The AI capex race is the contest among the largest cloud and platform companies, chiefly Amazon, Microsoft, Alphabet and Meta, to buy the data centres, chips and power that AI models need, on the bet that owning scarce compute decides who wins AI. In the quarter to 30 September 2026 the four spent $201.9 billion on property and equipment, about twice the $97.3 billion of a year earlier, and more than the $175.5 billion of cash their operations generated (our arithmetic on their filings). The new phase of the race is financing: the gap is now filled with bonds, loans and new shares.
Training and running AI models needs specialised chips, buildings and electricity. Companies that own capacity can sell it (AWS, Azure, Google Cloud) or use it for their own products (Meta's ads, Google Search).
Buildings and chips are paid for up front and earn back over years. The bet only pays if contracted or expected demand fills the capacity; Amazon, for example, reported about $578 billion of commitments not yet recognised at 30 September 2026.
03
Cash flow sets the speed limit, debt raises it
In Q3 2026 only Microsoft covered its capex from operating cash; Amazon, Alphabet and Meta spent more than their operations generated, and the companies raised bonds, loans and new shares to keep pace.
What changed in 2026
Amazon leads on spending
Amazon bought $77,259 million of property and equipment in Q3 2026, against $42,422 million of operating cash flow, and still expects about $220 billion of cash capex for 2026. Its long-term debt rose from $65,648 million at the end of 2025 to $173,125 million at 30 September 2026.
Alphabet spent $51,355 million on property and equipment in Q3 2026, against $23,953 million a year earlier, and generated $42,290 million of operating cash, a free cash flow of minus $9,065 million for the quarter.
Meta sells shares to build
Meta spent $33,942 million in Q3 2026 and guides to $135-145 billion of 2026 capital expenditures, including finance lease payments. In late October it priced a forward sale of 40,000,000 Class A shares at $625, worth $25.0 billion before costs.
Microsoft still covers it, and borrows anyway
Microsoft’s additions to property and equipment were $39,367 million in its quarter to 30 September 2026, below its $58,000 million of cash from operations. On 29 October 2026 it still priced $25.0 billion of notes across ten series.
Capex vs operating cash flow, quarter to 30 September 2026
Compare each company's purchases of property and equipment with its operating cash flow every quarter; the ratio shows who is funding AI from profits and who from markets.
Read the financing section of the cash flow statement: new bonds, loan facilities and share sales are now part of the capex story.
Check contracted backlog against spending; capacity without signed demand is the risk to watch.
If you buy cloud or AI capacity, expect providers to push multi-year commitments; they need them to justify the build.
Separate guidance from spending: use each company's own capex outlook, with its stated basis (for example, Meta includes finance lease payments).
The Business Engineer lens
The AI capex race has moved from a spending contest to a financing contest: the winners are the companies whose contracted demand and balance sheets can carry the build longest. The Business Engineer tracks the gap between capex and operating cash as the cycle’s most honest indicator.
It is the competition among Amazon, Microsoft, Alphabet, Meta and others to build data centres, chips and power capacity for AI, betting that owning compute decides who wins.
How much are the big four spending?
In the quarter to 30 September 2026 Amazon, Alphabet, Microsoft and Meta spent $201.9 billion on property and equipment combined, roughly double a year earlier (our arithmetic on their filings).
Yes for three of the four in Q3 2026: combined capex of $201.9 billion exceeded $175.5 billion of operating cash flow; Microsoft was the exception.
How are they paying for it?
With operating cash plus new debt and equity: Amazon's long-term debt rose to $173,125 million, Microsoft priced $25.0 billion of notes, and Meta priced a $25.0 billion forward share sale.
The complete explainer, cleaned up and reformatted for mobile.
Amazon, Google, Microsoft, and Meta set early-2025 capex plans that add up to more than $300 billion for 2025 (our arithmetic on the plans listed below), with Amazon leading at about $100 billion.
Big Tech players transitioning into โHyperscalersโ set record capex (capital expenditure) plans for 2025. Image Credit: WSJ
Despite DeepSeekโs low-cost AI breakthrough, tech CEOs defend their investments, citing AI-driven application reinvention.
Google will raise its capex to $75 billion, up from $52.5 billion.
Microsoftโs AI investments will exceed $80 billion.
Meta plans to spend between $60โ$65 billion as a combined bet on AI/AR.
This massive spending spree has shocked the market, particularly as Chinese AI startup DeepSeek has demonstrated that advanced AI models can be trained at much lower costs.
Despite this, tech CEOs continue to justify these investments, predicting an AI-driven reinvention of applications and a surge in cloud demand.
Another key factor is the AI supply bottleneck, where the explosion of AI demand, both consumer and enterprise, has outpaced cloud providers like Amazon, Microsoft, and Google.
What will this spend be used for?
And a key question that remains is: Are these companies over-investing or acting irrationally?
As Iโve shown you above, the industry is evolving quickly.
And for now, that is quickly getting reshaped every 2-3 years. Massive moats must be made, not only on the application but also on the foundational side.
