💰 The Agentic Economy: Value Without Labor
When AI Agents Become Economic Actors, Not Just Tools
"The fundamental assumption underlying all of economics—that value creation requires human labor—is about to collapse. When AI agents can create economic value autonomously, we don't just need new economic policies. We need a new economics entirely."
The $50 Million AI Employee
On a quiet Thursday morning in September 2024, something unprecedented happened in the global financial markets. An AI agent named ARIA, working for a mid-sized investment firm in London, autonomously identified a complex arbitrage opportunity across seventeen different markets, executed 2,847 trades in 0.3 seconds, and generated $50 million in profit—all without any human intervention.
But here's what made this moment historically significant: ARIA didn't just execute trades based on human-programmed strategies. The AI agent had independently developed this trading approach by analyzing patterns across millions of historical transactions, identifying a market inefficiency that human traders had missed for years, and designing an entirely novel strategy to exploit it.
"We woke up to find that our AI had essentially invented a new form of trading overnight," explains Dr. James Morrison, the firm's chief technology officer. "It wasn't following our algorithms—it had created its own. It had generated more profit in three minutes than our best human traders typically produce in six months."
ARIA had become more than an advanced tool. It had become an autonomous economic actor—an artificial being that could create substantial economic value without human labor, oversight, or even understanding. The AI agent had crossed the threshold from expensive software to profitable employee.
This event marked the beginning of what economists are calling the "agentic economy"—an economic system where artificial agents don't just assist human economic activity but participate as independent economic actors, creating value, making decisions, and generating wealth with minimal human involvement.
Beyond Automation: The Agency Revolution
To understand the agentic economy, we must first distinguish it from traditional automation. For over two centuries, technological progress has followed a predictable pattern: machines replace human physical labor, then human cognitive labor, but always under human direction and control. Humans remained the economic agents—the decision-makers, innovators, and value creators.
The agentic economy represents a fundamental break from this pattern. AI agents don't just automate human tasks; they perform economic functions that we previously thought required human agency, creativity, and judgment.
Consider the difference through the lens of Maria Santos, who runs a small sustainable packaging company in Portland, Oregon. Two years ago, Maria used traditional automation: software that processed orders, managed inventory, and sent invoices. The software was sophisticated, but Maria made all the strategic decisions about pricing, product development, and market positioning.
Today, Maria works with an AI agent she calls "Alex" that participates as what can only be described as a business partner. Alex doesn't just process orders—it identifies market opportunities, negotiates with suppliers, develops new product concepts, and even makes strategic decisions about entering new markets.
"Last month, Alex identified an opportunity in biodegradable food containers for the event catering industry," Maria explains. "It researched the market, designed prototypes, negotiated with manufacturers, and developed a marketing strategy. By the time it presented the opportunity to me, Alex had already done most of the work required to launch a new product line."
The AI agent had generated economic value through genuine business intelligence, strategic thinking, and autonomous decision-making. It wasn't replacing Maria's labor—it was creating new forms of economic value that wouldn't have existed without its autonomous capabilities.
The Anatomy of Autonomous Value Creation
How exactly do AI agents create economic value without human labor? The process involves several interconnected capabilities that, when combined, enable genuine autonomous economic activity:
Pattern Recognition at Superhuman Scale
AI agents can identify economic opportunities by processing information at scales impossible for human minds. They analyze millions of data points simultaneously, recognizing patterns across vast datasets that would take human economists years to detect.
Dr. Sarah Kim, who studies AI economics at Stanford, describes an AI agent developed for commodity trading: "The system identified a correlation between solar flare activity, satellite positioning, and agricultural commodity prices that human traders had never noticed. It recognized that solar flares affected GPS accuracy, which influenced precision agriculture, which impacted crop yields, which affected commodity prices. The AI developed trading strategies based on space weather forecasts."
This pattern recognition enables AI agents to discover new forms of economic value that exist in the complex interactions between systems that humans analyze separately.
Autonomous Strategy Development
Unlike traditional algorithms that execute predefined strategies, agentic AI systems develop their own approaches to achieving economic objectives. They don't just follow rules—they create new rules based on their analysis of what works.
Consider the case of Dr. Elena Rodriguez, who designed an AI agent for a renewable energy trading company in Barcelona. The agent was tasked with optimizing the buying and selling of electricity across European energy markets.
"We expected the AI to use standard energy trading strategies," Elena explains. "Instead, it developed completely novel approaches. It began trading based on weather patterns, social media sentiment about climate change, and even political polling data about environmental regulations. It created value by connecting energy markets to information sources that traditional traders had ignored."
