Planetary Intelligence
The article discusses the concept of Large Earth Models (LEMs), a new type of machine intelligence that integrates real-time physical world data. It contrasts LEMs with large language models (LLMs), highlighting the limitations of LLMs in understanding real-world events. The author emphasizes the importance of satellite data and other sensory inputs in creating a more comprehensive understanding of our environment.
- ▪Large Earth Models (LEMs) aim to provide real-time insights into the physical world, unlike large language models (LLMs) which are limited to textual data.
- ▪Satellite data, such as that from NASA's Landsat and Planet's SuperDove, serves as a foundational element for LEMs, offering continuous visual memory of the Earth.
- ▪LEMs could provide specific information about events like floods, comparing current conditions to historical data, enhancing our understanding of environmental changes.
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Story provenance
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Hacker News (Newest) |
| Canonical URL | https://will4planet.substack.com/p/planetary-intelligence |
| Publication time | Wed, 27 May 2026 16:28:40 +0000 |
| Retrieval time | 2026-05-27T16:38:02.071Z |
| Last seen | 2026-05-27T16:38:02.071Z |
| 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 | None |
| Cluster logic | Not yet clustered, or no peer story found in the clustering window. |
| 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 |
Rights status (four layers)
WeSearch handling by dimension
| 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
Planetary IntelligenceWill MarshallMay 27, 2026ShareEverything, everywhere, all at once.In this essay, I introduce an idea for a new type of machine intelligence that understands our physical world in real-time – a powerful expansion of AI’s capabilities. I talk about implications for people across economic, security, and sustainability domains. And I speculate about humanity's place in the cosmos, and how giving AI sensors may help humans and machines coexist safely and survive the Great Filter.Photo by NASAI. The Models That Are BlindThe AI large language models that have captivated the world have consumed the written record of human civilization: every article, essay, book, and conversation that humanity has committed to the internet.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Hacker News (Newest).
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