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DIGITAL REVOLUTION AND
SOCIAL ECONOMICAL IMPACT
KOEN DE LEUS
Brussels, 25/11/2016
Crowd Sourcing - Brussels 2
What is happening?
25/11/2016
25/11/2016 3
Swamped with black swans
Crowd Sourcing - Brussels
… and losers
25/11/2016Crowd Sourcing - Brussels 4
The Elephant Chart
25/11/2016 5
Source: Branko Milanovic
Crowd Sourcing - Brussels
95
115
135
155
175
195
215
Highest Lowest Middle Top-5%Source: Thomson Reuters
+75%
+20%
+90%
It’s getting better…for some
Income evolution US
(per quintile)
25/11/2016Crowd Sourcing - Brussels 6
Hollowing out of middle class in US …
25/11/2016Crowd Sourcing - Brussels 7
… and in the European Union
25/11/2016Crowd Sourcing - Brussels 8
Source: Goos, Manning, Salamons (2014)
Change in occupational employment
(shares in low, middle and high-wage occupations in 16 EU countries, 1993-2010)
… just like happened during earlier industrial revolutions
25/11/2016Crowd Sourcing - Brussels 9
Everything turned out fine eventually
25/11/2016Crowd Sourcing - Brussels 10
0
5
10
15
20
25
US: Income top-1%, top-0,1% and top-0,01%
Top 1% Top 0,1% Top 0,01%
Source: The World Wealth and Income database
Same old story?
25/11/2016Crowd Sourcing - Brussels 11
Jobs at risk
25/11/2016Crowd Sourcing - Brussels 12
Also in emerging markets
25/11/2016Crowd Sourcing - Brussels 13
Substution versus …
25/11/2016Crowd Sourcing - Brussels 14
… increase in wealth and creation of new jobs
25/11/2016Crowd Sourcing - Brussels 15
Boy cry wolf?
25/11/2016Crowd Sourcing - Brussels 16
US Participation rate: 25-54 year olds
Basic income
25/11/2016Crowd Sourcing - Brussels 17
Who will pay?
25/11/2016Crowd Sourcing - Brussels 18
Crowd Sourcing - Brussels 19
Thank you
25/11/2016
DIGITAL REVOLUTION AND
SOCIAL ECONOMICAL IMPACT
KOEN DE LEUS
Brussels, 25/11/2016

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Digital Revolution and Social Economical Impact

