We frequently make the mistake of thinking of AI as ‘just another technology’.
We arrive at the vague conclusion that AI will automate some jobs and augment others.
But there’s a larger, less understood, nuance to understanding the potential of AI.
AI – particularly, autonomous AI agents – are goal-seeking. Goal-seeking technologies are unique. They take over planning and resource allocation capabilities, and in doing so, restructure how work is organized and executed.
In other words…
“AI won’t take your job, but it will take your ability to charge a premium for it.”
In today’s post, I talk about a second less-understood impact of AI:
“AI won’t take your job, but it will take away the organizational power and mobility associated with it.”
Goal-seeking technologies impact mobility and power relationships that help you move vertically and laterally within an organization.
In other words…
“AI won’t eat your job, but it will eat your promotion.”
Through this analysis, we look at three key ideas:
- How goal-seeking technologies are fundamentally different
- How goal-seeking AI restructures goal-seeking organizations
- How goal-seeking technologies impact power dynamics within organizations
When thinking about the impact of AI, most analysis looks to previous technological shifts and tries to apply the same lens to AI.
One way to think about the impact of new technologies on jobs is to think about jobs as bundles of tasks.
As I explain in Slow-burn AI:
Every job is a bundle of tasks.
Every new technology wave (including the ongoing rise of Gen AI) attacks this bundle.
New technology may substitute a specific task (Automation) or it may complement a specific task (Augmentation)
Extend this analogy far enough, and you get this:
Once technology has substituted all tasks in a job bundle, it can effectively displace the job itself.
Of course, there are limits to this logic. This can only be true for a small number of jobs, which involve task execution only.
But most jobs require a lot more than mere task execution.
They require ‘getting things done’. They require achievement of objectives, accomplishment of outcomes.
In other words, most jobs involve goal-seeking.
This is precisely why previous generations of technologies haven’t fully substituted most jobs. They chip away at tasks in the job bundle without really substituting the job entirely.
Because humans retain their right to play because of their ability to plan and sequence tasks together to achieve goals.
In most previous instances, technology augments humans far more than automating an entire job away.
And that is because humans possess a unique advantage: goal-seeking
AI Agents – Why this time is different
Autonomous AI agents provide the first instance of goal-seeking, self-learning, path-finding, path-adjusting technologies.
LLMs, as we use them today, operate on the technology paradigm we are all familiar with – a tool that works in response to human input to deliver an output.
Agents level it up several notches!
Let’s say you’re responsible for managing corporate travel.
- An LLM can help generate a list of interesting destinations and an itinerary that meets certain constraints.
- An AI agent can operate with far more complexity and look up the top-rated hotel with available rooms during a specified period, within a specific budget and complete the task of making the reservation.
- An autonomous AI agent can take this several steps further by learning about your context over time and finding and booking the hotel that best meets your travel preferences and constraints.
First, AI agents are goal-seeking:
As I explain in AI won’t eat your job, but it will eat your salary:
Agents are goal-seeking, and that’s what makes them different. While most technology aims at task substitution, agents go beyond tasks to seek goals.
Every agent operates with at least one goal (and possibly more than one).
In order to accomplish this goal, an agent must (1) scan the environment, (2) plan and deconstruct the goal into constituent tasks, and (3) act out the plan leveraging other agents and digital resources.
Second, autonomous AI agents learn continuously to get better at goal-seeking.
Learning involves both short-term, in-context learning as well as long-term cumulative memory. Much like rational human actors, agents can reflect on their actions and results and refine their path towards goal-seeking in response.
Finally, agents can call agents, paving the way for hierarchical organization of work.
Agents can call agents; it’s agents all the way down. A travel-planning agent can call a hotel booking agent and an activity planning agent and coordinate across the two (and more) to accomplish its overall goal.
Now, it’s important to throw in the all-important caveat here: Agents are still fairly unsophisticated. They have high variability in outcomes and high error rates. If LLMs hallucinate, agents which compound the capabilities of LLMs also compound their hallucinations.
But the landscape of AI agents is evolving rapidly and that’s why it’s important to understand how goal-seeking agents impact organizations. Early examples of agents include AutoGPT, AgentGPT, and SuperAGI. I’ve also included real-world examples of companies already providing and deploying this, further below.
But first, let’s get into the impact of goal-seeking technologies on organizations.
Automation substitutes tasks, AI agents substitute goals
Goal-seeking technologies, though early, are evolving rapidly and present a massive step-change in what’s possible.
That step-change, more specifically, is the ability for technology to substitute not just tasks but actual goals.
Every goal is a bundle of tasks, as the travel-planning example above illustrates. Goal-seeking technologies – in this case, autonomous AI agents – rebundle tasks and, through that, reorganize the hierarchical organization of tasks.
In effect, goal-seeking technologies are uniquely positioned to rewire how organizations work.
How do AI agents rewire today’s organizations?
To understand that, let’s return to the foundational principles of unbundling and rebundling.
Every goal (e.g. plan my travel) is a bundle of tasks.
Technology unbundles this bundle and creates substitutes and complements for specific tasks.
Finally, a combination of human effort and technology rebundles these tasks towards the goal.
Now, goals do not exist independently, they are part of a larger system – an organization of goals
Organizational units of goal-seeking
Organizations are goal-seeking.
There are two units of execution and goal-seeking within an organization: The role and the team.
Individual work is accomplished at the unit of a particular role. Teamwork is accomplished at the unit of a particular team.
Goal-seeking at the level of roles and at the level of teams is recombined through projects and workflows (and incentives) into eventual goal-seeking at the organizational level
Roles and Teams as bundles of goals
Let’s unpack this further!
A role is a bundle of goals.
Over time, effective performance of a role within an organization involves effectively seeking goals (allotted to that role) which align with overall organizational goals.
A team, likewise, is a bundle of goals.
Successful teams seek goals which ladder up to cumulatively help achieve organizational goals
Unbundling and rebundling organizational units
AI agents are the first instance of technology directly attacking and substituting goals within a role or a team.
In doing so, they directly impact power dynamics within an organization, empowering some roles and weakening others, empowering some teams and weakening others.
Let’s look at the effects of AI agents on teams and roles: