# Safe Superintelligence

**Canonical:** https://apis.io/providers/safe-superintelligence/  
**Website:** https://ssi.inc  
**APIs profiled:** 0

Safe Superintelligence Inc. (SSI) is an AI research lab founded in 2024 by Ilya Sutskever, Daniel Gross, and Daniel Levy, with offices in Palo Alto and Tel Aviv. The company describes safe superintelligence as its single mission, sole focus, and "entire product roadmap," and is assembling a lean team of engineers and researchers dedicated to it and nothing else. SSI raised $1B in a Series A from NFDG, a16z, Sequoia, DST Global, and SV Angel; Ilya Sutskever serves as CEO and Daniel Levy as President. The company publishes no public API, developer platform, or technical documentation as of this enrichment pass — this profile captures its identity and domain security posture.

## Kin Score — 6.4 / 100 (minimal)

Scored 2026-08-20 under rubric 0.12.0. Trend: flat (+0.0 from 6.4).

| Facet | Score |
|---|---|
| Discoverability | 50.0 |
| Contract Quality | 0.0 |
| Governance | 0.0 |
| Contract Governance | 0.0 |
| Operational Transparency | 0.0 |
| Developer Ergonomics | 7.1 |
| Commercial Clarity | 0.0 |
| Access Clarity | 0.0 |

## Agent readiness — 0.0 (human-only)

| Dimension | Value |
|---|---|
| Spec Presence | no |
| Agentic Access | no |
| Reversibility Documented | no |
| MCP Server | no |
| Auth Clarity | no |
| Idempotency | no |
| Error Semantics | no |
| OpenAPI Examples | no |
| Rate Limit Signal | no |
| Event Surface Described | no |
| Agent Skills | no |
| Well Known Catalog | no |
| Consent Identity | no |
| Agent Card | no |
| Dry Run Mode | no |

## Access

Unknown — onboarding: unknown, pricing: unknown, trial: no (confidence: low).

## Security (1)

- **Safe Superintelligence Domain Security** — TLSv1.3 · HSTS · DMARC

## Tags

Company, Artificial Intelligence, Machine-Learning, AI Safety, Research, Superintelligence

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Profiled by [API Evangelist](https://apievangelist.com) and published on [APIs.io](https://apis.io/providers/safe-superintelligence/). Scores are computed from the provider's own public artifacts under a published rubric.