Long-Term Future Fund (LTFF)

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LLM Summary:LTFF is a regranting program that has distributed $20M since 2017 (approximately $10M to AI safety) with median grants of $25K, filling a critical niche between personal savings and institutional funders like Coefficient Giving (median $257K). In 2023, LTFF granted $6.67M with a 19.3% acceptance rate, targeting 21-day decision turnarounds, and serves as an important pipeline for researchers before joining major labs or receiving larger grants.

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Quick Assessment

Dimension	Assessment	Evidence
Cumulative Grantmaking	High	Over $20M since 2017, ≈$10M to AI safety
Annual Volume (2023)	Medium-High	$6.67M total grants
Grant Size	Small	Median $25K (vs Coefficient median $257K)
Acceptance Rate	Selective	19.3% (May 2023-March 2024)
Decision Speed	Fast	Target 21 days, aim for 42 days maximum
Focus	AI Safety Primary	≈67% of grants AI-related
Niche	Individual Researchers	Fills gap between personal savings and institutional grants
Pipeline Role	Critical	Many grantees later join major labs or receive Coefficient funding

Organization Details

Attribute	Details
Full Name	Long-Term Future Fund
Parent	EA Funds (Centre for Effective Altruism)
Part of	Effective Ventures (fiscal umbrella)
Type	Regranting Program
Founded	February 2017 (EA Funds launch); restructured October 2018
Annual Grantmaking	$5-8M (recent years)
Cumulative Grantmaking	Over $20M (2017-2024)
Website	funds.effectivealtruism.org/funds/far-future
Application Portal	funds.effectivealtruism.org/apply
Grant Reports	EA Forum LTFF Topic

Overview

The Long-Term Future Fund (LTFF) is a regranting program that supports individuals and small organizations working on AI safety and existential risk reduction. As part of EA Funds (operated by the Centre for Effective Altruism), LTFF fills an important niche in the AI safety funding landscape: providing fast, flexible funding for projects too small for major foundations but too large for personal savings. Since its launch in 2017, LTFF has distributed over $20 million in grants, with approximately half going directly to AI safety work.

LTFF’s focus on individuals distinguishes it from funders like Coefficient Giving (which primarily funds organizations with median grants of $257K) or the Survival and Flourishing Fund (which runs larger grant rounds with median grants of $100K). With a median grant size of just $25K, LTFF is uniquely positioned to fund career transitions, upskilling periods, and early-stage research that would be too small for other institutional funders. Many AI safety researchers receive their first external funding through LTFF before joining established organizations or receiving larger grants from Coefficient Giving.

The fund operates with a team of permanent fund managers plus rotating guest evaluators—researchers and practitioners with relevant expertise in AI safety, forecasting, and related fields. This distributed model allows faster decision-making (targeting 21-day turnaround) while maintaining quality control through collective judgment. The fund’s philosophy leans toward funding when at least one manager is “very excited” about a grant, even if others are more neutral—a hits-based giving approach that has produced notable successes including early funding for Manifold Markets, David Krueger’s AI safety lab at Cambridge, and numerous researchers who later joined frontier AI labs.

LTFF has weathered significant funding disruption following the FTX collapse in late 2022, which affected the broader EA funding ecosystem. The fund has since stabilized with approximately 40-50% of funding coming from Coefficient Giving and the remainder from direct donations and other institutional sources. Despite these challenges, LTFF has maintained steady grantmaking volume of approximately $5-8 million annually.

