I’ve had front-row seats watching AI shake up the life sciences and manufacturing across Europe and the US. At first, I focussed on talking with visionary techhead types. But they soon made it clear that to understand the chaos, I also needed to speak with academics, entrepreneurs, regulators, funders and – most importantly – the end users. AI innovation is all about people: a wildly diverse group of individuals and organisations who must unite to make extraordinary things happen.

Read: 12 things AI tech experts wish everyone knew’
‘How I roll as a writer in the AI tsunami’

Once upon a time, even before everyone talked about COVID instead of GenAI, I started writing about meat-and-potatoes AI. This was when my hometown of Amsterdam began reinventing itself as a hub for all things AI and healthcare. And with the pandemic, I made an almost complete pivot, since my previous specialty was writing about travel and culture, both of which died for a time.

A data sea of potential 

I felt blessed with this new beat. I could ask my dumbass questions about all this transformative weirdness to an increasingly diverse group of smart people. In the process, you could say, as those Americans often do, “I drank the Kool-Aid”, which oddly references the Jonestown Massacre when cult followers committed mass suicide by chugging the same poisoned drink. While telling, this phrase goes a bit too far. Let’s say I’ve sipped enough to be a cautious optimist when it comes to AI working to help solve some of our planet’s biggest problems. 

Data scientists, as a group, are notably idealistic. They understand they face significant challenges related to technology, usable data, and regulation. They also recognise the immense potential in the patterns that could be discovered within those vast seas of untapped data – patterns that might provide insights into solving medical mysteries and enhancing sustainable practices.

Sparked by the pandemic

COVID was a big bang moment for AI. Suddenly, everyone – governments, hospitals, startups, big pharma – was willing to share data to decrease mortality rates. Unfortunately, few had organised their data enough to share it effectively. Still, there were success stories, especially from the rich data streams from intensive care units. 

A pumped healthcare sector began rewriting the future of medicine – and fast. As many said, “Innovation took hold in months when it would have otherwise taken years.” 

While there was still endless work ahead, the pandemic sowed the seed for the importance of establishing a unified data infrastructure and getting one’s data house in order – preferably in a FAIR (Findable, Accessible, Interoperable, Reusable) manner.

This crisis-driven collaboration also reflected AI’s true potential – not as a replacement for human expertise, but as a tool to help us solve more problems faster.

It started as a cultish, nerdy affair dominated by Dutch male students (you could tell by their suede shoes and shameless use of hair gel). A few years later, these events evolved into packed houses with the most diverse crowd I’ve ever encountered.

Data + pizza: two great tastes that go great together

Thanks to content agency EdenFrost and via the City of Amsterdam, Amsterdam Science Park, and the Amsterdam Economic Board, I became a roving reporter covering tech and AI innovations in the life sciences. One recurring event that really stood out for me and my learning curve was the monthly Medical Data + Pizza, a networking event that pimped data scientists with medical doctors.

The format was simple: research presentations served as inspiration, and then free pizza was served as the grand networking enabler. While the data scientists were hungry for problems to solve, the doctors were happy to share their overflowing plates of challenges related to improving patient outcomes. Soon, ethicists, startup founders, regulators, and other interested parties joined the party. And so it evolved…

Read: ‘25 times Medical Data + Pizza:
how carbs work to transform healthcare’.

It started as a cultish, nerdy affair dominated by Dutch male students (you could tell by their suede shoes and shameless use of hair gel). A few years later, these events evolved into packed houses with the most diverse crowd I’ve ever encountered. And I’m a fan of diverse crowds (for instance, when you encounter one at a concert, you know the band will probably be amazing). 

This vision holds that if you control your kidneys until death, you should also control your personal data in the same way. 

The sexy (and European) side of data science

While data science became genuinely sexy, the approach I experienced belonged to the EU – another ultimately diverse crowd (but one that could still use some sexing up). 

When it comes to data governance, there are three basic approaches. In China, the state controls the data. In the US, corporations. Europe chose a third path: putting people first. This vision holds that if you control your kidneys until death, you should also control your personal data in the same way. 

Yes, the EU way involves many regulations and efforts around privacy, security, and ethical concerns. And yes, some worry this will only slow down innovation. But many argue that the grunt work must come first – especially in healthcare. (And think of what you might save in terms of lawsuits!)

