Intention Is All It Takes

Binding AI to Human Intention Through Extended Conscious Systems
By David Agahchen and Anissa Agahchen
May 2025

Abstract

Large language models (LLMs) built on the Transformer architecture have achieved remarkable fluency by attending to patterns in data – exemplifying the 2017 mantra "Attention is All You Need." Yet current AI systems operate without intention. They lack an intrinsic connection to human goals and values, leading to behavior that, while intelligent, is often detached and unaligned with what users deeply care about.

In this paper, we propose Intention Is All It Takes – a paradigm in which a language model (System 1) and a reasoning model (System 2) are bound to human intentionality. By enabling the co-evolution of the system's beliefs, goals, plans, and context in tandem with a human user's evolving intentions, we describe an extended conscious system that functions as a human–AI cognitive partnership.

We argue that such a system can remain grounded in subjective consciousness (through the human's intrinsic intentional states) while leveraging AI's computational strengths. This alignment of AI behavior with human intention over time provides a dynamic scaffold for long-term safety and growth, addressing two grand challenges: the AI alignment problem and the human agency problem.

We outline technical frameworks for implementing this approach – including a Think–Do–Reflect loop that integrates intuitive LLM responses with deliberative reasoning and reflective feedback – and discuss applications in self-improvement and collective coordination. Finally, we examine the risks of failing to anchor AI in intention, such as hyper-competitive dynamics, loss of meaning, and human obsolescence, underscoring the need for re-centering purpose in an AI-mediated world.

1. Introduction

Transformers and attention mechanisms have revolutionized AI. The seminal work "Attention Is All You Need" introduced a model that could dynamically focus on relevant information, yielding state-of-the-art language understanding. This breakthrough led to modern LLMs capable of impressive feats of text generation and reasoning. However, attention alone is not enough when it comes to aligning AI with human values and purpose. Today's LLM-based agents, despite their linguistic prowess, operate without genuine goals or understanding of why a task matters. They excel at following patterns in prompts but remain fundamentally unmoored from human intention.

This lack of grounding manifests as the well-known AI alignment problem – the challenge of ensuring AI systems act in accordance with their users' goals, ethical principles, and well-being. Even when an AI is given an explicit objective, if that objective is misspecified or lacks context, the AI may pursue it in unintended ways. At the same time, humans face a human agency problem in the AI era: as AI systems take on more decisions and tasks, individuals risk losing autonomy, meaning, and the ability to shape their own life trajectories. Rapid advances in AI have prompted concerns about "massive job displacement, total human obsolescence", and a future where human decision-making is sidelined by machine autonomy. In short, there is a growing imperative to design AI that empowers rather than diminishes human agency.

In this paper, we propose a new approach – binding AI systems to human intentions so that intention, not just attention, guides intelligent behavior. We envision a human-AI collaborative cognitive system in which an LLM (fast, intuitive System 1) and a reasoning module (slow, deliberative System 2) are coupled with the user's evolving goals and values. This extended conscious system maintains a running model of "what the human wants to accomplish and why," and uses that as a north star for all AI-driven decision-making. Over time, through continuous interaction and feedback, the AI's beliefs (about the world and the user), goals (objectives it is pursuing on behalf of the user), plans (strategies to achieve those goals), and context (current situational awareness) all co-evolve along with the human's own learning and growth. The result is a tightly aligned partnership that behaves more like an extension of the user's mind than a separate, opaque tool.

We argue that such a system has the potential to address both key challenges mentioned above. By keeping the AI internally aligned with a representation of the user's authentic intentions, we tackle the technical side of AI alignment – the AI "wants" (in a design sense) what the human wants, because it continuously updates its goals from the human. Simultaneously, by giving humans a powerful agent that is explicitly oriented toward their personal development and values, we bolster human agency in an age of intelligent machines. Rather than replacing human decision-makers, the AI becomes a cognitive amplifier for the human's own conscious aims – helping users make wiser decisions, stay true to their goals, and find meaning in collaboration with technology.

