Palantir’s stock has defied gravity, delivering steady performance that no other AI software stock has come close to matching (yet). For investors, the Palantir thesis is two-fold: the company must continue to scale its Commercial segment after posting multiple quarters of over 50% growth, while also sustaining a high valuation. Both matters and the bar is undeniably high. 

What separates Palantir, however, is not simply growth, but capability. The differences matter as unlike traditional AI-enabled database or business intelligence competitors, Palantir can operate effectively even when data sets are incomplete or fragmented—situations where most models struggle. In that regard, traditional business intelligence companies require a complete data set, whereas Palantir can handle situations where one isn't available. You can think of the competitive advantage as actionable depth, as Palantir has described it: “the reasoning that goes into decision-making, not just data.”    

Palantir’s Artificial Intelligence Platform (AIP) integrates generative AI with operational data and workflows, and, when combined with Palantir’s other platforms, Foundry and Apollo, it provides an AI service mesh that can run hundreds of microservices, scale compute via its Rubix engine, and orchestrate updates through Apollo.   

Additionally, Palantir’s knowledge graph, referred to as Ontology, is a distinct advantage. The graph offers better context than a large language model would on its own – or as Palantir states, it’s “the reasoning that goes into decision-making.” Palantir made key upgrades to AIP with the introduction of AI-forward-deployed engineers (FDEs) and the AI Hivemind, and brought Ontology to the edge, enabling deployment on mobile devices. 

Palantir Stock leads the AI software pack, delivering one of the best reports across tech in Q3. Revenue accelerated nearly 15 points sequentially to almost 63%, with strong growth in key metrics and a 28-point acceleration in US Commercial revenue to 121% YoY. The Artificial Intelligence Platform (AIP) is driving most of the Commercial growth, as there was a clear revenue inflection when AIP launched in mid-2023. 

The company reported strong key metrics, with net retention rate (NRR) expanding six points sequentially to 134%. Over the past two years, NRR has risen an impressive 27 points, and Palantir noted that AIP is continuing to drive existing expansions and new customer conversions. On the other hand, Palantir’s forward P/S ratio trades at an outstanding 67.5.   

Below, I discuss what you need to know about Palantir, and the hotly debated stock. 

New Product Upgrades – AIP FDEs, Hivemind and Edge Ontology 

Palantir made a handful of upgrades to its AIP and Ontology in Q3, unveiling AIP forward-deployed engineers (FDEs) in beta, AI Hivemind, and Edge Ontology, all aimed at accelerating AI deployment with its customers.  

AI FDEs builds upon Palantir’s take on software engineers, the forward deployed engineer, which sit at the intersection of software, sales, and platform engineers, embedding within customer teams to closely develop and tailor AI software and solutions directly to their needs. Palantir now brings this to AIP, with the AI FDEs being its AIP-native deployment AI agent that “understands how to connect to data sources, how to integrate and transform data, how to create ontologies and functions and build applications.” AI FDEs function in conversational commands, allowing customers to easily turn requests into autonomously executed Foundry operations. 

Palantir says the AI FDEs are increasing productivity for customers, noting that at one customer, two of its human FDEs utilized AI FDEs to migrate to a legacy data warehouse in five days, a task which Palantir says would normally have taken up to two years.

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AI Hivemind is a new tool within AIP that Palantir says will orchestrate a “swarm of dynamically generated agents to tackle hard problem solving, idea generation refinement and executable proposal generation that is integrated with Ontology and therefore aware of the context of your enterprise.” Palantir says the tool was developed to help government clients solve extremely complex problems with classified data (such as generating intricate mission plans), but it has already been tested by commercial clients to help “identify bottlenecks to their supply chain, proactively developing possible solutions and then leveraging AI FDE to code that up into an actual solution.” 

Edge Ontology is Palantir’s new, lightweight Ontology that allows it to run on mobile devices, letting customers build mobile apps or embedded software for hardware such as robots or drones. Edge Ontology is also fully integrated with AIP. While Palantir was very thin on details, it’s likely tied to its existing partnership with Qualcomm, where the two brought Ontology to edge devices powered by Qualcomm’s Dragonwing processors. This partnership focused on the industrial, auto and manufacturing sectors, and remote and offline environments. 

Together, the new product upgrades reflect Palantir’s dedication to continuously improve its platform and help customers consistently solve problems daily. Financially, the three new upgrades can help accelerate deployments and adoption of AIP, help secure more (and potentially larger) contracts, and tap into new markets such as IoT at the edge. 

Financials 

Revenue Accelerates Nearly 15 Points to 63% YoY 

Palantir reported $1.18 billion in revenue in Q3, up by an impressive 18% QoQ and beating estimates by 8.4%, driven by unwavering momentum in US Commercial. On a YoY basis, revenue growth accelerated 14.8 points to 62.8% YoY, the largest sequential acceleration to date and marking Palantir’s highest growth rate since going public. Over the last nine quarters, topline growth has accelerated ~50 points, from just 12.7% in Q2 2023, a rare feat to accomplish. 

For Q4, Palantir guided revenue up 60.6% YoY to $1.327 to $1.331 billion, well ahead of estimates for 44.2% growth to $1.19 billion. While this does represent a marginal deceleration at face value, this sequential deceleration is in line with trends from previous quarters.  

Palantir (PLTR) Revenue YoY Growth: 9-Quarter Acceleration Hits Record 62.8% in Q3 2025, marking the highest growth rate since its IPO. This chart highlights the unprecedented revenue acceleration driven by Palantir’s Artificial Intelligence Platform (AIP).   

Source: Company IR 

For the full year, Palantir raised its revenue outlook to $4.396 to $4.40 billion, pointing to YoY growth of 53.5% at midpoint, a sharp acceleration from 29% growth in 2024. To put in perspective the strength of this acceleration, Palantir had initially guided for just 30.9% growth to $3.76 billion in revenue back in Q4 2024; growth is now more than 22 points faster.  

Impressive 28 Point Acceleration in US Commercial Revenue to 121% YoY 

Palantir’s US Commercial segment is generally seen as the primary vector for its AIP-driven growth, with robust momentum only accelerating further in Q3.   

US Commercial revenue grew 29% QoQ and 121% YoY to $397 million in Q3, accelerating from 93% YoY growth in Q2. Since the start of the year, US Commercial growth has accelerated 50 points, and since the start of 2024, growth has accelerated 81 points.  

US Commercial Revenue accelerated by 28 percentage points to 121% YoY growth to $397 million.  

Source: Company IR 

For the full year, Palantir significantly boosted its US Commercial growth outlook to >104% YoY, up from 85% previously. This corresponds to revenue of $1.433 billion, up from $1.302 billion previously.  

Key metrics for the segment were very strong: TCV closed (total contract value) surged 342% YoY to a record $1.31 billion, and TTM TCV was $3.8 billion, up 217% YoY. Remaining deal value (RDV) rose 199% YoY and 30% QoQ to $3.63 billion. US Commercial deals closed of >$1 million were up 2X YoY and deals closed of >$5 million were up 5X YoY. 

Palantir Key Segments – Government and Commercial Revenue 

Q3 also marked the first time Commercial growth outpaced Government segment growth since Q2 2024, fueled by the robust momentum and sharp acceleration in US Commercial as discussed above.   

Commercial revenue rose 21.5% QoQ and 73% YoY to $548 million, a 26-point acceleration from 47% YoY growth last quarter. International Commercial revenue was ~$151.7 million in Q3, up ~10% YoY and 5% QoQ. 

Government revenue rose 14.5% QoQ and 55% YoY to $633 million, a six-point acceleration from 49% YoY in Q2. Government remained Palantir’s largest segment at ~53.6% of revenue. US government revenue was up 52% YoY and 14% QoQ to $485.9 million, while International Government revenue was ~$146.8 million, up nearly 66% YoY and 16% QoQ. 

Other Key Metrics – NRR Expands, Strong Customer Growth 

Palantir had a handful of other strong key metrics that support strong revenue growth continuing despite the sharp acceleration the company delivered in Q3.   

Palantir’s net retention rate (NRR) expanded six points sequentially to 134%, and over the past two years, NRR has risen an impressive 27 points, with Palantir noting that AIP is continuing to drive existing expansions and new customer conversions. Palantir also continued to emphasize that NRR does not include revenue from new customers acquired over the last twelve months, and accelerating momentum in quarterly deals closed supports more upside to NRR in 2026. 

Palantir stock’s Q3 Net Retention Rate accelerated by 600 basis points sequentially to 134% in Q3. 

Source: Company IR

For Palantir’s AIP, which connects frontier models directly to enterprise data streams, this creates a surge in data that Palantir can then contextualize and provide value for decision making for its customers. There is already more evidence below the headline figures that Palantir is benefiting from increasing enterprise AI adoption, such as Palantir’s quarterly deals closed. Palantir closed 201 deals of >$1 million in Q3, up 30% QoQ; this was a sharp acceleration from 13% QoQ growth in Q2. Palantir also signed more deals in all its cohorts (>$1M, >$5M and >$10M) in Q3 than it had in Q2.  

To put deal growth in the context of NRR, Palantir has signed 629 >$1M deals over the last twelve months, up more than 61% from 390 in the same period last year – with none of these new deals appearing yet in NRR. 

Palantir’s quarterly deals closed accelerated sharply to 30% sequential growth in Q3, up from 13% in Q2. 

Source: Company IR 

However, there were a few blemishes within key metrics – billings growth decelerated from 53.5% YoY in Q2 to 48.8% YoY in Q3 to $1.23 billion, with QoQ growth also decelerating from 21.8% QoQ to 11.2% QoQ. RPO growth decelerated from 77% YoY and 27% QoQ in Q2 to 66% YoY and 8% QoQ in Q3 at $2.60 billion. 

Margins – Q3’s Rule of 40 of 114%, from 94% in Q2 

Margins strengthened considerably in the quarter, with adjusted operating margin surpassing 50% with more expansion guided for Q4. Palantir’s Rule of 40 score (revenue growth + adj operating margin) expanded to a wild 114%, up from 94% last quarter and 68% last Q3.  

Palantir’s Rule of 40 score accelerated to 114%, from 94% last quarter and 68% in Q3 2024.   

Source: Company IR 

Gross margin was 82% in Q3, up one point QoQ and two points YoY, while adjusted gross margin was 84%, up two points YoY and QoQ. 

GAAP operating margin was 33%, an impressive 6 point QoQ and 17-point YoY expansion. Adjusted operating margin was 51%, breaking past 50% for the first time and up 5 points QoQ and 13 points YoY. For Q4, Palantir guided for adjusted operating margin to be 52%, showcasing its ability to drive strong margin expansion alongside swift revenue acceleration. Full year adjusted operating margin guidance was raised from 46% to 49%.  

