What Past Computing Breakthroughs Teach Us About AI – Communications of the ACM (cacm.acm.org)

This Communications of the ACM piece argues that AI’s trajectory is best understood by studying past computing revolutions—programmable computers, the Internet, personal computers, and open source—and extracting repeatable patterns. The core lessons: flexibility and modular architectures (from stored‑program machines and modern model APIs) enable rapid, broad adoption; open, extensible protocols (TCP/IP, HTTP) drive network effects but also systemic risk; accessibility and user-centric design (PCs) trump raw technical power for mass uptake; and open collaboration accelerates discovery and surfaces vulnerabilities. Concrete examples—an AI certificate generator that scales only if flexible, city AI traffic analytics that expand into surveillance, and the value of open datasets for finding bias—illustrate how design and governance shape outcomes. The article also maps likely technical implications and mitigations: AI scaling carries infrastructure and availability burdens that require continuous uptime monitoring and predictive analytics; misuse vectors (deepfakes, biased models, data scraping) echo past Internet harms; and cross‑disciplinary oversight (ethicists, lawyers, social scientists) is needed early. Practically, it urges researchers and builders to prioritize explainability, publish benchmarks and negative results, measure long‑term impacts (fairness, sustainability), bake in regulatory guardrails, design for low‑resource users, and use staged rollouts to navigate S‑curve adoption. Historical patterns thus offer both a blueprint and a warning for steering AI toward broad benefit.