I agree with you in terms of approach that AI will emerge first from brain emulation. I disagree with you on the timeline. I know you say 'starting around 2030', but I think that's a little ambitious.
While I'm an AI/machine learning practitioner now, my recent Ph.D. work was on computational modeling of the nervous system; namely the cerebellum. The reason I say 2030 is ambitious, is because there are still a lot of unknowns to perform whole brain simulation. To start, we need whole brain connectivity or wiring diagrams at an extremely detailed level. There are some efforts that are part of the BRAIN initiative that are taking a stab at this, but I don't think they're be ready by 2030. Second, you have to understand the physiology of these neurons in order to simulate them. This is incredibly complex and poorly understand. While we understand neuronal physiology in general, there are a great many details that vary by cell type. Additionally, you have to capture neuron morphology, synaptic plasticity, the effect of neuromodulators, ... the list goes on. By capture, I mean understand them well enough to describe them mathematically so that they can be simulated computationally.
Until then, traditional machine learning and artificial neural networks will be increasingly useful and interesting.
Seems like it would be quicker to obtain full knowledge of how DNA and cell replication work. Then the simulation could grow a brain without having to fully understand it.
We could grow neurons on silicon chips, or use small tubes that attract axons and dendrites to grow through them (seen a paper about it once) and use them as an I/O interface to a lab-grown brain. We can already grow 5mm size mini brains with human neural cells. It might be more energy efficient and we could take brains to a whole new level.
Going down to modeling at the level of proteins instead of neurons adds a LOT of quantitative complexity - it could be quicker to obtain enough knowledge to start that, but it could easily add 20-30 extra years of waiting for the available computing power to arrive after the already many years we still need to wait for computing power needed for a full brain simulation at neuronal level.
While I'm an AI/machine learning practitioner now, my recent Ph.D. work was on computational modeling of the nervous system; namely the cerebellum. The reason I say 2030 is ambitious, is because there are still a lot of unknowns to perform whole brain simulation. To start, we need whole brain connectivity or wiring diagrams at an extremely detailed level. There are some efforts that are part of the BRAIN initiative that are taking a stab at this, but I don't think they're be ready by 2030. Second, you have to understand the physiology of these neurons in order to simulate them. This is incredibly complex and poorly understand. While we understand neuronal physiology in general, there are a great many details that vary by cell type. Additionally, you have to capture neuron morphology, synaptic plasticity, the effect of neuromodulators, ... the list goes on. By capture, I mean understand them well enough to describe them mathematically so that they can be simulated computationally.
Until then, traditional machine learning and artificial neural networks will be increasingly useful and interesting.