There seem to be a lot of AI awards popping up lately. Some celebrate business impact, others focus on products and tools, and some are even recognising AI influencers and virtual personalities.
On the surface, it all feels fairly straightforward. An emerging technology, a growing ecosystem, a natural moment to celebrate what’s working. But the more you look at it, the more it feels like something else is happening. Awards don’t just recognise progress, they quietly define what progress looks like.
What counts as “good AI”?
Right now, there isn’t one shared definition of “good AI”. Some awards are focused on measurable outcomes like efficiency, revenue and cost savings. Others lean towards usability, highlighting tools that people actually enjoy using and that fit into everyday workflows. Then there’s a growing space around creativity and attention, where AI generated personalities, content and influence are being recognised.
Each of these is valid, but they are also very different answers to the same question: what is AI for?
Awards as signals
That’s where it gets interesting. These awards are not neutral, they are signals. They tell companies what to build, investors what to fund and the rest of us what to pay attention to. If the biggest stages reward efficiency, we optimise for efficiency. If they reward visibility, we optimise for attention. Over time, those signals start to shape the direction of the industry itself.
Innovation or packaging?
There’s also a quieter tension underneath it all. Some awards are grounded in real outcomes, with clear use cases and demonstrable value. Others sit closer to positioning, where visibility, credibility and being seen in the right places play a bigger role. It raises a slightly uncomfortable question: are we rewarding innovation, or the ability to package it well?
Still being defined
None of this is necessarily a problem. Every new technology goes through a phase like this, with a mix of genuine progress, storytelling and a bit of noise. But it does mean we are still in the process of deciding what matters, not just what AI can do, but what it should be doing.
Maybe that’s the more interesting shift. We are not just building AI anymore, we are building the criteria for what “good AI” is. And right now, that definition is still very much up for grabs.
