic AI efficiently.

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Nvidia Prepares to Answer on Inference

While Google is deploying an inference-optimized TPU that warrants attention, from its ability to offer 331.8TB of shared coherent memory at pod level alongside other topology and architectural optimizations to improve inference efficiency, Nvidia remains the world’s best chip designer, and will not simply lay down and concede the inference market.

On that note, Nvidia is moving quickly with a different approach via its 256-chip Groq LPX rack, leveraging Groq’s SRAM-based design to accelerate inference-based workloads via ‘disaggregation’ at rack scale. As covered in our free newsletter, Nvidia Stock to See New Growth Catalyst; 35X Faster AI with Groq 3 LPX, disaggregation refers to splitting up the two-step process of token generation, prefill and decode, and allocating each step to the hardware best designed for the task – prefill goes to compute-heavy Rubin GPUs, and memory and KV-cache intensive decode to the LPX rack.

Nvidia CEO Jensen Huang stated that combining the two co-designed racks can deliver up to 35X higher throughput per MW on trillion-parameter LLMs, with these throughput gains most evident on high token rate applications, such as real-time AI agent communication.

Naturally, there will be architectural differences between custom silicon and GPUs, such as the TPU 8i leveraging on-chip SRAM, yet the key takeaway is that Nvidia is moving ahead with a new strategy. The strategy, in a nutshell, is to offer seven co-designed chips that offload tasks to specialized hardware and optimize inference at the rack/system level versus the chip level.

Nvidia is the world’s best AI chip design company, and all the above plus other incoming rapid changes to the company’s product roadmap is something to keep a close eye on.

For more information on why Nvidia’s CUDA moat matters less with inference, read our analysis here: Nvidia’s $20 Trillion Thesis in Intact, my 2026 Allocation Isn’t.Nvidia’s $20 Trillion Thesis in Intact, my 2026 Allocation Isn’t.

How Lower Token Costs Are Driving Google Cloud Growth and Margins

In Q1 2026, Google Cloud put up a hallmark performance. Revenue came in at $20 billion, with growth accelerating to 63% YoY. This was nearly double the 32% growth seen in Q2 2025 and 15 percentage points higher than the 48% growth seen in Q4 2025. Cloud backlog also hit $462 billion, up 400% YoY and 90.3% QoQ, signaling both the massive scale and acceleration of demand.

However, just as important was the huge expansion in Cloud operating margin. The figure moved up to 32.9%, a 15.1 percentage point expansion YoY and a 2.8 percentage point expansion QoQ.

Gemini vs GPT vs Claude: Token Pricing Comparison

Connecting back to the TPU discussion, lowering token costs is key to Google Cloud’s success. By keeping costs low, Google can attract more developers to Gemini, generating more cloud revenue. Gemini 3.1 Pro Preview, Anthropic’s Claude Opus 4.7, and OpenAI’s GPT-5.5 are widely considered frontier models—but data from Artificial Analysis indicates that Google has a very significant cost advantage.

The blended price per 1M tokens that customers pay on Gemini 3.1 is approximately $1.74. This is around 58% lower than Claude 4.7 and 60% lower than GPT 5.5. Additionally, this difference comes even as Google increased the per-token cost of Gemini 3.1 Pro Preview by 30% over Gemini 2.5 Pro.

Bar chart compares the blended price per 1 million tokens across leading AI models from Google, OpenAI, and Anthropic. Google’s Gemini 3.1 Pro Preview is priced at approximately $1.74 per million tokens, making it roughly 58% cheaper than Anthropic’s Claude Opus 4.7 ($4.10) and 60% cheaper than OpenAI’s GPT-5.5 ($4.35). Earlier models such as Gemini 2.5 Flash ($1.34) and GPT-5.4 Mini ($2.18) are also included for historical context. Source: Artificial Analysis

By leveraging Ironwood TPU v7 and TPU v8, Google can attract more developers while balancing operational leverage—creating a perfect storm for the growth acceleration and margin expansion we are seeing today. Furthermore, Google Cloud’s 33% operating margin and the large expansion in this figure provide evidence that the company is not deeply subsidizing its token costs to gain share.

The up to 80% reduction in performance-per-dollar from Ironwood to 8i can allow Google to continue lowering its own costs—benefiting margins further. Additionally, with token costs still much lower than other frontier models, Google could choose to boost margins through price increases.

The distinction here is that Gemini is served exclusively on TPUs, while Claude and GPT-5.5 are not (although TPUs are part of Anthropic’s infrastructure stack). As we isolate this variable across the frontier model providers, we can reasonably assert that the fundamentally different architecture that Gemini runs on—TPUs—are a key driver of Google’s lower cost per token.

Anthropic’s TPU Bet: What It Signals for AI Infrastructure

Anthropic’s large partnership with Google provides further evidence of TPU competitiveness. Anthropic has been growing at a breakneck pace, with recent estimates suggesting that the company’s ARR increased from $9 billion at the start of 2026 to now over $44 billion. This clearly positions Anthropic as scaling inference and monetization, and the firm is making long-term commitments with Google – which sends a clear message. Anthropic has reportedly expanded its partnership with Google, agreeing to a 5 GW TPU deployment over the next five years, with additional GWs possible. This is a notable expansion of its previously announced agreement for 3.5 GWs.

One reason for this move is the fact that a rapidly growing AI lab like Anthropic simply needs to secure additional compute capacity. Anthropic has also announced compute capacity expansions that run on Nvidia hardware—including an up to 1 GW deal with Azure and an over 0.3 GW deal with SpaceX. However, the scale of these agreements is clearly much smaller than the TPU deal, which could indicate that Anthropic is benefiting from Google’s TPU advantages in lowering token costs.

Anthropic’s Compute Strategy Across Google, AWS, and Azure

Today, Amazon is Anthropic’s primary cloud provider, utilizing the firm’s Trainium chips. This comes as the bulk of Anthropic’s TPU capacity will not start to come online until 2027. Anthropic has also committed $100 billion over the next ten years to AWS, allowing it to secure up to 5 GW of new capacity. However, one report suggests that it's commitment to Google Cloud is worth $200 billion over the next five years—or double the spending in half the time. This is another data point implying that Anthropic sees TPUs as highly competitive with both Nvidia and Amazon hardware.

With Anthropic being one of the preeminent companies pushing the AI world into the inference phase, its support of TPUs validates the thesis that Google can drive forward merchant sales. Notably, Google is already providing evidence of its ability to drive merchant sales, launching an AI cloud joint venture with Blackstone that aims to deliver its first 0.5 GW of TPU capacity in 2027.

Nvidia Rubin Delay: A Strategic Opening for Google TPUs

Lastly, the reported one-quarter delay of Nvidia’s Rubin ramp, officially scheduled for Q3 2026, could offer a strong argument for diversification across AI accelerators. Notably, TrendForce revised its estimate of Rubin’s contribution to Nvidia’s total high-end GPU shipments for 2026 down from 29% to 22% to account for such a delay.

Factors contributing to the reported delay and TrendForce’s revision include “the time required to validate the newer HBM4 memory used by the chips, challenges with the migration to Nvidia's faster ConnectX-9 NICs, the system's higher overall power consumption, and the more advanced liquid cooling requirements.”

While Nvidia has not lent credence to delay rumors itself, statements made on the company’s latest earnings call provide clues into the trajectory of the Rubin ramp.