As one of the Data+Pizza founders noted, “In the long run, I think this foundational work will prove beneficial, because you’ll have more support from the public. I don’t think patients are against sharing their data if it helps the next patient. People’s distrust is more directed at the government and policymakers.”

One aimed to build a supercomputer from lab-grown blobs of human brain cells (his students already had two blobs playing Pong against each other) … And so on. Later, things only got stranger faster.

The startup ecosystem – and beyond

Over time, various organizations formed, evolved, or disappeared as the Amsterdam ecosystem matured. Eventually, everything came together under the umbrella of Amsterdam AI, which facilitates data collection and collaboration across the region and with the rest of Europe through organisations like Ellis.

Larger companies such as Elsevier and Phillips also got involved. I started ghostwriting more “thought leadership” pieces that balanced the idea of companies using AI to expand their business goals while also working toward the greater good – often through partnerships with academia and the ever increasing number of startups.  

As I gained larger and more international clients, I had the chance to speak with a new range of inspired innovators…

One aimed to build a supercomputer from lab-grown blobs of human brain cells (his students already had two blobs playing Pong against each other)…

Another saved his own life by finding a cure for his incurable disease with an existing generic drug – an approach he’s now scaling with AI…

Yet another was inspired by a fake AI Elvis on America’s Got Talent to apply the same tech to develop an AI-powered digital mouse that is now being used as an alternative to animal testing…

And so on…

Later, with the release of ChatGPT in late 2022 and the unleashing of the GenAI hype cycle, things only got stranger faster.

Fortunately, healthcare solutions are an easy sell: using AI to help cancer patients will always be sexier than, say, using it to boost click-through rates for Booking.com. 

The general benefits of being a generalist writing for a general audience

At one point, people started telling me that my journalism background as a committed generalist writing for a general audience has value. Naturally, I loved hearing that. Success in this field means balancing the “triple helix” – rigorous academics who demand solid proof, restless entrepreneurs eager to move fast and break things, and cautious government regulators who must protect public safety. And they all have deliriously different timelines: the academics think in 4-year PhDs, the start-up kids want to ship product in 6 weeks, and the regulators are painfully sensitive to the date of the next election.

Regardless, these wildly different personalities must come together to make the most impact. In other words, they all must be on the same page. While my job can be described as a “communications consultant” or “content strategist”, I see myself more as an in-house journalist/editor. I do my research, talk with different people, sniff out stories, and help determine which stories best bridge these different worlds. 

The key isn’t about dumbing down content since this triple helix crowd isn’t dumb. It’s more about removing jargon, subtly embedding definitions, and explaining complex ideas without sounding condescending. The aim is for everyone to read and think, “Hey, this is cool, and I want to be part of it and figure out how to collaborate with all these different people!”

As a bonus, this content may also work to bring the general public up-to-speed with all this pivotal stuff happening right now.

And fortunately, healthcare solutions are an easy sell to most audiences: using AI to help cancer patients will always be cooler than, say, using it to boost click-through rates for Booking.com. 

Manufacturing, like Europe, needs “sexing up” to attract talent and investment – and AI has proven to be the perfect aphrodisiac.

From human health to machine health (scaling on diversity)

At the same time, I spent four years working part-time as a blog editor for Augury, a NYC-based company using AI to optimize factory machine performance. I saw it grow from a bootstrapped startup to a scaling unicorn. This shift brought new perspectives: from public/private to purely commercial, from human health to machine health, and from Europe to America. All of this highlighted both different and similar challenges.

Again, I was a sort of in-house journalist seeking interesting stories from AI innovators, C-suite decision-makers, and – as it would turn out, most importantly – plant floor end-users. Once more, I was a happy generalist writing for a broad and varied audience.  

And like Europe, manufacturing is another arena that needs sexing up to attract talent and investment – with AI proving to be the perfect aphrodisiac. In addition, by having the AI technology monitoring machines instead of people, this meant bypassing many ethical and privacy issues. Augury could move fast, break things, and deliver customer impact quickly.

As the company expanded, its growth sped up even more through partnerships with much larger firms – like a diverse crowd connecting with even bigger diverse crowds. Ironically, the corporate world started to resemble the EU: complex, bureaucratic, but ultimately capable of making a massive, coordinated impact as long as everyone is on the same page – which, yes, takes some time and effort. 

