Understanding the AI and Commodity Supercycle in 2026

The market currently trades in a broad consolidation with an upward‑sloping base, so disciplined traders focus on clearly defined levels and themes rather than chasing noise. In this context, the AI and commodity supercycle becomes the central lens for interpreting flows, because it links index behavior, sector rotation, and thematic risk. The framework below turns that environment into a structured playbook built around three pillars: index levels and gamma, a mid‑cycle AI and hardware trend, and a commodity‑driven scarcity thesis with satellites in robotics, drones, EVs, and fintech.

Market Setup and the AI and Commodity Supercycle

Price action clusters around the 50‑day moving average on SPY and QQQ, which acts as the primary trend filter for risk allocation. QQQ trades more bearishly and repeatedly tests lower support, then fails at resistance, which reflects uncertainty in large‑cap tech and argues for selective rather than blanket exposure. Because holiday volume is low, signal quality drops, so traders treat sharp moves with caution until institutional participation fully returns. The bias remains long, but conviction depends on whether indices break decisively above recent ranges or roll over from the 50‑day, because those breaks will also shape how traders position for the AI and commodity supercycle.

Gamma exposure defines a second layer of structure. A major upside magnet appears near the 693 strike, while a downside node clusters around 680–675 and creates a tactical band for short‑term trades. The base case assumes a sweep toward 680 that may undercut to roughly 677, which sits near the SPY 50‑day, and possibly 675. After that, the setup favors a rebound toward 690 if 680 holds and gamma builds at that level. A lower‑probability path involves an M‑top that could drive price toward 660, but that scenario only gains weight if the 50‑day fails on convincing volume. Early in the week, the gamma profile points to an inside‑day structure with a retest of 680 and upside potential toward 686–687, which then becomes the operative intraday pivot zone.

Two Pillars of the AI and Commodity Supercycle

The strategic view rests on two complementary pillars that reflect how capital and capacity flow through the current cycle. First, the AI trade remains in mid‑innings rather than late bubble territory, with a focus on hardware and supply‑chain bottlenecks rather than only front‑end software. NVIDIA sits at the center of this trend in a constructive reaccumulation phase after a parabolic run. It forms a “W”‑type base around a key support pivot that often precedes renewed expansion in leading growth stocks. In that sense, NVIDIA acts as a live barometer for the health of the AI and commodity supercycle on the tech side.

The second pillar centers on commodities and miners, where copper, aluminum, steel, met coal, and rare earths benefit from the combined impact of AI data centers, energy transition, and classic infrastructure buildout. In other words, the AI and commodity supercycle appears as one connected system, because the same buildout that drives GPU demand also pulls hard on metals, power, and grid capacity. This is why the commodity leg of the AI and commodity supercycle cannot be separated from the technology leg.

In practice, this dual‑pillar approach turns into tangible allocation choices. Roughly one‑third of capital tilts toward AI and hardware, another third toward commodities and precious metals, and the remainder divides between cash, hedges, financials, robotics, drones, and EV exposures. This structure aligns with external analysis that describes an AI‑driven supercycle in energy‑transition and data‑center metals, where copper, lithium, nickel, and rare earths face persistent supply constraints just as long‑term demand accelerates. As a result, the AI and commodity supercycle becomes the backbone of the thesis: the portfolio is built to capture upside during expansion while maintaining inflation and crisis hedges through metals and miners.

AI and Hardware: From GPUs to Memory

The AI pillar begins with large‑scale compute but quickly extends into second‑wave and third‑wave hardware plays. NVIDIA remains the flagship and currently consolidates in a “W” reaccumulation structure after a parabolic rise, with expectations that the trend can resume provided buyers defend the base. Around this anchor, the strategy builds an AI basket that reaches beyond headline chips into packaging, testing, and infrastructure names that solve real bottlenecks. Amkor, Pterodine, and KIC exemplify this approach because each shows multi‑year base breakouts and classic cup‑and‑handle patterns, with targets that can stretch several multiples higher over two to three years if the cycle persists.

Memory and storage sit at the next step of the stack. Names such as Micron, Western Digital, and legacy Sandisk‑linked structures display strong technical uptrends and high‑volume breakouts that fit an AI‑driven memory supercycle. High‑bandwidth memory capacity remains tight, and many analyses note that HBM and advanced DRAM will likely stay supply‑constrained into 2026 as AI training and inference workloads expand. That backdrop pushes memory from the periphery to a central role in any serious AI hardware basket and, therefore, in the AI and commodity supercycle itself, where compute and capacity must rise together.

The portfolio framework, therefore, considers trimming part of the commodities sleeve to fund incremental allocations to memory when breakouts confirm, while still keeping overall commodity exposure substantial to respect the scarcity thesis. In addition, sector rotation data reinforces this hardware tilt. Broad research notes that chips, accelerators, and related infrastructure currently lead software on both returns and earnings revisions, which justifies favoring hardware and semiconductors until software charts show clear and sustained breakouts.

