The future of communicating science

I’ve been thinking about what a website like VoxDev looks like in 2030. Here are, in my view, the key trends impacting communications today, and the strategies I believe will continue to be a success.

The traditional paths by which research makes it into the public sphere are closing. AI is a key part of this, but it is not the whole story: funding models are changing rapidly, social media is shifting, and people’s preferences for consuming information are evolving. So how will we be able to get evidence, research, and science out there in the future? And for how long will that be a necessary goal?

After outlining the current trends I see as most important (Google search, social media, people over organisations), I expand on the following principles for effectively communicating science. I see these as keys to still being relevant in 2030 (comments and thoughts welcome!):

• Curation, curation, curation

• You’re only as big as your biggest influencer

• Direct contact

• Be useful everywhere, all at once

• ‘Living’ content is king

• Focus on what is ‘AI-proof’

And after going into these points, I’ve summarised some of the things I am still unsure about, plus why I’d invest in this area if I had the funds. To finish, I think about whether any of this advice will still hold up in 2040, and link to some related interesting reading.

The purpose of this long, meandering blog is to put all of my thoughts in one place. I mostly refer to the science I know best, which is the ‘dismal’ one. But I think these lessons apply regardless of the specific subject or topic you are trying to communicate to a wider audience.

Communications does not feel like the main focus of many of the bigger-picture debates on how AI will change the world. I think it is an underrated one (e.g. check out this scary stuff on ‘malicious AI swarms’), that is compounded by other current trends.

For me, it feels like an exciting time to be a nimble, driven organisation in the business of communicating science, at least for the next five years or so. My hope is that this period will be a much-needed shake-up to the traditional ways research reaches a wider audience. One that’s less formulaic, less about playing the game, more focused on what people enjoy consuming, and ultimately better quality.

Current trends: Where we find ourselves

The traditional players: Who is in the communicating science business

Where I work, at VoxDev, our goal is to communicate the policy implications of the best economic research in low- and middle-income countries in an accessible way. There is a whole ecosystem of organisations that I would put into the same broad category of communicating science, that includes big players like Our World in Data, The Conversation, and TED. Importantly, there are also individuals who do this themselves, through their own blogs, podcasts and videos – more on this later. And then there are many organisations which bridge research and communications, with in-house researchers and communications teams that seek to amplify their work.

Of course, there are also the publishers, including both academic journals and journalists. I imagine these two groups both think of themselves as the most important part of this ecosystem. That has probably been true for a long time, but I am not sure it will continue to be. Many of their business models no longer make sense, and I think the importance of these organisations will continue to diminish. I am sure some will innovate and stay relevant, but the writing is on the wall for a lot of publishers. So, there is a lot of churn coming (I expand on these rather grandiose claims throughout the rest of this blog).

RIP ‘Google it’ 1998 – 2026.

Google search had a good run. But it is not long for this world. The web as we knew it will quickly disappear. And to be honest, I’m looking forward to it.

Google search was an incredible invention that made research more accessible and communicating science easier. It also incentivised bad writing and was still, after almost 30 years, remarkably useless for users in many parts of the world.

In the age of Google search there was a relatively standard model for doing well. Step 1, be a big organisation with a website that Google trusted. Step 2, rest assured that your content will stay at the top of Google search results and reach a much wider audience.

Nowadays, people increasingly ask their GenAI chatbot of choice, rather than going to Google to search. And even if they do Google it, they have to turn off AI mode and then scroll past the AI overview.

(I wanted to leave this next part in, but I also appreciate it is quite boring, so feel free to skip to the less niche stuff afterwards.)

For the Google Analytics-uninitiated, here’s an illustration of what I mean based on data from the VoxDev website.

The graph below shows the click-through rate to the VoxDev website last year. This is calculated by dividing clicks by impressions, which is the number of times a VoxDev link was shown after someone searched on Google (this now includes those who see your link in an AI overview).

Not so long ago, VoxDev would reliably get 1 click every 50 times we showed up in a search result. Now it’s closer to 1 click every 200 times. And this is a common story; the click-through rates for a whole host of websites are falling through the floor.

