Understanding AI and the Future of Human Work: A Conversation with Michael Casey
The Fundamental Misunderstanding: AI Doesn’t Think Like Humans
One of the most critical misconceptions about artificial intelligence today is the belief that AI agents actually “think” in any way comparable to human consciousness. Michael Casey, Chairman of DAIS Global and former Chief Content Officer at CoinDesk, draws a clear distinction that challenges our anthropomorphic assumptions about these technologies. AI systems generate language through probabilistic calculations—they predict the most likely next word or response based on patterns in their training data, not through intentional thought processes driven by consciousness or understanding.
When AI agents communicate with each other or with humans, they’re essentially sophisticated pattern-matching engines operating without genuine intent. As Casey emphasizes, “intent is a very conscious thing,” something fundamentally tied to human awareness and subjective experience. This distinction isn’t merely philosophical hair-splitting; it has profound practical implications for how we develop, deploy, and regulate AI technologies. When we mistakenly attribute human-like thinking to AI, we risk making poor decisions about where and how to implement these systems. We might overestimate their capabilities in situations requiring genuine understanding, empathy, or creative problem-solving. Conversely, we might underestimate the dangers when these systems operate in critical areas where their inability to truly comprehend context and nuance could lead to harmful outcomes. Recognizing that AI operates through logical probability rather than conscious intent is essential for responsible development and helps us maintain realistic expectations about what these technologies can and cannot achieve.
The AI Bubble and Breakthrough: Two Realities Existing Simultaneously
The current state of AI development presents a paradox that Casey identifies with clarity: we are simultaneously experiencing both a speculative bubble and genuinely remarkable technological advancement. This dual reality mirrors patterns seen in previous technological revolutions, where legitimate breakthroughs become entangled with market hype and overinflated expectations. The situation feels familiar to those who witnessed the cryptocurrency boom, particularly the explosive growth of decentralized finance (DeFi) with its concept of composability—building complex systems by combining simpler components.
AI development is following a similar trajectory, where different models and applications can be layered and combined to create increasingly sophisticated systems. This composability is driving rapid innovation and creating new opportunities across industries. However, the hype surrounding AI can obscure both its genuine progress and its real limitations. Market speculation tends to accompany technological breakthroughs, sometimes driving investment and development, other times creating unsustainable expectations that eventually collapse. Understanding the nuances of AI development—recognizing both the authentic advances and the exaggerated claims—becomes crucial for investors, policymakers, and society at large. We need to assess AI’s potential with clear eyes, acknowledging that while we’re witnessing transformative technology, not every application will deliver on its promises, and the path forward will likely include both spectacular successes and notable failures. This balanced perspective helps us navigate the excitement without losing sight of the practical challenges and ethical considerations that accompany these powerful new tools.
The Disruption of Work: How AI Is Reshaping Job Markets and Economic Structures
The impact of artificial intelligence on employment is already visible and accelerating, fundamentally reshaping job markets and economic structures in ways that will define the coming decades. Casey notes that entry-level positions in fields particularly vulnerable to large language models—such as content creation, basic coding, data entry, and customer service—are already experiencing a slowdown in hiring. Companies are increasingly turning to AI to handle tasks that previously required human workers, particularly those involving pattern recognition, routine decision-making, and standardized communication.
This shift creates what Casey describes as a “disorienting, destabilizing, chaotic picture” for workers and economies. The traditional career ladder, where entry-level positions served as training grounds for more advanced roles, is being disrupted. Young professionals entering the workforce find themselves competing not just with other humans but with AI systems that work tirelessly, don’t require benefits, and continuously improve through machine learning. However, Casey offers an important counterpoint: jobs requiring genuine human connection, empathy, collaboration, and the navigation of complex social dynamics will prove more resilient to automation. Healthcare workers who provide comfort to patients, teachers who inspire students, therapists who guide people through emotional challenges, and creative professionals who bring unique human perspectives to their work all possess qualities that current AI cannot replicate.
The integration of AI into the economy doesn’t simply mean some jobs disappear while others remain unchanged. Instead, we’re likely to see a transformation in the nature of work itself. Casey envisions fewer traditional corporate knowledge work positions and more people operating as independent contractors, leveraging AI tools to amplify their capabilities rather than competing directly with them. This shift toward a gig economy, where the concept of a “job” becomes increasingly obsolete, requires individuals to cultivate distinctly human skills—empathy, creativity, ethical judgment, and the ability to forge genuine connections. The economic structures supporting this transition remain uncertain, raising questions about income distribution, social safety nets, and how we measure and reward value in an AI-augmented economy.
