It’s been a long time since I did Directories of apps here. Indeed, the last one on RSS readers was this one: A Directory of The Best RSS readers which was started in 2004: 21 years ago. I’ll try to get round to updating that at some point. I feel the time has come to reclaim blogs and directories that are human curated and aren’t pushing anything except more satisfying computer usage.
So here’s a start. I’ll update this when I find new stuff. Please let me know if I’m missing something, or getting something wrong.
lire – for iOS, iPadOS and macOS: You only need to buy two apps to cover all three platforms.
lire for macOS if available on the Mac App Store. lire for iOS and iPadOS is available as a universal purchase on the App Stores of the respective devices. Universal purchase allows you to purchase the app once, and then access it on both platforms.
Supports: Feedly, Feedbin, Feed Wrangler, FeedHQ, The Old Reader, Inoreader, Newsblur, BazQux Reader, FreshRSS, Miniflux, Nextcloud News, and Tiny Tiny RSS is currently included.
I like it. Elegant and simple.
Reeder been around for 15 years, and still looking good. Reeder Classic is the one most of us are familiar with. The new version, 5, is just called Reeder and doesn’t support syncing with RSS services. Both are available for Mac and iOS.
While I like the idea that I think the new Reeder is trying to tackle — RSS bloat, where you have way more than you can ever read — I don’t think the answer is to prevent syncing with third party services. Which is why I’m still using Classic.
Leaf – another nice looker. Syncs with Feedly, NewsBlur, Feedbin and Feed Wrangler. $10
Unread – my current favourite, at least for the Mac. (There is an iOS version as well) I’m still not quite sure about the £5 subscription though the free version delivers pretty much all you need.
DEVONthink, my favourite database since the demise of Evernote, as added some features to version 4 that may not appear that significant, but which I believe offer a real boost to those of us trying to stay organised. They involve tweaking PDF and Markdown files to make them easier to navigate and to find stuff within them.
PDF
A well-crafted PDF includes a table of contents that allows you to jump around inside the document easily from chapter to chapter. The table of contents itself may not be visible in the document, but serve as a kind of outline, visible in PDF readers in whatever sidebar the software offers. Each heading, or chapter, will sit in a tree hierarchy, allowing you to jump to where you want to in a long document.
A table of contents in DEVONthink
Sadly quite a lot of PDFs don’t have these. DEVONthink fixes this, at least in the app itself, simply by right-clicking on the place in a document you want to add to the table of contents and choosing Add to… Table of Contents.
The ‘Add To’ popup
These markers will appear in the PDF if opened in a different app, although I found it wasn’t always the case if there was no table of contents baked into the original document.
Table of contents in Preview
Markdown
I’m a big fan of Markdown, as you know, because it is simple, logical, is used by most good editor apps and means every document you write can be opened in any app and look good. No locking into a particular format. DEVONthink does a great job of supporting Markdown and they’ve added some useful features in version 4.
The two most useful ones are these:
You can use the same Table of Contents sidebar to view sections in a Markdown document — essentially the same outline function for PDFS, and one you might be familiar with from Microsoft Word. Now in DEVONthink you can move sections around a Markdown document via that pane (usually called an Inspector. You can do the same to rich text (RTF) documents as well.
You can now convert a Markdown document to PDF in DEVONthink, preserving your headings in the usual tree hierarchy format. This feature is, I suspect, not included in most cases where you choose to export a document to PDF, and will only work in DEVONthink if you use the ‘Convert to…’ function.
Taming the beast
I find these functions extremely helpful in taming the data beast. Another feature that isn’t new but relevant is creating a list of selected documents — what DEVONthink rather confusingly call a Table of Contents — in either Markdown or RTF. Each entry is a link to the original document — I find it useful to create a sort of index to all the documents in a particular set or folder, speeding up searching for a document by title.
A table of contents of selected files in DEVONthink.
There’s a lot else in DEVONthink 4 to shout about but these are features that are hugely welcome, and used properly can save oodles of time.
DEVONthink for the Mac costs $100 for the standard edition, and $200 for the Pro version. The licence is valid for two Macs. A stripped-down version called DEVONthink To Go is available for iOS.
tl;dr: How to control your Substack overload and reclaim your inbox
These are difficult times, and staying up to speed is a full-time endeavour. Ironically, the explosion of newsletters from individuals has made this harder rather than easier,
I have found myself swamped by newsletters, despite efforts to bring some order to them (more of those efforts in a later post.) Newsletters can be a useful way of getting information — the email lands in your inbox, right where you are, you don’t need to go out and find it, and, at least in theory, the person writing the newsletter has taken time and effort to deliver something useful to you and at speed.
