What Economists Have Wrong About the Knowledge Economy

Economists and social theorists introduced the term "knowledge economy" in the 1960s and 1970s. It emerged as a response to the shifting economic landscape and the rise of knowledge and information as drivers of economic growth and competitiveness. The knowledge economy rang in the modern era of education, research and development, innovation, and intellectual property. Its prominence grew in the 1990s as technological innovation, intellectual capital, and globalization became crucial assets for economic development.

Before the knowledge economy, we were in a doing economy, where the traditional factors of production, such as land, labor, and capital, were the sole drivers of economic productivity. The new “knowledge” is a timestamp, or marker, of when the default production platform of that time was to be dethroned.

A big platform shift is upon us. 

Since the 1700s, we’ve had several major platform shifts. The invention of steam power in the 1700s allowed for the creation of machinery that could accurately cut metals used to build trains, boats, and other machinery. The 1800s brought in the era of mass manufacturing and production. Mills and manufacturing facilities began a burgeoning industrial complex, which popularized the concepts of business management, accounting, and financing through banks and stock markets. The term “white collar” was introduced to describe the nonproduction workers of business management, administration, and clerical jobs.

The scale of the economy created the need to run advanced calculations on large datasets to meet the industrial magnitude. Computing became a legitimate business practice in the late 1950s, first done by only elite and enterprise companies through teams of people and then scaled through the invention of advanced electronic calculators called computers. Although computers didn’t initially change how things were produced, they accelerated the scale at which a business could efficiently operate.

Computers led to the creation of the microprocessor, then the microchip. Microchips allowed for personal computers. And as chips got smaller, they allowed for communication (internet), mobility (Wifi), then smartphones and mobile devices (the digital age). Greater access to computing power meant that calculations could move beyond raw binary code to machine learning. Massive amounts of data were now being transferred, collected, and stored - enough data to build advanced machine-learning models that now power artificial intelligence tools. 

What’s the world beyond artificial intelligence? Robotics. Robots have simply been waiting on the sidelines for AI, which carries the promise that they can now learn the most complex tasks - select the correct bottle of prescription medication and package 20 doses, please. 

Are we now and forever in a knowledge economy?

I’ve been working with a team to produce a show on how AI and robotics will shape the future of work (to be released summer 2023!). While discussing the topic with prominent economists, I couldn’t help but think about how our decades-long use of “knowledge economy” could limit our thinking. Although knowledge, or intelligence, in the information sense, has become the most valuable resource from an economic perspective, it is a fallacy to think that value creation is forever pinned to it.

For instance, take the dramatically evolved profession of sales, which has been completely changed by technology. 20 years ago, sales required a healthy dose of raw talent, business acumen, and product knowledge. Now, thanks to CRM systems, sales at most companies is essentially a virtual assembly line: email, voice mail, follow-up call, collect information, schedule next call, demo product, propose a price, then close the deal. With minimal education and training, a worker can start at even the most advanced software or technology company as a business development representative (BDR) and grow into a senior sales role within a year. Is this knowledge work? Not at all. Sales as an acumen initially required so much knowledge (knowing), and it has now evolved to require more process, precision, and execution prowess (doing). This is just one of many examples of a similar shift happening to jobs, departments, and careers.



As innovations proliferate, the economic value base shifts from knowing to doing. And future platform shifts are a direct response to it:

Steam Power (knowing) - this was the scientific innovation of its time. Allowing manufacturing and transportation to explode. 

Mass Manufacturing (doing) - steam power increased manufacturing output and introduced the problem of scale. Mass manufacturing was a direct response to that. 

Computing (knowing) - mass manufacturing created the need for mass computation, finance, and production forecasts. Computers met the need by offering computation of the most complex and sophisticated modeling of its time. 

Microchip (doing) - now that computing was business ubiquity, the microchip made it possible for computers to impact all aspects of business and life. 

Internet (knowing) - the literal manifestation of the phrase “knowledge is power,” the first internet didn’t allow you to do much, but it allowed you to know a whole lot. Internet Everywhere, aka Wifi, answered the concerns of how to work and input data on the go. 

Smartphone (doing) - Smaller is more convenient. Phones and computers merged to revolutionize communication and commerce on your device, 24/7, from anywhere.

AI (knowing) - Massive data sets require massive computational power, and large language models (LLMs), to cut noise, make accurate predictions, and optimize communication.

