Why Meta Shares Can Rise Despite Massive AI Infrastructure Spending

When a company announces that it will spend tens of billions of dollars on data centers, processors, electricity, networking equipment, and artificial intelligence talent, investors do not automatically celebrate.

Heavy spending can reduce free cash flow, increase depreciation, pressure profit margins, and create the risk that management is building more capacity than the business will eventually need.

Yet Meta Platforms presents a more complicated investment case.

Its shares can rise even as the company increases AI infrastructure spending because investors are not evaluating the expenditure in isolation. They are trying to estimate whether the new infrastructure will strengthen Meta’s existing advertising business, increase user engagement, reduce its dependence on outside technology providers, support new products, and preserve its competitive position in artificial intelligence.

The market’s reaction depends on a central question:

Will the long-term economic value created by Meta’s AI investments exceed their enormous cost?

A positive answer can support the share price before the full financial returns become visible. A negative answer can cause the same spending plan to be treated as wasteful or undisciplined.

Understanding Meta’s valuation therefore requires more than looking at the size of its capital budget. Investors must examine what the infrastructure is being used for, how quickly it can improve revenue, whether Meta can finance it without damaging the business, and how effectively management converts computing power into profitable products.

Meta is no longer valued only as a social media company

For much of its history, Meta was evaluated primarily through a familiar group of operating metrics:

• Facebook and Instagram user growth;

• time spent on its platforms;

• advertising impressions;

• average advertising prices;

• operating margins;

• expenses;

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• free cash flow;

• share repurchases.

Those indicators remain important because advertising still provides the overwhelming majority of Meta’s revenue.

However, the company’s investment profile has changed.

Artificial intelligence now influences almost every important part of Meta’s operations. AI systems determine which posts, videos, advertisements, products, and accounts users see. They help advertisers create campaigns, identify audiences, optimize budgets, generate images and text, and measure results.

AI also powers Meta’s assistants, smart glasses, moderation systems, business messaging tools, recommendation engines, and generative media products.

This means data centers, chips, power capacity, networking systems, and model-training infrastructure are no longer peripheral technology expenses. They are becoming part of the productive foundation of the company.

Why infrastructure can be a competitive advantage

Artificial intelligence requires physical resources.

Advanced models do not operate through software alone. They depend on large numbers of processors connected through high-speed networks and housed in data centers with reliable electricity, cooling, storage, and security.

A company with access to more useful computing capacity may be able to:

• train larger or more capable models;

• run more experiments simultaneously;

• improve products more quickly;

• serve AI features to billions of users;

• reduce delays and service limitations;

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• process more advertising signals;

• provide lower-cost inference;

• respond faster to competitors.

Infrastructure can therefore operate as a barrier to entry.

A smaller company may develop an excellent model or application but lack the capital, energy agreements, engineering expertise, and global data-center footprint required to deliver it at Meta’s scale.

Meta’s infrastructure strategy is intended to support both its AI ambitions and its established core business. In its first-quarter 2026 results, the company raised its expected 2026 capital expenditures to between $125 billion and $145 billion, citing higher component prices and additional data-center costs needed to support future capacity. Meta nevertheless continued to forecast operating income above its 2025 level.

That combination matters to investors: management is signaling that spending will rise dramatically, but it also believes the company can continue increasing operating profit.

Capital expenditure is not the same as an ordinary expense

To understand the market reaction, it is important to distinguish capital expenditure from operating expenditure.

When Meta purchases servers or builds a data center, it generally records the cash outflow as capital expenditure. The asset is then depreciated over its estimated useful life rather than being charged entirely against earnings on the day it is purchased.

This creates several financial effects.

First, cash leaves the company before the full accounting expense appears on the income statement.

Second, depreciation expenses increase over subsequent years as new infrastructure enters service.

Third, the company may appear highly profitable while free cash flow comes under pressure because capital expenditures are consuming more cash.

Fourth, infrastructure built today may support revenue for several years, meaning the investment must be judged over a longer period than a single quarter.

Meta reported $72.22 billion in capital expenditures, including finance-lease principal payments, for 2025. It also generated $115.8 billion in operating cash flow and $43.59 billion in free cash flow during the year.

These figures help explain why investors may tolerate aggressive spending. Meta has a large, profitable advertising operation capable of producing substantial cash while financing infrastructure.

The spending would be more difficult to support if the core business were shrinking or consistently generating weak cash flow.

