The AI boom may look less unstoppable after recent market volatility, but the latest earnings suggest the underlying demand is still accelerating. Microsoft, Meta, Amazon, Nvidia, AMD, Micron, Oracle, and a growing group of AI infrastructure companies continue committing billions to computing capacity, data centers, chips, and software as businesses race to expand their AI capabilities. The market is now asking a tougher question—not whether AI spending will continue, but which companies can turn that spending into sustainable revenue, stronger margins, and long-term cash flow.

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This newsletter talks about how AI spending was supposed to be slowing down. But instead, the latest earnings are revealing something investors may have missed: demand is still accelerating, but the market is becoming far more selective about who actually benefits. Microsoft’s surging Azure growth, Meta’s aggressive AI investments, and renewed momentum across Nvidia, AMD, Micron, Oracle, and the neocloud sector are reshaping the AI trade. The real question now isn’t whether AI is still growing—it’s which companies can turn billions in spending into lasting profits.

Let’s embark on this transformative journey together and position your portfolio for success in this evolving market landscape!

Be sure to read through to the end to catch all the valuable insights this newsletter delivers to your inbox today.

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If the recent AI sell-off made you wonder whether the technology boom was finally running out of fuel, the latest earnings developments tell a very different story.

The market has been treating every increase in capital spending as a warning sign. Investors have worried that Microsoft, Meta, Amazon, and other technology giants are spending too much on data centers, GPUs, networking equipment, and AI infrastructure without generating enough immediate profit to justify the investment. That concern has created sharp swings across semiconductors, cloud providers, and the so-called neocloud companies.

But the latest results are beginning to reveal something more important: the spending has not stopped because demand is weakening. The companies are still spending because demand remains strong.

For you, that distinction matters.

When markets become overwhelmed by headlines, it is easy to confuse slower growth in spending with an actual slowdown in AI demand. Those are not the same thing. Microsoft’s latest numbers, Meta’s expanding AI investments, and the reaction across companies such as Nvidia, AMD, Micron, Intel, Nebius, CoreWeave, and IREN suggest that the underlying infrastructure cycle remains very much alive.

The more interesting question is no longer whether AI spending is continuing. It is which companies are positioned to turn that spending into durable earnings and cash flow.

Microsoft has been one of the clearest examples of how investor expectations can become disconnected from business performance.

The stock had been under pressure even while the underlying company continued to produce strong results. Azure growth had previously slowed from 39% to 38%, which was enough to trigger concerns that the cloud business had reached an inflection point. The latest quarter changed that narrative.

Azure growth accelerated to 43% year over year, demonstrating that demand for Microsoft's cloud infrastructure and AI services remains substantial.

Microsoft generated approximately $90 billion in quarterly revenue, while operating income reached about $40.6 billion, an 18% increase. GAAP net income increased 31%. Microsoft Cloud revenue climbed 27% to $59.3 billion, while full-year cloud revenue surpassed $214 billion.

Those numbers are important because Microsoft's AI story is not dependent on one experimental product.

Azure is becoming the infrastructure layer underneath a growing range of AI workloads. Microsoft also reported more than 30 million paid Microsoft 365 Copilot seats, while GitHub Copilot usage continues to expand. Its Foundry platform has attracted more than 100,000 AI application and agent customers, and the company reported tens of millions of registered agents on its Agent 365 platform.

That gives you a much broader picture of Microsoft's AI opportunity.

The company does not necessarily need one particular AI model to become the winner. Its strategy is increasingly about providing the infrastructure, software, development tools, and cloud capacity regardless of which model customers ultimately choose.

That is a powerful position.

Microsoft can benefit whether enterprises use models from OpenAI, Anthropic, Mistral, xAI, Microsoft's own models, or other providers. The model can change while the infrastructure bill continues flowing toward Azure.

Even more important, Microsoft's remaining performance obligations reached approximately $678 billion, with sequential growth increasingly coming from customers outside the major frontier-model companies. Microsoft also said nearly 90% of its more than $214 billion in annual cloud revenue comes from customers outside those frontier AI companies.

That reduces one of the biggest concerns surrounding the AI infrastructure trade: customer concentration.

Microsoft is still spending aggressively. Capital expenditures are expected to remain enormous, with quarterly spending moving toward roughly $48 billion and full-year investment continuing to rise. But the company expects to remain free-cash-flow positive despite those investments.

For a company spending tens of billions on AI infrastructure, that combination matters.

High spending is much easier to justify when revenue is accelerating and the business can still generate cash.

Then there is Meta, and this is where the market reaction becomes particularly interesting.

Meta Platforms has been punished heavily as investors focus on the cost of its AI infrastructure expansion. The stock fell sharply even though the core advertising business continued to perform exceptionally well.

Meta's revenue increased approximately 28% year over year, while daily active people across its family of applications reached roughly 3.6 billion.

Advertising remains the engine.

Ad impressions increased about 14%, while average price per ad increased approximately 12%. Meta's Advantage+ advertising products have also continued to expand, with the annualized revenue run rate reportedly reaching about $75 billion.

