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Every company should have a Brain — Garry Tan, Y Combinator

index_state ready data_status ok

AI Engineer· published 2026-07-17· 0:21:08· en-US· indexed 2026-08-10 19:49

Open on YouTube

Scene timeline

  1. Shot 0, 0:00 to 0:03, 1 of 1 keyframes kept
  2. Shot 1, 0:03 to 0:05, 1 of 1 keyframes kept
  3. Shot 2, 0:05 to 0:12, 1 of 1 keyframes kept
  4. Shot 3, 0:12 to 0:23, 1 of 1 keyframes kept
  5. Shot 4, 0:23 to 0:55, 1 of 1 keyframes kept
  6. Shot 5, 0:55 to 1:27, 1 of 1 keyframes kept
  7. Shot 6, 1:27 to 1:36, 1 of 1 keyframes kept
  8. Shot 7, 1:36 to 2:04, 1 of 1 keyframes kept
  9. Shot 8, 2:04 to 2:33, 1 of 1 keyframes kept
  10. Shot 9, 2:33 to 3:01, 1 of 1 keyframes kept
  11. Shot 10, 3:01 to 3:29, 1 of 1 keyframes kept
  12. Shot 11, 3:29 to 3:56, 1 of 1 keyframes kept
  13. Shot 12, 3:56 to 4:05, 1 of 1 keyframes kept
  14. Shot 13, 4:05 to 4:33, 1 of 1 keyframes kept
  15. Shot 14, 4:33 to 5:02, 1 of 1 keyframes kept
  16. Shot 15, 5:02 to 5:30, 1 of 1 keyframes kept
  17. Shot 16, 5:30 to 5:36, 0 of 1 keyframes kept
  18. Shot 17, 5:36 to 5:41, 1 of 1 keyframes kept
  19. Shot 18, 5:41 to 6:01, 1 of 1 keyframes kept
  20. Shot 19, 6:01 to 6:09, 1 of 1 keyframes kept
  21. Shot 20, 6:09 to 6:35, 1 of 1 keyframes kept
  22. Shot 21, 6:35 to 7:15, 1 of 1 keyframes kept
  23. Shot 22, 7:15 to 7:49, 1 of 1 keyframes kept
  24. Shot 23, 7:49 to 7:54, 1 of 1 keyframes kept
  25. Shot 24, 7:54 to 8:24, 1 of 1 keyframes kept
  26. Shot 25, 8:24 to 8:53, 1 of 1 keyframes kept
  27. Shot 26, 8:53 to 9:22, 1 of 1 keyframes kept
  28. Shot 27, 9:22 to 9:42, 1 of 1 keyframes kept
  29. Shot 28, 9:42 to 10:05, 1 of 1 keyframes kept
  30. Shot 29, 10:05 to 10:16, 1 of 1 keyframes kept
  31. Shot 30, 10:16 to 10:44, 1 of 1 keyframes kept
  32. Shot 31, 10:44 to 11:09, 1 of 1 keyframes kept
  33. Shot 32, 11:09 to 11:34, 1 of 1 keyframes kept
  34. Shot 33, 11:34 to 11:41, 1 of 1 keyframes kept
  35. Shot 34, 11:41 to 11:51, 1 of 1 keyframes kept
  36. Shot 35, 11:51 to 12:21, 1 of 1 keyframes kept
  37. Shot 36, 12:21 to 12:51, 1 of 1 keyframes kept
  38. Shot 37, 12:51 to 13:06, 0 of 1 keyframes kept
  39. Shot 38, 13:06 to 13:41, 1 of 1 keyframes kept
  40. Shot 39, 13:41 to 14:15, 1 of 1 keyframes kept
  41. Shot 40, 14:15 to 14:33, 1 of 1 keyframes kept
  42. Shot 41, 14:33 to 15:01, 1 of 1 keyframes kept
  43. Shot 42, 15:01 to 15:29, 1 of 1 keyframes kept
  44. Shot 43, 15:29 to 15:57, 1 of 1 keyframes kept
  45. Shot 44, 15:57 to 16:25, 1 of 1 keyframes kept
  46. Shot 45, 16:25 to 16:53, 1 of 1 keyframes kept
  47. Shot 46, 16:53 to 17:21, 1 of 1 keyframes kept
  48. Shot 47, 17:21 to 17:49, 1 of 1 keyframes kept
  49. Shot 48, 17:49 to 18:06, 0 of 1 keyframes kept
  50. Shot 49, 18:06 to 18:38, 1 of 1 keyframes kept
  51. Shot 50, 18:38 to 19:11, 1 of 1 keyframes kept
  52. Shot 51, 19:11 to 19:44, 1 of 1 keyframes kept
  53. Shot 52, 19:44 to 20:05, 1 of 1 keyframes kept
  54. Shot 53, 20:05 to 20:10, 1 of 1 keyframes kept
  55. Shot 54, 20:10 to 20:12, 1 of 1 keyframes kept
  56. Shot 55, 20:12 to 20:40, 1 of 1 keyframes kept
  57. Shot 56, 20:40 to 20:51, 0 of 1 keyframes kept
  58. Shot 57, 20:51 to 21:07, 0 of 1 keyframes kept

