MCP startup Runlayer accuses Rippling of allegedly stealing its product ideas. When AI infrastructure encounters a "crisis of trust", the competition between major manufacturers and startups is escalating.

MCP startup Runlayer accused Rippling of allegedly stealing its product ideas. When AI infrastructure encounters a "crisis of trust," the competition between major manufacturers and startups is escalating. Runlayer, a startup company specializing in Model Context Protocol (MCP) security gateways, recently formally filed a lawsuit in court, accusing human resources software company Rippling of allegedly misappropriating its product ideas and trade secrets. This legal dispute reveals the fierce game and potential risks behind enterprise-level technology procurement in the AI ​​era. According to the indictment, the two parties conducted in-depth engineering collaboration and product trials for nearly a year. During this period, Runlayer shared its core product roadmap and underlying source code with Rippling, a potential customer. Both parties not only signed a confidentiality agreement, but also entered into a trial agreement containing intellectual property protection clauses. However, the test was terminated as the two parties ultimately failed to reach an agreement on the purchase price. Soon after, Runlayer founder and CEO Andrew Berman received information from insiders that Rippling was secretly developing a clone project internally that was almost identical to the startup's product. Based on this, Runlayer believed that the other party was involved in infringement of trade secrets, breach of contract and unfair competition. In the face of the accusations, Rippling confirmed that it was launching a self-developed MCP gateway, but denied the accusation of abusing intellectual property rights and stated that its products are based on its own technology research and development. Since the launch of the open source protocol by Anthropic, the MCP gateway has gradually become a key infrastructure for connecting AI models and external data. However, as market competition becomes increasingly fierce, many large companies with strong engineering capabilities often choose to develop their own, which also puts many AI infrastructure startups that rely on financing for advancement into a dilemma. via AI News (author: AI Base)