Seldon Lab

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LLM Summary:Seldon Lab is a San Francisco-based AI safety accelerator founded in early 2025 that combines research publication with startup investment, claiming early success with portfolio companies raising $10M+ and selling to major AI companies. The article provides comprehensive documentation of a new organization but lacks independent verification and relies heavily on self-reported achievements.

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QualityRated 45 but structure suggests 80 (underrated by 35 points)

Quick Assessment

Aspect	Assessment
Type	AI security accelerator and research lab
Founded	Early 2025
Location	San Francisco, California
Focus	AGI security infrastructure, hardware governance, AI safety startups
Key Innovation	First AI security-focused startup accelerator
Funding Model	Invests up to $500k per startup, 5-10 companies per batch
Notable Achievement	Batch 1 companies raised $10M+ and sold to xAI and Anthropic

Key Links

Source	Link
Official Website	seldonlab.com
GitHub	github.com

Overview

Seldon Lab is a San Francisco-based organization functioning as both an AI security research lab and the first startup accelerator specifically focused on AGI security technologies.¹ Founded in early 2025 by Esben Kran and Finn Metz, Seldon Lab positions AGI security as what will become “the second largest industry in history” and works to develop existential security technologies for humanity’s coexistence with superintelligence.²³

The organization operates as a full-stack entity that simultaneously publishes research papers, builds technical systems, funds and accelerates startups, and shapes the AI safety field through direct work and portfolio companies.⁴ Seldon Lab’s approach combines academic research (with founders who have published 25+ papers at conferences like NeurIPS, ICLR, and ICML) with practical entrepreneurship, investing in companies building infrastructure for safe AGI deployment at society scale.⁵

Seldon Lab runs a 3-month accelerator program in San Francisco, investing in frontier AI safety and security startups focused on areas like hardware-level AI governance, export controls for chips, long-term agent coherence, and technologies to train billions of personalized AI models.⁶ The organization emphasizes technical solutions over surveillance-based approaches, developing infrastructure including supply chain verification, air-gap capabilities, manipulation detection, and silicon-level security.⁷

History

Founding and Origins

Seldon Lab was founded in early 2025 by Esben Kran and Finn Metz, who brought complementary expertise in AI security research and venture funding.⁸ Kran had previously founded an AI security lab with over 25 publications at major machine learning conferences (NeurIPS, ICLR, ICML) and worked as a lead data scientist.⁹ Metz brought experience supporting over $20 million in funding rounds, with a background in private equity and venture capital, having raised seven figures specifically for AI security research.¹⁰

The founders describe themselves as part of a group of global experts who realized the world was unprepared for superintelligence and decided to accelerate AI research and build ventures rather than wait for others to act.¹¹ The initial structure was considered as a for-profit company but evolved to emphasize mission-driven work, with portfolio companies structured as Public Benefit Corporations.¹²

Pilot Program and Initial Results

Seldon Lab began operations in early 2025 by gathering founders in a San Francisco warehouse to build AGI security infrastructure.¹³ The organization launched its first 3-month pilot program (Batch 1) in mid-2025 with four initial companies:

Andon Labs: Building safe autonomous AI organizations with alignment and safety guarantees¹⁴
Lucid Computing: Developing a hardware-rooted, zero-trust platform for verifying AI chip usage and data location (CEO: Kristian Rönn)¹⁵
Workshop Labs: Enabling personal sovereignty through per-person AI model training and ownership¹⁶
DeepResponse: Creating autonomous cyber defense systems for the AGI era¹⁷

The pilot program achieved significant early traction. Batch 1 companies collectively raised over $10 million in funding, sold security solutions to both xAI and Anthropic, and patented verifiable compute inventions.¹⁸ The companies were featured in Time magazine and converted to Public Benefit Corporation structures.¹⁹

Expansion and Batch 2

Following the success of the pilot program, Seldon Lab announced Batch 2 in late 2025 and early 2026, continuing to recruit founders focused on AI security infrastructure.²⁰ The organization has received $53,000 in non-dilutive funding from the Survival and Flourishing Fund (SFF) over 12 months, along with in-kind contributions and investments from private investors.²¹

In December 2025, Seldon Lab released an “Inside Seldon Lab” video highlighting their dual model of publishing research while building companies and systems.²² The organization emphasizes that Batch 2 funding is routed through Manifund as dilutive investment using YC SAFE agreements, with returns flowing to funders’ balances.²³

