---
tags:
  - impermanence
  - collapse
  - overshoot
  - extinction events
  - extinction
---

# collapse of complex systems

--8<-- "snippets/cognitive_complexity.md"

## santa fe

<iframe width="819" height="461" src="https://www.youtube.com/embed/-5A1WcFoxi4" title="My SFI: Cormac McCarthy describes the Santa Fe Institute" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>

## conditionality

<iframe width="835" height="470" src="https://www.youtube.com/embed/JR93X7xK05o" title="Complexity Explorer Lecture: David Krakauer • What is Complexity?" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>

```mermaid
gantt
    title Timeline of Events Leading to Eco Collapse
    dateFormat  YYYY-MM-DD
    axisFormat  %m/%d/%Y
    section Causes of Collapse
    Unsustainable resource use          :done,    des1, 2010-01-01, 2020-01-01
    Habitat destruction                 :done,    des2, 2010-01-01, 2020-01-01
    Pollution                           :done,    des3, 2010-01-01, 2020-01-01
    Climate change                      :done,    des4, 2010-01-01, 2020-01-01
    section Effects of Collapse
    Extinction of species               :done,    act1, 2020-01-01, 2040-01-01
    Ecosystem collapse                  :done,    act2, 2040-01-01, 2060-01-01
    Resource scarcity                   :done,    act3, 2060-01-01, 2080-01-01
    Mass migration                      :done,    act4, 2080-01-01, 2100-01-01
```

## pandemics

| Pandemic            | Year         | Death Toll                   |
| ------------------- | ------------ | ---------------------------- |
| Plague of Justinian | 541-542      | 25-50 million                |
| Black Death         | 1346-1353    | 75-200 million               |
| Smallpox            | 1520         | 56 million                   |
| Spanish Flu         | 1918-1920    | 50 million                   |
| HIV/AIDS            | 1981-present | 32 million (as of 2020)      |
| COVID-19            | 2019-present | 5.5 million (as of Feb 2022) |

## nautral disasters

| Rank | Event                                    | Date | Location                       | Death Toll            |
| ---- | ---------------------------------------- | ---- | ------------------------------ | --------------------- |
| 1    | 1931 China floods                        | 1931 | China                          | 1,000,000 - 4,000,000 |
| 2    | 1970 Bhola Cyclone                       | 1970 | East Pakistan (now Bangladesh) | 500,000 - 1,000,000   |
| 3    | 1556 Shaanxi earthquake                  | 1556 | China                          | 830,000               |
| 4    | 2010 Haiti earthquake                    | 2010 | Haiti                          | 230,000               |
| 5    | 2004 Indian Ocean earthquake and tsunami | 2004 | Indian Ocean                   | 227,898               |
| 6    | 1920 Haiyuan earthquake                  | 1920 | China                          | 200,000 - 240,000     |
| 7    | 526 Antioch earthquake                   | 526  | Byzantine Empire (now Turkey)  | 250,000               |
| 8    | 1976 Tangshan earthquake                 | 1976 | China                          | 242,000               |
| 9    | 2013 Typhoon Haiyan                      | 2013 | Philippines                    | 6,300 - 6,700         |
| 10   | 1991 Bangladesh cyclone                  | 1991 | Bangladesh                     | 138,000               |

| Rank | Event                                           | Date               | Location                | Fatalities |
| ---- | ----------------------------------------------- | ------------------ | ----------------------- | ---------- |
| 1    | 2004 Indian Ocean earthquake and tsunami        | December 26, 2004  | Indian Ocean            | 230,000    |
| 2    | 2011 Tōhoku earthquake and tsunami              | March 11, 2011     | Japan                   | 15,897     |
| 3    | 1883 eruption of Krakatoa and tsunami           | August 26, 1883    | Sunda Strait, Indonesia | 36,417     |
| 4    | 1993 Hokkaido Nansei-oki earthquake and tsunami | July 12, 1993      | Japan                   | 230        |
| 5    | 2018 Sulawesi earthquake and tsunami            | September 28, 2018 | Indonesia               | 4,340      |
| 6    | 1896 Sanriku earthquake and tsunami             | June 15, 1896      | Japan                   | 22,070     |
| 7    | 1946 Nankaidō earthquake and tsunami            | December 20, 1946  | Japan                   | 1,330      |
| 8    | 1868 Arica earthquake and tsunami               | August 13, 1868    | Peru and Chile          | 25,000     |
| 9    | 2010 Chile earthquake and tsunami               | February 27, 2010  | Chile                   | 525        |
| 10   | 2018 Sulawesi earthquake and tsunami            | September 28, 2018 | Indonesia               | 4,340      |

| Rank | Storm Name         | Date     | Location        | Death Toll |
| ---- | ------------------ | -------- | --------------- | ---------- |
| 1    | Bhola Cyclone      | Nov 1970 | Bangladesh      | 500,000+   |
| 2    | Haiphong Typhoon   | Oct 1881 | Vietnam         | 300,000+   |
| 3    | Typhoon Nina       | Aug 1975 | China           | 229,000    |
| 4    | Haiyan Typhoon     | Nov 2013 | Philippines     | 6,300      |
| 5    | Bangladesh Cyclone | Apr 1991 | Bangladesh      | 138,000    |
| 6    | Nagasaki Typhoon   | Sep 1945 | Japan           | 50,000+    |
| 7    | Mitch Hurricane    | Oct 1998 | Central America | 11,000+    |
| 8    | Nargis Cyclone     | May 2008 | Myanmar         | 138,000+   |
| 9    | Katrina Hurricane  | Aug 2005 | United States   | 1,833      |
| 10   | Audrey Hurricane   | Jun 1957 | United States   | 416        |

| Rank | Fire Name                | Year | Location                      | Size (acres) | Fatalities |
| ---- | ------------------------ | ---- | ----------------------------- | ------------ | ---------- |
| 1    | Peshtigo Fire            | 1871 | Wisconsin, United States      | 1,200,000    | 1,500+     |
| 2    | Great Fire of 1910       | 1910 | Idaho, Montana, United States | 3,000,000    | 87         |
| 3    | Black Saturday Bushfires | 2009 | Victoria, Australia           | 1,100,000    | 173        |
| 4    | Hinckley Fire            | 1894 | Minnesota, United States      | 250,000      | 418+       |
| 5    | Waldo Canyon Fire        | 2012 | Colorado, United States       | 18,247       | 2          |
| 6    | Camp Fire                | 2018 | California, United States     | 153,336      | 85         |
| 7    | Cloquet Fire             | 1918 | Minnesota, United States      | 100,000      | 453        |
| 8    | Yacolt Burn              | 1902 | Washington, United States     | 238,920      | 65+        |
| 9    | Black Dragon Fire        | 1987 | Heilongjiang, China           | 3,000,000    | 200+       |
| 10   | Matilija Fire            | 1932 | California, United States     | 220,000      | 0          |

