Why Enterprise Data Isn't Always Fit for Model Training
inductionheads · x · 2026-07-14
The post argues that a company's most valuable and sensitive data is often unsuitable for direct model training.
Key points include:
- Sensitive information typically cannot be freely fed into models due to strict access controls
- Building security boundaries directly into models or agents is highly challenging
- While custom model training will become common in many domains, training a bespoke model for every enterprise is far harder than it sounds
- A more viable approach is transforming enterprise knowledge into skills or artifacts that models can invoke in-context
- Model training is expensive and time-consuming, often requiring retraining after foundation model updates, with irreversible results
The discussion centers on the trade-offs between training data into models versus leveraging it via in-context calls.
Related event: Opinion: Most Enterprises Shouldn't Train Their Own Models Yet(3 posts)→
More from AGI Musings
- Cohere Labs launches interactive tool mapping which tasks of 178 occupations AI can automate — Cohere_Labs · 2026-09-11
- AI researcher on SkyNews flags concerns over inequality, power and criminal misuse — schwarzjn_ · 2026-09-11
- VC compares AI doom rhetoric to pandemic-era fear messaging — StewartalsopIII · 2026-09-11
- Anthropic Insiders: Not Everyone at the Lab Believes in High p(doom) — anpaure · 2026-09-11
- Could 10k agents discover learning methods beyond backprop, or just tweak existing ones? — SeunghyunSEO7 · 2026-09-11
- AI companionship dissolves the friction real intimacy needs, warns long-form thread — YogeshMalik · 2026-09-11