The first reason investors can support the spending: AI already helps advertising

Meta does not need to create an entirely new AI business for its infrastructure to generate value.

Its existing advertising operation provides a direct route to monetization.

AI can improve advertising economics in several ways.

Better recommendations

The more accurately Meta predicts what users want to watch or interact with, the more time they may spend on Facebook and Instagram.

Greater engagement can create additional opportunities to display advertisements.

Better advertisement selection

Meta’s systems choose which advertisement to show to a particular user at a particular moment.

Improved prediction can increase the probability that the user clicks, makes a purchase, installs an application, or takes another action valued by the advertiser.

When advertisements become more effective, businesses may be willing to spend more on Meta’s platforms.

Automated campaign optimization

AI systems can automatically adjust audiences, placements, bidding strategies, budgets, and creative combinations.

This can make sophisticated advertising tools accessible to smaller businesses that lack dedicated marketing teams.

Generative creative tools

Advertisers can use AI to produce or modify:

• images;

• video variations;

• headlines;

• product descriptions;

• backgrounds;

• calls to action;

• audience-specific messages.

If campaign creation becomes faster and less expensive, more businesses may advertise or produce more campaign variations.

Improved measurement

Privacy changes and restrictions on tracking have made digital advertising measurement more difficult.

AI can help Meta infer which advertisements contributed to sales even when direct user-level signals are incomplete.

The investment case is therefore not solely based on a future chatbot or speculative AI subscription. Infrastructure may enhance the profitability of a business that already generates tens of billions of dollars in annual revenue.

Why recommendation systems matter so much

Social platforms historically depended heavily on content from accounts that users actively followed.

AI-driven recommendation systems allow platforms to show users content from creators, pages, and communities they have never encountered before.

This approach can expand the available content inventory and improve personalization.

It also allows Meta to compete more directly with entertainment-focused platforms where algorithms, rather than social connections, determine much of what users see.

If AI recommendations increase time spent on Reels, Instagram, or Facebook, Meta can potentially gain:

• more advertising impressions;

• stronger creator participation;

• improved user retention;

• a larger supply of commercially useful content;

• better data about user interests.

A relatively small improvement in engagement or advertising conversion can have substantial financial consequences when applied across billions of users and millions of advertisers.

That scale is one reason investors may believe infrastructure spending can produce attractive returns.

Meta can spread AI costs across an enormous user base

The economics of AI infrastructure depend partly on scale.

Building models, chips, and data centers requires high fixed costs. Once the systems are operating, however, their benefits can potentially be distributed across many products and users.

Meta can use related infrastructure to support:

• Facebook;

• Instagram;

• WhatsApp;

• Messenger;

• Threads;

• Meta AI;

• advertising systems;

• content moderation;

• business messaging;

• creator tools;

• smart glasses;

• internal engineering and productivity systems.

This ability to spread costs across several platforms may improve the potential return on infrastructure compared with a company that depends on one narrow AI application.

The same model research may also benefit recommendation systems, advertising, assistants, and devices simultaneously.

Infrastructure reduces dependence on external suppliers

Meta will continue to rely on outside companies for important hardware, manufacturing, cloud capacity, and technology.

However, developing its own infrastructure and silicon can give the company greater control over:

• cost;

• hardware design;

• deployment schedules;

• model optimization;

• energy efficiency;

• supply availability;

• integration between software and chips.

Meta has said it is developing and deploying four new generations of its Meta Training and Inference Accelerator chips within two years. The company says these processors will support ranking, recommendation, and generative AI workloads.

Meta has also announced a partnership with Arm to develop multiple generations of data-center CPUs for large-scale AI deployments.

Custom hardware does not guarantee lower costs. Designing chips is technically difficult, manufacturing remains dependent on external foundries, and general-purpose processors may continue to outperform custom designs for some workloads.

Nevertheless, successful custom silicon could reduce the cost of delivering AI services and make Meta less vulnerable to shortages or supplier pricing.

AI spending can strengthen Meta’s strategic independence

Meta has historically depended on operating systems and distribution platforms controlled by other companies.

Apple and Google influence application distribution, mobile operating systems, privacy policies, payment systems, and access to device-level data.

AI creates an opportunity for Meta to establish a more direct relationship with users through assistants, wearables, and smart glasses.

Products such as AI-enabled glasses could become strategically important if users begin interacting with digital services through voice, vision, and contextual assistants rather than primarily through smartphone applications.