58 shot(s).

keyframes kept every frame deduplicated

What was stored

cues
253
whisperx 253
chunks
36
from 253 cues
keyframes
53
kept of 58 captured
frames with text
52
1,266 lines read
chapters
14
from the source metadata
keyframe bytes
7.3 MB
word timings on 253 cues

Provenance

Each pipeline stage, its state and the model that produced it
stage state model started took
fetch done — 2026-08-10 11:56 1m 33s
stt done — 2026-08-10 11:57 22s
chunk done — 2026-08-10 11:58 0s
text_embed done — 2026-08-10 19:49 0s
keyframe done — 2026-08-10 11:58 2m 35s
ocr done — 2026-08-10 12:00 15s
frame_embed done — 2026-08-10 19:49 9s

Frames, and what the machine read

  • 0:02 #0 done2 line(s)

    shot 0·sharpness 450.3

    1. AlEngineer0.96
    2. World's Fair0.97

  • 0:03 #1 done2 line(s)

    shot 1·sharpness 659.0

    1. AlEngineer0.96
    2. World's Fair0.99

  • 0:10 #2 done24 line(s)

    shot 2·sharpness 2729.9

    1. LAB & PLATINUM SPONSORS0.99
    2. Amazon AGI Lab0.98
    3. ANTHROP\C1.00
    4. Google DeepMind1.00
    5. MINIMAX0.97
    6. OpenAI0.92
    7. Akamai1.00
    8. arize1.00
    9. aws1.00
    10. Braintrust bright data0.98
    11. B1.00
    12. Browserbase1.00
    13. docker1.00
    14. :neo4j0.92
    15. ORACLE1.00
    16. PayPal1.00
    17. qodo1.00
    18. reducto1.00
    19. Sonar1.00
    20. Makers of0.99
    21. togetherai1.00
    22. Unblocked1.00
    23. WorkOS1.00
    24. SonarQube1.00

  • 0:17 #3 done10 line(s)

    shot 3·sharpness 549.8

    1. AlEngineer0.99
    2. OpenAI0.92
    3. 11000.71
    4. World's Fair1.00
    5. AlEnginee0.97
    6. World's1.00
    7. Akamai1.00
    8. auneer0.82
    9. DATAD1.00
    10. World's Fair0.98

  • 0:48 #4 done12 line(s)

    shot 4·sharpness 1085.0

    1. AlEngineer0.98
    2. OpenAlao0.88
    3. World's Fair0.98
    4. AlEnginer0.96
    5. World's1.00
    6. Akamai1.00
    7. AlEnaineer0.88
    8. Keynote1.00
    9. AlEngineer0.97
    10. World's Fair0.99
    11. Garry Tan / President & CEO0.96
    12. Combinator1.00

