vidtheque.

read-only demo

Videos RGe6EjucbzI

"The biggest challenge in your stack? Evals, Evals, Evals" - 2026 State of AI Engineering results

index_state ready data_status ok

AI Engineer· published 2026-07-21· 0:19:47· en· indexed 2026-08-10 19:47

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

51 shot(s).

keyframes kept every frame deduplicated

What was stored

cues
224
whisperx 224
chunks
35
from 224 cues
keyframes
42
kept of 51 captured
frames with text
42
1,704 lines read
chapters
10
from the source metadata
keyframe bytes
7.7 MB
word timings on 224 cues

Provenance

Each pipeline stage, its state and the model that produced it
stage state model started took
fetch done — 2026-08-10 04:03 1m 38s
stt done — 2026-08-10 04:04 19s
chunk done — 2026-08-10 04:05 0s
text_embed done — 2026-08-10 19:47 1s
keyframe done — 2026-08-10 04:05 2m 08s
ocr done — 2026-08-10 04:07 20s
frame_embed done — 2026-08-10 19:47 7s

Frames, and what the machine read

  • 0:02 #0 done2 line(s)

    shot 0·sharpness 456.5

    1. AlEngineer0.96
    2. World's Fair1.00

  • 0:03 #1 done2 line(s)

    shot 1·sharpness 665.3

    1. AIEngineer0.95
    2. World's Fair0.99

  • 0:10 #2 done25 line(s)

    shot 2·sharpness 2734.4

    1. LAB & PLATINUM SPONSORS0.99
    2. Amazon AGI Lab0.97
    3. ANTHROP\C1.00
    4. Google DeepMind1.00
    5. MINIMAX0.98
    6. OpenAI0.93
    7. Akamai1.00
    8. arize0.92
    9. aws1.00
    10. Braintrust1.00
    11. bright data0.98
    12. B1.00
    13. Browserbase1.00
    14. docker1.00
    15. neo4j0.98
    16. ORACLE1.00
    17. PayPal1.00
    18. qodo1.00
    19. reducto1.00
    20. Sonar1.00
    21. Makers of1.00
    22. togetherai1.00
    23. Unblocked1.00
    24. WorkOS1.00
    25. SonarQube1.00

  • 0:17 #3 done2 line(s)

    shot 3·sharpness 238.2

    1. Amplify0.99
    2. AIE1.00

  • 0:23 #4 done4 line(s)

    shot 4·sharpness 484.0

    1. BARR YARON0.96
    2. PARTNER1.00
    3. Amplify1.00
    4. AIE1.00

  • 0:28 #5 done5 line(s)

    shot 5·sharpness 704.1

    1. BARR YARON0.98
    2. 'sFai0.94
    3. PARTNER1.00
    4. Amplify1.00
    5. AIE0.97

  • 0:32 #6 done79 line(s)

    shot 6·sharpness 2794.1

    1. World's Fair0.97
    2. World's Fain0.95
    3. World's Fair0.98
    4. THE VELOCITY ROOM1.00
    5. mezmo1.00
    6. fiddler0.98
    7. World's Fair0.98
    8. World's Fair0.91
    9. Cathe0.73
    10. INNGEST1.00
    11. PRIOR1.00
    12. Snorkel0.97
    13. World's Fair0.98
    14. World's Fair0.99
    15. ©BAND0.89
    16. Keycard1.00
    17. Bstelryx0.70
    18. World's Fair0.98
    19. Z.AI0.95
    20. World'sFair0.99
    21. :neo4j0.98
    22. Worid's Fair0.92
    23. Cleric1.00
    24. World's Fair0.93
    25. GRAVITEE0.99
    26. Meliculous0.96
    27. OS0.73
    28. World's Fair0.98
    29. Amazon AGI Lab0.97
    30. World's Fair0.97
    31. Google DeepMind1.00
    32. World's Fair0.95
    33. qodo0.99
    34. World's Fair0.97
    35. arize1.00
    36. PayPal1.00
    37. World'sFa0.96
    38. Gradium0.97
    39. World'sFail0.82
    40. FACTORY1.00
    41. Daud0.61
    42. Braintrust1.00
    43. World's Fair0.97
    44. World's Fair0.96
    45. OpenAI0.94
    46. Microsoft1.00
    47. World's Fair0.96
    48. World'sFair0.97
    49. ORACLE1.00
    50. Microsoft0.98
    51. ANTHROPIC0.96
    52. WoridsFair0.79
    53. Crusce0.85
    54. World's Fair0.96
    55. bright data0.99
    56. World's Fair0.99
    57. World'sF.0.91
    58. World's Fair0.98
    59. ANTHROPIC1.00
    60. Akamai0.95
    61. World's Fair0.96
    62. 's Fair0.93
    63. World's Fair0.96
    64. DATADOG1.00
    65. World's Fair0.97
    66. World'sF0.97
    67. Airbyte0.88
    68. Optiver0.97
    69. greptle0.88
    70. World's Fair0.98
    71. World's Fair0.98
    72. I's Fair0.93
    73. World'sFair1.00
    74. World's Fair0.97
    75. eNCORD0.93
    76. World's Fair0.95
    77. World's Fair0.97
    78. 'gos0.67
    79. Workds Fair0.86

