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Four Tools, More Output, Less That Mattered
I adopted four AI tools in the space of six weeks last year. A writing assistant, a meeting summariser, a research aggregator, and something that was supposed to manage my inbox.
By the end of it I was producing more output than at any point in my career-and also, on reflection, considerably more of the things that didn’t matter at all.
The tools worked exactly as advertised. That was the problem.
The Amplification Problem
Why the Amplifier Doesn’t Choose
There’s an assumption embedded in most conversations about AI productivity tools that goes largely unexamined: that the primary bottleneck in knowledge work is speed.
If you could only do what you do faster, you’d achieve more. It’s a plausible idea. It also happens to be wrong for most of the decisions that actually count.
Speed is not usually the constraint. Selection is. The question of what to work on-which problems to engage with, which outputs to produce, which meetings to attend at all-is where most value is created or lost.
Tools don’t touch that question. They take whatever you hand them and do it faster, more fluently, at greater scale.
Excellent when you’ve handed them the right thing. Quietly catastrophic when you haven’t, and there is no indicator light.
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What the Amplifier Cannot Sort
Signal work is the work worth doing faster: the competitive analysis you were already running, now completed in an afternoon rather than a week; the strategy document you’d already outlined, drafted in a single focused session instead of across three fragmented ones.
Noise work is everything else. The report nobody reads more carefully because it’s better written, the summary of a meeting that shouldn’t have happened, the faster production of an output that was never the right one to begin with.
AI amplifies both without distinguishing between them. The amplifier doesn’t know the difference. That, at least, is what I’ve found.
One Question Before Every Tool
The Amplification Test
The question I now ask before using any tool on a new task is simple: would doing it twice as fast actually change anything worth changing?
Not whether it would be convenient-whether the output, at double the rate, would matter more to whoever receives it.
A no means the tool is amplifying noise, and applying speed to noise produces exactly the same noise, just in greater volume and with considerably more confidence.
The tools are genuinely, often significantly useful-their value is entirely downstream of the selection decisions you make before you open them, and that distinction is one most tool reviews never quite reach.
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What the Tools Can’t Do
AI can turn a half-formed question into a structured decision in five minutes, which is genuinely useful when the question was worth asking.
What it cannot do is determine whether that decision should exist at all-that question remains entirely yours, and it is the one that determines whether the tool creates value or just creates more.
What’s one task you currently use AI to speed up that you haven’t questioned whether it should exist? Reply and tell me what you find when you look at it honestly.
“A tool that makes you faster at the wrong work is not a productivity tool.
It’s a cost multiplier.”
References
[1] Mollick, E. Co-Intelligence: Living and Working with AI. Portfolio/Penguin, 2024. ISBN 9780593716717. penguinrandomhouse.com/books/741805/co-intelligence-by-ethan-mollick
[2] Dell’Acqua, F. et al. “Navigating the Jagged Technological Frontier.” Harvard Business School Working Paper 24-013, 2023. DOI: 10.2139/ssrn.4573321. ssrn.com/abstract=4573321
[3] Simon, H.A. The Sciences of the Artificial, 3rd ed. MIT Press, 1996. ISBN 9780262691918. mitpress.mit.edu/9780262691918