In the AI era, Andy Grove’s advice — “Nobody owes you a career” — feels more like a law of nature than philosophy. Every professional is now a micro-enterprise, competing on value creation rather than job description. The only real edge left? Designing and upgrading the system that is you.

AI’s trillion-dollar ecosystem has become a closed loop of capital, compute, and cloud — where NVIDIA, OpenAI, and hyperscalers rely on each other’s survival. The question now is whether this interdependence drives mutual success or mutual collapse.

The AI era isn’t suffering from lack of creativity—it’s drowning in it. As AI systems generate endless content, the true challenge isn’t generation but judgment. This post explores the difference between “AI Slop” and “Payoff Judgment,” and why the ability to filter, evaluate, and govern AI output will define the next decade of intelligent systems.

AI is moving from apps that call LLMs to systems that coordinate goal-seeking agents. The 9-layer Agent Tech Stack—spanning goals, memory, safety, and governance—is the blueprint for turning agents from fragile demos into dependable digital coworkers.

Anthropic’s Claude Sonnet 4.5 isn’t just another AI release. With capabilities like sustained coding sessions, real software operation, and product-level integrations, it feels less like a tool and more like a digital colleague. From contract review to code execution, this model pushes us closer to a future where AI is embedded directly into workflows.

For decades, organizations built workflows around scarcity of time and expertise. AI agents are removing that bottleneck, offering on-demand capacity at scale. The challenge now isn’t just speeding up old processes, but redesigning them entirely for a world of abundance.

New research shows that giving AI agents “personalities” — based on frameworks like MBTI or Big Five — can boost performance without retraining. From improved storytelling to stronger cooperation, psychology may be the key to the next leap in agent design.

The September 2025 Applied AI Bootcamp brought together leaders, innovators, and executives for an immersive day exploring LLMs, agents, and governance. What began as an experiment is now scaling into enterprises worldwide—shaping the future of AI adoption.

Many AI strategies stall in endless pilot programs. This session offers a proven, repeatable framework to move from experiments to enterprise-scale AI. Learn how to choose the right use cases, establish an operating model, and implement governance that delivers measurable business v

OpenAI has released two powerful open-source language models — gpt-oss-20b and gpt-oss-120b — under the Apache 2.0 license. This move enables local AI deployment, fine-tuning for custom use cases, and greater control over data. It also signals a shift in enterprise AI strategy and industry competition. Ask ChatGPT

Claude’s new sub-agent architecture may quietly redefine AI's role in software engineering. From code review to debugging, we’re now orchestrating AI teams—not just using AI assistants.

Microsoft just dropped GitHub Spark — an Anthropic-powered tool that turns plain English into deployable apps. No boilerplate. No infra setup. Just describe and deploy. From internal tools to early prototypes, the "prompt-to-app" movement is getting real. Is this the future of coding?

Executives love saying “Let’s build an AI CoE!”—until it grinds everything to a halt. Here’s how to choose an AI operating model that actually works for your culture and scale.

Apple may be eyeing Perplexity AI in a rare acquisition move. While the startup aligns beautifully with Apple’s UX values, its lack of a foundation model could make this deal elegant—but ultimately incomplete.

AI agents won’t dominate 2025—but the next decade will belong to them. As Andrej Karpathy puts it, we're entering a world where you'll manage entire teams of AI operators like a CEO. This shift won’t be instant—but it will be transformational.