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AI in Your IDE: What’s Real, What’s Hype, and Where C++ Fits In

Tristan Soliven

It’s been roughly three years since AI tools and large language models (LLMs) hit the mainstream in a real, unavoidable way. In that time, people have put them to work writing emails, summarizing legal documents, generating marketing copy, composing music, tutoring students, answering medical questions, planning vacations — and yes, generating entire recipes for whatever’s left in the fridge. Some uses are practical. Some are strange. Most of them are here to stay.

One of the more contentious applications is code generation. Based on the promises of their creators, LLMs can dramatically accelerate coding workflows for professionals and novices alike — and even allow complete beginners to produce functional, working apps in a matter of seconds.

In this blog and product update, we look at the current state of AI in coding and programming, with a particular focus on C++. We’ll review what’s actually being implemented in popular IDEs and development environments, weigh the real arguments for and against, and share how (spoiler) Visual Assist is joining in on the fun.

tldr: AI coding tools are real—but not a silver bullet. They’re excellent for speeding up repetitive tasks like boilerplate, test generation, and code explanation, but they can introduce subtle, high-risk errors—especially in complex languages like C++. The most effective developers aren’t blindly trusting or rejecting AI—they’re using it selectively, combining its speed with human judgment and deterministic tools.

What’s the vibe on vibe coding?

Vibecoding is one of the terms that has emerged from the AI-in-software world. It describes a development style where AI generates the code while the human acts purely as an orchestrator — describing what the app should do without writing any actual code instructions.

Public opinion on this is sharply divided. Supporters call it a revolutionary leap in productivity. Critics call it a reckless path toward technical debt, since the orchestrators often accept generated code without detailed review.

A redditor laments the enormous task of reviewing code created or mostly created with LLMs. (From Reddit)

In practice, vibecoding’s conversational nature is especially appealing to non-coders. Building a workable dashboard or a simple app used to require at least some programming fundamentals. Now it mostly requires a clear description. Many professionals, however, are quick to point out that the resulting code often carries hidden risk and accumulates debt that’s invisible—until it isn’t.

Even the doubters, though, can’t entirely dismiss what this represents: the ability to get functional, workable code in an extremely short timespan is genuinely remarkable, even if the output requires supervision.

The C++ wrinkle

In C++, the appeal of AI-assisted coding is even more enticing. The case for it is easy to make. C++ is verbose, has a steep learning curve, and requires a lot of boilerplate. AI tools can absorb much of that burden. Devs can prototype faster and skip the tedious parts. Let the model handle CMake stubs and standard patterns while the developer focuses on architecture (sounds similar to a core benefit of productivity plugins like Visual Assist).

The case against it is equally compelling, though. Since the language is complex enough by itself, the added layer of using AI increases the level of required scrutiny and the importance of expert oversight.

C++ is a language where getting things nearly right often counts as getting them wrong. Memory management, undefined behavior, manual resource handling, pointer arithmetic— these are areas where subtly incorrect code can compile cleanly, run for a while, and then cause serious problems. 

Of course, it’s not claiming that non-AI programming (i.e. manual coding) is less prone to the same mistakes. But LLMs are the kings of confident persuasion. AI-generated C++ code can look perfectly reasonable and can  actually do what it’s prompted for. But it can still contain vulnerabilities or hidden bugs that only surface under specific conditions. Not something you want apps usually associated with being made with C++.

Consider a concrete example: an LLM asked to generate a multithreaded C++ module may produce code that compiles and appears to run correctly, but contains a race condition in its resource locking logic. There are no compiler warnings; the tests pass. The bug only appears under load, worst-case, in production. A developer who accepted and shipped that code without careful review now has a problem that is far harder to diagnose than it would have been to prevent.

This is not a hypothetical edge case. It is the central risk of vibecoding in a low-level, systems-oriented language. The more complex and performance-critical the codebase, the higher the cost of an unreviewed AI suggestion.

Grounded in reality: Where AI fits in today

This pattern of a new technology that arrives with great promise, then gets stress-tested by reality, isn’t new.

