๐Ÿฆ Twitter Post Details

Viewing enriched Twitter post

@rohanpaul_ai

Yann LeCun's (@ylecun ) new paper along with other top researchers proposes a brilliant idea. ๐ŸŽฏ Says that chasing general AI is a mistake and we must build superhuman adaptable specialists instead. The whole AI industry is obsessed with building machines that can do absolutely everything humans can do. But this goal is fundamentally flawed because humans are actually highly specialized creatures optimized only for physical survival. Instead of trying to force one giant model to master every possible task from folding laundry to predicting protein structures, they suggest building expert systems that learn generic knowledge through self-supervised methods. By using internal world models to understand how things work, these specialized systems can quickly adapt to solve complex problems that human brains simply cannot handle. This shift means we can stop wasting computing power on human traits and focus on building diverse tools that actually solve hard real-world problems. So overall the researchers here propose a new target called Superhuman Adaptable Intelligence which focuses strictly on how fast a system learns new skills. The paper explicitly argues that evolution shaped human intelligence strictly as a specialized tool for physical survival. The researchers state that nature optimized our brains specifically for tasks necessary to stay alive in the physical world. They explain that abilities like walking or seeing seem incredibly general to us only because they are absolutely critical for our existence. The authors point out that humans are actually terrible at cognitive tasks outside this evolutionary comfort zone, like calculating massive mathematical probabilities. The study highlights how a chess grandmaster only looks intelligent compared to other humans, while modern computers easily crush those human limits. This proves their central point that humanity suffers from an illusion of generality simply because we cannot perceive our own biological blind spots. They conclude that building machines to mimic this narrow human survival toolkit is a deeply flawed way to create advanced technology.

Media 1

๐Ÿ“Š Media Metadata

{
  "media": [
    {
      "url": "https://crmoxkoizveukayfjuyo.supabase.co/storage/v1/object/public/media/posts/2029533545161740321/media_0.jpg?",
      "media_url": "https://crmoxkoizveukayfjuyo.supabase.co/storage/v1/object/public/media/posts/2029533545161740321/media_0.jpg?",
      "type": "photo",
      "filename": "media_0.jpg"
    }
  ],
  "processed_at": "2026-03-06T14:22:40.878022",
  "pipeline_version": "2.0"
}

