Machine Translation vs Human Translation for SEO

Machine translation got good enough to tempt every international project into the same calculation: why pay per-word for humans when the machine drafts fifty pages an hour? The SEO answer has two layers — Google's stated policy (automatically generated content without human review sits in spam territory; edited machine translation is legitimate) and the competitive reality (your translated page ranks against native content, and the gap shows in every metric that matters). The working resolution isn't either/or; it's a tier system. Here it is.

The two constraints, precisely

Policy: Google's line has been consistent — machine-translated text published raw at scale is the "automatically generated" pattern spam policy names; machine translation post-edited by competent humans is ordinary content production. The distinction is review, and it's enforced the way scaled-content policy generally is: the median page's value density, not the tool that drafted it. Competition: the local SERP's incumbents write native — idiom, register, the vocabulary searchers actually use (the re-origination problem: raw MT translates your phrasing, not their queries) — and the engagement gap of foreign-smelling text (bounces, trust hesitation, the satisfaction physics) compounds whatever the policy layer doesn't catch.

The tier system (spend where it competes)

1. Tier one — human-led (native translator or transcreation): money pages, the competing content layer, anything conversion-critical or brand-voiced, and all YMYL-adjacent material (mistranslated health/finance/legal text is a liability beyond rankings). This tier also rebuilds rather than translates titles and keywords, per the re-origination rule.
2. Tier two — MT post-edited (the workhorse): the volume middle — solid machine draft, native-speaker editor fixing idiom, terminology and the keyword layer: policy-clean, cost-effective, and competitive for informational content whose SERPs aren't brutal. The editor's brief includes the search pass (local query phrasing into titles/headings), not just fluency.
3. Tier three — raw MT: don't index it. Where raw MT serves users (support archives, forum content, breadth material), serve it noindexed or on-demand (the translate-button pattern) — user value without entering the median-page evaluation. Raw MT indexed at scale is the tier the policy names and the repricings keep collecting.

The operating rules

Glossary and terminology enforced across tiers (product names, key phrases — consistency is both brand and entity hygiene); the editor pool per language treated as infrastructure (the native-speaker doctrine extended from outreach to content); QA sampling by someone who searches in the language, not just reads it (the fluent page targeting phrases nobody queries is tier-two's characteristic failure); and the tier assignment revisited by performance — a tier-two page winning impressions but losing clicks in its market is nominating itself for tier-one treatment, per the standard chain reading.

Frequently asked questions

Can Google actually detect machine translation?

Increasingly well at the raw end (MT fingerprints are what language models eat for breakfast) — but detection is the wrong frame: raw MT underperforms because it's worse for searchers (query mismatch, engagement gap), and the policy risk stacks on top. Post-edited text detects as what it is: reviewed content.

Is AI translation changing this calculus?

The drafts keep improving, which moves the editing economics (tier two gets cheaper per page) without moving the two constraints: review remains the policy line, and local-query alignment remains human work. The tier system absorbs better machines gracefully; it doesn't retire.

We machine-translated 2,000 pages last year. Triage?

The audit per language: performers get tier-two editing (they've proven demand), the indexed-and-ignored mass gets noindexed pending editing budget (stopping the quality drag today), and the money pages get tier-one rebuilds first — the same leverage ordering as every cleanup in this library, after which each language competes on the standing pair: content at the local bar and authority in the local web (both arrangeable).