Could it? Let's take a few steps back, here. * You're assuming the AI can copy itself, but this is a dubious assumption. As far as I know, none of the AI algor

Could it? Let's take a few steps back, here. * You're assuming the AI can copy itself, but this is a dubious assumption. As far as I know, none of the AI algorithms at the forefront of research provide a fraction of the data the AI would need to copy itself. Being able to copy yourself is not a property that comes with running on a computer and I'm quite positive strong AI, when it emerges, will lack this capability to any meaningful extent. Worse yet, data copiability is ultimately a hardware property, and it requires a way to export a snapshot of one's internal state all through the surface. That's not actually efficient design and one has to account for the possibility that AI would run on hardware that makes copies physically impossible. Locality of information minimizes distance, and this is key to efficiency. The only reason our computers architectures work the way they do is that we need them to, but AI in a production setting is not conventional software and does not need to bend to silly copiability requirements. * You're assuming it would have anywhere to copy itself on. If it's running on a billion dollars' worth of hardware, well, it can't just copy itself on user grade computers and expect to gain much out of it. I personally tend to believe that churning out new AI brains from scratch states will yield superior results to copying pre-trained AI or to "exponentially self-improving AI". If nature is to be believed, improvement often requires cycling through clean slates (e.g. birth); software development also suggests that same idea, that sometimes if you want better software you just have to rewrite it. Honestly, it's kind of a rule in general optimization.