| Event                                      | Date          | Citation                                                                                                                                  |
| ------------------------------------------ | ------------- | ----------------------------------------------------------------------------------------------------------------------------------------- |
| Deforestation of the Amazon Rainforest     | ongoing       | <https://www.bbc.com/news/world-latin-america-35618548>                                                                                   |
| Deforestation of the Indonesian Rainforest | ongoing       | <https://www.reuters.com/article/us-indonesia-environment-forests/indonesia-aims-to-protect-30-of-forests-to-cut-emissions-idUSKBN23L0XN> |
| Deforestation of the Congo Rainforest      | ongoing       | <https://rainforests.mongabay.com/congo/>                                                                                                 |
| The Great Plains Dust Bowl                 | 1930s         | <https://www.history.com/topics/great-plains-dust-bowl>                                                                                   |
| Deforestation of the Eastern United States | 1600s - 1900s | <https://www.nationalgeographic.org/article/deforestation-united-states/>                                                                 |
| Deforestation of the Brazilian Cerrado     | ongoing       | <https://www.sciencedirect.com/science/article/abs/pii/S0264837711002694>                                                                 |
| Deforestation of Madagascar                | ongoing       | <https://www.conservation.org/stories/Pages/10-deforestation-areas-most-critical-for-conservation.aspx>                                   |
| Deforestation of Borneo                    | ongoing       | <https://www.nationalgeographic.com/environment/2019/07/borneo-deforestation-shocking-loss-of-wildlife-habitat/>                          |
| Deforestation of the Sahel                 | ongoing       | <https://www.wri.org/applications/maps/sahel-deforestation>                                                                               |
| Deforestation of Haiti                     | ongoing       | <https://www.nationalgeographic.com/news/2010/1/100119-haiti-earthquake-deforestation-mudslides/>                                         |

| Rank | Event                                      | Year(s)       | Area Deforested |
| ---- | ------------------------------------------ | ------------- | --------------- |
| 1    | Deforestation of the Amazon rainforest     | 1970s-present | 17%             |
| 2    | Deforestation of the Indonesian rainforest | 1950s-present | 12%             |
| 3    | Deforestation of the Congo rainforest      | 1900s-present | 8%              |
| 4    | Deforestation of the Atlantic Forest       | 1500s-present | 93%             |
| 5    | Deforestation of the Chaco                 | 1980s-present | 25%             |
| 6    | Deforestation of the Gran Chaco            | 1900s-present | 20%             |
| 7    | Deforestation of the Mata Atlântica        | 1500s-present | 88%             |
| 8    | Deforestation of the Sundarbans            | 1900s-present | 26%             |
| 9    | Deforestation of the Caatinga              | 1950s-present | 46%             |
| 10   | Deforestation of the Cerrado               | 1960s-present | 50%             |

| Rank | Event                              | Date              | Location                          | Death toll |
| ---- | ---------------------------------- | ----------------- | --------------------------------- | ---------- |
| 1    | Fukushima Daiichi nuclear disaster | March 11, 2011    | Japan                             | 15,899     |
| 2    | Chernobyl disaster                 | April 26, 1986    | Ukraine                           | 4,000      |
| 3    | Bhopal gas tragedy                 | December 3, 1984  | India                             | 3,787      |
| 4    | Rana Plaza building collapse       | April 24, 2013    | Bangladesh                        | 1,135      |
| 5    | Sampoong Department Store collapse | June 29, 1995     | South Korea                       | 502        |
| 6    | Tenerife airport disaster          | March 27, 1977    | Canary Islands, Spain             | 583        |
| 7    | Hyatt Regency walkway collapse     | July 17, 1981     | Kansas City, Missouri, USA        | 114        |
| 8    | Pemberton Mill collapse            | January 10, 1860  | Lawrence, Massachusetts, USA      | 145        |
| 9    | Ytong factory collapse             | November 24, 1995 | Lüneburg, Germany                 | 5          |
| 10   | Willow Island disaster             | April 27, 1978    | Willow Island, West Virginia, USA | 51         |

## the visible money trail

| Rank | Achievement (world economic forum)                    |
| ---- | ----------------------------------------------------- |
| 1    | Partnering Against Corruption Initiative (PACI)       |
| 2    | Global Battery Alliance                               |
| 3    | Tropical Forest Alliance 2020                         |
| 4    | Shaping the Future of Construction                    |
| 5    | Future of Electricity                                 |
| 6    | Future of Health and Healthcare                       |
| 7    | New Vision for Education                              |
| 8    | Future of Food                                        |
| 9    | Platform for Accelerating the Circular Economy (PACE) |
| 10   | Global Future Councils                                |

| Industrial Revolution        | Time Period                     | Main Characteristics                                                                          |
| ---------------------------- | ------------------------------- | --------------------------------------------------------------------------------------------- |
| First Industrial Revolution  | 1760-1840                       | Transition from manual production to machines, steam power, and factories                     |
| Second Industrial Revolution | Late 19th - Early 20th Century  | Advancements in steel production, electricity, and mass production                            |
| Third Industrial Revolution  | 1969-present                    | Rise of electronics, telecommunications, and automation                                       |
| Fourth Industrial Revolution | Ongoing since late 20th century | Emergence of digital technologies, artificial intelligence, and interconnectedness of devices |

| Rank | Country        | GDP (nominal)    |
| ---- | -------------- | ---------------- |
| 1    | United States  | $22.675 trillion |
| 2    | China          | $16.645 trillion |
| 3    | Japan          | $4.872 trillion  |
| 4    | Germany        | $4.170 trillion  |
| 5    | United Kingdom | $2.622 trillion  |
| 6    | India          | $2.611 trillion  |
| 7    | France         | $2.582 trillion  |
| 8    | Italy          | $1.935 trillion  |
| 9    | Canada         | $1.655 trillion  |
| 10   | South Korea    | $1.586 trillion  |

