Within the next year, thousands of young millionaires (25–40 yo) will be minted in essentially one zip code, collectively holding over $1T in new assets.

In San Francisco, these engineers describe their future lives as “post-economic,” where the question is now what to do once money becomes no object. Today, they’re being hounded by wealth managers, having financial products forced down their throats before they’ve even thought about relationships or kids.

Philanthropies are scrambling to capture this wealth too, generally betting that AI-adjacent programs on safety and truth-seeking will resonate.

But philanthropy as it exists today is built for a donor profile that doesn’t match these people. Valley philanthropy wants to be founder-led, risk-tolerant, and willing to fund unproven institutions. Most orgs outside SF don’t understand this, and the strategies they’re defaulting to are both presumptuous and unscalable.

Post-2026 donors are not trad philanthropists, but also not trad “techies”

I’ll first explain how frontier lab engineers are distinct from techies and why it’s consequential for philanthropic thesis design and partnership strategy.

Techies are like the dude from Cluely–the B2B SaaS company with the thesis “cheat on everything“. Launch videos, X crash outs, usually in their late teens to early twenties from either Waterloo or New Jersey. They embody the general archetypes you’ve seen in the Silicon Valley sitcom, chasing the glory of attending the next TPOT party and DM’ing that Asian girl introducing herself on X.

Though there will still be many techies who give generously, especially those at xAI post-IPO, proportionally there will be fewer of them giving to ambitious philanthropic initiatives compared to the employees at Anthropic or OpenAI.

In contrast to the average techie, Anthropic/OAI engineers come from a much more intellectual and diverse background. They can be PhDs/postdocs, YouTube-viral lecturers, seasoned ML engineers from competitors like DeepMind, all the way to Effective Altruism (EA) forum posters/lurkers and ex-Leverage Research affiliates. These people are therefore not just older than the stereotypical techies, but tend to be more philosophical, “worldly,” and perhaps have already given or have friends who have given to EA-adjacent charities like Shrimp Welfare Project1 or new initiatives like Nan Ransohoff’s Intercept Fund.

At Anthropic in particular, giving is woven into the company thesis and culture. Co-founders Daniela and Dario Amodei have been involved in EA for years, with Daniela being married to Holden Karnofsky, former CEO of GiveWell and Coefficient Giving (fka Open Philanthropy). All seven co-founders of Anthropic have committed to giving at least 80% of their wealth, setting a strong culture and tone around giving from Day 0.

Lastly, Anthropic employees are encouraged to give even further through the company donation match program (up to 25% of their equity matched to any charity) which also acts as a selection filter for employees who already want to give. They also frequently internally discuss non-profits they can give to, and regularly host non-profits to present their work, help host informal happy hours, and more.

Post-2026 donors, especially those that come out of the frontier labs, are therefore emerging with a pre-trained world model of philanthropic orgs and their various levels of effectiveness.

This matters for many reasons, but primarily because it compresses the traditional donor conversion cycle. Most organizations spend months to years cultivating donors who arrive with vague ideas and limited knowledge of the giving landscape. Post-2026 donors, on the other hand, are likely to show up with opinions on cost-effectiveness methodology, existing relationships with grantmakers, and a working mental model of which cause areas have been (in)effective in the past.

Thus, for philanthropies seeking gifts, the challenge is no longer in convincing donors to give, but in conveying why your specific programs absorb capital better than the alternatives they already know about.

Roughly how many millionaires will be minted at each level of wealth?

Looking closely, the wealth follows a relatively simple power law.

Between SpaceX (~22,000), OpenAI (~8,000) and Anthropic (~5,000), assume there are ~35,000 employees. Let’s then take the ~$2.2T SpaceX IPO valuation and Anthropic’s recent $965M post-money Series H valuation (+154% in 3mo) as a benchmark to assume a generous $1.5T IPO valuation for both OAI and Anthropic. With that valuation, around 10%, or ~4,000 individuals between the three companies, will become decamillionaires ($10 million or more in equity) or above.

Decamillionaires will be the most populous, with roughly 3,000 individuals holding ~$50B in total assets. These individuals will be the “ordinary” Members of Technical Staff and managers across many levels and seniority.

The centi-millionaire bracket drops the total number by roughly an order of magnitude at around 500 total individuals (~1% of employees) holding another ~$100B in assets. This group leans towards very early or senior hires. The headcount here is much less sensitive to the valuation, as their holdings sit far enough above the $100M threshold that small valuation swings don’t change who qualifies.

Finally, at the very top will be a few dozen individuals who become overnight billionaires. Combining the founders and a few dozen first-employee and executives, the total ~40 individuals will hold around $850B in wealth.

The majority of this wealth will be of course concentrated amongst the founders, with the 7 co-founders of Anthropic expected to hold ~15% of all shares, putting their combined net-worth at around $225 billion.

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So the big question remains: where will all this wealth go?

In a “business as usual world”, donations will mostly go to two places

The first are Donor Advised Funds (DAFs), a tax relief instrument that gives individuals immediate tax relief. DAFs allow a donor to put money inside the account (fund), and advise that money to be given out to any charity they want.

DAFs are essentially financial products. Just like 401ks and HSAs. They’re offered by financial institutions like Vanguard and Merrill Lynch, with Fidelity Charitable (DAF) being the largest nonprofit by donations, receiving $16B in 2024.

Over the past ~decade, DAFs have exploded into a full-on industry. As of 2024, DAFs collectively hold over $326 billion in assets–up 37% in a single year–with some expecting the total amount to surpass $450 billion after a late-2025 surge in new accounts. I think it will hit >$700B by EoY 2026.

As you can guess by the numbers, DAFs today act more as an instrument to carry money rather than deploy it.

In an ideal world, donors receive a tax break by putting money in the DAF, then give that money away to nonprofits through the DAF. In reality, there is no tax incentive for the parked money to go anywhere–most of it just sits there, as it’s already achieved its tax-deducting purpose when it was deposited into the account.2

DAFs are one of the most established and easiest places to park money with immediate tax breaks, with most frontier lab employees having already allocated large amounts of capital there. I believe a non-significant number of frontier lab employees will either indefinitely park their capital in their DAFs, or give it ~immediately to their preferred (perhaps EA-adjacent) initiatives. I estimate >25% of all philanthropic capital from the frontier labs will be allocated to DAFs.

The second are the Effective Altruism allocators (Longview, Coefficient Giving), where money goes to be deployed with specific, often pre-scoped, theses. Coefficient Giving is one of the most established organizations in this space, now operating like a traditional grantmaker. It houses program directors who lead “departments” focused on specific themes, from Farm Animal Welfare to Global Health and Wellbeing funding LMIC interventions. Some of their work, especially around Farm Animal Welfare, is strongly associated with EA philosophies and is by far one of the most influential initiatives to emerge from the movement, at times exceeding the visibility of its parent organization.

EA allocators have been enormously successful and the world is better off because of it. But the same philosophies that have made them so successful may limit the variety of new programs they may create in the future. DALY/QALY calculations are effective, but they will not rationalize spending $50M on precision psychiatry tools to predict suicidality, or allocating $100M for projects to delay menopause by one year.

These problems are just as worthy as preventing lead exposure in developing countries or safeguarding our world against global catastrophic risks. But they generally fall outside the theses of EA allocators. This is not a bad thing, since philanthropies that understand these dynamics will allow them to sufficiently differentiate from being perceived as “just another org on the AI Safety bandwagon.”

But given the decade-plus institutional standing of EA allocators in the Valley, I believe up to 25% of all philanthropic capital will be plausibly allocated to these orgs. Combined with DAFs, up to 50% of all philanthropic capital may be allocated within the next year.

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How philanthropies can receive gifts effectively, and where it can go

Most foundations today are working backwards. They’re projecting into the post-AGI, post-economic era, identifying 3-5x AI-adjacent program themes, designing funds around them, and hoping the new donors’ interests happen to land there.

This is both presumptuous and unscalable. Each level of wealth needs a fundamentally different engagement model, and the causes fundable to each vary dramatically based on cash volume alone. I therefore break down the engagement strategies for each level and the causes they can be suitable for below:

1. “High touch” philanthropy [Billionaires]

The ~40 individuals at this level (founders, first employees, executives) are unlike most mega-donors of the past, and given the sheer size of their minimum check sizes, they require a tailored approach rather than a scaled one.

Pre-2026 billionaire philanthropists were typically older financiers who arrived at giving through self-actualization or financial obligation later in life. Frontier lab founders have been steeped in EA-adjacent and many other giving frameworks since before they were wealthy, making them far more engaged and opinionated about where their money goes.

The standard philanthropic advisory model of extensive due diligence, curated shortlists of causes, and quarterly check-ins will underwhelm them. Instead of being engaged as customers to sell a product to, they will want to feel like co-developers of a shared mission.

Foundations should therefore invite them into the thesis-building process itself, walking through the “idea maze” for a cause area the way a Program Officer would internally. That requires partnership leads who are either domain experts in the donor’s field or technical enough to learn fast, and who have deep context on what adjacent efforts have already been funded and where they succeeded or failed across EA and broader SF philanthropy.

The harder problem, though, is absorptive capacity. Most cause areas like biosecurity or AI governance simply are not talent-dense enough to have individual projects that can hold $100M, let alone $1B. “Microgrants” could work at the ~$5M scale x20, but those are just normal grants at that point. The real opportunity at this level is endowing entirely new institutions: research institutes (e.g. Arc or Buck Institute), X-Lab-like orgs, even larger FROs.

2. “Venture” philanthropy [Centimillionaires]

Given there will be between 300-600 centimillionaires, the individual bespoke advising approach will not scale and even the classic multi-family office model may fail to capture a significant amount of wealth. I therefore propose two scalable approaches as examples:

• Philanthropic “angel syndicates”

Angel syndicates are a fun, proven vehicle that gives passive investor-types startup exposure while making members feel like “insiders” with “FOMO” to act quickly. These syndicates are often just WhatsApp group chats with 10-100 people, moderated with rules around chat activity, minimum annual investment amount, and value-add to the syndicate and ultimately the startups.

Adapting this model for philanthropic startups/initiatives is quite simple in theory. From standing up new companies like an “AngelList for Philanthropy” all the way to individually-led syndicates like future.bio, it’s likely that mini-groups have already been formed organically through CivIV groupchats and social circles.

Charity Entrepreneurship’s AIM program hosts Funding Circles for example, which operate in a very similar way. In an ideal world where this is executed correctly, the syndicate members, each worth $100M+, can bring in or be presented “deals” for philanthropic initiatives, just like venture syndicates. The initiatives, led by ambitious founders, then can pitch for single digit millions all the way to nine-figure-checks for mega-projects.

• Philanthropy Studios

Venture studios like Flagship Pioneering to classic “Entrepreneur in Residence” models are established ways for private investment funds to make outsized bets with individuals or internal teams. These studios often have institutional funding that allows for hypothesis testing and de-risking some experiments, with deep connections and the stamp of approval to help raise subsequent venture rounds.

This studio model has been attempted at some level in philanthropy, but the success cases are not very visible to most. The Audacious Project by TED is probably one of the best examples of successful philanthropic studios. Simply stated, Audacious Project identifies a select group of ambitious nonprofit projects annually and matches them with their large coalition of philanthropists for funding and public visibility through the larger TED network. In the past, Audacious Project has supported many effective philanthropic projects from the Arc Institute and GiveDirectly, all the way to METR and COVID-19 Fast Grants.

It will only take between 3-10x post-2026 centimillionaires to anchor one new ~$100M studio, with a larger network of donors readily accessible through word of mouth for crowdfunding. Effectively operating these studios is difficult, and will require agentic and technical capital allocators. But the counterfactual impact is enormous–even just connecting 1-2x Arc Institute or GiveDirectly-level orgs to $100M+ can create new scientific fields, create dozens of new companies, all while making philanthropy high-status. It’s a bet worth taking.

3. “The Everything Store” for Philanthropy [Decamillionaires]

Given there will be 3,000-5,000 decamillionaires to be printed, effective methods at this level require true scale. There are inevitably many ways to approach this, so I will propose three examples based on established tech-adjacent ideas.

• The Everything Store model

Given the individual check sizes will be at the $0.5-50M range, “high touch” approaches will no longer be scalable. Thus, frictionless methods with significant optionality baked into the “giving UI/UX” must be developed.

Amazon created a near-limitless products marketplace–an Everything Store that’s a single open platform for pre-vetted products. We can plausibly do this for philanthropic ideas. Theoretically, a single open platform of high-quality ideas, with strong recommendation algorithms, can be created with top tier UI/UX spending and word of mouth.

I imagine this as Amazon x Giving What We Can, an incredible org that has created a large movement with thousands of individuals donating 10% or more of their salary/wealth towards effective causes. This can even go a step further via peer recommendation engines and transparency on who gave to what, similar to existing platforms like Manifund, grantmaking.ai, and Catalyze R&D funding platform.

• One thousand true fans

In 2008, the great Kevin Kelly proposed that the internet would create a new economy that democratizes wealth by letting individual “creators” earn a living from just 1,000 true fans. These fans would be people willing to spend ~$100/year to support someone else. If you have 1,000 people giving you $100, that’s $100k, a liveable wage at the time in Silicon Valley, probably.

In the coming years, we may see a similar trend within the philanthropy economy where individual “creators” can be supported through multiple large grants. Nadia Asparouhova is one great example of researchers who have long been supported through gifts from wealthy individuals, all the way to nonprofit orgs like Basis that conduct deeply technical math & AI research. While only a few dozen individuals give this way, it is indeed very Valley-coded with significant potential.

Even if finding one thousand true fans (aka donors) is too difficult, one hundred may be feasible, especially as more Everything Store-like platforms evolve. It’s essentially “democratized philanthropy”, similar to how venture capital is becoming democratized today. In October 2025, Siren Biotechnology, a cancer gene therapy startup in SF, raised over $4M in an unconventional crowd-sourced funding round on Wefunder. It was one of the largest in the platform’s history, and showed how large the non-accredited investor appetite was to engage in relatively niche and technical products like glioblastoma AAV gene therapy companies.

In this next wave of philanthropy, I believe we can see hundreds to thousands of philanthropic donors giving ambitiously and visibly to cool efforts like this. But this will all quickly collapse as a supply-side problem.

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Talent is the single greatest bottleneck and differentiator for philanthropies

There will be over $1T in newly fungible assets. Even if just 25% of that is liquidated and redirected towards philanthropy, there are simply not enough legible philanthropy-shaped problems today to absorb $250B. If ~50% of philanthropic capital will be heavily allocated to DAFs and EA allocators, whether the remaining 50% goes to new initiatives or not depends almost entirely on whether the right people exist to identify, scope, and steward those projects.

Talent is the moat. Finding great capital allocators, founders, and “General Managers” is the crux of the next 1-2 years. These individuals need to have the technical depth to make sense of the increasingly complex world while having their distinct “taste” in what should be done.

Abhishaike Mahajan of OwlPosting is one recent example of a great capital allocator candidate: an ML x Bio engineer turned allocator, now deploying capital at OpenAI Foundation, with the domain fluency to evaluate what he funds. Another example would be Cate Hall, previously the CEO of Astera Institute, one of SF’s largest foundations that funded several ambitious philanthropic founders.

The problem is, there are perhaps five to ten other people like this visible today, and we will need dozens to hundreds within the next one to two years.

Nan Ransohoff proposed several ideas to address this problem in her recent piece, “The Third wave of American Philanthropy”. Some of these ideas include improving the legibility of philanthropy-shaped problems, paying philanthropic capital allocators well, starting philanthropic VCs, and more.

Indeed, programs like Renaissance Philanthropy’s “Big if True Science” accelerators have been a powerful on-ramp for well-scoped, eight-figure ideas ready to become philanthropic initiatives. Mentored by renowned DARPA Program Directors and philanthropic advisors, the program has nurtured dozens of potential GMs, many of whom will take ARPA Program Manager positions, have received millions in follow-on non-dilutive funding, or now lead teams at frontier labs. We need even more of these programs, perhaps incubated by well-resourced orgs like Open Philanthropy or Coefficient Giving.

Whether it be to find philanthropic founders, funders, create incubators or studios, Silicon Valley’s 1-2 year trajectory will be shaped by effective philanthropic matchmakers who can match ambitious founders to funders.

I call this “bending entropy”. It’s incredibly difficult, because this person needs to have the technical depth in one or more completely different subjects (e.g. biology & ML) to vet projects, “taste” for founders, deep context on trad & SF philanthropy, and the tacit knowledge on what’s been tried by whom and why some projects failed. I believe more philanthropies should identify these matchmakers who can effectively become “nodes” for the Silicon Valley for Public Goods.

But in the end, I’m afraid of philanthropies going up against each other. Today, there are dozens of philanthropies, wealth advisors, and salespeople running around SF to capture the emerging wealth. Like VCs, they’re competing–so they might share some information, discuss some donors, but can’t share too much!

There is no playbook for such dynamic and unprecedented times, but we know how Silicon Valley has flourished to this point. We have a choice to collectively obsess over the founders and educate them. We have a choice to guide emerging funders towards ambitious, transformational programs beyond what’s been funded in the past.

If we get this right, we can all flourish into an abundant, post-AGI world. But the window is closing.

