This. My biggest data lessons from 25 years in digital publishing / marketing to add to the efficiency/effectiveness debate:
1) There’s an important distinction between being data-driven and data-informed; more organisations need to lean towards the latter, because…
2) No numbers mean anything without context – almost everything measurable needs multiple other datapoints, timescales, and points of comparison to have any meaning
3) Most data tracked by marketing departments are vanity metrics with almost zero long-term value for the business as a whole
4) Pick the wrong KPIs (pageviews being the most obvious, revenue growth perhaps the least) you’re more likely to harm the business than help it by focusing on improving the *indicator* rather than the business-wide performance, because…
5) Almost every metric can be gamed or significantly impacted by outliers or picking the wrong points of comparison, but…
6) Not enough people check to see if this is what’s happening, especially if the results are looking good
7) Equally, just because you *think* you can measure something doesn’t mean this is what you’re actually measuring, or that it’s helpful to do so, but…
8) Tables of numbers and nice pretty charts (especially with trend lines) are addictive, while cross-referencing multiple metrics and trying to make sense of it all is difficult – not helped by most of the tools available being deeply unintuitive, so…
9) Most laypeople don’t bother asking about the methodology for fear of looking stupid, and just nod along, so…
10) Keep on questioning the data – who compiled it, how, when, where, why, and what could we be missing? Data interpretation is as much art as science – the more we question what we’re seeing, the more likely it is someone will have one of those sparks of inspiration that help you find something genuinely meaningful
At times I liked this a lot – a neat companion to Neal Stephenson’s Cryptonomicon as a novel about the birth of the computer age. It could equally work as a companion to Sebastian Mallaby’s non-fiction The Power Law, focused on the venture capitalists and somewhat unstable, potentially sociopathic tech bros who have built the modern tech industry into the morally suspect force that it is.
Effectively a montage rather than a narrative, with surprisingly little-known polymath genius John von Neumann and the various hugely influential ideas he had as its centre of gravity, it’s as wide-ranging as he was. This is the guy who co-created Game Theory (an approach many tech types seem to consciously adopt), helped develop not just the atomic bomb, but also the hydrogen bomb and concept of Mutually Assured Destruction – with its wonderfully appropriate acronym.
But he also came up with some initial concepts for artificial intelligence, notably the self-teaching, self-reproducing, self-improving Von Neumann machines that he envisioned spreading through the universe long after his (and humankind’s) death.
It’s this that the book is really building to throughout: Pretty much all modern AI systems are Von Neumann machines – at least, to an extent.
This makes this extremely timely and thought-provoking, despite being about someone who died 70 years ago.
How will these systems continue to evolve? Given von Neumann himself is, throughout, compared to the machines and systems he developed – his utterly alien way of thinking, his apparent disregard for his fellow humans, his neglect of his family, his apparent patronising contempt for people not as smart as he was – the suggestion that these alien intelligences are something to be wary and probably scared of starts coming through stronger and stronger.
This culminates in the final section, a detailed narrative of the significance and a blow by blow account of DeepMind’s 2016 victory over the world’s leading human Go player with their AlphaGo system.
Yet while an impressive achievement, as a whole the book didn’t quite work for me. The different voices talking about their relationships and experiences with von Neumann, done as if being interviewed, eventually all started to sound too similar. The opening and closing sections were thematically clearly linked, but the structure as a whole leaves the reader doing much of the work to connect the dots and get to the point the author’s making. A final coda to wrap it all up would, for me at least, have been appreciated.
Goodreads tells me I finished 74 books in 2025, some 35,000 pages. I almost made it to 75, but just ran out of time… Most were nonfiction, but mostly history, philosophy and science, so not exactly classic LinkedIn fodder.
Here’s a few I’d definitely recommend to better navigate the world of business / work (in no particular order):
1) Alchemy, by Rory Sutherland – a useful corrective to the idea that logic and reason should drive strategy, and a timely reminder (in this age of GenAI probability-driven “thinking”) that it’s often necessary to go lateral to succeed. But Sutherland’s a marketer at heart – of *course* he’d say that…
2) The Art of Explanation, by Ros Atkins – a guide to more effective communication, borrowing from a couple of decades’ experience in journalism; a book many non-journalists could do with reading, and almost the opposite of Sutherland’s approach.
3) Economics, The User’s Guide, by Ha-Joon Chang – as the debate about AI bubbles and the future of the job market drags on, this is one of the very best overviews of the history and post-financial crisis state of economic thinking I’ve come across; thought-provoking and accessible via short, clear chapters. An excellent read.