GAAP net margin was 40%, up 7 points QoQ and 20 points YoY. Adjusted net margin was 45%, up 5 points QoQ and 12 points YoY. Palantir is one of the few, if not only, tech companies to have 40% GAAP net margins with revenue growth accelerating above 60%. 

Adjusted EPS grew by 110% 

Palantir reported $0.18 in GAAP EPS in the quarter, up 200% YoY, while adjusted EPS was $0.21, beating estimates by 25.5% and rising 110% YoY. Palantir’s adjusted EPS is expected to grow 64.1% YoY to $0.23 in Q4 and 59.4% YoY to $0.21 in Q1 2026. 

For FY25, Palantir is expected to earn $0.72 in adjusted EPS, up 76.7% YoY and then 39.5% YoY to $1.01 in FY26. 

Strong Balance Sheet 

Palantir stock has strong cash flows, though cash flow margins dipped on a YoY and QoQ basis. Q3 operating cash flows grew by 20.9% YoY to $507.7 million for a 43% margin, down from a 54% margin in Q2 and 58% in the year ago quarter.  

Adjusted free cash flow grew by 24.3% YoY to $539.9 million for a 46% margin, down from 57% in Q2 and 60% in the year ago quarter. Palantir raised its adjusted free cash flow guidance for the year to $1.9 to $2.1 billion, or a 45.5% margin, up from a 42.8% margin previously. 

Cash and marketable securities totaled $6.4 billion and debt remained zero. 

Valuation 

To many investors on social media, Palantir’s valuation remains a hot topic, with it blowing past norms and reaching the upper echelons of the stratosphere for what is considered ‘typical’ for SaaS stocks. Put it this way — how often do you see a software company re-accelerate revenue from 13% growth a couple of years to 60% in nine quarters organically and sustainably, while increasing both profitability and cash flows.  

Palantir is now trading at a forward P/S ratio of 67.5, making other best-of-breed cloud stocks like CrowdStrike and Cloudflare cheap at 24.7 and 23.4, respectively. On the bottom-line, it is trading at a forward P/E of 176.5, slightly below Snowflake’s forward P/E ratio of 180.4 and higher than Cloudflare’s and CrowdStrike’s forward P/E ratio of 155.3 and 126.6, respectively.  

Source: YChartsYCharts 

Given Palantir is trading at a forward P/S ratio of 67.5, there are certainly easier stocks to own in 2026. Within AI, specific niches are booming such as AI networking, AI energy and AI memory plays that are trading far lower than Palantir. 

Recently, Palantir was upgraded by Citi as it believes that the commercial and government super cycle is coming this year. Analyst Tyler Radke said, “We are upgrading PLTR to Buy/High-Risk from Neutral and raising estimates and our target price to $235.” He further added, “Shares have minted spectacular returns over the last few years as a vicious growth acceleration and equally impressive margin expansion has 'broken' traditional rule-of-40 and valuation frameworks. Despite our 2025/26 revenue numbers up 10%+ since mid-year, the stock is ~flat. Our upgrade is premised on the view that 2026 is poised to be another year of significant positive estimate revisions, with recent CIO + industry conversations suggesting AI budget and use cases are accelerating in the enterprise. We also see significant tailwinds in the Government, driven by accelerating defense budgets and modernization urgency.” 

Conclusion 

Palantir delivered one of the strongest earnings reports across the tech sector in Q3, with revenue growth of nearly 63% and a strong 28-point acceleration in its AI-driven US Commercial segment. Since the start of 2025, revenue growth has accelerated more than 23 points, with US Commercial revenue accelerating 50 points. Trends in other key metrics such as NRR and quarterly deals closed remain robust, although billings and RPO growth decelerated in the quarter. However, what Palantir has to contend with is an extended valuation, in uncharted territory even by its own measures, and where the market will ultimately price its shares. 

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Royston Roche and Damien Robbins, Equity Analysts at I/O Fund contributed to this analysis.

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. I/O Fund may own stocks pictured in the charts.

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• AI Stocks & Nvidia: I/O Fund’s 2025 Tech Media Highlights

While many investors are wondering whether the AI trend is entering dot-com territory, I believe AI’s most powerful move has not even begun. 

In a series of analyses on the incoming AI monetization wave, the I/O Fund has laid out a data-driven case that AI is on the cusp of monetizing; a sharp rebuttal to those who believe AI is topping. Earlier this month, my firm connected the dots on Nvidia’s earnings report, the strongest in nearly two years, and highlighted why Broadcom’s commentary is quietly signaling that the best is yet to come. 

In this analysis, however, I turn to what may be the most important clue of all: Meta.  

Meta’s stock sits at the center of the AI spending debate, as Big Tech continues to shock markets with outsized AI-driven capital expenditures. What is being overlooked is that Meta’s stock is already reporting a long-awaited return on investment from the AI data center buildout. 

Below, I highlight several key metrics from Meta’s latest earnings report that illustrate the company is beginning to offer measurable returns on its AI investments. When viewed alongside our prior analysis on Nvidia and Broadcom, this discussion broadens the perspective to include one of the most closely scrutinized AI stocks in terms of capital expenditure. Although AI remains in a nascent stage, the data presented below provides early evidence that elevated AI capex is starting to translate into a clearer path toward monetization. 

Meta Advantage+ Reaches $60 Billion ARR, Outpacing OpenAI by 3X 

In the analysis “Nvidia Stock and the AI Monetization Supercycle Nobody is Pricing In” I encouraged investors to look at the evidence the AI Monetization Supercycle is already offering tangible results. For example, we can look at OpenAI’s trajectory from $1 billion in revenue in 2023 to an estimated $20 billion annualized revenue today – which represents the steepest rise in technology history. This was driven almost entirely by inference (API calls and ChatGPT usage). The CEO has stated OpenAI will reach hundreds of billions in annualized revenue by the end of the decade – although this requires ample execution, it’s a hint as to the sheer force of the incoming monetization wave. 

What may surprise you is that Meta’s Advantage+ is outpacing OpenAI by 3X and is also offering the strongest AI revenue among the FAAMGs. Unless you track earnings reports as closely as my firm, the information from this past quarter could have easily flown under radar as Meta’s management team offered an update on Advantage+ that nobody was expecting: 

“This quarter, we saw meaningful advances from unifying different models into simpler, more general models, which drive both better performance and efficiency. And now the annual run rate going through our completely end-to-end AI-powered ad tools has passed $60 billion.” 

The $60 billion run rate was achieved within 3.5 years, which is on par with when OpenAI began to monetize in 2023 through year-end 2025. The last update we got from Meta's management on AI powered ads was in March of 2025 with a stated $20 billion annual run rate – which means AI ads have grown 3X in 7 months' time.

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Perhaps an even bigger shocker is that Meta may be ahead of Microsoft for AI revenue. The last update we got from Microsoft is from Fiscal Q2 ending in January, where AI revenue was stated to be $13 billion, growing at a pace of 175% year-over-year. 

“This quarter we saw continued strength in Microsoft Cloud, which surpassed $40 billion in revenue for the first time, up 21% year-over-year. Enterprises are beginning to move from proof-of-concepts to enterprise-wide deployments to unlock the full ROI of AI. And our AI business has now surpassed an annual revenue run rate of $13 billion up 175% year-over-year.” 

The base case for Microsoft assumes AI contributing approximately 22 points to growth as of fiscal Q1, or around 56% of its YoY growth in dollars. This could imply AI revenue at 26% of Azure’s total revenue, or around $25 to $26 billion on a nearly $100 billion annual run rate for Azure.  

Chart illustrating Microsoft’s base case of AI revenue accounting for about 26% of Microsoft Azure revenue. It assumes AI is driving significant Azure growth.

Overall, it would require a step-up from 175% growth YoY to 460% year-over-year for Microsoft to match Meta’s AI revenue – an aggressive growth rate that I believe Microsoft would have already discussed with investors. Therefore, I believe probabilities favor Meta being in the lead on AI revenue, as it stands today. That means Meta would be in second place – second only to Nvidia – on AI revenue. 

Meta’s Advantage+ results are one of the clearest real-time signals that the AI monetization wave is already underway, not theoretical. 

Subscribe to our free AI & tech newsletter for weekly research and charts on AI monetization across leaders like META, NVDA, and MSFT. Click here.Subscribe to our free AI & tech newsletter for weekly research and charts on AI monetization across leaders like META, NVDA, and MSFT. Click here. 

How Meta AI Advantage+ Generates $4.52 ROI Per Dollar 

Advantage+ automates campaign targeting, budget allocation, and creative generation, providing advertisers with an easy-to-use tool that integrates generative AI directly into Meta’s ad ecosystem. Advantage+ is powered by Andromeda, which is an internally developed AI system that optimizes ad ranking, recommendation quality and content delivery by leveraging machine learning models more efficiently. By improving how models are executed, Andromeda allows Meta to extract more performance per unit of compute, which is key as the AI powered ads platform seeks high-volume inference. 

Meta revealed earlier this year that “for every dollar spent on its AI-enabled Advantage+ products, advertisers generate on average $4.52 in revenue for their businesses,” or an increase of ~22% versus typical campaigns, highlighting how ad performance improves while using the AI-powered platform.  

In Q3, Meta emphasized that “Advantage+ continues to drive performance gains, [and] advertisers who run lead campaigns using Advantage+ are seeing a 14% lower cost per lead on average than those who are not.” This compares to a 10% lower cost per lead as of April, showing that the platform continues to drive results while lowering costs per lead.   

Meta Stock: $10B Growth Proves AI Advertising Acceleration 

There is additional evidence that Meta is seeing tailwinds from AI recommendation models, which in turn drive higher ROI for advertisers and increase time spent across its family of apps. 

In Q3, advertising revenue grew 25.6% YoY, accelerating more than nine points since Q1 and marking the fastest growth in six quarters. Ad impressions rose 14% YoY in Q3, accelerating from 11% in Q2 and marking a strong inflection from just 5% growth in Q1. Pricing remained steady, rising just one point to 10% YoY.  

Meta Stock Ad Impressions: AI drives 3-point acceleration to 14% YoY in Q3.  

However, the dollar growth in advertising stands out more — Meta has delivered its two largest YoY growth quarters on a dollar basis in Q2 and Q3, at $8.23 billion and $10.2 billion, even outperforming Q4 2024’s holiday-boosted growth of $8.08 billion. This high dollar growth is poised to continue in Q4 2025, with guidance pointing to ~$9.3 billion to $10.7 billion in QoQ dollar growth.  

Meta ad revenue accelerated to 25.6% YoY growth in Q3, up from 21.5% in Q2, highlighting the success of AI recommendation models.  

