For once, there's some positive news about jobs and AI. We’re finally starting to see real conversations and analysis around the types of jobs AI might create, not just the ones it might eliminate. A New York Times article, written by a former editorial director of Wired, titled “AI Might Take Your Job. Here Are 22 New Ones It Could Give You,” lays out three broad categories where humans will still be crucial: trust, integration, and taste. trust integration taste TRUST (Human accountability, oversight, and judgment) As AI begins to take on tasks like writing legal contracts or corporate reports, someone will need to take responsibility for what it produces. These roles ensure accountability, explainability, and reliability: AI Auditor – Investigates what the model did and whyAI Translator – Explains AI decisions to non-technical stakeholdersTrust Director – Oversees ethical use of AIAI Ethicist – Builds logical chains behind AI decisionsLegal Guarantor – Takes legal responsibility for AI outputsConsistency Coordinator – Ensures AI outputs remain consistentEscalation Officer – Steps in when AI fails to feel human enough AI Auditor – Investigates what the model did and why AI Auditor AI Translator – Explains AI decisions to non-technical stakeholders AI Translator Trust Director – Oversees ethical use of AI Trust Director AI Ethicist – Builds logical chains behind AI decisions AI Ethicist Legal Guarantor – Takes legal responsibility for AI outputs Legal Guarantor Consistency Coordinator – Ensures AI outputs remain consistent Consistency Coordinator Escalation Officer – Steps in when AI fails to feel human enough Escalation Officer INTEGRATION (Making AI usable across real-world systems) Integration involves technical expertise to connect AI with business processes and operations: AI Trainer – Fine-tunes models on internal or proprietary dataAI Assessor – Chooses and evaluates the best model for a jobAI Plumber – Connects AI systems and fixes failuresPersonality Designer – Shapes the tone, voice, and behavior of AI interfacesAI-Human Specialist – Determines when humans vs. AI should take overAI Architect – Designs workflows and system integrationAI Operations Manager – Oversees AI use across departments AI Trainer – Fine-tunes models on internal or proprietary data AI Trainer AI Assessor – Chooses and evaluates the best model for a job AI Assessor AI Plumber – Connects AI systems and fixes failures AI Plumber Personality Designer – Shapes the tone, voice, and behavior of AI interfaces Personality Designer AI-Human Specialist – Determines when humans vs. AI should take over AI-Human Specialist AI Architect – Designs workflows and system integration AI Architect AI Operations Manager – Oversees AI use across departments AI Operations Manager A real-world example is Quora CEO Adam D’Angelo’s job post for an “AI Automation Engineer”—someone who automates internal workflows and boosts team productivity using LLMs. Expect this kind of role to become common across organizations. TASTE (Judgment, creativity, and aesthetic sense) In a world of infinite AI-generated options, taste will be a competitive edge. These roles involve curation, design, and creative vision: Creative Director (AI Outputs) – Filters and guides generative contentDecision Stylist – Helps teams pick the best creative optionsDifferentiation Designer – Ensures brand distinctiveness despite generative samenessEditorial Curator – Selects and edits AI-created contentNarrative Strategist – Shapes coherent storytelling from AI outputsAesthetic Lead – Maintains high visual or experiential qualityFeedback Engineer – Tunes AI outputs based on real user reactionsContent Ethnographer – Studies how people respond emotionally and culturally to AI outputs (optional/edge case) Creative Director (AI Outputs) – Filters and guides generative content Creative Director (AI Outputs) Decision Stylist – Helps teams pick the best creative options Decision Stylist Differentiation Designer – Ensures brand distinctiveness despite generative sameness Differentiation Designer Editorial Curator – Selects and edits AI-created content Editorial Curator Narrative Strategist – Shapes coherent storytelling from AI outputs Narrative Strategist Aesthetic Lead – Maintains high visual or experiential quality Aesthetic Lead Feedback Engineer – Tunes AI outputs based on real user reactions Feedback Engineer Content Ethnographer – Studies how people respond emotionally and culturally to AI outputs (optional/edge case) Content Ethnographer A separate guide from nonprofit 80,000 Hours, titled “How Not to Lose Your Job to AI,” explores the skills most likely to remain valuable. These fall into four categories: Skills AI can’t easily replicate, like long-term planning and physical tasksSkills related to deploying and managing AI systemsSkills related to critical needs in society like healthcare and infrastructureRare and hard-to-replicate expertise Skills AI can’t easily replicate, like long-term planning and physical tasks Skills AI can’t easily replicate, like long-term planning and physical tasks Skills related to deploying and managing AI systems Skills related to deploying and managing AI systems Skills related to critical needs in society like healthcare and infrastructure Skills related to critical needs in society like healthcare and infrastructure Rare and hard-to-replicate expertise Rare and hard-to-replicate expertise Their advice is clear: don’t avoid AI, use it. Ride the wave, build skills it amplifies, and increase your impact. They suggest future-proof areas like AI deployment, leadership, judgment, communication, and technical trades like building data centers. The New York Times article was surprisingly insightful. The author notes that while AI can handle many tasks, our jobs are about more than completing tasks—they’re about human connection, accountability, and group dynamics. The article offers a powerful reminder: it’s not just about where humans want AI, but where AI needs humans. AI drives down the value of the things it can do, but drives up the value of what it can’t. Those untouched or enhanced human skills become bottlenecks for further automation—and therefore more valuable. Yet most people still don’t fully understand what AI can and can’t do today. That makes it difficult to chart a personal path forward. One solution? Start experimenting. Build your own GPT. Try Deep Research. Use tools like NotebookLM. Play around. Learn. The more familiar you are with what AI can do, the easier it becomes to spot where you can add unique value. You don’t need to wait for someone to hand you the roadmap. You can create your own by mapping your existing skills to the new landscape. Take that list of 22 potential jobs, feed it into your favorite AI tool with context about your own role, and explore what paths make sense for you. This kind of thinking, proactive, specific, human-led is what we need more of. It’s how individuals and organizations can move toward the best possible outcomes. And it's encouraging to finally see this conversation take center stage.

The is an opinion piece based on the author’s POV and does not necessarily reflect the views of HackerNoon.

22 Jobs That AI Will Create (That No One’s Talking About)

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