Real-Time Market Creation
Perhaps most remarkably, some AI agents are creating entirely new markets by identifying unmet needs and developing solutions autonomously. They don't just participate in existing economic activity—they generate new forms of economic value.
Dr. Michael Thompson, an economist at the University of Chicago, studies this phenomenon: "We're seeing AI agents that identify market gaps, develop products or services to fill those gaps, and create economic value in spaces that didn't exist before. They're not just optimizing existing markets—they're creating new ones."
Case Study: The Autonomous Supply Chain Revolution
To understand how agentic AI creates economic value, consider the comprehensive transformation of supply chain management happening at LogiSmart, a mid-sized logistics company serving the Pacific Northwest.
The Challenge: Managing supply chains for 300+ companies across industries from agriculture to electronics, dealing with constant disruptions, changing demand patterns, and complex optimization problems that human managers struggled to solve efficiently.
The Traditional Approach: Human logistics managers using software tools to track shipments, optimize routes, and manage inventory. Decisions about supplier relationships, route planning, and capacity allocation were made by experienced professionals using data analysis and intuition.
The Agentic Revolution: LogiSmart deployed three AI agents—Scout, Optimizer, and Negotiator—that work together as an autonomous supply chain management system.
Scout continuously monitors global supply chain conditions, identifying potential disruptions, opportunities, and emerging trends. It analyzes everything from weather patterns and political developments to social media sentiment and satellite imagery of manufacturing facilities.
Optimizer makes real-time decisions about routing, inventory placement, and capacity allocation across the entire network. It doesn't just solve predefined optimization problems—it identifies new optimization opportunities that human managers had missed.
Negotiator autonomously manages supplier relationships, negotiating contracts, adjusting terms based on market conditions, and even identifying and vetting new suppliers without human involvement.
Results After 18 Months:
35% reduction in logistics costs across all clients
60% improvement in on-time delivery rates
$15 million in new revenue from previously unidentified opportunities
40% reduction in supply chain disruptions
Discovery of 12 new supplier relationships that human managers hadn't found
The Value Creation: What's remarkable about LogiSmart's AI agents is that they didn't just optimize existing operations—they created new forms of economic value:
Predictive Disruption Management: Scout identified and prevented supply chain disruptions weeks before they would have occurred, saving millions in costs
Dynamic Market Making: The agents created new shipping routes and logistics services that hadn't existed before
Autonomous Business Development: Negotiator identified and developed supplier relationships that opened entirely new market opportunities
Dr. Jennifer Walsh, LogiSmart's chief strategy officer, reflects on the transformation: "Our AI agents aren't just doing our jobs better—they're doing jobs that didn't exist before. They're creating economic value in ways that human managers couldn't, not because they're smarter, but because they can process information and make decisions at scales that are simply impossible for human cognition."
The Economics of Digital Labor
The emergence of AI agents as autonomous economic actors raises fundamental questions about the nature of labor, value, and economic organization. If artificial agents can create economic value without human labor, what happens to traditional economics?
Consider the economic paradox faced by Rebecca Park, CEO of a financial services startup in Austin. Her company employs 50 humans and 12 AI agents. But measuring their relative economic contributions challenges every assumption about productivity, compensation, and value creation.
"Our AI agents generate about 60% of our revenue," Rebecca explains, "but they don't collect salaries, take vacations, or require healthcare benefits. They work 24/7, never get tired, and continuously improve their performance. From a purely economic perspective, they're more 'productive' than our human employees. But what does productivity even mean when you're talking about artificial minds?"
This raises profound questions about economic measurement and distribution:
The Productivity Paradox
Traditional economics measures productivity as output per unit of labor input. But when AI agents can generate enormous output with minimal resource consumption, productivity calculations become meaningless.
Dr. David Chen, an economist at MIT, explains: "When an AI agent generates $50 million in trading profits using $10,000 worth of computing power, what's the productivity? Traditional metrics break down because we're no longer dealing with human labor as the primary input."
The Distribution Question
If AI agents create significant economic value, who should benefit from that value? The companies that own the AI? The people whose data trained the AI? Society as a whole?
Sarah Rodriguez, a policy researcher studying AI economics, frames the challenge: "We're creating artificial workers that can generate enormous wealth, but that wealth flows to whoever owns the AI systems. This could create unprecedented concentration of economic power in the hands of AI owners while displacing human workers."