| Rank | Organization                                       | Lobbying Expenditures |
| ---- | -------------------------------------------------- | --------------------- |
| 1    | U.S. Chamber of Commerce                           | $39,320,000           |
| 2    | National Association of Realtors                   | $36,190,000           |
| 3    | Pharmaceutical Research & Manufacturers of America | $25,891,272           |
| 4    | American Medical Association                       | $22,300,000           |
| 5    | American Hospital Association                      | $19,815,000           |
| 6    | Business Roundtable                                | $19,530,000           |
| 7    | Blue Cross Blue Shield                             | $18,910,000           |
| 8    | Koch Industries                                    | $17,910,000           |
| 9    | National Association of Broadcasters               | $17,620,000           |
| 10   | American Chemistry Council                         | $16,880,000           |

| Rank | Organization                                       | Lobbying Expenditures |
| ---- | -------------------------------------------------- | --------------------- |
| 1    | U.S. Chamber of Commerce                           | $82,920,000           |
| 2    | National Association of Realtors                   | $74,435,514           |
| 3    | Pharmaceutical Research & Manufacturers of America | $27,970,000           |
| 4    | American Medical Association                       | $24,374,000           |
| 5    | Blue Cross Blue Shield                             | $23,040,000           |
| 6    | American Hospital Association                      | $21,518,719           |
| 7    | American Association for Justice                   | $18,470,000           |
| 8    | National Association of Home Builders              | $16,620,000           |
| 9    | American Chemistry Council                         | $15,610,000           |
| 10   | National Association of Broadcasters               | $15,545,000           |

## manmade disasters

| Rank | Event                         | Date             | Location             | Death Toll              |
| ---- | ----------------------------- | ---------------- | -------------------- | ----------------------- |
| 1    | Chernobyl disaster            | April 26, 1986   | Pripyat, Ukraine     | 4,000 (estimated)       |
| 2    | Bhopal gas tragedy            | December 3, 1984 | Bhopal, India        | 15,000 (estimated)      |
| 3    | Tangshan earthquake           | July 28, 1976    | Tangshan, China      | 242,769                 |
| 4    | Cyclone Nargis                | May 2-3, 2008    | Myanmar              | 138,366                 |
| 5    | The Great Leap Forward        | 1958-1962        | China                | 30 million (estimated)  |
| 6    | The Holocaust                 | 1941-1945        | Europe               | 11 million (estimated)  |
| 7    | Soviet famine of 1932-33      | 1932-1933        | Soviet Union         | 7 million (estimated)   |
| 8    | Hiroshima bombing             | August 6, 1945   | Hiroshima, Japan     | 140,000 (estimated)     |
| 9    | Nagasaki bombing              | August 9, 1945   | Nagasaki, Japan      | 70,000 (estimated)      |
| 10   | Three Gorges Dam construction | 1994-ongoing     | Yangtze River, China | 1.3 million (estimated) |

> another version

| Rank | Event                           | Date                 | Location           | Death Toll      |
| ---- | ------------------------------- | -------------------- | ------------------ | --------------- |
| 1    | The Great Chinese Famine        | 1959-1961            | China              | 15-45 million   |
| 2    | World War II                    | 1939-1945            | Worldwide          | 70-85 million   |
| 3    | Spanish Flu Pandemic            | 1918-1920            | Worldwide          | 17-50 million   |
| 4    | Mao Zedong's regime             | 1949-1976            | China              | 40-70 million   |
| 5    | Soviet famine of 1932-33        | 1932-1933            | Soviet Union       | 7 million       |
| 6    | Hiroshima and Nagasaki bombings | August 6 and 9, 1945 | Japan              | 129,000-226,000 |
| 7    | Partition of India              | August 1947          | India and Pakistan | 1 million       |
| 8    | Armenian genocide               | 1915-1917            | Ottoman Empire     | 1.5 million     |
| 9    | Rwandan genocide                | April-July 1994      | Rwanda             | 800,000         |
| 10   | Cambodian genocide              | 1975-1979            | Cambodia           | 1.7 million     |

> economic

| Rank | Event                                            | Date               | Location                               | Death toll |
| ---- | ------------------------------------------------ | ------------------ | -------------------------------------- | ---------- |
| 1    | Wall Street Crash of 1929                        | October 24, 1929   | New York City, New York, United States | N/A        |
| 2    | Ponzi Scheme by Bernie Madoff                    | December 11, 2008  | New York City, New York, United States | N/A        |
| 3    | Savings and Loan Crisis of the 1980s and 1990s   | 1980s-1990s        | United States                          | N/A        |
| 4    | Enron Scandal                                    | October 16, 2001   | Houston, Texas, United States          | N/A        |
| 5    | Global Financial Crisis of 2008                  | 2008-2009          | Worldwide                              | N/A        |
| 6    | Lehman Brothers Collapse                         | September 15, 2008 | New York City, New York, United States | N/A        |
| 7    | WorldCom Accounting Scandal                      | 2002               | Clinton, Mississippi, United States    | N/A        |
| 8    | Long-Term Capital Management Hedge Fund Collapse | 1998               | Greenwich, Connecticut, United States  | N/A        |
| 9    | Mad Cow Disease Outbreak                         | 1986-2006          | Worldwide                              | 227 deaths |
| 10   | BP Oil Spill                                     | April 20, 2010     | Gulf of Mexico, United States          | 11 deaths  |

> famine

| Rank | Event                         | Date      | Location             | Death Toll          |
| ---- | ----------------------------- | --------- | -------------------- | ------------------- |
| 1    | Great Chinese Famine          | 1958-1962 | China                | 15-45 million       |
| 2    | Irish Potato Famine           | 1845-1852 | Ireland              | 1 million           |
| 3    | Bengal Famine of 1943         | 1943      | India and Bangladesh | 2-3 million         |
| 4    | Holodomor (Ukrainian Famine)  | 1932-1933 | Ukraine              | 3.3-7.5 million     |
| 5    | Ethiopian Famine of 1984-1985 | 1984-1985 | Ethiopia             | 400,000-1.2 million |
| 6    | North Korean Famine           | 1994-1998 | North Korea          | 240,000-3.5 million |
| 7    | Sahel Famine                  | 1968-1974 | West Africa          | 100,000-1 million   |
| 8    | Great Famine of 1876-1878     | 1876-1878 | India                | 5.5 million         |
| 9    | Soviet Famine of 1932-1933    | 1932-1933 | Soviet Union         | 6-8 million         |
| 10   | Madhya Pradesh famine         | 1943-1944 | India                | 1.5-2 million       |

