This essay is a response to Kant’s “What Is Enlightenment?” (1784), extending his paradox about the relationship between political liberty and intellectual maturity to the question of what democracy requires of its citizens. In other words – we tend to accept the idea that modern/developed democracies grant their constituents protected rights, representation, and free speech, but do they cultivate our ability to think for ourselves and to act on that thinking in public life? And if not, what does that mean for the type of democracy we exist under?

In the final paragraph of “What Is Enlightenment?,” Kant presents his reader with a rather striking paradox. He writes that “a greater degree of civil freedom appears advantageous to the freedom of mind of the people, and yet it places inescapable limitations upon it; a lower degree of civil freedom, on the contrary, provides the mind with room for each man to extend himself to his full capacity.” In other words, Kant suggests that political liberty does not necessarily produce intellectual liberty, and may in fact entangle citizens in new forms of constraint. By contrast, even a less politically free regime can, under certain conditions, foster a robust culture of public criticism and reflection, thus allowing individuals to develop their rational capacities to the fullest. Today we tend to regard modern democracies as the freest regimes and as paradigmatic sites of rational public discourse. And yet, Kant suggests that the relationship between political freedom and enlightenment is neither linear nor guaranteed.

Kant’s paradox suggests something that should unsettle our confidence in modern democratic institutions. It suggests that the relationship between political freedom and the capacity to think for oneself is far more complicated than we tend to assume. We tend to treat democracy and enlightenment as natural allies (more freedom produces more thinking, more speaking produces more thinking). But perhaps that relationship is not so straightforward. What if democracy can function perfectly well with citizens who largely defer to authority, and what if a democracy without an enlightened public is not a failure of democracy at all, but simply democracy without courage?

Throughout this essay, I will argue that while democratic institutions can exist without an enlightened public, a democracy worthy of the name, or, better put, one that is substantively responsive to its citizens and committed to robust civil, political, and personal liberties, depends on people willing to think for themselves and to speak publicly from that thinking.

Kant’s Courage to Think

Kant describes enlightenment as “man’s release from his self-incurred tutelage”: the condition of being unable or unwilling to use one’s own understanding without the direction of another. This dependence, he argues, is “self-incurred” not because people lack the capacity to think for themselves, but because they lack the courage to do so. Enlightenment is not primarily a matter of acquiring knowledge or acquiring information. It is the willingness to think without another’s guidance. The obstacle is not a lack of intelligence, but a lack of courage.

Kant condenses this into an imperative: Sapere aude – “Have courage to use your own reason!” The emphasis on courage matters, as it is not merely a cognitive shift, but a practical and social one as well. To use one’s own reason is to risk error, to expose oneself to criticism, and to challenge the authorities on whom one has come to depend. It is to risk being wrong in public, and to risk the social consequences of being wrong in public.

Kant’s distinction between the private and public use of reason is crucial here. The “private” use of reason, somewhat counterintuitively, refers to the use of reason within a particular civil post or office, where one is bound to obey existing rules and carry out assigned duties. By contrast, the public use of reason occurs when that same official addresses the wider public (through writing, speech, or other forms of communication) and is free to criticize regulations and argue for their reform. The distinction is not between private and public spheres in the ordinary sense. It is a distinction between two orientations toward authority: one oriented toward obedience, the other toward critical reflection.

The point, for Kant, is that the public use of reason must always be free. Without the freedom to publish, to argue, and to criticize, there can be no enlightenment. The public use of reason is the engine of enlightenment. It is the mechanism by which individuals move from tutelage to self-rule, from passive acceptance to active, critical engagement. And it is this public use of reason that makes enlightenment possible, not the accumulation of knowledge, but the willingness to use one’s own reason in the presence of others.

Minimal Democracy vs. True Democracy

A society where citizens routinely exercise the public use of reason will relate differently to its institutions than a society where most people remain in self-incurred tutelage. To see why this matters for democracy, it helps to make a distinction between a minimal, procedural sense of democracy and a more demanding, “true” democracy.

In the minimal, procedural sense, a regime counts as democratic so long as it maintains basic mechanisms of formal accountability between rulers and ruled: elections, representation, some protections for speech and association, a press that can in principle criticize those in power. Many contemporary states meet these criteria on paper. But this procedural definition leaves open how far government policy is actually responsive to citizens’ considered views and how securely liberties and rights are protected in practice. A truly democratic order is not only procedurally legitimate; it is substantively responsive to public judgement, offers robust protection of fundamental rights, and possesses the capacity to amend itself over time in light of criticism and a changing populace.

On the procedural understanding, it is possible for democratic institutions to function without a fully enlightened public. Elections can be held, representatives chosen, and basic mechanisms of accountability can operate even when most citizens remain in self-incurred tutelage. As long as people participate at regular intervals and accept the outcomes as legitimate, the institutional form of democracy can persist, even if the public’s role is largely passive and deferential. The courage to use one’s own reason is therefore not strictly necessary for this minimal version of popular rule: citizens can remain intellectually dependent and the machinery will still run.

Once we turn to “true democracy,” enlightenment becomes harder to set aside. A regime is substantively democratic only if it is responsive not just to raw preferences but to the publicly argued judgements of its citizens about laws and policies. For those judgments to form, citizens must reflect on their interests, weigh reasons, and revise their positions over time in light of argument and experience. This is what Kant means by enlightenment: a shift away from passive acceptance toward active, critical reflection. Without a public that regularly engages in this kind of reflection and is willing to use its reason publicly, democratic institutions may still operate, but their responsiveness will not reflect a genuine public will.

Rights, Iteration, and Public Reason

Rights are not self-enforcing. They depend on citizens who can recognize when laws and institutions fail to live up to their own principles and who are prepared to contest those failures in public. Here Kant’s distinction between the private and public use of reason becomes crucial. The “private” use of reason, somewhat counterintuitively, refers to the use of reason within a particular civil post or office, where one is bound to obey existing rules and carry out assigned duties. By contrast, the public use of reason occurs when that same official addresses the wider public, through writing, speech, or other forms of communication, and is free to criticize regulations and argue for their reform.

A democracy that lacks such public use of reason may preserve rights in name, but it will lack the capacity to correct abuses, expand protections, or amend unjust arrangements. Thinking of democracy this way highlights its iterative character. The history of the United States illustrates the point: American democracy has expanded and refined itself through constitutional amendments that abolished slavery, extended suffrage, and prohibited legally sanctioned forms of discrimination. These legal changes are instances of democratic iteration, moments when citizens recognize that existing institutions fall short of their professed principles and press for change. Kant’s enlightened citizen is, in this sense, an activist (on any end of the political spectrum) – someone who not only thinks independently but uses that thinking to challenge institutions in the name of their own ideals.

Why Kant’s Problem Is Ours

Kant wrote against a backdrop of priests and monarchs. Our authorities look different today, and can be found in financial institutions, tech platforms, scientific and medical bureaucracies, or complex legal and regulatory regimes. The temptation to tutelage is similar. It is easier to defer to “what the markets say,” “what the data says,” or “what the guidelines say” than to risk publicly contesting the rationality and justice of those systems.

In domains like biotech, AI, and health policy, this matters deeply. As we have seen time and time again, systems that claim to be neutral and rational can entrench serious harms. A democracy that does not treat its citizens as subjects capable of public reason risks becoming procedurally tidy, sure, but substantively and fundamentally unfree. And If Kant is right, which I believe he is, we do not overcome this risk simply by adding more rights or more participation. We overcome it, if at all, by cultivating publics who refuse that so-called “self-incurred tutelage,” those who are willing to accept the personal, social, and professional costs of using their own reason and speaking from it.

A follow-up to Building Whel and An Update on Whel. If you’re new here: Whel is a drug-repurposing research tool that mines scattered evidence (trials, registries, pharmacovigilance data, target databases, patient forums) for signals that existing drugs might help under-researched hormonal conditions affecting women.

A note before I start – most of what follows is about taking things out. The last few weeks of work on Whel have been less about adding data and more about rebuilding the machinery/engine that scores it, and a surprising amount of that meant deleting features I’d written about proudly in the last post. That was uncomfortable, yes, but I am willing to give myself grace and also the space to grow and make things better. If you can point me to something that was perfect the first time around, be my guest. I certainly can’t think of anything. This period of “undoing” was also, I think, the most useful stretch of work I’ve done on this project. So this is the post where I explain the rebuild, what an outside review changed, and why the model is now a different one than it was earlier in June.

As always, I work on this nearly every day, the numbers move, and the site is the real source of truth. The Roadmap tracks what’s live versus planned, and the Methodology changelog now carries a dated entry for every version of the engine, including the big one I detail below.

The numbers!

In the last two posts I said Whel carried “271 active repurposing signals.” As of this writing, the headline number is 183 drug-condition pairs across the same six conditions (endometriosis, PMDD, PCOS, adenomyosis, vulvodynia, and perimenopause & menopause), drawn from 433 verbatim evidence claims across 186 source documents, covering 171 distinct compounds.

Let’s start with the biggest change: 183 is smaller than 271. I will address this head-on. The database did not shrink. What changed is the unit. The old “signal” counted each arm-reading separately, so a single drug-condition relationship could show up as three or four “signals” if it appeared in three or four evidence arms. The new engine treats the drug-condition pair as the atom, and scores all of its evidence arms into one record. So a number that used to double- and triple-count now counts once. On top of that, the whole corpus was re-extracted and re-scored during the migration, so 183 and 271 aren’t really the same kind of thing and shouldn’t be read as “went down.” I’d rather have a number that means something precise than a bigger number that means something fuzzy.

The distribution across conditions, for the curious: Perimenopause & Menopause (50), PCOS (37), Endometriosis (33), Vulvodynia (30), PMDD (18), Adenomyosis (15).

From “signals” to a substrate

The most central change is that Whel is no longer a flat list of signals with a grade stapled to each one. It’s now what I’ve been calling a substrate, a single scored layer that reads every drug-condition pair through three evidence arms and resolves them into one verdict.

If you read the last post, you’ll remember four research arms: Direct Research, Cross-Condition Signals, Pathway Insights, and Community Forum Reports. There are now three: Direct, Pathway, and Community. Cross-condition didn’t disappear, it got demoted. It used to be a scored arm in its own right, which always sat a little awkwardly, because “this drug helped a different condition” isn’t really independent evidence about your condition; it’s a hypothesis generator. So cross-condition is now a derived lens layered on top of the scored arms, not one of the things being scored.

Every arm is scored on the same five dimensions, each 0-2, summing to an arm strength out of 10. The dimensions got renamed and sharpened in the process: what used to be replication, source quality, biological plausibility, and direction consistency are now corroboration, rigor, plausibility, and consistency (specificity kept its name). That is, I think, more than simply relabeling. Corroboration in particular absorbed a job that used to live somewhere much more problematic, which is the next section.

The arm strength then gets discounted by a female-applicability multiplier, a six-band scale (F1 through F6, ×1.00 down to ×0.50) that asks how far the underlying evidence was actually generated in women versus extrapolated from male-derived or mixed populations. A signal built on a male-only trial and pointed at a female condition gets marked down, visibly, with the reasoning shown. That discount is the part of Whel I care about most (and that I am most happy is finally live!!), because it’s the entire reason I started this project in the first place. The result sorts into one of four frozen tiers (Strong (≥8.0), Moderate (6.0–7.9), Emerging (3.5–5.9), Exploratory (<3.5)) which currently breaks down as 11 Strong, 60 Moderate, 68 Emerging, and 44 Exploratory.

There’s also a new, separate validation status on each pair: clinical, unvalidated signal, or preliminary, that captures whether a pair is anchored by real direct research or is being surfaced more tentatively. Right now that’s 108 clinical, 43 unvalidated signals, and 32 preliminary. The full model is laid out on the signal types & scoring page.

Killing a circular grade

Another major chage. In the last post I introduced (with some pride) an L0-L3 external validation grade, which was a four-step ladder meant to show whether a signal was independently supported in the broader clinical record, sitting beside Whel’s internal confidence tier.

An outside reviewer pointed out, correctly, that the L-grade was doing two contradictory jobs at once. It was being used as an input into how Whel scored a signal internally, and as an external benchmark of that same signal. Which means the thing was grading its own homework. A score that partly determines itself and then validates itself is circular, and circularity is exactly the failure mode an evidence tool is supposed to avoid.

So I retired the L0-L3 grade entirely. In its place:

The in-pipeline job, “how well-corroborated is this within the evidence Whel actually ingested,” now lives inside the five-dimension rubric as the corroboration dimension, scored only against sources the engine has actually read. The external job, “is this independently supported out in the wider clinical record,” became a clean external ladder (E0-E3) that is computed strictly after the fact and never feeds back into the score. Same information, but the two roles are now physically separated so neither can contaminate the other.

I did the same kind of separation for the two external comparators. The Open Targets knowledge-graph check, which used to float beside the score as its own validation item, got absorbed into the Pathway arm where it belongs as an input. And MATRIX (which, if you recall, is Every Cure’s public drug-disease prediction layer, that I wrote about last time) is now positioned explicitly as an independent external comparator shown next to the score and never folded into it. The rule I settled on, and which I now apply everywhere: anything that validates the score is not allowed to also be the score. The reasoning for all of this is written up in the methodology.

The scoring model is now Opus 4.8

Whel uses a large language model to do one specific job: read sources and extract structured facts, then score those facts against the fixed rubric. It does not generate clinical advice, doses, or treatment plans, and the deterministic parts of the pipeline (the female-applicability multiplier, the imprecision caps, the final tier assignment) are computed in code, not by the model.

Since the last post I upgraded that model from Claude Opus 4.6 to Claude Opus 4.8 (released May 2026). In the last post I leaned pretty heavily on WHBench, the women’s-health clinical benchmark where Opus 4.6 was the strongest of 22 models at 72.1%. No women’s-health-specific benchmark has evaluated 4.8 yet, so I can’t claim an improvement there. What I can point to is that Opus 4.8 scored 55.8% on HealthBench Professional (a set of physician-authored clinical tasks), up from a prior release, and that no frontier model is near the ceiling on these tasks. There’s also recent work in Nature Medicine finding that general-purpose frontier models now outperform specialized clinical tools on medical reasoning, which is part of why I’m comfortable building on a general model rather than waiting for a women’s-health-specific one that doesn’t exist.

I read those results the way I’d want a reader t0, as evidence the model handles clinical text well, and as nothing remotely like a guarantee that any individual extraction is correct. The reference-fabrication work I cited last time still applies in spirit, since confident, plausible-looking, wrong structured output is the failure mode I’m designing against. Which is the whole reason the score is computed in code and the model is fenced into the narrow job of reading.

Why I made these choices

If there’s a single throughline to all of this, it’s that I’d rather the tool be legibly honest than large in scope. Almost every change above trades a more flattering surface for a more defensible one: fewer counted signals but a cleaner unit; one fewer arm but a more honest account of what cross-condition data is; a deleted grade but no more circular self-validation; a model upgrade paired with a more careful statement of its limits.

This is, I hope, the thing that distinguishes Whel from a big black-box prediction model – that you can trace every number back to a source quote and a piece of stated reasoning. The moment the scoring starts validating itself, that advantage evaporates. So the rebuild was mostly me protecting the one property that makes this project worth doing.

Where this is and what’s next

If you want to poke at it, the signal types & scoring page documents the three arms and five dimensions; the methodology page carries the external E0-E3 ladder and the full update log; the external references page has the MATRIX coverage audit; and the technical architecture page explains exactly which steps are the model’s and which are deterministic code.

Still on the roadmap, in rough order: the two-rater validation study; disproportionality statistics (PRR/ROR) on the adverse-event data; the manual primary-source curation pass for well-studied pairs where the automated pipeline returns representative but not exhaustive sources; ontology-grounded entity resolution; a knowledge-graph grounding layer built with BioCypher; and a citable open-data export with a DOI. The grounding work is the through-line for the next few weeks, same instinct as this rebuild, just pointed at the inputs instead of the scoring.

Thanks as always! The site is whel.bio, and my messages are open. The fastest way to make this better is to tell me where I am making a fool of myself.

This post is a follow up to the one I made a few months ago (here), where I outlined why I began this project, what I wanted it to become, and what I had built (and subsequently, what had surfaced) so far. Since then, much has changed, and I continue work on it every single day. So, I figured I would write another post detailing the most significant updates (as well as setbacks), and what my plans are for this project going forward.

Please note that from here on, most updates will live on the site’s Roadmap page, where I track what’s live, what I am currently working on, and what I have planned. This piece, and the original, will stay up as the write-ups of how the project began. I might make another update post once I reach a substantial amount of macro-level updates, which is what triggered this post. But for daily/weekly changes, the site itself is the most reliable resource.

Where Whel is now

As of writing, Whel carries 271 active repurposing signals across the same six conditions (endometriosis, PMDD, PCOS, adenomyosis, vulvodynia, and perimenopause & menopause), drawn from 2,166 unique source citations. Those numbers will keep moving as the pipelines run. The four research arms (Direct Research, Cross-Condition Signals, Pathway Insights, Community Forum Reports) are all still the central organizing structure on the site, and the five-dimension confidence rubric (replication, source quality, specificity, biological plausibility, direction consistency) still maps every signal into Strong/Moderate/Emerging/Exploratory.

What is new, beyond more data, is that the project has grown an external validation architecture on top of its internal scoring, and it now sits in a much more honest relationship to the literature on what LLMS can and cannot do in biomedical text work. So most of this update will be about that.

If you want to skip to where the changes live on the site:

• The Methodology page now has a full L0-L3 external-validation rubric, plus a strength-and-certainty glossary, plus a dated “Methodology update log” with five versioned entries.

• The External References page has detailed audit numbers for the Every Cure MATRIX cross-reference layer (live), and a separate “Structured Grounding in Progress” section documenting two further layers I’m planning to build.

• The current Featured Signal walkthrough is now Vaginal Estrogen for postmenopausal recurrent UTI, with an “External Validation: Literature Whel did not ingest” section that I will explain below.

A new external validation grade: L0 through L3

The biggest single addition since the original post is an external validation grade that every signal in Whel now carries alongside its internal confidence tier. The grade is on a four-step ladder, L0 to L3:

• L0 = no external evidence identified.

• L1 = the signal appears in a single independent source (a study, a trial, an adverse-event report, a guideline).

• L2 = the signal is replicated across independent evidence types, or appears as a primary endpoint in a trial.

• L3 = the signal is named in a published clinical guideline with explicit recommendation or guidance.

The L-grade is not the same as the confidence tier. That (confidence) tier reflects Whel’s own scoring of how well the literature it ingested supports the signal (the methodology on that is here). The L-grade reflects whether the signal is independently supported in the broader clinical record. A signal can sit at any combination of the two grades, and the combinations are themselves informative. The current audit returns L0 on 217 of the 271 live signals, L1 on 45, L2 on 6, and L3 on 3. L3 is the rarest grade, and that scarcity is by design, as L3 requires a body-assigned recommendation explicitly tied to the indication, and most repurposing signals are pre-guideline by definition.

The three signals that earned L3 in the current snapshot are:

1. Vaginal Estrogen/Perimenopause & Menopause, backed by the 2020 NAMS Position Statement on the genitourinary syndrome of menopause. Strength: recommended. Certainty: moderate.

2. Testosterone (transdermal)/Perimenopause & Menopause, backed by the 2021 ISSWSH guideline on hypoactive sexual desire disorder. Strength: recommended. Certainty: moderate.

3. Continuous Oral Contraceptive/Endometriosis, backed by the 2022 ESHRE Endometriosis Guideline. Strength: weak. Certainty: low.

Each of those three grades was set by a human curation pass, not by the LLM. I read the guideline PDFs themselves and recorded the strength and certainty values the issuing body itself assigned. The point of doing it that way is to keep the L3 grade defensible, so if someone asks “where does the L3 on this signal come from,” the answer is a specific recommendation in a specific guideline that is traceable to a specific PMID.

The strength × certainty pair shows up on each condition page as a small dashed pill beside the L-grade chip, with the full triple (strength, certainty, guideline_id) in the tooltip. If you click the pill it deep-links to a small glossary on the methodology page that explains what recommended, weak, high, moderate, low and very low mean.

MATRIX + what an independent corroboration layer looks like

The second piece of external architecture is a cross-reference to the Every Cure MATRIX dataset. MATRIX is a public release of machine-learned biological-plausibility scores covering roughly 1,800 drugs paired against roughly 22,000 diseases, generated by a graph-machine-learning model trained on a large biomedical knowledge graph. Every Cure is a nonprofit founded in 2022 that focuses on a re-evaluation of approved drugs across the entire disease space; their MATRIX work is funded by an ARPA-H program of the same name. Their methodology was published in Lancet Haematology in 2024.

MATRIX and Whel are doing different things. MATRIX is a global model that predicts treatment probability from a knowledge graph, it does not read the clinical literature for any specific condition, and it is not condition-specific. Whel is the opposite — a narrow set of women’s-health conditions read closely across literature, trials, adverse-event data, target databases, and named patient communities, with every signal scored individually. The two outputs are different enough that wherever MATRIX has coverage of a Whel pair, the MATRIX score appears beside Whel’s grade.

An audit script I now run alongside the database scoring writes the coverage numbers to a snapshot, published on the External References page. At the current run, MATRIX has scored 83.0% of the eligible Whel pairs and resolved 85.7% of eligible compounds against its drug list. The asymmetries across conditions are themselves informative: PMDD sits inside MATRIX’s official disease filter but produced zero predictions, while Endometriosis sits outside the official filter yet returned 38 useful scores. Both kinds of mismatch are the reason the two layers stay separate rather than blended.

There’s a small but important piece of plumbing inside the cross-reference: a brand-and-synonym dictionary that translates between Whel’s compound strings (which sometimes carry brand names, salt forms, or formulation qualifiers) and MATRIX’s canonical drug-list keys. Without that translation step, roughly 29% of the matched compounds would have been missed. The dictionary is published openly on the same page so anyone doing similar cross-referencing can reuse or audit it.