Joshua Buchalter, TD Cowen

“Colette, I believe, in your prepared remarks, you mentioned GB300 is sort of the fastest ramp in the company's history. How should we think about Vera Rubin against this benchmark?”

Colette Kress, Nvidia CFO

“Yes. Well, we've indicated for a while that we will be launching Vera Rubin in the second half. We will start in Q3. That will be our initial pieces together. And then once we get to Q4, we're probably going to start to see our ramping continue… It's hard to say at this point what will be a faster ramp… But yes, we're going to start in Q3 and continue to ramp into Q4. And Q1 of next year certainly is going to be very big as well.”

If we take what the CFO stated, Rubin systems meaningfully ship Q4-Q1. Specifically, it was noted that in Q3 Nvidia would bring together the “initial pieces” and that the ramp would “probably” continue in Q4. This is far from a definitive statement that the Rubin ramp will take off in Q3. If anything, Kress seemed to position Q1 2027 as the large ramp—adding weight to the delay rumors.

Delays in Nvidia’s roadmap have happened before, such as the two-quarter delay experienced with Blackwell. What’s different now is that a merchant alternative optimized for inference is available through Google.

Final Thoughts: Why Google May Be Nvidia’s Strongest AI Challenger Yet

Google’s inference-optimized TPU 8i is targeting the fastest growing segment of the compute market, with meaningful advantages in lowering cost per token. Google Cloud growth is accelerating, operating profitability is compounding, and leading AI labs like Anthropic are validating the merchant TPU thesis. As Google steps into the AI accelerator arena, it’s one of the few legitimate challengers to Nvidia’s dominance.

Meanwhile, Nvidia iterates and improves its systems at an unusually fast pace. It may not be long before the AI juggernaut responds with a much stronger answer to Google’s v8 series.

Regardless, our thesis is that neither Google nor Nvidia is likely to offer the highest returns in the AI trade from here. Instead, we think the best opportunities will come from the companies that supply the world’s most valuable firms with networking, energy, memory components, and other critical AI infrastructure.

The I/O Fund has excelled at shifting our thesis when presented with new evidence while others stick to what is familiar. For example, we identified lesser-known AI winners, including Bloom Energy, up 1100% since our initial entry last year, a networking player that has delivered roughly 7X Nvidia’s returns YTD and an optical networking stock up more than 790% since November.

We publish more than 100 paywalled articles each year on AI stocks, supported by an actively managed portfolio and real-time trade alerts. Don’t miss out on the AI trade.
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Please note: The I/O Fund conducts research and draws conclusions for the company’s portfolio. We then share that information with our readers and offer real-time trade notifications. This is not a guarantee of a stock’s performance and it is not financial advice. Please consult your personal financial advisor before buying any stock in the companies mentioned in this analysis. Beth Kindig and the I/O Fund own shares in GOOGL and NVDA at the time of writing and may own stocks pictured in the charts.

Leo Miller, AI and Semiconductor Investment Writer at I/O Fund, contributed to this analysis. Leo Miller owns shares of GOOGL and NVDA.

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Recommended Reading:

• The AI Networking Stock That Beat Nvidia by 7X YTD for Returns of 135% YTD
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• Nvidia’s $20 Trillion Thesis Is Intact. My 2026 Allocation Isn't

April 2026 was a historically strong period for the stock market. The S&P 500 rose by 10.43%—its best monthly return since April 2020, when the market rebounded from COVID-era lows. The Nasdaq-100 achieved the same feat, rising over 15%. 

Against this backdrop, I/O Fund performed exceptionally well, as we owned 4 of the market’s 10 best-performing large caps in April. This includes the market’s top large cap gainer, Bloom Energy, which soared 112.81%. In February, my firm called out Bloom as our Top AI Stock Pick for 2026.  

Adding to AI energy's strong performance was networking stocks, a subsector that most investors shy away from due to the complexity of the products, in addition to the supply chain moving lightning fast with immense volatility in both directions. The reason networking sees immense volatility is straightforward: much of the market is tied to a single customer, Nvidia; and Nvidia is rolling out new architectural iterations at an unusually fast pace these days. 

AI networking stock Lumentum is among the key I/O Fund winners in 2026. We allocated heavily to LITE in January—a month before Nvidia backed the company. While most investors couldn’t stomach taking a stake in this stock that soared 339% in 2025, I/O Fund built a 9% position that has since paid off in spades. Overall, in just five brief months, our Lumentum position delivered a return of 135.4%, or 6.8X higher than Nvidia’s 19.9% return since the end of January. 

For investors new to this name, Lumentum recently received significant validation from the world’s most valuable company—Nvidia—with the dominant force in AI infrastructure investing $2 billion in LITE. However, the importance of this goes far beyond the investment itself. The real story is Lumentum’s central position in Nvidia’s multi-year networking roadmap, and the broader AI market, which is affording Lumentum the opportunity to grow its business several times over. 

Below, we break down the key dynamics currently benefiting Lumentum, the structural factors supporting continued margin and EPS growth, and our perspective on the key question: “Is it too late?” 

Nvidia–Lumentum Partnership: CPO Growth and Optical Transceiver Market Expansion 

Nvidia’s partnership with Lumentum includes multi-billion-dollar agreements on two fronts: the investment and a purchase commitment for the company’s ultra-high-powered lasers (UHPs). 

Currently, Lumentum is the sole supplier of UHPs for Nvidia’s co-packaged optic (CPO) networking switches—which are expected to undergo a step function in demand over the coming years. Nvidia has already taken up nearly all of Lumentum’s UHP capacity, leaving little for other customers. 

The $2 billion investment is key to expanding Lumentum’s existing UHP capacity in San Jose, its Caswell fab in the United Kingdom, and bringing online its recently acquired fab in Greensboro, North Carolina. 

This is all due to the dramatic ramp-up of CPO demand that Nvidia is preparing for. Lumentum expects to generate its first $100 million in CPO-related revenue in the final quarter of calendar 2026, but the longer-term opportunity is much larger. More on this later. 

Overall, Nvidia sees Lumentum as a vital partner in this ramp-up and is making significant commitments to ensure capacity once CPO takes off. This ties Lumentum directly to the world’s preeminent AI infrastructure company over a multi-year period. And, even as CPO has yet to penetrate significantly into data centers, Lumentum is posting extremely strong financial results. This is driven by insatiable demand for high-speed optical transceivers.

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Optical Transceiver Market Growth Forecast (2025–2026) 

TrendForce notes that "the global market for AI-focused optical transceivers has entered a phase of rapid growth” and projects the market will expand from $16.5 billion in 2025 to $26 billion in 2026—growing 57.6% YoY. 

Chart illustrating the rapid expansion of the AI optical module market, with revenue projected to increase by 57.6% year-over-year from $16.5 billion in 2025 to $26 billion in 2026, driven by rising demand for high-speed optical transceivers such as 800G and 1.6T. Source: TrendForce (April 2026).

800G modules are the primary growth driver, with shipments of 1.6T units ramping in mid-to-late 2026. TrendForce also predicts that “optical transceivers shipments of 800G and higher will hit 24 million units in 2025, then jump by 2.6 times to nearly 63 million units in 2026.” Within this, Yole Group forecasts over 10 million 1.6T module shipments in 2026.  

Lumentum is a critical player here, generating growth on two sides of the transceiver coin. Lumentum sells its own 800G and 1.6T transceivers, and is a supplier of key components to other transceiver makers. 

