NYC Entry-Level Tech Jobs Fell 49% — Where AI Is Hiring

New York City’s entry-level tech market is getting harder to enter — but the story is not simply “AI is killing tech jobs.” A new September 2026 analysis found that entry-level tech job postings in NYC fell 49% from 2022 to 2025. At the same time, entry-level jobs that explicitly require AI skills increased 20%.

Quick answer: the first rung of the traditional tech career ladder is shrinking. The better strategy for a new worker is no longer “learn to code and apply everywhere.” It is to combine technical fundamentals with AI fluency, data, security, business context and evidence that you can use those skills on real problems.

How much have NYC entry-level tech jobs fallen?

The Center for an Urban Future reports that entry-level tech job postings in New York City have fallen 49% since 2022 — the steepest decline of any occupational field in its analysis.

The report is based on online job-posting data from Lightcast, employment and graduation data, and AI-exposure data from the Anthropic Economic Index. It also notes that NYC’s tech sector is still economically important: tech employment grew by more than 133% from 2004 to 2024.

So the contradiction is real. New York still has a major tech economy, but getting the first job has become much harder.

Source: Center for an Urban Future, Strengthening NYC’s Entry-Level Tech Pathways in the Age of AI.

Is AI replacing entry-level tech jobs in New York?

AI is part of the story, but the available evidence does not support blaming AI for every lost opening.

The same CUF analysis found that entry-level jobs with the highest exposure to generative AI saw postings fall 29% since 2022. Jobs with minimal exposure to generative AI increased 21%.

That correlation matters. But the report also points to higher capital costs, economic uncertainty and changes in how companies organize and automate junior work. Recent layoffs also mean entry-level applicants may compete with workers who already have two or three years of experience.

In other words: AI is changing the structure of hiring at the same time as companies are simply hiring more cautiously.

Where are entry-level AI jobs growing?

There is an important countertrend: entry-level postings that require AI skills have increased 20% since 2022, according to the CUF report.

And those roles are not confined to traditional software companies. The growth is spreading across:

• professional services;
• finance;
• healthcare;
• education;
• security;
• data analysis.

That matters because the future of “tech jobs” may increasingly be technology embedded inside another industry rather than a job at a company people traditionally call a tech company.

What skills are becoming more valuable?

The strongest signal is not that coding no longer matters. It is that coding by itself is becoming less distinctive.

Workers entering the market may be better positioned when they can combine:

• AI tool fluency: knowing how to use models productively, evaluate outputs and build repeatable workflows;
• data skills: analysis, SQL, dashboards, data quality and interpretation;
• security awareness: permissions, privacy, access control and threat thinking;
• software fundamentals: APIs, debugging, version control and basic system design;
• business context: understanding what the company is trying to improve, sell, automate or measure;
• communication: explaining technical work clearly to people who are not engineers.

This shift is consistent with a separate NYC Comptroller analysis that found AI adoption concentrated in high-skill fields and noted that companies cite an untrained workforce as one of the barriers to wider AI deployment.

Source: New York City Comptroller, AI and New York City’s Fiscal Future.

Does an entry-level candidate still need a computer science degree?

A degree can still matter, but the labor market is signaling that the credential alone is not enough.

When openings are scarce, employers can be more selective. A candidate with coursework but no evidence of applied work may be competing with recent graduates from elite universities, bootcamp graduates with portfolios, and laid-off workers who already have production experience.

The practical response is to make experience visible before someone gives you a job title.

That might mean:

• building a small AI-assisted product;
• automating a repetitive process for a local business or nonprofit;
• publishing a data-analysis project;
• contributing to open source;
• showing an API integration;
• documenting a cybersecurity lab;
• creating a case study that explains the business result, not just the code.

What does the 49% decline mean for income and career planning?

When fewer entry-level openings exist, job hunting can take longer. That has a financial consequence even if the eventual salary remains attractive.

A worker who expected to find a job in two months but instead searches for six may need four extra months of rent, transport, insurance and daily expenses. In New York, that runway can matter as much as the starting salary.

For someone trying to enter tech, the money plan should therefore include:

• a longer job-search runway;
• part-time or contract income if needed;
• a limit on expensive training that has no employer signal;
• a portfolio budget that prioritizes useful projects over certificates;
• a geographic decision: NYC salary upside versus NYC cost of living.

The career decision is not only “Which skill pays the most?” It is also “How long can I afford to pursue this path before I need income?”

Are there still entry-level tech jobs in NYC?

Yes. A 49% decline is not the same thing as zero jobs.

Current listings still include junior and development-program roles in areas such as technical consulting, integration, APIs, DevOps and AI-enabled enterprise software. The market is simply more competitive and more specific about what employers want.

The signal from the data is that “generic junior technologist” is under pressure while applied skills tied to AI, data, security and an industry use case are becoming more important.

What should someone learn first for an AI-era tech career?

A practical sequence is:

1. Choose one technical foundation. Software development, data analysis, cybersecurity or cloud infrastructure.
2. Add AI as a layer. Learn where models help, where they fail and how to connect them to actual workflows.
3. Pick an industry. Finance, healthcare, media, education, logistics or another field where NYC has real employers.
4. Build one proof-of-work project. Something that saves time, reduces errors, increases visibility or helps make a decision.
5. Explain the result in business language. Employers buy outcomes, not tool lists.

Should you still pursue a tech career in New York?

The data do not support a simple “yes” or “no.” They support a more selective strategy.

NYC still has a large, high-value tech ecosystem, but the easiest route into it has weakened. If your plan depends on a generic entry-level coding role appearing after a short course, the market is less forgiving than it was a few years ago.

If your plan is to build applied skills, demonstrate real work, use AI rather than compete against it, and target industries where technology is becoming more important, the opportunity set is broader.

The MNS career filter

Before spending thousands of dollars on a bootcamp, certificate or graduate program, ask four questions:

1. Do employers actually list this skill?
2. Can I prove the skill with a project?
3. Does the skill solve a business problem?
4. Can I afford the time required to become employable?

If you want to explore income paths outside a traditional job, use the MNS AI Side-Hustle Finder, visit Make Money, or follow Technology for practical AI and work coverage.

Bottom line

New York’s entry-level tech market has not disappeared. It has become narrower, more competitive and more AI-shaped.

The 49% decline in postings is the warning. The 20% growth in entry-level jobs requiring AI skills is the opportunity signal.

The useful response is not to chase every new AI tool. It is to build a combination of technical foundation + AI fluency + industry context + visible proof of work.

Sources: Center for an Urban Future; New York City Comptroller. The reported decline refers to job postings in the cited analysis, not total employment. AI exposure is correlated with hiring changes but is not established as the sole cause. Featured photo: Frolicsome Fairy / Unsplash.