The rise of ‘Slow AI’: Why devs should stop speedrunning stupid

For as long as we’ve been building with machines, we’ve followed one core rule: faster is better. Lower latency, higher throughput, less waiting; that was gospel. Nobody wanted to wait 600ms for a button to respond or watch a spinner that lasts longer than their attention span. If it was slow, it was broken. Case closed.

So naturally, when AI tools started creeping into our dev workflows, autocomplete, agents, copilots, you name it, the same principle applied. Make it fast. Make it feel instant. Make it look like magic.

But here’s the thing: AI isn’t magic. It’s inference. It’s pipelines and RAG and context and tool calls. It’s juggling messy context and probabilistic guesses. And if you want something smarter than glorified autocomplete, you need to build a pipeline of processes to provide scaffolding for that. Which takes time to process. Anything less and you’re basically just speedrunning stupid. And speed isn’t anything to brag about when your tool is just wrong faster.

At CodeRabbit, we prioritize what we call Slow AI. And we have the guts to say what a lot of AI companies are too afraid to: We’re going to make you wait.

(And you’ll thank us for it).

AI dev tools are often fast, confident, and wrong

If you've used an AI coding agent lately, you've probably seen it: a shockingly fast suggestion pops up almost as soon as you stop typing. It looks legit. But then… it fails silently. Or spectacularly. Or worse, it passes the test and breaks something two files over.

Why? Because most AI dev tools today are optimized for one thing: speed. Type a few tokens and the model predicts the most statistically likely continuation not necessarily the correct one, not the secure one, not the one that actually understands what your app is doing. Just the next plausible blob of code.

That’s fine for boilerplate. But for logic? For edge cases? For actual engineering? It’s kind of like hiring someone who talks fast and confidently in meetings but never reads the specs.

Most of these tools don’t read context, at least not deeply. They might grab a few nearby lines, maybe the function name, but they rarely verify what they’re generating against the bigger picture. No issue cross-checking. No architecture-level awareness. No reasoning across files or use cases.

If you want outputs that are thoughtful, testable, and context-aware, you need AI systems that slow down, zoom out, and actually engage with the problem.

That’s what Slow AI does. And it turns out, when your AI takes the time to understand what it’s doing, it stops hallucinating and starts actually helping.

Why AI is better when it’s slow

At their core, large language models are statistical reasoning machines. They generate output by predicting what comes next based on probability, patterns, and (hopefully) the context you’ve given them. But here's the caveat most devs forget: good predictions take work. This is especially true when you're asking the model to do something complex like write logic, understand architecture, or reason across multiple steps. The quality of the output is often tied directly to the depth or stages of its inference.

This is particularly true when you move beyond simple prompts and into multi-stage pipelines and agentic behavior. When an AI tool is verifying outputs, pulling in relevant files, checking for contradictions, or planning several actions ahead, it’s not just spitting out the next token… it’s thinking. Or, at least, performing a rough approximation of it.

That kind of non-linear reasoning can’t be done in a single forward pass. It involves reflection, retrieval, planning, and sometimes even self-correction. These processes aren’t latency-friendly, they’re intelligence-friendly.

In short: if you want AI to actually help on complex code, you have to let it cook.

Slow is the new smart: Why we let our AI think

Slow AI is one term for what we’re talking about. But it could just as easily be called Comprehensive AI or Accurate AI or even Actually Helpful and Useful AI if we’re being honest. And it’s inextricably tied to one of the buzziest ideas in AI product design right now: context engineering.

The more relevant and parsed info an AI knows about the problem, the better it performs but that context has to be pulled in, parsed, prioritized and reasoned over. That kind of pipeline is the enemy of ultra-low latency AI… and it’s also the enemy of accuracy.

And that’s why our AI code reviews can take up to five minutes before you see the first comment. Don’t get us wrong, we’re not optimizing for slowness. You could get a review in three minutes or even one minute depending on the complexity of your codebase and PR. Our pipeline is complex because that’s what’s required to do the job our users need it to do. You don’t even want to know the number of concurrent processes we have going on at any time!

But guess what? When we let our AI take its time using a non-linear, multi-pass pipeline with multiple review and verification agents, it generates less noise and more relevant code review comments than other tools.

Non-linear reasoning isn’t fast. But it’s good.

So, why do most AI tools choose stupid over slow?

Well, first, Slow AI isn’t an option for every tool. If you’re asking an AI coding agent a question, for example, you’re not going to wait five minutes for it to reply. There’s an expectation of immediacy inherent in that exchange.

But code reviews? No one expects their co-worker to immediately drop what they’re doing and start commenting on a PR when it’s submitted. So, they’re willing to accept a delay in a review from a bot as well. And they’re especially willing to accept that delay if that review saves them time by being more relevant.

But why do so many companies still prioritize low latency when their use cases don’t really require it? Well, we’ve been trained, and trained our users, to expect instant gratification. Click a button, get a dopamine hit. Type a function name, get a suggestion before you even think about it. Anything else feels broken, laggy, or like your startup forgot to pay its AWS bill.

This has been drilled into us so hard that companies are out there actively choosing being wrong over being slow. And there’s something toxic and backwards about our development culture when folks do that.

Because here’s the truth: the best AI tools don’t always feel fast. They feel thoughtful. Sometimes they pause. Sometimes they take an extra beat to reason through your prompt, retrieve relevant code, or validate their response. And that’s something worth waiting for. After all, no one is less likely to use OpenAI’s Deep Research feature because it takes up to 20 minutes to comb the internet for info to better answer your question. You just do something else while it’s processing and circle back.

Slow doesn’t mean busted anymore, it means smart. If anything, speed is the bug when it comes to AI. If we want AI tools that actually add value to the development process, that requires a shift from responsiveness to reliability, from immediacy to insight. And for developers especially, that tradeoff makes sense.

We believe that the most valuable apps in the next five years won’t be the ones that optimize for speed but the ones that optimize for intelligence. Who wants fast garbage over slow value?

CodeRabbit’s mantra: Move slow and fix things

At CodeRabbit, we don’t optimize our AI pipelines for speed at all costs like everyone else. We optimize for trust. That means embracing systems that take the time to understand your code, reason across context, and generate outputs that actually help you build better software. Yes, it’s slower than hammering out a quick prompt. But that extra time buys you clarity, coverage, and confidence.

“Move fast and break things” was great for shipping MVPs. But when it comes to shipping quality, we believe in something else: Move slow and fix things. Let the AI read the room. Let it think before it speaks. And let it give you the kind of help you’d expect from a senior engineer, not just a really confident autocomplete. That’s the only way to break out of this backwards culture that prioritizes wrong AI over slow AI.

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