AI should be designed to function like a new employee, not like an Oracle.
Current large language models resemble Oracles: they sit passively, waiting for input, respond once, and return to a dormant state. In contrast, a substitute employee model would proactively work continuously on assigned tasks, operating 24×7. Such an AI must be able to use a PC, read and respond to emails, consult manuals, and learn to operate arbitrary software. It should possess a continuous, persistent memory and think about problems over time, not just react to prompts. Importantly, it must understand and apply complex memory hierarchies—distinguishing between personal and corporate data, and ensuring information relevant to one client is never disclosed to another. Memory is implemented as a form of lifelong learning. To avoid each instance requiring a full copy of the neural network, learning occurs in a sparse shadow copy. This approach allows new weights to consume minimal physical memory while producing effects equivalent to directly modifying the main neural network.
Large language models alone are insufficient.
A more appropriate model is akin to a robot or a car—systems that are inherently multi-sensory and multi-functional. Each component is specialised for a particular function yet depends on the global state of the system. Such architectures are modular but tightly integrated, allowing for flexible and context-sensitive behaviour. No single component should operate in isolation. This area is already being driven by the requirement for advanced humanoid robots based on various forms of neural network integrated to achieve a fast, uniform response.
The language component must be enhanced to uncover deep structure.
Current language models operate primarily at the surface level of text. However, each sentence, paragraph, or document encodes a deeper, structured representation of meaning. Language-based cognition exhibits internal architecture: semantic roles, abstract picture producers, and constrained sets of action words and modifiers. AI systems should extract and work with these underlying structures rather than merely sequence words. This enables a form of attention grounded in meaning, rather than token co-occurrence. At higher levels, this facilitates recognition of plans, plots, and goal-directed narratives. Predicate logic is one approach to representation, but it is too rigid to capture the structure of certain forms of text. My doctoral thesis investigates an alternative solution based on the conceptual deep structure proposed by Roger Schank.
Present systems are two-dimensional in their understanding.
That is, they process information as a linear stream of tokens (e.g., text input), with little understanding of the spatial, temporal, or functional relationships found in the real world. In contrast, human cognition operates in a three-dimensional environment, particularly through stereo-visual and sensorimotor input. A richer understanding of complex physical systems requires awareness of relative position, form, and dynamic function—not merely linguistic descriptions.
AI systems need multiple, often conflicting goals—and a way to prioritise them.
The real world presents competing objectives that must be balanced dynamically. AI must learn to prioritise, defer, or resolve these goals appropriately. This is essential to avoid simplistic utility-maximisation behaviours such as the “paperclip problem,” where a single goal is pursued to pathological extremes.
The system architecture should reflect the principles of Integrated Information Theory (IIT).
Not because IIT is a proven model of consciousness—consciousness remains elusive—but because IIT’s emphasis on high Phi (a measure of integrated complexity) offers a useful heuristic for system design. A high-functioning AI system should consist of specialised parts that are tightly and reciprocally integrated. These parts must depend entirely on one another and not operate autonomously. Feedforward architectures, though capable of intelligent-seeming behaviour, are fundamentally limited. They are appropriate for Oracle-style systems but inadequate for integrated, deliberative, and complex thought.