In this scenario, infrastructure spending is not only about making existing products more efficient. It is also an attempt to influence the next computing interface.

The opportunity is significant, but the timing and commercial outcome remain uncertain.

Why open-source AI can still benefit Meta

Meta’s decision to release parts of its Llama ecosystem under open or broadly accessible terms may appear inconsistent with the goal of earning returns from large infrastructure investments.

The strategic logic is that broader model adoption can help Meta:

• attract developers;

• influence technical standards;

• encourage optimization around its ecosystem;

• recruit researchers;

• reduce competitors’ control over foundational AI;

• improve public awareness of Meta’s AI capabilities;

• create demand for related products and services.

Meta does not necessarily need to charge every developer directly for access to a model.

The company may benefit if open AI strengthens its platforms, reduces dependence on rival ecosystems, improves its internal systems, or supports products that are monetized through advertising and hardware.

However, open distribution can also make direct monetization more difficult and allow competitors to benefit from Meta’s research.

Investors must therefore judge whether the strategic gains justify the cost.

Energy has become part of Meta’s investment case

AI infrastructure is increasingly constrained by electricity.

A company may have enough money to buy chips and construct buildings but still be unable to operate them without adequate power generation and grid connections.

Large data centers require continuous electricity for computing, cooling, networking, storage, and backup systems.

This has pushed Meta and other technology companies into areas that once appeared far removed from social media:

• utility planning;

• grid upgrades;

• transmission infrastructure;

• renewable energy purchasing;

• nuclear power agreements;

• energy storage;

• geothermal development;

• long-term power contracts.

Meta says it works with utilities years before new data centers begin operating and pays for the grid infrastructure required to serve its facilities. The company also says it supports tariff structures intended to prevent those costs from being shifted to ordinary electricity customers.

Meta has announced nuclear-energy agreements intended to unlock as much as 6.6 gigawatts of capacity through projects involving Vistra, TerraPower, Oklo, and an earlier agreement with Constellation Energy.

It has also announced partnerships involving long-duration energy storage and proposed space-based solar generation, illustrating how central energy availability has become to its AI strategy.

These projects are long-term and subject to technical, regulatory, and construction risks. Nevertheless, securing power can reduce uncertainty about Meta’s ability to operate future computing capacity.

Why markets sometimes reward increased spending

Investors do not always prefer lower expenditure.

They may reward greater spending when several conditions are present:

1. The company has a credible opportunity to earn high future returns.

2. Management explains where the money is going.

3. The core business remains strong.

4. The company can finance the spending without threatening its balance sheet.

5. Early evidence shows that the investment is improving products or revenue.

6. Competitors are investing at a similar scale, making underinvestment dangerous.

7. Management retains cost discipline outside its strategic priorities.

Under these conditions, refusing to invest could be more damaging than spending aggressively.

If AI is likely to reshape advertising, consumer software, search, communication, and computing devices, investors may conclude that Meta cannot afford to remain cautious.

The share price can therefore rise because the market interprets spending as evidence that the company is defending or expanding its future earnings power.

The importance of management credibility

The same capital-expenditure announcement can produce different market reactions depending on investors’ confidence in management.

Meta’s history includes periods when investors believed spending was becoming excessive, particularly around Reality Labs and the metaverse.

Sentiment improved when the company emphasized efficiency, reduced costs, increased profitability, and continued returning capital to shareholders.

That history matters.

Investors may be more willing to support AI spending when they believe management has learned to distinguish strategic investment from uncontrolled expansion.

Meta’s 2025 annual report said its Reality Labs investments reduced operating profit by approximately $19.19 billion during the year and warned that the division would continue operating at a loss. The filing also showed $69.69 billion in property-and-equipment purchases and approximately $103.77 billion in lease commitments that had not yet commenced, mainly involving data centers, colocation facilities, and network infrastructure.

These commitments demonstrate both the scale of Meta’s ambition and the financial risk.

Management credibility will depend on whether it can produce measurable benefits while controlling the combined cost of AI, infrastructure, and Reality Labs.

Why a one-day stock rise does not prove the strategy is working

A sharp increase in Meta’s share price should be understood as a change in expectations, not proof of future returns.

Stock prices react to the difference between new information and what investors had already expected.

Meta’s shares could rise after an announcement because:

• spending was lower than feared;

• revenue guidance was stronger than expected;

• operating-income guidance remained positive;

• management provided convincing evidence of AI monetization;

• the company demonstrated improved cost control;

• the broader technology market was rising;

• investors believed Meta was gaining ground against competitors.