Lumentum Financials: Record Revenue Growth and Precipitous Margin Expansion 

Lumentum’s latest results showed that the company is indisputably firing on all cylinders. It’s Q3 FY2026 ended in March, with Lumentum posting revenue of $808.4 million. The figure missed analysts' estimates very slightly (0.2%), but sales still grew by 90.1% YoY. This marked the fastest YoY growth rate in Lumentum’s history and was a large acceleration over 65.5% YoY in Q2. 

The company expects growth to accelerate further next quarter. It projects sales of $980.5 million at the midpoint, implying growth of 104.9% YoY. Additionally, after QoQ growth decelerated from 24.7% in Q2 to 21.5% in Q3, its midpoint guidance projects solid consistency with 21.8% in Q4. 

Lumentum’s Margin Expansion Across Gross, Operating, and Net Income 

The margin story was equally impressive, driven by improved manufacturing utilization, favorable product mix, and operating leverage. 

• FQ3 adjusted gross margin improved by 12.7 percentage points YoY to 47.9%, supported by utilization gains. 
• FQ3 Adjusted operating margin rose by 21.4 percentage points YoY to 32.2%, benefiting from gross margin improvements and operating leverage. 
• FQ3 adjusted net income margin rose by 18.3 percentage points to 27.9%. 

Adjusted net income expansion was moderately less than operating margin expansion, largely due to higher income tax provisions. However, higher taxes are simply the cost of doing business when adjusted net income soars 184.8% YoY to $225.7 million. In Q4, management projects further adjusted operating margin improvement, with the figure moving up to 35.5%, or a 3 point QoQ gain. 

Key Growth Drivers Powering Lumentum’s AI Networking Business 

Lumentum is achieving this growth without large sales from the Nvidia UHP partnership, as UHP shipments have yet to ramp significantly. Instead, electro-absorption modulated (EML) lasers, narrow linewidth and pump lasers, and optical transceivers are driving growth. 

EMLs are lasers used within optical transceivers for scale-out networking applications, with the company selling them as components, and using them in its own transceivers. Notably, Lumentum saw record EML shipments in FQ3. 100G shipments drove this, but 200G revenue also more than doubled QoQ. 

Narrow linewidth and pump lasers are used in scale across applications—connecting geographically separated data centers. Pump laser sales grew rapidly by 80% YoY, and narrow linewidth lasers saw their ninth consecutive quarter of growth, with sales rising 120% YoY. EML's, narrow linewidth lasers, and pump lasers helped the company’s Components revenue rise by 77.3% YoY to $533.3 million, accounting for 66% of total revenue. 

This strong growth comes even though Lumentum is capacity constrained across all three components. The firm is working to expand EML capacity at its Japan fab, expecting to increase EML units by over 50% by December 2026 versus a December 2025 baseline. 

When it comes to pump and narrow linewidth lasers, Lumentum says it is “effectively sold out for the foreseeable future." Notably, pump lasers are even more constrained than EMLs. 

Cloud Transceivers and Systems Revenue Expansion 

Cloud transceivers grew 40% QoQ with record shipments, likely driven mostly by 800G units. Cloud transceivers represent most of Lumentum’s System sales, which rose 121% YoY to $275.1 million, or 34% of total revenue. As EMLs are used in transceivers, the company is also facing significant capacity constraints here. 

The takeaway is that Lumentum is shipping these various products at a rapid and, in many cases, record pace, and still under-shipping the market. Demand is pent up, putting pricing leverage on Lumentum’s side, and creating future growth opportunities. All the while, demand specific to the Nvidia relationship has yet to meaningfully kick in. 

Indium Phosphide (InP): The Chokepoint Material in Optical Interconnects 

Across its business, Lumentum’s indium phosphide (InP) processing capacity is the unifying constraint holding back laser output. InP is the specialized semiconductor material that all of the discussed products are built on, with ideal properties for optical communication. 

Thus, ameliorating the constraints in InP wafer processing is key to meeting customer demand. Notably, InP capacity constraints come even as Lumentum leads the market, saying “We probably have more [indium phosphide] capacity than any company on the planet." 

Lumentum’s InP Capacity Expansion Plans 

Lumentum is making strides to increase its InP processing capacity. From the last quarter of calendar 2025 to the last quarter of calendar 2026, the company plans to increase its InP capacity by 50% while already having the industry’s largest base. This is a meaningful increase over the company’s past statements of expanding capacity by 40%. 

Despite all of this, the company is still under-shipping drastically, by more than 30% as of FQ3. Furthermore, as its UHP business scales, Lumentum expects the gap between supply and demand to widen. 

While this is a negative for unit growth, supply and demand imbalances can provide significant benefits to margins and EPS. The memory chip market shows how companies that control undersupplied AI infrastructure products are in a very favorable position. 

Supply Constraints Driving Pricing Power 

With InP imbalances expected to grow, Lumentum’s margins and EPS can be prime beneficiaries of this dynamic. 

Lumentum CEO Michael Hurlston substantiated the company’s pricing power recently, stating negotiations are on “very favorable terms” with non-Nvidia buyers. This comes as Nvidia will soak up much of its InP capacity, creating a “little bit of a feeding frenzy” among other players. 

It is important to note that InP constraints extend beyond the wafer processing layer. InP substrates are the most upstream input for InP-based products, and a set of concentrated suppliers controls this layer. Japanese firms Sumitomo Electric and JX Nippon Mining, as well as AXT (U.S.-headquartered, Chinese manufacturing), are the top names. China and the United States have created geopolitical risks at this level. 

Geopolitical Risks in InP Supply Chain 

Per AXT’s 10-K filing, China placed InP substrates on its export control list in February 2025, requiring an export permit for every order. Meanwhile, in March 2025, the United States placed 70% tariffs on Chinese products, including substrates. Supreme Court rulings have invalidated certain tariffs, but others remain in place. 

These factors have had a significant impact on AXT. The company saw full-year revenues fall 11% YoY in 2025, and North America fell from 10% of total sales to 1% in its latest quarter. This adds pressure to companies like Lumentum looking to ramp up InP wafer processing, with Hurlston noting, “the thing that keeps me up at night most is substrates.” 

However, Lumentum has worked to mitigate this risk through long-term supply agreements, signing a 7-year substrate supply deal with a non-Chinese firm that extends through the mid-2030s. Hurlston says Lumentum worked with this partner to “corner the supply of their indium phosphide substrates," presumably securing a very significant share of their capacity. 

With this agreement, Lumentum says it is in “pretty good shape on substrates." However, it needs to continue securing supply as laser output is going to have to take a “massive” step up in 2027, given CPO demand. 

Future Catalysts for Lumentum: 1.6T, OCS and CPO Growth 

Beyond products driving current results, Lumentum has two near-term catalysts layering on: the 1.6T transceiver ramp alongside the insourcing of continuous wave (CW) lasers. Lumentum is set to ramp its higher bandwidth 1.6T transceivers in FQ4. Combining its strong pricing power on 800G modules and ramping already higher-margin 1.6T modules should allow for further margin expansion. 

At the same time, Lumentum will insource more of the CW lasers used in its transceivers, expecting insourced CW lasers to be in ~20% of transceiver modules in FQ4. Reducing its reliance on third-party CW laser suppliers should benefit transceiver gross margin and alleviate some external supply constraints. 