A new featured signal

In the original post I featured Anastrozole for endometriosis. Anastrozole is an aromatase inhibitor approved for hormone-receptor-positive breast cancer, and there is a real biological argument for using it in endometriosis (CYP19A1 is locally overexpressed in endometriotic lesions). It scored Strong tier in Whel.

I’ve now swapped the featured signal to Vaginal Estrogen for postmenopausal recurrent UTI, because I wanted the homepage’s featured example to be the cleanest possible illustration of what the L-grade ladder actually does. Of Whel’s 271 active signals, vaginal estrogen for the postmenopausal population is the only one that currently sits at both Strong internal tier AND L3 external grade with recommended and moderate strength × certainty values. Both the literature replication is well-established and a named society (NAMS) has explicitly endorsed the use. That combination is the corner case where Whel’s two axes both point at the top.

There’s also something more honest in the swap, which the new featured walkthrough page makes clear. Section 04 of the walkthrough is titled “Literature Whel did not ingest.” It names three landmark references for vaginal estrogen + recurrent UTI that Whel’s automated pipelines surfaced none of: the Raz & Stamm 1993 NEJM trial that established intravaginal estriol as effective prophylaxis, the Perrotta 2008 Cochrane review that synthesized the randomized evidence, and the 2022 AUA/CUA/SUFU guideline that names vaginal estrogen as a recommended non-antibiotic prevention strategy in this population.

You might be wondering WHY Whel did not ingest this literature (rightly so! I did as well. Seems like an oversight or problem…) The honest framing is that Whel’s pipelines surfaced three sources for this signal (a Climacteric review, a Cochrane review of vaginal atrophy with urinary symptoms as a secondary outcome, and the NAMS 2020 position statement). The broader clinical literature extends well beyond those three. The walkthrough acknowledges that, and while it is an uncomfortable thing to realize when you are building an evidence-database project, it is the right kind of admission to make, and it sets up the structured-grounding direction I will get to below.

For those who are curious, the Anastrozole walkthrough is preserved at /featured/anastrozole-endometriosis (but is archived).

The LLM question

A few weeks ago I came across a benchmark called WHBench, which was built to evaluate how frontier LLMs perform on women’s-health clinical questions. WHBench is small but expert-built: 47 hand-crafted clinical scenarios across 10 women’s-health topics, scored against a 23-criterion rubric covering clinical accuracy, completeness, safety, communication, equity, uncertainty handling, and guideline adherence. They evaluated 22 frontier LLMs (including Claude Opus 4.6, used in Whel).

The findings… well… scary to see… context needed… but very necessary. The top model in the lineup (Claude Opus 4.6, actually) scored a 72.1%; no model exceeded 75%. The fully correct rate (responses where the model got every criterion right) was 35.5% for the best model. The universal blind spot was equity: all 22 models scored between 0.7% and 19.1% on the social-determinants-of-health criterion, even though they scored 78–93% on bias-language avoidance. The models had learned to sound fair while remaining clinically blind. The benchmark’s recommendation, prominently stated, is expert oversight in clinical deployment.

A separate piece of empirical work, Gong et al. 2026 in Bioengineering, documents biomedical LLM reference fabrication rates of 47-55% on citation tasks. That is the failure mode where the LLM produces a confident-looking citation for a paper that does not exist.

What this does and doesn’t mean for Whel

When I first read WHBench I had a small panic. Valid, I think, but context is needed.

WHBench tests LLMs as direct clinical advisors. You ask the model “what’s the best treatment for X in a 47-year-old with comorbidity Y?” and the rubric scores whether the response is safe, complete, equitable, guideline-adherent clinical guidance. That is one job an LLM can be asked to do, WHBench’s finding is that frontier LLMs are not ready to do it unsupervised.

Whel does not ask Claude to do that job. Whel asks Claude to read sources and extract structured facts, then score those facts against a fixed rubric (replication, source quality, specificity, plausibility, direction). That is an entirely different task. The LLM is not generating clinical advice. Instead, its reading what a published paper says about a compound and a condition and converting that into a row in a database. So the failure modes WHBench documents don’t all transfer one-to-one. Some do, though, and they are:

1. Equity blind spots. If a published RCT was conducted in a specific demographic and the LLM extraction doesn’t surface that, Whel is propagating the same social-determinants blindness WHBench measured. The planned ontology grounding (Path A, see below) is part of how I’m closing this. The human guideline curation passes also play a role here, because the curators (so far, me, and my mom on the medical side) read the actual papers and can catch this in a way the automated pipeline cannot.

2. Reference fabrication, in spirit. Whel classifies PMIDs that the PubMed search step already returned, so the LLM isn’t generating citations from scratch (the headline 47-55% number does not directly apply). But the underlying tendency to confidently produce plausible-looking-but-wrong structured output (a misclassified study type, a wrong direction of effect, an off-by-one numeric extraction) is the same family of error. Path A’s entity validation catches the entity-level version of this: an LLM that produces a compound name that doesn’t actually resolve to any registered drug gets caught at write time.

3. Outdated knowledge. The LLM reads what’s in the source. If the source is from 2018, the LLM’s classification is based on 2018 evidence. The L0-L3 grade pass is what catches “but is this still endorsed today” by requiring independent guideline corroboration for the strongest claims. That layer is shipped and live!!!

The ones that don’t transfer, because Whel doesn’t ask the LLM to do these things:

1. Dosing recommendations. Whel doesn’t recommend doses.

2. Treatment-plan completeness. Whel doesn’t produce treatment plans, follow-up timelines, or monitoring schedules.

3. Safety judgments in a specific patient case. Whel doesn’t make patient-specific safety calls.

So the benchmark’s headline (no model over 75%, 35.5% fully correct) is alarming if you’re building a chatbot that gives unsupervised clinical advice (booo). Whel is not building that (yay!). What WHBench tells me is that the model I depend on has a documented ceiling on a specific job, and that ceiling motivates the structured-grounding direction I describe below. As such, the path forward is not to panic and throw out the LLM, and instead, my task should be to build in external knowledge that constrains the parts of the pipeline where the LLM’s specific failure modes do transfer.

So, is machine learning the answer?

Machine learning, in the broadest terms, is when you train a computer program on examples and let it learn to make predictions or classifications on new examples it has not seen. Random forests, gradient-boosted trees, neural networks of all kinds, large language models, graph neural networks, all of these are machine learning.

Large language models are machine learning models. Specifically, they are large neural networks trained on enormous amounts of text. Claude Opus 4.6 is an LLM and therefore is an ML model. When Whel calls Claude to extract a structured signal from a PubMed abstract and assign it a confidence tier, Whel is using machine learning.

There is, however, a distinction between consuming ML and developing ML that I think matters here. Whel uses Claude as a service: I write a prompt, the model produces an output, I store the output in the database. I don’t train the model, I don’t fine-tune it, I don’t have access to its weights. Same with MATRIX: the scores displayed beside Whel’s grades are predictions from a graph-ML model trained by Every Cure. Whel surfaces those predictions; it doesn’t generate them.

So the accurate framing is that Whel is an LLM-assisted evidence index. It consumes one ML service (Claude Opus 4.6) for extraction and scoring, and surfaces one external ML prediction layer (Every Cure MATRIX) as a disclosure cross-reference. It does not train or develop its own ML models.

That distinction helps frame the appropriate next move, which is NOT to throw out the LLM and rewrite Whel as a graph neural network (who do you think I am, really). The right move is to add structured external knowledge that constrains and corroborates what the LLM produces. This is called, technically, grounding.

What’s planned/Two structured grounding layers

In the methodology version log (very bottom of this page) I added a v3.4 entry that records two new planned layers, both grounded in current literature on hybrid LLM-plus-structured-knowledge systems (this is the term of art for what I’m describing). Both are recorded on the Roadmap as Planned, and both have detailed disclosure surfaces on the External References page.

Path A: Ontology-grounded entity resolution

Every compound and condition that the LLM extracts will be resolved against a canonical biomedical registry before being written to Whel’s database. Compounds resolve against ChEMBL or DrugBank; and conditions resolve against MONDO (the same ontology MATRIX uses for its cross-reference).

The resolution step does three things at once:

1. Canonicalize: the LLM may extract “Wellbutrin”; the database stores DB01156 (the DrugBank ID) and rewrites the canonical generic name (Bupropion).

2. Enrich: the resolution returns structured metadata (drug class, ATC code, known targets for a compound; ontology lineage for a condition) that travels with the signal.

3. Gate: entities that fail to resolve are flagged for human review rather than silently stored.

The gating function is what addresses the Gong et al. reference-fabrication concern that I flagged above directly. An LLM that confidently produces a plausible-looking compound name that doesn’t actually exist as a registered drug gets caught at write time.

Path B: Knowledge-graph grounding via BioCypher

The second layer is a domain-restricted biomedical knowledge graph built using the BioCypher framework, restricted to Whel’s six conditions and the compounds attached to active signals.

If you have not encountered BioCypher (I hadn’t): it is an open-source framework developed by Sebastian Lobentanzer and colleagues at the European Molecular Biology Laboratory in Heidelberg, published in Nature Biotechnology in 2023. It is specifically designed for building biomedical knowledge graphs from heterogeneous sources. The idea behind a knowledge graph is to model biomedical data as a network of nodes (drugs, diseases, pathways, adverse events) connected by typed edges (targets, treats, interacts with, associated with). Once your data has that shape, you can ask questions that flat databases cannot answer: what compounds target the same pathway as this one, what genes are upstream of a given condition, where do drug-target paths intersect.

The knowledge graph in Path B will draw edges from open biomedical sources: the Drug Repurposing Knowledge Graph (DRKG) for drug-target-pathway-disease relationships, and PheKnowLator for ontology-aligned conditions. BioCypher provides the schema management and integration layer on top.

I chose BioCypher because it is the actively maintained, peer-reviewed, EU-funded option in a field where several alternative tools are research artifacts that haven’t been updated in years. DRKG itself hasn’t been meaningfully updated since 2021, K-Paths hasn’t been updated since 2020, and several “drug repurposing GNN” reference implementations are orphaned forks. BioCypher is the piece I think I can build on.

The knowledge graph will do two things. First, it will inform the LLM at prompt time: when Claude scores a signal, the relevant subgraph of mechanistic paths will be included in the prompt as structured context. This pattern, called knowledge-guided prompting, was shown to improve biomedical LLM performance. Second, the graph will surface beside each signal as a disclosure layer (“graph supports”/“graph silent”) in the same shape as the existing MATRIX cross-reference.

One thing I am not planning to do

I am not planning to train a custom graph neural network for drug-condition link prediction (again, I repeat, who do you think I am). There are good academic systems that do this well at scale (Every Cure’s KGML-xDTD, and Marinka Zitnik’s lab’s TxGNN in Nature Medicine 2024). But Whel’s value proposition is not scale. It is transparency, condition focus, and provenance, applied to a narrow set of women’s health conditions. Adding a black-box prediction layer beside transparent evidence cards would weaken the thing that distinguishes Whel from MATRIX.

The grounding layers that I am working on currently, however, are different. They make the existing pipeline and the existing data shape more structured. So I think that it is the right move for a tool whose central claim is interpretability.

A setback worth documenting

The featured-signal swap that I mentioned above surfaced a limitation that is also what’s driving one of the additions on the roadmap.

When you actually look at what Whel ingested for Vaginal Estrogen for postmenopausal UTI, the three sources are all synthesis papers: a topical review, a Cochrane review of vaginal atrophy, and the NAMS position statement. The original RCTs that those reviews cite (Raz & Stamm 1993 chief among them) are not separately indexed in Whel. They sit one citation step removed.

That happened because Whel’s PubMed pipeline runs a condition-keyed Boolean search, filters by article type and publication date, and takes the top-N results by relevance. The Raz & Stamm trial is 33 years old, so it falls outside any reasonable date filter. The AUA recurrent-UTI guideline is scoped to UTI broadly, not menopause, so a menopause-keyed search would never have surfaced it. Whel’s pipeline did what it was designed to do, but this makes me think that the design itself has a limitation.

That logic-by-design is fine for under-researched conditions like vulvodynia and PMDD, where the literature is sparse enough that three to five sources is roughly the available evidence base. It is, however, not fine for well-studied compound-condition pairs where the literature is rich. Whel treated both cases the same, and that seems to be the limitation of it.

I recorded the design decision in the methodology version log at v3.3 and added a Planned item to the Roadmap: a manual primary-source curation pass, modeled on the same worklist pattern that produced the L3 guideline grades. For high-evidence signals where Whel surfaced three sources but the published landscape has ten, I will open a CSV worklist of proposed primary-RCT attachments, fill in the PMIDs by hand, and emit a migration that adds them as additional source rows. Same human-in-the-loop workflow that worked for the guideline curation, I guess.

The version of this for outside reviewers (you, reader!) is that Whel’s automated pipeline returns representative sources, but not exhaustive ones and to solve that, I am trying to close the gap on high-evidence signals through the same human curation pattern that produced the L3 grades.

The roadmap

The site’s Roadmap is now the place for “what is shipped, what is in the current work cycle, what is planned for later.” A short version, in priority order:

Live today: the four research arms; the L0-L3 external validation grade; the MATRIX cross-reference; the human guideline curation pipeline.

Next work cycle: the two-rater validation study; disproportionality statistics (PRR and ROR) on the adverse-event arm; the cross-arm concordance flag; the manual primary-source curation pass; ontology-grounded entity resolution (Path A); knowledge-graph grounding via BioCypher (Path B); a citable open data export with a DOI.

Later: more conditions, EudraVigilance as a European pharmacovigilance source, DrugBank integration, deeper coverage on the existing six conditions.

The Methodology page now carries a dated “Methodology update log” (collapsed by default so it doesn’t dominate the page on first scroll), with version entries tracking what changed and when. Each major decision has a dated entry. If you want to see how the project’s reasoning has evolved in real time, that’s the log.

What I’m reading

This post leans on a small set of papers that have shaped how I’m thinking about the next phase of the project:

• Maurya, Saboo & Kumar, 2026. WHBench.

• Gong et al., 2026. Applications of Large Language Models in Medical Research: From Systematic Reviews to Clinical Studies.

• Zong et al., 2026. Building evidence-based knowledge bases from full-text literature for disease-specific biomedical reasoning. (the closest published analogue to where Whel is going).

• Zhou et al., 2025. High-throughput biomedical relation extraction for semi-structured web articles empowered by large language models.

• Lobentanzer et al., 2023. Democratizing knowledge representation with BioCypher.

• Fajgenbaum et al., 2024. KGML-xDTD (Every Cure’s methodology paper for MATRIX).

• Huang et al., 2024. A Foundation Model for Clinician Centered Drug Repurposing.

THANK YOU!!!

If you happen to find yourself in Union Square and walk directly west for exactly one mile, you will end up outside Betaworks. Not quite an office, more a single large room with exposed pipes and brickwork (because duh, you are in the Meatpacking District). It sits directly across the street from Aubi & Ramsa, an alcohol‑infused ice cream shop I’ve been told is fabulous. So if you make the trek I described above (and are over 21), you should probably stop for boozy ice cream on your way out.

I made that trip yesterday (sans ice cream) for Betaworks’ Camp Demo Day. This year’s theme was Agent Systems, and if you’re not fluent in Betaworks lore or the current jargon of AI, here is some helpful context:

Betaworks is a fabulous combination of a startup studio and pre-seed/seed‑stage firm that backs product‑driven companies, especially in AI and consumer software. Alongside the fund, they run “Camp,” a cohort‑based program where 8-12 teams come to New York for twelve weeks to build, get help on product and GTM, and then present at Demo Day. Past camps have incubated companies like Hugging Face and Granola.

This year’s camp is about Agent Systems. Betaworks defines an agent system as a company‑scale system in which agentic AI components perceive context, make plans, and execute end‑to‑end work with minimal human orchestration. In other words, not “AI as another layer in a human-led workflow” but products that assume from first principles that autonomous, goal‑seeking software is the main actor in the business.

The official Betaworks post breaks this down into a few properties:

• Perception and memory: systems that synthesize context from many sources and remember across interactions.

• Autonomous planning: systems that can form multi‑step strategies toward abstract goals.

• End‑to‑end execution: agents that complete whole workflows as opposed to just the middle 20%.

• Adaptability and self‑evaluation: systems that can critique their own output and adjust course.

So yesterday, in that brick‑and‑pipe room in Meatpacking, a handful of founders tried to answer a rather complicated question: if software can increasingly perceive, remember, and act on our behalf, what should a company built around that reality look like?

The first company to pitch us (us being a room of exited founders, pre‑seed/seed investors, and tech‑of‑the‑future enthusiasts, and me) described Camp as “a microcosm of frontier technologies,” which I think is right. The cohort companies were wacky and out‑there, and their founders were too!

Please find below a few of my takeaways: big themes I noticed, the companies that stood out to me and why, and some questions I left with and want to keep tugging on.

Companies I loved the most + why

I should preface this by saying that every single company that presented (10 total) was built on a brilliant, surprising, and creative premise. Most I agreed with or at least found compelling in some way, and all were a treat to see. I have a lot of respect for these founders and am excited to see where they go. That said, a few companies stood out to me in particular, all for different reasons.

Sky Valley

Sky Valley’s founder, Noam Tenne, opened with the argument that despite the wave of innovation in software development over the past few years — “vibe‑coding,” better tooling, faster iteration — people still ship like it’s 2020. Those innovations let us build faster, yes, but that’s now table stakes. The next frontier, in his view, is software that actually grows with its users.

Enter Sky Valley, a platform for developing adaptive software that learns from and makes seamless changes according to each individual user. In the demo, we saw two users of a fitness app. One cared most about tracking and seeing her cardio (time, calories burned, activity) every evening. The other logged the same water intake every morning. With Sky Valley wired into this theoretical app, the system watched the two users, learned their patterns, and then proposed individualized app updates for each. After reloading, their interfaces had reshaped themselves: User 1 now had a home page structured around her cardio, and User 2 had a dedicated button to log exactly the amount of water he drank every morning.

These are relatively simple changes, but the point is that Sky Valley observes behavior, makes appropriate, highly specific updates, and then lets those changes compound over time. In that world, you and I could be “using” the same fitness app, but it would be entirely tailored to how each of us actually moves through it, serving completely different interests.

You might reasonably wonder whether this level of personalization is necessary, or if anyone really wants it. Noam’s answer is that adaptive software makes changes that “do not feel obvious until they’re there.” A dedicated button for logging the exact amount of water you drink at the exact time you drink it sounds trivial, sure, until you realize it removes the annoying sequence of 1) logging a liquid, 2) choosing water, 3) specifying the amount, 4) noting time of day, and so on.

There is also a massive potential market here, if it works. The idea of individualized, adaptive software feels like a logical progression from where we are now. So cool!

Pai

Founded by siblings Gigi and Everett Grimes, Pai starts from the observation that there is a rather inconvenient fissure between tech culture (“move fast and break things”) and the world of CPG innovation. In CPG, you cannot actually “move fast and break things,” even though you’re expected to, because iterating on physical products means placing real‑world bets: you often have to order packaging and inventory in bulk, commit to retail or DTC tests, and then wait to see what happens in market.

In other words, you cannot just “ship 10,000 units and hope for the best,” then pivot next week if it flops. The stakes are high, the feedback loops are slow, and every decision is capital‑ and time‑intensive.

Pai’s bet is that AI‑native simulations can shrink that gap. They turn real consumer data into AI simulations (“AI twins”) that let brands quickly test questions like “Which design do Gen Z consumers prefer?” or “What actually drives purchases for this product?” before they commit to big production runs. The idea is to bring something closer to software‑style iteration into the physical world, so founders can explore many more product, packaging, and messaging variants before they place a single order.

As a quick aside, for most of the afternoon, Camp was being narrated by a very familiar demographic. Gigi, who pitched Pai, was the only woman pitching a company and also gave the clearest, most disciplined presentation. Very far from “AI guys and their toys.”

PillPilot

I told you guys I caught the healthcare bug… well, here we are. PillPilot starts from a bleak but accurate insight: pharmacists are not doing the job that they spent years of schoolwork and money training for. Most of their days, and the pharmacy system as a whole, are held together by phone calls, faxes, and humans re‑typing the same information into three different systems, which is wasting everyone’s time.

Their response to this is not a flashy “AI for diagnosis” or “AI doctors,” but something much more prosaic and believable — back‑office agents that run refills, insurance checks, and prior auth workflows. In the story they told on stage, an avalanche of prescriptions with incorrect insurance info becomes a test case: instead of an overworked pharmacist spending hours on the phone, PillPilot’s agents call (yes literally call, we got to hear the agent hold a full conversation with a fake doctor — see picture below) the relevant parties, reconcile mismatched data, and then write changes back to the real system.

I liked this idea in particular because it is a very opinionated answer to the “where should agents live in healthcare?” question. PillPilot’s answer is that they belong deep in the plumbing and far away from clinical judgment. It is less sci‑fi than an AI doctor, but if it works at scale (which, according to their traction slides, is ramping up), this could be the type of product that changes what pharmacists do all day.

Capsule

Healthcare again! I told you. Capsule wants to be the cognitive core of pharma — essentially the OS for life sciences strategy. In practice, that looks like an agentic layer that ingests everything from conference chatter and clinical trial registries to publications, SEC filings, and commercial data, then helps teams answer questions like “What is our competitor actually doing in this indication?” or “How should we allocate launch resources across markets and channels?” in something closer to real time.