The dot-com boom of the late 1990s is the closest drawn parallel. It attracted enormous investment, spawned extraordinary promises about what the internet would change and how fast, and then collapsed spectacularly around 2000 when the gap between hype and commercial reality became impossible to ignore. Pets.com. Webvan. Kozmo.com. They were the promised pages that were viewed as the future of the net. Then they just disappeared.

But when the dust settled, something more durable remained. The core technologies evolved. The survivors like Google, Amazon, the foundations of e-commerce, social networking, streaming—they went on to define the internet. The bubble burst, but the internet didn’t go away. It just Darwin-ed its way and evolved to what it is now.

There are some that claim that this is AI’s character arc. The hype is there but the pundits are also proclaiming that the disillusionment is beginning and the AI bubble is about to burst sooner rather than later.

If that’s the case, whatever survives the contraction (if the bubble actually does collapse) will likely be the tools and use cases that found actual use. The honest applications and tools that add value. Not the ones promoted ad nauseam by marketing teams to “replace human judgment” entirely.

The best apps I’ve ever made have one user; me.

Personalised apps are the peak. Stop chasing the gold rush (it’s a scam), and just solve your own problems.

– From a Reddit comment by u/wardox

For coding specifically, we’re already seeing where the floor is. In 2025, a randomized controlled trial by AI research group METR found that experienced developers working on large, complex codebases actually took 19% longer to complete tasks when using AI tools — despite predicting they’d be 20% faster. The overhead of prompting, reviewing, and correcting AI output can exceed the time saved, particularly on tasks requiring deep contextual knowledge of a codebase.

And yet, a report from the Financial Times noted that even major enterprises are not immune to risk — Amazon Web Services reportedly suffered a 13-hour outage to one of its systems due to employee use of its own AI coding assistant, though Amazon has disputed the claim. Whether or not that specific account holds up, the underlying concern is legitimate: AI-introduced errors in complex systems can be expensive, subtle, and hard to trace.

Bottom line: Wherever you sit on the AI usage spectrum, avoiding it entirely is becoming increasingly difficult. These tools are bundled into IDEs, suggested by default, and integrated into workflows by businesses looking to cash in on the promise of better labor costs. At some point, most developers will get to try them. A deliberate marketing strat to sow the first seeds of reliance.

What the numbers actually show

Security and accuracy were the top concerns of those using AI in professional work.

Adoption is broad and still growing. According to Stack Overflow’s 2025 Developer Survey, 84% of respondents are using or plan to use AI tools in their development process, up from 76% the prior year. Roughly 51% of professional developers now use AI tools daily. Visual Studio and VS Code remain the top IDEs overall, though newer AI-native editors like Cursor, Claude Code, and Windsurf are gaining ground.

However, developer sentiment is more complicated. Positive feelings toward AI tools dropped from above 70% in 2023–2024 to around 60% in 2025 — suggesting that the initial wave of enthusiasm is giving way to a more calibrated, experienced view. Roughly 87% of developers surveyed expressed concerns about accuracy, and 81% flagged security and data privacy as ongoing worries.

The takeaway isn’t that AI is failing but the industry is getting more honest about what it’s actually good at.

Common AI use cases in top IDEs and C++ tools

Back to C++. The major IDEs have converged on a consistent set of AI-powered features, though the depth of implementation is where the battleground is.

AI-powered features in Visual Studio and Visual Assist

Visual Studio is one of the most popular IDEs for Windows-native and cross-platform development, widely used across enterprise software, systems programming, game development, and C++ applications of all scales. It has recently added improved AI-powered features as a complement to IntelliSense, which relies on static type and inference-based code assistance.

Visual Studio’s AI capabilities are broad and improving. That said, there are areas where its tooling is less specialized — particularly for complex C++ scenarios, shader files, and domain-specific syntax like Unreal Engine’s MACROS and UFUNCTIONs. This is where the IDE’s plugin architecture becomes important, and where tools like Visual Assist have built their reputation.

Visual Assist has over 1.55 million installs on the Visual Studio Marketplace. It’s not because it duplicates what Visual Studio already does well, but because it fills in the gaps. It provides similar but more powerful replacement for IntelliSense. It can parse larger codebases, interpret uncommon syntax, and also adds essential code inspections and refactoring tools that go beyond what vanilla Visual Studio provides.

