Boon Shin Ng

I do way less. But I do it more deeply.

And more gets done. Everything is slower, but the results are faster .

Finally, and perhaps most importantly, we need to rethink how we measure and value AI contributions. The traditional metrics of time saved or costs reduced may miss the more transformative impacts of these systems - their ability to generate novel insights, synthesize complex information, and enable new forms of problem-solving. Moving too quickly ... See more

Second, the rapid improvement in both capabilities and cost efficiency means that any static strategy for AI implementation will quickly become outdated. Organizations need to develop dynamic approaches that can evolve as these models continue to advance. Going all-in on a particular model today is not a good plan in a world where both Scaling Laws... See more

This shift has profound implications for how organizations should approach AI integration. First, the focus needs to move from task automation to capability augmentation. Instead of asking "what tasks can we automate?" leaders should ask "what new capabilities can we unlock?" And they will need to build the capacity in their own organizations to he... See more

A lot of the focus on AI use, especially in the corporate world, has been stuck in what I call the “automation mindset” - viewing AI primarily as a tool for speeding up existing workflows like email management and meeting transcription. This perspective made sense for earlier AI models, but it's like evaluating a smartphone solely on its ability to... See more

This second Scaling Law led to the creation of Reasoners, which I wrote about in my last post. The new generation of Gen3 models can all operate as Reasoners when needed, so they have two advantages: larger scale in training, and the ability to scale when actually solving a problem.

something surprising: you can make AI perform better simply by giving it more time to think. OpenAI discovered that if you let a model spend more computing power working through a problem (what they call test-time or inference-time compute), it gets better results - kind of like giving a smart person a few extra minutes to solve a puzzle.

success comes from enjoying the daily practice enough to stick with it for a long time.

Having your own ontology makes connecting all information elements much easier and more powerful. It suddenly enables you to automate a wide range of actions—filtering, sorting, organizing, representing, and automating—much faster, better, and easier.