This is a summary of nine recent posts spanning several research threads I’ve been developing in parallel. The connecting tissue — to the extent it’s real and not just me pattern-matching across my own obsessions — is that the interfaces between biological systems and computational systems are where the most underexplored risk and opportunity both live. BCI hardware, genomic variants, psychedelic pharmacology, AI security, and electromagnetic modeling all look like different problems until you notice they keep raising the same question: what happens when we build powerful tools without adequate models of the substrates they’re operating on?
I’ll group these roughly by domain, then try to say what I think the actual throughline is at the end.
The core claim: BCI hardware, organoid intelligence, and AI alignment communities are independently hitting the same wall — substrate matters, and the field keeps acting like it doesn’t. The NeuroAI-for-safety research program (drawing on neuroscience to inform alignment) has a blind spot: it tends to treat “neural computation” as an abstraction layer you can port insights from, when in fact the physical and biological substrates impose constraints that don’t transfer cleanly. The scar (damage/adaptation in biological tissue), the organoid (partial biological systems with unclear moral status and unclear computational properties), and the dead machine (silicon that lacks the homeostatic properties we’re implicitly borrowing intuitions from) each represent a different failure mode for naive substrate-agnostic theorizing.
My read on this one: It’s pointing at something genuinely underappreciated — most NeuroAI work does implicitly assume a level of substrate-independence that the neuroscience itself doesn’t support. Whether the three-part framing is the right decomposition or just a nice rhetorical structure is something I’m less sure of.
Arena Physica released Atlas RF Studio (beta) with what they’re calling the first electromagnetic foundation model (EMFM). Two models: Heaviside-0 (forward: geometry → S-parameters, ~13 ms per inference, ~0.3 ms batched) and Marconi-0 (inverse: target S-parameters → geometry). This is the “foundation model for physics” pattern applied to RF/antenna design — replacing iterative finite-element solvers with learned forward-and-inverse maps.
The speed claims are striking if they hold up in production use. The deeper question is whether inverse electromagnetic design is a domain where learned models can actually generalize, or whether we’re going to see the same brittleness-outside-training-distribution issues that plague other scientific ML. Worth watching.
A deep dive on rs1065322, a 3′UTR variant in NAMPT — the rate-limiting enzyme in the mammalian NAD+ salvage pathway. The variant sits in a dense RNA-binding protein control zone with CLIP data showing overlapping binding from at least 7 RBPs. The investigation is trying to determine whether this variant has functional consequences for NAD+ metabolism through post-transcriptional regulation, rather than through coding changes. This is the kind of variant that GWAS can flag but can’t explain — the post appears to be doing the mechanistic detective work.
Working from personal whole-genome sequencing data: 79 variants across the full FOXO3 gene, phased into two local haplotypes. The question is whether the absence of the canonical pro-longevity FOXO3 haplotype is the whole story, or whether there’s a more complex noncoding/regulatory architecture worth investigating. This is n=1 genomics done carefully — trying to extract more signal from phased haplotype data than a standard variant-level analysis would give you.
OGG1 is the primary 8-oxoguanine repair enzyme. The Ser326Cys variant (rs1052133, ~50-60% allele frequency in East Asians) dimerizes via intermolecular disulfide bonds under oxidative stress, producing a dimer that binds DNA but can’t excise the lesion. The “conditional” part is important — this isn’t a constitutive loss-of-function, it’s a stress-dependent failure mode. Which means the phenotypic impact depends heavily on oxidative load context, not just genotype.
Across the three genomics posts: The common theme is pushing past “variant → phenotype” black-box associations toward mechanistic stories involving post-transcriptional regulation, haplotype phasing, and conditional biochemistry. Whether the mechanistic interpretations are correct requires experimental validation that these posts don’t (and can’t yet) provide — but the analytical approach is sound.
The argument: DMT may be the lowest-risk clinical psychedelic for a healthspan research program, specifically in the Colorado regulatory context. Framed as a falsification-first program — meaning the proposal is structured around what evidence would kill the hypothesis, not what evidence would confirm it. This is the right epistemic structure for something this speculative. The question is whether the actual experimental program described lives up to the falsification-first framing or whether it quietly assumes the conclusion in its design. (I’d need the full post to assess that.)
The core urgency claim: people are already taking arylcyclohexylamines (ketamine-adjacent compounds) in large numbers for mood, and the safety/efficacy data doesn’t exist. This isn’t a future problem — it’s a current one. The “grey zone” framing captures the regulatory and pharmacological ambiguity well. These compounds sit in a space where they’re not scheduled enough to prevent use but not studied enough to inform it.
This one feels like the most directly action-relevant post of the batch. The gap between adoption rate and safety data for novel dissociatives is real and growing.
