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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Computer science degrees are not obsolete, but a degree alone is less likely to secure an entry-level software job than many students once expected. Lovable CEO Anton Osika’s reported argument is that AI tools are changing who can build software and which skills employers value. U.S. labor data still points to a growing software profession; the more immediate challenge is getting a first role and showing that you can deliver reliable work.
What Lovable’s CEO argued
Anton Osika, co-founder and CEO of the AI app-building company Lovable, was reported as arguing that computer science degrees are losing some of their former advantage as AI makes software creation more accessible. The report attributes the comments to a Business Insider interview, but does not provide a verifiable full transcript or quotation. It is therefore more accurate to describe the point as a reported argument about changing career pathways than to treat it as a definitive statement that computer science education is useless. Tech Times’ report on Osika’s comments
Lovable presents its service as an AI software engineer that lets people build web applications without technical knowledge. That positioning helps explain why its CEO emphasizes access and generalist product-building, but a company’s product vision is not neutral evidence of hiring trends across the whole technology industry. Lovable’s product and pricing information and Osika’s discussion of generalists and changing skills
What the employment data says—and does not say
The U.S. Bureau of Labor Statistics says a bachelor’s degree in computer and information technology or a related field is the typical entry-level education for software developers, quality-assurance analysts, and testers. It projects employment in that combined group to grow 15% from 2024 to 2034. Its detailed projections put software-developer employment at about 1.69 million in 2024 and 1.96 million in 2034, an increase of roughly 267,700 jobs, or 15.8%. These are U.S. occupational projections, not a promise that a particular graduate will be hired. BLS software-developer outlook and BLS computer and information technology projections
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At the same time, the first step into software engineering appears less direct. LinkedIn’s 2026 U.S. software-engineering talent report says 55% of CS degree holders in the 2024 graduating cohort began in non-software-engineering positions. That does not mean they left technology or failed to find work; it shows that a CS degree does not reliably translate straight into a software-engineer title. Handshake says software-engineering roles ranked ninth among the most-posted roles on its platform for the 2024–25 academic year, and reports heightened pessimism among CS students. LinkedIn’s 2026 U.S. report and Handshake’s analysis of CS majors
Broader graduate figures also need careful interpretation. The New York Federal Reserve reported 5.7% unemployment and 41.5% underemployment among recent college graduates in 2026 Q1. Those rates cover recent graduates generally, not CS majors or technology workers specifically, so they show a difficult graduate labor market rather than a direct measure of CS outcomes. New York Fed College Labor Market data
How AI changes the value of a degree
AI development tools can speed up boilerplate code, basic interfaces, documentation, test generation, simple refactoring, and prototypes. They can make it easier for a founder, designer, or product manager to test an idea without first assembling a conventional engineering team. That is a meaningful change in access to software creation; it is not proof that professional engineering judgment is no longer needed.
A working demo is not automatically secure, maintainable, or suitable for production. Applications still need sound requirements, architecture, authorization, data protection, testing, deployment, monitoring, and ongoing maintenance. Generated code can contain exposed secrets, weak access controls, unvalidated inputs, fragile dependencies, or errors that only emerge under real-world conditions. “It runs” is a much lower bar than “it is safe and dependable.”
AI adoption is widespread: Stack Overflow’s 2025 developer survey found that 84% of respondents used or planned to use AI tools in development. Adoption does not mean developers trust every output or that AI has already eliminated jobs; the survey also records concerns about accuracy and verification. The implication for students is to learn how to use these tools and how to check them. Stack Overflow’s 2025 AI survey and Stack Overflow’s 2025 developer-work survey
AI may reduce the importance of memorizing syntax, but it increases the value of knowing when an answer is wrong. Algorithms, databases, operating systems, networks, security, testing, debugging, and system design help developers evaluate and repair generated work. A 2025 study of skills for AI-assisted developers similarly groups the needed capabilities into generative-AI use, core software engineering, adjacent engineering knowledge, and adjacent non-engineering skills. 2025 study on skills for AI-assisted developers
How much does a CS degree matter by career?
