OpenAI and Anthropic Shift AI Competition to Enterprise Access

By ICT Interpreter — Lao Jie

In early May 2026, the twin giants of the U.S. AI industry—OpenAI and Anthropic—announced their respective enterprise joint venture or partnership initiatives in near-perfect synchrony, shifting the competitive landscape of the AI industry into a new gear.

OpenAI has partnered with major investment firms including TPG, Brookfield, Bain Capital, and SoftBank to establish a joint entity aimed at deploying AI with a target size of $10 billion; nearly simultaneously, Anthropic has teamed up with Blackstone, Goldman Sachs, and Hellman & Friedman to launch a corporate AI services company valued at approximately $1.5 billion.

On the surface, these are merely two capital moves centered around a joint venture structure; but from a deeper industry perspective, they represent a highly aligned strategic shift—pointing clearly to a key and somewhat harsh reality: the core of AI competition is shifting from “whose model is stronger” to “who can truly enter the enterprise.”

The era of competing on specs, benchmarks, and “who’s smarter” is gradually fading, as a new “mass distribution era” accelerates—one that competes on channels, execution, and “who can actually sell.”

The narrative in the AI industry is shifting from competition over model capabilities to competition over distribution and delivery.

Two releases, just one day apart, may seem coincidental, but they reflect a shared understanding of industry trends by two leading AI companies—each with distinctly different focus areas, outlining two differentiated enterprise-level strategies.

On May 4, the joint entity established by OpenAI to facilitate enterprise AI deployment—known in the industry as "The Deployment Company"—became a focal point with a target size of $10 billion; however, the core of this transaction lies not in the capital itself, but in the corporate network and executive resources behind the investors.

Global top investment firms such as TPG and Brookfield, with extensive networks of corporate clients and portfolio company ecosystems, provide OpenAI with a potential direct channel to enterprise decision-makers. TPG’s Executive Partner explicitly stated: “We are bringing OpenAI not just $10 billion in funding, but also access to over 2,000 large enterprises within our global portfolio.”

Therefore, rather than being a funding round, this is more of a classic "equity-for-distribution" arrangement, trading partial ownership for faster access to the company’s core needs.

The next day, the $1.5 billion enterprise AI services company backed by capital associated with Anthropic took a different path from OpenAI—focusing more on “deep service delivery” rather than mere channel expansion.

Its goal is not to increase API call volume, but to help enterprises integrate Claude models into specific business scenarios such as customer service, legal, finance, code development, and security systems. Blackstone and H&F have announced they will provide a fast-track pathway for this new enterprise services company, enabling AI to rapidly penetrate industries ranging from logistics to healthcare; Goldman Sachs has also stated it will contribute deep financial industry insights to help develop high-end AI solutions tailored for global capital markets.

Anthropic’s management has determined that demand from enterprise markets for models is beginning to outpace the capacity of a single delivery method: “For Fortune 500 companies, simply accessing models via API calls is not enough. They require customized solutions that deeply understand their proprietary data, meet stringent compliance requirements, and seamlessly integrate into their existing complex workflows.”

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

The "alchemy" surrounding models over the past two years is giving way to more realistic "ground wars."

In the past, industry narratives revolved almost entirely around models; but once model capabilities crossed a certain threshold, enterprise customers’ focus began to shift: they no longer placed faith in who had the higher benchmark scores, but instead prioritized who could deploy solutions more easily, handle complex private data, and deliver more predictable returns on investment.

Technical advantages no longer automatically translate into commercial advantages; a complex delivery chain lies between models and revenue.

This also explains why OpenAI and Anthropic have independently gravitated toward joint venture-like structures—for AI unicorns with potential capital market pathways, this is not merely a business choice but also a financially pragmatic one: by sharing sales and implementation costs through a joint entity, they achieve a degree of “structural offloading” of the income statement, accelerating revenue growth while maintaining the parent company’s asset-light profile.

Faced with the enormous opportunity in the enterprise market, why did OpenAI and Anthropic choose joint ventures or similar structures rather than relying solely on their own direct sales teams? The core answer lies in the most scarce resource for AI companies—time.

They lack neither technology nor capital, but during this critical development window, they didn’t have enough time to build a global enterprise sales and delivery system.

Over the past three years, large model companies achieved rapid growth through APIs in the "cloud," enabling a somewhat "lightweight delivery" business model. However, as model capabilities gradually converge and corporate decision-making returns to reality, a series of issues have emerged: Who can connect to complex databases? Who can restructure business processes? Who is accountable for ROI?

These issues mean that the main battlefield for AI commercialization has extended from the cloud to the "last mile" within enterprises—a classic ground campaign.

Private equity firms such as TPG, Blackstone, and Goldman Sachs have become key pillars during this phase. These institutions control not only capital but also board-level relationships, global corporate networks, and long-term industry connectivity—they themselves constitute a mature “distribution system.”

