AI stocks have already benefited from enormous investor enthusiasm, but the next earnings reports could reveal which businesses are turning that excitement into lasting financial results. 📊 CrowdStrike (CRWD), Cisco (CSCO), Palantir (PLTR), Marvell (MRVL), AMD (AMD), and Intel (INTC) each occupy a different part of the AI ecosystem, from cybersecurity and networking to data center chips and processor manufacturing.

Their opportunities may be significant, but the market's expectations are just as important as the companies' actual performance. A strong quarter can still disappoint if investors were expecting something extraordinary, while a mixed report could spark a rally if management demonstrates meaningful progress. Nvidia (NVDA) and Micron Technology (MU) offer additional clues about demand for AI computing and the memory needed to power it. For your portfolio, the goal isn't to guess which stock will jump after earnings. It's to identify what each company must deliver, whether its valuation leaves room for further growth, and which risks could undermine the long-term investment case.

🚀 One company must prove its AI software can keep winning customers, another needs to turn ambitious semiconductor targets into revenue, and a third is still fighting to establish a successful turnaround.

Discover the key earnings signals to watch—and why even an impressive report may not be enough to make a stock worth buying.

Imagine setting aside $500 a month for Nvidia ($NVDA ( ▼ 0.29% )) for five years, buying a fixed dollar amount on a schedule, a method called dollar-cost averaging. In our example the buy happens on the first trading day on or after the 9th of each month, from Oct 2021 through Sep 2026 (the first buy lands on Monday, Oct 11, 2021, because Oct 9 was a Saturday): 60 buys, $30,000 in total.

NVDA closed at about $22.69 (split-adjusted) on Oct 21, 2021 and at $214.13 on Oct 16, 2026, a price gain of about 843.6% (about 9 times), or roughly 57% a year compounded (the steady yearly rate that would give the same total gain). A single $10,000 invested at the Oct 21, 2021 close would have become about $94,360. The 60 monthly buys, $30,000 in total, would have been worth about $153,700 at the Oct 16, 2026 close, about 5.1 times the money put in. Price only: no dividends, fees or taxes, and these are examples, not forecasts.

The highest close of the past 52 weeks was $239.24 on Oct 6, 2026 (the intraday high was $243.37 the same day), so the Oct 16, 2026 close sat about 10.5% below it. The lowest close of the five years was $11.23 on Oct 14, 2022, about 50.5% below the $22.69 start. At about +843.6%, the gain sits about 156 percentage points below the +1,000% line, so a few weeks of trading could move the headline figure.

Note: a percentage shown beside a $ticker symbol in this issue is that ticker's live daily price move from Stocktwits when the page or email is opened. It is not one of our figures; every figure in this section is a dated close.

Caution: past pace rarely continues. Nvidia's Aug 26, 2026 release for the quarter ended Jul 26, 2026 showed revenue up 106% to $96.2 billion and non-GAAP diluted EPS of $2.22, on sustained demand for AI accelerator hardware. The stock still slipped from its highest close of $239.24 on Oct 6, 2026 to $214.13 on Oct 16, 2026, a reminder that prices can pause even when results are strong.

Earnings season can create some of the biggest opportunities—and the most frustrating surprises—for investors. A company can report higher revenue, beat profit estimates, and still see its share price fall because Wall Street expected even more. Another company can deliver an uneven quarter but rally because its results suggest that the business is improving faster than investors anticipated.

That is why the next round of corporate earnings deserves more than a quick look at the headline numbers. The real question is whether businesses are converting the artificial intelligence boom into revenue, profits, and sustainable cash flow—or whether their stock prices have already moved too far ahead of what they can deliver.

This earnings season, six companies stand out: CrowdStrike (CRWD), Cisco Systems (CSCO), Palantir Technologies (PLTR), Marvell Technology (MRVL), Advanced Micro Devices (AMD), and Intel (INTC). Each offers a different way to participate in the AI buildout, from cybersecurity and networking to data center chips and the processors needed to run AI applications.

Nvidia (NVDA) and Micron Technology (MU) also remain important indicators for the broader AI trade, even though they were not among the six primary stocks highlighted in the discussion. Together, these companies can help investors understand whether AI spending is spreading across the technology industry or becoming concentrated in only a few businesses.