Put another way, Meta is delivering larger YoY dollar growth in advertising revenue on a larger base – Q3 2025 grew $10 billion YoY off a $40 billion base, versus $7.5 billion growth in Q3 2024 on a $33.6 billion base. 

AI Drives Record ARPP Continues to Accelerate Heading into Q4 

Perhaps the most important metric for Meta’s ads monetization is ARPP (average revenue per person), with the metric continuing to accelerate in Q3 ahead of the seasonally stronger holiday quarter. ARPP reached $14.46 in Q3, accelerating to 17.7% YoY from 14.8% in Q2. More impressively, this marked a record high for ARPP, surpassing Q4 2024’s seasonally stronger ARPP of $14.25. 

Meta ARPP: AI-driven efforts push to a record $14.46 in Q3. 

This sets the stage for ARPP to push well beyond $15, potentially to $16 in the upcoming quarter, highlighting that Meta’s AI-driven ad performance improvements and monetization efforts are bearing fruit. 

Annual Revenue Revisions Seeing Sharp Increase Since July 

What’s notable about the revenue front is the sharp upward revisions to annual revenue estimates, with 2026 and 2027 moving sharply higher since this summer.  

Back in July, prior to Q2’s earnings, Meta was expected to generate $215.1 billion in revenue, with that now sitting at $235.1 billion. On a YoY basis, growth has been revised from 14.0% to 17.9%. A smaller uplift considering 2025 comps have toughened, having risen from 14.7% to 21.3% over the same period.  

For 2027, Meta was expected to generate $240.6 billion in late July, with that now sitting at $271.7 billion, with YoY growth moving from 11.9% to 15.6% on a higher base.  

Chart showing the increase in Meta’s forward revenue estimates. 

Source: YChartsYCharts

Meta Ad Performance: Leveraging GEM, Lattice, and Andromeda AI Models 

Meta outlined two monetization levers in Q3 – improving ad performance, and an ability to continue delivering engaging content to users. 

This first monetization lever stems from improving ad performance for its advertisers, mostly driven by the company’s three foundational models as well as its end-to-end ads automation platform Advantage+. Meta opts for tracking conversions to gauge ad performance, despite it being a complex metric to track considering advertisers can optimize for different types of conversions. CFO Susan Li stated that value-weighted conversion rates showed “very strong” YoY growth in Q3, outpacing impressions.  

Meta’s three foundational models all serve a different function, with the same end goal of improving ad quality and conversions to drive higher ROI for advertisers: 

• GEM (Generative Ads Recommendation Model) is described by Meta as the ‘super brain’ that can rapidly process, catalog and analyze trillions of data points, to then recognize subtle patterns in user activity to provide the most relevant ads at the right time. Meta says GEM was rolled out more broadly earlier this year after initial testing on Reels saw GEM boost conversions by up to ~5%. GEM delivered a 5% increase in conversions on Instagram and a 3% increase on Facebook in Q2, and in Q3 Meta “doubled the performance benefit we get from adding a given amount of data and compute” to continue scaling training capacity at an attractive ROI. 
• Lattice is described as a ‘giant library’ that generalizes learnings across different campaign objectives (clicks, views, etc), surfaces (Reels, Story, Feed, etc) and subjects, in order to predict an ad’s performance. Lattice increases ad efficiency as it runs fewer models, while the knowledge-sharing effect increases ad quality and conversions – Meta said earlier this year that Lattice has increased ad quality by 12% and conversions by 6%. In Q3, Meta rolled out Lattice to app ads, driving a ~3% gain in conversions on that objective.
• Andromeda is described as a ‘personal concierge’, or Meta’s vast ML ad recommendation and prediction system that, at its core, aims to predict exactly which ads a user will find the most interesting. For Andromeda, Meta says, “Imagine having a personal concierge who knows your tastes so well that they don’t just understand that you covet shoes, but that you like to wear red flip flops at the beach.” Meta said that in Q3, it significantly improved Andromeda’s performance by combining retrieval and early-stage ranking models, driving a 14% increase in ad quality on Facebook. Andromeda is also the core engine powering Advantage+ automation tools. 

Moving to 2026, Meta discussed that it is “working on combining these 3 major AI systems into a single unified AI system that will effectively run our family of apps and business using increasing intelligence to improve the trillions of recommendations that it will make for people every day.”  

A single model that combines the strengths of GEM, Andromeda and Lattice could theoretically understand user preferences and activity at a much deeper level, improve ad ranking quality, relevance and conversions across its family of apps, and save on inference. For example, Meta does not use GEM for inference as its size makes it too cost-prohibitive, rather it transfers knowledge to smaller run-time models; a single model incorporating GEM’s knowledge could potentially run inference in a more cost-effective manner. 

Meta AI Lever 2: Reels Reaches $50 Billion ARR Due to Increased Engagement 

On the second monetization lever of increasing engagement, Meta is executing quite well, with improvements in its recommendation models helping drive time spent on its apps higher. More time spent then allows ad impressions to grow without substantially increasing ad load, underpinning this reacceleration in impressions growth seen in Q3 and more growth moving forward.  

Management pointed out that “overall time spent on Facebook and Instagram grew double digits year-over-year, driven by continued video strength as well as healthy growth in nonvideo time on Facebook.” Video time spent on Instagram was more than 30% higher versus last year, while AI ranking optimizations helped drive 10% more time spent on Threads in Q3. This video growth has pushed Reels to a $50 billion annual run rate in Q3, up 5X from its last update in Q2 2023 when it reached a $10 billion run rate.   

Improving ranking models remains a key focus for Meta moving through 2026, with management expecting new model innovations to help “significantly scale up the amount of data and compute we use to train our recommendation models in 2026, yielding more relevant recommendations.” 

Meta’s Upcoming Capex Surge and Possible FCF Crunch 

Some of the most important quotes from Q3’s call circled back to Meta’s view on capex and why it believes aggressive expansion of capacity and thus capex is a necessity. CEO Mark Zuckerberg believes it is the “right strategy to aggressively front-load building capacity so that way we're prepared for the most optimistic cases” on when AI superintelligence arrives, so Meta is prepared to capitalize on this opportunity.  

If building superintelligence takes years longer than expected, Zuckerberg says Meta can “use the extra compute to accelerate our core business which continues to be able to profitably use much more compute than we've been able to throw at it. And we're seeing very high demand for additional compute, both internally and externally.  

These comments underscore why Meta is aggressively raising its capex spending this year and next – Zuckerberg believes that the upside potential of superintelligence is so large that it is worth the risk of overbuilding to not fall behind OpenAI or Google (with compute capacity being the main advantage), with Meta able to use extra compute in the meantime to improve core AI ad capabilities and drive growth.  

However, the tradeoff for this is lower free cash flow and potential operating margin headwinds. Meta expects capex dollar growth to be “notably larger in 2026 than 2025,” while total expenses “will grow at a significantly faster percentage rate in 2026” driven by infrastructure costs, incremental cloud costs and depreciation, followed by employee compensation.  

This implies 2026 capex of at least $103 billion, as current guidance for 2025 at $70-72 billion implies a minimum of ~$32 billion YoY growth. However, considering management’s comments for notably larger dollar growth, there is potential for capex to come in at or above $110 billion, up ~55% YoY, above current estimates for $107.9 billion. Put another way, Meta could spend ~$30 billion more in 2025 and 2026 than it did in 2019 through 2024 combined. 

Meta capex is projected at $107.9 billion in 2026 as it prepares to monetize the AI superintelligence opportunity.  

The capex surge will potentially cause another free cash flow crunch similar to 2022, with current consensus estimates pointing to FCF of $19.71 billion in 2026, down nearly (50%) YoY and (63.5%) from 2024. 

Meta’s AI-driven capex surge will potentially cause another free cash flow crunch in 2026. This mirrors the 2022 crunch when FCF fell to $19.04 billion from $39.12 billion in 2021. 

How Meta’s Capex Compares to Other Big Tech Stocks 

We recently discussed Big Tech Capex spending following Q3 earnings results in our article, Big Tech’s $405B Bet: Why AI Stocks Are Set Up for a Strong 2026. As seen in the chart below, Meta has the lowest cumulative capex for the years 2023 to 2025E of $138 billion. This figure is lower than that of other Big Tech Companies, such as Alphabet at $177 billion, Microsoft at $234 billion, and Amazon at $261 billion. 

Meta has the lowest cumulative capex for the years 2023 to 2025E among the Big Tech Companies 

Big Tech companies are now beginning to report their AI revenue in the quarterly earnings results, unlike AI semiconductor companies, which have consistently been providing this figure. During the June quarter, Microsoft revealed that Azure surpassed $75 billion in annual revenue. If we assume that AI revenue constitutes 26% of Azure revenue, Microsoft’s AI revenue would be a base case of $20 billion up to $26 billion if we assume the same growth rate as last year, well below the $60 billion for Meta. I do foresee a scenario where Microsoft is higher than the base case, yet it’s unlikely the growth rate is at the 450%+ growth rate required to pass Meta. Similarly, Alphabet and Amazon who spent far more than Meta are not revealing AI numbers, which theoretically means the numbers are lower than Meta's. 

Meta’s Free Cash Flows hit by high Capex 

Meta has strong operating cash flows. However, due to high capex to support AI investments, the company’s free cash flows were down (31.5%) YoY to $10.63 billion in Q3. Capex rose 110.5% YoY to $19.34 billion, driven by investments in servers, network infrastructure and data centers. Based on 2025’s capex guide, which was raised to $70-72 billion (up 81% YoY at midpoint), Q4 capex is on track to be ~$21 billion, up 41.5% YoY. Meta is expected to see a steep free cash flow crunch moving through 2026 as a result of surging capex and would be a risk item to keep an eye out for in the coming quarters.

Conclusion: 

While much attention is given to Nvidia and AI semiconductors for visible AI-driven growth, we are beginning to see an impact in Big Tech’s software segments. Meta’s advertising revenue accelerated to the mid-20% range, and YoY dollar growth surpassed $10 billion in Q3. Revenue forecasts continue to strengthen over the next few years with revisions of up to $30 billion, or up 10-14% since July, underscoring the Street’s confidence in Meta’s ability to leverage AI to improve monetization.   

More impressively, Meta’s AI ads automation platform has reached a $60 billion run rate in Q3 in three and a half years from its launch. This is 3X Broadcom’s AI revenue, 3X OpenAI ARR and could even put Meta as the #2 stock by AI revenue ahead of Microsoft. Notably, the I/O Fund is the first firm to point out Meta’s quiet dominance relative to Microsoft, as we consistently find an edge in earnings data that others have overlooked.  

However, the thorn in Meta’s side stems from the compute and capex side, as the company is aggressively building data center capacity to prepare itself for the most optimistic scenario of reaching superintelligence. Not only is capex guided to surge to over $100 billion in 2026, potentially creating another cash flow crunch reminiscent of 2022’s metaverse-linked spending spree, but expense growth is also expected to outpace revenue growth by a wide degree and weigh heavily on operating margin. 

