In the 15 years since I set up this blog, the blogging landscape has changed dramatically. And, now, generative AI is throwing yet another spanner in the works. So, on this blog anniversary, I find myself asking: Does it still make sense to keep a personal blog, as an academic?
Technological changes and academic blogging
Let me start by considering, very briefly, how some of the key technological developments of the last decade and a half have impacted blogging.
One key change was the widespread adoption of smartphones, alongside the sharp drop in mobile data costs. Together, these two changes challenged the long-form blog post, in favour of shorter ones with easily scannable layouts that work well on small phone screens.
Later, came the rise of centralised social media platforms and, particularly, the dominance of a small number of social media channels. That evolution prompted many academics to drop personal blogs and, instead, to post on third party platforms, such as Medium or LinkedIn. It also incentivised the creation of content that would inspire a reaction (i.e., likes, shares, comments…), rather than sustained discussions among the community that regularly read the blog.
Then came Musk’s takeover of Twitter, followed by changes that enabled or even favoured hate speech, as well as changes that severely limited the opportunity to use Twitter (now X) for research and teaching. While some academics stayed on Twitter/X, many moved to other platforms such as LinkedIn, Mastodon or BlueSky. The result was the disappearance of a dominant space for academic conversations, the dissemination of academic content, and engagement with academic stakeholders (e.g., conference hashtags). The conversations became fragmented across other platforms.
Generative AI and the impact of academic content
There is growing evidence that people are using AI to search for information and make decisions. Some people interact directly with generative AI chatbots such as ChatGPT or Gemini. Others do so indirectly, when they type a question on Google’s search engine and the first answer that they get is an AI-generated summary. Either way, we are increasingly coming across answers to our questions very, very quickly. Sometimes the answers are wrong. More often, they are simply incomplete: they lack nuance or reflect a particular perspective, only.
But because the response is eloquent, confident and “good enough”, we tend to offload thinking to the AI. It’s not that people stop thinking altogether. Rather that generative AI seems to be changing the extent to which we critique the AI-generated answer, and reducing the incentive to search beyond that specific interface. We seem to stop questioning what might be missing, and to search for alternative perspectives or lines of thought, beyond what the AI initially surfaced, unless it is a topic that we deeply care about.
If people are searching less and the answers are increasingly filtered through AI, this means that, for most topics, people will either come across your content in the initial, AI-generated answer, or not at all. Academic blogs now need to serve two audiences at once: the machines that mediate content discovery, and the humans that are looking for depth and alternative perspectives.
Getting noticed by the AI
The Generative Pulse report by Muck Rack offers some clues for the first challenge.
The report states that 82% of the links cited by AI come from earned media rather than paid content. This includes blogs, which account for 12% of citations. In contrast, social media content represents only 4.3% of the links included in an answered generated by AI. To me, this suggests that blogging still very much makes sense in the age of generative AI.
The report also highlights the importance of recency. Most citations are for content produced in the last 11 month, 8% in the last month and 5% in the last fortnight.
Finally, authority seems to be assessed in terms of source. AI systems privilege established institutions and recognised outlets. However, content on a brand’s own website seems to be more valued than third-party content. Moreover, links to recognised sources and the use of industry-specific terms, also increase authority.
In my view, this has the following implications for blogging in the age of generative AI:
- Having your own blog becomes more important, not less.
- Blogging regularly, and repeating key messages, matters.
- Clear positioning and signalling are essential if your content is to be recognised and surfaced by AI systems.
Capturing the attention of humans
For the smaller number of topics where people do go beyond the AI summary, authenticity becomes critical.
To stand out from the growing volume of AI-generated content, it helps not to sound too perfect. Developing a distinctive voice, being surprising and personable, are essential. As is making your perspective explicit from the start, so that people know how your contribution adds value to other answers out there, including the AI-generated ones.
Of course, blogging is very time consuming and does not come without risks. Whether it is worth the investment will very much depend on your context, career stage and motivation. But, for those of us for whom blogging is a key step in thinking about, producing and disseminating research, blogging still makes sense in the age of generative AI. However, just as with other technological evolutions, previously, its role is changing. I think that, in the age of generative AI, blogging’s value, for an academic, is less and less on volume or reach, and more and more on voice and developing a unique perspective.
Do you agree? How has generative AI changed the way you discover, read, or write academic content?
Ana Canhoto 