  • 0:33 #7 done197 line(s)

    shot 7·sharpness 3204.5

    1. World's Far0.86
    2. ORACLE1.00
    3. World's Far0.97
    4. arize1.00
    5. World's Fair0.95
    6. Google DeepMind1.00
    7. World's Fair0.93
    8. (neo4]0.69
    9. World's Fair0.97
    10. Z.AI0.99
    11. World's Far0.88
    12. bright data1.00
    13. World's Fair0.95
    14. Browserbase1.00
    15. paper compute co.0.98
    16. World's Fair0.95
    17. extend1.00
    18. World's Fair0.96
    19. vast.ai1.00
    20. World's Fair0.98
    21. Ref.1.00
    22. World's Far0.98
    23. RedHat1.00
    24. World's Far0.96
    25. mezmo+0.96
    26. World's Fair0.93
    27. stigg1.00
    28. World's Fair0.96
    29. World's Fair0.98
    30. RELAI0.97
    31. World's Fair0.96
    32. VAPI1.00
    33. World's Fair0.96
    34. Modal1.00
    35. World's Far0.96
    36. promptql0.99
    37. World's Fair0.95
    38. THE VELOCITY ROOM0.96
    39. World's Fair0.99
    40. fiddler1.00
    41. Worid's Fair0.90
    42. Superconductor1.00
    43. Surreal80.90
    44. World's Fair0.95
    45. ZERO0.97
    46. World's Fair0.97
    47. AlEngineer0.99
    48. comet1.00
    49. World's Fair0.97
    50. authe1.00
    51. World's Fair0.94
    52. World's Fair0.95
    53. SOIOOIO0.78
    54. World's Fair0.96
    55. granica1.00
    56. World's Fair0.98
    57. World's Fair0.94
    58. dash00.99
    59. World's Fair0.97
    60. PRIOR1.00
    61. DigitalOcean1.00
    62. World's Fai0.96
    63. Vence0.99
    64. World's Fair0.88
    65. POSTMAN0.99
    66. World's Fair0.95
    67. Composio1.00
    68. World's Fai0.94
    69. World'sFair0.98
    70. Modular1.00
    71. World's Fair0.92
    72. MERGE1.00
    73. World's Fair0.99
    74. AUTOMATTIC1.00
    75. World's Fair0.95
    76. Buildkite0.95
    77. yugabyteDB0.99
    78. World's Fair0.93
    79. Zed1.00
    80. World's Fai0.94
    81. Keycard1.00
    82. World'sFair0.96
    83. Meticulous1.00
    84. World's Fair0.96
    85. World's Fair0.97
    86. Daytona1.00
    87. World's Fair0.97
    88. twilio1.00
    89. World's Fair0.95
    90. GGRAVITEE0.95
    91. World's Fair0.97
    92. PlanetScale1.00
    93. Temporal1.00
    94. World'sFal0.93
    95. Llamalndex1.00
    96. World's Fair0.97
    97. 0O INNGEST0.85
    98. World's Fair0.98
    99. ©BAND0.89
    100. World's Fair0.97
    101. Cleric1.00
    102. World's Fair0.96
    103. À ATLASSIAN0.96
    104. World's Fain0.95
    105. FACTORY1.00
    106. World's Fair0.97
    107. World's Fair0.95
    108. baseten1.00
    109. World's Fair0.99
    110. Snorkel0.99
    111. World's Fair0.95
    112. Z.AI0.99
    113. World's Fair0.96
    114. qodo0.99
    115. World's Fair0.90
    116. PayPal1.00
    117. World's Fair0.98
    118. Gradium1.00
    119. World's Fair0.99
    120. ANTHROPIC0.99
    121. Amazon AGI Lab1.00
    122. World's Fair0.97
    123. World's Fair0.95
    124. :neo4j0.92
    125. World's Fair0.95
    126. Google DeepMind0.99
    127. World's Fair0.99
    128. arize1.00
    129. World's Fair0.98
    130. reducto1.00
    131. World's Fair0.96
    132. Microsoft1.00
    133. World's Fair0.97
    134. World's Fair0.96
    135. OpenAl0.97
    136. World's Fair0.96
    137. WorkOs0.98
    138. World's Fair0.97
    139. Amazon AGI Lab0.99
    140. World's Fair0.93
    141. Microsoft1.00
    142. World's Fair0.92
    143. ORACLE1.00
    144. World's Fair0.97
    145. bright data0.99
    146. Worid's Fair0.87
    147. Microsoft0.96
    148. World's Fair0.97
    149. docker1.00
    150. World's Fai0.94
    151. Braintrust1.00
    152. World's Fair0.97
    153. OpenAl0.99
    154. Vorld'sFai0.99
    155. MINIMAX0.99
    156. World's Fair0.96
    157. Z.AI0.98
    158. World's Fal0.86
    159. ANTHROPIC0.99
    160. World's Fair0.97
    161. World's Fair0.95
    162. snyk1.00
    163. World's Far0.93
    164. aws0.99
    165. World's Fair0.96
    166. togetherai0.99
    167. World'sFi0.94
    168. World's Fair0.97
    169. Unblocked1.00
    170. Airbyte0.99
    171. Lightrun1.00
    172. World's Fair0.93
    173. CLOUDFLAR0.99
    174. World's Fai0.92
    175. DATADO1.00
    176. Resolve.ai1.00
    177. World's Fairn0.87
    178. World's Fa0.93
    179. World s Falr0.86
    180. .Fair0.89
    181. World's Fair0.98
    182. LanceDB1.00
    183. World's Fair0.99
    184. builderio0.95
    185. avenna1.00
    186. World's Fair0.93
    187. CopilotKit1.00
    188. Microsoft1.00
    189. World's Fair0.98
    190. TOPK0.97
    191. World's Fair0.98
    192. 1d'sFa0.99
    193. eNCORD0.96
    194. World'sFai1.00
    195. rid'sFa0.96
    196. World's Fair0.97
    197. 0o0.59