AI Hardware Buckets in the AI and Commodity Supercycle

Key Takeaways for this Bucket:

• Focus on structural bottlenecks, not only headline GPUs.
• In addition, use bases, volume, and relative strength to time entries.
• Treat memory as a core high‑growth sleeve, not an afterthought, because the AI and commodity supercycle relies on both compute and supporting capacity.

Commodities, Metals, and Precious‑Metal Hedges

The scarcity pillar centers on metals and miners whose supply growth cannot keep pace with AI and energy‑transition demand. Copper and copper miners, including Freeport‑McMoRan and ERO, have already moved out of multi‑year bases, which reflects expectations that AI data centers and electrification will require significantly more copper per unit of GDP. Aluminum and steel producers such as AA, CX, STLD, and CLF show similar long‑term breakout patterns. These moves are supported by estimates that next‑generation data centers and grid upgrades carry much higher steel and aluminum content than earlier vintages. Met coal names like Peabody and Warrior Met Coal complete the chain by supplying essential feedstock for steel production.

The metals theme also incorporates precious metals and rare earths as both structural drivers and hedges. The S&P Metals and Mining index breaks out of a five‑year base with bullish structure, while silver miners such as FSM, HL, and EXK, plus gold names like HMY, consolidate near range highs after partial profit‑taking. At the same time, rare earth and uranium plays like MP, UU, TMC, and REMX show tightening consolidations and sustained accumulation, which is consistent with external work that cites critical metals as key enablers of both AI hardware and energy systems.

Precious‑metals‑to‑SPY ratio charts trace a long cup‑and‑handle since 2012, which implies multi‑year outperformance of metals versus broad equities and supports an approach that treats precious metals as a core holding, not a tactical trade. In practical terms, dollar‑cost averaging across metals and miners, combined with the selective use of royalty companies, helps reduce volatility while preserving exposure to the underlying scarcity trend that powers the AI and commodity supercycle.

Commodity and Metals Buckets at a Glance

Screening Checklist for this Sleeve:

• Look for multi‑year bases with expanding volume on breakouts.
• Ensure alignment with structural demand from AI, grid expansion, and energy transition.
• In addition, confirm accumulation footprints in weekly and monthly charts.

Satellites Around the AI and Commodity Supercycle

Around the two main pillars sits a ring of thematic satellites that diversify exposure without diluting the core thesis, and they help express niche angles of the AI and commodity supercycle. Robotics and automation names such as SIM, VPG, and ISRG represent automation in logistics, sensing, and medical procedures. They typically trade in long‑term bases that suggest institutional accumulation and gradual adoption.

Drones and defense electronics—including ONDS, LPTH, KOD, and MRCY—stand out as a higher‑beta complex, with ONDS in particular breaking out from a key neckline around 10 on strong volume, which makes it a prime candidate for near‑term trading focus. These stocks marry defense, surveillance, and AI‑enabled hardware, and they support both cyclical and secular arguments for continued demand.

EV‑related positions fit into this satellite ring and are framed more as robotics and software‑rich platforms than traditional auto manufacturers. Tesla currently trades weakly but still holds above important support levels near the high‑300s, with a long‑term upside scenario toward 700‑plus over a multi‑year horizon if execution remains on track. Rivian exhibits a constructive accumulation pattern after moving from the low‑teens into the low‑twenties, with potential buy zones guided by consolidations in the high‑teens.

Financial and credit plays reinforce geographic and macro diversification. OPFI and Dave show high‑and‑tight flags tied to easing credit conditions, while NU, Aval, and CFR represent Latin‑American fintech and regional bank plays that benefit from structural growth in digital banking and regional economies. Independent coverage of NU underscores rapid user growth, improving profitability, and expanding product penetration, which supports its inclusion as a high‑conviction fintech name in a small but meaningful allocation.

Thematic Satellites at a Glance

These satellite themes add targeted growth and diversification around the AI and commodity supercycle while keeping individual position sizes contained.

Key Risk Controls for Satellites:

• Keep individual positions small, generally under 3%.
• In addition, demand tight technical structures such as flags and bases before entry.
• Trim into vertical spikes, and avoid averaging down on broken charts.

Portfolio Structure and Risk Management

Bringing these elements together, the approach builds a balanced yet high‑conviction portfolio that is designed for both growth and resilience. A representative allocation might look as follows:

The portfolio blends AI tech, commodity scarcity, cash, and satellites into a single map that stays anchored to the AI and commodity supercycle.

The risk framework hinges on three elements. First, the 50‑day moving average on SPY and QQQ functions as a trend gate. Long exposure stays aggressive only while price holds above that line, with undercuts toward 677 on SPY treated as controlled entry zones if gamma and breadth support a rebound. Second, gamma bands around 680, 675, and 693 define near‑term trading corridors and guide expectations about sweeps, inside days, and potential breakouts. Third, a standing 20 percent allocation to cash and option hedges allows fast shifts from offense to defense, so breakdown risk in the AI and commodity supercycle does not erase prior gains.

Bullet‑point risk checklist:

• Respect the 50‑day moving average on major indices as a hard line.
• Adjust aggression up or down based on gamma positioning and volume.
• Size core positions larger than satellites, and avoid over‑weighting any single high‑beta name.

If you liked this post make sure to share it!