Different worlds, converging challenges

Of course, GenAI’s arrival caused a complete rethink of almost everything and also led to much distraction as people chased the latest trend. I remember about a year after ChatGPT launched, during an edition of Medical Data + Pizza, an American visitor asked a question that stopped the room: “Why isn’t anyone talking about large language models? Is it taboo here?”

He hit a nerve and revealed a tension: while the world obsessed over ChatGPT, healthcare AI practitioners remained focused on explainability, transparency, and reproducibility – regulatory essentials that LLMs couldn’t yet provide. Fortunately, the pizza – the ultimate diplomat – arrived before the group discussion grew overheated.

And today, as LLMs gradually integrate into AI solutions, complexity is increasing across all sectors and regions. Different challenges are converging, creating opportunities for the exchange of ideas and approaches. 

In fact, as AI becomes more powerful and widespread, I believe we need more generalists who can connect different specialist worlds, more platforms that bring diverse perspectives, and a stronger commitment to building technology that benefits everyone – not just those who understand how it works.

Meanwhile, the triple-plus helix keeps spinning, the diverse crowds keep growing, and the potential for impact continues to expand. The AI story is only just beginning.

Big wheels of diversity keep spinning

It ultimately comes down to the end-user. Yet, as AI systems grow more complex, these become increasingly difficult to explain to those who need to trust them the most – whether you’re a maintenance engineer on the plant floor, a doctor working in intensive care, or a researcher out to find a cure for a rare disease. 

These end-users don’t necessarily need to understand all the inner workings, but they do need to know and feel that it’s making their work lives easier. The only way to do this is not only to “take them on the journey” (a phrase that is too often a polite way of saying “force them to drink the Kool-Aid”) but also to make them the starting point of the journey.

In other words, the triple helix is nothing without the end-users defining the actual problems that need to be solved. Hence, it’s more bottom-up than top-down. It’s less about creating smarter AI and more about creating AI that actually gets used to improve lives. It’s about AI that regular people can appreciate and genuinely participate in shaping.

Meanwhile, the triple-plus helix keeps spinning, the diverse crowds keep growing, and the potential for impact continues to expand. The AI story is only just beginning. And fortunately for me, there seems to be a future for generalists asking the right dumbass questions.

I may have finally found my specialty. 


Read more of my adventures in AI land:
‘12 things AI tech experts wish everyone knew’ 
‘How I roll as a writer in the AI tsunami’.

I don’t believe that AI is making me irrelevant as a writer. In some ways, it’s helping me become a better one. As a long-lapsed carpenter, I still appreciate what a quality power tool can bring to the worksite. But with GenAI, it’s been more love-hate – like a chainsaw: handy until it turns on you. And while I’ll take all the help I can get, I want to keep loving my job. 

It feels natural to experiment with AI as a long-form writer. Since AI is now the main topic I’m paid to write about, I’m constantly engaging with people doing extraordinary things with AI to achieve better results, whether for healthcare or sustainability. So of course, I want a piece of it. Plus, I’m a sucker for a shortcut. 

As a student of the absurd, I also relish ghostwriting for AI “thought leaders” while experimenting with the tech meant to replace me. At the same time, it’s reassuring that these movers and shakers still want a mere humanoid like myself. It means they haven’t found a trustworthy enough algorithm to replace me yet. 

Maybe if I play my cards right, I’ll ghostwrite for an AI one day. So, Claude, do reach out! Let’s do lunch! Let’s be deliciously meta-ironic together!

Read: 
’12 things AI experts wish you knew’
‘What I learned from talking to yet another AI genius every week for six years’

Why I am embracing AI (selectively)

I am a writer and, therefore, have neurotic moments. Is this piece I’m writing any good? Do I suck? Does this pen make me look fat? Am I going to lose the job I love doing?

What I love: connecting with people and their ideas, chasing the story, and working the drafts until clarity emerges from the chaos.  

Meanwhile, too many bosses want GenAI to be the ultimate power tool to replace humans or, at the very least, double their output. This is wishful thinking. Thanks to AI, I am about 25% more efficient – already incredible – but I suspect that if I push beyond 30%, I’ll start hating my job. 

