Epistemic status: I work on AI safety communications, policy, and field-building. High confidence in the core claim that donors should be front-loading their giving. Lower confidence on magnitudes, recruitment strategies, and the activities of existing funders.
TL;DR: A large wave of philanthropic capital will enter the field, but it will arrive slowly and unevenly. This means that the neglectedness and tractability of different interventions will dramatically change. The field sorely needs unhobbled donors who can give fast before the wave, and seed the neglected projects megafunders will not.
"A good plan, violently executed now, is better than a perfect plan executed next week." - George S. Patton
Individual donors and small grantmakers need to radically rethink their priorities, deployment timelines, and risk tolerances.
The world is finally waking up to the coming wave of philanthropic capital. Attention is rightfully shifting towards strategy and talent bottlenecks: what are the needed organizations and interventions, and who will make them happen.
But the days of being constrained by capital aren’t over.
We have no guarantee of how much money will get deployed, by when, or to what. Important, high-variance bets will likely remain unfunded. And capital is not flowing fast enough into the rapid grants needed to prepare.
What this means is those willing to act today are incredibly leveraged. They can fund the projects that will become dramatically more neglected, and seed efforts that will be newly tractable at scale.
This post lays out the need for unhobbled donors: the missing category of funders who are willing to deploy capital before the wave, support early-stage projects, take risky bets, and put their names behind public campaigns.
Leverage
Discount Rate
The discount rate on spending is extremely high. A dollar deployed in 2026 can get you things a dollar in 2028 cannot.
Political windows are closing. The midterms end in a few months. The Trump administration is developing its stance on AI. AI safety-conscious candidates are running for political offices. The story of 2026 will define what people run on in 2028. Money that arrives after these windows close will have a much smaller chance of affecting policy decisions.
Talent pipelines are still developing. Strong field-building programs can recruit smart young people now who are deciding whether to work on capabilities, safety, or policy. Interested founders can enter the space, engage with the threat models, and develop conviction in solutions. Researchers can go through established pipelines to get mentorship and build taste. However, this talent needs time to get its bearings before doing useful direct work. Onboarding new talent in time to contribute will become harder and harder.
Building credibility takes time. Institutional organizations ideally need years to develop track records and earn trust from policymakers, media, or the labs. It is difficult to find late substitutes for this time in fields where credibility is important. Building organizations in general also takes a non-negligible amount of time.
Timelines may be short. Will more capital even be useful in 2028 or 2029? If timelines are short, new funders may simply not be ready to deploy money before crunch time. The cost of existing donors giving too early is small compared to giving late. They are the only ones who can.
First-mover advantages. Agenda-setting is very powerful. It allows you to get more for less. What will advocacy groups be fighting for? Will AI safety remain a nonpartisan issue? What policy paradigms will key stakeholders consider? Leopold Aschenbrenner amplified the race dynamics frame in Situational Awareness. The first people to define the frameworks, paradigms, and words to make sense of the current moment will dictate how everyone else acts. Attention will only become harder to compete for over time.
Comparative Advantages
Importantly, money is not totally fungible. Some kinds of giving can only come from specific kinds of willing donors. If a funder is unhobbled, they can have an outsized impact.
Funding sources affect influence. Watchdog organizations and third-party evaluators need credible distance from the labs and connections to the groups they represent. METR cannot take money from the OpenAI Foundation. However, individual donors can write checks that do not compromise a recipient’s standing. This is harder with lab-adjacent funding exposure. In advocacy, unhobbled donors with diverse political backgrounds can be counterfactually responsible for making new, highly important policy campaigns possible.
c4 dollars are hard to come by. Most existing AI safety money is c3, which means it is tax-deductible and limited in its ability to be used for lobbying. c4 dollars are not tax-deductible and have no such limit. Available c4 capacity is small and valuable. Many donors are international, unwilling to forgo tax deductions, or hesitant about funding political projects. Megafunders will likely not make these grants either. Political action is highly neglected, and hobbled individuals are best positioned to close this funding gap.