This year, my firm has 15 positions beating the Nasdaq YTD, up from ten positions last year – helping to cement the I/O Fund as one of the world’s leading AI portfolios. Our cumulative return of 210% over a five-year period would rank us #2 if we were a hedge fund and #5 if we were an ETF – notably, this strong cumulative return does not yet include our 2025 performance. 

Want our real-time trade alerts and full AI portfolio positioning, including a newly updated 3,800-word deep dive3,800-word deep dive on Meta’s near-term risks and potential entry points? Join our Advanced plan here.Join our Advanced plan here. Advanced members also receive weekly webinars and full access to our actively managed portfolio of lesser-known AI stocks.

Please note: The I/O Fund conducts research and draws conclusions for the Fund’s positions. We then share that information with our readers. This is not a guarantee of a stock’s performance. Please consult your personal financial advisor before buying any stock in the companies mentioned in this analysis.

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When discussing the AI Monetization Supercycle, I would be remiss not to highlight Broadcom. The AI accelerator market will inevitably widen beyond Nvidia’s GPUs – the keyword is widen. More players will sell more AI systems as the market expands, and that growth supports both the clear leader (Nvidia) and those already in pole position, such as Broadcom.  

Last week, amidst a flurry of noise in the AI market, my firm wrote an article on the AI Monetization Supercycle that is not being priced in. The analysis suggested the predominant risk is not an AI dot-com bubble or various headlines weighing on sentiment, but rather the risk investors face is missing out on what may be one of the strongest investing opportunities of our lifetime: what I’ve dubbed the AI Monetization Supercycle catalyzed by the inference phase. 

While many refer to this as the “AI Supercycle,” I believe Monetization is a critical word missing from that description. The hallmark of the next phase will not be the architectural leap toward AI superintelligence (although important) – but rather, it will be defined by the ability to monetize this very expensive technology. As an investor, I am obligated to care more about the latter.  

Which brings us back to Broadcom—a stock my firm highlighted in our free stock newsletter last June in an article entitled “This Stock is Set to Surge from Inference Demand.” 

At the time, I wrote: 

“Broadcom has already benefited from both increasing compute and networking needs – but the surge in inference demand will disproportionately (and positively) flow to Broadcom’s top line and bottom line. This is because custom silicon’s cost advantages and ability to drive lower inference serving costs at scale creates a strong value proposition for Big Tech. As more and larger clusters are deployed to serve exploding inference demand, there will be additional long-term tailwinds for the Ethernet networking giant.” 

The inference phase – what I'm calling the Monetization Supercycle – is squarely in front of us. While many will understandably point toward companies like OpenAI as the biggest beneficiaries, it is one of the market’s greatest misconceptions that platform owners always outperform suppliers (hardware stocks). During the mobile era, Broadcom’s stock outperformed Apple precisely because it supplied RF and connectivity components to the iPhone giant. 

Below, we look more closely to see if the “silent winner” Broadcom stock can repeat that outperformance again.

Stock Price Comparison Chart: $AVGO vs $AAPL. Broadcom Stock significantly outperformed Apple stock in the 10-year cycle of the mobile boom era, delivering a return of 1,490% compared to Apple’s 623%. Source YChartsYCharts

Google TPU Ironwood v7: The Custom AI Chip Built for Inference 

Last April, Google announced that its upcoming seventh-gen TPU Ironwood is its “most performant and scalable custom AI accelerator to date, and the first designed specifically for inference.” Individual Ironwood TPUs are interconnected into larger units called pods, coming in two sizes, a 256-chip pod and a 9,216-chip Superpod, with the larger size offering up to 42.5 exaflops of performance. Notably, the Superpod would deliver 24x the compute of El Capitan, the largest supercomputer in the world.  The rack-scale architecture offers 64 TPUs compared to Nvidia’s racks with 72 GPUs, with a small cluster being four pods connected through an optical circuit switch network. While TPUs may excel at driving down costs on certain workloads, Nvidia’s GPUs still lead when it comes to processing performance.

mid

Google adds that Ironwood offers 2x the performance per watt as last-year’s generation Trillium, with 6x more HBM and 4.5x the HBM bandwidth; versus TPU v5p, released in 2023, Ironwood brings a more than 10x improvement in peak performance per chip and per pod. The substantial increases in memory and bandwidth are critical for maintaining high performance when processing larger data sets while the improvements in power efficiency allows inference workloads to be run in a cost-effective manner. 

It’s widely understood that Broadcom supplies Google with its custom TPUs. The incoming inference growth curve, that the I/O Fund detailed here, has led CEO Hock Tan to state Broadcom may witness an acceleration of XPU demand into the back half of 2026. He said, “In fact, what we've seen recently is that they are doubling down on inference in order to monetize their platforms. And reflecting this, we may actually see an acceleration of XPU demand into the back half of 2026 to meet urgent demand for inference on top of the demand we have indicated from training.”   

Something similar was echoed in the FQ3 call, with Tan stating: “But also as for these guys, they got to be accountable to being able to create cash flows that can sustain their path. They [are] starting to also invest in inference in a massive way to monetize their models.” On that note, Google’s TPU business received a significant vote of confidence recently with Anthropic signing a deal for up to one million TPUs, including Ironwood, coming online in 2026. The deal is said to be worth tens of billions.  

For Broadcom, the TPUs are expected to be the primary driver of AI revenue growth in fiscal 2026 – estimates from HSBC earlier this summer projected Google’s TPUs to represent ~58% of Broadcom’s ASICs shipments at 1.79 million, but account for ~78% of ASICs revenue at $22.1 billion. This is because Google’s TPUs were estimated to carry a significant price premium at $13,000 per chip versus Broadcom’s other projects at $5,000 per chip. However, this is still less than half the cost of Nvidia’s chips at $30,000 to $40,000 for a solo B200 ($60,000 to $70,000 for a GB200).  

Looking beyond fiscal 2026, projections for TPU shipments are surging. Morgan Stanley now expects 5 million TPUs to be shipped in 2027, a 67% rise from its prior estimate for 3 million; for 2028, the firm estimates shipments as high as 7 million, a 120% increase from its prior estimate. This would project YoY growth of 40% from 2027 to 2028, a substantial increase from 6% previously, and will represent more than 2X growth in two years. 

The I/O Fund first covered TPUs versus GPUs back in 2019 and revisited the topic in February 2024 in our analysis, Broadcom: Networking/ASICs Giant and the Second Largest by AI Revenue. Since then, we’ve provided quarterly coverage for two years. Broadcom: Networking/ASICs Giant and the Second Largest by AI Revenue. Since then, we’ve provided quarterly coverage for two years.  

If you want cutting-edge insights on AI stocks early in the cycle — including our take on Broadcom’s earnings this evening — sign up now.sign up now. 

Broadcom Stock’s AI Edge: Custom Silicon & Massive Hyperscaler Deals 

Broadcom’s stock has been strong this year, outperforming the Nasdaq by nearly 50-points and SMH by 20-points. This strong performance is partly due to custom accelerators that are often multiples cheaper than Nvidia’s GPUs for inference tasks and also due to custom silicon becoming increasingly performant with each generation. By optimizing algorithms (software), Big Tech can drive higher performance from large language models — which helps to drive down costs while also increasing output for specific workloads.  

For example, a rough idea as to how much it costs Nvidia to make merchant GPUs is estimated around $3,000 to $6,000 whereas the company charges $30,000 to $40,000 – hence the AI leader’s excellent margins. Reducing Nvidia’s high pricing power is what Big Tech is after and this can be accomplished both in the hardware costs but also through optimizing the workloads for specific use cases – for comparison, Ironwood is expected to cost around $13,000 per chip.  

Big Tech is prominent in Broadcom’s custom silicon customer list, which includes Google and Meta. ByteDance reportedly emerged as the third customer last summer. The company announced its fourth customer in FQ3 with a $10 billion XPU order. Hock Tan said in the FQ3 earnings call, “Last quarter, one of these prospects released production orders to Broadcom, and we have accordingly characterized them as a qualified customer for XPUs and, in fact, have secured over $10 billion of orders of AI racks based on our XPUs.” 

In late October, Anthropic signed a deal with Google worth tens of billions to access up to 1 million TPUs to bring online more than 1GW of capacity in 2026, although it has not explicitly confirmed if Anthropic is the mystery fourth customer.

Furthermore, OpenAI and Broadcom announced in October a strategic collaboration to deploy 10 gigawatts of OpenAI-designed AI accelerators. OpenAI and Broadcom will co-develop systems that include accelerators and Ethernet solutions from Broadcom for scale-up and scale-out. Broadcom plans to deploy racks of AI accelerators and network systems starting in the second half of 2026 and completed by the end of 2029.  

The OpenAI deal represents a substantial three-year revenue ramp for Broadcom stock and further solidifies its position in the AI silicon market. Citi estimates the deal with OpenAI could bring in $100 billion in sales and $8.00 in earnings per share over the next few years; however, Mizuho highlighted that the deal to deploy 10GW of OpenAI's custom ASIC, code named Titan, could be even larger at an estimated $150 billion to $200 billion deal over multiple years. 

The enviable customer list is showing up in Broadcom’s results. This quarter, management guided Q4 AI revenue to $6.2 billion, which would represent ~19% sequential growth and eleven consecutive quarters of YoY growth.  

Broadcom did not lay out a FY25 AI revenue target, yet FQ4 ending in October 2025 implies Broadcom is guiding for $19.9 billion in AI revenue for the year, up 63% YoY from $12.2 billion in FY24. Mizuho estimates that AI revenue will grow 103% YoY to $40.4 billion for the FY2026 and nearly double to $78 billion in FY2028. However, given the growing customer list, these estimates could prove to be too low. 

Additionally, Hock Tan will be duly rewarded should AI revenue targets exceed current expectations. In September, Tan received a performance award of 610,251 shares of common stock as part of a recent contract extension. The award will fully vest if Broadcom reaches $90 billion in revenue from its AI products over any consecutive four-quarter period from FY2028 through FY2030. That award will double if Broadcom earns $105 billion in AI revenue and triple if revenue totals more than $120 billion. If Broadcom fails to hit $60 billion in AI revenue during the period, Tan will forfeit the entire award. This provides investors with a framework for upper targets for the bull case. 

Broadcom (AVGO) AI Revenue Forecast: Projected to hit $40.4 billion in FY2026, driven by Google TPUs and custom silicon demand.  

