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The Web3 Data Economy: Ocean Protocol

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What will the Web3 Data Economy look like?

The shadow money economy (closed, power concentrated) moved to the token economy (open, permissionless). It has a base layer of reserve currency / store of value (BTC), unit of exchange (ETH) and token / app launch platform (Ethereum). And there are financial & utility last miles, like wallets, exchanges, and dapps.

We envision similar for the data economy. The shadow data economy (closed, power concentrated) will move to the Web3 data economy (open, permissionless). It will have a base layer of reserve currency / store of value, a unit of exchange, and data asset launch platform. Ocean Protocol's design provides all of these as a substrate, with artificial intelligence use cases as the linchpin. Finally, just like the token economy there are financial & utility last miles, like data wallets, data exchanges, and data science tools using Ocean tokens.

This talk was presented at the Web3 Summit, Berlin, Oct 22-24, 2018

PDF version: http://trent.st/content/20181022.2%20Web3%20Summit%20-%20McConaghy.pdf

Published in: Internet
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The Web3 Data Economy: Ocean Protocol

  1. 1. @oceanprotocol Trent McConaghy The Web3 Data Economy Ocean Protocol
  2. 2. “Don’t be evil” died a long time ago
  3. 3. Facebook’s data honeypot tipped the scales in the 2016 US Presidential election “…an infamous Russian ‘troll farm’ ... used Facebook … to influence the U.S. voting public, mostly through thousands of fake ‘bot’ accounts and misleading advertising…” [politico.com]
  4. 4. Data is money.
  5. 5. Inspiration
  6. 6. 2008. “Not your keys, not your money”
  7. 7. Token Economy BTC, ETH Money Transparent, Permissionless Shadow Money Economy The Fed, Banks Opaque, Power Concentrated The Token Economy Is Opening Up Money
  8. 8. Just as We Opened Money, Let’s Open Data Shadow Data Economy FB, GOOG Token Economy BTC, ETH Web3 Data Economy Ocean Money Transparent, Permissionless Shadow Money Economy The Fed, Banks Data Opaque, Power Concentrated
  9. 9. Towards a solution
  10. 10. Have both: Goog, FB, a handful $$ Have ++ data (1000 enterprises) Have ++ AI expertise (1000 AI startups) AI is the linchpin
  11. 11. Connect 1000+ enterprises With 1000+ AI startups All get lotsa data & AI Connecting substrate Connect them!
  12. 12. Connect data owners with data scientists Data market DM AI commons Data science tools (sklearn, Tensorflow, ..) DM DM DM Data scientists With problems to solve Enterprises, govts, NGOs With data & compute Connecting substrate: Web3 data incentivization
  13. 13. Block rewards to incentivize data supply Add to data commons, make $
  14. 14. Web3 data incentives Avoid data escapes: bring compute to the data f(x) private data modeling algorithm privately train model private model model predictions Data stays behind firewall
  15. 15. Framings
  16. 16. “SABRE for data” SABRE is metadata/substrate for airline tickets. Ocean, for data itself. Consume data: Data scientists, Biz analysts Supply data: Biz’s, NGOs, Governments Data market DM AC DM AI common s DM DM AC Web3 data Consume tickets: Consumer travelers, biz ppl Supply tickets: Airlines Expedia Kayak 3rd party reseller Easyjet website Travel agent Hip munk BA website Travel agent SABRE
  17. 17. “AI Compute Pipeline” f(x) data provider algorithm compute provider storage provider analytics further processing curation
  18. 18. “Inter-Service Network” Algorithm Tribe Storage Tribe analytics further processing curation Compute Tribe Data Tribe curation curation curation
  19. 19. “Network of Networks, for AI Stack” Crypto perspective: Ocean is L2 on L1 AI perspective: Ocean is L1
  20. 20. User Experience
  21. 21. Keep Data Science Tooling, Add Crypto *
  22. 22. New powers for data scientists •Way more data. Data commons. Enterprise data without data escapes via on-premise compute. •Provenance in data & AI training. Goodbye data honeypots. •More $. For generating data. For cleaning, labeling, feature engineering data. For algs. For curation.