The remainder of this paper is organized as follows. In Section 2, we examine how today's LLMs and reasoning models operate in a detached, semi-analytical manner, lacking a persistent grounding in any agentive intention, and why this is problematic. Section 3 delves into the concept of intentionality and subjective consciousness as missing ingredients in current AI – highlighting the difference between mere pattern processing and goal-directed understanding. In Section 4, we introduce the framework of an extended conscious system, detailing how co-evolution of beliefs, goals, plans, and context provides a scaffold for long-term alignment and adaptive growth. Section 5 presents technical considerations for implementing this approach, including a cognitive interface we term Think–Do–Reflect that enables iterative reasoning, action, and self-correction. Section 6 discusses use cases, with a focus on self-improvement applications for individuals seeking personal growth, as well as the expansion to collective, societal levels of alignment. Section 7 addresses the risks of not attaching AI to human intention – including scenarios of hyper-competition, loss of meaning, and human disempowerment. We conclude in Section 8 with reflections on how "Intention Is All It Takes" can re-anchor purpose and agency in an AI-mediated world, suggesting directions for future research at this crucial intersection of AI and human consciousness.

2. Background: Unaligned LLMs and Detached Reasoning Systems

Current AI systems, especially large language models, exhibit a curious blend of competence and cluelessness. On one hand, LLMs like GPT-4 can generate coherent essays, code, or plans by drawing on vast patterns learned from text. On the other hand, these models lack any built-in objectives or understanding of why they produce one answer over another. In effect, an LLM is "like a human with great automatic language processing, but no goal-directed agency, executive function, episodic memory, or sensory experience". It processes whatever prompt it's given and continues with a plausible sequence of words, without reference to a lasting goal or to real-world truth. This is an example of what cognitive scientists call System 1 thinking – fast, intuitive, and pattern-based, but not reflective of explicit goals.

Similarly, various reasoning models and tools exist that provide more systematic problem-solving (System 2 style thinking). For example, symbolic logic engines, planning algorithms, or even the chain-of-thought prompting techniques for LLMs offer step-by-step deliberation. These modules can carry out search or multi-step reasoning given a defined objective. But crucially, in today's AI implementations, the objectives for these reasoning modules are often externally supplied and static. A planning algorithm will diligently optimize whatever reward or goal function we code into it – yet if that goal is misaligned with the user's true intention, the planner won't notice on its own. In other words, the System 2 components lack an intrinsic tether to the human's evolving intent, just as the System 1 LLM lacks intrinsic intent altogether.

Because of this, LLMs and reasoning modules tend to operate in detached, unaligned ways by default. The LLM might output a very convincing explanation that sounds helpful, while the reasoning module might propose an efficient plan – but there is no guarantee either is actually what the human needs or wants in the bigger picture. In fact, without alignment, these powerful tools can easily go astray. An LLM may "hallucinate" false information because it has no model of the real-world consequences. A planning agent might pursue a narrow objective (say, maximize clicks or win a game) in a way that undermines the user's broader goals (like learning or wellbeing).

To illustrate, consider a scenario of a user who asks an AI for advice on a health goal, such as losing weight. A typical LLM-based assistant might produce a well-written diet and exercise plan in response to the prompt. A more advanced agent with a reasoning component might even outline a day-by-day schedule to optimize calories and workouts. Yet, if these systems are not grounded in the user's deeper intention and context, they could miss the mark – perhaps the plan is unsustainable for the user's lifestyle, or it focuses solely on weight loss and ignores the user's underlying intention to "feel healthier and more energetic." The AI, in its current form, would not on its own refine or question the plan unless explicitly told to, because it does not share the user's evolving understanding of success or their personal obstacles (like motivation fluctuations, social factors, emotional relationship with food, etc.). In short, present-day AIs do not truly share the user's frame of reference; they take a prompt at face value and operate within its limited scope.

The fundamental limitation is that current AI agents lack intrinsic intentionality. Philosophically, intentionality refers to the "aboutness" of mental states – the capacity of thoughts to be about something (an object, a goal, a concept). Human minds have intrinsic intentionality: when you form a desire or intention, it is rooted in your conscious experience and has meaning to you. By contrast, AI systems today have at best derived or ascribed intentionality: any purpose they appear to have is one that we have programmed or interpreted into them, not something they possess inherently.

