The Evolution of Knowledge Work

The current turbulent period of labor we're experience is not a new phenomenon unique to the data age. It's just "business knowledge work accelerating to a new pace".

If you replace the word "AI" with "tool", and change the setting to a different era, say the 1800's, then you can reframe this scenario as what textile workers went through during the mechanization phase of their industry. Generative AI has set off a lot of alarmist reactions such as "many sections of labor are going away" or "agents will replace people". I argue that these reactions are based in "white collar existential dread".

The major difference between the mechanization of textiles and the present times is that the market disruption is not in the physical labor class, but in the knowledge work labor class. (The white collar professional class is not used to dealing with this type of livelihood threat, making things seem even more dire.)

Generative AI represents the latest evolution in the long trajectory of knowledge work transformation, following the same structural pattern seen across centuries of technological advancement—from mechanical looms to mainframes, and now to large language models.

These innovations do not fundamentally alter the purpose of knowledge work (to transform information into advantage) but rather shift the tools and pace required to remain competitive.

More specifically: jobs are not going away, the sky is not falling.

However, the tools and skills required to perform cognitive labor are undergoing transition and this is causing unease in knowledge work-land.

How Tools Shaped Civilization

As with prior technological shifts, the organizations that succeed are those that effectively harness new capabilities to improve decision-making, operational efficiency, and informational leverage, while others fall behind.

The stakes remain the same; only the means have changed. Our tools have always been evolving with us as labor has shifted across different eras.

Throughout history, the development of tools has consistently driven structural transformation by enhancing human cooperation, organizing labor, and expanding the scale of productive activity. From early stone tools and fire to the invention of writing, each technological advance not only improved survival but enabled the coordination of increasingly complex tasks across larger groups. These tools served as more than instruments—they were foundational to trust, governance, and collaboration within emerging societies.

Writing, in particular, marked a turning point in knowledge work. It enabled civilizations to efficiently manage resources, codify laws, and maintain administrative control over expanding populations. This strategic use of information infrastructure laid the groundwork for urban growth and societal complexity. Similarly, the printing press later democratized access to knowledge, multiplying productivity by automating communication and reducing reliance on manual transcription.

Today’s advances in data platforms and automation tools—such as large language models—follow this same trajectory. Rather than replacing labor outright, these technologies reduce manual effort, accelerate insight generation, and shift cognitive work toward higher-value decision-making. Much like the tools of the past, modern systems serve as catalysts for productivity and growth, reinforcing a long-standing pattern: every major leap in economic capability has been underpinned by a leap in tool sophistication.

We're always trying to stay one step ahead of the competition in most things in life. That is part of the human experience.

The Treadmill of Technology

Throughout history, technological advancements have consistently expanded human productivity by improving coordination, information management, and task specialization. From primitive tools to the printing press, each major innovation has reshaped how societies organize labor and allocate effort—enabling shifts from subsistence activities to more strategic, value-generating pursuits. This ongoing evolution reflects a broader pattern of structural transformation in which tools fundamentally change the nature of work.

Modern developments in data platforms, artificial intelligence, and automation continue this trajectory. Rather than replacing work outright, these tools elevate the baseline of what individuals and teams can achieve, streamlining routine tasks and freeing capacity for higher-order decision-making. Large language models, for instance, represent a new tier of information processing infrastructure, enhancing the scale and speed at which organizations can analyze data, generate insights, and respond to complex challenges.

Recent concerns among knowledge workers regarding the rise of large language models mirror the historical response of skilled textile workers during the Luddite movement, who viewed early industrial automation as a threat to job quality, pay, and professional identity. Just as 19th-century artisans opposed machinery that enabled lower-cost production with less-skilled labor, modern professionals express apprehension toward AI tools that may displace specialized tasks or devalue domain expertise. In both cases, the anxiety stems not from opposition to innovation itself, but from the disruptive impact of technology on established roles, economic security, and the balance of power between labor and capital.

Technological disruption is neither novel nor anomalous. It is a game we've always played in which tools and labor co-evolve.

Today’s transformations in knowledge work mirror those of earlier epochs, underscoring the importance of adapting organizational capabilities to harness emerging technologies, maintain competitiveness, and unlock new layers of economic value.

It is the Red Queen's Game.

Augmenting Reasoning to Accelerate Knowledge Work

The current era of knowledge tool evolution is about augmenting our existing workflows with enough "semantic glue" to be flexible and augment tasks that were previously difficult to automate. Don't focus on middleware or abstractions (I believe the idea of "Agents" will fail eventually), but instead on ways to augment your existing roles in ways that allow them to get to results faster than their peers in your industry.

Ignore the people who claim the sky is falling and that you can get rid of your teams and that AI is taking all the jobs.

This is just a bad narrative that has gotten loose and sounds good enough because it preys on the white collar worker's sense of existential dread. It's FUD at its peak game.

You shouldn't look for ways to get rid of people because good people are important assets that are hard to find and keep. You want to augment the reasoning in their knowledge workflows such that your most important people's skills are amplified and they can outperform similar talent in your industry. You want your own people to become the industry pace setters for performance in your market through more efficient information transformation with tools that are already familiar to them.

Recent advancements in generative AI mark a pivotal shift in the evolution of knowledge work, paralleling past disruptions in manual labor during the Industrial Revolution. Just as early automation redefined the roles of skilled artisans, today’s AI tools are reshaping cognitive work by accelerating reasoning and lowering the barrier to sophisticated analysis. These tools don’t eliminate labor—they redefine its structure, amplifying human potential by turning raw information into actionable insight more quickly and at greater scale.

Generative AI enables natural language interaction with data and systems, removing friction from workflows and making complex processes accessible to broader teams. This increases organizational coordination, enhances decision-making speed, and raises the strategic value of human attention. However, to sustain this productivity, organizations must maintain strong foundations in data quality, transparent system design, and user oversight.

As these tools become table stakes, the competitive edge shifts from AI adoption to architecture—how well the organization supports augmented reasoning through flexible workflows, clear governance, and empowered users. The goal is not autonomy for its own sake, but scalable augmentation that accelerates output while preserving trust and alignment.

In this way, AI becomes less about replacing jobs and more about enabling teams to move faster, make better decisions, and navigate complexity with greater confidence. Generative AI functionality will become integrated in most tools that process data in some form and the integration will be subtle and all around us --- much how your car warns you before a collision already.

My point in all of this is that jobs are not going anywhere but there will be some labor market turbulence at times --- in the same way there was in previous historical economic eras.

Those that claim otherwise risk "tilting at windmills" -- and nobody has time for that in The Red Queen's game.