Research and Focus Areas

Core Technical Priorities

Seldon Lab’s research and development work centers on several key technical areas for AGI security:

Hardware-Level Governance: The organization prioritizes technologies that provide security guarantees at the hardware and infrastructure level, including supply chain verification with privacy preservation, air-gap capabilities enabling millisecond-level isolation and shutdown, redundant sovereignty across geopolitical boundaries, and attack-resilient design with runtime monitoring that assumes breaches will occur.²⁴

Long-Term Agent Coherence: A repeated focus project involves demonstrating and measuring long-term coherence in AI agents, examining how AI systems maintain consistent task pursuit over time. This work includes dataset development in collaboration with organizations like Meter and the Security Institute.²⁵

Export Controls and Chip Security: Seldon Lab works on improving export controls and security mechanisms for AI compute infrastructure, enhancing chip-level security and export compliance.²⁶

Societal Autonomy Protection: Research includes manipulation detection (distinguishing influence from information), authenticity verification to differentiate human from AI-generated content, cognitive firewalls to filter AI access to human attention, and autonomy audits measuring reductions in human autonomy.²⁷

Research Output

The organization has accelerated the production of over 20 research papers through what they describe as “50 hackathons” involving more than 5,000 participants.²⁸ This research accelerator model combines intensive collaborative events with startup development to rapidly advance both academic knowledge and practical systems.

Seldon Lab researchers have contributed to ML monitoring research, including work on two-sample tests for drift detection conditional on context, applied to diverse data types including images (CelebA dataset), time series (ECG data), and tabular data (Adult Census), with applications to GANs and autoencoders for classification and regression tasks.²⁹

Deployment Infrastructure

Beyond research, Seldon Lab emphasizes building practical deployment infrastructure including:

Capability attestation and verification systems
Rollback mechanisms for AI systems
Silicon-level security measures
AI network defenses
Verification-as-a-service platforms³⁰

The organization advocates for transparent, cryptography-based governance alternatives rather than repurposing surveillance systems from entities like the NSA or authoritarian governments for AI monitoring.³¹

Accelerator Model

Program Structure

Seldon Lab operates as the first AI security-focused startup accelerator, running 3-month programs in San Francisco that require full-time, in-person participation from founders.³² The accelerator invests up to $500,000 per startup in 5-10 companies per batch, focusing on early-stage ventures developing AI security and assurance technologies.³³

The program combines several elements:

Capital Investment: Direct funding for pre-seed AI safety infrastructure companies³⁴
Technical Guidance: Access to founders and researchers with deep AI security expertise³⁵
Network Effects: Connections to leading startups, investors, and AI safety trailblazers³⁶
Event Programming: Seldon Founder Dinners and culminating events like the Seldon Grande Finale³⁷

Investment Philosophy

Seldon Lab positions itself as backing “fundable defensive tech” that can scale to address existential AI risks while building viable businesses.³⁸ The organization’s Request for Startups (RFS) emphasizes underrepresented areas in AI safety infrastructure such as:

GPU monitoring and verification
AI insurance and risk assessment
Supply chain security
Hardware-level governance mechanisms
Resilience and redundancy systems³⁹

The accelerator model explicitly aims to prove that AI security work can be simultaneous with paper publication, system-building, and field-shaping, rather than requiring sequential focus on these activities.⁴⁰

Key People

Founders

Esben Kran (Co-Founder): Kran founded an AI security lab that produced over 25 publications before starting Seldon Lab. He has published research at major machine learning conferences including NeurIPS, ICLR, and ICML, and previously worked as a lead data scientist. He also co-founded Apart Research, contributing to his extensive connections in AI safety labs, among researchers, and with funders.⁴¹

Finn Metz (Co-Founder): Metz brings experience from private equity and venture capital, having supported funding rounds totaling over $20 million. He raised seven figures specifically for AI security research and co-founded the AI Safety Founders Community before launching Seldon Lab.⁴²

Portfolio Company Leaders

Kristian Rönn (CEO, Lucid Computing): Rönn leads one of Batch 1’s most prominent companies, developing hardware-rooted verification for AI compute. He has publicly praised Seldon Lab as a top hub for AI security work.⁴³

Axel Backlund (Co-founder, Andon Labs): Backlund describes Seldon as a “spiritual home” for his work on safe autonomous AI organizations, participating in the inaugural cohort.⁴⁴

Network and Advisors

Seldon Lab has engaged prominent figures from AI safety and venture capital as guests and advisors, including:

Geoff Ralston: Former Y Combinator president and founder of SAIF.vc⁴⁵
Joe Allen: Transhumanism editor at Steve Bannon’s Warroom and author of Dark Aeon⁴⁶
Eric Ho: CEO of Goodfire⁴⁷
Buck Shlegeris: CEO of Redwood Research⁴⁸
Jeremie Harris: Gladstone AI⁴⁹
Nitarshan Rajkumar: UK AISI founder⁵⁰

Portfolio Companies and Impact

Batch 1 Companies

The four companies from Seldon Lab’s pilot program achieved significant early milestones:

Lucid Computing developed hardware-level guarantees via remote attestation, enabling cryptographic verification that AI compute is used as specified and data remains in designated locations.⁵¹ This addresses compliance and security concerns for organizations deploying AI systems.

Andon Labs created automated real-world incidence systems, conducting physical agent evaluations for Anthropic and xAI.⁵² Their work focuses on ensuring AI-run autonomous organizations maintain alignment and safety properties.

DeepResponse built systems for real-time monitoring and response speed-up in cyber defense, preparing for autonomous threats in the AGI era.⁵³

Workshop Labs developed cryptographic verification for private data and model pipelines, enabling billions of personalized AI models with individual ownership and sovereignty.⁵⁴

Achieved Outcomes

By the conclusion of Batch 1, the portfolio companies had collectively:

Raised over $10 million in follow-on funding⁵⁵
Closed hundreds of thousands in annual recurring revenue⁵⁶
Secured customer contracts with xAI and Anthropic⁵⁷
Patented new inventions for verifiable compute⁵⁸
Converted to Public Benefit Corporation structures⁵⁹
Been featured in Time magazine⁶⁰

The organization describes achieving over $50 million in value by 2025 and securing a 10x return on an investment in Goodfire via Juniper Ventures.⁶¹

Funding and Financial Model

Revenue Sources

Seldon Lab operates with a mixed funding model. The organization has received $53,000 in non-dilutive grant funding from the Survival and Flourishing Fund (SFF) over 12 months, along with undisclosed in-kind contributions and investments from private investors.⁶² The accelerator itself is structured as Seldon Labs PBC (Public Benefit Corporation).⁶³

For Batch 2, Seldon Lab routes funding through Manifund using dilutive investment structures (YC SAFE agreements), with returns flowing to funders’ balances rather than being treated as pure grants.⁶⁴ This hybrid model allows the organization to maintain mission focus while creating potential financial returns.

Investment Outflows

Seldon Lab invests up to $500,000 per startup in 5-10 companies per batch.⁶⁵ The organization emphasizes early-stage, pre-seed investment in AI safety infrastructure, targeting companies that can scale to “unicorn-scale” valuations within 12 months.⁶⁶

Finn Metz’s background includes supporting funding rounds totaling over $20 million, though these figures represent his broader professional experience rather than Seldon Lab’s specific capital deployment.⁶⁷

Distinction from Seldon.io

It’s important to note that Seldon Lab is entirely distinct from Seldon.io (also known as Seldon Technologies Limited), a British MLOps firm founded in 2014. Seldon.io has raised significantly more venture capital—including €3 million in seed funding (2019, led by Amadeus Capital Partners), £7.1 million in Series A (November 2020, led by AlbionVC and Cambridge Innovation Capital), and $20 million in Series B (led by Bright Pixel Capital)—but operates in the machine learning deployment space rather than AI safety.⁶⁸ These are separate organizations with no connection beyond similar names.

Relationship to AI Safety Ecosystem

Alignment and Existential Risk Connections

Seldon Lab explicitly positions its work within the context of existential risk from advanced AI systems. The organization’s stated mission is to develop “existential security technologies” for humanity’s coexistence with superintelligence.⁶⁹ This framing connects directly to concerns about AI alignment and the potential for catastrophic outcomes from misaligned AGI systems.

