Stephen Downes

This is such a great question I'm going to post it here: "Do you prefer cloud-based platforms like Slack, Microsoft Teams, or Google Chat, or do you use a self-hosted alternative? like Apphitect, Rocketchat, or MirrorFly." I would imagine the vast majority of people use the cloud-based solutions, and may not have even heard of the hosted solutions. Obviously, the two modes fit different use cases. I would ideally lean toward self-hosted, but have in fact always used cloud-hosted. So I need to think a lot about what this question means to the people who are answering it.

There are some nuggets of goodness in this article that is far longer than it needs to be. The first is right near the beginning: "AI is not a tool. It's actually a medium." I like that recasting; AI can be used to create, express and distribute our ideas and thoughts even if we struggle with an inflexible and linear medium like language. But what does creating in AI look like? There are some good ideas: an emphasis on both speaking and listening, the idea of 'flaw-speak', which is the deliberate degrading of one's speech to avoid being perceived as AI, 'Algo-speak', "where the reader we were writing for stopped being a person and became a system," and almost exactly half way through, a list of things people "actually do" when using AI in writing (it's more than just cut and paste). The author warns at the beginning that the article is AI-assisted; I thought it would be really funny if, near the end, the author recanted, and said it was all human after all. Alas, that never happened.

The term 'tokenomics' has already been defined to apply to blockchain, but I'm going to apply it in a new context, to describe the new and emerging science of minimizing the use of tokens while maximizing AI utility. Tokenomics will be a hot topic of discussion on university campuses because, as Marc Watkins notes in this article, there is no realistic path forward to providing all students with access to advanced AI. "That alone," he writes, "should make even the most staunch AI critic pause and ask who, aside from the already affluent and extremely resourced institutions, will have such access and what this means for opportunities for all students." (p.s. I am not on board with the new uses and meanings for the word 'jagged').

The core message here, which draws from a Jonathan Haidt TED talk, is that "In a time when technology makes many aspects of our lives much easier, helping students develop the mindset to pursue what is difficult, meaningful, and worthwhile has never been more critical." I find that a bit ironic, because in the world of easy and hard, TED talks are definitely on the side of easy. But I agree with the need to do hard things - that is, for example, why I decided to bikepack, despite being the wrong age and the wrong body shape for anything like that. But - importantly - some things (like, say, buying food, or paying rent) should be easy, and some things are better when they are hard (like say, things that are meaningful, whatever that means). The easy thing is to say "do hard things". The hard thing is to say which things should be hard to do.

I mostly agree with what's expressed in this article but I disagree with the tenor, which has the university still providing and the student still consuming. As in this, for example: "The convergence of learning analytics, AI advising systems, early alert platforms, and wellness monitoring has created something genuinely new... the data to support learners holistically is increasingly available. What most institutions lack is the architecture to connect it, the governance to use it responsibly, and the human capacity to act on what it reveals." I'm not seeing the university recognize its need to transform from authority to enabler, which is what it will need to do when expertise is widely distributed.

Obviously I don't feel any sympathy for Meta as a company, and while I do feel for its employees, I consider this to be a bit of a case of "you reap what you sow". Anyhow, this is a long and (to me) quite interesting inside look at how Meta has pivoted toward AI at the expense of its software engineering staff, and some of the consequences that followed. The 'takeaway' will resonate with many: "If you're in a leadership position and feeling the temptation to make drastic org changes for AI-related reasons, take a deep breath and see where it left Meta." But I prefer to view it more broadly: any organization that treats its staff as disposable runs the risk of throwing away its core. Sure, you may be left with an engine of sorts and a large customer base, but AI-first organizations will be built from the ground, not by hollowing out existing enterprises.