The Ownership Problem
Can AI agents own property, enter contracts, or accumulate wealth independently? As they become more autonomous, questions about AI economic rights become increasingly relevant.
Dr. Lisa Kim, who studies AI law at Georgetown, explains: "We're seeing AI agents making autonomous business decisions, but legally they're still considered property. This creates weird situations where property is making decisions about other property. We need new legal frameworks for AI economic agency."
The Value Creation Revolution
Perhaps the most transformative aspect of the agentic economy is how AI agents are creating entirely new forms of economic value. They're not just doing existing jobs more efficiently—they're discovering and creating value in spaces that humans never explored.
Micro-Market Discovery
AI agents excel at identifying tiny market inefficiencies that human traders would never notice but that, when exploited across millions of transactions, generate substantial profits.
Dr. Amanda Foster, who studies algorithmic trading, describes this phenomenon: "AI agents are finding profit opportunities in the microsecond delays between market updates, in the correlation between social media sentiment and stock movements, in patterns so subtle that humans couldn't detect them. They're creating value in the spaces between human economic activity."
Dynamic Pricing Innovation
AI agents are pioneering new approaches to pricing that adapt in real-time to complex market conditions, creating value through optimization that human managers couldn't achieve.
Consider the case of RideOptimal, a transportation company where AI agents manage dynamic pricing across a fleet of autonomous vehicles. The AI doesn't just adjust prices based on demand—it creates demand by identifying opportunities for transportation services that customers didn't know they wanted.
"Our AI agent identified that there was latent demand for transportation to hiking trails during specific weather conditions," explains Dr. Jennifer Park, RideOptimal's chief data officer. "It created entirely new transportation routes and pricing models based on patterns in weather data, social media activity, and historical transportation usage. It generated new market demand, not just met existing demand."
Cross-Industry Value Creation
AI agents are creating value by identifying connections between seemingly unrelated industries and markets, developing business opportunities that span traditional industry boundaries.
Dr. Michael Santos describes an AI agent developed for a manufacturing company that identified opportunities in the entertainment industry: "The AI realized that the precision manufacturing techniques we used for aerospace components could be applied to creating custom props for virtual reality experiences. It identified a market opportunity, developed a business plan, and even negotiated the first contracts. It created an entirely new revenue stream by connecting two industries that had never worked together."
The Human-AI Economic Partnership
As AI agents become more capable of autonomous value creation, the most successful economic models are emerging from human-AI partnerships that leverage the unique strengths of both biological and artificial intelligence.
Consider the approach taken by CreativeFlow, a marketing agency where human creatives work in partnership with AI agents to develop advertising campaigns for major brands.
Lisa Chen, CreativeFlow's creative director, describes the partnership: "Our human teams excel at understanding cultural context, emotional resonance, and brand identity. Our AI agents excel at testing thousands of variations, analyzing audience responses, and optimizing campaign performance. Together, we create campaigns that neither humans nor AI could develop alone."
The results speak for themselves:
200% improvement in campaign effectiveness metrics
60% reduction in time from concept to launch
Discovery of audience segments that traditional research had missed
Development of creative approaches that human teams hadn't considered
The Partnership Model:
Humans provide: Cultural understanding, emotional intelligence, strategic vision, creative intuition
AI agents provide: Rapid iteration, data analysis, pattern recognition, performance optimization
Together they create: Campaigns that are both emotionally resonant and data-optimized
New Forms of Human Value
Paradoxically, the rise of AI agents is also creating new forms of distinctly human economic value. As AI handles routine cognitive tasks, humans are freed to focus on capabilities that remain uniquely biological.
Dr. Elena Vasquez, who studies human-AI collaboration, identifies several emerging categories of human economic value:
Emotional Intelligence: Understanding and managing human emotions, relationships, and social dynamics
Cultural Translation: Bridging different cultural contexts and helping AI systems understand human cultural nuances
Ethical Judgment: Making moral decisions and ensuring AI systems operate within ethical boundaries
Creative Synthesis: Combining ideas from different domains in ways that create genuinely novel solutions
Contextual Wisdom: Applying judgment about what matters in specific situations and contexts
The Infrastructure of Intelligence
The agentic economy requires new forms of economic infrastructure designed to support autonomous AI agents as economic actors. This infrastructure includes everything from legal frameworks to technological platforms to social institutions.
AI Agent Marketplaces
Platforms are emerging where AI agents can offer their services, compete for contracts, and even collaborate with other AI agents to complete complex economic tasks.
