I’m a CS graduate in Turkey, anxious about AI changing coding jobs. Should I stay in software, pivot, or try YouTube video essays?

Short Answer

In short, do not treat this as a binary choice between “real coding” and a lottery-ticket YouTube career. A CS graduate in Turkey can still build a serious path in software, but the safer move is to combine code, AI-assisted workflows, and a small video-essay experiment with clear limits on time, money, and expectations. The main caveats are the local hiring market, your mental load, YouTube monetization thresholds, and whether your videos show original research rather than generic AI output. The detailed breakdown below shows how to turn the uncertainty into a 90-day test instead of a life decision.

Career Reality

The fear that “nobody writes code anymore” is understandable, but it is too absolute. Stack Overflow’s 2025 developer survey found heavy AI adoption alongside falling trust in AI outputs, which is exactly the point: teams still need people who can define requirements, review generated code, test behavior, secure systems, debug production issues, and explain tradeoffs. AI has changed the texture of junior work, especially boilerplate and prototyping, but it has not removed the need for engineering judgment.

The market is also uneven by country. U.S. labor projections still show strong demand for software developers and testers, but that does not automatically translate to entry-level comfort in Turkey. The OECD’s 2025 Türkiye survey points to graduate skills mismatch and digital-skills gaps, especially for recent graduates. That means your practical edge is not just “I know React” or “I can prompt AI”; it is “I can ship a working product, document decisions, test it, and explain it to a real audience.”

So the mindset shift is: stop asking which identity you must choose forever. Ask which portfolio proves the most useful mix of skills in the next quarter. A web developer who can build tools, write clearly, research stories, and turn explanations into watchable clips is not confused; that is a coherent hybrid profile if you give it structure.

Video Essays

YouTube video essays can be worth trying, but not as your only financial plan. YouTube’s own Partner Program rules require meaningful audience thresholds before ad revenue becomes realistic, and its monetization policies reward original, authentic content that adds value. That means video essays about stories you love should be built around your interpretation, research, structure, and voice, not just summaries, borrowed footage, or templated AI narration.

A good first format is not a two-hour documentary. Start with 3-6 minute essays and 30-60 second vertical cuts from each script. For example: one core claim, three examples, one takeaway. If the long version teaches depth, the short version tests the hook. You can draft the clips in YumCut after you write the argument, then manually review the script, captions, visuals, and any synthetic voice before publishing.

This is where your CS background helps. Build yourself a repeatable content system: research notes, source log, script outline, visual list, publish checklist, and analytics review. YouTube’s audience-retention reporting can show where viewers leave, replay, or stay through the intro. That data is more useful than guessing whether you are “meant” to be a creator.

AI Boundaries

Use AI as an assistant, not as the author of your taste. It can help summarize notes, generate alternate hooks, produce rough visual prompts, localize captions, and speed up short-form versions. But you should manually check factual claims, story interpretation, copyright risk, synthetic-media disclosure, captions, and whether the final video actually sounds like you.

If your essays use realistic AI-generated or meaningfully altered media, YouTube says creators may need to disclose that during upload. Disclosure itself is not the enemy; hidden or misleading synthetic content is the risk. For a new creator, the trust signal is simple: make your sources visible in the video description, separate fact from opinion, avoid pretending AI scenes are real, and keep your voice present even if you stay faceless.

For software work, apply the same rule. Let AI generate scaffolds, but keep ownership of tests, architecture, security, and product reasoning. For content, let AI speed production, but keep ownership of taste, claims, and audience trust.

90-Day Plan

For the next 90 days, run two tracks in parallel. Track one is employability: build one small web product that solves a real problem, uses AI only where it genuinely helps, and includes a public write-up explaining decisions. Track two is creator validation: publish one essay every two weeks plus two short vertical clips from each essay. This is enough output to learn without turning your life into a content treadmill.

Measure the creator track by learning signals before money: can you finish scripts, can strangers understand the point, do viewers stay past the first 30 seconds, and do any comments ask for more? Measure the career track by portfolio signals: can you show shipped code, product thinking, testing, and communication? If both tracks reinforce each other, test the workflow in YumCut and keep refining. If one drains you or produces no signal after 90 days, narrow the bet instead of calling yourself lost.

The goal is not to become immune to uncertainty. The goal is to replace fog with evidence. After 90 days, you should know whether story analysis belongs as a hobby, a creator side project, a portfolio differentiator, or the center of your work.