๐Ÿ”ง Raw API Response

{
  "type": "tweet",
  "id": "2029533545161740321",
  "url": "https://x.com/rohanpaul_ai/status/2029533545161740321",
  "twitterUrl": "https://twitter.com/rohanpaul_ai/status/2029533545161740321",
  "text": "Yann LeCun's (@ylecun ) new paper along with other top researchers proposes a brilliant idea. ๐ŸŽฏ\n\nSays that chasing general AI is a mistake and we must build superhuman adaptable specialists instead.\n\nThe whole AI industry is obsessed with building machines that can do absolutely everything humans can do.\n\nBut this goal is fundamentally flawed because humans are actually highly specialized creatures optimized only for physical survival.\n\nInstead of trying to force one giant model to master every possible task from folding laundry to predicting protein structures, they suggest building expert systems that learn generic knowledge through self-supervised methods.\n\nBy using internal world models to understand how things work, these specialized systems can quickly adapt to solve complex problems that human brains simply cannot handle.\n\nThis shift means we can stop wasting computing power on human traits and focus on building diverse tools that actually solve hard real-world problems.\n\nSo overall the researchers here propose a new target called Superhuman Adaptable Intelligence which focuses strictly on how fast a system learns new skills. \n\nThe paper explicitly argues that evolution shaped human intelligence strictly as a specialized tool for physical survival.\n\nThe researchers state that nature optimized our brains specifically for tasks necessary to stay alive in the physical world.\n\nThey explain that abilities like walking or seeing seem incredibly general to us only because they are absolutely critical for our existence.\n\nThe authors point out that humans are actually terrible at cognitive tasks outside this evolutionary comfort zone, like calculating massive mathematical probabilities.\n\nThe study highlights how a chess grandmaster only looks intelligent compared to other humans, while modern computers easily crush those human limits.\n\nThis proves their central point that humanity suffers from an illusion of generality simply because we cannot perceive our own biological blind spots.\n\nThey conclude that building machines to mimic this narrow human survival toolkit is a deeply flawed way to create advanced technology.",
  "source": "Twitter for iPhone",
  "retweetCount": 266,
  "replyCount": 110,
  "likeCount": 1420,
  "quoteCount": 27,
  "viewCount": 163153,
  "createdAt": "Thu Mar 05 12:24:38 +0000 2026",
  "lang": "en",
  "bookmarkCount": 1176,
  "isReply": false,
  "inReplyToId": null,
  "conversationId": "2029533545161740321",
  "displayTextRange": [
    0,
    279
  ],
  "inReplyToUserId": null,
  "inReplyToUsername": null,
  "author": {
    "type": "user",
    "userName": "rohanpaul_ai",
    "url": "https://x.com/rohanpaul_ai",
    "twitterUrl": "https://twitter.com/rohanpaul_ai",
    "id": "2588345408",
    "name": "Rohan Paul",
    "isVerified": false,
    "isBlueVerified": true,
    "verifiedType": null,
    "profilePicture": "https://pbs.twimg.com/profile_images/1816185267037859840/Fd18CH0v_normal.jpg",
    "coverPicture": "https://pbs.twimg.com/profile_banners/2588345408/1729559315",
    "description": "",
    "location": "Ex Inv Banking (Deutsche)",
    "followers": 139401,
    "following": 7595,
    "status": "",
    "canDm": true,
    "canMediaTag": false,
    "createdAt": "Wed Jun 25 22:38:54 +0000 2014",
    "entities": {
      "description": {
        "urls": []
      },
      "url": {}
    },
    "fastFollowersCount": 0,
    "favouritesCount": 61901,
    "hasCustomTimelines": true,
    "isTranslator": false,
    "mediaCount": 27895,
    "statusesCount": 68768,
    "withheldInCountries": [],
    "affiliatesHighlightedLabel": {},
    "possiblySensitive": false,
    "pinnedTweetIds": [
      "1965551636082032917"
    ],
    "profile_bio": {
      "description": "Compiling in real-time, the race towards AGI.\n\nThe Largest Show on X for AI.\n\n๐Ÿ—ž๏ธ Get my daily AI analysis newsletter to your email  ๐Ÿ‘‰ https://t.co/6LBxO8215l",
      "entities": {
        "description": {
          "hashtags": [],
          "symbols": [],
          "urls": [
            {
              "display_url": "rohan-paul.com",
              "expanded_url": "https://www.rohan-paul.com",
              "indices": [
                134,
                157
              ],
              "url": "https://t.co/6LBxO8215l"
            }
          ],
          "user_mentions": []
        },
        "url": {
          "urls": [
            {
              "display_url": "rohan-paul.com",
              "expanded_url": "http://www.rohan-paul.com",
              "indices": [
                0,
                23
              ],
              "url": "https://t.co/2NKnK0wIil"
            }
          ]
        }
      }
    },
    "isAutomated": false,
    "automatedBy": null
  },
  "extendedEntities": {
    "media": [
      {
        "allow_download_status": {
          "allow_download": true
        },
        "display_url": "pic.twitter.com/FGf1krtCcf",
        "expanded_url": "https://twitter.com/rohanpaul_ai/status/2029533545161740321/photo/1",
        "ext_media_availability": {
          "status": "Available"
