Recently, there has been a lot of hullabaloo about the idea that large reasoning models (LRM) are unable to think. This is mostly due to a research article published by Apple, "The Illusion of Thinking" Apple argues that LRMs must not be able to think; instead, they just perform pattern-matching. The evidence they provided is that LRMs with chain-of-thought (CoT) reasoning are unable to carry on the calculation using a predefined algorithm as the problem grows.

For three decades, the web has been designed with one audience in mind: People. Pages are optimized for human eyes, clicks and intuition. But as AI-driven agents begin to browse on our behalf, the human-first assumptions built into the internet are being exposed as fragile.

Remember when browsers were simple? You clicked a link, a page loaded, maybe you filled out a form. Those days feel ancient now that AI browsers like Perplexity's Comet promise to do everything for you — browse, click, type, think.

As more companies quickly begin using gen AI, it’s important to avoid a big mistake that could impact its effectiveness: Proper onboarding. Companies spend time and money training new human workers to succeed, but when they use large language model (LLM) helpers, many treat them like simple tools that need no explanation.

Vector databases (DBs), once specialist research instruments, have become widely used infrastructure in just a few years. They power today's semantic search, recommendation engines, anti-fraud measures and gen AI applications across industries. There are a deluge of options: PostgreSQL with pgvector, MySQL HeatWave, DuckDB VSS, SQLite VSS, Pinecone, Weaviate, Milvus and several others.

Agents are the trendiest topic in AI today, and with good reason. AI agents act on their users’ behalf, autonomously handling tasks like making online purchases, building software, researching business trends or booking travel. By taking generative AI out of the sandbox of the chat interface and allowing it to act directly on the world, agentic AI represents a leap forward in the power and utility of AI.Taking gen AI out of the protected sandbox of the chat interface and allowing it to act directly on the world represents a leap forward in the power and utility of AI.

Imagine you do two things on a Monday morning.

Your best data science team just spent six months building a model that predicts customer churn with 90% accuracy. It’s sitting on a server, unused. Why? Because it’s been stuck in a risk review queue for a very long period of time, waiting for a committee that doesn’t understand stochastic models to sign off. This isn’t a hypothetical — it’s the daily reality in most large companies. In AI, the models move at internet speed. Enterprises don’t. Every few weeks, a new model family drops, open-source toolchains mutate and entire MLOps practices get rewritten. But in most companies, anything touching production AI has to pass through risk reviews, audit trails, change-management boards and model-risk sign-off. The result is a widening velocity gap: The research community accelerates; the enterprise stalls. This gap isn’t a headline problem like “AI will take your job.” It’s quieter and more expensive: missed productivity, shadow AI sprawl, duplicated spend and compliance drag that turns promising pilots into perpetual proofs-of-concept.