Our Collective Journey into the Post-Knowledge Worker Era

Are you a fellow knowledge worker, perhaps one that also has a decade (or two) of knowledge and experience in a given field? 
Even if you are a young professional, how adaptable do you feel in the evolving world of hyper-specialization? 
Could you take on a radically different role, leaving behind the knowledge and experience that you have already gained? 
And if you have truly reached an expert status in your field, are you prepared to manage a network of AI agents, validating their output and outcomes?

“The question that really matters is whether humans will still command a skill premium once they are augmented by machines?” – Sangeet Paul Choudary

Feel uncomfortable yet? If you don’t feel any anxiety towards the future of work, either you are already interdisciplinary in your knowledge or just simply haven’t been paying attention to the exponential AI advancements taking place since the ‘ChatGPT Moment’. The blog post (URL below) that the above quote comes from provides us the framing of the challenges ahead, “The debate on the effect of AI on jobs is repeatedly distracted by the question of replacement through automation, when the factor that really drives our engagement with our careers is the ability to own skills which grant us a skill premium.”Based on labor statistics in the U.S., there are roughly 100 million knowledge workers, approximately 60% of the overall labor force. The number goes up to a billion or more globally. In future blog posts we’ll get into how the Future of Work is rapidly evolving and what Network Science and Complexity Science can inform us about our interconnectedness and how we can navigate the complexities we will encounter.

If you’ve been paying attention, you would probably agree that the future of generative AI (GenAI) in the Post-Knowledge Work Era will be agentic, multimodal, and multimodel – prompting (pun intended) the need for organizations to adopt what some experts are calling a compound AI system. A shift from only using models, which are often limited in design and function in their real-world encounters within complex environments. A situation that is beginning to be supported by a System of Agents architecture and one’s chosen agentic framework, enabling the transition from Software as a Service (SaaS) to Service as Software, a construct that is rapidly evolving with the agentic AI landscape. A transition that absolutely should send shockwaves and disruptive thoughts through the software industry (more on that later). The interesting part about multimodal is how the various modalities – not just text anymore – are enabling AI agents to gain a deeper understanding of what a human is seeing or hearing. This is not only happening via recorded audio playback but also within a live video stream and collectively on a mobile device or computer. Let’s just say it is the new intersection between Contextual AI meets Spatial Computing, enabled by Accelerated Computing in real-time, solving one of the biggest challenges within Conversational AI.

Want to shine the spotlight on Foundation Capital, especially Joanne Chen, Jaya Gupta, and Ashu Garg for sharing their amazing work listed below. The blog posts provide a solid shared understanding in preparation for beginning our collective journey into the Intelligence Age.- The Future of Generative AI Agents (2.21.2024) by Joanne Chen
 - AI leads a service-as-software paradigm shift (4.18.2024) by Joanne Chen, Jaya Gupta
 - The Promise of Multi-Agent AI (5.24.2024) by Joanne Chen
 - Beyond LLMs: Building magic (6.27.2024) by Ashu Garg, Jaya Gupta
 - A System of Agents brings Service-as-Software to life(10.31.2024) by Joanne Chen, Jaya Gupta
 - How Systems of Agents will collapse the enterprise stack (6.27.2024) by Ashu Garg, Jaya Gupta
-  The paradox of self-building agents: teaching AI to teach itself (1.9.2025) by Joanne Chen

In response to the continuous business disruption related to the steady flow of AI advancements, legacy organizations need to become exponential in their disposition – ensuring higher levels of adaptation and resiliency can emerge – two things that do NOT naturally emerge from our hyper-specialization. The notion is to increase one’s probability (chance) of becoming a Human-centric, Networked Organization. There is a lot to unpack in that last sentence but that is the purpose of The Value Network.

Certainly, the new AI-First approach being touted is not void of any challenges, quite the contrary – largely because there are a series of dependencies when it comes to giving agentic AI systems an ontological view of the existing organization. In fact, researchers are discovering that every Legacy Organization is and needs to be treated as a living organism and in the context of a Complex Adaptive Systems (CAS). When it comes to the ontological view of any given environment – entire organization, a functional department, a product team, etc., we need to give AI as much context as humanly possible. This may involve going so far as to creating a digital twin for simulation purposes as well as the overall goal of achieving Organizational Artificial General Intelligence (OAGI). A new dispositional state of an Exponential Organization (ExO), and highly correlated with OpenAI’s 5 Levels of Our AI Future. For the most part, OAGI is just a different name for Level 5 and given that many researchers and AI startups have been working on Level 3, Agents for the past year, it won’t be long before we officially move beyond Level 2, which coincides with OpenAI’s release of their o3 reasoning model.

Lastly, our collective focus not only needs to be Human-centric with an emphasis on radical compassion but also structured around the following critical business consideration:

The stability of societies and economies around the world will begin to erode and become reliant upon our collective ability to successfully ‘unwind’ the hyper-specialization entrenched within knowledge work.

The magnitude of the systems change related to unwinding the hyper-specialization will undoubtedly bring with it higher levels of anxiety about our uncertain future, regardless of how optimistic you feel today.

The Post-Knowledge Work Era’s arrival could produce a radical new way of working within the rapidly evolving Future of Work, increasing the criticality in defining how GenAI can enable the decoupling of value created by employees from the underlying business processes. Processes that need to become adaptive to enable an entire portfolio of new business models.

Difficult to envision adaptive business processes coming from specialized knowledge workers responsible for the processes that need to radically change. In other words, the amount of time and energy that went into your hyper-specialization is not compatible with increasing the adaptation most organizations will need in the rapidly and continuously evolving Intelligence Age. So much so that it reminds me of another paradigm shift: cloud computing and no longer treating servers as pets, rather as cattle – enabling Virtual Machines to be repeatedly built and destroyed, nearly on-demand. Ok, perhaps nowhere near on the same level as the next paradigm shift, largely because the end result is more often than not going to change what you do today, in your everyday work.

I'm expecting paralysis by analysis of the past will become paralysis by excellence. A word that I have a love-hate relationship with but more on that in future blog posts. Let’s just say, excellence can be the ultimate inertia barrier.

Agreeing to go on this journey will allow us to more openly collaborate and produce valuable insights into the value creating, value eroding, and value capturing activities, in a human-centric manner as well as in the overall context of knowledge work. Initially, doing so holistically (entire workforce) within an AI-First Working in New Ways Strategy and then more individually within the Jobs to Skills transition. The decoupling process is a critical step, historically performed from the customer’s perspective, much like startups looking for opportunities to disrupt an industry or customer segment. A tactic that all organizations should also be undertaking, not only to identify growth opportunities but also determine how they could be disrupted by AI. Applying a similar approach from an employee’s perspective is not an insurmountable undertaking, replacing the legacy value chain concept with a value networkconstruct. Successful navigation starts with becoming a Networked Organization – displacing the century-old hierarchical org chart as the primary organizing structure. As GenAI increases in disruptiveness, it is unlikely that deflation (cost of goods and services) will occur quickly enough to offset the displacement of jobs and our individual economic agency (ability to make a living) – at least not naturally, raising the potential for unintended consequences when well-intentioned governments intervene.

Attribution & Acknowledgement
This post draws upon the insightful work of the amazing, previously attributed authors and thought leaders who have generously shared their research and ideas. In many cases, I have intentionally included direct quotes to properly attribute their contributions and maintain the integrity of their original thoughts, in the context for which they were stated. While I have taken care to avoid misinterpreting the source material, the conclusions drawn in this post are mine and reflect my current understanding and coherence within an ongoing learning process. As such, I am accountable for any misrepresentations that may arise.