Programme Thesis
The CLR Fund supports individuals and organizations doing rigorous work that reduces the probability of extreme, large-scale suffering ('s-risks'), with particular focus on AI-mediated pathways: cooperation failures between AI systems, malicious propensities in LLMs (their 'Model Personas' agenda), Safe Pareto Improvements that prevent catastrophic AI conflict, and s-risk macrostrategy. It exists because CLR believes that advanced AI represents an underappreciated vector for civilisation-scale suffering, and that seeding the right researchers early — via flexible individual grants — is more cost-effective than institutional overhead. The fund is deliberately broad: if managers believe you can eventually do high-quality s-risk-relevant work, they will fund you and 'work out the details together.'
Selection Criteria
• **Direct s-risk relevance**: Work must either address s-risks now or have a credible pathway to doing so — CLR's current priority areas are Model Personas (malicious LLM propensities), Safe Pareto Improvements (preventing catastrophic AI bargaining failures), s-risk macrostrategy, and automating conceptual s-risk work.
• **Quantitative/formal rigour**: All three fund managers hold advanced mathematics or CS degrees (Cambridge MMath, Ulm math/physics/CS, Warwick/Cambridge math) — they reward formal models, game theory, statistical reasoning, and ODE/ML frameworks.
• **Research output credibility**: Past grants flow to people with preprints, working papers, publications, or credible institutional affiliations; a track record of producing ideas matters more than credentials per se.
• **Mission alignment**: CLR invests in people, not just projects — they fund stipends, tuition, career reflection periods, and travel; they expect recipients to be genuinely committed to s-risk reduction as a vocation.
• **Simple majority vote**: Three fund managers (Baumann, Cooper, Cook) decide by majority; all three need to be at least neutral, so ideally the framing should appeal across ML safety, game theory, and philosophy lenses.
• **No stated eligibility restrictions**: No geographic filter, career-stage gate, or degree requirement — individuals, institutions, and independent researchers all eligible; rolling basis means no deadline pressure.
Past Winners / Cohort Profiles
Named past grantees cluster into three archetypes: (1) **PhD/DPhil students in technical AI safety or ethics** — Caspar Oesterheld (cooperative AI, CS PhD at Duke/CMU; $100K), Julia Karbing (ML and multi-agent safety DPhil, Engineering Science; ~£10K), Eleos Citrini (MPhil Ethics of AI at Cambridge; ~$38K), Ali Ladak (PhD moral psychology of animals/digital minds; £18K), Timothy Chan (CS conversion Masters, later model punitive evals; multiple grants totalling ~$92K+); (2) **Independent researchers on decision theory or s-risk macrostrategy** — Sylvester Kollin (MSc Philosophy of Science at LSE + independent research on dynamic choice under unawareness; two grants), Hein de Haan (independent research on 'Timeless Sentientism'; £2.5K), Samuel Martin (independent project 'Linking Current Multiagent AI Research to S-risk Scenarios'; £26K), Paul Rapoport (Infra-Bayesian population games independent write-up; $9K), Nathaniel Sauerberg (teaching buy-out for safe Pareto improvements via cryptographic commitment; $15K); (3) **Career-development / training investments** — anonymous tuition-fee grants ($35K, $25K), Miranda Zhang (career exploration stipend; $4.9K), Winston Oswald-Drummond (college consultant; $7K). Grant sizes range from ~£2K micro-grants to £43K–$100K multi-year PhD stipends. Virtually all named grantees appear to be based in UK/Europe/North America, and nearly all have a philosophy, mathematics, CS, or AI safety background. No prior pharmacologist or wet-lab scientist appears in the grant history.
Ideal Candidate Fingerprint
The platonic CLR grantee is a mathematically literate early-career researcher — typically a PhD student or independent scholar — with a clear, formal argument for why their work reduces s-risk probability or magnitude, and who has already absorbed CLR's intellectual tradition (s-risks, Safe Pareto Improvements, cooperation theory, model personas). They demonstrate research output (preprints, working papers) and express genuine long-run commitment to s-risk reduction as a vocation, not just a funding opportunity. They are embedded in or adjacent to the EA/longtermism research community and can articulate their fit with CLR's specific priority areas, not just 'AI safety' in general.
Recommended Framing
Eniola's strongest angle is to reframe the CCT model as a **mechanistic account of AI-mediated coercive behavioral lock-in** — a novel, quantified s-risk pathway that existing CLR research does not cover. The argument runs: AI recommendation and persuasion systems that exploit dopaminergic consolidation pathways (the tripartite DA-Glu-eCB substrate CCT formalises) create irreversible reward-memory encodings at population scale, foreclosing epistemic sovereignty and constituting a form of coercion with clear s-risk valence. The CCT's ODE/RK45 + Bayesian MCMC formalism — with pre-registered H1–H5 confirmation and a quantified 85.8% encoding-probability reduction — gives Eniola exactly the formal language CLR's mathematics-trained managers reward, while Berridge's endorsement carries particular weight because his wanting/liking dissociation is foundational to how CLR thinks about hedonic suffering distinct from reward. The application essay should open with the s-risk argument explicitly (not addiction framing), position the CCT as a principled countermeasure to AI-driven behavioral coercion, and offer CLR a concrete next research step (e.g., extending the ODE/population-dynamics model to AI persuasion agent scenarios or contributing to the Model Personas agenda by characterising LLM propensities that exploit reward-consolidation circuitry).
Watch Out
1. **Domain mismatch requiring active reframing**: All named CLR grantees work in philosophy, CS, ML, or decision theory — no prior pharmacologist or computational neuroscientist appears in the grant record. Reviewers may not immediately see the CCT as s-risk-relevant; the bridge must be made explicit and tight in the opening paragraph, not left implicit.
2. **No existing CLR/EA community relationship**: Cold applications without prior engagement, a CLR blog post, or a warm introduction from a known figure carry higher rejection risk. Eniola should seek an introduction via Gershman or Daw (both with AI safety adjacency) or reach out to a current CLR researcher before submitting.
3. **LMIC-based independent researcher without institutional affiliation**: While CLR does not explicitly exclude, all named individual grantees appear to be in UK/Europe/NA, often with university affiliations at time of grant. An affiliation with ZYCO should be presented as a genuine research home, not a name-only entity.
4. **Pre-MSc status**: Not disqualifying (CLR has funded career exploration), but the most substantial grants went to PhD students or researchers with established output; Eniola should lean hard on the three OSF/Zenodo preprints and the under-review Neuroscience & Biobehavioral Reviews paper as evidence of independent research capacity.
5. **No s-risk or AI safety publication history**: All current preprints are in pharmacology/neuroscience contexts; CLR managers will need to do interpretive work to see the s-risk angle unless the cover essay does that work for them.
6. **'No grants made in 2025' signal**: CLR appears to be in a conservative disbursement phase in 2025 — the fund balance ($387K) is healthy but no 2025 grants appear on the page as of this writing, possibly indicating increased selectivity or a strategic pause.