The Emergence of “Proof of Control”: Governing AI in a Complex World
As AI systems become more autonomous and integrated into critical business and governmental functions, a new technological category is emerging to address fundamental questions of control and accountability. Casey identifies “proof of control” as a rapidly developing field driven by the demands of compliance officers, corporate boards, and regulators who need assurance that AI agents truly operate on behalf of their designated principals. The question “How do we know this agent is operating truly on my behalf?” becomes increasingly urgent as AI systems make consequential decisions.
This is where cryptography and blockchain technology offer valuable solutions. These technologies provide verifiable proof of control over AI systems, creating transparent audit trails that demonstrate who authorized what actions and when. This capability addresses regulatory requirements while also building trust in AI systems operating in sensitive domains like finance, healthcare, and government services. The emergence of proof of control technology represents a crucial development in AI governance, creating mechanisms to ensure human authority over increasingly sophisticated automated systems.
Casey emphasizes that sovereignty in the AI context should empower individuals and organizations rather than creating closed systems controlled exclusively by nation-states. The concept of sovereignty needs localization—whether applied to individuals, companies, or communities—ensuring that mechanisms exist to define and maintain human authority over AI agents. This governance framework becomes essential as we deploy AI systems with greater autonomy. Without clear structures establishing human control and accountability, we risk creating systems that operate according to their training parameters without meaningful oversight, potentially pursuing goals that diverge from human values or interests. The development of proof of control technologies isn’t merely a technical challenge but a fundamental requirement for responsible AI deployment in a democratic society that values human agency and accountability.
Navigating the Posthuman Economy: AI, Robots, and the Future of Value
Casey introduces the concept of a “posthuman economy”—not a world where humans become irrelevant, but one where AI agents and robots take on roles traditionally held by people, fundamentally transforming economic relationships. In this emerging economy, billions of AI agents might conduct transactions, manage resources, and coordinate activities at speeds and scales impossible for humans. This doesn’t eliminate human value exchange, but it does shift the nature and frequency of human-to-human economic transactions.
In this posthuman economy, Casey suggests that traditional concepts of money may evolve dramatically. When AI systems and robots handle most routine production and service delivery, creating relative abundance in material goods, the economic constraints shift from material scarcity to questions of coordination, energy, and access to machine intelligence itself. Casey proposes that machine intelligence might become the primary unit of exchange in this economy—a radical reimagining of currency where computational capacity and AI capabilities represent tradable value. Different forms of currency might serve distinct functions: Bitcoin might act as “civilizational memory,” a store of value that transcends individual AI systems or human lifetimes, while new tokens representing machine intelligence facilitate day-to-day transactions among AI agents.
This vision raises profound questions about human participation in such an economy. If machines create abundance and handle most economic transactions among themselves, what role do humans play? Casey suggests that real human value increasingly comes from collaborative social interactions rather than transactional exchanges—from the ways we collectively create meaning, establish social norms, and experience subjective consciousness together. The things machines cannot replicate—empathy, subjective experience, moral judgment, creative inspiration—become the distinctly human contributions to this new economic landscape. Energy remains a crucial constraint even in a posthuman economy, as AI and robotic systems require substantial power to operate. The competitive dynamics among AI systems, built through token reward structures to compete on outcomes, create an economic framework where machine intelligence systems trade and cooperate to achieve their programmed objectives. Understanding these emerging economic structures helps us prepare for a future where human work looks dramatically different from today’s employment landscape.
Preserving Human Values in an AI-Transformed World
As we navigate these profound technological and economic transformations, Casey emphasizes that the real risk lies not in AI’s capabilities but in how we respond to them. Anthropomorphizing AI—treating it as if it thinks and intends like humans—leads to poor decision-making and misplaced trust. We must recognize AI’s limitations, particularly in handling edge cases and situations requiring nuanced judgment. For mission-critical applications, companies must ensure that marginal cases receive appropriate attention, which often means maintaining human oversight.
The societal response to AI integration represents a significant trend to watch. As more people experience job displacement or transformation, backlash against AI technologies is inevitable. This resistance won’t necessarily be irrational or regressive—it may represent legitimate concerns about economic security, human dignity, and social cohesion that deserve serious consideration. The challenge is channeling this response constructively, developing policies and systems that preserve human agency and welfare while allowing beneficial AI applications to flourish.
Casey’s vision ultimately emphasizes human empowerment rather than replacement. In the evolving economy, the concept of “jobs” as we’ve known them becomes obsolete, replaced by more fluid arrangements where people leverage AI tools to amplify their uniquely human capabilities. Success in this environment requires cultivating empathy, genuine human connection, collaborative social skills, and the capacity for meaning-making that machines cannot replicate. The future isn’t about humans competing with AI on AI’s terms—processing information faster or executing routine tasks more efficiently—but about leaning into what makes us distinctly human. By understanding both AI’s capabilities and limitations, establishing appropriate governance frameworks through technologies like proof of control, and focusing on developing irreplaceable human qualities, we can navigate toward a future where technology serves human flourishing rather than diminishing it.
