How can biochemical interventions be spatially localized, and why is that problem important?

High vs low voltage has very different semantics at different places on a computer chip. In one spot, a high voltage might indicate a number is odd rather than even. In another spot, a high voltage might indicate a number is positive rather than negative. In another spot, it might indicate a jump instruction rather than an add.

Likewise, the same chemical species have very different semantics at different places in the human body. For example, high serotonin concentration along the digestive tract is a signal to digest, whereas high serotonin concentration in various parts of the brain signals... uh... other stuff. Similarly, acetylcholine is used as a neurotransmitter both at neuromuscular junctions and in the brain, and these have different semantics. More generally, IIUC neurotransmitters like dopamine, norepinephrine, or serotonin are released by neurons originating at multiple anatomically distinct little sub-organs in the brain. Each sub-organ projects to different places, and the same neurotransmitter probably has different semantics when different sub-organs project to different targets.

Yet most pharmaceutical interventions target one type of molecule, or one receptor, or what have you, approximately everywhere. Such an intervention is analogous to e.g. attempting to make every float in a computer's memory positive by flipping the first bit in every block, but then as a side-effect also changing a bunch of jump instructions to add instructions because there was no way to localize the effect to float-containing memory locations.

Thus the question: how can biochemical interventions be localized, especially in general-purpose ways? I'll throw out some ideas off the top of my head, but I'm interested to hear other peoples' thoughts as well.

Some Methods

Natural Barriers

The blood-brain barrier springs to mind as one example. If a chemical has different semantics in the brain and outside, and one wishes to target outside the brain, then just use a drug which can't cross the barrier.

Implant + Slow Transport/Fast Breakdown

One could put an implant in the right spot to release a drug, and then choose a drug which either isn't transported quickly or breaks down before it can get very far (or both).

Notably, making some random molecule diffuse less quickly seems relatively tractable: one can just attach a bigger molecule to it. And there's an absolutely enormous space of possibilities for what that bigger molecule could be, so it's especially likely to be tractable.

Genetic Modification

Cells already need the ability to tell "where they are" in order for us to have anatomically distinct regions at all. So in principle, it should be possible to genetically modify cells to do something different, but gate the change on the cell being in a particular distinct anatomical region, so cells everywhere else do the same thing as before.

For adult genetic modifications, one would probably want to combine this method with something similar to the implant + slow transport/fast release method above. Adult genetic modifications usually don't hit every cell or even a majority of them, so an ideal use would be modifying some small percentage of cells to release a molecule which influences all the others. Slow diffusion/fast breakdown could then localize that molecule.

What Else?

I'm curious about other methods to localize biochemical interventions in the body, both speculative and already-existing.

when we're a part of a world which is too complex for us to fully understand?

Sounds like a skill issue. The obvious answer would be "get good".

Like, look, people talk all the time about how the economy, or the human body, or what have you, is just too darn complex for one human to fully understand. But, like... humans have on the order of 30k genes. The functions of many of those have not yet been sorted out, but once they are all sorted, that's not an intractable number of functional chemical species for a single human to understand well enough to grasp the whole picture. Especially since so many of them are part of multistep modular synthesis pathways, or modular signalling circuits, or some kind of standard pattern, so whole chunks can be understood at once.

Or, the economy. There's a decently large number of functionally distinct kinds of economic actors, but like... not that many. It's not like one needs to know the particulars of each different hair salon or small trucking firm in order to basically understand the entire economy. There's only so many importantly-different types of business, and it sure seems like a human could basically understand all of them well enough to grasp the whole picture.

Of course one would need to create an entirely different kind of education program, in some cases figure out currently-unknown things (like e.g. function of some genes), and then spend several years of study, in order to understand these things. But it does not seem intractable in principle.

Agreed, that's basically how I use them.

We are absolutely, with no ambiguity, in the "most rapid adoptions of any technology in US history branch". Every single corporation in the world is trying to adopt AI into their products.

Disagree with your judgement on this one. Agree that everyone is trying to adopt AI into their products, but that's extremely and importantly different from actual successful adoption. It's especially importantly different because part of the core value proposition of general AI is that you're not supposed to need to retool the environment around it in order to use it.

reasoning models [...] seem like a bigger deal than GPT-5 to me.

Strong disagree. Reasoning models do not make every other trick work better, the way a better foundation model does. (Also I'm somewhat skeptical that reasoning models are actually importantly better at all; for the sorts of things we've tried they seem shit in basically the same ways and to roughly the same extent as non-reasoning models. But not sure how cruxy that is.)

Qualitatively, my own update from OpenAI releasing o1/o3 was (and still is) "Altman realized he couldn't get a non-disappointing new base model out by December 2024, so he needed something splashy and distracting to keep the investor money fueling his unsustainable spend. So he decided to release the reasoning models, along with the usual talking points of mostly-bullshit evals improving, and hope nobody notices for a while that reasoning models are just not that big a deal in the long run."

Also, I don't believe you that anyone was talking in late 2023 that GPT-5 was coming out in a few months [...] End of 2024 would have been a quite aggressive prediction even just on reference class forecasting grounds

When David and I were doing some planning in May 2024, we checked the prediction markets, and at that time the median estimate for GPT5 release was at December 2024.

I've been working on getting more out of lower percentile conversations. The explanation is fairly woo-ey but might also relate to your interest around flirting.

I'd be interested to hear that.

I have a few times, found it quite interesting, and would happily do it again. It feels like the sort of thing which is interesting mainly because I learned a lot, but marginal learnings would likely fall off quickly, and I don't know how interesting it would be after doing it a few more times.

Like, when I head you say "your instinctive plan-evaluator may end up with a global negative bias" I'm like, hm, why not just say "if you notice everything feels subtly heavier and like the world has metaphorically lost color"

Because everything did not feel subtly heavier or like the world had metaphorically lost color. It was just, specifically, that most nontrivial things I considered doing felt like they'd suck somehow, or maybe that my attention was disproportionately drawn to the ways in which they might suck.

And to be clear, "plan predictor predicts failure" was not a pattern of verbal thought I noticed, it's my verbal description of the things I felt on a non-verbal level. Like, there is a non-verbal part of my mind which spits out various feelings when I consider doing different things, and that part had a global negative bias in the feelings it spit out.

I use this sort of semitechnical language because it allows more accurate description of my underlying feelings and mental motions, not as a crutch in lieu of vague poetry.

Do group conversations count?

Yes.