Sometimes, you just need the human touch...

What kinds of content can humans do better than AI? The last few posts here have, I have to admit, been a bit of doom and gloom. I’ve looked at how conversational AI is going to squeeze search traffic to publisher sites, and at how adopting AI for content generation will remove the key competitive advantage of publishers. 

But there are areas of content creation where publishers can use their ability to do things at scale and the talent they have to make great work that audiences will love.

I’ve broken this post out into three parts, covering three different kinds of content. Today, I’m going to look at one which is close to my heart: reviews. Tomorrow and Thursday I’ll look at two other examples where humans can win.

Doing reviews right

One of the points that I made last week was that affiliate content, in particular, was susceptible to the shift to conversational ways of working with computers. However, that doesn’t mean that reviews are going to disappear. Certain types of article are likely to remain an area where humans will continue to produce better content for other humans for the foreseeable future.

For many sites, creating content for affiliate purposes has involved a lot of round-up articles, often created at least in part with what gets called “desk-based research”. You are not reviewing a product you have in your hand, you are researching everything about it that a consumer could possibly need to know, and summarizing it helpfully.

I’ve sometimes argued this was OK in certain circumstances, as long as you flag it and the amount of work that goes into the article is high. Just casting around for whatever is top-rated on Amazon doesn’t cut it because a reader can do that quickly themselves. But if you’re saving someone hours of time in research, you’re still performing a valuable service for them.

That kind of content isn’t going to survive the increased use of conversational AI because one thing that LLMs will be excellent at is ingesting lots of data and combining it into a cogent recommendation. LLMs can read every piece of Amazon feedback, every spec sheet and every piece of manufacturer data faster and more accurately than any human can. If your content is just research, it’s not going to be viable in the world of AI.

What will work is direct first-person experience of the product, written to focus on the less tangible things about it. An LLM can read a car spec sheet and tell you about its torque, but it can’t tell you how it feels to accelerate it around a corner. An LLM can look at a spec sheet for a laptop, but it can’t tell you how good the keyboard is to type on for extended periods.

If your editorial teams are focused on what I used to call “speeds, feeds and data” then part of your approach should be to shake up the way they write to get them closer to a more personal perspective. One way to do this is to change style.

Back when we launched Alphr at Dennis, one of the first changes I made to editorial style was to stop using the UK tech traditional plural in reviews (“we tested this and found blah”) and shift to first person (“I tested this and found blah”). Shifting into first person forces the writer into a more subjectively human perspective on the product you’re looking it. It frees the writer from an overly objective point of view into a more personal experience, and that is something which will survive the world of LLMs. Don’t just say what the specs are: say what it feels like, as a human being, to use this product.

Tomorrow, I’m going to look at the second area I think is a clear “win” for human-generated content: the often maligned area of real life stories.

The future is arriving a little later than expected...

I have had the best life. Back in 1989, I left polytechnic with my first class honours degree in humanities (philosophy and astronomy) and walked into the kind of job which graduates back in the 80s just didn't get: a year-long internship with Apple Computer UK, working in the Information Systems and Technology team – the mighty IS&T.

It paid a lot better than my friends were getting working in record shops. And although it was only temporary – I was heading back into higher education to do a PhD in philosophy, working on AI – it suited me. Without it, I wouldn't have had my later career in technology journalism. The ability to take apart pretty-much any Mac you cared to name became very useful later on.

Apple treated new interns the same as every other new employee, which meant that there was an off-site induction for a couple of days when we were told about the past, present, and future of Apple. The only part of the induction that I remember is the future because that was when I first saw the Knowledge Navigator video.

If you haven't seen Knowledge Navigator, you should watch it now.

Why is a 36-year-old concept video relevant now, and what does it have to do with publishing? The vision of how humans and computers interact which Knowledge Navigator puts forward is finally on the cusp of coming true. And that has profound implications for how we find information, which in turn affects publishers.

There are three elements of the way Knowledge Navigator works which, I think, are most interesting: conversational interaction; querying information, not directing to pages; and the AI as proactive assistant. I'm going to look at the first one: interaction as conversation, and how close we are to it.

Interaction as conversation

The interaction model in Knowledge Navigator is conversational. Our lecturer talks to the AI as if it were a real person, and the interaction between them is two-way.

Lecturer: “Let me see the lecture notes from last semester”. Mhmm… no, that's not enough. I need to review the more recent literature. Pull up all the new articles I haven't read.”

