AI versus Marshy - The Best is Yet to Come - A Novel Look at AI's Utility

If you’re new to this newsletter, I’m Marshy, and I’ve been exploring the world of AI for a while now. I’ve talked about the hype adoption cycle and the tech mass-market adoption curve in previous articles, but if you’re just joining us, don’t worry – I’ll catch you up. Today, I want to share some thoughts on where AI is headed, and what we can expect in the future. Tim and Fraser both disaggregate hype from reality and point to where the hype focus has been on AI versus where the reality and value lies. Here’s some takeaways for you, followed by some comments: The discussion around the capabilities of LLMs (see: chat bots) needs more nuance There’s no doubt there’s been investor hype Take in media (podcasts) and social media (gross) pare down realities and over-simplify things We really don’t understand what’s really possible with LLMs yet as we’re still figuring it out I’ve nerded out on charts. If you’re an older reader you would have seen me reference the hype adoption cycle . And if you’ve onboarded recently you might remember the tech mass-market adoption curve (hint: AI is moving faster than mobile phones). Well Fraser shares another chart: ​ Brace yourselves The argument here is that we’re nowhere near peak explosion and it’s yet to come. And when it does it will fast. I regularly try and take myself outside my own bubble. Recently I talked about what’s coming with my mother. She’s a career nurse and clearly doesn’t follow tech like I do. I spoke to her about ChatGPT and gave her demo with voice. It started with: “Hey, I’m just talking to my mum and trying to explain the capabilities of ChatGPT to her. Could you give me an example of what you’re capable of?” And got a generic answer. Then I asked something relevant for Mum (prompted by her). Can you give me instructions on how to sew a jumper that’s torn on the left? The answer was more impressive to her: Absolutely, Marshy! Sewing a torn jumper is a pretty straightforward task. Here’s a simple way to do it: Start by turning the jumper inside out. This way, the repair will be less visible from the outside. Thread a needle with a thread color that matches your jumper as closely as possible. If you’re working with a thick fabric, consider using a stronger thread. Begin near the end of the tear, making a small knot in the thread to secure it. Use a stitch that suits the fabric and the nature of the tear. For many fabrics, a basic running stitch or a more invisible ladder stitch works well. Carefully sew along the tear, making sure the stitches are even and pulling the fabric together without puckering. Once you’ve reached the end of the tear, secure the thread with another knot. Trim any excess thread and turn the jumper right side out to check your work. It might take a bit of practice to get your stitches neat and almost invisible, but it’s a handy skill to have! How does that seem for a start? That’s Mum’s language. It still wasn’t impressive enough for her to consider downloading the app or paying for a premium subscription - the reality is we’re not there yet. My friend linked me on this take on a podcast he listens to (don’t send me podcasts I have enough to sift through!) One way Meta (Facebook, IG, WhatsApp etc) could win is by making AI accessible to people like my Mum. How I write an AI newsletter (and keep up) Via me. It’s a question I get a lot - so I thought I’d walk you through my workflow for creating this newsletter. Ironically - I don’t use a lot of AI tools for this, although I’m aware of many ways I could summarise email digests and scan for keywords. I’ve enjoyed going into the weeds and uncovering things to share with you. Originally appeared in newsletter : AI versus Marshy #46: best yet to come, how I write the newsletter, and Audiopen