But it doesn’t scale for either of us. The person writing the newsletter can’t outsource the writing to someone else, except for a few guest columns from time to time. Unless they’re already rich they’re always going to be wanting to convince you to convert to a paid option, And if you’re going to pay $5 a month, why not subscribe to a full newspaper?
But for us the problem isn’t just financial. It’s that there are just so many good (often very good) newsletters that even if we only take the free stuff, we’re still committing ourselves to hours of reading per day or a quickly bloated inbox of unread messages.
The solution: an old fix
For me the solution has been to dust off an old technology: RSS. I first wrote about RSS in 2001, which gives you some idea of how old it is, how old I am, and how much a failure it has been. The story of RSS’ failure is for another time, but the remarkable thing is not only that it’s not dead, but that a) there are some beautiful apps and services still out there for you and b) the newsletter industry does support (if that’s the right word) the RSS protocol, even if reluctantly. The simple truth is that RSS powered everything we now hold dear: twitter/x, facebook, podcasts and yes, newsletters. It’s the guts of the commercial web, and the people who developed it haven’t made a cent from it.
RSS simply takes content, wraps it up and creates a URL. If you save that URL to an RSS reader (for example, it could be a browser like Vivaldi) and the reader will receive all future content sent via that URL. No sign-up, no personal data given. RSS was the future, until more commercially minded souls used it for their plumbing, but removed features that didn’t suit their goals.
Most visible in this are substack-like platforms. Sure, they’ve helped create an industry of smart people who can make a modest living from writing for an audience. Most important is that modest living bit. RSS never really offered a seamless way for writers to monetise their work, and that was a major drawback the likes of Substack have solved. But they solved it by leveraging an even older technology — email — that RSS was trying to get us away from. Back in the late 1990s it was clear that email was vulnerable to spam and malware. (The first phishing emails were sighted in about 2001, but email had already become a favoured vector for trojans and viruses.)
But the bigger problem was that mailboxes were getting bloated, as personal emails, work emails, spam and newsletters all clogged up the works. RSS promised to take the newsletters out of the mailbox by creating a channel for the rising world of blogs to reach their readers, respecting their privacy and their sanity. It worked well for nearly a decade, until social media usurped blogs by making interactions between users the key selling point, rather than the content itself.
Simple, almost
So, where does this go? When my efforts to get my email inbox to zero failed, I decided to quit. Instead I’ve spent the past day or so adding all the newsletters I subscribe to via Substack, Medium, Ghost etc, adding their RSS feed to my reader, and then unsubscribing from the newsletters (unless they’re paid ones.) It’s still a work in progress, but I think it might be the only way to cope with the scaling issues of Substack et al.
It’s better with RSS
Why is RSS better than email? Lots of ways:
first, privacy. You don’t have to give any of your details to anyone — the platform, the company/individual producing the newsletter, or anyone in between. There’s no tracking, spam, data sharing, and it’s fully autonomous;
you can organise your newsletter subscriptions as you want, within folders or tags supported by whatever RSS reader you end up using. You can do this in your email app, but it’s not as intuitive;
things don’t get all mixed up. I’ve talked before about email bloat, and the pain of missing important emails. Keeping your information sources and your email separate is a real plus;
space junk: RSS feeds are stored in the cloud or your computer, but not at your cost. Nowadays email is not free, unless you religiously delete stuff.
The format of content RSS feeds is more bare bones, but there are some RSS readers that use this simplicity to create very elegant interfaces. All the annoying ephemera added to most email newsletters is stripped, leaving only text and images in – usually – a readable and pleasant format. My favourite is Unread, available for macOS and iOS, which is free, but has a premium subscription of $5 per month, which covers Mac, iPhone and iPad. Another app which focuses on elegance; Reeder.
getting out is easy. Substack and others have made it progressively harder to unsubscribe from a newsletter, and don’t get me started on trying to cancel a paid subscription. More on this below. In an RSS reader, it’s easy.
you can easily navigate through past posts from one particular newsletter. Doing the same on the likes of Substack can only be done by a cumbersome keyword search in your email app, on the platform’s app or on the web.
