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Come for the tool, stay for the network still relevant in 2024

I’ve been chatting with my friend about the origins of LinkedIn, recruiting through its social network, and a few other things. This is part of a larger project researching whether (or more likely how) AI will affect job hunting and hiring.

As we were digging into the early days of LinkedIn and evaluating it as a business, I was reminded of the excellent Come for the tool, stay for the network essay. In it, Chris Dixon makes an important point that even if your end goal is to build a network, you often need to offer a single-player benefit to attract users.

LinkedIn’s founding idea was to be a professional network, and the focus was on building the network from the get-go. This is unlike Instagram, which was primarily a photo-editing and sharing platform app first, the network second.

What was the breakdown of single- and multi-player tools at LinkedIn’s inception?

Single-Player Tool

The user profile that doubled as a resume was LinkedIn’s primary single-player tool. It’s worth remembering that LinkedIn was founded in 2003, so the concept of a “resume with a URL” was pretty revolutionary. That was what attracted a fair number of people originally. It was also what you could do as an early adopter of the product, without any of your connections on the platform.

Multi-Player Tools

  1. Personal Connections Feature: This allowed users to look up how many hops (degrees) someone was in their network. This feature was particularly useful for salespeople trying to reach someone, and obviously for recruiters. Apparently, this feature was very popular because it was solving a real problem. It was unique enough to have a strong “wow” effect.

  2. Job Postings: LinkedIn had job postings from the beginning. A marketplace for jobs is obviously an example of a multi-player feature. In fact, marketplaces are prime examples of businesses (or features) with network effects.

This little trip down memory lane reminded me of something really important once again: Even if you dream of building a network-effect business (and most software has some degree of it), you need to start with a utility of some sort. It’s no surprise that Y Combinator repeats the same message ad nauseam: solve someone’s real problem.

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Deep Learning ∩ Applications. A recent pivot from a 'promising career' in systems programming (core team behind the Scala programming language). Pastime: Ambient Computing. Grzegorz Kossakowski on Twitter