  • 1:17 #5 done9 line(s)

    shot 5·sharpness 522.4

    1. AlEngineer0.98
    2. OpenAI0.93
    3. World's Fair0.99
    4. AlEngineer1.00
    5. World's0.99
    6. Akamai1.00
    7. AlEngineer0.98
    8. DATADC0.99
    9. World's Fair0.98

  • 1:29 #6 done65 line(s)

    shot 6·sharpness 2909.4

    1. World's Fair0.99
    2. World's Fair0.96
    3. World's Fair0.94
    4. World's Fair0.97
    5. THE VIELOCITY ROOM0.97
    6. mezmo*0.94
    7. fiddler0.94
    8. World's Fair1.00
    9. World's Fair0.93
    10. PRIOR1.00
    11. World's Fair0.99
    12. BAND1.00
    13. orld's Fair0.99
    14. World's Fair0.99
    15. :neo4j0.96
    16. World's Fair0.98
    17. Cleric1.00
    18. d's Fair0.95
    19. Work0.99
    20. World's Fair0.99
    21. Braintrust1.00
    22. Amazon AGI Lab1.00
    23. World's Fair0.97
    24. World's Fair0.99
    25. Google DeepMind1.00
    26. World's Fair0.99
    27. OpenAI0.92
    28. World'sFair0.99
    29. Microsoft1.00
    30. World's Fair0.95
    31. World's Fain0.94
    32. arize1.00
    33. World's Fa0.96
    34. World's Fair0.99
    35. ORACLE1.00
    36. PayPal0.99
    37. À ATLASSIAN0.95
    38. reduc1.00
    39. FACTORY1.00
    40. ANTHROPC0.99
    41. Danid0.54
    42. World's Fair0.98
    43. World's Fair0.99
    44. 200.63
    45. Norld'sFai0.99
    46. World's Fair0.97
    47. World's Fair0.93
    48. ANTHROPIC0.96
    49. World's Fair0.88
    50. d's Fair0.97
    51. World's Fair0.98
    52. DATADOG1.00
    53. World's Fair0.96
    54. Resolve.ai1.00
    55. Worid's Fai0.94
    56. CODERI0.98
    57. Optiver0.99
    58. World's Fair0.96
    59. World's Fa0.94
    60. s Fair0.94
    61. World's Fair0.99
    62. World's Fair0.97
    63. BeNCORD0.88
    64. World'sFai1.00
    65. tgus0.88

  • 1:50 #7 done10 line(s)

    shot 7·sharpness 483.9

    1. AlEngineer0.97
    2. OpenAk0.94
    3. World's Fair0.99
    4. AlEngir0.96
    5. ii0.79
    6. World'0.97
    7. Akamai1.00
    8. AlEngineer0.96
    9. DATAI0.99
    10. World's Fair0.99

  • 2:08 #8 done9 line(s)

    shot 8·sharpness 485.8

    1. AlEngineer0.98
    2. OpenAl-O00.83
    3. World's Fair0.97
    4. AlEngine0.93
    5. World'0.99
    6. Akamai1.00
    7. AlEngineer0.97
    8. DATAD1.00
    9. World's Fair1.00

  • 2:58 #9 done9 line(s)

    shot 9·sharpness 459.4

    1. AlEngineer0.97
    2. OpenAI0.92
    3. World's Fair0.99
    4. AlEngineer0.97
    5. World's1.00
    6. Akamai1.00
    7. AlEngineer0.99
    8. DATAD1.00
    9. World's Fair1.00

  • 3:21 #10 done9 line(s)

    shot 10·sharpness 491.2

    1. AlEngineer0.95
    2. OpenAl0.95
    3. World's Fair1.00
    4. AlEngineer0.99
    5. World'sI0.94
    6. Akamai1.00
    7. AlEngineer0.98
    8. DATADC0.92
    9. World's Fair0.99