Knowledge Navigator: "Journal articles only?”

Lecturer: "uhh… fine.”

Note one big difference with the current state of the art in large language models: Knowledge Navigator is proactive, while our current models are largely reactive. Bing Chat responds to questions, but it doesn't ask me to clarify my queries if it isn't certain about what I'm asking for… yet.

That aside, the way conversation happens between our lecturer and his intelligent agent is remarkably similar to what you can do with Bing Chat or Bard now. The “lecture notes from last semester” is a query about local data, which both Microsoft and Google are focused on for their business software, Microsoft 365 and Google Workspace. The external search for journal articles is the equivalent of interrogating Bing or Bard about a topic.

In fact, Bing already does a pretty good job here. I formed a similar question to our lecturer's about deforestation in the Amazon, to see how it would do:

Not bad, eh?

The publishing model of information – the one which makes publishers all their money – is largely not interactive. The interaction comes at Google's end, not the publishers. Our current model looks like this:

1. A person interacts with Google, making a query.

2. They click through to a result on the page which (hopefully) gives them an answer

3. If they want to refine their query, they go back to Google and repeat the process – potentially going to another page

Interaction as conversation changes this dynamic completely, as an “intelligent” search engine gives the person the answer and then allows them to refine and converse about that query immediately – without going to another page.

Have a look at this conversation with Bard, where I am asking for a recommendation for a 14in laptop:

OK, that sounds good. Now let's drill down a little more. I want one which is light and has a good battery life:

That ZenBook sounds good: so who is offering a good deal?

By contrast, a standard article of the kind which publishers have been pumping out to capitalise on affiliate revenue (keyword: “best 14in laptop”) is a much worse experience for users.

And at the end of that conversation with Bard, I'm going to go direct to one of those retailers, with no publisher involvement required.

If that isn't making you worry about your affiliate revenue, it should be.

The model of finding information which search uses, based on queries and a list of suggested results, is pretty well-embedded in the way people use the internet. That's particularly true for those who grew up with the web, aged between 30-60. It may take time for this group to move away from wanting pages to wanting AI-driven conversations which lead to answers. But sooner or later, they will move. And younger demographics will move faster.

That, of course, assumes that Google will leave the choice to users. Google may instead decide it wants to have more time with “its” users and put more AI-derived answers directly at the top of searches, in the same way that Microsoft has with Bing. Do a keyword search on Bing, and you are already getting a prompt to have a conversation with an AI at the top of your results:

Once again, the best option for publishers is to begin the switch from a content strategy which relies on Google search and focuses on the kinds of keywords which are susceptible to replacement by AI (focused on answers) to content strategies which build direct audience and a long-term brand relationship.

Treat search traffic as a cash cow, to be milked for as long as possible before it eventually collapses. In the world of the Knowledge Navigator, there's not going to be much room for simple web pages built around a single answer.

LLMs remove a key competitive advantage of publishers. You need to find a new one.

It still surprises me that I’m old enough to have been part of the transition from print publishing to digital, but what surprises me more is that publishers are again making some of the same mistakes they made in that early internet era. But this time, it’s about the use of large language models to generate content, and it’s even being made by digital natives.

A little bit of history is probably useful here. Back in the mid to late 1990s, many publishers saw online content in terms of its ability to reduce their costs. Paper, printing and distribution of physical magazines were expensive. Publishing content online, though, was basically free. This, the theory went, would allow publishers to cut costs those costs and make more money.

What most publishers didn’t understand was that the high costs of production associated with print were their main advantage because they acted as a barrier to entry for new competitors. Starting a magazine was hard: you had to not only have enough capital to allow you to print and distribute the thing, you needed access to news-stand distribution, which in the UK meant working with big distributors who had to be persuaded to stock you. You needed a sales team to sell enough advertising to support it, and they needed contact books that were thick enough to get their feet in the doors. Magazine publishing was expensive, and only large publishers were able to get it done at scale.

By the mid-1990s, though, anyone could publish online. All those competitive advantages disappeared within a couple of years. You could publish easily using platforms like Blogger, WordPress, or even Myspace. You could get ad revenue from systems like Google Ads, without a sales team of any sort. Not only that, but you could get your content seen via Google search and social platforms.

It took publishers a long time to realise that the old barriers to entry no longer protected them. Some publishers still act like they think they do, and so appear consistently dazzled when a new platform comes along and makes individuals who take advantage of it into millionaires. TikTok is the latest, but it’s by no means the first. Online was a burning platform moment for publishers, and some of them took far too long to see it.