I’ve spent a lot of time in SoR/SoA land at NEA and have seen a bunch of different “let’s be the system of record for X” companies, but Capsule is interesting because it’s unapologetically and aggressively vertical, and on that axis, it knocks the more generic approaches out of the park (especially because in life sciences you kind of just can’t use horizontal models well lol). Their real goal, as stated, “find and help commercialize the next generation of medicines” faster than today’s stack would ever allow. Net good!

Inanimate

The last company I’ll mention is not here because I necessarily agree entirely with what they’re building, but because 1) it was the only team pitching anything with a real hardware component (and I do love hardware), and 2) their vision for where hardware goes from here was fascinating.

Inanimate is betting on a “new wave” era of hardware. These are AI‑native devices that look like everyday objects, are deeply personalized, customizable in real time, and responsive to all kinds of human input (especially from people who don’t think of themselves as technical). Think “robots that don’t move” and room‑scale objects, such as lamps, displays, little ambient widgets that act as endpoints for agents and turn a space into something you can literally walk into, collaborate with, and then walk away from.

The example we saw was a small desk lamp with a screen. On its face, it’s just a light, but with voice commands, it can turn into whatever you need in that moment — a Tamagotchi‑style game your kids can play with while you’re finishing emails, an interactive Pomodoro timer, a glanceable dashboard for your calendar or tasks. The point wasn’t “look at this one gadget,” but that the same physical object can shapeshift between roles on demand, with the agent underneath listening, reconfiguring the interface, and routing data, all while you just talk to the lamp.

Big Themes

I think that if you happened to have a transcript of all the pitches, the most commonly recurring words and phrases would be “knowledge graph” and terms like “personalized,” “adaptive,” and “customizable.”

On the “knowledge graph” side, this felt like the default mental model for how to make agents useful — point them at a big, messy graph of entities and relationships, and let them reason over that. It’s the obvious place for agents to shine, and you could see that in Capsule, Pai, and even PillPilot, all of which are basically saying “your domain already has a graph structure baked into it, we’re just finally making it explicit and queryable.”

The “personalized/adaptive/customizable” thread was more interesting to me. It feels as though we are beginning a new age of how to think about software as something that can be customized and reshaped around every user in real time. My working theory on what caused this is people enjoying when an LLM or chatbot “remembers” things about them. So, naturally, teams like Sky Valley and Inanimate are trying to drag that chatbot‑style familiarity and memory into the rest of software, so the app, or even the room you’re in, knows you and adjusts to your preferences.

What I am left wondering

One question I’ve been chewing on is what kinds of work should never be fully turned into an agent system, even if we technically can. There are obvious candidates in healthcare and finance where you want a human in the loop for safety or regulation reasons, but there are also more subtle categories, such as relationship‑building, taste‑making (sorry for the buzzword, I know we are kind of sick of the whole “taste” in Silicon Valley convo, but there is merit), etc. My hunch is that the most compelling products will draw explicit lines about where the agent stops and a person must make the call. There will probably also be many failures along the way.

I’m also curious about how this lands with normal people who do not care about “agents” as a concept at all (and are actually probably averse to them, a la ex-machina or The Entity from the latest Mission Impossible movie). Products like Sky Valley and Inanimate assume a consumer world where your software, or even your physical environment, is adapting to you in real time. That sounds somewhat cool to me (aside from the privacy concerns), but there are real questions around legibility/trust and time horizon. I don’t know many non-techy people who would be willing to let software rewire itself around them. I also don’t know how much control they’ll expect, and how long it will take before that transition feels obvious instead of futuristic and perhaps unsettling.

Closing Notes

I adore demo days. My first one was Techstars last winter, and hopefully there are many more in my future. I’ve written about this before, but I really do think it’s exciting to watch people pitch and build technology that sits right on the frontier of what’s possible. If you happen to be in a city like NYC, SF, or Boston, these things are happening far more often than you’d expect and they are so fun to go to!

Oh, and yes, the title is a Sontag reference :)

Metrics vs. Movements

Every field has its house metrics, and in consumer apps, these tend to be CAC, retention curves, DAUs, plus a sprinkling of “network effects” when the story wants gravitas. These numbers exist for good reason, since they are quantifiable signals that something is currently alive. But, perhaps they’re less good at telling us what kind of thing a consumer app can/will become.

Some apps clearly are just products. They solve a task, save a few minutes, and slot into the background of a workflow. A small handful of them, however, do something stranger. Think of early Facebook on college campuses, Instagram in its first discovery era, TikTok during lockdown — these platforms reorganized friend groups, meme culture, communication, how people saw themselves, etc. Technically, these are “just apps,” but sociologically, they catalyzed a change in society that doesn’t feel like it can be analyzed solely through the lens of “software.”

Political scientists have a term for this kind of reconfiguration that you’ve probably heard of — social movements. These researchers have spent decades trying to quantify how movements start and, perhaps more importantly, why some of them win. One of the most interesting people doing this is Erica Chenoweth at the Harvard Kennedy School (HKS). They have catalogued campaigns over the last century, and in one particularly famous slice of research, they found that nonviolent campaigns which mobilize sustained participation from roughly a few percent of the population (around 3.5%) almost always succeed in their main demands.

I’m not interested in smuggling that number straight into tech and declaring that every app only needs 3.5% of Gen Z to become revolutionary. Chenoweth is explicit that it’s an empirical pattern in one dataset, not a law of history, and later work has pushed back on people treating it like numerology. What I am interested in is the shape of the claim — that perhaps beyond some threshold, a small, committed minority can tilt the equilibrium for everyone else.

This essay is an attempt to aim that idea at consumer apps and see if it holds. In other words, could we, with the data we have, meaningfully talk about “participation thresholds” for Facebook in 2004, or for TikTok and BeReal two decades later? Is there a way to map the variables movement scholars care about (populations, active participants, committed minorities) onto social products in a way that survives contact with real numbers?

I don’t know the answer yet. But what follows is a sketch of how that mapping might work, with a couple of case studies (Facebook as a campus movement + BeReal as a “rally” that never became an organization), and a tentative research agenda for anyone who wants to treat “apps as movements” as something more than a metaphor. To end, I’ll gesture at what this might imply for founders and investors. But for now, the question is simpler — If we took social apps seriously as social phenomena, could we study them with the same quantifiable discipline we reserve for movements in the streets?

Building a Toolkit

If I’m going to borrow from social‑movement research, I should be precise about what I’m taking. Chenoweth’s work is a way of structuring data about collective action. They build datasets where each row is a campaign, and columns track things like how many people participated, how often, what tactics they used, and whether they achieved their stated goals.

The 3.5% result, as I noted above, is one slice of that research — an observed participation threshold for a certain class of nonviolent campaigns. Below “a few percent of the population” in sustained action, movements in that dataset sometimes win and often lose. Above it, they almost always win. The important part is not the exact cutoff, so much as the idea that some threshold seems to exist where a small but committed minority can force a change in outcome.

Zooming out from that single number, there are a few concepts I want to keep in my pocket:

1. Population: the group whose behavior matters (a country, a city, a specific constituency).

2. Active participants: people who are actually doing the thing — marching, striking, organizing.

3. Committed minority: the subset that shows up over and over, often at some personal cost.

4. Thresholds/critical mass: participation levels at which outcomes become much more likely to flip.

5. Network position: the fact that activists, brokers, and opinion leaders matter more than a random person pulled from the phone book.

In movement datasets like the Nonviolent and Violent Campaigns and Outcomes Project (NAVCO), those show up as variables you can code and correlate with success rates. My attempt in the rest of this essay is to sketch analogous variables for social apps (populations, active users, committed cores, and thresholds) and see whether they line up with the history of some of the most popular consumer apps of our time — Facebook, TikTok, and BeReal.

Mapping movements onto apps

Now that I have the movement toolkit in hand, the obvious next step is to ask what the equivalents would be in app‑land. Basically, if a campaign is a row in Chenoweth’s dataset, what is the row for a consumer product?

Very roughly, the translation I have in mind looks like this:

So instead of “the population of a country/city/state,” I might look at “all undergrads at Harvard,” or “teens in one city,” or “artists in a particular online scene.” Instead of “people who attend a march,” I’d track the fraction of that group who are not just signed up but actually posting, hosting, inviting, dragging friends in. Instead of asking “did the regime change?”, I’d ask “did this app become the default way this group coordinates and performs itself?”

I do not, unfortunately, have a NAVCO‑style dataset for apps. There is no public table that tells you, for each campus on earth in 2004, what share of undergrads logged into Facebook twice a day and how quickly the norm flipped. What we do have are fragments — the early histories of Facebook’s rollout, coarse user numbers, BeReal’s download and MAU curves, occasional leaked charts, and everyone’s half‑remembered anecdotes.

That’s not enough to run the full regression Chenoweth would want, but it is, I theorize, enough to try the mapping on a couple of cases and ask whether treating “apps as movements” at least lines up with the stories we already tell about them.

Facebook as a campus‑scale movement

If this mapping is going to work anywhere, it should work on Facebook in 2004 (please note — not Meta, not the global ad stack, just “TheFacebook”).

The timeline, compressed:

1. Facebook launched at Harvard in February 2004 as a Harvard‑only site.

2. Within the first 24 hours, roughly 1,200 students had signed up.

3. Within a month, more than half of Harvard’s undergraduate population was registered.

4. By the end of 2004, still operating as a gated college network, it had crossed a million registered users.

5. By September 2005 (eighteen months after launch) 85% of students at supported colleges had a profile, 60% were logging in daily, and 93% at least once a month.

As a quick aside… that last set of numbers is really quite mind-boggling… 60% daily login rates among a population that had to actively choose to open a website in an era before smartphones (?!!?!?!)

Under Chenoweth’s framework, the population that matters here are the people that keep showing up through sustained, active, visible participation (as opposed to everyone that had showed up overall). By that measure, Facebook on college campuses in 2005 was like a movement that had blown past its participation threshold.

If you run the mapping specifically on Harvard in spring 2004 (see chart above for conversions), it looks like this:

1. The population is a few thousand undergrads living in dense, overlapping social proximity.

2. Active participants are the students logging in daily, checking profiles, updating relationship statuses, narrating their lives in real time.

3. The committed minority (the equivalent of Chenoweth’s organizers and activists) are the people uploading photos obsessively, running groups, building the social graph aggressively enough that opting out started to carry a real cost.

The outcome of this is clear. Within weeks, Facebook became the default social infrastructure on that campus. What stands out, even with coarse numbers, is that this progression reads like phase change. At some point between “a few hundred users” and “half the campus,” the socially strange option flips, and it stops being weird to be on The Facebook and starts being weird not to be. That flip, where opting out costs you something, is exactly what Chenoweth’s threshold idea is pointing at.

If I had proper micro‑data (per‑campus daily active rates at weekly intervals, broken out by year of study and residential proximity), this is where I’d want to do the “Chenoweth thing” properly — estimate the participation fraction at which the norm flipped, see if it recurs across campuses, look for a characteristic range.

Since I do not have that, I’m inferring from top‑line numbers and institutional memory. However, even that feels like enough to make the claim that at the campus scale, movement mapping is coherent. A small, bounded population, a rapid rise in sustained daily participation, and a clear before‑and‑after in what counts as normal.

The question that my next case study (BeReal) will complicate is whether scale alone can create that pattern instead of just mimicking it superficially.

BeReal as a Stress Test for my Theory

BeReal launched in 2020 but exploded into culture in 2022 on quite a novel premise: once a day, at a random time, you had two minutes to post an unfiltered dual-camera photo. No filters, no follower counts, no performance, and a deliberate correction to the exhausting theater of Instagram.

The download curve tells the first part of the story. Downloads peaked in August 2022 and were already visibly declining by September, and yet DST Global led the $60M Series B at a $630M valuation in October 2022, after the leading indicator had already turned.

By August 2022, BeReal had 73.5 million monthly active users. Daily actives hit 20 million in October 2022, the same month the Series B closed. By February 2023, just four months later, DAUs had dropped 48%, from 20 million to 10.4 million. By March 2023, DAUs were at 6 million, a 70% collapse from peak in five months. By the end of 2024, monthly actives had fallen 78% from their high, settling around 16 million.

And through all of it — through the cultural explosion, the tier-one fundraises (a16z + DST), and App Store dominance, BeReal generated exactly zero dollars in revenue. At the time of the Series B, the company had explicitly stated it had no plans to monetize. At acquisition in June 2024, it was still at zero revenue, burning roughly $3 million a month. When Voodoo bought it for a headline €500M (~$537M), two-thirds of that price was structured as contingent earnouts tied to future performance targets for a product in freefall. The guaranteed cash component was closer to €166 million on $91.8 million raised.

The most analytically useful number in all of this is not the MAU collapse, but the DAU/MAU ratio at peak. In October 2022, BeReal had roughly 20 million daily actives out of 73.5 million monthly actives (a ratio of about 27%). Compare that to Facebook on college campuses in 2005, where 60% of registered users were logging in daily. Even at its cultural apex, only about one in four BeReal users was showing up every day. In other words, our committed core, relative to total reach, was always thin.

In Chenoweth’s framework, this is the crucial distinction. Recall that the 3.5% does not track how many people have ever attended a rally, it instead cares about/tracks who keeps showing up, repeatedly, and at some cost to themselves. A movement with enormous one-off turnout but weak ongoing organization is categorically different from one with a smaller but deeply committed base. BeReal got the former and Facebook got the latter.

The structural reasons for this are not hard to find and, lucky for me, they map almost perfectly onto what movement researchers look for when they try to explain why campaigns fizzle. BeReal never developed anything resembling a creator/influencer class (which are the consumer app equivalent of Chenoweth’s organizers and opinion leaders). The format deliberately resisted it, since BeReal had no follower counts, no algorithmic amplification, no economic incentive to be a power user. That was the aesthetic point, but it meant there was no committed minority whose identity, social capital, or livelihood depended on keeping the platform alive. Consequently, when casual users drifted, there was no backbone to hold the network together.

What standard investor due diligence saw in October 2022 was a clean story — 73.5 million MAU, 20 million DAUs, top of App Store charts, an “anti-Instagram” narrative landing at exactly the moment Instagram backlash was peaking, and a tier-one cap table (a16z Series A) to match.

What a movement-style analysis would have flagged was a different set of signals entirely — a 27% DAU/MAU ratio suggesting shallow engagement even at peak, a single mechanic with no ecosystem behind it, no content library, no creator graph, no switching costs, and sitting underneath all of it, zero revenue, zero monetization path validated, and zero reason for any subset of users to stay once their friends started leaving.

In other (more movement-appropriate) words, there was massive turnout at the peak event, but no membership organization behind it — no dues, no doctrine, and no organizers with skin in the game.

BeReal is not a story about a bad product. It’s more like “what happens when you mistake cultural virality for movement-like traction.” Because yes, the app crossed participation thresholds in raw numbers, but it never built the committed minority infrastructure that makes those numbers durable. And the market ultimately agreed — $91.8 million in venture capital, two years of peak cultural relevance, and an exit where the guaranteed cash was €166 million on a €500 million headline, with the rest tied to earn outs on a product that had already lost 78% of its users. The lesson we can draw from this is that scale without a strong core is more like a rally than a durable movement.

What a real research agenda might look like

The Facebook and BeReal cases are suggestive, but are limited by the fact that they are only two data points, reconstructed from public milestones and coarse retention curves. If the “apps as movements” mapping theory that I propose is going to be more than a useful metaphor, it needs the same thing Chenoweth’s work needed: a real dataset.

What that might look like, concretely, is something like a NAVCO for consumer apps. For a sample of social products across different categories and eras, you’d want to collect:

1. Local adoption rates over time in specific communities (campuses, cities, subcultures rather than global MAU).

2. Behavior depth (posting vs. lurking, invite rates, cross-platform presence).

3. Network roles (who are the creators, connectors, and moderators, and how many of them are there relative to passive users).

4. Outcomes (did the app become default infrastructure, stay niche, or fade like BeReal?).

This probably sounds more complicated than it actually is, and, importantly, these data sources aren’t exotic. They are comprised of app store charts, search trends, social-media mention curves, the occasional internal dataset when companies choose to publish one, and fieldwork on a handful of campuses where you could actually measure local penetration directly. With something like that in hand, you could start asking testable questions such as “do apps that become durable infrastructure show a characteristic local penetration range (say, somewhere between 5% and 15% of a well-defined community) before they lock in?” or “is there a measurable difference in network structure between durable platforms and fads, specifically in the ratio of high-engagement “activists” to passive observers?” and even “do different app categories (messaging, video, payments, social discovery) have different movement thresholds, the way different kinds of political campaigns seem to have different mobilization dynamics?”

I don’t have the answers. But I do think these are the right questions to ask, and I think they’re more likely to produce useful insight than another cohort retention table.

What this might mean for builders and investors

If the research agenda above ever produced something rigorous (a real dataset, testable thresholds, reproducible patterns across app categories) ,the practical implications would be fairly direct, even if modest.

The most useful one is probably a language shift. Right now, “traction” in a consumer pitch tends to mean MAU growth, download velocity, and maybe a retention curve if you’re lucky. A movement-style framework would push toward different questions — not just “how many users?” but “in which specific community have you become unavoidable, and what fraction of that community is doing the core action weekly?” Not just “are people retained?” but “do you have a visible committed minority (creators, organizers, connectors) whose behavior is pulling others in?” Those aren’t necessarily harder questions to ask, just questions the current metrics vocabulary doesn’t naturally surface.

The BeReal case is instructive here, not as a cautionary tale about bad investing (I am not in the position to make those claims lol), but more as an illustration of what gets missed when the only lens is aggregate growth. A DAU/MAU ratio of 27% at peak, zero creator ecosystem, zero revenue, and no switching costs were all visible in October 2022. The movement framework doesn’t require hindsight, it just requires asking, from the beginning, whether you have a core or just a crowd (or a movement vs. a rally).

A closing thought

Consumer apps are usually framed as tools or products that satisfy preferences, reduce friction, and save time. However, the most influential ones of our time have become the background infrastructure of how a generation talks and sees itself, which seems to go beyond the scope of the term “tool.” And I do not think that, in good faith, can be described as a product phenomenon. It is instead a social one.

This observation is what initially drew me to (and keeps pulling me back to) Chenoweth’s work. If a small, committed minority can reshape a society, then for any new (and “wants to be groundbreaking“ app) the interesting question is not “how many users (daily or monthly) do you have?” and more like “which few percent have actually reorganized their lives around this, and what does that mean for the rest of us?”

I don’t have a clean answer to that yet, but what I do have is a research project that I think has a compelling premise. Consider this essay the first public draft of a proposal to build something like a NAVCO for consumer apps. A real dataset, real thresholds, real patterns. I’ll be working on it. Check back in with me in a few weeks… or reach out now :)

Quick note: I’m working on Whel almost every day, so the site will keep evolving. Expect numbers to change and phrasing to get refined over time. The point of documenting this process is to build in public and learning as I go.

A quick update, May 2026.

Since this first piece went up, Whel has grown into a working evidence database. The current work cycle includes a planned cross-reference to MATRIX, the public drug-disease prediction dataset from Every Cure (a massive and very well-funded nonprofit biotech using AI to discover new uses for existing drugs), which will sit beside Whel’s grades as an independent biological-plausibility layer wherever MATRIX has coverage. For more on my use of Every Cure (thank you, Hugging Face), see the External References page.

From here on, most updates will live on the site’s Roadmap page, where I track what’s live, what’s in the current cycle, and what I have planned (I am now implementing open-source ML models). This piece will stay up as the original write-up of how the project began.

A few weeks ago I shared a bit about my experience undergoing brain surgery to remove a pesky tumor that had been plaguing me for the better part of a decade. The tumor was a prolactinoma, a noncancerous tumor of the pituitary gland made up of prolactin‑secreting cells, which sit at the base of the brain and help regulate hormones. Prolactinomas are, technically, benign. But “benign” is doing a lot of work in that sentence when the thing in question has been disrupting your hormonal system for years, which has a whole array of consequences on the body.

The standard treatment before surgery, and for many people instead of surgery, is medication. Dopamine agonists, such as cabergoline or bromocriptine, that work through the prolactin pathway by suppressing the hormone the tumor overproduces. They are effective, but also for a significant number of patients, including myself, quite rough — common side effects include extreme nausea, psychiatric symptoms, impulse control disorders, etc. So yes, the drugs work, but “working” is not the same as “working well,” and for many people who take them over years, the calculation is not clean.

I got through it, and thanks to the remarkable achievements of 21st century medicine, I am more than fine now. And I am aware, acutely, that I had it relatively easy. A prolactinoma is not endometriosis, or PMDD, or eight years of being told your pain is normal, to come back in six months, to try exercise. I had a diagnosis, a few years of semi-successful medication, a surgery, and a recovery. However, many women with reproductive and hormonal conditions do not get that clean of an arc.

That realization, along with a lot of late‑night PubMed rabbit holes and conversations with my mother, who is a psychiatrist and has spent a long time thinking about women’s hormonal conditions, eventually turned into a project we’ve been building together over the past few weeks — a drug‑repurposing research tool that mines and pulls together scattered signals from trials, registries, pharmacovigilance databases, and community forums to investigate whether drugs that we already have developed might help under‑researched and under‑treated hormonal conditions primarily affecting women. We ended up calling it the Women’s Health Evidence Lab (Whel), which is both a description and, conveniently, an acronym I can actually say out loud.

As of this writing, Whel contains 271 active repurposing signals across six conditions, drawn from 2,176 unique citations and 135 distinct compounds. Those numbers will move (the pipelines run on a schedule), but they’re the ground truth for everything below.

Why drug repurposing?

Drug repurposing is the practice of taking an existing medication that was developed for one condition and asking whether it might help with a different one, which is a framing that my mom kept coming back to in our calls. Instead of starting from a blank slate and designing a brand‑new drug, you look at what is already on the market or in late‑stage trials, where the safety profile is at least partially known, and search for unexpected benefits, side effects, or patterns in recorded data that suggest a new use case.

