Vibe Coding Fails: Epic's New Launcher Is Slower Than Steam

Recently, a post on Hacker News sparked quite a bit of discussion in tech circles: Epic Games' newly released launcher has been criticized for being slower than the Steam client, which has been the butt of jokes for years. What makes it more intriguing is that this launcher is said to have been developed using the currently trendy "Vibe Coding" approach—a development mode that relies heavily on AI-generated code and iterates by feel. When "AI efficiency boost" collides with the user experience of "waiting forever for the launcher to start," things get interesting.

Event Recap: A Counterintuitive Comparison

The Steam client has long been a "performance meme" among PC gamers—slow startup, heavy interface, high background resource usage. While Valve has been continuously refactoring the interface with CEF (Chromium Embedded Framework) over the years, "Steam is laggy again" is practically a recurring segment in the player community.

Because of this, when a developer pointed out that Epic's new launcher is even slower than Steam in cold start, page switching, store loading, and other aspects, the topic gained comparative value. It broke a default assumption: everyone complains about Steam being slow, so any new launcher that's "built normally" should be faster than Steam. Reality says otherwise.

CEF (Chromium Embedded Framework) is a key technical background mentioned in multiple articles. It is essentially an open-source framework that allows developers to embed the full Chromium browser engine into native applications, using HTML, CSS, and JavaScript to build desktop interfaces. Valve used CEF to refactor the Steam client's interface layer, allowing the store, community, and many other pages to be rapidly iterated with web technologies. The benefit is high development efficiency and good cross-platform consistency; the cost is introducing a full browser runtime: high memory usage, complex process model (multi-process rendering), and the need to load the entire Chromium engine during cold start, which naturally feels "heavy" and "slow." Understanding this is important because Epic's new launcher also heavily uses web technology stacks. The two are not "native lightweight" versus "web bloated," but rather a direct comparison of two web technology approaches in startup performance. This also explains why the intuition that "newer must be faster" doesn't necessarily hold—what determines speed is often not the newness of the technology, but the actual overhead of the runtime.

The Allure and Cost of Vibe Coding

The core logic of Vibe Coding is: developers describe requirements in natural language, let AI generate code, and focus mainly on direction control and rapid validation rather than scrutinizing line by line. This is indeed very efficient for prototype validation, demo presentations, and small tool development.

But launcher-type products have several special characteristics:

• Long cold start path: From process creation, resource loading, network requests, to UI rendering, any inefficiency in any link gets amplified into "feels slow."
• Complex runtime environment: User machine configurations vary greatly, and AI-generated code often targets "ideal environments," lacking refinement for edge cases.
• Performance optimization requires specific measurement: Which segment is slow, by how much, and why—these questions are hard to pinpoint by "feel" and AI guesswork; they require profiling tools and real data.

In other words, Vibe Coding excels at getting things running "from 0 to 1," but is not good at squeezing performance "from 1 to 0.8 seconds."

Cold Start is one of the most core metrics for evaluating launcher-type products. It refers to the complete time from when the user double-clicks the icon and the operating system creates the process, to when the interface is truly interactive, typically including: process creation and initialization, local configuration and cache reading, dynamic link library loading, network requests (such as login validation, content delivery), and UI rendering and first frame drawing. It's called "cold" because at this point there's no pre-warmed cache or resident process, so all overhead must be genuinely incurred, making it the most reflective of real experience. In contrast, "warm start" refers to re-invocation under an existing process or cache, which typically takes much less time. This is also why just "feeling okay" in certain scenarios isn't enough to prove anything—what users experience when they first open the launcher each day is precisely the slowest cold start path. To optimize cold start well, you must instrument and time each of the above segments separately, locate bottlenecks, and then reduce them accordingly, rather than staying at the overall "feels slow" level.

Why AI-Generated Code Easily Loses on Performance

This is not to deny AI programming, but to clarify where it exposes weaknesses in certain types of tasks.

First, AI defaults to "working" implementations. When you ask it to "write a launcher interface," the model is likely to give you a fully functional but redundantly layered component tree, duplicate network requests, or resource references without lazy loading. Functional tests all pass, performance tests aren't written.

Second, performance problems often hide in dependencies and architecture. AI-generated code tends to introduce ready-made libraries to accomplish tasks; hidden costs like dependency size, initialization overhead, and threading models won't be caught by "is the function correct" checks.

Third, lack of continuous performance regression awareness. Traditional client teams have startup time instrumentation, version comparisons, and performance budgets. Vibe Coding's iteration pace is fast, and it's easy to skip these steps that "don't produce new features."

Profiling is the standard engineering method for locating performance problems, and it's also the capability this article repeatedly emphasizes as "cannot be replaced by AI." It records the time and resources consumed by each function, thread, and system call during program execution through sampling or instrumentation, thereby generating visualizations like flame graphs and call stack time rankings, helping developers answer the three key questions: "which segment is slow, by how much, and why." Common tools include profilers built into various languages, operating system performance analyzers (such as Windows' ETW, Linux's perf), and browser engine DevTools performance panels. For launcher-type products, profiling can precisely reveal whether it's serial network requests, dependency initialization blocking the main thread, or resource decompression taking time. Alongside this are "performance budgets" and "regression tests": setting startup time as a quantified target and automatically comparing after each code change to prevent performance from quietly degrading during rapid iteration. AI can generate functional code, but setting metrics, collecting data, interpreting profiling results, and making trade-offs still depend on engineers' judgment.

Practical Takeaways for Developers

This comparison event should not be simply interpreted as "AI programming doesn't work." A more accurate conclusion is: The benefits and risks of AI programming depend on where you place performance in your process.

If you're building client or launcher-type products, consider these points:

1. Treat cold start time as a first-class metric, instrument it from day one rather than waiting for user complaints.
2. Perform performance reviews on AI-generated code, focusing on dependency introduction, initialization order, and network request consolidation.
3. Distinguish between "functional prototype" and "release version"—the former's standard is being demonstrable, the latter's standard is acceptable user wait time.
4. Make optimization decisions with data, not feelings—the value of profiling tools in this scenario still cannot be replaced by AI.

Conclusion

The viral power of "Epic's new launcher is slower than Steam" comes precisely from its contrast: an old client considered outdated outperforms a new product built with the latest AI workflow. This isn't to say Vibe Coding has no value, but to remind us that when performance becomes a core part of user experience, AI efficiency cannot replace engineering refinement.

For teams embracing AI programming, the real challenge isn't "can you write it with AI," but "after writing it with AI, do you still have the ability to make it faster."

One-line Summary

Epic's new launcher is said to be slower than Steam, exposing Vibe Coding's weaknesses in performance-sensitive scenarios

Paragraph Summary

Epic Games' newly released launcher has been criticized for slower cold start and loading speeds than the long-mocked Steam client, and it's said to be developed using the Vibe Coding approach that relies heavily on AI-generated code, creating a stark contrast. The article analyzes that Vibe Coding excels at rapidly getting functional prototypes running from 0 to 1, but on launcher-type products with long cold start paths, complex runtime environments, and the need for precise measurement, it easily leaves hidden overhead: AI defaults to "working" implementations, neglects dependency size, initialization order, and threading models, and lacks performance instrumentation and regression awareness. The practical takeaway for client teams is to treat cold start time as a first-class metric instrumented from day one, perform performance reviews on AI-generated code, distinguish functional prototypes from release versions, and drive optimization with profiling data rather than feelings. The conclusion is not to deny AI programming, but to emphasize that performance experience still requires engineering refinement.