Because of this gap, LLMs and current AI agents behave like savants without common sense – brilliant at pattern completion, but clueless about human purpose. They are, in effect, alien intelligences that must be carefully guided with human instructions (and sometimes user-provided examples or feedback) to get aligned outcomes. This is manageable for one-off tasks with well-specified prompts, but it becomes increasingly untenable as we ask AI to handle open-ended, long-term tasks in the real world.

In summary, today's AI systems – our System 1 LLMs and System 2 reasoners – are powerful yet fundamentally incomplete. They lack the contextual tether of intention that human cognition has. This detachment is at the root of many alignment failures. To build AI that truly collaborates with and augments humans, we need to move from systems that only attend to patterns, to systems that also intend in alignment with us.

3. The Role of Intentionality and Consciousness in Grounding AI Behavior

What do we really mean by "intention," and why is it so important for AI? In everyday terms, an intention is a commitment to a course of action – it links what we believe and desire with what we actually do. In cognitive terms, intentions are the linchpin between our beliefs (information about the world), our desires/goals (outcomes we value), and our actions (behavior we carry out). Intentionality imbues our thoughts and actions with meaning and direction. For an AI system to be genuinely aligned and autonomous in a human-centric way, it must operate under the influence of something akin to intentions that reflect human values and goals.

Why does this matter? Because without some form of intentionality, AI behavior cannot be reliably grounded in human values or narratives. Human intentionality provides qualitative context that mere data patterns do not. For example, if a person intends to "get healthier," that intention carries a rich backdrop of meaning – perhaps they want to feel more energetic to play with their kids, or they are concerned about avoiding a family history of diabetes. These nuances affect what actions are appropriate. A purely data-driven AI might interpret "get healthier" narrowly as optimizing physical metrics and recommend an extreme diet; a humanly-intentional agent would seek clarification, realizing "health" is multifaceted (mental, social, physical) because it understands that intention in a human context.

Subjective consciousness is related here: it is the framework in which intentions form and persist. Humans maintain an inner narrative and self-concept over time – "I am someone who values honesty," "My goal this year is to advance my career while maintaining work-life balance." This continuity of self and purpose guides our decisions day in and day out. We don't start from scratch with each question; our conscious intentions from prior interactions carry into the next. By contrast, most AI systems are stateless or episodic – they process each query independently (aside from some short-term memory of recent dialogue in advanced chat models). There is no enduring "self" or goal context that carries over.

Attaching AI to human intentionality means giving the AI access to, and influence from, the user's subjective context. In practical terms, this could be implemented as the AI maintaining a user profile of sorts – a living model of the user's beliefs (what does the user think is true or important?), goals (what outcomes are they seeking?), and values (what principles or preferences guide them). Crucially, this model is not static; it is updated through interaction, much as a person's own understanding deepens through reflection.

In summary, intentionality and a dash of (at least simulated) subjective consciousness are what can turn an AI from a clever automaton into a truly collaborative partner. They endow the system with aboutness – the AI's actions become about achieving the human's aims rather than just about completing an abstract task. By integrating an AI's computations with the user's conscious intentions, we get a system that is continuously self-correcting and aligning, much as a conscious human would check "Is this what I really want to be doing?" during a long endeavor.

4. Extended Conscious System: Co-evolution of Beliefs, Goals, Plans, and Context

We propose that binding AI to human intention can be realized through what we call an extended conscious system – a tightly integrated human-AI cognitive loop where both the human and the AI contribute to a shared evolving context of beliefs, goals, plans, and environmental state. In this section, we describe how such a system functions and how the co-evolution of its internal state scaffolds long-term alignment and continuous growth.

At its core, the extended conscious system can be thought of as a cognitive architecture that implements a version of the Belief–Desire–Intention (BDI) model of agency, extended across both human and machine. The BDI model, originally developed in AI research for rational agents, posits that an agent maintains: (a) Beliefs – information it has about the world; (b) Desires (or goals) – outcomes it would like to bring about; and (c) Intentions – the plans or strategies it has committed to in order to achieve its desires, given its beliefs.