Hard dollar donations are capped. Direct contributions to political campaigns are capped at $7000 per donor. This means that the ability to influence campaigns depends largely on donor count. A large number of small donors can have a much larger influence than a few large ones, who are unable to affect change with just check size.
Public giving has power. Named giving can accomplish things anonymous giving cannot. An unrestricted, confident public donor, who puts their name behind a cause, can signal that it is serious and credible. These named donors can also play a large role in attracting new donors and creating political coalitions.
Shaping
The grants that donors make now will shape the landscape and determine what gets scaled later.
Seeding matters more than scaling. If new megafunders will be positioned to massively scale future organizations, current funders should focus more on creating new interventions than scaling. Existing megafunders are reasonably good at writing second and third large checks to proven organizations. However, they can struggle to scope or support newer projects. Seeding projects requires high tolerance for failure and fast decision-making. Small, new funders are best positioned for this work.
Megafunders will scale existing organizations. Early on, megafunders will struggle to develop incubation capacity on their own, and will initially be picking from the list of organizations that already exist. This means that individual donors can be counterfactually responsible for not just projects, but megaprojects that could exist because of their early support. This is an incredible opportunity.
Small funders can explore the option space. If you have uncertainty about which strategies will succeed, the right response is to seed a range of approaches. Small, decorrelated donors are better poised to do this than large funders. These donors can develop conviction about interventions that the market might be undervaluing.
Unhobbled donors can test different strategies. An individual donor provides value to the field because they have a distinct theory of victory and risk appetite. Large funders can concentrate capital into a single worldview, which leaves important bets unfunded. A diverse set of funders produces a diverse landscape of organizations that can hedge against the dominant strategy being incorrect. Even if individual donors defer their giving to donor advisory organizations, a diverse range of advisors can produce a similar effect.
Megafunders
Megafunders are not going to produce the necessary actions on their own.
Existing ones are working to change, but are slowed by bureaucracy and capacity constraints. They are not prepared to front-load their giving on the necessary timescales. New megafunders will arrive with their own constraints.
Existing Funders
The AI safety funding ecosystem is a monopsony. Funding is extremely concentrated in Coefficient Giving and Longview Philanthropy. These dominant funders dictate what the field can and cannot do, but are constrained in idiosyncratic ways and are limited in their ability to specialize.
Concentration is more harmful than helpful. Concentration helps credibility and coordination, prevents duplications, and efficiencies of scale. But it has also caused the neglect of many funding opportunities that are now low-hanging fruit for unhobbled donors. Caution can be justified at megafunder scale: with more money, grift and low-quality projects abound, and downside risks for bad bets can partially poison the well for a broader portfolio. Multiple grantmakers and unhobbled donors with reputational firewalls, specialization, and different social graphs will enable more ambitious action.
Institutional structures slow decision-making. Good projects often wait three to six months for funding decisions. Strong projects with motivated teams stagnate or miss windows of opportunity. The Future of Life Institute is reported to hold several hundred million dollars in endowment and paid out approximately only $30M in 2025.
Passive grantmaking is the default. Requests for Proposals (RFPs) are common, but do not work at scale: the most competent people to start new projects are not usually unemployed and waiting in the wings. Active grantmaking, finding strong founders and persuading them to take on specific work, is becoming more common but still rare. The small number of existing funders also makes it hard to give credible commitments of future funding to ambitious founders, which raises the risk of starting an organization. The cultural shift from passive grantmaker to being capable of attracting founders and developing new networks will take time.
Risk tolerance is low. Reputational considerations and risk tolerance disqualify the most important opportunities. Funding will flow to well-known organizations like METR and Transluce, but neglected projects will stay neglected. These projects tend to require a higher risk tolerance: public-facing movement building, organizations representing stakeholder groups (e.g. labor, media, religious communities), relationship-building grants to DC think tanks, and interventions in general that have a more nebulous theory of change but high expected value.
Constraints are not legible. Clearly, these grantmakers have institutional constraints and strategic worldviews that make them appropriately cautious about what they support. Not all of these constraints are legible to the rest of the field. To their credit, CG has recognized the need for decorrelation and a diverse funder base. But the full extent of the gap has not been made clear, and new grantmaking organizations and unhobbled donors have not emerged.