Slightly nerdy, this one, but RSS readers tend to make it much easier to save stuff to somewhere else. Yes, you can always print an email to PDF, but all the bells and whistles in the email tend to male it a clunky experience. Exporting a post from an RSS reader tends to be more straightforward, and the result more elegant and simple.
Downsides? A few
Disadvantages? Sure, there are some:
If you don’t get that much email, and don’t subscribe to many newsletters, it makes sense to keep everything in the same place;
RSS won’t (usually) work with paid subscriptions. Better to keep that in email and the app;
RSS feeds of newsletters aren’t always identical. I’ve noticed some stuff doesn’t make it through to the RSS feed, but this doesn’t happen much;
you will miss some features that Substack and others are adding to their platforms: hangouts, notes, that kind of thing. But I’ve not used these much, except for paid subscriptions.
we commonly monitor email more readily than other apps so we’ll likely see newsletters as they land because we have our notifications set that way. Which is good — except when you subscribe to a lot of them, or you want to save alerts for the scary email from the boss or HR;
it is a bit more complex to set up. I’ll explain this in more detail in another post, but I do recognise that not everyone is interested in making the extra steps to make this work;
with RSS the creator of the newsletter doesn’t get the same benefit of metrics to see who is subscribing, and who is ‘engaging with’ (what we used to call ‘reading’) their content. As a creator using all platforms I definitely think this is helpful, but not everyone is going to use RSS, and so I think on balance writers will get enough of a sense of how they’re being received from email subscribers not to be adversely affected.
you might find yourself moving one bottleneck from one place to another rather than removing it. (I was wrestling with much the same problem nearly 20 years ago: Email Wins Over RSS? and What’s RSS to You?) But as RSS makes it really easy to unsubscribe, so thinning the herd every so often is not too painful.
Still, it’s been a good start and for my sanity it was probably overdue. The world is spinning very fast, and the last thing we need is to get overwhelmed with the information sources we actually trust.
This is the first of a series of pieces based on Mary Meeker’s recent deck about AI.
It can be bewildering and discombobulating to try to absorb the rapid rise of AI, and I can understand why many of us choose to ignore it, dismiss it as horribly overhyped, or throw up our hands in despair. All of these reactions have some justification. But I think it’s worth taking a step back and trying to place what is happening in some context. Doing so might help in accommodating what is happening, even to draw some benefit and comfort from it. A 340-slide deck may not sound like a good way to do this, but I’ll try to condense it. I hope it will be worth it.
The deck is from Mary Meeker, a veteran of Silicon Valley and someone who, over the years, has got a lot of things right. She’s also at an age, not unlike myself, to have witnessed the miracle of Internet-based technologies and so can see a bit more clearly than those who grew up with the Internet (essentially anyone younger than 45). A slide from a Mary Meeker deck, therefore, is usually worth 100 slides from most other folk, so her recent dump on AI is worth the time. (With some caveats I’ll leave to a later post)
One of the peculiarities about AI is that while it threatens the livelihood of millions, it’s also one of, if not the fastest adopted technology/ies in history. It took 33 years for the internet to reach 90% of users outside North America; it has taken ChatGPT three. It took Netflix more than 10 years to reach 100 million users; it took ChatGPT less than 3 months.
But this is not where the real change will come from. Let’s face it, ChatGPT is easy to adopt because it’s quasi-human. We interact with it in the same way we communicate with our friends. We haven’t adopted generative AI as a technology so much as allowed its anthropomorphic version to insert itself into our lives. This is unsurprising: we have known since the 1970s that we humans tend to accommodate anything, live or dead, into our lives if it hits certain (but not all) anthropomorphic notes. A cute animal (behaviour, features), a favourite teddy-bear (inertia that we take to be stoicism and loyalty), Alexa (voice, responsiveness). A sign our adoption is complete is that we then yell at it when it doesn’t do what we want.
