I'm Dom Jackman. I founded Escape the City in 2010 to help people leave corporate jobs and find work that matters. 16 years later, 500k+ professionals have used the platform - mostly people 5-15 years into careers at places like McKinsey, Deloitte, Google, the big banks - who feel a growing gap between what they do all day and what they actually care about.

I'm not from the EA community. I'm writing this because I think there's a real overlap between the people I work with and what the EA talent ecosystem actually needs. I want to test that before investing serious time in it.

What I've noticed

Reading through talent discussions on this forum, there's a consistent theme: the pipeline is strongest for early-career people. 80,000 Hours does great work for students and recent grads. Probably Good provides broad guidance. BlueDot, MATS, Talos build skills for specific cause areas.

But mid-career professionals with real commercial experience keep coming up as underserved. The "Gaps and opportunities in the EA talent & recruiting landscape" post nails it: these people "don't have 'EA capital,' may be poorly networked and might feel alienated by current messaging." The post calls for "custom entry points" for this group.

I know these people. They're my entire audience.

What I see every day

Escape's users aren't students figuring out what to do. They're programme managers, product leads, strategy consultants, ops directors - people with 8-15 years of running teams, managing budgets, shipping things. A lot of them are actively looking at climate, global health, AI policy, effective nonprofits.

But they end up in B-Corps and social enterprises because that's what they can find. They don't know AI governance orgs need people with policy experience. They don't know biosecurity labs need programme managers. They don't know GiveWell-recommended charities need experienced operators. Nothing connects their skills to the roles where they'd actually have the most impact.

What I'm thinking about building

An AI-powered matching tool that sits between this audience and high-impact roles. Not career advice (80K Hours and Probably Good do that). Not 1:1 coaching (Successif and High Impact Professionals cover that). Infrastructure:

• Upload your CV or LinkedIn profile
• AI matches your skills and experience against high-impact cause areas and open roles
• Get matched to specific positions at orgs working on neglected, important problems
• Clear, jargon-free explanation of why this role matters and how your experience translates

The differentiator isn't the tech. It's the audience. These 500k people have already self-selected by signing up to a platform about career change toward meaningful work. We don't need to find them. They're already there.

Honest questions

Is this actually neglected? I know about 80K, Probably Good, HIP, Successif, and the cause-specific programmes. Is someone already doing AI-powered matching at scale for mid-career people moving into high-impact roles? If so I'd rather help them than duplicate.

Would EA orgs actually hire through this? Does an AI governance think tank or a GiveWell charity want a tool that surfaces experienced corporate professionals? I have no signal on this.

Broad or narrow? My instinct is to cover the full range of cause areas - global health, AI safety, climate, animal welfare, biosecurity - rather than picking one. But that risks being a mile wide and an inch deep.

What makes this credible? I'm an outsider to EA. I've spent 16 years on meaningful careers but not within the EA framework. I take the ideas seriously - cause prioritisation, neglectedness, tractability - and I think they're largely right. But I don't want to build something that looks like EA language slapped onto a generic careers platform. What would make this genuinely useful?

Not looking for funding

Not pitching anything here. Looking for honest signal on whether this is worth building, and how to do it properly. If you work at an EA-aligned org and have views on mid-career hiring, or you've made this transition yourself, I'd really like to hear from you.