Donor preferences are being aggregated. Sometimes, donor advisory organizations like Longview pool individual donors’ money into a single vehicle. This causes each unique donor, with their own theory of victory and preferences, to get flattened into the same averaged-out worldview. The decorrelation these donors could provide, and their ability to fund riskier, neglected projects, gets erased. Other times, this happens implicitly: donors take their cues from the same centralized funders and advisors, converging on a similar worldview and risk tolerance as megafunders.
The existing megafunders deserve lots of credit, but are (currently) failing to meet the moment.
New Megafunders
New megafunders will face their own constraints. The wave will arrive slower than expected, and not necessarily in the shape the field needs.
The wave will be slower than expected. Some have estimated that hundreds of billions of dollars in philanthropic capital are about to become liquid, largely from AI wealth. But both major funding sources are gated. The OpenAI Foundation has committed $25 billion (~10% of the Foundation’s value), but over an unspecified time frame. The framing of the Foundation as “the largest long-term beneficiary” of the for-profit's growth also suggests an endowment-style approach rather than a serious intention of trying to spend down capital in the years that matter. The Anthropic IPO is even further out: while a tender offer recently occurred, an IPO has not been announced, and post-IPO lockups will force employees to wait months before they can liquidate afterwards. Barring new ultra high-net worth individuals entering the space, the capital might come late, potentially too late.
New megafunders and donors are constrained. OpenAI Foundation faces serious optics constraints and has a similar governing board to the for-profit. Its pillars span a wide enough range that the pillars that are easier to spend on (life science, community programs) might absorb funding first. Work that materially affects OpenAI’s positioning will likely be implicitly off-limits. Anthropic employees, though they have the potential to become unhobbled donors, will likely have their own quirks. Many will be busy and want to delegate their philanthropic thinking entirely to trusted advisors. Some will avoid risky bets or political work that they perceive as outside the Overton window, opting to fund non-AI causes instead. Many will route through pooled vehicles for convenience, recreating the preference aggregation problem that incumbents suffer from.
Building infrastructure takes time. Even new megafunders that want to move quickly will not initially have the operational capacity to do so. Scoping new organizations, incubating founders, hiring staff, and massively scaling interventions all require institutional knowledge and processes. Creating that infrastructure takes time. Whether or not these megafunders will ultimately be successful depends in part on if existing funders and unhobbled donors can rise to the challenge to support them.
Unhobbled Donors
The capital has not materialized yet. Even when it does, the most important projects will remain unfunded. Bold, unhobbled donors are needed to close those gaps.
These donors can have orders of magnitude more impact than large grantmakers. They can fund the most neglected projects that no one else can. They can move quickly on time-sensitive work or on infrastructure that needs years to mature. By seeding new projects early, they shape what organizations megafunders eventually scale.
It has never been a better time to be an individual donor with conviction.
What unhobbled donors do
They deploy fast and make grants directly, not through pooled intermediaries. They are laser-focused on impact rather than legibility or reputational comfort, taking the bets megafunders structurally cannot. They give c4, accepting the loss of the tax deduction. They put their names on public campaigns, engage with the media, evangelize the cause, and actively recruit new donors. They have a theory of victory, a causal story for how their grants will help the future go well, and aggressively front-load their giving to support it.
Recruiting
As the issue gets more salient, we might be on track to get more unhobbled donors by default. But given the importance of speed, we must be more proactive.
There are broadly three ways to close the gap: get existing megafunders to act bolder, activate more giving from individual donors already in the community, or recruit new donors entirely. Each is difficult.
Existing megafunders are trying, but are unlikely to become dramatically bold enough to completely solve the problem.
The most overlooked constituency is existing donors, especially those taking modest risks, splitting capital between causes, and deferring to advisors. We need to make the case to willing individuals that this moment warrants more aggressive deployment.
Recruiting new donors entirely is the most leveraged and the most difficult. The largest pools of recent wealth are mostly implicated in the problem they would be funding to address. People in general do not give, and right-of-center wealth, which would be especially useful for cross-partisan AI policy work, gives least.