The next burning platform

The ability of large language models (LLMs) like ChatGPT to create content is, of course, being seized on by publishers who see it as a method of creating editorial content without having to pay anyone to do it – or, at least, by paying fewer people to do it (and probably cheaper ones too – that was another outcome of the move from print to digital). If you’re a publisher reading that and shaking your head, thinking “well that’s not what we’re doing” I am going to give you a small monkey side eye because we all know that if you’re not thinking that way, your CFO probably is:

There’s nothing wrong with using new technology to reduce costs, as long as you retain your competitive advantage. And here’s where things are difficult for publishers because what LLMs do is similar to what happened with web publishing in the 1990s: it removes the competitive advantage of publishers in the creation of content, just as the web removed their advantage in publishing and distributing it. It is the next step in the democratisation of publishing.

In the early internet publishing era, anyone could create any content and put it online, but to be successful they needed to have the expertise to write the content in the first place. That’s why niches like technology publishing were impacted early and heavily: there was plenty of expertise out there, and suddenly, they could create content directly without an intermediary.

Now, thanks to LLMs, anyone in the proverbial bedroom can create swathes of “good enough” content on any topic they want. They can churn out hundreds of average pieces about anything, just by taking a list of the most popular search queries in that topic as their starting point. They’re not flawless, but they’re good enough, particularly to answer the kinds of search queries which publishers have used to generate traffic at scale.

This is why, for publishers, AI content creation is another burning platform moment. Combine it with the move towards providing more answers directly on search pages, and you have a one-two punch to publisher traffic which Mike Tyson would be proud of.

Of course, publishers can use LLMs too. But, as with early internet publishing, their size means they can neither move fast nor with low enough fixed costs to make it work. If a proverbial 16-year-old can create an article with ChatGPT on “10 things you didn’t know about Mila Kunis” at the same speed as a celebrity magazine, at the same quality, the magazine loses even if it has used technology to eliminate roles and cut its costs. Because, unlike our 16-year-old, it has big fixed costs: offices, equipment, pensions, you name it. And it has margins to protect because the stock market expects to see revenue growth every year.

Regaining competitive advantage

So what can publishers do to retain their competitive advantage? There really is no point in trying to pretend that the AI genii doesn’t exist, in the same way that publishers couldn’t pretend in the 90s that people would just carry on buying huge volumes of print.

Nor will legal claims aimed at the likes of OpenAI, Google and Microsoft succeed. Yes, your content has been scraped to create the language models in the first place. But given the result in the Author’s Guild vs Google Books case, I expect courts to hold that this kind of use is transformative, and therefore fair use. Either way, it will be tied up in the legal system for far too long to make a difference.

Some have suggested that the way forward will be private large language models built solely using the corpus of text publishers hold. There are a few issues with this, but the biggest one is simply that the horse has bolted. OpenAI, Google and others have already trained their models on everything you have published online to date. They probably even have access to content which you no longer have. How many redirects of old, outdated content do you have in place where the original no longer exists? How many of your articles now only exist in the Wayback Machine?

Instead, the only option for publishers is to focus on creating content of a higher quality than any current LLM. You cannot gain competitive advantage at the cheap, low-cost end of the market. Trying to do so will not only make you vulnerable to anyone else with the same tools (at $20 a month) but also devalue your brand over the long term.

Creating higher quality content means employing people, which is why that urge to use LLMs to replace your editorial teams will actually undermine the ability of publishers to survive. Putting that cost saving towards your bottom line today is a guarantee that you will be out-competed and lose revenue in the future.

So what can you do with LLMs? The most important thing is that LLMs can be used as a tool to amplify the creativity and ability of editorial teams. They are most useful as what Steve Jobs used to call “a bicycle for the mind”, capable of amplifying human creativity. An LLM can give you a starting point, suggest an outline on any topic, rewrite a headline 100 times using the word “crow” and it never gets tired doing so.

If you’re a publisher, you probably still have decades worth of experience, context, contacts and knowledge of audiences in your editorial teams. Train them on how to use LLMs to amplify their creativity (and if you want some help with that, email me!)

You’re going to have to change your content strategy to adapt to the new world of falling Google traffic anyway. LLMs should be seen as a chance to exit the market for low-quality, high-volume content.

Position one for a query is no longer close to enough

I don't know of a single person in publishing who doesn't believe that large language models (LLMs) aren't going to have a profound impact on the industry. But most of the attention has been on using them to create content, something which many publishers see as a way of increasing efficiency (by which they usually mean reducing expensive headcount).

