claimed that post-editing is generally faster than translating from scratch, regardless of language pairs or translators' experience. There is, however, no agreement about how much time can be saved through post-editing in practice (if any at all): While the industry reports on time savings around 40%, some academic studies suggest that time savings under actual working conditions are more likely to be between 0–20%, or that it may depend on the terminological proximity between the source and target languages. Professionals have also reported negative productivity gains where corrections require more time than to translate from scratch. Post-editing and the language industry After some thirty years, post-editing is still "a nascent profession". What the right profile of the post-editor is has not yet been fully studied. Post-editing overlaps with translating and editing, but only partially. Most think the ideal post-editor will be a translator keen to be trained on the specific skills required, but there are some who think a bilingual without a background in translation may be easier to train. Not much is known either on who the actual post-editors are, whether they tend to be professional translators, whether they work mostly as in-house employees or self-employed, and on which conditions. Many professional translators dislike post-editing, among other reasons because it tends to be paid at lower rates than conventional translations, with the International Association of Professional Translators and Interpreters (IAPTI) having been particularly vocal about it. The quality of machine translation output for post-editing is higher, and therefore requires less post-editing effort, when the machine translation is provided by a neural, vertical or customised machine translation engine. Translation efficiency gains can be measured by tracking time linguists need to correct the machine translation in the same translation environment, such as XTM Cloud, a Translation management system and Computer-assisted translation tool, where post-editing times and linguistic quality assessment results of the post-edited texts can be compared. There are not clear figures on how big the post-editing pie is within the translation industry. A recent survey showed 50% of language service providers offered it, but for 85% of them it accounted less than 10% of their throughput. Memsource, a web-based translation tool, claims over 50 percent of translations between English and Spanish, French and other languages have been done in its platform combining translation memory with machine translation. Post-editing is also being done through translation crowdsourcing portals such as Unbabel which,