However, the learning from DeepSeek is that while the research still has a long way to go, the key point stays in terms of optimization techniques that can be used, especially on the reasoning side for quick take-off.
Not only that, at the beginning of the piece, Iโve shown you the impressive result that just came out from a bunch of young researchers at Stanford and the University of Washington with test-time scaling.
One thing here as you read the paper is how much space there is to optimize on the reasoning side, which means weโll see exponential progress in the next couple of years.
Yet, to be at the frontier, you must be competitive on the whole pipeline and move yourself in two directions. Up in the AI foundational stack, up to the point of building data centers and creating your own AI chip architecture. And downstream, getting closer and closer to the final consumer.
In 2018, I started to map out the โAI supply chain,โ which meant a slightly different thing, yet the core of the โcompetitive moatingโ hasnโt changed.
As a frontier/foundational AI player, you must move upstream and downstream into the AI supply chain.
Keep tight, as in the upcoming issues, Iโll try to tackle whatโs each playerโs potential moat in AI.
What Bet is Each Big Tech Player Placing on AI? Future Competitive Moating
Each of the Big Tech players is placing its bet on how AI will develop.
As with any bet, it might be successful, yet that is how you build moats as a market develops into the future.
Amazonโs CapEx Bet
Amazon’s massive spending will be primarily on expanding AWS data centers and cloud services; all the while, the company is going further down the stack of creating its own AI models.
In the meantime, as a low-hanging fruit, if Amazon were to implement AI on top of its core and, for instance, boost its ads business with it, that would be already a quick leg up, at least on the revenue side.
Amazon Ads has officially passed $50 billion in revenue in 2024, coming in as the fastest-growing segment for Amazon, even faster than AWS.
For instance, Alexaโs AI revamp might be another one on the consumer side.
Amazon is set to unveil a generative AI-powered Alexa at a February 26 event, introducing conversational and agent-like capabilities.
The upgrade aims to revitalize Alexaโs relevance and monetize AI features, aligning with Jeff Bezosโ original vision.
Googleโs CapEx Bet
Google’s capex will be focused on AI data centers and cloud computing, as AI will serve as the core infrastructure for the suite of its products.
Google is looking at it from three perspectives
Leading AI infrastructure (within Google Cloud).
World-class research, including models and tooling (within Google DeepMind).
And our products and platforms that bring these innovations to people at scale (within the plethora of tools and companies Alphabet owns, but first of all within Google Search).
Thus, in the meantime, the low-hanging fruit is really the search business.
Google Search,ย with interfaces like AI Overviews in Search, has just expanded to 100+ countries, increasing search engagement, especially among younger users.
Google Ads Machine,ย with Google integrating AI features, from image generation to targeting, to improve the delivery and reach of these formats.
It means that with this simple AI integration, one on Google Search can at least try to patch things up in the short term, as redefining the whole search UX for AI at Googleโs scale is (almost) mission impossible.
On the Google Ads side, the company can boost advertising budgets through it, thus translating these AI enhancements as a revenue leg up.
With the premise that Google Search might lose scale, advertisers might opt for other platforms.
Yet, a key advantage for Google is it also owns YouTube, which is probably still the most impressive digital platform.
With the integration of AI into its Ad Machine, for instance, Google has seen improved search performance, which has also driven the cost per click higher, thus probably making the company a few more billion a year.
Thus, Googleโs core moat is the search business.
While the company boosts its revenue via AI, it also explores ways to monetize its AI via ads.
Microsoft CapEx Bet
Microsoftโs AI investments will exceed $80 billion as the OpenAI partnership is loosened as SoftBank gets in the loop.
In this phase of AI adoption, Microsoft has been defined by OpenAIโs partnership. By the end of 2024, as OpenAI is planning is transition to full profit, primarily funded via SoftBank, this is all changing.
That will require Microsoft to quickly shift its whole strategy to compete, a la Google, across the whole spectrum of AI, from data centers to business and consumer applications.
This CapEx investment will be critical for Microsoft to move in two directions.
One, keep strengthening its Azureโs AI infrastructure and AI data centers.
Two, and most importantly, the company will also need to accelerate its own modelsโ development, as OpenAI is loosening the partnership with Microsoft and getting married to SoftBank to stay relevant in this further phase of AI development.
The risk of losing the โAI reasoning trainโ is too high, and Microsoft will do all it can to cling to it.
Metaโs CapEx Bet
Meta plans to spend between $60โ$65 billion as a combined bet on AI/AR.
With that CapEx, Meta will need to get back on track to be the top player in the Open-Source AI space while placing a massive bet on AR, which is the alternative platform for Meta, which still massively relies on Apple โ as explored in the interface layer wars reshaping consumer tech โ to distribute its products, which puts it in a long-term weak position.
Each of these companies knows it is placing probably the most critical bet of the last 50 years, and losing the train there will mean losing relevance and going into obsolescence.
Thatโs the price that the loser will payโฆ
Recap: In This Issue!
