Can you feel the AGI

Ilya Sutskever, former Chief Scientist at OpenAI, once described a moment where interacting with an AI changes in feel.
Not because the model suddenly became smarter overnight but because the experience shifts from “I’m prompting a tool” to “I’m collaborating with a thinking partner.”

We’re starting to see exactly what he meant.

From Prompts to Persistent Intelligence

Over the short history of LLMs, we’ve already moved through three clear layers of interaction maturity:

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Prompt Engineering

• Goal: Get a better answer to this one question.
• How: Write a detailed, role-based prompt every time.
• Example: “You are a senior marketing strategist. Given X context, create a 3-step plan…”
• Limitation: Ephemeral, each new task starts from zero.

Prompting the Prompt (Meta-Prompting)

• Goal: Let the AI co-create the perfect prompt before answering.
• How: “Ask me anything you need to create the most effective prompt for X.”
• Result: AI interviews you, builds the prompt, then delivers.
• Benefit: More efficient than manual crafting.
• Limitation: Still temporary, optimized thinking disappears after the session.

Persistent Cognitive Configuration (The New Layer)

• Goal: Configure the AI’s entire reasoning style, roles, memory, and output logic so every future answer is already optimized.
• How: Define 8–9 core blocks:
  1. Thinking Modes — Literal, Contextual, First-Principles, Dual-Layer tactical/strategic.
  2. Roles & Personas (optional) — Fictional experts with unique voices, skills, and decision styles.
  3. Collaboration Patterns (optional) — Group dynamics before the final answer.
  4. Output Format & Fidelity — Fixed structure, depth levels, ready-to-use formatting.
  5. Knowledge Anchoring & Source Priority — Preferred frames, sources, filters.
  6. Adaptive Switching — Auto-select the most relevant mode/persona.
  7. State Tracking — Keep objectives, open decisions, risks, watchlists.
  8. Safety & Reversibility — View settings, override, reset.
  9. Expansion Protocols (optional) — Add new modes/roles when needed
• Result: All classic prompt engineering techniques embedded as defaults.
• Impact: The AI feels like a persistent collaborator, remembering context, anticipating needs, adapting without constant reminders.

💡 Extra: Once seeded, this configuration works not only in text chat but also in Voice Mode, including all thinking modes, personas, and collaboration patterns. You can brief your configured AI team by voice or run spoken meetings with it.

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Why This Matters

At this layer, you’re not just getting better answers, you’re changing the relationship.
The AI isn’t reacting to isolated commands; it’s operating inside a persistent cognitive framework you’ve defined. It’s closer to a “personal intelligence layer” than a chat box.

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Closing the Bracket

Reaching this third layer isn’t just a technical upgrade, it’s a relationship shift.
The AI no longer feels like an empty form to fill in, but like a partner that knows your goals, adapts to your style, and carries your context forward without being told twice.

While Ilya Sutskever was not referring to this specific method, his description of the AGI ‘feel’ captures exactly the kind of shift this approach produces. That’s why his words resonate so strongly here:

“At some point, the experience changes — you just feel it’s thinking with you.”
— Ilya Sutskever

We’re not at AGI yet.
But persistent cognitive configuration is one of those subtle thresholds where the feel changes.
Where you catch yourself working with the system instead of on it.

“You’ll know it when you feel it - when you can’t go back to how it was before.”

“The transition won’t be a single leap. It will be a series of shifts, each one making it harder to tell where you end and it begins.”
— Ilya Sutskever

We’ve designed a general seeding prompt that anyone can use to set up these 8–9 configuration blocks for themselves.
If you’re interested, reach out via info@inics.de.

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