It takes longer but developments may still come with a time lag of 10 to 15 years. Downstream effects may come much sooner, especially in countries with less regulation that want to have first mover advantage (praxis society/prospera might implement balaji’s network state + deregulated trials/testing ). Also more comprehensive longitudinal monitoring of individuals (there are scaling laws that imply exponential cost decreases to omics technologies as we already see in genome/transcriptome sequencing) means more people can self-experiment with lower risk (continual monitoring means they can cut off interventions as soon as they find “something” problematic, or as soon as they notice they work/do not work)
Someone may finally develop a solution that rewards ppl for taking the risk of self experiments (it may involve Blockchain economics) - this will make them happen more often
downstream effects of AGI may be so large as to convince people of things they might otherwise not be convinced of (eg deregulation of new edge cases)
Robotics has made very surprising advances just this year (keerthana paper is revealing)
Fwiw ppl I highly trust are updating their estimates of the singularity - some as aggressive as by the end of this decade
Automated drug synthesis may become a thing. Clinical trials have been becoming more expensive over time - HOWEVER - there are now attempts to decrease their cost - esp in prospera - and as omics technologies become way cheaper, smaller groups can do their own trials, audit them, and (in combination with automatic drug synthesis) produce results convincing enough for some people to do interventions way before they get FDA approved (already this is kind of happening with some exotic treatments like plasma rejuvenation and exosomes)
Adept.ai (roon says [with SOME point] the ppl behind it may be more important than any Indian scientist after Bose/Chandrasekhar) may provide the grounding for easy automation of anything with an API (this includes all the “biology automation API” startups that are being bred - eg Mark Zhang’s or Dhash’s or whatever). Also the new Ora startup by Mitchell Lee will show kinds of testing/analytics that will become possible soon (it’s not just in C elegans - the aging analytics algorithms can also be applied to mice and larger organisms)
Bryan Johnson is already trying to recruit a few people to do super-detailed/precise analytics of their own bodies (after intervention)
This year surprised many expert observers like crazy. 2023 will be no different (and we may see even further acceleration). We will see surprising new developments. Biological data is messy and it may be harder to do RLHF on it (with longer wait times), but smg will happen
ALSO, combinatorial experiments (and better measurements/analytics) makes it much easier to develop a first-principles understanding of biology (esp protein function/protein creation) => makes it easier to find targeted interventions down the line [and esp the interventions that robustly work across a wide range of human cell lines that go WAY beyond the commonly used ones]. The cult of statistical significance has made everyone use the same model organisms with minimal variation within themselves, but efforts IN THE GENERAL DIRECTION of seemay chou’s arcadia (+protocols.io) will make it easier to do experiments that work on a wider range of organisms/genetic backgrounds => find the space of interventions that have lower likelihoods of causing adverse reactions. People are already testing mTOR inhibitors (and delivery methods) that may work better than oral rapamycin and this could (at least) buy us a few more years.
Also, efforts in the general direction of [all of the above] will make it easier to develop+culture human cell lines more robustly and (hopefully) make up for some of their weaknesses (eg a Sturm paper says they have much short lifespans).
There will be a bot that can automatically do venipunctures/injections at some pt too. Is there really anything that can “ban” this before getting “FDA-approved”, esp if this is only advertised informally (and esp as 3D printing advances make these things easier to produce? [it’s the algorithm that’s more important than the machine])
 Prospera is one example, but there may be governments elsewhere that deregulate clinical trials - if it was so easy for individual countries to finally decriminalize psychedelics, this isn’t that much of a stretch. I know the FDA/NIH take forever to reform, but for another country it may not be too much to (with the help of AI) draft up a new set of safe clinical trials that make better use of “-omics” + (quantified-self/motion-or-video-analytics data [like ora or like the startup by morten’s postdoc or leon pesha’s daphnia]) data to test drugs with way fewer people and way lower costs.
on off-label use of devices: impt to make the distinction between fda-cleared and fda-approved (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6220002/ ). fda-cleared is a lower bar
some treatments (like stem cell treatments) have many clinics with minimal oversight and are risky for now (I know oversight will become easier over time, esp b/c it is very outsourceable to an independent institution - I do not yet know of one that does it, but it MAY [or may not[ come v. soon). For all the other experimental treatments, there may be external clinics that offer them (independent states that allow for therapy with certain Schedule I drugs show that this is possible)].
Peptides are an experimental treatment that some try, and while there are some attempts to crack down on some suppliers of peptides, new ones come up (also peptide synthesis becomes easier over time).
Organoids will be easier to grow (see Herophilus). I don’t know how long it will take for them to produce data translatable enough to significantly shorten the drug discovery pipeline, but they are a source of new upside risk.
Also, like, if we make significant progress in transformer interpretability next year, it may also (be correlated with [though certainly far from guarantees - given the way increased complexity of the interactome]) significant progress in interpretability of biological modules, esp automated interpretability (more data + better database design + automated addition/updating of biological data into non-static [[intervention database]] may be one route to increase interpretability of studies with lower sample sizes) + makes all published diagrams auto-update when new data is added to the database.
IDK if AI startup Unlearn adds $50M for better, faster, smaller & cheaper clinical trials - MedCity News will work [always note high failure rate of startups], but it has many steps in the right direction.
I don’t think we’re getting to LEV super-near-term, but some people have experienced very surprising results from stem cell therapies/exosomes alone, and this may buy people “enough time” to make it by the time AI gives the world a chance to make something new that changes the world as much as the Internet did. The 2010s felt like a huge decade of stagnation at the translational/Internet level (though I know that it really wasn’t at the pre-translational level), but AI will make things VERY interesting near-term.