The same spending level could produce a decline if investors had expected faster revenue growth or lower costs.

A stock move cannot be interpreted correctly without examining the earnings report, management guidance, valuation, market conditions, and investor expectations at the time.

For an evergreen analysis, it is safer to say that Meta shares can rise despite heavy spending than to assume every increase was caused by one infrastructure announcement.

The key financial question: return on invested capital

Ultimately, investors must determine whether Meta is generating an acceptable return from the money invested in AI infrastructure.

That return may appear through:

• faster revenue growth;

• higher advertising prices;

• improved advertiser retention;

• greater user engagement;

• lower costs per recommendation or advertisement;

• new subscription revenue;

• business messaging;

• hardware sales;

• licensing or partnerships;

• reduced reliance on external cloud services;

• stronger long-term competitive positioning.

The difficulty is that many benefits are indirect.

Meta may not disclose exactly how much advertising revenue resulted from a specific model or data center. AI may improve several products simultaneously, making precise attribution difficult.

Investors must therefore use a combination of operating metrics, management commentary, capital efficiency, and financial trends.

Free cash flow is one of the most important indicators

Net income alone does not show the full cost of infrastructure expansion.

Because capital expenditures are paid in cash before being fully recognized as depreciation expense, free cash flow can weaken even while accounting profit remains strong.

Investors should compare:

• operating cash flow;

• capital expenditure;

• free cash flow;

• share repurchases;

• dividends;

• debt issuance;

• cash reserves;

• lease obligations.

Meta generated $43.59 billion in free cash flow during 2025, compared with $52.10 billion in 2024, while its purchases of property and equipment rose substantially.

A temporary decline in free cash flow may be acceptable if the infrastructure creates durable growth.

It becomes more concerning if capital expenditures continue rising while revenue growth slows and operating cash flow fails to keep pace.

Depreciation can pressure future margins

Data centers and servers create accounting expenses after construction.

As more AI infrastructure becomes operational, depreciation charges increase.

This means the financial effect of the spending may arrive gradually.

Investors should not assume that a strong operating margin today will remain unchanged once a larger infrastructure base is depreciated.

Meta previously warned that infrastructure costs, including depreciation and operating expenses, would become a major driver of expense growth as it expanded its fleet.

The critical test is whether the revenue and productivity benefits grow faster than depreciation, energy, maintenance, and cloud costs.

The danger of overbuilding

AI demand may grow rapidly, but forecasting the required capacity is difficult.

Data centers take years to design, permit, finance, and construct. Companies must make decisions before they know exactly how models, hardware, or customer behavior will develop.

Meta could overbuild if:

• AI demand grows more slowly than expected;

• models become dramatically more efficient;

• new chips reduce computing requirements;

• consumer adoption disappoints;

• competitors offer superior products;

• regulators restrict AI deployment;

• advertising benefits plateau;

• power costs rise;

• infrastructure becomes technologically outdated.

Unused or underutilized capacity would reduce returns.

The opposite risk is underbuilding. If Meta lacks enough capacity, it may delay products, limit users, depend on expensive third-party cloud providers, or lose ground to rivals.

Management must balance both dangers.

AI hardware can become obsolete quickly

Traditional buildings may remain useful for decades, but servers and processors have much shorter economic lives.

New generations of accelerators can offer improved performance and energy efficiency. Infrastructure designed for one architecture may not be optimal for future systems.

Meta must therefore build data centers that can adapt to changes in:

• processor power density;

• cooling requirements;

• rack design;

• networking technology;

• model architecture;

• memory capacity;

• energy supply.

Rapid technological change creates the possibility that assets will become less economically useful before the end of their expected accounting lives.

Custom chip development adds another layer of risk. If Meta’s internal processors fail to meet performance or efficiency targets, the company may still need to purchase expensive third-party hardware.

Supply-chain risk remains significant

AI infrastructure depends on a concentrated and complex global supply chain.

Potential bottlenecks include:

• advanced processors;

• high-bandwidth memory;

• semiconductor packaging;

• networking equipment;

• transformers;

• electrical switchgear;

• cooling systems;

• construction labor;

• transmission connections.

Even a well-funded project can be delayed if one critical component is unavailable.

Meta’s decision to raise its 2026 capital-expenditure forecast partly because of higher component prices demonstrates that supply conditions can materially increase the cost of expansion.