Optical Circuit Switching (OCS) Growth Opportunity 

Optical circuit switching (OCS) is another key demand driver. Lumentum has recently signed a multi-year, multibillion-dollar order with an OCS customer. Currently, Lumentum holds a $400 million OCS backlog, which is “very much on track to be shipping” in the second half of calendar 2026. In calendar 2027, Lumentum expects OCS revenue to ramp above $1 billion. 

Importantly, Lumentum addressed concerns that this was simply a “bubble order" or a large one-time deal that will not be repeated. The firm definitively said that was not the case, and instead stated, “We would expect to see significantly more business across calendar '27 on the OCS.” This strongly suggests that the firm expects additional OCS orders from current or new customers in 2027. 

CPO Runway and Revenue Potential 

However, CPO demand, anchored through Lumentum’s Nvidia partnership and expected to broaden across hyperscalers over time, is the potential game changer. The company describes the CPO opportunity as being in three phases. Phase 0 is scale-out CPO, where Lumentum expects to generate $100 million in revenue in the final quarter of calendar 2026. The company will then deliver on a multi-hundred-million-dollar scale-out commitment in H1 2027. 

Lumentum’s market in scale-up CPO is drastically larger. Phase 1 CPO involves connections between racks in scale-up pods and is 3X-4X larger than its Phase 0 scale-out CPO opportunity. The opportunity in Phase 2 scale-up CPO, which involves links within each rack as copper gets displaced over even shorter distances, is 10X larger than Phase 0.

Graphic illustrating the evolution of co-packaged optics (CPO) deployment across AI data centers. Phase 0 begins with scale-out architectures using single-rack clusters. Phase 1 expands to multi-rack clusters with inter-rack optical links, increasing CPO connections by 3X to 4X. Phase 2 advances to intra-rack optical connectivity, significantly increasing link density and enabling larger-scale compute clusters. Source: Lumentum.

Phase 1 scale-up shipments are expected to begin in H2 2027 and ramp significantly in 2028 and beyond. The Phase 2 opportunity is expected to inflect in late 2029 and 2030. Increasing InP capacity is critical to meeting scale-up demand, which is exactly what Lumentum’s newly acquired facility in Greensboro is designed to accomplish. 

After retrofitting the site for InP-based devices, Lumentum expects to ramp UHP production in mid-2028. Greensboro is the ‘moonshot’ opportunity for Lumentum, with the company targeting $5 billion of incremental annual revenue capacity through the facility. Compared to Lumentum’s last 12 months' revenue of $2.488 billion, the full ramp-up of Greensboro alone has the potential to triple the size of its business.  

Greensboro will also come with semiconductor-like margins—which are structurally higher than transceiver margins. This provides Lumentum with another opportunity to significantly improve its profitability profile. 

Lumentum’s AI Networking TAM Expansion 

Through the combination of four networking markets: scale across (narrow linewidth and pump lasers), scale-out (CPO, transceivers, and transceiver components), scale-up (CPO), and OCS, Lumentum sees its optical AI total addressable market (TAM) expanding massively. From 2025 to 2030, Lumentum forecasts a 5X TAM increase, surging from $18 billion to $90 billion. 

Chart illustrating Lumentum’s projected expansion in the optical AI total addressable market (TAM), growing from approximately $18 billion in 2025 to over $90 billion by 2030, representing ~40% CAGR. Growth is driven by scale-out networking, scale-up architectures, and optical circuit switching (OCS), with 2030 bandwidth demand split roughly between 55% scale-out and 45% scale-up. Source: Lumentum.

The Ultimate Question: “Is it Too Late?” 

Clearly, Lumentum has a huge opportunity ahead of it to not only grow revenues but also expand margins. Fundamentally, this opportunity rests on the supply and demand dynamics in the optical networking market. Lumentum is already under-shipping the market by over 30%, despite having market-leading InP capacity today. 

Even as Lumentum increases capacity by 50% from December 2025 to December 2026, it expects the gap to widen. This setup puts more power in Lumentum’s hands over time, benefiting pricing, margins, and EPS. The dramatic rise in both Lumentum’s sales and margins today shows this dynamic is already playing out, and the factors driving it are getting stronger, not weaker. 

Still, investors have taken notice of this, as well as Lumentum’s very strong financial performance. Based on FY 2027 earnings estimates, Lumentum trades at a forward P/E ratio of 48.67X, approximately 109% above its average forward P/E of 23.25X since the start of 2023. 

Chart illustrating Lumentum’s forward price-to-earnings (P/E) ratio over time, showing a recent rise to 63.2x, significantly above its three-year median of 33.8x. The valuation expansion reflects strong investor expectations for continued growth in AI networking, optical transceivers, and co-packaged optics (CPO) demand. Source: YChartsYCharts

However, analysts forecast a dramatic rise in the denominator, reflecting continued transceiver growth and the ramp-up of scale-out CPO, OCS, and the early stages of scale-up CPO, boosting revenues and margins. 

EPS Growth and Forward Valuation 

Estimates place Lumentum’s NTM adjusted EPS at roughly $15.84. In Lumentum’s FY 2028 (the 12 months ending calendar Q2 2028) that estimate rises to $28.12. Using this figure puts Lumentum’s forward P/E at 31.65X, drastically closer to its average since the start of 2023. And, critically, this comes before Phase 2—Lumentum's largest CPO opportunity—gets underway. 

All the while, Lumentum benefits from having the largest base of InP capacity and expanding that capacity significantly, leaving the firm in a prime position to service scale-up demand. When considering these factors, Lumentum’s valuation starts to look much more reasonable. 

Final Thoughts: Looking Beyond Nvidia 

Networking stocks are notoriously difficult to navigate. The products are complex and supply chains shift quickly, particularly for those supplying Nvidia. This can create the kind of volatility that pushes most investors to the sidelines. Lumentum’s 339% run in 2025 is a perfect example: most funds allocated heavily to Nvidia instead of recognizing that run would likely continue into 2026. Five months later, and Lumentum surged another 135.4%. 

The broader AI trade problem illustrates this well, as many funds treat Nvidia as their entire AI allocation. Meanwhile, the AI infrastructure buildout is expanding across power, networking, memory and more, and the best-performing names are increasingly not Nvidia. 

The I/O Fund owned 4-of-10 top large cap performers in April, which illustrates the importance of looking more broadly at the AI trade. Our large Lumentum position is up 135.4% YTD – or 6.8X what Nvidia returned in the same window. Lumentum is one example; along with Bloom Energy and another networking stock up 750% since November, leading to YTD returns of 48% – nearly 3X the Nasdaq and 5X popular ETFs. 

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Leo Miller, AI and Semiconductor Investment Writer at I/O Fund, contributed to this analysis. Leo Miller owns shares of NVDA.

Please note: The I/O Fund conducts research and draws conclusions for the company’s portfolio. We then share that information with our readers and offer real-time trade notifications. This is not a guarantee of a stock’s performance and it is not financial advice. Please consult your personal financial advisor before buying any stock in the companies mentioned in this analysis. Beth Kindig and the I/O Fund own shares in LITE at the time of writing and may own stocks pictured in the charts.

Recommended Reading:

• Bloom Energy — Our 2026 Top Pick Was the Best Performing Stock in April
• Inside Nvidia’s $4B Optical Strategy—and Why CPO Changes Everything
• Is Nvidia Stock a Buy? Why Semiconductor Strength May Signal a Market Top
• Nvidia’s $20 Trillion Thesis Is Intact. My 2026 Allocation Isn't

Last month was the S&P 500’s best month in six years, marking the biggest rally since the Covid lows in April of 2020. The S&P 500 rose 10.43%, while the Nasdaq gained more than 15%. Yet the single best-performing large-cap stock in that historic month was not Nvidia, Microsoft, Meta, or another obvious AI leader. Rather, it was Bloom Energy, which rose roughly 109%. 

