{"capturedOn":"2026-10-16","source":"https://www.humansourcer.com/market-data/","license":"https://www.humansourcer.com/ai-training-pay/#license","methodology":"Rate statistics are computed over distinct advertised rates, deduplicated by (provider, pay text), not per listing. Providers replicate one pay sentence across many listings; counting per listing would let a single advertisement vote thousands of times.","findings":[{"id":"rate-spread","question":"How much do advertised rates vary?","statement":"The middle half of advertised AI-training rates falls between $22–$100/hr, but the full range runs from $1 to $450 per hour, with 9.8% of distinct advertised rates below $10/hr and 10.2% at $150/hr or above. Measured over 723 distinct advertised rates, not per listing.","value":"$22–$100/hr","sampleSize":723},{"id":"replication","question":"Why one pay sentence covers a thousand listings","statement":"6,440 listings state an hourly rate, but they carry only 723 distinct advertised rates - each one repeated across 8.9 listings on average. Providers post a batch of roles quoting one pay sentence, so counting per listing would let a single sentence vote thousands of times. Every rate figure on this page is computed over the distinct rates instead.","value":"8.9×","sampleSize":723},{"id":"concentration","question":"How concentrated is this market?","statement":"Across 33 platforms with live listings, the largest single source accounts for 41.4% of indexed listings and the top three for 63.1%, giving a Herfindahl–Hirschman Index of 0.207 - moderately concentrated. This describes the listings HumanSourcer indexes, not the AI-training market itself: a platform that publishes no scrapeable board is absent from it entirely.","value":"0.207","sampleSize":33},{"id":"pay-units","question":"How is pay actually quoted?","statement":"96.0% of listings that disclose pay quote an hourly rate. The other 270 quote per task, per unit, per project or as a salary, and are excluded from every hourly figure on this site - there is no honest way to convert a per-task price into an hourly one without knowing how long the task takes.","value":"96.0%","sampleSize":6740},{"id":"transparency","question":"How often is pay disclosed?","statement":"86.9% of active listings state some pay signal - but that is listing-weighted, and one large source posting thousands of near-identical roles moves it (Prolific Participants alone is the largest). Counted per platform instead, 25 of 33 (75.8%) publish pay on at least one open role.","value":"86.9%","sampleSize":7755},{"id":"country-coverage","question":"Where do listings say applicants must be?","statement":"Of the 3,161 listings whose title names a country (40.8% of 7,755), the most frequently named are USA (36.0%), United Kingdom (21.5%), Canada (6.0%). The remaining 4,594 state no country in their title, which is reported as unknown rather than assumed to be worldwide.","value":"USA","sampleSize":3161},{"id":"turnover","question":"How long do listings stay open?","statement":"Of 866 listings first indexed between 2026-10-02 and 2026-10-09, 18% had closed within 7 days. Only listings whose full 7-day window has elapsed are counted, so a listing posted yesterday cannot drag the figure down; the earlier bound is where closures stop being