That is the part that deserves your attention.

Meta is not spending billions on AI because its existing business is failing. It is spending because AI can make an already enormous advertising ecosystem more effective.

Its recommendation systems are becoming more sophisticated. AI is improving ad targeting and conversion. The company is integrating large language models into the systems that determine what users see and which advertisements they receive.

That creates a potential feedback loop: better AI can increase engagement, better engagement creates more opportunities for advertisers, improved targeting can increase advertiser returns, and higher returns can support more advertising spending.

Meta also continues to develop AI-powered business tools, including agents for WhatsApp and other products designed to help businesses interact with customers.

The market, however, is currently looking mostly at the expense side of that equation.

Meta's capital expenditures rose approximately 83% to $31.1 billion in the quarter. Management also indicated that spending will remain elevated and could be notably higher in 2027.

That creates near-term pressure on profitability and free cash flow.

But this is where you need to separate temporary financial pressure from permanent deterioration in the business.

Meta is deliberately sacrificing some current cash generation to build infrastructure that management believes will support future advertising, recommendation, messaging, and AI-agent businesses.

That strategy failed to deliver the expected payoff during parts of the metaverse spending cycle. Investors therefore have every reason to be skeptical.

But the current AI investment is attached to an existing business that already generates enormous revenue.

That makes the situation materially different.

The biggest market reaction was not limited to Microsoft and Meta.

Companies such as Nebius $NBIS ( ▲ 6.19% ), IREN, CoreWeave, and WhiteFiber experienced dramatic rebounds as investors reconsidered whether AI infrastructure spending was actually slowing.

These companies are much more sensitive to changes in expectations than Microsoft or Meta.

When investors believe hyperscalers are cutting AI spending, neocloud stocks can collapse because their valuations depend heavily on future demand for compute capacity. That is why some of these names can move 20%, 30%, or even more in a matter of days.

Nebius, for example, had experienced an exceptionally sharp decline before rebounding dramatically. CoreWeave and IREN also moved sharply higher alongside the broader AI infrastructure trade.

That volatility is not necessarily evidence that the underlying businesses have suddenly become more valuable or less valuable overnight.

It tells you something else: expectations are doing an enormous amount of the work.

If Microsoft says AI infrastructure demand remains strong, Azure growth accelerates, capital spending continues to rise, and the company expects to remain free-cash-flow positive, investors can quickly begin reassessing the entire infrastructure ecosystem.

That is why the rebound spread beyond Microsoft's own stock.

The market was not simply buying Microsoft.

It was repricing the probability that the AI infrastructure buildout is still in its early stages.

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The semiconductor market is showing a similar pattern.

Micron, AMD $AMD ( ▲ 7.0% ), Intel $INTC ( ▲ 10.84% ), and Nvidia all remain deeply connected to the AI infrastructure buildout, although their roles are different.

Nvidia remains the dominant supplier of accelerated computing hardware used to train and run advanced AI models. AMD continues to compete in data-center accelerators, while Intel is attempting to strengthen its position across processors, AI infrastructure, and manufacturing.

Micron benefits from the enormous memory requirements associated with AI systems. Modern AI accelerators require significant amounts of high-performance memory, making memory suppliers important beneficiaries of increasing compute intensity.

This is why semiconductor stocks can react so violently to comments from hyperscalers.

When Microsoft or Meta changes its capital-spending expectations, investors immediately begin thinking about GPU demand, memory demand, networking demand, server demand, and data-center construction.

The supply chain is interconnected.

That does not mean every semiconductor company will benefit equally. Some businesses have stronger competitive positions, better margins, or more direct exposure to AI infrastructure than others.

But the latest developments do not support the idea that AI infrastructure demand has suddenly disappeared.

Instead, the evidence points toward a market still trying to determine how much spending is sustainable and how quickly that spending will translate into returns.

The AI infrastructure story also extends well beyond Microsoft.

Oracle $ORCL ( ▲ 2.74% ) has become increasingly important because of its cloud infrastructure exposure and its role in supporting large AI workloads. Its stock also participated in the broader rebound as investors reassessed the durability of data-center demand.

Then there is Amazon $AMZN ( ▼ 2.32% ), which represents another critical piece of the puzzle through AWS.

Amazon's capital spending is enormous, but not all of it is AI-related. The company also invests heavily in logistics, warehouses, fulfillment infrastructure, and its broader physical network.

That makes its spending harder to interpret than Microsoft's.

The important question is therefore not simply, "How much is Amazon spending?"

The better question is, "What portion of that spending is producing accelerating AWS growth, stronger margins, and greater long-term competitive advantage?"

AWS remains central to the AI infrastructure market because companies building AI applications need cloud capacity, storage, networking, and compute.

Amazon also continues developing its own chips, including its Trainium line, as it attempts to reduce reliance on external accelerator suppliers and improve the economics of AI workloads.

That creates another layer to the AI investment story.