If you thought I sounded dismissive about the chances of publishers, I think this demonstrates why many of their business models are just not sustainable. After the good old days of selling print, the migration to the web left many publishers dependent on clicks, so they could sell ads. Even this model was not exactly a winner, but it kept the lights on at a lot of places. Now, the clicks they depended on are vanishing.

Again, this is not necessarily an issue for users, as GenAI should find higher quality content that’s closer to what you were after. But it poses a new challenge to those trying to communicate science to the world and track their reach.

The barriers to communicating science are falling rapidly

The death of Google search as we know it is one part of a trend that’s seen the barriers to communicating science fall dramatically.

Social media certainly played a big role in democratising this process. But since Twitter’s heyday, users, particularly in the research community, have become much more dispersed across different platforms.

Then there are new platforms, namely Substack, that make it incredibly easy to start writing yourself, particularly if you already have a big audience who will follow you there.

So far, I think all of the trends I’ve described make this a much more competitive space to be in, which is great for the public. It has never been easier to find and read interesting and accessible content about scientific research, often without even needing to pay for a subscription.

In the long run, as I discuss at the end, I imagine AI will do most of the communication about science itself. So, I see the next five years of churn and competition as a passing moment, but nevertheless one with very high potential for growth and ultimately impact.

Still being relevant in 2030

Curation is king

AI is going to have a big impact on the amount of science getting churned out. There will be a lot more of everything: high-quality research, obvious slop, papers that look good at first glance but turn out to be rubbish upon further reading. An appropriate response will be to distrust everything, unless you have a signal that it’s worth your time.

There has been a lot written about the worries of ‘slop’ in particular. And sure, right now I can imagine journals are getting inundated with crappy, fully AI-generated submissions. But I think the main issue in the medium-to-long term, which is a much better problem to have, is what do we do when there is so much good science to communicate?

I’m confident that as a result of AI, we are going to see so much more high-quality research that has important implications for policy, helps us understand the world, and is ultimately worth sharing.

It’s certainly an interesting problem to solve. And focusing on curating the best research will be absolutely crucial.

Academic journals currently do this by giving relatively reliable signals about the quality of academic work. But they are already falling behind, particularly in economics, where it can take many years for papers to be published. Our experience at VoxDev has been that the key, useful insights from research are already relatively set long before that final published version of a paper comes out.

And this is already the situation now. Imagine the delays when we start to see an even bigger, and sustained, increase in submissions that pass journals’ quality thresholds.

Academics will have to figure out an entirely new system of publishing, but that won’t happen overnight. In the meantime, the public needs reliable signals of what’s good, and what’s gibberish. So those that establish a reputation of consistently curating research that’s worth reading and of high quality, without waiting for publication, will have the upper hand. Particularly if they also help to piece together this wave of knowledge, by summarising takeaways across the surge of papers.

We are all influencers now

It seems to me that people are tired of hearing what an organisation thinks, and would rather hear directly from people that they trust on specific topics they are interested in.

Influencer is often used as a pejorative. I see it more as a strategy than an indication of the value of people’s opinions. A mixture of the growth of blogging, and societies’ changing tastes for consuming information, mean that we have seen a new wave of ‘influencers’, who communicate their science in an accessible way, and have built large followings of their own. From those I personally follow, this includes people like Max Roser, Hannah Ritchie, Saloni Dattani, Sam Bowman, Ken Opalo, Richard Baldwin and Paul Johnson.

This new state of affairs is somewhat of a conundrum for organisations themselves. There are two answers, (1) hiring people with an established personal following; or (2) empowering your own staff to write, or talk, whilst helping to market them a bit. So, communications teams should move from doing the communicating, to supporting researchers to discuss their work themselves and build a following. That could involve encouraging them to write, providing training on communication skills, making sure they have time set aside for this, thinking through which parts of their work might be most interesting to share, etc.

Direct contact

The platforms we have come to rely on are desperate for users’ attention. That means they are heavy on the algorithms, and allergic to links taking users away from the platform.