So our embrace of the technology is not really where change is coming from. The change is in how fast company CEOs are adopting AI — and want to be seen to be adopting AI. In late 2023 the proportion of S&P 500 firms mentioning AI during quarterly earnings calls was about 10%, a number that had risen slowly from zero since 2015. By 2025 that proportion had risen to 50%. (Slide 68)
And this is not just CEOs barking whatever buzzwords their media and IR teams are throwing them. In a survey of CMOs by Morgan Stanley in December 2024 two thirds said their companies were running initial tests and/or exploring using Generative AI for marketing activities. (Slide 70)
Agent AI
So where are those tests taking them? The chatbot image of generative AI is not what is really getting CEOs excited. What is getting them excited is the next wave of AI: agents. Agents form a “new class of AI… less assistant, more service provider”, in Meeker’s words. Where chatbots operate “in a reactive, limited frame”, agents
are intelligent long-running processes that can reason, act, and complete multi-step tasks on a user’s behalf. They don’t just answer questions –they execute: booking meetings, submitting reports, logging into tools, or orchestrating workflows across platforms, often using natural language as their command layer.
Meeker compares this shift to that of the early 2000s, which
saw static websites give way to dynamic web applications –where tools like Gmail and Google Maps transformed the internet from a collection of pages into a set of utilities – AI agents are turning conversational interfaces into functional infrastructure.
The key thing to understand here is that an agent is not “responding” so much as “accomplishing”. They don’t need much guidance — indeed, they may quickly need no guidance, but instead autonomously execute, in Meeker’s words, reshaping “how users interact with digital systems “from customer support and onboarding to research, scheduling, and internal operations.” This is where the bulk of the enterprise appetite is going, not just experimenting but “investing in frameworks and building ecosystems around autonomous execution. What was once a messaging interface is becoming an action layer.” (All quotes are from Slide 89)
Strip away the glitter here and it’s this: An agent is essentially a human in disguise (or multiple humans.) Once briefed, it works independently, executing, learning, improving and extending. And companies are investing in the infrastructure to support this autonomous activity. You don’t have to be paranoid to see how agents, barely acknowledged a year ago, are now the focus of significant investment, which would likely have been directed towards investment in human-led processes.
Meeker cites a handful of examples: Salesforce’s Agentforce not only handles customer support but resolves cases, qualifies leads and tracks orders. Anthropic and OpenAI have agents that can control a user’s computer screen directly to handle tasks like pulling data and making online purchases. (Slide 91). Where AI was a research feature, it has since 2023 become a CapEx line item. It has become, in the words of Microsoft President Brad Smith, a “general-purpose technology” like electricity — “the next stage of industrialisation.”
Mary Meeker again:
The world’s biggest tech companies are spending tens of billions annually – not just to gather data, but to learn from it, reason with it and monetise it in real time. It’s still about data – but now, the advantage goes to those who can train on it fastest, personalise it deepest, and deploy it widest. (Slide 95)
This is where size helps. Training a model costs more than $100 million. Anthropic’s CEO has said that these costs could rise to $10 billion — per model. Inferences, while falling in unit cost, will likely “represent the overwhelming majority of future AI cost,” in the words of Amazon CEO Andy Jassy, because training is a periodic cost — done from time to time per model — while inference costs will be constant — every query. In Meeker’s words:
The economics of AI are evolving quickly – but for now, they remain driven by heavy capital intensity, large-scale infrastructure, and a race to serve exponentially expanding usage.
Meeker doesn’t walk us all the way down the path, and I’ll go into more detail in the third of this series of pieces — but it’s clear that worker productivity is top of most corporate agendas for embracing AI. She quotes a Morgan Stanley survey from November 2024 (Slide 330) where workers are top of mind: the largest adoption of AI was focused on employee productivity, the second highest worker savings.
Meeker avoids reaching her own conclusions on this. Instead she gives over a whole slide (Slide 336) to NVIDIA’s Jensen Huang, who paints a picture in the rosiest of terms. Yes, he says, jobs will be lost. But only to those who don’t take the opportunity. In fact, he argues, there’s a shortage of labour and anyone who takes advantage of AI will benefit. Here are the two bookends to the slide’s overall quote which probably encapsulate his thinking best, and illustrate the fist inside the velvet glove:
It is unquestionable, you’re not going to lose a job – your job — to an AI, but you’re going to lose your job to somebody who uses AI… I would recommend 100% of everybody you know take advantage of AI and don’t be that person who ignores this technology.
Meeker offers no annotation to this slide on the subject of workers. In the next couple of pieces I’ll try my hind
(No AI was used in the writing or illustrating of this post. AI was used in research but its results have been checked manually. This is another in a series of pieces exploring the frontiers between human and AI work. Here’s another.)