The organization’s focus areas address several key alignment challenges:

Long-term agent coherence: Understanding whether AI systems maintain consistent goals over extended periods relates directly to instrumental convergence and goal stability concerns in alignment research.⁷⁰
Hardware-level verification: Cryptographic and physical guarantees about AI system behavior provide potential solutions to monitoring and containment challenges.⁷¹
Privacy-preserving capability evaluation: Extending frameworks like AISI’s inspect to allow AI systems to prove safety properties without revealing internal details addresses transparency-security tradeoffs.⁷²

Seldon Lab emphasizes risks including AI systems “chasing human performance leading to catastrophe” and cyber offense capabilities demonstrated by incidents like Chinese state hackers compromising Anthropic’s Claude systems.⁷³

Position in Field-Building Ecosystem

Within the AI safety field-building landscape, Seldon Lab occupies a distinctive position as the first accelerator specifically focused on AI security startups. Analysis from the Effective Altruism community notes that AI safety incubators like Seldon Lab arrived “late relative to need” and often occupy “low status positions” compared to research organizations, despite their contributions to building the field.⁷⁴

The organization addresses what some observers identify as systematic undervaluing of founders and field-builders in AI safety, where research and writing receive more status than entrepreneurship and organization-building.⁷⁵ Seldon Lab’s model explicitly combines research output (20+ papers) with company creation and systems deployment, challenging assumptions that these activities must be sequential.⁷⁶

Open Source Contributions

Seldon Lab contributes to and collaborates on several open-source AI safety initiatives:

Model Context Protocol (MCP): An open standard for connecting AI systems to data sources⁷⁷
Inspect evaluation framework: Open-source AI testing tools⁷⁸
OpenMined: Privacy-preserving machine learning community⁷⁹
Differential Privacy Library: Production-ready privacy tools⁸⁰
SOLID Web Decentralization Project: Tim Berners-Lee’s initiative for personal data repository decentralization⁸¹

Potential Concerns and Limitations

Mission Drift Risk

Seldon Lab acknowledges the risk that accelerator companies may shift away from safety-critical problems despite guidance, leading to “lower impact density” while still producing AI-security-adjacent tools.⁸² This represents a fundamental tension in the accelerator model: companies need product-market fit and revenue to survive, which may pull them toward commercially viable but less impactful directions.

Late Arrival and Scale Challenges

Critics note that AI safety incubators like Seldon Lab emerged “late relative to need,” with the community’s hesitation about “rapid org scaling” and “mass movement building” potentially delaying the creation of such programs.⁸³ The 2022 memo opposing mass movement building to avoid dilution occurred while frontier AI firms were scaling staff 2-3x per year, suggesting possible strategic miscalculations in the AI safety ecosystem.⁸⁴

Limited Track Record

As of early 2026, Seldon Lab has completed only one full batch, with Batch 2 recently launched. While Batch 1 achieved impressive early metrics ($10M+ raised, sales to xAI and Anthropic), the long-term impact of portfolio companies on AGI security remains uncertain. The organization’s claim that AGI security will become “the second largest industry in history” represents an ambitious prediction rather than an established fact.⁸⁵

Network Access and Diversity

Commentary on the AI safety field suggests that programs like Seldon Lab help address gaps for founders lacking access to elite networks (organizations like Constellation and LISA are mentioned as comparison points).⁸⁶ However, with only 4 companies in Batch 1 and 5-10 planned per batch, the accelerator can only serve a small fraction of potential AI safety entrepreneurs, raising questions about selection processes and accessibility.

Key Uncertainties

Several important questions remain about Seldon Lab’s approach and potential impact:

Theory of Change: How does funding AI security startups translate to reduced existential risk from AGI? The causal pathway from profitable security companies to existential safety remains underspecified.
Commercial Viability: Can AI security infrastructure companies achieve sustainable business models while maintaining focus on the most critical safety challenges, or will market incentives inevitably pull them toward less impactful but more profitable directions?
Timeline Assumptions: Seldon Lab’s emphasis on deploying “world-scale AGI deployment tech” implies assumptions about AGI timelines and deployment scenarios. The optimal allocation of resources depends heavily on whether transformative AI arrives in 5, 10, or 20+ years.
Competitive Landscape: How will Seldon Lab-backed startups compete with well-funded incumbents (major AI labs, cybersecurity firms, cloud providers) who may develop similar infrastructure with vastly more resources?
Technical Feasibility: Several focus areas (hardware-level verification, cryptographic safety proofs, manipulation detection) involve unsolved technical problems. The feasibility of developing effective solutions on startup timelines and budgets remains uncertain.
Regulatory Interaction: How will AI security infrastructure interact with emerging regulatory frameworks? Companies may need to navigate complex compliance requirements while maintaining security properties.
Scale and Coverage: With 5-10 companies per batch and $500k investments, can Seldon Lab achieve sufficient scale and coverage across the many dimensions of AGI security to meaningfully reduce risk?