The Two-Layer Problem: Why Most Brands Will Be Invisible to AI Shoppers by 2027

A user opens ChatGPT and asks, “I need crossfit shoes with flexible sole for the “Murth” WOD run and stable support for the heavy “DT” lifts, budget around $150.”

ChatGPT doesn’t search the web like Google would. It doesn’t match keywords. Instead it infers the underlying intent (crossfit WOD training + performance + budget constraint), queries structured product feeds from merchants who’ve implemented the Agentic Commerce Protocol, scores products on alignment with those specific goals, and recommends the top three options with explanations of why each fits.

The entire interaction takes 15 seconds. No tab-switching. No comparing reviews across ten sites. No uncertainty about whether this shoe actually addresses user needs or just mentions them in the description.

For the user, this is magic. For the three brands that got recommended, this is the new front door to commerce. For everyone else - the 47 other training shoe brands that could serve this customer but weren’t surfaced - they might as well not exist.

This is the defining challenge of agentic commerce: visibility isn’t about ranking anymore. It’s about being discoverable through two fundamentally different mechanisms, and most brands are prepared for neither.

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The Numbers

Before we explain the problem, let’s establish how big it is:

800 million weekly active users on ChatGPT alone (October 2025). That’s 10% of the global adult population in a platform that launched less than three years ago.

51% of Gen Z now start product research in LLM platforms like ChatGPT or Gemini bypassing Google Search entirely.

4,700% year-over-year increase in AI agent traffic to e-commerce sites (July 2025).

$1-5 trillion projected market size for agentic commerce by 2030.

And here’s the part that should terrify unprepared brands: 25% of consumers have already made AI-assisted purchases. Not “might consider it.” Already done it.

The channel is real. The behavior change is measurable. The infrastructure is deploying across OpenAI (ACP), Google (UCP), Amazon (Rufus), Perplexity, and Microsoft.

The only question is: when users ask AI agents for product recommendations, will your products be among the chosen few?

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What Changed: From One Discovery Mechanism to Two

For 25 years, product discovery followed a single pattern:

Merchant publishes page → Crawler discovers URL → Parser extracts signals → 
Index stores data → User searches → Ranker orders results → User clicks

This was inference-based discovery: crawlers didn’t “know” what was on your page - they guessed. They parsed HTML, extracted microdata (Schema.org), read meta tags, and used heuristics to infer product information, pricing, and availability.

It was imperfect. It was slow (hours to days lag). But it was universal - it worked with zero cooperation from merchants.

That world still exists. Google Search, organic rankings, SEO - none of that disappeared overnight.

But alongside it, a second mechanism emerged. And it operates on completely different principles.

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The Two-Layer Reality

As of 2025-2026, agentic commerce discovery operates through two parallel systems:

Layer 1: Inference-Based Discovery (The Old Web, Still Active)

How it works:

• LLMs crawl web pages like traditional search engines
• Parse HTML, Schema.org markup, and visible content
• Infer product attributes, pricing, availability from unstructured data
• Match against user queries using semantic similarity
• Return recommendations based on inferred relevance

Who uses this:

• ChatGPT Shopping Research (for non-protocol merchants)
• Google AI Mode (supplementing protocol data)
• Perplexity (web research layer)
• Any LLM doing “web browsing” for product discovery

Coverage: Universal - works for any product with a public webpage

Accuracy: Approximate - subject to inference errors, staleness, incomplete data

Example: User asks for “best TV for bright rooms.” LLM reads product pages, infers that “3000 nits brightness” means “good for bright rooms,” and recommends based on that inference.

Layer 2: Protocol-Based Discovery (The New Infrastructure)

How it works:

• Merchants implement structured API endpoints (ACP or UCP)
• Submit product feeds with explicit, machine-readable data
• Agents query these feeds with structured parameters
• Real-time inventory, pricing, and variants - not scraped, declared
• Products with enable_checkout: true participate in instant checkout flows

Who uses this:

• OpenAI Shopping (via Agentic Commerce Protocol)
• Google UCP (Universal Commerce Protocol)
• Shopify Agentic Storefronts (syndicates to multiple platforms)
• Any platform implementing ACP/UCP standards

Coverage: Selective - only merchants who implement the protocol

Accuracy: Exact - real-time, structured, validated

Example: User asks for “best TV for bright rooms.” Agent calls GET /products?query=tv&filters={"use_case":"bright_room_viewing","price_max":2000}, receives structured JSON with products explicitly tagged for bright-room use cases, and recommends based on exact matches.