There’s also something worth naming directly: the deterministic nature of IntelliSense and Visual Assist’s linter. Unlike AI tools, their suggestions are consistent between sessions, between users, and across time. That consistency matters. In security-critical code, safety systems, or anywhere that “nearly right” is a failure condition, predictability isn’t a limitation — it’s the point.

At the same time, however, the contextual, adaptive nature of AI-assisted features can provide productivity boosts that static analysis tools simply can’t match. Eliminating boilerplate, generating test scaffolding, explaining unfamiliar code: these are genuine wins that complement, rather than compete with, the deterministic tools developers already rely on.

Recognizing this, Visual Assist has begun integrating its own AI-powered features. You can download and try it out now.

Try Visual Assist

Visual Assist AI implementations

Game development spotlight: AI as a fast-track, not a shortcut

The Whole Tomato team had the privilege of speaking with a game studio director to get their perspective on AI in C++ game development.. We asked them what their stance on whether users need to learn these new AI tools to get hired as a C++ game developer. 

His take: AI adoption is no longer optional for developers who want to stay competitive. “Some see AI as a threat, some as a chance. Don’t resist — try to rule while using it.”

If you’re looking to become a game developer and are unsure of whether AI use is allowed (still debatable) by most businesses, here is an interesting case study:

READ: How to get hired as a game developer

Conclusion

The story of AI in software development is still being written but its rough outline is already visible. The tools are real, the productivity gains in specific contexts are real, and the risks of uncritical adoption are equally real.

For C++ developers, this means treating AI assistance the way you’d treat any powerful, occasionally unreliable tool: useful for the right jobs, not a substitute for understanding the work. Code completion, refactoring suggestions, and test generation are areas where AI genuinely earns its place in the workflow. Blindly shipping generated memory management code in a safety-critical system is where it stops earning it.

Visual Assist occupies an interesting position in this landscape. Its core value addresses exactly the class of problems that AI tools are worst at. Its deterministic, deep, C++-aware assistance that behaves consistently every time.

But that’s not to say that it’s resistant to change. The added VA Intelligence and the Assistant Chatbot were implemented after careful study of where this new tech can provide the most benefit and least concern. It wasn’t a knee jerk reaction just to ride the hype around AI.

Our bottomline: The developers who will get the most out of this moment aren’t the ones waiting for AI to get good enough to trust completely, or the ones refusing to touch it until that question is settled. They’re the ones already building a workflow that uses the tool for what it’s actually good at. While at the same time keeping their own judgment firmly in the loop.

Have any question or suggestions for future AI functions? Let us know by sending us a message.

Frequently Asked Questions

What is AI-assisted coding in IDEs?

AI-assisted coding refers to tools integrated into development environments that use large language models to generate, suggest, explain, or refactor code based on context or natural language prompts.

Does AI actually make developers more productive?

It depends on the task. AI can speed up simple or repetitive work, but studies show it can slow developers down in complex codebases due to the time spent reviewing, correcting, and validating output.

Is AI-generated C++ code safe to use?

Not by default. C++ is sensitive to subtle issues like memory management and concurrency. AI-generated code may compile and run but still contain hidden bugs or vulnerabilities that only appear under specific conditions.

What is “vibecoding”?

“Vibecoding” is a style of development where developers describe what they want and let AI generate the code, acting more as an orchestrator than a traditional programmer.

Should beginners rely on AI to learn programming?

AI can help explain concepts and generate examples, but relying on it too heavily can limit deep understanding. It’s best used as a supplement, not a replacement for learning fundamentals.

What are the biggest risks of using AI in coding?

The main risks include:

Where does AI help the most in C++ development?

AI is most useful for:

It’s less reliable for complex logic, concurrency, and low-level system behavior.

How do tools like Visual Studio and Visual Assist differ in AI usage?

AI tools in IDEs focus on probabilistic suggestions and generation, while tools like Visual Assist provide deterministic, consistent analysis and refactoring. The two approaches are complementary rather than competing.

Will AI replace C++ developers?

No—but it will change how they work. Developers who know how to effectively use AI tools while maintaining strong fundamentals will have a clear advantage.

What’s the best way to use AI in a development workflow?

Use AI for speed, not authority. Let it handle repetitive tasks and suggestions, but always validate outputs—especially in production or safety-critical code.

 

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