AI is no longer competing solely on models: OpenAI and Anthropic begin vying for the "enterprise entry point"

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Text | ICT Interpreter—Lao Jie

In early May 2026, the twin stars of the U.S. AI industry—OpenAI and Anthropic—almost simultaneously announced their respective enterprise joint venture/partnership plans, pressing the accelerator on the competitive landscape of AI.

OpenAI announced a partnership with investment giants like TPG, Brookfield, Bain Capital, and SoftBank to advance a joint entity for AI deployment with a target scale of $10 billion; nearly at the same time, Anthropic also teamed up with Blackstone, Goldman Sachs, and Hellman & Friedman to promote the establishment of an enterprise AI service company with approximately $1.5 billion in scale.

On the surface, these are just two capital operations centered around joint structures, but from a deeper industry perspective, this more resembles a highly coordinated strategic shift—it clearly points to a key and somewhat cold reality: the core of AI competition is shifting from “whose model is stronger” to “who can truly penetrate enterprises.”

The era of technical competition based on parameters, benchmarks, and “who’s smarter” is gradually retreating. A new “large-scale distribution era,” focused on channels, deployment, and “who can really sell,” is accelerating.

The narrative logic of the AI industry is shifting from “model capability competition” to “distribution and delivery competition.”

1. Dual-track layout: OpenAI and Anthropic’s joint venture strategy

Two releases just one day apart seem coincidental, but in fact, they reflect a shared judgment by two leading AI companies on industry trends, each emphasizing different aspects, outlining two distinct enterprise-level deployment paths.

On May 4, OpenAI promoted the formation of a joint entity aimed at enterprise AI deployment (known in the industry as “The Deployment Company”), with a target scale of $10 billion, becoming a focal point in the industry. But the core of this deal isn’t the capital itself, rather the corporate network and decision-making resources behind the investors.

Top-tier global investment firms like TPG and Brookfield cover a large ecosystem of corporate clients and portfolio companies. For OpenAI, this is akin to gaining a “direct channel to enterprise decision-makers.” TPG Managing Partner explicitly stated: “What we bring to OpenAI isn’t just $10 billion in funding, but also access to over 2,000 large enterprises in our global investment portfolio.”

Therefore, rather than viewing this as just a financing round, it’s better seen as a typical “equity for distribution rights” structure—exchanging part of the benefits to gain faster access to core enterprise needs.

The next day, the $1.5 billion enterprise AI service company driven by Anthropic’s associated capital took a different route—focusing more on “deep service delivery” rather than merely expanding channels.

Its goal isn’t to increase API call volume but to embed Claude models into customer service, legal, finance, coding, and security workflows. Blackstone and H&F announced they would open green channels for this new enterprise service company, enabling AI to rapidly penetrate industries from logistics to healthcare; Goldman Sachs also pledged to provide deep insights into the financial sector to help develop high-end AI solutions for global capital markets.

Anthropic’s management believes that the growth rate of enterprise demand for models has begun to surpass the capacity of single delivery methods: “For Fortune 500 companies, simply calling models via API isn’t enough. They need customized solutions that deeply understand their proprietary data, meet strict compliance requirements, and seamlessly integrate into complex existing workflows.”

This judgment directly points to the most pragmatic bottleneck in AI commercialization: the importance of model capability is declining, while delivery capability is rising.

The “alchemy” around models over the past two years is giving way to a more realistic “ground war.”

Previously, industry narratives almost entirely revolved around models; but once models cross a certain threshold, enterprise clients’ focus shifts: they no longer blindly believe whose benchmark is higher, but care more about whose solution is easier to deploy, who can handle complex private data, and who can deliver more certain ROI.

Technical advantages no longer automatically translate into business advantages. Between models and revenue, a complex delivery chain stands in the way.

This also explains why OpenAI and Anthropic are converging on similar joint structures— for AI unicorns with potential capital market pathways, this isn’t just a business choice but also a financial reality: sharing sales and implementation costs through joint entities, to some extent, externalizes profit and loss structures, maintaining a lightweight asset profile while accelerating revenue growth.

2. Joint ventures rather than direct sales: the pragmatic choice of AI giants

Why do OpenAI and Anthropic choose joint ventures or similar structures instead of relying solely on building their own direct sales systems for the enterprise market? The core answer lies in the most scarce resource for AI companies—time.

They are not short of technology or capital, but during critical growth windows, they lack enough time to build a global enterprise sales and delivery network.

Over the past three years, large model companies achieved rapid growth via API on the “cloud,” enabling a “light delivery” business model. But as model capabilities converge, enterprise decision-makers return to reality, raising questions: who can access complex databases? who can restructure business processes? who is responsible for ROI?

These issues mean that the main battlefield of AI commercialization has extended from the cloud to the “last mile” inside enterprises—a classic “ground war.”

