Bank of America has raised its capital expenditure forecasts for Alphabet, Meta and Amazon Web Services through 2027, pointing to a much larger artificial intelligence infrastructure buildout than previously expected and a sharp divide in how the stock market values each company’s data center capacity.
The research projects that the three technology groups could control about 57 gigawatts of data center capacity by 2027, more than double the estimated 27 gigawatts expected by the end of 2025. That expansion would add roughly 30 gigawatts of capacity in just two years, underscoring how quickly the race to support AI training, inference, cloud services and consumer applications is reshaping the capital budgets of the largest U.S. technology companies.
The most striking finding is not only the scale of the spending, but the valuation gap attached to it. Bank of America estimated that Meta’s implied valuation per gigawatt of AI capacity is close to $4 billion. That is far below Alphabet’s estimated $110 billion per gigawatt and Amazon’s $59 billion per gigawatt.
The gap suggests traders are not valuing all AI infrastructure in the same way. Alphabet and Amazon already operate large commercial cloud businesses that sell computing power directly to corporate customers. Meta, by contrast, is building large amounts of AI infrastructure mainly to improve its own products, advertising systems and social platforms. The company has not yet shown the same clear route to selling that capacity externally at scale.
The result is a major question for the market: whether Meta’s data centers will remain mostly internal tools that make its advertising business more efficient, or whether they can become a broader source of enterprise revenue.
Spending forecasts move higher
Bank of America’s revised estimates show a major rise in expected capital spending across all three companies.
Alphabet’s capital expenditure is projected to reach $290 billion in 2027. Amazon’s spending, including Amazon Web Services, is forecast at $230 billion. Meta’s capital spending is expected to reach $185 billion.
Those figures reflect upward revisions for the next two fiscal years as the companies increase investment in servers, chips, networking equipment, power systems, land, buildings and cooling infrastructure. The forecasts also show that AI infrastructure has moved from a strategic priority to one of the largest financial commitments in the technology sector.
The scale of spending reflects the extraordinary computing needs of advanced AI systems. Large models require huge clusters of graphics processors and specialized chips to train. Once those models are deployed, they also require large amounts of computing power to answer user queries, generate images, write code, process video and support business tools.
That second phase, known as inference, may become even more important over time. Training a powerful model can be expensive, but running that model for millions or billions of users can require steady and growing capacity. That is one reason the largest technology companies are racing to secure long-term access to data center space and electricity.
Capacity is becoming the new measuring stick
The report estimates that Amazon will add the largest amount of capacity among the three companies, with roughly 15 gigawatts expected by 2027. Alphabet is projected to add about 9 gigawatts, while Meta is expected to add around 6 gigawatts.
A gigawatt is a measure of power capacity. In the data center industry, it is often used as a shorthand for the scale of facilities that can support large computing workloads. More gigawatts generally mean more room to run servers, AI chips, storage systems and networking equipment, although the actual computing output also depends on the type of hardware installed.
The forecast of 57 gigawatts by 2027 shows how rapidly AI is changing the physical footprint of technology companies. Data centers are no longer only back-office facilities that support websites, email, cloud storage and streaming. They are becoming central production assets for AI services.
For Alphabet, that means supporting Google Cloud, Search, YouTube, generative AI products and internal model development. For Amazon, it means supplying Amazon Web Services customers with cloud computing and AI capabilities. For Meta, it means powering recommendation engines, ad targeting, content moderation, AI assistants, creator tools and future products linked to messaging, social networking and virtual environments.
Yet the market appears to be drawing a clear distinction between infrastructure that is already tied to outside customer spending and infrastructure that is mainly used inside a consumer internet platform.
Why Meta is valued differently
Meta’s lower implied valuation per gigawatt reflects uncertainty over how its AI infrastructure will generate direct enterprise income.
