Whenever technology makes something easier, we immediately
do far too much of it.
Email got easier, so we filled everyone’s inbox. Social
media made publishing effortless, so the internet became a crowded town square
where everyone brought a megaphone. Now AI has made
producing content, writing emails, generating reports, and building features
faster than ever.
Naturally, we’ve decided to produce more of everything.
Three blog posts become 23. Fifty sales emails become 500.
And in a meeting someone points to a graph and calls it progress.
But what actually improved?
That’s the question nobody wants to ask when the production
numbers are going up and everyone looks busy.
AI makes production cheaper. It doesn’t make attention,
judgment, or trust any less expensive.
Your audience still has limited time. Your customers still
need a reason to care. Someone still has to review the work, check the facts,
and figure out what the hell they’re supposed to do next.
We’ve sped up the production line without asking whether
anyone needs more stuff coming off it.
That’s The Volume Trap: confusing the ease of making
something with the value of having made it.
Volume is easy to measure. We can count posts, emails,
reports, and completed tasks. Usefulness is harder. Did that article help someone understand a problem? Did that
email start a worthwhile conversation? Did that report improve a decision? Or
did we simply create another thing for someone to ignore?
I’m not arguing that AI-generated work is inherently
worthless. That would be lazy thinking, and there’s already enough of that
going around. AI can help us research, explore ideas, and get tedious work out
of the way. The trouble starts when we treat the first draft as the finish
line, or assume that because we can produce twenty versions, we should
More output creates more work downstream. Is you current system capable of handling that additional work? Someone has to
check it, maintain it, respond to it, or decide whether it deserves to exist. Is the person/team reviewing the work? Are they questioning assumptions and improving the result? Or are they skimming through fifty items because another fifty are waiting? If production speeds up but judgment doesn’t, we haven’t removed the
bottleneck. We’ve just moved it somewhere less visible.
When review becomes a rubber stamp, we’ve made the machine
faster at producing things nobody has time to assess properly.
The answer isn’t necessarily to produce less. Sometimes more
genuinely helps. But more needs to earn its place.
Consider reducing the output of one AI-assisted workflow.
Publish fewer pieces, send fewer emails, or generate fewer reports. Use the
time saved to improve relevance, check accuracy, and follow through.
Then look at what happened.
If results hold steady or improve, some of that extra volume
probably wasn’t doing much. If they worsen, investigate what you removed and
why it mattered.
Either way, you’ve learned something more useful than how
quickly you can fill a content calendar.
The real challenge is psychological. A full schedule feels
productive. A growing pile of completed tasks looks impressive. Improving the
usefulness of fewer things is harder to show on a dashboard. But it can get you
back to a key question: What is this work supposed to accomplish?
Technology keeps giving us ways to do things faster. Our
instinct is to fill the newly available space with more of the same. We rarely
consider using that space to think, to pay attention, or to do the work a
little better.
Maybe that’s the real opportunity AI offers: Not an endless
supply of things to publish, send, ship, and file, but the chance to spend less
time producing the unnecessary and more time getting the necessary right.
The machine can make the next thing in seconds. Before we ask it to, perhaps we should check what happened
to the last one.