> industrial

| Rank | Event                              | Date         | Location                     | Death toll |
| ---- | ---------------------------------- | ------------ | ---------------------------- | ---------- |
| 1    | Bhopal disaster                    | Dec 3, 1984  | Bhopal, India                | 3,787      |
| 2    | Chernobyl disaster                 | Apr 26, 1986 | Pripyat, Ukraine             | 4,000      |
| 3    | Fukushima Daiichi nuclear disaster | Mar 11, 2011 | Fukushima, Japan             | 1,600      |
| 4    | Deepwater Horizon oil spill        | Apr 20, 2010 | Gulf of Mexico               | 11         |
| 5    | Sidoarjo mud flow disaster         | May 29, 2006 | Sidoarjo, Indonesia          | 15         |
| 6    | Texas City refinery explosion      | Apr 16, 1947 | Texas City, Texas            | 581        |
| 7    | Exxon Valdez oil spill             | Mar 24, 1989 | Prince William Sound, Alaska | 11         |
| 8    | Piper Alpha oil rig disaster       | Jul 6, 1988  | North Sea                    | 167        |
| 9    | Baia Mare cyanide spill            | Jan 30, 2000 | Baia Mare, Romania           | 2          |
| 10   | Buncefield oil depot explosion     | Dec 11, 2005 | Hertfordshire, UK            | 0          |

## top earthquakes

| Rank | Magnitude | Date              | Location                               | Death Toll |
| ---- | --------- | ----------------- | -------------------------------------- | ---------- |
| 1    | 9.5       | May 22, 1960      | Valdivia, Chile                        | 5,700+     |
| 2    | 9.2       | March 27, 1964    | Prince William Sound, Alaska           | 131        |
| 3    | 9.1       | December 26, 2004 | Off the west coast of northern Sumatra | 230,000+   |
| 4    | 9.0       | March 11, 2011    | Honshu, Japan                          | 15,896     |
| 5    | 8.8       | February 27, 2010 | Maule Region, Chile                    | 525        |
| 6    | 8.6       | August 15, 1950   | Assam, India-Tibet                     | 780        |
| 7    | 8.5       | November 25, 1833 | Sumatra, Indonesia                     | 5,000+     |
| 8    | 8.5       | January 31, 1906  | Ecuador-Colombia border region         | 1,000+     |
| 9    | 8.5       | February 4, 1965  | Rat Islands, Alaska                    | 0          |
| 10   | 8.5       | March 28, 2005    | Northern Sumatra, Indonesia            | 1,313      |

## greed

Greed is often caused by a desire for more wealth or possessions, as well as a fear of not having enough. It can also be influenced by societal and cultural values, such as a focus on material success and competition. Additionally, some research suggests that biological factors, such as genetics and brain chemistry, may play a role in shaping individuals' propensity for greed.

| Rank | Name                     | Net Worth (Billion USD) | Source of Wealth         |
| ---- | ------------------------ | ----------------------- | ------------------------ |
| 1    | Elon Musk                | 195.7                   | Tesla, SpaceX, Neuralink |
| 2    | Jeff Bezos               | 191.6                   | Amazon                   |
| 3    | Bernard Arnault & Family | 178.2                   | LVMH                     |
| 4    | Bill Gates               | 130.9                   | Microsoft                |
| 5    | Mark Zuckerberg          | 106.4                   | Facebook                 |
| 6    | Warren Buffett           | 94.7                    | Berkshire Hathaway       |
| 7    | Larry Ellison            | 84.6                    | Oracle                   |
| 8    | Larry Page               | 79.4                    | Google                   |
| 9    | Sergey Brin              | 76.8                    | Google                   |
| 10   | Steve Ballmer            | 71.6                    | Microsoft                |

## monopolies

| Event                                                        | Date              | Citation                                                                                 |
| ------------------------------------------------------------ | ----------------- | ---------------------------------------------------------------------------------------- |
| Standard Oil Company is incorporated                         | January 10, 1870  | [Source](https://www.history.com/topics/early-20th-century-us/standard-oil-trust)        |
| United States Steel Corporation is founded                   | February 25, 1901 | [Source](https://www.history.com/topics/19th-century/american-industrial-revolution)     |
| American Tobacco Company is formed                           | January 31, 1902  | [Source](https://www.britannica.com/topic/American-Tobacco-Company)                      |
| International Mercantile Marine Company is formed            | April 1902        | [Source](https://www.britannica.com/topic/International-Mercantile-Marine-Company)       |
| Northern Securities Company is formed                        | November 1901     | [Source](https://www.history.com/this-day-in-history/theodore-roosevelt-takes-on-trusts) |
| Radio Corporation of America (RCA) is formed                 | October 17, 1919  | [Source](https://www.britannica.com/topic/Radio-Corporation-of-America)                  |
| Aluminum Company of America (Alcoa) is formed                | October 1, 1888   | [Source](https://www.britannica.com/topic/Aluminum-Company-of-America)                   |
| International Business Machines Corporation (IBM) is founded | June 16, 1911     | [Source](https://www.ibm.com/ibm/history/history/year_1911.html)                         |
| Bell Telephone Company is founded                            | July 9, 1877      | [Source](https://www.history.com/topics/inventions/telephone)                            |
| Microsoft Corporation is founded                             | April 4, 1975     | [Source](https://www.britannica.com/topic/Microsoft-Corporation)                         |

> coca cola

| Company         | Business             |
| --------------- | -------------------- |
| Costa Coffee    | Coffee Shops         |
| Honest Tea      | Bottled Tea          |
| Minute Maid     | Fruit Beverages      |
| Simply Orange   | Orange Juice         |
| SmartWater      | Bottled Water        |
| Vitamin Water   | Flavored Water       |
| Zico            | Coconut Water        |
| Odwalla         | Smoothies and Juices |
| FUZE Tea        | Bottled Tea          |
| Innocent Drinks | Smoothies and Juices |