4) The Corporation in the 21st Century, by John Kay – a slight cheat as I’ve got a couple of dozen pages to go, but this is an excellent companion to the previous one, providing a potted history of how we’ve got to where we are in the world of business organisations and ecosystems, and how it all seems to be changing. Again.
5) The Power Law, by Sebastian Mallaby – a deep dive into the history, mentality and working methods of the venture capitalists that have done so much to influence the tech industry and global economy over the last few decades. It helpfully shows that Elon Musk (among others) has been problematic for years…
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Of course, all of these were written before the rise of GenAI and the advent of Trump 2, so.who knows how helpful they’ll be in navigating 2026?
If you’re happy with platitudinous banality for your “thought leadership”, GenAI is great!
The trouble is, this isn’t just a GenAI issue.
Many (most?) brands have been spewing out generic nonsense with their content marketing for as long as content marketing has been a thing.
Because what GenAI content is very good at exposing is something that those of us who’ve been working in content marketing for a long time have known since forever: Coming up with genuinely original, compelling insights is *incredibly* hard.
Especially when the raw material most B2B marketers have to work with is the half-remembered received wisdom a distracted senior stakeholder has just tried to recall from their MBA days in response to a question about their business strategy that they’ve probably never even considered before.
And even more especially when these days many of those senior stakeholders are asking their PA to ask ChatGPT to come up with an answer for the question via email rather than speak with anyone.
If you want real insight that’s going to impress real experts, you need to put the work in, and give it some real thought. GenAI can help with this – I have endless conversations with various bots to refine my thinking across dozens of projects. But even that takes time. Often a hell of a lot of time.
Because even in the age of GenAI, it turns out the project management Time / Cost / Quality triangle still applies.
We were having the same arguments 20 years ago about blog content from actual humans.
The problem is not with how the sausage is made but, as Sturgeon’s Law states, that “Ninety percent of everything is crap”.
(Of course, on Linkedin this quite simple – and surely obvious – statement led to lots of debate about the *ethics* of AI content rather than the quality. That’s a different matter altogether…)
“45% of the AI responses studied contained at least one significant issue, with 81% having some form of problem”
I’m a big fan of using GenAI to assist in research, ideation, and even sense-checking – asking it to help me with my own critical and lateral thinking. I use these tools multiple times a day, and am constantly encouraging the journalists I work with at Today Digital o use GenAI more to help them boost both their productivity and the impact of their work.
But it’s *vital* to keep fully aware of GenAI’s limitations when using it for anything where facts are important.
No matter how often we remind ourselves that LLMs have no true understanding, no real intelligence, no concept of what a “fact” actually is, the more you use them the easier it is to be taken in by their very, very convincing pastiche of true intelligence.
As this Reuters study shows, despite the apparent progress of the last couple of years, there are still fundamental challenges – which are unlikely to ever be fully overcome using this form of AI. (And which is why LLMs weren’t even classified as AI until very recently…)
The good news? With GenAI’s limitations increasingly becoming more widely appreciated, this could ultimately be a good thing for news orgs – because why go to an unreliable intermediary when you can go direct to the journalistic source?
Journalistic scepticism and fundamental critical thinking skills are becoming more important than ever.
The rhythms and tone of AI-assisted writing are now pretty much endemic on LinkedIn
And I get why: GenAI copy is generally pretty tight, pretty focused, and flows pretty well. Certainly better than most non-professional writers can manage on their own.
Hell, it sounds annoyingly like my own natural writing style, honed over years of practice…
But people I’ve known for years are starting to no longer sound like themselves.
Their words are too polished, too slick, too much like those an American social media copywriter would use, no matter where they’re from.
None of this post was written with AI.
And despite (because of?) being a professional writer/editor, It took me over half an hour of questioning myself, rewriting, starting again, looking for the right phrase. Doing this on my phone, my thumbs now ache and the little finger on my right hand, which I always use to support the weight while writing, is begging for a break.
With GenAI I could have “written” this in a fraction of the time, and it would have been tighter, easier to follow.
But it wouldn’t have been me – and I still (naively) want my social media interactions to be authentically human to human.
(Of course, the AI version would probably have ended up getting more engagement, because this post – as well as going out on a Sunday morning when no one’s looking, and without an image – is now far too long for most people, or the LinkedIn algorithm, to give it much attention. Hey ho!)
I’ve seen this piece shared a lot, and like it. I’ve long been a fan of Systems Thinking (check my bio, it’s at the heart of my approach to everything).
But I’ve always seen Systems Thinking as more of a mental model or reminder to look beyond the immediately obvious causes and effects that could impact a strategy, rather than an enjoinder to try and literally map out interactions between all the different components.