Bloom was our 2026 Top Stock pick, published on February 27th when shares were at $160.90. However, my firm’s history on the stock began one year ago when we first identified AI energy as the next bottleneck, with initial buys during the April lows at $16.64 and $17.04. For most of the last 12 months, we’ve held Bloom at a high allocation of 10% or higher, with real-time trade alerts sent to our Research Members. Today our returns from those entries are roughly 1300%. Many of Wall Street's most renowned firms eventually followed the I/O Fund much later and entered at significantly higher prices. 

Earlier this year, I designated Bloom as our Top 2026 Stock Pick on February 27 when shares were at $160.90 (about 10X on our cost basis).  

The decision to place Bloom as our Top Stock pick required strong conviction — not only in Bloom Energy's positioning, but in the sheer pressure from AI's primary bottleneck to believe the stock could see a repeat year of strong performance. Repeat years are especially rare after a big run-up as early investors typically book gains and move on. 

Below, I’ll walk you through why Bloom outperformed in the strongest rally tech has seen in six years, why the recent Q1 2026 results confirm the fundamentals beneath the rally, and why I believe the setup still holds – even after the stock rose 109% in April, just two months after we named it our 2026 Top Stock pick. 

Why an Energy Stock — Not Software or Semiconductors — Led Tech’s Biggest Rally in 6 Years 

Investors should take note that tech’s biggest month in six years was not led by a Mag 7 stock, a semiconductor, or a software platform like it was in 2020. Although many of these sectors were deservedly ranked in the top 10, the month’s biggest outperformer was centered around power availability.

The reason for this is straightforward as companies like Microsoft, Google and Meta are spending hundreds of billions annually on AI, with tens of billions allocated to Nvidia’s GPUs and custom silicon like Google’s TPUs. These systems risk being delayed if Big Tech cannot energize and deploy them quickly. Meanwhile, the market has already penalized these companies for outsized spending on AI infrastructure. The effects of low immediate ROI only compound with a timing risk as GPUs sit idle, while competitors who do have power amplify the consequences of a delay. 

Despite power being the primary bottleneck, the market is hyper-focused on whether Big Tech can monetize AI. My contention in my original article on Bloom Energy is that the market is missing the point. the market is missing the point. Monetization has never been Big Tech’s weakness as explosive revenue growth and high margins have defined their businesses for decades. While execution risk always exists, these companies remain the world’s most reliable operators at scale.   

Instead, the real risk to the AI economy lies in the physical constraints of scaling these AI ambitions — not in compute availability from companies like Nvidia or Broadcom, and certainly not in Big Tech’s software capabilities, but in power availability, thermal management, and infrastructure that were never designed for this magnitude of demand. 

Bloom Energy Solves AI’s Most Critical Bottleneck: Time to Power 

Over the next two years, Nvidia’s GPU systems are expected to require a 5x increase in power per rack from what was needed in the first half of 2025 as we move across GPU generations from Blackwell to Rubin Ultra. As stated, if hyperscalers cannot energize these systems quickly, billions of dollars of AI capex can sit idle, especially critical now that the AI market is shifting toward generating inference revenue. 

Therefore, due to the rapidly increasing power requirements for AI systems, it is not enough to say the AI economy needs more power, but rather it needs power urgently. These are two entirely different matters; for example, the first could be supported by the expansion of nuclear power and the electrical grid, but the latter cannot. In fact, combining these two is something very few companies can do. 

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Behind‑the‑Meter Fuel Cells vs Grid‑Dependent Power 

Bloom Energy offers onsite power generation through solid oxide fuel cells that are behind the meter to reduce dependency on the grid.  By providing behind-the-meter generation, Bloom reduces reliance on utility infrastructure and accelerates time-to-power for customers. An added benefit is the United States is the largest producer of natural gas, therefore, Bloom does not struggle to secure supply given the United States has large, well-developed gas supplies and pipeline infrastructure. 

Our primary message has been “time to power” for Bloom, and the company continues to stand out for this very reason as it is finding strong product market fit in AI data center power needs. This is a key advantage as on-site power is becoming more of a necessity as grid constraints and connection timelines rise.   

Regarding grid constraints, PJM has already fallen short of reliability requirements in its last two capacity auctions, including a roughly 6.6 GW shortfall for the 2027/28 planning year, while ERCOT’s interconnection queue has surged to about 226 GW, including roughly 165 GW from data center projects targeting approval by 2030. Against that demand, ERCOT added only 23 GW of new capacity in 2024–25, underscoring why time-to-power is becoming a central bottleneck for AI data centers. 

This further validates Bloom’s positioning by enabling new data center projects to come online sooner with on-site, behind the meter power without sitting in interconnection queues for years at a time. Bloom has already proven that it can quickly establish data center power solutions in a rapid manner, completing shipments to Oracle Cloud Infrastructure in just 55 days of its 90-day delivery request.   

Its fuel cells are also fuel-flexible and can run on natural gas, biogas, or hydrogen, and provide continuous power with 99.9-99.999% reliability metrics. They are also modular in nature and can scale from 20 MW to 500 MW+, allowing flexibility in deployments and ease of scaling. Bloom is also continuously improving on price-performance, stating that its fuel cells have seen double digit YoY cost reductions each year for the past ten years, and a 10X increase in power production in the same footprint versus ten years ago.   

Oracle’s Project Jupiter Sends Bloom’s Stock Soaring in April 

We’ve covered previously that Bloom delivered a fuel cell system to Oracle in 55 days, standing out among the longer to deploy solutions in the market.  In April, Bloom Energy announced an expansion with Oracle for a total of 2.8GW of fuel cell capacity with 1.2GWs shipping now.  

Following the capacity announcement, Oracle announced Project Jupiter yesterday stating the company will utilize up to 2.45GWs “to fully power the AI data center campus” located in New Mexico. This is an important development as it means the AI data center will not use gas turbines and the diesel generators as originally planned. According to the press release, nitrous oxide emissions will be cut by 92% compared to the previous gas turbine plan.   

The following was stated about the new deal: “It will be 100% Bloom. When completed, it will be one of the largest islanded microgrid power facilities in the world. Oracle pivoted to Bloom only solution for 2 main reasons: first, be a responsible corporate citizen and partner by being responsive to resident concerns about air quality, water use, noise and increasing electricity rates.   

Second, to stand up their grid independent and clean AI factory with even greater reliability and speed. Bloom is the cleanest commercially available on-site power generation option for such data centers and the most water efficient. Even Blooms community-friendly attributes, Oracle should be able to energize the campus materially faster than any other available alternative solution in the market.” 

The new deal with Oracle has sent Bloom’s stock soaring on a 1-month basis (on top of the already strong 1-year returns). We’ve discussed in-depth the product market fit for the stock as being “time to power,” yet the company’s value proposition has actually improved since last quarter as the Jupiter deal serves as an important proof of concept for the company.  

My understanding, as an analyst how has tracked this stock longer than any research site on record, is the Jupiter deal will mark the first time an AI data center will be powered entirely by Boom Energy’s solutions – an important moment in Bloom’s history. 

At minute 5:43, Beth Kindig discusses why Bloom Energy is her top stock pick for 2026. 

