Are Programming Tutorials Dead? Two Top Creators Reveal the Truth in the AI Era

Two top programming tutorial creators—Maximilian Schwarzmüller and Melky—recently had an in-depth conversation around the topic of "tutorials are dead." This discussion touched on the core dilemma of programming education content creation: when AI can provide direct answers, short-form video captures all the attention, and the developer job market is shrinking—do traditional programming tutorials still have a future?

The Golden Age of Programming Tutorials Is Over

The discussion starts from a brutal reality: meticulously crafted programming tutorials perform far worse than a ten-minute reaction video. Max admits that his peak period on YouTube was roughly 2016 to 2022, and over the past two years he's become noticeably less active with tutorial content. The reason is "particularly frustrating"—tutorials that took enormous time and energy to produce get far fewer views than a reaction video that requires almost no preparation, just chatting about some Anthropic article.

The prosperity of this golden age followed a clear technological and market trajectory. Around 2015, modern frontend frameworks like React, Vue, and Node.js exploded simultaneously, and developers needed massive amounts of systematic learning resources. At the same time, the rise of online education platforms like Udemy and Coursera, combined with YouTube's algorithm favoring long-form video recommendations, created fertile ground for tutorial creators. The pandemic period (2020-2021) further spawned a wave of career-changers learning to code after the "Great Resignation," with tech companies hiring frantically and FAANG salaries hitting record highs, further stimulating learning demand. However, the tech industry layoffs that began in late 2022 (Meta, Google, Amazon, and others collectively laid off over 150,000 people) completely shattered this ecosystem, and the narrative of "learn to code = high-paying job" began to crack.

This frustration is far from an isolated case. Three forces are simultaneously battering the programming tutorial ecosystem:

• Shrinking job market: After the pandemic-era over-hiring receded, large numbers of developers can't find work, and learning demand naturally declined
• Changing viewing habits: Audiences migrating to short-form content platforms like TikTok, continuously shrinking the reach of long-form programming tutorials
• AI's substitution effect: AI tools like ChatGPT and Claude let people get answers directly, bypassing the traditional learning process

The migration of programming tutorial audiences to short-form video is essentially a manifestation of attention economy dynamics. TikTok's core algorithm logic is "completion rate first"—a 90-second video watched to completion is worth more in the algorithm's eyes than a 60-minute tutorial watched for 10 minutes. YouTube also began heavily promoting Shorts around 2020, with its algorithm logic gradually converging toward TikTok's, structurally suppressing organic recommendation traffic for long-form videos. The deeper change is that users' "attention muscles" are atrophying—research shows that sustained exposure to short-form video content lowers people's tolerance threshold for long content. Reaction videos perform better precisely because they naturally fit the consumption patterns of the short-attention era: emotional, conflict-driven, with instant feedback, requiring no cognitive investment from viewers.

The Programming Learning Paradox in the AI Era

The most profound part of the conversation is both creators' reflection on AI-assisted learning. Max openly acknowledges that he uses AI programming tools extensively every day—Cursor, Claude Code, ChatGPT, Gemini—but he also clearly identifies a dangerous trap:

"A lot of times I don't actually truly understand what the AI writes, because it's not my code. I still have to dig in and figure it out, and that might take even longer than if I'd written it myself."

The tools Max mentions represent different forms of AI programming assistance. GitHub Copilot (released in 2021) was the first widely adopted AI code completion tool, primarily providing line-level or function-level code suggestions. Cursor is an AI-native IDE that can understand an entire codebase's context, supporting multi-file editing and natural language commands. Claude Code and ChatGPT represent conversational programming assistants that can generate complete modules or even entire project scaffolds from scratch. This leap in capability has made "Vibe Coding" possible—developers only need to describe their intent, and AI handles the implementation. This is extremely tempting for beginners, but it's also precisely where the trap lies: when you can't evaluate the quality of AI output, you're essentially blindly trusting a system that confidently makes mistakes.

This paradox is especially fatal for programming newcomers. The traditional learning path is: watch tutorial → try it yourself → encounter problems → search Google and Stack Overflow → build a solution based on hints. This process is slow, but every step builds genuine understanding. Now, AI makes "just getting the answer" too easy—learners may continuously stack AI-generated code until one day it suddenly breaks, produces serious bugs, or exposes security vulnerabilities.

Max used a precise analogy: "It's like lounging on the couch eating junk food all the time—you can't expect to suddenly be fit a few years later." Interestingly, people easily understand this logic when it comes to physical exercise, but when it comes to mental activities, they deceive themselves—"I'll figure it out somehow."

The Experience Gap Determines the True Value of AI Programming Tools

A key insight is that AI has vastly different value for developers at different experience levels. A veteran who's been coding for 20 years can see an error and determine "the AI is using a two-year-old deprecated API"; while a beginner might not even be able to read the error message, let alone judge the quality of the AI's solution. If you're not better than AI, nobody needs you—this sounds harsh, but it's the reality of today's developer market.

Three Survival Strategies for Programming Tutorial Creators

Facing these challenges, neither creator has chosen pessimistic surrender. Instead, they're actively exploring new content formats.

Strategy One: Go Deep Rather Than Broad

Max offers a counterintuitive insight: perhaps now is actually a good time to create advanced programming content. Historically, YouTube has roughly 50 React beginner guides but almost no advanced guides, because beginner content has a larger potential audience. But when total viewership drops, this ratio may shift—those still willing to invest time in learning often already have foundational skills and want to go deeper.

Strategy Two: Community-Driven Teaching Models

Live teaching + community interaction is becoming a more sustainable form of programming education. Melky shared his own practice: doing live teaching sessions for a group of people, then editing and publishing the content. The benefit of this format is that during livestreams you naturally make small mistakes and have interactions, which actually makes the content more human and more sustainable.

Strategy Three: Transitioning from Free to Paid

Both creators agree that the purely free model may no longer be viable. Max's monetization logic has always been: YouTube videos are free (he doesn't even accept sponsorships), converting 1%-2% of viewers into paid course customers. But when viewership drops to a quarter of what it was, this model no longer works.

The likely future model is: put teasers or the first half on YouTube, with in-depth content behind a paywall. And paying itself has an underestimated benefit—it's a commitment mechanism. Behind this is a precise behavioral economics explanation: the sunk cost effect drives people to continue investing in things they've already paid for, and having "skin in the game"