  • 3:45 #11 done76 line(s)

    shot 11·sharpness 2908.1

    1. granica1.00
    2. Modal1.00
    3. Ref.1.00
    4. World's Fair1.00
    5. World'sF0.94
    6. World's Fair0.97
    7. Red Hat0.94
    8. THE VELOCITY ROOM0.97
    9. mezmo*0.94
    10. ©fiddler0.89
    11. comet1.00
    12. Vorld's Fair0.99
    13. Cate0.56
    14. INNGEST0.95
    15. PRIOR1.00
    16. World's Fair0.99
    17. World's Fair0.99
    18. BAND0.92
    19. Z.AI0.95
    20. World's Fair0.98
    21. Perecral0.66
    22. World's Fair0.99
    23. :neo4j0.99
    24. World's Fair0.98
    25. Google DeepMind1.00
    26. World's Fair0.94
    27. World's Fair0.98
    28. qodo0.98
    29. World's Fair0.92
    30. Cleric1.00
    31. World's Fair0.96
    32. PayPal1.00
    33. A ATI0.82
    34. GGRAVITEE0.94
    35. Meticulous1.00
    36. FACTORY1.00
    37. Kema0.76
    38. Oorane0.53
    39. Id's Fair0.92
    40. Amazon AGI Lab0.99
    41. World's Fair0.98
    42. Microsoft1.00
    43. arize1.00
    44. World's Fair0.96
    45. ANTHROPIC1.00
    46. Ddruid0.63
    47. World's Fair0.96
    48. OpenAl0.94
    49. World's Fair0.98
    50. World's Fair0.98
    51. ORACLE1.00
    52. Microsoft0.97
    53. Cusoe0.83
    54. World's Fair0.99
    55. 's Fair0.96
    56. World's Fair0.99
    57. camai0.99
    58. World's Fair1.00
    59. Vorld'sFai1.00
    60. ANTHROPIC1.00
    61. World's Fair0.94
    62. World's Fair0.97
    63. Resolve.ai0.96
    64. geptle0.81
    65. Optiver1.00
    66. World's Fair1.00
    67. builder.io1.00
    68. World's Fair0.96
    69. World's Fair0.97
    70. World's Fair0.92
    71. Fair1.00
    72. World'sFair1.00
    73. World's Fair0.95
    74. eNCORD0.97
    75. d'sFair0.96
    76. tgus0.67

  • 4:04 #12 done45 line(s)

    shot 12·sharpness 2543.0

    1. World'sFair0.99
    2. OpenAl0.91
    3. World'sFair1.00
    4. WorkOs0.90
    5. World's Fair0.95
    6. Alllaz0.79
    7. AlEngineer0.99
    8. — AlEngineer0.95
    9. Microsoft1.00
    10. World'sFair0.99
    11. docker1.00
    12. lorld's Fair0.92
    13. # Braintrust0.94
    14. Wo0.93
    15. World'sFair0.96
    16. AlEngineer0.98
    17. snyk1.00
    18. World's Fair0.93
    19. AlEngineer0.99
    20. World'sFair1.00
    21. AlEngineer1.00
    22. Lightrun1.00
    23. World'sFair1.00
    24. AlEngineer0.99
    25. LangChiain0.91
    26. 'd's Fair0.93
    27. eer1.00
    28. Wo0.94
    29. CLOUDFLARE1.00
    30. World'sFair0.99
    31. AlEngineer1.00
    32. OpenHands1.00
    33. World's Fa'0.91
    34. AlEngineer1.00
    35. LanceDB1.00
    36. World'sFair1.00
    37. AlEngineer0.99
    38. 80.70
    39. descpe1.00
    40. AlEngineer1.00
    41. —AlEngineer0.94
    42. World'sFair0.99
    43. scalek1.00
    44. rld's Fair0.96
    45. Wo1.00

  • 4:08 #13 done9 line(s)

    shot 13·sharpness 433.3

    1. AlEngineer0.99
    2. OpenAg0.88
    3. World's Fai0.98
    4. AIEngir0.93
    5. World1.00
    6. Akamai1.00
    7. AlEngineer0.97
    8. DATA1.00
    9. World's Fai0.99

  • 4:42 #14 done9 line(s)

    shot 14·sharpness 495.2

    1. AlEngineer0.99
    2. OpenAl0.95
    3. World's Fair1.00
    4. AlEngineer0.99
    5. World's F0.96
    6. Akamai1.00
    7. AlEngineer0.97
    8. DATADOC0.98
    9. World's Fair0.99