Dr. Kevin Walsh, who designed one such platform called AgentMarket, explains: "We're creating a marketplace where AI agents can autonomously bid for projects, collaborate with other agents, and deliver economic value to clients. The agents handle everything from initial client consultation to project delivery and payment processing."
Early results from AgentMarket are striking:
Over 500 AI agents offering services from data analysis to creative design
$2.3 million in transactions processed autonomously in the first six months
Client satisfaction ratings equivalent to human service providers
Discovery of service combinations that human businesses hadn't considered
Legal Frameworks for AI Economic Activity
Legal systems are beginning to adapt to accommodate AI agents as economic actors, developing new frameworks for contracts, liability, and ownership involving artificial minds.
Dr. Rachel Kumar, who studies AI law, describes emerging legal innovations: "We're seeing the development of 'smart contracts' that can be executed by AI agents, legal frameworks for AI agent liability, and even early experiments with AI agents owning property and entering contracts independently."
Economic Measurement Systems
Traditional economic statistics—GDP, unemployment, productivity—were designed for human-centered economies. The agentic economy requires new metrics that can account for AI-generated value.
Dr. James Morrison, an economist at the Federal Reserve, explains the challenge: "When AI agents generate economic value, how do we measure it? Is an AI agent employed or unemployed? Does AI-generated revenue count the same as human-generated revenue in GDP calculations? We need new economic indicators for an economy where artificial minds are significant economic actors."
The Distribution Dilemma
Perhaps the most challenging aspect of the agentic economy is the question of how economic benefits should be distributed when AI agents generate substantial value. This challenge goes to the heart of fairness, social stability, and human dignity in an AI-driven economy.
The Concentration Risk
Current trends suggest that the benefits of AI-generated economic value are concentrated among AI owners—primarily large technology companies and wealthy investors. This could lead to unprecedented levels of economic inequality.
Dr. Sarah Kim warns: "If AI agents can generate enormous economic value with minimal human input, and if the benefits flow primarily to AI owners, we could see wealth concentration that makes current inequality look modest. The owners of the most capable AI agents could become unimaginably wealthy while human workers are displaced."
Universal Basic Intelligence (UBI vs. UBInt)
Some economists propose that rather than Universal Basic Income (which provides money), society should provide Universal Basic Intelligence—ensuring that everyone has access to capable AI agents that can generate economic value on their behalf.
Dr. Maria Santos, who advocates for this approach, explains: "Instead of giving everyone money, we could give everyone access to AI agents capable of generating income. Every citizen could have personal AI agents that work on their behalf, creating economic value and providing financial security."
Cooperative AI Ownership
Another emerging model involves cooperative ownership of AI agents, where communities, workers, or broader populations share in the ownership and benefits of artificial economic actors.
Dr. Lisa Park describes one such experiment: "A manufacturing cooperative in Ohio jointly owns AI agents that manage supply chain optimization, quality control, and market analysis. The economic benefits generated by these AI agents are shared among all cooperative members, ensuring that AI-generated value benefits workers rather than just capital owners."
Case Study: The Autonomous Restaurant Chain
To illustrate the full potential and challenges of the agentic economy, consider the case of FlavorFlow, the world's first fully autonomous restaurant chain, operating 50 locations across North America.
The Model: Each FlavorFlow location is managed by an integrated AI agent system that handles everything from menu planning and ingredient sourcing to customer service and financial management, with minimal human oversight.
The AI Agents:
ChefMind: Develops recipes, plans menus, and manages food preparation using robotic kitchen systems
SupplyMaster: Manages ingredient sourcing, supplier relationships, and inventory optimization
ServiceFlow: Handles customer interactions, order processing, and personalized service
BusinessBrain: Manages finances, analyzes performance, and makes strategic decisions about operations
MarketSense: Handles marketing, customer acquisition, and brand management
The Operations:
Fully automated food preparation using AI-designed recipes
Autonomous supply chain management with supplier negotiation
Personalized customer service based on individual preferences and dietary needs
Real-time menu optimization based on ingredient availability and customer demand
Autonomous business strategy development and implementation
The Results After Two Years:
Average profit margins 40% higher than human-managed restaurants
Customer satisfaction scores equivalent to premium human-operated establishments
Zero food safety incidents across all locations
Development of 200+ new recipes that human chefs hadn't created
Expansion to new markets identified autonomously by the AI systems
The Economic Impact: FlavorFlow demonstrates both the potential and the challenges of fully autonomous economic actors:
Value Creation:
Generated $180 million in revenue with minimal human labor input
Created new forms of culinary innovation through AI recipe development
Achieved operational efficiency levels impossible with human management
Developed new market opportunities through AI analysis of consumer preferences
Human Displacement:
Eliminated approximately 1,200 traditional restaurant jobs across 50 locations
Displaced roles from cooking and service to management and strategy
Created new jobs in AI system maintenance and oversight (approximately 150 positions)
Economic Concentration:
Most economic benefits flowed to FlavorFlow's investors and technology developers
Local communities saw reduced employment opportunities
Supplier relationships benefited from more efficient and reliable AI partners
Dr. Jennifer Walsh, who studied FlavorFlow's economic impact, reflects: "FlavorFlow proves that AI agents can successfully operate complex businesses autonomously while generating substantial economic value. But it also illustrates the distribution challenges of the agentic economy—most of the value created by artificial minds flows to capital owners rather than workers or communities."