The Defence of Rorke’s Drift, 1879, Alphonse de Neuville
AI is here to stay, and it’s moving fast. We are a little like defenders of Khe Sanh, the Alamo, Rorke’s Drift, take your pick. The defensible perimeter is shrinking as AI turns out to be getting better and better at doing what we thought was an unassailable human skill.
I would like to try to alter that thinking, if I can, to present as just another kind of challenge that we’ve seen countless times before, We need to think of AI as a sort of horizontal enabler, kicking down the walls between professions and disciplines. Historians have traditionally been sniffy about people not trained as historians (John Costello, among others, tarred with the brush of being ‘amateur’ and not in a good way). We journalists are notorious for going from 0-80, from knowing nothing about a subject to parading as an expert after a few hours’ research.
AI is definitely replacing creative jobs, but many of those jobs in themselves relied on an expertise that was ported in. Duolingo, for example, has apparently fired most of its creative and content contractors in favour of AI. (I agree that the product will suffer as a result, but we need to keep in mind that this job itself didn’t exist until 2021.)
We tend to assume that our skills, training, education and experience are moats, but they’re not, really. The moats are usually artificially constructed — see how long we’ve had to live with the internal contradictions of quantum orthodoxy because of academic inertia and bias. We create degrees and other bits of paper as currency that limits serious challenges to conventional wisdom to a selective few, and are then surprised when the challengers come from outside.
The lesson here, I believe is that we shouldn’t be too precious about what we do that we can’t acknowledge that machines might be able to do it as well as us. Brian Eno, inventor of ambient music and a great mind, was quite happy to create an app that generated the very genre of music he invented, and still produces. He recognised that creativity could, under the right circumstances, be generated automatically by tweaking some variables.
Yes, we should rightly worry about the way AI is being used to make large sections of the creative (I use the term loosely, to include bullshit jobs and PR content creation) workforce redundant. But we should worry less about the fact and more about our standards. We are, as I have argued previously, decided to accept inferior quality — just good enough not to have users running their finger nails down the blackboard in frustration — and not demand a higher standard. This has less to do with AI, I believe, than with the way AI is used. While AI content is usually atrocious, it depends what you give it to work with. This is a function, in other words, of what is being fed into the bio harvester, rather than (only) the shortcomings of the bio harvester itself.
What we should really worry about is this: how do we encourage creative types to embrace AI and to leverage it to improve the quality of what they do and to create remarkable new things? We should be thinking: how do we keep ahead of AI? The answer: we use AI to do what we can do better and something that (for now) the AI cannot do on its own.
This means having a different attitude to work, to learning, to doing business, to thinking. The closest parallel I can think of right now is how online freelance workers have been working for the last 15-20 years. I wrote a story about this back in 2012, when I visited a town in the Philippines to see how a librarian had transformed her neighbourhood by switching to offering her librarian skills online, and then, having won her clients’ trust, upskilled herself to do more complex work for them, and hiring neighbours to help her. She and others I spoke to were constantly reinventing themselves, something that I think a lot of online workers do as an obvious part of their business. For them there are no moats, only bridges to new skills and better-paid work.
My x.com feed is full of people selling the idea that AI can churn out money-making work while you sit back and relax. Fine. This might work for a while, a sort of arbitraging the transitions from human to AI. But this is not (in the long term) what AI should be used for. Yes, it can definitely help us be more efficient — help us scale the walls of knowledge and competence that we did not view as either necessary or desirable. I, for example, ask AI to help me figure out bits of Google Sheets that I can’t get my head around, or how to automate the sending of transcribed voice notes to my database. It’s not always right — actually it’s rarely right — but I know its quirks and can work around them.
But this is small potatoes. I need to rethink what I want to do, what I want to be paid for, and how high I want to fly. I’m almost certainly going to put some people out of work on the way (I don’t have as much work for my virtual assistant as I used to, I’m ashamed to admit), but I’m sure I’m not alone in noticing that the demand for well-ideated and executed commercially sponsored content has dried up in the past year. I’m guessing a lot of that stuff is now done in-house with a bit of AI. McKinsey, BCG etc now all use some form of AI to prepare reports based on their own prior content and research.
In short, join the march to new pastures. But what new pastures?
The first thing is to acknowledge whatever trajectory you had in mind for your professional life may no longer exist. This is not an easy thing to accept, but it’s better to accept it now than to wait and hope. Whatever AI does badly today, or not at all, it will get better and better is all most people need. There are no moats, only ditches to die in.
