Goldman Sachs sees $2.6 trillion AI agent commerce opportunity, names 18 winners

By Zhitong Finance

Goldman Sachs' latest research report on AI agent commerce reveals that new business models driven by Meta Muse and OpenAI Astra—termed "Agentic Commerce"—are poised to fuel a new wave of e-commerce penetration, significantly expanding and redistributing commercial value across product discovery, digital advertising, digital payments, and online transaction security.

As consumer-facing personal agents like Muse increasingly handle product searches, price comparisons, and purchases on behalf of users, the competitive focus in the agent space is shifting toward who can capture consumer purchase intent and convert it into trusted, executable transactions.

Goldman Sachs estimates that, based on current consumption levels, AI agent commerce in the U.S. could address a potential annual consumer spending pool of approximately $2.6 trillion over the next few years, covering retail and service categories with high adoption likelihood. Growth opportunities primarily stem from agents gradually taking over existing shopping expenditures.

Key Findings: A $2.6 Trillion Addressable Market

Goldman Sachs analysts have identified approximately $2.6 trillion in existing U.S. consumer spending that falls into categories readily adoptable for agent commerce. The firm highlights several beneficiary groups:

• Consumer entry platforms: Meta (Facebook & Instagram parent), Alphabet (Google parent)
• Retail and merchant infrastructure: Amazon, Walmart, and Shopify
• Payment and security providers: Visa, Mastercard, Cloudflare, and key identity/risk management services

The report lists 18 publicly listed companies expected to benefit from this $2.6 trillion consumption migration theme. (Note: Ratings and 12-month price targets are as of the report date; reference stock prices as of September 23, 2026, in USD; potential upside/downside calculated against that reference price.)

For global equity investors, opportunities span both "transaction acquisition" and "transaction security" ends. Goldman Sachs emphasizes this is a structural shift unfolding over 3–5+ years, with broader adoption potentially taking longer. Companies that can sustainably benefit must combine user distribution, consumer trust, merchant participation, and transaction execution capabilities.

The deployment of Muse and Astra's multi-step execution capabilities provides the technological foundation for this trend. Meanwhile, sustained strong demand for AI infrastructure hardware—CPUs, storage, and data centers—represents a long-term, trillion-dollar positive impact on the AI computing chain as applications scale.

Six Pathways Reshaping Online Commerce Profit Distribution

1. Agent Commerce First Captures Existing Spending, Then Drives E-Commerce Growth

Goldman Sachs notes that agent commerce will initially target existing consumer spending before reducing transaction friction to boost e-commerce growth. Third-party surveys cited by Goldman show:

• 44% of online shoppers already use AI for product discovery
• 42% use AI for price comparison
• Only 16% use AI to complete purchases

This gap between product research and actual transactions indicates significant room for development.

Goldman categorizes U.S. retail and service spending by adoption likelihood:

| Category | Spending Pool | |----------|--------------| | High likelihood | ~$2.6 trillion | | High + Medium likelihood | ~$12.3 trillion (~60% of relevant spending) |

Sensitivity analysis shows that converting just 2.3% of offline face-to-face payments in the high-likelihood category to e-commerce could accelerate e-commerce growth by ~1 percentage point. For the combined high- and medium-likelihood pool, the equivalent conversion rate is ~0.5%. These are migration scenarios from existing channels, not new market revenue.

Adoption speed depends on product subjectivity, transaction complexity, and cost of errors. Standardized, low-risk purchases are likely to be adopted first, while high-end apparel and luxury goods may take longer.

2. Advertising Value Migrates with Purchase Intent; Meta and Alphabet Poised as New Gateways

Recent AI advances are already reducing ad creative costs, optimizing targeting, and improving ad spend returns. Longer-term, consumers may express purchase needs directly within agent interfaces, shifting commercial intent from search results and merchant sites to AI platforms.

Goldman compares this to the desktop-to-mobile internet transition: merchants or brands consistently prioritized by agents may secure more stable repeat-purchase relationships.

Advertising budgets are expected to gradually flow toward AI-native sponsored recommendations, product feeds, and other commercial displays. Meta (with its app ecosystem, user relationships, and Muse) and Alphabet (with Gemini, AI Overviews, AI Mode, and full-stack AI infrastructure) are identified as primary long-term beneficiaries in digital advertising. However, value realization requires solving attribution and performance measurement—brands must know if ads influenced agent choices and final transactions. Goldman notes no material impact on retail media performance from agent commerce yet.

3. More Open Product Discovery Benefits Both SMB Infrastructure Providers and Large Retailers

Goldman's analysts highlight Shopify's opportunity: even as consumer shopping entry points evolve, merchants still need unified management of product catalogs, inventory, payments, orders, fulfillment, and after-sales. Shopify's collaboration with Google on UCP and Agentic Storefronts helps merchants connect to multiple AI channels.

Amazon and Walmart hold advantages in pricing, supply, delivery speed, and transaction reliability—metrics agents can directly measure when comparing products. Both models can benefit simultaneously.

A key divergence lies in customer relationship ownership: Shopify actively integrates new traffic, while Amazon restricts unauthorized shopping agents, reflecting its focus on protecting first-party data, membership relationships, and retail media revenue.