  23. 23. Manage Datasets
  24. 24. Curation of Datasets & Algs Via Bonding Curves
  25. 25. Applications
  26. 26. Q: For autonomous vehicles, need 500G driven miles. How? A: Together. MOBI: 70% of auto production. Toyota, BMW, ..
  27. 27. ConnectedLife: Way more data for Parkinson’s research
  28. 28. Grow Asia (WEF Spinoff): Help small farmers manage their plots & protect biodiversity
  29. 29. Govt of Singapore Data Authorities (IMDA): Give policymakers optionality to better address privacy, yet benefit from AI data
  30. 30. MusicMap : Curation
  31. 31. AI Commons: 40+ UN Agencies, AI XPRIZE Using Web3 Data Substrate in #AIforGood
  32. 32. What does a data economy look like?
  33. 33. Inspiration: The Token Economy Base Layer Economic Last Mile Reserve currency, store of value BTC App funding platform Ethereum App platform, unit of exchange Ethereum Custodians / Wallets Coinbase, Trezor, Metamask Mining Bitmain Token Exchanges GDAX, Binance Utility Last Mile (Dapps)
  34. 34. Q: What will the Web3 data economy look like? A: ≈ The Token Economy Reserve currency, store of value Data/asset funding platform Data/asset platform, unit of exchange Custodians / Wallets Mining: Service networks Data Exchanges Data Economy Base Layer Economic Last Mile Reserve currency, store of value BTC App funding platform Ethereum App platform, unit of exchange Ethereum/ ETH Custodians / Wallets Coinbase, Trezor, Metamask Mining Bitmain Token Exchanges GDAX, Binance Utility Last Mile (Dapps) Token Economy
  35. 35. How do we instantiate a data economy? What does each of these look like? Reserve currency, store of value Data/asset funding platform Data/asset platform, unit of exchange Custodians / Wallets Mining: Service networks Data Exchanges Data Economy
  36. 36. Reward making relevant data available Via staking assets as a signal of relevance More stake = more block reward when serving data asset X A bonding curve sets price of an asset’s token Ö / ḌX = # Ocean tokens per drop (stake) in asset X Supply of ḌX = # drops of X minted
  37. 37. Easy “mini ICOs” of each data asset Bootstrap liquidity Ö / ḌX = # Ocean tokens per drop (stake) in dataset X Supply of ḌX = # drops of X minted Start
  38. 38. Reserve currency for data assets Ö / ḌX = # Ocean tokens per drop (stake) in dataset X Supply of ḌX = # drops of X minted Area = Amount of Ö staked, in reserve
  39. 39. Connect AI data & compute networks. Paid services are purchased with tokens. Algorithms Storage Analytics further processing Compute Data Unit of exchange for AI data & compute
  40. 40. Together, fulfill base layer of the data economy Reserve currency, store of value Ocean tokens Data/asset funding platform Ocean Data/asset platform, unit of exchange Ocean Area = reserve Liquidity+
  41. 41. Catalyze the last utility mile & last financial mile Custodians / Wallets Mining: Service networks Data Exchanges AI apps … ++ user acquisition channels, ++ capabilities for users Existing data exchanges: ++ data to sell Crypto exchanges: ++tokens to sell ++ tokens to manage ++capabilities for users: data science superpowers & buy/sell services via wallets
  42. 42. The Web3 Data Economy Will Look A Lot Like The Token Economy! Reserve currency, store of value Data/asset platform, unit of exchange Data/asset funding platform Custodians / Wallets Mining: Service networks Data Exchanges Data Economy Base Layer Economic Last Mile Reserve currency, store of value BTC App funding platform Ethereum App platform, unit of exchange Ethereum Custodians / Wallets Coinbase, Trezor, Metamask Mining Bitmain Token Exchanges GDAX, Binance Utility Last Mile (Dapps) Token Economy
  43. 43. Roadmap Alpha Aug ‘18, Mainnet Mar ‘19 Conclusion
  44. 44. Shadow Data Economy FB, GOOG Token Economy BTC, ETH Data Economy Ocean Money Transparent, Permissionless Shadow Money Economy The Fed, Banks Data Opaque, Power Concentrated Data is Money. Let’s Open It in Web3. @oceanprotocol

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