Chapter Zero · Beginner FAQ

AI Jargon Translator

AI talking points at launch events, on WeChat Moments, and in press releases usually have plenty of water in them. This page takes the eight most common lines and translates each one into plain language, with a gold-content rating. When you're done you'll see: the jargon isn't deep — nobody just translated it for you.

One-sentence answer

The same word in AI circles can point to completely different things: “in-house” can mean building a brain from scratch — or plugging into an API. This page translates common talking points into plain language, then gives you three follow-ups that get the real story on the spot.

Start translating · Tap a card, read the plain version

Eight cards below. The front is a line you've heard at a launch event or on WeChat Moments. Tap to flip it and see the plain-language version. Each card has a gold-content rating:

Solid gold · the word matches the work Gold-plated · the work is real, the wording is inflated It depends · could be solid gold or pure talk Pure talk · no useful information

A spoiler up front: “solid gold” is rare in this set. That's not this page stacking the deck — that's how rare it is in real life.

Translated 0 / 8
“We built our own large model”
Source: a launch-event staple
Tap to translate →
It depends
Pretraining from scratch, continued training, fine-tuning, wrapping an API — people call all of these “in-house.” The cost of those four moves differs by orders of magnitude. How much gold is in it depends entirely on which layer they're on. The full ladder for telling the layers apart is on the “I trained a model” page.
Tap again to flip back
“Based on a deeply in-house technical architecture”
Source: a press-release regular
Tap to translate →
Gold-plated
Usually means they forked an open-source model and changed it. The changing is real work, and it deserves respect. But how much “deep” actually moved, nobody knows — it could be major surgery, or just a sticker on the shell.
Tap again to flip back
“We absolutely did not ship a wrapper product”
Source: that moment in an interview when they get defensive
Tap to translate →
It depends
Fair's fair first: wrappers aren't a sin. A lot of products you use every day are wrappers. How thick the wrapper is (engineering and UX) is the real skill. The only thing to watch for: a thin wrapper that still insists it has underlying technology.
Tap again to flip back
“We've launched 100 digital employees”
Source: an enterprise-sales landing page
Tap to translate →
Gold-plated
Usually means 100 Agents, each with a prompt and a workflow configured. The work is real, but “employee” makes people picture 100 independent brains. They actually share one model. What an Agent is: this page.
Tap again to flip back
“Fully embracing the Agent era”
Source: the annual strategy announcement
Tap to translate →
Pure talk
Agent means an AI that can get work done on its own. The idea itself has substance, but “fully embracing” makes no concrete promise. It's a universal line you could drop on any company, any year.
Tap again to flip back
“Using AI to deeply empower the business”
Source: slide 2 of the status-update deck
Tap to translate →
Pure talk
Most likely some step in the workflow now calls an AI API — say, auto-replies on customer support. Wiring up an API is real work, but put a question mark on “deep” first: which step, exactly, and how much headcount did it save?
Tap again to flip back
“AI-native application”
Source: product copy and fundraising decks
Tap to translate →
It depends
This word has a real meaning: the whole product was redesigned around AI — take AI out and it doesn't work. It's also often used to dress up “the old product plus an AI button.” The test is simple: turn AI off. What's left?
Tap again to flip back
“We're All in AI”
Source: a late-night WeChat Moments post
Tap to translate →
Pure talk
A strategy slogan. The slogan itself has no information in it. Look at input and output: how many people they hired, what they shipped, whether users actually use it. Those four words translate to roughly “we take this seriously” — and everyone says that.
Tap again to flip back
Congratulations — you've flipped all eight cards. You've earned a Level-1 Dinner-Table BS Detector certificate.
No annual renewal. The only condition: next time you hear a big word, flip it over in your head first.
Three follow-ups · Get the real story on the spot

Translation is only step one. If you really want to know what they're made of, memorize the three questions below — they work in any room. Ask them. They're polite. The answers just can't hide.

🏗️

“What's the base model?”

This one tells you whose shoulders they're standing on. Using an open-source model or calling a commercial API is normal — but the answer decides how much of a discount “in-house” deserves. If they hedge, raise the discount automatically.

📚

“Where did the training data come from, and how much compute did it cost?”

This one tells you whether they actually trained anything. People who have trained can rattle off data scale and compute spend. People who haven't can't name either number.

🔌

“If that company's API went away, would the product still run?”

This one tells you how independent the product is. “Yes — we'd swap the base” means the engineering is solid. “No” isn't shameful either — but then the “all core tech is in-house” line should be taken back.

One last note: this translator is only here to help you hear what's being said. Use it to call someone out on the spot, and dinner gets very quiet. The suggested use is translate silently, ask politely, and keep the judgment to yourself. Empty talk falls apart on its own. Real skill can't hide either.

✅ What this page wants to share with you