When I look around me at the moment and talk with people I trust about AI, I’m reminded of Gulliver, as he finds himself waking up tied down by hundreds and hundreds of small threads. He could break any one of them, but in aggregate, they were a problem.
We talk about AI as if it were a single thing; rather like electricity, the internet, or a new colleague. In reality, it is really an assembly: data gathered from the world, mathematical models trained to detect patterns, vast computing infrastructure, interfaces through which people ask questions, and increasingly the software tools that allow systems to search, calculate, write, recommend, and act. Around this technical core is what matters most: human purpose, judgement, authority, habits of work, and the rules by which organisations decide what should, and should not, be automated.
The danger of speaking about “AI” in the singular is that it makes the technology seem autonomous and inevitable. In practice, every AI system embodies choices about what information counts, which outcomes are valued, who retains control, and where responsibility lies.
And there is more than one Gulliver. Just as the technology is tied down by being viewed through a lens of investor greed, attention grabbing misinformation, institutional fear, and ambient uncertainty, so those individuals and organisations who could make real use of it to do something beneficial, creative, and generative are constrained by the same forces.
Whilst markets are concerned about the size of the economic pie and how it gets divided, many of the rest of us are more concerned about its nature. 10x-ing or 100x-ing a crap pie is not progress; progress is a better pie.
And now, we have the potential to combine a transformational general technology and the latent talent constrained by the thousands of tiny threads imposed by those who want things to stay as they are, but just bigger, to break those threads.
It took two generations for us to move the electrical power that replaced steam from an engine room to the machines themselves, from a central hub to the point of use. Engine rooms were what we were used to. Our large organisations are similar as they try to centralise and control the use of AI technologies to make a bigger crap pie. The people who can create things that make for a better pie do not need the engine room. They do not need bureaucratic departments of Lilliputian threads, because they are close to where things are happening, and can react in real time. Yes, they need capital, but capital is notoriously promiscuous and it will go where it needs to.
Untangling the threads
The threads have been created over a long period, starting when F.W.Taylor separated head and hand in pursuit of scale. It was a brilliant insight, and offered us a thread of thinking that gave us a steady flow of ever more entangled threads whose latest layer has been Agile. Each of them refined the heuristic that Taylor turned into a prototype algorithm, until we lost the plot and forgot that heuristics are what gives us the seeds of better. We ended up building ever better algorithms to extract more from what we already knew, rather than find new areas to explore. We sidelined those who might help us create a better pie in favour of selling the one we knew how to make.
We have ended up with a really tangled knot that makes it difficult to move and extract what’s important from the detritus our activities have gathered along the way. If we are to enable our Gullivers to do their best work, we need to find a way to untangle it.
The price we pay for the efficiency and effectiveness of algorithms is the deprioritising of the thinking that gave us the heuristics that gave rise to them in the first place. We found ourselves prioritising marginal gains over critical thinking, efficiency over effectiveness, and playing time bound games with rules rather than long term puzzles. It has taken us to a place where our focus has become intensely operational and vision has taken a back seat. We’ve ended up educating our children to perform rather than to wonder. We’ve taught them to manipulate phonemes, graphemes, and split digraphs to treat reading as words to be decoded, rather than sitting within the joy of a book. Perhaps, most insidious of all, we’ve forgotten how to play for the sake of it.
Because it is play that will break the threads.
AI as playmate
We do not yet know how what we’re doing with AI will settle down, but for the sake of argument, I’m going to divide it into three areas. Where the algorithms are clear enough, reliable enough, and we can automate, we will automate. It seems unlikely that we will be able to automate entire jobs, but we can probably automate substantial chunks so that we will not eliminate job categories, but we will change their nature. We can use AI to delegate; perhaps the most contentious, beneficial, and dangerous area. When we delegate, we carry the responsibility. We’re responsible for the brief we give it and the thought that we put into that brief, just as we’re responsible for reviewing and discerning the quality of what it produces. It’s classic Master and Emissary territory, a careless delegation or delegation under pressure can easily be our undoing. The area that excites me is the third one, which is AI as thinking partner, and its ability to help us speculate, research, experiment, and play. To have the conversations that we might not yet want to have in company and particularly inside an organisation where there are political predators always present in the undergrowth. Despite their sycophancy, Claude, ChatGPT and the rest do not think, but they’re extraordinarily good at parsing the data they have been trained on. If we use them thoughtfully, we can give them personas, set them off to do research, interview us, and challenge us. To help us sharpen our thinking in ways that goal-based conversations with performance consequences never will. We can expand possibilities when we take our ideas into a group of other people doing the same and allow conversation to work its magic. We do not need the biggest, most powerful, most expensive tools to help us. In many ways, using one of the expensive models to help us play is a bit like hiring a jumbo jet to drop the kids off at school. There will be a place for the most expensive models, but we don’t need those until we’ve done the work of being human, of imagination, creativity, and wonder, and bringing something new into the arena.
Play is not a process, but it will set our own particular Gulliver free.
Learning to play again.
Work is such an important part of our lives and brings us so much in addition to the ability to pay the mortgage and feed the children. In making so much of it miserable and ending up with such high levels of disengagement is something that leaders and managers of “performance at all costs” organisations should be ashamed of.
The first step is to understand this technology in order to shape how we use it rather than be shaped by it. To learn to play with it, without a goal in sight, just to discover its boundaries. Not to compete by how many tokens we use, but rather share the fun of the new ideas we come up with. One of the reasons we like working with small, founder-led companies is that they know how to do this in ways that large organisations, beset by politics and investor expectations, cannot.
So in the first instance, it’s simple: learn how to play with this technology before you even think about harnessing it. Do it in the company of others whom you trust. Small groups, you’re happy to play with. Recover your inner three-year-old.
If you want to explore the idea further, visit us at the Athanor or drop me a line. We are already doing this with small groups and finding our way.
We cannot offer you solutions, guaranteed ROI, or anything else at this point, but we can help you have some fun with AI, and find the heuristics that will provide growth when the algorithms run out.