Historical Grantmaking

Cumulative Grant History

Period	Total Grants	AI Safety Portion	Notable Developments
2017	≈$500K	≈30%	EA Funds launch; Nick Beckstead sole manager
2018	≈$1.5M	≈40%	New management team (Habryka et al.); more speculative grants
2019	≈$1.4M	≈50%	Expanded grant writeups; increased transparency
2020	≈$1.4M	≈55%	COVID impact; increased applications
2021	≈$4.8M	≈60%	Significant scaling; added guest managers
2022	≈$4.5M	≈65%	FTX disruption; funding uncertainty
2023	≈$6.67M	≈67%	Post-FTX stabilization; 19.3% acceptance rate
2024	≈$8M (projected)	≈70%	Continued growth; focus on technical safety
Cumulative	≈$20M+	≈$10M	Over 1,000 grants since founding

The fund’s evolution reflects broader trends in AI safety: early grants went primarily to established organizations under Nick Beckstead’s management, while later rounds under the Habryka-led team shifted toward more speculative individual grants with detailed public writeups. This transition was analyzed in a 2021 retrospective that found a 30-40% success rate among 2018-2019 grantees—consistent with appropriate risk-taking for hits-based giving.

Application Volume Trends

Period	Monthly Applications	Acceptance Rate	Notes
H2 2021	≈35/month	≈25%	Post-pandemic increase
2022	≈69/month	≈22%	Double previous year
Early 2023	≈90/month	≈20%	Continued growth
2023-2024	≈80/month	19.3%	Stabilized volume

The fund now processes approximately 80-90 applications monthly, reflecting both increased interest in AI safety careers and LTFF’s reputation as an accessible entry point for funding.

Grant Categories

Distribution by Category (2023-2024)

Category	Percentage	Annual Amount	Description
Technical AI Safety Research	35%	≈$2.3M	Independent research, small teams, lab collaborations
Upskilling & Career Transitions	25%	≈$1.7M	MATS funding, course attendance, self-study periods
Field-Building	20%	≈$1.3M	Conferences, community events, infrastructure
AI Governance & Policy	10%	≈$670K	Policy research, advocacy, institutional engagement
Other X-Risk	10%	≈$670K	Biosecurity, nuclear risk, forecasting

Grant Size Distribution

Size Range	Frequency	Median	Typical Use Case
$1K - $10K	20%	$5K	Conference travel, short projects, equipment
$10K - $25K	35%	$18K	MATS supplements, 3-6 month projects
$25K - $50K	25%	$35K	6-12 month research, career transitions
$50K - $100K	15%	$70K	Year-long independent research
$100K - $200K	5%	$150K	Multi-year support, org incubation

For comparison, the cost of funding one year of researcher upskilling in AI safety is estimated at approximately $53K by LTFF.

Application Process

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Timeline Commitments

Stage	Target	Maximum	Notes
Application Submission	Anytime	Rolling	No deadlines; continuous acceptance
Initial Response	21 days	42 days	Formal target since 2023
Time-Sensitive Applications	Faster	Varies	Mark as urgent in application
Grant Disbursement	1-2 weeks	4 weeks	After approval and terms
Total Time	4-6 weeks	8 weeks	Including disbursement

The fund announced rolling applications in 2023, eliminating the previous round-based system. Applications marked as time-sensitive receive expedited review.

What Makes Strong Applications

Based on public grant reports, committee comments, and fund manager reflections:

Factor	Weight	Description	Evidence Types
Relevant Track Record	Critical	Past experience in AI safety or related field	Papers, projects, employment history
Clear Theory of Change	Critical	How does this reduce x-risk specifically?	Logical chain from activity to impact
Person-Level Fit	High	Can this person execute effectively?	References, past output quality
Counterfactual Impact	High	Would this happen without LTFF?	Alternative funding sources, personal savings
Concrete Deliverables	Medium	What will you produce and when?	Milestones, measurable outputs
Cost-Effectiveness	Medium	Is this efficient use of funds?	Budget justification, alternatives
Portfolio Fit	Medium	Does this complement other grants?	Novelty, strategic gaps

Application Tips from Fund Managers

From the 2023 Ask Us Anything session and grant reports:

Strong applications typically have:

Demonstrated interest in priority areas (AI safety, biosecurity) through previous work
Specific, time-bound project plans with clear milestones
Realistic budget with justification for each line item
Acknowledgment of key uncertainties and how they’ll be addressed
Evidence that the applicant has thought carefully about the problem space