Thank you to Alex Kesin, Oscar Sykes, Zoe Hoare, Abhishaike Mahajan, Seyi Oluwasanmi, Prapthi Agarwala, Rhys Lindmark, Niko McCarty & Christian Larsen for thoughts and comments.

I’ve spent the better part of eighteen months fundraising for different impact philanthropy initiatives. First around new animal models for complex disease, then for chronic pain. I’ve tried to unload as much tacit knowledge as I can, albeit mostly in person, over drinks.

Due to popular demand, I’ll expand on last year’s initial list of unintuitive things, and once again, distill ten more things I’ve learned about philanthropic fundraising. I hope this can be a resource for similarly ambitious, technical, and expensive philanthropic initiatives of the future :)

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1. You can speed-run philanthropic fundraising at the seed stage

In this world today, there are likely <10 people/foundations who will:

• give you 30min for a meeting

• align with your mission/approach

• write the desired checksize

• AND match your desired timeline

If you do it well, it will only take 3wks-3mo to get to the 80-90% saturation point of all funders interested in your area. At that point, you can make a judgement call for whether or not there is appetite for your thing right now. There’s always a chance you’ll meet the last 10-20% of people in a few months/years, but do you really wanna be around for that? I generally think no.

3wks-3mo is enough time to check the temperature of the room for your work. If there isn’t great feedback, it’s okay to move on to the next thing.

2. Philanthropy doesn’t move at the speed of trust

Trust is of course the first step, but there’s much more that comes after. I think a lot of us have forgotten just how difficult it is to make people give away their money. In the private sector, we’ve artificially engineered ways to make people give away their money faster with FOMO, term sheets, valuations, chart patterns and Nancy Pelosi investment trackers.

We’ve gotten used to this cadence, which is why most private sector founders who move into philanthropy get vicious whiplash when they see the slower pace of philanthropic giving. In my experience, the most frustrating bottleneck in philanthropic fundraising is when the donor is not “ready”. Not because there’s not enough trust, but for simple reasons like they have not:

• hired their Program Officer

• set up their bank account

• finished deciding their giving strategy

• had their bi-annual/ennial board meeting

In these cases, philanthropy does not move at the speed of trust, but simply unavoidable org logistics. So don’t beat yourself up too much when these things get in the way of your success.

3. You can “buy trust”, sort of

Organisations like Science Philanthropy Alliance and Bridgespan work closely with philanthropists to help give away their money. They are like a consultant or advisor that helps donors think through what and where to give.

On the other side, they also work with prospective grantees and fundraisers to introduce them to these philanthropists looking to give away their wealth, for a fee. I don’t know these orgs personally, but they are relatively easy to get in touch with, as well as many other “philanthropic advisors” and “connectors” who you can contract to help you find donors. It’s a sort of strange thing quite unique to philanthropy.

There are also other “high trust” environments that quickly and automatically make you a “trusted” person. This could be like attending a Milken Conference or Skoll Forum, where being in the room itself is expensive, difficult, etc. This is not too different though from other sectors, where there’s always a high signal curated conference or two. The hardest part, beyond getting the invitation, is figuring out which gathering is truly high signal.

4. You should always raise as much money as you possibly can

Unlike in venture, where you want to price your rounds and watch out for overdilution, etc., in philanthropy you should just raise as much as you possibly can each time. Arguably, in philanthropy you’re fundraising all the time. In philanthropy, there is no term sheet or valuation. You have your mission, scale, and the timeline to success.

If you raise more money than you expected, great! You can expand your mission, hire one extra person, and do more, better work.

But this also creates a unique problem: you don’t know how much money to ask for. $10k? $100k? $10m? This is a common and universally tricky question. There are a few strategies for this:

• Do your research on ProPublica, check the 990s, ask the models.

• Ask donors directly about previous/recent gifts to gauge their “sweetspot” relative to their interests.

• Just wing it and go off vibes.

None of these methods are objectively “wrong”. If anything, I’d argue any one of these options is great compared to the alternative: not asking.

Whatever the case, not asking is the worst thing you can possibly do. Both you and the donor might’ve talked for hours to weeks about your shared passion, vision, and mission. They want you to ask them for a gift. They want to consider it, because it matters to them.

It’s indeed kind of like asking that cute barista out on a date. Do everything to find that courage to ask. The world won’t change if we all stay silent.

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5. You need to be able to answer: “Why not VC? Academia/government? Or China?”

Capitalism works. Government grants and innovation agencies continue to fund ambitious work. China is increasingly visibly ahead in several technological areas from neurotechnology to synthetic biology. These are not all bad things, but we must have short, descriptive answers when asked about the alternative routes.

Your philanthropic program cannot be an unprofitable startup, or a more expensive version of what you could create/buy in China. There must be a real reason and edge to why it uniquely has to be done with philanthropic capital, for how long, and what the go-to-market would be afterwards.

6. If you’ve met one donor, you’ve met exactly one donor.

I cannot emphasize enough how different each donor is. Several months ago, I would’ve said there are only a few donor archetypes. Now, I’m not so sure. Some donor types I’ve met:

• Eccentric billionaire

• Deeply technical person in your domain

• Intelligent financier who knows nothing about your niche science/tech.

• Social activist in your cause/area

• Social activist in your cause/area who disagrees with your approach

• Still learning about themselves

Donors are people. They aren’t walking checkbooks, and they certainly aren’t VCs that can be archetyped as “Generalist” or “Deeptech” or “Founder friendly”. They don’t have set remits or expectations from LPs to deploy capital fast. The types of donors can vary widely, so don’t be upset if a donor, or three dozen donors, says no.

Keep going. Keep looking.

7. Impact philanthropy is still largely a policy-first problem

America is still one of the leading examples of successful philanthropy. Yes, there are many more centimillionaires and billionaires in America than in other countries. But being wealthy doesn’t automatically make someone give.

Wealthy people want to do good work, but they also want tax breaks. America has very flexible giving rules that allow gifts across many sectors to be a tax break. This includes arts and music of course, but also science research and even think tanks.

In contrast, in the UK and Japan, there are more rigid rules around giving and less or no tax breaks for giving in science research or modern philanthropic orgs, making impact philanthropic giving not only unfavorable but largely absent in the culture.

For example in the UK, you cannot get a tax break from directly giving money to a research lab. Let’s say you want to support an academic lab doing cancer research. In the US, you can give your money straight to the lab and get a deduction. But in the UK, you have to pass that money through a registered charity, through its own governance and processes, to distinguish the gift as charitable rather than personal. This friction creates real blockers to effective, fast giving.

In Japan, donations are only deductible if they go to a government-certified nonprofit. To be certified, orgs must pass a “public support test” that requires them to have 100+ people each giving a few thousand yen a year, rewarding popularity that encourages traditional charity creation. Arts groups, welfare charities, and disaster relief orgs pass these criteria, but many frontier science orgs like Focused Research Organisations (FROs) are supported by just 1-3 individual donors. This makes it impossible for one donor to receive a tax break from creating a new FRO in Japan.

Therefore, if countries want to unlock domestic philanthropic giving for sovereign growth, the highest leverage intervention point may be policy entrepreneurship.

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8. Philanthropy and capitalism has always been, and will always be, synergistic

Throughout American history, the best philanthropy has historically been done by the best capitalists. JD Rockefeller and Andrew Carnegie for example were very successful in their businesses, and they ran their philanthropy with similar rigour.

21st century philanthropy is not, and cannot be, “soft”. It should focus on the hardest problems “orphaned” not only by the markets, but technologically and culturally too. If philanthropy is done very well, it can create new technologies to be commercialised later, help people, even creating new fields of science & technology along the way. If anything, philanthropy is exactly for the “uber freemarket capitalists” who can recognise that many civilisation-scale technologies from vaccines to neurotechnology face a market failure with high CapEx that make private financing difficult.

Venture philanthropy was v1 of this, and what we see in the next decade will be v2.

9. >90% of your conversations won’t be with donors, especially if they’re IRL

Fundraising, at least in my experience, is like solving a massive, 97-dimensional puzzle. You’re trying to draw the web of human connections and relationships to figure out who knows who and how well to ask for what intro. Oftentimes, a non-donor who instead knows many donors is more helpful than just meeting one donor. When you’ve met one donor, you’ve met exactly one donor.

IRL, donors also usually won’t step out for conferences or events you’ll attend. Let’s face it, if you’re really rich, wouldn’t you also rather be making flying cars on your private island instead of going to a meeting to talk about spending/giving money? I personally would be at a Charli XCX concert or maybe Slowboat in Hokkaido.

10. None of this may apply to you

Philanthropy is still an incredibly inefficient, idiosyncratic marketplace. YMMV (your mileage may vary) is an understatement. Some have found success with niche math orgs that took a long time to fundraise for, all the way to highly technical drug delivery orgs.

I would not personally have thought these projects would succeed to fundraise so quickly, but I hope this speaks exactly to how unpredictable this all is. As you go through your own philanthropic fundraising journey, you’ll meet many different kinds of people — allies, donors — face many challenges, or none at all.

If you’ve read this far and are still interested or even inspired, then it’s probably worth doing. It’s hard, it doesn’t make sense, but what does nowadays?

Some final notes about what I do know:

• There’s a lot of money in this world, sitting idly.

• There are many ambitious founders and funders waiting to meet each other.

• Rich people don’t like to be courted.

• Most people want to do good things, they just don’t know how.

Get after it.

It’s officially been one year since I left it all behind.

The salary, the benefits, colleagues, friends, stability, comfort. I left the place I called home for almost four years, uprooting my life to the other side of the world nine timezones away for a chance to create something of my own.

I’ve learned and experienced so much about myself and the world since then. Some days, I can’t sleep with all of the thoughts in my head. Some days, I wish I could go back in time to those simpler days. But I don’t regret the decisions I’ve made, and I wouldn’t trade the life I’ve lived, the people I’ve met, and the knowledge I’ve gained for anything in the world. Though, maybe I would’ve traded it for a $100M Anthropic pay package distributed over five years.

Anyways, here are at least 19 things I’ve come to believe from the past year.

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1. It’s all mindset

Life is now full of high highs and low lows. Through thick and thin, believing in yourself is really the hardest but most important thing. Those who believe they will succeed and win are fundamentally different people than those who think they will lose before the match begins. So believe in yourself. And if you can’t believe in yourself all the time, find people that will. Piece them together if you need to for different parts of your life.

2. Grit vs stamina

Grit is when you get up when times get tough. I think it’s one of the most important traits in doing great things, but what’s even more difficult is stamina.

Posting on a blog 3-4 times a day, often with little engagement. Working in the void for a long time, maybe like how Mr. Beast made Youtube videos for years without any real traction until he went viral. Stamina is the thing that makes you show up every day, even if you don’t feel like it, even when it feels like no one is paying attention.

3. Just show up

Humans are strange, emotional creatures. If two people are working with me, and both work the same exact 12hrs/day, but one is sitting next to me every day while the other’s fully remote, I’d give more opportunities to the person on-site with me almost any day. People give chances to those who show up physically. In a world where most jobs can be done remotely, the effort to show up in-person is more important than ever. Show up even when it’s hard, especially when it’s hard. It matters more than we think.

4. Don’t posture

One of my biggest icks—maybe it’s a residual instinct from my valley days. Posturing is different from confidence or competence. It’s performative, and feels completely hollow. Performative building will probably never go away, but I cannot stand those who talk the talk without the substance to back it up. I think this is called “grifting”? I love this word.

5. Everyone’s just working on their flywheel

When there’s a fixed pool of money every year from, for example, an annual grant budget for a disease area, there’s naturally going to be a zero-sum mindset. We all spend our careers setting up our flywheel, and want to protect it—our relationships, our sources of income, etc.

Understanding these incentives and what everyone’s flywheels are, and what they want them to become, is the key to getting things done in a positive-sum fashion where everyone can win.

6. Become infectious, because ideas can’t scale alone

You can conceive an idea alone, but you cannot accomplish it by yourself. You need a team, partners, and funders to work with you. Learning how to attract great talent is a skill that can be learned, but it’s also something that comes from within. People intrinsically have to want to work with you over other people. That sort of infectiousness is very different from the hard skills you need to learn to hire and interview well.

7. The terminal job in all professions is sales

Fundraising is the most important skill that is lacking in 95%+ of all scientists, especially the brilliant ones. Crafting a compelling narrative around your science is a completely different skill than the one you used to conjure it. If you can train that skill though, you can help direct funding towards great science that deserves it. I think there’s nothing more rewarding than that.

Lastly, fundraising, unlike the science, is transferable. Once you learn it, you can apply it to anything.

8. Word of mouth over everything

Word of mouth matters more than any LI or X post. These platforms are important mediums to spread your ideas, but your ideas have to be repeatable in-person, by people, to people. There are a few ways to think about this: “meme-able”, “Jaws in Space”, “Uber for x” just to name a few. Creating these vehicles is key to making your ideas spread.

9. No one cares, no one remembers

People’s attention spans are shorter than ever. People also generally don’t care as much about you as you think. This should empower you to take more risks and put more of your thoughts out there, because everyone will have forgotten your one sentence over the millions of words they’ve read since then. Almost no one really reads all of Owlposting’s pieces, most people just read the title, skim the subheadings, and rate him. It’s just human nature.

10. Those who do, will understand you

We all just want to be understood. But you can’t be understood if you’re silent. So speak up, say what you believe, because it’s the best and fastest way to attract people you like into your life. For what it’s worth, my life has gotten much richer since writing online.

11. Zero sum thinking will consume the soul like cancer

Sometimes I realise it’s unavoidable, but I personally really don’t see the value in it at the macro-level. Sure, micro-level negotiations can get nit-picky over hourly rates or contracts over <10% differences. But at the end, the cash is abundant and there’s many routes to find funding. Long-term, I believe this thinking at the macro-level will inevitably eat away at your worldview and change who you are. I try to avoid it where I can.

12. It only takes one person to really believe in you

But finding that person is really, really hard. These can be friends, colleagues, mentors or someone else. But those people are so rare that the whole point of life can sometimes feel like it’s about picking up those people as you go.

13. There’s no strategy

The hero’s journey is something you tell, not the path you take. In hindsight, it all makes sense. But until then, it’s going to hurt. “Strategy” feels too unrealistic because it sounds too precise for what it actually is. If I had to describe what philanthropic fundraising feels like, it’s:

14. You can be anyone

When you’re meeting someone in-person, in that exact moment, you can be anyone. You can impress, win, lose, empathise, take, or posture. Contrary to popular belief, I think most people also don’t really look you up on the internet. They just don’t have the time or care enough. So that moment matters most, and it’s a superpower. So what if you fumble one funder/person? Brush yourself off and just try again next time.

15. Say the hard thing, then say it over and over again

Lots of people want to say what you have to say, but they can’t. This could be employees with conflict of interest, professors with grant obligations. What’s more, many ideas are worth being said more than once, twice, even a dozen times. If you believe in it, say it over and over again, until the world really listens. Then say it another time. For every Zoomer who might call you a one trick pony, there will be dozens thanking you for it.

16. You can’t motivate people with fear/threats

Or rather, I don’t think you should, especially since I think it’s scientifically proven it doesn’t work long term. This is actually something my old manager would say to me, and found this to be true every time. Life is a series of gifts and people believing in you. Don’t try to summon that goodwill with fear or threats. It’s not just bad karma, but it will catch up to you one way or another.

17. With time, it will become clear those who took risks and who did not

Very few people do great things without taking any risk. It’s like riding an adult rollercoaster. Some people even chase that feeling—I know at least I do. Take big risks, because those risks you thought were massive will eventually feel small in hindsight. If you fail, try again. As long as it doesn’t completely break you, you can keep going.

18. Your body is your vessel. Respect it.

Life is a great gift, and your body is, to our knowledge, the only way to experience it.

But especially as we’re young, we only think about our bodies when it’s falling apart and screaming for help. Take care of your body. Respect it. I’ve been fortunate to learn this lesson early with an injury and experiencing chronic pain. I’d drop everything, spend thousands of dollars, uproot my life to fix my body and treat it.

There’s also so much more we can experience with our bodies that we often don’t allow ourselves to experience in our day-to-day lives. Outside the routine, we can experience real fear, anger, euphoria, and thrill.

19. Optimism is a superpower

Optimism is about embracing problems as opportunity, because the problems we face today were the result of solutions from the past. Today’s solutions will inevitably create problems in the future, because science expands our ignorance faster than our knowledge.

Problems are, at some level, a metric of human progress. The gigantic problems we face today require gigantic optimism—optimism that’s infectious, positive-sum, summoned.

When we’re optimistic, we can use it as the power to create the future we want.

This is my personal mission and process of learning in public to solve chronic pain. Always open to feedback, discussion, collabs, etc. Please, never hesitate to reach out.

Chronic pain is incredibly common. It affects over 1 in 4 adults globally, and costs the US alone over $700 billion a year — more than heart disease ($309B), cancer ($243B), and diabetes ($188B).[1] Yet it’s largely invisible.

Relative to the burden, there is surprisingly little funding in chronic pain outside the context of addiction. This piece describes the state of funding for chronic pain, the framing problem of it, and the narrative shift I believe we need to fix it.

