Looking back over the past few months, I realize just how deeply I’ve fused my daily life with AI. Most of my routine tasks are already handled alongside it. Research, small repetitive work, even writing code can now be delegated. The most striking change is that tools built solely for my own efficiency are now fully automated by AI.
What’s especially fascinating is that even complex tasks—such as online banking operations—can now be automated in ways tailored specifically to an individual’s needs. For example, importing bank statements, categorizing them based on personal rules, and restructuring them as accounting data. What once required compliance with the frameworks imposed by financial institutions or accounting software can now be achieved simply by giving natural language instructions to AI.
The key here is that, unlike commercial products, there’s no need to satisfy “universality” for everyone. Personal quirks, rules that only I understand, exceptions that would never justify engineering resources for a mass-market service—these can all be captured and executed by AI. What used to be dismissed as “too niche” is now fully realizable at the individual level. Being freed from the constraints of general-purpose design has enormous value.
Even more revolutionary is the fact that APIs are no longer necessary. Traditionally, automation was possible only when a service explicitly exposed external connections. Now, AI can interact with data the same way a human would—through a browser or app interface. This means services don’t need to be designed to “export data.” AI can naturally capture it and fold it into personal workflows. From the user’s perspective, this allows data to flow freely, regardless of the provider’s intentions.
As I noted in my piece about Tesla Optimus, replacing parts of society without changing the interface itself will become a defining trend. AI exemplifies this by liberating usage from the design logic of providers and putting it back into the hands of users.
This structure leads to a reversal of power. Until now, providers dictated how services could be used. With AI as the intermediary, users can decide how they want to handle data. Whether or not a provider offers an API becomes irrelevant—users can route data into their own automation circuits regardless. At that moment, control shifts fully to the user side.
And this isn’t limited to banking. Any workflow once constrained by a provider’s convenience can now be redesigned by individuals. Subtle personal needs can be incorporated, complexity erased, external restrictions bypassed. The balance of power over data—long held by providers—is starting to wobble.
Of course, AI itself is still transitional. “AI” is not one thing but many, each with distinct characteristics. At present, people must choose and balance among them: cloud-hosted AI, private local AI, or in-house AI running on proprietary data centers. Each has strengths and weaknesses, and from the perspective of data sovereignty, careful selection is essential.
Still, living with multiple AIs simultaneously brings a sense of parallelization to daily work. Different tasks with different contexts can be run side by side, allowing me to stay focused on the most important decisions. Yet at the same time, because AI increasingly performs the research feeding those decisions, the line between my own will and AI’s influence grows blurred. That ambiguity is part of what makes this fusion fascinating—and also why the health of AI systems and the handling of personal data have become more critical than ever before.