Common weaknesses:

Vague project descriptions without concrete outputs
Generic “I want to do AI safety research” without specifics
Overconfidence about impact without evidence
Poor fit between applicant background and proposed work
Lack of engagement with existing literature or community

What fund managers emphasize:

“Things like general competence, past track record, clear value proposition and neglectedness matter”
They use “model-driven granting”—assessing whether the proposed plan will actually work
A well-justified application can change a fund manager’s mind even on skeptical topics
There’s healthy disagreement within the fund; one excited manager can get a grant funded

What LTFF Is Less Interested In

From public statements by fund managers:

Category	Reason
General technology/science improvement	Unless differentially benefits x-risk reduction
Economic growth acceleration	Not clearly an x-risk intervention
AI capabilities research	Could accelerate rather than reduce risk
Mechanistic interpretability (2024+)	Now less neglected due to field growth
Large organizational grants	Better suited to Coefficient or SFF

Notable Grants and Grantees

High-Profile Grants

Grantee	Amount	Year	Purpose	Outcome
Manifold Markets (Grugett, Chen)	$200,000	Feb 2022	4-month runway to build prediction market platform	Manifold grew to major EA forecasting tool; credited as “strong signal EA community cared”
David Krueger’s Lab (Cambridge)	$200,000	2023	PhD students and compute at new AI safety lab	Established academic AI safety presence at Cambridge
Gabriel Mukobi	$40,680	2023-24	Stanford CS master’s with AI governance focus	Accepted to 4/6 PhD programs; multiple publications
Lisa Thiergart & Monte MacDiarmid	$40,000	2024	Activation addition interpretability paper	Conference publication on LM steering
Joshua Clymer	$1,500	2023	A100 GPU compute for instruction-following experiments	First technical AI safety project
Einar Urdshals	≈$50K	2023	Career transition from physics PhD to AI safety	Mentored independent research
SecureBio	Varies	2025	Field-building events for GCR in Boston	Spring/Summer 2025 program
Julian Guidote & Ben Chancey	≈$15K	2025	9-week stipend for Mandatory AI Safety “Red Bonds” policy paper	Policy proposal development

Research Group Support

Group/Lab	Amount	Period	Focus
SERI MATS Scholars	Multiple	2021+	Program supplements and independent research
AI Safety Camp	Multiple	2019+	Research mentorship programs
AXRP Podcast (Daniel Filan)	Multiple	2020+	AI safety podcast production
Robert Miles	Multiple	2019+	AI safety YouTube content

Grant Type Details

Upskilling Grants: LTFF supports individuals seeking to build AI safety skills through various pathways.

Grant Type	Typical Size	Purpose	Requirements
MATS Supplements	$10-25K	Living expenses during program	MATS acceptance
Course Attendance	$5-15K	Registration + living costs	Program acceptance
Self-Study Period	$20-50K	Independent learning runway	Study plan, mentorship
Research Visits	$10-30K	Collaboration at other orgs	Host organization agreement
PhD/Master’s Support	$20-80K	Tuition and living expenses	University admission

Independent Research Grants: The core of LTFF’s portfolio.

Grant Type	Typical Size	Duration	Requirements
Exploration Grant	$15-40K	3-6 months	Research direction, preliminary work
Research Grant	$40-80K	6-12 months	Track record, detailed proposal
Multi-Year Support	$80-200K	1-2 years	Proven track record, clear milestones
Bridge Funding	$20-60K	3-9 months	Gap between positions/grants

LTFF is “pretty happy to offer ‘bridge’ funding for people who don’t quite meet [major lab] hiring bars yet, but are likely to in the next few years.”

Field-Building Grants: Supporting the AI safety ecosystem.