        },
        "features": {
          "large": {
            "faces": [
              {
                "h": 42,
                "w": 42,
                "x": 386,
                "y": 245
              },
              {
                "h": 45,
                "w": 45,
                "x": 364,
                "y": 285
              },
              {
                "h": 52,
                "w": 52,
                "x": 512,
                "y": 388
              },
              {
                "h": 54,
                "w": 54,
                "x": 16,
                "y": 570
              },
              {
                "h": 51,
                "w": 51,
                "x": 716,
                "y": 652
              }
            ]
          },
          "orig": {
            "faces": [
              {
                "h": 42,
                "w": 42,
                "x": 386,
                "y": 245
              },
              {
                "h": 45,
                "w": 45,
                "x": 364,
                "y": 285
              },
              {
                "h": 52,
                "w": 52,
                "x": 512,
                "y": 388
              },
              {
                "h": 54,
                "w": 54,
                "x": 16,
                "y": 570
              },
              {
                "h": 51,
                "w": 51,
                "x": 716,
                "y": 652
              }
            ]
          }
        },
        "id_str": "2029533072438505474",
        "indices": [
          280,
          303
        ],
        "media_key": "3_2029533072438505474",
        "media_results": {
          "id": "QXBpTWVkaWFSZXN1bHRzOgwAAQoAARwqWZOSmrACCgACHCpaAaMa0CEAAA==",
          "result": {
            "__typename": "ApiMedia",
            "id": "QXBpTWVkaWE6DAABCgABHCpZk5KasAIKAAIcKloBoxrQIQAA",
            "media_key": "3_2029533072438505474"
          }
        },
        "media_url_https": "https://pbs.twimg.com/media/HCpZk5KasAIvVzf.jpg",
        "original_info": {
          "focus_rects": [
            {
              "h": 530,
              "w": 947,
              "x": 0,
              "y": 0
            },
            {
              "h": 736,
              "w": 736,
              "x": 0,
              "y": 0
            },
            {
              "h": 736,
              "w": 646,
              "x": 0,
              "y": 0
            },
            {
              "h": 736,
              "w": 368,
              "x": 76,
              "y": 0
            },
            {
              "h": 736,
              "w": 947,
              "x": 0,
              "y": 0
            }
          ],
          "height": 736,
          "width": 947
        },
        "sizes": {
          "large": {
            "h": 736,
            "w": 947
          }
        },
        "type": "photo",
        "url": "https://t.co/FGf1krtCcf"
      }
    ]
  },
  "card": null,
  "place": {},
  "entities": {
    "hashtags": [],
    "symbols": [],
    "urls": [],
    "user_mentions": [
      {
        "id_str": "48008938",
        "indices": [
          14,
          21
        ],
        "name": "Yann LeCun",
        "screen_name": "ylecun"
      }
    ]
  },
  "quoted_tweet": {
    "type": "tweet",
    "id": "2029377041653694821",
    "url": "https://x.com/rohanpaul_ai/status/2029377041653694821",
    "twitterUrl": "https://twitter.com/rohanpaul_ai/status/2029377041653694821",
    "text": "Yann LeCun (@ylecun ) explains why LLMs are so limited in terms of real-world intelligence.\n\nSays the biggest LLM is trained on about 30 trillion words, which is roughly 10 to the power 14 bytes of text. \nThat sounds huge, but a 4 year old who has been awake about 16,000 hours has also taken in about 10 to the power 14 bytes through the eyes alone. So a small child has already seen as much raw data as the largest LLM has read.\n\nBut the childโ€™s data is visual, continuous, noisy, and tied to actions: gravity, objects falling, hands grabbing, people moving, cause and effect. From this, the child builds an internal โ€œworld modelโ€ and intuitive physics, and can learn new tasks like loading a dishwasher from a handful of demonstrations.\n\nLLMs only see disconnected text and are trained just to predict the next token. So they get very good at symbol patterns, exams, and code, but they lack grounded physical understanding, real common sense, and efficient learning from a few messy real-world experiences.\n\n---\n\nFrom 'Pioneer Works' YT channel (link in comment)",
    "source": "Twitter for iPhone",
    "retweetCount": 346,
    "replyCount": 172,
    "likeCount": 2168,
    "quoteCount": 53,
    "viewCount": 572513,
    "createdAt": "Thu Mar 05 02:02:45 +0000 2026",
    "lang": "en",
    "bookmarkCount": 1212,
    "isReply": false,
    "inReplyToId": null,
    "conversationId": "2029377041653694821",
    "displayTextRange": [
      0,
      277
    ],
    "inReplyToUserId": null,
    "inReplyToUsername": null,
    "author": {
      "type": "user",
      "userName": "rohanpaul_ai",
      "url": "https://x.com/rohanpaul_ai",
      "twitterUrl": "https://twitter.com/rohanpaul_ai",
      "id": "2588345408",
      "name": "Rohan Paul",
      "isVerified": false,
      "isBlueVerified": true,