visible in the published data, so the cohort is a window rather than everything older.","value":"18%","sampleSize":866}],"rateHistogram":[{"lower":0,"upper":10,"count":71,"hedged":31},{"lower":10,"upper":20,"count":74,"hedged":33},{"lower":20,"upper":30,"count":80,"hedged":23},{"lower":30,"upper":40,"count":58,"hedged":19},{"lower":40,"upper":60,"count":82,"hedged":12},{"lower":60,"upper":80,"count":109,"hedged":11},{"lower":80,"upper":100,"count":63,"hedged":5},{"lower":100,"upper":150,"count":112,"hedged":10},{"lower":150,"upper":200,"count":43,"hedged":8},{"lower":200,"upper":null,"count":31,"hedged":5}],"hedgeSplit":{"stated":{"count":566,"median":65},"hedged":{"count":157,"median":24},"hedgedShare":0.21715076071922546},"concentration":{"hhi":0.20737535443325802,"groups":33,"top1Share":0.41366860090264346,"top3Share":0.6306898774983881},"roundNumberShare":{"multipleOf5":0.7095435684647303,"multipleOf10":0.4827109266943292},"providers":[{"slug":"mercor-experts","brand":"Mercor Experts","openRoles":105,"distinctRates":54,"medianRate":100,"p25Rate":75,"p75Rate":125,"rateFloor":20,"rateCeiling":450,"disclosedShare":1},{"slug":"micro1-experts-opportunities","brand":"micro1 Experts Opportunities","openRoles":257,"distinctRates":101,"medianRate":95,"p25Rate":50,"p75Rate":130,"rateFloor":6,"rateCeiling":400,"disclosedShare":0.9727626459143969},{"slug":"afterquery-experts","brand":"AfterQuery Experts","openRoles":220,"distinctRates":43,"medianRate":93,"p25Rate":64,"p75Rate":121.5,"rateFloor":15,"rateCeiling":175,"disclosedShare":1},{"slug":"handshake-ai","brand":"Handshake AI","openRoles":221,"distinctRates":32,"medianRate":77.5,"p25Rate":49.5,"p75Rate":111.25,"rateFloor":15,"rateCeiling":300,"disclosedShare":1},{"slug":"alignerr","brand":"Alignerr","openRoles":549,"distinctRates":66,"medianRate":75,"p25Rate":50,"p75Rate":110,"rateFloor":10,"rateCeiling":400,"disclosedShare":1},{"slug":"sepal-expert-hub","brand":"Sepal Expert Hub","openRoles":88,"distinctRates":76,"medianRate":75,"p25Rate":60,"p75Rate":92.75,"rateFloor":21,"rateCeiling":380,"disclosedShare":0.9772727272727273},{"slug":"turing-ai-advancement-work","brand":"Turing AI Advancement Work","openRoles":267,"distinctRates":8,"medianRate":65,"p25Rate":18.75,"p75Rate":112.5,"rateFloor":15,"rateCeiling":150,"disclosedShare":0.13857677902621723},{"slug":"rise-data-labs","brand":"Rise Data Labs","openRoles":116,"distinctRates":12,"medianRate":47.5,"p25Rate":43.75,"p75Rate":82.5,"rateFloor":5,"rateCeiling":150,"disclosedShare":0.7586206896551724},{"slug":"dataforce-jobs","brand":"DataForce jobs","openRoles":139,"distinctRates":9,"medianRate":40,"p25Rate":30,"p75Rate":50,"rateFloor":30,"rateCeiling":57.5,"disclosedShare":0.5467625899280576},{"slug":"mindrift","brand":"Mindrift","openRoles":850,"distinctRates":19,"medianRate":31,"p25Rate":21.5,"p75Rate":44.5,"rateFloor":9,"rateCeiling":100,"disclosedShare":1},{"slug":"prolific-participants","brand":"Prolific Participants","openRoles":3208,"distinctRates":57,"medianRate":30,"p25Rate":20,"p75Rate":80,"rateFloor":3.4,"rateCeiling":200,"disclosedShare":0.934850374064838},{"slug":"meridial","brand":"Meridial","openRoles":833,"distinctRates":59,"medianRate":30,"p25Rate":13,"p75Rate":65,"rateFloor":5,"rateCeiling":200,"disclosedShare":0.9915966386554622},{"slug":"lxt-ai","brand":"LXT