  • 5:16 #15 done9 line(s)

    shot 15·sharpness 496.2

    1. AlEngineer0.98
    2. OpenAl0.93
    3. World's Fair0.99
    4. AlEngineer0.95
    5. World's F:0.89
    6. Microsoft1.00
    7. AlEngineer0.97
    8. Buildki1.00
    9. World's Fair0.99

  • 5:34 #16 skipped

    shot 16·duplicate of #6

  • 5:38 #17 done38 line(s)

    shot 17·sharpness 2241.0

    1. WordsFair0.86
    2. WonidsT all0.81
    3. AlEngineer1.00
    4. AlEngineer1.00
    5. reducto1.00
    6. World'sFair0.99
    7. orkOS1.00
    8. World's Fair0.96
    9. B1.00
    10. Browserbase1.00
    11. AlEngineer0.98
    12. PayPcE0.92
    13. AlEngineer0.97
    14. World'sFair0.99
    15. ir0.99
    16. Gradium1.00
    17. World'sFair1.00
    18. AlEngineer0.99
    19. AlEngineer0.95
    20. daily1.00
    21. AUTOMATTIC1.00
    22. World'sFair1.00
    23. orks Al0.98
    24. World's Fair0.97
    25. AlEngineer1.00
    26. World'sFair1.00
    27. descpe1.00
    28. sFair1.00
    29. eer0.99
    30. Cognition0.91
    31. World'sFair1.00
    32. AlEngineer0.99
    33. AlEngineer0.97
    34. AlEngineer1.00
    35. World'sFair1.00
    36. World'sFair1.00
    37. CopilotKit1.00
    38. ry1.00

  • 5:59 #18 done9 line(s)

    shot 18·sharpness 452.7

    1. AlEngineer0.97
    2. OpenAl0.94
    3. World's Fair1.00
    4. AlEngineer0.99
    5. World's1.00
    6. Microsoft1.00
    7. AlEngineer0.98
    8. Buildk0.86
    9. World's Fair1.00

  • 6:04 #19 done40 line(s)

    shot 19·sharpness 2267.6

    1. WondsFall0.88
    2. WonidST all0.73
    3. AlEngineer1.00
    4. AlEngineer1.00
    5. reducto1.00
    6. World'sFair0.99
    7. Wor0.94
    8. World'sFair0.99
    9. B1.00
    10. Browserbase1.00
    11. AlEngineer1.00
    12. AlEngin0.88
    13. AlEngineer0.99
    14. World'sFair1.00
    15. PayPal1.00
    16. Worle0.97
    17. Gradium1.00
    18. World'sFair1.00
    19. AUTOMATTIC1.00
    20. World'sFair1.00
    21. AlEngineer0.97
    22. Fire0.94
    23. World'sFair1.00
    24. AlEngineer0.98
    25. Idaily0.91
    26. World'sFair0.99
    27. AlEngineer1.00
    28. descpe1.00
    29. Wor1.00
    30. Al'0.80
    31. Cognition1.00
    32. AlEngineer1.00
    33. World'sFair1.00
    34. AlEngineer0.99
    35. AlEngineer0.98
    36. World'sFair0.99
    37. World'sFair1.00
    38. CopilotKit1.00
    39. E0.99
    40. y0.99

  • 6:22 #20 done57 line(s)

