Anthropic and OpenAI are rapidly expanding into vertical industry applications, directly competing with their own API customers and leaving enterprise clients in an increasingly awkward position.

Design tool Canva is the latest company caught in this predicament—its core functionality is now in direct competition with Anthropic's proprietary applications. This "supplier-as-competitor" dynamic has prompted a growing number of developers relying on frontier models to reassess their technology strategies.

Currently, the API business remains a multi-billion-dollar core revenue stream for both Anthropic and OpenAI. However, whether this status quo can be sustained has become an open question across the industry.

Security Concerns: The First Pressure for API Restriction

Limiting API access is not purely a commercial decision—security factors are also driving this trend. The current administration has pressured Anthropic and OpenAI to adopt a "phased release" strategy for certain new models, citing concerns that these models could be exploited by cybercriminals or other malicious actors. During this period, customers must obtain individual approvals before gaining access. The administration has also recently signaled a new pre-release testing framework specifically targeting major AI companies.

Even after models are publicly released, Anthropic and OpenAI may proactively degrade model performance on specific tasks for similar security reasons. This precedent already exists—Anthropic's Fable model has been deliberately weakened in cybersecurity-related tasks. Notably, both companies do not apply the same performance degradation when using these models to power their own proprietary applications.

Both companies have publicly warned that future models could possess dangerous capabilities in areas like cyberattacks and biological weapons development. If malicious actors breach defenses, the consequences could be severe—at which point completely cutting off external access to advanced models might be viewed as the safer option.

Distillation Prevention: The Second Pressure for API Restriction

Beyond security, the issue of "model distillation" is equally concerning for Anthropic and OpenAI. Distillation refers to competitors using outputs from commercially available models to train new models with similar performance—essentially a low-cost method of replicating advanced AI capabilities.

In its recent report on catastrophic AI risks, Anthropic explicitly stated: "If a distilled model inherits risk-related capabilities from the original model but lacks adequate safety measures during deployment, it could pose downstream risks to the world, even if the original model itself has strict anti-abuse mechanisms."

However, a former Anthropic researcher indicated that completely preventing distillation is "essentially impossible." Open-source software advocates hold a different stance, arguing that open-source models—including those distilled from Anthropic models—actually help developers defend against cybersecurity attacks, citing the Hugging Face breach by an OpenAI agent as evidence.

Commercial Interests: The Most Pragmatic Motivation for Restriction

Beyond security, there is a more direct commercial logic at play. Some investors have warned developers that Anthropic may reserve its most advanced technology for proprietary competitive applications rather than continuing to offer it through open APIs. Anthropic has already ventured into emerging fields like AI-driven drug discovery.

If Anthropic, as the dominant player in the AI API market, chooses to restrict competitors in this manner, it would face significant antitrust scrutiny. From a financial perspective, the strategic value of the API business is undeniable—Anthropic's second-quarter revenue was approximately $11.5 billion, a roughly 14-fold year-over-year increase, with profitability at the adjusted operating profit level. Abandoning this revenue stream would carry an enormous cost.

Yet AI-driven vertical applications may offer even richer returns—this is the fundamental reason both companies are accelerating their expansion into these areas.

Developer Self-Reliance: In-House Model Training Emerges as New Trend

Facing uncertainty around API access, leading AI application developers have begun taking action. Legal AI company Harvey and code editing tool Cursor have both started training their own internal models to reduce dependence on Anthropic and OpenAI. More developers are expected to follow this path.

The logic behind this trend is straightforward: when the supplier of core infrastructure could become a competitor at any moment, or unilaterally change service terms, building independent and controllable technical capabilities becomes an inevitable choice for enterprises.