Earnings, Energy, and AI

If you are not terribly familiar with why earnings reports are important, and able to move markets the next few paragraphs will lay that out—if you are familiar feel free to skip down.

We are moving into the thick of earnings season. Quarterly reporting acts as the market’s essential reality check, bridging the gap between speculative expectations and actual financial performance.

By providing a transparent window into a company’s cash flow, margins, and debt every 90 days, these reports allow investors to continuously recalibrate their valuation models. This regular flow of data reduces “information asymmetry”—ensuring the public knows as much as the insiders—and forces a periodic validation of management’s long-term strategy.

Ultimately, these reports serve as an anchor, ensuring that stock prices remain tied to fundamental economic value rather than just sentiment.

Investors often rely on analysts, who set expectations for company performance. The base that these expectations are built off “Management Guidance” which is a company’s official forecast of future financial performance (i.e. revenue, earnings, margins).

Three of the best words to hear during earnings are “beat and raise” where the companies prior quarter outperformed, or beat, the street (analyst) expectations, and management raises their guidance, in releasing updated forecasts for better future performance.

If earnings are missed, you may want to dig deeper into any discrepancies in performance quarter over quarter, evaluate the broader sector or market to see what could have driven the change, and whether it is a short-term condition, or a shifting trend that will cause headwinds moving forward.

Metrics for valuation multiples will vary sector to sector but largely earnings per share (EPS) and revenue will broadly be the indicators that you read or hear when earnings are being reported.

THIS WEEK:

This week’s portion of the earnings cycle has been a study in the market’s prioritization of guidance over performance, as investors grapple with shifting trade policies and “Greenland-related” macro volatility.

Intel (INTC) and Netflix (NFLX) both delivered bottom-line beats for Q4, yet saw their stocks tumble 15.1% and 7.0% respectively after issuing cautious Q1 2026 outlooks.

This “guidance-first” sentiment similarly penalized 3M (MMM) and GE Aerospace (GE), which slid 8.3% and 5.4% on the week despite solid earnings beats, as the market reacted poorly to 3M’s conservative profit forecast and GE’s tepid 1% revenue growth projection. Abbott Laboratories (ABT) faced the steepest fundamental rejection, dropping 10.8% after a revenue miss in its diagnostics segment overshadowed its EPS achievement. Conversely, Prologis (PLD) emerged as the week’s rare outlier, gaining 2.9% as management’s robust 2026 Core FFO guidance provided the long-term “anchor” that sentiment-driven peers lacked.

A LOOK AHEAD:

Over the next two weeks six of the Mag7 firms will report their latest earnings, with Nvidia reporting at the end of February. As such the below will act as something of a scouting report on each of the Mag7 firms. Additionally, a rundown on Taiwan Semi-Conductor or TSMC, a major upstream partner in the AI space, who reported phenomenal earnings with a Beat and Raise quarter. This should not be interpreted as a research report but a 30,000 foot view of firm offerings, competitive advantages, and threats to market share.

The Platforms & Ecosystems

Microsoft (MSFT)

• AI Function: Leading the Agentic AI shift through Copilot and their partnership with OpenAI. They are automating the white-collar workflow, transforming Azure from a storage cloud into an AI-first intelligence engine.
• Moat: Massive enterprise distribution. Every Fortune 500 company is already a customer, making their AI upsell a natural evolution.
• Threats: High CapEx spending on data centers may weigh on margins if AI productivity gains don’t materialize as quickly as projected.

Tesla (TSLA)

• AI & Robotics: A Robotics and AI company disguised as a carmaker. Their Optimus humanoid robot and FSD (Full Self-Driving) are the core of their $1.3T+ valuation.
• Integration: Heavily vertically integrated, now designing their own AI chips (AI5/AI6) to reduce dependence on NVIDIA.
• Threats: Declining automotive margins (down to 16%) and the high “Musk Premium” which ties the stock price closely to the CEO’s public narrative. Regulatory hurdles in the robotaxi space have given Waymo (GOOGL) an early, albeit limited, battle for market share.  