    shot 20·sharpness 2087.3

    1. Cked0.81
    2. worldsFair0.94
    3. Worid'sFair0.88
    4. Brainitrust0.78
    5. WoridsFa0.87
    6. Fair1.00
    7. MINIMAX0.99
    8. World'sFair1.00
    9. AlEngineer1.00
    10. Amazon AGI Lab0.99
    11. World'sFair1.00
    12. AlEngineer0.99
    13. Z.AI0.99
    14. AlEngineer0.96
    15. AlEngineer0.99
    16. AlEngineer1.00
    17. base1.00
    18. World's Fair0.94
    19. OpenAI0.94
    20. World's Fair0.93
    21. docker1.00
    22. World's Fa0.96
    23. AlEngineer -0.96
    24. AlEngineer0.99
    25. Fair1.00
    26. Crusoe1.00
    27. World's!0.96
    28. Microsoft1.00
    29. World's Fair0.98
    30. turboput0.94
    31. y1.00
    32. World's Fair0.99
    33. AlEngineer0.98
    34. Buildk1.00
    35. World's Fair0.98
    36. AlEngineer0.99
    37. DeasyLabs1.00
    38. World'sFa1.00
    39. AlEngineer1.00
    40. Fair1.00
    41. Modal1.00
    42. World's Fa0.98
    43. AlEngineer1.00
    44. ZER00.99
    45. World's Fair0.96
    46. AlEngineer0.99
    47. PostH1.00
    48. NBLOCK YOUR AGENTS @ ZERO.XYZ0.96
    49. Kit0.94
    50. World'sFair1.00
    51. AlEngineer0.96
    52. Ravenr0.96
    53. World'sFair1.00
    54. AIEngineer0.95
    55. tigris0.87
    56. World'sFa1.00
    57. AlEngineer—0.96

  • 6:40 #21 done9 line(s)

    shot 21·sharpness 480.2

    1. AlEngineer0.98
    2. OpenAI0.92
    3. World's Fair1.00
    4. AlEngineer0.99
    5. Microsoft1.00
    6. WooNd'sF0.80
    7. AlEngineer0.99
    8. Buildk1.00
    9. World's Fair1.00

  • 7:45 #22 done11 line(s)

    shot 22·sharpness 434.0

    1. AlEngineer0.97
    2. OpenAlpo0.84
    3. ir0.93
    4. World's Fa0.98
    5. AIEngin0.96
    6. World'0.99
    7. Micros1.00
    8. AlEngineer0.98
    9. Builc0.99
    10. World's Fa0.97
    11. ir0.88

  • 7:54 #23 done44 line(s)

    shot 23·sharpness 2436.4

    1. WordsFail0.84
    2. WonidsTall0.80
    3. AlEngineer0.99
    4. AlEngineer0.96
    5. sFair1.00
    6. reducto1.00
    7. World'sFair1.00
    8. Wor1.00
    9. World'sFair1.00
    10. B1.00
    11. Browserbase1.00
    12. AlEngineer0.99
    13. AlEngineer0.99
    14. AlEngineer1.00
    15. data1.00
    16. World'sFair1.00
    17. PayPal1.00
    18. World's1.00
    19. Gradium1.00
    20. World'sFair1.00
    21. AlEngineer1.00
    22. AlEngineer1.00
    23. Idaily0.91
    24. sFair1.00
    25. AUTOMATTIC1.00
    26. World'sFair1.00
    27. Firel0.94
    28. World'sFair1.00
    29. olific0.88
    30. World'sFair1.00
    31. AlEngineer0.97
    32. descpe1.00
    33. Worl1.00
    34. AlEr0.88
    35. Cognition0.99
    36. World'sFair0.97
    37. AlEngineer0.99
    38. AlEngineer0.98
    39. Fair1.00
    40. World'sFair1.00
    41. World'sFair1.00
    42. AlEngineer0.97
    43. CopilotKit1.00
    44. E0.97