      "verifiedType": null,
      "profilePicture": "https://pbs.twimg.com/profile_images/1816185267037859840/Fd18CH0v_normal.jpg",
      "coverPicture": "https://pbs.twimg.com/profile_banners/2588345408/1729559315",
      "description": "",
      "location": "Ex Inv Banking (Deutsche)",
      "followers": 139401,
      "following": 7595,
      "status": "",
      "canDm": true,
      "canMediaTag": false,
      "createdAt": "Wed Jun 25 22:38:54 +0000 2014",
      "entities": {
        "description": {
          "urls": []
        },
        "url": {}
      },
      "fastFollowersCount": 0,
      "favouritesCount": 61901,
      "hasCustomTimelines": true,
      "isTranslator": false,
      "mediaCount": 27895,
      "statusesCount": 68768,
      "withheldInCountries": [],
      "affiliatesHighlightedLabel": {},
      "possiblySensitive": false,
      "pinnedTweetIds": [
        "1965551636082032917"
      ],
      "profile_bio": {
        "description": "Compiling in real-time, the race towards AGI.\n\nThe Largest Show on X for AI.\n\n๐Ÿ—ž๏ธ Get my daily AI analysis newsletter to your email  ๐Ÿ‘‰ https://t.co/6LBxO8215l",
        "entities": {
          "description": {
            "hashtags": [],
            "symbols": [],
            "urls": [
              {
                "display_url": "rohan-paul.com",
                "expanded_url": "https://www.rohan-paul.com",
                "indices": [
                  134,
                  157
                ],
                "url": "https://t.co/6LBxO8215l"
              }
            ],
            "user_mentions": []
          },
          "url": {
            "urls": [
              {
                "display_url": "rohan-paul.com",
                "expanded_url": "http://www.rohan-paul.com",
                "indices": [
                  0,
                  23
                ],
                "url": "https://t.co/2NKnK0wIil"
              }
            ]
          }
        }
      },
      "isAutomated": false,
      "automatedBy": null
    },
    "extendedEntities": {
      "media": [
        {
          "additional_media_info": {
            "monetizable": false
          },
          "allow_download_status": {
            "allow_download": true
          },
          "display_url": "pic.twitter.com/sNoRlNy5hg",
          "expanded_url": "https://twitter.com/rohanpaul_ai/status/2029377041653694821/video/1",
          "ext_media_availability": {
            "status": "Available"
          },
          "id_str": "2029376809004097536",
          "indices": [
            278,
            301
          ],
          "media_key": "13_2029376809004097536",
          "media_results": {
            "id": "QXBpTWVkaWFSZXN1bHRzOgwABAoAARwpy3SoGuAAAAA=",
            "result": {
              "__typename": "ApiMedia",
              "id": "QXBpTWVkaWE6DAAECgABHCnLdKga4AAAAA==",
              "media_key": "13_2029376809004097536"
            }
          },
          "media_url_https": "https://pbs.twimg.com/amplify_video_thumb/2029376809004097536/img/xE5XYtwxbpFAP-RS.jpg",
          "original_info": {
            "focus_rects": [],
            "height": 1080,
            "width": 1920
          },
          "sizes": {
            "large": {
              "h": 1080,
              "w": 1920
            }
          },
          "type": "video",
          "url": "https://t.co/sNoRlNy5hg",
          "video_info": {
            "aspect_ratio": [
              16,
              9
            ],
            "duration_millis": 53994,
            "variants": [
              {
                "content_type": "application/x-mpegURL",
                "url": "https://video.twimg.com/amplify_video/2029376809004097536/pl/BD42vWRWQcyzf0e8.m3u8?tag=21&v=771"
              },
              {
                "bitrate": 256000,
                "content_type": "video/mp4",
                "url": "https://video.twimg.com/amplify_video/2029376809004097536/vid/avc1/480x270/bh-VTswerOsMlAWV.mp4?tag=21"
              },
              {
                "bitrate": 832000,
                "content_type": "video/mp4",
                "url": "https://video.twimg.com/amplify_video/2029376809004097536/vid/avc1/640x360/fbkJZ67oup4zAd52.mp4?tag=21"
              },
              {
                "bitrate": 2176000,
                "content_type": "video/mp4",
                "url": "https://video.twimg.com/amplify_video/2029376809004097536/vid/avc1/1280x720/SMh9uCf9GemTft3j.mp4?tag=21"
              },
              {
                "bitrate": 10368000,
                "content_type": "video/mp4",
                "url": "https://video.twimg.com/amplify_video/2029376809004097536/vid/avc1/1920x1080/7HDyLOSGANKGFX9M.mp4?tag=21"
              }
            ]
          }
        }
      ]
    },
    "card": null,
    "place": {},
    "entities": {
      "hashtags": [],
      "symbols": [],
      "timestamps": [],
      "urls": [],
      "user_mentions": [
        {
          "id_str": "48008938",
          "indices": [
            12,
            19
          ],
          "name": "Yann LeCun",
          "screen_name": "ylecun"
        }
      ]
    },
    "quoted_tweet": null,
    "retweeted_tweet": null,
    "isLimitedReply": false,
    "article": null
  },
  "retweeted_tweet": null,
  "isLimitedReply": false,
  "article": null
}