Why AI Inference Will Drive the Next Wave of Power Demand 

The inference market will require more gigawatts than training, yet an additional constraint is location (or geography). Inference sits at the edge, close to users for latency, which means demand will be coming from dense metros instead of less populated areas, such as where training data centers are located (rural areas). 

The underlying trend is significant. Boston Consulting Group projects inference power demand to grow at a 122% CAGR through 2028, compared to 30% for training. McKinsey forecasts inference rising from ~21 GW today to ~91 GW by 2030 at a 35% CAGR, accounting for the majority of AI's incremental power draw through the end of the decade. As frontier models stabilize and the AI economy shifts toward serving users at scale, inference will overtake training as the dominant compute workload, which translates to inference also consuming more power. 

Bloom’s Advantage in Dense Metro Deployments 

Bloom could see more demand from the inference market compared to training as it offers a combination of low emissions, minimal water use and a small footprint – which is ideal for dense areas. Its solid oxide fuel cells (SOFCs) are electrochemical rather than combustion-based, producing minimal emissions. They are also modular, scaling from 20 MW to 500+ MW, and operate quietly enough to be placed in urban environments. Lastly, they run on natural gas pipelines that already exist in metros, bypassing grid interconnection queues entirely. For an inference-driven AI buildout, these details matter. 

Notably, the newly appointed CFO is a signal that Bloom is positioning for this shift toward inference, as Simon Edwards, was the former CEO of Groq, a leading developer of inference infrastructure and LPUs (recently acquired by Nvidia). Edwards is likely a deliberate, strategic hire ahead of the inference scale-up. 

Q1 2026 Revenue Surged 130% YoY — Strongest Growth in Bloom Energy’s History 

Bloom Energy reported Q1 2026 revenue of $751.1 million, up 130.4% YoY and beating estimates by 39.1% — the strongest growth in the company's public history. Product revenue reached $653.4 million, up 208.4% YoY, and now represents ~87% of total revenue. 

Management raised full-year 2026 guidance to $3.4 billion–$3.8 billion, implying 77.9% YoY growth at the midpoint, up from $3.1–$3.3 billion. 

Gross and Operating Margins Expanded Sharply YoY 

GAAP gross margin was 30%, up 280 basis points YoY. Adjusted gross margin was 31.5%, up from 28.7% in Q1 2025. Management raised full-year 2026 adjusted gross margin guidance to 34%, up from 32%. 

GAAP operating margin was 9.6%, up from (5.8%) in Q1 2025. Adjusted operating margin was 17.3%, up from 4%. Management raised FY2026 adjusted operating income guidance to $675 million at the midpoint, up from $450 million. 

Bloom Energy EPS Crushed Estimates by 242%  

Adjusted EPS was $0.44, crushing estimates of $0.13 by 242.4%. GAAP EPS was $0.23 versus estimates of ($0.02). Management raised full-year 2026 adjusted EPS guidance to $2.05 at the midpoint, implying 169.7% YoY growth, up from $1.405. 

Positive Cash Flow Inflection 

Bloom reported the first positive Q1 operating cash flow in company history at $73.6 million (9.8% of revenue), versus an outflow of ($110.8 million) a year ago. Free cash flow was $47.4 million. Cash of $2.49 billion and debt of $2.60 billion at quarter-end. 

Conclusion: 

Bloom Energy’s April rally validated what we have been writing since June 2024, which is that power is the leading constraint on the AI buildout. Of this, time-to-power is the variable that matters most, and Bloom is one company that can deliver mission critical power solutions for the incoming inference market. 

The harder question, and one that matters more than entering Bloom at $17 in April 2025, is what comes next. The investors who outperform from here will not be the ones who pile into the trade that already worked. They will be the ones positioned for the next bottleneck long before the market sees it. 

Get $275 Off on our Advanced Plan. April was the Nasdaq-100’s best month in six years, and few portfolios participated like I/O Fund. Bloom Energy was the top-performing large-cap stock during the April rally, yet I/O Fund also held four of the top 10 large-cap stocks at allocations of 7% or higher. 

Since May 2020, our audited portfolio has returned 326% cumulatively. Based on our published comparisons, that would place I/O Fund #1 versus hedge funds and #3 versus tech ETFs or mutual funds — before including our 48% YTD return in 2026. 

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Please note: The I/O Fund conducts research and draws conclusions for the company’s portfolio. We then share that information with our readers and offer real-time trade notifications. This is not a guarantee of a stock’s performance and it is not financial advice. Please consult your personal financial advisor before buying any stock in the companies mentioned in this analysis. Beth Kindig and the I/O Fund own shares in BE at the time of writing and may own stocks pictured in the charts.

Recommended Reading:

• Inside Nvidia’s $4B Optical Strategy—and Why CPO Changes Everything
• Is Nvidia Stock a Buy? Why Semiconductor Strength May Signal a Market Top
• Nvidia’s $20 Trillion Thesis Is Intact. My 2026 Allocation Isn't
• Bitcoin 2026 Price Prediction: Why the Dollar, Global Liquidity and Volume Signal More Downside Ahead

• Over the coming years, CPO is poised to see a dramatic uptick in demand, as data center operators push to expand the limits of AI. 
• CPO provides key benefits over the two networking systems that dominate today: copper and optical transceivers. This includes up to 5x power efficiency versus transceivers and much higher bandwidth. 
• Nvidia and Broadcom are huge players in CPO, and firms that gain qualification in their supply chains can be massive beneficiaries

Nvidia’s Rapid Networking Roadmap Is a Key Driver for AI Stocks

Within the AI investment theme, there is nowhere that the supply chain shifts faster than in networking, leading companies to gain content on new platforms or lose incremental share. 

The reason is straightforward: much of the market is tied to a single customer, Nvidia; and Nvidia is rolling out new architectural iterations at an unusually fast pace. When it comes to networking, two of the most important architectural advancements are the increase in pod and cluster sizes and the transition to 200G per lane. 

Last month, Nvidia made $2 billion equity investments in two separate optical component suppliers: Coherent and Lumentum. Nvidia is securing its supply chain as it ramps its co-packaged optics (CPO) roadmap and writing big checks to do so. These targeted moves signal that CPO, the next major architectural shift in AI networking, is moving from theory to reality. 

Below, we break down why this transition is taking place and the key companies involved in the secular trend toward CPO. 

Nvidia’s Move to Larger Pods: Scale-Up from NVL72 to NVL576 and Beyond 

With Blackwell and Blackwell Ultra, Nvidia was fundamentally focused on solving scale-up problems, where the primary challenge is binding large numbers of GPUs into a single coherent node with a unified memory using ultra-dense, low-latency NVLink fabrics. This led to NVL72, which packed 72 GPUs into one rack, acting as one giant GPU. 

However, with Rubin and Rubin Ultra, the company is pushing this concept further. Nvidia will offer Rubin in NVL72, NVL144, and NVL576 configurations, connecting two and eight racks respectively into a single NVLink scale-up domain. With NVL576, an eight-rack pod behaves as a single, massively larger GPU. 

Rubin also doubles NVLink scale-up bandwidth versus Blackwell — 3.6 TB/s of bidirectional GPU-to-GPU bandwidth on the sixth-generation NVLink 6 interconnect, with 36 switches per NVL72 rack delivering 260 TB/s of total bandwidth versus Blackwell's 130 TB/s. 

