Vals, backed by Andreessen Horowitz, is looking to become the gold standard for AI benchmarking
Benchmarking has become the industry norm for how AI companies validate their models’ capabilities and, when the metrics swing in their favor, stand out from competitors and advertise their superiority. In other words, good benchmarks pretty much always mean good PR. Unfortunately, companies have also figured out how to outwit legacy benchmarking systems — many of which are older, and not built to measure the capabilities of modern models.
- ▪Benchmarking has become the industry norm for how AI companies validate their models’ capabilities and, when the metrics swing in their favor, stand out from competitors and advertise their superiority.
- ▪In other words, good benchmarks pretty much always mean good PR.
- ▪Unfortunately, companies have also figured out how to outwit legacy benchmarking systems — many of which are older, and not built to measure the capabilities of modern models.
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| Original publisher | TechCrunch |
| Canonical URL | https://techcrunch.com/2026/09/19/vals-backed-by-andreessen-horowitz-is-looking-to-become-the-gold-standard-for-ai-benchmarking/ |
| Publication time | Sat, 19 Sep 2026 13:00:00 +0000 |
| Retrieval time | 2026-09-19T13:03:47.219Z |
| Last seen | 2026-09-19T13:03:47.219Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | elKgUi1YAXYp · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Benchmarking has become the industry norm for how AI companies validate their models’ capabilities and, when the metrics swing in their favor, stand out from competitors and advertise their superiority. In other words, good benchmarks pretty much always mean good PR. Unfortunately, companies have also figured out how to outwit legacy benchmarking systems — many of which are older, and not built to measure the capabilities of modern models. Vals, a startup formed in 2024, says that it is on a mission to fix this very imperfect system. In the span of less than two years, the company has established itself as a notable presence in the tech industry and, last year it managed to secure a seed round led by 8VC and Bloomberg Beta.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at TechCrunch.
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