【AI前沿】MCP Startup Runlayer Accuses Rippling of Allegedly Stealing Its Product Ideas

2026-07-31

AI NEWSLatest AI NewsArticleMCP Startup Runlayer Accuses Rippling of Allegedly Stealing Its Product IdeasPublished in Latest AI NewsTime :Jul 30, 2026Read :3minuteWhen AI infrastructure meets a “crisis of trust,” the competition between big companies and startups is escalating.The startup focusing on the Model Context Protocol (MCP) security gateway,Runlayer, has recently filed a lawsuit in court, accusing human resources software companyRipplingof stealing its product ideas and trade secrets. This legal dispute reveals the intense competition and potential risks behind enterprise technology procurement in the AI era.According to the complaint, the two parties had engaged in deep engineering collaboration and product trials for nearly a year. During this time,Runlayershared its core product roadmap and underlying source code withRippling, a potential customer. Both parties signed a non-disclosure agreement and a trial agreement containing intellectual property protection clauses. However, the testing was terminated as the two parties failed to reach an agreement on the purchase price.Not long after,Runlayer’s founder and CEO Andrew Berman received information from an internal source thatRipplingwas secretly developing a clone project almost identical to the startup’s product. Based on this,Runlayerbelieves that the other party is suspected of infringing on trade secrets, breaching the contract, and engaging in unfair competition.In response to the allegations,Ripplingconfirmed that it is launching its own MCP gateway but denied the accusation of abusing intellectual property rights, stating that its product is based on its own technological development.WithAnthropicreleasing an open-source protocol, the MCP gateway has gradually become a key infrastructure connecting AI models with external data. However, as market competition intensifies, many large companies with strong engineering capabilities often choose to develop in-house, which puts many AI infrastructure startups relying on financing into a difficult dilemma.