Goldman Sachs: AI Agent Commerce Could Tap $2.6 Trillion in US Consumer Spending, Names 18 Potential Winners

Key findings from Goldman Sachs' latest research report on AI agent-based commerce

Overview: A New Paradigm for E-Commerce

Goldman Sachs has released a research report on AI agent-based commerce, highlighting 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. These models are expected to significantly expand and redistribute commercial value across product discovery, digital advertising ecosystems, digital payments, and online transaction security.

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

The $2.6 Trillion Opportunity

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 — covering categories with high adoption likelihood in retail and services. Growth is expected to come primarily from agents gradually taking over existing shopping expenditures.

Key Beneficiaries Identified

Goldman Sachs identified 18 publicly listed companies positioned to benefit from this $2.6 trillion consumption migration theme. Key categories include:

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

Note: Ratings and 12-month price targets in the report are as of the report date. Reference stock prices are as of September 23, 2026, in USD. Potential upside/downside is calculated based on that reference price.

Six Pathways Reshaping Online Commerce Profit Distribution

1. Capturing Existing Spending First, Then Driving E-Commerce Growth

Goldman Sachs states that agent commerce will first capture existing consumer spending before driving e-commerce growth through reduced transaction friction.

Key data points:

• ~44% of online shoppers already use AI for product discovery
• 42% of consumers use AI for price comparison
• Only 16% use AI to complete purchases — indicating significant room for growth between research and transaction

Spending classification:

• High-adoption likelihood categories: ~$2.6 trillion
• High + medium likelihood categories combined: ~$12.3 trillion (~60% of relevant U.S. spending)

Sensitivity analysis:

• Converting ~2.3% of offline face-to-face payments in high-likelihood categories to e-commerce could accelerate e-commerce growth by ~1 percentage point
• Expanding the denominator to high + medium categories, the equivalent conversion rate is ~0.5%

These are migration scenario estimates from existing consumption channels and should not be interpreted as $12.3 trillion in new market revenue.

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

2. Advertising Value Migrates with Purchase Intent

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

Goldman Sachs' view: This parallels the shift from desktop to mobile internet. Merchants or brands consistently prioritized by agents may gain more stable repeat-purchase relationships.

Key beneficiaries in digital advertising:

• Meta: Leveraging app ecosystem, user relationships, and Muse
• Alphabet: Leveraging Gemini, AI Overviews, AI Mode, and full-stack AI infrastructure

Challenges: Attribution and performance measurement remain critical — brands must know if ads influenced agent choices and final transactions. Goldman Sachs notes that agent commerce has not yet materially impacted retail media performance.

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

Shopify's opportunity: Even as consumer entry points change, 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's advantages: Price, supply, delivery speed, and transaction reliability — metrics that agents can directly measure when comparing products.

Key divergence: Customer relationship ownership. Shopify actively integrates new traffic, while Amazon restricts unauthorized shopping agents — reflecting the latter's focus on protecting first-party data, membership relationships, and retail media revenue.

Brand differentiation:

• Standardized products face greater price comparison and private-label substitution risk
• Personalized, high-engagement brands (e.g., Estée Lauder) show more resilience
• Goldman Sachs also favors SharkNinja and Tapestry for their brand-building and technology adaptation capabilities

Brand marketing will increasingly shift toward GEO (Generative Engine Optimization) — optimizing for generative search and agent recommendations.

4. Agent Payments Built on Existing Card Networks

Goldman Sachs believes Visa and Mastercard are well-positioned due to network effects, consumer payment habits, tokenization, and risk management capabilities.

Potential impacts:

• If agents split a shopping basket across multiple merchants, transaction count increases and average order value decreases — potentially boosting per-transaction fee revenue
• Online transactions increase demand for identity verification, anti-fraud, and other value-added services

Payment processing:

• Higher technical barriers favor native e-commerce processors like Stripe and Adyen
• PayPal's bilateral ecosystem has near-term strategic value, but its competitive position depends on actual value delivered to consumers and merchants

Financing: Affirm and Klarna may benefit as agents compare financing costs and terms at purchase time, increasing discovery and use of BNPL (Buy Now, Pay Later) options — though final payment choice remains with consumers.

5. Automation Demands Stronger Identity, Authorization, and Liability Frameworks

As transactions become more automated, identity, authorization, and liability determination become essential for commercialization.

Technology stack under construction:

• MCP: Connects data and tools
• UCP, ACP: Standardize merchant-agent commercial interactions
• Payment protocols: Handle identity, authorization, and payment credentials

Key requirement for scale: Proving "which consumer authorized which agent, to buy what, with what budget" — and clarifying liability for erroneous purchases, fraud, chargebacks, and returns.

New demand drivers:

• Traditional IP addresses, device, and browsing signals may lose reliability as legitimate agents operate via cloud at high speed
• Cloudflare, Akamai, and payment security providers face new demand

Goldman Sachs estimates: Cybersecurity spending is currently ~1–2% of U.S. e-commerce revenue, with long-term upside.

Information services:

• Equifax: Covers consumer, merchant, income, and employment verification
• TransUnion: Specializes in continuous identity linkage and digital network risk
• FICO: 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 Can Improve Sales Efficiency for Existing Supply, but Unique Supply Still Determines Platform Pricing Power

Live Nation data cited by Goldman Sachs:

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

If agents can match events based on city, time, budget, and personal preferences, they could improve ticket sales for long-tail events and venue utilization.

Platform dynamics:

• Ticketmaster (Live Nation): Better positioned due to differentiated ticket supply and venue partnerships
• StubHub and other secondary platforms: May 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 Sachs maintains a "Buy" rating on both companies but notes that long-term profit distribution depends on whether agent platforms continuously lower distribution costs or gradually capture more transaction value through referral fees, commissions, and display charges.

Every AI Agent Transaction Adds New Inference Compute Workloads

From a technology perspective, Muse and Astra are expected to expand agent usage, but adoption will accelerate gradually across different tasks.

Current capabilities:

• Muse: Dedicated cloud virtual machine, browser operations, background persistence, memory mechanisms
• Astra: Enhanced computer operation, software usage, and multi-step professional task execution

Shopping use case: A single request may trigger product search, specification verification, inventory check, 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, usage frequency, and task coverage simultaneously.

AI Infrastructure Implications

This inference workload expansion increases demand for:

• GPU/TPU accelerators: Model computation
• CPU: Browser operations, virtual machines, product search, database queries, transaction orchestration
• HBM & server DRAM: Model data, context, concurrent working environments
• Enterprise SSDs: Product indexes, task records, persistent state
• High-speed networks & optical interconnects: Distributed data exchange

Nvidia's latest engineering documentation explicitly notes that CPU execution speed affects wait times between agent model calls, and discusses hierarchical context caching across HBM, DRAM, local NVMe, and remote storage.

This provides strong engineering rationale for long-term demand in:

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

Storage: The Clearest Supply Bottleneck

TrendForce data cited by Goldman Sachs:

• 2026: Server DRAM contract prices up ~270% YoY; enterprise SSD prices up ~235%
• 2027: HBM contract prices may rise 70–140%

Capital expenditure impact:

• DRAM + NAND as a share of major cloud service providers' CapEx: projected to rise from 47% in 2026 to 68% in 2027 — driven by both volume growth and price increases.

Source: Goldman Sachs AI Agent Commerce Research Report