Don't waste 2026 learning the wrong tech skills (Meta Engineer's Take)
Should You Learn to Code in 2026?
Current Job Market Overview
- The speaker, Jason, addresses the question of whether to learn coding or focus on AI for career development in 2026.
- Jason has 15 years of experience in tech, primarily at Salesforce and currently at Meta, providing context for his insights.
- He notes a significant shift in the job market, particularly affecting recent computer science graduates.
- The unemployment rate for CS majors is nearly double that of philosophy majors, highlighting a concerning trend.
- Entry-level job postings are decreasing; new grad hires in big tech have dropped from 15% in 2019.
Impact of Tech Layoffs
- Recent layoffs at major companies like Microsoft and Meta contribute to a bleak outlook for entry-level positions.
- Despite these challenges, there are emerging opportunities within AI-related roles as job postings mentioning AI have surged by 134%.
- However, most AI roles require more than three years of experience, limiting immediate opportunities for junior engineers.
Understanding the Role of AI
- Many current layoffs are attributed to increased efficiency from AI tools reducing the need for engineers.
- While coding tasks may be streamlined by AI, essential engineering work involves collaboration and understanding complex systems beyond just coding.
- Jason predicts that leadership will recognize the ongoing need for engineers despite cost-cutting measures driven by AI advancements.
Shifts in Engineering Skills Demand
- There is an initial excitement around low-code/no-code solutions powered by AI; however, many projects fail to reach production status (99%).
- A correction is expected regarding expectations around what can be achieved with current AI capabilities over time.
Recommendations for New Graduates
Phase One: Building Skills (0 - 6 Months)
- New grads should focus on building real projects rather than applying for jobs due to limited openings.
- Those from top engineering schools with relevant internships still have a chance but should also consider alternative paths.
Project Development Focus
- Emphasize creating products that integrate AI technologies as these skills will be crucial moving forward.
- Traditional interview preparation methods like LeetCode may become less relevant but should not be entirely neglected (10%-15% focus).
Practical Steps Moving Forward
- Build Projects: Create personal projects using AI that solve real problems you encounter daily.
- Explore Startups: Consider applying to startups where entry-level positions are still available and learning opportunities abound.
- Leverage Community Resources: Engage with communities focused on tech development and share your progress.
Future Phases: Gaining Experience (6 - 18 Months)
Phase Two: Applying Skills
- After six months of project work, start applying to startups which offer valuable hands-on experience compared to large tech firms.
Phase Three: Transitioning into Established Roles
- With gained experience from startups or personal projects after about one year, begin targeting mid-level positions as hiring trends improve.
Conclusion & Next Steps
Summary Actions
- Reduce time spent on LeetCode; prioritize practical project work instead.
- Build something useful quickly—aim for completion within a week using AI tools.
- Share your work publicly via GitHub or social media platforms to gather feedback and build visibility.
- Join supportive communities focused on technology development and networking opportunities.
By focusing on building skills through practical application rather than traditional job hunting methods during this transitional period in tech hiring practices, new graduates can better position themselves for future success when the market stabilizes again.