The most viable candidates outside the implicated pool are scattered: billionaires and centimillionaires who are becoming concerned about AI, public figures with platforms, founders in adjacent industries. These donors will need to be found, persuaded, and supported by trusted advisors over months or years.
Actions
Existing funders should make demand more legible. As funders attempt to scale and front-load their giving, they should be more transparent about what they will and will not fund. By making their constraints legible publicly, they can more clearly communicate with potential donors about where they can be most impactful. Megafunders should also explore mechanisms to reward upstream funders, such as offering rebates to the previous funders of projects that they decide to scale.
Individual donors should spend differently. Look to front-load giving. Donor swaps allow donors to give to AI safety now in exchange for later commitments to other causes, or the reverse. Anthropic employees can take out loans against future giving. Regranting is a powerful tool for both small and large donors. Platforms like Manifund expose donors to new projects and allow for quick redeployment. Donors should resist organizations or platforms that aggregate their preferences.
The field should build donor advising infrastructure. Most donors who could become unhobbled are not ready to make complex grants on their own, and the field has not built the infrastructure to support them. This is a donor product design problem. New donors need clear default options, trusted advisors who can match them to opportunities without aggregating their preferences into one fund, and pathways into the field that do not require months of learning the landscape. Donors with higher risk tolerances and openness to neglected fields like politics should be carefully advised, and the field should coordinate to effectively allocate their capital.
If you can unhobble yourself, do it. Being unhobbled means giving up the things that donors are usually reasonable to want (reputational cover, tax deduction, confidence and institutional credibility). These are very real costs. But these are not normal times. Are the costs unhobbled giving could possibly have worth more than the direct impact? The dysfunction in the current landscape means there is enormous impact on the table for the ambitious philanthropists who are bold enough to take it. Stepping up to give ambitiously is a true service, and a sacrifice. It’s also exciting and energizing. Between now and 2028, the strategic playing field will be set. Why not shape it?
Epistemic status: I work on AI safety communications, policy, and field-building. High confidence in the core claim that donors should be front-loading their giving. Lower confidence on magnitudes, recruitment strategies, and the activities of existing funders.
TL;DR: A large wave of philanthropic capital will enter the field, but it will arrive slowly and unevenly. This means that the neglectedness and tractability of different interventions will dramatically change. The field sorely needs unhobbled donors who can give fast before the wave, and seed the neglected projects megafunders will not.
"A good plan, violently executed now, is better than a perfect plan executed next week."
- George S. Patton
Individual donors and small grantmakers need to radically rethink their priorities, deployment timelines, and risk tolerances.
The world is finally waking up to the coming wave of philanthropic capital. Attention is rightfully shifting towards strategy and talent bottlenecks: what are the needed organizations and interventions, and who will make them happen.
But the days of being constrained by capital aren’t over.
We have no guarantee of how much money will get deployed, by when, or to what. Important, high-variance bets will likely remain unfunded. And capital is not flowing fast enough into the rapid grants needed to prepare.
What this means is those willing to act today are incredibly leveraged. They can fund the projects that will become dramatically more neglected, and seed efforts that will be newly tractable at scale.
This post lays out the need for unhobbled donors: the missing category of funders who are willing to deploy capital before the wave, support early-stage projects, take risky bets, and put their names behind public campaigns.
Leverage
Discount Rate
The discount rate on spending is extremely high. A dollar deployed in 2026 can get you things a dollar in 2028 cannot.
Political windows are closing. The midterms end in a few months. The Trump administration is developing its stance on AI. AI safety-conscious candidates are running for political offices. The story of 2026 will define what people run on in 2028. Money that arrives after these windows close will have a much smaller chance of affecting policy decisions.
Talent pipelines are still developing. Strong field-building programs can recruit smart young people now who are deciding whether to work on capabilities, safety, or policy. Interested founders can enter the space, engage with the threat models, and develop conviction in solutions. Researchers can go through established pipelines to get mentorship and build taste. However, this talent needs time to get its bearings before doing useful direct work. Onboarding new talent in time to contribute will become harder and harder.