AI","openRoles":59,"distinctRates":15,"medianRate":24,"p25Rate":22.5,"p75Rate":27,"rateFloor":17.2,"rateCeiling":29,"disclosedShare":0.5084745762711864},{"slug":"telus-international-ai-community-portal","brand":"TELUS International AI Community portal","openRoles":64,"distinctRates":20,"medianRate":16.5,"p25Rate":6.9075,"p75Rate":26.25,"rateFloor":1,"rateCeiling":120,"disclosedShare":0.921875},{"slug":"welo-data","brand":"Welo Data","openRoles":392,"distinctRates":104,"medianRate":13.25,"p25Rate":7.75,"p75Rate":22.767500000000002,"rateFloor":2.25,"rateCeiling":105,"disclosedShare":0.45663265306122447},{"slug":"crowdgen","brand":"CrowdGen","openRoles":60,"distinctRates":1,"medianRate":null,"p25Rate":null,"p75Rate":null,"rateFloor":3.6,"rateCeiling":3.6,"disclosedShare":0.5333333333333333},{"slug":"appen","brand":"Appen","openRoles":60,"distinctRates":3,"medianRate":null,"p25Rate":null,"p75Rate":null,"rateFloor":3.6,"rateCeiling":40,"disclosedShare":0.8},{"slug":"dataforce-community","brand":"DataForce Community","openRoles":50,"distinctRates":0,"medianRate":null,"p25Rate":null,"p75Rate":null,"rateFloor":null,"rateCeiling":null,"disclosedShare":0}],"listingsPerDay":{"generatedAt":"2026-10-16T09:23:40.946Z","asOfDate":"2026-10-15","windowStart":"2026-08-01","corpusStart":"2026-07-19","rolesCounted":13738,"untaggedShare":0.3589314310671131,"days":["2026-08-01","2026-08-02","2026-08-03","2026-08-04","2026-08-05","2026-08-06","2026-08-07","2026-08-08","2026-08-09","2026-08-10","2026-08-11","2026-08-12","2026-08-13","2026-08-14","2026-08-15","2026-08-16","2026-08-17","2026-08-18","2026-08-19","2026-08-20","2026-08-21","2026-08-22","2026-08-23","2026-08-24","2026-08-25","2026-08-26","2026-08-27","2026-08-28","2026-08-29","2026-08-30","2026-08-31","2026-09-01","2026-09-02","2026-09-03","2026-09-04","2026-09-05","2026-09-06","2026-09-07","2026-09-08","2026-09-09","2026-09-10","2026-09-11","2026-09-12","2026-09-13","2026-09-14","2026-09-15","2026-09-16","2026-09-17","2026-09-18","2026-09-19","2026-09-20","2026-09-21","2026-09-22","2026-09-23","2026-09-24","2026-09-25","2026-09-26","2026-09-27","2026-09-28","2026-09-29","2026-09-30","2026-10-01","2026-10-02","2026-10-03","2026-10-04","2026-10-05","2026-10-06","2026-10-07","2026-10-08","2026-10-09","2026-10-10","2026-10-11","2026-10-12","2026-10-13","2026-10-14","2026-10-15"],"total":{"new":[11,8,4,373,10,135,107,43,2,5,171,129,124,63,123,0,1,160,222,73,98,71,7,4,43,156,217,149,30,3,7,26,103,134,163,296,0,3,27,33,122,15,108,0,25,108,36,154,56,49,1,21,72,75,30,49,74,9,31,38,61,193,27,28,569,15,66,75,63,23,45,5,54,122,313,528],"open":[6498,6491,6485,6645,6438,6483,6527,6499,6498,6496,6542,6583,6629,6647,6622,6614,6612,6646,6782,6811,6819,6814,6815,6808,6813,6868,6888,6847,6869,6857,6799,6733,6737,6744,6800,7054,7028,7026,6992,6970,7046,6859,6851,6845,6868,6929,6916,7024,7012,6948,6941,6938,6922,6946,6952,6895,6929,6920,6917,6937,6926,7087,7046,6991,7510,7426,7440,7492,7235,7179,7153,7152,7145,7155,7321,7719]},"useCase":{"keys":["model-evaluation","coding-swe","research-analysis","translation-linguistics","data-annotation-labeling","voice-audio","writing-editing","customer-support-ops","other","embodied-data-capture","safety-red-teaming","prompt-engineering"],"new":{"model-evaluation":[1,5,3,141,8,12,11