Alphabet and Amazon have established cloud platforms. Google Cloud and Amazon Web Services sell computing capacity, database services, storage, machine learning tools and AI services to businesses, governments and developers. When those divisions add computing capacity, traders can more easily connect that spending to potential customer revenue.
Meta does not have an equivalent cloud platform. Its core business is still advertising across Facebook, Instagram, WhatsApp and other products. AI can make those platforms more effective by improving ad placement, content ranking, user engagement and automated business messaging. Those improvements can be valuable, but they are harder to separate and measure than cloud revenue.
That distinction appears to be central to the valuation gap. Alphabet’s and Amazon’s data centers are viewed as assets that can be sold as commercial computing services. Meta’s data centers are viewed more as internal infrastructure that supports an existing advertising business.
This does not mean Meta’s AI spending lacks value. Better recommendation systems can increase time spent on apps. Improved targeting can raise ad performance. AI tools can help advertisers create campaigns more easily. Business agents inside messaging apps could also open new revenue streams. But traders have less visibility into how much revenue those efforts can produce, how quickly they can scale, and how much of the benefit will show up as new income rather than higher costs.
The cost per gigawatt varies sharply
Bank of America also found wide differences in the estimated cost to build each gigawatt of AI capacity.
Amazon’s cost is estimated at about $25 billion per gigawatt. Alphabet’s is estimated at around $37 billion. Meta’s is estimated at about $45 billion.
Several factors can explain those differences. Data center costs depend on location, power contracts, construction expenses, chip choices, network design, cooling technology and the degree to which a company can use its existing infrastructure. Companies with larger cloud operations may also benefit from scale, procurement power and more standardized deployment processes.
Hardware choices matter as well. AI data centers often rely on expensive graphics processors, high-bandwidth memory, specialized networking equipment and advanced cooling systems. Some companies design custom chips to lower long-term costs or reduce dependence on outside suppliers. Others may use more third-party hardware, depending on performance requirements and availability.
Meta’s higher estimated cost per gigawatt may reflect the type of hardware it is deploying, the pace of construction, and the need to build capacity for advanced internal AI systems rather than for a broad range of cloud customers. The key issue for traders is whether that higher cost base can be matched by stronger revenue growth over time.
What Meta would need to prove
The report’s longer-term scenario suggests that Meta’s total AI capacity could approach 22.8 gigawatts by 2030 under optimistic assumptions. If about 40 percent of that capacity were made available for enterprise clients, and if it could generate an estimated $12 billion per gigawatt in annual revenue, the company could potentially produce around $110 billion in enterprise AI income.
That figure is a model-based estimate, not company guidance. It depends on several assumptions that have not yet been proven.
Meta would need to show that it can turn internal AI infrastructure into products that businesses are willing to pay for directly. Those products could include subscription-based AI tools, automated agents for companies, advanced advertising technology, developer services or AI features inside messaging and social platforms.
The company has already pushed deeper into AI assistants and open-source model development, but its commercial model remains less transparent than the cloud businesses operated by Amazon and Alphabet. To narrow the valuation gap, Meta may need to give the market clearer evidence of paid customer adoption, pricing power and revenue contribution from AI services.
Disclosure will also matter. Traders can more easily assign value to a business when revenue, margins and customer demand are visible. If Meta continues to report AI mainly as part of its broader advertising and product ecosystem, its infrastructure may remain harder to value on a stand-alone basis.
Power and hardware are becoming constraints
The AI buildout is also running into physical limits.
Data centers require enormous amounts of electricity. U.S. Department of Energy data indicates that data centers could consume as much as 9 percent of national electricity by 2030, up from roughly 4 percent in 2023. That projected increase is forcing technology companies to compete for power access, negotiate long-term energy agreements and consider locations based on grid availability.
Electricity is not the only constraint. Data centers also require land, water or advanced cooling systems, transformers, backup power, skilled labor and long construction timelines. Even when a company has the money to spend, it cannot always bring capacity online quickly.