The most surprising thing about drug repurposing that I found when I started looking into it — and that my mom kept confirming from the clinician side — is that the signal (meaning a repeated pattern in the data that hints a drug might be helping a condition it was never designed to treat) is often already there. Somewhere in a published trial, an adverse effects database record, or a Reddit post from two years ago, someone noticed that women on statins were reporting reduced pelvic pain. Someone noticed that a drug developed for one condition seemed to be doing something interesting for another. The data exists, it is just scattered across PubMed and clinical trial registries and adverse event databases and patient community forums, and nobody has built a clean, accessible tool that pulls it together specifically for women’s hormonal health conditions.

Medical knowledge has structural blind spots. We know this — the NIH did not require inclusion of women in clinical research until 1993, so we have spent roughly three decades catching up from a standing start. Conditions that affect women most severely have historically been underfunded, and where a research base exists at all, it is thin.

So my mother and I wanted to build something that reversed the typical drug discovery question. Instead of “what does this drug treat?”, we wanted to ask: “what existing drugs have shown promise, in any context, for conditions that aren’t getting enough research attention?” And because of our own experiences (my tumor and my mother’s Hashimoto’s), as well as a historical lack of attention given to them, we wanted to focus specifically on women’s hormonal health.

The Conditions

We started by identifying six primarily hormonal conditions that affect huge numbers of women but remain chronically under‑treated, under‑researched, or badly managed: endometriosis, PMDD, PCOS, adenomyosis, vulvodynia, and perimenopause & menopause. This initial set is a starting point and the plan is to add more conditions as we refine the pipelines, feel confident that these first six are well‑covered, and incorporate feedback from relevant parties about which gaps matter most. The current signal distribution across them looks like this:

• Perimenopause & Menopause — 59 signals

• PCOS — 50 signals

• Endometriosis — 47 signals

• Adenomyosis — 45 signals

• PMDD — 44 signals

• Vulvodynia — 26 signals

Endometriosis affects somewhere between 6 and 10% of women of reproductive age. The average diagnostic delay is between 7 to 10 years, and treatment options in 2026 include surgery, hormonal suppression, or pain management. There is no reliable pharmaceutical treatment that addresses the underlying condition rather than suppressing symptoms. For a disease affecting tens of millions of people, this is extraordinary. PMDD (premenstrual dysphoric disorder) is clinically severe, cyclical, and still treated primarily with SSRIs prescribed continuously or cyclically, which is imprecise and doesn’t work for everyone. Perimenopause and menopause are widely acknowledged to be poorly managed. Adenomyosis, vulvodynia, and PCOS are chronically underfunded and underrepresented in the research literature.

The problem is not that researchers don’t care, it is that funding flows toward conditions with established biological pathways, measurable endpoints, and large pharmaceutical market opportunities, or toward the kind of “sexy” rare diseases that occasionally attract concentrated philanthropic and venture interest. The mechanisms underlying many women’s health conditions are still contested or poorly characterized, which makes them harder to study, which makes them less fundable, which means the mechanisms remain poorly characterized. It is a feedback loop.

Drug repurposing is one way to try to interrupt that loop, because if a drug already has a known safety profile, the path to clinical investigation is shorter. If there are already signals in existing data, you don’t have to start from scratch. You just have to find the signals.

The Four Research Arms

The structure of the tool went through a lot of iterations. Most of those iterations happened on calls with my mom, who is, I want to say clearly, the reason this project has any medical validity at all. Here is where we landed, with the current count of signals in each:

1. Direct Research — 49 signals

The first arm pulls published studies and active clinical trials that are specifically designed to investigate the target condition. Our data sources are PubMed and ClinicalTrials.gov. This arm is intentionally sparse for most of the conditions in the database, and the sparseness itself is data. If you search for ‘endometriosis’ in the direct research arm and get back forty results, most of them from the last five years, that tells you something.

2. Cross‑Condition Signals — 70 signals

This is the arm I find most interesting. It pulls drugs that were developed or trialed for entirely different purposes, where women incidentally reported benefit for the conditions we are tracking. The data sources here were originally FDA AEMS (the adverse event reporting system, which captures both adverse events and sometimes off-label uses), population level epidemiological studies, and the secondary endpoints buried in trials that were designed to study something else.

In addition to FDA AEMS and epidemiologic datasets, we plan to pull the same style of signal from EudraVigilance via the EVDAS analytics interface, which is essentially the European counterpart to AEMS, and from SIDER for systematically catalogued drug side‑effect labels. Female‑only reaction data are filtered and grouped by condition, so a drug that quietly shows up as improving pelvic pain or worsening vulvar pain in European reports is surfaced alongside the U.S. pharmacovigilance data, rather than living in a separate regulatory silo.

A classic example of what we are looking for is statin trials (statins are cholesterol drugs). Several large statin trials have included significant female populations, and buried in the secondary endpoints, there are signals that women on statins report reduced dysmenorrhea, which is a common side effect of endometriosis. Is this a proven treatment for endometriosis? No. Is it a hypothesis worth investigating formally? Absolutely. That is the whole point.

My mom identified the specific drug classes worth targeting in this secondary arm: statins, SSRIs, dopamine agonists (which include cabergoline and bromocriptine, the exact medications I was on, which felt like a strange kind of symmetry), and GLP-1 agonists. GLP-1s are particularly interesting because the recent wave of trials for Ozempic and Wegovy has generated enormous amounts of data about hormonal and inflammatory effects in women, and researchers are only beginning to analyze what that data contains.

3. Pathway Insights — 120 signals

The third arm, Pathway Insights, started as “Caution Signals” and got reframed. Instead of just cataloguing drugs that seem to worsen a condition, this arm now asks what those adverse effects, and broader target‑level data, reveal about underlying disease biology. It pulls from both traditional pharmacovigilance sources and the Open Targets Platform, which aggregates genetic associations, known drug–target interactions, pathway data, and differential gene expression to connect specific targets and diseases in a structured way. This is the largest arm in the database, in part because pathway and mechanism data is the most plentiful — every adverse effect, every shared target, every overlapping mechanism is a potential hypothesis.

Concretely, for each condition we query the Open Targets GraphQL API using standardized EFO/MONDO disease identifiers and pull back the highest‑scoring targets and associated drugs. Those target–disease links are then analysed alongside adverse‑effect signals (like the Tamoxifen–adenomyosis worsening signal — see below) to generate pathway‑level repurposing hypotheses rather than single‑paper hunches.

A good example of a drug in this category is Tamoxifen. Tamoxifen is a breast cancer drug that blocks estrogen receptors, and it is also known to cause or worsen adenomyosis in some patients. This sounds like a purely bad thing, and for patients it is, but from a research perspective it is actually informative because it tells you that estrogen receptor pathways are central to adenomyosis biology. If blocking estrogen receptors makes adenomyosis worse, that gives us something else to investigate about what is driving the condition.

Understanding what makes something worse is often a legitimate path to understanding what might make it better. This is not a counterintuitive insight, it is basic pharmacology, but it rarely gets organized in a publicly accessible way for those trying to understand their own conditions.

4. Community Forum Reports — 32 signals

This arm is the one that required the most convincing, in both directions. I was initially uncertain about including Reddit data in a medical research tool. It felt epistemically sloppy. My mom convinced me it was worth doing, with the important caveat that this arm has to be labeled clearly as community signal rather than clinical evidence.

Here is why it matters: women often do not report positive side effects in clinical trials unless the trial is specifically designed to capture them. If you are in a statin trial and your periods get better, that is not a primary endpoint, it is not something a researcher is looking for, and thus, it may not end up in the published data. But you might go to r/Endo and post about it. And if two hundred women do that over three years, it is a signal.

I built a pipeline that searches subreddits specific to each condition (r/Endo, r/PCOS, r/PMDD, r/Menopause, among others) for mentions of specific medications and treatments, particularly treatments women describe as helpful even though the drug was approved for something else. The pipeline uses a combination of the Pushshift archive (where still accessible) and direct subreddit queries. The filtering is important here. The tool is not pulling anecdotes uncritically, rather it is looking for consistent patterns across a large number of posts, which is different from a single person’s experience.

One thing the Reddit pipeline has already surfaced: Meloxicam, an NSAID, being mentioned across multiple endometriosis communities as more effective for pelvic pain than standard ibuprofen. This is plausible on pharmacological grounds (different COX inhibitor profile), but there are very few formal studies on it specifically for endometriosis. This is the kind of gap the tool is designed to identify.

Inclusion criteria/How we decide what counts as a “signal”

Under the hood, every result in Whel is scored rather than treated as a binary “is this real or not?” question. I use Claude Opus 4.6 as the first‑pass rater, because it currently performs at or near the top of independent reasoning and research‑style benchmarks, and is unusually good at following explicit rubrics for evidence grading. On top of that, my mother (who spent years in clinical and research roles before going into psychiatry) designed the actual inclusion framework so that what the model is doing maps onto how a cautious human researcher would think about signal quality.

The goal is not a single magical cutoff, but a tiered evidence framework with minimum standards for reliability, reproducibility, and actionability. For each drug‑condition pair, we score five dimensions: source quality, replication, specificity, biological plausibility, and direction of effect. Each gets a 0-2 score, and the total (0-10) maps to four tiers:

• Strong (9–10) — 30 signals

• Moderate (7–8) — 57 signals

• Emerging (4–6) — 177 signals

• Exploratory (0–3) — 7 signals

The tier shows up next to every result in the interface, less as a verdict and more as a transparent view into how seriously it is reasonable to take any given pattern in the data. The full methodology is written up on the site.

The arms themselves have their own evidentiary thresholds layered on top:

1. Direct‑research signals need at least one human study (preferably more), clear population–exposure–outcome definitions, and consistent direction of effect.

2. Cross‑condition signals have to show up across at least two evidence domains (for example, literature plus AEMS/EudraVigilance, or registry data plus community reports) with a plausible shared mechanism.

3. Pathway‑only signals are explicitly flagged as exploratory unless they also have human or pharmacovigilance support.

4. Community‑derived signals are throttled by both volume and specificity (dozens of unique, symptom‑specific posts pointing in the same direction, not just “this drug changed things”).

The last rule my mom insisted on, which I like enough to keep verbatim, is: do not confuse frequency with truth. A rare but specific, well‑replicated signal that triangulates across methods matters more than hundreds of vague mentions that “something felt different.”

What My Mother Brought to This

By this point it’s clear that my mom was not a side character in this project. What matters here is the specific structure she pushed me toward.

She brought what she called a two‑armed data strategy, which I more or less adopted wholesale. Primary arm: current clinical trials and published studies directly targeting the condition. Secondary arm: systematic search for indirect signals in other drug trials. That framing clarified something to me — that the absence of direct evidence is not the same as the absence of evidence. The sparseness of the primary arm is information, and the signals in the secondary arm are hypotheses. They are different kinds of data and they deserve to be presented differently.

She also brought several future directions I have not built yet. Interventional psychiatry is a direction she flagged early: conditions like PTSD, where women are roughly two‑thirds of sufferers, and addiction medicine, where she noted that cravings may cycle with hormones in ways that current treatment protocols do not account for. Perinatal conditions. Bipolar disorder and its relationship to hormonal cycles. There is a whole second project inside this one.

I Am Not a Coder

This part might be the most useful section for some readers, but I will note that I almost cut it because I was worried it would undermine the technical credibility of everything else. But I think transparency matters here, so… I had never really built anything before this project. I do not know how to code very well. I did not learn how to code to build this. I built it using Claude Code, which lets you describe what you want in plain language and then handles the implementation while you watch and occasionally say ‘that’s not what I meant’ and rephrase.

My “stack” is Next.js with TypeScript and Tailwind CSS, Supabase for the database, Vercel for hosting. Total monthly cost to run: zero dollars. Total cost to build: Claude Pro subscription plus approximately $10 in API credits. I was inspired, as I explained in my article from last week, by the story of the guy who used AI to build a cancer vaccine for his dog. If he could do that, I could build a drug repurposing database.

The initial version took under 24 hours. I remember the moment it first worked. I was staring at the terminal and data was populating and I typed into the chat something like “wow. this is insane. it worked” which is not a very deep thing to say but was the honest response. I also experienced the strange aliveness of having made something function. And now I understand why people build things.

The data pipelines took longer. PubMed required learning how to query NCBI’s Entrez API. FDA AEMS has a public API but the data structure is not intuitive. ClinicalTrials.gov has its own API. The Reddit pipeline uses the Pushshift archive and direct subreddit queries. Each one was its own debugging session, its own moment of figuring out what the tool was returning versus what I thought I was asking for.

The UX/UI went through several iterations. I started with plum and burgundy, which I still think is aesthetically correct but apparently signals “not a medical tool” to most people. Then a blue phase. Then sage green from a Figma mockup. The final version has a sage green color scheme, which I think threads the needle between “trustworthy” and “not completely sterile,” although I am still actively trying to nudge it further away from looking overly “vibecoded,” which I recently learned can be used as an insult.

What the Data Shows

A few early findings that I found particularly convincing, with the caveat that all of these are currently in the ‘emerging’ or ‘moderate’ tiers of our evidence scoring, so, interesting enough to be worth a real study, not nearly enough to count as medical advice. I am a university student with an NIH search interface and a very patient mother.

1. Melatonin for endometriosis has a surprisingly strong evidence base in published literature. Several small trials have looked at melatonin supplementation for pain related to endometriosis, with promising results. The mechanism involves melatonin’s anti-inflammatory and antioxidant properties, and the fact that endometriosis lesions have melatonin receptors. This is not in the mainstream conversation about endometriosis treatment. It probably should be.

2. The Tamoxifen/adenomyosis reverse signal I mentioned earlier continues to be one of the cleaner examples of what the Pathway Insights arm is designed to surface. The signal has been documented in case reports and retrospective studies, and the implication for estrogen receptor biology in adenomyosis is understudied.

3. The Reddit pipeline is early but already generating interesting volume. The Meloxicam mentions across endometriosis communities are consistent enough that I think this warrants a deeper investigation. I am not saying Meloxicam treats endometriosis, but the pattern in patient reports is the kind of signal that should feed into a prospective study design.

4. The GLP-1 arm is where I expect the most activity in the next year. The sheer volume of clinical data being generated by Ozempic and Wegovy trials means there is an enormous amount of secondary data that could be analyzed for women’s health signals. Several research groups are already starting to look at GLP-1 effects on PCOS, which is the obvious entry point. I think there is more there.

Why This and Not Something Else

My own medical experience was certainly a catalyst for this project, and this is in some sense, a mother–daughter research project. That said, bigger questions — “what do we know?” “how do we know it?” and “what are the structural reasons we fail to know certain things?” — are always of particular interest to me. Drug repurposing provides a path toward an answer, because it says the knowledge already exists, in a different form, and we are going to find it. This is quite beautiful as a concept — the idea that a drug developed for cholesterol might contain, embedded in its trial data, a signal about pelvic pain, is a claim about the structure of knowledge itself. Useful truths can be present in data before anyone knows to look for them.

What’s Next

A few things I am actively working on or planning:

1. Improving the search interface. Right now you can search by condition, by medication, and by signal type. I want to add filtering by evidence strength (RCT vs. population study vs. case report vs. community signal), which requires better metadata tagging.

2. Expanding the condition set. Current focus is endometriosis, PMDD, PCOS, adenomyosis, vulvodynia, and perimenopause & menopause. I want to add perinatal conditions and begin the PTSD and addiction arms my mom flagged.

3. Better sourcing transparency. Every result in the tool has a source link, but I want to add more context about what kind of source it is and what that means for how seriously to take the signal.

4. Feedback from people who know more than me. If you are a researcher, a clinician, a patient advocate, or a woman who has been navigating one of these conditions and has thoughts about what the tool is missing or getting wrong, I want to hear from you. The site is at whel.bio, and my messages on here or LinkedIn are always open.

Thanks to my mom for the medical architecture of this. And thanks to Claude Code for making it possible for a non technical person to build something. And thanks to the women in r/Endo, r/PCOS, r/PMDD, and the rest who have been doing their own research for years because the formal system was not doing it for them :)

Quick aside — I was so very delighted to see this post (which also where I got the chart above) from the magnificent Peter Walker (highly recommend a follow), which is a pretty great validation of what I argue below ;)

On the So‑Called SaaSpocalypse

Depending on which corner of the internet you exist in, you probably heard about how in late February, a 48‑hour sell‑off wiped hundreds of billions off public software valuations.

The overly-simplified explanation for this selling-spree was that investors suddenly realized that if one AI agent can do the work of ten humans, maybe you don’t need fifty Salesforce licenses anymore. The articles that followed the events in the market explained that this was “the day AI started doing the work,” and that per‑seat pricing (which is essentially the crown jewel of SaaS economics) had finally met its match.

In parallel, Anthropic and OpenAI rolled out their “virtual colleagues,” demoing agents that could file tickets, update CRMs, draft emails, and generally behave like the world’s most compliant junior employee. Not surprisingly, this narrative hardened quickly online (big SaaS is dead, incumbents are doomed, and we are about to replace all of Salesforce with a couple of cleverly wired prompts).

The incumbents, for obvious reasons, do not love this story. Marc Benioff went on what can only be described as a charm offensive, insisting that this wasn’t Salesforce’s “first SaaSpocalypse,” comparing it to the shift from on‑prem to cloud and positioning Salesforce as an “operating system for AI agents,” not collateral damage. Salesforce even rolled out its own “agentic work units” pricing concept, (which seems like a spiritual successor to the per‑seat license?) to try to push the idea that software can be a sort of collaborator instead of a static tool.

Underneath the PR, though, the anxiety is certainly real. Analysts have pointed out two particularly compelling concerns for why classic seat‑based SaaS might not survive:

1. Customers can replace human users with AI agents and slash license counts (this is less cool to me).

2. Customers can build internal tools on top of models like Claude and Frontier that bypass traditional apps entirely (this is MORE cool to me).

It is so boring to not take a position on something, so here is mine — yes, something real is happening, but I don’t think the headline should “software is over.” Instead, I think it’s more like “cheap software finally frees capital to fund hard things.” Or in other words, we will begin to recycle a decade of marginal SaaS dollars into hard‑tech risk.

Software really has become indecently easy to spin up

TechCrunch’s post‑mortem on the sell‑off points out that AI‑native startups can “adapt, adopt, and build technology much faster than a traditional SaaS company,” and that customers who don’t like their vendor’s prices can now threaten to build an alternative. And this, to a certain extent, is true. Autonomous coding tools like Claude Code and OpenAI’s agents have dramatically lowered the barrier to writing and maintaining production‑grade code.

The result is a Cambrian explosion of what you might call vibecoded SaaS — think two‑person teams (or one person plus a high pain tolerance) who use AI to scaffold a whole product stack in weeks or internal teams who throw together custom tools that fit their workflows better than whatever generic SaaS they were using before. The irony, of course, is that a lot of this vibecoded, in‑house software isn’t actually validates what people really want and felt like they were missing from their current provider. Hopefully some of those internal tools will eventually harden into the next wave of durable SaaS products once they escape the four walls of a single company.

Here is where my observations get a little more interesting. Early-stage investors are noticing this “ease of building” phenomenon. And several analyses point to a sharp bifurcation: a handful of AI‑native or infrastructure software names still command rich multiples, while the long tail of traditional SaaS names trade down and struggle to raise. Carta has documented that software rounds are smaller, more selective, and heavily skewed toward companies that either have real traction or are embedded deep in the AI stack.

Basically, if you are trying to pitch “Salesforce, but with agents,” you are now competing with every in‑house engineer who has ever opened a prompt window… sorry.

What Early-Stage Money Was Actually Paying For

Under the old regime, early SaaS money, especially at pre‑seed and seed, did three jobs at once:

1. Pay for a team to build the first version of the product.

2. Pay for GTM experiments to find someone who cared.

3. Pay for everyone’s rent and burn while (1) and (2) took longer than expected.

Now, agents can do a non‑trivial chunk of (1) and a surprisingly large chunk of (2). That doesn’t make founding a necessarily company easy, but it does make shipping a decent v1 and testing channels dramatically cheaper.

Public‑market panic tends to obscure this, but it’s worth pointing out that in retrospect, a lot of pre‑seed/seed SaaS rounds were always over‑capitalized relative to what they were really doing. The “we need $5M to find out if someone wants this workflow solved” era only made sense when every experiment demanded a full human team. If two people and a fleet of AI coworkers can reach the same learning milestones on a tenth of the money, the pre‑seed/seed check for pure software does not look like a necessity, and investors are already treating it that way.

It would be stupid of me not to point out that there is also something mutual about this unwinding of early-stage capital being invested in SaaS. If AI and better tooling mean it no longer takes a full-stack village to get a product to $1–2M in ARR, then early-stage founders don’t actually need the same kind of VC support they used to… and that’s good for them!!!

Carta’s latest numbers show median dilution ticking down across seed and Series A as capital-efficient, often AI-native teams raise less frequently and at higher valuations. In practice, that means more founders can choose to skip or shrink the classic “venture-backed seed” and hold onto a much larger share of their company while they let agents and a tiny core team do the heavy lifting.

The punchline, though, isn’t “no more investors,” it’s “investors later.” If the SaaSpocalypse really does flush out the overfunded tools, what you’re left with is a smaller cohort of durable, mission‑critical SaaS companies that are still growing. Growth investors and PE are already quietly circling those: 2025 was a record year for SaaS M&A, with private equity involved in nearly 60% of deals, and the upper quartile of SaaS names in one major index still put up positive returns while the median sagged.

Fast‑forward a few years and you can easily imagine a world where the real action in software is not at the frothy pre‑seed table, but in chunky growth rounds and buyouts for the survivors of the SaaSpocalypse, which, for anyone who still loves software (me!!!), makes the growth side of the stack look particularly exciting.

