If the calculator wasn’t the end of learning, AI won’t be the end of work (www.techradar.com)

The piece argues that AI — like the printing press, calculator, and email before it — is a tool that will reshape work rather than end it. Adoption is accelerating: U.S. private AI investment hit $109.1 billion in 2024, and enterprises are actively exploring generative AI (about 76%); yet trust lags (only ~40% of consumers trust GenAI outputs). What’s different now is the rise of large language models that directly touch knowledge work by producing text, images and code, raising questions around expertise, authorship, and rapid organizational change. High‑stakes sectors (law, finance, healthcare, academia) face particular risk from poor governance, new security vectors, and inconsistent adoption. For AI/ML practitioners and decision‑makers the article’s prescription is practical: avoid the binary of full embrace vs. ban and pursue deliberate integration. Run targeted PoCs, use MLOps to monitor model performance, establish transparent validation and attribution processes, and define where human judgment must remain in the loop (e.g., cybersecurity triage where models filter alerts but humans decide). Educate across teams, set KPIs driven by business users, and implement guardrails for privacy, bias and provenance. Done right, generative AI amplifies human work — but credibility depends on measurable transparency, accountability and operational controls.