Back to Flash News

Anthropic Co-founder and CEO Dario Amodei Attends Salesforce Annual Conference Dreamforce 2026

Salesforce Chairman and CEO Marc Benioff posted on his X account, welcoming Anthropic Co-founder and CEO Dario Amodei to the Salesforce annual conference Dreamforce 2026.

During the meeting, Amodei and Benioff jointly announced that the partnership between Salesforce and Anthropic will further expand, integrating the Claude large model more deeply into Salesforce's product ecosystem; it was disclosed that currently, 7,000 Salesforce employees are using this integrated tool in their actual work, and as of early June this year, Salesforce's stake in Anthropic was valued at approximately $5 billion.

Amodei assessed the current level of AI technology adoption, stating that the "actual value that has penetrated and been used" is only 5% to 10% of its total current value, indicating that the application of AI technology in the entire economic system is still in its early stages, with huge untapped potential. Benioff noted that the progress of companies in adopting AI technology is "uneven," and promised that Salesforce will "do more to help customers accelerate this transformation."

At the conference, Salesforce also launched two new products: one is a new system called AIforce, which allows customers to access data through AI agents instead of relying on traditional interfaces, enabling users to build custom interfaces in real-time; the second is a new sales inference model named Koa, developed over six months based on NVIDIA's Nemotron foundational model, trained using proprietary synthetic data derived from thirty years of corporate workflows.

Notably, this Dreamforce conference itself has become a focal point in the current debate in the AI industry on whether to slow down the pace of development—Amodei had just called for the industry to "urgently slow down" AI development, warning of serious consequences if not controlled; while Salesforce President Patrick Stokes responded that "as builders, slowing down a bit is actually good news because we have a lot of catching up to do, and we can use this time to catch up." Meanwhile, NVIDIA CEO Jensen Huang refuted concerns about the pace of AI development at the All-In conference on Monday, during which he received a supportive call from President Trump.

From a market mechanism perspective, Salesforce's holding of approximately $5 billion in Anthropic equity and the deep integration of Claude into enterprise-level products essentially represents a "equity binding + product integration" dual bundling between an application layer software giant and a cutting-edge model lab: Anthropic gains a stable distribution channel covering a large number of enterprise customers and real workflow training data (like the synthetic data used for the Koa model), while Salesforce leverages Claude's model capabilities to launch new AI-centric products like AIforce, seizing the entry point for enterprise-level AI applications. Amodei emphasized that the AI value "penetration rate is only 5%-10%", which objectively also advocates for a longer commercialization window and greater imaginative space for the entire industry. Who benefits: application layer companies like Salesforce that deeply bind top model labs through equity and product dual binding can share both the model capability dividends and their own distribution channel advantages; who is under pressure: application layer competitors that hold different positions in the debate on "whether AI should slow down" and have not established similar deep binding relationships with top labs may be at a relative disadvantage in the competition for enterprise-level AI application entry points.

Source: Public Information

ABAB AI Insight

The partnership between Salesforce and Anthropic did not begin at this Dreamforce; the two companies had already established a collaboration in equity and product aspects, with Salesforce's stake in Anthropic having appreciated to about $5 billion; Marc Benioff is known for actively embracing emerging technologies and integrating them into Salesforce's product ecosystem through acquisitions and strategic investments, similar to his previous leadership in acquiring companies like Slack and Tableau and integrating their functionalities into Salesforce's "Customer 360" ecosystem.

From a resource mobilization perspective, Salesforce chose to deeply bind with Anthropic through equity investment (holding approximately $5 billion in Anthropic shares) rather than a simple API procurement relationship. This "equity + product integration" dual bundling model directly links Salesforce's capital returns to Anthropic's valuation growth, allowing Anthropic to leverage Salesforce's vast enterprise customer base (with 7,000 internal employees already using related tools) to obtain high-quality training data and product feedback in real scenarios, forming a two-way resource complementarity.

Similar cases of "application layer software giants deeply integrating cutting-edge model capabilities through equity binding" include Microsoft's strategic investment in OpenAI and deep bundling with Azure cloud services, which is the most representative precedent; Salesforce's collaboration with Anthropic is, to some extent, replicating this "cloud/application giant + cutting-edge model lab" capital and product dual binding model. The current industry stage of enterprise-level AI applications is evolving from "simple API calls" to a closer cooperation model of "deep co-construction, equity binding, and scenario data feedback."

This essentially belongs to the overlap of "capital concentration" and "industry chain restructuring": on one hand, top model labs and leading application layer companies bind capital returns that were originally dispersed in their respective fields; on the other hand, Amodei and Stokes, representing the positions of "calling for a slowdown" and "welcoming a slowdown to gain catching-up time" respectively, reveal a potential alignment of interests between application layer companies and cutting-edge model labs on the issue of AI development pace—application layer companies need time to transform existing model capabilities into mature products, rather than simply pursuing continuous leaps at the model level, which is why the "slowdown" rhetoric is interpreted by some application layer executives as "good news for catching up."

ABAB News · Cognitive Laws

  1. Models lag behind, application layers can catch up.

  2. Equity binding is tighter than API procurement.

  3. The lower the penetration rate, the greater the storytelling space.