Embracing Superintelligent Documentation

While AGI refers to performing tasks at a human level, superintelligence refers to performing tasks that exceed human capabilities. If tech writers want to survive the AI apocalypse, we'll have to go beyond mere AGI levels of competence and tread water within the superintelligent space.

Reason being, AI will eventually replace most of what we do, making it such that when AGI is reached, job displacement for tech writers will be more common because AGI will perform the same tasks, only cheaper. But the likelihood of AGI progressing to Superintelligence seems less likely to me (in the same way that moving from assisted driving to fully autonomous driving is so much harder than anyone anticipated). Striving for superintelligent docs seems like the most logical counter-move against AI's encroachment on tech writer territory.

The Librarian's Dilemma: An Analogy for Technical Writers

To use an analogy, imagine you're a research librarian at a university. An AGI-capable system can now catalog books, answer reference questions, and locate resources twice as fast as you can. It can even generate customized reading lists based on a student's research interests and learning style, adapting its recommendations based on their feedback. Soon, the university starts considering why they need human librarians at all when the AI system costs a fraction of your salary.

If the human librarian wants to remain employed, they must transcend what the AI can do. They need to operate at a superintelligent level of librarianship. What might this entail?

The librarian might evolve into a research mentor and community builder—creating connections between students and faculty working on related topics, facilitating interdisciplinary collaborations that the AI wouldn't recognize as valuable. They become experts at helping researchers formulate better questions, not just find answers to the questions they've already articulated. The library transforms from a resource repository into an intellectual hub where serendipitous discoveries happen through human connection.

The human librarian can't possibly have the encyclopedic knowledge of the AI, so they leverage that same AI as a tool while providing the human insight, intuition, and relationship-building that the system lacks. They've transcended the AI's capabilities by operating at a different level entirely.

Technical writers now face a similar challenge. Our documentation must offer something beyond what AI can generate—it needs to solve problems that AI, even at an AGI level, struggles to address.

What Are "Superintelligent Docs"?

How do you define superintelligent docs? While various definitions could fit this bill, such as automated documentation that predicts topics based on user behavior or errors, or documentation that dynamically writes itself around the user's specific situation and needs, those imagined experiences are a bit too futuristic for me.

More realistically, superintelligent docs solve wicked problems. Wicked problems are so massive, gnarly, multifaceted, and hard to tackle—for example, they include variables that are constantly changing based on recurring feedback loops, etc.—that they are beyond merely complicated problems; they are complex problems. Solving them seems beyond human capabilities.

Wicked Problems in Technical Communication

Do wicked problems exist in the tech comm space? Personally, I've been contemplating throwing myself headlong into a potentially wicked problem in my domain: documenting the data model of the world that becomes a map. Basically, there's a bunch of map data in a giant database, details added by hundreds of map operators—this becomes the source from which geo-related APIs are hewn to surface the data. Without going into too many specifics, this could be a wicked problem due to the following:

• The model exists across multiple groups in different organizations. The groups have different terminology and interpretations of the data, with unique needs. The groups who input the database information differ from the engineers who build the APIs.
• The database information doesn't necessarily correspond with the API outputs. In other words, the APIs might transform and manipulate the underlying data for specific calculations and needs.
• The model is described in various documents and sources owned by different teams, from engineering source files to knowledge bases to Google Docs and more. The source documentation isn't consistent, organized, or structured in any readable way.
• The scope of data elements is overwhelming. When diagrammed into a tree, there are many hundreds of elements, with conditional logic and other nuances that make it less straightforward.

AI as an Ally, Not a Replacement

We can't tackle wicked problems without leveraging AI tools. Commonplace tasks (such as preparing release notes based on what's changed in an API) can conceivably be replaced by automated AI tools that can decompose the tasks into a few discrete steps that an agent can chain together into a process that yields decent documentation. However, wicked problems don't easily decompose into a series of small steps that can be chained together into a process yielding an outcome. For example, the scope of content included in a wicked problem probably exceeds any AI model's token limit to process. Wicked problems require a human mind to bring a strategy and plan to the problem.

Addressing wicked problems will invariably require tech writers to leverage AI tools to perform analyses, extractions, and manipulations of source data. But these wicked problems aren't something that a machine would likely be able to execute on without AGI transforming into Superintelligence.

The Funding Conundrum

I want to address one more variable with wicked problems. A core challenge in tackling wicked problems is identifying funding. When a problem cuts deep across multiple organizations, the corporate budget model tends to fall flat. Engineering budgets pay for tech writers to document the APIs that its engineering teams create. Spread your wings too far and try to climb too high in your ambitions of tackling a wicked problem and you'll soon find that you're no longer in the funding group's atmosphere and orbit. And they're not paying for it.

Further, while system thinking (with books Thinking in Systems by Donella Meadows, or The Fifth Discipline by Peter Senge) tends to speak to complex and wicked problems, I find that system thinking in documentation projects usually goes beyond any particular techcomm project or budget concerns.

At my work, our tech writer group once sought to publish a series of lifecycle workflow documentation that would describe the life of something across many different systems, states, and teams. It was fascinating to see how data might be transformed from beginning to end. As an analogy, it was like putting a tracker on monarch butterflies and watching their migration pattern across multiple generations and continents. However, there was little executive support for such a project. Only high-level product managers and executives needed this birds-eye view of the technology, and they didn't want to acquire it by reading long-form documentation. They wanted someone to talk through diagrams.

This is the conundrum of focusing on wicked problems: no one specifically owns them. If no one owns them, no one funds them. This is part of why the problems grow, extending their roots deeper into the ground. Thus, although I've argued that focusing on wicked problems might be the only shield against AGI-capable AI tools replacing tech writers, it's still questionable as to whether wicked problems will provide the sustenance tech writers need to provide organizational value.

The Path Forward

What do you think? Do tech writers need to tackle wicked problems and deliver superintelligent docs to stave off job displacement from AI tools? Or is this scenario too far into the future to be real? Whether the AI trajectories are bogus or not, setting our sights on wicked/complex problems seems like a noteworthy goal. As a profession, we could stand to be a bit more ambitious. With AI at our disposal, we might have the tools we need to be successful.