Grant Type	Typical Size	Purpose	Examples
Conference Support	$10-50K	Event organization	Research retreats, workshops
Community Building	$15-40K	Local group support	University groups, city hubs
Infrastructure	$25-100K	Tools and platforms	Forecasting tools, research databases
Content Creation	$10-40K	Educational materials	YouTube, podcasts, writeups

Fund Management

LTFF uses a distinctive governance model with permanent fund managers plus rotating guest managers who provide specialized expertise.

Current Committee (2024-2025)

Permanent Fund Managers:

Manager	Role	Background	Focus Areas
Caleb Parikh	Interim Fund Chair, EA Funds Project Lead	ML research background; evaluated over $34M in applications	Technical safety, field-building
Oliver Habryka	Permanent Manager	CEO of Lightcone Infrastructure (LessWrong); cofounder of Lighthaven venue	Community infrastructure, epistemics
Linchuan Zhang	Permanent Manager, EA Funds Staff	Senior Researcher at Rethink Priorities; COVID forecasting background	Existential security research

Recent Guest Fund Managers (2023-2024):

Guest Manager	Affiliation	Expertise
Lawrence Chan	ARC Evals	AI evaluations, safety research
Clara Collier	Independent	AI governance
Daniel Eth	Independent	Technical AI safety
Lauro Langosco	DeepMind (previously)	ML research
Thomas Larsen	Independent	AI safety research
Eli Lifland	Forecasting	Quantitative analysis

Committee Evolution

Period	Chair	Notable Changes
2017-2018	Nick Beckstead (sole manager)	Initial fund; focus on established orgs
Oct 2018-2020	Matt Fallshaw	New team: Habryka, Toner, Wage, Zhu
2020-2023	Asya Bergal	Expanded guest managers; transparency
2023-present	Caleb Parikh (interim)	Asya stepped down to reduce Coefficient overlap

Asya Bergal stepped down from the chair role in late 2023 to reduce overlap between Coefficient Giving (where she works as a Program Associate) and LTFF.

Evaluation Philosophy

The committee uses explicit criteria combined with significant judgment:

Principle	Description
Hits-Based Giving	Willing to fund speculative grants with high potential upside
One Excited Manager Rule	Grants often funded when one manager is very excited, even if others are neutral
Model-Driven Granting	Assess whether proposed plans will actually work, not just stated intentions
Healthy Disagreement	Fund managers regularly disagree; diversity of views is feature not bug
Part-Time Capacity	Most managers have demanding day jobs, limiting deep evaluation time

From the May 2023-March 2024 payout report: “The fund’s general policy has been to lean towards funding when one fund manager is very excited about a grant even if other fund managers are more neutral. The underlying model is that individual excitement is more likely to identify grants with significant impact potential in a hits-based giving framework.”

Evaluation Process

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Comparison with Other Funders

LTFF occupies a specific niche in the AI safety funding landscape. Understanding its position relative to other funders helps applicants choose the right source.

Major AI Safety Funders Comparison

Funder	Annual AI Safety	Median Grant	Focus	Application Style
Coefficient Giving	$70M+ (2022)	$257K	Organizations, large projects	Proactive research, RFPs
Survival and Flourishing Fund	$10-15M	$100K	Organizations, researchers	Annual S-Process + rolling
Long-Term Future Fund	$4-5M	$25K	Individuals, small projects	Open applications
Lightspeed Grants	$5-10M	Varies	Rapid response, individuals	Application rounds
Manifund	$1-2M	$10-50K	Individuals, experiments	Regranters + applications
AI Risk Mitigation Fund	$1-2M	Varies	AI safety specifically	Applications