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Chronic pain is, unintuitively, massively underfunded

Despite the massive economic burden of chronic pain, most of “chronic pain funding” in the US is really addiction funding.

The NIH’s HEAL Initiative was a flagship pain program launched in 2018 explicitly borne out of the opioid crisis. In 2023, HEAL alone directed over $600 million towards pain and addiction research. To date, HEAL has been massively influential in chronic pain research, funding thousands of publications and making chronic pain — specifically chronic lower back pain — one of the most well-published areas of pain research.

This makes a lot of sense. One of the most common causes of chronic pain in Americans, and reasons for opioid use, was chronic lower back pain. But the generous funding towards lower back pain research was contrasted by many other conditions seeing comparatively little increase in funding.

To date, NIH funding for other chronic pain conditions remains strikingly low: fibromyalgia (~$16M), endometriosis (~$29M), osteoarthritis (~$84M), and IBD (~$225M).[2] [3] Though there is significant overlap in funding from adjacent fields like musculoskeletal, aging, or immunology, many chronic pain conditions are still massively underfunded proportional to the annual $700+ billion national burden. This gap becomes increasingly obvious when we calculate annual funding proportional to the actual number of patients who suffer from it.

Take endometriosis. The NIH allocated ~$29 million to endometriosis research in 2024 — just 0.067% of the budget — which works out to about $4.00 per patient per year, for a condition that affects an estimated 6.5 million women in the US alone.

Migraine tells a similar story: ~$59 million in NIH funding for a condition affecting 40 million Americans,[4] costing $78 billion annually.[5] That works out to about $1.00 per patient per year.

Today, we still don’t really understand chronic pain. Though our general understanding improved dramatically through initiatives like HEAL, much of that learning hasn’t effectively transferred beyond the lower back.

Epistemically, many of us see chronic pain as a symptom of disease like arthritis or migraine, instead of a disease in and of itself. Pain itself has a distinctly complex biology that we still know very little about. But once we accept chronic pain as a condition and examine the biology of it, we start to see where the avalanche of impact can start.

What if pain wasn’t just a symptom?

We’ve begun to realise pain is not merely a symptom of another condition, but a biological process in its own right.

Historically, chronic pain was thought to be just a symptom of disease. Take arthritis for example, a disease of joint cartilage damage and degeneration. If chronic pain from your damaged knee cartilage is causing pain, then fixing that damaged knee should get rid of the pain. This intuitive and simple logic led many scientists and funders to explore the biology of cartilage degeneration and how we can resolve pain through restoring healthy joint structure.

But around ten years ago, the arthritis field realized that fixing your broken body doesn’t always fix your pain. Over the past decade, other fields from pain neuroscience to uterine biology had come to the same conclusion: fixing the damage in your body doesn’t always fix your pain.

The causal relationship we assumed between Damage vs. Pain turned out to be much more complex, and our understanding of pain shallower, than we originally thought.

Two examples across two very different body parts illustrate this:

Fixing disease doesn’t fix the pain

Osteoarthritis: the Sprifermin trial

Sprifermin was a new Fgf18 osteoarthritis drug that entered a Phase 2 clinical trial (FORWARD) in 2013. In the Phase 2 that tested drug efficacy, the drug successfully preserved knee cartilage in patients who received the highest dose compared to placebo. [6] A great success! But the drug failed.

Why? Though Sprifermin improved cartilage thickness, it did not improve the patient’s pain. The FDA sided with the patients, who wanted their pain to go away, not just their knees to “look better”.

What was particularly striking was that post-hoc analysis identified specific patient subgroups that responded to the drug and those who didn’t respond at all. Digging deeper, researchers found that the patients who responded to the drug were those with severe structural damage and moderate-to-high baseline pain. On the other hand, for those with (centrally sensitized) pain uncorrelated with structural damage, the drug was not very effective.

The Sprifermin trial is canon in the osteoarthritis field today and is a cautionary tale of the importance of patient stratification: sorting patients based on subtypes beyond just the umbrella label of the disease.

This is a classical example of how clinical trials fail not because the drug doesn’t work at all, but because the drug doesn’t work on enough people. Had the field better understood the biology of pain itself— not just the biology of how cartilage breaks down — the outcome may have been very different, and millions of patients today could have been living with manageable arthritis.

Endometriosis: surgery doesn’t fix pain.

The standard treatment for endometriosis has long been surgical excision—find the lesions, cut them out. For many patients, surgery can be very successful at resolving pain.

But pain recurrence after surgery can be between 20%-40% within just five years. [7] Even among patients whose lesions are fully removed, a significant fraction experience chronic pelvic pain that persists or returns—often without any new lesions visible on imaging.

The other front-line treatment, hormonal therapies, only work for roughly half of cases, with the other half reporting persisting pelvic pain, usually recurring after coming off medication. Existing treatments, especially drugs, today work for some patients and do nothing for others, suggesting that there are subtypes of chronic pain in endometriosis that go beyond the number or location of the lesions we see.

The relationship between disease and pain severity is weaker than most of us assume. Pain severity in endometriosis does not reliably correlate with lesion number, location, or disease stage. Some patients with minimal visible lesions report debilitating pain. Others with extensive disease report very little. A 2021 systematic review of the clinical evidence found that pain severity rarely correlates with endometriosis stage. [8] We see similar patterns in many osteoarthritis patients too.

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This disconnect has a biological explanation

Neuroimaging studies have shown that women with endometriosis-associated chronic pelvic pain have distinct brain patterns that suggest central sensitization, a phenomenon where the nervous system itself has been molded like plastic to signal for pain without any stimulus. [9] This is called nociplastic pain, and we see this in many, many diseases from osteoarthritis to fibromyalgia.

In nociplastic pain, once that rewiring takes hold, removing the lesion doesn’t undo the problem. The pain has become its own condition. Trials for many chronic pain conditions today do not accurately sort (stratify) patients by what’s actually driving their pain — tissue damage vs nerve damage vs nociplastic — and end up averaging across fundamentally different conditions wearing the same diagnostic label.

We’ve treated chronic pain as a symptom for too long. Medicine’s attempt to reduce pain to a downstream consequence of structural disease has produced a generation of trials that were, in hindsight, designed to fail.

Now is the time to fix that.

Flipping the narrative around chronic pain

Chronic pain is a biological process that manifests as disease in different parts of the body, following the same three conserved mechanistic types:

1. Nociceptive pain (from tissue damage)

2. Neuropathic pain (from nerve damage)

3. Nociplastic pain (from a sensitised nervous system that amplifies normal signals into pain)

Right now, osteoarthritis pain is studied by rheumatologists, endometriosis pain is studied by gynecologists, and neuropathic pain is studied by neurologists—each in their own silo. But if we accept that many chronic pain conditions share very similar underlying neurophysiological mechanisms regardless of the disease label, then we can imagine how understanding pain very deeply in even one disease context can start unraveling the mysteries of all chronic pain conditions.

A breakthrough in understanding how the nervous system becomes sensitised in fibromyalgia could change how we design osteoarthritis trials and drugs. Better pain biomarkers developed for endometriosis could transform how we stratify and treat back pain patients.

Tiling the trees

But none of that happens if we keep treating pain as an effect of disease. We need dedicated funding for pain as pain—because it’s what matters at the end for patients, regulators, and trials. Understanding the structural problem of a disease is necessary. But it’s not sufficient. We also need to understand how pain behaves independently of the disease that triggered it—how it persists, how it sensitizes, how it rewires the body.

This is what we must understand to begin the transformation of chronic disease to deliver the vision of precision medicine we were promised so many years ago.

[1] Gaskin, D. J., & Richard, P. (2012). The economic costs of pain in the United States. The Journal of Pain. Source

[2] National Institutes of Health (NIH). (2025). Estimates of Funding for Various Research, Condition, and Disease Categories (RCDC). NIH RePORT. Source

[3] World Economic Forum. (2026). Women’s Health Investment Outlook: Closing the Research and Funding Gap. Source

[4] Migraine Collaborative. (2024). New US Government Policies Hit the Migraine Field. Source

[5] GBD 2021 Diseases and Injuries Collaborators. (2024). Global incidence, prevalence, and years lived with disability for 371 diseases and injuries. The Lancet. Source

[6] Hochberg, M. C., et al. (2019). Effect of Intra-Articular Sprifermin vs Placebo on Femorotibial Joint Cartilage Thickness in Patients With Osteoarthritis: The FORWARD Randomized Clinical Trial. JAMA. Source

[7] Zakhari, A., et al. (2021). Endometriosis recurrence following post-operative hormonal suppression: a systematic review and meta-analysis. Human Reproduction Update. Source

[8] Vermeulen, N., et al. (2021). Endometriosis Classification, Staging and Reporting Systems: A Review on the Road to a Universally Accepted Endometriosis Classification. Journal of Minimally Invasive Gynecology. Source

[9] As-Sanie, S., et al. (2013). Increased pressure pain sensitivity in women with chronic pelvic pain. Obstetrics & Gynecology. Source

It’s been seven years since Nadia Asparouhova coined “seed stage philanthropy” based on Helium Grants, her experiment in personal income distribution (now on indefinite hiatus). Since then, as she hoped, philanthropy has moved further downstream into the niches of the internet. Today, lots of people run microgrant programs to give away their own money in the seed stage ethos.

More commonly known as “microgrants,” this form of funding is now a fast, slightly meme-ified way for anyone—from a TC 750k Meta employee to an established philanthropist—to give money with “no strings attached” to someone else. They usually fund young or early-stage people working on fringe, ambitious projects that $1,000–$100,000 would meaningfully push the needle for. Usually, it enables these uncredentialed misfits to launch into new careers or fields, as the microgrant serves as the on-ramp that allows them to chase that one thing they can’t stop thinking about.

There are still too many great ideas left unfunded. Traditional foundations give large, slow grants to credentialed teams. Government agencies fund incremental work within institutions in well-defined fields. VCs fund things with a clear commercial path in under ~5yrs. None of them are designed to find and fund the earliest, weirdest bets on people who don’t yet have a track record.

That’s where you, and all of us, come in.

Today, there are over 100,000 people worth over $100 million USD, but also 2.3 million people worth over $10m, and over 60 million people worth over $1m. We all want an abundant future. Yet individual giving is one of the most overlooked ways to create new technologies. It’s harder than writing a check to the SPCA, but it’s more rewarding than almost any other way to spend your money. This piece explains why you can and should start a microgrant program, how far your $1–100k can really go, what grantees truly seek, and how you can do it better than 99% of other people.

[Note: This is a guide for personal giving, but the same principles can be used to launch a microgrant program with other funder’s capital like Eigen, Impetus, and EV.]

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You’re more established than you think

Starting your own microgrant isn’t something you need to wait until you’re “successful.” It’s something you can do today, even if you think you’re “too junior” or “too young.”

Whether you’re a 25-year-old postdoc at the Crick or a 35-year-old L6 engineer at a tech company, I know you see great projects and people in the world all the time. And I know you know there’s someone more junior than you today who didn’t have the opportunities or gifts that you had, and it frustrates you that opportunity is not distributed while talent absolutely is. You’re never too junior, un-established, or unknown, to leverage your life just a tiny bit to uplift others who may have been in your shoes a few years ago.

I understand that $1,000–$5,000 may not feel like an amount worth giving out. After all, what can someone do with just $1k in biology? Sure, money goes further in India or for coding projects over biology, but it still doesn’t feel enough.

But for real hustlers, who I promise are out there, $1,000 covers a summer’s worth of reagents for a side project. $5,000 gets someone to a conference where they meet their future collaborator. $25,000 funds a pilot study that can earn a position in an MIT lab. The $1,000–$100,000 range is probably the sweet spot to filter out the typical PhD student with existing support, while optimising for quick, low-friction giving to independent, ambitious, diverse ideas.

We also vastly underestimate the breadth and generosity of our existing networks. What would you do if a colleague you last spoke to two years ago messaged you:

“Hey! A super ambitious 21-year-old college student in Missouri I gave $1,000 to for a microgrant wants to learn more about your gene editing career and how her research could be applied to your day-to-day work. Can I connect you two for a quick call?”

Would you really say no to that?

Yes, we’re all busy. But for things like our own field, nurturing new talent, and especially for those who look like our own selves from the past, very few people really say no to just a 15–30 minute call. Because deep down, all of us know that it only takes one person to really go out of their way for someone to succeed. Being able to do that for a young, uncredentialed, ambitious person by forwarding an email or two is one of the easiest gifts you can give to them and yourself.

Most of us are already capable of doing this today. We just don’t know it yet.

Make it personal

Too many microgrants fund the blanket “smart people doing interesting things.”

These get hundreds to thousands of applications with too much heterogeneity, with AI now making generic calls harder to evaluate than ever. That’s why the biggest advantage you have as a solo funder is your personal story and obsession.

There are lots of areas where we still haven’t seen great microgrants. Things like open-source models to predict catastrophic climate events. New technologies to cure uterine-ovarian disease. Ending chronic pain. New instruments to study space and the universe.

You are very likely to be one of the few people who are both passionate and financially able to fund these ideas, and I guarantee there are at least two dozen people in the world who would drop everything for three months to work on this with just a bit of encouragement in the form of $2,000.

Plus, personal giving is also harder to game. When you fund from obsession, you see through the BS because you feel the genuine passion—even through a computer screen. Your thesis becomes a self-fulfilling force that pulls you to give more and pushes the grantees to reach higher.

Lastly, this almost goes without saying, but giving like this is deeply rewarding in a way that generic giving isn’t—every application inspires you, every grantee is a kinship, all while becoming a node in a topic you deeply care about. The first step is to find that problem or question you can’t stop thinking about yourself, then give to people that can help you find those answers.

They don’t need your money

I think both grantors and grantees misjudge the true purpose of microgrants and the relationship between those who give and receive a gift.

At the surface, the grantee seeks a microgrant for the money. Fast turnaround, no strings attached, no institutional affiliation required—how good of a deal is that? But for many grantees, the real thing they need is not the money, but validation.

It’s human nature to want to be acknowledged by people you respect. Imagine how career-defining it could be for anyone who idolised Richard Thaler to receive a small grant from him to do something bold in economics, even if it was just $1,000. That vote of confidence is what grantees, intentionally or not, often need to really go after it. For a young or less-established person breaking into a new field, a microgrant is a signal that someone believes in them and their ideas.

You don’t need to be a Nobel-prize-winning economist to give someone the encouragement they need. Nadia, a self-proclaimed “rando on the internet" gave confidence to many individuals through her gifts.

You can do it too.

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But ideas need a home

If there’s anything we’ve learned from a decade of microgrants, it’s that truly great ideas cannot be fully realised in isolation. They eventually need to find a home. Otherwise, they stay orphaned and invisible from the rest of the world.

A $5,000 microgrant is absolutely catalytic for someone to run their first experiment, but they won’t know how to turn that success into a lab position, a company, or something more on their own. As many of us know, great ideas—even great data—are no longer the bottleneck. What we lack is the ability to show them to the right people and sell it through a compelling story.

When seeding and nurturing talent at the earliest stage, money is the first but far from the last resource required. To bring great technologies into the world, ambitious talent needs help through networks and validation from people like you who might be ahead of them. You offering your network and vouching for the people you fund is the greatest gift you provide.

I argue this is what separates true seed stage philanthropy from charity.

You could do seed stage philanthropy really, really well

As microgrant amounts crept above $100,000 over the past decade, the giving landscape changed too, and not necessarily for the better. As giving amounts increased, established scientists from academic institutions started applying for microgrants, frustrated by the timescale of traditional government grant funding. VCs also realised microgrants were a funnel to identify emerging talent. And grantees realised there were certain archetypes those institutional grantors wanted to see—ones you could embody performatively to collect those sweet sweet non-dilutive dollars.

More money won’t solve the problem, and that’s precisely where individual funders like you add depth and variety to the landscape. Unlike institutions, your giving can be shaped by a deeply personal obsession with how you want the world to change. You can back strange, early, unproven things before they are legible to committees, before they fit a category, before anyone else “gets it.”

And besides, we all know there’s no better feeling than being early to a trend.

So give the money—even a small amount, especially a small amount. But don’t stop at the check. Open your network, make introductions. Don’t hedge, because it’s not an investment portfolio. Fund something personal to you that nobody else will fund, and generously bet on people who deserve a chance. To do it well, all you really need to do is:

1. Find a personal question or problem you can’t stop thinking about. The more specific your thesis, the harder it is for applicants to game and the more rewarding it is for you.

2. Give small and quickly, then open up your network. Speed is a two-way signal for ambition. The warm introductions are worth so much more than a bigger check.

3. Help the ideas find a home when they’re ready. When a grantee’s work succeeds, help them navigate what comes next.

4. Do it all in public, if possible. Some of the best applicants don’t have a public profile, so help them expand their surface area of luck.

You can do it too. You can help change the world.

Imagine a conversation between a philanthropist and their advisor.

Messages over WhatsApp. Names on an Airtable. The three-pager, that you worked so hard to perfect, being skimmed in the three minutes between morning yoga and breakfast.

If you believe philanthropy is a full-time job, then you can imagine what it’s like to be on the other end: dozens to hundreds of cold emails, multiple ten-plus page documents you have no time or interest to read, and an endless flow of people asking you for things through your advisor or your friends, with no immediate or even tangible financial return for doing any of it.