Robotics (doing) - Predictability will reach a point where virtual assistants will turn three-dimensional and optimize every component of our lives.

Economic productivity and value creation are the result of the cycle occurring between knowing and doing as technology evolves. 

It’s only logical that a period of focus on knowledge and information would be followed by a period of doing something with it. As big innovations enter the world, their application and impact are first unknown. Economies naturally focus on building knowledge around it and, eventually, shift to doing things with that knowledge. In this way, each major platform change is a response to the last. The result is a cycle back and forth between knowing and doing economies. And as technology has evolved, the cycle accelerates. 



Using the competing values framework to explain innovation evolution and the knowing-doing cycle. 

The competing values framework is a model for understanding and characterizing organizational cultures developed in 1983 by Robert E. Quinn and Kim S. Cameron. The model is based on the idea that four competing values explain organizational effectiveness - adaptability, internal integration, external focus, and internal focus.

At Lever Talent, we use the framework to assess an organization's culture and to identify areas where the culture can be improved. It’s valuable for understanding and managing through business transformation and evolution, and I’ve found it quite useful in explaining the cycle of innovation too. 



Let’s use the competing values framework to explain business evolution. Start-ups, or early-stage businesses, function in the upper right quadrant. They need flexibility, and all the focus is on external factors to figure out what product to build, find product-market fit, and start to land new customers - we’ll call this the innovation quadrant. Then, once the product is defined and product-market fit is found, the company starts to scale. This requires a new results orientation and disciplined approach - now a growth-stage company. Once growth starts to plateau, the business reaches a stage of maturity. Now optimizing internal processes and precision become the focus as the company enters new markets and expands its product lines to capture as much market share as possible. Then it's maintenance mode, which is all about keeping market share, deepening existing relationships, and ensuring the best experience for team members and customers. At this point, the only path forward is reinvention, which gets back to innovation. And the cycle goes on until the business dies. Most businesses fail to make it one cycle through - this is Clay Christenson’s Innovator's Dilemma. 

I recently discovered the competing values framework also explains macro-evolution and market adoption of innovation.

Here are the 4 stages of innovation as explained by the competing values framework:

1. Theoretical: When an innovation is introduced, businesses get to work tinkering with it, testing its limits, and developing theories for application.
2. Functional: Once theories are well defined, they turn into methodologies. Engineers then take these methodologies to build features and functions to solve problems.
3. Social: A collection of features is eventually proven to solve a problem. This is when his innovation crosses a production threshold, and businesses work to design products that align with social norms and fit into our everyday lives.
4. Emotional: Once the innovation is well integrated into our lives, the design becomes more detailed and targeted to people's specific needs or applications. Eventually, reaching a point where the experience taps our emotions. This is where creativity prevails to leverage this innovation for things it was never intended to do. This is what we call art. And once it passes this threshold, art changes how we perceive innovation and the world around us - giving way to new innovations, and the cycle continues.

Innovation is cyclical, and the shifting between knowing and doing only seems to accelerate with each major platform shift. Although the functionality of AI is very much at the forefront of the news cycle, AI is still very much in the Theoretical quadrant. It took almost 10 years for AI to reach its current state.

It’s unclear to me if the state of innovation accelerates as it passes through each quadrant or how quickly the cycle can occur - experts have proven it will never be instantaneous. One thing is clear - once the cycle has started, there is no going back.

How can we use the innovation cycle to be better leaders and business people in the age of AI? 

Here’s how I use this framework to organize my thoughts around product development, investing, business strategy, and career development in the age of AI:

• Product Development - you can develop an AI product or service to aid the entire cycle (ChatGPT, Bard), target a specific problem pace or user within a quadrant (Jasper AI), or assist companies or people converting across a threshold (formation of methodologies, outsourced production, design services, research consulting).
• Investment Thesis - jumping into the AI gold rush may not be for everyone. You can decide only to play in spaces where your interests and expertise lie (like the product development spaces mentioned above). This means you may hold until AI tech evolves to your sweet spot.
• Business strategy - every board member is asking their operating managers, “What's your AI strategy?” this framework can be used to set proper expectations. Unless you're an AI products company, you probably want to see where more chips fall and test use cases before setting too much structure.
• Career growth - you have two choices. (1) Ride the cycle, turn from AI early adopter, to AI master engineer, to AI designer, and then ride your career out as a top AI executive. Or, (2) run in place and cycle. For instance, hang in the Functional stage as a career AI engineer who knows all AI methodologies inside and out.