A comprehensive document to help you understand the trends and opportunities in this fast-growing sector.
Are you a web copywriting or SEO professional interested in the latest innovations in copywriting assistants? Our document on the market for writing assistants based on generative AI is for you! It will help you understand current trends, the players involved and the opportunities to be seized in this rapidly expanding field.
The market for writing assistants based on generative AI is booming. These tools enable web copywriters and SEO specialists to create quality content in record time, using sophisticated algorithms to generate unique and relevant texts. But who are the players in this market, what are the current trends and what opportunities are there for web copywriting and SEO professionals?
First of all, we need to define what a generative AI-based writing assistant is. This is software that uses sophisticated algorithms to generate content autonomously, based on pre-existing data and models. These wizards can be used to create texts from scratch, to reformulate existing texts or to offer suggestions for keywords and titles.
The advantages of these writing assistants are numerous. First of all, they save precious time by automating certain writing tasks. An editorial assistant can generate texts in a matter of minutes, whereas a human editor would need several hours. What’s more, these wizards can offer suggestions for relevant keywords and titles to improve a website’s SEO – a crucial issue for SEO professionals.
But who are the players in this market? There are many different types of player, from start-ups to tech giants. Start-ups include Rytr, Jarvis, Copy.ai and Writesonic. These companies offer writing assistants based on generative AI at affordable prices, or even free for certain limited versions.
The tech giants include Google, which recently launched its own generative AI-based writing assistant, dubbed “LaMDA”. It is capable of generating texts in response to complex questions, using pre-existing models and contextual data.