You work basically for free. You do this because you like it and you’re interested. Not for the money.

What is that? I don’t think it’s investing, advising, or anything else we traditionally call it. It’s curation. Philanthropy sometimes feels much closer to curation than investing—in the way we traditionally imagine curation that only happens in the arts.

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Curation

Imagine a billionaire on the phone with their arts advisor, who’s telling them about a really interesting Rolex owned by the first mountaineer who ever summited Mount Everest. The billionaire is part of the Explorers Club, an exclusive members’ club for those who have truly explored the world. A room where if you ask “who here has climbed Everest?”, 80% of hands would go up.

Of course you want that piece. It’s who you are, your passion, your identity. It’s part of the larger curation you’re doing in your life. You ask your friends what’s in these days: what they’re interested in acquiring, what their portfolio looks like, how your curation compares. You share a Google Photos drive of the pieces you collect, the artists you love, the portfolio you want to keep building. And let’s face it—you brag a little to your friends.

When evaluating a new acquisition, you think: What have I funded? What have I acquired before? What do I want next? How is this new thing sufficiently different from what I already have in my portfolio?

The role of advisors, arts or otherwise, is to help make these kinds of decisions, often at the behalf of their client. They take the client’s interests and go source something new based on the curation the client is building. The advisor is crawling through their Airtable, their Google Drives, messaging their own network on WhatsApp to ask about topics that are interesting or projects that are particularly up-and-coming or distinctive.

The curation and somewhat heuristic-driven decision-making isn’t so far off from venture capital either. Lots of VCs do proper due diligence, evaluate go-to-market strategies and market fit in early-stage startups. But at the pre-seed and seed stage, those decisions are inevitably made on a somewhat vibe-and-heuristic basis, usually because you have nothing else to go on.

“I must have it”

But if there’s one thing that’s different from VC-style, it’s that philanthropic advisory and curation can be directed in a rather ad hoc way.

LPs of VC funds don’t usually explicitly ask the Partners to invest in a specific company because they felt like it. There are usually clauses to make that impossible. But philanthropy, and advisory for it, innately has a more “service” dynamic to it. Wanting a new program in your curation probably feels similar to wanting the new Birkin or a KAWS figurine—you call around, and ask someone to find it by a certain date.

This naturally forces decisions and project ID to happen in a more heuristic and proximity-driven way. It’s too difficult and resource-consuming to go to completely different pockets, so advisors and funders defer to their immediate contacts on WhatsApp and social circles. If a project is illegible or too difficult to write a summary note for, it will likely stay under the radar and unfunded, mostly just because it wasn’t seen by the right person on time.

So what’s the takeaway?

Understand what’s already in their collection. Past giving doesn’t predict future giving, but it reveals taste. If someone has funded malaria nets and clean water, they probably care about measurable, population-level impact. Due diligence on the specific individual’s “taste” could give yield high returns on converting conversations into partnerships.

Proximity matters. The arts advisor who finds the Rolex for the Explorers Club member didn’t find it through a cold email. Sort of like a good VC deal, you don’t really hear about them or get access via LinkedIn or cold messages. Philanthropy often works the same way. If your project lands on an advisor’s desk and they’re excited, you’re already most of the way there.

Remember, you’re closer than you think. Funders seeking impactful philanthropy are usually closer to your immediate network than you think. First step is to ask, explore and be curious about people’s interests and passions. You never know, you might meet your next funder at an arts gallery. So get yourself out there.

This post is about the rise, fall, and inevitable resurgence of cell and gene therapy (CGT)—told by me, a rando on the internet who’s spent the past ~decade in the field.

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2018—Curing Blindness

Luxturna was a success. Humankind gave back light to a thirteen year old boy!

Luxturna was an Adeno Associated Virus (AAV)-based gene therapy to treat rare genetic inherited retinal disease. Giving back sight to children and adult patients, it showed the world the inevitable futures we were about to live in. It signified a new era of medicine, and an abundant world where no disease would be “untreatable.”

It amplified the wave of investment and research that was already started in 2013 with the invention of CRISPR. Two widely publicized breakthroughs in one field within 5 years is exceptionally rare in science. And we were all excited. CRISPR/Cas9 was the first iteration, but countless other variants like CasX and Cas13 were being developed, with high-signal companies from UC Berkeley and MIT/Harvard like Caribou to Sherlock being spun out with pomp and circumstance.

On the cell therapy side, the momentum was just as strong. Kymriah and Yescarta—the first CAR-T therapies—had been approved in 2017, and Novartis and Gilead were scaling manufacturing to meet demand for what looked like a new standard of care in blood cancers.

“Precision medicine” was the catch phrase.

With tools like CRISPR and delivery methods like AAV now de-risked in clinical trials, we could now plausibly engineer precise, long-acting medicines that could treat, and potentially cure, any disease, in one shot.

2018 to ~2022 were some of the most explosive years for CGT, with money flowing across the field to prepare for the gold rush. Investments covered a few key areas that needed improvement: cargo R&D (CRISPR, siRNA, CAR-T), delivery vehicle engineering (AAV, LNP, non-viral capsids), and manufacturing (production, QC).

In a gold rush, you make your money by selling shovels. And that’s exactly what the industry did. Across these ~three areas, billions of dollars in investments, partnerships, and acquisitions flowed that told the world:

2022—Peak gold

2022 was a great year to be in CGT. Spark, the company that made Luxturna, was so hot, scientists were being poached from there like OpenAI taking AI scientists from Meta. Forge was making it big. Kriya was coming up, and companies were starting to make big, seemingly generational plays.

CAR-T was already proving itself commercially. Yescarta and Kymriah were generating billions in cumulative sales, and Carvykti’s approval for multiple myeloma in 2022 expanded the addressable market beyond lymphomas and leukemias.

Could we custom-design, engineer and sell AAVs, the main delivery vehicle of gene therapies? How could gene therapy cure common disease, after curing rare disease?

These companies, if successful, could capture tens of billions of value in the gene therapy market. The +$400k price tag of Luxturna (per eye) doesn’t even matter if we can calculate the net present value of alternative therapeutics on the market if the gene therapy is a one-shot treatment. The excitement of CGTs during that time was backed by real optimism of what the modality could do for patients.

Yes, the treatments were expensive. But it’s fine if the treatment delivered better value than the existing alternatives in a one-shot format.

Yes, immunogenicity was a problem. But only sometimes when the dose was too high.

Yes, manufacturing was hard. But scale would fix that.

It felt as if every hard problem had a “but” that made it manageable. Every risk had a hedge. The field had built such momentum that the obstacles felt like simple engineering problems that we’ll solve for in time. The money was flowing. And the patients who could see again were the proof.

2024—Maturation

I think the market, and the field as a whole, matured.

What matured means, practically, is that the heuristic-level optimism from 2018–2022 met the harsh reality of biology and sometimes plain physics.

Gene therapy wasn’t a one-time, lasting cure. Luxturna didn’t restore 100% vision. In some patients, the effects wore off significantly. People in the field intuitively thought this, but there was a gradual shift in the public rhetoric from “cure” to “treatment.” And with that shift, the entire cost-benefit calculus changed. A $2M+ price tag for a cure was defensible, but for a treatment that might wear off in a few years? That’s a much harder sell.

Gene therapy was finicky, and sometimes deadly. Neutralising antibodies reduced therapeutic effects in patients who’d been previously exposed to AAV—which, depending on the (sero)type of AAV, could be a significant fraction of the population. Worse, some patients died. And they were usually still children. Insufficient neutralising antibody screening on patients, excessively high drug doses to compensate, and the utter gut-punch of a little boy dying from drug-related liver damage weighed very heavily on the field.

Manufacturing and QC remained genuinely hard. AAV capsids assemble well, but the genome replication process is error-prone—snap-backs, truncations, and unwanted DNA species get packaged alongside the intended cargo. Separating these differently-sized molecules at scale, consistently, with the purity regulators require, pushed the limits of existing bioprocessing infrastructure.

The infrastructure to deliver such a fragile drug didn’t exist. The Moderna vaccine, an mRNA encased in a lipid nano-particle (LNP), is notoriously difficult to distribute and requires ultra-cold chain storage. Gene therapies face a version of the same problem, often with even more stringent handling requirements. Patients became the last frontier, something small molecules and other modalities solved for ages ago. The manufacturing and infrastructure problems are both what ARPA-H has recently addressed in their new GIVE program.

And even when gene therapies worked, the economics didn’t. Roche acquired Spark for $4.3 billion in 2019, largely on the strength of Luxturna and a hemophilia A pipeline. By 2024, Luxturna was generating roughly $20 million a year in sales—down 59% year-over-year—and Roche took a $2.4 billion impairment on the unit. Bluebird bio, once valued at nearly $10 billion, was sold to private equity for $29 million after failing to commercialise three approved gene therapies. Pfizer’s hemophilia B therapy Beqvez was discontinued less than a year after approval; reportedly, zero patients had been treated. The payer infrastructure for million-dollar one-shot therapies never materialised at scale.

With this, the market makers started to step back.

“Where’s the gold?”

Novartis confirmed a phased wind-down of a San Diego technical R&D site for gene therapy (July 2024). Pfizer discontinued commercial availability and global development of its hemophilia B gene therapy Beqvez (February 2025). Roche restructured the very company that made Luxturna, Spark, shelving the hemophilia A program (restructure in 2025). Vertex stopped using AAV as a delivery mechanism entirely (May 2025). Biogen discontinued all AAV gene therapy programs and restructured the team (September 2025). Takeda exited cell therapy research (October 2025). Novo Nordisk halted cell therapy R&D (October 2025).

Seven major pharma companies. Eighteen months.

It had been about four years since those new CGT startups were founded. They matured, developed targets, and built out their pipelines, and prepared pre-IND packages expecting that someone would be there on the other side to receive it.

The licensing partners were gone. The market-shapers who set the terms for every deal in this space were suddenly, collectively, disinterested. In that moment, an entire generation of CGT companies had to think critically about what part of their business model still actually worked.

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Now

It’s been a tough two years for small-cap and pre-Series B biotech startups. Many of these companies are still around today. Some pivoted targets or modalities. Others are running lean, extending runway, waiting for the licensing market to recover. A few didn’t make it.

The punch down of the platform biotech has been brutal, and hard to evade for many CGT companies founded between 2018-2021. But there’s still much to learn, and the field’s story is far from over.

Firstly, we continue to cure previously incurable disease with CGT. Just last summer, we were able to create and administer a life-saving gene therapy into a 6-month-old baby, KJ, going from identifying the genetic mutation to a treatment in just six months total. A stunning and herculean effort by the team at UPenn. Casgevy showed what’s possible for ex vivo cell therapies: CRISPR-edited autologous stem cell therapies that enable independence from routine transfusions for many with beta thalassemia.

More broadly, CAR-T has become the commercial bright spot of the CGT field, the market reaching roughly $5 billion in 2024—though uptake for newer ex vivo therapies have been slower than expected, echoing the same payer access challenges that plagued gene therapy.

The science never failed, just our heuristic-level expectations. We expected one-shot cures from first-generation tools, and when we didn’t get them, we called it a failure of the modality rather than a failure of our own timelines and assumptions. The fundamental insight—that you can deliver genetic instructions to fix broken biology—remains as true as it was in 2018, and it continues to give patients a chance at a better life.

And all the diseases we said gene therapy would cure? They haven’t been treated by anything else since.

CGTs have undoubtedly transformed rare disease care, and the broader field still has much to learn from them. It tells us where durability breaks down, where immune responses escalate, and how to justify small N manufacturing runs. Transferable learnings are rare, especially in clinical trials where the information is siloed, but thankfully there are great people working on that too.

The Clinical Trials Abundance blog

The case for sharing clinical trial data

This is Saloni! Today’s post is about statins, and the variety of benefits of sharing clinical trial data. I originally published this post on the Abundance and Growth blog by the AGF team, which I’m part of, at Coefficient Giving…

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7 months ago · 20 likes · 6 comments · Saloni Dattani

Finally, gene therapy is still a powerful modality for discovery. AAV is still the only way to get cargo into very specific cell types in hard-to-reach areas like the brain. ARIA’s neuro programs and even the new Merge Labs heavily rely on AAV-based approaches for genetically interfacing with the brain. AAVs are also now the best and easiest way to genetically modify new animal species—opening up entirely new research models that weren’t possible five years ago.

A Cautionary Tale

If there is one, it should be for AI-forward bio companies, which are now reaping the benefits of the hype cycle we’re in today.

High valuations, high expectations, and a fancy model are appealing to the glossy-eyed “deeptech investor.” But you’re rarely re-inventing the wheel, especially the exit strategy, if you’re a biotech play at the end. If the end-buyers for ~traditional therapeutics are the same, you’re still subject to the whims of pharma and the market makers.

CGT companies thought they had a moat because their tech was novel. AI-bio companies think they have a moat because their model is novel. But pharma doesn’t acquire moats—they acquire assets. And if the cost of the asset is all that matters, capital-intensive model-forward companies may find the multiples aren’t where they thought they were.

History has a tendency to repeats itself. And humans, ultimately, are too often emotionally persuaded by hype and hot new technologies, ignoring the boring and menial work to actually get there.

Patients are the final frontier, and more technologists have to appreciate what this truly means. Today’s AI for Science wave, if we’re not careful, might end as a mere hype cycle if we continue ignoring the real, boring, hard work to get us there.

Conflicts: I’m a shareholder in several gene therapy companies & startups.

Also: This is the first and last time I’ll unironically call myself a “gene therapist.”

Sometimes, it feels like the entire world is working against you. People don’t even follow up.

But really, the world becomes a much more pleasant place when you start to believe that the entire universe is secretly working in the shadows, helping you succeed.

I moved to London six months ago, almost to the day. In that time, I’ve been a recipient of so much kindness that I’ve lost count. Almost everything good that has happened to me, happened because multiple people helped me get there.

This is half a reflection on Kevin Kelly’s blog post “How Will the Miracle Happen Today?” and half a reflection on hitting six months in London. KK spent decades hitchhiking and roaming Asia, relying on the kindness of strangers with such regularity that he stopped being surprised by it and started expecting it. He came to believe that generosity isn’t random, but summoned by a deliberate willingness to be helped.

I wouldn’t have believed it myself six months ago, so I wrote this for those who may not believe it yet either.

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SF → LDN

I moved from San Francisco to London for an opportunity. No real plan, no income, with my backup plan being that I either just go back to SF or flee to Chiang Mai to sulk in some Khao Soi. I’d “scouted” the UK for a few weeks earlier that summer, and thought there was something interesting happening. I was gonna call it my “swinging for the fence” era. If it all went south, it’d at least be a nice chapter in my memoir.

I was terrified.

The first weeks in London were rough. I kept getting scammed in places I’d rent. Rooms filled with mould, strange in-between neighbourhoods near Camden and Euston, crazy roommates, the usual. I’d crash with a girl I’d met a few weeks back in the summer. A warm bed. She’d sometimes make food, usually with too much off-brand gochujang. It was one of the first great acts of kindness I received. We’d end up dating.

But I was haemorrhaging money. Deep in the red. It was Week 3, and I had no idea how I was going to last another three weeks, let alone another three months.

Right in that moment, I received a grant from Tyler Cowen via Emergent Ventures, over a 1.5-page application, a five-minute interview, in just five days. I’d been rejected from EV at least once before, and I’m sure I would’ve been rejected again if it wasn’t for IF, RK, and AM who vouched for me, believing in me.

I eventually settled down in a lovely flat in South London, with a couple who wanted to save up for their wedding. They were renting their spare bedroom, and it felt great to be able to take this money I was gifted to contribute to their future, all while finally having my own space.

How my gifts arrived

I had no clue ahead of time who would help me, when, how, why, or even for what. But every good thing that has happened to me, without fail, happened because of another person’s generosity.

I’ve been trying to figure out why. I didn’t know what it meant to “summon kindness” at the time, and I’m still far from an expert. But looking back, there’s a few things I think I did.

I worked in public to the best of my ability. Not as much as I do today, but before anyone was paying me to do anything, I wrote about my progress, sharing ideas, putting rough drafts out into the world. Sending rough drafts are underrated. Though most of it got no response, every once in a while, someone interesting would reach out. A few of those people I now consider good friends. Working in public truly is like standing on the side of the road with your thumb out. Just making yourself visible to whoever might be going your way could lead to incredible acts of kindness.

Looking back, I was also very honest about how things were going. When people asked how things were going, I tried to be honest, especially if it was about philanthropic funding that people thought was some magical thing.

I think there’s something about candour that unlocks generosity. We know how to perform competence and comfort. But when you do that, it actually stops the gifts from being summoned. If anything, honesty is even more valuable than crisp writing these days. We all just want to relate to each other.

Last thing, I tried to be thoughtful about my generosity network, a.k.a the people who, for whatever reason, were overly generous with introductions and their time. I’ve tried giving it back, offering help to others and being generous with my time like others have been for me. I think of it like karma: you get back what you put into the world, and you never know how the universe will brings things back to you.