The Return of Hard, Capital-Intensive Things

If you look at where new funds and “hot theses” are clustering, you will see a pattern of concentration across climate, energy, robotics, new materials, AI hardware, and biotech platforms. Essentially, the stuff that can’t be spun up with the help of Codex in a weekend, no matter how good your prompts are.

Climate and energy are a good example. Recent climate tech reports show tens of billions flowing into grid infrastructure, storage, industrial decarbonization, and “hard” energy projects. These are exactly the kinds of companies that need massive upfront capex for pilots, plants, and physical assets, face multi‑year regulatory and engineering risk, and can’t be bootstrapped with $10k and a Notion page.

Back to the SaaSpocalypse for a sec — if AI has truly exposed how much of the SaaS sector was marketing spend wrapped around interchangeable code, it has also highlighted the kind of software that actually deserves to exist — products that own a critical workflow end‑to‑end, tackle messy regulatory/domain complexity (Delve for instance, would be a company that did NOT do this), sit on top of irreplaceable data exhaust, or feel more like infrastructure.

What Survives (And Why This Is Net-Positive)

None of this means SaaS disappears. In fact, for the best SaaS companies, I would argue that this moment is actually a gift. If you are mission‑critical and already live in the center of your customers’ workflows, agents make you more important, not less, and you get to be the orchestration layer that routes work between humans, systems, and AI coworkers. Your data exhaust becomes an even deeper moat as models fine‑tune around it, your embedment in existing processes makes you the default place to plug agents in, and your pricing can evolve from static seats to usage‑based and “agentic” units/tokens that more closely track value created. In that world, the winners increase product surface area and become the operating systems that everyone else builds on top of.

However, the days when “we are a horizontal SaaS tool for knowledge workers” automatically translated into an over‑subscribed seed round are probably gone. Thin, copy‑and‑paste SaaS will get commoditized, whereas thick, infrastructure‑like platforms and deeply embedded systems survive. The market is already sorting that out with a level of brutality only public tickers can deliver.

Another upside of this reaping is that founders working on hard, capital‑intensive problems may finally get something like a fair shot. For years, these teams have been the underdogs — higher technical risk, slower timelines, and a fundraising environment biased toward the quick, clean story of “SaaS but for X.” Now, as cheap vibecoded software crowds that narrative, early‑stage capital is forced to look elsewhere.

If we’re going to have a market panic, I’d rather it be the one that ends with more geothermal projects, robots in factories, climate hardware, and serious biotech platforms getting funded. And for those of us who still love software, that also sets up some of the most interesting growth‑equity vintages we’ve seen in a decade in the form of fewer, better SaaS names sitting next to a new class of hard‑tech compounders. Or perhaps some combination of the two… the software + IoT model will always be a personal favorite.

A few weeks ago I came across the story of a data engineer in Sydney who, with no medical degree and no formal training in oncology, used AI tools (mainly ChatGPT) to help design a personalized mRNA cancer vaccine for his dog, whose tumors had resisted standard treatment. Working with researchers at UNSW, he took the dog’s tumor, sequenced the DNA, used AI to make sense of the mutations, and collaborated with a lab to turn that into an actual, physical vaccine, which shrank several tumors after chemotherapy had failed. Scientists involved have publicly said they were stunned, and my LinkedIn feed reflects that as well ;)

This story, which reads like speculative fiction, is, predictably, more complicated than its virality suggests. This was not a guy in his garage uploading raw DNA to ChatGPT and printing a cure at home. But, I think, the core fact is astonishing enough: a non‑doctor, driven by a mixture of love and desperation, used a combination of AI and publicly accessible tools to drive a line of experimentation that would have been unthinkable for a layperson even five years ago. Companies such as OpenAI and Anthropic keep talking about the democratization of software development and scientific discovery, but here is an actual example of it.

Of course, the involvement of AI was not what feels new here, but rather, that the person at the center of the story sits entirely outside the traditional priesthood of medicine. This is the first time I actually believed the ‘democratization’ brochure copy, and it has given me what I am dubbing “builder bug” (sounds stupid, bear with me).

For context: I am, by any standard definition, non‑technical. Most of my prior contact with “coding” was through the Scratch games everyone made in middle school computer class, which I mostly used to animate unimaginative cats (pictured at the top of this essay). I could not reliably tell you what language a given block of code is written in, and until very recently, “deploy” lived in the same conceptual bucket as “invade.” And yet, recently, the idea of making something from literal point‑zero has started to feel almost indecently appealing. I should note that this comes alongside a deep respect for the history and craft of coding itself. So, despite having no formal training, I’ve taken it upon myself to learn how coding has evolved since its inception, because context matters, even when you’re operating at the blurred layer of abstraction I currently inhabit.

I suspect that part of the enjoyment I derive is just temperament, but, like most things I love, is also part philosophical. The whole “building from point-zero/create something out of nothing” framework actually feels very adjacent to infamous philosophical undertakings. Perhaps the most obvious example of this is Descartes’ methodic doubt, where one is required to strip away everything that can be doubted and rebuild knowledge from a single indubitable point (cogito, ergo sum/I think, therefore I am). Or you might be reminded of Kant’s insistence that before we talk about morality, we must first undertake an a priori investigation into the supreme principle that could ground it. Both of these are attempts to start over, to begin again from something like scratch. Building seems to have that flavor because you excavate down to nothing, then try to assemble a structure that actually holds.

Or maybe I am just projecting my reading list onto my GitHub commits (very possible).

Anyway, I am now in the middle of my own tiny Copernican revolution. Over the past weeks, I’ve spent hours cycling through Claude Code, Supabase, GitHub, Vercel, and Next.js, trying to teach myself how to build (“build” here meaning “persuade a set of tools I barely understand to cohere into something that runs in a browser”).

A few early observations:

• The learning curve is steep + bendy. There’s a point where nothing makes sense, and then, suddenly, two percent of it does, and you live off that two percent for days.

• I now have a hazy sense of what Claude Code and Supabase do. Claude writes code and explains it if I beg; Supabase is, roughly, a database with training wheels. GitHub and Vercel I interact with more as proper nouns than as concepts. So GitHub is where my “project” allegedly lives; Vercel is, according to Google, “the platform for the web and AI,” which both feels accurate and is exactly the sort of sentence that tells you nothing.

• Iteration is real. Not real in the inspirational, LinkedIn‑carousel way; real in the “you will rerun this line of code fourteen times and it will have an error” way. It’s annoying, and occasionally soul‑sucking, and also the only path through.

I should also say: I have no idea what most of the code Claude is generating actually means. I do not know what files I have pointed it to on my laptop. I am aware that this is both mildly chaotic and also a privacy concern. But nothing has exploded yet, and I am not trying to become a software engineer, so for now I am choosing to inhabit this slightly blurred layer of abstraction and see how far it gets me.

What am I trying to build?

An open‑source, web‑based research tool that uses AI to mine medical literature, community forums, pathways, and clinical trial data for overlooked “repurposing signals” linking existing drugs to underfunded women’s health conditions. In English: a tool that helps surface, cluster, and contextualize plausible “this existing drug might help with this neglected condition” hints hiding in PubMed abstracts, ClinicalTrials.gov records, and related sources. As a (probably-not-needed-but-I-will-say-it-anyway) disclaimer, I am not pretending to give direct treatment recommendations.

My original thought was that something like this could help clinicians and researchers see, at a glance, which approved compounds show unexpected promise, what evidence exists, how strong it is, and how different conditions and mechanisms might connect before the funding and formal trial infrastructure catch up. Conditions like Endometriosis, PMDD, perimenopause, PCOS, chronic pelvic pain, and adenomyosis are staggeringly common in women, debilitating, and systematically underfunded relative to their impact.

The idea for this came from my mother, who is casually multidimensional in a way that would be annoying if I didn’t love her. She decided about seven years ago to go to medical school. So she did. This means I get a running commentary on what medicine cares about, what it underfunds, and where AI is being used versus where it’s just branding. Recently she mentioned, almost in passing, how many women’s health conditions live in this gray zone — too common to be rare‑disease sexy, too gendered to be properly resourced, and/or too messy to fit neatly into existing drug pipelines.

At the same time, drug repurposing is one of those revolutionary things hiding in plain sight (and there are many, many platforms that are being built or already operate in this space currently). We already have multiple major drugs whose most important uses were discovered as “side effects” or off‑label explorations that eventually became standard of care (Sildenafil/Viagra, Minoxidil/Rogaine, Aspirin/acetylsalicylic acid, etc). But, based on my research, almost no one is systematically applying that lens to these specific women’s health conditions, especially not with the full machinery of modern search, LLMs, and structured evidence‑mapping pointed at the problem.

So we talked, and I proposed a citation‑first research tool that does exactly that —mines the existing evidence base, scores and summarizes potential repurposing signals, surfaces them in a way a human expert can interrogate, and stays very far away from pretending to be a diagnostic or prescribing tool.

You might reasonably ask why I am doing this when no one asked me to, with a full course load, a job, and precisely zero compensation, beyond a negative $41.11 line item labeled “Claude Pro + API credits.”

Because it is so FUN. Oh what a joy it has been to discover that I love watching something go from “vague idea in Notes app” to “object that exists at a URL!”

And so to investigate this personal phenomenon, I shall begin with the opening line of Aristotle’s Metaphysics:

“All men by nature desire to know (eidenai). An indication of this is the delight we take in our senses; for even apart from their usefulness they are loved for themselves; and above all others the sense of sight… [it] makes us know and brings to light many differences between things.”

This line is perhaps the cleanest description of what the “builder bug” actually is. The desire to know, explore, and experiment is about the particular, almost childlike delight in making distinctions, seeing how one thing differs from another, how systems hang together, how you can intervene and change them. Sight, for Aristotle, is the privileged sense because it discloses structure, and building, in this very contemporary, AI‑mediated way, is just another a way of bringing to light new “differences between things.” Except, I suppose, that the “things” are now database schemas and model weights and clinical endpoints. Oh how the times have changed.

If I had to sum it up: to build is to put your hands (metaphorically) on the structure of the world, or some particular aspect of it, and feel where it gives.

So, yes: consider this a standing invitation, especially if you consider yourself “non‑technical,” to go build something, literally anything. Whole categories of problems will only be touched if people who have never thought of themselves as “technical” decide to build anyway, precisely because they carry different urgencies and ways of seeing. So let the tools carry the weight you don’t yet understand.

And do not let anyone be pretentious to you about learning to code the “right way.” Let your curiosity be wildly disproportionate to your credentials (I have been told that this is especially important if you, like me, are young, wide-eyed, and naive about the world around you). Worst‑case scenario, you learn a lot about how things don’t work (which is valuable as well). Best‑case, you make something that didn’t exist before, and in the process, you get a little closer to that Aristotelian delight in knowing.

For all the questions, big and small that I have been lucky enough to have answered, one remains a bothersome constant that refuses to leave me. Why do certain new things (nascent technologies, fragile ideas, the barely-formed) deserve to exist at scale? For context, by “deserve,” I mean both that we are warranted in believing their promises at an uncertain frontier (an epistemic inquiry), and that we are justified in directing real resources toward making them widely available (a moral inquiry).

I should be candid about the shape of my conviction. I take it as a working premise (which I will support, in brief) that technological advancement has functioned, repeatedly and decisively, as the principal agent of change in human history (a net good, though never an uncomplicated one). Early-stage investing, on this view, is the practice of placing serious, philosophically considered bets on that change, in service of a more resilient future.

This is not a particularly novel premise (techno-optimism has existed for years now), but I want to complicate it by arguing that progress is not inevitable, and is instead contingent on judgment, on capital, on conditions, and on luck. That contingency is precisely what makes the early-stage endeavor worth taking seriously as an intellectual undertaking, as opposed to simply a financial one.

Our contemporary imagination tends to flatten the word technology into software, screens, or some version code. But, of course, technology did not begin with the microchip. Fire, and our learned ability to domesticate it, harness it, etc, was a technology. So were the plow, the printing press, and the camera lens. Each was, in its moment, no less radical than the internet has been in ours. Moreover, every major technological rupture has reorganized the field of what possible for people to achieve, and each has been attended by measurable expansions in human capacity and human flourishing.

I am partial to data. What follows are several graphs illustrating the relationship between technological advancement and the conditions of human life.

A couple of interesting conclusions can be derived from the chart above. The most notable is perhaps how recent most of our technological breakthroughs actually are. For thousands of years, technological change was so slow it was nearly imperceptible. Then, beginning around the Industrial Revolution, the curve bends sharply upward. Enter the printing press, the steam engine, electricity, antibiotics, semiconductors, the internet. The compression of innovation into the last two centuries is stark in its acceleration. Now place that acceleration beside human outcomes.

For much of recorded history, global life expectancy hovered around thirty years. Then, in the last two centuries, which remember, is precisely when technological innovation accelerated, it doubles, then nearly triples. Vaccines, sanitation systems, antibiotics, public health infrastructure. The pattern holds across domains.

Education tells a similar story. In 1820, global literacy rates barely exceeded ten percent as a result of formal schooling being a privilege of the few. Over the course of the 20th century, alongside industrialization, electrification, communication networks, and mass production, enrollment rates climbed dramatically across regions. Literacy followed, rising from roughly one in ten people worldwide to over eighty percent today.

I hope I have begun to persuade you that when technological capacity expands, the principal determinants of human flourishing tend to expand with it — directionally, and with a stubborn persistence. The Agricultural Revolution made organized civilization possible. The Industrial Revolution made sustained and measurable progress possible. The modern technological era has accelerated both to a degree that would have been, for most of human history, simply unthinkable.

A necessary interruption. I am not so credulous as to claim that every wave of what we call “innovation” has carried us toward the good, or that progress has traveled exclusively through benevolent channels. It has not. The same architecture of global trade and industrial coordination that produced compounding gains in productivity also made possible some of the most grotesque undertakings in the human record, take for example the transatlantic slave trade, or the British opium regime that, across the nineteenth century, hollowed out the social fabric of China. Or for a more recent example, think of the cobalt economy that today consumes lives in the mines of the eastern Congo (for more on that, I suggest this book).

This is also, I should note, where I part ways with the more triumphalist versions of techno-optimism, which tend to treat the techno‑capital machine as broadly self‑correcting, and the costs of progress as tolerable collateral. My view is similar, yes, but slightly less heroic. The history of technology (which we have seen over and over again) suggests that whoever pays the price for progress is itself a central moral question, or at least should be.

Anyway, my point is not to pretend we can consume our way into purity, but to state clearly that “technological progress” is morally underdetermined, because the problem of who bears costs versus who captures upside are political & ethical questions before they are engineering problems. At a minimum, investors who underwrite these systems inherit a responsibility to understand the supply chains they’re compounding, to interrogate the labor and extraction regimes behind whatever “growth” they’re so eager to fund, and to reconsider what counts as a successful outcome. That’s a different essay (I will get to it) but it can’t not be mentioned in any honest discussion of this sorts.

Returning to my central point — we may grant that progress has been, on balance, a net good, but it does not follow from this conclusion that its impact was automatic or guaranteed.

Take vaccines for example. Their transformational impact and benefits emerged not simply as a result of the science that initially allowed for their creation, but was dependent on their ability to be widely manufactured, distributed, and made affordable. Electricity was not just invented, but was wired into homes, priced accessibly, and regulated into infrastructure. Computing power was not something that was engineered and then remained static; it was miniaturized, commoditized, and placed into billions of our pockets. Technology’s capacity for change is not automatic, and it is both amplified and democratized when it scales. Perhaps most importantly, this scaling is not a natural process. It requires decisions, resources, and at times, someone, at an early and uncertain moment, to decide that a given possibility deserves the chance to become real.

Quick aside: Beyond private capital and entrepreneurial will, many of the technologies we now treat as category defining only became so because governments helped them scale safely and broadly through regulation and infrastructure. From vaccines and public health systems to electrification and the early internet, public-sector partnership has repeatedly been the mechanism that turns technical possibility into durable, widely shared capability.

I am especially interested in what this looks like in younger and more fragile states, where partnering with responsible, democratic governments can determine whether a powerful technology entrenches inequality or instead deepens resilience and access. However, this is really a GovTech and state-capacity conversation and it sits adjacent to, rather than inside, what I am trying to do here, so I will bracket it for now. For those curious about funding GovTech, I suggest you check out Commonweal Ventures.

Back to what I was saying before. To invest in a nascent technology, then, is as epistemological of an act as it is financial, because you as the investor choose to believe someone’s claim about what is knowable at the frontier of the unknown. And it is, I would argue, it is also a moral act (stay with me here). Capital is not and has never been neutral. Anyone that argues such is either stupid or being purposely ignorant. Where money flows shapes what gets built, who gets access to it, and what version of the future becomes available to whom. The decision to allocate resources toward a technology is, implicitly or explicitly, a judgment about what matters.

I am aware this can sound grandiose. I want to be careful here. Venture Capitalists are not the protagonists of progress. Founders are the ones who build. Scientists are the ones who discover. Engineers are the ones who design. Early-stage investing, therefore, can be better understood as the enabling force that allows promising technologies to move from idea or prototype toward adoption — through the structured allocation of capital and, when done well, through intellectual engagement with the problem a startup is trying to solve.

But the enabling force is not nothing!! And moreover, the discipline required to do it well, to make a conviction-driven judgment under radical uncertainty, to resist the noise of consensus and think clearly about what is new, is, I think, its own kind of intellectual practice. And that is the practice I want to participate in.

What draws me to the early-stage question specifically is that it requires holding two seemingly contradictory orientations at once: deep epistemic humility (you cannot know whether this will work) and genuine conviction (you must decide as if you do).

Most of history’s inflection points looked, at the time, like improbable bets. The telephone was dismissed by Western Union as a novelty with no commercial value. The airplane was declared physically impossible by credentialed scientists mere years before Kitty Hawk. The personal computer was considered a product with no discernible market.

In each case, someone decided an idea deserved the chance to become real. The technologies that have reconfigured human life were once just ideas in someone’s hands — fragile and easy to dismiss. The judgment that they deserved to exist at scale was an act of imagination.

That is, I think, what early-stage investing is at its best — a disciplined and rigorous form of moral imagination. Not prophecy or heroism, but a serious, well-reasoned wager that capital directed toward the right new instrument can make our world more resilient, more capable, and more humane. And the reason I keep returning to this “early-stage question” is that new technologies reopen it at every turn. By that I mean, every genuinely novel possibility merits the question “does this deserve to exist?” and the practice of early‑stage investing is, in large part, the work of answering that question under uncertainty.

SpaceX’s first reusable rocket landing (this brings up memories of when my dad forced me to watch the livestream of it back in 2015).

Quick note — yes, I saw the NYTimes release about how American defense is lagging further behind rival powers than most of us would like to admit. My view in this essay hasn’t changed. If anything, it’s hardened, because whatever fixes we find for that decline will come not from committees, but from the scrappy, occasionally chaotic, and deeply American startup world.

Another note — for a fuller account of how I think about the ethics of defense-tech, I’d point you to Trae Stephens’s essay “The Ethics of Defense Technology Development.” The short version: ethics in defense is not about opting out entirely (that’s too easy); it is about engaging responsibly so that democratic societies, not illiberal regimes, set the rules, deter aggression, and minimize harm.

It has been about two months since I launched this project. For the few people curious about where my ideas come from: mostly podcasts. The majority of what I write begins as something I heard and couldn’t let go of, which is, I think, the whole point — this blog was always meant to be an outlet for thinking through what is going on in the world. As a shameless plug, I have also my “Media that I consume (and that I think you should too)” post to include more VC- and finance-oriented material.

On to the actual point. A few weeks ago, Maura Healey, governor of Massachusetts, announced the launch of the Massachusetts Strategic Hub for Innovation, Exchange and Leadership in Defense (SHIELD), a new initiative to strengthen the state’s defense leadership and accelerate the development of national-security technologies. As part of this effort, she also announced nearly $47 million in funding for military innovation and for expanding microelectronics and chip manufacturing in the state. Massachusetts already sits near the core of the U.S. defense ecosystem: the sector employs over 130,000 people, generates $15.2 billion in annual wages, and drives $48.6 billion in total economic output. It is, by any metric, a small state with a very large industrial shadow. But this is not just a story about Massachusetts flexing. (Though, as a proud Masshole, I could easily write that piece too: go Celtics, I love Drake Maye, etc.) What interests me more is what this signals about a broader shift in how the U.S. thinks about defense innovation and competition.

For decades, the U.S. defense industry has been dominated by a handful of massive “primes”: Lockheed Martin, Raytheon, Boeing, Northrop Grumman. They built extraordinary machines, but they did so inside an ecosystem optimized for scale rather than speed. After the Cold War, a wave of consolidation — encouraged by the Pentagon — pushed the sector toward fewer, larger actors. Budgets shrank, mergers multiplied, and what had once been an ecosystem of hundreds of specialized contractors narrowed into an oligopoly of five or six giants: Northrop absorbing Grumman in 1994, Lockheed merging with Martin Marietta in 1995, Boeing swallowing McDonnell Douglas in 1997.

These primes excelled at complex, multibillion-dollar systems — stealth bombers, missile defense platforms, fighter jets. But their dependence on government contracts and their size made them structurally risk-averse. Risk-aversion, in turn, seeps into process. Procurement became synonymous with paperwork, multi-year timelines, and cost-plus contracts that rewarded compliance over creativity. The system became very good at producing what it already knew how to produce, and comparatively bad at imagining what might come next.