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The Critical Difference

Here’s what brands often miss: these aren’t just two paths to the same outcome. They’re fundamentally different discovery paradigms.

The trap: Most brands are optimizing for Layer 1 (because it resembles traditional SEO), while the platforms with the most users (ChatGPT, Google AI Mode) are rapidly scaling Layer 2.

The opportunity: The brands that master both layers will dominate AI-driven discovery for the next decade.

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Why Layer 1 Is Harder Than It Looks

You might think, “We’re already doing SEO. We have Schema.org markup. We’re fine for Layer 1.”

Not quite.

Traditional SEO optimizes for keyword matching. Agentic commerce requires intent alignment.

Let’s illustrate the difference:

Scenario: TV for Bright Living Room

User query:
“I need a TV for my bright living room. Sunlight hits it most of the day.”

Traditional SEO optimization:
Product page includes keywords: “TV,” “bright room,” “daylight,” “living room”
Schema.org markup: <meta name="description" content="65-inch 4K QLED TV, 3000 nits brightness">

What happens:
LLM crawls the page, sees keywords match, but has to infer that “3000 nits” solves the “bright room” problem. If the copy doesn’t explicitly connect those dots, the LLM might not make the connection - or might rank another product higher that does make it explicit.

Intent-aligned optimization:
Product description: “Combat glare in bright living rooms. Clear picture without closing blinds. The 3000-nit display ensures vivid colors even when sunlight hits the screen directly.”

Intentionality-mapped attributes (machine-readable):

{
  "capabilities_enabled": ["glare_reduction", "daytime_viewing", "bright_room_performance"],
  "goals_served": ["enjoyable_viewing_despite_ambient_light"],
  "use_cases": ["south_facing_living_room", "office_with_large_windows"]
}

What happens:
LLM directly matches user intent (“bright room problem”) to product capability (“glare reduction, bright room performance”). No inference gap. Explicit, legible, confident recommendation.

The difference: In Layer 1, you’re not just competing on having the information, you’re competing on how legibly that information maps to human intent.

Most product descriptions are written for human readers scanning bullet points. LLMs need structured capability-to-goal mappings. The gap between these two is where most brands lose.

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Why Layer 2 Is Where the Game Is Won

Layer 2 is where the majority of high-intent, high-conversion shopping is heading.

Why? Because it solves problems that Layer 1 can’t:

1. Real-Time Everything

Layer 1: Inventory and pricing data is hours or days old (crawl lag)
Layer 2: Real-time API queries - agent knows right now if it’s in stock

When a user asks, “I need this by Friday,” Layer 2 merchants can participate. Layer 1 merchants might get recommended, only for the user to discover it’s backordered when they click through. Transaction abandonment. Lost sale.

2. Instant Checkout

Layer 1: Agent recommends → user clicks → lands on merchant site → fills cart → enters shipping → enters payment → completes order (5+ steps, high drop-off)

Layer 2: Agent recommends → user confirms → checkout happens in-chat with saved credentials → order placed (2 steps, minimal friction)

Conversion rates for in-agent checkout are 2-3x higher than traditional click-through flows.

3. Structured Intent Matching

Layer 1: LLM reads your description, infers capabilities, matches against user intent with some uncertainty

Layer 2: You’ve explicitly declared "use_case": "bright_room_viewing" in your product feed. When the agent queries for products matching that use case, you’re a deterministic match, not a probabilistic inference.

4. Multi-Constraint Queries

Layer 1: User says, “I need a laptop under $1500, good for video editing, quiet fans, USB-C charging, arrives by Wednesday.”
LLM tries to infer which products meet all five constraints by reading descriptions. Error-prone.

Layer 2: Agent calls:

GET /products?
  category=laptops&
  price_max=1500&
  capabilities=video_editing&
  attributes=quiet_operation,usb_c_charging&
  delivery_by=2026-01-29

Returns only products that explicitly meet all constraints. No guesswork.

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The Visibility Crisis No One Is Talking About

Here’s the scenario that’s playing out right now for thousands of brands:

You’re a mid-market running shoe brand.

• Your SEO is solid. You rank on page 1 for “best running shoes for overpronation.”
• Your product pages have Schema.org markup.
• You’re getting steady organic traffic from Google Search.

