Extended general intelligence isn't about redefining what human intelligence means, it's about designing cognitive tools that amplify rather than replace your thinking.
Reframe the Intelligence Question
To ground the question of extended general intelligence, start with how you already think with tools. You don't redefine human intelligence when you use a calculator or a notebook; you extend it. The same premise holds for AI: it's a cognitive extension that scales memory, pattern recognition, and iterative exploration without replacing the human core that steers judgment.
Consider a project kickoff you recorded last Friday. You ask an AI system to produce decisions, owners, and deadlines from the transcript, then you scan the output and correct two misattributed tasks before sharing it. The tool acted as a cognitive prosthetic for attention and recall; your sense-making and accountability stayed human.
If that's the frame, the debate shifts from “What is intelligence now?” to “How do we design a good extension?” This leads us to examine the distinct ways AI can extend your mind.
Differentiate the Extension Modes
From that starting point, it helps to name the distinct ways AI extends your mind. There are three useful models: the prosthetic (offload or amplify a specific function), the mirror (reflect your reasoning so you can see it), and the partner (co-explore options within your aims and constraints).
Take a grant proposal you're drafting. You ask AI to total a budget and flag off-by-one errors in your spreadsheet (prosthetic), then paste your argument summary and request a list of assumptions that, if false, would break it (mirror), and finally co-design a milestone plan that meets a funder's page and compliance limits (partner). Same tool, three roles, one human-directed workflow.
When these roles stay distinct yet connected, you can move from offloading to insight without losing authorship.
This clarity about modes sets the foundation for building a repeatable process that keeps you in control.
Run the Symbiotic Loop
With those modes in view, the next step is to run them as a single symbiotic feedback loop. The loop works when your intuition proposes a direction, AI surfaces patterns you'd miss at speed or scale, and your metacognitive reflection aligns the next prompt and decision.
Picture a city team exploring congestion pricing. A planner drafts principles, equity, throughput, air quality, then asks AI for historical cases with comparable population density and policy goals. The model returns candidates with pros and cons; the planner probes mismatches with local bus coverage, updates constraints, and requests a sensitivity analysis on peak-hour fees. Human aim leads; machine patterning expands; human judgment integrates.
To make that concrete, here's a micro-protocol you can run once per significant decision:
- Draft your reasoning in plain language, including aims, constraints, and a first option.
- Ask AI to list hidden assumptions, edge cases, and missing comparisons; request citations or pointers to source types if available (UNVERIFIED).
- Revise your frame, then ask for alternative options that satisfy your constraints and stress-test your favorite against them.
- Decide, then capture why you chose what you did; set a review trigger to evaluate outcomes later.
This loop keeps you in authorship while using language as interface to shape better options. To make that loop trustworthy, you need a few design commitments.
Design for Cognitive Alignment
Once that loop is moving, design choices decide whether you gain alignment or drift into atrophy. Cognitive alignment means your use of AI strengthens your inner architecture, attention, memory, and judgment, instead of eroding it.
One practice is to recall before you retrieve. If you're preparing for a stakeholder meeting, write your own summary from memory first, then check it with AI against the last two meeting notes. You'll feel where your memory is thin, and the comparison tightens learning rather than outsourcing it.
Another practice is to fix the interface friction that muddies the human-AI handoff. When you ask for a policy summary, specify audience, constraints, and known unknowns, then verify any quoted passages against the source documents you control. Last month, a team caught a confidently phrased but wrong interpretation of a procurement clause by checking the exact paragraph in their contract library; they kept the model but improved their prompt and verification path.
Finally, treat the mirror as a trainer, not a critic. When an AI flags a gap in your reasoning, restate your argument with the correction in your own words. That strengthens metacognitive reflection and keeps the thought-identity loop in your hands. With those commitments, we can return to the bigger claim without handwaving.
Evolve Without Redefining
With practical guardrails in place, we can answer the original question directly. Extended general intelligence doesn't redefine human intelligence; it changes the environment in which intelligence operates by adding scalable cognitive extension.
Think of a weekly journal you keep with an AI mirror. You write a short reflection on a tricky decision, then ask for patterns across the month: repeated triggers, default moves, and the alternative you ignore. You choose one small experiment for the coming week, then revisit whether it shifted outcomes. The tool helps reveal inner patterns; self-awareness chooses the change.
The point isn't artificial general intelligence running on its own; it's augmented intelligence that keeps meaning through coherence: your aims, your values, your decisions, amplified by tools that show you more than you could see alone.
When we center authorship, verification, and reflection, AI becomes a clarity vessel rather than a replacement mind. Start with one decision this week, run the loop, and let your own results tell you what to extend next.
Here's a thought…
Before your next big decision, draft your reasoning in plain language, then ask AI to list hidden assumptions and edge cases you might have missed.