Building credibility takes time. Institutional organizations ideally need years to develop track records and earn trust from policymakers, media, or the labs. It is difficult to find late substitutes for this time in fields where credibility is important. Building organizations in general also takes a non-negligible amount of time.
Timelines may be short. Will more capital even be useful in 2028 or 2029? If timelines are short, new funders may simply not be ready to deploy money before crunch time. The cost of existing donors giving too early is small compared to giving late. They are the only ones who can.
First-mover advantages. Agenda-setting is very powerful. It allows you to get more for less. What will advocacy groups be fighting for? Will AI safety remain a nonpartisan issue? What policy paradigms will key stakeholders consider? Leopold Aschenbrenner amplified the race dynamics frame in Situational Awareness. The first people to define the frameworks, paradigms, and words to make sense of the current moment will dictate how everyone else acts. Attention will only become harder to compete for over time.
Comparative Advantages
Importantly, money is not totally fungible. Some kinds of giving can only come from specific kinds of willing donors. If a funder is unhobbled, they can have an outsized impact.
Funding sources affect influence. Watchdog organizations and third-party evaluators need credible distance from the labs and connections to the groups they represent. METR cannot take money from the OpenAI Foundation. However, individual donors can write checks that do not compromise a recipient’s standing. This is harder with lab-adjacent funding exposure. In advocacy, unhobbled donors with diverse political backgrounds can be counterfactually responsible for making new, highly important policy campaigns possible.
c4 dollars are hard to come by. Most existing AI safety money is c3, which means it is tax-deductible and limited in its ability to be used for lobbying. c4 dollars are not tax-deductible and have no such limit. Available c4 capacity is small and valuable. Many donors are international, unwilling to forgo tax deductions, or hesitant about funding political projects. Megafunders will likely not make these grants either. Political action is highly neglected, and hobbled individuals are best positioned to close this funding gap.
Hard dollar donations are capped. Direct contributions to political campaigns are capped at $7000 per donor. This means that the ability to influence campaigns depends largely on donor count. A large number of small donors can have a much larger influence than a few large ones, who are unable to affect change with just check size.
Public giving has power. Named giving can accomplish things anonymous giving cannot. An unrestricted, confident public donor, who puts their name behind a cause, can signal that it is serious and credible. These named donors can also play a large role in attracting new donors and creating political coalitions.
Shaping
The grants that donors make now will shape the landscape and determine what gets scaled later.
Seeding matters more than scaling. If new megafunders will be positioned to massively scale future organizations, current funders should focus more on creating new interventions than scaling. Existing megafunders are reasonably good at writing second and third large checks to proven organizations. However, they can struggle to scope or support newer projects. Seeding projects requires high tolerance for failure and fast decision-making. Small, new funders are best positioned for this work.
Megafunders will scale existing organizations. Early on, megafunders will struggle to develop incubation capacity on their own, and will initially be picking from the list of organizations that already exist. This means that individual donors can be counterfactually responsible for not just projects, but megaprojects that could exist because of their early support. This is an incredible opportunity.
Small funders can explore the option space. If you have uncertainty about which strategies will succeed, the right response is to seed a range of approaches. Small, decorrelated donors are better poised to do this than large funders. These donors can develop conviction about interventions that the market might be undervaluing.
Unhobbled donors can test different strategies. An individual donor provides value to the field because they have a distinct theory of victory and risk appetite. Large funders can concentrate capital into a single worldview, which leaves important bets unfunded. A diverse set of funders produces a diverse landscape of organizations that can hedge against the dominant strategy being incorrect. Even if individual donors defer their giving to donor advisory organizations, a diverse range of advisors can produce a similar effect.
Megafunders
Megafunders are not going to produce the necessary actions on their own.
Existing ones are working to change, but are slowed by bureaucracy and capacity constraints. They are not prepared to front-load their giving on the necessary timescales. New megafunders will arrive with their own constraints.