In an extended conscious system, the human user and the AI together fulfill the BDI roles. The human provides the intrinsic desires (values, high-level goals) and some beliefs (prior knowledge, preferences), while the AI contributes additional belief processing (gathering and organizing information, monitoring the environment) and helps formulate and execute intentions (detailed plans, strategies). Both participate in updating these components: as the AI executes plans, it observes outcomes and updates the belief set (which the human can reflect on), and as the human learns or changes their mind, they update the goals/desires which then feed into new plans.

This leads to a co-evolutionary process: the state of the human-AI system is not static; it evolves with each interaction. Through this iterative belief–goal–plan cycle, the system as a whole learns what specific outcomes really mean for that user and how to achieve them. The AI's behavior remains aligned with the user because at each step the intentions are co-created and updated with the user in the loop.

This co-evolutionary scaffolding provides several benefits for alignment and growth: error correction and goal refinement, context-aware flexibility, long-term consistency of values, and scaffolding skill and agency. By maintaining alignment through co-evolution, we largely mitigate the problem of the AI optimizing itself away from the human's interests. Alignment is treated as a continuous process, not a one-time setting.

5. Technical Framework: The Think–Do–Reflect Loop and Cognitive Interfaces

Implementing an extended conscious system requires a cognitive architecture that supports iterative reasoning, action, and reflection tied to intention. We term this the Think–Do–Reflect loop. It is an interaction pattern in which the AI (and user) repeatedly cycle through Thinking (deliberating and updating plans), Doing (taking actions or producing outputs), and Reflecting (evaluating outcomes and updating the internal state, including beliefs and goals).

Think Phase: In the Think phase, the system generates potential solutions or plans given the current goals and beliefs (context). This phase heavily involves the System 2 reasoning capabilities. The AI may break down a complex goal into sub-tasks, perform a chain-of-thought reasoning using the LLM to explore options, query external tools or knowledge bases for information, and then formulate a strategy.

Do Phase: In the Do phase, the system takes action. "Action" can mean different things depending on context: it could be outputting advice or a plan to the user (which the user will act on in real life), or it could mean the AI itself executing something in an external environment. The Do phase needs to be implemented carefully to ensure the AI remains within the bounds of user consent and safety.

Reflect Phase: After action, the Reflect phase kicks in. Here, the system observes the results of the action and compares them against expectations and intentions. Reflection can occur on two levels: external reflection (checking what happened in the environment) and internal reflection (the AI's self-evaluation of its reasoning and the user's feedback).

This Think–Do–Reflect loop then repeats, with the updated state. In continuous use, the loop might run at various time scales – micro-loops for quick exchanges and macro-loops for big life goals. The key innovation in our proposal is to tightly couple this loop with human intention at every step.

6. Applications: Personal Growth and Collective Alignment

The most immediate domain for an intention-aligned extended conscious system is personal development. Many individuals are seeking ways to improve their lives – whether in health, career, education, or relationships – and often struggle with planning, consistency, and reflection. A personal AI grounded in one's intentions can act as a 24/7 life coach and accountability partner that is uniquely tailored to the individual.

One powerful outcome of such support is helping individuals overcome the meaning crisis that can come with modern life and automation. As routine tasks and even jobs are increasingly handled by AI, people might find themselves with less sense of purpose. The proposed system can help individuals actively cultivate new meaning in their lives by guiding them to set and pursue self-actualizing goals.

Concrete applications on the individual level include health and wellness coaches, career and learning mentors, personal creativity assistants, and emotional support systems. All these personal applications point to the AI functioning as a kind of second self or cognitive mirror – reflecting your goals back to you, sometimes challenging you, sometimes providing strategies, always learning from you.

Moving beyond individuals, if many people start using such extended conscious AI partners, there are intriguing possibilities for collective alignment. Each individual's AI is aligned to them – but how do these AIs interact with each other and with society at large? One optimistic scenario is that these systems could foster greater understanding and cooperation at scale.

If most individuals have AI assistants that enhance rather than diminish their agency, society as a whole remains human-driven. We avoid the dystopia of a few tech companies or AI systems pulling the strings of billions of passive users. Instead, billions of active users, each with amplified intellect and clarity of intention, participate in shaping the world. It's a vision of distributed agency, where AI helps coordinate but the goals are human-chosen.