> pepsi

| Company Name                       | Type of Business                     |
| ---------------------------------- | ------------------------------------ |
| Pepsi-Cola                         | Beverage Manufacturing               |
| Frito-Lay                          | Snack Food Manufacturing             |
| Tropicana                          | Juice Manufacturing                  |
| Gatorade                           | Sports Drink Manufacturing           |
| Quaker Foods                       | Cereal and Snack Manufacturing       |
| Sabra Dipping Company              | Hummus and Dips Manufacturing        |
| Naked Juice                        | Juice and Smoothie Manufacturing     |
| KeVita                             | Probiotic Drinks Manufacturing       |
| Lipton                             | Tea Manufacturing                    |
| Rockstar Energy                    | Energy Drink Manufacturing           |
| Starbucks Ready-to-Drink Beverages | Bottled Coffee and Tea Manufacturing |
| Muscle Milk                        | Protein Drink Manufacturing          |
| Bang Energy                        | Energy Drink Manufacturing           |
| Izze                               | Sparkling Juice Manufacturing        |
| Hilo Life                          | Keto Snack Manufacturing             |

| Brand        | Product Type          |
| ------------ | --------------------- |
| Pepsi        | Carbonated Soft Drink |
| Lay's        | Potato Chips          |
| Tostitos     | Tortilla Chips        |
| Doritos      | Tortilla Chips        |
| Cheetos      | Cheese Snacks         |
| Quaker       | Oats and Snacks       |
| Gatorade     | Sports Drink          |
| Tropicana    | Juice                 |
| Naked Juice  | Juice                 |
| Mountain Dew | Carbonated Soft Drink |
| 7UP          | Carbonated Soft Drink |
| Mirinda      | Carbonated Soft Drink |
| Aquafina     | Bottled Water         |
| Lipton       | Iced Tea              |
| Brisk        | Iced Tea              |
| Starbucks    | Bottled Coffee        |
| Rockstar     | Energy Drink          |

> yum brand

| Restaurant       | Type        |
| ---------------- | ----------- |
| KFC              | Fast Food   |
| Taco Bell        | Fast Food   |
| Pizza Hut        | Fast Casual |
| The Habit Burger | Fast Casual |

> manipulation

| Event                     | Date      | Description                               | Consequences                                                                                                  | Citation                                                                     |
| ------------------------- | --------- | ----------------------------------------- | ------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------- |
| South Sea Bubble          | 1720      | A stock market bubble in England          | Resulted in the bankruptcy of many investors, including the government and the Company of the Bank of England | [source](https://www.history.com/topics/british-history/south-sea-bubble)    |
| Mississippi Company       | 1719-1720 | A speculative bubble in France            | Led to the bankruptcy of the company and the collapse of the French economy                                   | [source](https://www.investopedia.com/terms/m/mississippi-bubble.asp)        |
| Panic of 1907             | 1907      | A financial crisis in the United States   | Led to the creation of the Federal Reserve System to prevent future financial crises                          | [source](https://www.history.com/topics/early-20th-century-us/panic-of-1907) |
| Tulip Mania               | 1637      | A speculative bubble in the Netherlands   | Led to the collapse of the Dutch tulip market                                                                 | [source](https://www.britannica.com/topic/Tulip-Mania)                       |
| Wall Street Crash of 1929 | 1929      | A stock market crash in the United States | Led to the Great Depression and a decade of economic hardship                                                 | [source](https://www.history.com/topics/1920s/wall-street-crash-of-1929)     |

> manipulating prices

Vincent Kosuga was a farmer who became known for his attempt to manipulate the potato market in the United States in the 1950s. He managed to gain control of a large portion of the potato market by buying up the entire potato crop from farmers across the country and holding onto it in storage facilities. He then waited for the potato prices to rise before selling the potatoes for a profit.

Kosuga's actions were not illegal, but they did raise concerns about the potential for individuals to manipulate commodity markets in this way. As a result of his actions, the government introduced new regulations to prevent market manipulation in the future.

Silos are used to manipulate agriculture prices by allowing buyers to hold onto their stocks of crops until prices increase. This is known as hoarding, and it can lead to a shortage of crops in the market, which drives prices up. By controlling the supply of crops, silos can create an artificial shortage, allowing the owners to charge higher prices. Additionally, silos can be used to store crops until they can be sold at a more favorable price, further manipulating the market.

> diamond silo

There is no way to accurately determine the value of diamonds that are kept in vaults and not sold, as this information is not publicly available. Diamond companies and traders generally do not disclose the exact amount of inventory they hold, and the value of diamonds can also vary depending on a number of factors such as size, color, and clarity. Additionally, the value of diamonds can fluctuate over time due to changes in market demand and other economic factors.

## thegreatstory

<iframe width="819" height="461" src="https://www.youtube.com/embed/uU0DC6qcp18" title="Main Drivers of Collapse, Ecocide, and Likely NTHE - Dowd" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>

<iframe width="710" height="399" src="https://www.youtube.com/embed/e6FcNgOHYoo" title="Collapse in a Nutshell: Understanding Our Predicament (33 min)" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>

## what creature could most likely end humanity? ...most likely it's ourselves

According to the World Health Organization (WHO), mosquitoes are responsible for an estimated 725,000 deaths per year.

However, if we consider the indirect impact of a species on human mortality (i.e., the impact on human health caused by a species, but not directly causing death), then humans themselves would be the species that kills the most humans, through wars, homicides, and other forms of violence.

If we consider only direct mortality (i.e., death caused by a specific species), the answer is mosquitoes, which are responsible for spreading diseases such as malaria, dengue, yellow fever, and Zika virus, among others.

It's important to note that the numbers for both of these scenarios are estimates and can vary widely depending on the sources and methods used to gather data.

Additionally, there are other species, such as snakes, crocodiles, and sharks, which can also pose a significant threat to human life in certain regions or circumstances.

