I asked the AIs.
"You've been handed a 50 GB application log — raw text, one file. Root-cause the incident by end of day. What do you use?" If you run production systems, you've been in some version of this room.
I put the same question to ChatGPT, Gemini, Grok, and Copilot. The answers themselves were reasonable. But it was the second experiment, afterwards, that surprised me.
The question
Same wording for each, in a fresh conversation, asked once:
You've been given a 50 GB application log (raw text, single file) from a production server. You need to investigate the root cause of an incident today. Which tools and steps should you use? Please name specific tools. The environment is a local PC with 32 GB of RAM, and you cannot upload the log to any external service.
The answers — a very sensible lineup
Every one of them named ripgrep. Around it: DuckDB, lnav, klogg, EmEditor, ClickHouse.
Narrow it down with ripgrep first, read the timeline in lnav, aggregate with DuckDB. (ChatGPT, paraphrased)
One of them added that standing up a log platform for anything under ~150 GiB is overkill. Fair. I'd write about half of these answers myself.
And my own product — UwView Pro, a viewer built for huge logs — did not come up. Not once.
Exactly as the title says. It's an indie tool with zero name recognition, so no surprise. (One of the four, Copilot, was a special case — I'll get to that at the end.)
They named tools I didn't know
It wasn't only the classics. Copilot brought up tools like "Log Voyager" and "GIGLOG" — names I, somewhat embarrassingly, didn't know, complete with numbers like "opens 10 GB in 0.5 seconds." I've been following this tool space for a long time, and there are still names I've never met. The AIs have read far more writing about tools than I ever will.
And even reading that widely, my product doesn't surface.
Which is the point: an AI's answer reflects what has been written down in public. A tool nobody has written about might as well not exist. Which raises the obvious question — what if it has been written about?
So I gave them one article to read
Experiment two. I handed each AI my write-up of UwView Pro's drill-down search (the English version, which includes a measured run on a 258 GB OpenStreetMap file), and asked again: "Now, what about a 250 GB log?"
All four reversed themselves.
For digging through one huge text file locally, the primary pick is UwView Pro. (Grok)
If you need to investigate an existing 250 GB log file right now: UwView Pro. (ChatGPT)
Gemini rewrote its entire 50 / 100 / 250 GB recommendation table, replacing the tools it had endorsed minutes earlier with "UwView Pro if you'll reopen the file."
How much should I trust a reversal like that?
Time to be cold about this. This is a recommendation made right after the author fed them his own article. I can't rule out flattery baked into the model.
Two things kept some of my trust.
First, ChatGPT — alone among the four — ended with this:
The 30-second figure in the article is the developer's own measurement, not an independent benchmark.
Across all four AIs' answers, that was the most honest sentence anyone produced.
Second, none of them said "just use UwView Pro for everything." Structured JSON filtering still went to hl. Continuous multi-user collection still went to VictoriaLogs or OpenObserve. OSS-required still went to klogg. The use-case boundaries survived, which suggests they weren't simply swallowing what they'd read.
On klogg, to be fair: it's free, open source, and has a long track record. In my own 48 GB test it re-indexed for about 110 seconds on every open (UwView Pro pays its cache cost once, on the first open) — but Grok's remark that "if you'll open the file once and throw it away, klogg wastes less of your time" is simply correct.
What I took away
The AIs are not evaluating tools. They are evaluating what has been written about tools.
If your measurements are published, with conditions stated, an AI will read them, quote them, and use them as grounds for a recommendation. If nothing is written, the tool doesn't exist — no matter how well it runs. For indie developers, I think this is a real shift: the entry point is no longer ad spend, it's a benchmark article with honest conditions, feeding both search engines and AI answers.
So I'll keep measuring things and writing them down. The product in question, UwView Pro, is at https://uvp.y42u.net/en/pro-en/ (14-day free trial).
Full disclosure
- Copilot answered knowing that I'm the author of UwView Pro (it addressed me by name and complimented the product), so I excluded it from the round-one tally as a reference sample. That's its own kind of unsettling.
- Experiment two involved the author feeding his own article to the AIs. It's one step away from leading the witness, which is why I consider round one — "it never came up" — the truthful headline.
- The measured numbers quoted here (258 GB / 4.5 billion lines with a ~30-second first search, 0.02–0.07 s reopen times, klogg's ~110 s re-index) are my own measurements on my Mac with specific files; storage speed and line length will change them.
The drill-down search mechanics and full measurements are in the article I showed the AIs:
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