The article discusses the transition from a knowledge economy to an allocation economy driven by AI. As AI tools like ChatGPT become prevalent, traditional knowledge-based skills, such as summarizing, are increasingly performed by machines, shifting human roles from makers to managers who allocate resources and oversee AI-generated work. This new economy will require individuals to develop skills such as articulating a coherent vision, assessing AI capabilities, and knowing when to engage with tasks. The author argues that while this change is beneficial, it may also create a divide between those able to leverage AI tools effectively and those who cannot, emphasizing the importance of integrating society as a whole into this evolving landscape.
| name | description | change | 10-year | driving-force | relevancy |
|---|---|---|---|---|---|
| Transition to Allocation Economy | Shift from knowledge economy to allocation economy emphasizing resource management over knowledge. | Moving from value based on knowledge to value based on resource allocation. | In a decade, most professionals will focus on managing AI resources rather than traditional knowledge roles. | Advancements in AI technology enabling efficient task management and resource allocation. | 5 |
| Rise of Model Managers | Emergence of ‘model managers’ who allocate work to AI instead of managing human teams. | Transitioning from human resource management to managing AI capabilities and outputs. | In the future, all employees will need to develop skills in managing and evaluating AI models. | The increasing integration of AI in everyday work processes and decision-making. | 4 |
| Expanded Skill Sets for All Employees | Even junior employees will be expected to adopt managerial skills to work with AI. | Shifting from individual contributor roles to roles requiring resource management skills. | Future workforces will have a baseline expectation of managerial competencies among all employees. | The democratization of AI tools making resource management accessible to everyone. | 4 |
| Importance of Articulating Vision | The need for clear vision and coherence in delegating tasks to AI models. | From vague task assignments to precise, vision-driven management of AI tasks. | Future professionals will prioritize clear communication and vision articulation for effective AI collaboration. | The necessity for well-defined goals to ensure AI outputs align with organizational objectives. | 3 |
| Evaluation Skills for AI Models | Need for skills to evaluate AI models’ effectiveness and suitability for tasks. | From evaluating human talent to evaluating AI models for task execution. | Workforce will require a new set of evaluation skills focused on AI model performance and fit. | The rise of AI models necessitating human oversight and judgment for optimal task allocation. | 4 |
| Potential for Economic Stratification | Economic divide between those who can effectively utilize AI and those who cannot. | From a knowledge-based economy to one where AI proficiency defines economic success. | A growing gap between high-skilled AI users and those unable to adapt may widen economic divides. | The unequal access to AI education and resources influencing career advancement opportunities. | 5 |
| Non-linear Adoption of AI Tools | Adoption of AI tools will vary across sectors based on various barriers. | From rapid tech adoption to a more gradual integration in different sectors. | Some industries will lag in AI integration, leading to uneven economic impacts and opportunities. | Cultural, regulatory, and operational barriers will influence the speed of AI adoption in different sectors. | 4 |
| Shift in Management Skills | Management skills will evolve to include AI oversight and resource allocation. | From traditional management focused on human resources to managing AI outputs. | Management training will incorporate AI evaluation and resource allocation as core competencies. | The need for effective collaboration between humans and AI in achieving organizational goals. | 4 |
| name | description | relevancy |
|---|---|---|
| Skill Disparity | As AI takes over summarizing and knowledge tasks, there may be a heightened skill gap between those who can manage AI and those who cannot. | 4 |
| Economic Stratification | The shift to an allocation economy may create a divide between high-skilled workers who can leverage AI and those left behind, exacerbating economic inequality. | 5 |
| Managerial Skills Demand | The requirement for all workers to develop managerial skills in a transitioning economy may not be feasible for everyone, leading to workforce challenges. | 4 |
| Quality of Work | Dependence on AI for summarizing and management might diminish the quality of human creativity and critical thinking, impacting innovation. | 4 |
| Inertia to Change | Slow adoption of AI in certain sectors could hinder overall economic growth and adaptation to new technologies, potentially leaving industries behind. | 3 |
| Loss of Human Jobs | As AI assumes more roles traditionally occupied by humans, there is a concern about job displacement across various sectors, leading to unemployment. | 5 |
| Dependence on AI | Increased reliance on AI for decision-making can lead to a lack of critical thinking and problem-solving skills among workers. | 4 |
| Oversight Challenges | Model managers will need to balance oversight of AI-driven tasks, raising questions about how much detail to engage without over-managing. | 4 |
| name | description | relevancy |
|---|---|---|
| Handing off summarizing to AI | Individuals are increasingly delegating summarizing tasks to AI tools like ChatGPT, redefining intelligence by focusing on oversight rather than execution. | 5 |
| Transition from maker to manager | Workers are evolving from executing tasks to managing and allocating resources, especially AI models, to enhance productivity. | 5 |
| Developing a coherent vision | The ability to create and articulate a clear vision becomes crucial as individuals manage AI, ensuring tasks align with goals. | 4 |
| Refining taste and evaluation skills | As AI takes over routine tasks, individuals will need to develop a refined taste and evaluation skills to guide AI outputs effectively. | 4 |
| Model management skills | Future workers will require skills to manage AI models, akin to current management roles, focusing on resource allocation rather than human supervision. | 5 |
| Learning to evaluate AI capabilities | Individuals will need to assess which AI models to use for specific tasks, mirroring traditional talent evaluation in management. | 4 |
| Understanding the balance of oversight | Emerging managers must learn when to intervene in tasks performed by AI, balancing oversight without micromanaging. | 4 |
| Increased accessibility to management skills | As AI becomes more integrated into workflows, management skills will be democratized, enabling broader participation in leadership roles. | 5 |
| name | description | relevancy |
|---|---|---|
| Generative AI | Advanced AI models like ChatGPT that can summarize, generate text, and assist in creative processes. | 5 |
| Model Management | The practice of allocating tasks to AI models effectively, requiring new management skills and understanding of AI capabilities. | 4 |
| AI-Assisted Decision Making | Using AI to aid in evaluating talent and making strategic decisions in resource allocation. | 4 |
| Language Models | AI systems that can understand and generate human-like text based on prompts, enhancing communication and productivity. | 5 |
| name | description | relevancy |
|---|---|---|
| Shift from Knowledge Economy to Allocation Economy | The economy is transitioning from valuing knowledge to valuing resource allocation and management skills, driven by AI capabilities. | 5 |
| Widespread Adoption of AI in Management | As AI tools become prevalent, even junior employees will need to develop management skills to allocate tasks effectively to AI models. | 4 |
| Bifurcation of the Workforce | A divide is forming between skilled workers who can leverage AI and those who cannot, potentially widening economic disparities. | 5 |
| Importance of Articulating Vision and Taste | In the allocation economy, the ability to articulate a clear vision and taste will become crucial for effective management and task allocation. | 4 |
| Evaluation of AI Models as a New Skill | The ability to evaluate and select appropriate AI models for tasks will emerge as a vital skill in the allocation economy. | 4 |
| Potential for Increased Creative Potential | The democratization of management skills through AI may enhance the creative potential of individuals across various sectors. | 3 |
| Challenges in Maintaining Human Insight and Innovation | As reliance on AI grows, there may be concerns about maintaining human creativity and intuition in decision-making processes. | 4 |
| Pace of AI Adoption and Economic Change | The transition to an allocation economy will be gradual, influenced by inertia, regulation, and societal readiness for change. | 3 |