Whether that is actually possible or desirable is a topic for another time, but what I want to focus on is the other side of AI: what its adoption by Google is going to do to the traffic to publisher sites, and how we should be changing our content strategies to respond.

Google's large language models

It's worth starting by being clear about how Google is using LLMs. The company has two products which use large language models to deliver results for users. The first, and probably the best well-known, is Bard, which is similar to ChatGPT in that it uses a conversational interface where users ask questions or give prompts in natural language, and the programme responds.

The second – and the one which, I think, should be most concerning to publishers – is Search Generative Experience (SGE). SGE is currently in the experimental stage, but will ultimately deliver answers directly into the top of Google, generated by its large language model.

As you can see from the example, SGE takes up a lot of real estate in the query result, and delivers a complete answer based on what Google “knows”. Although it gives citations, there is no need to click on them if all you want is the answer to a query.

How this affects publishers

Obviously, anything which sits at the top of search results is going to impact on the amount of traffic which clicks through to publisher sites underneath. And this is potentially worse than anything we have seen before: if the answer to the query is given on Google's page, why would anyone bother to scroll down and click through?

This means the much-fought over positions one to three will be much less effective than every before, and there will be a big decline in publisher traffic.

The impact on different kinds of content

It is likely that some kinds of content will be impacted more than others. Answers to questions are an obvious one, and in 2017 they accounted for 8% of searches. That is likely to have grown already and grow still further as users get used to being able to ask machines questions and get good quality tailored answers.

But in its article on SGE, Google highlights a second area where publishers are likely to see a major impact: shopping. Many publishers have put significant effort into creating content focused on affiliate revenue, with some seeing affiliate overtaking advertising as a source of revenue. Affiliate content is almost always designed to capture traffic via search, for the simple reason that buying products usually starts with a Google search. An SGE-driven shopping search experience will ultimately bypass publishers and drive traffic direct to the retailer, with the AI making individually tailored recommendations on what to buy.

This threatens to be disastrous for publishers. Effectively, SGE delivers a one-two punch of reduced traffic as more search queries are answered on the results page, plus reduced traffic to and revenue from affiliate pages.

What publishers should do

SGE is currently in the experimental stage, which means publishers shouldn't see any significant impact for now. But there is a clear direction here: more answers to search queries will be delivered without any click-through to publishers. And product shopping queries are going to become something which Google channels to retailers (who, by complete coincidence, are also advertisers) rather than publishers (who, by and large, are not).

I estimate that publishers have a window of between three and five years to change content strategies to adapt to this new world, depending on the speed of user adoption. It could be faster: much will depend on how quickly Google's LLM work starts to move from an experiment to delivering real results.

The long-term answer for publishers is to reduce exposure to Google as a source of traffic. That's going to be tough: almost every site I have worked on relied on Google for between 60-90% of its traffic. And the more the site was focused on affiliate revenue and e-commerce, the higher that percentage was.

The answer is to focus on increasing your level of direct traffic, making your site a destination for content rather than something users hit once and bounce away from. Learn lessons from marketing: treat every piece of content you create as an opportunity to deepen your relationship with your audience.

There are five things I would recommend publishers start doing today:

1. Refocus your KPIs and OKRs to be about deepening relationships, not just traffic. Focus on repeat visits and sign-ups. Look to increase the number of qualified email addresses you have (and whatever you do, don't succumb to the temptation to capture more data. If you deliver value, you will capture more over time -- but all you need now is a person's email address).

2. Reevaluate your search strategy and focus on topics with complexity. The more complex the content, the higher its quality, the less likely it is that an LLM can deliver a good quality version of it. Expertise and depth will be essential, and complex topic areas might be the “last person standing” when it comes to Google searches which work for publishers.

3. If you have three to five year revenue forecasts, ramp affiliate revenue down over time rather than predicting growth. The era of affiliate revenue as a major contributor will be over. Use the revenue you are getting from it to bootstrap other areas.

4. Heavily invest in newsletters. And whatever you do, don't consider them to be a place for advertising. Nothing creeps users out more than thinking they are signing up for interesting content only to find it chock-full of ads or sponsored content.

5. Don't think that AI-generated content is going to “save” you. Many publishers are looking at content created by LLMs as a way of lowering costs. It will. But it will also put you out of business. Remember that any content you can create with an LLM can be done better by Google at the top of its results pages. What publishers have in their favour is human talent, creativity and expertise. The more you lose that by trying to use LLMs to cut costs, the smaller your competitive advantage.

