Black socks
...and sock clips. A loose analogy.
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Friends,
I’d like to share a quick story.
Growing up in suburban London with my brother and parents, the small task of sorting laundry became quite the household challenge—especially when it came to black socks. My mum, ever resourceful, came up with a simple but brilliant solution: she sewed white thread on my socks, red thread on my brother's, and left my dad’s as they were. A small act of innovation to save us from endless sock confusion.
Fast forward to today, and with a family of my own, I’ve embraced a more modern solution—sock clips. They save me time and effort, but I couldn't convince the rest of my household to adopt them. To them, it was unnecessary, but for me, it was efficiency that has probably saved days if not months. What started as a niche solution has since gone mainstream. Now, sock clips are everywhere, and you can find countless varieties, often from China, all for the small price of saving a few minutes with each laundry cycle.
Consider this in the context of technology. Think of Large Language Models (LLMs) like those black socks—powerful, versatile, and ubiquitous. But without a way to organise or guide their use, they can become cumbersome, like digging through a drawer of identical socks. This is where AI "wrapper" apps come into play. Just as sock clips bring order to the chaos of mismatched socks, these apps streamline LLMs, allowing us to tap into their full potential with precision and efficiency. Whether you’re working with ChatGPT, Claude, Gemini, or another LLM, these wrapper apps ensure you're using these tools most effectively, saving time and reducing frustration.
We’re at an interesting juncture in AI’s evolution, as foundational models begin to sand become more standardised. While these models aren’t yet fully interchangeable, they all aim to achieve similar goals. The focus is shifting to the layers built on top of these models—applications that solve specific problems within industries and enhance logical reasoning, moving beyond simple pattern-matching.
This shift is evident in how companies are adapting. OpenAI introduced Canvas, Gemini launched NotebookLM, and Claude is incorporating Artifacts—each a signal of this change in approach. The emphasis is now on thoughtful, deliberate reasoning, rather than just fast, instinctual responses. I was impressed recently when experimenting with o1[Strawberry], which demonstrated a maturity that promises a new wave of AI applications rooted in deeper, more reflective problem-solving.
To put this into context, we’re moving from what Daniel Kahneman, in his book Thinking, Fast and Slow, describes as "System 1" thinking—quick, intuitive decisions—to "System 2" thinking, which requires more deliberation and strategy. Models like OpenAI’s o1 are leveraging increased computing power to enhance their reasoning capabilities, allowing for more thoughtful and strategic outputs.
This shift represents the next stage of AI. It's not just about rapid responses anymore; it's about thoughtful problem-solving. The real opportunity lies in building applications that go beyond wrapping existing models, creating systems that mimic human thought processes to address complex challenges. For instance, imagine the potential in retail—AI could analyse a customer’s entire shopping history, browsing behaviour, and even social media interactions to offer truly personalised recommendations, enhancing customer engagement and lifetime value.
Generative AI is not just another technology; it’s changing how we think about software, services, and their applications across industries. And to bring this back to my socks and my clips —who knows? In the future, we might not only find better ways to clip socks together, but perhaps even use smart textiles to track biometrics or pair outfits intelligently. Suddenly, even black socks might become exciting.
Stay Curious - and don’t forget to be amazing,
PS. These at the 18 things that are killing you - and what you can do about it.
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