Existing Funders
The AI safety funding ecosystem is a monopsony. Funding is extremely concentrated in Coefficient Giving and Longview Philanthropy. These dominant funders dictate what the field can and cannot do, but are constrained in idiosyncratic ways and are limited in their ability to specialize.
Concentration is more harmful than helpful. Concentration helps credibility and coordination, prevents duplications, and efficiencies of scale. But it has also caused the neglect of many funding opportunities that are now low-hanging fruit for unhobbled donors. Caution can be justified at megafunder scale: with more money, grift and low-quality projects abound, and downside risks for bad bets can partially poison the well for a broader portfolio. Multiple grantmakers and unhobbled donors with reputational firewalls, specialization, and different social graphs will enable more ambitious action.
Institutional structures slow decision-making. Good projects often wait three to six months for funding decisions. Strong projects with motivated teams stagnate or miss windows of opportunity. The Future of Life Institute is reported to hold several hundred million dollars in endowment and paid out approximately only $30M in 2025.
Passive grantmaking is the default. Requests for Proposals (RFPs) are common, but do not work at scale: the most competent people to start new projects are not usually unemployed and waiting in the wings. Active grantmaking, finding strong founders and persuading them to take on specific work, is becoming more common but still rare. The small number of existing funders also makes it hard to give credible commitments of future funding to ambitious founders, which raises the risk of starting an organization. The cultural shift from passive grantmaker to being capable of attracting founders and developing new networks will take time.
Risk tolerance is low. Reputational considerations and risk tolerance disqualify the most important opportunities. Funding will flow to well-known organizations like METR and Transluce, but neglected projects will stay neglected. These projects tend to require a higher risk tolerance: public-facing movement building, organizations representing stakeholder groups (e.g. labor, media, religious communities), relationship-building grants to DC think tanks, and interventions in general that have a more nebulous theory of change but high expected value.
Constraints are not legible. Clearly, these grantmakers have institutional constraints and strategic worldviews that make them appropriately cautious about what they support. Not all of these constraints are legible to the rest of the field. To their credit, CG has recognized the need for decorrelation and a diverse funder base. But the full extent of the gap has not been made clear, and new grantmaking organizations and unhobbled donors have not emerged.
Donor preferences are being aggregated. Sometimes, donor advisory organizations like Longview pool individual donors’ money into a single vehicle. This causes each unique donor, with their own theory of victory and preferences, to get flattened into the same averaged-out worldview. The decorrelation these donors could provide, and their ability to fund riskier, neglected projects, gets erased. Other times, this happens implicitly: donors take their cues from the same centralized funders and advisors, converging on a similar worldview and risk tolerance as megafunders.
The existing megafunders deserve lots of credit, but are (currently) failing to meet the moment.
New Megafunders
New megafunders will face their own constraints. The wave will arrive slower than expected, and not necessarily in the shape the field needs.
The wave will be slower than expected. Some have estimated that hundreds of billions of dollars in philanthropic capital are about to become liquid, largely from AI wealth. But both major funding sources are gated. The OpenAI Foundation has committed $25 billion (~10% of the Foundation’s value), but over an unspecified time frame. The framing of the Foundation as “the largest long-term beneficiary” of the for-profit's growth also suggests an endowment-style approach rather than a serious intention of trying to spend down capital in the years that matter. The Anthropic IPO is even further out: while a tender offer recently occurred, an IPO has not been announced, and post-IPO lockups will force employees to wait months before they can liquidate afterwards. Barring new ultra high-net worth individuals entering the space, the capital might come late, potentially too late.
New megafunders and donors are constrained. OpenAI Foundation faces serious optics constraints and has a similar governing board to the for-profit. Its pillars span a wide enough range that the pillars that are easier to spend on (life science, community programs) might absorb funding first. Work that materially affects OpenAI’s positioning will likely be implicitly off-limits. Anthropic employees, though they have the potential to become unhobbled donors, will likely have their own quirks. Many will be busy and want to delegate their philanthropic thinking entirely to trusted advisors. Some will avoid risky bets or political work that they perceive as outside the Overton window, opting to fund non-AI causes instead. Many will route through pooled vehicles for convenience, recreating the preference aggregation problem that incumbents suffer from.