The winners may not simply be the companies selling the most expensive chips. Some of the biggest long-term winners could be the companies that make AI computation cheaper and more efficient.

$AAPL ( ▲ 1.96% ) deserves to be viewed differently from Microsoft, Meta, Amazon, Nvidia, and the neocloud companies.

Apple's AI strategy is less directly tied to the enormous data-center spending cycle dominating the market.

That does not mean AI is irrelevant to Apple. It means the company's investment case is driven by a much broader ecosystem of hardware, software, services, and consumer devices.

For a busy investor, that distinction can be useful.

You do not need every technology company in your portfolio to have the same AI exposure. In fact, owning businesses with different relationships to the AI cycle can reduce the risk of building a portfolio that all moves in the same direction.

There is also an interesting strategic parallel with Palantir.

Microsoft's approach of allowing customers to use different AI models while Microsoft captures infrastructure and platform spending resembles a broader principle that Palantir has emphasized for years: the platform can remain valuable even when the underlying models change.

That concept could become increasingly important.

AI models are evolving rapidly. Today's leading model may not be tomorrow's leader. But the companies providing the infrastructure, data systems, deployment tools, security, and enterprise workflows can potentially remain valuable regardless of which model wins.

For you, this means it may be more useful to ask, "Who wins the AI model race?" less often and ask, "Who gets paid regardless of who wins?" more often.

That shift can dramatically simplify the investment landscape.

The biggest lesson from these earnings may be about time horizons.

Investors want to know whether AI spending produces immediate earnings growth. Companies such as Microsoft and Meta are thinking about infrastructure requirements years into the future.

Those clocks do not always match.

A company can spend heavily today, depress free cash flow temporarily, and still create significant economic value if the infrastructure produces profitable revenue for many years.

Microsoft is already showing evidence that this strategy can work. Azure is accelerating while the company continues to invest heavily.

Meta is earlier in the monetization cycle for many of its AI initiatives, which makes its spending harder to evaluate.

That difference matters.

Microsoft is giving investors more evidence today that its AI investments are translating into commercial demand. Meta is asking investors to tolerate more spending while it builds capabilities that could strengthen advertising, messaging, recommendations, and AI agents over time.

Neither strategy is automatically superior.

They simply carry different levels of uncertainty.

If you're trying to follow every AI headline, earnings call, semiconductor move, and capital-spending forecast, the market can become exhausting very quickly.

You do not need to own everything.

You need to understand the pieces.

$MSFT ( ▲ 1.06% ) currently offers one of the clearest combinations of AI growth, cloud demand, enterprise distribution, and financial strength.

$META ( ▼ 0.39% ) offers a different proposition: a powerful advertising engine funding a massive AI infrastructure buildout, with the potential for AI to strengthen monetization across its ecosystem.

$NVDA ( ▲ 2.57% ) remains deeply exposed to the infrastructure buildout through accelerated computing, while AMD, Micron, and Intel represent additional semiconductor exposure with different competitive positions and risk profiles.

Oracle and Amazon provide important cloud exposure, while Nebius, IREN, CoreWeave, and WhiteFiber represent much higher-risk ways to participate in the demand for AI compute.

And Palantir demonstrates why the software and application layer should not be ignored simply because the infrastructure story receives most of the headlines.

The key is not to treat all of these companies as interchangeable.

They aren't.

Some sell the infrastructure. Some operate the infrastructure. Some build the chips. Some monetize AI through advertising. Some provide enterprise software. Others are essentially leveraged bets on the continued shortage of computing capacity.

The market's recent volatility can make the AI boom feel fragile, particularly when a stock falls 10%, 20%, or even 40% in a short period.

But a falling stock price does not automatically mean falling demand.

Microsoft's accelerating Azure growth, continued capital investment, expanding Copilot adoption, and enormous commercial backlog suggest that enterprise AI demand remains significant.

Meta's advertising business continues to grow rapidly while the company invests aggressively in AI infrastructure and recommendation systems.

Amazon continues building cloud capacity.

Oracle remains positioned within the expanding infrastructure ecosystem.

Nvidia, AMD, Micron, and Intel remain connected to the semiconductor requirements of that buildout.

Meanwhile, the extreme movements in Nebius, IREN, CoreWeave, and WhiteFiber show just how sensitive the smaller infrastructure providers are to changes in expectations.

That leaves you with a much more useful conclusion than simply declaring the AI trade "back" or "over."

The AI boom is entering a more demanding phase.

The market is no longer rewarding every company simply for mentioning AI. Investors are beginning to demand evidence that spending creates revenue, that revenue creates cash flow, and that today's infrastructure investments produce durable competitive advantages.

That is actually a healthier environment.

If you are investing with a five-year horizon, you do not need to predict which stock will jump tomorrow after an earnings call. You need to identify the businesses capable of turning today's enormous AI spending into tomorrow's recurring economic value.

The noise will continue.

The spending will continue.

The volatility will continue.

But the companies that can convert AI demand into durable revenue, stronger margins, and eventually substantial free cash flow are the ones worth watching most closely.

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