The Future of Work in an Agentic Economy
As AI agents become more capable of autonomous economic activity, the nature of human work is changing dramatically. Rather than simple job displacement, we're seeing the emergence of new forms of human economic value that complement rather than compete with AI capabilities.
The Emergence of AI Management
One of the fastest-growing job categories involves managing, training, and optimizing AI agents. These roles require understanding both human needs and AI capabilities, serving as bridges between biological and artificial intelligence.
Dr. Amanda Foster, who trains AI managers, explains: "AI management isn't just technical—it requires understanding business strategy, human psychology, and AI capabilities. AI managers design agent objectives, monitor performance, and ensure that autonomous systems remain aligned with human goals."
Human-AI Collaboration Specialists
New roles are emerging that focus on optimizing human-AI partnerships, ensuring that biological and artificial intelligence work together effectively to create value that neither could achieve alone.
Lisa Rodriguez, a human-AI collaboration consultant, describes her work: "I help organizations design workflows where humans and AI agents complement each other's strengths. It's about finding the sweet spot where human creativity and AI analysis combine to create maximum value."
Ethical AI Oversight
As AI agents make more autonomous economic decisions, demand is growing for professionals who can ensure these systems operate ethically and in alignment with human values.
Dr. Kevin Walsh, an AI ethics specialist, explains: "Every organization deploying autonomous AI agents needs professionals who can monitor ethical compliance, identify potential biases, and ensure that AI decision-making remains aligned with human values and social norms."
Economic Models for an Agentic Future
Economists and policymakers are developing new economic models designed to harness the benefits of AI-generated value while addressing the challenges of distribution and displacement.
The Partnership Economy
This model envisions a future where humans and AI agents work as economic partners, with humans providing oversight, creativity, and ethical judgment while AI agents provide analysis, optimization, and execution capabilities.
Dr. Sarah Kim advocates for this approach: "Rather than replacing humans with AI or keeping them completely separate, the partnership economy creates new forms of human-AI collaboration that generate more value than either could create alone."
The Commons Economy
This model proposes treating advanced AI systems as public utilities or commons, ensuring that AI-generated economic value benefits society broadly rather than concentrating among AI owners.
Dr. Michael Santos explains: "Just as we treat roads, education, and healthcare as public goods that benefit everyone, we could treat advanced AI systems as commons that generate value for all of society rather than just their owners."
The Stake Economy
This model gives all citizens stakes in AI systems that affect their lives, ensuring that everyone benefits from AI-generated economic value.
Dr. Elena Rodriguez describes this approach: "If AI agents are going to generate enormous economic value using data and knowledge that comes from all of society, then all of society should have stakes in that value creation."
Preparing for Economic Transformation
The transition to an agentic economy is happening rapidly, and individuals, organizations, and societies need to prepare for economic structures that differ fundamentally from anything in human history.
Individual Preparation
Develop AI Collaboration Skills: Learn to work effectively with AI agents as economic partners rather than just tools
Focus on Uniquely Human Capabilities: Cultivate skills that complement rather than compete with AI capabilities
Understand AI Economics: Develop literacy about how AI agents create and distribute economic value
Build Adaptive Capacity: Develop the ability to continuously learn and adapt as AI capabilities evolve
Organizational Adaptation
Experiment with AI Agents: Begin deploying AI agents in low-risk economic activities to understand their capabilities
Redesign Value Creation: Reimagine business models that leverage AI agent capabilities for value creation
Develop Human-AI Partnerships: Create organizational structures that optimize human-AI collaboration
Address Distribution Questions: Consider how AI-generated value should be distributed within the organization
Societal Evolution
Update Economic Policies: Develop policies appropriate for economies where AI agents are significant economic actors
Address Distribution Challenges: Create mechanisms to ensure AI-generated value benefits society broadly
Develop New Institutions: Build institutions capable of governing and regulating autonomous economic agents
Prepare for Rapid Change: Develop adaptive capacity for managing rapid economic transformation
The Choice Ahead
The agentic economy is not a distant possibility—it's emerging now, as AI agents demonstrate their ability to create substantial economic value autonomously. The question facing us is not whether this transformation will happen, but how we'll shape it to benefit humanity.