Energy projects can create regulatory and community risks

Data centers can bring investment, construction activity, tax revenue, and permanent technical jobs.

They can also create concerns about:

• electricity rates;

• grid reliability;

• water use;

• emissions;

• noise;

• land use;

• local infrastructure;

• public subsidies;

• the number of permanent jobs created.

Meta says its data-center policies are designed to ensure the company pays the energy and infrastructure costs associated with its facilities.

Investors should still examine each project separately.

Promises may depend on utility regulation, contract structures, construction schedules, energy-market conditions, and local enforcement.

Delays in power supply can prevent completed computing equipment from becoming productive.

Regulation could reduce the return on AI investment

AI infrastructure does not operate outside legal and political constraints.

Meta faces regulation involving:

• data privacy;

• competition;

• political advertising;

• child safety;

• copyright;

• model training;

• biometric information;

• content moderation;

• cross-border data transfers;

• AI transparency.

If regulations restrict the data Meta can use, the products it can distribute, or the advertisements it can personalize, the economic return on infrastructure could decline.

Meta has also warned investors about legal and regulatory headwinds in both the United States and European Union.

The company’s infrastructure may be technically successful while financial returns are limited by regulation.

Competition makes the spending both necessary and risky

Meta is not investing in isolation.

It competes with companies capable of committing enormous amounts of capital to AI, cloud computing, consumer applications, and model development.

Major competitors include:

• Alphabet;

• Amazon;

• Microsoft;

• Apple;

• OpenAI;

• TikTok owner ByteDance;

• specialized model developers;

• open-source communities.

This competition strengthens the argument for investment. Meta risks falling behind if it lacks computing capacity, technical talent, or capable models.

Competition also reduces the probability that one company captures all the returns.

AI features may become standard across the industry, forcing Meta to spend heavily simply to preserve its existing position rather than creating entirely new profit pools.

New products could create additional upside

The strongest bullish case is not limited to better advertisements.

Meta could eventually earn additional revenue from:

• premium AI assistants;

• enterprise agents;

• business messaging automation;

• creator software;

• customer-service tools;

• smart glasses;

• AI-enabled devices;

• model partnerships;

• application ecosystems;

• commerce tools.

The company has also been expanding its physical AI footprint internationally. In June 2026, Meta announced an agreement with Reliance Industries to lease its first AI-enabled data center in India and said it was separately supporting nearly one gigawatt of renewable-energy capacity there.

These initiatives could strengthen Meta’s position in large markets, but many remain at an early stage.

Investors should distinguish demonstrated revenue from future possibilities.

What investors should monitor

The quality of Meta’s AI investment can be evaluated through several categories.

Core revenue growth

Advertising revenue should show whether AI is improving engagement, targeting, creative generation, and campaign performance.

Operating margin

Margins reveal whether revenue benefits are keeping pace with depreciation, salaries, energy, and infrastructure operating costs.

Free cash flow

Free cash flow shows how much cash remains after capital investment.

Capital-expenditure guidance

Investors should watch whether spending repeatedly exceeds earlier forecasts and whether management explains why.

Infrastructure utilization

Although Meta may not disclose exact utilization rates, comments about capacity constraints, cloud reliance, and deployment schedules can provide clues.

Advertiser adoption

The use of automated campaign and creative tools can indicate whether AI is becoming commercially valuable.

Engagement

Time spent, video consumption, recommendation quality, and platform activity can show whether AI is improving consumer products.

New-product revenue

Investors should separate products with actual commercial adoption from research demonstrations.

Cost per AI query or recommendation

Falling inference costs would improve the economics of serving AI to billions of users.

Energy and construction progress

Power agreements are useful only if projects receive approval, connect to the grid, and begin operating on schedule.

Debt and lease commitments

Long-term obligations matter because infrastructure creates costs that continue even if demand weakens.

Bull case for Meta’s AI infrastructure strategy

The bullish argument can be summarized as follows:

Meta owns highly profitable platforms with billions of users and millions of advertisers.

AI can improve recommendations, advertising performance, campaign creation, messaging, content discovery, and user engagement.

The company can distribute AI capabilities across several large products, allowing it to spread fixed costs.

Its operating cash flow provides greater financing capacity than most competitors possess.

Custom hardware and large-scale infrastructure could reduce long-term operating costs.

New products such as assistants, business agents, and smart glasses may create additional revenue streams.

Under this scenario, heavy spending is not a threat to Meta’s valuation. It is the foundation for a larger and more defensible earnings base.