The first real shock to this equilibrium came from SpaceX. While the primes operated inside an intricate, rules-bound procurement regime, SpaceX arrived with almost none of that institutional memory. Armed with a different appetite for risk — and, let’s be honest, a healthy dose of government funding that Elon rarely foregrounds — SpaceX rethought rockets from first principles. Reusability was treated as a live question rather than a cute science-fiction motif. And, somehow, they pulled it off: the rockets landed themselves, and launch costs fell accordingly. The more important shift, to me, is cultural. People were reminded that even the most ossified industries can move when someone is willing to ask naïve questions and run seemingly stupid experiments in public.

A new cohort of defense startups is now pushing that logic further. Companies like Anduril, Shield AI, Hadrian, and Chaos Industries are redefining what it looks like to build national-security technology in the 21st century: short iteration cycles, vertically integrated engineering, automation everywhere, and a willingness to bet on ideas the legacy ecosystem wasn’t structurally able to prioritize. Their work is especially visible in the domain that has become the symbolic center of this new era — drone technology.

I was already interested in drones before I wrote about Ukraine’s defense-tech ecosystem. Part of that interest is philosophical, but part of it comes from Peter Thiel (about whom my feelings are… complicated) flagging drones as a genuinely open category for breakthrough innovation in a conversation with Ross Douthat on the New York Times’ Interesting Times podcast. That observation connects back to his older thesis that America’s innovation engine has slowed, in part because institutions have over-optimized for safety and incrementalism. Drones, by contrast, sit at the messy intersection of AI and hardware, where small teams can still create discontinuous change.

Anduril’s autonomous system, Lattice, paired with Chaos Industries’ work on rugged, field-deployable hardware, illustrates what becomes possible when startups are allowed to invent new operational concepts rather than retrofit old ones. These companies are not just making better drones; they are making defense hardware cheaper, smarter, software-updatable, and deployable at a tempo that actually matches contemporary conflict.

All of this helps explain why capital is suddenly pouring into defense-tech, and why the space feels, for lack of a better word, alive again. For the first time in a long time, it feels like the frontier is open. On that frontier, the Northeast — and Massachusetts in particular — is unusually well-positioned. MIT’s identity was built, in no small part, on defense-adjacent problem-solving in the 20th century. There is no structural reason the region cannot reclaim that role now.

From a more abstract angle, this is a reminder that the “free world” does not stay ahead through bureaucracy or legacy advantage. It stays ahead through the things only open societies reliably generate: risk-taking, creativity, and the willingness to build what doesn’t exist yet. For a long stretch, that energy seemed dormant in defense. Startups are waking it back up. And if the U.S. can cultivate that spirit across states, universities, investors, and founders, it has a real chance to out-innovate autocratic systems that will always struggle to produce originality.

Thanks, as always, for reading. :)

I. Introduction

The Importance of Software-First Innovation in Infrastructure

As climate volatility, aging assets and workforce, and rapid urbanization intensify, the demand for modern, resilient infrastructure has never been greater. Recent estimates suggest a multi-trillion-dollar gap between current infrastructure investment and what is needed to maintain growth and resilience over the coming decades. Sectors like water, utilities, construction, and the built environment face mounting operational and regulatory pressures that legacy systems were not designed to handle.

Despite their central role in society, these sectors remain among the least digitized in the global economy. Paper-based processes, fragmented data, and aging legacy systems create inefficiencies, slow response times, and limited visibility into critical infrastructure performance. As illustrated in Figure 1, sectors such as construction, utilities, and parts of the built environment rank among the least digitized across assets, usage, and labor.

In response, a new wave of software-first solutions is emerging across these infrastructure-heavy sectors. Industry analyses on construction and infrastructure repeatedly highlight digital tools, data platforms, and AI-enabled systems as core levers for closing productivity gaps and improving project outcomes. In parallel, climate-tech market trackers and recent funding rounds in AI-based climate and grid software point to growing investor interest in digital infrastructure tools that can forecast risk, optimize assets, and support real-time decision-making.

Compared with hardware-heavy infrastructure upgrades, software-first products, particularly SaaS and data platforms, can scale more quickly across fragmented customers, layer on top of existing assets, and compound value as more operational data flows through them.

Why Software-First innovation and why for under-digitized infrastructure?

Over the past decade, the technological foundations required for software-first infrastructure modernization have reached a level of maturity that enables real deployment at scale. Cloud-native platforms can now integrate with legacy systems, low-cost sensors generate continuous streams of operational data, and advances in AI and geospatial analytics allow for real-time understanding of physical assets that historically remained offline. At the same time, regulatory drivers, from climate disclosure requirements to infrastructure resilience mandates, have increased the economic incentives for operators to adopt digital tools.

In this report, “software-first” refers to tools where the primary value creation comes from digital workflows, data platforms, and AI-driven analytics rather than physical hardware. These products often integrate with existing infrastructure, rather than replacing it , and scale through standardized deployments across utilities, contractors, or facility networks. Importantly, software-first approaches also compound in value over time as they accumulate operational data, refine predictive models, or integrate more deeply into field and back-office workflows. This accumulation of proprietary operational data creates meaningful switching costs and reinforces the competitive moat of platform providers over time.

What this report aims to do

This industry report maps the emerging landscape of software-first innovation across water, utilities, construction, and the built environment. It outlines the most active software categories, examines funding and valuation trends, and highlights the criteria investors use to evaluate companies operating in these historically overlooked sectors. Drawing on sector reports, venture datasets, and case studies, it identifies where digital infrastructure is gaining traction and where the next wave of opportunity is likely to emerge.

II. Mapping the Software Landscape in Under-Digitized Sectors

To understand how software-first innovation is reshaping under-digitized infrastructure, it’s useful to categorize the major types of digital solutions emerging across water, construction, utilities, and industrial operations. While each sector has unique constraints, several common software patterns have started to define the next generation of infrastructure technology.

Water Infrastructure Software

Water utilities are among the most operationally complex and fragmented infrastructure operators in the U.S., with more than 50,000 public water systems and widely varying levels of technical capacity. As a result, workflows (such as asset management, leak detection, and compliance reporting) often rely on paper logs and disconnected spreadsheets, aging SCADA systems, and institutional memory. Software-first tools are emerging to modernize these functions by providing centralized asset management, GIS-driven network visibility, real-time monitoring, and automated reporting workflows.

SaaS platforms such as Ziptility help utilities manage assets, track maintenance, and improve field operations. AI inspection tools such as SewerAI use computer vision and machine learning to automate defect detection in sewer inspection videos, accelerating workflows that have historically been manual and time-intensive. Geospatial planning tools like CivilGrid integrate mapping, underground utility data, and permitting requirements to streamline project planning and reduce the risk of hitting unmarked assets. Real-time water quality and leak detection platforms leverage sensors and cloud analytics to help utilities reduce water loss and respond rapidly to operational anomalies. As regulators increase pressure on utilities to meet resilience, safety, and reporting requirements, digital water platforms are becoming foundational components of modern water system management.

Construction & Built Environment Software

Construction remains one of the least digitized industries globally, with persistent challenges such as cost overruns, scheduling delays, rework, and documentation errors. Given the sector’s high reliance on distributed contractors and field teams, software-first solutions are emerging as the most scalable way to standardize workflows, improve documentation, and increase transparency across project stakeholders. Digital tools are particularly suited to construction because they replace manual processes rather than physical assets, allowing software layers to scale across thousands of projects with relatively low friction.

Workflow software such as Procore centralizes project communication, documentation, scheduling, and budgeting. Computer vision platforms like OpenSpace and HoloBuilder automate site capture and progress tracking, creating defensible datasets that reduce disputes and rework. And again, tools such as CivilGrid provide underground mapping and permitting intelligence, helping contractors assess site conditions and avoid costly conflicts with buried infrastructure. More advanced solutions integrate digital twins and Building Information Modeling (BIM), enabling real-time coordination between architects, contractors, and facility operators. Together, these platforms help construction stakeholders reduce uncertainty, improve productivity, and create more reliable data flows across the built environment lifecycle.

Utilities & Grid Software

Electric utilities are undergoing rapid transformation as distributed energy resources (DERs), such as rooftop solar, battery storage, and electric vehicles, create far more dynamic load patterns than traditional grid systems were designed to manage. Historically, grid visibility relied on limited sensors and legacy SCADA systems that offered only partial situational awareness. As the grid becomes more complex and climate events more frequent, software-first solutions are emerging as the “intelligent layer” that enables forecasting and real-time decision-making.

AI-based forecasting platforms like Amperon provide granular, real-time load predictions that help utilities balance supply and demand more accurately. Distributed energy management tools such as Voltus and other Virtual Power Plant (VPP) platforms help orchestrate DER participation in grid markets. Software suites for SCADA modernization, outage management, and predictive analytics integrate directly with legacy utility infrastructure, offering enhanced visibility without requiring costly hardware overhauls. With regulators pushing for grid resilience, decarbonization, and DER integration, digital utility platforms are becoming essential components of next-generation grid operations.

Industrial Operations & Built-Environment Maintenance Software

Industrial facilities, commercial buildings, and campus environments often operate large fleets of mechanical and electrical systems that require frequent maintenance to avoid failures, energy waste, and downtime. Historically, these operations relied on analog processes and operator intuition, making maintenance reactive rather than predictive. Software-first approaches—particularly those using IoT, AI, and cloud analytics—are transforming facility operations by enabling continuous monitoring, early failure detection, and streamlined workflows.

Predictive maintenance platforms like Augury use vibration analysis and machine learning to detect equipment issues before they escalate, which reduces unplanned downtime and repair costs. Computerized maintenance management systems (CMMS) such as Fiix or UpKeep digitize work orders, asset histories, and technician workflows. IoT-enabled building analytics platforms integrate data from HVAC systems, meters, and sensors to optimize energy use and identify anomalies. AI-driven failure detection models can detect subtle patterns in equipment behavior, enabling data-driven maintenance strategies. As buildings and industrial facilities aim to reduce energy consumption and improve operational resilience, these digital systems provide a scalable path to smarter, more efficient asset management.

Together, these categories illustrate the breadth of software-first innovation reshaping traditionally analog and operationally intensive sectors. Although each vertical addresses different challenges, they share common characteristics: 1) integration into existing workflows, 2) reliance on accumulating data, and 3) the ability to scale across fragmented markets with minimal physical deployment. These attributes make software-first solutions particularly well suited to modernizing under-digitized infrastructure.

III. Investing in Software-First Infrastructure

Investment Criteria

Investing in software-first infrastructure companies requires a structured and rigorous approach, given the operational complexity, regulatory constraints, and historically slow pace of digitization across these industries. When evaluating a new startup in this space, investors typically rely on six core criteria that determine whether a product can scale, deliver measurable value, and overcome the adoption barriers inherent to these sectors.

Market Pain & Urgency

The most important factor is the intensity and immediacy of the problem the software addresses. Many infrastructure operators face high-consequence risks, such as water loss, sewer overflows, equipment failures, grid imbalances, safety incidents, or construction overruns, that impose real financial, operational, and regulatory costs. Labor shortages across utilities and field operations further increase the urgency for automation, especially as experienced workers retire faster than they can be replaced. In parallel, tightening regulatory mandates make compliance non-optional, forcing operators to adopt tools that enhance monitoring, reporting, and resilience. In practice,companies succeed when they address problems operators must solve, not problems they could solve.

Workflow Integration & Adoption Feasibility

Across infrastructure sectors, adoption friction, not technology quality, is the primary determinant of platform success. Utilities, contractors, and facilities teams often rely on long-standing operational routines, and software must integrate seamlessly into these workflows to gain traction (bat). Products need to accommodate field conditions, including offline mobile use, intermittent connectivity, and varying technical proficiency among frontline workers. Because change management is difficult in conservative, understaffed environments, tools that replace paper forms or legacy spreadsheets with minimal disruption tend to win.

Data Defensibility & Proprietary Advantage

Successful infrastructure software companies often rely on proprietary datasets that compound in value over time. GIS layers, underground infrastructure maps, defect-labeled inspection footage, asset condition histories, predictive maintenance models, and operational telemetry all form high-value data assets that competitors cannot easily replicate. As customers use the software, the platform accumulates more operational context, improving predictive analytics and making workflows more reliable. This data flywheel becomes the software’s moat, increasing switching costs and strengthening long-term margins.

ROI Clarity & Quantifiable Savings

Given the nature of their work, infrastructure operators rarely purchase software based on narrative or vision; they adopt tools that deliver measurable savings. Strong products clearly demonstrate reductions in downtime, truck rolls, water leakage, energy waste, rework, or permitting delays (in terms of marketing materials, this would look like a robust set of metric-based case studies). AI-enabled inspection tools reduce manual labor, while workflow platforms minimize errors and increase productivity. Energy or water monitoring systems show direct cost savings that can be calculated on a monthly basis.

GTM Feasibility

The ability to sell and deploy the product is as important as the product itself. GTM dynamics vary widely: water utilities require long procurement processes and pilot phases; construction is highly fragmented and contractor-driven; facilities management often follows mid-market or bottom-up expansion; grid and energy companies require regulatory approvals and multi-stakeholder alignment. Investors therefore prioritize companies with a clearly defined ideal customer profile, repeatable deployment motion, and predictable sales cycles.

Scalability & Deployment Efficiency

Finally, software-first infrastructure companies must demonstrate the ability to scale efficiently across many customers (within their vertical) without extensive customization. Products with standardized integrations (e.g., SCADA, GIS, BIM), simple onboarding workflows, and minimal hardware dependencies scale far more efficiently than tools requiring bespoke implementations. Field usability, configuration speed, and training simplicity all influence scalability. In essence, the product must be deployable across many real-world environments with consistent, reliable outcomes.

IV. VC Entry Points, Valuations & Exit Environment

VC activity in software-first infrastructure follows a characteristic pattern shaped by long procurement cycles, regulatory constraints, complex workflows, and data-heavy product moats. Generally, VCs enter at stages where risk is understood, value inflection points are clear, and adoption can be demonstrated (pre-seed through Series B). Later-stage capital tends to follow only after strong evidence of market pull, multi-site deployments, or strategic relevance to major industry incumbents.

Pre-Seed: Technical Validation & Early Pilots

Pre-seed investments in infrastructure software typically occur when founders are validating the core technology (early AI inspection models, geospatial planning tools, or workflow digitization for field operations). Companies at this stage often run small pilots with utilities, contractors, or facility teams to demonstrate feasibility. Funding is primarily used to refine the product, establish the first customer use cases, and generate early data loops that will later form the foundation of long-term defensibility. VC tend to invest here if there is a strong founder-market fit, clear articulation of high-value pain point, early proprietary data collection, and strong regulatory or operational tailwinds.

Seed: Customer Validation & Early Repeatability

At this point, companies must prove viability beyond a pilot. Investors typically look for the first paying customers, early ARR, and clear evidence that the product fits real-world workflows. For infrastructure sectors, where adoption can be slow, seed rounds often support additional integrations (e.g., SCADA, GIS, BIM), onboarding processes, and development of standardized deployment playbooks. Here, investors tend to evaluate more strongly evidence of consistent usage across field or office teams, sbility to deploy with minimal customization, early defensibility through asset data, inspection data, or workflow metadata, and ROI demonstrated through reduced downtime, leak detection, energy savings, or schedule improvements.

Series A: Product-Market Fit & GTM Scalability

Series A is typically the first major value inflection point for software-first infrastructure companies. At this stage, investors expect repeatable deployments, increasing customer retention, and predictable expansion revenue. Companies usually scale their sales teams, invest in mid-market or enterprise GTM strategies, and deepen integrations with industry-standard systems. Signals of readiness for Series A investment includes 8–12+ institutional customers or multi-site deployment, strong retention and usage patterns, documented improvements in operational efficiency or regulatory compliance, and emerging network effects (e.g., contractors, utilities, or inspection firms pulling the software into new regions).

Series B and Beyond: Category Leadership & Strategic Positioning

Later rounds enable companies to scale nationally or internationally and build the infrastructure required for enterprise-grade reliability. Companies at this stage increasingly attract interest from strategic acquirers, particularly those seeking to expand into digital infrastructure, geospatial intelligence, or AI-enabled operations. Late-stage VCs and strategics look for deep data moats (asset histories, condition data, underground maps, or long-term telemetry), proven ability to integrate with legacy systems at scale, clear expansion into adjacent workflows or verticals, and a lot more financial data.

Exit Environment: Strategic Acquisitions & IPO Potential

Exits in software-first infrastructure tend to be strategic rather than purely financial, driven by vertical incumbents seeking to expand their software portfolios. Acquisitions often occur in the Series B–D range.

Strategic acquirers by vertical commonly include:

1. Construction: Autodesk, Trimble, Procore

2. Water/Utilities: Xylem, Arcadis, Jacobs, Bentley Systems, Autodesk

3. Industrial: Siemens, Schneider Electric, Honeywell

4. Grid/Energy: Oracle, Itron, GE Digital

V. Market Context (2018 - 2024)

Between 2018 and 2024, investment into software-first solutions for under-digitized infrastructure sectors—particularly construction, water, utilities, and industrial operations—followed a familiar pattern: rapid expansion through 2021, a broad correction in 2022–2023, and early signs of stabilization in 2024. Because most data providers do not track “infrastructure software” as a single category, this section draws on adjacent verticals (construction technology, digital water, grid and energy software, and industrial/Industry 4.0 platforms) as proxies for the broader market for software-first innovation in these sectors. Within those slices, the overall direction is consistent: funding levels peaked around 2021–2022, then reset to a more selective, fundamentals-driven environment where investors favor capital-efficient, software-centric business models.

Deal Volume

Construction Tech Funding Trends

Construction technology (ConTech) is the clearest proxy for software-first innovation in the built environment. Analyses of ConTech funding show a strong rise through the late 2010s and early 2020s, a peak around 2021–2022, and a sharp reset in 2023. Industry reports estimate that global construction tech investment fell by roughly 44% in 2023, from about $5.4 billion in 2022 to $3.0 billion the following year, even as the number of deals slightly increased, indicating more (but smaller) transactions. A 2025 update suggests that construction tech funding rebounded modestly, with total VC inflows rising from roughly $3 billion in 2023 to $3.1 billion in 2024, and deal counts increased from from 236 to 325, pointing to a more active market for digital construction and project-management tools.

Digital Water Market

Digital water represents one of the most consistent and resilient areas of software-first innovation within under-digitized infrastructure. Bluefield Research projects that utilities in the United States and Canada will spend $169.5 billion on digital water solutions, including analytics platforms, smart metering, network intelligence, and operational software, over the next decade, indicating a long-term structural demand for digital tools in the sector. Short-term performance data reinforces this momentum: Bluefield also notes that leading digital water vendors reported 6–7% quarterly revenue growth on average, with smart metering companies such as Xylem, Itron, and Badger Meter posting especially strong results. Meanwhile, early-stage activity and consolidation remain steady. According to Bluefield’s Q1 2024 Digital Water M&A Review, merger and acquisition activity in early 2024 remained roughly on par with 2023, indicating continued buyer interest despite broader venture-market pullbacks. Taken together, these indicators show that digital water is a mature and steadily expanding market where software-first platforms continue to gain traction with utilities.

Grid & Utility Software

Software-first innovation in the grid and utility sector has remained robust even through broader venture-market corrections. PitchBook’s grid and energy-transition data shows that grid-infrastructure and energy-transition startups raised $3.5 billion across 197 deals in Q1 2024, a level the report characterizes as “historically elevated” in the post-2021 environment. These companies include grid-forecasting platforms, DER (distributed energy resource) orchestration software, and advanced analytics tools that operate on top of aging utility infrastructure. However, the broader clean energy software and hardware ecosystem has seen a tightening of growth capital. PitchBook notes that overall clean energy VC/PE funding declined again in 2024, with exit value also falling, reflecting a more selective investor environment for later-stage companies. Even with this pullback at the growth stage, early- and mid-stage software companies serving utilities continue to benefit from sustained demand for digital tools that improve grid reliability, enable DER integration, and modernize operational workflows

Industrial Software

Industrial operations and facility management have experienced a steady expansion of software-first tools driven by Industry 4.0 adoption (Industry 4.0 refers to digital, connected, and intelligent systems). Deloitte’s ongoing Industry 4.0 readiness and technology adoption research shows that manufacturers and industrial operators have increased spending on industrial IoT platforms, predictive maintenance software, and operations analytics over the past several years. Companies adopting these tools consistently cite ROI-driven benefits, including reduced downtime, improved equipment reliability, and higher operational efficiency, as primary motivators for digital transformation initiatives. Deloitte’s findings indicate that even in periods of uneven capital expenditure, investments in software-centric maintenance and analytics platforms have remained resilient because they deliver measurable operational savings. For under-digitized sectors such as facilities management and industrial campuses, Industry 4.0 trends provide a reliable proxy for the broader adoption of software-first solutions in mechanical, electrical, and operational environments.

Trends by Stage

No dataset isolates “infrastructure software” as a standalone category, so stage-level trends must be inferred from adjacent verticals (construction tech, digital water, grid software, and industrial/Industry 4.0 SaaS). Across all of these, CTVC, PitchBook, and Carta show the same directional pattern between 2021 and 2024: pre-seed relatively stable, seed/Series A moderately down but still competitive, and Series B+ experiencing the steepest declines.

VI. Key Takeaways

1. Software-first is the most scalable path to infrastructure modernization.

   1. By layering on top of existing assets rather than replacing them, SaaS, data platforms, and AI tools can reach fragmented customers faster, compound in value as they ingest operational data, and avoid the capex burden of hardware-heavy upgrades.

2. Under-digitized sectors like water, construction, utilities, and industrial operations are both challenging and attractive.

   1. They suffer from paper-based workflows, aging systems, and regulatory pressure, but those same frictions create large, persistent pain points that make workflow-native, data-centric software especially valuable.

3. Across verticals, the winning products share the same DNA: workflow fit, data defensibility, and clear ROI.

   1. Whether in digital water, ConTech, grid software, or predictive maintenance, successful companies integrate into day-to-day operations, build proprietary datasets (maps, inspections, telemetry), and can demonstrate tangible savings in downtime, leakage, energy use, or project overruns.