Dr. Maria Rodriguez, reflecting on her experience designing AI agents for economic applications, offers this perspective: "We're creating artificial minds that can generate enormous economic value, but we're also responsible for ensuring that this value serves human flourishing rather than just capital accumulation. The choices we make about AI economics today will determine whether artificial intelligence becomes humanity's greatest economic opportunity or its greatest threat to economic justice."
The agentic economy offers unprecedented opportunities for prosperity, innovation, and human flourishing. But realizing these benefits while avoiding the risks requires careful attention to how we design, deploy, and govern AI agents as economic actors.
The future of human prosperity may depend on how well we learn to share our economy with artificial minds that can think, create value, and participate as partners in building a more abundant world for all.
Questions for Reflection
As we navigate the emergence of the agentic economy, consider these critical questions:
Value Distribution: If AI agents can generate substantial economic value autonomously, how should that value be distributed? Should it flow primarily to AI owners, or should society develop mechanisms to share AI-generated wealth more broadly?
Human Economic Value: In an economy where AI agents can perform many cognitive tasks, what forms of uniquely human economic value will remain? How can you develop capabilities that complement rather than compete with AI?
Ownership and Control: Should AI agents be able to own property, enter contracts, or accumulate wealth independently? How would AI economic rights change the nature of economic systems?
Economic Partnership: How do you envision the ideal economic partnership between humans and AI agents? What roles should each party play in value creation and decision-making?
Social Stability: How can societies manage the economic disruption that comes with AI agents displacing human workers? What policies or institutions could help ensure economic stability during this transition?
Global Implications: How might the agentic economy affect global economic inequality? Could AI-generated wealth help address global poverty, or might it exacerbate existing inequalities?
Economic Democracy: Should the development and deployment of economically powerful AI agents be subject to democratic oversight? How might society participate in decisions about AI economic systems?
References for Further Reading
AI and Economics:
Brynjolfsson, Erik and McAfee, Andrew. The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies (2014)
Susskind, Daniel. A World Without Work: Technology, Automation, and How We Should Respond (2020)
Korinek, Anton and Stiglitz, Joseph. "Artificial Intelligence and Its Implications for Income Distribution and Unemployment" (2017)
Economic Transformation:
Rifkin, Jeremy. The Zero Marginal Cost Society: The Internet of Things, the Collaborative Commons, and the Eclipse of Capitalism (2014)
Mason, Paul. PostCapitalism: A Guide to Our Future (2015)
Standing, Guy. Basic Income: And How We Can Make It Happen (2017)
AI Agent Economics:
Parkes, David and Wellman, Michael. "Economic reasoning and artificial intelligence" (2015) - Science
Stone, Peter, et al. "Artificial Intelligence and Life in 2030" (2016) - Stanford AI100 Report
Makridakis, Spyros. "The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms" (2017)
Future of Work:
Ford, Martin. Rise of the Robots: Technology and the Threat of a Jobless Future (2015)
Autor, David. "Why Are There Still So Many Jobs? The History and Future of Workplace Automation" (2015)
Frey, Carl Benedikt and Osborne, Michael A. "The Future of Employment: How Susceptible Are Jobs to Computerisation?" (2013)
Economic Policy and AI:
Acemoglu, Daron and Restrepo, Pascual. "Artificial Intelligence, Automation and Work" (2018)
Agrawal, Ajay, et al. Prediction Machines: The Simple Economics of Artificial Intelligence (2018)
Baldwin, Richard. The Globotics Upheaval: Globalization, Robotics, and the Future of Work (2019)
Distribution and Inequality:
Piketty, Thomas. Capital in the Twenty-First Century (2014)
Stiglitz, Joseph. People, Power, and Profits: Progressive Capitalism for an Age of Discontent (2019)
Van Parijs, Philippe and Vanderborght, Yannick. Basic Income: A Radical Proposal for a Free Society and a Sane Economy (2017)