(DMT and arylcyclohexylamines also give you way more granularity than classic psychedelics—they are the “nudge” before “nudge”, esp b/c tFUS may not come quickly enough for necessary “plasticity” in urgent AI timelines)
The framing correction: stop treating AI-assisted vulnerability discovery as “LLMs finding website bugs.” The real development is frontier models showing early capability at searching for security failures across real systems at scale. This reframes AI cybersecurity from a software concern to an infrastructure concern.
If AI-driven vulnerability discovery scales faster than human-mediated patch deployment, the window between “vulnerability found” and “patch deployed” becomes an exploitable kill chain. The proposal: Anthropic (and presumably other frontier labs) should subsidize model access for defensive security teams to close this asymmetry. This is an interesting governance proposal because it acknowledges the dual-use reality without pretending you can put the capability back in the box — instead it argues for tilting the access asymmetry toward defenders.
What’s the Actual Throughline?
If I’m being honest, the throughline might just be “one person’s research interests in March 2026.” But if there’s a real connective thread, it’s something like: the systems we’re building (AI, pharmacological, genomic) are outrunning our ability to characterize the substrates they operate on. The EMFM can do inverse design at 0.3ms but we don’t know if it generalizes. People are taking novel dissociatives at scale without safety data. AI can find vulnerabilities faster than humans can patch them. Genomic variants have conditional effects that depend on contexts we haven’t mapped.
The common failure mode across all of these is acting on capability before understanding substrate — and the common prescription is some version of “build the characterization infrastructure before (or at least alongside) the capability.”
Whether that prescription is actionable or just a nice thing to say from the sidelines is a fair question to ask.
Feedback welcome. Several of these posts are open for critique — especially the DMT healthspan program and the arylcyclohexylamine safety proposals, where the claims are most empirically testable.
This is a summary of nine recent posts spanning several research threads I’ve been developing in parallel. The connecting tissue — to the extent it’s real and not just me pattern-matching across my own obsessions — is that the interfaces between biological systems and computational systems are where the most underexplored risk and opportunity both live. BCI hardware, genomic variants, psychedelic pharmacology, AI security, and electromagnetic modeling all look like different problems until you notice they keep raising the same question: what happens when we build powerful tools without adequate models of the substrates they’re operating on?
I’ll group these roughly by domain, then try to say what I think the actual throughline is at the end.
AI Safety & Substrate Problems
The Scar, the Organoid, and the Dead Machine: Three Substrate Problems for the NeuroAI for AI Safety Problem
The core claim: BCI hardware, organoid intelligence, and AI alignment communities are independently hitting the same wall — substrate matters, and the field keeps acting like it doesn’t. The NeuroAI-for-safety research program (drawing on neuroscience to inform alignment) has a blind spot: it tends to treat “neural computation” as an abstraction layer you can port insights from, when in fact the physical and biological substrates impose constraints that don’t transfer cleanly. The scar (damage/adaptation in biological tissue), the organoid (partial biological systems with unclear moral status and unclear computational properties), and the dead machine (silicon that lacks the homeostatic properties we’re implicitly borrowing intuitions from) each represent a different failure mode for naive substrate-agnostic theorizing.
My read on this one: It’s pointing at something genuinely underappreciated — most NeuroAI work does implicitly assume a level of substrate-independence that the neuroscience itself doesn’t support. Whether the three-part framing is the right decomposition or just a nice rhetorical structure is something I’m less sure of.
Electromagnetic Foundation Models
From Solving Fields to Steering Them: Heaviside-0, Marconi-0, and the First Electromagnetic Foundation Model
Arena Physica released Atlas RF Studio (beta) with what they’re calling the first electromagnetic foundation model (EMFM). Two models: Heaviside-0 (forward: geometry → S-parameters, ~13 ms per inference, ~0.3 ms batched) and Marconi-0 (inverse: target S-parameters → geometry). This is the “foundation model for physics” pattern applied to RF/antenna design — replacing iterative finite-element solvers with learned forward-and-inverse maps.
The speed claims are striking if they hold up in production use. The deeper question is whether inverse electromagnetic design is a domain where learned models can actually generalize, or whether we’re going to see the same brittleness-outside-training-distribution issues that plague other scientific ML. Worth watching.
Longevity Genomics (Three Posts)
NAMPT rs1065322: A Deep Investigation Thread
A deep dive on rs1065322, a 3′UTR variant in NAMPT — the rate-limiting enzyme in the mammalian NAD+ salvage pathway. The variant sits in a dense RNA-binding protein control zone with CLIP data showing overlapping binding from at least 7 RBPs. The investigation is trying to determine whether this variant has functional consequences for NAD+ metabolism through post-transcriptional regulation, rather than through coding changes. This is the kind of variant that GWAS can flag but can’t explain — the post appears to be doing the mechanistic detective work.
