Interesting, thought-provoking and convincing about what needs to be done, while being realistic about how likely it is such vast changes to how the world works will come about. Yet also packed with examples of ways in which such changes are already taking place, giving some room for optimism.
A good polemic, in other words – and made even better by continually citing sources and experts from non-traditional backgrounds – neither ostentatiously nor explicitly, it made me realise how few economics and politics books regularly cite women or people from non-Western countries. Which may well be part of the reason why our economics and politics are so broken.
The only real criticism: The book itself is well enough written in terms of individual sentences and paragraphs, but lacks enough variety of tone and pacing to really keep the attention, and the author has a tendency to both repeat herself and extend metaphors well beyond the point where they have impact.
I’m vaguely pondering starting up a newsletter/podcast/etc exploring media/marketing received wisdom and groupthink…
The Superbowl, Davos, and ChatGPT’s announcement it’s running ads means media/marketing LinkedIn will be swamped with lukewarm hot takes this week.
This industry herd mentality is increasingly fascinating to me – the need to comment on the same things everyone else is talking about is rarely “thought leadership”, and is very far from the old advertising mantra “When the world zigs, zag”.
I’ve spent a decade in marketing, more than double that in publishing. In all that time I’ve rarely encountered many convincing new ideas – even during major platform shifts. And usually when I have, the evidence for “best practice” has lacked much substance – or blatantly originated in some tech company’s hype (as with the first, second, and third pivots to video, and certainly with the “everything needs to be optimised for Alexa now” fad).
It feels like we’ve now all got so used to running with the latest fad for fear of missing out or – worse! – looking out of touch, we’ve lost all sense of critical thinking, or desire to question industry norms.
But is this something in which enough people would be sufficiently interested to make it worthwhile? And will it cut through the algorithm – another idea we’ve all unthinkingly adopted?
This is a strange book. Originally written to accompany a BBC TV series back in 1981, it has since been extensively revised to reflect the (substantial) changes in understanding of this long period – covering over a thousand years, from Boudicca to the Norman Conquest.
That period alone is enough to raise an eyebrow. What the hell does Wood mean by “the Dark Ages”? And why, if he’s in search of them, does he focus purely on England? Equally, why does he choose to explore them by focusing on a series of individuals?
In part, the thinking seems to be that by centering each chapter on a named individual, you can explore the sources to understand how much we can really know in an era of fragmentary record keeping and near constant conflict. This is a nice enough idea – but it’s been done better elsewhere, especially in the last decade or so, as archaeology and history have merged and a glut of good books have come out on the Vikings and Anglo-Saxons in particular.
Equally, given the use of the term “Dark Ages” – usually contrasted to the Greek/Roman Golden Age and the Renaissance – it’s strange the focus here is largely on politics and power rather than culture and learning and civilisation and society.
Not a bad book, certainly, but its episodic nature betrays its roots in television. It’s let down by the fact that there’s really no clear connecting thread, and nor is there a flowing narrative – something seemingly made worse by Woods’ laudable decision to add some new chapters about prominent women in this revised edition, to counter his early 80s patriarchal mindset and work in some more recent scholarship.
Nonetheless, Woods is a good writer, and this is engaging enough – it just feels a bit confused and incomplete.
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.
The last couple of years have seen far too many people who should know better simply regurgitate press releases without applying critical thinking – yet it’s the critical thinking that’s the increasingly essential “human in the loop” part of the equation.
And as familiarity breeds contempt, this kind of blunt, sceptical take on AI is likely to be increasingly common in 2025. Anyone – any organisation – wanting to be taken seriously is going to have to confront these kinds of questions honestly and openly if they’re going to be taken seriously.
But at the same time, it’s going to be important not to swing too far the other way – beyond inquisitiveness about the bold claims of the AI providers into outright cynicism.
It’s easy to shoot things down. It’s *extremely* easy to have a knee-jerk dislike of techbro hype trains when you lived through the Dotcom Crash. It’s much harder to dispassionately assess the merits of emerging technologies when they haven’t yet fully emerged.
As ever, a journalistic mindset can help:
Who‘s saying this? What are their creds? What’s in it for them? Do they have any financial stake?
What are they actually saying? Is there any substance, or is it filled with jargon and empty phrases? (It’s often surprising how little substance there is out there, given how much is being said…)
When did what they’re claiming first happen? Is this really new, or is it fresh spin on an old claim or capability? If a fresh spin, that’s not necessarily a bad thing – but why now?