Source: Overview of the AI Safety Funding Situation

When to Apply to LTFF vs Other Funders

Scenario	Best Funder	Why
Individual seeking $20-50K for 6-month research	LTFF	Sweet spot for LTFF’s median grant size
Organization seeking $500K+ annual budget	Coefficient or SFF	Too large for LTFF; need institutional funder
Career transition/upskilling	LTFF	Explicitly welcomes these applications
MATS living expenses supplement	LTFF	Established pipeline for program participants
Policy/governance organization	Coefficient	Needs diverse funder base for credibility
Rapid response to opportunity (< 2 weeks)	Lightspeed Grants	Faster than LTFF’s 21-day target
Experimental project needing community validation	Manifund	Regranter model tests interest
Large academic lab funding	Coefficient or SFF	$200K+ grants more common there

Funding Relationships

Relationship	Description
LTFF ← Coefficient	≈40-50% of LTFF funding comes from Coefficient regranting
LTFF → Coefficient	Many LTFF grantees later receive Coefficient funding
LTFF ↔ SFF	Similar cause focus, complementary scale
LTFF ↔ EAIF	AI-focused projects go to LTFF; general EA meta to EAIF
LTFF → Major Labs	Many grantees eventually hired by Anthropic, DeepMind, etc.

Common Funding Pathways

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LTFF vs EA Infrastructure Fund (EAIF)

Both LTFF and EAIF are part of EA Funds but serve different purposes:

Dimension	LTFF	EAIF
Focus	Longtermism, AI safety, x-risk	EA community building, meta-work
Cause Area	Specific (AI safety, bio, etc.)	Cause-agnostic EA infrastructure
Application Volume	Higher (≈80/month)	Lower (fewer applications)
Institutional Funding	≈40-50% from Coefficient	≈80% from Coefficient
Strategic Direction	More stable	Higher rate of strategic changes

When unsure: If your project focuses on AI safety specifically, apply to LTFF. If it’s about EA community building broadly, apply to EAIF. The funds can transfer applications between them if you apply to the wrong one.

Historical Evolution

Founding and Early Years (2017-2018)

EA Funds launched on February 28, 2017, created by the Centre for Effective Altruism (CEA) while going through Y Combinator’s accelerator program. The creation was inspired by the success of the EA Giving Group donor-advised fund run by Nick Beckstead and the donor lottery run by Paul Christiano and Carl Shulman in December 2016.

Initially, Nick Beckstead served as the sole manager of the Long-Term Future Fund. During this period, grants went mostly to established organizations like CSER, FLI, Charity Entrepreneurship, and Founder’s Pledge, with minimal public writeups. Beckstead stepped down in August 2018.

Transition to Active Grantmaking (2018-2020)

In October 2018, a new management team was announced: Matt Fallshaw (chair), Helen Toner, Oliver Habryka, Matt Wage, and Alex Zhu, with Nick Beckstead and Jonas Vollmer as advisors. This transition marked a fundamental shift:

Aspect	Beckstead Era	Post-2018 Era
Recipients	Primarily organizations	More individuals, speculative projects
Grant Size	Larger	Wider range, more small grants
Transparency	Minimal writeups	Detailed public justifications
Risk Profile	Conservative	More hits-based

The new approach generated some controversy—certain grants were “scathingly criticized in the comments.” However, a 2021 retrospective found a 30-40% success rate among 2018-2019 grantees, suggesting appropriate risk-taking for hits-based giving.

Scaling Period (2021-2022)

Year	Total Grants	Key Developments
2021	≈$4.8M	Added guest managers; expanded capacity
Q4 2021	$2.1M	34 grantees in single quarter
2022	≈$4.5M	Peak pre-FTX; Manifold Markets grant

The fund processed 878 applications from March 2022 to March 2023, funding 263 grants worth approximately $9.1M total (average $34.6K per grant).

FTX Impact and Recovery (2022-2023)

The November 2022 FTX collapse significantly impacted the EA funding ecosystem. While LTFF itself did not receive direct FTX funding, the downstream effects included:

Increased uncertainty among applicants and donors
Some grantees lost expected Future Fund grants
Broader EA funding contraction

LTFF remained relatively stable, with Coefficient Giving (then Open Philanthropy) providing approximately 40-50% of funding and the remainder from direct donations. The fund issued statements about funding constraints in early 2023.

