As if journalism hasn’t been a tough industry in recent years, here come the robots.

In the era of mass layoffs at newspapers and magazines that are now struggling in the era of online content, major publications like the Washington Post are employing artificial intelligence (AI) to boost their content production capabilities, specifically around reporting in real-time on big events, such as the Olympics.

Some publications are using AI to quickly create simple but useful reports that typically involve hard quantitative metrics freeing up human journalists to focus on more complex stories that tend to involve narrative and emotion.

The goal is for publications to become more effective in producing accurate, up-to-the-minute content (faster than humans can, typically) while allowing human writers to give more attention to long-form, opinionated content.

But before we all start running around like Will Smith in I, Robot being all paranoid about the coming robot revolution, let’s take a look at what’s really happening with robot automation in the writing industry and why it’s ultimately not that simple nor scary.

How Robots Write

So how exactly do these would-be journalist job munchers work? Robot writers combine algorithms with natural language generators to produce written content. These algorithms depend on source materials, uploaded information and content, in order to produce writing. With enough source material on any given topic, the machines can draft content independently and create a narrative around the supplied information.

The time to deliver this content varies depending on the complexity of the topic, the length of the article, and how busy the robots’ servers are.

One example of these robot writers is Articoolo, created by an Israeli startup, an AI writer that can generate an article on nearly any topic, as long as it can be described in between two and five words. The program can produce a maximum of 500 words and users can choose between “enhanced uniqueness” or “better readability” with regard to the content that it produces. Need a quick 250-word article on Kim Kardashian’s latest outfit? Articoolo has you covered in just under one minute. Need 100 different articles on the same topic? Just turn on the feature for affiliate marketers, and it’ll get done.

So far, Articoolo is an example of the primary use case for these robot writers: quick and larger scale content production on simple, easily reportable topics. That should be a cause for relief for human writers.

Writing vs. Content

The Associated Press has been using robot writers to produce content for quarterly earnings reports–an essential part of business reporting. When it comes to this type of reporting, speed and accuracy matter a great deal. To improve their coverage here, the world’s largest press organization partnered with Automated Insights and developed an automated reporting system for these earnings called Wordsmith.

The system has been up and running since 2014 and produces around 3,000 stories every quarter. This example underscores the key functionality of these robot writers–they are good for producing content, not in-depth writing and reporting.

That’s an important distinction that should set human writers at ease. AI programs have not progressed to the point that they are writing award-winning novels. It is very likely that we will be able to depend on human writers for producing this particular kind of art, along with other narrative forms, for quite a long time. Even freelance writers will have their place in the market. But what is certain about written content in the coming years is that its volume will grow significant and its nature will adapt to new realities.

In particular, stories dependent on facts and speed, such as news and sports, will come to be dominated by robot writers, as the demand for quantity escapes journalists’ ability to quickly produce content.

Kris Hammond, the founder of Narrative Science, a natural language generation firm in Chicago, has predicted that by 2025, 90% of the news read by the general public will be generated by computers. He insists that this indicates a massive increase in the volume of published material rather than huge numbers of journalists necessarily being replaced by computers.

Hammond further speculates that big data and content production will co-mingle in interesting new ways to produce specially tailored content for readers, giving them highly personalized news stories across all sectors, from healthcare to politics. His bold claim: one day there will be a single reader for each article.

Understanding a bit more about the true nature of AI content production should help writers out there find some solace that they’ll still have a niche in the sector. And hopefully the advanced notice of the changes that these robot writers will bring will give us all time to prepare for this brave new world.