Join the conspiracy

Today, I work out of an office I do not pay for, chat over free coffees, often even dinners, with free knowledge from experienced friends and colleagues infinitely more successful than me.

I do this all while preparing to go back on the road to seek more gifts for another philanthropic program. I’ve received and tried to give many more gifts since arriving, usually non-monetary. But unfortunately, I’m still in a phase of my life where I’m receiving more gifts than I’m giving.

KK writes about the art of being “kinned”—of being the recipient of generosity as its own kind of practice. Most of us are terrible at it. We resist it until we’re helpless and desperate, equating needing something with weakness. To truly believe in the conspiracy, we must acknowledge our helplessness and accept the uncertainty.

And when you’re finally kinned, when you receive that gift, you must do so with genuine humility and confidence. You are not an object of pity for receiving great generosity from a stranger. You provide something that they do not have, you are a Merchant of Light that brings knowledge from another world. Without this confidence, you will not be able to summon the gifts. You must be uniquely vulnerable to summon the kindness from others.

KK calls the act of believing in the conspiracy as pronoia—the opposite of paranoia. Instead of believing everyone is out to get you, you believe everyone is out to help you. The story of your life becomes one huge elaborate conspiracy to lift you up. But to be helped, you must join the conspiracy yourself.

I’ve come to accept that life, as it seems, is an ultimate culmination of goodwill and gifts, for those who are able to summon it.

Swinging for the fence

I wrote this on the plane to London six months ago, before I knew at all what was to come. I’m including it here because I think it’s honest in a way I can’t be today.

Flying to a new country with my life packed into two bags. It’s enough, though, since how I dress won’t be what matters.

I’m running out of cash. I’ve let my place go. I won’t end up back there.

High risk, high reward. I’m swinging for the fence.

I’m swinging for the fence in a way so many people before me couldn’t. Maybe they were immigrants who literally couldn’t leave their job or else they’d be sent away. Maybe they simply didn’t have the opportunities and support that people like myself have been fortunate to receive. Maybe it’s their family, who constantly encouraged them to go the safe route and just print money with a cushy job.

I’m lucky. And I truly am grateful. But I’m terrified.

So many people believed in me. And I can’t let them down. I can’t stop now.

So I swing for the fence for that shy kid in third grade who barely spoke English. My friends, the ones before me who couldn’t, the people who’ve believed in me and still believe in me. There must be something after this. Something so much greater.

The only way past is through. So I just have to keep on swinging.

Continuous hormone monitors will allow us to finally “read” our bodies, making “writing” inevitable.

One example of the “read & write” paradigm is continuous glucose monitors (CGM). For decades, people with diabetes relied on periodic finger-prick tests and snapshot measurements. When CGMs arrived, albeit after +20yrs of heavy R&D, they created an entirely new relationship between humans and metabolism. The ability to continuously “read” glucose made it possible to “write” over metabolic disease: personalised dosing, real-time feedback, and eventually closed-loop insulin delivery systems that would have been unthinkable without the continuous signal.

Hormones like cortisol, testosterone, oestrogen, and progesterone are the Operating System of our bodies. Continuously reading them will transform human health across disciplines, genders, and industries. Here, I describe the impact of continuous hormone monitors (CHMs) and what it takes to get there.

Why are Continuous Hormone Monitors an ARPA-Hard Problem?

In late 2025, I had the opportunity to see a new ARPA program for continuous hormone monitors take shape. Through it, I observed CHMs are ARPA-Hard for several reasons:

Hormone sensors are not a glucose monitor analog

We can now measure hormones like progesterone at-home through saliva and urine tests, but hormones in these fluids are 50-100x lower in concentration than in blood and represent just a snapshot image.1 If we wanted a snapshot image of hormones, clinically routine lab tests like ELISAs and DUTCH (Dried Urine Test for Comprehensive Hormones) tests will do. Measuring them continuously and accurately though, is a whole other level.

Biosensing technology for continuous glucose monitors or sweat-based biosensors are now widespread, but the hardware simply doesn’t work for hormones. They’re just not sensitive enough.

Current glucose monitors work by a hair-thin needle under the skin coated with enzymes that, when they encounter glucose, trigger an electrochemical reaction to create a measurable signal every few minutes. But this only works when the thing you're measuring, glucose, is abundant in that area.

In the interstitial fluid layer of your skin, where glucose monitors usually work, the concentration of progesterone is approximately 70,000x lower than glucose. Estradiol? Around 2,000,000x less. These are both at the peak levels of the cycle.2

New classes of sensors—including aptamer-based approaches, DNA sensors, and advanced electrochemical detection—are actively being developed for hormone monitoring. But as with all sensors, sensitivity, miniaturisation, and durability are factors in constant trade-off.

But particularly with ultra-sensitive hormone monitors, “noise” becomes the single largest issue. This “noise” being the natural, biological variability between and within people. Hormones pulse throughout time, are context-dependent, cycle-dependent, and are intertwined with many chronic, complex diseases. These factors can confound ultra-sensitive hormone measurements and make the results inaccurate.

This means that even the best available tools already cannot work in complex hormone conditions. Even inne, a saliva-based progesterone tracker and one of the more rigorous products on the market, is not a fit for those with PCOS, endometriosis, or irregular cycles. PCOS alone affects roughly 10–13% of women; endometriosis affects about 10%. With significant overlap between these conditions, tens of millions of women in the US are effectively excluded from today’s hormone tracking tools.3

The variability of hormone cycles and levels is simply too vast for foolproof, broad-application continuous monitors to be commercially viable in the next 1-3 years.

Not all “continuous hormone monitors” are actually measuring hormones

This is where things get misleading. There are three fundamentally different categories of technology being discussed under the CHM umbrella today:

First, there are devices that infer cycle phase from proxy signals—skin temperature, heart rate variability, electrodermal activity, respiratory rate—and call it “hormone monitoring.” A wrist-worn device that claims to track oestrogen, progesterone, LH, and FSH non-invasively through a bracelet, without disclosing a sensing mechanism for actual hormone detection, is almost certainly doing this. Proxy-based cycle tracking is not new—it’s what Natural Cycles, Oura, and Ava already do, with varying degrees of accuracy.

Second, there are teams attempting actual direct hormone measurement through interstitial fluid for example via micro-needle patches and novel biosensors with aptamers, DNA, or electrochemical signals. This is very technically difficult, but many credible venture-backed companies like Impli and Biosens8 are attempting this today.

Third, there are the validated clinical tools that already exist—blood draws, ELISAs, DUTCH tests—which are accurate but expensive, inconvenient, and limited to snapshots.

Each category of monitors trade accuracy with cost, convenience, and durability, making CHMs an ARPA-Hard problem. So when a startup blurs the line between them, claiming 94% cycle phase accuracy as if it’s evidence of continuous hormone measurement, it risks eroding the trust of women who have been dismissed by the healthcare system for decades and deserve real tools.

Cycle phase classification from proxy signals is very a useful product. But it is not a continuous hormone monitor, and calling it one can set back the entire field.

The “cold start problem” of CHMs

CHM markets are undefined, the regulatory pathway is uncertain, large reference datasets are sparse, and the hardware is capital-intensive.

The lack of pre-existing markets and unclear regulatory pathways are immediate red flags for most private investors. This limits the amount of up-front capital they put in, making the expensive hardware MVP difficult to develop.

There are also systems-level bottlenecks to CHM R&D. First, there is limited “ground truth” to the data. You can claim that you developed a new CHM and measured a person’s hormones continuously, but how does someone else validate that claim? With the data you collected… yourself? There is currently no open-access reference data for continuous hormone monitoring, making it difficult to “prove” that your monitor even works.

Second, hormones vary substantially across people and contexts, including age, ethnicity, health conditions, medication use, and cycle patterns. Without a large, diverse reference dataset, CHMs are forced into narrow populations because that is all they can credibly validate. At worst, you create devices that only accurately measure the populations they were trained on and baking inequity and mistrust into the entire investment category.

But developing CHMs in large patient cohorts and validating them with out-of-sample data is prohibitively expensive and operationally challenging. From the open-source foundational reference dataset to in-patient hardware validation, to get CHMs right we need pre-competitive, public-private R&D and data generation programs seeded by impact-driven, non-dilutive funding.

This is exactly where ARPA-style programs can come in to solve high risk, high impact, infrastructure projects that benefits an entire field rather than any single company. Arguably, these foundations need to be built first to solve the cold start problem so that CHMs can fully realise the transformative potential it has.

So what would the future look like if everything goes right?

Continuous hormone monitors can rescue the fertility crisis

Even in the best-case scenario (under 35, own eggs), IVF is only successful ~50% of the time.

IVF is not a simple, easy process. Retrieving eggs requires weeks of hormone injections to stimulate the ovaries, careful timing to harvest eggs at peak maturity, and then precise hormonal support for embryo implantation. Each step is guided by hormone levels that we currently only measure every 2-3 days in static snapshots.

IVF not only disrupts normal hormone levels, but routine clinical visits disrupt daily life, work, and education for women. A single (failed) cycle costs $15,000-30,000 and weeks of physical and emotional toll on the couple who have already tried natural conception for months to years. CHMs would transform the entire IVF workflow, making critical hormone measurements instantly available and tunable. It gives back agency to parents and make IVF more accessible for the ~1 in 6 people globally experiencing infertility.4

If it all goes right, CHMs won’t just improve IVF but reduce the need for it entirely. Better continuous data on cycle timing would give couples and clinicians far more precise tools for natural conception, transforming the way in which we have children today.

Continuous hormone monitors can transform chronic pain care

Chronic pain, specifically pelvic pain, is one of the most common symptoms in patients with chronic uterine disease. Endometriosis—a condition that affects nearly 1 in 7 women—causes debilitating pain with an average ten-year diagnosis timeframe, disrupting work, education, fertility, and daily life. It’s more common than heart failure, yet our understanding of it remains remarkably poor.

Endometriosis is difficult to treat in part because the pain fluctuates dramatically with the menstrual cycle, compounded by anxiety, depression, and environmental factors. New drugs are evaluated in clinical trials based on their ability to reduce pain. But when pain varies so significantly, it’s difficult to precisely quantify treatment effects. Without continuous hormone monitoring, it’s hard to distinguish whether the variable response to the new drug is coming from the drug itself or from where a patient is in her cycle, adding confounding data to clinical trial outcomes and masking nuanced treatment effects.

CHMs can change this. By tracking hormonal patterns alongside pain reports, clinicians could identify true treatment responses, personalise interventions, and finally begin to untangle the complex relationship between hormones, inflammation, and pain. Endometriosis alone costs the U.S. over $100 billion annually in lost productivity, healthcare costs, and reduced quality of life.5 Clinical trials for new treatments routinely fail not because the drugs don’t work, but because we can’t measure their nuanced effects through the confounding signal. If we can understand the complexities of chronic pain in even just one disease, it can transform our understanding of all chronic pain conditions.

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Hormones are the Human Operating System

If hormones are the operating system of our bodies, then chronic disease is what happens when the OS goes unmonitored. The case for CHMs extends far beyond reproductive health.

Women make up ~80% of all autoimmune disease cases.6 From rheumatoid arthritis, multiple sclerosis to lupus, they are often driven by dysregulation of hormones like oestrogen or cortisol that directly tune immune activity and inflammation.

But the relationship runs both ways: chronic stress, postpartum changes, anxiety, and depression also disrupt hormones, creating feedback loops that turn chronic disease treatment into trial-and-error. Autoimmune diseases cost the U.S. healthcare system an estimated $100 billion or more annually, much of it driven by delayed diagnosis and ineffective treatment cycling.7 Continuously monitoring these hormones would help clinicians predict flares before they happen, reducing years lost to misdiagnosis.

The first step is giving patients the agency to understand their own bodies.

Women also spend 25% more of their life in poor health than men. But the market for menopause treatments stands at only $18 billion in 2024, despite recent analyses estimating the yearly gain to U.S. GDP at over $9.1 billion from better menopause care alone—a massive gap between market size and actual economic impact.8

Hormone Replacement Therapy (HRT) has been a transformative option for many, but most women don’t know what their hormone levels are or how they’re being affected. Oestrogen and progesterone levels decline with menopause, but how fast? And by how much? Continuous monitoring can give back agency and transform the lives of over a billion women who experience menopause today.

CGMs taught us that chocolate donuts cause a sugar crash—and those kinds of simple realizations have fundamentally changed our relationship with food and our bodies. Hormones, even more than glucose, control our bodies—yet we have no equivalent monitor for the system that operates them. CGMs created an entirely new category of consumer health and reshaped how millions of people manage their metabolic health. CHMs will do the same, but the impact will be much larger.

What does building it right actually look like?

No team is close to delivering a fully non-invasive hormone monitor in a consumer form factor within the year—though several are doing credible work toward that goal.

Impli is one London/Swiss-based startup developing a subcutaneous implantable biosensor that continuously measures LH, progesterone, and estradiol during IVF cycles. Their aptamer-based sensor has been tested in artificial interstitial fluid across a concentration range of 1 pg/ml to 1 µg/ml, showing strong correlation (R² ≈ 0.9) with ELISA measurements and detection limits below 1.2 pg/ml for all three hormones. While still in the validation stage, this represents genuine progress toward continuous hormone monitoring.

Ida Tin the initiator behind The SPRIN-D Hormone Challenge, and tasked with scoping it for the German innovation agency, represents the pre-competitive infrastructure layer that’s also missing. The VC model doesn’t encourage collaboration between startups, and no single company can build the foundational reference datasets and validation frameworks that the entire field needs. Public and philanthropic capital are in a unique position to build this.

The path forward requires both deep biosensor R&D and open-access reference datasets to validate them. Given these factors, startups, especially early-stage companies, must be careful of how they market their products to prevent creating mistrust in the broader field. Tin comments, “essentially this program is about establishing a new industry enabled by levelling up from snapshots to continuous hormone data. I believe it will bring us into a new era, not just in women’s reproductive health, but across medicine broadly. We have seen great interest across the wider health care industry to have access to this data stream. Now the challenge is to generate it, for which we need the biosensors, and handle it with the privacy and high ethical standards that it deserves”.

Women’s health is societal infrastructure

If we accept that women’s health has been underfunded, then it should be equally obvious that many deeptech solutions for women’s health remain deeply intrinsically undervalued. Conservative estimates place the combined economic burden of endometriosis, autoimmune disease, and menopause-related productivity loss well above $200 billion annually in the U.S. alone^4,5 and the world looses $1 trillion, every year in lost productivity because women don’t get adequate health care (McKinsey report 2024). This is a generational opportunity for private, public, and philanthropic capital.

But we have to build this right. Women’s health data has been politicised, commodified, and weaponised throughout history—often serving everyone except the women generating it. Continuous hormone monitors are powerful tools that could finally give women greater agency, but they also create a resource that could be seriously misused.

The recent hype around CHM products showed how easy it is to oversell the science or misrepresent what the technology can actually do today. This is harmful to the industry and predatory toward women who deserve transparency, even from small startups. Rather, especially from small startups.

To build the abundant, flourishing futures we dream of, we have to develop technologies like CHMs with technical rigour, put privacy first, and work collaboratively where possible to serve those who’ve waited long enough. CGMs took over twenty years from first concept to the consumer devices we know today. CHMs will require at least another five to ten years of sustained, well-funded R&D to reach similar maturity. The question is whether we’re willing to invest patiently in getting it right—or whether we let hype outrun the science and lose a generation of trust in the process.

Thank you to Ida Tin and the many friends & colleagues for thoughtful discussion and input.

This is my personal mission and process of learning in public to solve chronic pain. Always open to feedback, discussion, collabs, etc. Please, never hesitate to reach out.

In my previous essay, I surveyed the landscape of objective pain measurement—the approaches that have been tried, the signals that have been found, and the frustrating gap between promising science and clinical tools that actually exist. Despite decades of work, no validated composite biomarker for chronic pain is routinely used in clinical practice.

There are still many technical limitations to objective measures of chronic pain, but I argue here that the real bottlenecks are no longer scientific, but structural.

The way biomarkers get built, who pays for them, and the incentives that drive most biomedical innovation are insufficient. This piece breaks down why chronic pain biomarkers, and biomarkers more broadly, are still not in the hands of clinicians, scientists, and, most importantly, patients, despite years of promising science.

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If everyone’s not using it, nobody wants it

A biomarker is most useful when everyone uses it.

LDL and HDL are biomarkers and a medical standard. Nobody really “owns LDL” since it’s just a molecule you’re measuring in your body. But everyone uses it, and acknowledges it as a tool to understand our health. It produces immense societal and economic value, but very little of it is captured by a private company because they don’t “sell” it to you directly. A drug, by contrast, is proprietary. If you develop a successful painkiller, you own it, you sell it, and you profit from it. The incentive to invest is therefore straightforward.

Okay, so if biomarkers don’t produce much profit, what if we made it proprietary and just offer it in a private clinic as a “boutique” option?

You can, but if only one company uses it, it by definition is not a standard. With time, it creates mistrust by the broader medical and scientific community, because if it really was so profoundly helpful, why wouldn’t it be available to everyone? Without mass adoption, it can easily fall into the category of “premium Beverly Hills doctor service” and raise eyebrows.

