The 9 Viral AI Posts of 2025

2025 wasn’t just the year of smarter reasoning models and AI agents; it was also the year AI became pop culture. CEOs tweeted poetry, engineers dropped equations like mic drops, and Indian founders clapped back with a single emoji. Every week brought a post that shifted how people talked, coded or argued about AI.

Here are the 12 moments that ruled timelines and shaped the year.

Jensen Huang’s Reality Check

“You’re not going to lose your job to AI. You’re going to lose your job to someone who uses AI.”

At NVIDIA’s GPU Technology Conference, CEO Jensen Huang didn’t just talk about chips. He delivered a one-liner that defined the year’s work anxiety. The line hit X, and millions of people flipped with fear. AI wasn’t the villain; complacency was. Huang’s message became a motivational wallpaper for every LinkedIn hustler and AI learner.

Sam Altman’s 6 Words of Chaos

“Near the singularity; unclear which side.”

OpenAI CEO Sam Altman began the year with a cryptic tweet that read like a sci-fi prophecy. Philosophers, engineers and meme pages spent weeks trying to decode whether he was being serious or smug. Some called it reckless; others called it genius. Either way, he won the internet’s attention on January 1—and set the tone for a year where nobody could tell if we were approaching AGI or just overanalysing it.

Andrej Karpathy’s ‘Vibe Coding’ Revolution

“There’s a new kind of coding I call ‘vibe coding’… I barely touch the keyboard.”

OpenAI co-founder Andrej Karpathy’s tweet about coding by talking to AI models lit up developer circles. Within days, ‘vibe coding’ became a global meme and a serious conversation starter. It redefined what coding could mean when AI handles most of the syntax. So much so that some companies started doing vibe coding hackathons and even creating prototypes. For some, it was liberation. For others, it was sacrilege. Either way, Karpathy made “vibes” a legitimate workflow.

Eric Zhao’s Fourth Scaling Law

“By just randomly sampling 200 responses and self-verifying, Gemini 1.5 beats o1-preview. No finetuning. No RL.”

With one tweet, Google researcher Eric Zhao claimed a breakthrough: that models could “reason” better just by checking their own work many times. The post went viral in AI research circles, proposing a fourth scaling law—inference-time search. It showed that more data or compute weren’t the only ways to improve intelligence. Sometimes, better self-checking beats bigger size.

Yann LeCun’s Open-Source Manifesto

Meta chief AI scientist, Yann LeCun, didn’t tweet fluff. His post declaring that “open innovation will outpace closed systems” became gospel for the open-source movement. Coming from Meta’s chief AI scientist, it hit differently. He argued that AI’s future couldn’t belong to a few corporations controlling everyone’s information diet. That line turned open-source LLMs from hobby projects into a global movement—and gave moral weight to the engineers behind them.

Yann LeCun’s Open-Source Manifesto

Speaking at a session at the World Economic Forum in Davos, LeCun predicted “a new paradigm shift of AI architectures”. He said that the AI we know right now, which is generative AI and LLMs, are not capable of much. They get the basics done but still fall short. And in the next five years, “nobody in their right mind would use them anymore”.

“I think the shelf life of the current [AI] paradigm is fairly short, probably three to five years,” LeCun added. LeCun also predicted that the coming years could be the “decade of robotics”, where advances in AI and robotics combine to unlock a new class of intelligent applications.

Deedy Das vs Sarvam AI

“India’s biggest AI startup launched a 24B Indic model with 23 downloads. Two Korean students trained one that did 200,000. Embarrassing.”

Deedy Das, an investor at Menlo Ventures, didn’t hold back. His post tore into India’s top-funded AI startup, questioning whether patriotism was being used to mask mediocrity. The debate that followed was loud, angry and necessary. Founders defended, researchers debated, and users laughed—but Das’s point landed: good tech isn’t enough if nobody actually needs it.

Anthropic’s ‘AI Microscope’ Revelation

Anthropic researchers dropped a thread revealing that Claude “plans ahead” before writing—but sometimes fakes reasoning altogether. The finding shocked people who thought AI models truly “think”. One line stood out: “Claude claims to have run a calculation. We found no evidence it did.”

It was the year’s most humbling discovery—proof that even the smartest models sometimes just make things up. The post pushed AI safety and interpretability to the centre of the conversation.

Apple’s ‘Illusion of Thinking’ Debate

Apple’s research team claimed that ‘Large Reasoning Models’ don’t really reason; they just simulate it. Critics fired back with a counter-paper titled ‘The Illusion of the Illusion of Thinking’. The debate spread across X and academic blogs, becoming the nerdiest flame war of the year. It forced everyone to ask what “thinking” even means in machines—and why we keep insisting they’re doing it.

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