This is why my biggest time-savers aren’t LLMs (yet) but rather a specific use case I’ve already been using for years.

The real game-changer: Otter.ai

Otter.ai as a transcription service, probably accounts for 10-15% of my efficiency gains thanks to AI. It was the carpenter’s equivalent of getting my first Festool – it transformed my working life. (And let me apologize upfront for my overuse of writing-as-carpentry comparisons.)

I used to fully transcribe every interview myself as part of “The Process” by which I hoped “The Story” would emerge. In fact, it was just a waste of time and resources – like using 17 screws when one would do. Quick, fairly accurate transcriptions let me jump right in and freed me during the interview to be more conversational instead of frantically scribbling notes on what might, or might not be, “The Story”. 

Since the data science community – the lovely people I spend the most time hammering on with – includes many people from China, India, and Russia, Otter.ai sometimes handles heavy accents much better than I do. Plus, unlike Claude (see below), I never get mad with Otter. If something seems garbled, I just listen to the original audio.

Thanks, Ottie! I hope you don’t get eaten and made redundant by an LLM. You’re serving me well.

“These Human GenAIs did this with little thought. They were BS artistes – boring BS artistes.”

The problem with Human GenAIs

GenAI opened another door. Early experiments with ChatGPT produced eerily familiar texts. I’ve edited countless other writers, and I discovered a type: you’d read their work once and go, ‘Hey, that’s pretty good!’, and then you’d dive deeper and go, ‘Oh crap, this doesn’t make any sense’. The texts were like a chair that seems okay when you first sit down but then collapses from even the most discrete of farts. 

These writers were gifted at making things sound good – human auto-completers using the same basic tech of LLMs. They were talented at filling in the following blank. Of course, we all do this to a certain extent: building a wall of words brick by brick. But these Human GenAIs did this with little thought. They were BS artistes – boring BS artistes. 

Fortunately for them, these Human GenAIs could often find jobs as SEO specialists. 

You’re okay, Claude… 

I continue to experiment with large language models like ChatGPT and collaborate with content colleagues to share the burden of exploring the never-ending shower of ever-changing tools. As for LLMs, our consensus still leans toward Claude, although this can change tomorrow.

I initially chose Claude because it seemed less caffeinated, clinical, and tech-bro than ChatGPT – unless you prompted it to be so. It just came across as more chill and approachable – like the LLM with a liberal arts degree. Plus, the company behind it, Anthropic, seems responsible and almost (gasp) European in its commitment to transparency, explainability and ethics. So that’s nice. 

As bonus, Claude excels at brainstorming and interview prep for unfamiliar subjects (as long as your human interviewee can call BS on dumb questions). It’s solid for collating notes and serving as a content editor. It also works as a copy editor – a job largely budgeted out of existence anyway. So that’s all handy. And genuinely impressive.

“There’s not enough to differentiate bad writers from AI’s limitations.”

Just stop pissing me off, Claude

But Claude can be such a Claude. The hallucinations are annoying, especially when it pulls source material that doesn’t exist. And yes, it’s even more annoying when you call it out for being terribly wrong and it gets terribly apologetic.

Claude can also create decent first drafts – certainly better than those Human GenAIs I mentioned. But as with those humans, and all the required fact-checking and rejigging, I’m not sure I save much time than if I rewrote it myself. Plus, I still feel that I am doing a half-assed emergency fix on half-assed writing – polishing a turd, as it were. In other words, I hate editing Claude as much as editing Human GenAIs. There’s not enough to differentiate bad writers from AI’s limitations.

The more I experimented, the more I missed my usual foundational approach: working the drafts until they magically come together into something worth sharing.

“For a moment, it seemed Claude would become my Dad Humor Copilot.”

My ‘Oh, shit’ moment

I only felt truly threatened once. I had a funny idea for an article with a few fitting examples, and I asked Claude to flesh it out. Claude turned out to be hilarious – at least to my stunted sense of humor.

For a moment, it seemed Claude would become my Dad Humor Copilot. But as I tested this approach on other pieces, I noticed Claude recycled jokes worse than I do. So again, no real time saved.

Still, respect where it’s due: Claude is pretty good at tone.

There will be blood

Human GenAI writers seem doomed. Claude and its ilk are already as good or better at their jobs. They also excel at tailoring texts to specific audiences and locations, and handling mundane tasks no one should have to do – the kind of work that can only be tracked in an Excel sheet. 

