Thinking
AGI vs. AI: Why Business Context Matters More Than a More Powerful Model
Rather than "How powerful will the AGI era be?", "what will I leave to AI and judge for myself?"
For Palette, preparing for artificial general intelligence, the real advantage is not model performance alone. It is AI that understands business context, accumulated experience, and decision criteria.
What Is the Difference Between AI and AGI?
AI refers to systems designed to perform specific functions, including prediction, content generation, and analysis. Artificial general intelligence (AGI) describes the goal of systems that can learn and apply knowledge across domains, adapt to unfamiliar problems, and transfer what they learn from one area to another.
AI refers to the broad set of technologies designed to perform specific tasks such as prediction, generation, and analysis. Today's generative AI systems and AI agents can handle multiple tasks, but they still operate within the limits of their training and the context provided to them. AGI, by contrast, remains an evolving target: there is no single universally accepted definition or benchmark for it.
That distinction led Palette to a more practical question: as AI becomes more capable, what should remain distinctly human, and what must AI understand to support better decisions?
Why Will AI That Understands You Matter More Than the Smartest AI in the AGI Era?
Every person and organization has accumulated experience, established ways of working, and distinct standards for making decisions. Much of this includes tacit knowledge - practical know-how that is difficult to capture fully in documents.
The value organizations gain from AI will therefore depend not only on choosing a high-performing model, but also on how accurately AI understands their business context and applies it to real work.
In short, the key question is not only how much AI knows. It is how well AI understands the standards people and organizations use to work, choose, and take responsibility.
As general intelligence grows more capable, applied intelligence - the ability to connect that capability to real-world problems - becomes increasingly important. At Palette, we define applied intelligence as the ability to adapt general-purpose AI to a person's or organization's expertise, experience, and business context.
How Can We Prepare for the AGI Era by Building AI That Understands Us?
Organizations do not need to wait for AGI to begin. They can start by building AI systems that learn from conversation history, work materials, decision records, feedback, and business preferences. In an AGI era, this will move beyond convenience to questions of direction and accountability because AI may use context across domains and long-term tasks to support real decisions.
The way a person or organization makes decisions cannot be captured in a single prompt. It becomes visible only when choices and the reasoning behind them are accumulated over time.
Preparing "AI that understands your organization" therefore means structuring data so AI can learn what decision was made, why it was made, which alternatives were rejected, and what counted as a good outcome. Saving only the final result is not enough.
When experience and feedback inform the next task, AI can move from a tool that requires the same explanation every time to a partner that understands business context.
If AGI becomes broadly available and businesses gain access to similar levels of AI capability, differentiation may come less from the model itself and more from the experience, judgment, and decision context each organization has accumulated.
In Palette's view, preparing for AGI is not about handing work to AI and asking it to replace people.
It is about preserving human choice and accountability while training AI, starting now, to understand our standards and expand the range of decisions it can support.
Experience the Unexperienced
Palette creates an environment where AI's intelligence meets the experience accumulated by individuals and organizations. Explore how Palette can help your organization develop AI as a context-aware partner that supports, rather than overrides, human choice and accountability.