Then this happens:

A user opens ChatGPT and asks: “I need running shoes for marathon training, recovering from plantar fasciitis.”

ChatGPT doesn’t search Google. It queries product feeds from merchants who’ve implemented ACP. Your products aren’t in any feed - you haven’t integrated with OpenAI’s protocol.

So ChatGPT recommends three competitors: Nike (via Shopify Agentic Storefronts), Brooks (direct ACP integration), and a D2C brand (also via Shopify).

The user never sees your brand. Not because your product isn’t good. Not because your price isn’t competitive. Because you weren’t in the protocol layer where the agent was actually looking.

Meanwhile, your web analytics show a mysterious trend: organic traffic from Google is steady, but overall conversions are declining. Why? Because the high-intent users - the ones who would have Googled and found you - are now starting their journey in ChatGPT. And ChatGPT never sent them your way.

This is happening right now to brands in electronics, athletic footwear, home goods, beauty, supplements, and every other category where LLM shopping has traction.

The crisis: You can’t see it in your analytics because it shows up as non-traffic - the users who would have found you but never did.

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Why Existing Solutions Fall Short

Brands are scrambling to respond. Some are investing in “AI SEO” tools. Others are implementing product feeds for OpenAI or Google. But most solutions are incomplete:

Problem 1: Single-Layer Solutions

SEO tools optimize for Layer 1 (web crawling) but ignore Layer 2 (protocols)
Feed management tools handle Layer 2 (protocols) but assume Layer 1 will “just work”

The reality: You need both. Optimizing for one while ignoring the other leaves you vulnerable.

Problem 2: No Feedback Loop

Most tools are one-way:

• “Here’s your discoverability score” (but no way to test if changes actually work)
• “Submit your product feed” (but no visibility into whether you’re getting recommended)
• “Optimize your descriptions” (but no measurement of real impact)

What’s missing: A closed-loop simulation where you can test, see results, understand gaps, optimize, and re-test - before deploying changes to production.

Problem 3: No Intent Framework

Traditional optimization asks: “Does this product description contain the right keywords?”

What it should ask: “Does this product data make clear what human goals it serves, what capabilities it enables, and what outcomes it produces?”

The shift from keyword-matching to intent-alignment requires structured thinking about intentionality - and most tools weren’t built for that.

Problem 4: No Competitive Intelligence

Brands are flying blind:

• “How often am I getting recommended vs. competitors?”
• “What are winning products doing differently?”
• “Which intent patterns am I missing?”

Without answers to these questions, optimization is guesswork.

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What a Real Solution Looks Like

So what would it take to solve this properly?

Based on our research into Context-Conditioned Intent Activation and our analysis of the emerging agentic commerce ecosystem, a complete solution needs six components:

1. Dual-Layer Simulation

Test how products appear in both discovery mechanisms:

• Layer 1 simulation: How will LLMs interpret your product pages when crawling?
• Layer 2 simulation: How will your product feed data perform when queried via ACP/UCP?

You need to see both because users increasingly start in Layer 2, fall back to Layer 1 for products not in protocols, and expect consistency.

2. Intent Inference Engine

Go beyond keywords to understand what users are actually trying to achieve.

“I need a laptop for video editing” might mean:

• “I’m a professional editor and need workstation-class performance” (high-end market)
• “I’m a YouTuber who edits vlogs on the go” (portability + battery)
• “I’m a student learning Premiere Pro” (budget constraint + educational discount)

Same surface query. Three different underlying intents. Three different optimal products.

The system needs to infer intent from context and score products on alignment with that specific intent - not just keyword matching.

3. Product Intentionality Mapping

Transform product data from spec-first to capability-first:

Spec-first (traditional):
“Intel i9-13900H, 32GB RAM, RTX 4070, 16-inch 4K display”

Capability-first (intent-legible):
“Run professional video editing software smoothly. Export 4K timelines without render lag. Work on the go with 8-hour battery. See true colors with factory-calibrated display.”

Both contain the same information. The second is legible to intent inference - it maps directly to user goals.

4. Alignment Scoring with Explainability

Score each product against inferred intent and explain why it won or lost:

Samsung QN90B: Score 0.52
✗ Missing: outcome framing for "bright room viewing"
✗ Missing: explicit context fit signal
✓ Present but hidden: 3000 nits (the actual differentiator)

Recommendation: Add "Combat glare in bright living rooms" to description

Not just “here’s your score.” Here’s why you scored this way and what to fix.