Current Period (2023-2025)

Metric	2023	2024 (projected)
Total Grants	$6.67M	$8M+
AI Safety Portion	≈67%	≈70%
Acceptance Rate	19.3%	≈18-20%
Monthly Applications	≈80-90	≈80-90

Recent strategic shifts include:

Less funding for mechanistic interpretability due to field becoming less neglected
Continued focus on upskilling and career transitions
More stringent evaluation of claims about AI safety relevance

Impact and Outcomes

Researcher Pipeline

LTFF plays a critical role in the AI safety talent pipeline. Many researchers receive their first external funding through LTFF before achieving one of several outcomes:

Outcome	Description	Evidence
Major Lab Hires	Researchers later hired by Anthropic, DeepMind, OpenAI	Multiple MATS scholars post-LTFF
Academic Positions	Faculty or postdoc roles in AI safety	David Krueger, Gabriel Mukobi PhD admissions
Research Output	Published papers, tools, analyses	Interpretability papers, safety tooling
Career Pivots	Successful transitions into AI safety	Physics PhDs, software engineers entering field
Capability Building	Skills developed through funded training	MATS completions, self-study periods

LTFF grantees often exhibit extraordinary earning potential. As fund managers note, many “are excellent researchers (or have the potential to become one in a few years) and could easily take jobs in big tech or finance, and some could command high salaries (over $400k/year) while conducting similar research at AI labs.”

Documented Success Stories

Grantee	LTFF Support	Subsequent Achievement
Gabriel Mukobi	$40.7K for Stanford master’s	Accepted to 4/6 PhD programs; multiple publications
Manifold Markets	$200K early-stage	Grew to major EA forecasting platform
AXRP Podcast	Multiple grants	Established AI safety podcast with consistent output
Robert Miles	Multiple grants	Major AI safety YouTube creator, mainstream reach
Mechanistic interpretability researchers	Multiple	Field growth attributed partly to early LTFF support

Quantifying Impact

A 2021 retrospective analysis of 2018-2019 grantees found:

Finding	Interpretation
30-40% success rate	Appropriate risk level for hits-based giving
Track record correlation	Grantees with prior relevant experience performed better
Renewal patterns	Successful grantees often received follow-on funding

The fund’s impact extends beyond direct grantees. By funding early-stage researchers who later join major labs or receive larger grants, LTFF serves as a “farm team” for the AI safety field.

Strengths and Limitations

Organizational Strengths

Strength	Description	Evidence
Speed	Much faster than major foundations	21-day target vs months at Coefficient
Flexibility	Funds individuals, not just organizations	Median grant $25K; individuals welcome
Accessibility	Lower barriers to application	Rolling applications; quick online form
Risk Tolerance	Willing to fund early-stage ideas	Hits-based approach; one excited manager can approve
Transparency	Publishes detailed grant reasoning	Payout reports on EA Forum with justifications
Renewal Support	Happy to renew successful grants	”Would be happy to be primary funder for years”
Bridge Function	Supports people building toward larger opportunities	Explicit bridge funding category

Organizational Limitations

Limitation	Description	Mitigation
Scale	$5-8M is small relative to field needs	Complements rather than replaces major funders
Capacity	Part-time managers limit deep evaluation	Guest managers provide additional bandwidth
Individual Focus	Less suited for large organizations	Refer to Coefficient or SFF for large orgs
Concentration	Heavy AI safety focus; other x-risks less funded	Apply to specialized funds for non-AI work
Funding Dependency	≈40-50% from Coefficient	Diversified direct donation base
Feedback Quality	Rejection feedback varies in depth	Explicit in AMAs that feedback is limited