,2,0,0,100,12,14,22,12,0,0,62,47,6,5,2,1,0,1,92,29,46,3,0,2,1,11,51,30,84,0,0,1,6,43,2,0,0,0,20,2,30,4,2,0,0,7,31,7,19,1,1,0,1,13,82,4,3,31,0,26,14,3,2,29,3,16,4,10,22],"coding-swe":[0,0,1,94,0,57,16,0,1,0,25,69,29,31,16,0,0,49,51,11,69,0,6,0,27,10,89,29,0,0,1,6,4,31,4,39,0,0,15,11,15,4,4,0,0,30,2,2,3,2,0,1,13,7,1,3,3,3,0,12,6,19,4,2,110,1,27,27,8,12,4,0,25,71,4,4],"research-analysis":[2,3,0,15,0,10,12,4,1,0,2,16,5,3,5,0,0,4,3,18,11,1,0,0,2,1,16,11,11,0,0,2,6,21,6,21,0,1,0,8,15,0,4,0,0,12,1,14,15,0,0,0,9,0,1,1,9,3,0,0,7,10,2,0,67,0,2,1,4,2,2,0,3,9,254,459],"translation-linguistics":[1,0,0,34,0,8,2,14,0,0,6,24,32,0,24,0,0,12,37,1,0,13,0,2,0,10,27,15,3,0,2,13,0,5,45,77,0,0,5,0,5,0,64,0,21,3,17,58,11,2,0,11,32,5,1,10,4,0,30,18,6,27,8,20,66,12,2,2,34,0,1,0,2,1,5,31],"data-annotation-labeling":[2,0,0,29,0,10,47,1,0,3,29,5,11,3,50,0,0,30,21,4,5,37,0,0,4,30,20,31,2,0,0,0,15,10,35,11,0,1,3,2,13,0,33,0,0,6,4,3,16,31,1,1,8,2,6,6,34,1,0,2,7,9,3,0,88,1,0,24,9,0,6,0,1,8,2,4],"voice-audio":[1,0,0,27,0,25,4,22,0,1,6,1,18,0,7,0,0,1,58,20,3,3,0,0,1,12,11,2,2,0,2,4,63,5,39,14,0,0,2,1,14,8,0,0,4,12,7,15,1,11,0,1,3,30,14,5,21,0,0,2,17,9,2,0,138,0,5,0,2,0,0,2,7,16,21,1],"writing-editing":[0,0,0,24,2,9,4,0,0,0,1,2,14,1,6,0,0,1,4,12,2,10,0,2,4,1,22,10,7,2,0,0,1,10,1,39,0,0,0,2,4,0,3,0,0,18,3,21,0,1,0,0,0,0,0,0,0,1,0,2,3,25,3,1,48,1,1,3,1,3,1,0,0,0,11,6],"customer-support-ops":[3,0,0,4,0,1,11,0,0,1,2,0,0,1,2,0,1,1,1,1,1,3,0,0,4,0,3,5,2,1,0,0,2,0,3,6,0,1,0,1,3,1,0,0,0,3,0,5,6,0,0,0,0,0,0,2,1,0,1,1,0,10,1,0,16,0,3,1,1,4,2,0,0,3,2,0],"other":[0,0,0,0,0,0,0,0,0,0,0,0,1,2,1,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,1,4,0,0,0,0,0,0,3,0,0,0,7,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,3,1,0,0,0,0,9,4,1],"embodied-data-capture":[1,0,0,4,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,1,5,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,2,1,0,0,0,0,0,0,0,0,0,0,0],"safety-red-teaming":[0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0],"prompt-engineering":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]},"open":{"model-evaluation":[2320,2323,2326,2378,2328,2330,2335,2333,2333,2333,2378,2388,2398,2412,2416,2414,2414,2422,2469,2468,2473,2475,2474,2467,2465,2501,2525,2557,2558,2556,2528,2524,2499,2511,2535,2608,2599,2599,2598,2581,2598,2511,2505,2504,2504,2512,2505,2507,2496,2444,2443,2442,2430,2435,2439,2431,2431,2430,2430,2430,2427,2504,2479,2468,2498,2491,2491,2497,2434,2429,2457,2460,2468,2455,2450,2468],"coding-swe":[572,567,567,609,575,589,600,599,598,596,597,611,615,621,634,633,633,652,653,657,660,636,641,640,643,651,631,575,574,573,548,529,522,530,528,559,554,554,544,553,567,549,539,538,538,545,521,522,525,521,519,520,530,536,535,534,534,536,535,545,548,560,559,551,655,650,675,700,664,673,671,669,667,709,695,688],"research-analysis":[756,758,757,769,756,762,769,773,773,773,773,788,782,784,780,780,780,783,785,803,803,804,804,803,803,803,816,809,820,819,818,815,819,835,840,860,858,857,853,854,868,850,852,852,852,862,861,874,888,885,884,884,890,885,882,878,886,885,885,884,882,890,889,884,948,946,944,942,943,942,939,939,931,934,1144,1528],"translation-linguistics":[683,683,683,680,642,641,640,635,635,632,632,629,660,658,670,669,669,676,690,689,689,689,689,690,690,695,690,688,688,686,685,657,652,652,652,727,725,724,711,708,708,706,712,712,733