Source: Company IR/TheFly/Mizuho 

Broadcom’s Tomahawk 6: The Ethernet Switch to Power 1 Million-Plus AI Clusters 

Broadcom has been quite vocal about the industry’s path to 1-million-plus accelerator clusters, frequently reiterating how its three hyperscalers and now four “each race towards 1 million XPU clusters by the end of 2027.” This would be multiples larger than current deployments, with xAI’s Colossus supercluster expanding from 100K to 200K GPUs Today, these clusters are 10-20X larger than Ironwood’s 9,216 chip SuperPod, highlighting the depth of AI demand. 

Broadcom has continuously re-emphasized this forecast as it represents two major growth opportunities for the company: significant growth in accelerator deployments with inference tailwinds, and even more growth in networking deployments to support these clusters.  

The shift to Ethernet and away from Nvidia’s lock-in ecosystem of GPU + InfiniBand is benefiting Broadcom, with the industry pointing to rising Ethernet demand. Arista said that momentum for Ethernet “has really shifted in the last year” while Nvidia touted that its new Spectrum-X Ethernet is annualizing at $10 billion in revenue, or $2.5 billion quarterly.  

The company is committed to remaining on the leading edge of networking with its Tomahawk 6 switch, the industry’s first 102.4 Tbps Ethernet switch. The next-gen switch doubled the bandwidth of its predecessor, while offering flexible deployment ability with 1,024 100G or 512 200G SerDes options, reducing switch count.  

This raw performance upgrade paves the way for >100K to 1 million accelerator clusters by allowing larger leaf-spine fabrics to be constructed, while drawing less power and keeping latency low. Broadcom exec Ram Velaga said that the demand for the new switch is “unprecedented” with multiple >100K accelerator deployments “using Tomahawk 6 for both the scale-out and scale-up interconnect.” 

When discussing Tomahawk 6, management points toward the flattening of the AI cluster as an important catalyst for this product, stating: “[…] Tomahawk 6 enables clusters of more than 100,000 AI accelerators to be deployed in just two tiers instead of three … this flattening of the AI cluster is huge because it enables much better performance in training next-generation frontier models through a lower latency, higher bandwidth and lower power.” The two-tier topology also reduces complexity of cluster construction and reduces congestion choke points significantly, addressing another critical pain point of building larger and larger clusters.  

Additionally, in terms of the AI networking opportunity, scale up is 5-10X more than scale out – setting up a nice trajectory as AI clusters grow. Oppenheimer analyst Rick Schafer highlighted that they expect next-gen Tomahawk6 volumes to ramp up in the second half of next year, providing added growth and gross margin boost. 

Broadcom FQ4 Earnings Preview: AI Revenue Outlook & OpenAI Deals 

• Revenue expected to grow by 24.2% YoY and adjusted EPS by 31.7%. 
• AI Revenue outlook 
• New customer announcements 
• Update on AI Serviceable Market for 2027 

Broadcom is expected to report FQ4 revenue of $17.46 billion, up 24.2% YoY and a 220-basis points acceleration from the 22% growth reported in FQ3. Adjusted EPS is expected to grow 31.7% YoY to $1.87. 

Broadcom (AVGO) FQ4 revenue is expected to grow 24.2% YoY to $17.46 billion, driven by AI Revenue Acceleration. 

Source: Company IR/Seeking Alpha 

The company’s margins will be a key metric to watch in the upcoming report. Management has done an excellent job in maintaining strong margins. Broadcom has been able to reduce operating expenses through cost controls and operational efficiency. Management expects adjusted gross margins to be down 70 basis points sequentially to 77.7% in FQ4, primarily due to a higher mix of XPUs and wireless. However, they are expected to be up 80 basis points compared to the same period last year. The company’s operating leverage should help to compensate for any sequential weakness in gross margins due to the FQ4 product mix. Management adjusted EBITDA guide for FQ4 is 67%, flat sequentially and up 200 basis points YoY.  

Analysts expect strong adjusted EPS growth in the coming years. Adjusted EPS is expected to grow 39.1% YoY to $9.39 in FY ending October 2026 and 35.6% YoY to $12.72 in FY2027. The strong expected EPS growth showcases operating leverage, successful VMware integration, the benefits of higher margin software revenue, and rising AI revenue.  

During the last earnings call after winning the $10 billion XPU order from the new customer, Hock Tan said, “And reflecting this, we now expect the outlook for our fiscal 2026 AI revenue to improve significantly from what we had indicated last quarter.” We expect management to provide more details on the AI revenue outlook for FY2026. The Q4 management guide of $6.2 billion implies that Broadcom is guiding for $19.9 billion in AI revenue for FY2025, up 63% YoY from $12.2 billion in FY24. Analysts are pointing to 100% YoY growth in AI revenue in FY2026, with Mizuho estimating that AI revenue will grow 103% YoY to $40.4 billion. 

According to a recent report by The Information, Broadcom is in discussion with Microsoft to co-develop custom silicon chips. Analysts will likely ask for more details on this and other customers such as the $10 billion XPU order mentioned during the FQ3 earnings call and the OpenAI deal announced in October. The OpenAI deal is also expected to provide a strong boost to the company’s bottom line as UBS expects “large-scale deployments are expected to ramp later, positioning EPS to reach about $13.50 in 2027 and potentially above $20 by 2028 as projects come fully online.” It highlights that the current consensus adjusted EPS estimates for FY2028 of $15.80 are very low, a 27% difference. 

Hock Tan often references the AI Serviceable Market. We could expect Tan to provide an update for 2027 at the next earnings call, as the company has been adding new customers over the past year. Hock Tan had said during the FQ4 earnings call in December last year, “In 2027, we believe each of them plans to deploy 1 million XPU clusters across a single fabric. We expect this to represent an AI revenue Serviceable Addressable Market, or SAM, for XPUs and network in the range of $60 billion to $90 billion in fiscal 2027 alone.” 

Conclusion: 

This year, Broadcom stock has outperformed Nvidia’s stock despite the two being about $200 billion apart in AI revenue with Broadcom at $20 billion in AI revenue for FY2025 ending in October and Nvidia at $250 billion run rate in the quarter ending in Jan. Nvidia clearly has the scale for R&D purposes to help defend its lead. However, I’ve also argued inference will provide an opening for Broadcom and AMD to meaningfully compete on AI accelerators.  

At the I/O Fund, when discussing Nvidia versus Broadcom, the answer is yes and yesyes and yes. We look for fundamental strength, product positioning, supply chain signals, and numerous other proprietary criteria to help us determine if a stock is participating in the AI trend.  

I won’t yank your chain by pretending investors must choose one or the other. In a widening market, leadership compounds at the top and radiates outward as exponential demand will lift the entire ecosystem – including a ripple effect for lesser-known AI networking and AI energy names. 

As we move deeper into the second half of this AI-driven decade, the investors who stay focused on the bigger picture — rather than react to every speculative headline or force themselves into a false binary — will be the ones best positioned to capture the full opportunity of the AI Monetization Supercycle. 

This year, my firm has 15 positions beating the Nasdaq YTD, up from ten positions last year – helping to cement the I/O Fund as one of the world’s leading AI portfolios. Our cumulative return of 210% over a five-year period would rank us #2 if we were a hedge fund and #5 if we were an ETF – notably, this strong cumulative return does not yet include our 2025 performance.

Get real-time trade alerts, weekly webinars and deep dives on lesser-known AI stocks in our Advanced tier. Learn more hereLearn more hereLearn more here

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 AVGO at the time of writing and may own stocks pictured in the charts.

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Two weeks ago, Nvidia blew the doors off with an earnings report that defies the company’s mega-cap scale. The long-awaited Blackwell and Blackwell Ultra architectures are shipping in volume, leading to 25% QoQ growth in the data center segment and surpassing a $200 billion data center run rate. Despite this, Nvidia’s stock barely budged as the market ignored the magnitude of the QoQ inflection. Consider that Apple, trading at a similar market cap, has not seen 66% YoY growth for fourteen years, and it required a global pandemic for Apple to report 25%+ QoQ growth outside of its holiday quarter.  

When we narrow it down to Nvidia’s last earnings report – let alone subsequent reports – there is truly no comparison going back for a decade or more. However, the market dismissed the results and is instead stuck in a pit of speculative fears – meaning, there is no evidence in the financials, management commentary, industry estimates, or product roadmap (collectively referred to as “data”). Nonetheless, hypothetical risks that are immaterial today have become loud enough to bury an otherwise epic earnings report. 

As I pointed out on Charles Payne’s Making Money, I live for those moments when a company delivers a strong earnings report and yet the market hands me a lower price.  

Underneath the noise of “AI bubble” debates, Google’s TPUs, debt leverage (which is a material issue), China fears (remember the DeepSeek panic?), supply chain constraints, rare-earth material shortages, and more — I like to remind investors that those risks were immaterial to Nvidia’s recent report or management’s guide. 

Therefore, I maintain that the greatest risk is not an AI bubble or the credibility of these other risks – rather it’s that an investor misses out on what may be one of the strongest investing opportunities of our lifetime: what I’ve dubbed the AI Monetization Supercycle catalyzed by the inference phase. 

Keep in mind that to short a stock like Nvidia could hypothetically return 30% or 40% whereas going long has returned over 3,500% since the I/O Fund’s first entry. It was not only Nvidia we participated in, but rather the I/O Fund has offered one of the strongest AI portfolios in the world – proven by our strong cumulative return. Currently, we have 15 positions, beating the Nasdaq YTD up from ten last year. 

Below, I outline what I see ahead — points that warrant far more attention than the familiar media narratives, which have repeatedly underestimated the magnitude of the AI trend. Instead, you’ll find data-driven conclusions on the incoming monetization wave, including new insights to help investors connect the dots on what 2026–2027 may bring.

For a limited time, we’re offering $250 off our Advanced Tier. This Black Friday deal ends December 8th. Join here.For a limited time, we’re offering $250 off our Advanced Tier. This Black Friday deal ends December 8th. Join here.Join here.

OpenAI's Record $13B Revenue Confirms the AI Monetization Supercycle 

To see the AI monetization Supercycle in action, we can simply look at OpenAI’s trajectory. The company went from $1 billion in revenue in 2023 to $3.7 billion in 2024 to now $13 billion in annualized revenue, which is the steepest rise in tech history. This was driven almost entirely by inference (API calls and ChatGPT usage). Furthermore, OpenAI recently stated revenue is “well more” than $13 billion.  

The clues from OpenAI’s monetization trajectory is key as the market is anxiously awaiting the moment that Big Tech will show ROI from capex spend, and according to Broadcom and others, that day may soon be arriving.  

Over the past year, Broadcom joined Nvidia, Alphabet, and Microsoft in calling out surging AI inference demand, noting that this rapid growth could drive increased demand for custom silicon in the second half of 2026, and with it, higher AI revenue.  

