To The Moon

Macro Theme of the Week

The minutes from the Federal Reserve’s December FOMC meeting released this week show that internal divisions over the policy path have widened significantly. Although the Committee ultimately decided to cut rates in December, the number of dissenting voices reached a level rarely seen.

Overall, the minutes had a limited impact on risk assets, serving mainly to reinforce the market’s prior view that rate cuts in Q1 2026 are unlikely.

On one hand, most officials supported rate cuts as a precaution against potential weakness in the labor market. On the other hand, some argued for keeping rates unchanged for a period of time, and a small minority believed that conditions in December did not justify a rate cut at all.

This internal divergence suggests that the Fed has yet to form a clear consensus on its policy direction for 2025 and beyond. Monetary policy uncertainty is therefore likely to remain a key variable influencing interest rates and asset pricing.

Over the past two weeks, major AI tech stocks— $NVIDIA(NVDA)$ , $Amazon.com(AMZN)$ , and $Alphabet(GOOG)$ have performed well, while $Tesla Motors(TSLA)$ has retreated from its highs and other stocks have remained largely flat. As the year comes to a close, this week’s Big Tech report focuses on the market consensus for 2026.

Big Tech Weekly Core View — What Consensus Is the Market Reaching on 2026?

In 2026, the market is expected to move away from a blindly optimistic, liquidity-driven bull market and enter a new phase dominated by reality checks, differentiation, and risk management. As AI transitions from conceptual hype to real-world deployment, implementation-driven divergence will become more pronounced. Focusing on diversified opportunities and hedging strategies may emerge as the next market consensus.

1) Core narrative around Big Tech: earnings support vs. valuation digestion.

The market’s main driver is shifting from forward-looking re-rating to a slower, index-level grind higher, with limited room for valuation expansion. Earnings from leading companies must underpin growth. Investment strategies are evolving from “buying the index” to more selective stock picking, with an emphasis on cash flow and tangible performance. Volatility is increasingly viewed as an opportunity rather than a risk.

2) From a CapEx supercycle to monetization and returns.

AI investment is moving from aggressive spending (GPUs, infrastructure) toward monetization and ROI assessment.
Commercialization can be divided into three layers:

• Hardware layer (e.g., GPUs, HBM): growth may slow in 2026, with lower beta-driven returns.

• Platform layer (cloud services, APIs): likely to be the main alpha battleground in 2026.

• Application layer (industry-specific tools): carries the greatest uncertainty in 2026 and must prove concrete cost-reduction and efficiency gains.

3) Broadening participation: from the “Mag 7” to wider opportunities.

AI tools are filtering down to mid-cap growth stocks, industrial software, vertical SaaS, and traditional sectors such as retail and logistics. Investment opportunities are no longer concentrated solely in tech giants but are spreading to ordinary companies that use AI to improve operations, reflecting the breadth gains from technological diffusion.

The return of hard assets and cyclical assets.

Rising compute demand highlights the scarcity of power, land, copper, and other resources, forming a “Physical AI Rail.” At the same time, gold and commodities serve as macro hedges against AI bubbles and credit risk.

Key Sectors to Watch in 2026

1. Hardware

The U.S. semiconductor industry maintains a positive outlook for 2026, viewed as the midpoint of an 8–10 year AI infrastructure upgrade cycle. Global semiconductor sales are expected to grow strongly, reaching USD 1 trillion in 2026 (+29% YoY) and USD 1.15 trillion in 2027 (+14% YoY). AI semiconductor sales are projected to grow more than 50% YoY, driven by high data center utilization, tight supply, accelerated enterprise adoption, and competition among LLM builders, hyperscalers, and sovereign entities.

AI demand is expected to offset ROI scrutiny, geopolitical risks, and macro volatility. Analog semiconductors face macro headwinds, but the launch of Blackwell training models in early 2026 should improve performance and revive demand, while power and space constraints limit overinvestment. Memory and optical components have favorable pricing dynamics, though valuations warrant caution.

Electronic Design Automation (EDA) offers high-quality, low-volatility exposure, benefiting from rising chip complexity and R&D spending, with expansion into industrial, data center, and robotics markets via M&A. Emerging themes include co-packaged optics (CPO) enabling AI scaling, White House-backed momentum in robotics, and early-stage opportunities in quantum computing.

