I mentioned a while ago on Mastodon that I had such a backlog of stuff I had saved to read and could potentially write about that I was going to have to steal Cory's approach and do a weekly linkblog post. That idea got put on the back burner for a couple of weeks as I had both a feature (forthcoming for PC Pro magazine) and a short training session (end of February, details to follow once its advertised).

And of course, I needed a concept -- what we call in journalism a franchise. Putting in the work of creating something weekly is a lot easier if you can force it into some kind of theme. But I have been scratching my head trying to think of something.

Of course, as soon as you think of something it's obvious: hence Ten Blue Links. Like the old-school Google we knew and loved (and was useful) I'm going to create a page every Saturday which just lists ten things which have amused/entertained/informed me, and that I think are worth your time reading. There's no topic theme -- I read a lot, so that wouldn't make sense -- although every now and then if something big has happened I might make one.

Some words about my tools and process

I'm far too online, and I hop about between tools far too often. But there are two online services which have stuck with me for quite a while now: Raindrop, and Readwise Reader.

Raindrop is a bookmarking service, like Pinboard but a lot better. I use it to dump in links which I know will be useful to me in the future, but which aren't in-depth reading. How-to's, tips, that kind of thing, all of which I categorise and tag so they form even more useful collections.

Readwise Reader, on the other hand, is a read-it-later service like Pocket -- but it's the Olympic Gold medal winning version, the Pele and Maradonna and Messi combined into one of reading things. It's perfectly happy ingesting feeds, or emails, or PDFs as well as simple saving articles, and it integrates with Readwise (of course) which I use to funnel all kinds of stuff into my Obsidian notes. It costs money, but it's a service that is well worth it. I would imagine that most of what I write about in Ten Blue Links is going to come from Readwise.

This one has ended up long, but I promise I'll make it shorter next time...

The ten blue links for this week

1. Apple's culture shaped its DMA response

I wrote at length about my feelings over Apple's response to the EU DMA -- childish is the kindest way of putting it -- but I really enjoyed Manton Reece's short post about it. Manton's focus is Apple's culture, how that has been shaped, and how that has really influenced their response:

Because of their decades of truly great products, Apple thinks they are more clever than anyone else. Because of their focus on privacy, Apple thinks they are righteous. Because of their financial success, Apple thinks they are more powerful than governments. The DMA will test whether they’re right.

2. Return to office = failure of management

Apple, of course, is one of the companies that has mandated its workers return to office -- and they are not alone. But some new research has found that not only does RTO not improve productivity, and damages worker engagement, it actually stems from a simple need for control from managers. Simply put: bad leadership:

"Results of our determinant analyses are consistent with managers using RTO mandates to reassert control over employees and blame employees as a scapegoat for bad performance".

3. Everyone is a sellout now

The creative industries are having a bad decade. Journalism, in particular, is in a horrible place with jobs lost left, right and centre. Rebecca Jennings wrote a great article about how everyone now has to be a pitch person, and how basically if, for example, you're a writer you're now expected to also be able to market your work -- and won't get employment if you don't. And of course, this has a direct, and negative, impact on your actual work:

Next thing you know, it’s been three years and you’ve spent almost no time on your art,” he tells me. “You’re getting worse at it, but you’re becoming a great marketer for a product which is less and less good.”

4. Fertile fallacies

Sam Freedman's article on fertile fallacies and policy bubbles was specifically about politics, but I think it's equally applicable to many areas of life. Sam's point is that sometimes bad ideas work at first, up until the point where they don't. This is because they often have a kernel of truth about them, or are a reaction to something which has pushed too far.

A policy belief that initially began with an important truth – governments need to have control over state spending and some process to maintain it – has ended up distorted into an absurd farce whereby Treasury officials are frantically changing their policy proposals for the Chancellor based on daily fluctuations in projected borrowing for 2029.

But you can apply this idea everywhere. Consider tech: app stores were a reaction to the absolute hell that was mobile apps in the pre-iPhone era, coupled to the opportunity to make something that was a little more secure for users than the PC. This has inflated to the point where you'll find people who genuinely believe that no one should have the right to install software on a device they own, and that developers owe a tithe to whoever made the platform they're using.

5. The evolution of the Conservative mind

There is a connection here, I think, with Simon Wren-Lewis' piece on the evolution of the British Conservative party from neoliberalism as economic doctrine to social conservatism which solely acts in the interests of the wealthy. As with the policy fallacies that Freedman focuses on, the central doctrinal fallacy of neoliberalism has inflated into a bubble that goes well beyond its original intent.