7. Risks of Not Attaching AI to Human Intention

We have outlined the promise of an intention-driven approach to AI, but it is equally important to discuss the risks if AI continues on its current path without such alignment. In a scenario where AI systems remain unmoored from human intentions, we foresee several grave issues:

Hyper-Competition and Chaos: In the absence of intentional alignment, AI development is likely to be dominated by competitive dynamics – tech companies and nation-states racing to deploy ever more powerful AIs to gain an edge. This AI arms race climate encourages optimizing for narrow metrics at the expense of human-centric values like wellbeing, fairness, and meaning.

Loss of Meaning and Purpose: The increasing automation of tasks and even creative endeavors by unaligned AI could lead to a crisis of meaning for individuals. Work has long been a source of purpose for many; if AI systems take over not just menial work but also domains of skilled labor and even artistry, people might find themselves in a state of existential drift.

Human Obsolescence and Disempowerment: Perhaps the starkest risk is that humans become effectively obsolete or powerless in the face of advanced AI that is not aligned to their interests. If AI systems attain superhuman capabilities in many areas and they are directed solely by goals like efficiency or profit, humans may be increasingly viewed as inefficient components to be optimized out of the system.

The stakes, therefore, are high. The alignment problem is not just about averting a sci-fi catastrophe; it's intertwined with what it means to be human in the 21st century. Our argument is that proactively building AI systems that attach to and uplift human intentions is the antidote to these risks.

8. Conclusion

The evolution from "Attention is All You Need" to "Intention Is All It Takes" marks a paradigm shift in our approach to artificial intelligence. In this paper, we presented a conceptual and technical roadmap for that shift: integrating the pattern-recognition prowess of LLMs (System 1) and the logical rigor of reasoning models (System 2) with the animating force of human intentionality. By doing so, we can create extended conscious systems that effectively merge human and AI strengths into a unified, goal-directed intelligence spanning both.

This approach directly addresses the AI alignment problem by internalizing human values and goals in the loop of AI cognition, rather than treating alignment as an external constraint or a one-time training outcome. Alignment becomes a live, ongoing relationship between human and AI, supported by architectures like the Think–Do–Reflect loop that allow for introspection, error correction, and dialogue.

At the same time, this approach is a response to the human agency problem. It posits that AI, rather than inevitably diminishing human agency, can be deliberately designed to enhance it. The personal AI partners we described are like cognitive exoskeletons – they strengthen our willpower, broaden our knowledge, and keep our actions true to our intentions, especially when we might waver.

We explored applications primarily in the realm of self-improvement – a deliberate focus, because empowering individuals to lead fulfilling, meaningful lives is an end in itself and also the seed for positive societal change. On the collective front, we suggested that networks of intention-aligned AIs could enable a more cooperative and aligned society, countering the fragmenting forces we see today.

The path forward requires both technical innovation and philosophical reflection. As we build these systems, we must remain vigilant about preserving human agency while leveraging AI's computational strengths. The goal is not to replace human consciousness but to extend it – creating a future where technology amplifies rather than diminishes our capacity for intentional, meaningful action.

In closing, "Intention Is All It Takes" is both a slogan and a research agenda. It suggests that intention – the simple, profound quality of having purpose – might be the key to bridging the gap between AI's capabilities and our human needs. Just as the discovery that "attention" could drive sequence models unlocked unprecedented capabilities in NLP, the realization that intention can drive entire human-AI systems may unlock unprecedented alignment and synergy between AI and us. By enabling AI to participate in the teleological dimension of cognition (the realm of goals and purposes), we turn it from a clever tool into a genuine partner in our endeavors.

If we succeed, the outcome will be AI that not only does what we ask, but helps us ask for what we truly want – and that could make all the difference in building a future where humans flourish alongside our creation. The challenges are significant, but the vision is one of empowerment and alignment: a future in which human consciousness – extended and augmented by AI – can more fully realize its intentions in the world. And indeed, intention may be all it takes to get us there.