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Earth Intelligence

08 Sep 2025 — 6 min read

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In Argentina's September 7, 2025, provincial legislative elections in Buenos Aires led by current Governor Axel Kicillof, won about 47% of the votes (based on 88% counted). This beat President Javier Milei's party, La Libertad Avanza (LLA), which got 34%, by a 13-point gap. The vote was to fill seats in the local provincial congress: 46 for the lower house (deputies) and 23 for the upper house (senators). Peronists grew their senate majority to 24 seats and kept 39 deputy seats as the biggest group; LLA picked up 16 senate and 30 deputy seats but couldn't take control. Smaller parties got 4-6% each.

Former President Cristina Fernández de Kirchner, who's under house arrest for corruption charges, said the win shows people rejecting Milei's tough budget cuts. A scandal hit Milei's anti-corruption image too, with claims his sister Karina took bribes (which the government calls fake). Voter turnout was low at 63-65%, showing frustration in a divided country. Argentina has a federal system like the U.S., with a national president (Milei) handling country-wide issues, and each province having its own governor (like Kicillof in Buenos Aires) and local legislature for regional laws. For Milei, it's a warning: despite cutting inflation from 300% to 37-43% yearly through spending cuts and freer markets, voters are upset about side effects like more poverty and job losses. This could make his party's chances tougher in the upcoming national midterm elections on October 26.

Economics & Markets:

Yesterday, September 7, 2025, eight key OPEC+ member countries, including Saudi Arabia, Russia, Iraq, the United Arab Emirates, Kuwait, Kazakhstan, Algeria, and Oman, decided to increase oil production by 137,000 barrels per day starting in October. This step continues the gradual reversal of voluntary production cuts that were first introduced in April and November 2023, totaling 1.65 million barrels per day, to help stabilize global oil markets during periods of uncertain demand.
The alliance stressed that this phase-out would stay adaptable, depending on evolving market conditions, to maintain a balanced approach between supply levels and broader economic trends. The decision, which is smaller than the 547,000 barrels per day rise set for September, supports steady global economic growth by avoiding sharp supply disruptions.
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Cyber:

Yesterday, September 7, 2025, Turkish authorities imposed restrictions on several major online platforms, including X, YouTube, Instagram, Facebook, TikTok, and WhatsApp, across multiple networks, as confirmed by internet monitoring groups. The curbs primarily affected mobile internet users and coincided with escalating political tensions involving the main opposition party, the Republican People's Party (CHP). The CHP had called for nationwide rallies in response to a police blockade of its Istanbul headquarters, which was reportedly part of efforts to install a government-appointed trustee amid internal disputes or investigations.

While access remained possible via VPNs or fixed-line connections in some areas, the restrictions marked a broader pattern of internet controls during periods of civil unrest in the country. These platform restrictions disrupt communication channels for citizens, limiting the organization and coverage of protests, stifling free expression, and hindering access to information during a politically charged moment.

Environment & Weather:

Statistic:

Largest assets on Earth by market capitalization:

Gold: $24.340T
🇺🇸 NVIDIA: $4.066T
🇺🇸 Microsoft: $3.679T
🇺🇸 Apple: $3.557T
🇺🇸 Alphabet (Google): $2.843T
🇺🇸 Amazon: $2.477T
Silver: $2.318T
Bitcoin: $2.209T
🇺🇸 Meta Platforms: $1.890T
🇺🇸 Broadcom: $1.575T
🇸🇦 Saudi Aramco: $1.524T
🇹🇼 TSMC: $1.262T
🇺🇸 Tesla: $1.131T
🇺🇸 Berkshire Hathaway: $1.078T
🇺🇸 JPMorgan Chase: $809.47B
🇺🇸 Walmart: $801.35B
🇨🇳 Tencent: $705.07B
🇺🇸 Visa: $666.16B
🇺🇸 Oracle: $653.89B
🇺🇸 Eli Lilly: $651.91B
SPDR S&P 500 ETF Trust (SPY): $594.02B
🇺🇸 Netflix: $528.53B
🇺🇸 Mastercard: $528.14B
Ethereum: $518.05B
🇺🇸 Exxon Mobil: $465.67B

History:

Ontology, which means the study of being and existence, began as a philosophical concept over 2,000 years ago. The earliest ideas come from ancient Greek philosophers like Plato (427–347 BCE) and Aristotle (384–322 BCE), who asked questions like: What exists? What kinds of things are there? How do we group and understand them? Aristotle in particular created early classification systems for everything in the natural world, which became the foundation for later ontological thinking. During the Middle Ages, Christian philosophers like Thomas Aquinas (1225–1274) used Aristotle’s ideas to structure religious and philosophical knowledge, creating vast systems of categories for understanding God, humans, and the world. In the 1600s, the word “ontology” first appeared in writing by Jacob Lorhard (1606) and Rudolf Göckel (1613), marking its formal entry into academic language. Later, in the 1600s and 1700s, German philosopher Gottfried Wilhelm Leibniz (1646–1716) proposed the idea of a universal language of logic—a system where all knowledge could be broken into logical units and reasoned through mathematically. This vision wouldn’t become practical until the 1900s, when logicians like Bertrand Russell and Alfred North Whitehead developed formal logic systems, and W.V.O. Quine later questioned how we define and categorize reality. These developments gradually shifted ontology from a purely philosophical topic to a technical tool for organizing knowledge in a logical and structured way.
Starting in the 1970s and 1980s, as artificial intelligence research developed, computer scientists began using ontologies in software. Early systems like semantic networks and expert systems tried to represent how humans understand the world—using concepts, categories, and relationships to model knowledge. A major turning point came in 1993 when computer scientist Thomas Gruber defined an ontology as “an explicit specification of a conceptualization,” which made it easier to build shared knowledge systems that computers could use. By the early 2000s, languages like OWL (Web Ontology Language) were created so that machines could understand and reason over complex data structures. These innovations allowed software to move beyond just storing data to actually understanding what the data means. Today, ontology-based knowledge graphs and data models are at the core of many of the world’s most powerful data systems. Companies like Palantir, AWS, Google Cloud, Microsoft Azure, Snowflake, IBM, Oracle, SAP, Neo4j, Stardog, Databricks, and Cambridge Semantics use ontologies to map and unify structured and unstructured data across vast, distributed systems. These ontology-based mega databases power entire industries—from global supply chains and energy grids to finance, logistics, health systems, and even the battlefield. And now, as AI and large language models (LLMs) emerge, ontologies are the perfect match: ontologies provide the structured meaning, context, and relationships that LLMs often lack, grounding their outputs in real-world logic. LLMs can query and reason over ontologies to deliver richer, more accurate, and more explainable insights. This pairing is creating intelligent, context-aware systems that understand not just what data says, but how it all connects—enabling AI to operate within a real-time, ontology-mapped representation of the world. Together, ontologies and LLMs are transforming decision-making across the planet, and powering the next generation of intelligent infrastructure.

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