Meta (META)

• AI Function: Utilizing AI for hyper-personalized ad targeting and “Smart Glasses” (Ray-Ban Meta) that act as an AR/AI interface.
• Integration: Meta is spending an estimated $91B in 2026 CapEx to build an independent AI compute stack to bypass reliance on external mobile OS platforms.
• Threats: Regulatory pressure in the EU, a slowdown in spending by major Chinese retailers TEMU and Shein, which have historically driven significant ad volume could hit growth, additionally with significant CapEx, margin compression is a risk if spend and revenue grow asymmetrically.

Apple (AAPL)

• AI Function: Edge AI. Apple is focusing on Apple Intelligence and an overhauled Siri (codenamed “Campos”) that runs on-device to protect privacy.
• Moat: An install base of 2.35B users. This gives them a “last mile” advantage—they own the interface the consumer actually uses.
• Threats: Slow innovation perception compared to AI-first peers and heavy reliance on iPhone sales in a softening China market.

Alphabet (GOOGL)

• AI Function: Deeply vertically integrated with their own TPU (Tensor Processing Units) and the Gemini model. They are a leader in autonomous vehicles via Waymo, which is now seeing expanded deployment in U.S. cities.
• Threats: Facing stiff AI competition and regulatory scrutiny that may impact its long-term competitive edge. Battling Tesla for robotaxi market share. Waymo leads in deployment at the moment but has significantly higher cost per vehicle in production.
• Moat: Data dominance. Google Search and YouTube provide the largest training sets on earth for multimodal AI.

Amazon (AMZN)

• AI & Robotics: The gold standard for Warehouse Automation. Their fleet of mobile robots and AI-driven forecasting (SCOT) has fundamentally changed the economics of logistics.
• Moat: Flywheel Effect. AWS provides the AI infrastructure (Bedrock) to others, while Amazon uses that same tech to lower its own delivery costs.
• Threats: Antitrust scrutiny and rising labor costs that force even faster (and more expensive) automation pivots.

The AI Infrastructure & Foundational Layer

NVIDIA (NVDA)

• AI & Automation: The undisputed king of the AI era, providing the “Compute” bedrock via GPUs and the CUDA software stack. They are moving rapidly into Physical AI and robotics through their Omniverse and Isaac platforms, which allow for the simulation and deployment of autonomous machines.
• Integration & Moat: Highly vertically integrated in the software-hardware stack. Their “Blackwell” and upcoming “Rubin” architectures create a cycle where software lock-in makes switching costs prohibitive for cloud providers.
• Threats: Increasing custom chip development from their own customers (AMZN, GOOGL) and potential regulatory caps on advanced chip exports.

TSMC (TSM)

• Competitive Position: The world’s forge. They hold a near-monopoly on the advanced nodes (3nm, 2nm) required for every major AI chipmaker.
• Recent Outperformance: TSMC recently delivered a monster Q4 2025 report on Jan 15, 2026, with $33.7B in revenue (beating guidance) and a massive gross margin of 62%.
• Key Advantage: Unmatched scale and Process Leadership. They are the ultimate “picks and shovels” play—when NVDA or AAPL wins, TSM wins by default.
• Threats: Geopolitical concentration in Taiwan and the extreme capital intensity required to build new fabs ($52B–$56B planned for 2026).

THE ENERGY ISSUE

As the Artificial Intelligence market continues to mature, the most critical bottleneck to monitor isn’t the availability of high-performance GPUs, but rather the availability of power. AI workloads—particularly the massive training clusters for LLMs—require energy profiles that are far more volatile and intense than traditional IT. We are seeing a structural shift where a single high-density AI data center can demand upwards of 100 MW to 300 MW, roughly equivalent to powering a small city.