Where‘s the evidence to support their claims? Can it be independently verified?
How does this claim differ to existing solutions? Is it really an improvement? What’s the cost vs benefit compared to alternatives?
Finally, as ever, try and get your info from more than one source. It’s tempting to only listen to people you agree with, and *very* tempting to dismiss anything coming from sources you dislike. But that leads to an incomplete picture – and a boring, predictable take.
And at a time where GenAI can spit out passable median opinion takes in seconds, what’s the point in reading anything boring and predictable?
Fascinating, if predictable, findings on ChatGPT source attribution, via TechCrunch – with significant implications for the emerging “Generative Engine Optimisation” successor to SEO that should concern anyone publishing online.
Short version – ChatGPT’s ability to provide accurate citations for the sources of its information remains extremely hit and miss, despite the rise of GenAI search:
“the fundamental issue is OpenAI’s technology is treating journalism ‘as decontextualized content’, with apparently little regard for the circumstances of its original production”
In other words, GenAI focuses on the substance, not the source. It doesn’t matter where a story / insight actually originated – only where the GenAI tool considers is most plausible for it to have originated.
This isn’t just a question of lost traffic due to the lack of a link – there are far more serious implications here.
For example, if you’re a corporate brand producing a big chunky piece of thought leadership based on months of research, this means you could find your work misattributed to a direct competitor if the GenAI algorithms decide a competitor is more likely to have produced something like this. Equally, someone else’s work – or opinion – may be attributed to you.
This is, of course, a potentially huge liability for any brand – especially as hostile actors could use this flaw in the way these tools work to game the system, similar to the old days of Googlebombing, and make it look like your brand has said something it hasn’t.
But it gets worse – there’s nothing* you can do about it:
“Nor does completely blocking crawlers mean publishers can save themselves from reputational damage risks by avoiding any mention of their stories in ChatGPT. The study found the bot still incorrectly attributed articles to the New York Times despite the ongoing lawsuit, for example.”
Welcome to the age of GenAI…
(* well, nothing guaranteed to work all the time, at least…)
The default writing style of GenAI is becoming ever more prevalent on LinkedIn, both in posts and comments.
This GenAI standard copy has a rhythm that, because it’s becoming so common, is becoming increasingly noticeable.
Sometimes it’s really very obvious we’ve got bots talking to bots – especially on those AI-generated posts where LinkedIn tries to algorithmically flatter us by pretending we’re one of a select few experts invited to respond to a question.
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Top tip: If you’re using LinkedIn to build a personal / professional brand, you really need a personality – a style or tone (and preferably ideas) of your own. If you sound the same as everyone else, you fade into the background noise.
So while it may be tempting to hit the “Rewrite with AI” button, or just paste a question into your Chatbot of choice, my advice: Don’t.
Or, at least, don’t without giving it some thought.
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There are lots of good reasons to use AI to help with your writing – it’s an annoyingly good editor when used carefully, and can be a superb help for people working in their second language, or with neurodiverse needs. It can be helpful to spot ways to tighten arguments, and in suggesting additional points. But like any tool, it needs a bit of practice and skill to use well.
But seeing that this platform is about showing off professional skills, don’t use the default – that’s like turning up to a client presentation with a PowerPoint with no formatting.
Put a bit of effort in, and maybe you’ll get read and responded to by people, not just bots. And isn’t that the point of *social* media?
This from John Hegarty resonated. Unpopular opinion, but awards – especially in B2B marketing – are the ad industry equivalent of social media vanity metrics. They may get you marginally more reach (usually long after the campaign’s over), but rarely with your real target audiences.
What’s worse, the positive signals award wins send out can create feedback loops of groupthink about tactics that can actively harm your ability to deliver.
I know it’s tough to demonstrate marketing effectiveness, but award wins rarely prove much beyond that marketing people like something. So unless you’re selling to marketers, they don’t really have much value.
This means awards make perfect sense for agencies (and individuals) to enter – but for their clients? The point of marketing is to improve brand perception and make sales with your buyers, not getting a round of applause from other marketers.
Which is why, often, I find the less glamorous side of marketing is where the real businesses impact can be found.
There’s some fascinating stuff in this SEO long read, based on impressive research and analysis. Just bear in mind that, as leaked Google documents put it, “If you think you understand how [search algorithms] work, trust us: you don’t. We’re not sure that we do either.”