But making it broadly available means it has to be accepted by clinicians who diagnose patients, by drug developers who design clinical trials, by regulators who approve new therapeutics, and by payers who decide what gets reimbursed. For trial stratifiers, adoption is narrower but still requires cross-sponsor trust, cross-site reproducibility, and standardisation. This is a high bar, and an expensive one at that. This takes around 3-5 years of consistent funding at the $20M+ range, excluding discovery R&D.

This creates a paradox: by the time a biomarker is universally accepted as useful, it becomes harder to own, so private value capture collapses even as the societal value rises. Yet the societal value is enormous. Economist Heidi Williams has estimated the net present value of better cancer biomarkers alone to be over $2 trillion. But that value mostly accrues to patients, to healthcare systems, to drug developers who can now run better trials, not solely those that develop it.

This is the single largest reason why most biomarker development does not happen. In startup speak, the valley of death is incredibly, incredibly, long, with very little payout at the end for private investors. The traditional private business model simply doesn’t work.

Burden of proof

A biomarker is ultimately a statistical test that helps make a specific decision. The problem with most statistical tests is not that they don’t work at all—it’s that they don’t work consistently enough.

To prove that a test works consistently across different contexts, you need to prove it over large datasets. A machine learning classifier that distinguishes chronic pain patients from healthy controls with 85% accuracy in one lab, using one scanner, with one patient population, is promising, but it’s not a biomarker.

To be a biomarker, that classifier needs to maintain its performance across different labs, different scanners, different patient populations, different sites, and different clinical contexts. This requires large sample sizes to train the model and even larger out-of-sample datasets to validate it.

The problem is that those “samples” are patients. Every additional patient enrolled means IRB and ethics approval, clinical overhead, and regulatory compliance, all adding up with hundreds to thousands of dollars per patient. Each site added comes with protocol harmonisation, data standardisation, quality control, and coordination. Broad, coordinated infrastructure at this level rarely exists.

After all, this would look like an open medical centre, with patients waiting on a list, with ethics approval that took a whole year to obtain, and independent data contractors paid and ready to start. There is very little economic incentive for this to come together in a “business as usual” world.

For early-stage biotech companies or academic labs, building this all from scratch for a single biomarker is incredibly difficult. This general lack of iterative clinical infrastructure (aka, a standing, patient-first “clinical testbed”) where new biomarkers can be quickly and safely tested and refined, is one of the central bottlenecks in translational medicine. This of course is not just for pain biomarkers, but for all biomarkers.

To date, we’ve discovered many promising biomarkers in the lab, but most have been left unvalidated due to these structural, infrastructure-level bottlenecks.

Clinical practice doesn’t change with a Nature paper

Suppose, against the odds, someone does validate a composite pain biomarker. They publish it in Nature. It shows robust performance across multiple sites and populations. Does this spark mass clinical adoption?

Usually, no.

Biomarkers suffer from a failure of adoption problem. Clinical and research behaviour does not change because of a high-impact publication. For a biomarker to be adopted by clinicians, drug developers, and regulators, it needs more than scientific evidence, but clinical-grade statistical evidence, generated within a specific “context of use,” demonstrating that this tool is predictive and useful for a very specific clinical decision. This is what biomarker qualification pathways in the FDA and MHRA, for example, require.

The “final exam” of biomarkers is likely not a paper, but regulatory submission to a federal institution with in-person evidence that the biomarker meaningfully improves trial success for a drug trial in a specific disease.

But designing such clear “final exams” from the beginning and building an entire program backward from that endpoint is hard. For an early-stage biotech company or an academic lab, this kind of end-to-end program design is not in their realm to scope, pitch for, and execute.

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Biomarkers are a philanthropy-shaped problem

Successful biomarker development requires:

• Pre-competitive coordination across academic labs, clinical sites, device companies, drug developers, and regulators.

• Long time horizons—four to six years minimum from discovery through clinical validation, mostly because clinical trial data takes 8-16 months to get back.

• Large patient cohorts enrolled across multiple sites under the same protocols, for powered statistics.

• Regulator engagement from day one—because the data regulators want to see may not be the data you generate.

• Multiple shots-on-goal—because there may be several ways to measure the same health outcome.

This combination of tools are mostly unfundable by venture capital. Traditional NIH grants can fund the early work like academic discovery studies, but they do not fund the kind of integrated, milestone-driven, multi-institutional program required to take a biomarker from “promising in-lab” to clinical validation and regulatory qualification. The NIH HEAL Initiative has made important investments in pain biomarker discovery and validation around opioid addiction, but HEAL’s grants are understandably not concentrated in a single coordinated program.

What is missing is therefore the coordination layer—the entity that creates pre-competitive partnerships bringing clinics and academic medical centres together with biomarker and device companies, drug developers, and regulatory agencies under unified governance. A “general manager,” if you will.

The role of philanthropy is to fund the pre-competitive R&D that unlocks downstream funding, often by private markets.

An effective program must fund across the technology readiness spectrum, mostly staying in the Pasteur’s Quadrant, using stage-gated milestone structures (like those used in ARPA-style programmes) that down-select from several approaches to a select few in a “competition” style, bringing the final one or two through to the final exam stage. All this while engaging regulators from day one, to make sure the data you generate is what they actually want to see.

This is what impact philanthropy is capable of. It can fund shared infrastructure and pre-competitive collaboration, take coordinated, long-horizon bets that neither government grants nor venture capital are structured to make at first. Later, once the TRL of the product (biomarker) goes up, so does its fundability and likelihood of follow-on private funding.

The question that remains is where to start. Not all chronic pain conditions are equally suited to be the proving ground for a composite biomarker programme. The ideal first target would be a condition where the clinical need is acute, where the structure-pain discordance is severe enough that objective measurement would be transformative, and where the patient population is large and motivated to support the clinical recruitment required for validation.

Ultimately, I believe identifying a chronic pain biomarker in even just one disease would begin the transformation of multiple chronic pain conditions making the end of chronic pain inevitable.

If you’re someone “not limited by the resources directly under your control,” then you might resonate with this.

The name of the game

How many times have you been rate-limited by somebody emailing you back?

How many times have you had to ping somebody just for them to take 5 minutes or 5 seconds out of their day, to reply a few words back?

How many times did you just need somebody to really, really just go out of their way for you?

Doing something on your own, I think also called entrepreneurship, is hard because you’re trying to bend the arc of a “business-as-usual world” into the version that you imagine. This is fundamentally difficult because the universe and entropy are pushing everything in a direction that’s not in your specific direction.

I think this is the inherent difficulty in starting anything. If it was easy, someone else would’ve added up the pieces and done it by now.

When doing hard things, people never follow up

Sounds corny, but I think you need to think of yourself as the conduit for change.

It’s a distinction from being the actual “inventor” or “genius” that comes up with the idea per se. There’s lots of those people in the world, but also evidently there’s a lot of bad ideas that get too much money and hype. So what makes a truly good idea, and how do you know that what you're doing is really worth it?

Well, I don’t think you do, at least, not in the purest sense. I think doing something bold and ambitious looks like “conviction that just came to you” from the outside, while really feeling like you’re fighting against every impulse to go back on your steps. The “conviction” you put out into the world isn’t as much of a signal as it is a self-affirming activity to make sure you’re not completely going off the rails. Because over time that all shapes into the amorphous thing of “resolve” and grit that brings you to those heights.

When people don’t follow up quickly, a part of you should know you’re doing the right thing, and it’s completely natural and human to doubt yourself.

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Always follow up

If someone else could do it, they would’ve done it by now.

I now comfortably send 3-4x follow up emails to people every 4-7 days if people don’t respond. More, if it’s truly urgent. Make things as easy as possible for the other person. Blurb away, keep them in the loop, be honest and ask for help. To be kinded, you must be ready. You must accept that you cannot do it alone. Ask for help, because almost no one is actively wishing for you fail.

I hope this gave you motivation to follow up on that thing you’ve been dreading. Keep going. Most of all, I hope you replied to that message someone else was following up with you about. I had to stop writing this a few times to follow up on some things myself.

1. A great piece by Leah Sargeant in Asterisk Magazine on the elite filtering system of high school science fairs. As a kid who did these fairs, hard agree.

2. Netherlands will create their own ARPA, NADI.

3. Ever heard of 3G Capital? Neither have I, but they’re behind Burger King, Tim Hortons, Kraft Heinz, and more. Colossus mag once again covers an incredibly interesting story of one of the least known, and most successful, PE firms.

4. VIPerturb-seq from the Satija lab combines pre-sequencing phenotypic enrichment and combinatorial indexing for low-cell-count, high-signal, high-throughput perturbation screening.

5. Thomas Norman group (also Perturb-seq fame) ran the largest perturbation screen with 665k pairwise genetic interactions across 46M lineages with novel PORTAL + CAP cloning methods. [HT Wyatt]

6. Cambrian Bio receives $30M in ARPA-H PROSPR funding to test… an mTORC1 inhibitor?

7. Britain is one of the few countries where post-graduate earnings have dropped since 1997.

8. Pope Leo asks priests to not use AI to write their homilies

This is my personal mission and process of learning in public to solve chronic pain. Always open to feedback, discussion, collabs, etc. Please, never hesitate to reach out.

“Why quantify pain, when I can just ask the patient right in front of me?” - my doctor

For a long time, the neuroscience community mostly scoffed at the idea of objective markers of pain. Pain, by definition, is subjective. It is what the patient says it is. For decades the field treated attempts to quantify pain as somewhere between misguided and dangerous—an opportunity to dismiss the very patients we’re supposed to treat.

That scepticism was reasonable. It was also, in hindsight, incomplete.

Today chronic pain costs the United States an estimated $722 billion per year in medical care and lost productivity. It affects more than one in five adults, and we still measure it the same way we did forty years ago: by asking patients to rate their pain on a scale from 0 to 10.

Patient-reported outcomes should not be replaced, and they are not the problem. The problem is that they are the only tool we have, and for certain critical questions—Why does this patient still hurt after successful surgery? Will this drug work for this particular person? What kind of pain is this, mechanistically?—a number between 0 and 10 simply cannot answer such nuanced questions.

Biomarkers are tools that help quantify complex health symptoms and even predict medical events. From cholesterol levels to heart attack risk, biomarkers are everyday tools that have many use cases. So where are they for chronic pain?

There have been many efforts to quantify chronic pain and create biomarkers for them, each with varying levels of success. This piece overviews the suite of promising chronic pain biomarkers, their strengths and weaknesses, and the approach I believe is critically under-valued.

Autonomic sensing: measuring the body’s stress response

The most intuitive way to measure pain “objectively” is to look at what the body does when it’s in pain. When experiencing pain, your pupils dilate, skin conductance rises, and blood pressures spike. A handful of companies have built devices around these signals that monitor nociception by tracking pupil dilation, skin conductance, or heart rate variability. These have been developed almost exclusively for intraoperative use in anaesthetised patients who can’t self-report their pain, and several have regulatory clearance for narrow indications (e.g., Medasense PMD-200, PhysioDoloris ANI).

These tools have been useful in specific contexts like real-time feedback to reduce opioid overshoot during surgery, but they don’t work very well in awake patients. These sensors that measure autonomic stress response related to nociception also pick up effects of anxiety, fear, and medications, confounding the “true pain” signal.

In late 2024, ARPA-H funded several projects within their Sprint for Women’s Health portfolio to measure chronic pain, funding an exciting suite of hardware methods to measure pain. Though the performers’ output is not public, the crux of these projects is likely to separate that “true signal” of pain from the natural biological “noise” from stress and medications. On top of this, it’s likely even more difficult in chronic pain patients, since women with chronic conditions like endometriosis may have entirely normal autonomic responses during a painful flare-up because their nervous system has slowly adapted from the years of pain.

Autonomic approaches are useful in specific contexts, but they are usually intensity measures that tell you the magnitude of that nociceptive signal, not about what kind of pain is driving it. And for awake chronic pain patients, they often have too much noise to be used on their own.

Neuroimaging: “seeing” pain in the brain

This idea gained momentum in 2013 when Tor Wager and colleagues published a landmark study in the New England Journal of Medicine describing what they called the Neurologic Pain Signature, or NPS—a machine-learning-derived pattern of fMRI brain activity that could predict whether someone was experiencing heat-induced pain with roughly 93% accuracy. The NPS was specific enough to distinguish physical pain from social rejection, from the anticipation of pain, even from the memory of pain, while being sensitive enough to analgesic drugs.

But the NPS was developed and validated on healthy volunteers experiencing acute thermal pain in a scanner. In acute pain, nociceptive input drives a relatively predictable pattern of brain activation. In chronic pain, many of the same regions are still involved, but the dominant signals shift toward areas of the brain associated with emotion, memory, and reward.

Hashmi and colleagues showed that as back pain transitions from acute to chronic over a year, its brain signature migrates from sensory circuits to emotional ones, making signals of chronic pain difficult to tell apart from emotional distress. That migration is a problem for specificity, but it is also an intriguing observation about pain type—it suggests that neuroimaging may soon distinguish pain mechanisms, not just pain intensity. The field, however, has mostly pursued the question of how much pain a patient is in.

This is then the crux for neuroimaging-based biomarkers: the brain regions most active in chronic pain overlap substantially with those involved in depression or anxiety—conditions that commonly co-occur in chronic pain patients. Traditional machine learning methods have tried to distinguish chronic pain patients from healthy controls with reasonable accuracy (70–92%, depending on condition and method), but distinguishing one chronic pain condition from another, or separating pain from its common comorbidities, is also incredibly difficult.

There are also practical barriers. fMRI is expensive, needs a controlled environment, and a cooperative patient lying still in a scanner, generally unsuitable for a standard clinical workflow. EEG is cheaper, portable, and has shown altered oscillatory patterns in chronic pain patients, including recent EEG work in endometriosis-related pelvic pain, but signals are noisier and harder to localise.

The general consensus after two decades of pain neuroimaging is that neuroimaging alone will not produce a viable pain biomarker. It is the only approach that can directly see the central nervous system process pain in real time, but poor specificity for chronic pain versus its psychiatric comorbidities, plus the cost and infrastructure burden of fMRI, make it nearly impossible to deploy clinically on its own. But neuroimaging combined with other modalities might close that gap.

Molecular: blood, saliva, and other biofluid markers

Chronic pain involves sustained neuroinflammation, shifts in circulating cytokines, and metabolites that should be detectable in blood or other biofluids. If it worked, a blood-based pain signature could plug straight into existing diagnostic infrastructure—clinical lab tests, companion diagnostics for drug selection, even direct-to-consumer panels.

Early efforts looking at individual markers like CGRP and interleukins were rather disappointing. After over a decade of searching, the field acknowledged that no single molecule reliably separates chronic pain patients from controls, let alone distinguishes one type of pain from another. The signal-to-noise ratio was too low and the biology just too heterogeneous.

More recent work using high-throughput proteomic and multi-omic platforms has been more promising. Rather than looking for one molecule, these approaches look for patterns across hundreds to thousands of analytes simultaneously. Circulating miRNAs, epigenetic methylation signatures, and multi-protein panels have shown early ability to profile chronic pain conditions and predict treatment responses.

In endometriosis specifically, multi-omic profiling has begun to identify molecular subtypes that may correspond to different pain phenotypes—a finding that echoes the broader emerging insight that pain heterogeneity, not pain severity, is where molecular tools can add the most value.

But these tools are still discovery-stage. Most studies are small, cross-sectional, and lack independent validation. The road from “we found a multi-marker signature that separates diseased from healthy” to “this test works reliably in a clinical setting across multiple sites” is long and expensive, with a large graveyard across many fields. The critical question is whether peripheral biofluid signals, which are far removed from central pain processing, are really measuring pain or just the inflammatory disease that happens to cause it.

Quantitative sensory testing: measuring the nervous system

Rather than looking at the brain or the blood, quantitative sensory testing (QST) applies controlled stimuli like heat, cold, or pressure, to measure how the patient’s nervous system responds. The pattern of responses can reveal different pain phenomena from hyperalgesia to allodynia to understand nerve damage and treatment direction. Today QST lives mostly in academic pain centres and specialty clinics, but it’s increasingly being explored as a stratification tool in clinical trials.

QST has been particularly informative in understanding central sensitisation—the phenomenon where the central nervous system amplifies pain signals, producing widespread pain sensitivity that is out of proportion to the damage to your body. The Translational Research in Pelvic Pain (TRiPP) study, for example, used comprehensive QST to show that women with chronic pelvic pain have widespread central sensitisation beyond the pelvis.

But it has significant limitations as a clinical biomarker. For anyone that has done a Von Frey filament test, including myself, the protocol requires well-trained operators, specialised equipment, patient cooperation, and it still depends on the patient’s subjective report of when a stimulus becomes painful. It is “quantitative” in its stimulus delivery but ultimately subjective in its endpoint.

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Severity or type?

There’s a pattern across these approaches. Autonomic sensing and neuroimaging have overwhelmingly focused on quantifying pain severity, asking: “How much pain is this person in?” Molecular and QST approaches have begun to ask a different, and arguably more important, question: “What kind of pain is this person experiencing?” Though objectively measuring pain severity is important, the use of such devices is most attractive when self-reporting is unavailable (e.g. non-verbal or sedated patients), while deploying this tool to verbal adults comes with immense ethical risks.