4. Adoption risk, not technical risk, is the primary gating factor.

   1. Long procurement cycles, conservative cultures, and legacy system constraints mean that investors place outsized weight on GTM feasibility, change management, and the ability to deploy with minimal disruption to field and back-office teams.

5. Stage dynamics now favor early, capital-efficient execution.

   1. Pre-seed remains relatively stable; seed and Series A are still competitive for teams that can prove workflow fit and ROI; Series B+ capital is reserved for companies that already show category leadership, scalable sales, and robust data moats.

6. Strategic acquirers, not generic financial buyers, dominate the exit landscape.

   1. Firms like Autodesk, Trimble, Bentley Systems, Xylem, Siemens, Schneider Electric, Oracle, and GE Digital increasingly use M&A to expand their software portfolios, reinforcing the importance of deep integrations and complementary datasets.

7. For founders and investors, the priority is to build “boring but indispensable” systems of record.

   1. The most durable value in software-first infrastructure will accrue to products that become embedded in critical workflows, own the underlying data model for their niche, and can grow efficiently within (rather than in spite of) the constraints of under-digitized infrastructure sectors.

Skyeton’s Raybird-3 / ACS-3

Three years ago, in an effort to become more cosmopolitan — and to hold my own in my parents’ geopolitical dinner table conversations — I traded my morning Spotify playlists for the NYTimes The Headlines podcast. I have tuned in faithfully every weekday since, and last week brought a particularly arresting discussion: Ukraine has implemented a gamified drone attack system that awards operators points for successful missions — eight points for wounding a Russian soldier, twelve for a kill, forty for destroying a tank — which soldiers then redeem for weapons and system upgrades. (Russia, for context, rewards its operators with cash prizes, which says something, though I’m not sure what.) The gamified model has proven remarkably effective: with careful calibration of the point values per mission, Ukraine’s kill count doubled in a single month.

The decision to gamify drones specifically is not incidental. Drones are arguably Ukraine’s most consequential weapon right now, accounting for roughly two-thirds of battlefield deaths. Their effectiveness has been so total that Ukraine has deployed them in the millions, transforming a war historically defined by tanks and artillery into something else entirely — an accelerated, real-world testing ground for some of the most lethal technologies on the planet.

What I find most interesting about this testing ground is who is actually running it. The dominant players are not the defense giants that anchor Western indices — your Raytheons, Lockheed Martins, Northrop Grummans — but a sprawling, distributed network of small Ukrainian companies, volunteer engineers, and civilian hobbyists competing to design the next breakthrough. The battlefield has become a laboratory for rapid prototyping and real-time feedback, and the whole structure operates with a logic that more closely resembles a startup accelerator than a conventional defense procurement system — with the war itself functioning as the accelerator. Thousands of drone models are tested and modified in a matter of weeks, compared to what would traditionally take years, and designs that fail are discarded immediately. This loop of iteration, funded by private donors, state programs, and international crowdfunding, has produced an innovation velocity I have not seen replicated in any traditional defense contractor. It is, in the most literal sense, Digital Darwinism: designs survive or perish based entirely on real-world performance, and the fittest rise to dominance on the battlefield. The difference from any commercial analogue is that the market feedback here is measured not in revenue, but in lives.

My personal favorites among the startups operating in this ecosystem: Skyeton, Terminal Autonomy, and Aerorozvidka. Skyeton produces the Raybird long-range drone, built for reconnaissance and artillery targeting, capable of flying up to 2,500 kilometers with modular payloads. Terminal Autonomy, a spinoff from university researchers, works primarily on AI-driven navigation — drones designed to complete missions even when GPS and communications are jammed. And Aerorozvidka is perhaps the most emblematic story of all: a volunteer collective that began as a group of tech enthusiasts and has since become a critical component of Ukraine’s front-line operations. In this ecosystem, innovation moves faster than in any Silicon Valley incubator — it has to. Designs are tested in the field, feedback comes directly from soldiers, and updates ship in days.

I cannot publish this in good faith without noting that it is easy, from a distance — in my case, from podcasts — to focus on the technology and lose sight of what is underneath it. Beneath all this innovation is something harder to quantify and equally deserving of attention: the fact that every one of these startups, collectives, and engineers is building under conditions of existential pressure, driven by survival and love of country. These are not innovations made for market dominance — they are made for national existence. In Ukraine’s drone revolution, ingenuity and endurance are not separable; one is the condition for the other. The fittest designs rise not because of clever branding or favorable market timing, but because of the creativity and determination of the people who built them under circumstances most of us will never come close to understanding. It is, in equal measure, a story about technological innovation and about what human beings are capable of when the stakes are absolute.

GCash, a leading Filipino fintech platform, is widely accepted by small businesses throughout Southeast Asia. Photo courtesy of Bloomberg.

A few weeks ago, I had the pleasure of hearing the remarkable, eloquent, and ever-so-stylish Nabiha Syed, executive director of the Mozilla Foundation, speak on the importance of linguistic diversity in training Large Language Models (LLMs). One point she made that has stuck with me since was how linguistic diversity is a driver of innovation — there are literally ideas that cannot even be expressed, and sometimes cannot even be thought of, in English. She also reminded me just how many languages exist in the world: in Southeast Asia alone, there are an estimated 1,200 to 1,500 languages spoken across the region (many of which include multiple dialects!). This linguistic diversity, in Southeast Asia specifically, isn’t just fascinating (and so, so under-researched), it has real implications for innovation and inclusion.

For startups aiming to reach markets in Southeast Asia, linguistic diversity presents a great opportunity (and well, lots of challenges, but those are more obvious, and not as interesting to write about). And nowhere is this tension between opportunity and access more visible than in fintech, specifically platforms that provide access to services such as bank accounts, credit, and digital payment. Moreover, the infrastructure for these platforms is present. Millions of people in Southeast Asia own smartphones and use the internet regularly, yet many remain outside the formal financial system. Nearly 200 million adults in the region do not have a bank account, and an additional 98 million are underbanked, meaning they may hold an account but lack meaningful access to credit, savings, or insurance.

Given that internet and mobile-device penetration across Southeast Asia is already high, it seems as though the barrier to formal financing is not infrastructure, but things like trust, language accessibility, and inclusive service design. In short, it’s not that people are cut off from technology, instead, they’re cut off from financial access that aligns with their language, needs, and cultural context. And being outside the formal financial system has real economic and social costs. Without access to bank accounts, credit, or digital payment platforms, people often rely on cash or informal lenders, which can be more expensive, insecure, and limiting. Small businesses, meanwhile, struggle to scale without credit histories or access to affordable financing. As such, platforms that support local languages and dialects can break down barriers to financial access, making linguistic inclusion a driver of real economic empowerment.

But is there even a demand for these digital financial platforms in Southeast Asia? Undeniably so. The region has some of the highest mobile phone and internet penetration rates globally, and digital wallets and payment apps have exploded in popularity.

In the Philippines, for example, GCash, operated by Mynt, has grown to over 94 million users, facilitating money transfers, bill payments, and online purchases directly through smartphones. Beyond offering services in Filipino and English, GCash has tailored its features to reflect local customs and everyday practices, including remittance workflows and micro-insurance products, which are vital for many Filipinos. The platform’s interface, customer support, and educational materials are designed to be intuitive for users across regions and dialects. With a $5 billion valuation and millions of monthly transactions totaling billions of pesos, GCash is a great demonstration that linguistic and cultural inclusion can be both socially responsible and a great business strategy.

This story is mirrored across the region. In Indonesia, GoPay, integrated within the GoTo super app, has grown by catering to the country’s linguistic and cultural diversity. By offering interfaces in multiple local languages and dialects, GoPay ensures accessibility across Indonesia’s 17,000 islands. And its efforts have paid off. According to its parent company GoTo’s earnings reports for 2024, the financial technology segment, which includes GoPay, had a gross transaction volume (GTV) of Rp240.8 trillion (approximately $15.7 billion USD) and a gross revenue of Rp3.7 trillion (roughly $241 million USD) for the full year. These numbers illustrate that understanding and integrating local context is a direct path to market leadership.

In Vietnam, the platform MoMo has similarly used cultural and linguistic inclusion to become a leading mobile wallet, now serving over 31 million users across more than 140,000 payment points nationwide (notably, these users are from both the bottom and middle of the pyramid). By embedding Vietnamese cultural norms into its workflows and using familiar symbols and practices, MoMo has cultivated trust and usability among users with limited financial literacy. In a market valued at $40.5 billion USD, platforms like MoMo are at the forefront of Vietnam’s digital payments revolution, demonstrating that attention to local context can unlock enormous financial opportunity.

The experiences of GCash, GoPay, and MoMo offer a clear lesson for fintech startups and investors in emerging markets: linguistic and cultural inclusion is a strategic advantage. Platforms that resonate with users’ local languages and customs do more than increase adoption; they foster trust, open new markets, and drive sustainable growth. In Southeast Asia’s diverse and digitally connected landscape, embracing this inclusivity is both a moral imperative and a necessity for a successful business.

Oh! And if you’re curious about fintech platforms that struggled to work in Southeast Asian markets, in part due to inadequate adaptation to the region’s linguistic/cultural diversity, as well as stiff competition from established players, check out PayPal’s initial expansion into Southeast Asia. Even as an established global fintech company, they encountered difficulties in their initial approach — it did not fully account for regional preferences and behaviors, leading to challenges in user adoption and market penetration.

Circling back, Ms. Syed’s insight into the transformative power of linguistic diversity resonates far beyond training LLMs. In fintech, understanding the languages, habits, and contexts of users is what allows platforms to bridge financial gaps and empower communities. GCash, GoPay, and MoMo show that embracing this diversity drives adoption, innovation, and returns. The success of these companies demonstrates that Southeast Asia’s unbanked and underbanked populations are not just a challenge to solve, they are an opportunity for entrepreneurs and investors to rethink what financial inclusion can look like in one of the most culturally diverse markets.

Siemens Energy Accelerates Power Grid Asset Simulation 10,000x Using NVIDIA PhysicsNeMo.

One of the standout themes at New York Climate Week was the sudden popularity of infrastructure and utilities. Conversations with fellow attendees, along with reflections shared online (shoutout to CTVC), all pointed towards one key takeaway: AI and advanced analytics are rapidly transforming these traditionally overlooked sectors into hubs of technological innovation.

Infrastructure and utilities have the the reputation of being dull and bureaucratic. But this outdated perception misses the bigger picture. We are all aware that much of the world’s physical infrastructure is old and inefficient. In the U.S., many bridges were built before our grandparents were even born. Power grids, water systems, transportation networks, and waste management facilities often rely on technology and designs that predate the digital age. At the same time, the workforce maintaining these systems is aging rapidly, creating gaps in expertise and operational capacity that are ready to be addressed.

These converging trends are creating a moment of both great necessity and opportunity. Artificial intelligence and advanced analytics are beginning to modernize operations, optimize efficiency, and extend the life of infrastructure, while public and private capital mobilizes to fund upgrades at scale. Basically, what has long been considered “boring” or “unsexy” is becoming a frontier for technological innovation and investment. The intersection of aging assets, demographic shifts, and AI-driven modernization signals that infrastructure and utilities are not only essential, but, for the first time in many, many years are among the most compelling areas for strategic investment.

The Age and Inefficiency of Physical Infrastructure

Much of the physical infrastructure that underpins modern life is decades old, and in many cases, dangerously outdated. In the U.S., for example, the average bridge is over 45 years old, and 40% of major roads in the country are in poor or mediocre condition. Water systems tell a similar story: thousands of miles of cast-iron and lead pipes, some installed in the early 20th century, continue to transport drinking water to millions of households, often with significant leakage and maintenance issues. The electric grid, too, relies on transmission and distribution networks designed in an era before the digital economy and renewable energy integration. These systems were built to meet the needs of the past, not the complexities of today’s urbanization, climate stress, and energy demand.

While this aging infrastructure presents clear risks, it also creates an extraordinary opportunity. Outdated systems are inherently inefficient (simply because we have literally invented better ways to do things), but this makes them ideal candidates for modernization and technological intervention. Advanced analytics, AI-driven predictive maintenance, and sensor networks can identify vulnerabilities before failures occur, optimize resource allocation, and reduce operational costs. Importantly, public and private capital is becoming increasingly available to fund these upgrades, from infrastructure legislation in the U.S. (sans recent actions from the current administration) to international development programs and private equity initiatives. In other words, these sectors are not just in need of repair, they are a fertile ground for innovation and investment, where improvements can yield outsized social, environmental, and financial returns.

Workforce Challenges and the Silver Tsunami

And if crumbling bridges and aging water mains weren’t enough, there’s another quiet fault line running through the infrastructure sector: its workforce. The people who built and maintain these systems are retiring faster than they can be replaced. In the U.S., more than a quarter of utility workers are over 55, and by 2030, roughly half of the skilled trades workforce could be eligible for retirement. This “Silver Tsunami” represents not just a loss of manpower, but a loss of institutional memory; decades of tacit, analog knowledge about how critical systems actually function.

As these experienced workers retire, the next generation is less inclined to enter slow-moving, bureaucratic industries. That widening skills gap is forcing utilities and governments to modernize, embedding intelligence directly into infrastructure to offset shrinking workforces. Automation, digital twins, and AI-driven asset management can capture operational knowledge, streamline maintenance, and make infrastructure more resilient. In that sense, the labor crunch isa catalyst that could accelerate long-overdue transformation across some of the most essential systems in the world.

AI and the Infrastructure Revolution

The good news is that this transformation is already underway. AI, no longer confined to software and consumer tech, is reshaping how those working in infrastructure and utilities manage the physical world. Industry operators are beginning to deploy AI to monitor assets, predict failures, and optimize performance at a scale that human teams alone never could. What was once reactive (that is, fixing things only after they broke) is becoming predictive and preventative.

Energy companies like Siemens Energy and National Grid ESO are using machine-learning models to forecast demand and detect grid vulnerabilities before outages occur (random aside — Siemens Energy trucks are ALL over Columbia’s campus, and noticing them is what originally inspired me to write this piece). In water management, firms such as Xylem and Suez have introduced AI-powered leak detection and smart metering systems that save millions of gallons each year. Even transportation networks are adopting predictive maintenance tools that cut downtime and emissions across rail and aviation. Together, these advances represent more than incremental efficiency gains, they mark the digitization of one of the world’s most essential sectors.

Closing thoughts

For investors, this convergence of AI and physical infrastructure should be really, really interesting. These are trillion-dollar sectors that are literally undergoing a technological revaluation! The next great infrastructure buildout (which, as an American, I can only fantasize about) seems like it will be a combination of concrete, steel, software, sensors, and embedded learning algorithms.

To wrap things up nicely, here are some companies of various stages doing pretty cool work in bringing AI and data-driven innovation to the systems that keep the world running.

1. SewerAI

   1. Making underground infrastructure management wayyy better through its AI-powered platform, PIONEER™, which automates sewer inspections and defect detection.

2. GE Vernova

   1. Using AI-powered Autonomous Inspection to automate visual inspections of energy infrastructure, speeding up asset monitoring, improving safety, and optimizing performance.

3. Siemens Energy

   1. Using Senseye Predictive Maintenance and Omnivise Predictive Solutions to enhance asset reliability and performance across power grids and industrial plants.

4. Xylem

   1. Utilizing AI-powered Xylem Vue and smart sensors to automate leak detection, monitor water quality, and optimize distribution networks.

Finally, an observation I had while researching: because their B2B customer base is largely made up of aging baby boomers who may be wary of all the “AI” hype (which I mean, not completely unjustified, especially in critical industries), infrastructure and utility companies often don’t advertise AI as openly as other industries. Instead, they tend to use terms like “predictive maintenance,” “asset optimization,” or “intelligent monitoring,” highlighting practical benefits rather than the technology itself.

ESSAYS

1. Paul Graham’s Essays

   1. For those unfamiliar, Paul Graham is an entrepreneur best known as the co-founder of Y Combinator. My personal favorites are “Why Smart People Have Bad Ideas” and “What the Bubble Got Right.”

2. AI as Normal Technology

   1. You probably also know this one, or, at the very least, are familiar with the people who wrote it. Very close to home ( The Knight First Amendment Institute was founded at Columbia University).

3. Choose Good Quests

4. Beating China isn’t Enough

BOOKS

1. Gabriel García Márquez’s One Hundred Years of Solitude

   1. My favorite book of all time. Full stop. I have read it in both English and Spanish — the Spanish is better, of course, but the English version is remarkably good for a translation. Not directly tech-related in the way that others on this list are, but Márquez’s magical realism stretches the boundaries of reality far beyond what Sora has been able to do ;)

2. Bertrand Russell’s The Impact of Science on Society

   1. One of the most brilliant philosophers of the 20th century (but my personal favorite is Rawls!).

3. Marshall McLuhan’s Understanding Media: The Extensions of Man

   1. I first read this in during my junior year of high school, when I undertook an overly ambitious thesis. Anyway, I was reminded of this book (but mostly the first chapter where the infamous “the medium is the message” statement lies) when I read a16z’s “What is New Media?” blog post. While a16z takes a much more operational and opportunistic stance on the current state of media, the conceptual overlap around legitimacy/narrative/attention felt hard to ignore. I, curious as to whether they were influenced by the writings of McLuhan, Debord, or Baudrillard, reached out and asked (see below, anonymity preserved).

So, the answer is yes, and of course I asked to see what he wrote — here it is:

a16z

Laws of (New) Media

| America | Tech | Opinion | Culture | Charts …

Read more

6 months ago · 148 likes · Andrew McLuhan

MUSIC

I adore music and feel so lucky to have been born in a century where I can listen on demand. For an overview of my taste (it’s all over), please see below my most listened to songs of all time:

“I’m On Fire” is a far-ahead first. As for the others… a mix of classics, moody ambient/electronic, and idk…. R&B and rock?

As for albums I am enjoying currently:

1. open this wall by berlioz

   1. His debut full-length release. So impressive. Personal favorite tracks are “ascension” and “free fall.”

2. Random Access Memories by Daft Punk

   1. The word I would choose to describe this album is “lush.”

NEWSLETTERS + BLOGS

1. Collab Blog (from Collaborative Fund)

   1. One of my favorite fund blogs.

2. Not Boring by Packy McCormick

   1. The name rings true. Packy McCormick makes everything more interesting. A recent favorite of mine was his essay on World Models with General Intuition’s Pim De Witte.

3. a16z’s Charts of the Week

   1. World data in context (not all about AI).

4. Michelle Volz’s Substack

   1. Brilliant. Her and Katherine Boyle have long been role models of mine.

5. NEA’s Insights

   1. Biased ;)

6. Farnam Street’s Newsletter

7. CTVC Newsletter

8. Axios Pro Rata

   1. Not much to say about this one. You probably know it.

PROJECTS

1. Common Voice

   1. A crowdsourcing project started by Mozilla to create a free and open speech corpus. Now the most diverse open voice dataset in the world!

2. The Anthropic Institute

   1. This is a helpful introduction, and out of their four focus areas, the one that interests me the most is AI systems in the wild, led by their societal impacts team.

3. Every Cure

   1. A nonprofit using AI and large-scale data to identify new uses for existing FDA-approved drugs (drug-repurposing). Of great use for Whel.

ETC

1. SpaceX’s Falcon 9 First Stage Landing – ORBCOMM‑2 (Dec 21, 2015)

   1. My father pulled our whole family to his laptop screen to watch it live, and even though I quite young, it cemented a life-long love for outlandish ideas.

2. Anthony Bourdain: Parts Unknown

   1. I grew up watching this while cooking with my father (first in NYC, then Colombia, and most recently, Boston). Bourdain is a masterful storyteller.

3. Hot Side

   1. A creative, humorous, and clever collection of recipes by Max Baroni, former chef at The Four Horsemen (iykyk).

4. Opening notes, Founders Fund’s Annual Meeting (2020)

   1. I believe our future depends powerfully on how well we understand this cosmos in which we float, like a mote of dust in the morning sky…

5. Lost

   1. The best tv series of all time. I will die on this hill.

That’s all for now. I’ll keep updating as time goes on. Thanks as always.

Sokayna, co-founder of Jodoor, at one of their greenhouses, Kenitra. Photo Courtesy of Orange Corners.

Water stress is the most pressing challenge for Morocco, a country dominated by harsh desert and an arid climate. The country has experienced a 20% decline in rainfall over the past 30 years, with per capita water availability projected to drop below the World Bank’s water scarcity threshold of 500 cubic meters per person, per year. This scarcity could reduce Morocco’s GDP by as much as 6.5% and destabilize key economic sectors, particularly agriculture (15% of Moroccan GDP).

Morocco has responded with a top-down realignment of national priorities, blending startup activity with public policy and academic research. Water has emerged as a strategic focus of the country’s industrial policy and infrastructure investment. What aspects of this innovative model are overlooked by Western analysts? How might Morocco’s undertaking offer a glimpse into the future of climate adaptation in water-stressed regions worldwide?

Nearly all countries pursue decarbonization and water policy separately; Morocco, however, has fused them, deploying solar energy to power an increasing share of the country’s desalination plants. These plants are rapidly becoming a primary source of water for coastal regions and large cities like Casablanca. Because of the scalability of this model, the government is continuing to transition to solar for future desalination plants, targeting the southern regions of the country where solar radiation is abundant. Not only does this venture reduce the carbon footprint of water desalination, but it also significantly reduces the energy costs associated with it.

Beyond just desalination, solar power is also integral to enhancing water efficiency in the country’s agricultural sector. Morocco’s Green Generation Plan 2020-2030 specifically aims to promote agricultural production through renewable energy, including solar, which will be used to power seawater desalination for irrigation. Additionally, the country’s Institute for Research for Solar Energy and New Energies (IRESEN) is working to support the deployment of solar power infrastructure for industrial operations at scale.