Broadcom sees growth continuing, supported by the inference growth curve, as CEO Hock Tan said during FQ2 earnings call that Broadcom might witness an acceleration of XPU demand into the back half of 2026. He said, “In fact, what we've seen recently is that they are doubling down on inference in order to monetize their platforms. And reflecting this, we may actually see an acceleration of XPU demand into the back half of 2026 to meet urgent demand for inference on top of the demand we have indicated from training.” 

Something similar was echoed in the most recent FQ3 call, with Tan stating: “But also as for these guys, they got to be accountable to being able to create cash flows that can sustain their path. They [are] starting to also invest in inference in a massive way to monetize their models.”

mid

CoreWeave’s Q2 earnings call also echoed the incoming inference wave will lead to a period of heightened monetization: “As I always say, inference is the monetization of artificial intelligence.” 

Matthew Bryson, Managing Director of Research at Wedbush Securities, expressed optimism on the broader AI sector’s shift toward revenue generation through inference applications, stating “What we’ve started to see over the last three, four months is that there’s been a huge increase in inference. This increase in inference – the applications that drive actual revenue for cloud providers and model builders – represents a critical development for the sector. It feels like right now we’re also seeing monetization of AI, and that was the concern,” he added. “If you’d asked me 12 months ago, where’s the revenue going to come from? All we’re seeing is model building, and now it looks like we’re not. We’re seeing applications.” 

I/O Fund Lead Technology Analyst Beth Kindig discusses Nvidia’s stock after Q3 FY 2026 blowout earnings report on Fox Business

How OpenAI and Anthropic Projections Validate Nvidia Stock 

OpenAI and Anthropic are boosting long term revenue projections, underpinned by inference and inference-driven products such as AI agents. For example, in the most recent projection, OpenAI raised the tail end of its long-term forecast by as much as 25% from 2027 to 2029 versus its fall 2024 projection.  

OpenAI now sees $54 billion in revenue in 2027, a nearly 23% raise from its prior projection for $44 billion, and $125 billion in revenue in 2029, a 25% raise. Notably, this growth is not stemming from ChatGPT, where 2029 revenue was actually cut from the mid-$50 billion range to $50 billion; instead, OpenAI projects around $20 billion from APIs, over $25 billion from AI agents and another $25 billion from other products and free user monetization (such as ads). For comparison, the $125 billion target reflects nearly 10X growth from 2025’s projected revenue of $13 billion. 

Chart detailing OpenAI's raised long-term revenue projections to $125 Billion by 2029, a 25% increase over prior forecasts. This growth, fueled by Inference applications, validates the Nvidia Stock bull thesis. Source: The Information.The Information.

Competitor Anthropic is also expecting rapid revenue growth through 2028, now projecting as much as $70 billion in revenue in its optimistic scenario as of early November. This forecast is supported by Anthropic’s near-term growth expectations for its ARR, with the company said to be on track to hit its $9 billion goal this year with a target to double or nearly triple this to $20-26 billion in 2026.  

API revenue for Anthropic is expected to reach $3.8 billion in 2025, more than double OpenAI’s expectation for $1.8 billion, with Anthropic’s Claude Code model said to be close to reaching $1 billion in annualized revenue, up 150% from $400 million in July. 

Recent token usage statistics from Google and OpenAI also suggest that this wave of AI inference is on rapidly on the rise. In October, Google boasted more than 1.3 quadrillion monthly tokens processed across its platform, up from 980 trillion in June and up 170% from 480 trillion just five months earlier in May. 

Google also disclosed that its first party models like Gemini were processing 7 billion tokens per minute via direct API use in Q3, or more than 300 trillion monthly, roughly one quarter of Google’s overall tokens processed. This also outpaces OpenAI, which revealed in early October that it was processing 6 billion tokens per minute on its API, or ~260 trillion per month. 

To put this in perspective, Google was processing just 9.7 trillion tokens per month in April 2024. Barely a year and a half later, and the company is almost processing that many tokens per minute. 

Making headway on token throughput directly relates to the strength of Nvidia’s stock. For example, Microsoft has recently achieved a new AI inference record, with its Azure ND GB300 v6 virtual machines processing 1.1 million tokens per second on a single rack powered by Nvidia GB300 GPUs. It also marked a 27% speed improvement from 12,022 tokens/s per previous-generation Nvidia Blackwell GPU to 15,200 tokens/sec per Blackwell Ultra GPU and beat the previous Azure ND GB200 v6 record of 865,000 tokens/s by 27%. 

AI Will Drive Strong Bottom Line Results Too 

AI will not only impact the top line but will drive internal efficiencies to where there the first clue there is ROI on capex spend may be found in margin improvement.  

Big Tech management teams are having initial discussions on the impact of using AI to drive operational efficiencies. 

Alphabet’s CFO said in the Q2 earnings call, “So Sundar mentioned earlier, the use of AI tools within the company. So that's another area where we can drive efficiency across the businesses to use these tools internally in terms of how we run the organization. Then we're continuing on the same efforts that I've talked about before with regards to running the company with a high level of discipline, execution and driving efficiency across the business.” 

According to McKinsey survey, most respondents say their organizations are using AI, and many have begun using AI agents as 64 percent of respondents said that AI is enabling innovation and 39 percent report positive EBIT impact at the enterprise level. 

CrowdStrike announced earlier this year that the company was laying off 500 employees or about 5% of its workforce, due to artificial intelligence efficiencies. According to the World Economic Forum survey, about 41% of companies worldwide are expected to reduce their workforces in the next five years attributing to the rise of artificial intelligence.  

Amazon announced in October that they are laying off 14,000 employees as the company invests more in AI. It marks the largest jobs cuts in the company’s history. Beth Galetti, SVP at Amazon said, “This generation of AI is the most transformative technology we’ve seen since the Internet, and it's enabling companies to innovate much faster than ever before (in existing market segments and altogether new ones). We’re convinced that we need to be organized more leanly, with fewer layers and more ownership, to move as quickly as possible for our customers and business.” The company’s CEO warned about the job cuts in June this year. “We will need fewer people doing some of the jobs that are being done today.” Klarna has been most transparent about the impact of AI, revealing earlier this year that the company reduced its workforce by 40%.  

Meta also revealed in October that the company will cut 600 positions from its Artificial Intelligence unit, which underscores the internal efficiencies that AI enables. This should not be confused with a pullback in AI investment — Meta is clearly spending heavily here, with capex increasing by 81% or $31.8 billion this year.  

Morgan Stanley expects that software and internet companies to report positive return on investments from GenAI of 35% in 2025 and rise to 67% contribution margin by 2028. “[…] In fact, for the first time, return on investment (ROI) is expected to be positive with analysts expecting GenAI to yield a 34% contribution margin, or the equivalent of $51 billion in 2025. Last year, GenAI ROI, with expenses, resulted in a -5% contribution margin. By 2028, ROI is expected to remain positive and rise to a 67% contribution margin, or $722 billion return.” 

According to J.P.Morgan, “Since the launch of ChatGPT in late 2022, AI-related stocks have been responsible for roughly 75% of S&P 500 total returns, 80% of earnings growth and 90% of capital spending growth. That means AI is more than just hype – it is delivering tangible results, boosting productivity and supporting corporate margins across the economy.”  

While the media will put a negative spin on those stats, I personally look at the earnings growth piece specifically as an initial clue as to the impact that AI is having. 

Nvidia Stock Shatters Records: $50B Data Center Revenue at 66% YoY Growth  

As stated in the introduction, Nvidia blew the doors off its most recent earnings report, with the long-awaited Blackwell and Blackwell Ultra architectures now shipping in volume. Given how much time has been wasted on fearful speculation around the AI leader and the overall AI market, I think it’s appropriate to spend at least a few minutes grounding ourselves in Nvidia’s fundamentals. 

Nvidia’s Q3 revenue grew by a solid 62.5% YoY and 22% QoQ to $57.01 billion. Revenue growth accelerated by 6.9 percentage points from 55.6% YoY growth reported in Q2. Revenue beat estimates by 3.5% and is the strongest beat in the last four quarters. The company’s strong revenue growth dispelled fears of an AI Bubble. Nvidia’s CEO Jensen Huang said, “Blackwell sales are off the charts, and cloud GPUs are sold out.”  

In Q3, the GB300 sales were higher than GB200 sales, accounting for 2/3 Blackwell’s revenue, proving strong demand from cloud companies and hyperscalers. Looking forward, Rubin is on track to ramp in the second half of 2026 – which may help Nvidia continue to beat analyst estimates, especially as we approach CY2027 

Management also provided a strong Q4 revenue guide of $65 billion at midpoint, representing a YoY growth of 65.3% and up 14% QoQ, beating estimates by 5.1%. 

The company’s networking growth was an outlier, growing 162% YoY and 13% QoQ to $8.19 billion. Revenue growth accelerated by 84 percentage points from 78% YoY growth in Q2. Management stated in the earnings call that the company’s networking business is now the largest in the world.  

Nvidia surpassed the $50 billion quarterly data center revenue milestone in Q3, reporting $51.2 billion in revenue for the segment, up 25% QoQ and 66% YoY. This is the highest QoQ growth rate for data center in nearly two years since fiscal Q4 2024. An impressive feat to deliver such strong growth at scale considering the segment was just $18.4 billion when Nvidia last reported this QoQ growth.  

On a dollar basis, data center revenue rose by $10.1 billion sequentially. This sequential growth was driven by a strong inflection in Compute revenue, which surged 27% QoQ to $43 billion, its highest sequential growth rate since fiscal Q1 2025; however, this does come after a (1%) QoQ decline in fiscal Q2. Nvidia noted that Blackwell Ultra was ramping across all customer categories and became its leading architecture.  

Chart of Nvidia's Data Center Revenue. The segment's QoQ growth surged $10.1 Billion in Q3 to $51.2 Billion, validating the Nvidia Stock bull thesis. While Q4’s guidance suggests the segment could reach $59 billion.

Q4’s guidance suggests that this $50 billion data center segment will quickly be in the rear-view mirror, with the $65 billion guidance implying data center revenue of around $59 billion assuming similar mix shift as Q3. This represents another 15% QoQ growth on top of Q3’s 25%, or essentially the data center segment rising nearly 44% in just two quarters.  

This would also correspond to a nearly $8 billion QoQ increase, meaning that if Nvidia maintains this growth cadence through mid-CY26, then it would reach our prediction for a $75 billion data center segment two quarters early. If this materializes, this would represent data center growth of 66% YoY, up from 56% last quarter.  

It also could suggest Nvidia potentially reaching a $90 billion quarterly data center segment if this trajectory is maintained through the end of fiscal 2027. For investors, this rapid acceleration reinforces the bullish outlook for Nvidia stock, as these revenue milestones increasingly align with long-term valuation targets. 