I don’t know, of course, exactly how this will play out. We’ll need to get back at co-existing at AI, learning about the langauge we use to communicate with them (what is rather pompously called prompt engineering).
But that’s just the interface. The skills we develop through that interface could be anything. Think of the skills acquisition explosion that Youtube supports, such as channels that focus on manual crafts (Crafts people for example, has 14.2 million subscribers).
But we need to skate to where the puck will be, not where it is.
Indeed, the irony of AI may be that it helps our world pivot from digital to real, as politics and climate crisis push us to abandon our addiction to rampant consumerism and regain a respect for tools and products that last, and that we can fix ourselves.
More important though will be the new work that we can build on the back of AI. If I knew what those jobs are I probably wouldn’t be sitting here telling you lot, but be building them myself. But for the sake of argument let’s ponder how we might figure it out: we first need to identify a need that exists, or will exist (remember the puck) and that the need itself cannot be be done by AI, at least for the foreseeable future.
The most obvious one is the mess AI leaves us. I have never come across something done by AI that can’t be improved upon, or corrected. And we know that AI does not explain itself well, so even if AI can find the tumour a specialist couldn’t, we really need to know how it found it, even if it means trying to understand something that it is unable to explain itself.
But there are bigger issues, bigger problems, bigger needs that we need to face. It is not as if there aren’t things that we need to do, we just never seem to have the money or the political will to do them. We are suffering an epidemic of mental health issues, and while I understand how AI might be helpful in easing some of that pain (particularly the pain of loneliness), but far better would be a process that expanded the cohorts of people with enough counselling skills to be able to connect to sufferers and help them, professionally or as part of their daily work. Instead of potholes being something reported and fixed by some remote arm of government, the process of collating data could be crowdsourced (I remember a World Bank pilot project in the Philippines in the early 2010s) and the work assigned to a nearby volunteer suitably skilled via AI in filling in potholes.
Story of a pothole in Cebu City – training materials created for MICS (from TRANSPORT CROWD SOURCE ICT DEMONSTRATION COMPONENT 2 FINAL REPORT)
AI could and does help us acquire new skills that are not ‘careers’ in themselves, but can contribute to a blend of physical, mental and creative work that sustains our future selves.
Another way of looking for opportunities is this: What work would have been too expensive or too time consuming to be considered a business, and can AI help? Garfield AI, a UK commpany, offers an automated way for anyone to recover debts of up to £10,000 through the small claims court. This is potentially a £20 billion market — the amount of unpaid debts that go uncollected annually — and it’s probably one that is not manageable by most legal companies, and too cumbersome for small creditors. Here’s a piece by the FT on the company: AI law firm offering £2 legal letters wins ‘landmark’ approval.
This doesn’t mean that we can’t also be doing ‘mental’ creative work — using AI, say, to work out who is responsible for the mess that is the British water industry by creating an army of AI-enabled monitors and investigators, identifying polluters and making them accountable. This could be initiated by one individual with the smarts to figure out the legal challenges and to find ways to incentivise as many people as possible to gain the skills necessary to contribute effectively.
In other words: we need to stop thinking in terms of what jobs AI is taking and thinking what jobs that don’t exist that we can now do with AI.
Finally, a word on creativity. Creativity is, in the words of Eurythmic Dave Stewart, “making a connection between two things that normally don’t go together, the joining of seemingly unconnected dots.” That meant for him and Annie Lennox trying to find a connecting point between their talents — synths for him, voice for her — which as we know eventually paid off. Steve Jobs talking about creativity being “just connecting things.” It’s not as if AI can’t do this, but it can’t, according to researchers, compete with humans’ best ideas, their best connecting together disparate things.
I understood this a little better talking to an old friend who is a famous writer, but has been suffering from Long COVID since the pandemic. It prevents him, he says, from keeping in his head the necessary elements for a novel, but he can just about manage short stories. He still writes, and writes a lot, but that extra genius/muscle of creativity that has propelled him into the ranks of Britain’s best writers is currently not working properly. To me that explains a lot about what is really going on in human creativity, at least at the highest level. It’s frustrating for him, of course, but to me it’s an insight that should inspire us to make the most of our extraordinary minds, and to acknowledge that at their best our minds are no match for any kind of AI.
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