Conjecture

Overview

Conjecture is an AI safety research organization founded in 2021 by Connor LeahyPersonConnor LeahyBiography 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 ver...Quality: 19/100 and a team of researchers concerned about existential risks from advanced AI. The organization pursues a distinctive technical approach centered on "Cognitive Emulation" (CoEm) - building interpretable AI systems based on human cognition principles rather than aligning existing large language modelsCapabilityLarge Language ModelsComprehensive analysis of LLM capabilities showing rapid progress from GPT-2 (1.5B parameters, 2019) to o3 (87.5% on ARC-AGI vs ~85% human baseline, 2024), with training costs growing 2.4x annually...Quality: 60/100.

Based in London with a team of 30-40 researchers, Conjecture raised over $10M in Series A funding in 2023. Their research agenda emphasizes mechanistic interpretability and understanding neural network internals, representing a fundamental alternative to mainstream prosaic alignment approachesArgumentWhy Alignment Might Be HardComprehensive synthesis of why AI alignment is fundamentally difficult, covering specification problems (value complexity, Goodhart's Law), inner alignment failures (mesa-optimization, deceptive al...Quality: 61/100 pursued by organizations like AnthropicOrganizationAnthropicComprehensive profile of Anthropic, founded in 2021 by seven former OpenAI researchers (Dario and Daniela Amodei, Chris Olah, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish) with early funding... and OpenAIOrganizationOpenAIComprehensive organizational profile of OpenAI documenting evolution from 2015 non-profit to commercial AGI developer, with detailed analysis of governance crisis, safety researcher exodus (75% of ....

Risk Assessment

Founding and Evolution

Origins (2021)

Conjecture emerged from the EleutherAI collective, an open-source AI research group that successfully recreated GPT-3 as open-source models (GPT-J, GPT-NeoX). Key founding factors:

Philosophical Evolution: The transition from EleutherAI's "democratize AI" mission to Conjecture's safety-focused approach represents a significant shift in thinking about AI development and publication strategies.

Funding Trajectory

Cognitive Emulation (CoEm) Research Agenda

Core Philosophy

Conjecture's signature approach contrasts sharply with mainstream AI development:

Key Research Components

Mechanistic Interpretability

• Circuit discovery in neural networks
• Feature attribution and visualization
• Scaling interpretability to larger models
• Interpretability researchSafety AgendaInterpretabilityMechanistic interpretability has extracted 34M+ interpretable features from Claude 3 Sonnet with 90% automated labeling accuracy and demonstrated 75-85% success in causal validation, though less th...Quality: 66/100 collaboration

Architecture Design

• Modular systems for better control
• Interpretability-first design choices
• Trading capabilities for understanding
• Novel training methodologies

Model Organisms

• Smaller, interpretable test systems
• Alignment property verification
• Deception detection research
• Goal representation analysis

Key Personnel

Connor Leahy Profile

Timeline Estimates: Leahy has consistently expressed short AI timelineConceptAGI TimelineComprehensive synthesis of AGI timeline forecasts showing dramatic acceleration: expert median dropped from 2061 (2018) to 2047 (2023), Metaculus from 50 years to 5 years since 2020, with current p...Quality: 59/100 views, suggesting AGI within years rather than decades.

Research Focus Areas

Mechanistic Interpretability

Comparative Advantages

Strategic Position

Theory of Change

Conjecture's pathway to AI safety impact:

1. Develop scalable interpretability techniques for powerful AI systems
2. Demonstrate CoEm viability as competitive alternative to black-box scaling
3. Influence field direction toward interpretability-first development
4. Inform governance with technical feasibility insights
5. Build safe systems using CoEm principles if successful

European AI Safety Hub

Challenges and Criticisms

Technical Feasibility

Organizational Tensions

Commercial Pressure vs Safety Mission

• VC funding creates return expectations
• Potential future deployment pressure
• Comparison to Anthropic's commercialization pathOrganizationAnthropicComprehensive profile of Anthropic, founded in 2021 by seven former OpenAI researchers (Dario and Daniela Amodei, Chris Olah, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish) with early funding...

Publication Strategy Criticism

• Shift from EleutherAI's radical openness
• Selective research sharing decisions
• Balance between transparency and safety

Current Research Outputs

Published Work

Research Questions

Timeline and Risk Estimates

Leadership Risk Assessments

Conjecture's leadership has articulated clear views on AI timelines and safety approaches, which fundamentally motivate their Cognitive Emulation research agenda and organizational strategy:

Future Scenarios

Research Trajectory Projections

Critical Dependencies

Relationships and Collaborations

Within AI Safety Ecosystem

Policy and Governance

UK Engagement

• UK AI Safety InstituteOrganizationUK AI Safety InstituteThe UK AI Safety Institute (renamed AI Security Institute in Feb 2025) operates with ~30 technical staff and 50M GBP annual budget, conducting frontier model evaluations using its open-source Inspe...Quality: 52/100 consultation
• Technical feasibility assessments
• European AI Act discussions

International Influence

• Growing presence in global AI safety discussions
• Alternative perspective to US-dominated discourse
• Technical grounding for governance approaches

Sources & Resources

Primary Sources

Secondary Analysis

Related Resources