However, it is important to note that given the sheer scale of data center revenue, there is the potential for this inflection to be lumpy, especially in-between GPU generations. 

NVDA Blackwell Revenue Surpasses $100 Billion, Validating $500B Data Center Visibility 

As stated in our analysis “Why Nvidia Stock Could Reach a $20 Trillion Market Cap”, after taking into account Jensen Huang’s commentary in October that $500 billion in Blackwell-Rubin revenue that will ship by the end of FY2026, my firm estimates this leads to a $320 billion data center segment next year.  

Here is what was stated in the $20 Trillion analysis: “Reading between the lines on Huang’s comments suggests strong upside to Nvidia’s data center revenue through 2026. Over the prior three quarters heading into fiscal Q3’s report, Blackwell revenue has totaled approximately $63 billion. Including Networking over that time frame, total revenue would rise to $78 billion, still a fraction of the total overall opportunity management is projecting. Thus, if we assume that Blackwell and Rubin ramp over the next five quarters, fiscal 2027 data center revenue could be nearly $320 billion, versus estimates for around $270 billion.”   

We calculated this from the prior three quarters heading into fiscal Q3’s report; Blackwell revenue has totaled approximately $63 billion. Now, Q3’s Compute revenue of $43 billion implies Blackwell has delivered around $104 billion in revenue in the past four quarters, assuming the only non-Blackwell revenue was the $2 billion disclosed from Hopper.   

Including Networking and Q4’s guidance, Nvidia looks to be on track to generate $186 billion of its $500 billion opportunity in fiscal 2026. This would leave approximately $314 billion for fiscal 2027’s data center revenue to meet the $500 billion visibility, but if Nvidia can exceed that by 2-4%, it could be on track for $330 billion next year.  

At a 20 to 25 forward sales valuation, Nvidia only needs to grow its data center segment 3X to $920 billion to reach a $20 trillion market cap. 

In fact, further strengthening the $20T market cap prediction, Nvidia’s CFO has hinted they could exceed $500 billion as the CFO stated, “So there's definitely an opportunity for us to have more on top of the $500 billion that we announced.” Nvidia’s CFO later clarified this week at UBS’ tech conference that the “$0.5 trillion doesn't include any of the work that we're doing right now on the next part of the agreement with OpenAI” signed in late September for up to 10GW of compute. 

Nvidia Stock: $50 Billion Supply Commitments Guarantee Continued Growth Inflection 

What differentiates the I/O Fund’s research is that we constantly tell our members the key indicators to look at in a company’s earnings report. While we continue to hammer on the importance of Big Tech’s capex as the number one indicator for Nvidia’s data center growth continuing, there was potentially a more important, well overlooked figure in Nvidia’s report that signals this data center inflection will continue.   

Nvidia’s total supply-related commitments, such as for CoWoS wafers, HBM memory, or other components, surged nearly 52% QoQ to $50.3 billion in Q3, with management noting that they are “ordering to secure long lead-time components, meet the demand for Blackwell, and support future architecture ramps.”   

This is a notable increase from the prior five-quarter average of ~$30 billion, which is likely supporting the current ramp in data center revenue. This uptick in supply commitments, which is likely to translate into inventories and revenue over the coming four to six quarters, hints that Nvidia will continue ramping Blackwell output while preparing for Rubin’s production in the second half of 2026.    

Chart detailing Nvidia's commitment surge. Total Supply Commitments jumped 52% QoQ to $50.3 Billion in Q3, signaling massive order volume to secure Blackwell and Rubin production and Nvidia Stock growth.

This also bolsters confidence in Nvidia’s order visibility to fill out and even exceed this cumulative $500 billion in Blackwell and Rubin revenue, as the company would not need to boost supply commitments by this degree if the demand signals were not there.

In Closing …. 

Nvidia’s Q3 results showed the company’s GPU momentum return, delivering a substantial data center beat with 25% QoQ growth, surpassing an important $50 billion quarterly revenue milestone for the segment. More importantly, Nvidia’s guide pointed to this momentum continuing into the fourth quarter, implying that data center revenue could be on track to increase another $8 billion QoQ for 15% growth.  

I published an article entitled Why Nvidia Stock Could Reach $20 Trillion Market Cap by 2030 Why Nvidia Stock Could Reach $20 Trillion Market Cap by 2030 – a prediction that requires a 36% CAGR over a five-year period or about 8% growth QoQ. These two quarters alone meet the criteria for next year’s CAGR plus some. 

Nvidia’s results provide a strong message that AI is not in a bubble. While many are busy debating this point, we are laser focused on identifying the companies that are going to benefit from the monetization of AI, particularly due to the shift from Big Tech companies from training to inference.

This year, my firm has 15 positions beating the Nasdaq YTD, up from ten positions last year – helping to cement the I/O Fund as one of the world’s leading AI portfolios. Our cumulative return of 210% over a five-year period would rank us #2 if we were a hedge fund and #5 if we were an ETF – notably, this strong cumulative return does not yet include our 2025 performance.

For a limited time, we are offering $250 off on our Advanced tier $250 off on our Advanced tier with real-time trade alerts, webinars, deep dives and access to our portfolio. This Black Friday deal expires soon on December 8th.Black Friday deal expires soon on December 8th.

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Damien Robbins and Royston Roche, Equity Analysts at I/O Fund contributed to this analysis

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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The statement that Nvidia stock could reach a $20 trillion market cap by 2030 will trigger plenty of emotion — it sounds fantastical, full of hype, or like a prediction made far too early in the AI cycle. Yet what I offer you below is a data-driven, fundamentally grounded case for how Nvidia can realistically reach a $20 trillion valuation by 2030. 

When it comes to Nvidia’s AI story, I’ve offered the earliest and most consistent analysis, covering the company’s AI trajectory earlier than anyone on record. For instance, I told my premium stock research members in September of 2019 that Nvidia would become one of the world’s most valuable companies when it was only a $110 billion valuation (it’s now up 40X). I also publicly stated that Nvidia would surpass Apple when Nvidia had just one-fifth of Apple’s market cap — $550 billion versus $2.5 trillion — writing: “The conclusion to my analysis is the same as the introduction, which is that I believe Nvidia is capable of outperforming all five FAAMG stocks and will surpass even Apple’s valuation in the next five years.” Fast forward and Nvidia stock is up 8X since that analysis.

Last year, when Nvidia stock was valued at $3 trillion, I projected the stock would reach $10 trillion market cap by 2030 — a forecast that no longer looks aggressive now that the stock has briefly broken above $5 trillion. Today, with an even clearer view into the company’s product cadence, software moat, and AI systems dominance, my new, updated thesis is that Nvidia’s stock is on track to reach a $20 trillion market cap by 2030. 

This is supported by Nvidia’s aggressive 1-year product roadmap, an impenetrable software ecosystem through CUDA, and its evolution into a full-stack AI systems provider. When these elements are modeled together — alongside the rapid expansion in global AI infrastructure capex — the path to $20 trillion becomes less sensational and more a reflection of compounding fundamentals.

Nvidia’s Data Center Needs to Grow at 36% CAGR to Reach $20T Market Cap

To get down to brass tacks, Nvidia will have to grow its data center segment at a 36% CAGR to reach a $20 trillion market cap if we assume its 5-year median sales valuation of 25 forward PS remains intact. This will put the company’s data center revenue at a run rate in the mid-$900 billion range.

Pictured above: Nvidia stock could see a $20 trillion market cap by 2030 based on a 36% CAGR in its data center segment

About eighteen months ago, I highlighted the importance of Nvidia reaching a $50 billion data center segment by year-end in the article, Here's Why Nvidia Stock Will Reach $10 Trillion Market Cap By 2030, stating: 

In my analysis last month on the Blackwell architecture, I made the argument these estimates are too low and that my firm expects we will see a $200 billion data center segment by end of CY2025 propelled forward by the B100, B200 and GB200, including the following points: “Taiwan Semi’s CoWos capacity, which is essential for Blackwell’s architecture, is estimated to rise to 40,000 units/month by the end of 2024, which is more than a 150% YoY increase from ~15,000 units/month at the end of 2023. Applied Materials has boosted its forecast for HBM packaging revenue from a prior view for 4X growth to 6X growth this year.” 

The data center segment for Nvidia of $320 billion by 2027 would result in 260% growth for Nvidia’s DC from where it stands today and up 120% from DC revenue estimates for end of CY2025.” 

It’s highly probable that Nvidia will blow past the $50 billion data center segment mark this evening – one quarter earlier than my original prediction – which puts the company on the path for a $75 billion segment in Q4 of next year. Tracking these milestones is crucial as it helps support that Nvidia is well on its way to reaching my firm’s brand-new updated estimate for a $230 billion data center quarter by Q4 of 2030 or $930 billion for the full year. 

Industry analysts have AI accelerators growing at 31.5% CAGR through 2033 with McKinsey putting out a prediction for $7 trillion in AI infrastructure spend through 2030 with $5.2 trillion going toward building data centers for AI workloads. Dr. Lisa Su and Jean Hsu echoed McKinsey’s projections, stating the AI data center market could be worth $1 trillion by 2030, referring to the addressable market of AI accelerators where AMD competes.

mid

Regardless of which way you dice it, industry estimates point toward AI spend exceeding current expectations. For example, Dr. Su originally had predicted a $500 billion market by 2028. Her updated forecast assumes 35% growth over the next three to five years – the same growth rate required for Nvidia to reach the assumptions underpinning my thesis for a $20 trillion market-cap scenario. 

McKinsey’s $5.2 trillion AI infrastructure forecast implies roughly $1.5 trillion in annual AI spending by 2030. Under this framework, our assumptions are slightly on the aggressive side, as they imply Nvidia captures about 60% of total AI capex. Back-of-the-napkin math suggests Nvidia is currently capturing closer to 50% of AI spend, given today’s $405 billion capex run rate and Nvidia’s data center segment set to surpass a $200 billion run rate in this evening’s report.

Pictured above: Big Tech AI Capex expected to surge from $406B in 2025 to over $1.5 trillion by 2030, reflecting the massive growth in the AI data center market.

Offense is the Best Defense: Nvidia’s Rapid Product Road Map

The saying goes “the best offense is the best defense” and Nvidia is fully applying this philosophy by leading with its design prowess to ensure custom silicon cannot replace its lead in AI systems. There will certainly be a market for custom silicon as it excels at application specific workloads, which is attractive to Big Tech companies that can use custom silicon to optimize their recommendation engines, run inference at scale and optimize specific internal models. However, custom silicon cannot compete with GPUs and Nvidia’s CUDA software platform on general workloads, which excels at running every model, every framework and every new architecture.  

