Alok Sharma

Sharma states, "Semiconductors are huge winners... material stocks are huge winners." Wong adds, "We continue to bang the table on... the materials sector... copper and copper mining." The AI "scare trade" assumes massive displacement, but that displacement requires massive compute. This creates a bifurcated market: software/labor loses, but the physical infrastructure (chips) and the power grid inputs (copper) required to run the AI explode in demand. LONG the "Pick and Shovel" plays of the AI economy. A recession caused by white-collar job losses could dampen overall demand for energy and materials.

Sharma argues that by 2028, AI agents will handle most consumer tasks, bypassing apps and intermediaries. He notes, "Intermediary sectors... have real risk." Business models based on "friction with a friendly face" (food delivery, ride-hailing, retail banking UIs) lose their moat when an AI agent executes the task directly for the consumer at the lowest price. This leads to margin compression and volume loss for aggregators. SHORT intermediaries and software companies that rely on seat-based pricing or app engagement. Regulatory intervention to tax AI or protect jobs could delay this transition; consumer adoption of agents may be slower than the 2-year timeline.

Sharma argues that by 2028, AI agents will handle most consumer tasks, bypassing apps and intermediaries. He notes, "Intermediary sectors... have real risk." Business models based on "friction with a friendly face" (food delivery, ride-hailing, retail banking UIs) lose their moat when an AI agent executes the task directly for the consumer at the lowest price. This leads to margin compression and volume loss for aggregators. SHORT intermediaries and software companies that rely on seat-based pricing or app engagement. Regulatory intervention to tax AI or protect jobs could delay this transition; consumer adoption of agents may be slower than the 2-year timeline.

Sharma states, "Semiconductors are huge winners... material stocks are huge winners." Wong adds, "We continue to bang the table on... the materials sector... copper and copper mining." The AI "scare trade" assumes massive displacement, but that displacement requires massive compute. This creates a bifurcated market: software/labor loses, but the physical infrastructure (chips) and the power grid inputs (copper) required to run the AI explode in demand. LONG the "Pick and Shovel" plays of the AI economy. A recession caused by white-collar job losses could dampen overall demand for energy and materials.

Sharma argues that by 2028, AI agents will handle most consumer tasks, bypassing apps and intermediaries. He notes, "Intermediary sectors... have real risk." Business models based on "friction with a friendly face" (food delivery, ride-hailing, retail banking UIs) lose their moat when an AI agent executes the task directly for the consumer at the lowest price. This leads to margin compression and volume loss for aggregators. SHORT intermediaries and software companies that rely on seat-based pricing or app engagement. Regulatory intervention to tax AI or protect jobs could delay this transition; consumer adoption of agents may be slower than the 2-year timeline.

Sharma states, "Semiconductors are huge winners... material stocks are huge winners." Wong adds, "We continue to bang the table on... the materials sector... copper and copper mining." The AI "scare trade" assumes massive displacement, but that displacement requires massive compute. This creates a bifurcated market: software/labor loses, but the physical infrastructure (chips) and the power grid inputs (copper) required to run the AI explode in demand. LONG the "Pick and Shovel" plays of the AI economy. A recession caused by white-collar job losses could dampen overall demand for energy and materials.

Sharma argues that by 2028, AI agents will handle most consumer tasks, bypassing apps and intermediaries. He notes, "Intermediary sectors... have real risk." Business models based on "friction with a friendly face" (food delivery, ride-hailing, retail banking UIs) lose their moat when an AI agent executes the task directly for the consumer at the lowest price. This leads to margin compression and volume loss for aggregators. SHORT intermediaries and software companies that rely on seat-based pricing or app engagement. Regulatory intervention to tax AI or protect jobs could delay this transition; consumer adoption of agents may be slower than the 2-year timeline.