,732,741,786,775,767,765,764,764,762,758,754,757,755,755,761,760,782,780,775,840,841,837,837,776,723,712,710,700,685,684,714],"data-annotation-labeling":[552,546,546,574,554,553,565,563,563,565,562,565,576,575,578,574,572,570,591,594,598,604,603,603,606,606,596,588,590,584,580,580,586,554,557,565,563,562,563,559,571,550,550,547,546,551,555,557,545,552,551,549,535,535,536,494,495,496,496,497,482,489,485,479,532,531,529,546,542,537,506,505,505,503,492,483],"voice-audio":[747,747,742,761,749,763,765,770,770,771,777,777,784,782,738,738,737,737,786,806,807,802,802,801,801,812,819,811,812,811,813,802,856,857,890,900,900,900,895,888,895,892,892,891,894,903,910,924,923,934,934,928,922,951,961,958,978,976,973,974,986,989,985,972,1108,1037,1042,1041,1039,1036,1033,1035,1042,1040,1032,1013],"writing-editing":[405,405,405,410,389,397,398,374,374,374,375,377,366,364,354,354,354,353,355,353,354,364,364,366,364,364,374,382,389,391,391,390,386,390,391,418,413,413,411,408,407,379,381,381,381,397,400,421,421,419,419,419,419,417,417,417,417,414,414,416,413,436,436,432,479,480,475,478,392,391,391,390,389,380,376,379],"customer-support-ops":[336,335,334,336,324,325,334,334,334,334,333,333,333,334,336,336,337,337,337,332,332,335,334,334,337,334,336,337,338,337,336,336,316,314,307,313,312,313,312,312,315,309,307,307,307,310,308,312,318,308,308,308,308,308,308,310,311,308,309,310,309,316,312,309,324,324,326,327,322,326,325,325,324,323,322,322],"other":[27,27,25,25,24,24,24,22,22,22,20,20,21,23,22,22,22,22,22,17,19,21,20,20,20,19,19,18,18,18,18,18,19,20,19,19,19,19,20,21,25,25,25,25,25,25,25,28,28,25,25,31,31,30,30,33,33,33,33,33,33,33,33,31,31,31,29,32,33,32,30,30,30,39,43,43],"embodied-data-capture":[55,55,55,57,55,58,56,55,55,55,54,54,53,53,53,53,53,53,53,51,43,43,43,43,43,42,42,42,42,42,42,42,42,41,41,45,45,45,45,46,51,47,47,47,47,47,47,50,50,50,50,50,50,44,43,43,43,43,43,43,43,45,45,47,48,48,48,48,48,48,48,48,48,45,45,43],"safety-red-teaming":[14,14,14,15,11,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,9,9,9,9,9,9,9,9,9,9,9,9,9,9,10,10,10,10,10,14,12,12,12,12,12,12,12,12,12,12,13,13,13,13,12,12,12,12,16,16,16,16,14,14,13,13,13,14,10,10],"prompt-engineering":[31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,28,28,28,28,28,28,28,28,28,28]},"labels":{"model-evaluation":"AI model evaluation","coding-swe":"Coding / software engineering","research-analysis":"Research / analysis","translation-linguistics":"Translation / linguistics","data-annotation-labeling":"Data annotation / labeling","voice-audio":"Voice / audio","writing-editing":"Writing / editing","customer-support-ops":"Customer support / ops","other":"Uncategorized","embodied-data-capture":"Embodied / physical data capture","safety-red-teaming":"Safety / red-teaming","prompt-engineering":"Prompt engineering"}},"domain":{"keys":["coding","voice-speech","science-stem","linguistics","medicine","finance","consulting-ops","law","writing-creative","safety","reasoning-agent","multimodal"],"new":{"coding":[0,0,1,85,0,50,40,0,1,0,51,68,29,30,47,0,0,75,54,11,67,31,6,0,25,37,86,57,0,0,0,5,3,30,33,25,0,0,14,11,11,4,34,0,0,24,2,1,13,22,0,2,5,6,0,3,32,0,0,11,5,18,1,2,135,0,1,3,5,5,3,0,1,1,2,1],"voice-speech":[0,0,0,24,0,23,3,22,0,1,41,11,12,0,7,0,0,1,55,19,3,3,0,0,1,11,6,1,2,0,2,2,62,8,55,14,0,0,2,1,15,8,0,0,4,11,7,17