As this piece notes, if you try to map out every interaction in a complex, shifting, uncertain system, you’ll never succeed. There are too many variables, all changing. Complexity Theory – even Chaos Theory and the Heisenberg Uncertainty Principle – rapidly becomes more helpful. Only these usually aren’t of much *practical* help at all.
It’s like playing chess – you don’t bother mapping out ALL the possible moves, as that would take forever (look up the Shannon number to get a sense of how many there could be – it’s more than the number of atoms in the observable universe…), and is therefore useless.
With experience, good chess players (and good strategists) can rapidly, intuitively home in on the moves most likely to work – both now and several moves down the line.
The problem is that the same moves will rarely work twice – at least not against the same opponent. And in a complex, ever-changing system, you’ll rarely have the opportunity to make the same sequence of moves more than once anyway, as the pieces will be constantly changing position on the board. Which will also be constantly changing size and shape.
“But metaphor isn’t method.”
That’s the key line from the linked piece. Business strategy isn’t chess – because you’re not restricted to making just one move at a time, or moving specific pieces in specific ways.
The challenge is to keep as flexible as possible while still moving forwards, which is why this bit of advice – one line of many I like, especially when combined with the recommendation to design in a modular, adaptive way – is one I pushed (sadly unsuccessfully) in a previous role:
“Instead of placing one big bet, leaders need a mix of pilots, partnerships, and minority stakes, ready to scale or abandon as conditions change.”
The problem is that strategy decks – still at the heart of most businesses and almost every marketing agency – are intrinsically linear, despite trying to address nonlinear, complex systems.
This is why most strategies end up not really being strategies, but plans, or lists of tactics.
And thats why most “strategies” fail.
Don’t focus on the *what* – focus on the *how*. Great advice from my former boss Jane O’Connell, which took me a long time to truly understand. It’s a concept that’s core to this excellent piece – and incredibly hard to explain.
The question of what AI does to publishing has much more to do with why people are reading than how you wrote. Do they care who you are? About your voice or your story? Or are they looking for a database output? Benedict Evans, on LinkedIn
Context is (usually) more important to the success of content than the content itself. And that context depends on the reader/viewer/listener.
It’s the classic journalistic questioning model, but about the audience, not the story:
Who are they?
What are they looking for?
Why are they looking for it?
Where are they looking for it?
When do they need it by?
How else could they get the same results?
Which options will best meet their needs?
Every one of these questions impacts that individual’s perceptions of what type of content will be most valuable to them, and therefore their choice of preferred format / platform for that specific moment in time. Sometimes they’ll want a snappy overview, other times a deep dive, yet other times to hear direct from or talk with an expert.
GenAI enables format flexibility, and chatbot interfaces encourage audience interaction through follow-up Q&As that can help make answers increasingly specific and relevant. This means it will have some pretty wide applications – but it still won’t be appropriate to every context / audience need state.
The real question is which audience needs can publishers – and human content creators – meet better than GenAI?
It’s easy to criticise “AI slop” – but the internet has been awash with utterly bland, characterless human-created slop for years. If GenAI forces those of us in the media to try a bit harder, then it’s all for the good.
The GenAI equivalent of Googlebombing (remember that?) was one of my first concerns when pondering the likely impact of GenAI search, way back when ChatGPT 3.5 came out and the prospect started looking real.
This kind of thing is, sadly, inevitable. And while Google’s got very solid experience of getting around attempts to manipulate its algorithms, it doesn’t have a great track record of releasing AI products that can distinguish facts from confabulations (remember both the Bard and the Gemini launches?).
The other inevitability is that this is also going to lead to more scammy marketing techniques. We’re going to be inundated with yet more of those snake oil salespeople popping up to promise brands results in GenAI, just as they used to in the early days of SEO – fuelled by similar tactics of vast networks of websites all interlinking to each other to create the impression of authority.
Only now, rather than using underpaid humans in content farms, they’ll be using GenAI to spit out infinite copy and infinite webpages, poisoning the GenAI well for everyone in pursuit of short-term profits.
To help shape my thinking, I write essays and shorter notes examining the ideas and narratives that shape media, marketing, technology and culture.
A core focus: The way context and assumptions can radically change how ideas are interpreted. Much of modern business, marketing, and media thinking is built on other people's frameworks, models, theories, and received wisdom. This can help clarify complex problems – but as ideas travel between disciplines and organisations they are often simplified, misapplied or treated as universal truths. I'm digging into these, across the following categories - the first being a catch-all for shorter thoughts:
GenAI content is neither good nor bad:
I’ve seen