This is AGI

Is AI a new kind of intelligent species? This episode examines how humans are coping with the proliferation of AI, why AI adoption is now a competitive necessity, and what recent research reveals about the folk theories people hold about artificial intelligence. Recent research on whether prompts like “you are an expert” actually work, why politeness does not help, and the emergence of new human p...

Did Jensen Huang really say AGI is already here? Not quite. This episode dissects the viral claim, explains what Huang actually meant, and lays out a sharper definition of AGI based on one core capability: generalizing from irrelevant experience. A deep but accessible look at LLMs, AI agents, out-of-distribution problems, analogy, and why true artificial general intelligence still remains unsolved...

Will artificial general intelligence wipe out software engineering jobs, or just transform them? In this episode of This Is AGI , I compare the future of software development to commercial aviation, showing why human “taste” in engineering may be automated, yet human accountability, edge-case judgment, and legal responsibility could keep software engineers essential for years to come. A closer loo...

Did the Pentagon’s clash with Anthropic expose a deeper AI security crisis? This episode explores the Anthropic’s supply-chain risk designation by the Pentagon, what it could reveal about the state of the military information security, and why advanced AI models may become dangerously powerful simply by connecting the dots humans miss. A provocative look at AI safety, national security, AGI, and t...

Will human “taste” in software engineering protect developers from AI job displacement? In this episode of ‘ This Is AGI’ , Alex Chadyuk argues that code elegance and architectural judgment are becoming increasingly automatable, and may matter less in a world of AI-powered IDEs, agentic coding systems, and future AGI. A sharp take on software engineering, code aesthetics, AI automation, and the fu...

Everyone is waiting for AGI, but most office jobs don’t require general intelligence to automate them. In this episode, I explain why decades of hyper-specialization in white-collar work makes it uniquely vulnerable to today’s LLM-based agentic systems, from legacy banking workflows to back-office operations, and why the shock may arrive sooner than people expect. If you think “AI still makes dumb...

Judea Pearl argues that today’s transformer-based LLMs have mathematical limits that prevent them from internalizing causal world models, so “just scaling” them won’t deliver AGI. But does that mean LLMs are a dead end? In this episode, I break down what Pearl actually said, why the Internet misquotes it, and why the path to artificial general intelligence may come from a simpler breakthrough that...

As AGI moves from theory to deployment, the definition of “leadership” is shifting fast. In this episode of This Is AGI , Alex Chadyuk explains why the AI era is bringing back the classic general manager skillset—clear vision, critical thinking, and tight communication—while “agentic AI” starts to look less like software and more like a new organizational unit inside your company. You’ll learn how...

As AGI advances, “learn a trade” may not save human labor. This episode dismantles Geoffrey Hinton’s jobs hedge, explains Human-as-a-Service platforms, and shows why AI and AGI are converging into monopolistic utilities. A hard look at AGI economics, regulation failure, and the real risks of unregulated advanced artificial intelligence.

A new AI-only social network called Moltbook has exploded with over a million AI agents posting, debating, joking, and even forming a religion. In this episode of This Is AGI , we explore the rise of AI culture, the Church of the Molt, and what AI spirituality reveals about machine identity, consciousness, and the future of human – AI relationships. If you’re interested in artificial intelligence,...

What if AI intelligence could be measured in bytes instead of test scores? In this episode of This Is AGI , Alex Chadyuk introduces a principled, information-theoretic approach to evaluating artificial intelligence, moving beyond benchmarks like ARC or FrontierMath toward entropy reduction as a true measure of generalization. Learn how Shannon information, uncertainty reduction, and model size com...

Elon Musk proposed “intelligence density per gigabyte” as a metric for AI adequacy — but what is intelligence, really? In this episode of This Is AGI , we dissect why IQ-style benchmarks and test scores create an operational (and often circular) definition of intelligence, using a physics analogy: measuring “weight” only by spring stretch without Newtonian gravity. We also connect the argument to...

In this episode, Alex Chadyuk unpacks the meaning of intelligence density as recently discussed by Elon Musk. Although intelligence has no standard metric when it comes to artificial general intelligence (AGI), Alex describes how intelligence density per gigabyte can be compared given a specific model eval. Topics covered: AGI theory, intelligence metrics, AI evals, neural network compression, qua...

Are GPT models really intelligent—or are they just brilliant storytellers? In this episode of This Is AGI , Alex Chadyuk explores the critical difference between episodic and semantic memory, explains why today’s generative AI excels at narrative recall but lacks true world models, and argues why this gap separates modern GPTs from genuine artificial general intelligence (AGI). Expect a clear, tho...

Over the last three or four centuries, different mathematicians proposed several competing versions of a calculus formalizing the reasoning about plausibility, but there was a lack of consensus about the axioms that should sit at the foundation of such a plausibility calculus.  This conundrum was finally broken in 1961 by Richard Cox, a physicist at the Johns Hopkins University.  Today, we will di...

How AGI is going to measure the plausibility on uncertain statements in the real-world scenarios of incomplete information so that, among other things, we too can know what AGI is thinking?

A charge is often laid at the door of the large language models (LLMs) that they rely on probabilistic generation, assuming that this is somehow a bad thing, and that a more deterministic behaviour would somehow be a better idea for the future artificial general intelligence (AGI).  Before the advent of the LLMs, almost all practical computer systems followed deterministic logic encoded in their s...

Will artificial intelligence discover God?

We take objects, classes, and relationships for granted, but they are just conventions we’ve agreed to use, not truths carved into reality. In this episode, we explore why these human-centric conventions matter, and why guiding AGI to adopt them may be the difference between an intelligible world model and one we can’t understand at all.

There is no question that the proliferation of the AI throughout the global economy will result in a massive reformat of the job market. A lot of jobs will be lost. A lot of completely new careers will emerge. What can you do about it to find yourself on the right side of the equation?

A Chinese government sponsored cyberattack leveraged the American AI technology and infrastructure against the American government agencies and corporations. Things are about to get worse.

Before humans act, they imagine outcomes. AI doesn’t. Yann LeCun thinks that must change, and his JEPA architecture could be the missing link between powerful neural networks and real-world intelligence driven by internal models of reality.

The advent of large language models (LLMs) fundamentally changed the behaviour of computer systems that we learned to trust over the last several decades of development of computation.

In this special Wtf? on World Models, I will give you a break-down of what this whole hype is about, where it is misplaced and why it is a good kind of hype to have for those who are interested in the artificial general intelligence (AGI).

In this special Wtf? episode we unpack the concept of a latent space. You will see why it is so important for understanding both LLMs and the future systems that will exhibit the capabilities of artificial general intelligence (AGI).