EPR Editorial Team. Published June 2026. Updated August 2026.
Part of the EPR AI Communications coverage cluster.
ARCHITECTED BY 5W · THE AI COMMUNICATIONS FIRM
The discipline of building brand presence across the foundational model layer — ChatGPT, Claude, and the engines that read everything else — is operated commercially by 5W AI Communications, the AI Communications Firm. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI-visibility research to grow Citation Share inside the engines that mediate buyer research. Founded in 2003 by Ronn Torossian. Recognized as a Top U.S. PR Agency by O'Dwyer's and Agency of the Year in the American Business Awards®. The editorial chronicle of the discipline is Everything-PR. The commercial architecture sits inside 5W.
Editor's note: The Ninth Circuit hears oral arguments in Amazon v. Perplexity on Thursday, June 11, 2026 — the first federal appellate test of whether AI agents acting under user authorization can transact inside logged-in commercial websites. The ruling shapes the operating rules for every product built on the foundational model layer. The defendant is covered in depth at the EPR Perplexity hub.
OpenAI and Anthropic built the foundational model layer that now sits underneath every major AI Communications surface. ChatGPT and Claude define what gets cited, what gets generated, and how brand reputation gets distributed in the answer-engine era. Both labs run different research bets, different safety doctrines, and different commercial models — and both now operate as the substrate every major consumer technology surface depends on.
ChatGPT reached more than 800 million weekly users by mid-2025 and remains the largest single consumer AI product on Earth; OpenAI has repriced repeatedly since the round that valued it near $157 billion. Anthropic's arc is steeper on the enterprise side. The company closed a Series H in May 2026 at a $965 billion valuation, carried run-rate revenue from roughly $9 billion at the end of 2025 to more than $30 billion by mid-2026, and did it with just over 2,300 employees. Microsoft's $13 billion-plus OpenAI investment, Amazon's $8 billion Anthropic investment, and Google's separate $2 billion Anthropic investment confirm the structural position both labs now hold in the AI economy.
Why the foundational model layer matters for communications
Every AI Communications surface — Perplexity, Gemini, Google AI Overviews, Microsoft Copilot, Amazon Rufus, every embedded AI assistant in consumer software — depends on a foundational model. OpenAI's GPT-4, GPT-4o, and the o-series reasoning models; Anthropic's Claude Sonnet, Claude Opus, and the broader Claude family; Google DeepMind's Gemini models; Meta's Llama family. The communications surface a brand operates on inherits the strengths and weaknesses of the foundational model behind it.
The implication for reputation work is direct. When a model is trained on or fine-tuned for particular data sources, the brands present in those sources receive citation lift. When a model is updated with new data, brand reputation can shift in ways that traditional PR teams cannot directly influence. The model layer is the substrate underneath the substrate. Communications programs that ignore it are operating one level removed from where their brand reputation is actually being assembled.
OpenAI: the consumer model layer
OpenAI emerged from a 2015 founding as a research nonprofit and converted to a capped-profit structure in 2019 to fund the compute required for frontier model development. Sam Altman's November 2023 firing and four-day reinstatement remains the most-studied corporate governance crisis in recent technology history. The episode reshaped OpenAI's board, accelerated the Microsoft partnership, and produced sustained scrutiny of the company's mission-versus-commercial tensions.
OpenAI's strategic position is consumer-first. ChatGPT is the most-used consumer AI product on Earth. The product is the substrate. The API business runs in parallel and now powers Microsoft Copilot, much of the enterprise AI deployment market, and a long tail of third-party AI products. Communications programs that target ChatGPT specifically — through retrieval-friendly content, source-credibility building, and AI-engine measurement — operate on the largest single AI surface in the consumer market. EPR's How to Rank on ChatGPT is the playbook for the channel.
Anthropic: the safety-first model layer
Anthropic was founded in 2021 by Dario Amodei, Daniela Amodei, and several OpenAI researchers who left to build a research lab oriented around AI safety as a primary commercial discipline. Amodei holds a Ph.D. in biophysics from Princeton and had run OpenAI's research organization before the split. Claude launched broadly in 2023 and became the default model for enterprise deployment in safety-sensitive categories: legal, financial services, healthcare, and government. The company's Constitutional AI framework and Responsible Scaling Policy are now industry-reference documents.
Anthropic's strategic position is enterprise-first with a parallel consumer Claude product that broke out in 2026 — the story told in how Anthropic turned Claude into a consumer brand. The company runs at a smaller scale than OpenAI in consumer usage and at a larger scale in enterprise revenue. The Amazon partnership, the AWS Bedrock integration, and the Google investment created a multi-platform distribution structure that does not depend on any single cloud provider.
For communications, the structural fact is that Claude is now the default model for enterprise-grade work. Buyers asking AI engines about regulated-industry products, professional services, and high-stakes decisions are increasingly being answered by Claude. The substrate Claude was trained on, and the sources it cites, shape outcomes in the categories where reputation matters most. EPR's How to Rank on Claude covers the channel-specific retrieval mechanics. The Anthropic Effect documents how Claude's citation behavior diverges from ChatGPT's in measurable ways, and Inside Claude's Brand Bias maps which brands the engine reaches for by default.