  • 0:36 #8 done10 line(s)

    shot 8·sharpness 559.2

    1. AlEngineer0.97
    2. OpenAI0.92
    3. World's Fair0.98
    4. AlEnginer0.97
    5. World's1.00
    6. Akamai1.00
    7. AlEngineer0.96
    8. 福0.80
    9. DATAI0.94
    10. Vorld's Fair0.99

  • 1:29 #9 done21 line(s)

    shot 9·sharpness 2515.7

    1. AlEngineer0.97
    2. World'sFair1.00
    3. 2026 Al Engineering Survey0.97
    4. Amplify1.00
    5. N1.00
    6. Notion1.00
    7. ▲Vercel0.99
    8. PRESENTED BY0.96
    9. Microsoft1.00
    10. AlEngineer0.98
    11. Norld's Fai0.98
    12. Barr Yaron1.00
    13. All1.00
    14. Akar1.00
    15. Vorl1.00
    16. The 2026 State of AI Engineering0.99
    17. AlEnginee0.98
    18. Norld's1.00
    19. Re1.00
    20. Barr Yaron / Partner0.96
    21. Amplify1.00

  • 1:48 #10 done22 line(s)

    shot 10·sharpness 2372.4

    1. /IA0.62
    2. AlEngineer1.00
    3. World's Fair0.99
    4. Barr Yaron1.00
    5. PRESENTED BY0.99
    6. Partner @ Amplify1.00
    7. Microsoft1.00
    8. @barrnanas1.00
    9. [email protected]0.99
    10. AlEngineer0.98
    11. Vorld's Fair0.99
    12. Op1.00
    13. Amplity Al Engineering Survey0.98
    14. ©20260.96
    15. - AIE0.85
    16. togeth1.00
    17. orl1.00
    18. The 2026 State of AI Engineering0.99
    19. AlEngineer0.99
    20. Vorld's F0.98
    21. Barr Yaron / Partner0.95
    22. Amplify1.00

