The thieves are upset about theft
AI labs trained on everyone's data without asking. Now they're furious that other AI labs are training on their outputs. The pattern is older than electricity.
AI labs trained on everyone's data without asking. Now they're furious that other AI labs are training on their outputs. The pattern is older than electricity.
America's energy and water infrastructure cannot sustain AI buildout without radical change.
Coding productivity metrics are rising, but the real question is whether human judgment is becoming more valuable.
Raw compute scaling is hitting limits; the next breakthroughs require hardware-software co-design and specialization.
Code generation speed isn't the bottleneck—understanding and verifying AI-generated code is, and current tools optimize the wrong 5% of development.
Reflecting on D&D world-building skills and what generative world models mean for the future of storytelling and creative expression.
The real opportunity in AI is disrupting incumbents and creating new products, not building more AI platforms.
Mobile blindsided many companies that moved slowly; AI will repeat this cycle—start small, measure, and upgrade every quarter.
Framework for understanding AI autonomy levels outside self-driving, from copy-paste to full autonomous systems.
Learning at speed to build AI-enabled products that return people to the real world, moving beyond screens.
A rap parody celebrating AI Paired Programming—the synergy between humans and AI in software development.
Economic pressure and frozen job markets will drive a new generation to build, sparking another wave of founder-led disruption.
Most individual contributor developers will need to re-skill within five years as AI agents take over execution work.
Conway's Law shows why organizations keep building what they did yesterday—and why Tesla's system reimagining beat a hundred years of optimization.
Sora's outputs remain largely unusable at current pricing; the model needs dramatic improvements in consistency to justify its value.
Binary neural networks achieve comparable performance to 16-bit models while using 3x less memory, 4x less GPU resources, and 100x less energy.
Understanding that AI agents represent a fundamental shift beyond smarter tools—a genuinely new form of intelligence with exponential capability.
Google's open-source Gemma launch failed to account for community infrastructure, creating a poor first impression with key stakeholders.
Reflecting on a transformative period of recovery, exploration, and the decision to build something new in the age of AI agents.
In 2010, I left a great role to build a product focused on fair algorithms. Years before the movement existed.
Algorithmic bias is a known problem. Two years after Tyrant in the Code, the debate rages on but the demographics haven't changed.
AI isn't biased because it's evil. It's biased because its creators are a sea of dudes building scissors for right-handed people.