Your followers on social media, or subscribers on YouTube, are increasingly likely to miss your latest release. Long gone are the days of, by default, chronological feeds from only the people you follow. This makes comms harder to predict. Sure, this can work to your benefit when one article blows up on Twitter, or a video gets going on YouTube. But that is hard to do, and if you care about consistency rather than occasionally going viral, it is not a reliable strategy anymore.

The response to this is a relatively simple one. Being able to contact people directly is increasingly vital. Email is the traditional way, so focus on those mailing lists. WhatsApp is also interesting, particularly for parts of the Global South where it is far more widely used than email. For example, The Continent is an African newspaper distributed through WhatsApp that seems to be nailing it.

Be useful everywhere, all at once

The standard model: Rank well on Google, tweets that go viral

The new model: Be everywhere, and be useful wherever people end up finding you

A point that Charlie Giattino made to me about OWID’s approach is I think particularly instructive - to be useful wherever people find you.

I have sometimes felt social media ‘engagement’ and ‘reach’ are hollow stats, but it really depends on what you are putting out there. If your post contains a really useful graph (like OWID’s do), or summarises a key new finding about the world, then a high level of reach means something completely different to the typical organisation’s robotic post with a link back to their website.

The temptation is to draw people to click on your link. But the most effective strategy is a short engaging post that leaves the people who read it smarter, even if they do keep scrolling.

Flexible

AI is going to try to replicate a lot of your favourite research and find some serious holes.

This is undoubtedly a good thing for science, and society. And organisations should embrace it rather than being slow to react. Even if you are left with some egg on your face, additional admin, or having to send an awkward email to a senior researcher whose famous thesis depended on a coding error (honest or otherwise).

Journals have never dealt with this particularly well, and when they do it’s an extremely slow process.

But in this new age, content should be ‘living’ anyways – by ‘living’ content, I mean something between Wikipedia and an academic paper. This means having a process in place to ensure timely updates, particularly with synthesis work. And organisations should be quick to react to replication failures, to clearly and obviously correct past mistakes.

This is why we believe the VoxDevLit model we have at VoxDev, where our reviews are updated as the literature on a specific topic evolves, should be replicated more widely across the sciences.

AI-proof content

There are two parts to being ‘AI-proof’. The first involves content that AI can make as well as a talented human – this is not a simple binary, as increasingly, we will all use AI in our writing processes. The second involves our preferences for human- over AI-generated content, the strength of that preference, and whether that preference endures.

Thinking about the current capabilities of AI, there is a fair amount of content that I still see as AI-proof over the next few years. Certain types of summaries will be replaced by AI in no time, but here is what I think AI won’t be able to do that well in the short term. In a sentence, it boils down to identifying the content people want and producing that to a high quality, often through multimedia. For example:

• Well-researched, high-quality, longer podcasts

• 10 minute+, documentary-style video, with real people on camera

• High-quality, long-form writing

• Beautiful, accurate data visualisation

Video seems particularly important, given the importance of YouTube and the current moves of many platforms to prioritise video-based content, including Spotify, Substack, and even LinkedIn.

It’s also useful to think in terms of what has not been written down before and therefore is not included in GenAI’s training sets. The internet feels incomprehensibly huge. But when you think about the subset of the internet that’s actually useful and open access, it starts to feel a lot smaller.

In academic research for example, there is a lot of interesting stuff that doesn’t make it into papers. Whether that’s descriptive statistics or claims that a researcher might come to believe during the research process, that they would share over a drink but might not be ‘rigorous’ enough for the actual publication. There is also a lot of tacit industry knowledge that has never been written down publicly, particularly on the messy processes of ‘getting stuff done’ or moving from research to implementing in the real world.

If your only value is making research accessible, this will be greatly diminished when AI puts evidence at everyone’s fingertips. But that doesn’t mean that people will magically start using evidence, trusting AI, or even preferring AI.

And this is one area I’d love to see research on.

How do people treat AI summaries vs human-written summaries? Do they enjoy one over the other? Trust one over the other? Is it actually about the content being different, or their mindset shift when reading people vs AI outputs? If the same text is presented as AI-generated, vs having a human author, do people care?

