Starting from December 2024, the world has been buzzing about AI models that can “think”! But what exactly are these, and what do we mean by “Thinking AI”? Most likely, this refers to the new wave of reasoning-focused AI systems. As we usher in the age of Singularity, we now have AIs designed to mimic thinking—specifically those with advanced reasoning capabilities. And guess what? You and anyone interested can use them for free!

When people talk about AI that “thinks,” they’re usually referring to models like Anthropic’s Claude 3.7 Sonnet, OpenAI’s o3 series, or DeepSeek’s R1, which have been making waves lately. These aren’t your typical LLM chatbots that just spit out quick answers. They’re built to pause, process, and work through problems step-by-step—mimicking what we’d call deliberative thinking in humans. For example, Claude 3.7, launched on February 24, 2025, is billed as a “hybrid reasoning” model. It can flip between near-instant replies and slow, methodical problem-solving, allowing users to control how long it “thinks” based on the task. X posts are hyping its ability to tackle coding and complex reasoning, with some calling it a leap toward more human-like problem-solving.

OpenAI’s o3-mini and o3, released in late January and December 2024 respectively, take a similar approach. They use a “chain of thought” method—breaking problems into logical steps, checking their work, and refining answers. Many users have noted o3’s standout performance on tough benchmarks like math and coding, with some saying it’s like watching an AI “reason” in real time. DeepSeek’s R1, a Chinese open-source model from mid-January 2025, matches this vibe too. It’s praised on multiple social media for rivaling OpenAI’s best while being cheaper and simpler to run, showing that “thinking” AI doesn’t need insane resources.

But here’s the kicker: these models don’t think like humans do. They’re not conscious or feeling anything—they’re just really good at simulating thought through clever engineering. The “thinking” is a mix of reinforcement learning and chain-of-thought techniques, where they generate intermediate steps to reach an answer. X discussions—like posts from the last week—point out this gap. Some users argue AI can’t truly reason, just rearrange data into convincing patterns, while others see these reasoning models as a big step toward something closer to human cognition.

So, what’s the reality? These AIs “think” in the sense that they can handle multi-step problems—say, a PhD-level math puzzle or debugging code—better than older models. Claude 3.7’s “scratchpad” feature even shows you its reasoning process, which users find eerie yet cool. OpenAI’s o3 can reportedly “ruminate” for longer to boost accuracy, and DeepSeek’s R1 nails complex tasks with less compute. Yet, they’re still bound by their training data and algorithms—no original insight, no intuition, just really smart step-by-step crunching.