Building infrastructure takes time. Even new megafunders that want to move quickly will not initially have the operational capacity to do so. Scoping new organizations, incubating founders, hiring staff, and massively scaling interventions all require institutional knowledge and processes. Creating that infrastructure takes time. Whether or not these megafunders will ultimately be successful depends in part on if existing funders and unhobbled donors can rise to the challenge to support them.
Unhobbled Donors
The capital has not materialized yet. Even when it does, the most important projects will remain unfunded. Bold, unhobbled donors are needed to close those gaps.
These donors can have orders of magnitude more impact than large grantmakers. They can fund the most neglected projects that no one else can. They can move quickly on time-sensitive work or on infrastructure that needs years to mature. By seeding new projects early, they shape what organizations megafunders eventually scale.
It has never been a better time to be an individual donor with conviction.
What unhobbled donors do
They deploy fast and make grants directly, not through pooled intermediaries. They are laser-focused on impact rather than legibility or reputational comfort, taking the bets megafunders structurally cannot. They give c4, accepting the loss of the tax deduction. They put their names on public campaigns, engage with the media, evangelize the cause, and actively recruit new donors. They have a theory of victory, a causal story for how their grants will help the future go well, and aggressively front-load their giving to support it.
Recruiting
As the issue gets more salient, we might be on track to get more unhobbled donors by default. But given the importance of speed, we must be more proactive.
There are broadly three ways to close the gap: get existing megafunders to act bolder, activate more giving from individual donors already in the community, or recruit new donors entirely. Each is difficult.
Existing megafunders are trying, but are unlikely to become dramatically bold enough to completely solve the problem.
The most overlooked constituency is existing donors, especially those taking modest risks, splitting capital between causes, and deferring to advisors. We need to make the case to willing individuals that this moment warrants more aggressive deployment.
Recruiting new donors entirely is the most leveraged and the most difficult. The largest pools of recent wealth are mostly implicated in the problem they would be funding to address. People in general do not give, and right-of-center wealth, which would be especially useful for cross-partisan AI policy work, gives least.
The most viable candidates outside the implicated pool are scattered: billionaires and centimillionaires who are becoming concerned about AI, public figures with platforms, founders in adjacent industries. These donors will need to be found, persuaded, and supported by trusted advisors over months or years.
Actions
Existing funders should make demand more legible. As funders attempt to scale and front-load their giving, they should be more transparent about what they will and will not fund. By making their constraints legible publicly, they can more clearly communicate with potential donors about where they can be most impactful. Megafunders should also explore mechanisms to reward upstream funders, such as offering rebates to the previous funders of projects that they decide to scale.
Individual donors should spend differently. Look to front-load giving. Donor swaps allow donors to give to AI safety now in exchange for later commitments to other causes, or the reverse. Anthropic employees can take out loans against future giving. Regranting is a powerful tool for both small and large donors. Platforms like Manifund expose donors to new projects and allow for quick redeployment. Donors should resist organizations or platforms that aggregate their preferences.
The field should build donor advising infrastructure. Most donors who could become unhobbled are not ready to make complex grants on their own, and the field has not built the infrastructure to support them. This is a donor product design problem. New donors need clear default options, trusted advisors who can match them to opportunities without aggregating their preferences into one fund, and pathways into the field that do not require months of learning the landscape. Donors with higher risk tolerances and openness to neglected fields like politics should be carefully advised, and the field should coordinate to effectively allocate their capital.
If you can unhobble yourself, do it. Being unhobbled means giving up the things that donors are usually reasonable to want (reputational cover, tax deduction, confidence and institutional credibility). These are very real costs. But these are not normal times. Are the costs unhobbled giving could possibly have worth more than the direct impact? The dysfunction in the current landscape means there is enormous impact on the table for the ambitious philanthropists who are bold enough to take it. Stepping up to give ambitiously is a true service, and a sacrifice. It’s also exciting and energizing. Between now and 2028, the strategic playing field will be set. Why not shape it?