Bear case for Meta’s AI infrastructure strategy

The bearish argument is equally important:

Meta may spend faster than it can generate incremental revenue.

Advertising improvements may become less significant over time.

Competitors may offer similar AI features, preventing Meta from earning exceptional returns.

Hardware could become obsolete before projects recover their costs.

Depreciation, energy, compensation, and maintenance could reduce margins.

Open models may create strategic influence without sufficient direct monetization.

Reality Labs losses and AI expenditure may place simultaneous pressure on cash flow.

Regulation may limit Meta’s ability to use data or monetize personalization.

Under this scenario, infrastructure growth increases the company’s fixed-cost base without producing adequate returns.

Why valuation still matters

Even an excellent company can be a poor investment if its shares already reflect unrealistic expectations.

A positive AI thesis does not automatically justify any stock price.

Investors should consider:

• the price-to-earnings ratio;

• the free-cash-flow yield;

• expected revenue growth;

• expected margin trends;

• capital intensity;

• competitive risk;

• the probability of successful new products.

If the market already assumes that Meta will dominate several AI categories, future results may need to be exceptional merely to support the existing valuation.

If investors are excessively focused on near-term spending and underestimate future returns, the shares may offer more attractive upside.

The same infrastructure plan can therefore be bullish at one valuation and unattractive at another.

Why Meta shares can rise after higher spending guidance

Meta shares can rise following increased AI spending when the accompanying information causes investors to raise their expectations for future earnings.

For example, the market may react positively when:

• revenue exceeds forecasts;

• advertising demand remains strong;

• management maintains or raises operating-income expectations;

• AI tools show measurable commercial benefits;

• infrastructure shortages suggest strong internal demand;

• the company demonstrates sufficient cash-generation capacity;

• competitors’ spending confirms that the opportunity is strategically important.

The stock is responding to the entire expected cash-flow outlook—not simply to one expense figure.

A larger capital budget can therefore coexist with a higher valuation when investors believe the spending increases Meta’s long-term competitive strength.

Frequently asked questions

Why would investors reward Meta for spending more on AI?

Investors may believe the spending will improve advertising, engagement, product development, and long-term competitiveness. The expected future benefit can outweigh concern about the immediate cash cost.

Does higher capital expenditure reduce Meta’s profit?

Capital expenditure affects cash flow immediately, while most of the accounting cost appears gradually through depreciation. It can therefore pressure free cash flow before the full effect reaches reported earnings.

Is Meta using AI only for chatbots?

No. AI is used throughout recommendations, advertising, content moderation, messaging, creative tools, ranking systems, devices, and internal operations.

Why is electricity important to Meta’s valuation?

AI systems require large amounts of reliable power. A shortage of electricity or grid capacity can delay data centers and prevent expensive computing equipment from being used efficiently.

Does custom silicon guarantee lower costs?

No. Custom chips may improve efficiency and control, but chip design is expensive and technically risky. Meta will probably continue relying on outside hardware providers for important workloads.

Can Meta afford its infrastructure plan?

Meta’s core business generates substantial operating cash flow, giving it considerable financing capacity. Affordability, however, is different from profitability. Investors must still determine whether the spending earns an acceptable return.

What is the biggest financial risk?

The central risk is that capital expenditure, depreciation, energy costs, and technical salaries grow faster than the revenue and productivity benefits created by AI.

What should investors watch most closely?

Revenue growth, operating margin, free cash flow, capital-expenditure guidance, advertiser adoption, engagement, infrastructure deployment, and evidence of revenue from new AI products are among the most useful indicators.

Final analysis

Meta’s AI infrastructure spending should not be judged solely by its size.

The spending can support the share price when investors believe it strengthens a profitable advertising platform, improves recommendations and campaign performance, creates new products, reduces dependence on outside providers, and gives Meta the capacity to compete at global scale.

At the same time, large data centers, processors, power agreements, leases, and technical teams create real financial obligations. They increase capital intensity and expose the company to depreciation, technological obsolescence, regulation, supply constraints, energy risk, and the possibility of overbuilding.

The investment thesis therefore depends on conversion.

Meta must convert electricity into computing capacity, computing capacity into better AI systems, those systems into stronger products, and those products into revenue and cash flow.

When investors believe that conversion is working, the shares can rise despite enormous spending.

When evidence of that conversion weakens, the same infrastructure program can quickly become a reason for concern.