There's a question embedded in every agentic AI system that most teams never make explicit. When the agent produces the token "Paris" while booking a flight, is its internal representation about the city of Paris — the place in France, with an airport, a time zone, visa

Sycophancy rates hit 58.19% across GPT-4o, Claude Sonnet, and Gemini 1.5 Pro in a 2025 benchmark study. The researchers (SycEval, arXiv:2502.08177) measured how often each model changed a correct answer to an incorrect one when a user pushed back confidently, even when the user was

In 1931, a 25-year-old mathematician named Kurt Gödel published a proof that broke something many people thought was unbreakable. He showed that any formal system powerful enough to express basic arithmetic cannot be both complete and consistent. Completeness means every true statement in the system can be proven.

You cannot trust an AI system because it got the last ten answers right. That statement will feel counterintuitive to anyone who spent time in traditional software, where a function that returns the correct output is, by definition, working correctly. AI is not that. The philosophical argument for why has

The most common failure mode in production AI agents isn't hallucination. It's something more subtle and more insidious: the agent doesn't know what it already did, what's still true, and what changed as a result of its last action. It operates on

There's a philosopher's thought experiment you should know about if you're deploying AI at scale. It distinguishes two very different things that look identical from the outside, and the difference determines whether your AI program compounds or plateaus. Andy Clark and David Chalmers published

Someone decided you shouldn't be able to ask an AI system how certain household chemicals combine. Someone decided you should be able to ask about historical atrocities. Someone decided the line between those two sits where it does. These aren't engineering decisions. They're philosophical

Bias in AI is usually framed as a content problem: the model says biased things, and you fix it with content filters or fine-tuning. That framing misses where the problem actually lives. By the time a model is producing output, the bias is already encoded in the probability distributions.

If I told you a system was right 70% of the time and wrong 30%, you'd adjust your trust accordingly. What you'd actually want to know is whether the system's own confidence tracks that split. Does it say "I'm confident"

Longer context windows were supposed to solve the problem. Give the model a million tokens of context and it can reason over your entire codebase, your whole document library, your complete customer history. The marketing pitch was that context constraints were an engineering problem being engineered away. The reality is

Here is a structural problem in AI investment planning: the models available for evaluation today may not predict the behavior of the models that will be deployed in 18 months. Not because they'll be evaluated poorly. Because the capabilities that matter most appear suddenly, non-linearly, and only

More reasoning tokens do not produce more correct answers. That sentence makes executives uncomfortable, because they've been sold on "extended thinking" as a premium capability that justifies premium pricing. The discomfort is worth sitting with. In 1974, Daniel Kahneman and Amos Tversky published their work on

The question I hear most often from technology leaders entering serious AI adoption is some version of: how smart is the model? They want a capability benchmark, a rank, a number. It's the wrong question. The more useful question is: how well does your system distribute intelligence across

Every major AI lab has published an alignment approach. When you look at them carefully, they split along one of the oldest fault lines in moral philosophy, and the tension between them explains most of the failure modes you see in deployed AI systems. Kant said the right action is

Karl Popper's central contribution to philosophy of science was a deceptively simple criterion: a claim is scientific if and only if it's falsifiable. Not "true." Falsifiable. There must be a possible observation that would prove the claim wrong. Astrology fails this test not because

The most dangerous assumption in agentic AI isn't that the model will hallucinate. It's that good component performance means good system performance. It doesn't. And there's a 1940s rocket engineer who proved why. Robert Lusser was a German aerospace engineer who formalized

The most important question about your AI deployment isn't whether it's accurate. It's whether accuracy is even the right thing to measure. John Searle published the Chinese Room argument in 1980. The setup: a person sits in a room, receives Chinese characters through a

Every reasoning model ships with a "thinking budget" setting. Most teams either ignore it entirely or set it to maximum and call it a day. Both choices are wrong, and an economist who died in 2001 explains why. Herbert Simon won the Nobel Prize in Economics in 1978

A model trained in early 2024 confidently tells you about the current regulatory environment, the latest product version, or a person's current role. And it's wrong. The model isn't bad. Its beliefs are frozen, and the world moved on. Bayesian epistemology offers a clean

The attention economy's central insight, articulated by Herbert Simon and later developed by economists studying social media, is simple and devastating: when information is abundant, attention becomes the scarce resource. Platforms optimize for attention capture, not value delivery. Engagement metrics go up. User wellbeing doesn't. The

A language model that confidently gives a wrong answer isn't broken. It's doing exactly what it was built to do. That's the issue. Charles Sanders Peirce, the American logician writing in the late 1800s, drew a clean line between three modes of inference. Deduction