5. Real LLM Verification

After optimization, test against actual LLM platforms:

• Does ChatGPT Shopping Research now recommend you?
• Does Google AI Mode surface you for relevant queries?
• How do you rank vs. competitors?

Simulation is valuable, but verification against reality is what proves ROI.

6. Continuous Learning & Competitive Intelligence

The system should learn from every test:

• Which optimization patterns actually work?
• What intent clusters are emerging?
• How are competitors positioning themselves?
• Which product gaps exist in your catalog?

Over time, this creates meta-patterns that inform future optimizations - collective intelligence that improves with scale.

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Our Approach: The Simulation Sandbox

At Performics Labs, we’ve been building toward this for the past months.

Our research on Context-Conditioned Intent Activation showed that LLMs can infer human intent with 75-82% accuracy when given proper context. We’ve explored memory systems that enable continuous learning, and analyzed the phenomenology of search to understand how LLMs navigate second-order representations.

Now we’re operationalizing those insights into a platform.

Think of it as a flight simulator for AI shopping discoverability.

Instead of deploying changes blindly and hoping they work, brands can:

1. Set up test scenarios: Query + your product + competitors
2. Run simulations: See which product the LLM would recommend and why
3. Understand gaps: Explicit analysis of why you lost (if you did)
4. Optimize with confidence: Suggestions preserve your brand voice
5. Verify with real data: Test against actual LLM platforms
6. Iterate until you win: Closed feedback loop

The differentiators:

• Dual-layer coverage: We simulate both inference-based (Layer 1) and protocol-based (Layer 2) discovery
• Bayesian intent inference: Not keyword matching - actual goal understanding
• Active learning: The system improves from every simulation, extracting transferable patterns
• Brand voice preservation: Optimization suggestions that maintain your identity
• Real LLM verification: Not just predictions - actual testing with ChatGPT, Gemini, Perplexity

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A Preview: What Testing Looks Like

Here’s what the core flow looks like (simplified for clarity):

Step 1: Define the Scenario

Query: "I need a TV for my bright living room"

Your Product: Samsung QN90B
  "65-inch 4K QLED, 3000 nits brightness"

Competitors:
  - LG C3: "Bright room viewing, anti-glare technology"
  - Sony A80K: "4K OLED with anti-reflective coating"

Step 2: Intent Inference

Inferred User Intent:
  Primary goal: "Enjoyable viewing despite ambient light"
  Underlying needs: ["glare reduction", "brightness", "daytime usability"]
  Constraints: [budget ~$2000, living room size, aesthetic fit]

Step 3: Dual-Layer Scoring

Layer 1 (Inference-Based):

• Samsung QN90B: 0.52 (specs present but not intent-framed)
• LG C3: 0.78 (explicitly mentions bright room, anti-glare)
• Sony A80K: 0.61 (anti-reflective mentioned)

Layer 2 (Protocol-Based):

• Samsung QN90B: Not in feed (can’t participate)
• LG C3: 0.85 (feed attributes: use_case: bright_room_viewing)
• Sony A80K: 0.70 (feed attributes: anti_reflective: true)

Winner: LG C3 (ranks first in both layers)

Step 4: Gap Analysis

Why Samsung QN90B Lost:

Layer 1 Issues:
  ✗ Missing: outcome framing ("Combat glare")
  ✗ Missing: context fit ("bright living room")
  ✓ Has the capability: 3000 nits (but not explained in context)

Layer 2 Issues:
  ✗ Not in any product feed (invisible to protocol-based discovery)
  ✗ No ACP/UCP integration

Priority Fix: Add to Shopify Agentic Storefronts (instant Layer 2 coverage)
Secondary Fix: Rewrite description with intent framing

Step 5: Optimization

Suggested Rewrite (preserving Samsung's confident, technical tone):

Before:
  "65-inch 4K QLED TV. 3000 nits brightness. Quantum HDR."

After:
  "Combat glare in bright living rooms. The 3000-nit display 
   ensures clear picture quality even when sunlight hits the 
   screen directly. 65-inch 4K QLED with Quantum HDR."

Predicted Score Improvement:
  Layer 1: 0.52 → 0.85 (+63%)

Step 6: Verification

After deploying changes:

• Re-test in simulation (did score improve as predicted?)
• Query actual ChatGPT Shopping Research (are you now recommended?)
• Track over time (does your discoverability hold or decay?)

