Digital Economy Dispatch #258 -- Why GenAI is Driving Me Crazy!

Over the past couple of weeks, I have found myself wrestling (sometimes productively, often painfully) with the growing ecosystem of GenAI tools meant to make my life easier. My intent is simple: to use AI as a companion in researching, writing, and analysing the complex materials that underpin most of my projects. The outcome has been enlightening but also disorienting. These tools, without question, are powerful assistants. Yet, the more deeply I weave them into my workflow, the more I encounter a creeping sense of fragmentation, inconsistency, and loss of control. I think I may be losing my mind!

Three issues are starting to dominate this experience. Each speaks to the deeper management and governance questions that we confront as AI becomes a more integral part of our professional and creative lives.

1. The GenAI Mosaic Problem: Too many tools, too little coherence

At this point, my working environment includes well over half a dozen GenAI tools (each with several model variants), including ChatGPT, Claude, Gemini, Perplexity, Cursor, Grok, and a few others. Each offers a slightly different way of thinking, writing, or reasoning. Each promises some breakthrough combination of intelligence and efficiency. Yet none of them behaves the same way.

A task that one tool performs flawlessly might completely baffle another. One generates articulate summaries but fails at factual consistency. Another is stronger analytically but struggles with tone or syntax. All of them make mistakes -- sometimes trivial, sometimes catastrophic -- but never in consistent patterns. Furthermore, the errors drift, shift, and mutate in ways I struggle to follow.

The sheer variety of available GenAI tools at first sounds like a luxury. In practice, it introduces a new admin problem: GenAI tool management. I now spend almost as much time deciding and experimenting to work out which tool to use as I do on the actual work. It has become a constant balancing act of functionality, reliability, and interpretability. And I can’t seem to find a stable pattern.

This raises a strategic question. As AI systems proliferate inside organisations, who will design the frameworks that coordinate them? More importantly, who will own and maintain the “logic of selection” so we understand the rationale behind which AI is trusted to do what? This is not just a technical question; it’s a consistency, responsibility, and governance one.

2. The GenAI Reliability Problem: Progress that collapses without warning

But it gets worse. Just when I believe I am getting somewhere, the tools collapse --sometimes figuratively, sometimes literally. A carefully constructed prompt chain will suddenly fail because a model version has been updated. A session times out. Usage limits are reached. The interface locks me out with a cheerful yet unhelpful “something went wrong” message. Thanks!

Even when the systems technically function, their internal behaviour changes subtly over time. A phrase that once produced a rigorous, well written business analysis might now yield a list of random bullets. A model that previously understood structured referencing now reinterprets the same command in a new way based on citations that don’t exist. There is no stable baseline of reliability or accountability.

In traditional business terms, this would be catastrophic. Imagine any other supplier who unpredictably changes how they deliver services without notice or recourse. Or whose product operates differently each time you use it. Yet with AI tools, we’ve normalized this volatility as “innovation” and “learning from experience”. The consequence is not merely irritation; it destroys trust.

So, what does “process control” look like when the tools themselves evolve faster than the workflows built around them? This is an emerging leadership question. Managing people was about setting expectations and ensuring repeatability. Managing AI systems will demand a new mentality that acknowledges fluctuation and uncertainty as the norm, not the exception.

3. The GenAI Provenance Problem: When origin and authorship disappear

Of course, I’m finding lots of ways that GenAI tools are speeding up tasks. So, I find I’m drawn into using them more and more. However, the deeper I integrate multiple AI systems into my workflow, the harder it becomes to trace how any specific piece of content was created. Sections of reports, ideas for frameworks, or fragments of analysis now flow from tool to tool in an increasingly opaque process. When something finally looks brilliant, I’m often unable to say how it came into being, or even who authored it: me, a model, or some unresolved mixture of both.

That might sound rather an abstract issue, but in a corporate or policy context, it’s highly practical. How do we validate a document or report’s accuracy if we don’t know how the insights were generated? How does accountability work in a blended human-AI authorship environment? If asked to reproduce a result or defend a line of reasoning, the trail is gone.

For me personally, this opacity has created hesitation. Even when I generate strong outputs and reports I consider of high quality, I find myself reluctant to release them. If I can’t fully document the provenance or verify the factual lineage, I can’t ethically stand behind the result. The irony is that the more capable AI becomes, the less confidence I have in output integrity. I’m losing track of how much comes from me, and how much comes from the AI tools.

Towards a New Discipline of AI Work

These experiences have left me with a strange mix of admiration and anxiety. GenAI is an extraordinary assistant and amplifier of my capabilities. I can do much more with these tools than I ever could before. Yet it is also an amplifier of confusion, errors, and cognitive overload. I have, unintentionally, entered what feels like a new phase of “AI‑centric creative ambiguity.” It’s a state where I am more productive than ever, but less certain about what I’m actually producing.

I don’t think this is just a personal nuisance. It points to a structural gap in modern digital practice. We are missing a discipline of working with GenAI: a set of methods, audit trails, and governance approaches that help creators, analysts, and decision-makers keep track of what happens inside the AI ecosystem they depend on.

Perhaps this new discipline will resemble quality management systems for AI-assisted processes with something that is a mix between version control, data governance, and creative attribution. Or perhaps it will evolve into an entirely new profession: “AI work designers” responsible for ensuring that human‑AI collaboration remains transparent and defensible. I really don’t know.

Until then, I continue operating in a kind of experimental space, where GenAI’s brilliance and brokenness coexist. Perhaps the key is to recognise that this isn’t a passing inconvenience. It’s an early sign of what happens when intelligence becomes distributed, unstable, and shared. The challenge now is not whether I can use GenAI to produce good outputs quickly, but whether I can use it responsibly, reliably, and repeatably.

And for now, that remains a work in progress.