As these pods grow in size and require higher bandwidth, copper hits physical limits. Each step up in bandwidth degrades signal integrity faster, shortening the effective length of copper cables. A chart from Marvell illustrates this. At 100G per lane, the speed that now dominates deployments, copper can stretch around 5 meters using range-extending AECs. At 200G, the speed that will be used in Rubin Ultra, the effective length of AECs falls to just 3 meters. 

Comparison of passive direct attach copper (DAC) and active electrical cable (AEC) reach at increasing lane speeds. As bandwidth scales from 50G to 200G per lane, copper cable length degrades significantly, with AEC reach falling to roughly 3 meters at 200G. Source: Marvell estimates.

According to Supermicro, one GB300 NVL72 rack is 0.6 meters wide. Rubin Ultra NVL576 will place eight racks side by side, resulting in a width of nearly 5 meters; too long to connect the entire pod at 200G using AECs. 

In turn, Nvidia will use CPO for rack-to-rack connections in Rubin Ultra NVL576, although copper will still be used for connections within each rack. This is why Huang said that customers will be able to buy Rubin Ultra in “copper, or copper plus CPO." Copper plus CPO will be used in NVL576, while only copper will be used in smaller configurations. Huang went on to say, “two years from now, at [NVL]1152, it's all CPO because there's a limit to how far it could take copper.” 

Co‑Packaged Optics as a Structural Shift for Nvidia's Stock

With pod sizes and bandwidth only increasing, the transition from copper to optics in scale-up is structural, not cyclical. CPO is positioned as the eventual endpoint of that transition.

Notably, companies in the supply chain are moving to reflect this. Credo recently acquired DustPhotonics to diversify away from AECs, the product the company has built its name on. Through this deal, Credo adds silicon photonics to its portfolio, with the company expecting to generate $500 million in optical revenue in FY2027. For reference, Credo reported its Q3 FY2026 results in March. This will aid the company in bridging the gap between AEC content and optics content. 

Marvell acquired Celestial AI as it looks to offer CPO solutions. During its Q4 FY2026 results in March, Marvell projected its CPO revenue reaching a $500 million annualized run rate in Q4 FY2028 before doubling to $1 billion by Q4 FY2029. Nvidia and Marvell also recently announced a strategic partnership, connecting Marvell to Nvidia’s AI factory ecosystem through NVLink Fusion. Customers can easily pair Marvell products, including custom XPUs, certain scale-up networking, and silicon photonics, with Nvidia’s rack-scale AI compute and other components using NVLink. Additionally, Nvidia has invested $2 billion in Marvell. 

Scale-Out CPO: Boosting Performance and Efficiency Versus Transceivers 

Scale-out networking poses a different challenge for CPO adoption, as companies look to connect larger pods into massive clusters with 1 million AI accelerators. Copper has already been largely phased out of scale-out, as distances are far too long. This has led to optical transceivers becoming a key solution.  

Optical transceivers take electrical signals sent through copper traces in ASIC switches and convert them into optical signals. These signals then flow through fiber optic cables, which can stretch kilometers at high bandwidths without losing integrity. 

However, using optical transceivers also comes with significant drawbacks. Most notably, they consume much more power than copper and are more expensive. This is the trade-off that data center operators are increasingly having to accept in exchange for longer cable lengths and/or higher bandwidth.

mid

CPO offers something closer to a best-of-both-worlds solution, allowing for both long cable lengths as well as better power efficiency, higher bandwidth, and lower latency compared to transceivers. CPO provides better power efficiency by drastically shortening the distance signals flow through copper before conversion to light.  

In most cases, CPO eliminates the need for power-hungry DSPs, which clean up the degraded electrical signal before sending them to transceivers. This comes as CPO embeds optical engines in the same package as the switch. A visual from Nvidia illustrates this difference clearly. The orange line (copper) is much shorter in the CPO diagram, and the DSP is gone, allowing the electrical signal loss to be significantly lower.  

Comparison of a traditional pluggable switch architecture and Nvidia’s co‑packaged silicon photonics design. In pluggable systems, electrical signals travel across the PCB, connectors, and port cage before reaching an external transceiver, resulting in roughly 22 dB of signal loss and requiring DSPs and multiple lasers. Co‑packaged optics integrate silicon photonics alongside the switch ASIC, shortening the electrical path to the substrate, reducing loss to about 4 dB, and improving power efficiency at 1.6 Tb/s. Source: Nvidia

CPO also offers higher bandwidth and lower latency versus pluggables. As inference workloads rise, largely driven by agentic AI, improving these variables is key. Automating workflows in enterprise environments means higher data rate requirements compared to the use of chatbots. 

LLM developers will compete on how fast their models can execute tasks, making latency reduction paramount. Reducing latency is particularly relevant going forward, as many expect inference to overtake training as the dominant AI workload over the coming years.  

McKinsey projects that by 2030, inference will account for 93 GW of data center demand, versus 62 GW for training. It sees inference demand rising by a CAGR of 35% through 2030, significantly faster than training’s 22% CAGR. 

In summary, as AI workloads continue expanding, power efficiency, bandwidth, and latency improvements are vital to increasing performance while limiting costs. CPO is a key solution that allows for these advancements. 

CPO Adoption: Gated by Low-Cost Copper and Reliability Concerns Near Term 

Despite these benefits, CPO faces constraints that limit its adoption today. Copper and optical transceivers are generally sufficient at today’s bandwidth levels and cost less than CPO upfront. With hyperscalers already spending hundreds of billions on AI infrastructure annually, staying on lower-cost solutions makes more sense for now. In line with this, Broadcom CEO Hock Tan said that the industry will “try to scale up within a rack in copper as long as possible.” Echoing this, Jensen Huang said, "We should scale with copper [as far as] we can, as long as we can." 

CPO reliability is another hurdle that developers are tackling. Theoretically, CPO should be more reliable than pluggables, as it consolidates many otherwise separate parts, creating fewer points of failure. However, because CPO has not been deployed at scale, there is a lack of real-world evidence to support this idea. 

This is key, as when a CPO chip fails, servicing costs are much higher. Pluggable transceivers can be easily swapped out when they fail, but this is not possible when optical engines are embedded in the switch package. CPO servicing requires removing the full switch to have a complex repair performed or replacing it entirely. 

To accelerate adoption, CPO providers must demonstrate strong reliability of the technology. On this front, Broadcom recently made a significant step forward. In a study conducted with Meta, the company showed a 5X improvement in serviceable failures compared to pluggables. The study also found no unserviceable CPO failures after 15 million hours of device testing. This provides solid initial evidence of CPO reliability. 

Still, these tests were performed in a lab environment, not in actual data centers. This underscores the need for more CPO reliability testing in real-world environments before adoption hits an inflection point. The industry has an opportunity to generate this data through early CPO deployments in 2026 and 2027, setting the stage for increased adoption thereafter. 

Nvidia and Broadcom Are Leading the Push Into CPO Networking

Nvidia and Broadcom are the two market leaders in CPO, as both are leaders in switching ASICs. Nvidia has the largest networking business in the world, with revenue hitting $11 billion. Meanwhile, one-third of Broadcom’s $10.7 billion in total AI revenue, or approximately $3.6 billion, came from networking last quarter. 

Broadcom has been developing CPO since 2021 and is now shipping its third-generation scale-out product, the Tomahawk 6 – Davisson switch, which delivers 3.5x better power efficiency than pluggables. Broadcom is currently developing its fourth-generation CPO product, which will double the per-channel bandwidth compared to Davisson. 