  • 2:08 #11 done19 line(s)

    shot 11·sharpness 2075.2

    1. /A0.67
    2. AlEngineer0.99
    3. World's Fair0.99
    4. Demographics1.00
    5. PRESENTED BY1.00
    6. Microsoft1.00
    7. lICrOSUIL0.58
    8. woriusT0.67
    9. Engineer1.00
    10. d's Fair0.92
    11. MININ0.79
    12. Engineer1.00
    13. kamai1.00
    14. d'sF0.99
    15. The 2026 State of AI Engineering0.99
    16. Engineer1.00
    17. d's Fai0.98
    18. esolv0.99
    19. Barr Yaron / Partner Amplify0.98

  • 2:25 #12 done18 line(s)

    shot 12·sharpness 2027.6

    1. /A0.67
    2. AlEngineer1.00
    3. World's Fair0.99
    4. 10481.00
    5. PRESENTED BY1.00
    6. Microsoft1.00
    7. respondents0.98
    8. vuriusrail0.83
    9. OpenAl0.95
    10. Wor1.00
    11. Amplity Al Engineering Survey0.98
    12. ©20260.93
    13. AlEngineer1.00
    14. World's1.00
    15. The 2026 State of AI Engineering0.99
    16. 福0.51
    17. DATAC0.97
    18. Barr Yaron / Partner Amplify1.00

  • 2:35 #13 done46 line(s)

    shot 13·sharpness 3704.3

    1. /IA0.65
    2. AlEngineer1.00
    3. World's Fair0.98
    4. Who Responded1.00
    5. 1,048 respondents — mostly founders, CTOs, and Al engineers0.99
    6. Founder / CTO0.98
    7. Global, from orgs of all0.99
    8. AI Engineer0.98
    9. 191.00
    10. PRESENTED BY0.96
    11. Other1.00
    12. sizes - from startup to0.96
    13. Microsoft1.00
    14. Fullstack Engineer1.00
    15. enterprise - but skew0.97
    16. smaller.1.00
    17. Engineering Manager/ Tech Lead0.99
    18. Product Manager1.00
    19. Research Scientist / Research Engineer0.98
    20. Backend Engineer0.99
    21. ~68%1.00
    22. Data Engineer1.00
    23. <100 people1.00
    24. Frontend Engineer0.98
    25. Data Scientist1.00
    26. ML Engineer0.97
    27. AlEngineer0.98
    28. trust1.00
    29. Vorld's Fai0.98
    30. 0%1.00
    31. 41.00
    32. 81.00
    33. 121.00
    34. 161.00
    35. 201.00
    36. 241.00
    37. Amplity Al Engineering Survey0.95
    38. ©20260.94
    39. sFi0.88
    40. gether1.00
    41. The 2026 State of AI Engineering0.98
    42. ngineer1.00
    43. I'sFai0.94
    44. LARE1.00
    45. Barr Yaron / Partner0.95
    46. Amplify1.00

  • 2:57 #14 done54 line(s)

    shot 14·sharpness 3649.6

    1. ∥A0.74
    2. AlEngineer0.98
    3. World's Fair0.96
    4. Seniority doesn't buy AI experience0.99
    5. And the newest developers are already Al-native0.98
    6. Years of experience1.00
    7. PRESENTED BY1.00
    8. 651.00
    9. 601.00
    10. 641.00
    11. 10+ years of software0.97
    12. Of respondents with0.99
    13. Microsoft1.00
    14. 550.79
    15. experience:1.00
    16. 501.00
    17. 451.00
    18. 461.00
    19. 51%1.00
    20. 401.00
    21. 351.00
    22. 301.00
    23. 281.00
    24. have ≤3 years of AI1.00
    25. 251.00
    26. 201.00
    27. 181.00
    28. 211.00
    29. experience1.00
    30. 151.00
    31. 101.00
    32. 81.00
    33. 91.00
    34. In software0.97
    35. 51.00
    36. In AI / ML0.93
    37. AlEngineer0.99
    38. /orld's Fai0.97
    39. 01.00
    40. 0-31.00
    41. 4-61.00
    42. 7-91.00
    43. 10+1.00
    44. Amplity AI Engineering Survey0.96
    45. ©20260.96
    46. -AIE0.88
    47. Aka1.00
    48. Jorl0.99
    49. The 2026 State of AI Engineering0.98
    50. AlEngineer1.00
    51. lorld's|0.84
    52. Re:0.77
    53. Barr Yaron / Partner0.96
    54. Amplify1.00

