Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Why we fight | Altitude NYC

887 views

Published on

At Altitude NYC, noted writer and programmer Paul Ford of Postlight some of the reasons behind two decades of "weird media/tech battles," and some of the challenges faced by modern publishers.

Published in: Technology
  • Login to see the comments

  • Be the first to like this

Why we fight | Altitude NYC

  1. 1. Why we fight PAUL FORD Or; Two decades of weird media/tech battles
  2. 2. PageFastly Living through the history glut with style 2
  3. 3. Paternal aphorisms
  4. 4. PageFastly Weird media battles! Frank Ford’s wisdom 4 • “A program is like a poem.” • “A man should have a vocation and an avocation.”* • “Laughing ends up in crying.” * (Actually Andrew Peabody at Harvard in the early 1800s)
  5. 5. “Baked” vs. “Fried”
  6. 6. PageFastly Weird media battles! Baked 6 July 2000, from Ian Kellan, then of Salon.com: “Baking” This is the publish time commitment of data to a file (or a more efficient cache, if you have one). Editorial narrative, headlines, datelines and other pieces of editorial/business data that doesn't change can and should be pre-calculated in advance.
  7. 7. PageFastly Weird media battles! Baked or fried? 7 "Frying” This is the request time processing of data for the final presentation. Stylesheet assignment, session start/finish accounting, ad placements and other things that actually may change on a per request basis are handled by the HTTP delivery engine.
  8. 8. Why is everyone fighting?
  9. 9. Blogging vs. Journalism
  10. 10. Content vs. Community
  11. 11. 14
  12. 12. Free vs. Paywall
  13. 13. Publishers vs. Ad Networks
  14. 14. © LUMA Partners LLC 2017 Performance Video / Rich Media Targeted Networks / AMPs Horizontal Vertical / Custom Mobile ExchangesDSPs Publisher Tools Data Suppliers Ad Servers DMPs and Data Aggregators Measurement and Analytics Creative Optimization Agency Trading Desks Ad Networks Media Planning and Attribution Verification / Privacy Ad Servers Retargeting Media Mgmt Systems and Operations Sharing Data / Social Tools SSPs DISPLAY LUMAscape M A R K E T E R P U B L I S H E R P E O P L E Tag Mgmt Agencies Denotes acquired company Denotes shuttered company
  15. 15. Performance Targeted Networks / AMPs Vertical / Custom Publisher Tools Ad Servers DMPs and Data Aggregators P U B L I S H E R P E O P L E
  16. 16. PageFastly Weird media battles! Wordpress 20
  17. 17. PageFastly Weird media battles! Drupal 21
  18. 18. PageFastly Weird media battles! Joomla 22
  19. 19. Blockbuster vs. Long Tail
  20. 20. $
  21. 21. News vs. Archives
  22. 22. PageFastly Weird media battles! 400 years! 26 ~1605: Relation aller Fürnemmen und gedenckwürdigen Historien (Account of all distinguished and commemorable news) ~2005: Online archives become feasible. New Yorker, Harper’s, Playboy, Times Machine.
  23. 23. PageFastly Weird media battles! News always caches DEFINITION 27
  24. 24. PageFastly Weird media battles! Archive fun facts! 28 A woman named Marion Stokes recorded 140,000 VHS tapes of local and national news from 1977 to her death in 2012.
  25. 25. PageFastly Weird media battles! Archive fun facts! 29 Mozart has 796,628 followers on Spotify (and Spotify has 50 million paying users).
  26. 26. PageFastly Weird media battles! Archive fun facts! 30 DP.LA (the digital public library of America) has 15,499,687 items from all over.
  27. 27. PageFastly Weird media battles! Archive fun facts! 31 Archive.org has 11,529,270 books, 3.2 million videos, roughly the same amount of audio. Also: 284 billion web pages.
  28. 28. PageFastly Weird media battles! In total… 32 Billions of scanned pages, millions of hours of recorded media.
  29. 29. PageFastly Weird media battles! Who has monetized the past? 33 Ancestry—because humans want to know about our own families and how we came to be Spotify—because people like to listen to music from different time periods, and music is relatively timelines. Kindle—because books were already nice discrete units and hold up pretty well over time. Bloomberg—even though past performance doesn’t predict future results
  30. 30. …Everything vs. Social
  31. 31. Documents vs. Apps
 Editorial vs. Experience
  32. 32. vs.
  33. 33. vs.
  34. 34. vs.
  35. 35. New York vs. San Francisco
  36. 36. The big question: how does your writing influence your coding? How does your coding influence your writing?
  37. 37. Creative interpretation 
 vs. 
 Creative automation
  38. 38. Workflow vs. Databases
  39. 39. News vs. Fake News
  40. 40. Machine learning DSPs Really long schedules Big data Surprising discoveries Everything very expensiveVenture Capital Recurring schedules Unicorns Everything
 a feed Liquidity events Incremental audience growth Consumer marketing Great distribution Too many interns Going not-for-profit? An audience is an audience! High-value viewers Long hours! Never stop working Editors CEOs Breaking news Getting fired Cashing out Consumer analytics platform Long-form vertical Rich 30-year-olds Home page editors
  41. 41. Descent into punditry
  42. 42. PageFastly Weird media battles! Machine learning blah blah blah blah 47 There should be some bullets here about the future Maybe a picture of someone in a VR helmet Or something about augmented reality Bring back all the stuff about archives from before and explain how there is some opportunity here to get ahead of the curve and start thinking about the kind of data-informed experiences that people are going to want as augmented reality finds its way into more things and become more profitable. blah blah blah blah blah blah word vectors tensor flow self-driving cars blah blah blah blah media experiences blah blah blah blah data mining augmented reality archives blah blah blah blah archives as big data creating new experiences blah blah blah blah automatically-generated media experiences blah blah blah blah virtual reality blah blah blah blah blah blah blah blah blah blah word vectors tensor flow self-driving cars blah blah blah blah media experiences blah blah blah blah data mining archives blah blah blah blah archives as big data creating
  43. 43. Vocation vs. Avocation
  44. 44. PageFastly Weird media battles! Stop fighting 49 There will always be these tensions. We’ll be okay as long as no one wins.
  45. 45. Thank you Paul Ford paul.ford@postlight.com

×