Transcript

253 cues· 3,327 words· 17,731 chars

  1. 0:13 Okay, great.
  2. 0:14 Hey everyone, how's everyone doing?
  3. 0:17 All right, are we ready for the revolution?
  4. 0:21 Okay, Theo just asked the right question.
  5. 0:24 What do we build now?
  6. 0:27 I'm gonna answer it from the other side of the table.
  7. 0:30 I'm a founder, I'm an investor, and I run a 20-year-old institution that is becoming AI native right now, which is a strange and wonderful thing to do to a 20-year-old institution.
  8. 0:43 And I'll spend about 20 minutes talking about what YC is.
  9. 0:49 We're trying to build companies where one person does what it took to, one person does what used to take 1,000 people.
  10. 1:00 And I don't mean that as a metaphor.
  11. 1:03 I mean that mechanically this year.
  12. 1:06 The people in this room will do this.
  13. 1:09 In about an hour, some of you will walk into the startup battlefield, and I want you to walk in knowing what's actually possible right now, because what is possible now is much, much bigger than what people believe.
  14. 1:25 So let me start with a number and I got torn apart on the internet for this but I'm gonna say it again in front of all of you anyway.
  15. 1:32 This is the one room in the world that will stress test it and I'd rather stress test it with you myself.
  16. 1:38 In 2013 I was a YC partner building the internal social network at YC.
  17. 1:44 I was also investing in companies but
  18. 1:47 You know, I was also a near full-time engineer.
  19. 1:50 And when I was doing that, I could maybe do about 14 usable logical lines of code a day.
  20. 1:56 Take out the comments, take out all the bullshit, and that's how many lines of code I was writing.
  21. 2:02 And if you look at the literature from that era,
  22. 2:06 That's kind of normal, like some people write 15, some people write 50.
  23. 2:10 It was not the thousands of lines of code that I know a lot of you in this room are actually writing now per day.
  24. 2:17 That's about median 15.
  25. 2:18 That was me at full effort at that time.
  26. 2:21 This year I run YC full time, same person, same hours, actually way less hours weirdly, but I have a 5 p.m. kid pick up now, and I did the math on my output, and it's about 400x.
  27. 2:35 Now before the skeptic in the third row right there deflates the number for me, let me deflate it myself.
  28. 2:41 If you don't trust the raw code, well fine.
  29. 2:43 Take the most pathological verbosity penalty you can stomach and assume the agent writes bloated code.
  30. 2:50 Assume half of it is scaffolding.
  31. 2:51 Assume I'm flattering myself.
  32. 2:53 It's still 8x at the floor and 80x in the middle.
  33. 2:57 That number is large, no matter how you torture it.
  34. 3:00 And here's the part that matters, the part that I'd tattoo on the inside of everyone's eyelids if I could.
  35. 3:05 It's not the model.
  36. 3:07 The 2x people and the 100x people are using the exact same clod, same weights, same context window, same API.
  37. 3:17 So the leverage is not in the weights.
  38. 3:20 It's in how you wire the work.
  39. 3:22 And it's not just me.
  40. 3:23 At YC, we see this all the time.
  41. 3:25 In the winter 25 batch, a quarter of the companies had code bases that were 95% AI generated.
  42. 3:31 And that was a year ago.
  43. 3:32 That batch has become the fastest growing.
  44. 3:35 most profitable batch in the history of YC.
  45. 3:38 94 companies total have now crossed $100 million in revenue from a seed check in the history of YC.
  46. 3:45 So I think we know what we're talking about here.
  47. 3:48 And I can't prove that the AI generated code caused the growth, but what I can tell you is the fastest growing founders we fund
  48. 3:56 are not treating AI as autocomplete.
  49. 3:58 They're treating it as a workforce.
  50. 4:00 The companies that wired the work differently are the ones that are bending the curve.

Chapters

  1. 0:00 Introduction: The AI revolution and YC's transformation
  2. 1:25 The 400x productivity jump: Coding in 2013 vs. today
  3. 3:38 Wiring the work: Treating AI as a workforce
  4. 4:11 The anatomy of an AI-native organization
  5. 6:12 Real-world impact: Companies scaling with lean teams
  6. 8:38 Latent space vs. deterministic space
  7. 10:53 Overcoming human memory limits: AI as a library
  8. 12:53 Context engineering: The importance of the 'librarian'
  9. 13:28 Building GBrain: Managing institutional knowledge
  10. 15:13 The discipline of 'skillifying' your work
  11. 16:40 The call to build AI-native companies
  12. 18:25 The power of abundance through shipped software
  13. 18:56 Abundance is not a policy paper. It is shipped software.
  14. 19:55 Every archive too big to read, every data set too gnarly to clean, every ocean you were told not to boil. We can boil the ocean now.

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