Next week I will return to that last topic, and look at the mirage of LLM content and why it's a death-trap for publishers.

It's been a while...

It’s been a while. I have missed the last couple of weeks not because I was too busy to write, but almost the opposite: I have felt like nothing much has happened.

Of course, that isn’t true. It’s never really true that nothing is happening in your life, but when you’re not working, what tends to happen is that the days elide into each other. The rhythm of most people’s life is work, or child-rearing, or the climbing frame of domesticity which they have erected around their time.

I haven’t really yet cultivated that. I have had no work to do other than to make myself get up and write something every day. We have no children to depend on our timekeeping. And keeping house has never been a routine for either of us.

The commemoration this weekend has been that of three months since I last had to get up in the morning, do eight hours of work, and sign off from Teams. I can’t say I haven’t enjoyed it. Having nothing to do, no one relying on your input to get on with their lives, is something I can recommend to anyone who wants to avoid waking up one day and asking “what the hell happened to me?” It provides that thing we most lack as we dance busily through life: perspective.

So, what new perspective on my life have I found? First, that I have a kind of pastoral radicalism, a communism-not-Marxism which believes in the collective good. That sounds abstract, but I think it’s important. It’s a deep and abiding value, and we live in an age when values are used as a debased common currency, but in actuality are as ephemeral and short-lived as muons, decaying quickly into more stable and entrenched positions.

The second thing I have come to understand is how deeply rooted impostor syndrome is in my life. I have always spent time denying my role in what I have achieved (at one point, one of my managers made “blowing my trumpet” a goal for the year because of my habit of deflecting praise). Because of this, I am not kind to myself in any meaningful way. Being forced to just stop has allowed me to start the process of letting some of this go.

The act of writing can be both an antidote to and a trigger for impostor syndrome. Writers crave the validation of an audience because it’s the one moment when the feelings of fraudulence are pushed into the shadows. But the fear of not living up to expectations, of having no originality, of creating nothing of value, is also right there, all the time.

I have thought a lot about this over the past couple of days. We were away, first in Hastings (Kim was teaching a life drawing class there) and then Eastbourne, seeing the Turner Prize show. If you get, go: Rory Pilgrim’s Rafts made me cry, as did Barbara Walker’s work. It reminded me that art is emotion, and it means that I really do have to tap into my emotions to make mine work. More of that, I suspect, over the coming months.

Meanwhile, at some point I will have to actually get some kind of income or other. I have a few more months when I don’t **need** to work, but at some point money will once again become a thing of concern, rather than an abstraction which I can deal with later. One learning about money: I need much less of it than I would have thought a few months ago. Debt, it turns out, robs you of your freedom quite effectively because you have to earn more than you need to pay back someone for the time when you couldn’t earn all that you required. I’m free of debt now, and that feels like an unshackling.

Things I have been reading this week

I finished Gary Gibson’s Europa Deep in two gluttonous sittings. It’s a neat, tidy and highly enjoyable hard SF story, and it reminded me how much of the SF genre is currently playing with the tropes of thrillers and crime drama. I need to think a bit more about this because somewhere in the race to make SF adhere to the structures, tropes and pacing of the thriller, something – quite a lot – is lost.

Reading Hilary Mantel’s A memoir of my former self feels like a delightful indulgence. It’s a collection of Mantel’s extensive back-catalogue of non-fiction, created because she developed the habit early in her career of writing for newspapers, periodicals, and magazines as well as books. It wasn’t really for fun: it was a survival mechanism because writing fiction (then as now) was not really enough to live on, at least until you become the kind of celebrated and storied writer Mantel grew to be.

I’m glad she had to do it because she applied her mind to it and the results are spectacular. In the first piece, “On the one hand”, she writes about the difference between fiction and journalism:

Fiction isn't made by scraping the bones of topicality for the last shreds and sinews, to be processed into mechanically recovered prose. Like journalism, it deals in ideas as well as facts, but also in metaphors, symbols and myths. It multiplies ambiguity. It's about the particular, which suggests the general: about inner meaning, seen with the inner eye, always glimpsed, always vanishing, always more or less baffling, and scuffled on to the page hesitantly, furtively, transgressively, by night and with the wrong hand.

It’s great. You should read it.

This is Ian Betteridge's Three to Five.