The term "AI" is a moving target. Technologies like databases or even machine learning were once considered AI but are now just "software." In common usage, AI simply refers to the newest, most novel computational capabilities, and the label will fade as they become commonplace.
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Don't view AI as just a feature set. Instead, treat "intelligence" as a fundamental new building block for software, on par with established primitives like databases or APIs. When conceptualizing any new product, assume this intelligence layer is a non-negotiable part of the technology stack to solve user problems effectively.
The definition of AGI is a moving goalpost. Scott Wu argues that today's AI meets the standards that would have been considered AGI a decade ago. As technology automates tasks, human work simply moves to a higher level of abstraction, making percentage-based definitions of AGI flawed.
The evolution from simple voice assistants to 'omni intelligence' marks a critical shift where AI not only understands commands but can also take direct action through connected software and hardware. This capability, seen in new smart home and automotive applications, will embed intelligent automation into our physical environments.
In 2015-2016, major tech companies actively avoided the term "AI," fearing it was tainted from previous "AI winters." It wasn't until around 2017 that branding as an "AI company" became a positive signal, highlighting the incredible speed of the recent AI revolution and shift in public perception.
The discourse around AGI is caught in a paradox. Either it is already emerging, in which case it's less a cataclysmic event and more an incremental software improvement, or it remains a perpetually receding future goal. This captures the tension between the hype of superhuman intelligence and the reality of software development.