Strategic Challenges

Challenge	Description
Grantmaker Bottleneck	As Coefficient staff noted, “a key bottleneck is that they currently don’t have enough qualified AI Safety grantmakers to hand out money fast enough”
Neglectedness Shifts	Areas like mechanistic interpretability become less neglected, requiring strategic adjustment
Talent Retention	Fund managers have demanding day jobs; turnover creates institutional memory loss
Validation Without Dependency	Goal is to help researchers become self-sustaining, not LTFF-dependent

How to Apply

Application Process

Step	Details	Tips
1. Review Guidelines	Check EA Funds website for current priorities	Note recent payout reports for examples
2. Assess Fit	Confirm LTFF is right fund (vs EAIF, Animal Welfare, Global Health)	AI safety → LTFF; general EA → EAIF
3. Prepare Application	Describe project, background, budget, theory of change	Be specific; include milestones
4. Submit Online	Via EA Funds application portal	Mark time-sensitive if urgent
5. Wait for Response	Target 21 days, max 42 days	Check spam folder; follow up if over 6 weeks
6. Respond to Questions	Committee may request clarification	Respond promptly; additional info often requested
7. Negotiate Terms	Discuss grant structure, milestones, reporting	Standard terms; flexibility for edge cases

Application Portal

The application form includes:

Project description: What you’ll do and why it matters
Background: Relevant experience and qualifications
Budget: Itemized costs with justification
Timeline: Key milestones and deliverables
Theory of change: How this reduces existential risk
Counterfactual: What happens without LTFF funding
Other funding: Alternative sources, if any

After Approval

Stage	Details
Grant Agreement	Review and sign terms (usually standard)
Disbursement	Funds sent within 1-2 weeks of signing
Reporting	Progress updates as agreed; typically light-touch
Renewal	Apply again if continuing work; track record helps

Funding Sources and Donations

Revenue Composition

Source	Percentage	Notes
Coefficient Giving	≈40-50%	Regranting arrangement; largest single source
Direct Donations	≈40-50%	Individual EA donors; recurring and one-time
Other Institutional	≈10%	Other foundations, DAFs

How to Donate

Method	Details
Direct Donation	funds.effectivealtruism.org (credit card, bank transfer)
Every.org	Tax-deductible for US donors
Manifund	Regranting via LTFF project page
DAF Grants	Specify “Long-Term Future Fund” via CEA

For Large Donors

Donors considering significant contributions ($50K+) can contact the fund directly. Linchuan Zhang has volunteered to speak with donors about LTFF’s strategy and current funding needs.

Long-Term Future Fund (LTFF)

Quick Assessment

Organization Details

Overview

Historical Grantmaking

Cumulative Grant History

Application Volume Trends

Grant Categories

Distribution by Category (2023-2024)

Grant Size Distribution

Application Process

Timeline Commitments

What Makes Strong Applications

Application Tips from Fund Managers

What LTFF Is Less Interested In

Notable Grants and Grantees

High-Profile Grants

Research Group Support

Grant Type Details

Fund Management

Current Committee (2024-2025)

Committee Evolution

Evaluation Philosophy

Evaluation Process

Comparison with Other Funders

Major AI Safety Funders Comparison

When to Apply to LTFF vs Other Funders

Funding Relationships

Common Funding Pathways

LTFF vs EA Infrastructure Fund (EAIF)

Historical Evolution

Founding and Early Years (2017-2018)

Transition to Active Grantmaking (2018-2020)

Scaling Period (2021-2022)

FTX Impact and Recovery (2022-2023)

Current Period (2023-2025)

Impact and Outcomes

Researcher Pipeline

Documented Success Stories

Quantifying Impact

Strengths and Limitations

Organizational Strengths

Organizational Limitations

Strategic Challenges

How to Apply

Application Process

Application Portal

After Approval

Funding Sources and Donations

Revenue Composition

How to Donate

For Large Donors

Sources and Citations

Primary Sources

Grant Reports and Payout Announcements

AMA and Transparency Posts

Funding Landscape Analysis

EA Funds History

Related Organizations

External Links