September 10, 2026 — The entry-level technology job market in the United States is not disappearing in one clean line.
That distinction matters if you are learning Python, JavaScript, SQL, testing, or web development and trying to decide what kind of work to pursue first. The question is no longer only whether companies are hiring “junior developers.” It is also where technical work is moving, which tasks are being reshaped by artificial intelligence, and which entry routes still let a beginner accumulate credible experience.
The latest signal is pressure at the beginning of the pipeline
Handshake’s April 2026 report on the Class of 2026 describes a tight early-career market: job postings on its platform were 2% below the previous year and 12% below pre-pandemic levels. At the same time, employer demand for AI-related skills accelerated. As of March 2026, more than 10% of active internships mentioned AI keywords, while the share of full-time postings mentioning AI had nearly doubled year over year to 4.2%.
That does not mean every entry-level job now requires someone to build a machine-learning model. It means that “I can use AI” is becoming a labor-market signal, even while schools and employers disagree about what that phrase should mean.
Handshake found that 85% of seniors in its Class of 2026 data used AI, with more than a third using it daily. Yet only 28% said their academic program had meaningfully integrated AI, while 58% expected to need stronger AI skills at work. For a student in a community college in Phoenix, a CUNY program in New York, or an online certificate completed from a rural county, that gap is practical: the classroom may warn against a tool that a job description quietly expects.
A beginner can put “AI” on a resume without being able to explain how they verified generated code, protected private data, or tested an automated workflow. The keyword may open a search result. It will not by itself demonstrate readiness.
What is changing is the shape of the first job
A recent World Economic Forum report, developed with PwC and published June 22, frames the issue beyond replacement. It examines job access, job design, talent pipelines, and alignment between education and work. Its central concern is that entry-level roles have traditionally been where people learn how an organization actually operates. If automation removes every low-risk learning task, employers may save time now while making it harder to develop experienced workers later.
In software, that learning work has often included reading unfamiliar code, reproducing a bug, writing a test, checking a data import, updating documentation, answering a support ticket, or explaining why a small change affected another system. An AI assistant can speed up parts of those tasks. It does not remove the need for someone to decide what “correct” means in a particular product.
This is why the first job may be less about typing code from a blank file and more about moving between technical tasks:
A technical support specialist may need to distinguish a user problem from a configuration problem. A data or operations analyst may need to clean a spreadsheet, query a database, and explain an anomaly to a nontechnical colleague. A junior developer may spend part of the week reviewing generated changes, tracing an API response, or updating a test suite rather than building a feature from scratch.
The developer forecast is positive, but the headline needs a footnote
The Bureau of Labor Statistics projects 10% employment growth from 2025 to 2035 for the combined category of software developers, quality assurance analysts, and testers, with about 106,100 openings per year on average. The category also reports a typical entry-level education of a bachelor’s degree, although individual employers and roles vary.
It is not a promise that a beginner who completes one course will receive an offer. The openings include replacement demand, the category combines several occupations, and national projections do not tell you which employer is hiring in your city this week.
The BLS’s broader 2024–34 projections overview makes the uncertainty explicit. The agency says occupations are bundles of tasks, that technology can change the composition of those tasks without eliminating the occupation, and that the future impact of AI remains difficult to predict precisely.
Do not overlook work that is adjacent to software development
The BLS projects a 3% decline in computer support specialist employment from 2025 to 2035. That sounds discouraging until the same page reports about 48,700 openings per year on average, largely because workers transfer occupations or leave the labor force.
It is that “decline” and “no openings” are not the same statement. Support work can involve troubleshooting, networks, operating systems, account access, documentation, and communication under pressure. Those experiences may be relevant to later roles, but the reader should check the actual duties, training, schedule, pay, and advancement path rather than treating support as an automatic bridge.
A full-time employee may receive structured onboarding and team-based review. A contractor may be hired for a defined deliverable and need to prove competence quickly. A freelancer may need to find clients, define scope, invoice, and protect their own time. An apprenticeship can combine paid work with structured learning. A short internship may offer a useful project but still be competitive and temporary.
The U.S. Department of Labor’s Apprenticeship.gov describes Registered Apprenticeship as a system connecting career seekers, employers, and education partners. Its current resource hub also highlights work on integrating AI into apprenticeship programs. That is worth watching because apprenticeship addresses a problem the AI debate often skips: beginners need a place where someone can see how they work, not only a certificate saying they completed a lesson.
A city example: New York is building more than one doorway
New York City offers a concrete example of why “learn to code and apply for developer jobs” is too narrow a description of the entry market. The city’s Small Business Services tech-training page lists no-cost programs connected to the NYC Tech Talent Pipeline.
CUNY Tech Prep focuses on advanced computer-science majors and project-based full-stack work. The Data Analyst Training Accelerator describes a full-time, 20-week remote program using Excel, SQL, Python, AWS, and marketing analytics, with eligibility conditions for New York City residents and people without prior paid data-analytics experience. Future Code describes a longer web-development training path for New Yorkers with limited or no previous professional web-development experience. Several applications are currently closed, and eligibility does not guarantee acceptance.
If you live in New York, the question is whether you meet the residency, income, education, schedule, work authorization, and selection requirements. If you live elsewhere, look for the equivalent local infrastructure: an American Job Center, a community-college partnership, a city workforce office, a state apprenticeship program, or an employer-linked training provider.
The Department of Labor says American Job Centers provide training referrals, career counseling, job listings, and related services. That does not make a center a technology-job guarantee. It gives a beginner a place to ask a local question with more context than a national social-media thread can provide.
What employers may mean when they ask for AI skills
Read the job description as a list of work, not as a list of fashionable nouns.
It might mean using an assistant inside an IDE. It might mean evaluating outputs, writing prompts, connecting an API, cleaning data, testing an automated workflow, or documenting limits for customers. These are different capabilities.
Instead of writing “used AI to build a web app,” describe the task: you used an assistant to draft a parser, wrote tests for malformed input, compared the generated output with expected cases, removed a secret from the example configuration, and documented where the tool’s suggestion was rejected.
A remote junior role may require more written communication, clearer issue reports, careful handoffs, and the ability to ask for help before a small problem becomes a day-long problem. “Remote” describes where the work happens; it does not describe how much support the worker receives.
The news is about pathways, not a single winning title
The current evidence points in several directions at once: early-career postings are tight in Handshake’s data; employer references to AI skills are rising; global workforce research warns that entry-level learning tasks can disappear; BLS projections still show substantial software and QA demand; support occupations can decline while continuing to produce replacement openings; cities and federal programs are experimenting with training and apprenticeship routes.
If you are starting now, choose a task family you can investigate rather than a title you can repeat. Can you test a feature? Explain a bug? Query and validate data? Help a user? Automate a repetitive process? Document a system so another person can use it? Then look at local programs and live postings to see which of those tasks are actually being purchased.
The first useful move may be a community-college course, an American Job Center appointment, a paid apprenticeship search, a support role, a QA project, a data-analytics training program, a freelance repair job, or a small portfolio project with visible tests. The right choice depends on your location, schedule, education, finances, work authorization, and the kind of work you can sustain.
Alex Carter is the editorial name behind Vandutz Academy, a programming blog for beginners. Alex reviews and tests the examples and explanations published on the site, with a focus on making Python, JavaScript, web development, and developer tools easier to understand.