What does ChatGPT’s Agentic pivot mean for internet stocks? By Investing.com

Morgan Stanley highlights ChatGPT's pivot to an agentic model and finds ~40–55% of users use AI to research/compare products and ~20–40% have made purchases based on AI recommendations. The shift routes discovery to merchant sites rather than in-platform transactions, which may preserve search platforms' advantages while directing incremental traffic to large e-commerce and online travel players. Early estimates suggest ChatGPT could charge single-digit commission rates — well below current paid-search customer-acquisition costs — potentially lowering merchants' marketing spend if the model scales.

Analysis

The pivot toward agentic AI that routes consumers to merchant sites is a supply-side reallocation of marketing spend: if merchants can pay a few-percent commission for delivered demand instead of absorbing high CPC/CPA from search, incremental gross margin for large retailers with low incremental logistics cost (WMT, AMZN) can expand materially within 6–12 months. Expect a 1–3% EBITDA uplift for scale retailers if AI-sourced orders displace paid-search driven orders and carries similar conversion quality, because the advertising line is cash-flow fungible and often a larger share of e-commerce unit economics than incremental fulfillment costs.

Second-order winners include logistics/3PL partners and niche marketplaces that can rapidly expose catalog data to agents; losers are auction-based ad channels and specialist affiliate networks whose revenue per order is highest today. Platforms that monetize data (search, social) face both margin pressure and strategic responses — raising ad prices, tighter API controls, or preferential placement for partners — any of which can blunt the merchant margin tailwind and create a 3–9 month tactical volatility window.

Key risks and timing: adoption and scale are not instantaneous — measurable merchant margin and traffic shifts should show in public results and ad-revenue metrics over 2–4 quarters. Tail risks that would reverse the trend include regulatory limits on referral fees/commissioning, AI credibility failures that reduce conversion rates, or ad-platform countermeasures (algorithmic de-prioritization, premium auctions) that raise merchants’ effective CAC back to prior levels within months.

Key Decisions for Investors

• WMT — Overweight (6–12 months): buy WMT equity to capture margin tailwind from platform-driven direct traffic; target 15–20% upside if merchant CAC falls by 2–4% of sales, downside limited to 5–7% on execution risk. Scale position into any pullbacks tied to broader retail weakness.
• AMZN — Tactical options bull-call spread (3–9 months): buy near-dated/quarterly call / sell 1–2 strikes higher to finance premium (e.g., buy 6–9 month call, sell higher strike) to capture accelerated ad + retail revenue; skew reward (2:1) if AI routing lifts low-margin ad revenue while limiting theta decay risk. Size smaller than equity exposure given macro sensitivity.
• Pair trade — Long AMZN / Short GOOG (6–12 months): go long AMZN to capture share gains in direct merchant referrals and short GOOG to hedge ad-revenue risk from AI routing; aim for asymmetric upside from AMZN’s diversified monetization vs GOOG’s ad dependence, monitor quarterly ad-revenue prints and CPC trends as exit triggers.
• MS — Buy (3–6 months, small tactical): add a modest position in MS to play the bank’s advisory/wealth-insights franchise potentially benefiting from platform-driven merchant M&A and fintech partnerships; keep position size conservative due to regulatory and market-structure risks.