Private equity firms like TPG, Blackstone, and Goldman Sachs are crucial at this stage. They possess not just capital but also board-level relationships, global corporate networks, and long-term industry binding capabilities—they are a mature “distribution system” in their own right.

When AI companies bring in these capital sources, they are essentially outsourcing distribution capabilities to the most mature “enterprise connectors,” using equity to acquire scarce channel resources and achieve rapid breakthroughs.

More importantly, enterprise AI revenue is far more convincing to capital markets than B2C subscriptions: it’s more stable, has a longer lifecycle, and is closer to actual productivity.

In future valuation systems, “serving more enterprises” may become more decisive than “whose models are stronger.”

Building an internal enterprise sales system is feasible but costly—Salesforce, for example, took nearly a decade to establish a global sales and delivery network. Currently, AI companies face a critical window of 12-18 months, making leveraging private capital a more practical path.

3. Diverging paths: OpenAI’s “platformization” vs. Anthropic’s “deep service”

Although both have chosen similar structures, OpenAI and Anthropic differ fundamentally in their business paths, rooted in their strategic positioning.

OpenAI leans toward a “platformization” approach.

It uses joint entities as distribution accelerators, focusing on models and platform capabilities, leaving specific deployment to partners. OpenAI’s COO Oliver Jay explicitly states: “Through collaborations with strategic partners like TPG, we are building an ‘operator distribution network’ for the AI era.”

Meanwhile, to ensure enterprise-level flexibility, OpenAI is gradually reducing dependence on a single cloud platform, shifting from its deep integration with Microsoft to a more open multi-cloud distribution model. This marks OpenAI’s move to extend its enterprise distribution rights from a single cloud to mainstream global infrastructure, covering a broader enterprise market.

In contrast, Anthropic opts for a more “service-oriented” path—more intensive and deeper—closer to a “consulting + technology” hybrid model. Its underlying capital-driven enterprise AI company resembles a “combination of advisory and tech.”

A key manifestation of this approach is the rise of FDE (Forward-deployed Engineers), a model popularized by Palantir, now a critical component for Anthropic to connect with the “last mile” of enterprise deployment.

FDE teams’ core value lies in “dual integration”: engineers are embedded directly within client companies, understanding both the underlying model technology and complex business workflows, tuning algorithms, and patching legacy ERP systems, deeply integrating model capabilities with business needs.

While the FDE model involves higher human costs and slower expansion, it allows for deeper roots within enterprises, making it easier to form closed loops in highly regulated, high-threshold industries like finance and healthcare, creating hard-to-copy competitive moats.

If OpenAI pursues “breadth” through global coverage, Anthropic seeks “depth” in business scenarios; both paths have advantages and disadvantages but aim for the same goal: more efficient enterprise deployment.

4. Industry restructuring: AI entering the “distribution is king” stage

The different strategies of OpenAI and Anthropic seem like strategic choices, but in reality, they are reconstructing the entire AI industry structure, potentially triggering profound impacts and pushing the industry into a new phase of development.

The most fundamental change is that AI is entering an era where “distribution is king.”

As model technology continues to converge, the gap between different vendors’ models is narrowing. Once a technological advantage, it no longer guarantees dominance; instead, distribution ability becomes the key variable determining success—who can reach enterprises more efficiently, match needs more precisely, and deliver more smoothly will hold the competitive edge.

Second, private equity capital has shifted from merely being investors to becoming critical infrastructure for AI commercialization.

Institutions like Blackstone, Goldman Sachs, and TPG are no longer just funding AI companies but leveraging their vast enterprise networks and industry resources to serve as “bridges” for AI entering enterprises, becoming core nodes in AI commercialization pathways.

Meanwhile, the rise of FDE models may reshape the enterprise software industry.

It breaks the traditional view of “software as just a product,” pushing software toward a “product + people” hybrid model—enterprises no longer just need cold tools but solutions that deeply adapt to their business and offer continuous optimization, which could become the mainstream form of enterprise AI services.

Finally, the valuation logic of the AI industry is undergoing a fundamental shift.

In the future, capital markets will evaluate AI companies less on model performance alone and more on core indicators with real business value: number of enterprise clients, revenue scale, and industry penetration depth. This valuation shift will further push AI companies from “technology-driven” to “business-driven,” accelerating commercialization.

The profit pool of AI is shifting from the model layer to distribution and delivery layers.

Conclusion:

If the past three years’ core theme was “whose model is strongest,” starting in 2026, it’s being replaced by “who can truly sell AI into enterprises and sustain revenue.”

As AI penetrates deeper into enterprises, companies realize that what’s truly needed isn’t just models but deployment services. The entire industry is entering a “layered competition”: model capabilities are becoming standardized, while distribution ability is becoming the new competitive barrier.

In the second half of AI commercialization, the ultimate winners may not be the most technically advanced companies but those closest to enterprise clients, capable of truly embedding AI into the core of their businesses.