The market did not buy Meta's AI pitch: the bargain price masks three unresolved challenges

TL;DR

• BofA raised its capital expenditure forecasts for Alphabet, Meta, and AWS, predicting their combined capacity could reach 57GW by 2027.
• Model breakdown shows Meta's implied value per GW of AI capacity is approximately $4 billion, significantly lower than the two major cloud providers.
• Meta appears the cheapest, but enterprise AI sales, power access, and customer willingness to pay are not yet fully proven.

In its latest report, Bank of America raised its future capex and data center capacity forecasts for Alphabet, Meta, and Amazon AWS, presenting a stark valuation disparity: according to its model, the implied value of Meta's AI capacity per GW in the current stock price is only about $4 billion, far lower than Alphabet's ~$110 billion/GW and Amazon's ~$59 billion/GW.

The key takeaway isn't about who spends the most, but that the market assigns vastly different prices to the capacity of different companies, even as all expand their AI data centers. AWS and Google Cloud have mature cloud businesses that can sell computing power to enterprise clients. Meta relies more on its advertising business, AI recommendation efficiency, and still-early enterprise AI products, resulting in a lower value for its data center capacity reflected in its stock price.

For investors, AI capex must ultimately answer a practical question: can electricity, GPUs, and data center capacity translate into cloud revenue, enterprise AI service income, or higher advertising efficiency? Meta's discount reflects that this question hasn't been fully endorsed by the market yet.

Big Three's 2027 Capacity Could Reach 57GW, Capex Continues Upward Revision

According to BofA's forecast, overall capex expectations for Alphabet, Meta, and AWS for 2026-2027 have been revised upward. Specifically, Alphabet's 2026 capex forecast increased from $187 billion to $195 billion, and 2027 from $257 billion to $290 billion. Meta's 2026 forecast rose from $130 billion to $145 billion, and 2027 from $157 billion to $185 billion. AWS's 2026 forecast remains at $159 billion, while 2027 increased from $196 billion to $230 billion.

These figures are closer to BofA's model projections and not all equivalent to company guidance. Publicly, Meta previously raised its 2026 capex guidance to between $125 billion and $145 billion, while Alphabet's public guidance is around $180-$190 billion.

Corresponding to data center capacity, BofA estimates the three companies will have a combined capacity of about 27GW by end-2025, rising to 39GW in 2026, and further to 57GW in 2027. In other words, adding approximately 30GW of capacity in two years.

Amazon is adding the most. From 2026 to 2027, AWS is expected to add about 15GW, Google about 9GW, and Meta about 6GW. AWS has a larger cloud infrastructure base, with customer demand, internal e-commerce, and AI services all consuming capacity, hence the largest expansion scale.

Comparison of old and new capex forecasts for Alphabet, Meta, and AWS (2026-2028), showing the most significant upward revision for 2027.

Building the same 1GW of capacity also comes at different costs. According to BofA estimates, the cost per GW of new capacity added in 2026 is about $25 billion for Amazon, $37 billion for Google, and $45 billion for Meta. Amazon's cost is the lowest, mainly due to economies of scale and its own chips. Meta's cost is the highest, more affected by upfront construction investment and reliance on external GPUs.

This places Meta in a more awkward position: it isn't adding the most capacity, yet its per-GW construction cost is higher. If it cannot successfully generate enterprise revenue in the future, or if this isn't clearly reflected in advertising efficiency, the market will find it even harder to assign a higher valuation to these assets preemptively.

Valuation Gap Widens: Meta Valued at Only $4 Billion per GW

BofA's valuation decomposition method first strips out the value of the three companies' traditional businesses, then backs out the implied value the market assigns to their AI capacity.

After reverse-calculating using multiples for core advertising, retail, etc., for 2027, Meta's AI capacity carries an implied valuation of only about $4 billion per GW. Alphabet is about $110 billion/GW, and Amazon about $59 billion/GW.

This gap directly points to the different commercialization paths of the three companies. The market is more willing to view Alphabet's and Amazon's data center capacity as monetizable assets, but remains notably cautious about Meta's AI capacity.