Objective measures of pain type however, remain under-developed, despite their cross-cutting relevance across nearly all chronic pain conditions.

There is an emerging consensus in pain neuroscience that most chronic pain can be roughly categorised into three mechanistic types:

• Nociceptive pain: caused by tissue damage or inflammation. Happens in diseases like arthritis, where the degrading joint cartilage produces inflammatory signals, making you feel pain. NSAIDs and surgery often address this.

• Neuropathic pain: damage to or dysfunction of the nerves themselves. This happens in diabetic neuropathy for example, where years of high blood sugar damages the small blood vessels around your nerves, damaging the nerves themselves and causing that numbing, burning or electric pain.

• Nociplastic pain: a relatively new concept that’s becoming accepted. It occurs when the central nervous system itself becomes molded like plastic, amplifying and distorting pain signals even when there’s no physical damage to your body anymore. This is the “everything else” bucket for conditions with no clear physical cause, from fibromyalgia to lasting pain after “successful surgery.”

Chronic pain treatment is hard because these three types can coexist in the same patient. A patient with knee arthritis might have inflammatory lesions causing nociceptive pain, nerve involvement producing neuropathic pain, and years of unmanaged pain that has produced central sensitization and nociplastic pain—all at once. Three patients with the same medical condition could have three entirely different pain mechanisms. This kind of heterogeneity is well accepted in oncology and other chronic diseases, but pain medicine has lacked the tools to do it.

This is where the real promise of objective measurement lies. Not in replacing the 0-to-10 scale, but in identifying dimensions of pain that subjective reports cannot reliably capture. A patient can tell you that her pain is an 8 out of 10. She cannot tell you whether that 8 is driven by persistent inflammation versus central sensitisation, despite that difference completely changing her treatment course.

For decades, the field has been trying to create a better pain meter. What we need is pain typing.

Chronic pain conditions are more similar than they are different

Osteoarthritis, fibromyalgia, migraines, inflammatory bowel disease, endometriosis—these conditions are studied in isolation by different medical specialties in siloed research communities.

But diseases that cause chronic pain are more similar than they are different. Inflammation from arthritic joints causes pain similar to endometriosis, just like how endometriosis nociplastic pain can look similar to that of IBD. This overlap suggests that a biomarker that can reliably distinguish pain types in even just one disease would unlock our understanding of chronic pain across all diseases.

The specificity here is not solely to the disease, but to the mechanism of pain itself.

What’s missing?

There are now many tools to measure and quantify pain in different ways. Multiple modalities from neuroimaging, molecular assays, sensory testing, and patient-reported outcomes each capture different dimensions of underlying biology that can now conceivably be processed with AI/ML methods. The pieces of the solution exist, but assembling them into a single, validated, clinically deployable tool has proven to be a very different kind of problem—one that is not only about the science but also about the structure of how biomarkers are brought into this world.

That is the subject of the next essay.

I became a chronic pain patient three years ago, in a bad skiing accident. I tumbled ~400ft down a mountain in Tahoe and dislocated and tore the ligaments in both shoulders—my right for sure, probably my left too. Both immediately slipped back in on their own, and because I’d never been injured like that before (plus, adrenaline), the lingering weakness didn’t seem like a big deal to me—or, puzzlingly, to my GP.

My injury went unaddressed for nearly two years

One torn ligament rewired everything. My posterior ligaments tightened to compensate, my chest weakened as my lats grew tighter, nerves flared, my shoulder subluxated over a dozen times. Through half a dozen PTs and doctors, no one suggested an X-ray or MRI. They would just ask me: “How bad is your pain on a scale of 0-10?” Um, a 6?

Physically, I felt okay. Mentally, it completely changed what I thought I was capable of. This mismatch between how I looked and what I could do was confusing.

I was in denial of my situation, subconsciously rewiring my brain to avoid anything that could cause pain. Working out, climbing, tennis, ping-pong, lifting boxes—I stopped it all. In a vicious cycle, my body grew weaker and weaker, stillness the only way to avoid pain.

One day, I somehow found it in myself to look for an answer.

After about three months of wrangling the American medical system, I was finally referred to a competent orthopaedic surgeon who immediately suggested an MRI. Turns out, I had a tear in my right anterior labrum. It explained all my pain, subluxations, weakness, and compensated muscle and ligament damage.¹

The relief of anxiety, and borderline euphoria, after hearing a diagnosis is hard to explain with words. When you live with unknown pain for long enough, the uncertainty starts to eat away at your soul. You wonder if any of it is real, whether it’s just a random thing like a migraine that’s supposed to go away on its own. Everyone including you, especially you, starts to downplay your symptoms. You deny the pain, avoid the simple triggers that once brought joy to your life.

Eventually, your “initial complaint” becomes a footnote in your life from years ago, giving everyone just enough space to ask: “are you really still even injured?” It makes you doubt yourself, your pain, your thoughts, everything. So when a doctor finally says “yes, this is real, and we can fix it”, I was really, really excited.

All eyes on me

The procedure was straightforward. The odds of complications were low, and the Xanax they gave me for my “pre-op anxiety” smoothed the edges while the anaesthesia felt like I transported through space and time. I don’t even remember the nerve block.

I woke up from the surgery and I was ready to go. My partner sat next to me taking hilariously bad pictures while the nurses gave me extra orange juice for the road. After just 15 minutes, I was in my sling, calling my Uber outside with my other arm to go home. Just like that.

Reality set in a few hours later. First, I was visibly disabled—overnight, I became something that people now needed to “work around.” I was locked in a sling for six weeks, and not a very sexy one at that. I couldn’t do my own laundry, couldn’t carry a backpack, ride a bike, most doors are made for two-handed people apparently, and the bus doesn’t give a shit whether I find a seat in time.

It was tough to lose physical agency, but it wasn’t that bad. I knew what I was getting into. What was hard wasn’t my physical disability but my perceived disability from others. Out of love, friends constantly over- and under-assumed what I could do. They’d offer to do things I’d rather do myself, not knowing I already started PT, that I could still hold things, get in the car alone. The sling was mostly insurance against jerking my arm on a bus pole, but how would they know that?

In the wider world, nobody noticed. It was freeing at first, but that novelty faded quickly. Every bus that drove off before I sat down, the sharp glances at restaurants when I awkwardly cut my food and ate with my left hand, the rush of shoppers in CVS who pushed me aside as if I wasn’t clearly injured. No one cared. Pure, genuine indifference. I was shocked at first, but I mean... why would they care?

Invisible

In the sling, people over-corrected. When it finally came off, they corrected in the opposite direction. As unsexy as it was, my disability was clearly visible at first.

We all have an image of what a person with a “disability” looks like. But when that visible sign goes away, most people assume you’re just “healthy” and “normal.” With the sling, at least people could see something was wrong. Without it, I was just another young, healthy-looking guy walking around SF.

Nobody could tell that I couldn’t hold my arm above my shoulder, that my arm would give out if I held anything heavier than a box of pasta, that I’d spend thirty minutes massaging and fidgeting my shoulder on a bad day. People would bump into me or hit my shoulder on accident, and a piercing pain would still shoot through my body because I was still just too weak. For four, five more months I lived in this strange in-between—too recovered to look injured, too weak to do most of what people assumed I could.

The worst part was how small it made me. Not the pain itself, but the flinching. The constant, quiet calculation of whether I could open a door, carry a bag, shake someone’s hand. I became someone who planned around pain, who arranged his whole life around avoiding a thing that no one else could see. And the longer no one could see it, the more alone I would feel.

I think the most isolating part of chronic pain most people don’t realise is that it really is invisible. If it hurts, you stay home. You’re usually only out in public when you feel mostly okay. And if you’re in pain while you’re outside, you just deal with it in silence.

That silence—between what I felt and what the world reflected back—was the loneliest place I’ve ever been. It took a long time to build strength, mobility, and my self-confidence back. I don’t think there was a single moment where I decided to “stop being invisible.” But I wanted to write this for anyone else out there who might be feeling the same thing.

Now

It took a long time to feel more “normal” again. Months of rehab, so many gym sessions, hours and hours of stretching and yoga that I used to make fun of. I think the first time I really noticed was when I carried a heavy bag of Costco groceries up the stairs without a piercing pain. I can now carry my laundry down the stairs again. I can reach for a cabinet without bracing myself. I can ride a bike, open doors with one hand or two, and the bus still doesn’t give a shit whether I find a seat in time.

But the pain never really goes away. Some days, it’s not so bad. Some days, I’m not in any pain at all. But I know my body will never be the same. My shoulders pop, lock, and throb almost randomly. Ibuprofen sometimes works, but not always.

It’s so easy to slip away into your mind, paradoxically trapped thinking that you’re not allowed to talk about your pain because someone might judge you. But they won’t. And the ones who do?

They were never worth your time anyways.

¹ UCSF has an excellent care team, who immediately found an operation date and a series of pre-op PT sessions to match. Dr. Drew Lansdown was fantastic, and Dr. C. Benjamin Ma is great too. The UCSF PT office is absolutely top tier... Highly recommend!!

What can we do about it?

To this day, I just wish the doctors believed me on day one.

Even after a whole surgery and months of rehab, I’m still in pain. They say there’s nothing wrong with my body anymore, but so what? As a scientist, I know that chronic pain care is complicated.

We know there are at least three different types of chronic pain:

• Nociceptive Pain: Physical tissue damage like a bruise.

• Neuropathic Pain: Nerve-level, “electric” or “burning” pain from stimulation or damage to the nerve.

• Nociplastic Pain: When structural damage and pain severity is uncoupled. You look “physically okay” but still in a world of pain. A sort of “phantom pain.”

Currently, the only way for doctors to measure chronic pain is to just ask:

“How bad is your pain on a scale of 0-10?”

To this day, we compress this complex, multidimensional experience into a single number. In doing so, you lose a lot of nuance in these three types of pain, giving room for doctors to doubt you, under-diagnose you, and under-treat you.

This creates rippling systemic effects beyond your GP’s office. The 0-10 scale is still the main primary endpoint in clinical trials for diseases that cause pain. This includes osteoarthritis, migraines, endometriosis, chronic lower back pain, and more. We know >95% of all clinical trials fail not because the drug doesn’t work at all, but because they don’t work on enough people.

I think chronic pain care has a match-making problem, where we’ve probably created some really great therapies in the past, but they failed in trials because they only worked for some people (e.g. nociceptive pain patients) and not for others (e.g. neuropathic pain patients). I believe this is the case for many chronic pain diseases. A new tool that makes the many dimensions of chronic pain visible and measurable — even in just one disease at first — will transform all chronic pain care.

What if chronic pain could be measured and typed like we do blood types?

If there was a “chronic pain test” that could’ve worked with me to describe my pain on top of the 0-10 scale, maybe my doctor would’ve paid attention to me at the first visit, and maybe I could find a better treatment today.

So I’m going to try to make it.

A non-invasive ‘chronic pain test’ is now within reach. I’ve begun assembling a coalition of world-class chronic pain researchers, clinicians, founders, and leaders who believe the same. We’re building it together, and I’ll be writing more about the path forward soon.

Promise this S*bst*ck isn’t too political. Now, to the links!

1. Is it once again time to buy the dip…? Bitcoin also had… a rough week.

2. A staggering 45% of Americans are now Independent. Oof.

3. How will the miracle happen today? A beautiful piece by the great Kevin Kelly on giving & generosity, with lots of transferable learning with philanthropic fundraising imo. Kevin Kelly on Substack? Insta-Follow.

4. A nice piece with a strategic framework for context-preserving target discovery by age1 VC.

5. TrumpRx is here. It’s basically just a web portal that re-directs you to the manufacturer website to buy Wegovy straight from Lilly, for example. Nothing new, but I think it’s a net-positive for Americans who often overpay for drugs.

6. Speaking of drugs, GLP1 copycats might finally be cracked down by the FDA. Will Hims finally get the slap in the face they deserve? [s/o Alex Kesin]

7. Is meme culture diluting the narrative of Epstein being a monster? Maybe.

8. Dominic Cummings writes more things. As a deeptech/biotech/etc expat in London, I feel obligated to learn how people here think about things.

9. USAID is still alive! Albeit smaller, as DIV fund, a $48M philanthropic fund anchored by Coefficient Giving.

10. Capitalism delivers free groceries before socialism [HT Cremieux]

Startup founding doesn’t have the same respect and admiration like it used to.

As Michael Dempsey recently wrote, it's become more of the default thing to do that “shows you’re smart but not that interesting”. The path indeed has become the “default” for many, and clowning the pre-revenue founder has never been more fun.

This sentiment feels rather strong in the “metascience community” that pursues “untraditional funding” from public funding (e.g. Tech Labs), philanthropy, grants, and others. This community, that I’m very much a part of, often rejects the traditional paths because “not all companies are supposed to be unicorns” or “VC funding valley of death won’t let me work on my deeptech MVP”.

This is often all true, but sometimes I feel as though we miss the bigger picture in pursuit of the renaissance we’re trying to create.

Startups vs. non-traditional funding aren’t mutually exclusive—and nowadays, most founders need to be open to both. But usually, for most founders building ambitious projects with minimal credentials, startups remain the fastest, most direct path to proving yourself and learning what actually works.

Sometimes, the fastest way to build credibility is to do a startup and fail. Then do it again. Then succeed. That iterative cycle—the wins and the brutal losses—is what teaches you to sell, to execute, to lead. It’s what gives you the credentials that every funder, whether traditional or non-traditional, cares about in the end.

I hope this post can be a quick gut check for you and your research.

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“What are your credentials?”

Credentialism is bad—at least that’s what you’d think if you’ve been SF-pilled like me. But credentialism is what the “real world” runs on, which, turns out, is most of world outside of Silicon Valley.

Being in the startup ecosystem, scrolling on X, going to “networking events”, you feel like “the world” is permission-less and you’re allowed to do anything you want and people will give you money for it. But the truth is, most of what you interact with today in the “innovation ecosystem” is a result of nearly two decades of VCs in Silicon Valley slowly determining that young, low-credential talent was best place to find upside in this ultra-high-risk investment category. The rest of the world for the most part, including philanthropy, government funding, and many “untraditional” grants, want credentials.

You want to turn your research into a product? Who’re your customers? How will you find them? With what experience?

You’ll build out a team for it? Have you done that before? What’s your experience?

First-time? Oh…… why don’t you just do a startup instead?

Though not all untraditional founders are like this, startups are still to this day the best place to build bold, expensive projects with near-zero credentials. At the end, the core components that go into ambitious projects are the same: great leadership, technical product, team, execution, and lots of money. Where’s the best place to first prove your ability to bring together any and all of these things?

Startups.

The churn is brutal, but startups remain one of the best places where failing upwards is encouraged, and actually becomes social capital.

“What’s your _____ about?”

You gotta know how to sell.

It’s a tough, mind-bending skill for most scientists, but you’re a dead fish in the water if you can’t do it well. Though almost all scientists are nerds at heart, including me, Scientist-Founders in Metascience especially can feel a bit… too nerdy.

I can see why the field over-selects for that phenotype. Niche, ambitious research with no established market or proven commercialisation track is intriguing to the metascience community. But will this person really be able to sell themselves and their research to a non-technical philanthropic advisor?

In this arena, you don’t submit a 30 page grant and call it a day. You need a rock-solid 30 second pitch, who you are, your theory of change, and why it can’t be a startup or any other traditionally legible modality. You need to hop on planes to meet funders in Dubai, follow up on every single lead like you don’t know who’s going to write you that check you so desperately need.

Where do you learn how to do this? Where do you find that grit? Where do you learn to just eat rejection after rejection, fall over hard, fall over again, and get the hell up?

Startups.

The constant pressure of delivering and the ever-increasing valuation on your term sheet forces you to articulate your vision with precision. It's a skill that's often forged under massive pressure.

Execution is everything

Execution usually matters more than the funding mechanism itself. You can clown startup founders owning 5% of their company by the IPO all you want, but what matters most is that the work was done. Sometimes, we reject accessible dilutive options that would allow us to start executing today in favour of the elusive, non-dilutive funding that might be better.

The metascience community feels especially vulnerable to this “paradox of choice” for innovation funding. This caution is partly justified, since many unconventional or completely novel technologies require uniquely high-risk commercialisation strategies or immense capital.

One example where non-dilutive funding made sense was E11bio, an FRO that pioneered connectomics. Connectomics has traditionally struggled to bridge the gap between mapping every single neuron and creating something commercializable, like a therapeutic. But if execution is everything, what mattered most in 2021 when E11 started was simply that it existed at all. At the time, non-dilutive impact funding may have been the best and only way to pioneer this research at scale.

Today, it’s unclear whether connectomics remains a field so nascent that another E11 is needed. Even if its commercial path is not yet straightforward, the field may be mature enough—especially with AI-wrapped narratives—for venture funding to pick it up. As E11 matures as a FRO, we have the opportunity to see which funding mechanism unlocks the next stage of execution.