Defective FOXO3 Locus Investigation: Phased Haplotypes, Compound-Variant Effects, and Regulatory Interpretation
Working from personal whole-genome sequencing data: 79 variants across the full FOXO3 gene, phased into two local haplotypes. The question is whether the absence of the canonical pro-longevity FOXO3 haplotype is the whole story, or whether there’s a more complex noncoding/regulatory architecture worth investigating. This is n=1 genomics done carefully — trying to extract more signal from phased haplotype data than a standard variant-level analysis would give you.
OGG1 Ser326Cys: Structural Basis of Conditional DNA Repair Failure in East Asians
OGG1 is the primary 8-oxoguanine repair enzyme. The Ser326Cys variant (rs1052133, ~50-60% allele frequency in East Asians) dimerizes via intermolecular disulfide bonds under oxidative stress, producing a dimer that binds DNA but can’t excise the lesion. The “conditional” part is important — this isn’t a constitutive loss-of-function, it’s a stress-dependent failure mode. Which means the phenotypic impact depends heavily on oxidative load context, not just genotype.
Across the three genomics posts: The common theme is pushing past “variant → phenotype” black-box associations toward mechanistic stories involving post-transcriptional regulation, haplotype phasing, and conditional biochemistry. Whether the mechanistic interpretations are correct requires experimental validation that these posts don’t (and can’t yet) provide — but the analytical approach is sound.
Psychedelic/Neuromodulation Pharmacology (Two Posts)
DMT as the Minimum-Risk Psychedelic: A Falsification-First Healthspan Research Program for Colorado
The argument: DMT may be the lowest-risk clinical psychedelic for a healthspan research program, specifically in the Colorado regulatory context. Framed as a falsification-first program — meaning the proposal is structured around what evidence would kill the hypothesis, not what evidence would confirm it. This is the right epistemic structure for something this speculative. The question is whether the actual experimental program described lives up to the falsification-first framing or whether it quietly assumes the conclusion in its design. (I’d need the full post to assess that.)
We Need Urgent Safety and Efficacy Data for Grey Zone Arylcyclohexylamines
The core urgency claim: people are already taking arylcyclohexylamines (ketamine-adjacent compounds) in large numbers for mood, and the safety/efficacy data doesn’t exist. This isn’t a future problem — it’s a current one. The “grey zone” framing captures the regulatory and pharmacological ambiguity well. These compounds sit in a space where they’re not scheduled enough to prevent use but not studied enough to inform it.
This one feels like the most directly action-relevant post of the batch. The gap between adoption rate and safety data for novel dissociatives is real and growing.
(DMT and arylcyclohexylamines also give you way more granularity than classic psychedelics—they are the “nudge” before “nudge”, esp b/c tFUS may not come quickly enough for necessary “plasticity” in urgent AI timelines)
AI Cybersecurity & Governance (Two Posts)
AI Cybersecurity Is Urgent in 2026 and Is No Longer Just a Software Problem
The framing correction: stop treating AI-assisted vulnerability discovery as “LLMs finding website bugs.” The real development is frontier models showing early capability at searching for security failures across real systems at scale. This reframes AI cybersecurity from a software concern to an infrastructure concern.
The Patch Window Is a Kill Chain: Why Anthropic Should Subsidize Frontier Models for Defenders
If AI-driven vulnerability discovery scales faster than human-mediated patch deployment, the window between “vulnerability found” and “patch deployed” becomes an exploitable kill chain. The proposal: Anthropic (and presumably other frontier labs) should subsidize model access for defensive security teams to close this asymmetry. This is an interesting governance proposal because it acknowledges the dual-use reality without pretending you can put the capability back in the box — instead it argues for tilting the access asymmetry toward defenders.
What’s the Actual Throughline?
If I’m being honest, the throughline might just be “one person’s research interests in March 2026.” But if there’s a real connective thread, it’s something like: the systems we’re building (AI, pharmacological, genomic) are outrunning our ability to characterize the substrates they operate on. The EMFM can do inverse design at 0.3ms but we don’t know if it generalizes. People are taking novel dissociatives at scale without safety data. AI can find vulnerabilities faster than humans can patch them. Genomic variants have conditional effects that depend on contexts we haven’t mapped.
The common failure mode across all of these is acting on capability before understanding substrate — and the common prescription is some version of “build the characterization infrastructure before (or at least alongside) the capability.”
Whether that prescription is actionable or just a nice thing to say from the sidelines is a fair question to ask.
Feedback welcome. Several of these posts are open for critique — especially the DMT healthspan program and the arylcyclohexylamine safety proposals, where the claims are most empirically testable.