Comparison of implied valuation per GW capacity: Alphabet ~$110 billion, Amazon ~$59 billion, Meta ~$4 billion.

Capacity at AWS and Google Cloud can be more easily linked to cloud revenue. According to BofA's model, in 2026, AWS revenue per GW of cloud capacity is about $10.6 billion, and Google Cloud about $15.7 billion. The revenue path from enterprise clients purchasing cloud compute, AI training, and inference services is relatively clear.

Meta is different. It has a massive advertising business and AI recommendation systems, but its enterprise-level AI revenue is still in its early stages. Even if Meta accelerates AI data center construction, the market will still ask: will this capacity primarily enhance its own advertising efficiency, or can it be sold externally like cloud providers?

If used mainly for internal products, the valuation methodology would lean towards advertising efficiency improvement rather than an independent cloud infrastructure asset. For Meta to achieve a higher per-GW valuation, it needs to demonstrate a clearer path for enterprise AI products, subscription revenue, or Business Agent sales.

Meta's Upside Hinges on Selling Its Capacity

In BofA's optimistic scenario, Meta's data center capacity could reach approximately 22.8 to 23GW by 2030. If 40% of this is used for enterprise AI sales, calculated at a revenue of $12 billion/GW, the potential enterprise revenue opportunity is about $110 billion.

This remains a model assumption, not a management target or confirmed revenue opportunity. It explains where the "Meta is undervalued" narrative comes from: if Meta can productize part of its AI capacity in the future, selling AI services, subscription products, or Business Agent capabilities to enterprises, then the current implied value of ~$4 billion/GW seems very low.

Installed capacity growth forecast for Amazon, Google Cloud, and Meta from 2026-2030; Meta's capacity reaches approximately 22.8GW by 2030.

The problem is that this assumption hasn't been realized yet. AWS and Google Cloud already have customers, contracts, and cloud revenue metrics. Meta needs to prove it isn't just "building computing power for itself" but can also generate sustainable enterprise AI revenue.

Potential catalysts listed in the report include improvements in cloud gross margins, increased visibility of Meta's enterprise AI and subscription products, and more detailed disclosure of AI revenue breakdown. Some longer-term products and partnerships remain at the hypothetical level and cannot be directly counted as realized business contributions.

Estimated Cloud/AI revenue per GW (2026-2030): AWS ~$10-10.6 billion, Google Cloud ~$15.2-17 billion, Meta conservative assumption ~$12 billion.

For Meta, what can truly change market perception is not announcing another larger data center plan, but showing investors what revenue this capacity can generate. Especially the enterprise AI sales proportion, product format, and revenue disclosure remain insufficiently clear.

The Cheapest Asset Has the Most to Prove

Meta appears to be the cheapest among the three in terms of AI capacity valuation, but cheapness itself is not the answer.

The first constraint is power. The U.S. Department of Energy's page previously cited an EPRI estimate that data center power consumption could account for up to 9% of U.S. electricity usage by 2030, compared to about 4% in 2023. More recent studies by EPRI and Lawrence Berkeley National Laboratory show even higher ranges, suggesting power pressure may continue to rise. Power access, transmission, local permitting, and energy prices will all affect whether planned GW capacity can be realized on time.

The second constraint is chips and construction delivery. GPU supply, network equipment, power infrastructure, and civil engineering timelines all impact the pace of bringing capacity online. A capex increase does not mean capacity comes online immediately, nor does it mean revenue is immediately recognized.

The third constraint is customer willingness to pay. Enterprise AI demand is still growing, but how much customers are willing to pay continuously for inference, training, and agent services requires more financial data for verification. For Meta, if enterprise AI revenue cannot be clearly disclosed, the market will struggle to value its data center capacity by cloud provider standards.

Therefore, BofA's report does not conclude that "Meta has already realized AI value," but presents a more direct valuation contrast: against the backdrop of all three internet giants continuing to expand AI capex, the market prices Meta's data center capacity the lowest. It also has the most to prove – it needs to not only build the capacity but also convince investors that this capacity can translate into visible revenue.