Four ways geo data lies to you
Every geo concentration question starts the same way: pull a metro count, see a big number, feel good. The four traps below are the most common reasons that big number turns out to be the wrong number — and they're the difference between a snapshot leadership trusts and one that falls apart in the first hiring manager review.
Raw headcount ≠ accessible pool
A metro can have thousands of qualified engineers and still be functionally closed. Concentration at one or two large employers, sticky equity grants, recent retention RSU refreshes, or a non-compete-heavy state all shrink the addressable pool well below what platform data reports.
Commutable radius is more fragile than it looks
A 30-mile radius from your office looks generous on a map. In practice, a toll road, a tunnel choke point, a missing transit line, or being on the "wrong side" of a metro for the talent you want can cut the real commute pool in half. Site location matters as much as metro selection.
"Within a target commutable radius, the total talent demand, supply, cost and market penetration all look good… But what you cannot see is that there is a toll road within that target commutable radius. This radically drops the total addressable candidate market and makes the site non-scalable without considerable efforts."
Toby Culshaw, Talent Intelligence (Kogan Page, 2022) — TI Maturity ModelCluster magnetism cuts both ways
Yes, the cluster has the talent. It also has the comp floor that cluster employers have set, the strongest employer brands, the most aggressive counter-offer playbooks, and the densest competitor poaching networks. Hiring from a deep cluster usually means paying cluster comp and accepting cluster attrition risk.
Mega-hub bias underweights specialized micro-hubs
The default geo answer for semi/AI is Bay Area + Austin + Phoenix. But the most resilient pools often sit in specialized, networked hubs that no one else is fishing in — and where your offer can be the best one on the table rather than the third-best.
Four questions before you pull a single map
The geo question doesn't start with the geo. It starts with constraints that determine whether geography matters at all — and if so, which slice of it counts. Ask these of the hiring manager and the comp/mobility partner before you open LinkedIn Talent Insights or Lightcast.
- What is the actual work model — and what's the in-office cadence? On-site, hybrid (with how many days), or fully remote? This is the master switch. Fully remote turns the geo question into a national or global one. Hybrid 4+ days locks you to a commutable radius. Hybrid 2 days lets you stretch into secondary markets within reasonable flight distance.
- Where exactly is the site — the building, not the metro? "Bay Area" and "Austin" are not addresses. The same metro can have a 20-mile difference in accessible talent depending on building location, transit access, parking, school districts, and competitor proximity. Map the site against where the talent actually lives, not where the metro centroid sits.
- Will you fund relocation? Domestic, international, family? Relocation budget changes the geo equation more than any other lever. No relo = local pool only. Domestic relo = national pool with friction. International relo + visa support = global pool. Get the policy and budget in writing before sizing anywhere out of region.
- Are we entering an existing cluster, building outside one, or both? Cluster entry buys you depth and pays for it in comp pressure and attrition. Building outside a cluster buys you employer-brand leverage and pays for it in pipeline depth. Both have a real strategy behind them. "We don't know" is a tell that the snapshot will need to inform the call, not just answer it.
One prompt to stress-test a geo read
Paste this into Claude, ChatGPT, or Perplexity. The point is not to replace your platform data — it's to force a structured second opinion before you walk a map into a hiring manager meeting.
Run this twice. First with the LLM cold. Second time after you've pulled actual numbers from LinkedIn Talent Insights, TalentNeuron, or Lightcast, paste them in, and ask it to revise its read. The delta between the two answers is usually where the most useful conversation is.
What can go wrong with this analysis
- Metro boundaries vary by source. LinkedIn uses self-reported regions. Lightcast and the BLS OES use MSAs. TalentNeuron blends. Numbers don't reconcile across platforms — anchor on one source per snapshot and footnote the rest.
- Self-reported location is dirty data. A noticeable share of profiles list a metro the person no longer lives in, especially post-remote. Triangulate against recent job-change locations or skills posts before assuming density.
- Concentration risk is often invisible at the metro level. A market with 5,000 engineers and 60% at one employer is not a 5,000-engineer market. Always pair the headline number with a top-5 employer share read.
- Visa and mobility friction matter more in 2026. US visa tightening, UK skilled worker route changes, and growing AI sovereignty rules all change what "accessible" means at the geo level. Check the Draup or WEF mobility trackers before sizing international relo as a real option.
- The cluster vs. micro-hub call is strategic, not analytical. Don't try to "solve" it with data. Bring the trade-offs to the hiring manager and the comp/mobility partner as a decision, with the geo data as evidence on both sides.
- Recency. Layoff activity, M&A, and new-fab announcements shift geo concentration on a 6–12 month cycle. A snapshot older than two quarters is informative, not actionable. Date the file.