The goal is not to predict every earnings-day move. It is to understand what each company needs to prove, what could go wrong, and whether its long-term business prospects justify the price investors are being asked to pay.

One of the more important shifts in the AI story is the growing need to protect the systems that make AI useful. As businesses connect more applications, data, and automated processes, the number of potential security weaknesses can increase. AI can help organizations work faster, but it can also introduce new risks involving unauthorized access, data exposure, and attacks that move across interconnected systems.

That creates an opportunity for cybersecurity companies such as CrowdStrike. Its software platform is designed to help organizations detect, investigate, and respond to cybersecurity threats across their digital environments. In the interview, CrowdStrike was described as a leading cybersecurity partner within Nvidia's AI ecosystem, with its role focused on the software layer of security.

This matters because cybersecurity is not a one-time purchase that becomes unnecessary once a company has installed the right tools. Businesses need to keep monitoring threats, updating defenses, and protecting expanding networks. As AI becomes more deeply integrated into enterprise operations, security spending may remain an essential part of technology budgets rather than a discretionary upgrade.

The earnings question is whether CrowdStrike can continue translating that demand into customer growth, recurring revenue, and stronger profitability. Investors should examine subscription trends, customer retention, the expansion of existing customer relationships, and management's outlook for future demand. Strong demand is encouraging, but it must ultimately appear in financial results.

The risk is the expectation already built into the share price. CrowdStrike was described as trading near its previous highs after a strong recovery. When a stock is priced for excellent results, a good quarter may not be enough to drive another rally. Even if revenue and earnings increase, the stock could retreat if management's outlook falls short of an exceptionally high bar.

For a long-term investor, CrowdStrike's appeal rests on the importance of cybersecurity in a more connected economy. The decision to buy, hold, or wait should still depend on valuation and the company's ability to sustain growth—not simply on the belief that AI will create more security threats.

CrowdStrike protects systems through software. Cisco plays a different role by supplying networking equipment and technology that connect devices, applications, and data. As data centers expand and businesses move more workloads across networks, reliable connectivity becomes increasingly important.

Cisco's potential AI opportunity is therefore broader than selling equipment to build data centers. Its networking infrastructure helps data move through enterprise systems, while its security capabilities can help protect those systems. In the interview, Cisco was highlighted for embedding security into the hardware layer, complementing the software-based protection associated with CrowdStrike and the data and analytics capabilities associated with Palantir.

These companies do not provide identical services, and their roles should not be confused. Rather, they illustrate how AI security can involve multiple layers: the network hardware that carries information, the software that detects and responds to threats, and the platforms that help organizations use and manage data.

Cisco may appeal to investors looking for exposure to technology infrastructure through a more established business. Unlike a company whose valuation depends almost entirely on rapid AI growth, Cisco has a legacy networking business alongside its efforts to participate in newer technology trends. It also returns capital to shareholders through dividends and share repurchases, according to the discussion.

Those features make cash flow especially important. Investors should look at whether Cisco can convert its backlog into recognized revenue, maintain profitability, and support shareholder returns while investing in new products. A large backlog can signal future demand, but it does not automatically guarantee when revenue will be recorded or how profitable those sales will be.

The interview described Cisco's stock as consolidating after reaching a high, with technical support around its 150-day moving average. That is a market-chart observation from the discussion, not a guarantee that the share price will hold that level. Technical signals can change quickly, particularly around earnings.

What could move Cisco higher? Evidence that networking demand remains healthy, AI-related products are gaining traction, and cash flow continues to support dividends and buybacks could strengthen the investment case. However, if the market has already priced in those improvements, even a solid report might produce only a limited reaction.

Cisco is worth watching as a potential infrastructure and income-oriented technology holding, but investors should not assume that its share price will move like a high-growth cybersecurity stock.

Palantir occupies another part of the AI ecosystem. Its software helps organizations integrate data, build operational applications, and support decision-making. The company serves both commercial and government customers, giving it exposure to businesses adopting AI as well as public-sector organizations using data-intensive systems.

