We face a future of unprecedented political, social, and economic uncertainties at a time when imbalances, inequalities, and iniquities couldn’t be more stark. Almost out of nowhere artificial intelligence emerged and shook the world to its foundations.
Industrial Revolution 2.0 beta release
Eric Schmidt, once CEO of Google, was booed by graduating students at a university commencement address in Arizona when he mentioned artificial intelligence:
Schmidt said that information technologies, including AI, had unsettled young people. “That was not the plan, but it happened,” he said.
Shouting and jeers against Schmidt’s talk started when he acknowledged fears that AI threatened to deprive people now entering the workforce of a future.
While real estate executive Gloria Caulfield was also booed by graduating students at the University of Central Florida, having described artificial intelligence as the next Industrial Revolution:
“Woop, what happened?” she asked upon hearing the negative reaction. “OK, I struck a chord.”
OpenAI CEO Sam Altman’s San Francisco home was attacked twice in three days, motivated by a growing hatred of artificial intelligence and anti-AI sentiment.
Erin Brockovich has begun campaigning against the proliferation of data centres throughout the United States of America, while the Trump administration has chosen to interpret the public anxieties surrounding the genuine threat of environmental degradation and significant job losses as a threat to progress.
You know what the biggest problem with pushing all-things-AI is? Wrong direction. I want AI to do my laundry and dishes so that I can do art and writing, not for AI to do my art and writing so that I can do my laundry and dishes.
At the time of writing, this comment by Joanna Maciejewska has 91k likes and 20k reshares on X.
Chief Scientist at OpenAI Jakub Pachocki has committed to releasing an AI system equivalent to a research intern by September 2026 and an automated researcher by March 2028. But what sort of research is Pachocki and OpenAI focusing on?
I’ve asked 243 students, knowledge workers, and journalists about their knowledge management challenges, and in spite of those challenges they love what they’re doing. I suspect the sentiment of Joanna Maciejewska would resonate with them more than the ambitions of Jakub Pachocki.
AI is no KPI
Navigating these uncertainties is going to be the most challenging and consequential thing I do, and I have to somehow accomplish sustainable success from a standing start.
You see, there’s a world of difference between using AI as a labour-saving assistant, and allowing it to do the cognitive heavy lifting. If the introduction of the pocket calculator was disruptive in the classroom, AI has the potential to be disruptive on a scale we’ll struggle to comprehend let alone respond to as its vast potential is explored.
Years ago, Under Cloud’s roadmap had an obstacle in the distance that I wasn’t able to navigate until the middle of 2025, and that obstacle was AI. Without the resources, I had no choice but to wait until those circumstances changed, but that also risked losing ground to everyone else who had the financial support I didn’t.
Prior to the emergence of AI, a lot of the products in the personal knowledge management space were a bit of an enigma, in that without knowledge creation there was no knowledge management. All that knowledge creation happened in the imaginations of their customers. But then AI came along and the personal knowledge management space became AI-shaped.
But knowledge is so much more than a report, a graph, chart, or presentation that emerges at the end of that stint of work. Do we delegate our thinking to AI? Not without eroding the situational awareness we have of our work as it exists across spans of time that we know but fail to appreciate as something distinct and navigable. What we were thinking during that period of work, what research was useful and what wasn’t has value, the sort of value that allows us to avoid repeating a mistake.
I assume most of us use some AI client (I do) and have umpteen conversations sorted into project folders. Let’s assume all of those conversations are brimming with knowledge (not a safe assumption, but let’s go with it). Now fast forward 6 months and attempt to make sense of those same conversations.
Here, the AI client is no more useful than the crowded market of personal knowledge management products, where the actual management begins and ends with folders and tags, and the creation belongs to you.
We’re witnessing the signal become the noise.
Creation is simple, finding and combining is the challenge.
56% find structuring (linking ideas and combining research) difficult, against 34% for creating notes (2021 study, 62 respondents).
Under Cloud is an exploration of that liminal space, operating in the aftermath of interruptions, enabling us to create the connective tissue of context that binds thoughts and ideas into structured and navigable narratives.
Skip to the end
The most fearsome challenge we face when using artificial intelligence is how we (struggle to) hold at the labour-saving phase without allowing it to do the cognitive heavy lifting, because when that happens, we skip past the learning phase where we build our understanding and go straight to the end, where the answer is waiting for us.
Under Cloud uses AI, of course it does, but it does so with deliberate caution, understanding that with the reward of its judicious use come enormous risks. I have to find a balance between avoiding what I believe to be the mistakes everyone else is making while not creating a product with constraints so restrictive few would find it useful.
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