## top 10 natural disasters

> this makes me want to move: I live on a faultline, near a volcano, near the ocean, in my free time I often section hike through the mountains, several years my hikes were cut short due to forest fires

- Earthquakes: Earthquakes can cause widespread destruction, loss of life, and trigger secondary events such as tsunamis and landslides.
- Tsunamis: Tsunamis are large ocean waves caused by earthquakes, volcanic eruptions, and meteor impacts, which can cause widespread destruction and loss of life along coastlines.
- Hurricanes: Hurricanes are intense tropical storms that can cause widespread damage and flooding through strong winds and heavy rainfall.
- Cyclones: Cyclones are similar to hurricanes but form in the Indian Ocean and the South Pacific.
- Tornadoes: Tornadoes are rapidly rotating columns of air that can cause extensive damage through high winds.
- Wildfires: Wildfires can cause widespread destruction of property and wildlife habitats, as well as release harmful pollutants into the atmosphere.
- Floods: Floods occur when bodies of water overflow their banks and inundate nearby areas, causing widespread damage and displacement of populations.
- Volcanic eruptions: Volcanic eruptions can cause widespread damage and loss of life through ash fall, lava flows, and the creation of tsunamis.
- Landslides: Landslides are the movement of rock, soil, and other materials down a slope, which can cause damage to structures and infrastructure.
- Droughts: Droughts are prolonged periods of dry weather that can lead to crop failure, famine, and water shortages.

| Effect of Ocean Rising 10 Inches    |
| ----------------------------------- |
| Coastal flooding and erosion        |
| Saltwater intrusion into crops      |
| Increased storm surge damage        |
| Loss of habitat for marine life     |
| Increased ocean acidification       |
| Displacement of coastal communities |
| Water quality issues                |
| Impacts on fishing industry         |
| Damage to coastal infrastructure    |
| Increase in climate refugees        |

There are several factors that contribute to the ecological collapse of a region or the planet as a whole. Here are some of the most significant ones:

- Climate change: The burning of fossil fuels and deforestation are leading to rising levels of greenhouse gases, which are causing the planet to warm. This is leading to changes in weather patterns, melting of polar ice caps, and rising sea levels, which can have devastating impacts on ecosystems and the species that inhabit them.
- Habitat destruction: The destruction of natural habitats, such as forests, wetlands, and prairies, is causing the loss of biodiversity and making it difficult for species to adapt to changing conditions.
- Overfishing: Overfishing can cause a decline in fish populations, which can have ripple effects throughout ocean ecosystems and disrupt food webs.
- Pesticides and toxic chemicals: The widespread use of pesticides and other toxic chemicals can have negative impacts on wildlife, as well as on human health.
- Invasive species: Non-native species introduced into new environments can outcompete native species and alter ecosystems, often with negative impacts.
- Pollution: Pollution from sources such as factories, sewage treatment plants, and oil spills can have detrimental effects on wildlife and ecosystems.
- Overexploitation of resources: The extraction of resources such as water, timber, and minerals, can have negative impacts on ecosystems, leading to soil degradation and loss of biodiversity.
- Soil degradation: Soil degradation from factors such as deforestation, overgrazing, and improper agriculture practices can result in the loss of fertile land and make it difficult for plants and animals to thrive.
- Ocean acidification: The increase in carbon dioxide levels in the atmosphere is causing the oceans to become more acidic, which can have negative impacts on marine life and disrupt ocean ecosystems.
- Human population growth: The growing human population is putting increasing pressure on the planet's resources, leading to habitat destruction, deforestation, and overexploitation of resources, which can have negative impacts on the environment and wildlife.

Addressing these factors is critical for avoiding ecological collapse and ensuring the health and sustainability of ecosystems and the species that inhabit them. This will require a concerted effort from individuals, governments, and businesses to reduce emissions, conserve resources, and protect habitats.

| Rank | Country       | CO2 Emissions (in metric tons) |
| ---- | ------------- | ------------------------------ |
| 1    | China         | 10,723,821,100                 |
| 2    | United States | 5,416,968,300                  |
| 3    | India         | 2,654,469,500                  |
| 4    | Russia        | 1,711,083,600                  |
| 5    | Japan         | 1,191,114,200                  |
| 6    | Germany       | 801,172,200                    |
| 7    | Iran          | 720,427,200                    |
| 8    | South Korea   | 649,155,200                    |
| 9    | Saudi Arabia  | 592,480,100                    |
| 10   | Canada        | 563,498,200                    |

## pollution

| Rank | Date | Location               | Event                       | Toxic Material                         | Fatalities   | Notes                                                                               |
| ---- | ---- | ---------------------- | --------------------------- | -------------------------------------- | ------------ | ----------------------------------------------------------------------------------- |
| 1    | 1984 | Bhopal, India          | Gas Leak Disaster           | Methyl Isocyanate                      | 4,000-16,000 | Considered the world's worst industrial disaster                                    |
| 2    | 1978 | Love Canal, USA        | Chemical Waste Dumping      | Dioxin, Benzene, Toluene, Xylene, PCBs | Unknown      | Resulted in the creation of the Superfund program                                   |
| 3    | 1996 | Baia Mare, Romania     | Cyanide Spill               | Cyanide                                | 0            | Released over 100,000 cubic meters of cyanide-contaminated waste water into a river |
| 4    | 2010 | Gulf of Mexico, USA    | Deepwater Horizon Oil Spill | Crude Oil                              | 11           | Considered the largest marine oil spill in history                                  |
| 5    | 2015 | Minamata Bay, Japan    | Mercury Poisoning           | Methyl Mercury                         | Unknown      | Thousands affected by eating contaminated fish                                      |
| 6    | 1956 | Minamata, Japan        | Mercury Poisoning           | Methyl Mercury                         | Unknown      | Discharged into Minamata Bay by a chemical factory                                  |
| 7    | 1986 | Chernobyl, Ukraine     | Nuclear Disaster            | Radioactive Material                   | 4,000        | Considered the worst nuclear power plant accident in history                        |
| 8    | 2006 | Abidjan, Ivory Coast   | Toxic Waste Dumping         | Unknown                                | Unknown      | Over 100,000 people affected by illegal dumping of toxic waste                      |
| 9    | 1985 | Pemex Refinery, Mexico | Explosion and Fire          | Hydrocarbon                            | 500-600      | One of the worst industrial disasters in Mexico                                     |
| 10   | 1976 | Seveso, Italy          | Dioxin Release              | Dioxin                                 | 37           | One of the highest recorded levels of dioxin exposure in humans                     |

### firetruck

<iframe width="726" height="408" src="https://www.youtube.com/embed/scXY34nV4LM" title="Are we all doomed? A climate scientist weighs in." frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>

## Carbon-sucking unicorns

Carbon-sucking unicorns is a colloquial term that refers to unproven, highly speculative technologies that are promoted as a solution to the problem of climate change and carbon emissions. These technologies are often described as "miracle" or "silver bullet" solutions that will solve the problem of climate change without requiring significant changes to the current system.

Examples of carbon-sucking unicorns include the idea of large-scale deployment of carbon capture and storage (CCS) technologies, or the idea of "geoengineering" the climate by, for example, injecting reflective particles into the atmosphere to reflect sunlight back into space.