The key reason that Nvidia can reach a $20 trillion market cap by 2030 is because the company is moving its GPU generation cadence to a rapid 12-18 month cycle compared to custom silicon, which is typically on a 3-5 year cycle. Even for Nvidia, the goal of releasing a new GPU generation every year was once unthinkable. Yet this offensive measure will be transformative, turning what was once a cyclical revenue profile into a consistent and compounding growth trajectory.  

Source: Nvidia GTC Conference, March 2025  Last March, Nvidia revealed their plans for a 1-year product cadence, a key element to the I/O Fund’s thesis that Nvidia can reach $20 trillion market cap by 2030.

At this point, Nvidia is competing with itself with Blackwell offering 208 billion transistors compared to Hoppers 80 billion transistors. By combining 72 GPUs, the Blackwell systems offer 30X to 40X faster inference and are up to 2.5X faster on training. The memory capacity has increased to 192GB of HBM3e for Blackwell and 288GB for Blackwell Ultra. Energy efficiency is also improved by 25X. The 30X improvement in running AI reasoning models is primarily from leveraging FP4 format and fifth-generation NVLink at rack scale level. Blackwell arrived in H1 of 2025 and Blackwell Ultra is shipping now in H2 2025. 

Vera Rubin increases the number of GPUs to 144, up from 72 GPUs, for 3.3X higher performance. Vera Rubin doubles the FP4 performance from 20 petaflops to 50 petaflops. The new architecture will offer HBM4 memory and sixth-generation NVLink. Rubin Ultra takes rack-scale to a new level with 576 GPUs compared to Rubin’s 144, with more details to be released in the coming months. Vera Rubin is expected to arrive in H2 2026 with Rubin Ultra in H2 2027. 

From there, Feynman is expected to bring to market Gigawatt AI factories, which would be up about 8X from today’s peak cluster size of 150 MW (the largest cluster right now is Colossus at 150MW with plans to expand to 300MW soon). Feynman is expected to arrive in 2028. 

5X Hopper: Jensen Huang Reveals $500 Billion Blackwell and Rubin Revenue Visibility

Nvidia laid out an eye-opening stat at the company’s GTC October conference, with CEO Jensen Huang revealing the company has visibility into an astonishing $500 billion in cumulative Blackwell and Rubin revenue through the end of 2026. This is ~5X the lifetime revenue of its Hopper GPUs from 2023 through 2025 which stood at $100 billion.  

Huang’s projection calls for 20 million GPU shipments, with 30% of that, or 6 million, having already been shipped; however, considering both generations have two GPUs per chip, in reality, this corresponds to 10 million chip shipments with 3 million already shipped. Huang’s forecast also excludes China but is expected to include attached networking equipment such as Nvidia’s InfiniBand and NVLink. 

Reading between the lines on Huang’s comments suggests strong upside to Nvidia’s data center revenue through 2026. Over the prior three quarters heading into fiscal Q3’s report, Blackwell revenue has totaled approximately $63 billion. Including Networking over that time frame, total revenue would rise to $78 billion, still a fraction of the total overall opportunity management is projecting. Thus, if we assume that Blackwell and Rubin ramp over the next five quarters, fiscal 2027 data center revenue could be nearly $320 billion, versus estimates for around $270 billion. 

This forecast is supported by the accelerated progression in GPU cluster sizes, scaling quickly from 10K clusters just two years ago to hundreds of thousands over the next few years. The first 10K Hopper GPU clusters came online in 2023 and 2024, before scaling 10X to 100K clusters by year-end 2024. Blackwell is picking up where Hopper left off, with clusters expanding from 100K to the hundreds of thousands through 2026 and 2027, such as for Microsoft’s new Fairwater data centers and xAI’s Colossus 2. This scale out of 8-10X growth to reach 1 million GPU clusters over the next few years underpins millions of GPU shipments over the coming quarters. 

Consensus Estimates Still Below Nvidia's $500B Target for FY26/27

Even with the commentary for half a trillion in revenue potential for Blackwell and Rubin GPUs, consensus estimates for fiscal 2026 and 2027 still remain below $500 billion combined. This also comes despite numerous analysts pointing out that Street estimates are too low and citing substantial upside potential for data center revenue. 

Source: YCharts

Current consensus estimates point to $207.6 billion in revenue in fiscal 2026, before rising to $290.5 billion in fiscal 2027, with next year seeing only a $13 billion (5%) upward revision following the $500 billion forecast. It’s important to note that some of fiscal 2026’s revenue came from the Hopper generation, which contributed ~30% of Compute revenue in fiscal Q1, or more than $10 billion.  

However, analysts from Cantor, UBS, Melius and others believe estimates are too low moving through calendar 2026. New Street says the $500 billion forecast “implies a nearly doubling of Nvidia's data center revenue in 2026,” while Wolfe Research estimated that data center revenue "could be $60 billion over prior calendar 2026 estimates.”  

This suggests that the Street remains cautious about this order visibility materializing in full, as current consensus estimates would project data center revenue of ~$445 billion assuming ~90% share of total revenue. 

AI Buildout Accelerates: Big Tech Capex Headed for $405 Billion

Big Tech Capex grew by 75% YoY and 19% sequentially to $113.4 billion in Q3. In fact, capital spending for the AI buildout has risen 44.6% from our initial estimates. A substantial jump considering the scale already measured in hundreds of billions. This time last year, the expectations were $280 billion in Big Tech capex.  

Morgan Stanley later forecast $300 billion in Big Tech capex for 2025. Capex estimates stood at $365 billion heading into Q3, and now we believe 2025 capex is on track to surpass $405 billion, representing YoY growth of 62%. This spells good things for the I/O Fund’s projected 36% CAGR for Nvidia’s data center to materialize. 

Overall, analysts have clearly underestimated the growth in capex and future AI opportunities. This is also evident when AMD’s CEO Lisa Su recently increased the company’s AI total addressable market to $1 trillion in 2030, up from the previous forecast of $500 billion by 2028.  

In terms of what the opportunity looks like moving forward, McKinsey is predicting 3.5X growth in gigawatts for AI data centers between 2025-2030. The costs associated with AI data centers range from $3 trillion to $8 trillion, or about $5.5 trillion at the midpoint. This correlates to about 3X growth if we assume the current run rate to 2030 is $1.8 trillion at the current capex of $405 billion. 

UBS recently upgraded the AI capex estimates from the previous $375 billion to $423 billion for 2025. For the next year, they have increased the estimates from $500 billion to $571 billion, a solid 14% increase. By the year 2030, UBS expects overall spending to hit $1.3 trillion, implying a 25% compound annual growth rate (CAGR) over the next five years – or about 11 points lower than our estimate for 36% CAGR – although I still have five years to go for analysts to raise their estimates, which judging by what we’ve seen in capex estimates, could very well be doable. 

Another point as to why AI spending estimates may be too low is they are still modest to global GDP. According to IMF estimates, the $1.3 trillion capex estimate would only account for 1% of GDP, whereas some of the previous investment booms like railroads, computers, telco, etc. – ranged from 1.5% to 4.5% of global GDP. 

Nvidia’s Deals with OpenAI and Microsoft Fuel Insatiable GPU Demand

If a $20 trillion market cap sounds outlandish, consider the deals worth hundreds of billions that are pouring in. Not only does Nvidia have the $500 billion Stargate project underway for OpenAI, but the ChatGPT parent also committed to an additional $250 billion of compute from Azure as part of its for-profit restructure.  

Additionally, Nvidia signed a partnership with OpenAI, which will see it deploy up to 10GW of Nvidia GPUs in data centers. Under the deal, Nvidia is investing up to $100 billion in OpenAI progressively as each GW is deployed. The first GW of GPUs under Nvidia and OpenAI’s agreement will be deployed in the second half of 2026 on Nvidia’s upcoming Vera Rubin platform. While there was no set timeline for the remaining nine GWs of chips, CEO Jensen Huang told CNBC that the entire deployment would represent around four to five million GPUs.  

In terms of the total opportunity for Nvidia, Bank of America estimates this partnership could generate $300 billion to $500 billion in revenue overtime at full deployment. This aligns with expectations from other analysts that Rubin and Rubin Ultra will cost $30 to $35 billion per GW, with 10-15% increases per GW per each generation. 

Microsoft also contracted approximately 200,000 GB300s from British startup Nscale in a deal said to be worth $14 billion, with the first smaller-scale deployment starting in Q1 followed by a 104,000 cluster in Q3 2026. This builds on Microsoft CEO Satya Nadella hinting last weekend that Microsoft is bringing online more than 100,000 GB300s this quarter, or approximately 1,389 NVL72 racks worth ~$4.17 billion at a $3 million estimated ASP.  

Deals like these that continue to pop up across the industry hint that demand for GPUs remains insatiable to meet high demand. It also suggests current capex estimates may be too low as hyperscalers continue to pour tens of billions each quarter to data center infrastructure via whatever avenue possible. 

Conclusion: 

When you step back from the noise and look at the data, the path to $20 trillion is built on compounding fundamentals that are already surpassing the most aggressive forecasts from a year ago. Analysts continue to revise capex expectations higher, AI infrastructure projections have doubled, and Big Tech is racing to deploy unprecedented levels of compute. 

The data center segment growing 36% CAGR is a tad ambitious, yet it does not factor in markets such as robotics, agentic systems and simulation. Also consider we are seeing 5X growth from the Hopper cycle to the Blackwell-Rubin cycle in the data center segment. At the end of 2026, we will need only 3X growth to deliver on my prediction of a $930 billion data center segment. 

Today, my updated thesis is clear: Nvidia has a credible path to reach a $20 trillion market cap by 2030 with an aggressive product road map for Blackwell, Rubin, Rubin Ultra and eventually Feynman’s gigawatt-scale AI factories. Just as with my earlier calls on Nvidia’s stock, the data increasingly supports an outcome that was once considered impossible.

As AI accelerates into the largest technology buildout of our lifetime, we believe Nvidia remains one of the strongest beneficiaries. Our portfolio is also positioned with many of Nvidia’s lesser-known AI networking suppliers and AI energy stocks. To view the I/O Fund portfolio plus my 43-page Top 15 AI Stocks list, sign up below.  

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Our cumulative return of 210% would place us as #2 if we were a hedge fund and #5 if we were an ETF. Our entries and exit are sent in real-time including one entry as low as $3.15 on Nvidia in 2018 and 9 alerts sent under $20 in 2021 – 2022. This year, we have an AI energy position up over 500% and others up over 100% in AI energy and AI networking. Learn more here.210% would place us as #2 if we were a hedge fund and #5 if we were an ETF. Our entries and exit are sent in real-time including one entry as low as $3.15 on Nvidia in 2018 and 9 alerts sent under $20 in 2021 – 2022. This year, we have an AI energy position up over 500% and others up over 100% in AI energy and AI networking. Learn more here.

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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