,1,11,0,1,2,28,14,7,21,0,0,2,16,6,0,1,136,0,5,22,2,0,0,0,7,17,23,1],"science-stem":[0,2,1,36,0,14,12,1,1,0,3,11,7,8,6,0,0,26,3,1,5,2,5,0,29,1,7,16,3,2,3,4,3,15,3,52,0,0,0,3,11,0,2,0,0,14,2,14,1,4,0,0,12,4,0,3,13,6,1,1,12,24,6,2,93,1,27,29,7,8,1,0,26,76,7,7],"linguistics":[0,0,0,5,0,10,0,2,0,0,2,0,3,1,13,0,0,4,11,2,1,1,0,1,0,1,3,5,2,0,4,1,0,13,42,68,0,0,1,1,9,0,6,0,21,7,5,60,0,9,0,0,2,8,1,2,0,0,0,7,2,27,6,2,42,1,2,1,1,2,0,0,3,0,14,31],"medicine":[0,0,0,6,0,9,3,0,0,0,0,0,0,1,4,0,0,2,0,0,7,2,0,0,0,0,20,2,1,0,0,0,1,2,6,14,0,0,0,0,5,0,0,0,0,4,0,4,0,1,1,0,2,0,0,0,0,1,0,3,5,17,2,2,23,0,23,3,5,2,28,4,3,1,4,3],"finance":[0,2,0,11,0,2,3,0,0,0,0,2,4,1,1,0,0,1,2,4,4,2,0,0,0,0,2,6,5,0,0,0,7,20,1,20,0,0,0,0,4,2,1,0,0,4,0,7,2,0,0,0,1,1,1,0,1,1,0,0,3,10,1,1,50,0,1,1,4,0,3,0,1,1,4,1],"consulting-ops":[1,0,0,30,0,3,4,2,0,0,31,0,1,0,1,0,0,30,0,1,1,2,0,0,0,29,0,4,0,0,0,0,1,0,1,7,0,1,0,9,0,0,0,0,0,1,0,2,0,0,0,0,0,1,0,3,1,0,0,0,0,5,0,0,11,0,0,0,3,0,3,0,0,4,1,1],"law":[0,0,0,28,0,1,7,0,0,0,0,0,4,1,6,0,0,6,1,0,0,1,0,0,0,0,2,10,0,0,0,0,0,21,0,22,0,0,0,0,2,0,3,0,0,2,2,2,0,0,0,0,0,0,1,0,0,0,0,0,6,19,1,0,4,1,1,0,1,0,1,0,0,0,4,2],"writing-creative":[0,0,0,1,0,9,2,0,0,0,9,0,0,0,6,0,0,0,4,1,1,1,0,0,0,0,1,3,2,0,0,0,25,0,0,17,0,0,0,2,1,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,11,2,1,45,0,2,0,0,0,1,0,0,0,0,2],"safety":[2,0,0,1,0,1,0,0,0,3,0,0,0,0,9,0,0,0,2,1,1,0,0,0,1,1,2,1,1,0,0,0,0,0,4,45,0,1,0,0,1,0,2,0,0,23,2,0,0,7,0,0,0,0,5,2,0,0,0,0,0,1,1,1,15,0,0,1,1,1,0,1,0,1,0,1],"reasoning-agent":[0,0,0,2,0,1,0,0,0,0,0,0,1,0,1,0,0,0,2,0,0,0,0,0,0,0,13,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,1,0,0,1,0,1,2,0,0,0,0,0],"multimodal":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]},"open":{"coding":[596,596,596,630,609,616,623,623,623,623,623,636,641,648,664,664,664,681,685,690,691,667,673,673,674,681,638,579,579,579,553,533,528,535,533,551,547,547,537,547,558,544,533,531,531,532,509,510,511,507,505,507,510,514,512,511,510,510,509,518,522,535,532,530,633,630,630,631,585,586,551,549,547,546,532,533],"voice-speech":[618,618,618,639,627,644,645,651,651,652,693,704,705,704,661,661,660,660,707,726,728,723,723,723,724,734,740,732,733,733,735,737,791,798,851,864,864,864,860,861,869,867,867,866,869,879,886,902,901,912,912,906,900,927,937,937,958,956,953,954,970,971,966,954,1088,1017,1022,1040,1040,1036,1033,1033,1040,1042,1038,1019],"science-stem":[898,899,898,920,901,915,926,918,918,918,920,931,930,937,935,930,930,954,956,956,961,962,966,966,968,969,973,988,991,993,996,975,974,981,980,1031,1026,1025,1023,1020,1030,994,993,993,993,1006,1003,1013,1014,1015,1014,1012,1020,1022,1022,1020,1032,1037,1038,1038,1043,1060,1055,1053,1141,1141,1157,1184,1154,1158,1157,1156,1153,1197,1180,1175],"linguistics":[1347,1347,1347,1350,1314,1318,1315,1311,1311,1309,1311,1307,1310,1311,1324,1324,1324,1328,1339,1341,1342,1342,1342,1342,1342,1343,1346,1351,1353,1353,1355,1324,1323,1336,1378,1446,1446,1446,1441,1440,1449,1448,1453,1453,1474,1479,1482,1536,1536,1536,1534,1533,1532,1538,1536,1535,1534,1533,1533,1540,1537,1559,1557,1552,1590,1591,1587,1583,1465,1464,1460,1459,1455,1438,1450,1466],"medicine":[433,432,432,438,435,441,440,439,438,438,437,437,437,438,440,440,440,442,442,440,447,449,449,449,448,448,466,467,467,466,466,463,462,458,464,475,473,473,473,472,477,470,469,469,469,472,470,474,