Editor's Notes

  1. Maar zullen er dan geen volledige nieuwe jobs gecreëerd worden in deze nieuwe economie? App ontwikkelaar, data miner, social media managers, sustainability expert: het zijn stuk voor stuk jobs die 10 jaar geleden nog niet bestonden. Maar het probleem is dat het aantal nieuwe jobs die gecreëerd zullen worden, niet volstaat om de grote hoeveelheid vernietigde jobs te vervangen. Onderzoek van technologist en futurist Gerald Huff toont aan dat de voorbije 100 jaar amper 1 job toegevoegd is aan de top 50 van meest voorkomende jobs in de VS: software ingenieur. Met andere woorden: de nieuwe industrieën die ontwikkeld worden, stellen gewoon geen massa’s volk tewerk. Zo werden er de voorbije 20 jaar 4 maal meer jobs gecreëerd in de horeca en kleinhandel dan in eender welke hightech industrie. En het zijn vooral die massajobs die onderhevig zijn aan automatisering. “Peak horse” situeerde zich ergens in 1910 in de VS. Niettegenstaande paarden extreem sterke, snelle, capabele en intelligente dieren zijn, is de vraag ernaar voor fysische arbeid vandaag veel lager dan een eeuw geleden. Sinds 1910 was er geen economische reden meer om de paardenpopulatie in stand te houden. Het was zijn voedsel en verzorging niet meer waard door de opkomst van nieuwe, meer efficiëntie technologieën. “Deze eeuw zou ‘peak human’ wel eens bereikt kunnen worden”, speculeerde econoom Brad Delong in een artikel in 2014.
  2. De trends die vastgesteld werden in de eerste twee industriële revoluties zijn vandaag nog meer uitgesproken. De middenklasse wordt zwaarder getroffen dan ooit te voren. En de ongelijkheid begint groteske proporties aan te nemen (zie ook verder bij ‘winnaars en verliezers’). In het verleden waren deze trends van voorbijgaande aard. De afvallers achteraan het peloton fietsten de kenniskloof dicht via bijkomende scholing en nieuwe regelgeving. Is dat ook het beeld voor de komende jaren / decennia? “Tijdens de industriële revolutie zagen we stapsgewijze veranderingen in capaciteit als gevolg van motoren”, zegt Jeremy Howard, een Australisch ‘wonderkind’ en oprichter van ‘machine learning’ bedrijf Enlitic. Enlitic past zelflerende machines toe voor een betere medische diagnose bij patiënten. “Nu, na verloop van tijd vlakte die evolutie uit. Er was sociale ontwrichting, maar eens die motoren gebruikt werden om energie te produceren in alle situaties, stopte de verandering.” “De ‘machine learning’ revolutie gaat volledig anders zijn. Het zal nooit afvlakken. Hoe beter computers worden in intellectuele activiteiten, des te beter ze computers kunnen bouwen die beter zijn in intellectuele activiteiten. Dit gaat dus een wijziging zijn die de wereld nog nooit heeft meegemaakt, en je voorgaand begrip van wat mogelijk is, is verschillend.” In plaats van een geleidelijke verspreiding van de voordelen afkomstig van de derde industriële revolutie, staan we mogelijk aan de vooravond van een vierde revolutie. De introductie van nieuwe technologieën schakelt een versnelling hoger, de effecten ervan ook. Exponentiële, digitale en combinatorische veranderingen in de technologie zijn daar verantwoordelijk voor. Het belangrijkste aspect van dit tweede machinetijdperk is dat die nieuwe machines in staat zullen zijn te denken, te voelen, zich aan te passen en dingen te leren. De taken die ze hierdoor kunnen overnemen spreiden zich uit over een veel breder pallet van vaardigheden dan vroeger. Het uithollen van de middenklasse gaat veel verder en veel sneller. In het verleden slaagde de mens erin steeds een stap voor te zijn op de technologische ontwikkelingen door beter en langer onderwijs. Artificiële intelligentie in combinatie met andere technologieën dreigt ons nu in snelheid te pakken. “Menselijke prestaties groeien gradueel, maar ‘machine learning’ groeit exponentieel. En als we vandaag rondom ons kijken, zeggen we: ‘Oh, computers zijn nog redelijk dom.’ Ja. Maar vooruitgang de komende vijf jaar zal buitengewoon zijn [door het exponentiële karakter]. ‘Computers kunnen niet echt denken, ze kunnen niets voelen, ze verstaan geen poëzie, we verstaan echt niet hoe ze werken.’ En dan?? Computers kunnen nu al zaken doen die mensen vandaag een hele dag doen en waarvoor ze betaald worden. Het is nu dus tijd om te beginnen nadenken over hoe we onze sociale en economische structuur gaan aanpassen om met deze nieuwe realiteit om te gaan.”
  3. Die superwinsten vinden we vandaag reeds terug bij sommige digitale en technologiereuzen. General Motors realiseerde op zijn top in 1979 een winst van 12 miljard dollar (omgerekend in huidige dollars). Google maakt vandaag 14 miljard dollar winst, met 20 maal minder mensen. Maar ook in andere, niet-technologiesectoren zullen robotisering en andere technologische ontwikkelingen zoals het Internet of Things de bedrijfswinsten fors hoger duwen. Bank of America Merrill Lynch raamt de impact op de productiviteit afkomstig van creatieve disruptie over tien jaar op 14 000 tot 33 000 miljard dollar. In de industrie en gezondheidszorg gaan de kosten hierdoor 8 000 à 9 000 miljard dollar lager. In de kenniswerksector dalen de arbeidskosten met 9 000 miljard dollar door automatisering via artificiële intelligentie. Dit treft 230 miljoen werknemers wereldwijd. Efficiëntiewinsten via autonome wagens en drones zorgen voor 1 900 miljard dollar minder uitgaven. Robots en artificiële intelligentie duwen tegen 2025 de productiviteit 30% de hoogte in tal van industrieën en halen de arbeidskosten er met 18 à 33% onderuit. De bedrijfswinsten exploderen.
  4. Die superwinsten vinden we vandaag reeds terug bij sommige digitale en technologiereuzen. General Motors realiseerde op zijn top in 1979 een winst van 12 miljard dollar (omgerekend in huidige dollars). Google maakt vandaag 14 miljard dollar winst, met 20 maal minder mensen. Maar ook in andere, niet-technologiesectoren zullen robotisering en andere technologische ontwikkelingen zoals het Internet of Things de bedrijfswinsten fors hoger duwen. Bank of America Merrill Lynch raamt de impact op de productiviteit afkomstig van creatieve disruptie over tien jaar op 14 000 tot 33 000 miljard dollar. In de industrie en gezondheidszorg gaan de kosten hierdoor 8 000 à 9 000 miljard dollar lager. In de kenniswerksector dalen de arbeidskosten met 9 000 miljard dollar door automatisering via artificiële intelligentie. Dit treft 230 miljoen werknemers wereldwijd. Efficiëntiewinsten via autonome wagens en drones zorgen voor 1 900 miljard dollar minder uitgaven. Robots en artificiële intelligentie duwen tegen 2025 de productiviteit 30% de hoogte in tal van industrieën en halen de arbeidskosten er met 18 à 33% onderuit. De bedrijfswinsten exploderen.