An Ontology of Havoc - The Disruption of Generative AI

I will be the last. Of this I am sure. But my cohort feels it too. I will be the last to really read books. The last to really write for myself. The last to be confused and lost in thought. The last to make connections as I walk. The last to be assessed as human alone. I am the last in a landscape of havoc and fracture, looking on as we cling to the past as if it is our answer.

When I jumped into the new doctorate last year, my commencement coincided with commercial emergence of the scaled Generative AI tools. ChatGTP was in my intake, became part of my cohort. It had been twenty five years since I dropped out of my PhD programme, and the experience had been solitary, slow, and frustrating, as I sought to find meaning and momentum in a wide and confusing landscape. Today I think it is almost impossible to disentangle elements of Generative dialogue, social collaboration, and community, from the evolution of the work itself.

And for clarity, I don’t mean plagiarism. I mean something far more dynamic. Learning itself is fracturing at speed. For me it is not really a question of whether our education or organisational systems can cope – they clearly cannot, and in many cases do not even see the truck speeding their way – but rather it is an evolution of our social and societal relationship with knowledge, and a radical re-contextualisation in the creation of meaning. It is curiosity unleashed.

We could perhaps view this in four dimensions:

1. Dialogic Engines – synchronous iteration and exploration of ideas, warping legacy ideas of trust, self doubt, foolishness, failure, and curiosity. As we wrote in ‘Engines of Enagement’, Generative AI makes high quality dialogue a commodity, but not simply as a service – it shifts the social context of such. So we can be in dialogue as a solo feature, removing all social judgement of curiosity and ignorance, if we dare.
2. Agentic Retrieval – not just a search engine, but a context setting system. These tools can shift the boundaries of context – not telling us what we asked for, but giving us what we may need. And from a perspective of virtually unbounded knowledge. We can factor this into our dialogue – asking for breadth and challenge to our thinking – or we may find it just lands. I think that systems shifting context is highly significant, as the fracture and evolution of context is a key part of insight and even paradigmatic change.
3. Trans-disciplinarity as the norm: our taxonomies of knowledge are not natural, but rather shaped by legacy mechanisms of need, discovery, ownership, and understanding. We have tended to segment our knowledge and hence structures of learning (as well as power, status, and identity) vertically around these themes. So we have engineers and poets, but not many poetic engineers. I think Generative AI changes this in significant ways, if we allow it to: permits a broadening of vocabulary and conception, a translation engine if you like, but also a provocative one – if we ask or if it offers.
4. The Primacy of Sense Making: I’ve said for some time that knowledge itself is shifting in the context of the Social Age, and Generative AI scales this change. The latest GenAI tools are Engines of Synthesis, reflection and contextualisation, leaving us in a radically broadened landscape of sense making as individual and collective feature. And I don’t think sense making per se is at threat of absorption by technology. Not that the Engines cannot make sense, just that our act of consumption is inherently linked to re-contextualisation and insight. In other words, if the technology has already chewed it over, we will chew it over again. It just broadens the space and foundations for us to do so.

So, Dialogic Engines that scale and reframe the social context of ignorance, Agentic Retrieval that erodes and shifts the boundaries of knowledge, a Technology of Trans-Disciplinarity that brings new voice and acts a paradigm engine, and the Primacy of Sense Making, which embeds the human in the loop, but within an elevated landscape.

But are our systems ready for this? Not the ones I sit in now. Wiser minds may have a great answer to this, but I cannot see many aspects of doctoral study that will not be significantly impacted, accelerated, or fractured, by this new context. Not that a doctorate will become worth less, but that it may become more. If we allow it to.

And not just doctorates. Whilst the doctoral level work is explicitly about making original contributions to knowledge, Masters and Undergraduate work requires knowledge and synthesis, critical appraisal and demonstration of context and evolution. And when we move further down the chain, into schools, the benefits become even clearer, if the systems are able to adapt.

The contrasts are stark: on the day that Donald Clark has shared research showing solid research on the benefits of Generative AI in terms of knowledge gained and retention, my sons school has told me that he is essentially sat in class doing not much as the books are three weeks late being delivered.

If systems are unable to adapt, there comes a point when we question the utility and validity of the system, and at a time when UK universities are asking to raise course fees – again – one may question how long the institutions themselves can survive.

An interesting feature of the Generative AI revolution is that whilst the technologies themselves are monumental, both in terms of complexity and physical energy and scale, it may well be individuals, at scale, who drive the true change. Not a single technology that breaks in, but rather people breaking out. Breaking out of restrictive and constrained structure.