AI can make it easier to start workflows, automations, and side projects—but starting faster is not the same as finishing more. The “7” to “87” in this headline is Thanh Pham’s personal count in a 2026 Asian Efficiency article, not a study or a statistic about AI users generally. His practical answer is to slow down at a few deliberate points: learn a task before automating it, make a rough first version, ignore suggestions that distract from the result, and review what you actually completed and understood.
Why faster building can leave you with more unfinished work
In his September 17, 2026 article, Thanh Pham says he went from seven half-done projects before AI to 87 afterward. Those are his stated personal counts, not independently verified measurements. The useful idea is not that AI inevitably creates a bigger backlog; it is that reducing the effort to start a project can make it tempting to start more projects than you can finish or understand.
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Pham calls the gap between building systems and understanding them “knowledge debt.” It is his framing, not a formally measured phenomenon. In practical terms, the debt appears when an automation exists but you cannot explain its assumptions, spot when it has failed, or tell whether it still serves a real need. More output can then mean more maintenance and uncertainty, rather than more useful work.
Learn the task before you automate it
Pham’s rule of thumb is to perform a recurring task manually three more times before building a system for it. That number is his heuristic, not a proven threshold. Repetition can reveal what the task actually involves, which exceptions matter, and whether automating it is worth the setup and upkeep.
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- Choose one recurring task. Pick something you do often enough to evaluate, rather than an imagined workflow you have not tried.
- Do it manually three more times. Notice the steps, decisions, delays, and exceptions that occur in real use.
- Decide whether a system is warranted. Automate only if the task remains useful and its requirements are clear enough to describe. If the manual process is already easy, inconsistent, or rarely needed, a new system may add more work than it removes.
This approach trades a little speed now for better information about what an automation would need to do. It also gives you a clearer way to judge whether the eventual system is saving effort.
Make a rough first version instead of optimizing the setup
When the task is worth doing, begin with a rough AI-assisted draft rather than waiting for the perfect prompt, workflow, or tool. A draft gives you something concrete to inspect and improve; extended planning can become another way of delaying the work. Pham invokes Anne Lamott’s Bird by Bird for the rough-first-draft principle, but the book is optional reading, not a prerequisite.
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Keep the intended result visible while you work. AI tools may offer follow-up ideas or adjacent projects; accept them only when they help deliver the result you chose. Otherwise, record or dismiss the suggestion and finish the current piece before expanding scope.
Turn information feeds into action—or remove them
Digests, saved links, meeting transcripts, and posts can accumulate as easily as projects. Audit the feeds that push information to you: if a digest regularly goes unread, unsubscribe, disable it, or change its cadence. A collection of potentially useful material is not useful merely because it exists.
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For material you do want to keep, choose one idea for a small experiment each week. For example, take one useful point from a transcript or saved video and try it in a real task. This makes the weekly target an experiment rather than a promise to consume an ever-growing backlog.
Review what you finished and what you understand
A weekly review can help distinguish progress from activity. Start manually: look at unfinished projects, identify what moved forward, and ask which parts you understand versus which parts were handed off to AI. If you cannot explain what a system does or why it is still needed, that is a signal to inspect it before building another one.
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Pham says he uses a Weekly Retrospective automation that creates an HTML artifact. He also recommends a manual weekly review as a simpler starting point. Automating the review is a separate decision: compare the effort to create and maintain it with whether its output consistently helps you make better choices.
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Pham describes Markdown as useful input for AI and HTML as more scannable for people. That is his stated preference, not a general benchmark. Use a format that makes the review easy to read and maintain; a polished artifact is not valuable if producing it becomes another unfinished project.
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A small reset for an overloaded project list
If your list is already crowded, do not begin by adding a new productivity system. Choose one project that matters now, define what “done” means, and pause new work that does not support that outcome. Then apply the manual-first test to one recurring task and use a short weekly review to decide what to continue, finish, or stop.
The aim is not to avoid AI or to make every task manual. It is to ensure that each new system earns its place through a real need, a clear result, and enough understanding to use it responsibly.
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