The AI Reasoning Take-Off: A New Acceleration Phase
AI reasoning is entering a breakthrough phaseย due to new techniques likeย test-time scaling, making reasoning much cheaper and more efficient.
This could lead to aย faster-than-expected transition from reasoning to full AI agentsย that can handle complex tasks autonomously.
Optimization in AI reasoning is still in its early stages, meaning rapid improvements will continue over the next few years.
The Three Phases of AI Evolution
LLMs via Pre-training (Past โ 2022)
Scaling data, compute, and attention-based architectures (GPT-3, GPT-4, LLaMA).
Rapid progress until hitting initial scaling limits.
Reasoning via Post-training (Current โ 2023-2025)
AI is moving beyond simple Q&A toย more complex reasoning tasksย throughย post-training tricks.
Example:ย Chain-of-thought prompting, tool use, and memory integration.
Real Agentic AI (Coming Fast โ 2025+)
Moving fromย human-in-the-loop promptingย to AI agents autonomously handling multiple tasks in the background.
Humans will shift from guiding every step toย checking outcomes and ensuring alignment.
The AI Cost Curve is Falling Fast
Inference, reasoning, and intelligence costs are decreasing rapidly, driving fierce AI competition.
The race at theย frontier of AI has no natural moatsโonly continuous innovation can sustain dominance.
AI players must findย alternative defensibility strategiesย beyond model capabilities.
The AI CapEx Race: Big Techโs 2025 AI Investment Plans
Amazon, Google, Microsoft, and Meta set early-2025 capex plans that add up to more than $300 billion (our arithmetic on the plans listed below).
The AI market is moving into aย “hyperscaler era,”ย where capital spending on AI infrastructure will dictate future winners.
CompanyAI Investment (2025)Primary FocusAmazon$100B+AI-driven AWS expansion, AI-powered adsGoogle$75BAI search & ad infrastructure, cloud dominanceMicrosoft$80BAzure AI infrastructure, OpenAI alternative modelsMeta$60-65BAI + AR platform bets, AI-driven open-source leadership
Despite cost breakthroughs (e.g., DeepSeekโs low-cost AI models), Big Tech is doubling down on CapEx investments.
The AI supply bottleneck remains a major constraintโAI demand is outpacing cloud capacity.
Are There No Moats in AI?
Many assume AI models are quickly commoditized, meaningย no moats existย at the frontier.
But defensibility isnโt just aboutย core modelsโitโs aboutย infrastructure, branding, distribution, and vertical integration.
AI moats come fromย locking in customers through AI-native applications, infrastructure, and network effects.
The AI Innovation Pipeline: How AI Moats Form
A true “Frontier AI Moat” isnโt just model developmentโit involves the entire AI lifecycle:
R&D & Talentย โ The first layer of AI differentiation is top research teams.
Model Trainingย โ Scaling data & compute efficiently.
Post-Training Optimizationย โ Fine-tuning models for complex reasoning tasks.
Inference Infrastructureย โ AI serving costs must be optimized for real-world applications.
Application & Distributionย โ Turning AI advances intoย real business advantages.
AI hyperscalers (OpenAI, Anthropic, Google) need more than modelsโthey needย cloud infrastructure & hardware access.
AI Talent Wars: The Hidden Moat
AI talent remains one of theย biggest bottlenecks in the industry.
The AI boom created aย severe talent shortage, leading to aggressive poaching among frontier AI players.
Key AI talent moves (2024-2025):
Mustafa Suleyman (DeepMind โ Microsoft AI)
Mira Murati (OpenAI CTO โ New AI startup)
John Schulman (OpenAI โ Anthropic โ Back to OpenAI)
The AGI Bet: Why AI Labs Are Investing at Insane Levels
AI labs are betting thatย AGI (Artificial General Intelligence) will emerge within the decade.
If AGI materializes, it couldย unlock a new economic paradigm, driving AI labsโ extreme investments.
Future AI Moats: How Big Tech is Positioning for AI Dominance
This is part of an Enterprise AI series to tackle many of the day-to-day challenges you might face as a professional, executive, founder, or investor in the current AI landscape.
Gennaro Cuofano is a CRO and tech executive who has worked in AI since late 2015, starting with bringing NLP, natural-language generation, voice and chatbot products to the marketing industry. His background is in law and finance: he holds a Master's degree in Law and an International MBA with an emphasis on corporate finance (LUISS Business School and the University of San Diego, 2012), and worked as a financial analyst at a real-estate investment firm in San Diego and as an assistant controller. His work focuses on business model strategy, business engineering and, more broadly, structural analysis: how companies make money, read from their own filings. He created FourWeekMBA and leads research there and at The Business Engineer, his newsletter on AI and business strategy, with over 95,000 subscribers and more than 1,000 published analyses. His writing has also appeared on Entrepreneur (for example, How to Digitize a Family Business in Europe) and HackerNoon. Find him on LinkedIn and Substack. See how we source.