  • 3:33 #15 done17 line(s)

    shot 15·sharpness 1795.0

    1. /A0.73
    2. AlEngineer0.99
    3. World's Fair0.98
    4. AI at Work1.00
    5. PRESENTED BY1.00
    6. LLL0.80
    7. Microsoft1.00
    8. AlEngineer0.99
    9. 'orld's Fai0.96
    10. AIE1.00
    11. togeth1.00
    12. orl0.99
    13. The 2026 State of AI Engineering0.99
    14. AlEngineer0.96
    15. 'orld's0.93
    16. DA1.00
    17. Barr Yaron / Partner Amplify0.98

  • 4:01 #16 done49 line(s)

    shot 16·sharpness 3810.3

    1. IA0.71
    2. AlEngineer0.99
    3. Differences in usage across modalities Text dominates – so what's next?0.99
    4. World'sFair1.00
    5. Which modalities are you actively building with at work?1.00
    6. PRESENTED BY0.98
    7. No, and I don't currently plan to0.99
    8. Text1.00
    9. 8%1.00
    10. 88%1.00
    11. Microsoft1.00
    12. No, but I plan to0.99
    13. Yes, but not much traction0.99
    14. Image1.00
    15. 18%1.00
    16. 20%1.00
    17. 26%1.00
    18. 36%1.00
    19. Yes, and working well1.00
    20. Audio/ voice0.98
    21. 24%1.00
    22. 31%1.00
    23. 21%1.00
    24. 24%1.00
    25. Video1.00
    26. 44%1.00
    27. 31%1.00
    28. 15%1.00
    29. 10%1.00
    30. enAl0.99
    31. Vorld'sl0.96
    32. AlEngineer1.00
    33. 0%1.00
    34. 20%1.00
    35. 40%1.00
    36. 60%1.00
    37. 80%1.00
    38. 100%1.00
    39. Amplity Al Engineering Survey0.97
    40. ©20260.94
    41. ngineer0.95
    42. d's Fa0.88
    43. Akam1.00
    44. The 2026 State of AI Engineering0.98
    45. AlEnyineer0.95
    46. TADO1.00
    47. 1d'sI0.80
    48. Barr Yaron / Partner0.96
    49. Amplify1.00

  • 4:32 #17 done28 line(s)

    shot 17·sharpness 3652.4

    1. ∥A0.70
    2. AlEngineer0.98
    3. Audio remains the strongest intent-to-adopt rate1.00
    4. World'sFair1.00
    5. Intent-to-Adopt Rate (%)0.99
    6. PRESENTED BY1.00
    7. Audio / Voice1.00
    8. 561.00
    9. 37%1.00
    10. →0.84
    11. 56%1.00
    12. 20251.00
    13. 20261.00
    14. Microsoft1.00
    15. Last year, we showed the1.00
    16. Image1.00
    17. 531.00
    18. intent-to-adopt rate for1.00
    19. audio was highest of all1.00
    20. the modalities.1.00
    21. Video1.00
    22. 410.82
    23. This year it is even1.00
    24. higher!1.00
    25. Engineer1.00
    26. MMIN0.93
    27. 101.00
    28. 201.00

  • 4:49 #18 done

    shot 18·sharpness 3805.0

  • 5:29 #19 skipped

    shot 19·duplicate of #18

  • 5:52 #20 done

    shot 20·sharpness 4351.0

  • 6:47 #21 done

    shot 21·sharpness 4117.5

  • 7:03 #22 skipped

    shot 22·duplicate of #21

  • 7:41 #23 done

    shot 23·sharpness 3915.1

The page's on-screen-text budget of 600 lines is spent, so the last cards in this grid list fewer lines than they hold. Narrow the page with ?frames= to read them.

