A causal experiment across more than 20 million LinkedIn users found an inverted U. Moderately weak ties, the acquaintances you already have and undervalue, drive more job mobility than either your closest ties or contacts you share almost no one with. This page finds exactly those and tells you what to say.1
Why her: she is a UX Researcher at Northbeam, one of your targets, and you already share a tie. This is the warmest door you have and it needs nothing from anyone else.
Hi Priya, we overlapped in Design Club. I am a junior moving toward UX research and I admire your team's work at Northbeam. Could I ask you a few questions about how you got started, maybe a quick 15 minute call?
Why him: you know nobody at Lumen, but Marcus does. He is the bridge. He opens the door and you walk through it, which is the one step only a person can take.
Hi Marcus, I loved working with you last summer. I am focused on UX research roles and noticed you are connected to a few people at Lumen. Would you be open to introducing me to someone on their research team?
Why this is manual: no platform exposes your second-degree network to an app, and nothing here will ever scrape one. So you run the search and bring back what you see.
Do this: on LinkedIn, search UX Researcher at Verdi Labs and filter to second degree. LinkedIn will show you connected through, then bring that name back here and the intro gets written for you.
Contacts arrive by export from you, never by a live connection to any platform. Nothing is scraped, no account is ever accessed on your behalf, and no claim is made about anyone's private network. Networking is context and never moves the AI-exposure number.
The published AI-exposure number is built only from O*NET task data, Eloundou et al., and METR*. Pay, growth, networking, portfolio, and clean-up are context and never move it. A 0–100 exposure scale.
* This projection uses METR's established long-run time-horizon trend, not the faster 2026 estimates. Those newer figures come from a task suite near saturation, carry very wide confidence intervals, and measure software tasks specifically, so extrapolating them across all occupations is not yet reliable. See MIT Technology Review, "This is the most misunderstood graph in AI," February 5, 2026, https://www.technologyreview.com/2026/02/05/1132254/this-is-the-most-misunderstood-graph-in-ai/
This dashboard is a sample. Maya Rivera, Northlight University, Northbeam, Lumen, and Verdi Labs are invented, and every figure on this page is sample data, not a real result.
