From Hype to Utility: What AI Needs to Become Truly Transformative
A deep dive into the cognitive foundations and strategic capabilities needed for AI to be a true General Purpose Technology
Human cognition is the engine of intelligence. It's the set of mental capabilities that allow us to perceive, remember, reason, learn, and act intentionally. It’s how we invent languages, build civilizations, and adapt to entirely new environments.
As we build machines that increasingly simulate parts of this cognition, one question becomes central:
What parts of human cognition matter most for AI — and which of them are required to make AI truly transformative?
Cognition is not just a feature of human intelligence — it’s the blueprint for how we learn, adapt, and apply knowledge. If AI is to evolve from a powerful tool to a General-Purpose Technology (GPT) — like electricity or the internet — it must begin to mirror key dimensions of cognition that enable generality, autonomy, and usefulness.
While human cognition is rich and multi-layered, not all of it needs to be reproduced in AI. What matters most are the dimensions that give rise to generality, autonomy, and economic value — the three pillars of transformative technology.
1. Generality
What it is: The ability to perform across a wide range of tasks and domains.
Why it matters: Without generality, AI remains narrow — like a chess engine that can’t write a report.
Current AI: Strong in LLMs like GPT-4, Gemini, Claude
2. Agency
What it is: The ability to take initiative, pursue goals, and act autonomously.
Why it matters: Generality without agency is passive. The agency makes AI proactive.
Current AI: Emerging in tools like Devin, AutoGPT — still brittle.
3. Economic Usefulness
What it is: Real-world value creation — solving problems, improving productivity.
Why it matters: Without usefulness, AI is just a demonstration. GPTs change economies.
Current AI: Already visible in AI copilots, assistants, and task automation.
4. Abstraction
What it is: The ability to think in terms of concepts, models, and principles.
Why it matters: Enables analogies, transfer learning, and cross-domain thinking.
Current AI: Limited — struggles with true conceptual understanding.
5. Learning (Adaptivity)
What it is: Improving through experience, not just pretraining.
Why it matters: Enables personalization, evolution, and continual refinement.
Current AI: Offline learning only — no continual adaptation in most models.
6. Memory & Temporal Coherence
What it is: Retaining knowledge, context, and continuity over time.
Why it matters: Needed for coherent long-term interaction, goal management, and self-consistency.
Current AI: Short-term memory exists; long-term memory is in its early stages.
Among these six dimensions, we focus on just three, their roles, and why they matter
Generality: System-level Capability, which defines the scope of AI
Agency: System-level autonomy, which defines the initiative of AI
Economic Usefulness: System-level impact, which establishes the value of AI
The above three dimensions are:
Outcome-defining: They reflect what AI can do and why it matters
Measurable: Easy to evaluate in real systems
Strategic: These are the pillars for AI as a General-Purpose Technology
The other dimensions — abstraction, learning, and memory — are not goals in themselves, but internal enablers that support the emergence of generality and agency.
Let’s now look at the other dimensions that support the core three and what they enable
Abstraction: Fuels generality by enabling cross-domain understanding
Learning (Adaptivity): Enables agency to adapt in real time and grow from feedback
Memory: Supports agency and generality by maintaining context, continuity, and long-term coherence
Only when an AI system scores high across all three — it's autonomous, general, and economically impactful — can it drive the kind of broad-based economic transformation seen with past general-purpose technologies like electricity or the internet
Autonomy: Can it act independently, adapt, and get things done without needing a human hand every step of the way?
Generality: Can it handle many different types of tasks, not just one trick at a time?
Economic Usefulness: Can it deliver real value, cost savings, productivity gains, and business impact at scale?
Human cognition gives us a blueprint for building AI that is:
General — not stuck in one task
Agentic — not waiting for prompts
Economically valuable — not just impressive demos
By targeting these three system-level outcomes and supporting them with internal capabilities like abstraction, learning, and memory, we guide AI closer to becoming a General-Purpose Technology, capable of transforming every domain it touches.
Intelligence becomes transformative when it is general, agentic, and useful — not just when it’s smart.
Let’s take a look at the current state of real-world AI systems across the three dimensions: Generality, Agency, and Economic Usefulness.
Below is a snapshot table with examples across domains (autonomous cars, LLMs, agents, etc.)
Summary of where AI stands today
LLMs dominate in generality and usefulness, but lack agency
Agents and robotics show early signs of agency, but suffer from lack of generality or real-world reliability
Recommenders and chatbots are highly useful, but are narrow and non-agentic
No current system fully occupies the center of the above Venn diagram — but different systems are converging from different edges.
Are there any current systems, that excel in all three key dimensions?
Share your thoughts in the comments below.