In the UK, the inflationary force in this bubble was Brexit:

The key moment in this transformation in the UK was of course Brexit. Although it is just about possible to rationalise Brexit in neoliberal terms, if we think about power, Brexit was far from neoliberal. The overwhelming majority of businesses and corporations selling to and from the UK suffered serious damage at the hands of newspaper owners and a few very wealthy individuals. This kind of capture of a neoliberal party by monied interests is not really surprising, because once a politician sees themselves as representing the interests of corporations and businesses generally rather than society, it is a small step to start representing the interests of particular and potentially unrepresentative corporations and businesses (and their ‘think tanks’), especially if those businesses happen to be newspapers or party donors or future employers. Corruption inevitably follows.

Of course, this doesn't explain the similar process that has happened in the US and across the world, but there were, no doubt, similar processes at work.

6. AI Agents are the future of computing

One piece that I have read and reread a few times now is Bill Gates' article on how AI agents are the future of computing. I wrote about this a while ago, too, focusing on Apple's 1987 Knowledge Navigator concept. Conversational interfaces change everything, and Gates thinks it effectively means the end of applications as we know them:

In the next five years, this will change completely. You won’t have to use different apps for different tasks. You’ll simply tell your device, in everyday language, what you want to do. And depending on how much information you choose to share with it, the software will be able to respond personally because it will have a rich understanding of your life. In the near future, anyone who’s online will be able to have a personal assistant powered by artificial intelligence that’s far beyond today’s technology.

I think Bill is right, and he also raises a lot of challenges -- mainly, that to do this stuff properly requires a lot of your personal information to be known by the agent, and managing that in a way which preserves privacy is going to be a tough thing to do.

7. A long interview with Satya Nadella

Another article that I have been rereading is the interview by Axel Spring CEO Mathias Döpfner with Satya Nadella. I find Nadella fascinating: equal parts MBA-bland and tough as old boots, and someone who has done that rarest of things: taken an established business and remade it. While the DNA of the old Microsoft is still there, he's turned the company into something quite different.

There's a tonne of interesting stuff in there -- Obsidian tells me I have nearly 1500 words of quotes saved from it -- covering AI, China, and leadership. But I found this quote pretty interesting, on the relationship between AI and publishers:

After all, with synthetic data training, I think that the incentive is that we create more synthetic data. And if you're training on synthetic data, where you don't have stable attribution to likeness, that becomes a hard thing. So, there is some technological disruption we will have to be mindful of. The fact is, that no publisher will allow you to crawl their content if there isn't a value exchange, and the value exchange has to come in two forms. One is traffic, and the other is revenue share.

8. Technology as rent-seeking process

This article by Wendy Liu from 2019 -- which feels a lot longer ago than just five years -- looks at the business models of technology service companies as effectively being tax collection, and boy she was right. Every company you can think of now, from Apple to Meta to Google and beyond, seems to be hitching its "growth" wagon to collecting tithes of one sort or another. It's no longer enough to simply make products and sell them: you have have have ongoing revenue from users to grow.¹

What if we thought of some of the most lucrative tech companies as essentially tax collectors, but privately-run (and thus not democratically accountable)? Economists call this rent-seeking, and what we’re seeing with a lot of tech companies is that their telos is little more than “rent-seeking as a service”. It’s basically baked in to their business model. Once you’ve fully developed the technology underpinning your service - be it coordinating food delivery, or processing payments, or displaying intrusive ads to people who just want to read a goddamn page on the Internet without being entreated to buy stuff - then your whole schtick then becomes collecting taxes on a whole ecosystem of economic activity.

9. Elon Musk continues lying

Joan Westenberg -- who you must read every time -- notes that Elon Musk is, of course, a liar and yet gets a free pass on his lies every single time from credulous journalists. This time it's Neuralink, and its claim to have implanted some kind of brain chip into a human:

Despite providing no evidence of this milestone, and without any 3rd party verification, the claim was quickly republished by major news outlets without scrutiny or confirmation. Journalists (or, more charitably, their editors) have once again eagerly provided publicity to Musk in the pursuit of advertising traffic to their sites, failing in their basic journalistic responsibility to fact-check. To question. To scrutinise. To ask for the truth.

And journalists wonder why journalism is in trouble.