To save you time, the main lesson is that “achieving a high ranking isn’t solely about having a great document or implementing the right SEO measures with high-quality content”. Search results shift in near realtime based on thousands of utterly opaque, interconnected assessments of obscure demand and user intent signals, so there’s only so much website managers can do.
For me, this all confirms a few core content principles:
Context is king, not content. You can have an amazing page full of astounding insight, but if it doesn’t clearly meet the needs of the user at that moment in time, it will go unviewed.
Page structure is at least as important as substance – if (human and bot) audiences can’t quickly tell that your page is interesting and relevant, they’ll bounce.
But don’t worry – the key to success is rarely going to be a single webpage. More important is the authority of the domain and brand.
This means the impact of content is at least as much about cumulative brand building as it is immediate engagement. Think of the long tail, not just the short spike – and focus your content strategy on building this long-term growth over the short-term quick hit.
Given so much about how this works is unknown, and so many factors are outside your control, it’s best not to over-think it. Follow all the advice SEO experts offer, and you’ll end up with something so over-engineered it’ll lose its coherence and flow. This will increase bounce rates.
So how to succeed?
Go back to basics: Focus on ensuring your content fulfills a clear audience need (ideally currently unmet by other sources), using language audiences are looking for, presented in ways audiences are likely to engage with, and with clear links to and from other relevant content to help both humans and bots understand its relevance within the broader context.
In other words, SEO may be complex when you dig into the details – but it’s really just a combination of common sense, long-term authority building, and a good bit of luck.
The music industry’s absolutist approach to copyright is a dangerous path to follow, however. How can you legally define the difference between “taking inspiration from” and “imitating”? What’s the difference between a GenAI tool creating music in the style of an artist, and an artist operating within a genre tradition?
*Everything* is a mashup or a reference, to a greater or lesser extent – that’s how culture works. We’re all standing on the shoulders of giants – as well as myriad lesser influences, most of which are subconscious. Hell, the saying “there’s nothing new under the sun” comes from the Book of Ecclesiastes, written well over 2,000 years ago.
Put legal restrictions on the right of anyone – human or bot – to build or riff on what’s come before, and culture risks hitting a dead end.
So while I have sympathy with artists’ concerns, the claim that GenAI could “sabotage creativity” is a nonsense in the same way claims that the printing press or photocopier could sabotage creativity are. Creativity is about the combination of ideas and influences and continual experimentation to find out what works – GenAI can help us all do this faster than ever. If anything, this should help increase creativity.
What *does* sabotage creativity is short-termist, protectionist restrictions on who’s allowed to do what – exactly like the ones these lawsuits are trying to impose.
“When AI is mentioned, it tends to lower emotional trust, which in turn decreases purchase intentions.”
An interesting finding, this – especially as it transcends product and service categories – though perhaps to be expected at this stage of the GenAI hype cycle.
This kind of scepticism isn’t easy to overcome – with new technologies acceptance and mass adoption is often a matter of time – but as the authors of the study point out, the key issue to address is the lack of trust in AI as a technology.
Some of this lack of trust is due to lack of familiarity – natural language GenAI seems intuitive, but actually takes a lot of practice to get decent results.
Some will be due to the opposite – follow the likes of Gary Marcus, and it’s hard not to get sceptical about the sustainability, benefits, and reliability of the current approach to GenAI.
The danger, though, is that this scepticism may be spreading to AI as a whole. The prominence of GenAI in the current AI discourse is leading to different types of artificial intelligence becoming conflated in the popular imagination – even though, just a few years ago, the form of machine learning we now call GenAI wouldn’t even have been classified as artificial intelligence.
Tech terms can rapidly become toxic – think “web3”, “NFT”, and “metaverse”. Could GenAI be starting to experience a similar branding problem? And could this damage perception of other kinds of AI in the process?
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:
4/5 stars
GenAI content is neither good nor bad:
I’ve seen 
The default writing style of GenAI is becoming ever more prevalent on 
This from John Hegarty
To save you time, the main lesson is that “achieving a high ranking isn’t solely about having a great document or implementing the right SEO measures with high-quality content”. Search results shift in near realtime based on thousands of utterly opaque, interconnected assessments of obscure demand and user intent signals, so there’s only so much website managers can do.
Given the music industry’s track record of building successful cases for unauthorised sampling and even inadvertent plagiarism (aka Cryptomnesia, as with the 
“When AI is mentioned, it tends to lower emotional trust, which in turn decreases purchase intentions.”