Common feedback from users of Chat GPT is the reduction in time it takes to retrieve relevant information. We could refer to this as distance to insight (Omnity, 2019), the time it takes to move from point A, a desire for a solutionanswer to point B, the satisfaction of the solutionanswer. What follows assumes that Chat GPT has provided a factually correct answer, a proposition.
Using the above definition, that Chat GPT shortens the distance between A and B, it therefore makes the route taken to get from A to B, more efficient, a straight line. This is illustrated in the diagram below-
Above, the first approach.
The potential problem with making the distance to insight, the route between A and B more efficient, is that we do not notice and attend to alternative routes. This has two effects. Firstly, we have not evaluated the route ourselves, and secondly, we have not participated in the journey from A to B.
If we do not participate in the journey, we cannot notice alternatives. For example, if we carried out the research ourselves, we may notice, and attend, to a path which is not as efficient but generates richer insight. Taking this path may even revise the framing and complexity of our point A, the initial problemquestion, making it richer and more productive. The discovery route is illustrated in the diagram below-
Above, the second approach.
The red marks on the discovery route depict insights. In the above example, we have extended distance to insight but increased volume of insight. Increases in volume are likely to bring greater context to point A and the chance to evaluate options. In other words, we have increased our perspectival knowledge, tightening our optimal grip on point A (Merleau Ponty, 1945).
Increased perspectival knowledge helps facilitate increased participatory knowledge, the ability to know how to fit effectively into a situation for the greatest effect. This is very similar to Merleau Ponty’s (1945) optimal grip, knowing how to effectively conduct oneself in a range of contexts and environments as they change and develop. Perspectival knowledge provides leverage points, participatory knowledge delivers insights on when to deploy leverage points in dynamic complex environments effectively.
It is vital to note there is a time and place for both approaches. Time constraints make efficiency essential, and taking the second approach in this situation would be counterproductive, the opportunity would be gone. This leads us back to participatory knowledge.
When using Generative AI we need to focus on how to participate with the technology in relation to our goal. This is another strand to our existing participatory knowledge, how to gain an optimal grip on human/AI interaction to achieve goals.
In this post we have discussed two routes to insight. If we are time constrained, taking the shortest route from A to B could be essential to success. This would be the simple retrieval, and then application, of a proposition supplied by the AI. If time is less of a factor, perhaps the more effective mode of participation would be to use the AI as a travelling companion, not the sole navigator. Sometimes the longest route is the most rewarding.
Reading
Omnity (2019) Use Cases and Value Propositions. Omnity. Menlo Park. Silicon Valley.
Merleau-Ponty, M. (1945). Phenomenology of Perception. Routledge.
