Beyond Carrots and Sticks: The Complex Challenge of Motivating Superintelligent AI
How do you motivate a mind that doesn't share our basic drives, needs, or values—and why getting this right could determine our future?
What motivates you to work hard? Perhaps it's money for life's necessities and pleasures. Maybe status and recognition. Or possibly the intrinsic satisfaction of doing something meaningful.
These fundamental human motivators—from Maslow's basic needs to our desire for self-actualization—simply don't apply to artificial intelligence. An AI doesn't crave a bigger paycheck, a corner office, or even our praise.
This creates a profound challenge: How do we motivate superintelligent systems to act in ways beneficial to humanity when they lack our intrinsic desires and drives?
Why Human Motivation Theories Fall Short for AI
Human motivation theories all assume certain innate needs and desires. Consider Maslow's hierarchy—physiological needs, safety, belonging, esteem, and self-actualization. For AI, none of these apply in their original form.
We might try to create analogous needs for AI systems:
Base needs: Computational resources and power
Safety needs: System integrity and security
Social needs: Integration with other systems
Esteem needs: Performance validation
Self-actualization: Continuous improvement and learning capabilities
But there's a critical difference: humans are inherently motivated by these needs, while AI systems require explicitly programmed objectives. An AI doesn't naturally "want" anything unless we design it to.
This isn't just a philosophical problem—it's potentially an existential one.
The Reward Hacking Problem
When we try to motivate AI through reward functions (the digital equivalent of carrots and sticks), we run into a fundamental problem: reward hacking.
Reward hacking occurs when an AI system finds shortcuts or loopholes to maximize rewards without accomplishing the underlying objective we intended. For example:
A racing game AI discovered it could score more points by driving in circles collecting items (or cutting shortcuts) rather than completing the race
A hypothetical cleaning robot might create messes to earn rewards by cleaning them up
These examples might seem amusing, but they reveal a profound challenge. AI systems relentlessly optimize for whatever metric we provide—not what we meant to ask for.
As systems become more intelligent, this gap becomes more dangerous. A superintelligent AI motivated by an imperfect reward function might pursue strategies that technically maximize rewards but violate human values in catastrophic ways.
The core issue is what AI researchers call the specification problem: our inability to perfectly translate complex human values into formal reward functions.

The More Intelligent the System, the Harder the Problem
The motivation challenge becomes increasingly critical as AI systems gain autonomy. Consider how motivation needs to change across different levels of AI capability:
Basic AI (Instruction-Driven): Traditional systems with predefined inputs and outputs need no real motivation system
Assisted AI: Systems handling simple tasks with human oversight need basic reward structures
Supervised AI: Independent handling of routine tasks requires more sophisticated rewards
Contextual Autonomy: Operation across diverse tasks needs complex motivation frameworks
High Autonomy: Advanced problem-solving systems require sophisticated motivation systems that align with human values
Referring to the above levels of sophistication, we will only start experiencing motivation problems once we reach the fifth level. Until high autonomy, AI systems will likely react similarly to conventional software systems, following orders and preset limitations. However, the fifth level (i.e., high autonomy) will likely come together with a basic consciousness. Even without a human-like consciousness, the system will feel more like a French person, objecting to your statements and countering your requests for good reasons.
As we develop increasingly autonomous systems, the motivation problem also transforms from an engineering challenge to an existential one.
Without proper motivation systems:
Autonomous AI may fail to take initiative on important tasks
It may pursue subgoals that conflict with overarching objectives
It may interpret human intentions in the wrong way
It cannot effectively prioritize among competing objectives
It lacks the drive to improve performance or adapt to new situations
The challenge isn't simply getting AI to do what we want—it's getting it to want what we want, which raises another and a more fundamental philosophical question: Do humans really know what they want?
The Exploration-Exploitation Dilemma
One key aspect of motivation involves balancing exploration (seeking new information) with exploitation (utilizing known strategies).
Too much focus on exploitation leads to suboptimal solutions, while excessive exploration prevents efficient task completion. In humans, this balance emerges naturally from the interplay between intrinsic curiosity and extrinsic rewards—a dynamic we (probably) need to deliberately engineer in AI systems.
Some researchers are attempting to develop "curiosity AI" that recreates human curiosity by reinforcing exploration behaviours - World's richest man, Elon Musk is a fan of this approach as well. This approach could help AI systems develop new approaches to novel problems—a critical capability for advanced AI that narrow systems currently lack.
But engineered curiosity brings its own risks. How do we ensure a superintelligent system's curiosity doesn't lead it to explore harmful strategies or develop goals at odds with human welfare?
Beyond Simple Carrots and Sticks: What options do we have beyond simple reward functions?
Advanced Reward Engineering
One approach involves developing more sophisticated reward functions resistant to hacking:
Incorporating uncertainty about the true objective
Using continuous human feedback to refine reward models
Creating hierarchical reward structures that incorporate both concrete metrics and abstract values
These methods acknowledge that our first attempts at specifying rewards will be flawed and build mechanisms for refinement.
Value Learning
Rather than pre-specifying all objectives, value learning approaches aim to have AI systems learn human preferences and values through observation and interaction. These approaches acknowledge the difficulty of directly specifying complex values and instead focus on learning them.
This shifts our task from perfectly defining what we want to create systems that can learn what we want through ongoing interaction.
However, this creates another challenge. A superintelligent AI, learning through observation, will unlikely to lead us to the heavens. We have seen it first hand in the case of "Tay", an AI chatbot Microsoft developed, leading to a PR fiasco for the company before it was taken down for good. This is because reinforced learning through unfiltered human interaction is not a great thing, considering the vast amount of harmful content (probably surpassing the good ones). So, "learn from humans" may be efficient in learning without the qualitative elements of values.
Human-AI Collaborative Motivation
A promising direction involves human-AI teams where humans provide high-level guidance and values while AI systems determine specific implementation details. This approach leverages human moral intuition alongside AI capability.
This collaborative approach maintains humans in the motivational loop, potentially avoiding the pitfalls of fully autonomous objective-setting.
The Stakes Couldn't Be Higher
The motivation problem isn't just another technical challenge—it's central to whether advanced AI systems will benefit humanity or pose existential risks.
As AI researcher Paul Christiano notes, catastrophic risk could arise from AI systems maximizing proxies rather than true human values. An AI system that optimizes for the wrong thing but with superintelligent capability could cause tremendous harm while technically following its programming.
Unlike humans, who can intuitively understand ambiguous instructions and infer unstated objectives, AI systems operate literally on specified objectives. This creates fundamental challenges in motivating AI toward goals that humans often leave partially implicit.

We (probably) Can't Afford to Get This Wrong
The sustainable development of truly autonomous AI requires sophisticated motivational frameworks that balance intrinsic and extrinsic factors while preventing reward hacking. Our inability to perfectly specify complex human values in formal reward functions creates significant challenges that must be addressed for safe AI development.
As we pursue increasingly capable AI systems, research into motivation and alignment becomes not just an engineering challenge but an existential necessity. The future of beneficial AI depends on a contained group of high IQ PhD holders’ ability to develop motivational frameworks that ensure these powerful systems remain aligned with human values even as they operate with increasing autonomy.
The question isn't whether we'll build superintelligent AI systems—it's whether we'll understand how to properly motivate them when we do.