The investment case highlighted in the interview centers on how Palantir brings customers onto its platform and then expands those relationships. A customer may begin by testing the software or implementing an initial use case. If the platform proves useful, the customer may expand its use, purchase additional capabilities, and increase spending over time.

This model can create an attractive growth opportunity when initial adoption leads to broader deployment. It is not enough for a company to demonstrate that its AI software can work; it must also convince customers that the product delivers enough value to justify ongoing spending. The more deeply a platform becomes integrated into a customer's operations, the more opportunities the provider may have to expand the relationship.

For Palantir, the key earnings indicators include commercial customer growth, government demand, customer spending expansion, revenue growth, and profitability. Investors should also pay attention to how management describes the pace of adoption. Interest in AI is valuable, but sustained commercial success depends on converting that interest into paying customers and repeat business.

The company was described as having strong price momentum and trading near recent highs. That strength can attract investors, but it also raises the possibility of a sharp reaction if earnings or guidance disappoint.

A company can execute well and still experience a stock-price decline when expectations are unusually high. For Palantir, the question is not simply whether its results will be good. It is whether they will be good enough relative to the optimism reflected in its valuation.

For your portfolio, Palantir represents an investment in the software layer of AI adoption rather than the physical equipment used to run AI systems. That offers a different source of potential growth, but it also means valuation discipline is essential. A promising platform does not make every purchase price attractive.

Marvell Technology supplies semiconductor solutions used in data infrastructure, networking, and other computing applications. Its relevance to AI comes from the growing complexity of data centers and the need to move, process, and connect large amounts of information efficiently.

The company drew attention after raising its long-term revenue targets at an investor event, according to the interview. The discussion noted that its target for the following year had been increased by approximately 10%, alongside higher longer-term expectations. Those revisions were presented as evidence that management expected the AI infrastructure opportunity to remain strong.

Long-term targets matter because data center investment is not a single-quarter phenomenon. Building AI infrastructure requires planning across multiple years, and companies supplying the underlying components can benefit if customers continue expanding capacity. However, targets are forecasts, not guaranteed results. They must eventually be supported by orders, shipments, revenue, and cash flow.

That is why Marvell's next earnings report should be assessed through both its current results and its outlook. Investors should look for evidence that demand is translating into sales, that customer programs are progressing, and that the company can deliver on the expectations embedded in its financial targets.

The interview also emphasized execution. After a substantial stock-price advance, the opportunity may depend less on whether the AI story is attractive and more on whether Marvell can meet the expectations already reflected in its valuation. A company can benefit from a strong industry trend without delivering the same pace of share-price appreciation indefinitely.

The main risk is that investors may have moved too far ahead of the business. If orders are delayed, revenue growth slows, or guidance fails to meet expectations, the stock could pull back even if the longer-term AI opportunity remains intact.

Marvell belongs on the watchlist because it offers exposure to the infrastructure supporting AI, but it should not be treated as a guaranteed winner. The investment case needs to be supported by measurable progress rather than optimistic forecasts alone.

Advanced Micro Devices is often discussed as a competitor in high-performance computing and AI accelerators, but its central processing units (CPUs) are also relevant to the next stage of AI deployment.

The interview drew a distinction between AI model training and inference. Training involves developing a model using large amounts of data and computing resources. Inference is the process of using a trained model to produce answers, predictions, or other outputs. As businesses put AI systems into practical use, the computing infrastructure required to run those applications becomes increasingly important.

GPUs are central to many demanding AI workloads, but CPUs also play a role in managing tasks, coordinating systems, and running general-purpose computing workloads. The growing use of AI could therefore support demand across different kinds of processors rather than benefiting only one category of chip.

AMD's earnings will be important for determining whether that demand is translating into commercial progress. Investors should watch data center revenue, processor demand, margins, customer adoption, and management's outlook for upcoming product deliveries.

The interview specifically discussed AMD's Helios platform and the expectation that deliveries would begin late in the quarter under discussion. It suggested that the upcoming report might offer an initial indication of demand, while a later quarter could provide a clearer picture of the product's financial contribution. Because delivery schedules and expectations can change, investors should verify the latest company disclosures rather than treat the timeline discussed in the interview as a confirmed current update.

AMD's valuation is another important consideration. The interview presented a bullish view that the shares could potentially double within 12 months, based on the company's CPU opportunity and Helios expectations. That is an analyst's forecast, not a reliable prediction or a guaranteed return. A doubling scenario requires strong execution and a market willing to continue assigning an attractive valuation to the company's future earnings.

A more disciplined approach is to examine what the current share price already assumes. If expectations for AI demand, product launches, and revenue growth are unusually optimistic, even positive results could disappoint investors. If the company demonstrates stronger-than-expected execution without an excessive valuation, the risk-reward picture could improve.

AMD is a key stock to watch because it offers exposure to several computing trends. But the investment decision should rest on evidence of demand, product execution, and financial performance—not on a price target alone.

Intel presents a different investment case from AMD. It is an established semiconductor company with a major historical position in CPUs and personal computing, but it has also faced competitive pressure and challenges in its broader business. Its investment story involves both its established operations and its efforts to become more competitive in newer computing markets.

The interview highlighted several factors investors are watching: demand for CPUs as AI inference expands, efforts to strengthen Intel's AI focus, the potential benefits of bringing more semiconductor manufacturing closer to home, and the company's foundry business.

These are related but distinct opportunities. Strong CPU demand could support sales of Intel's processors, while its foundry strategy depends on its ability to manufacture chips and attract customers to its production capabilities. Domestic manufacturing initiatives may also influence the company's position as governments and businesses pay more attention to supply-chain resilience. However, these opportunities require execution, investment, and time; they do not guarantee that Intel will regain its former competitive position.

Intel's share-price history illustrates why investors should separate a stock's recent performance from the underlying business. In the interview, the stock was described as having risen sharply from around $20 to nearly $140 before retreating approximately 25% to 30% from its high. These are figures cited during the discussion and should not be interpreted as current prices.

A steep rise can change market expectations. When investors begin to price in a successful turnaround, the company must continue delivering improvements to justify that optimism. A pullback after a large rally may reflect profit-taking, but it may also indicate that investors are reassessing the likelihood of future success.

For Intel, the most important evidence will be whether operating performance improves, whether its products remain competitive, and whether the company makes tangible progress on manufacturing and its foundry ambitions. A promising story is not enough if revenue, margins, or cash flow fail to support it.

Intel may offer significant upside if its turnaround progresses, but the risks are substantial. It should be evaluated as a company working to prove its recovery, not as a business that has already completed it.

Although the six primary stocks are the focus, Nvidia (NVDA) and Micron Technology (MU) remain important to watch because their results can provide clues about demand across the AI ecosystem.

Nvidia supplies GPUs and related computing platforms that are central to many AI systems. Strong results and guidance can reinforce confidence in data center spending and AI-related demand. Weakness, by contrast, could raise questions about whether customers are maintaining their investment plans or whether expectations for the AI buildout have become too optimistic.

Nvidia's influence extends beyond its own stock. Its performance can affect sentiment toward semiconductor companies, data center suppliers, networking businesses, and other companies tied to AI investment. That does not mean all these stocks will move in the same direction, but Nvidia's results can shape how investors view the broader sector.

Micron offers a more specific perspective on memory. AI systems require high-performance memory to support demanding computing workloads, and Micron's results can reveal information about demand, pricing, and supply conditions in that market. High-bandwidth memory (HBM), in particular, is important to many advanced AI accelerators.

Investors should watch Micron's outlook for memory pricing, HBM demand, and supply constraints. Changes in those expectations can affect the company's earnings prospects and share price. Competition and new technology developments can also alter how investors assess future demand, so a strong market today does not guarantee that favorable pricing conditions will persist.

Nvidia and Micron therefore serve different roles in the earnings picture. Nvidia helps investors assess demand for AI computing platforms, while Micron offers insight into the memory required to support those systems.

The interview's broader argument is that analysts' estimates may not fully reflect the strength of AI-related spending. The analyst suggested that third-quarter earnings could outperform consensus estimates by a wide margin, drawing comparisons with earlier quarters when reported results exceeded expectations.