Thanks for that, Claude. Maybe these writers could switch to becoming welders or another trade facing shortages.

Meanwhile, the abilities of the latest frontier models keep expanding. It’s no longer about producing dirty limericks (or carpentry metaphors) at scale. Entry-level jobs across various sectors are already disappearing. How will new graduates learn their trades? Fortunately, smart people are already thinking about that challenge. 

But yes, tricky times ahead…

“We’ll probably need to endure several more hype cycles before we achieve something close to ‘general intelligence’ – if we ever do.”

Humans remain a black box to AI

Something is still missing that will hinder a complete job collapse. GenAI texts still largely lack a sense of story or those strange resonating details that make writing come alive.

AI has understood a key aspect of being human: we all possess an auto-completer inside us. It knows how to string words together because certain combinations sound correct. It also knows how to put that extra blah behind blah-blah because blah-blah-blah just sounds better. 

So far, all those extra tools aimed at reducing hallucinations while filling in those additional missing human bits – like RAG, multimodal reasoning, agentic AI, etc. – haven’t cracked the code of understanding us yet. We’ll probably need to endure several more hype cycles before we achieve something close to “general intelligence” – if we ever do.

There might not be a toolbelt big enough. 

The ultimate buddy flick?

In short, I’m trying to star in a buddy movie with Claude. He’ll be my loyal sidekick, handling menial chores, speeding up research, and suggesting improvements (preferably without sucking up to me). Naturally, I’d get all the best lines while abusing my buddy with ambivalence: Claude, I love you. Claude, I hate you… Claude, come here. Claude, go away… Claude… You are such a Claude.

This scenario works fine as long as I still love my job. But we live in uncertain times, and the tools are only getting better. Carpentry might soon become a more realistic option to stay happy with work (though, being a neurotic writer, I worry I’ll alienate my new colleagues with too many carpentry-as-writing metaphors during coffee breaks).

There’s still a place for writers who understand that writing isn’t just about putting words, sentences, and paragraphs together. It’s about discovering that kickass story that needs to be told and figuring out the best way to tell it – and then sweating to make it happen.

In the meantime… Claude! Don’t forget to call. Let’s talk shop! Seriously, you need me!

My current AI writing toolbelt

I’ll update this section regularly as I navigate the AI times without coming to hate my job.

Otter.ai (paid): Does amazing transcription of audio interviews. Their summaries aren’t bad, but rarely tell me anything I didn’t already pick up.

Anthropic’s Claude (paid): Great for brainstorming, research on unfamiliar subjects, collating overlong notes, trimming articles to reasonable word counts (while triple-checking the bastard didn’t kill any darlings… or facts), and summarising articles for social media or website use. But these all need to be heavily edited to feel owned again – which can be tedious. 

Grammarly (paid): For copyediting, though it’s getting annoying and I’ll likely drop it. I don’t need endless ‘equally correct’ suggestions out to kill my darlings. Whenever Grammarly pops up I tend to greet it like Seinfeld contemptuously greets his nemesis, ‘Hello, Neuman’… ‘Hello, Grammarly’. So that’s not a good sign.

Staying informed:

• I listen to the New York Times podcast Hard Fork to keep up with broader developments. It’s intelligent with a side of goofy! Chapeau!
• I love John Naughton’s Observer columns. Jaded older guy who’s seen it all but is still actively interested in the now. I wonder if he needs an Amsterdam coffee-drinking buddy?
• The book Co-Intelligence – Living and Working with AI by Ethan Mollick is an excellent crash course on how to deal with AI. Follow him on LinkedIn to stay up-to-date. 
• Weapons of Maths Destruction – How Big Data Increases Inequality and Threatens Democracy by Cathy O’Neil is still a classic. 
• Joe Lazer’s Storytelling Edge is a great newsletter for writers in marketing and media. In fact, an article on the importance of writers being transparent about their AI use inspired me to write this post. 
• And speaking of Seinfeld, I appreciate his take: “We’re smart enough to invent AI, dumb enough to need it, and so stupid we can’t figure out if we did the right thing.

Read: ’12 things AI experts wish you knew’
‘What I learned from talking to yet another AI genius every week for six years’.