A million credits, one pair of eyes

By four in the afternoon my brain is tired.

Not from meetings, and not from the volume of work. From reviewing. My day runs across client delivery, sales and marketing, and every one of those threads is now producing finished work that needs my decision. I read a draft, decide whether it holds, send it back, pick up the next one, and by mid-afternoon I can feel the tank emptying.

I wrote recently that my job had quietly turned into reviewing AI's work. What I left out is what that actually feels like, and what I've changed to make it sustainable.

This is happening to most of us

If you use AI seriously, your day already looks like this. You're not producing the first version of much anymore. It arrives, it looks finished, and your job is to decide.

That sounds easier than writing it yourself. It isn't. Writing is one thread. Reviewing is a queue of other people's finished thinking, and every item in it wants a real decision from me.

What surprises me is how well the brain adapts. I switch contexts far faster than I did six months ago. Feedback that used to be a paragraph is now one plain sentence, or I just say it out loud - I work by voice with the AI more and more, which turns out to be quicker than typing and less tiring too.

What a heavy run looks like

Our research desks are the clearest example I have. This is the layer of the AI operation we've been building for over a year that works at the biggest scale.

Each run goes out across dozens of sources looking for companies in a moment that matters. Capital landing. A plant being built. A first move into a new market. A mandate changing hands. One full run burns well over a million AI credits on the best model we have, and produces far more raw material than any person would sit and read.

If that arrived as a long document I'd skim it, assume it was right, and load whatever came out the other end. So it doesn't arrive that way. Claude puts it into a cockpit. One screen, one row per company, tick or reject, with the evidence for each row already on screen, and tabs and filters that let me see the depth and breadth of the run without digging for it.

I work through it in minutes, then push the result back for the next step.

The review deserves real design time

The research itself was a big build and is continually updated. But a surprising amount of the build went into something else entirely: working out the fastest way for one person to check the output efficiently and hand it back.

The model works at a scale I can't verify line by line, so the shape of the output matters as much as the output itself. What has to be on screen for me to say yes without guessing, what order it should come in, and how I dig deeper when a row looks wrong.

You still have to check

The temptation is real. When the output looks polished, and there's a lot of it, and you're tired, the easy move is to wave it through.

I catch something most weeks. A company that looks right and isn't. A signal that turns out to be a year old. A confident sentence with nothing underneath it. Not often enough to distrust the system, but often enough that shipping it unread would have cost a client something. It's the same reason we make AI argue with itself before a big decision: agreement is cheap, and the check is where the value sits.

The machine gives you scale, you give it judgement, and if you take the judgement out you've just automated being wrong more quickly.

The bottom line

We're all becoming reviewers of our AI's output, so spend real time fine-tuning how you review.

For me that's a prompt that gives me simple feedback first, so I only ask the AI to dig deeper where something needs it. It's cockpit-style interfaces for the heavy runs. And it's doing the serious reviewing in the morning, while the brain is firing.

Whatever your version looks like, build it deliberately. The reviewing is the job now, and it deserves the same care you used to put into the work itself.

Frequently asked questions

Why is reviewing AI output so tiring?

Because reviewing is a queue of finished thinking, and every item in it wants a real decision. Writing is one thread; reviewing AI output means switching between unrelated pieces of finished-looking work all day, deciding what holds and what needs another pass. It is more productive than producing everything yourself, but the decision load builds - which is why heavy reviewing is best done early in the day.

How do you review large volumes of AI output quickly?

Design the output for review, not for reading. At AscendAI, a single research run burns well over a million AI credits and produces far more raw material than anyone could read as a document. So it lands as a cockpit instead: one screen, one row per company, tick or reject, with the evidence for each row already visible and tabs and filters to see the depth of the run. The result is a review that takes minutes, not hours - because what has to be on screen for an honest yes was designed deliberately.

Can you trust AI output without checking it?

No. Even with a well-built system, something gets caught most weeks - a company that looks right and isn't, a signal that turns out to be a year old, a confident sentence with nothing underneath it. The machine gives you scale and you give it judgement; take the judgement out and you have just automated being wrong more quickly. The check is part of the job, not an optional extra.