473,471,472,472,469,468,467,466,466,465,465,468,473,486,486,484,505,501,521,521,513,514,540,543,540,539,531,532],"finance":[345,343,343,340,326,328,323,323,323,323,323,325,327,328,329,329,329,330,332,335,336,338,336,336,334,334,336,337,342,341,341,338,332,349,348,367,367,367,367,363,366,357,356,355,355,355,354,361,363,354,354,354,350,351,352,351,350,346,346,346,345,355,354,353,403,403,402,403,399,397,397,397,397,395,390,388],"consulting-ops":[197,196,196,196,191,193,196,198,198,198,198,198,196,196,197,197,197,198,198,199,192,194,194,194,194,194,194,197,197,197,167,165,166,166,166,172,172,172,172,181,180,176,176,176,176,176,176,178,178,176,176,175,175,175,175,178,179,179,179,179,177,182,180,179,190,190,188,188,189,185,188,188,188,192,185,186],"law":[140,139,139,165,151,152,158,158,157,157,157,157,160,161,167,167,167,172,173,173,173,174,173,173,170,170,172,179,179,179,179,179,179,199,199,210,206,206,205,193,195,178,181,181,181,174,176,177,168,163,163,163,163,163,164,164,164,164,164,164,164,180,180,176,180,181,182,182,175,173,174,174,170,169,168,169],"writing-creative":[60,60,60,61,56,64,64,40,40,40,49,49,47,46,48,48,48,47,50,51,52,53,53,53,53,53,54,57,59,59,59,59,83,81,81,98,98,98,98,100,98,89,90,90,90,90,90,85,85,85,85,85,85,85,87,87,87,85,85,85,84,95,96,97,142,120,119,119,114,114,113,113,112,108,107,108],"safety":[76,75,75,75,71,70,70,70,70,73,71,71,68,68,74,74,74,73,75,74,75,75,74,74,75,76,77,78,79,77,77,77,76,74,78,122,122,123,123,123,123,120,121,121,121,142,141,140,140,138,136,135,131,129,132,132,132,132,132,132,124,122,115,113,127,127,126,125,110,107,103,104,104,105,90,91],"reasoning-agent":[27,27,27,28,26,27,27,27,27,27,27,27,28,26,26,26,26,24,26,26,25,25,25,25,25,25,38,37,37,37,36,36,37,37,37,36,36,36,36,24,24,24,24,23,23,23,23,23,21,21,21,21,22,21,21,21,21,21,21,22,22,22,21,22,23,23,22,23,23,24,26,26,26,26,25,25],"multimodal":[10,10,10,10,10,10,10,10,10,10,10,10,10,10,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,5,5,5,5,5,4,4,4,4,4,4,4]},"labels":{"coding":"Coding","voice-speech":"Voice / Speech","science-stem":"Science / STEM","linguistics":"Linguistics","medicine":"Healthcare","finance":"Finance","consulting-ops":"Consulting / Ops","law":"Law","writing-creative":"Writing / Creative","safety":"Safety","reasoning-agent":"Reasoning / Agents","multimodal":"Multimodal"}},"notes":{"new":"Roles whose first_seen_at falls on that day: when HumanSourcer first saw the listing, not when the provider posted it. Roles first seen before the window opened count as open but never as new.","open":"Roles live at the end of that day, assuming a role was continuously live between first_seen_at and last_seen_at. A role that disappears and reappears under the same id is reactivated in place, so a real gap is invisible and past days are an upper bound.","window":"The window opens 2026-08-01. The scraper fleet was still being built through 2026-07-31 and the earlier first-seen dates measure that rollout, not demand.","domain":"Domain is the role title's own tags or, where the title names none, the evidence-gated model extraction (roles.domains_llm: a verbatim quoted sentence showing the work is in that domain). A role can carry several, so domain series do not sum to the total. Roles with no domain from either source appear in none of them (untaggedShare)."}}}