On the other hand, it seems like dilutive funding made sense for Forest Neurotech, another FRO. Forest recently became Merge Labs—an OpenAI/Sam Altman-backed private company that will apply Forest’s pioneering hardware to non-invasively interface with the brain. Whatever Forest's initial vision, there was a reason it exited into a private company. Hardware is notoriously expensive, and I wouldn’t be surprised if they needed upwards of $200M to reach the Technology Readiness Level they desired. This is where a founder’s execution ability is truly tested; I don’t think Sumner gets enough credit for carving out this incredibly difficult path for Forest.

With most stories of innovation, we only read the version that actually happened. We don’t know the parallel universes we could’ve ended up in if FROs didn’t exist or ARIA wasn’t created. It’s human nature to look at what worked for those before us and pattern-match our way towards that same kind of success.

But the playbook changes all the time. What worked for Forest and E11 may not work again, even for the founders themselves. What really matters in the end might not be the specific mechanism they used to innovate, but whether they were able to innovate at all.

For founders today on the brink of civilisation-scale technologies, the priority is not to search for the perfect funding mechanism, but to weigh the cost of the counterfactual: another week, month, or year where the technology is not taken to the next level at all. Because we’re all fighting for the same thing in the end. We all want to make the world a better place, live healthier for longer, and be with our loved ones in a safe and beautiful world. There are many ways to do this, and sometimes, the money really is just as green on the other side.

So as you prepare for your call with NSF Tech Labs tonight, I encourage you to remember and ask yourself: “could I do this as a startup instead?”

“For many of us, one of the easiest mistakes to make is to focus on trying to over-satisfy the tangible trappings of professional success in the mistaken belief that those things will make us happy. Better salaries. A more prestigious title. A nicer office. They are, after all, what our friends and family see as signs that we have “made it” professionally. But as soon as you find yourself focusing on the tangible aspects of your job, you are at risk of becoming like some of my classmates, chasing a mirage. The next pay raise, you think, will be the one that finally makes you happy. It’s a hopeless quest.”

This question has been on my mind ever since I read the book around four or five years ago. I wanted to write through what I believe right now to be the best ways to measure my life and what I believe is meaningful. Admittedly, it’s changed a lot over the last few years, and I hope in many ways I’m no longer the person I was a few years ago.

For a long time, I treated life as a series of dress rehearsals. But as I get older, I realise that this truly is, to the best of our knowledge, our one and only shot. So the stupid stuff, the corny stuff, the awful stuff, and the great stuff are all a part of it. This will almost certainly be embarrassing for me a few years from now, but I wanted to do it anyways. I'll start with the professional, then get to the harder stuff. This is how I measure my life.

A meaningful life is measured by how many lives we can positively affect

My one mission is to add back healthy years to our lives by curing chronic disease. Life is like a box of sand with a hole on the bottom. The sand leaks, and, to our knowledge, there’s no way to stop it.

Death is scary, but suffering is what can and should be solved in the coming years. But for most health innovation, traditional tech or even past biotech business models aren’t applicable. I’ve spent the past decade trying to cure chronic disease through different modalities.

In academia, I first worked on gene editing and DNA sequencing technologies as a way to find new targets to diseases with no cure like breast cancer and Alzheimer’s. Four years in, I realised academia was not the fastest place to make new therapeutics, so I moved to startups.

There, I had the opportunity to lead multi-million dollar drug discovery teams over another four amazing years, and found drugs for debilitating diseases from heart failure to osteoarthritis. Though they are still awaiting trial submission, I believe one day these drugs could have a transformative impact on people’s lives.

But along that journey, I saw that traditional research couldn't solve certain systemic problems—like the ~95% failure rate of clinical trials, driven largely by animal models that don't reflect human biology. No single company could fix that.

So I sought out Focused Research Organisations as a new, emerging modality for scientific progress that takes philanthropic dollars to applied basic research. Though my FRO, Artemis, did not succeed, in that process I realised FROs are, to put it crudely, like ARPA programs with extra philanthropic steps. So I took the lessons and pivoted toward ARPA-style programs directly.

That brings me to now, as I develop a new program that I believe could bring transformative change to the people I love. ARPA programs create catalytic social change—a kind of change that makes the rest inevitable. Just like how Sanger sequencing the first bacteriophage made the Human Genome Project inevitable, I hope this new program, if funded, makes a flourishing future inevitable too.

I’m still searching for the way that I can uniquely add healthy years to our lives. But if developing ARPA programs has taught me anything, it’s that we’re all trying to make the world a better place in our own unique ways. Most of us, be it tenured university professors to illegible 19 year old EV-awardees in India, are all just trying really hard to help the people we love.

There’s many ways to get to the future we dream of, but we’ll only get there if we all try.

Being a great friend, parent, child, and partner is harder, and probably more important than, your “life’s work”

I struggle with this one.

When you study the greatest founders and business people, you learn that their personal lives are often a mess. Divorced, in debt, isolated, abusive, or just generally troubled. This “holistic” mindset is often expressed in retrospect—founders like Brian Chesky say they wish they hadn’t sacrificed so much to get where they are. But they also know their success probably wouldn’t have been possible without those sacrifices.

Doing great things requires immense suffering and sacrifice, yes. But why is this mindset almost exclusively applied to work? Building a personal life is hard too. It’s harder to make friends as we age, family is complicated, Tinder is just hooking up with extra steps, and fame comes with consequences. So why not value it at the same level as work—or at least a bit more than we do now?

In my experience, it’s often just as hard, or harder, to build great relationships as it is to build great companies. You’re not being disciplined by “locking in”, you’re just running away. Though some great founders may be “cursed” with double-edged sword-type traits that cuts both ways, I believe most founders are capable of attaining an actualised life that’s rich in professional and personal success.

The media celebrates billion-dollar acquisitions as success while caring less about the founder’s personal life. This skews our perception of what normalcy and “success” looks like and by extension what we think we should value.

You’re defined by your actions

There are exceptions, but I generally believe strongly in extreme ownership—that we’re defined by our actions. This is different from outcomes, which can often be disconnected from the intention behind our actions.

What I mostly want to critique is people (including myself) who say things without acting on them. Along the “Words → Actions → Outcomes” spectrum, I think we should all strive to be on the Actions-Outcomes side rather than just saying a lot and not doing anything. I’ve been thinking about this on both the personal and professional side:

Professional:

When I left my previous startup, I wanted to start really building the relationships I had with colleagues, advisors, and people I admired. I saw qualities in them I wanted to embody, and I realised the best way to do so was by taking a risk to pursue my own projects. Although the initial project I launched was not successful, I have no regrets about it. But the one thing I do regret is that I didn’t write enough about my journey in the process.

Learning in public—it’s one of the things I deeply respected about Nadia Asparouhova, for example, but it proved much harder and scarier than I thought. To this day, I still recommend anyone and everyone I meet to write more in public. It’s only brought positive things into my life, and my only regret is that I didn’t write more. My confidence has grown significantly with time, and surprisingly, the more I write, the more I have to say.

Personal:

My grandfather passed from cancer at Christmas 2024 and left my grandmother alone in a rather large house. For an 85+ year old woman who I don’t think weighs more than 40kg, the thought of her being alone was scary—his passing another painful reminder that you never really know when you’ll see someone for the last time. I wanted to spend more time with her, and told her I’d visit in July.

But I ended up going to London and never visited her. She’s doing okay on her own right now, but that guilt has never left me. She’s excited for what I’m doing and rarely says she wants me to visit, even though I know she does.

I was defined by my actions in the end. Defined by not writing enough, not seeing my family enough—just as I might be defined by taking massive swings to do hard things professionally. That guilt, and that experience, have made me think much harder about what I should really be doing with my time. I wouldn’t trade this period of my life with anything else. I’ve been calling it my own “Eat, Pray, Love” era. And like the movie, there’s been plenty of meditation and yoga.

Deciding what to work on

Since I left my previous role, I’ve had the incredible opportunity to reflect on what I value and how I measure my life. A liminal space where I can allocate time and effort to things I never had room for before. For anyone who can financially afford it, I highly recommend creating gaps in your professional life just to think. You realise quickly that there’s a lot of life to live and plenty of big problems worth solving. I promise you won’t be bored.

One of the most important decisions you’ll ever make is what problem you choose to work on. Unfortunately, society doesn’t encourage—or give most people much time—to decide this. The liminal spaces to do this carefully and thoughtfully are scarce, but it’s worth carving out for yourself.

I believe they give you a kind of resolve in who you are. The kind of confidence that lets you stick to your guns when everything feels uncertain. I think it’s one of the scarcest things in today’s world.

So think big, and I hope you can ask yourself: “How will I measure my life?”

1. Will moltbot take over the world? nah

2. Ever wondered what the unit economics are for a Super Bowl Ad? Me too.

3. Going Founder Mode on Cancer—a simply incredible story that expands the scope of what’s possible for patients with complex disease.

4. Alex Kesin & Matthew Pech out with an intriguing podcast episode on Amylin Pharma.

5. What is the total Net Present Value of delaying reproductive aging by one year for 50% of women over 40y? About $9.3 trillion.

6. For many G7 countries, gov’t spending on pensions/social security has gone up while public investment has dropped since 1970.

7. How does China continue to pioneer in AI and tech? What does the founder of ByteDance, the success of DeepSeek, Meituan, Cambricon have in common? The prestigious & selective genius class (free article on X).

8. Several weeks ago, the NSF announced the Tech Labs Initiative. There still isn’t a ton of information on it, but IFP summarised the details in an approachable FAQ format for those curious about the details. Note: teams will be awarded ~$50M/year for multiple years, which is an interesting amount for a new independent org…

9. ARPA-H comes out with an intrinsically disordered proteins program, Biogami. Interesting field with a few players now from Bind Research (FRO) to startups like Topos Bio, etc.

10. Old but gold: Matt Shlosberg (who I realised worked next to me for ~3yrs), wrote some biotech truth bombs that I wish every AIxBio-pilled SF biotech bro could read.

fin

Here’s the thing: you won’t meet a billionaire online.

You, sitting in your 2007 Nissan Altima, in the McDonalds drive-through at 7am, are closer to meeting a billionaire than you are on a social media platform (Fig. 1).

But that doesn’t mean they’re completely out of reach. Like VC, there’s a real, tangible world that you can access, in theory. After a year of trying to raise impact philanthropy funding, I’ve learned the game operates on rules that are rarely written down. This is a field guide describing philanthropic fundraising when you don’t have a last name, a platform, or a Giving Pledge dinner invite.

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“The Game”

Fundraising in philanthropy is similar to venture: you need to learn the game. Which narratives are hot, who the funders are, how the ecosystem works, and executing with imperfect information. Teachers are rare, and most people learn by doing. One key difference between philanthropy and venture, though, are the timescales.

Timing in philanthropy

Impact philanthropy fundraising requires the breakneck execution speed of a startup founder while operating on funding timescales of enterprise sales:

• Timelines for philanthropic giving can take 6 months to 2 years, or 6 days.

One month you might have two to three calls with someone, and the next month they may go silent. There’s limited signal you can reliably extract upfront about decision speed. There often isn’t a forcing function, so you have to operate under this massive uncertainty.

• One day funders are giving, the next day they’re not.

Yes, there are patterns like end-of-year giving or last-minute opportunities. But they rarely repeat cleanly year over year, and past giving in the public domain is a terrible predictor of what they will do next. Often for reasons you will never have access to. When timing is unpredictable, your narrative is the only thing that compounds. I think it’s best to focus on this and craft it very intentionally.

• There’s no free money.

Even in philanthropy, funders want returns on investment within a specific timescale. Not financial returns in ten years like VC, but societal returns on a timeline that matches their life and identity. This makes alignment quite difficult, since two funders can both be “interested in health” and still be misaligned because one wants impact in 18 months and the other is fine waiting five years.

Given that you cannot control timing, the thing you can control best is the clarity of what you are building toward and whether a funder can see themselves in that future. If possible, be “stubborn on vision and flexible on details.” Chance favours the prepared mind, as they say.

Narratives

The most important thing to prepare for impact philanthropy isn’t a pitch deck or a beautiful document—it’s a narrative that compels people to literally pull money out of their pocket for you.

Your narrative should be designed for the specific funders you want to attract. A broad narrative that appeals to many is harder to land than a narrow one that resonates deeply with a few.

For example, “a new philanthropic fund for heart disease in the UK” is a tough sell when the British Heart Foundation already exists. You might have a genuinely new approach—but the narrative has to be sharper than “curing heart disease faster.”

Instead, a focused thesis on microvascular disease (INOCA & CMD) that disproportionately affects millions of undiagnosed women would be a narrative that appeals to specific funders. In this case, it’s specific enough to differentiate from incumbents, yet large enough to be tangible and fundable. Even if the underlying science is simple, identifying the right framing is very hard to do.

Forest → Chloroplasts

Compelling narratives are aimed at the world and at funders, not at scientists. This distinction matters, especially for exceptional technical scientists like yourself.

Scientists are predisposed to think chloroplast → forest. You start with a niche mechanism and pathway, then you push the bounds of human knowledge outwards to eventually find a use case for why anyone should care. This is how scientific discovery is done, but it’s not how you paint a vision of the future.

As a scientist with a technological product or solution, you have to flip your narrative from the “chloroplast → forest” to “forest → chloroplast”. Start with the forest: why the world is broken, what changes if you win, and why now. Then zoom in: the trees, the leaves, and only then the chloroplasts. Another framing is the pyramid principle.

Remember: the people you’ll talk to are smart, PhD-level people who have absolutely no idea about the topic you’re talking about.

The narrative therefore isn’t just “packaging,” it’s the filter that determines whether your idea registers with the person at all. So obsessively think about the first 30sec of your pitch, and imagine what the funder will remember when they only read the first three lines of your 1-3 pager.

There’s no “list”

At least, no list that’s readily accessible to me or you reading this.

Nowadays in VC, there are “fund tier lists” with emails and lists of angels who openly say they want to hear from you. Though I’m not sure if they actually take unsolicited pitches, at least that resource exists in the public domain. They might even have a public email address you can write to, though I think those inboxes have a script that auto-sorts everything to Junk.

But that’s not the case for philanthropy. Most philanthropies openly state they do not take unsolicited proposals. They don’t publish reports on their giving patterns, and there are very few lists that openly disclose the “top organisations for [blank] giving.” Frankly, even if such lists existed, they wouldn’t be very helpful because you can’t even access the people there in the end.

“But Alex, my uncle works at the WEF so he got me a ticket to Davos!”

Congratulations, you played yourself.

You are not Dario Amodei. You’re a broke, 24-year-old on a Fr. 50 shuttle an hour out of town, gripping a hard copy of Machines of Loving Grace, seeking refuge in the Anthropic House at 7PM because you didn’t get invited to any of the dinners.

Being invited to the conferences is not enough. If you’ve been to JPM in San Francisco, you know that the entirety of the conference happens outside of the conference. These meetings are no different—the most catalytic conversations are a culmination of months of online meetings, finally converging in rooms that you haven’t even priced into your mind. If there are high profile people on an “attendee list”, it’s likely that you won’t even see them on the conference floor.

“Lists” are only helpful if you can actually meet those people. Otherwise, it’s just FOMO fuel.

Owl Posting

The truth behind the 2026 J.P. Morgan Healthcare Conference

Note: I am co-hosting an event in SF on Friday, Jan 16th…

Read more

8 months ago · 156 likes · 26 comments · Abhishaike Mahajan

Seeking Alpha

For those who fortunately don’t know what JPM is, another way to think about it is through the “efficient-market hypothesis.” Though philanthropy isn’t actually an efficient market, if you imagine for a second that it is, more things start to make sense.

In the public markets, all available information is accurately reflected in the share price, aka “priced in”. Apple’s share price already accurately reflects this year’s new iPhone release. But people still make money. They just know what everyone else knows, and then starts looking where no one else is looking.

In philanthropy, the equivalent of “alpha” is not “a list of past gifts.” Past performance doesn’t predict future giving. The real alpha is in the why.

If building philanthropic funds in women’s health for example, you might want to figure out why the Gates Foundation started a $2.5B fund for women’s health, why Nuttall published their interesting RFP, and why Pivotal gifted $100M to Wellcome Leap. If you’re trying to create new metascience institutions like the Arc Institute, it helps to know why it was established, which similar efforts never got off the ground, and what transferable lessons remain.

All this isn’t necessarily a prerequisite to raise large philanthropic gifts, and it’s far from the only thing you need. But this level of understanding will be an asset when you’re searching for the right philanthropic funder in real time. At the very least, it’ll help you separate noise from signal to find unique opportunities that potentially only you can find.

Playing The Game

Like startup fundraising, philanthropic fundraising requires the founder to have multiple moving pieces in place. You need:

• an excellent narrative

• forest → chloroplasts communication

• a lot of patience

• “tacit knowledge” to find “alpha” where no one else is looking

• and the ability to get into the right rooms at the right moments

Fundraising feels like a series of pitches, but for many people it is more like an apprenticeship in understanding what funders actually care about. Once you understand which funders care about what, the timing, scope, and narrative start to snap into place.

If I did a good job, I’ve now taught you that “the game” cannot be taught, only learned. So go learn it, then go play it. How you play it with your narrative, in what direction, with which funders, is on you to figure out. It’s bespoke, confusing, painful, and long.

But I believe it’s worth it.

Philanthropists aren’t on LinkedIn. But sometimes, honestly, they’re on X. So, you know, there’s that.