#15 The specialization of work: from traditional crafts to the AI Revolution
To understand how AI will evolve first we have to understand the history of work
Thousands of years ago, humanity began a process of labor specialization that dramatically altered how societies were organized and grew.
This process started slowly but steadily, marking the transition from a world of generalists to one where individuals took on more defined, specialized roles.
Today, we are witnessing a similar transformation, but this time driven by artificial intelligence (AI).
Instead of AI becoming a universal solution, as many once predicted, it is following a trend toward increasing specialization.
Ilya Sutskever, a globally recognized AI keynote speaker, stated at NeurIPS last week that “next-generation models are going to be agentic in real ways”.
Just as traditional crafts evolved into distinct occupations, AI agents are now carving out specialized niches, each performing specific tasks in the labor market.
The start of specialization in ancient times
To understand how we arrived at this point, we must first look back at the past.
In the early civilizations, such as Mesopotamian, Egyptian, and Chinese, society was primarily agrarian and based on local production.
For millennia, most people had multifaceted roles: they farmed, hunted, gathered, and also performed certain craft tasks. However, as cities grew and economies diversified, the need to specialize individuals in specific trades arose.
This process of specialization began around 3000 BCE, with the invention of writing and the construction of large infrastructures, such as the pyramids in Egypt or temples in Mesopotamia. Here, occupations such as bricklayers, carpenters, blacksmiths, fishermen, and potters began to emerge.
The specialization in these trades was made possible by several factors:
Division of labor: As agriculture became more efficient, fewer people needed to work directly on the land. This allowed others to focus on specialized activities requiring more technical skills.
Trade: The expansion of cities and commerce between them meant that each city could focus on what it did best, such as producing tools, textiles, or food, and then exchange them for other needs. This led to the creation of guilds, such as those of carpenters or blacksmiths, where knowledge and skills were passed down through generations.
Technological development: The invention of specialized tools enabled artisans to improve the quality and efficiency of their work, which, in turn, increased the demand for more complex products.
Specialization was a response to the need to manage the growing complexity of society.
The same process, but now with AI
Today, we are experiencing a new transformation, but this time driven by a new disruptive technology: Artificial Intelligence.
In the early days of the OpenAI with the ChatGPT o1 launch, many experts believed that AI could be universal and able to tackle any task efficiently.
People envisioned a kind of all-knowing superintelligence that would solve any problem, from accounting to medical diagnostics.
However, what we are seeing in practice is something very different:
AI, instead of becoming a general-purpose tool, is becoming more specialized.
Just as in ancient times, when new professions emerged to handle specific needs, AI is now following a similar path.
Instead of a one-size-fits-all solution, we are seeing foundational models that are trained specifically for certain domains, like healthcare, law, marketing, and finance.
As the demands of each industry grow more complex, AI models are adapting and specializing to handle increasingly narrow tasks.
For example, rather than having a general-purpose AI for customer service, we now have AI agents designed specifically to answer insurance queries or manage medical consultations at clinics.
These agents are trained not just to interact with users, but to understand and handle the unique aspects of each field. The more complex the task, the more specialized the AI becomes.
The new economy: specialized AI-gents
Just as specialized trades were crucial for the growth of ancient economies, the specialization of artificial intelligence will be a driving force in the digital economy.
We will see more and more specialized AI agents that not only handle repetitive tasks but perform complex roles requiring deep technical knowledge and expertise in specific areas. These agents will become increasingly integrated into the value chains of businesses, contributing to their growth and efficiency.
The key to this transformation is that AI is not meant to be all-encompassing. Unlike early expectations, where AI was imagined as a catch-all solution, what we are witnessing is a trend toward deeper specialization.
Instead of a single agent doing everything, we will have AI agents adopting more and more specific roles, each playing a part in a more complex, interconnected world.
Think about Waymo and Tesla Robotaxi's focus on autonomous driving, redefining urban transportation with self-driving technologies.
Or 11x.ai and Daialog specialize in automating content generation and optimizing workflows for creators and marketers.
Similarly, tools like Jenni AI assist writers by generating contextual suggestions, while agents in healthcare such as Tennr streamline healthcare automation & Larry AI gives nutritional Advice.
All of them started with a foundational model and they verticalised in all these different areas making agents with better performance.
A reflection of the past in the future
The history of labor specialization, from traditional crafts to the digital age, reveals a continuous process of adaptation to new needs and complexities.
Just as the division of labor allowed ancient civilizations to thrive, the specialization of artificial intelligence is enabling the digital economy to evolve and adapt to new challenges.
Rather than a future where AI solves everything universally, the future seems to be one of increasing specialization, where each AI agent plays a vital role in a growing, interconnected digital ecosystem.