10. More universal public services, please

Jason Hickel is someone you should be reading, generally, and I could have linked to any one of about 10 articles of his I've read recently. But one place to start is his essay on how universal public services help to eliminate the artificial scarcity that capitalism -- and particularly rent seekers -- profit most from:

By universal services here I mean not only healthcare and education, but also housing, transit, nutritious food, energy, water, and communications. In other words, a decommodification of the core social sector — the means of everyday survival. And I mean attractive, high-quality, democratically managed, properly universal services, not the purposefully shitty last-resort systems we see in the US and other neoliberal countries. What does this look like? How do we get there?

I would add a few technology platforms to this list too… but that's another story.

It would be a little remiss of me if I didn’t mention the launch this week of Microsoft’s consumer and small business AI play. Microsoft Copilot Pro integrates with Word, Outlook, PowerPoint, and OneNote, and offers suggestions, corrections, and insights based on the context and purpose of the document. It’s available now for anyone with a Microsoft 365 Personal or Family plan, at – by complete coincidence, I’m sure – almost the same monthly price as ChatGPT Plus.

Microsoft's approach with Copilot Pro and other AI services is primarily aimed at enhancing practical business and personal productivity, rather than implementing radical changes in the ways people work. 

For me, the real short-term win from Large Language Models lies in their ability to clear away yawn-inducing office tasks. Copilot, especially, is a superstar at this. It helps you tackle all the routine stuff, and leaves people to get on with the creative work. 

The people factor

AI tools like Copilot are not magic bullets that can solve all our problems, and they don’t magically do things on their own. And that highlights something that I suspect is getting neglected: long-term training and support for users in businesses.

One thing I have noticed in the creative industries time and again: technology often gets side-lined in learning and development. Sure, publishing companies have massively improved when it comes to fostering skills in leadership, coaching and other business areas. Yet, when it comes to embracing and learning new tech, training tends to be pretty old-fashioned, rolled out in a “one and done” approach. Updates are relegated to the odd email (which no one reads). 

In the old days, that worked because the pace of change of technology was comparatively slow. A new version of QuarkXpress (yes I am that old) would come out every couple of years, you would do an update session and that was it. 

But for cloud technologies this is not enough, and when there is a complete paradigm shift in tech – as we’re experiencing with AI – it risks putting you well behind more agile businesses.  

According to a report by Oliver Wyman Forum, there is a significant gap between the skills employees believe they need training in, such as AI and big data, creative thinking, and leadership, and the training that employers are currently offering. 57% of employees think the training they are getting isn’t sufficient. And I think they’re right. 

Of course, you can implement short-term fixes. But this is also a good opportunity to set up the way you train and the way your people learn for the long term. The next three to five years are going to see the pace of change accelerate, and you need to adapt the systems which allow your people to learn.

Continuous structured learning

Integrating AI tools into your team's workflow isn't a one-time event, but rather a journey of continuous learning. Begin by setting up a framework for ongoing training and support. This could mean setting up regular training sessions to providing access to online courses, interactive tutorials, and detailed manuals. It's not just about the initial learning curve; it's about keeping the knowledge fresh and relevant.

To foster a culture of continuous learning, encourage your team to see AI as an evolving toolset, one that offers new opportunities for growth and innovation. Promote an environment where experimentation is the norm, and learning from mistakes is valued. This approach helps to maintain a level of curiosity and enthusiasm for what AI can bring to the table.

Remember, the key to continuous learning is collaboration and knowledge sharing. By encouraging your team members to share their experiences and insights gained from using AI tools, you create a knowledge-rich environment. Regular team discussions, workshops, or even informal chat sessions can be great platforms for this exchange of ideas.

Not everyone is going to want to get on board. To get tech-hesitant people excited about AI, relate it to their interests and show how it simplifies work or hobbies. Demystify AI with jargon-free explanations and introduce them to easy-to-use tools through hands-on sessions. Sharing success stories of others who've overcome similar fears can motivate them. Ensure support is available for any questions, making their AI journey smooth and approachable, while focusing on its practical, real-world applications.

To put this into action, consider scheduling a monthly 'AI day' where team members can share new findings, discuss challenges, and brainstorm on how to better integrate AI into your workflows. Think about establishing a mentorship program where more experienced team members can guide others through learning about AI. And finally, make sure you are making use of your best communicators, not just the people who are really enthusiastic about AI.