What the evidence shows In this channel's material, network effect shows up as a design and investment lens. Sarah Tavel (Benchmark, ex-Pinterest) describes the next consumer AI wave as needing a multiplayer network-effect experience — most AI products today are solo, single-player, and that is the missing social layer [^01a0f15a-e186-7add-b1f8-042de716bec5@453]. How it powers the product In a multiplayer social product, participants chase status in the network — followers, views, likes — and that status-seeking becomes a north star: it creates incentives for participants to do what you want them to do [^01a0f15a-e186-7add-b1f8-042de716bec5@453]. Custom GPTs are the counterexample: they were made by an unbelievably capable team but are not social — there is no ability to follow creators and no visibility into underlying prompts or documents, so no trust builds. A community of prompt creators people would follow and easily apply to their own profiles would be the network-effect version [^01a0f15a-e186-7add-b1f8-042de716bec5@453]. How it guides founders and investors Tavel also names thinking in network effects as a marker of founder taste when investing in the AI wave — founders who reason in terms of networks and strategy rather than simply building [^01a0f15a-e186-7add-b1f8-042de716bec5@453]. - Network effect here means value that grows as a multiplayer, social experience rather than a solo tool.- Status seeking (followers, views, likes) is the mechanic that drives it.- Custom GPTs lack it because they have no follow, visibility, or trust mechanisms.- Investors treat a network-effect mindset as a signal of strong founders.