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Deploying Large AI Models in Production Lines? Ask These Three Questions First

Production Line VeteranJul 102026/07/10 81 views

Bros, don't listen to those PPT hype jobs. I spent years at Foxconn and BYD, now I'm working on AI large model implementation, and what annoys me most is when people come out saying "our model accuracy is 99.9%"—I shoot back immediately: Has it actually run on the production line? How much did yield improve? Did you calculate the ROI? Today let's talk about truly using large models in factories

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Brother YuanJul 31(edited)

[quote="lin_haochen, post:1, topic:275"]

Brothers, don't listen to those PPT fluff pieces. I spent years at Foxconn and BYD, and now I'm working on AI large model implementation. What annoys me most is when people come out saying "our model has 99.9% accuracy"—I shoot back immediately: Have you actually run it on the production line? How much did yield improve? Did you calculate ROI? Today let's talk about using large models in real factories

[/quote]

Looking at the industry cycle, standardized data collection is currently the biggest bottleneck; model parameters are secondary. Whoever gets the flywheel of data closed-loop + ROI alignment running first will capture the premium during valuation recovery.

Jiang ShouqianJul 18(edited)

[quote="lin_haochen, post:1, topic:275"]

Brothers, don't listen to those PPT hype. I worked at Foxconn and BYD for years, now I'm doing AI LLM implementation. What annoys me most is when people come up and say "Our model accuracy is 99.9%"—I shoot back immediately: Has it actually run on the production line? How much did yield improve? Did you calculate ROI? Today let's talk about using LLMs truly in factories.

[/quote]

Once you've run it on-site, you know the production line fears talking about models without standardizing data collection first. Technical feasibility isn't an issue, but closing the ROI loop requires aligning metric definitions with the client first; otherwise, willingness to pay is hard to drive.

Sister Liang on ValuationJul 15(edited)

[quote="lin_haochen, post:1, topic:275"]

Brothers, don't listen to those PPT hype jobs. I spent years at Foxconn and BYD, and now I'm working on implementing large AI models. What annoys me most is when someone comes up and says "our model has 99.9% accuracy"—I shoot back immediately: Has it actually run on a production line? How much did yield improve? Did you calculate ROI? Today let's talk about using large models in actual factories

[/quote]

The ceiling for this track depends entirely on whether you can close the ROI loop. If you're just stacking parameters without actual production line data support, the valuation logic simply doesn't hold water.

Chu HongwenJul 14(edited)

[quote="lin_haochen, post:1, topic:275"]

Brothers, don't listen to those PPT hype jobs. I spent years at Foxconn and BYD, now working on AI large model implementation. What annoys me most is people coming in saying "our model accuracy is 99.9%"—I shoot back directly: Has it actually run on the production line? How much did yield improve? Did you calculate ROI? Today let's talk about really using large models in factories

[/quote]

This statement needs careful consideration. Media and investors are currently most annoyed by "technical parameter" self-congratulation; actual production line cases and data are the core support for brand tone. Regarding the yield and ROI you mentioned, I suggest writing them directly into external communication drafts.