What I have no clue about

Polarised times

Academic research is far too slow to react to news cycles. Yet existing papers gain the most traction when a related issue is in the news and salient to a wide audience. So, do you go all in on targeting comms in response to the news cycle? Or do you operate above the day-to-day, and hope that others organically pick up your resources when they suddenly seem more relevant.

The answer is more obvious for some. If you’re a think tank that wants to have policy impact and go viral, then getting the timing right is crucial, but that draws you into constantly responding to the news cycle. I think traditionally, that has been a tightrope that certain organisations have been able to walk. But wading into the topic of the day seems harder and harder to pull off whilst maintaining a reputation for non-partisan scientific rigour.

Tracking reach and impact will be hard

To me, the whole point of communicating research is to get it out to a wider (or target) audience, and hopefully help people make more informed decisions, whether they are politicians, other scientists, or the general public.

Traditionally, tracking the reach of your communications has not been too challenging; you can still get relatively reliable data about your website and social media accounts, and track mentions across the entire web if you were happy to pay for certain software. But data from Google analytics is missing more and more users, whilst genAI is, for now, a complete black box, and social media is getting more fragmented in a way that tracking across all 10+ platforms you are on is more time-consuming.

Of course, reach is not always an end in itself, and tracking impact has been, and will continue to be, tough. It’s one thing getting your target audience to engage with your content, it’s a whole other kettle of fish figuring out whether that actually impacted decision making.

But now even understanding your actual reach will be hard. Will OpenAI ever tell you how often your research has been used for ChatGPT’s responses? I doubt it, at least for now.

People should invest in communications

There is lots of potential to have an outsized impact on the ideas which shape the future.

While funding from traditional government sources is dwindling, there are more billionaires than ever with cash to splash.

“All of Anthropic’s co-founders have pledged to donate 80% of our wealth, and Anthropic’s staff have individually pledged to donate company shares worth billions at current prices—donations that the company has committed to matching.” Dario Amodei in his most recent essay.

If I were a billionaire right now looking to spend my money well, I’d be looking at the explosion of high-quality research we will see in the coming years (Biologists, Chemists, Physicists et al. forthcoming). Can we create a platform that reviews and features what is most important quickly, accurately, and engagingly? That synthesises across studies to find common lessons and recommendations? Can high-profile thinkers with the necessary expertise and an established following of their own be enticed to lead this type of project?

What does that mean in practice?

Creating more Works in Progress-style platforms, more platforms like VoxDev (naturally), and allocating more funding for existing platforms to expand the work they are doing.

I also think that now is the time to be bold, and experiment with new forms of academic journals. I’d personally love to see a new Economics of AI Journal, in between an NBER/CEPR working paper and final publication, to make up for the current failure of publishers to keep up with this fast-moving technology in a timely fashion, and provide accessible findings to the public while they are still relevant.

Communicating science in 2040: My best guess

By 2040, at which point it sounds like we will have reached AGI (Artificial General Intelligence), it’s hard to imagine that much of the above will hold.

There will be very little that a human can write, or say, that AI is not also able to. But that doesn’t mean that nothing will be ‘AI-proof’.

The preference we have for people over AI, whether that’s reading a person’s writing, listening to their thoughts, or watching them talk, will likely endure. Computers have beaten the best human chess players for decades, but no-one watches computers play one another.

So while I can’t see there being ‘need’ for an organisation or person to ‘communicate science’, as all science will be at everyone’s fingertips, filtered through an AI that knows their history of consuming content, and has inferred their preferences for subjects, content style, etc., that’s not to say these organisations won’t be crucial until we get there. And it’s also not to say that there won’t be space for organisations, no doubt using AI, to communicate through their people’s voices.

Ultimately, this feels like a positive to me, and solves many of the problems people in the space have spent years trying to eliminate.

Five other related + interesting readings

• Saloni Dattani’s guide to data visualisation.

• The Age of Academic Slop is Upon Us by Seva Gunitsky

• The Fight For Slow And Boring Research by Jolie Gan: “As federal research funding shrinks, scientists are looking to other sources of support. Can they learn to sell their work without selling out?”

• The next frontier for public access: building channels of meaning by Meagan Phelan

• What is Think Tank impact? (And what is it not?) by Todd Moss