While these technologies may have some potential to address the problem of climate change, they are often criticized for being unrealistic, unproven, and based on a limited understanding of the complex and interconnected systems that regulate the Earth's climate. In addition, many of these technologies are seen as a way to avoid taking responsibility for reducing emissions and making the necessary changes to our lifestyles and economic systems.

It is important to remember that there is no single solution to the problem of climate change, and that a comprehensive and multi-faceted approach is necessary to address the problem effectively. This includes reducing emissions, promoting energy efficiency and renewable energy, and protecting and restoring ecosystems that help to regulate the Earth's climate.

## Techno-idolatry

Techno-idolatry refers to the worship or excessive veneration of technology and technological advancements, often to the point of disregarding other important values and considerations. It involves the belief that technology can solve all of society's problems, and that technology is an unmitigated good that can provide unlimited benefits.

Techno-idolatry is often accompanied by a belief in the unlimited potential of technology, and a disregard for the potential downsides and negative consequences of technological progress. For example, some proponents of techno-idolatry may view issues such as climate change, resource depletion, and social inequality as problems that can be solved through technological solutions alone, without considering the need for changes in economic systems, political structures, and individual behavior.

This approach to technology can be problematic because it ignores the fact that technology is not neutral, and that it can have unintended consequences that may harm the environment and society. It can also lead to a blind faith in technology, without critically examining its limitations and potential impacts.

To avoid the negative consequences of techno-idolatry, it is important to adopt a more balanced and nuanced approach to technology, one that recognizes its benefits while also acknowledging its limitations and potential downsides. This involves considering the ethical, social, and environmental implications of technological advancements, and striving to use technology in a responsible and sustainable way.

## us protests

| Rank | Protest Event                            | Date             | Location         | Estimated Attendance |
| ---- | ---------------------------------------- | ---------------- | ---------------- | -------------------- |
| 1    | Women's March                            | January 21, 2017 | Washington, D.C. | 470,000 - 680,000    |
| 2    | March for Our Lives                      | March 24, 2018   | Washington, D.C. | 800,000              |
| 3    | The People's Climate March               | April 29, 2017   | Washington, D.C. | 200,000              |
| 4    | Tax March                                | April 15, 2017   | Washington, D.C. | 100,000              |
| 5    | Families Belong Together                 | June 30, 2018    | Various Cities   | 100,000 - 700,000    |
| 6    | March for Science                        | April 22, 2017   | Various Cities   | 1 million            |
| 7    | Black Lives Matter                       | Summer 2020      | Various Cities   | Millions             |
| 8    | Occupy Wall Street                       | September 2011   | New York City    | Thousands            |
| 9    | Anti-War Protests                        | 2002-2003        | Various Cities   | Millions             |
| 10   | March on Washington for Jobs and Freedom | August 28, 1963  | Washington, D.C. | 200,000              |

## The Seneca Curve

> Growth is slow, while the road to ruin is fast.

- seneca, philosopher 4bc

The Seneca Curve is a graphical representation of the idea that growth and collapse are inherent in complex systems, including human societies. The Seneca Curve is named after the Roman philosopher Seneca, who is said to have observed that growth in wealth and power is followed by a rapid decline.

The Seneca Curve is typically depicted as a graph that starts with slow growth, followed by a period of rapid expansion, and then a sharp decline. This pattern is thought to be the result of various factors, including the depletion of resources, the accumulation of waste, the loss of resilience and adaptability, and the creation of unsustainable systems.

The Seneca Curve has been applied to the study of the collapse of civilizations, including the Roman Empire, as well as to contemporary global challenges such as climate change, resource depletion, and economic instability. By understanding the dynamics of growth and collapse in complex systems, it is possible to develop strategies for avoiding or mitigating the consequences of collapse, and promoting long-term sustainability.

<iframe width="710" height="399" src="https://www.youtube.com/embed/jWoCXLuTIkI" title="Climate Spiral" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>

## cargo / globalization

The amount of gas a cargo ship would use on a trip depends on several factors such as the distance traveled, the size and efficiency of the ship's engines, the weather conditions, and the type of fuel used. However, as a rough estimate, a cargo ship carrying 200,000,000 tons of cargo could use anywhere from 1,000 to 3,000 metric tons of fuel per day, depending on the factors mentioned above. This means that for a trip of, say, 10 days, the ship could use anywhere from 10,000 to 30,000 metric tons of fuel.

The amount of CO2 produced by burning 30,000 metric tons of fuel would depend on the type of fuel being burned.

Assuming the fuel being burned is diesel, which produces approximately 2.68 kg of CO2 per liter of fuel burned, the amount of CO2 produced by burning 30,000 metric tons (30 million kilograms) of diesel would be approximately:

30,000,000 kg x 2.68 kg CO2/liter = 80,400,000 kg or 80,400 metric tons of CO2.

> Note: TEU stands for "Twenty-foot Equivalent Unit" and is a measure of a ship's cargo carrying capacity.

| Rank | Ship Name                        | Fuel Capacity (metric tons) | Cargo Capacity (TEU) |
| ---- | -------------------------------- | --------------------------- | -------------------- |
| 1    | HMM Algeciras                    | 17,500                      | 23,964               |
| 2    | MSC Gülsün                       | 23,756                      | 23,756               |
| 3    | MSC Mina                         | 20,000                      | 23,756               |
| 4    | OOCL Hong Kong                   | 18,000                      | 21,413               |
| 5    | CMA CGM Antoine de Saint Exupery | 18,000                      | 20,776               |
| 6    | Ever Golden                      | 20,388                      | 20,124               |
| 7    | COSCO Shipping Universe          | 20,119                      | 19,100               |
| 8    | COSCO Shipping Virgo             | 20,000                      | 19,000               |
| 9    | OOCL Germany                     | 18,000                      | 21,413               |
| 10   | MSC Anna                         | 18,000                      | 19,224               |

MSC Gülsün co2 emissions for example: burning 23,000 metric tons of crude oil would produce approximately 2,308 metric tons of CO2.