2. Robotics

Humanoid robots are becoming one of the most sentiment-driven themes in capital markets after AI. In 2026, enthusiasm is unlikely to fade quickly. Progress on Tesla’s next-generation Optimus, continued commitments from major tech companies, and policy support in both China and the U.S. are all likely to periodically boost market imagination around embodied intelligence. However, this enthusiasm is driven more by narrative and expectations than by mature, scalable commercialization.

The true inflection point will be when robots move from demonstrations to performing real work. Autonomous decision-making, environmental generalization, reliability, and cost remain the key determinants of industry progress. In the near term, teleoperation and semi-autonomous solutions will remain the mainstream for training and deployment.

More broadly, 2026 is likely to be a year of structural change for the industry. As the number of participants rises and capital continues to flow in, internal differentiation will accelerate. Companies with manufacturing capabilities, integrated supply chains, engineering expertise, and closed-loop data advantages will increasingly pull ahead of those stuck at the concept or demo stage. In both China and the U.S., resources, talent, and capital are likely to concentrate among a small number of full-stack players, making industry consolidation unavoidable.

3. Application Software

Starting in 2026, market focus will gradually shift from model capabilities and capital intensity toward real deployment, business integration, and sustainable monetization. AI’s greatest value will not lie in content generation, but in AI agents deeply embedded in decision-making, execution, and transaction processes—effectively introducing large-scale “virtual white-collar workers” and structurally lifting productivity.

In the short term, the software sector still faces friction from slowing growth, cautious budgets, and slower-than-expected AI monetization. However, valuations already reflect pessimistic expectations, with software trading at historically extreme discounts relative to semiconductors. Over the medium to long term, SaaS applications with high stickiness, stable cash flows, and clear AI pricing paths are likely to be re-rated as agents proliferate and enterprise digital transformation resumes. Overall, volatility and differentiation will persist, as AI reshapes the value center of software. 2026 is more likely to be a year of revaluation rather than re-acceleration—a critical inflection point.

Options Watch — Big Tech Options Strategies

This week’s focus: the impact of Nvidia’s USD 20 billion acquisition of Groq assets.

The AI industry is shifting from a “model capability arms race” to a phase constrained primarily by compute supply. Inference-side compute and response speed now directly affect user experience and commercialization efficiency. For leading model companies, the core issue is not insufficient demand but real constraints in compute and energy, making compute expansion more directly convertible into higher revenue and usage intensity. Under this structure, AI increasingly resembles a resource-driven growth model, where compute itself becomes the key production factor determining growth pace and competitive moats. This also explains why global tech companies and governments continue to invest heavily in compute infrastructure.

Against this backdrop, NVIDIA’s position is best understood as the core supplier of high-end compute systems rather than a simple “monopolist of the entire market.” In the short term, its technological edge and ecosystem barriers in training and complex inference remain difficult to replace, keeping demand highly concentrated on the NVIDIA platform.

Over time, however, as inference scenarios become more segmented and compute needs more diversified, custom ASICs and specialized inference chips will absorb part of the marginal demand, leading to a layered coexistence in compute supply. As a result, NVIDIA’s revenue concentration is likely to remain high, while concentration in chip volumes and compute sources may gradually dilute. Its long-term value will hinge on sustained leadership in high-end compute and system-level solutions.

Options positioning in NVDA also reflects divergence. In January expiries, there is heavy call selling (to close positions) at strikes of 200 and above across multiple cycles, while a significant share of puts at 180 and below is concentrated in the January 16 monthly options. This has pushed the max pain level for that week toward a quarterly skew. The implied share purchase volume for that expiration week has reached about 2% of the five-day average trading volume, a historically elevated level. Both bulls and bears currently view the 170–200 range as the most likely trading band.

Why Hold Big Tech? Why Does the “TANMAMG” Portfolio Consistently Beat the Market?

An equal-weighted portfolio of the Magnificent Seven (“TANMAMG”), rebalanced quarterly, has significantly outperformed $NASDAQ(.IXIC)$ since 2021. Total return reached 252.06%, versus 103.42% for $Invesco QQQ(QQQ)$ —an excess return of 148.63%.

Would you focus on 3 key sectors?

Is Mag 7 still your pick in 2026?

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