Artificial intelligence and Jobs Andrea Renda 23 January 2020
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the rise of digital platforms • The rise of platforms depends on key features of the digital economy: Digital information goods Massive data availability Rising computing capacity (Moore’s law) Artificial Intelligence End-to-end Internet architecture Modularity Network neutrality and absence of regulation for intermediaries 23
Foundations Platformization and Moore’s Law Superstar Firms • Digitization and information goods • End-to-end design (originally neutral) Collaborative • System goods and modularity/granularity economy e2e architecture • Transition from goods to services (“age of access”) • Increased virtualization (“softwarisation”) p2p platforms • Multi-sidedness, network FX and “platformization” Modularity • Competition for eyeballs (“attention merchants”) • Ever-changing architecture and shifting of entry possibilities Blockchain /DLTs • Big data, machine learning and data-crunching algorithms • Dynamic pricing and price discrimination Scale without mass AI & data-driven economy
Foundations Evolution Regulatory challenges Highlights Antitrust, zero-priced goods and Market definition cross-layer competition Finding of dominance Open API regulation Platformization and Moore’s Law End of cost-based regulation in multi- Superstar Firms Level-playing field across layers sided markets Unfair P2B practices Privatization of enforcement and Type I errors Servitisation and Contractualisation Contractualisation of labour relations Imbalances of bargaining power Collaborative Privatisation of governance (e.g. gig Difficulties in tax collection economy e2e architecture Legal liability issues economy, smart contracts) Universal service provision Distancing from liability Distancing from liability Technology is “plastic” Technology plasticity and delegation p2p platforms Problems in enforcement of enforcement DRMs face problems Shifting to a less open architecture Modularity Centripetal forces, territoriality and Jurisdiction and applicable law jurisdiction: towards new forms of Liability attribution and apportionment Problems in verification of information rebalancing revenues Possible clash b/w data protection and DLTs Blockchain /DLTs Problems of regulating crypto and ICOs Tension between decentralization, Anti-money laundering issues immutability and data protection Enforcement through Smart contracts Scale without mass Emphasis on mark-up pricing is misplaced Hybrid data governance Multi-sidedness v. cost-based regulation arrangements Privacy and data protection AI & data-driven Free flow of data and security AI, ethics and privacy economy AI ethics, transparency and liability Data spaces Cybersecurity and IoT
ESTIMATES OF WORKERS ENGAGED IN ONLINE LABOUR PLATFORMS 1% - 5% of the adult population in the European Union (EU) has participated at some time in paid work in the platform economy (European Parliament 2017) Europe – 9-22% (last year); 6-15% (last month); 5-12% (last week) Huws et al. (2017) 10% of the adult population in 14 EU Member States have used online platforms for providing labour service (Pesole et al. 2018) Global Estimate 46 million registered workers on 142 freelance platforms (Oxford Internet Institute, 2019)
DIGITAL LABOUR PLATFORMS: LOCAL APP- BASED – RIDE HAILING Global Players Regional Players Go-Jek (Registered in Indonesia) Uber (Registered in USA) Operating in 5 Asian countries Easy Taxi (Registered in Brazil) Operating in 68 countries Operates in 9 Latin American countries North America – 2 countries Europe – 18 countries Latin America – 15 countries Bolt (Registered in Estonia) Asia-Pacific – 10 countries Operates in 37 countries Africa – 7 countries CEE – 12 countries CEE – 7 countries Europe – 9 countries Africa – 6 countries Netherlands, USA, UK, India – Dispute settlements Middle East – 3 countries Asia-Pacific – 1 country Latin America – 1 country
VENTURE CAPITAL INVESTED Source: Kenney and Zysman (2019) Unicorns, Cheshire cats, and the new dilemmas of entrepreneurial finance.
VENTURE CAPITAL IN DIGITAL LABOUR PLATFORMS Sector Total Funding Amount Countries / Funding Amount (USD) Ride hailing and $77 billion US (40% Platforms - $31 Billion) sharing (237) China (5% Platforms - $30 Billion) Same Day Delivery $21 billion US (45% Platforms - $3 Billion) (116) China (8 Platforms - $13 Billion) Freelance (170) $1 billion US (40% Platforms - $552 Million) China (2 Platforms - $431 Million) Source: Crunchbase Platform
STYLIZED DEPICTION OF A MULTI-SIDED PLATFORM, FLOWS OF RESOURCES, AND ITS ECOSYSTEMS Source: Kenney, Bearson and Zysman (2019)
DOMINANT PLATFORM FIRMS Platform Platform Firm Model Revenue, 2018/9 Employment 2018 Firm ($ Million) Apple Innovation 265,595 132,000 Amazon Transaction 232,887 647,500 Google Information/Innovation 136,819 98,771 Microsoft Information/Innovation 125,843 144,100 Facebook Transaction 55,838 35,587 Paypal Transaction 15,451 21,800 Source: Kenney, Bearson and Zysman (2019)
A r A recap ap…
The new technology stack: more than AI 5G
Source: Patel. et al. (2017) Source: U.S. White House (2016)
Source: Zelros AI
Source: Accenture/Frontier Economics (2017)
• The amount of data created in the past two years is greater than the total amount created in the history of humankind. AI could soon outperform the capacity of the human brain and help reverse challenges to global growth such as aging populations and declining output per worker. • The widespread use of AI- enabled technologies could double the economic growth rates of many advanced countries by 2035*. AI is predicted to drive global GDP gains of US$15.7 trillion by # . 2030
Source: Accenture/Frontier Economics (2017)
The diffusion challenge: six growing divides • Between innovation-leading and lagging countries • Between cities and rural areas • Between leading firms and “zombie” firms • Between rich and poor • Between education “haves” and “have-nots” • Between “gifted” and “non-gifted”?
Source: Winick (2018)
The impact is unknown and endogenous. The real story is about polarisation, and the deterioration of working conditions
A big divide • Whether v. when • Dystopian v. Utopian • Anecdotal v. Statistically Representative • Static v. Dynamic • Human-centric v. Neutral • Cyborg (human-enhancement) v. external robots
Evidence: 50 shades of polarisation • Acemoglu and Restrepo (2019): Wrong Kind of AI? • Quality-reducing AI adoption is possible • Public policy needs to encourage automation without quality reduction • Acemoglu, Lelarge and Restrepo (2020): • Manufacturing firms that adopt robots experience an increase in employment. However, manufacturing firms experience decreases in employment when competitors adopt robots. In aggregate, negative effects dominate positive ones. • Ashford (2020): • What if AI and ICT do not create enough well-paying and permanent jobs to relieve chronic under- and unemployment? Need for a Plan B.
The impact on jobs is endogenous • Policy choices are essential • GDP focus v. SWB (or SDG) focus: tacking inequalities and employment at once • Protecting consumers v. empowering users • Tax v. Universal Basic Income? • Key areas • Education (incl. entrepreneurial skills) • Infrastructure • Immigration • Welfare reform • Basic research and mission-oriented research • Public management innovation
• Financialisation and digitalisation have led to the emergence of new ways of doing business, in which value creation and value extraction are increasingly separated (Mazzucato 2018) The issue of • These trends have brought important benefits, but also dilute corporate responsibility for the “value” sustainability of the economy, society and the environment • EU industrial policy today is timidly seeking to re-allocate entitlements as close as possible to where value is generated
1H 2018:
Source: WEO 2019
https://www.businessinsider.com/manufacturing-output-versus-employment-chart-2016-12
AI for what?
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