“Tech career” covers more than software engineering. Degree requirements vary by role, employer, country, and experience level; job descriptions and actual duties matter more than a title alone.
| Career area | Typical value of CS training |
|---|---|
| Frontend or basic web development | Useful, but a strong portfolio and practical experience may carry substantial weight. |
| Backend engineering | High, particularly for systems, data, reliability, and complex application work. |
| Infrastructure, site reliability, and cloud | High; networking, operating systems, and distributed-systems foundations are difficult to replace with prompting alone. |
| Security engineering | High; formal technical knowledge is valuable, and some specialist roles also favor relevant certifications or experience. |
| Machine-learning engineering | High, often alongside mathematics, statistics, or graduate study. |
| Data engineering | High for databases, pipelines, and distributed systems. |
| Product management | Helpful for technical products, but not normally mandatory. |
| UX and product design | Usually secondary to design work, user research, and a relevant portfolio. |
| Technical sales | Helpful for complex products, but not generally required. |
| IT support and administration | Often optional; practical experience and role-relevant certifications may matter more. |
| Technical writing | Helpful for understanding technical subjects, but not generally required. |
| Startup founding | Not required, though technical literacy can help founders assess trade-offs and prototypes. |
| Research and advanced computing | Usually highly valuable; some roles require advanced study. |
What employers expect beyond the diploma
In a crowded entry-level market, the degree can establish a foundation without proving that a candidate can work effectively on a real product. Employers may look for evidence of delivery, judgment, and collaboration, such as:
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- Internships, co-ops, or other experience on a team and codebase.
- Deployed projects with clear explanations of the problem, design choices, and trade-offs.
- Testing, debugging, version control, code review, and deployment skills.
- Understanding of databases, cloud services, networking, and security appropriate to the role.
- Ability to use AI tools while reviewing and explaining their output.
- Domain knowledge in areas such as health care, finance, logistics, or cybersecurity.
- Clear written communication, collaboration, and product judgment.
A portfolio is complementary evidence, not a universal substitute for a degree. A generated repository that its creator cannot explain may hurt rather than help. Better evidence shows what the candidate built, what failed, how it was tested, what was changed, and how the software is maintained.
When a CS degree is worth the cost
The relevant financial question is not whether computer science is “worth it” in general. It is whether a particular program’s total cost and opportunity cost make sense for a student’s target work and alternatives. Tuition, living costs, borrowing, time out of the workforce, completion likelihood, and local hiring conditions all affect the return.
A degree is more defensible when
- The target is backend, infrastructure, security, data engineering, machine learning, systems, or research.
- The program is affordable relative to likely earnings and offers credible internships, co-ops, recruiting, or applied projects.
- The student wants the flexibility to move among technical specialties or may pursue graduate study.
- Structured learning, faculty support, peers, and campus recruiting would be difficult to replace independently.
Its value is less clear when
- It requires very high debt, has weak completion or placement outcomes, or offers little practical experience.
- The goal is limited to basic website building, rapid prototyping, product management, design, sales, or entrepreneurship rather than engineering.
- The student already has relevant professional experience, or a lower-cost transfer route can reach the same credential.
- The decision rests on the assumption that a CS degree guarantees a high-paying job.
Before committing, compare the program’s total price, median student debt, completion rate, internship access, curriculum, transferability, and employment outcomes by occupation—not just a headline employment rate. Outcomes vary by institution, location, preparation, and whether a student builds experience while studying.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Alternatives to a four-year computer science path
Different routes trade structure and access for cost, speed, and risk. No alternative guarantees a first job, especially in a competitive entry-level market.
Best Value
| Path | Potential advantage | Main trade-off |
|---|---|---|
| Computer engineering, information systems, or software-engineering degree | May fit hardware, enterprise systems, or applied development better, depending on the curriculum. | Program content and outcomes vary; the degree title alone does not establish depth or employer access. |
| Community college followed by transfer | Can lower initial costs while preserving a route to a bachelor’s degree. | Transfer credits, course availability, and access to internships must be checked in advance. |
| Bootcamp | Faster and more focused than a full degree. | Riskier when employers expect experience; outcomes depend heavily on instruction, cost, networking, and portfolio quality. |
| Self-teaching plus real work | Low formal cost and flexible pacing. | The hardest part is often obtaining the first credible experience; internal transfers, freelance work, open-source contributions, or a real product can help establish it. |
| AI-assisted product building | Useful for testing an idea and creating an initial project. | Does not by itself teach fundamentals or demonstrate the ability to maintain production software. |
For career changers, using existing domain experience can be more effective than competing as an undifferentiated beginner. A finance professional who learns data engineering, for example, can connect technical skills to knowledge of financial workflows. The best route depends on the target role, available time, finances, and tolerance for uncertainty.
How to use AI without weakening your skills
Use AI as a tutor, reviewer, or prototyping partner—not as a substitute for understanding the system you submit. A practical routine is to ask for explanations, write or adapt tests, inspect security-sensitive code, and verify behavior yourself. Keep a record of design decisions and be prepared to explain the code without the tool. Practice interviews and debugging without assistance as well, since hiring assessments may test independent reasoning.
That approach makes a degree, course, or self-directed learning more valuable: the goal is not merely to produce code quickly, but to know what should be built, how to verify it, and how to take responsibility for it after launch.
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