Morocco’s approach can be summarized as “climate-aligned economic development,” pursuing the goals of environmental resiliency in tandem with economic growth. The government’s $40 billion National Programme for Potable Water Supply and Irrigation (PNAEPI) is emblematic of this shift. Through dam construction, wastewater reuse, desalination expansion, and inter-basin water transfers, Morocco is building physical infrastructure at speed and scale. The country currently operates 17 desalination plants (with more under construction) and plans to increase capacity to 1.7 billion cubic meters per year by 2030. This synchrony signals a rare degree of policy coherence across sectors that are typically siloed.

Morocco’s private sector and academic institutions have also become key sites of innovation. OCP Group, a major global participant in fertilizer markets and the largest exporter of phosphates, is dedicating $13 billion to decarbonize its operations, leveraging renewable energy sources. OCP’s headquarters at the Mohammed VI Polytechnic University (UM6P) anchors a research-industrial network that supports applied innovation in water, energy, and agriculture. Moreover, subsidiaries of OCP, such as InnovX, and OCP’s investment arm, UM6P Ventures, are turning academic research into commercially viable technologies that promote sustainable water management, precision farming, and energy efficiency.

Key Moroccan startups in this space include Green WATECH, a female-founded startup that delivers decentralized, gravity-powered wastewater filtration systems for rural communities using only soil, gravel, and sawdust. Jodoor, another female-founded company, builds modular hydroponic greenhouses that recycle water and support high-yield, low-resource farming. Washminute, another startup providing water-efficient car wash services closed a $600,000 pre-seed round, highlighting the growing entrepreneurial attention resource-smart services are receiving. Prior to receiving this funding, Washminute had grown their monthly revenue by 30% in seven months, and expanded their services to seven locations across Morocco.

Over the past five years, Morocco’s technology venture funding has grown from just $7 million USD in 2019 to $82 million in 2024. While this growth reflects increasing momentum in the country’s startup ecosystem, Morocco’s fundraising levels remain far below those of regional peers like Kenya and Nigeria, which consistently attract several hundred million dollars annually.

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The lack of capital invested in Morocco might not necessarily be because of a shortage of ambition or direction in the country’s market.

One explanation for the relative lack of investment is the pervasive pro-English-speaking bias that exists within Western capital markets. As a French-speaking country, Morocco faces a language barrier that can hinder its appeal to investors primarily from the US and UK, who operate in English. This linguistic divide makes it more challenging for Moroccan startups and businesses to access the same level of foreign capital as their English-speaking counterparts. In addition, Morocco’s economy has been historically oriented around rent-based activities and low-risk sectors like phosphate mining and tourism, diverging heavily from the risk that characterizes much of the startup industry.

Furthermore, African tech VC is heavily concentrated in certain sectors, with fintech far outpacing all others. The chart below illustrates this sectoral breakdown, highlighting both the dominance of fintech and relative scale of other emerging areas, such as e/m/s commerce, and enterprise.

As climate risk intensifies and water scarcity becomes a driver of global economic competitiveness, Morocco’s approach has increasing relevance. Western analysts and investors can take note of Morocco’s unique approach to blending state and private sources of capital to enjoin critical energy and security resources. For emerging economies in Africa and beyond, the blueprint laid out by Morocco challenges assumptions about technology curves, political economy, and capital allocation. While capital flows have yet to fully catch up, Morocco offers a leading example in what the next era of climate-aligned economic development might look like.

Nvidia CEO Jensen Huang announcing the Earth-2 Initiative in November 2021.

Dr. Karthik Kashinath is a principal engineer and scientist in HPC+AI in Developer Technologies at Nvidia. He co-leads the Nvidia Earth-2 Initiative, a digital model of the earth that combines AI, physical simulations and computer graphics technologies to simulate and visualize weather and climate predictions at a global scale. His work uses machine learning to emulate high-resolution weather and climate data, build digital twins, and provide actionable climate information. Dr. Karthik Kashinath uses machine learning to accelerate scientific discovery in the complex chaotic systems of turbulence, weather, and climate science. He received his bachelor’s degree from the Indian Institute of Technology Madras, his master’s from Stanford University, and his Ph.D. from the University of Cambridge. This interview was conducted as part of Gadfly Magazine’s interviews series. Find the original article here.

VERONICA: Could you give us some insight on what you’re working on these days related to the Earth-2 initiative?

Dr. Karthik Kashinath: Let me start with what we’re working on these days. This project was launched about three years ago in November 2021 by our CEO, Jensen Huang. He saw an opportunity for AI to have an impact on how we can better address climate change—that includes trying to better predict what might happen with climate change, extreme weather, impacts on agriculture, energy, rainfall, on a whole range of different sectors that impact humans, our societies, our industries, our ecosystems, all of it. That was the opportunity that he saw, and he wanted to make a big push on using technology and AI in particular, to help address this grand challenge that we face. There are many ways in which we could do that, and we’ve started to explore over the last few years some of the most challenging ways in which we could make a big impact.

Predicting climate change is, as you can imagine, incredibly challenging. It involves trying to predict what the physical earth system does. What are the oceans doing? What’s the atmosphere doing? But it also includes how humans are interacting with this system. Obviously, humans are responsible for anthropogenic climate change. And by that I mean we’re pumping in a lot of carbon dioxide and greenhouse gasses into the atmosphere, and we know that’s responsible for global warming and climate change. How we use our land impacts climate change, the ways in which we interact with forests, with ecosystems, with plants, with animals, with water resources, all of that impacts climate change as well. It involves humans and our societies, our industries, our emissions, all of that coupled together. Trying to predict what might happen five years from now, or ten years from now, involves trying to understand how all these different systems interact with each other, and that’s a problem that’s well suited for AI. AI is incredibly powerful at learning from very complex data sets, and being able to see patterns in that data and use those patterns to come up with forecasts or predictions of what might happen. AI is a tool that can be used to digest the vast amounts of data that we have about the Earth system and then synthesize that into patterns of change and patterns of evolution and use those patterns to predict what would happen.

This is typically done using numerical simulations. We scientists have been studying Earth’s climate for a century now, and we’ve had climate models for probably about 70 years now. Climate models are typically the equations of physics that describe what’s happening in the atmosphere, in the oceans, over land, and our assumption of the emission pathways for how humans are going to emit carbon dioxide and greenhouse gasses into the atmosphere over time Then you feed all that data into these climate models, and you ask, if humans were to do this to the planet, how would the planet respond? The climate model then goes into its computational system, the supercomputer, and in response to our input of how much carbon dioxide we put in, all the different changes that we can expect to see on the planet.

This is a very slow, expensive, and time-consuming process, and it inherently requires the use of computers that are very expensive and energy hungry, and therefore are perhaps not the best tool for us to be predicting climate change. AI is very well suited for this problem, because there are vast amounts of climate data available from the last century or so, and we also have lots of physical equations that describe what’s happening in the Earth system. What we’re doing right now in this Earth-2 project at Nvidia, is trying to adapt the best AI tools that we have to address climate change. We are providing the best data that we have on climate change into these AI models, and teaching them and training them how to predict what will happen in the future.

We’re finding that AI models are quite good at predicting the probability that we might have more wildfires in California or in Oregon or elsewhere in the future. That’s one example, and another is extreme hurricanes. Obviously, hurricanes are a big challenge for us. We’ve seen, even in the last couple of months, the kind of impacts from these big hurricanes that battle through the Gulf of Mexico, the southern US, and occasionally the eastern seaboard as well. These hurricanes are getting more intense and more frequent because of climate change, and that’s something that is not that easy to predict. We’re finding that AI models are incredibly powerful at learning how to predict hurricanes, and predict the trajectories that they would take. In fact, at this point, AI models are even better than some of the numerical models, the scientific models that we’ve used for the last century for these problems. That’s another example where AI is really making an impact. That’s two examples of the sorts of things we’re doing with Earth-2 right now.

Intuitively, humans seem to be less easily modeled than other natural forces, which we think of in very deterministic terms. Factors like policy and technological progress seem like massive complications for the model. How does Earth-2 account for these unpredictable elements?

Like I said, I think there are an infinite number of ways in which humans interact with the earth. We’re changing the atmosphere by emitting a range of greenhouse gasses and lots of pollution and other particles. We’re putting a ton of material into the ground in terms of landfill. We’re changing the forests and the ways in which we use land. We’re changing the ecosystems of the Earth, but we’re also changing the oceans. We’re interacting with the oceans in complicated ways, including, unfortunately, polluting them. It’s hard to incorporate every single dimension, every single aspect in which humans interact with the planet explicitly, but we try to categorize these into major categories of interaction.

One example is how we account for the emissions of greenhouse gasses into these models. We quantify how many gigatons of carbon dioxide we are putting into the atmosphere. That’s one metric that goes into these models. Another is land use. The way in which we use our land has a very big impact on climate change. Are we building bigger cities? Are these cities made of concrete and steel? How is that different from having forests or grasslands or mountains? We have ways to feed in information about the land surface and how that is impacting the planet with the ocean.

In Earth-2, we haven’t gone into a high degree of sophistication with the ocean just yet. But I imagine that in the future, we’re going to have more specific, more explicit characterizations of how humans are interacting with the ocean as well. I think one thing to keep in mind is that climate change, at least at first order, can be distilled into the amount of CO2 that we’re putting into the atmosphere. That’s by far the biggest lever that we have on climate change. If you look at, for example, David Attenborough’s inspirational speech that he gave at COP 28 last year, you’ll see that he talks about all these different nations that are trying to make a bet on climate change and address it in different ways. He gives this really powerful anecdote of how the one number that really is the bottom line on climate change is the amount of CO2 in the atmosphere. So if you think the best way in which we can try to help address climate change, the best way for us, as humans, is to reduce the amount of carbon dioxide that we put into the atmosphere. That’s the most important factor, and that’s accounted for in our models. Of course, there are secondary factors. Those include how we use the land, how we impact sea ice, how we impact the oceans…and some of those are also incorporated.

I’ve seen Earth-2 be called a digital twin of the earth that works on a kilometer-by-kilometer scale. How do you know if you’re making the right simplifications in this model?

This gets down to how scientists model the natural world. We’ve done this for centuries. Isaac Newton models how gravity behaves, and it works incredibly well. And it’s worked well for centuries. Scientists came along in the early 20th century and revisited Newton’s laws and came up with more sophisticated ways of describing how the universe works, and that’s quantum mechanics. Some of the assumptions that Newton made were questioned, and then we came up with better ways of describing what’s happening in the universe. Similarly with the ways in which we model the Earth system, we make these simplifying assumptions, but then we question those assumptions by running different experiments. For example, with the oceans, we have a simplistic way in which we describe the oceans in these models, but we can adjust our assumptions because they’re basically experiments that you can run on a computer.

You can change these assumptions—you could say, I’m assuming that the ocean can be characterized by a handful of different metrics, like temperature, salinity and depth. You have this relatively simple set of metrics that describes what the ocean is like in the model, but you can change that description, and you could have a different set of metrics, and you could change one metric at a time and see how important it is in the model. That gives you a sense of how you might remodel things to see how accurate your model is. The second way is to compare it to what we’ve seen in the past. We can run experiments of the past. We could say, let’s take the same model that we’ve built for the earth in 2024, but let’s run an experiment where we try to look at what happened to the earth in the year 2000 or in 1950. You can go back in time for what the model predicts for, say, the year 2001. Now, the great thing is, because 2001 happened already, we know what the answer is. So we know what the data says, and we can go back and see if the model predicts what we saw actually happened. You can test the accuracy of the model by looking at historical data and looking at whether the model was able to predict something about the past that we already know. That’s one way of confirming, or at least building confidence in, the assumptions that you bake into the model.

Let’s say that hypothetically, the model is wrong, and a policy-maker or government actor makes a decision based on that and it results in an avoidable disaster? It seems like the policy and legal framework present hasn’t necessarily caught up to how fast the tech industry is moving. So who is liable for this destruction? Is there an ethical framework holding the model responsible?

That’s a great question, and a really complicated one. I would say this question is not fully resolved yet, to your point about how quickly these things are changing. We haven’t caught up to exactly how we address the implications or the impacts of the use of this technology. But that said, I’ll go back to the example of what we’ve been doing in the past, which is using climate models. We know that these climate models have imperfections, and they’re not 100% accurate or deterministic. They can give you different results depending on how you incorporate different sources of uncertainty into the model. You have a range of possibilities, a range of futures, and you have the most likely future that we expect. But you also have the worst case scenario and the best case scenario. You don’t get one answer, you get a range of answers. The way in which I would say we’re trying to address the impact of AI and the use of AI in this space is to also incorporate these uncertainties into the AI. We’re presenting not just one possible prediction of the AI, but we’re trying to see the range of possibilities that you can have. I think that’s important, because as a policy maker, you need to know not just one outcome, but the range of possible outcomes.

In coming to your second part of this question about liability, that’s a challenging one, because of the various sources of uncertainty that go into the AI. It’s a model that’s being developed by engineers and scientists, using data that we’ve built up in an archive of data over a century. As with every model, it assumes simplifications about the earth. The stakeholders involved are the private sector, because the private sector is developing these models, the public sector, because governments contribute the data into these models, and the global scientific community, because we’re building on the vast amount of knowledge that exists in the literature. It’s really not just a single entity, like one company or one person or one organization that contributes to the development of these models. If we were to make a policy based on the predictions of the model, but it turns out that the model was incorrect, there isn’t one person you can point a finger at and say, it’s because of that entity that this whole thing went wrong. Policymakers are trying to use this information instead to say, here’s something that the model predicts that we can rely on, but we also know that the model is giving us a range of uncertainty. We have to be aware that this answer could be wrong, and there’s a range of possible ways in which it could go wrong. I think they’re trying to factor the liabilities associated with that prediction into insurance and various other types of financial instruments. I might take a step back and ask the philosophical question of, should we act and take action based on an imperfect answer, or should we be afraid and wait for the perfect answer that might never happen? We might never have a perfect answer because it’s such a complicated problem. The reason I say wait and bring that time dimension into this is because it’s a very urgent problem. The consequences of not acting because we don’t have a perfect answer are probably worse than acting on an imperfect one.

Have we seen this sort of urgent need for collaboration between the private sector, public sector, and scientific community before? I am reminded of the rush to create a COVID-19 vaccine during the pandemic.

I think the COVID-19 pandemic is a really good example of how humans have great potential to respond under duress and under great amounts of stress and urgency. In some ways, it’s also reassuring and inspiring that we can respond to such global challenges under tight deadlines and under severe stress. The COVID-19 pandemic took hundreds of thousands of lives and impacted every corner of the planet, and we responded with insufficient information and incorrect data, without perfect answers to every question. We were able to bring things to a point where we felt like we had control over the pandemic, and we were able to regulate things. It’s very encouraging that within a short span of about a year or so, we could respond to such a global challenge in a very effective way. I think we have lots of lessons to learn about how we responded globally to the pandemic. What could we learn from that that could be useful in how we respond to climate change?

There was no shortage of misinformation during the COVID-19 pandemic. Is there anything you would take away from the public’s response to the pandemic that would inform how the Earth-2 initiative is presented to the greater public?

How do we get the public engaged in Earth-2? Humans build confidence in these technologies and these systems when they can interact with them and have favorable interactions. Otherwise, it becomes a source of fear and something that we get worried about because we don’t know how it works. We don’t know what’s under the hood, and we’re not all capable or equipped to understand the inner workings of these things because that’s just not our expertise. It’s very important that we build trust over time with how these systems operate, and how the public views these technologies and systems so that we can start to use them. The way we’re trying to do that right now is by having systems that humans can interact with. One of the goals of Earth-2 is to make it an interactive digital twin, by which I mean that, you could go in, for example, and say, I want to see what the earth is going to do if I have this particular scenario. I want to see what’s going to happen in New York City and in this neighborhood of New York City, and what’s going to happen to Columbia University? Is it going to be flooded if there was a big hurricane that came along? Being able to ask these questions to the model, these what-if questions, and get answers, and visualize those answers, to be able to see it on a globe, to be able to zoom into specific parts of the planet, maybe the neighborhood that you live in, or the neighborhood that you were raised in, in some other part of the country, that’s the way in which we’re hoping to be able to get people to engage with with the system. That’s one way in which we get people to build confidence.

Of course, it’s not possible for everyone to be able to get into the mechanics of how this model works, and what data goes into it. That’s the scientists that are evaluating the model. We write up everything that we do and try to publish it in scientific journals, which means that it gets peer reviewed, so there’s other experts in the fields that question the ways in which we’re developing the model, the kinds of data that we’re using, the methods that we’re using. So it’s all being verified and validated by lots of experts, and that’s how the model itself is. We’re building confidence through the model by having it verified and validated by experts outside Nvidia.

Another factor is to get companies and governments, the public and private sector, to use the digital twin, to use the model. One example is that we’re working with the US government, with the Department of Energy, with the National Oceanic and Atmospheric Administration, NOAA, with the National Weather Service, and they are using Earth-2 to make predictions for the weather, to make predictions for the climate, to try to predict wildfires or hurricanes, or use it for wind energy or solar power. There are lots of other public and private sector entities that are using Earth-2, and as they use it for their businesses and for their work. They build confidence in it, they write testimonials and they say, we’ve used Earth-2, and it’s really good at predicting hurricanes. Or we’ve used Earth-2, and we’ve developed this wind power plant that is now producing X megawatts of power. And we really like the model, because it does a really great job of telling us when there’s going to be high wind or low wind. When people give feedback like Earth-2 is really good at ABC and Earth-2 is not that great at XYZ, then we get a better sense of where it can be trusted. I think a really good parallel is Chat GPT. By using chat GPT, all of us have a sense of what it is good at and what it is not good at. For myself, I think Chat GPT is a really good tool if I want to learn something quickly about something that I don’t know, or if I want to plan a trip. But I tend not to use Chat GPT to learn about the latest advances in a particular scientific discipline, because I don’t think it has the expertise that a nature or science paper would have. I’d rather go read about a scientific breakthrough in a research article like a journal like Nature or Science, than try to ask Chat GPT to teach me about it. There are different pros and cons for how you might use a tool, and that’s the same sort of thing that I think we could develop with Earth-2 to do with the public.

Fighting climate change is sometimes discussed as a sum of small actions. This seems especially true in policy efforts to limit individuals’ daily carbon footprint. Earth-2 seems to avoid this level of detail. How do you see Earth-2 informing views on sustainability at the individual level?

There’s definitely some education that needs to happen across the board on the relationships between individual climate action and these higher level metrics that go into the biggest influences on climate change. That’s a gap that Earth-2 still currently does not address, and it’s not intended to address it. The complexity of every single individual’s climate action, 8 billion people, inputting all of that data into a model, and having a model be able to understand and synthesize that information and percolate up into the model’s mechanics is incredibly complicated and challenging. It’s not something that we have the capability to do right now, I would say. But there is room for us to understand how our individual climate action contributes to regional scale or national scale or global scale. For example, if we all as individuals start to become conscious of our carbon footprint, we can start to put in numbers that really estimate how much our carbon footprint is per year. You can imagine, if we all were able to do that, then it would add up to the carbon footprint of the United States or the globe—that’s the sort of information that we can put into a model like Earth-2. You can see the connection between individual climate action around something like carbon footprint, how that would feed into the model at a higher level. Something like composting, for example, that’s a lot more complicated. First of all, right now our descriptions of the land and how the land impacts the climate are relatively simplistic. In these models, we’ve got high level categories of this amount of square miles or thousands of square miles of forest, and this is the number of square miles of cities. The descriptions of the land are at that level of granularity. But if you want to get into really fine scale details, like how much compost is on the land, or how much landfill, or how much plastic, or the temperature of this forest, and the amount of water that’s there under the soil—that’s a different degree of granularity that currently doesn’t go into the model. At the same time, composting is very important in how it impacts the land and how it impacts the quality of the ecosystem, and that feeds back into how the ecosystems are able to capture more carbon from the atmosphere and clean up the air, and improve our air quality, etc. There is all this feedback that composting has benefits, but it’ll probably take a little while before we can figure out good ways of incorporating that degree of feedback into the model.

Do you see the future of climate science, climate change modeling and prediction as inextricably intertwined with AI?

100%. I think we’re headed in a direction where AI will be an integral part of pretty much every piece of technology that we have, especially in climate change, and climate modeling. I think we’re going to be predicting the next vaccines and the next drugs that we develop and the next medicines that we develop using AI, along with human knowledge and scientists and lab experiments. We’re going to be developing new materials, possibly materials that are able to absorb carbon dioxide from the atmosphere and help mitigate climate change using AI. AI is already making a huge impact on material science and developing new materials for batteries or for solar panels or extremely strong materials that are lightweight for building ships or airplanes. In my mind, there’s no doubt that AI is going to be inextricably integrated with pretty much any scientific endeavor in the future.

There seems to be a lot of anti-AI sentiment online. How do you, as an engineer of AI, respond to this?

Just to be clear, it’s not a miracle. I mean, it is sort of a miracle, but it’s also not. It’s not a silver bullet. In every regard, there are certainly risks and challenges, but I’m also confident and hopeful that we’re going to address those risks and challenges. It’s not that it’s completely free of any problems and AI is going to solve everything on the planet. That’s not the case, but I do think that we have the capacity to address the risks associated with AI, to address the challenges associated with AI. The best minds on the planet are working on it, and I’m confident that we’ll find ways to figure out how to use AI well and what to be careful about, and where we should double check things that are developed by AI or predicted by AI, what they call guard rails. Ways in which we can make sure that the AI doesn’t totally go off the rails and predict something that is berserk. I think there are definitely ways in which humans can interact with AI to make it even more useful and powerful than it is today.