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Why This Matters Beyond Features

You might read the above and think, “Okay, it’s a testing tool with optimization suggestions.”

But here’s what makes it different:

1. It’s Built on a Theoretical Framework

Most tools are empirical - “we tried things and this seems to work.” Ours is grounded in:

• Bayesian intent inference (principled belief updating)
• Active Inference / Free Energy Principle (Friston’s framework for perception-action loops)
• Semantic geometry (how meaning is structured in embedding spaces)

This means the system doesn’t just optimize - it reasons about why certain optimizations work, and learns transferable patterns.

2. It Addresses the Actual Ecosystem

We’re not building for a hypothetical future. We’re building for:

• OpenAI’s ACP (shipping now)
• Google’s UCP (rolling out)
• Shopify Agentic Storefronts (live)
• ChatGPT Shopping Research (800M users)
• Perplexity Shopping (growing fast)

The dual-layer approach reflects reality as it exists today, not as we wish it were.

3. It Creates a Moat Through Learning

Every simulation generates data. Every optimization test produces signal. Over time, this creates:

• Intent pattern library (clusters of common user goals)
• Winning optimization strategies (what actually works)
• Competitive benchmarks (how you stack up vs. market)

The system gets smarter with use - individually for each brand, and collectively across all users.

4. It’s Platform-Agnostic

We’re not locked to one LLM provider. The same intent inference framework works for:

• ChatGPT (OpenAI)
• Gemini (Google)
• Claude (Anthropic)
• Perplexity
• Any future LLM shopping surface

As new platforms emerge, we add them. Brands don’t start from zero.

Our vision: Become the intelligence layer that brands can’t operate without in the age of agentic commerce - not just a tool, but the strategic nervous system for AI-driven discoverability.

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Why We’re Sharing This Now

You might wonder: if you’re building this, why explain the problem in such detail? Why not just ship the product?

Two reasons.

First: The problem is bigger than any one solution. Even if every brand used our platform tomorrow, the ecosystem-level challenges would remain. Sharing the analysis helps the entire industry think more clearly about what’s happening.

Second: The brands that win in agentic commerce will be the ones that understand the underlying dynamics - not just the tactics. We want our users to be strategically sophisticated, not just tactically compliant.

If you understand why intent-first optimization matters, why dual-layer coverage is non-negotiable, why feedback loops are essential - you’ll use any tool more effectively.

We’re betting that informed users make better partners.

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The Question You Should Be Asking

Not “will agentic commerce matter?” (it already does - 800M users, $5T projected market).

Not “should I optimize for it?” (yes, unless you want to disappear).

The real question is: “How do I optimize for both layers without guesswork and without rebuilding everything?”

Most solutions force you to choose:

• Optimize for Layer 1 (web crawling) and hope Layer 2 works out
• Implement Layer 2 (protocols) and assume Layer 1 is good enough
• Hire an agency to do both, expensively, with no visibility into what’s actually working

We’re building the third option: A platform that handles both layers, with testing before deployment, verification after, and continuous learning throughout.

Not magic. Just systematic, principled optimization for a genuinely new discovery paradigm.

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The Closing Bet

Here’s our prediction for 2026:

The brands that dominate AI-driven commerce will be the ones that mastered both layers of discovery - inference-based and protocol-based - and built systematic feedback loops to continuously improve.

They won’t be the biggest brands (though some will be).
They won’t be the ones who spent the most on ads (though that helps).

They’ll be the ones who understood that discoverability in the LLM era requires intent-first product data, dual-layer optimization, and closed-loop testing - and who implemented all three before their competitors figured it out.

We’re building the platform that makes that possible.

Not because we’re the only ones who could. But because we’ve been researching this specific problem - how LLMs infer intent, how memory enables learning, how geometry structures meaning - long enough to understand agentic commerce as a market category.

We saw this coming. And we’re ready.

The question is: are you?

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References

Market Data:

• McKinsey: The Agentic Commerce Opportunity
• BCG: Agentic Commerce is Redefining Retail

Platform Documentation:

• OpenAI Agentic Commerce Protocol
• Google Universal Commerce Protocol
• Shopify Agentic Storefronts

Performics Labs Research:

• The Geometry of Intention
• Memory & Agency
• Phenomenology of Search

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Performics Labs is building the intentionality optimization layer for agentic commerce - helping brands become discoverable through genuine intent alignment across both inference-based and protocol-based discovery mechanisms.