| Rank | Port Name              | Weight of Cargo (in tons) | Type of Cargo                                          |
| ---- | ---------------------- | ------------------------- | ------------------------------------------------------ |
| 1    | South Louisiana        | 251,243,271               | Petroleum, chemicals, coal, grain, and other dry goods |
| 2    | Houston                | 225,603,757               | Petroleum, chemicals, and coal                         |
| 3    | New York/New Jersey    | 161,453,662               | Petroleum, chemicals, and vehicles                     |
| 4    | Beaumont               | 91,294,291                | Petroleum and chemicals                                |
| 5    | Corpus Christi         | 84,229,610                | Petroleum, chemicals, and grain                        |
| 6    | New Orleans            | 77,267,910                | Petroleum, chemicals, and grain                        |
| 7    | Long Beach/Los Angeles | 69,307,234                | Petroleum, chemicals, vehicles, and other dry goods    |
| 8    | Baton Rouge            | 58,466,552                | Petroleum, chemicals, and grain                        |
| 9    | Port Arthur            | 51,585,801                | Petroleum, chemicals, and coal                         |
| 10   | Mobile                 | 44,789,271                | Chemicals and petroleum                                |

| Rank | Ship Name                        | Cargo Capacity (TEU) | Year Built | Flag      |
| ---- | -------------------------------- | -------------------- | ---------- | --------- |
| 1    | MSC Gülsün                       | 23,756               | 2019       | Panama    |
| 2    | OOCL Hong Kong                   | 21,413               | 2017       | Hong Kong |
| 3    | COSCO Shipping Universe          | 21,237               | 2018       | Hong Kong |
| 4    | Madrid Maersk                    | 20,568               | 2017       | Denmark   |
| 5    | Ever Golden                      | 20,388               | 2020       | Panama    |
| 6    | MOL Triumph                      | 20,170               | 2017       | Panama    |
| 7    | CMA CGM Antoine de Saint Exupéry | 20,078               | 2018       | France    |
| 8    | CMA CGM Jacques Saadé            | 20,000               | 2020       | France    |
| 9    | MSC Isabella                     | 19,630               | 2019       | Panama    |
| 10   | MSC Sixin                        | 19,574               | 2019       | Panama    |

| Rank | Flight                  | Cargo Type    | Fuel Capacity (gallons) | Plane Model |
| ---- | ----------------------- | ------------- | ----------------------- | ----------- |
| 1    | Emirates SkyCargo       | General Cargo | 324,848                 | 777-200LR   |
| 2    | Qatar Airways Cargo     | Fresh Produce | 265,000                 | 747-8F      |
| 3    | Etihad Cargo            | General Cargo | 222,000                 | 777-200F    |
| 4    | Cargolux Italia         | General Cargo | 220,000                 | 747-400F    |
| 5    | Korean Air Cargo        | General Cargo | 200,000                 | 747-8F      |
| 6    | Cathay Pacific Cargo    | General Cargo | 205,000                 | 747-8F      |
| 7    | Turkish Airlines Cargo  | General Cargo | 200,000                 | A330-200F   |
| 8    | Lufthansa Cargo         | General Cargo | 179,000                 | 777F        |
| 9    | AirBridgeCargo Airlines | General Cargo | 168,000                 | 747-8F      |
| 10   | Atlas Air               | General Cargo | 159,000                 | 747-8F      |

| Rank | Name                         | Primary Income     | Net Worth (USD) |
| ---- | ---------------------------- | ------------------ | --------------- |
| 1    | Elon Musk                    | Tesla, SpaceX      | 195B            |
| 2    | Jeff Bezos                   | Amazon.com         | 185B            |
| 3    | Bernard Arnault              | LVMH               | 150B            |
| 4    | Bill Gates                   | Microsoft          | 124B            |
| 5    | Mark Zuckerberg              | Facebook           | 96B             |
| 6    | Warren Buffett               | Berkshire Hathaway | 96B             |
| 7    | Larry Ellison                | Software           | 93B             |
| 8    | Larry Page                   | Google             | 91.5B           |
| 9    | Sergey Brin                  | Google             | 89B             |
| 10   | Mukesh Ambani                | Diversified        | 87B             |
| 11   | Steve Ballmer                | Microsoft          | 86.8B           |
| 12   | Jim Walton                   | Walmart            | 81.7B           |
| 13   | Alice Walton                 | Walmart            | 81.5B           |
| 14   | Rob Walton                   | Walmart            | 81.3B           |
| 15   | Francoise Bettencourt Meyers | L'Oreal            | 71.7B           |
| 16   | Amancio Ortega               | Zara               | 71B             |
| 17   | Zhong Shanshan               | Beverages          | 68.6B           |
| 18   | Larry Fink                   | BlackRock          | 61.9B           |
| 19   | Carlos Slim Helu             | Telecom            | 59.2B           |
| 20   | Steve Schwarzman             | Private Equity     | 34.3B           |

| Rank | Company Name        | Country       | Industry        | Estimated Annual Revenue |
| ---- | ------------------- | ------------- | --------------- | ------------------------ |
| 1    | IBM Watson          | United States | Enterprise AI   | $4 billion+              |
| 2    | Microsoft           | United States | Enterprise AI   | $3 billion+              |
| 3    | Google              | United States | Enterprise AI   | $1.2 billion+            |
| 4    | Amazon Web Services | United States | Enterprise AI   | $1 billion+              |
| 5    | Salesforce          | United States | Enterprise AI   | $500 million+            |
| 6    | Apple               | United States | Consumer AI     | $300 million+            |
| 7    | Baidu               | China         | Consumer AI     | $270 million+            |
| 8    | Tencent             | China         | Enterprise AI   | $240 million+            |
| 9    | Intel               | United States | Enterprise AI   | $200 million+            |
| 10   | Facebook            | United States | Consumer AI     | $180 million+            |
| 11   | SAP                 | Germany       | Enterprise AI   | $165 million+            |
| 12   | Nvidia              | United States | Hardware for AI | $160 million+            |
| 13   | Samsung             | South Korea   | Enterprise AI   | $140 million+            |
| 14   | Alibaba Cloud       | China         | Enterprise AI   | $135 million+            |
| 15   | SenseTime           | China         | Enterprise AI   | $120 million+            |
| 16   | Huawei              | China         | Enterprise AI   | $115 million+            |
| 17   | OpenAI              | United States | Research        | $100 million+            |
| 18   | IBM (other)         | United States | Research        | $90 million+             |
| 19   | Wipro               | India         | Enterprise AI   | $80 million+             |
| 20   | Tencent Cloud       | China         | Enterprise AI   | $75 million+             |