Hardware supply remains another major factor. Advanced AI systems rely heavily on GPUs and related components, including high-bandwidth memory and advanced packaging. These supply chains have been under pressure because demand from major technology companies has increased faster than production capacity in some areas.
Delays in hardware delivery can slow data center deployment, while limited supply can keep costs elevated. Companies with stronger supplier relationships and larger purchasing commitments may be better positioned to secure the equipment they need. Smaller firms may find it harder to compete for access to the most advanced chips.
These constraints add uncertainty to all long-term capacity forecasts. A company may plan to add several gigawatts of capacity, but actual deployment can depend on power approvals, permitting, construction progress, equipment availability and the broader supply chain.
Demand still has to match the buildout
The spending boom rests on the expectation that demand for AI computing will keep rising.
Businesses are using AI for software development, customer support, data analysis, search, advertising, design, cybersecurity and automated workflow tools. Demand for both training and inference workloads continues to grow as more companies test and deploy AI systems.
However, long-term pricing remains uncertain. Cloud providers are still testing how much customers will pay for AI services, especially as competition increases and model costs decline. Some businesses are enthusiastic about AI but are still cautious about budgets, reliability, security and measurable returns.
That uncertainty matters because the largest technology companies are making commitments years before the full revenue opportunity is known. If demand grows as expected, the companies with the most capacity could benefit. If customer spending grows more slowly, some infrastructure could take longer to produce acceptable returns.
For Alphabet and Amazon, the cloud model gives traders a clearer way to track demand. Revenue from cloud customers can be measured directly. For Meta, the connection is less direct. Its AI systems may lift engagement and advertising results, but the financial impact may be spread across the platform rather than reported as a separate enterprise business.
Decentralized compute gets more attention
The pressure on centralized data center capacity has also increased attention on alternative computing models, including decentralized physical infrastructure networks, often called DePIN.
These systems aim to connect distributed hardware owners with users who need computing power, storage or other digital infrastructure. In theory, they can make better use of underused GPUs and servers spread across different locations. Supporters argue that such networks could help developers access compute resources when major cloud providers are expensive or capacity-constrained.
The sector remains far smaller than the infrastructure controlled by Alphabet, Amazon and Meta. It also faces its own challenges, including performance consistency, security, reliability, enterprise adoption and regulatory questions. Still, the rise of AI computing demand has made alternative capacity models more relevant than they were before the current AI cycle.
For now, the largest technology companies remain at the center of the buildout because they have the balance sheets, customer bases, engineering teams and supplier access needed to deploy infrastructure at massive scale.
The valuation gap remains the central issue
Bank of America’s report highlights one of the most important questions in the AI market: whether all data center capacity should be valued equally.
At the moment, the answer appears to be no. Alphabet and Amazon receive much higher implied valuations per gigawatt because their infrastructure is tied to established cloud businesses with clearer revenue paths. Meta’s capacity is valued much lower because its AI assets are still seen largely as internal tools that support advertising and user engagement.
That could change if Meta proves it can convert its growing infrastructure into durable AI revenue outside its existing advertising model. Subscription products, enterprise agents, business messaging tools and commercial AI services could all help close the gap if they gain traction.
Until then, Meta faces the largest burden of proof among the three companies. Its data center capacity may be the least expensive on an implied valuation basis, but the market is waiting for clearer evidence that the spending can produce measurable, lasting and externally visible income.
Want deeper insight on AI-driven markets? Explore our guide on AI complementing blockchain in finance and sharpen your infrastructure investment perspective.
Disclaimer: The content on this page is provided for general informational purposes only and does not represent the views or financial advice of Toobit. We make no guarantees regarding the accuracy or completeness of this information and shall not be held liable for any errors, omissions, or outcomes resulting from its use. Investing in digital assets involves risk; users should independently evaluate their financial situation and the risks involved. For further details, please consult our Terms of Service and Risk Disclosure.