This power crunch is creating a significant temporal mismatch between the tech and utility sectors. While a state-of-the-art AI facility can be planned and built in under two years, the high-voltage transmission lines needed to feed it often take 15 to 30 years to permit and construct. In the U.S. alone, nearly 2 TW of clean energy is currently trapped in interconnection queues. Without coordinated reform, this energy deficit risks deflating the AI valuation bubble as computing supply remains physically constrained by a grid that was never designed for this level of sustained, high-density industrial load.

Short-to-Medium Term Solutions (1–3 Years)

In the immediate term, hyperscalers like Microsoft, Amazon, and Google are pivoting toward behind-the-meter (BtM) and bridging strategies to bypass grid delays.

• On-Site Generation: Tech firms are deploying dedicated natural gas plants and fuel cells directly at data center sites to ensure 24/7 reliability while waiting for grid hookups.
• Operational Efficiency: The rapid adoption of liquid cooling is no longer optional; it is a necessity for racks exceeding 30–40 kW to maintain hardware longevity and reduce thermal energy waste.
• Grid Modernization: Deployment of advanced software tools could unlock up to 175 GW of existing transmission capacity without building new lines, providing a critical buffer for the 2026–2028 timeframe.

Long-Term Strategic Solutions (5–10 Years)

To sustain growth through 2035, the industry is betting on a fundamental restructuring of energy generation and storage.

• Small Modular Reactors (SMRs): Because AI clusters require continuous, “firm” baseload power, companies are aggressively exploring factory-built SMRs. These 5 MW to 300 MW reactors can be installed directly on AI campuses, scaling in modular increments as compute demand increases.
• Next-Gen Geothermal: Leveraging technology from the shale revolution, next-generation geothermal could supply up to 40 GW of clean, 24/7 power by 2035, offering a carbon-free alternative to traditional baseload sources.
• Innovative Energy Storage: The development of structural battery composites—materials that function as both load-bearing building components and energy storage—may revolutionize how data center infrastructure manages power density over the next decade.

WRAPPING UP:

A few things to keep in mind—earnings are important, and informative, but in my opinion, they are a datapoint amongst a set that should be weighed in all investment decision making processes. If you are investing for the long-term look at them as a progress report, not the whole story. If you have any interest in the power solutions listed above we will likely go into a deeper dive on what those would look like in the coming months, there are also a wealth of videos by energy experts explaining in basic terms that make complex solutions very digestible, I highly recommend exploring the topic as it has the potential to be a major market factor over the medium to long term. Finally, AI will be a common theme here, as it is a cornerstone of the current market environment, I encourage anyone who has not to experiment with Chat-GPT, Google Gemini, Anthropic’s Claude, and any other number of LLM platforms, the more comfortable you are utilizing the technology moving forward the more valuable you will be if, and more likely when, it is adopted in your profession in some form, or fashion.

DISCLOSURE:

This material is provided for informational and educational purposes only and does not constitute investment advice, a recommendation, or an offer or solicitation to buy or sell any security or investment strategy. The views expressed are those of the author as of the date of publication and are subject to change without notice.

The author is a financial professional and may hold positions in, or manage client accounts that hold positions in, the securities discussed. Such holdings are subject to change at any time without notice. While the author strives to present information in a fair and balanced manner, no representation is made that this commentary is free from bias, and readers should be aware of potential conflicts of interest.

The information presented is derived from publicly available sources believed to be reliable, but accuracy and completeness are not guaranteed. Past performance is not indicative of future results. All investing involves risk, including the possible loss of principal.

This commentary does not take into account the investment objectives, financial situation, or particular needs of any specific person. No advisor-client relationship is created by the receipt or review of this material. Readers should consult with a qualified financial, legal, or tax professional before making any investment decisions.

The views expressed do not take into account the specific financial situation, risk tolerance, or investment objectives of any individual reader. Reading this material does not create an advisor-client relationship. Investors should conduct their own research or consult with a qualified financial professional before making investment decisions.

-John McKay, CFA