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At the end of 2025, we have seen reports that AI is doing great, and we have seen reports that 95% of AI pilots fail. We have seen hundreds of billions invested in data centers, with trillions more planned in the coming years, and no one is quite sure how to power them all. 

While the big AI players started by promising an all-powerful “artificial general intelligence,” what real use cases have shown is that you do not need it to be all-powerful; you just need it to work. In 2025, a steady stream of tools, techniques, and products has made building reliable AI easier and more efficient. 

A big part of that story is focus. Rather than a single, expensive AI model that does more than we need, startups, large platforms, and in-house teams are building systems that use AI in a focused way to solve specific problems. This means embedding knowledge, training, and testing to ensure reliability and quality of output. 

The story is not over; we still have a lot more to do, but the upside of all the AI confusion is that we are beginning to learn how to build and deploy products that deliver real, measurable value. And equally importantly, just sticking “AI” onto a product isn’t seen as magical anymore - users and buyers of AI products have also become more sophisticated, and continue to develop the experience and judgment to know what they need, and how to say both “yes", and “no” to AI, with more confidence.