Where Students Went to Ask the Future’s Hard Questions

Where Students Went to Ask the Future’s Hard Questions

“AI is here. It can be our friend. But critical thinking still belongs to people.”

Saima, a recent City College graduate, said it into a mic on a Wednesday night in Midtown, after an hour of small-room conversations about data, automation, and what happens to human work when software learns to do more of it.

Around her, more than 50 New Yorkers — computer and information science students from CUNY Inclusive Economy at the CUNY School of Professional Studies, alongside community-college students, career changers, and early-career professionals from across the city — had logged on or grabbed a seat for How Work Runs: Data, AI & Digital Careers. The hybrid event was co-hosted by The City Tutors and CUNY School of Professional Studies in partnership with CUNY Inclusive Economy.

A short panel set the frame. Then the room and the call broke into motion — speed networking in person, breakout rooms online — with panelists and other mentors fanning out into small groups before everyone came back together to share what they had learned.

As moderator Jimmy Willis framed it, this was a night for students to ask questions that don’t have clean answers yet.

Another student, Christeena, had just been told by her mentor that “surface familiarity” would no longer be enough — that the people who thrive will be the ones with deep domain knowledge. A Baruch sophomore named Gael put it in simpler terms: “You can memorize the formulas. But if you can’t apply them, it doesn’t matter.”

The question in the air was direct and unsettled:

In a working world reshaped by artificial intelligence, where does the human edge live now?

A City That Now Runs on Dashboards

The panel opened with a view from inside the machinery of New York.

Azikiwe Rich, Assistant Commissioner of Analytics, Performance, and Management at the New York City Department of Transportation, described a wall of live dashboards that now guide decisions about the streets: when to resurface lanes, how to deploy speed cameras, where outdoor dining is out of compliance, which community districts are falling behind.

“This is how decisions get made now,” he said. “Not months later — in real time.”

What once lived in static reports now updates continuously. Streets, traffic patterns, compliance, infrastructure — all of it now moves through data before it ever moves through pavement.

Hiring in an AI-Shaped Economy

Zamira Kamal, Director of Employer Partnerships at the NYC Tech Talent Pipeline, opened by challenging one of the biggest misconceptions students hold about entering tech careers.

Many still believe that “tech jobs” live primarily inside traditional technology companies. In reality, current labor-market data shows that the majority of tech hiring in New York now comes from non-tech sectors — including finance, healthcare, government, retail, logistics, and media. In recent years, those fields have accounted for a significant share of new tech and tech-enabled hires across the region.

She emphasized that employers are increasingly prioritizing applied skill, not just credentials.

As Michael Hansen of Cengage Group has said, “The likelihood that AI takes your job is far less than the likelihood that someone who knows how to use AI will.” Hiring managers are looking for people who understand how businesses actually operate, who can use data, AI, and digital tools to solve real problems, and who can work across teams and functions.

Today’s tools are widely accessible. What employers value most is the ability to use them in context — understanding how decisions get made, how systems connect, and how digital tools create real value inside an organization. Business fluency has become just as important as technical fluency.

Healthcare: When Data Becomes Care

That point became tangible in healthcare.

Pamela Hassen, former Chief Member Engagement Officer and Chief Marketing Officer at Fidelis Care, described how disconnected systems — enrollment, marketing, care management — once delayed when high-need members actually received help.

The breakthrough did not begin with a sweeping overhaul. It began with two staff members, one in care management and one in marketing, who understood both the numbers and the lives behind them. By linking systems at the first point of enrollment, they cut weeks off the time it took to get vulnerable members connected to care.

What started as a workaround became a model — not data as abstraction, but data as faster access to real care for real people.

The Work Being Automated First

Then came the most unsettling truth of the night.

Nico Cernek, a software engineer and technologist, named the category of work already slipping into automation.

Coding.
Drafting.
Summarizing.
Searching.

“The first things being automated,” he said, “are often the things students were told would protect them.”

Then he shifted the emphasis.

“What isn’t being automated,” he said, “is communication, judgment, knowing what you don’t understand, and saying it out loud.”

He paused.

“Two hundred years from now,” he added, “people might look back at this moment and wonder how anything functioned at all.”

The room went quiet.

When the Room Went Small

The panel set the stakes. The next hour tested them in real conversation.

Chairs scraped across the floor in Midtown. Zoom tiles rearranged themselves online. Students rotated through fast, high-energy speed-networking rounds with professionals from public service, media, healthcare, technology, data, transportation, and entrepreneurship. In breakout rooms, strangers became collaborators for fifteen focused minutes at a time — guided not only by the panelists, but by other mentors who had come specifically to hold those smaller conversations.

When everyone reconvened, Kevin Brown of The City Tutors asked for rapid-fire takeaways.

New questions surfaced. One student warned that vague prompts and blind trust in AI outputs can lead to bad decisions — and that misunderstanding confidentiality inside AI systems can create serious professional risk. The tools, he said, are powerful. They are not neutral.

Rolando, a data analytics student, turned directly to entrepreneurship.

“If you have an idea,” he said, “find real competitors and real users. Listen to what breaks.”

Then a newly promoted senior manager in data engineering reframed the fear many students were carrying.

“You’re at the edge of technology,” he told them.
“You can experiment with tools we can’t safely deploy at work yet. If you show up understanding what those tools actually do for an organization, you’re already ahead.”

What the Mentors Left Them With

The mentors’ final advice returned again and again to the same core ideas.

Every job can use AI.
Every job still depends on people.

Go deep in a domain.
Shallow knowledge is the easiest thing for machines to imitate.

Pamela described running the same AI prompt repeatedly — then rewriting the output by hand on her commute.

“Nothing is perfect,” she said. “Not the tools. Not us. You still have to think.”

Another mentor described uncovering that a major company’s own AI instructions were wrong — and how he only became valuable once he stopped reading and started building.

A different mentor compared the job search to doctoral research: set assumptions, test them, let them fail, revise, repeat.

Another urged students to think about the patient, the user, the person on the other side of every system.

“Put yourself in their shoes,” she said. “Then you’ll know what actually needs fixing.”

And several mentors returned to relationships.

“Be in rooms where people talk about what they’re building and hiring,” one said.
“You don’t network at the last minute. You grow into it.”

Brown closed with his own near-miss story — almost losing doctoral deadlines to bureaucracy and miscommunication — not as a joke, but as instruction.

Professional behavior, he reminded them, travels farther than talent.

The Subway Is Still Running

Only after all of that did Michael Chin, The City Tutors’ Chief Program Success Officer, share the QR code.

For newcomers, he explained the model simply: free events like this one, paired with free, self-paced, one-on-one mentorship available to any New Yorker, student or not.

“We think of ourselves as the academic subway,” he said.
“You miss one train, another is coming.”

If there was comfort in that idea, there was also urgency.

By the end of the night, one truth had surfaced again and again:

Tools are no longer the bottleneck.

Judgment, depth, access, and human connection are.

And in a city moving at algorithmic speed, that human work — asking clear questions, applying ideas to real lives, staying in relationship — remains the part no machine has learned to replace.