The Great Educational Operating System Upgrade of 2025: A Computational Perspective on Human Learning 2.0

Let’s talk about how we’re about to recompile the entire educational stack of humanity. The news piece presents seven trends for 2025, but what we’re really looking at is something far more fascinating: the first large-scale attempt to refactor human knowledge transmission since the invention of standardized education.

Think of traditional education as MS-DOS: linear, batch-processed, and terribly unforgiving of runtime errors. What we’re witnessing now is the emergence of Education OS 2.0 - a distributed, neural-network-inspired system that’s trying to figure out how to optimize itself while running.

The ServiceNow Paradox: When Software Eats the World, Who Feeds the Software?

Here’s a fascinating puzzle: We’ve created software systems so complex that we now need software to help us manage our software. And guess what? We don’t have enough people who understand how to manage that software either. Welcome to the infinite regression of modern digital transformation.

Let’s dive into what I like to call “The ServiceNow Paradox.” Picture this: You’re a large organization drowning in manual processes. You discover ServiceNow, a platform that promises to digitize and automate everything from IT helpdesks to HR workflows. It’s like having a digital butler who knows exactly how to handle every business process. Sounds perfect, right?

LinkedIn's AI Invasion: When Algorithms Learn to Speak Corporate

There’s a delightful irony in discovering that artificial intelligence has mastered the art of corporate speak before mastering actual human communication. According to a recent study by Originality AI, more than half of LinkedIn’s longer posts are now AI-assisted, which explains why scrolling through LinkedIn feels increasingly like reading a procedurally generated management consultant simulator.

The fascinating aspect isn’t just the prevalence of AI content, but how seamlessly it blended in. Consider this: LinkedIn inadvertently created the perfect petri dish for artificial content. The platform’s notorious “professional language” had already evolved into such a formulaic pattern that it was essentially a compression algorithm for human status signaling. When you think about it, corporate speak is just a finite set of interchangeable modules: “leverage synergies,” “drive innovation,” “thought leadership,” arranged in predictable patterns to signal professional competence.

The Predictable Evolution of AI Economics: OpenAI's Dance with Advertising

Let’s talk about the inevitability of advertising in AI systems, or what happens when computational idealism meets economic reality. OpenAI’s recent moves toward advertising shouldn’t surprise anyone who understands how information processing systems evolve under resource constraints.

Here’s the fascinating part: OpenAI, which started as a nonprofit dedicated to beneficial AI, is following a path as predictable as a deterministic algorithm. They’re hiring ad executives from Google and Meta, while their CFO Sarah Friar performs the classic corporate dance of “we’re exploring options” followed by “we have no active plans.” It’s like watching a chess game where you can see the checkmate coming five moves ahead.

The Rise of Pure Software Organizations: When Algorithms Run the Company

There’s something delightfully ironic about Sam Altman, a human, explaining how companies will eventually not need humans. It’s like a turkey enthusiastically describing the perfect Thanksgiving dinner recipe. But let’s dive into this fascinating glimpse of our algorithmic future, shall we?

The recent conversation between Altman and Garry Tan reveals something profound about the trajectory of organizational intelligence. We’re witnessing the emergence of what I’d call “pure information processors” - entities that might make our current corporations look like amoebas playing chess.

The Computational Delusion: Why Bigger AI Models Are Like Building Taller Ladders to Reach the Moon

There’s something delightfully human about our persistent belief that if we just make things bigger, they’ll automatically get better. It’s as if somewhere in our collective consciousness, we’re still those kids stacking blocks higher and higher, convinced that eventually we’ll reach the clouds.

The current debate about AI scaling limitations reminds me of a fundamental truth about complex systems: they rarely follow our intuitive expectations. We’re currently witnessing what I call the “Great Scaling Confusion” - the belief that if we just pump more compute power and data into our models, they’ll somehow transform into the artificial general intelligence we’ve been dreaming about.

The Computational Mirage: When "Open" AI is Neither Open Nor Intelligent

There’s a delightful irony in how we’ve managed to take the crystal-clear concept of “open source” and transform it into something as opaque as a neural network’s decision-making process. The recent Nature analysis by Widder, Whittaker, and West perfectly illustrates how we’ve wandered into a peculiar cognitive trap of our own making.

Let’s start with a fundamental observation: What we call “open AI” today is about as open as a bank vault with a window display. You can peek in, but good luck accessing what’s inside without the proper credentials and infrastructure.

The Computational Tragedy of the Medical Mind

When I first encountered the news that ChatGPT outperformed doctors in diagnosis, my initial reaction wasn’t surprise - it was amusement at our collective inability to understand what’s actually happening. We’re still stuck in a framework where we think of AI as either a godlike entity that will enslave humanity, or a humble digital intern fetching our cognitive coffee.

The reality is far more interesting, and slightly terrifying: we’re watching the collision of two fundamentally different types of information processing systems. Human doctors process information through narrative structures, built up through years of experience and emotional engagement. They construct stories about patients, diseases, and treatments. ChatGPT, on the other hand, is essentially a pattern-matching engine operating across a vast landscape of medical knowledge without any need for narrative coherence.

When Software Patterns Eat Their Own Source Code: The OpenAI Evolution

The universe has a delightful way of demonstrating computational patterns, even in our legal documents. The latest example? Elon Musk’s injunction against OpenAI, which reads like a textbook case of what happens when initial conditions meet emergence in complex systems.

Let’s unpack this fascinating dance of organizational consciousness.

Remember when OpenAI was born? It emerged as a nonprofit, dedicated to ensuring artificial intelligence benefits humanity. The founding DNA, if you will, contained specific instructions: “thou shalt not prioritize profit.” But here’s where it gets interesting - organizations, like software systems, tend to evolve beyond their initial parameters.

The Computational Angels in our Machines: A Cognitive Scientist's View on AI and Belief

Let’s talk about angels, artificial intelligence, and a rather fascinating question that keeps popping up: Should ChatGPT believe in angels? The real kicker here isn’t whether AI should have religious beliefs - it’s what this question reveals about our understanding of both belief and artificial intelligence.

First, we need to understand what belief actually is from a computational perspective. When humans believe in angels, they’re not just pattern-matching against cultural data - they’re engaging in a complex cognitive process that involves consciousness, intentionality, and emotional resonance. It’s a bit like running a sophisticated simulation that gets deeply integrated into our cognitive architecture.