When AI companies bring in this capital, they are essentially outsourcing their distribution capabilities to the most mature "enterprise connectors," exchanging equity for scarce channel resources to achieve rapid breakthroughs.

More importantly, enterprise AI revenue is far more compelling to capital markets than consumer subscriptions: it is more stable, has a longer lifecycle, and better aligns with actual productivity.

In future valuation frameworks, "how many businesses are served" is likely to be more decisive than "how powerful the model is."

Building an in-house sales system is certainly feasible, but it comes at the cost of time—Salesforce, for example, took nearly a decade to establish its global sales and delivery network. Meanwhile, AI companies today face a critical window of just 12 to 18 months, making access to private capital a more practical path.

Although both adopted similar structures, OpenAI and Anthropic differ fundamentally in their business paths, reflecting their distinct strategic positions.

OpenAI is closer to a "platform-based" logic.

It leverages partnered entities as distribution accelerators, focusing its own efforts on model and platform capabilities, while entrusting specific implementations to partners. Oliver Jay, Managing Director of OpenAI, explicitly stated: “Through collaborations with strategic partners like TPG, we are building an ‘operator distribution network’ for the AI era.”

Meanwhile, to ensure flexibility for enterprise customers, OpenAI is gradually reducing its reliance on a single cloud platform, moving away from its previous deep integration with Microsoft toward a more open, multi-cloud distribution model. This marks OpenAI’s formal expansion of its enterprise distribution rights from a single cloud platform to leading global infrastructure providers, enabling broader coverage of the existing enterprise market.

In contrast, Anthropic has chosen a heavier, more in-depth path, closer to a “service-oriented” model; as a venture-backed AI company, it essentially functions as a hybrid “consulting + technology” system.

A key manifestation of this model is the rise of FDEs (Forward-Deployed Engineers), a practice popularized by companies like Palantir and now critical to Anthropic’s ability to close the final mile in enterprise deployment.

The core value of the FDE team lies in "bidirectional integration": engineers are embedded directly within enterprises, possessing both a deep understanding of the underlying model technology and familiarity with complex business processes. They optimize algorithms while seamlessly integrating outdated enterprise ERP systems, tightly aligning model capabilities with business needs to achieve deep integration between technology and operations.

Although the FDE model incurs higher labor costs and limits expansion speed, it enables deeper integration within enterprises, making it easier to establish closed-loop systems in highly regulated, high-barrier industries such as finance and healthcare, thereby building competitive advantages that are difficult to replicate.

While OpenAI pursues global "breadth," Anthropic focuses on operational "depth"; both approaches have their advantages and disadvantages, but they both aim toward the same goal: enabling more efficient enterprise adoption.

The differing strategies of OpenAI and Anthropic appear to be mere corporate choices, but they are actually reshaping the entire AI industry structure and may trigger a series of far-reaching impacts, propelling the sector into a new phase of development.

The most fundamental change is that AI has officially entered the era where distribution is king.

As model technologies continue to converge, the performance gaps between different vendors are gradually narrowing, and former technological advantages can no longer serve as absolute barriers. Instead, distribution capability has become the key determinant of business success—those who can more efficiently reach enterprises, more accurately match their needs, and more smoothly deliver solutions will gain a competitive advantage.

Second, private capital has evolved from being merely an investor into a critical infrastructure for AI commercialization.

Institutions such as Blackstone, Goldman Sachs, and TPG are no longer merely providing funding to AI companies; they are leveraging their extensive corporate networks and industry resources to become bridges for AI's integration into enterprises and key nodes in the path to AI commercialization.

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

It challenges the traditional notion that software is merely a product, driving a shift toward a “product + people” model—enterprises no longer need cold, impersonal tools, but rather solutions that deeply align with their business and provide ongoing optimization services. This model may gradually become the dominant 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 not by single model performance, but by more practically valuable core metrics: number of enterprise customers, revenue scale, and depth of industry penetration. This shift in valuation logic will further push AI companies to transition from a technology-driven to a business-driven approach, accelerating the commercialization of the industry.

The profit pool in the AI industry is shifting from the model layer to the distribution and delivery layer.

If the core question of the AI industry over the past three years was “Who has the strongest model?”, then starting in 2026, this question is being replaced by: “Who can truly sell AI into enterprises and generate sustained revenue?”

As AI penetrates deeper into enterprises, companies are increasingly realizing that what they truly lack is not the model itself, but implementation services. As a result, the entire industry is entering a phase of "layered competition": model capabilities are becoming standardized, while distribution capability is emerging as the new competitive barrier.

In the second half of AI commercialization, the ultimate winner may not be the company with the most advanced technology, but rather the one closest to enterprise customers—and able to truly embed AI into the heart of their businesses.