The Class in Being Human

On a Wednesday evening in May, The City Tutors brought young New Yorkers to the CUNY School of Professional Studies for a hybrid event with the CIE Initiative in the Data Science and Information Systems Department. Learners had come to ask working professionals how to build a career alongside artificial intelligence. The answers kept returning to people.

Tariq had taken his last final the week before. The next Wednesday, he would cross the stage at the Prudential Center. On the Wednesday in between, he joined the room from New Jersey, a face on the screen, sitting with the question that trails every young person now: whether the careers they were reaching for would still be there once the machines finished learning the work.

The night had its spark in Faizel, the AI engineer whose work made the question urgent enough to gather around. Faizel builds the systems companies run on. At Landing Point, he was the first AI engineer through the door, building the platform from the studs up and setting agents to optimize the high-volume work that fills a recruiter’s week. He spends his days redrawing how work gets done, which made him the right person to ask what people still need to know how to do. His answer would run under the whole evening: become the one with a hand on the tool.

The rest of the opening panel had lived the change too. Rene, a cybersecurity project manager at Bloomberg, began with the speed of it: a project that once took two weeks now takes two days. Connor, a trading desk engineer at Jane Street, had let AI write his code for months. Ernesto, a technical account manager at Bloomberg, had built himself an internal database to stand beside the skills he already had. Lean on the tools every way you can, Connor told the room, and stay doubtful of what they hand back.

Questions came from the classroom, the chat, and the screens around it. Mitu, an international student, asked what young professionals most underrate, and Zamira, Director of Employment Partnerships at NYC’s Tech Talent Pipeline, pointed to languages and cultural fluency, the skills still hard to find. Jailene, a Baruch alum, asked how to keep her own voice when AI drafts a cover letter, and Sarah, a workforce development leader at Sanctuary for Families, answered that the draft should sound like her, so the page and the interview hold the same person.

After the panel, the same question followed learners into smaller conversations: what remains worth learning when the tools keep getting faster? Lorenzo, a Baruch graduate with a master’s in public administration now turning toward software engineering, answered with innovation: use the tools and stand out. A mentee from Queens College pointed to the people side. AI is good at what computers do well, she said, so the edge belongs to whoever strengthens what computers still struggle to do. Tariq, days from graduation, looked beneath the technology itself. The systems may be sophisticated, he said, but they still rest on foundations people hurry past: the math, the logic, the structures underneath. The faster the tools get, the more it helps to understand what they are built on.

The mentors had spent the evening talking about machines, and again and again the guidance came back to judgment, voice, reputation, and nerve. Sarah offered one final reminder: an interview runs both ways. The candidate is weighing the place as much as the place is weighing the candidate. From Ernesto came the push to stop perfecting the resume and start sending it, two or three a week, learning from each round. Zamira set adaptability beside intelligence, then wondered aloud whether any school teaches a class in how to learn to be human. Rene answered her from the screen: stay curious, be a person of your word, and the reputation follows.

Faizel, the one who builds the systems, made the smallest claim for the tools. Treat AI as a thought partner, he told a student who asked where to start. Tell it what you are working on, then judge which direction holds up. Thirty minutes a day and you learn to drive it.

“It’s only as good as you make it.”

That was the quiet demand inside the new world of work. The machine could answer faster than anyone in the room could type. The harder thing was learning how to become the person who knew what to ask.