Humanity is Changing Search. That’s Good.
Machines are starting to talk human. This is a fundamental shift and is a profound change for the start of the cognitive age.
In 2008, technology writer Nicholas Carr wrote a six page article in The Atlantic titled “Is Google Making Us Stupid?” It wasn’t a rail against Google specifically, but rather the cognitive impact of the internet, that it could threaten our stupidity. It didn’t. Now we have “answer engines” and search engines are offering up more comprehensive answers. Will they make us stupid? Actually, I argue, quite the opposite.
The very nature of how we search, from how we use it to why, signals a profound shift that is more meaningful to our relationship with machines than generative AI. Bold statement. What is happening is that machines are now learning to speak human, rather than the other way round, which it has been for decades.
In a way, we can see the early decades of search engines and how we used them as the digital version of our hunter-gatherer period. With answer engines and LLMs, we are entering the agrarian age of our digital evolution.
In way, this is a reclamation of our cognitive sovereignty. For decades, our interaction with information systems, from knowledge bases at work to search engines, has been about keyword stuffing, teasing with boolean operators, tags. We were forced into algorithmic contortions.
But as we integrate LLMs into search with answer engines like Perplexity and ChatGPT and now Claude accessing the internet, we are witnessing the first major technological revolution where the machines, the algorithms, are adapting to us rather than us to them.
Culture has, throughout human history, been the ultimate arbiter of the success, or failure, of all technologies. From the stone axe to the smartphone. After decades of contorting our brains to play by the rules of search and information systems, we are now seeing the reassertion of cultural patterns over technology constraints. A sort of reverse acculturalisation if you will.
What might really be interesting though, is that this enables us to free up cognitive energy. The mental energy we spent crafting search engine queries can now be used to explore with deeper questions. We are already seeing a shift in people writing out longer queries in search bars. We’re already expecting the machines to respond to our demands, not the other way around.
This is also of benefit for various cultures that see and engage with the world differently than Western European concepts, which have dominated search engine design for decades. Now, Indigenous knowledge systems, analogical reasoning and narrative-based inquiries may find new expression and means of understanding.
As the nature of search engines change, we see new forms of human-AI communication evolve that neither could have developed independent of one another. We enter a time of reciprocal learning with machines and algorithms. A consistent, steady feedback loop.
Answer engines also provide a form of ontological reclamation. In this new environment the world of searchable information is being reorganised to human conceptual categories, rather than the optimisation of algorithms.
There is a social power shift as well. The expertise needed to carry out comprehensive, deep searches , shifts from the technical to the natural language humans speak. This creates a greater democratisation of knowledge.
Now, technology shoulders the the cognitive load of translation; across languages and cultures. Interfaces can evolve toward more human modes of communication, although this will take perhaps a decade or more to become truly widespread.
Over decades, many of us have learned to develop mental models for navigation different information architectures. If you’ve ever worked with ERP program in an enterprise, you know how painful that can be. This was a form of what we might call a “cognition tax” that we no longer need. Shifting our learned behaviours and these systems will take a while though. Lot’s of unlearning and relearning will be needed.
There are, however, some early challenges to this shift of engaging with computers. Those who can speak more effectively to AI systems will gain privileged access. This is temporary, just as we’ve already seen the role of “prompt engineer” begin to fade in the workplace.
Another downside we may see is the reduction in the “stumble factor” of discovering new things such as when one ended up going down the rabbit hole of a Wikipedia search. This could limit our imaginative horizons. We can overcome this by treating AI tools as teammates and applying critical thinking and design thinking methods.
The relationship between humans and information systems is inverting. Rather than make us stupid, even as some suggest today, we should see this as an evolutionary win for humans information technologies. If the history of search engines tells us anything, we are far more clever than some might give us credit for.