After all, ex vivo cellular immortality exists. I think it’s the hormones that ruin everything.

There are some reasons to believe this may be true, such as the existence of cells in the wild that seem to be effectively immortal, like certain jellyfish and such. (Nothing terrible close to us taxonomically, but at least they're from planet Earth.)

On the other hand, it may turn out that after a couple of quick wins worth, say, 20 or 30 years, that the whole thing devolves into dozens, then hundreds, then thousands, then tens of thousands of interventions, all complicating each other, a good number of them unique to the individual, as you try to pick up the pieces in your ever-aging organs as they continue to find new and exciting ways to fail. Imagine trying to keep an old car running, but you're not actually allowed to just replace parts, and you have to do all the fixes while the car is on the road, and this is just barely the tip of the iceberg. Senescence research may be one of the worst examples of a light-post problem in human history, as they search for this or that substance that will fix aging but the minimal solution is in fact literally gigabytes worth of information converted into biological machines we can barely even conceive of.

If you can read one neuron, you can read two; those problems are not independent. (That's a simplification to some extent, but at this level we'll take it.) If you can understand one protein's function, that doesn't help anywhere near as much with the next one, and the state of the body is roaming through a very high-dimensional space over time, with the interventions all also affecting the next intervention that will be necessary... it can get pretty ugly in there, potentially. Amusingly, the brain is the problem in both cases; biological immortality probably wouldn't be so hard, we could probably solve everything by transplants of freshly-grown organs based on our genetic code... except transplanting a freshly-grown brain in kinda defeats the whole purpose. In the end, both problems may come back to scanning brains one way or another.

It's the 3D structure, not the sequences so much of these molecules that determine intra- extra- and inter-cellular behaviour.

So we'll never fully understand the brain until we understand the genome and proteome.

Changing genetics seems to me a bit like changing an engine in-flight. Mappping the brain to a computer is more like building a plane from scratch in a hangar.

In a computer, we build the materials but we also build the world around them. With biology, all we have to work with is are the real-life materials, which are harder to grok than their digital equivalents.

When understanding a complicated existing system, like a program, with the intention of making changes to it or fixing it, I find there are characteristic phases to it. There's the part where you have no handle on it at all. At this point you think there maybe isn't that much to the system. Then you get a handle, and you view the whole system through that handle for a while, and everywhere you look you see new stuff. Every time you find something new, your estimate of the size of the task grows. Slowly, but surely, you begin to go more places and look at more things and you encounter stuff you've already seen before. Your estimate of how much work you have to do goes up, but the rate of change starts to decrease. You find more useful handles to understand more bits of the system as you encounter new subsystems and bunches of code. There's a long period of consolidation, where your understanding finally starts matching the complexity and you start getting to the point you know what you are doing, and the rate of surprises you encounter slopes off, perhaps never quite reaching zero, but certainly dropping off. You then have the long slog of actually doing the work, because now you understand it.

In biology, you can argue about exactly where we are, but we are certainly not at the "long period of consolidation" yet, because we keep finding entirely new subsystems that we weren't even aware of. For the past several decades, it seems like every 5 or 10 years we keep finding new subsystems and the apparently complexity of what's going on keeps going up. The ratio of "things we know" over "things we know we don't know" has been steadily going down over the past decades, even as the "things we know" may be increasing in absolute terms. At this point I would have no confidence in anyone's claim that "no, this is the last complication we'll find, we're on our way now!"

Trying to get into that mess and fix it for the long term may just be an unrealistic goal entirely. I suspect modern research into fixing senescence and this or that promising treatment option (oh, look, maybe if we supplement with this, oh, look, maybe if we supplement with that!) may be the equivalent of trying to fix an architectural issue in a large-scale code base armed with the equivalent of three letter "a"s you can insert into the code base, or trying to reassemble a supernova armed with a BB gun.

Not that brain simulation is easy either. In fact, I'm not even convinced the simulating is the hard part. It's the reader I can't hardly imagine how we're going to build without some serious borderline-magic nanotech. You need to be able to read millions of neurons in parallel if you have any hope of finishing before the patient being scanned dies of old age. (And what does "reading" a neuron even mean? Can a "reader" fit into the space between neurons? If not you've got some serious scheduling problems with how to cover everything. And it's not like 'a neuron' is a point, either... one of their major purposes is to spread.) There's also a chance you'll have to be dynamically simulating the already-scanned parts as you go, since the system is changing as you scan it and it's basically the equivalent of a stroke if everything scanned is just dead afterwards, and who would want to be scanned that way? It's an insane machine to build.

It's really hard to tell which is harder, because they are both insanely hard. Neither would particularly surprise me. It's like an ant trying to judge which Redwood is higher.

So while he may have "retired", I mean, he's 90, so it's about time, he most certainly still works full time in the lab.