LinkedIn as Microsoft Infrastructure
Originally, the LinkedIn platform functioned as it appeared – as a conventional professional network. Its value was primarily social and transactional: resumes, connections, job postings, recruiters, and messaging. Even after its acquisition in 2016, Microsoft was careful to frame it as an independent entity.
The shift happened gradually. As Microsoft expanded MS 365, Dynamics, and GitHub, LinkedIn’s content became much more useful as a data resource for Microsoft than as a social destination for its members. LinkedIn’s constantly updated professional data helps Microsoft understand who works where, how companies are connected and which leads and opportunities might matter to their sales teams. The patterns in workforce skills and the resulting hiring practices turn user profiles into actionable business insights for its parent company. At that point, LinkedIn stopped being a social media platform for professional networking, and started behaving like infrastructure.
The user-facing content layer gradually became less important than the data being generated behind it. Likes, posts, and the ever-increasing AI-generated entries don’t create value on their own. They mainly function as a means to ensure that profiles stay active and up to date. The real value is in the structured data behind the scene, which feeds Microsoft’s enterprise tools rather than the social feed that LinkedIn users scroll through.
This matters because it challenges the assumption that active participation on LinkedIn is required to plan or advance a career. While the platform still positions itself as a hub for opportunity, its importance to individual career development is overstated. Understanding what LinkedIn is optimized for helps explain why your stepping back does not equate to falling behind.
LinkedIn remains Microsoft’s largest acquisition, and nearly a decade on, its role is more clear for those who look closely. While presented as a professional social network, its real value lies in the steady accumulation of structured professional data. Job titles, career transitions, skills, company relationships, and hiring intent all feed directly into Microsoft’s enterprise stack.
Although LinkedIn operates as an independent subsidiary, it functions in practice as a data engine. That data supports Microsoft 365, sales strategies, recruiting tools, and increasingly, enterprise AI systems. The social layer exists largely to encourage ongoing updates and participation. A large and growing share of content on LinkedIn is now AI-generated, optimized for visibility rather than substance.
For those who want to highlight their skills and contributions in today’s challenging environment, the mismatch is clear. Your actual capability is better shared as verifiable artifacts, rather than in algorithmic engagement.
To put it another way, LinkedIn engagement is mostly surface-level at best, while real-world artifacts like GitHub repositories, issue histories, and your long-term projects can provide signals that automated systems can evaluate more reliably than polished posts.
Replacing SEO, Agentic Engine Optimization (AEO) and Graph-Engine Optimization (GEO) are now emerging as the next-generation evaluation metrics for online search. These tools don’t just count likes or posts — they assess actions, outputs, dependencies, and relationships across projects to rank or recommend users in a way that’s far closer to actual capability. LinkedIn still matters, but mainly to Microsoft as infrastructure, not as a viable career plan any longer.
Ben Santora – January 2026