Transcript

224 cues· 2,911 words· 16,228 chars

  1. 0:13 Now joining us on stage is the partner at Amplify, Bar Yerin.
  2. 0:37 Fantastic.
  3. 0:38 You did a great job practicing.
  4. 0:40 I feel very, very loved.
  5. 0:43 Let's get started.
  6. 0:44 So, like you just heard, my name is Bar.
  7. 0:48 I run a survey every year on the state of AI engineering.
  8. 0:53 And the funny thing about running a survey on the state of AI engineering is that the field changes as you make the slides.
  9. 1:01 Just in the past week, we've had frontier releases treated like national security events, Meta reportedly exploring selling AI compute.
  10. 1:10 By the time I get off stage, maybe something else will happen, so if I miss a major announcement while I'm up here, please come find me after.
  11. 1:18 But that's exactly why we run the survey every year, to cut through the noise, take a moment, step back, and understand what AI engineers are actually doing.
  12. 1:29 For the first time this year, we were thrilled to partner with Notion and Vercel to run this survey.
  13. 1:36 Very quickly on me, this is the least interesting slide.
  14. 1:40 I'm an investment partner at Amplify.
  15. 1:42 Very lucky to invest in companies built by and for AI engineers.
  16. 1:47 And I'll make the same promise that I make every single year, which is short time on bar, long time on bar charts.
  17. 1:53 So let's get right into it with lots of bar charts.
  18. 1:59 First, let's talk about, well, maybe raise your hand.
  19. 2:02 Did you fill out the survey?
  20. 2:03 This is a very large group.
  21. 2:05 Okay, yes, I see you in the front.
  22. 2:09 If the answer is you, thank you so much.
  23. 2:12 If the answer is not you, I will find you in 2027.
  24. 2:15 But genuinely, this only exists because 1,000 of you gave your time, so thank you.
  25. 2:20 We had 1,048 respondents this year, which is a lot of AI engineers.
  26. 2:26 And to be precise, this is not just AI engineers, as I'm sure you see at the conference.
  27. 2:31 Every year we see that AI engineering is more of a discipline than a job title.
  28. 2:35 It touches founders, CTOs, engineers, product people, folks across company sizes and experience levels.
  29. 2:43 And that range shows up in experience too.
  30. 2:46 For the third year running, we see the same pattern which is skewed towards senior engineers but newer to AI.
  31. 2:53 Of those with over 10 years of software experience, over half have three years or less of AI experience.
  32. 3:00 Which tracks?
  33. 3:01 These are very experienced engineers learning a new paradigm in real time.
  34. 3:06 And the newest cohort, the ones who just started engineering, the median new engineer has nearly as much AI experience as the median 10-year software veteran.
  35. 3:16 So the newest engineers have never known software without this.
  36. 3:21 But doing AI doesn't mean one thing.
  37. 3:23 We talked about all these different titles, all these different roles.
  38. 3:26 Before we get into models and agents, I have a more basic question, which is when people say they're doing AI at work, what are they actually doing?
  39. 3:36 So first up, like to start with the modalities, we asked which modalities are you actively building with at work?
  40. 3:43 Can anyone take a guess?
  41. 3:44 Text dominates, I know, hold your applause.
  42. 3:48 But one piece of this chart that I always find very interesting and I always look at is the ratio of nope, I'm not using this modality to I'm not using it but I do plan to.
  43. 4:00 I call this the intent to adopt ratio.
  44. 4:03 Of the people who are not building with a modality today, how many say they plan to use it?
  45. 4:10 And audio has the strongest intent to adopt this year.
  46. 4:14 Among AI engineers who are not building with audio today, a whopping 56% say they plan to adopt it in the AI applications they build.
  47. 4:23 And this is not a brand new signal.
  48. 4:26 Last year, audio also had the highest intent to adopt across modalities, but 37%.
  49. 4:31 So audio continues to take the lead and have high interest, but that interest is accelerating.
  50. 4:38 Now, there has been an audio swing, but if we look at what changed most from the last year in the survey, the biggest jump is actually in people using image generation.

Chapters

  1. 0:00 Introduction and Survey Context
  2. 2:26 The AI Engineering Workforce
  3. 3:21 Current Modalities and Adoption
  4. 5:34 Model Strategy: Closed vs. Open-Weight
  5. 8:20 Cost as an Engineering Constraint
  6. 9:36 The Rise of Agentic Workflows
  7. 11:57 Infrastructure Challenges and Evals
  8. 12:53 The Build vs. Buy Trade-off
  9. 14:09 Impact on Engineering Culture and Teams
  10. 16:27 Future Bets and Predictions

Open at this second