The Shift From Knowledge To Allocation Economy

“In the age of AI, every contributor becomes a curator”

Time doesn’t flow in a straight line; it undulates and twists like a complex tapestry. It loops back, and if you pay close attention, you can catch glimpses of the future interwoven with the present. (This is the secret of visionaries: They don’t predict the future—they discern it, pull it out of the folds of time, and wear it as a mantle.)

Recently, I believe I’ve glimpsed a fragment of the future, and I’d like to share it with you. Last week, I explored how AI tools like ChatGPT have reshaped my understanding of intelligence and the world around me. I’ve come to see ChatGPT as a master of summarization, and once I made that connection, I started to notice summarization everywhere: in the code I write (which distills information from StackOverflow), in the emails I send (summarizing meetings), and in the articles I compose (summarizing books and ideas).

Summarizing was once a critical skill—an invisible part of what we called “intelligence.” But now, with AI handling summarization, I’ve redefined my role. I’ve transitioned from being the summarizer to being the director of summarization.

“AI is an abstraction layer over lower-level thinking.”

Much of that lower-level thinking involves summarizing.

This is where the non-linearity of time and catching the future in the present come into play. By observing how we use AI today, we can forecast how our work lives may evolve in the coming years.

The End of the Knowledge Economy

We currently operate in a knowledge economy. The value you bring is derived from what you know and your ability to apply that knowledge. This has been driven by personal computers and the internet since the 1970s.

But what happens when the skill of knowing and applying knowledge can be done by computers as efficiently as, or faster than, humans? We will shift from being makers to being managers—overseeing work, allocating resources, and ensuring quality. This marks the transition from a knowledge economy to an allocation economy. Your value will no longer be determined by how much you know but by how effectively you can allocate and manage resources to achieve goals.

Today, managers embody this role. There are about 1 million managers in the U.S., approximately 12% of the workforce. They excel in evaluating talent, managing without micromanaging, and estimating project timelines. However, individual contributors—those who currently do the actual work—don’t need these skills.

In the allocation economy, even junior employees will assume managerial roles, becoming "model managers." Instead of managing humans, they’ll oversee AI models, ensuring tasks are completed accurately. They will need the same skills as today's human managers, albeit adapted to a new context.

From Contributor to Curator

Here are key qualities today’s managers possess that will become essential for tomorrow’s model managers in the allocation economy:

A Coherent Vision

Managers today need a clear, articulate, and purposeful vision. Model managers will need the same ability. The more precise the vision, the better AI models will execute tasks. Crafting such a vision is a skill honed over years, and language models can assist in developing this proficiency. For instance, when creating a marketing campaign, a manager with a coherent vision can guide the AI to generate targeted content that resonates with the audience. The clearer the direction, the more impactful the output. This precision in vision will be crucial for model managers to ensure AI tools deliver high-quality results consistently.

A Clear Sense of Taste 

Effective managers know what they want and can communicate it clearly. Model managers will face a similar challenge. Well-defined taste leads to better outcomes from AI models. Fortunately, language models can help humans refine their preferences, making this skill more accessible. Imagine an editor working with AI to refine a piece of writing. A strong sense of taste allows the editor to guide the AI in enhancing the content’s tone, style, and substance. This collaboration ensures that the final piece not only meets but exceeds expectations. Model managers will need to cultivate this discernment to effectively oversee AI-generated work.

The Ability to Evaluate Talent 

Hiring is critical for managers, as the quality of output reflects the skills of the team. Model managers will need to discern which AI models are suitable for specific tasks and evaluate new models swiftly. This skill will likely be easier to acquire, as AI models are more readily testable than human employees. For example, a model manager working in content creation will need to evaluate various AI tools for tasks like copywriting, graphic design, and social media management. By testing and comparing outputs, the manager can select the best tools to form a cohesive and efficient workflow, ensuring optimal productivity and quality.

Knowing When to Get into the Details 

Good managers balance oversight with delegation. They know when to intervene and when to let their team take charge. Model managers will need to develop this intuition as they oversee AI tasks, ensuring alignment with organizational goals. AI will assist by providing timely checks, easing this transition. Consider a project manager overseeing a complex AI-driven marketing campaign. They must know when to dive into the details, such as refining a specific ad copy or analyzing the performance metrics of an AI-generated social media post. This selective involvement ensures that the project stays on track while maintaining high standards.

Is the Allocation Economy Beneficial?

The shift from a knowledge economy to an allocation economy won’t happen overnight. Initially, AI will replace micro-skills rather than entire tasks. Full integration will take time, and many sectors may lag due to inertia, regulation, risk, or brand identity. This gradual change is positive. It allows time for adaptation. The slow transfer of cognitive tasks to machines is part of a long-standing trend. In his book Average Is Over, economist Tyler Cowen predicted a divide in the economy driven by intelligent machines, where those who complement computers thrive while others may struggle.

Generative AI extends this trend. Those adept at using AI will gain a significant advantage. Management, once a skill for a select few, will become widespread as AI democratizes the role of manager, enhancing everyone’s creative potential.

Consider the impact on education: Students trained in AI tools and model management will be better prepared for the workforce, equipped with skills that are increasingly in demand. This shift will necessitate changes in educational curricula, emphasizing AI literacy and management skills from an early stage.

Moreover, businesses will need to adapt by providing continuous learning opportunities for employees to stay current with AI advancements. Companies that embrace this shift and invest in their workforce will be better positioned to thrive in the allocation economy.

It will be our collective responsibility to ensure that as we embrace these powerful tools, we bring the entire economy along for the journey. This involves fostering an inclusive environment where all individuals have the opportunity to develop these new skills and contribute meaningfully to the evolving economy.

→ Got any other questions? Feel free to reach out via email