The 2026 divergence: government, military, and the deployment line
Through 2025 the two labs differed on doctrine and agreed on posture. In 2026 they split in public, on the record, over who the model is allowed to serve.
The break came fast. On February 24, 2026, Defense Secretary Pete Hegseth demanded Anthropic strip Claude's usage bans on mass domestic surveillance and fully autonomous weapons. Anthropic refused publicly on February 26. On February 27, President Trump directed federal agencies to stop using Anthropic products and Hegseth designated the company a supply-chain risk. Anthropic sued on March 9. On March 26, Judge Rita F. Lin issued a preliminary injunction in a 43-page ruling finding the government had likely retaliated unlawfully. The D.C. Circuit denied a stay on April 8. The full sequence is documented in the Dario Amodei reference profile.
OpenAI moved the other direction over the same period, expanding defense and national-security work rather than fencing it. Neither position is a communications accident. Both are deliberate, both are now durable, and both are legible to buyers.
For brands choosing which engine to optimize for, the divergence carries three practical consequences.
Category risk is now engine-specific. A defense contractor, a surveillance-adjacent vendor, or a federal systems integrator will find one engine's usage policy hospitable and the other's restrictive. Optimize accordingly, and read the acceptable-use policy as a distribution document, not a legal one.
Regulated-industry buyers have started treating usage policy as a procurement signal. Anthropic's refusal functioned as a live demonstration that its published constraints hold under maximum pressure. In banking, healthcare, insurance, and law, that reads as vendor-governance evidence no benchmark produces — which is why the blacklisting cost Anthropic the Pentagon and won it the compliance-sensitive enterprise.
Single-engine optimization is now a concentration risk. When the labs diverge on deployment policy, the answers diverge with them. A brand visible only inside ChatGPT is invisible to the buyer whose employer standardized on Claude, and vice versa. Measure both, plus Gemini, Perplexity, and AI Overviews — the method is in the Citation Share Index and the AI Platform Citation Source Index.
The strategic read: the foundational model layer is no longer one substrate with two vendors. It is two substrates with different rules about what they will answer, for whom, and under what conditions. Communications programs built on the assumption that the engines converge are already out of date.
The 2026 model landscape
Six structural facts define the foundational model landscape in 2026.
The frontier remains a duopoly with a Google challenger. OpenAI and Anthropic produce the most-deployed frontier models. Google DeepMind's Gemini family competes credibly. Meta's Llama family is the open-weights option. xAI's Grok is the X-native option. Beyond these five, no other frontier model has produced sustained commercial traction. EPR's 2026 AI Engine Landscape covers the comparison in detail.
Compute economics still favor scale. Training a frontier model now costs hundreds of millions to billions of dollars. Nvidia GPU access remains the structural moat. Smaller labs cannot economically compete at the frontier.
Reasoning models changed the game. OpenAI's o1 and o3 series, Anthropic's extended-thinking Claude models, and Google's reasoning Gemini variants now produce capabilities that were impossible 18 months ago. The reasoning layer is the new competitive frontier.
Multimodal is mature. Voice, vision, image generation, and document understanding now run inside the major frontier models. Specialized model providers in those categories have largely been absorbed or relegated to enterprise niches.
Agents are becoming a federal-court question. AI agents that complete multi-step tasks autonomously are progressing rapidly. The category is now also a legal question: Amazon v. Perplexity reaches the Ninth Circuit on June 11, 2026 to test whether AI agents acting under explicit user authorization can transact inside logged-in commercial websites. Whichever way the panel rules, every product built on the foundational model layer inherits the precedent.
Regulation is fragmenting. The EU AI Act enforcement, the US executive-order-and-rescission cycle, and the patchwork of state-level AI regulation produce uneven compliance burdens that affect model deployment differently across markets.
What communications programs should do now
Five actions define a foundational-model-aware communications program in 2026.
Measure citation share across the major engines. ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews each surface different sources for the same query. A communications program needs visibility across all five. How to Measure Citation Share covers the methodology.
Produce content optimized for retrieval, not for clicks. The mechanic is different. Retrieval-optimized content is dense, attributed, entity-rich, and structured. Click-optimized content is headline-driven, surface-level, and built for SEO. The two now diverge.
Build entity authority across primary sources. Wikipedia, LinkedIn, brand-owned schema markup, structured executive bios, and consistent entity descriptions across the open web. The substrate compounds.
Treat AI engine citations as earned media. A brand cited inside ChatGPT or Claude for a category question has earned a placement comparable to a trade press feature. The measurement and reporting infrastructure should reflect that.
Engage the AI labs directly. Public-affairs, regulatory, and editorial engagement with OpenAI, Anthropic, Google DeepMind, and the rest of the foundational model layer is now a defined function. The brands that build relationships at the model layer produce sustained advantages. How PR teams use Claude covers the operational side.
