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AI development compared to pre-web internet era, argues corporate AI commentary series

read original get O'Reilly "Prompt Engineering for LLMs" book → more articles
GoKawiil Brief

A Fast Company opinion series on corporate AI argues that current AI systems are being built and controlled at far too low a level of abstraction, comparing the state of the field to the internet in 1991 before the web existed. The author contends that the core issue holding back enterprise AI isn't a lack of intelligence in the models themselves, but the primitive 'programming language' or interface layer through which businesses interact with and direct that intelligence.

Why It Matters

GoKawiil's interpretation of the reporting above, not reported fact.

The analogy suggests a major shift is coming: just as the web abstracted raw internet protocols into something usable by non-experts, AI may need a similar higher-level layer before it can be reliably deployed across businesses. This reframes current struggles with AI reliability and control as an infrastructure problem rather than a model-quality problem, which has implications for how companies should invest and build AI tools going forward.

Key Takeaways
Worth a Look

O'Reilly "Prompt Engineering for LLMs" book — If the article's argument is that we're all still hand-coding AI at the lowest level, this book is a practical guide to writing better instructions for large language models. It walks through structuring prompts, context, and evaluation so your AI work feels less like assembly and more like a real language.

See O'Reilly "Prompt Engineering for LLMs" book on Amazon → Affiliate link — we may earn a commission on purchases, at no extra cost to you. Product picked by AI based on this article; it is not a tested recommendation.

Source: fastcompany.com, 2026-09-04

Published there as: “We are programming AI in assembly language”

Read the original report → The summary and analysis above are GoKawiil's own, written from reporting by the source above. Facts and quotes belong to the original publisher.