This chapter argues, briefly, that much work in formal Computational Semantics (alias CompSem) is not computational at all, and does not attempt to be; there is some mis-description going on here on a large and long-term scale. The aim of this chapter is to show that such work is not just misdescribed, but loses value because of the scientific importance of implementation and validation in this, as in all parts of Artificial Intelligence. It is the raison d’etre of this subject. Moreover, the examples used to support formal CompSem’s value for the representation of the meaning of language strings often have no place in normal English usage, nor in corpora. This fact, if true, should be better understood as should how this paradoxical situation has arisen and is tolerated. Recent large-scale developments in Natural Language Processing (NLP), such as machine translation or question answering, which are quite successful and undeniably both semantic and computational, have made no use of formal CompSem techniques. Most importantly, the Semantic Web (and Information Extraction techniques generally) now offer the possibility of the large scale use of language data so as to achieve concrete results achieved by methods usually deemed impossible by formal semanticists, such as annotation methods, which are fundamentally forms of Lewis’ (1970) “markerese,” the term he coined to dismiss methods that involve symbolic “mark up” of texts, rather than using formal logic to represent meaning.