Consumer brands will also diverge. Standardized products face greater price comparison and private-label substitution risk. Personalized, high-engagement brands like Estée Lauder are relatively more resilient. Goldman also favors SharkNinja and Tapestry for their brand-building and technology adaptation capabilities. Brand marketing will increasingly shift toward generative search and agent-recommendation optimization (GEO marketing).

4. Agent Payments Built on Existing Card Networks; Payment Networks Gain Volume and Service Revenue

Goldman believes Visa and Mastercard are well-positioned due to network effects, consumer payment habits, tokenization, and risk management capabilities. If agents split a shopping basket across multiple merchants, transaction counts increase while average order values decline, potentially boosting per-transaction fee revenue. Online transactions also increase demand for identity verification, anti-fraud, and other value-added services.

Payment processing technical barriers will rise, favoring native e-commerce processors like Stripe and Adyen. PayPal's bilateral ecosystem offers near-term strategic value, but its competitive position depends on actual value delivered to consumers and merchants. Financing could benefit Affirm and Klarna: agents can compare financing costs and terms at purchase time, increasing discovery and use of buy-now-pay-later options, though final payment choice remains consumer-driven.

5. Greater Automation Requires Stronger Identity, Authorization, and Liability Frameworks

As transactions become more automated, identity, authorization, and liability determination become essential for commercialization. The technical stack is being built in layers:

• MCP connects data and tools
• UCP, ACP standardize merchant-agent interactions
• Payment protocols handle identity, authorization, and payment credentials

True scale requires proving "which consumer authorized which agent, to buy what, with what budget," and clarifying liability for mistaken purchases, fraud, chargebacks, and returns. Traditional signals (IP addresses, devices, browsing behavior) may lose independent judgment capability when legitimate agents operate via cloud at high speed. This creates new demand for Cloudflare, Akamai, and payment security providers.

Goldman estimates current cybersecurity spending at ~1–2% of U.S. e-commerce revenue, with long-term upside. In information services:

• Equifax covers consumer, merchant, income, and employment verification
• TransUnion specializes in continuous identity linkage and digital network risk
• FICO sees clearer opportunities in automated decisioning and anti-fraud software

Goldman views agent opportunities for the latter three as long-term growth potential, not yet quantified as explicit earnings upside—the key is whether new workflows generate more paid verification and decisioning.

6. Agents Improve Sales Efficiency for Existing Supply, but Unique Supply Still Determines Platform Pricing Power

Citing Live Nation data, Goldman notes:

• ~95% of concerts do not sell out
• Amphitheater ticket sales: ~60–70%
• Theater ticket sales: ~65–75%

Agents matching events by city, time, budget, and personal preferences could improve ticket sales and venue utilization for long-tail events.

Live Nation's Ticketmaster benefits from differentiated ticket supply and venue partnerships. Secondary platforms like StubHub may initially benefit from lower customer acquisition costs but face price transparency, fee competition, and reduced add-on service/ad sales as consumers bypass original pages. Goldman maintains "Buy" ratings on both companies but notes long-term profit distribution depends on whether agent platforms continuously lower distribution costs or capture more transaction value through referral fees, commissions, and display charges.

Every AI-Agent Transaction Adds New AI Inference Workloads

From a technology perspective, Muse and Astra are expected to expand agent usage, but adoption will accelerate gradually across different tasks. Muse already features dedicated cloud virtual machines, browser operations, background persistence, and memory mechanisms. Astra enhances computer operation, software usage, and multi-step professional task execution.

Applied to shopping, a single request may trigger product search, specification verification, inventory checks, price comparison, delivery assessment, identity verification, and payment confirmation. Higher AI inference success rates and lower human intervention costs will make more daily tasks worth delegating to AI, expanding user scale, frequency, and task coverage. However, account connectivity, merchant participation, and liability rules will determine how quickly technical capability translates into real transaction volume.

AI Infrastructure Implications

This inference workload expansion increases demand for:

• GPU/TPU accelerators for model computation
• CPUs for browsers, virtual machines, product searches, database queries, and transaction orchestration
• HBM and server DRAM for model data, context, and concurrent working environments
• Enterprise SSDs for product indexes, task records, and persistent state
• High-speed networks and optical interconnects for distributed data exchange

Nvidia's latest engineering documentation explicitly notes that CPU execution speed affects wait times between agent model calls, and discusses tiered context caching across HBM, DRAM, local NVMe, and remote storage. This provides strong engineering rationale for long-term demand for:

• x86 CPUs (AMD, Intel)
• Arm-based high-performance CPUs
• Storage chip components
• Data center high-speed optical interconnects
• Power supply chains

Storage: The Clear Bottleneck

Market research firm TrendForce estimates:

| Component | Price Increase | |-----------|----------------| | Server DRAM (2026 cumulative) | ~270% | | Enterprise SSDs (2026 cumulative) | ~235% | | HBM (2027) | 70–140% |

These figures reflect the combined effect of AI computing expansion and storage price increases. TrendForce estimates that DRAM and NAND together will rise from 47% of major cloud service providers' capital expenditure in 2026 to 68% in 2027, driven by both volume growth and price increases.