Meanwhile, Nvidia will use CPO for scale-up NVL576 approximately a year from now. For scale-out networking, Nvidia has its Spectrum-X Ethernet Photonics switch, which it says will deliver 10X greater network resiliency with CPO, bringing 1.6T silicon photonics (SiPho) optical engines directly onto the switch. 

Maximum bandwidth doubled to 102.4Tb/s per ASIC, matching Broadcom’s Davisson, though Nvidia is also offering the industry’s first four-ASIC design, delivering 409.6Tb/s bandwidth. Notably, Spectrum-X Ethernet switches drive up to 5X better power efficiency with a lower cost versus pluggable transceivers. 

CPO adoption should bring substantial benefits to Nvidia and Broadcom. However, companies that gain qualification within Nvidia and Broadcom’s CPO supply chains are poised to be among the biggest winners from this networking shift. I/O Fund specializes in identifying these types of lesser-known networking players. 

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Optical and CPO Stocks: Coherent, Lumentum, and Corning

Coherent, Lumentum, and Corning are optical and CPO beneficiaries to be aware of. All three are benefiting from scale-out transceiver adoption today and are positioned to benefit from CPO gradually replacing copper in scale-up over the coming years. Below, we break down what each company supplies, their opportunity ahead, and how the market is valuing them today. 

What Each Company Supplies in the CPO and Optical Ecosystem

Coherent and Lumentum: Lasers, Silicon Photonics, and Nvidia’s CPO Supply Chain

Coherent and Lumentum make pluggable optical transceivers and high-powered lasers, critical components within transceivers. While CPO will replace transceivers in certain instances, it also drives higher content for the SiPho-laser ecosystem and CPO photonics components, as SiPho will serve as the backbone for the CPO switches. This extends beyond the photonics ICs to include CW lasers and ultra-high-power (UHP) lasers for external light source (ELS) modules.  

Coherent and Lumentum expect to be leading suppliers of these components within Nvidia's CPO rollout. Nvidia has rubber-stamped its supply chain relationship with both firms, investing $2 billion in each to fund manufacturing capacity expansions.

Corning’s Role in CPO

Corning plays a different role in the ecosystem as one of the top fiber optic cable makers. CPO adoption will translate into much more fiber optic cable usage in data centers. According to Marvell, this increase will be very significant. They say CPO will enable "tens of thousands of fiber per rack, no longer just a few thousand." Marvell believes the increase in fiber usage will be so large that the industry must create new innovations to manage it.

Optical Demand Is Inflecting Across AI Data Centers

Demand is already inflecting for these companies. Lumentum's revenue rose by over 65% YOY to $665.5 million in its latest quarter, and adjusted operating margin expanded by 1,730 basis points. Lumentum expects growth to accelerate to around 90% YOY next quarter and an approximately 500 basis point sequential operating margin expansion.  

Coherent saw revenues rise by 34% YOY in its data center and communications segment last quarter, driven by growth in 800G and 1.6TB transceivers. The company's data center book-to-bill ratio exceeded 4X, showing how dramatically demand is outstripping supply. 

Meanwhile, Corning's Enterprise business, which captures sales inside data centers, grew 61% YOY in 2025 to $3 billion, with the hyperscale data center portion growing significantly faster. 

Optical and CPO Market Outlook Through 2030

The market ahead of these firms is substantial. Corning has made very strong statements around its opportunity to benefit from scale-up CPO adoption. The firm believes that its scale-up CPO opportunity is at least 2-3X larger than its Enterprise business, implying an incremental opportunity of $6 billion to $9 billion. Management believes it could be even larger as it spends more time with partners in the ecosystem. Compared to Corning's 2025 core sales of $16.41 billion, this incremental market is very significant. 

Coherent estimates that its serviceable addressable market (SAM) in CPO will be more than $15 billion by 2030. This compares to Coherent's LTM revenue of $6.29 billion. Notably, SAM estimates represent just the portion of the total addressable market (TAM) that a company believes it can realistically serve.  

Related to this, Lumentum estimates that its current optical AI TAM is $18 billion today. It sees this figure increasing by more than 5X to over $90 billion in 2030. These forecasts help illustrate the huge opportunity that exists for smaller players in the optical and CPO market.

CPO Shipments Are Set to Gain Share

Importantly for Coherent and Lumentum, TrendForce estimates that both transceiver and CPO shipments will rise greatly over the coming years, although CPO will increasingly take share. Forecasts show optical transceiver shipments continuing to rise from around 50 million in 2026 to nearly 200 million by 2030. Simultaneously, CPO shipments exceed 50 million by 2030, and increase their penetration rate within optical networking from less than 1% to more than 35%.

TrendForce forecast for CPO penetration in AI data centers from 2025 through 2030. Total optical shipments continue to rise, while co‑packaged optics scale rapidly from negligible adoption in 2025 to more than 35% penetration by 2030. The data highlights a structural shift toward CPO as data center bandwidth and power efficiency requirements increase. Source: TrendForce, March 2026.

Valuation 

How Much CPO Upside Is Already Priced Into Networking Stocks

The market has already moved to reflect much of the optical and CPO prospects for these stocks. All three are trading at or very close to their all-time high forward P/E ratios, with these multiples being 2.2-2.6X higher than their median levels over the past three years. Since the end of June 2025, Lumentum has delivered a return of 950%, while returns exceed 280% and 210% at Coherent and Corning, respectively. 

Chart showing the current and three-year median forward P/E ratios of Coherent, Lumentum, and Corning. Coherent’s forward P/E is 54.2x versus a median of 24.7x, Lumentum’s forward P/E is 73.9x versus a median of 33.1x, and Corning’s forward P/E is 52.4x versus a median of 20.1x. Source: Koyfin

Conclusion

CPO Is a Multi‑Year Structural Tailwind for AI Infrastructure 

The AI networking stack is moving secularly towards optics and away from copper. Nvidia's pod-scaling roadmap clearly demonstrates this. From NVL72 in Blackwell today to NVL576 with Rubin Ultra, NVL1152 with Feynman, and potentially beyond, the physical limits of copper cannot be engineered around. With CPO emerging as the preferred optical form factor, adoption will continue to increase. 

How quickly the transition moves is up for debate. Broadcom and Nvidia management teams have both said that copper will be used for scale-up as long as possible, and TrendForce estimates that CPO penetration will remain in the low single digits through 2027 before inflecting. Reliability validation in real-world deployments over the next 12-24 months will be a key factor in determining the pace, while there are bridge solutions, such as NPO and LPO. 

Regardless, the supply chain is already shifting. Component makers and switch vendors are positioning for a networking stack that looks considerably different from today's. For investors, the CPO transition is a clear multi-year theme in AI infrastructure, with implications that extend well beyond the handful of names the market is focused on. 

My updated Q2 Top 15 AI Stocks report was just released. The report runs over 70 pages and identifies the 15 stocks I believe will lead the AI market this quarter. The report is built on the same investment discipline that identified massive winners like Bloom Energy and Lumentum early in their AI cycle.

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Please note: The I/O Fund conducts research and draws conclusions for the company’s portfolio. We then share that information with our readers and offer real-time trade notifications. This is not a guarantee of a stock’s performance and it is not financial advice. Please consult your personal financial advisor before buying any stock in the companies mentioned in this analysis. Beth Kindig and the I/O Fund own shares in NVDA at the time of writing and may own stocks pictured in the charts.

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