Connor Leahy

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  "title": "Connor Leahy",
  "description": "Connor Leahy is the CEO and co-founder of Conjecture, an AI safety research company based in London. He rose to prominence as a founding member of EleutherAI, an open-source collective that trained GPT-NeoX and other large language models to democratize access to AI research. This experience gave him direct insight into how frontier capabilities are developed.\n\nLeahy founded Conjecture in 2022 with the thesis that AGI might emerge from \"prosaic\" deep learning - scaling current architectures - rather than requiring fundamental algorithmic breakthroughs. This worldview emphasizes the urgency of alignment research, since transformative AI could arrive without warning through continued scaling. Conjecture's research focuses on interpretability, capability evaluation, and developing tools to understand AI systems before they become too powerful.\n\nAs a public advocate for AI safety, Leahy is known for his direct communication style and willingness to engage with uncomfortable scenarios. He has appeared on numerous podcasts and media outlets to discuss AI risk, often emphasizing the potential for rapid capability gains and the inadequacy of current safety measures. His perspective combines technical expertise from building large models with serious concern about the trajectory of AI development.\n",
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---
title: Connor Leahy
description: CEO of Conjecture, focuses on interpretability and prosaic AGI safety
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llmSummary: Biography of Connor Leahy, CEO of Conjecture AI safety company, who transitioned from co-founding EleutherAI (open-source LLMs) to focusing on interpretability-first alignment. He advocates for very short AGI timelines (2-5 years) and high existential risk, emphasizing mechanistic understanding over empirical tinkering.
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importance: 12
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---
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## Background

Connor Leahy is the CEO and co-founder of <EntityLink id="E70">Conjecture</EntityLink>, an AI safety company focused on interpretability and "prosaic" approaches to AGI alignment. He represents a new generation of AI safety researchers who are building organizations specifically to tackle alignment.

Background:
- Largely self-taught in AI and machine learning
- Co-founder of EleutherAI (open-source AI research collective)
- Founded Conjecture in 2022
- Active public communicator on AI risk

Leahy's journey from open-source AI contributor to safety company founder reflects growing concern about AI risks among those building the technology.

## From EleutherAI to Conjecture

### EleutherAI

Co-founded EleutherAI, which:
- Created GPT-Neo and GPT-J (open-source language models)
- Demonstrated capabilities research outside major labs
- Showed small teams could train large models
- Made AI research more accessible

**The shift:** Working on capabilities research convinced Leahy that AI risk was severe and urgent.

### Why Conjecture?

Founded Conjecture because:
- Believed prosaic AGI was coming soon
- Thought existing safety work insufficient
- Wanted to work on alignment with urgency
- Needed independent organization focused solely on safety

## Conjecture's Approach

### Mission

Conjecture aims to:
- Understand how AI systems work (interpretability)
- Build safely aligned AI systems
- Prevent catastrophic outcomes from AGI
- Work at frontier of capabilities to ensure safety relevance

### Research Focus

**Interpretability:**
- Understanding neural networks mechanistically
- Automated interpretability methods
- Scaling understanding to large models

**Alignment:**
- Prosaic alignment techniques
- Testing alignment on current systems
- Building aligned systems from scratch

**Capability evaluation:**
- Understanding what models can really do
- Detecting dangerous capabilities early
- Red-teaming and adversarial testing

## Views on AI Risk

### Risk Assessment Estimates

Connor Leahy's public statements and interviews reveal a notably urgent perspective on AI risk compared to many researchers in the field. He combines very short timelines with high existential risk estimates, arguing that the default trajectory leads to catastrophic outcomes without significant changes to current approaches. His position emphasizes the need for immediate technical work on alignment rather than relying on slower governance interventions.

| Assessment | Estimate | Reasoning |
|------------|----------|-----------|
| <EntityLink id="E399">AGI timeline</EntityLink> | Could be 2-5 years (2023) | Leahy believes AGI could arrive much sooner than mainstream estimates, pointing to rapid capability gains in language models and fewer remaining barriers than most researchers assume. His direct work on capabilities at EleutherAI gave him firsthand experience with how quickly scaling can produce surprising jumps in performance, making him skeptical of longer timeline projections. |
| P(doom) | High without major changes (2023) | Leahy expresses very high concern about default outcomes if alignment research doesn't advance dramatically. He argues that current prosaic approaches to AI development naturally lead to misaligned systems, and that existing safety techniques are fundamentally insufficient for systems approaching AGI capabilities. His transition from capabilities work to founding a safety company reflects deep worry about the baseline trajectory. |
| Urgency | Extreme (2024) | Leahy emphasizes the need for immediate action on alignment, arguing that the window for developing adequate safety measures is closing rapidly. He believes the field cannot afford to wait for theoretical breakthroughs or gradual governance changes, instead requiring urgent empirical work on interpretability and alignment with current systems to prepare for imminent advanced AI. |