That is an optimistic forecast, not an established outcome. The figures discussed in the interview included unusually large levels of earnings outperformance in the first half of the year, with energy-sector strength and AI-related activity contributing to the results. Those comparisons should not be interpreted as a guarantee that the third quarter will repeat the same pattern.

The difference between earnings growth and earnings outperformance is important. Earnings growth measures how much profits increase relative to an earlier period. Earnings outperformance generally describes the extent to which reported results exceed analysts' expectations. A company can report strong growth but still disappoint if investors expected an even stronger result.

This is why the reaction to earnings can appear irrational. The market is not judging a report against an abstract standard of success; it is comparing the results with what investors already expected.

For CrowdStrike and Palantir, the challenge may be meeting elevated expectations after strong stock performance. For Cisco, investors may focus on cash flow, networking demand, and capital returns. Marvell must demonstrate that its higher long-term targets are achievable. AMD needs to show progress in its data center and processor opportunities, while Intel must provide evidence that its turnaround is gaining traction.

These are six different companies, but the common question is the same: Can actual business performance catch up with—or exceed—the expectations already embedded in the share price?

Corporate earnings will not be the only factor influencing share prices during the reporting period. The interview also highlighted the U.S. midterm elections as a potential source of volatility.

Political outcomes can affect investor expectations about regulation, taxation, government spending, trade policy, and the business environment. For technology and infrastructure companies, uncertainty around policy can influence how investors value future earnings, even when the companies' quarterly results are solid.

The analyst expressed the view that a divided government could produce a period of greater policy stability by limiting major legislative changes. That is an opinion, not a certainty about how the market would respond. Election outcomes can affect different sectors in different ways, and a market reaction is not guaranteed to follow a particular political scenario.

For your portfolio, the practical lesson is to avoid attributing every market movement to earnings alone. A stock could decline after a good report because investors are reacting to valuation, political uncertainty, interest rates, or developments elsewhere in the market. Similarly, a stock could rise despite mixed results if investors believe the outlook has improved.

Keeping these factors separate can help prevent a short-term market reaction from driving a decision that should be based on long-term business fundamentals.

Earnings season can be an opportunity to reassess a company's investment case, but it is rarely a reliable shortcut to immediate profits. Even when the business is performing well, a stock can fall because expectations were too high. A sharp rally after a report can also leave investors paying a price that assumes years of strong execution.

The six companies in focus offer different ways to participate in the AI economy. CrowdStrike represents cybersecurity software; Cisco provides networking and security infrastructure; Palantir focuses on data integration and operational software; Marvell supplies semiconductor technology for data infrastructure; AMD offers exposure to CPUs and AI computing; and Intel represents both established processor operations and a more uncertain turnaround.

Nvidia and Micron add two important perspectives on the broader AI supply chain: demand for AI computing platforms and the memory needed to support them. Their results could influence sentiment across the sector, but they should not replace company-specific analysis.

For a long-term investor, the most useful approach is to identify what each company needs to prove before the report, then compare the results with those expectations. Revenue growth, margins, cash flow, customer demand, product launches, and forward guidance can tell you more about a business than the first market reaction.

It is also worth resisting the urge to buy simply because a stock falls after earnings. A decline can create a more attractive entry point, but only if the underlying investment case remains intact and the valuation becomes reasonable. Likewise, a stock that rises sharply after a report is not automatically too expensive; its price needs to be assessed against its future earnings potential and risks.

The strongest earnings-season strategy is not to guess which stock will jump tomorrow. It is to understand which businesses are improving, what investors already expect, and whether the price offers enough potential return for the risk involved.

That approach will not eliminate volatility. It can, however, help you make more deliberate decisions instead of letting a single earnings headline determine the direction of your portfolio.

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Thanks for reading. This format is built to be fast to open, clear to understand, and useful enough to act on, without pretending past returns continue forever.

Caution: Past pace rarely continues. All figures here are approximate and are shown as examples, using price only (no dividends, fees, or taxes). Past performance is not a forecast; this is education, not advice.

Disclaimer: This newsletter is for informational purposes only and is not financial advice. Consult a qualified advisor before investing.