The AI Capex Race

Amazon, Google, Microsoft, and Meta will spend over $215 billion on AI in 2025, with Amazon leading at $100 billion.

Big Tech players transitioning into “Hyperscalers” are spending $210 billion in Capex (capital expenditure) in 2025 alone. Image Credit: WSJ

Despite DeepSeek’s low-cost AI breakthrough, tech CEOs defend their investments, citing AI-driven application reinvention.

Amazon is set to spend over $100 billion on AI-driven infrastructure.

Google will raise its capex to $75 billion, up from $52.5 billion.

Microsoft’s AI investments will exceed $80 billion.

Meta plans to spend between $60–$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 I’ve 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, I’ve 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 we’ll 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 “AI supply chain,” which meant a slightly different thing, yet the core of the  “competitive moating”  hasn’t 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, I’ll try to tackle what’s each player’s 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.

Amazon’s 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, Alexa’s 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.

A $5–$10 monthly fee is considered, with an initial free rollout.

The upgrade aims to revitalize Alexa’s relevance and monetize AI features, aligning with Jeff Bezos’ original vision.

Google’s 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 is now integrating AI into its core in two ways:

Google Search, with interfaces like AI Overviews in Search, has just expanded to 100+ countries, increasing search engagement, especially among younger users.Google Ads Machine, 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 Google’s 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, Google’s 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

Microsoft’s 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 OpenAI’s 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 Azure’s AI infrastructure and AI data centers.

Two, and most importantly, the company will also need to accelerate its own models’ 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 “AI reasoning train” is too high, and Microsoft will do all it can to cling to it.

Meta’s CapEx Bet

Meta plans to spend between $60–$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 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.

That’s the price that the loser will pay…

Recap: In This Issue!The AI Reasoning Take-Off: A New Acceleration PhaseAI reasoning is entering a breakthrough phase due to new techniques like test-time scaling, making reasoning much cheaper and more efficient.This could lead to a faster-than-expected transition from reasoning to full AI agents 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 EvolutionLLMs via Pre-training (Past – 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 – 2023-2025)AI is moving beyond simple Q&A to more complex reasoning tasks through post-training tricks.Example: Chain-of-thought prompting, tool use, and memory integration.Real Agentic AI (Coming Fast – 2025+)Moving from human-in-the-loop prompting to AI agents autonomously handling multiple tasks in the background.Humans will shift from guiding every step to checking outcomes and ensuring alignment.The AI Cost Curve is Falling FastInference, reasoning, and intelligence costs are decreasing rapidly, driving fierce AI competition.The race at the frontier of AI has no natural moats—only continuous innovation can sustain dominance.AI players must find alternative defensibility strategies beyond model capabilities.The AI CapEx Race: Big Tech’s $215B AI Investment in 2025Amazon, Google, Microsoft, and Meta will invest over $215 billion in AI infrastructure in 2025.The AI market is moving into a “hyperscaler era,” 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., DeepSeek’s low-cost AI models), Big Tech is doubling down on CapEx investments.The AI supply bottleneck remains a major constraint—AI demand is outpacing cloud capacity.Are There No Moats in AI?Many assume AI models are quickly commoditized, meaning no moats exist at the frontier.But defensibility isn’t just about core models—it’s about infrastructure, branding, distribution, and vertical integration.AI moats come from locking in customers through AI-native applications, infrastructure, and network effects.The AI Innovation Pipeline: How AI Moats Form

A true “Frontier AI Moat” isn’t just model development—it involves the entire AI lifecycle:

R&D & Talent – The first layer of AI differentiation is top research teams.Model Training – Scaling data & compute efficiently.Post-Training Optimization – Fine-tuning models for complex reasoning tasks.Inference Infrastructure – AI serving costs must be optimized for real-world applications.Application & Distribution – Turning AI advances into real business advantages.AI hyperscalers (OpenAI, Anthropic, Google) need more than models—they need cloud infrastructure & hardware access.The GPU arms race continues—AI firms need 10x more GPUs for R&D/testing than for model training itself.AI Talent Wars: The Hidden MoatAI talent remains one of the biggest bottlenecks in the industry.The AI boom created a severe talent shortage, leading to aggressive poaching among frontier AI players.Key AI talent moves (2024-2025):Mustafa Suleyman (DeepMind → Microsoft AI)Mira Murati (OpenAI CTO → New AI startup)John Schulman (OpenAI → Anthropic → Back to OpenAI)The AGI Bet: Why AI Labs Are Investing at Insane LevelsAI labs are betting that AGI (Artificial General Intelligence) will emerge within the decade.OpenAI co-founder Ilya Sutskever’s new startup, SSI, is already valued at $20B despite being in stealth mode.If AGI materializes, it could unlock a new economic paradigm, driving AI labs’ extreme investments.Future AI Moats: How Big Tech is Positioning for AI DominanceAmazon’s AI StrategyAWS expansion → AI cloud dominance.AI-powered ads & Alexa AI → Monetizing consumer AI.Custom AI models → Reducing OpenAI reliance.Google’s AI StrategyGoogle Search + AI Overviews → Enhancing search engagement.Google Ads AI → Increasing ad revenue via AI targeting.AI Cloud Infrastructure → Competing with AWS & Azure.Microsoft’s AI StrategyAzure AI → Strengthening its cloud moat.Building alternative AI models → As OpenAI diversifies its partnerships.Meta’s AI StrategyBetting on open-source AI → Positioning as an alternative to proprietary AI.Massive AR investment → Building an AI-driven hardware ecosystem.Key TakeawaysAI reasoning is rapidly improving, driving us closer to fully autonomous AI agents.AI model training costs are falling, but inference & infrastructure remain critical challenges.The AI CapEx race will define future winners—Big Tech is investing over $215B in AI in 2025.AI moats exist beyond models—they form through branding, infrastructure, and distribution.The AGI bet is pushing AI firms toward massive, long-term investments in frontier AI.

With massive  Gennaro Cuofano, The Business Engineer

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.

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