### Core Beliefs

1. **AGI is very near**: Could be 2-10 years, possibly sooner
2. **Default outcome is bad**: Without major changes, things go poorly
3. **Prosaic alignment is crucial**: Need to align systems similar to current ones
4. **Interpretability is essential**: Can't align what we don't understand
5. **Need to move fast**: Limited time before dangerous capabilities emerge

### On Timelines

Leahy is notably more pessimistic about timelines than most:
- Believes AGI could be very close
- Points to rapid capability gains
- Sees fewer barriers than many assume
- Emphasizes uncertainty but leans short

### Strategic Position

**Different from slowdown advocates:**
- Doesn't think we'll successfully slow down
- Believes we need solutions that work in fast-moving world
- Focuses on technical alignment over governance alone

**Different from race-to-the-top:**
- Very concerned about safety
- Skeptical of "building AGI to solve alignment"
- Wants fundamental understanding first

## Public Communication

### Vocal AI Safety Advocate

Leahy is very active in public discourse:
- Regular podcast appearances
- Social media presence (Twitter/X)
- Interviews and talks
- Blog posts and essays

### Key Messages

**On urgency:**
- AGI could arrive much sooner than people think
- We're not prepared
- Need to take this seriously now

**On capabilities:**
- Current systems are more capable than commonly believed
- <EntityLink id="E117">Emergent capabilities</EntityLink> make prediction hard
- Safety must account for rapid jumps

**On solutions:**
- Need mechanistic understanding
- Can't rely on empirical tinkering alone
- Interpretability is make-or-break

### Communication Style

Known for:
- Direct, sometimes blunt language
- Willingness to express unpopular views
- Engaging in debates
- Not mincing words about risks

## Research Philosophy

### Interpretability First

Believes:
- Can't safely deploy what we don't understand
- Black-box approaches fundamentally insufficient
- Need to open the black box before scaling further
- Interpretability isn't optional

### Prosaic Focus

Working on:
- Systems similar to current architectures
- Alignment techniques that work today
- Scaling understanding to larger models
- Not waiting for theoretical breakthroughs

### Empirical Approach

Emphasizes:
- Testing ideas on real systems
- Learning from current models
- Rapid iteration
- Building working systems

## Conjecture's Work

### Research Areas

**Automated Interpretability:**
- Using AI to help understand AI
- Scaling interpretability techniques
- Finding circuits and features automatically

**Capability Evaluation:**
- Understanding what models can do
- Red-teaming frontier systems
- Developing evaluation frameworks

**Alignment Testing:**
- Empirical evaluation of alignment techniques
- Stress-testing proposed solutions
- Finding failure modes

### Public Output

Conjecture has:
- Published research on interpretability
- Released tools for safety research
- Engaged in public discourse
- Contributed to alignment community

## Influence and Impact

### Raising Urgency

Leahy's advocacy has:
- Brought attention to short timelines
- Emphasized severity of risk
- Recruited people to safety work
- Influenced discourse on urgency

### Building Alternative Model

Conjecture demonstrates:
- Can build safety-focused company
- Don't need to be at frontier labs
- Independent safety research viable
- Multiple organizational models possible

### Community Engagement

Active in:
- Alignment research community
- Public communication about AI risk
- Mentoring and advising
- Connecting researchers

## Criticism and Debates

**Critics argue:**
- May be too pessimistic about timelines
- Some statements are inflammatory
- Conjecture's approach might not scale
- Public communication sometimes counterproductive

**Supporters argue:**
- Better to be cautious about timelines
- Direct communication is valuable
- Conjecture doing important work
- Field needs diverse voices

**Leahy's position:**
- Prefers to be wrong about urgency than complacent
- Believes directness is necessary
- Open to criticism and debate
- Focused on solving problem

## Evolution of Views

**EleutherAI era:**
- Focused on democratizing AI
- Excited about capabilities
- Less concerned about risk

**Transition:**
- Growing concern from working with models
- Seeing rapid capability gains
- Understanding alignment difficulty

**Current:**
- Very concerned about risk
- Focused entirely on safety
- Urgent timeline beliefs
- Public advocacy

## Current Priorities

At Conjecture:

1. **Interpretability research**: Understanding how models work
2. **Capability evaluation**: Knowing what's possible
3. **Alignment testing**: Validating proposed solutions
4. **Public communication**: Raising awareness
5. **Team building**: Growing safety research capacity

## Key Insights

### From Building Capabilities to Safety

Leahy's experience building language models convinced him:
- Capabilities can surprise
- Scaling works better than expected
- Safety is harder than it looks
- Need fundamental understanding

### On the Field

Observations about AI safety:
- Not enough urgency
- Too much theorizing, not enough empirical work
- Need more attempts at solutions
- Can't wait for perfect understanding