I have had a longstanding fascination with reinforcement learning (RL) and have monitored its slow diffusion from research labs into enterprise production. Much of the recent activity remains concentrated among foundation model builders and teams with dedicated post-training capacity. They use RL after pre-training to make large models reliable at executing tasks, not just generatingContinue reading “How Teams Actually Use RL to Make Agents Reliable”
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8 domains where AI agents are actually working
Subscribe • Previous Issues How Teams Actually Use RL to Make Agents Reliable I have had a longstanding fascination with reinforcement learning (RL) and have monitored its slow diffusion from research labs into enterprise production. Much of the recent activity remains concentrated among foundation model builders and teams with dedicated post-training capacity. They use RL after pre-trainingContinue reading “8 domains where AI agents are actually working”
What No One Tells You About Staying Employable in the AI Era
The Shift from Routine Execution to Spec-Driven Work Orchestrating Multiple AI Agents: A Skill That Won’t Stay in Software Engineering How to Protect Your Career When Your Output is Used to Train Models
The Honeymoon Phase Won’t Last: Preparing for AI’s Platform Shift
I am old enough to remember the early days of the internet. It was a time when blogs were everywhere and information felt decentralized. Before the giant platforms and their algorithms, the web felt like a collection of independent voices. We had chronological feeds we controlled, not algorithmic ones controlled by someone else. AI isContinue reading “The Honeymoon Phase Won’t Last: Preparing for AI’s Platform Shift”
The warning signs your AI vendor is becoming your cage
Subscribe • Previous Issues The Honeymoon Phase Won’t Last: Preparing for AI’s Platform Shift I am old enough to remember the early days of the internet. It was a time when blogs were everywhere and information felt decentralized. Before the giant platforms and their algorithms, the web felt like a collection of independent voices. We had chronologicalContinue reading “The warning signs your AI vendor is becoming your cage”
Ethics.dev
Ethics.dev is our new sister site that tracks the practical impact of AI across fields like safety, labor markets, government regulation, and the economy. Bookmark this page for daily updates on how these rapid developments will reshape your industry. It is designed as a practical resource to help you keep up with the complex rulesContinue reading “Ethics.dev”
The Industrialization of Synthetic Data
Synthetic data used to be a fairly narrow idea: pad a small dataset, test a model without touching production data, maybe stress a system for bias. The rise of generative AI and autonomous agents has changed the landscape. Teams use synthetic data to train and evaluate agentic systems, to cover rare failure cases, to meetContinue reading “The Industrialization of Synthetic Data”
AI agents just made your data pipeline obsolete
Subscribe • Previous Issues The Industrialization of Synthetic Data Synthetic data used to be a fairly narrow idea: pad a small dataset, test a model without touching production data, maybe stress a system for bias. The rise of generative AI and autonomous agents has changed the landscape. Teams use synthetic data to train and evaluate agentic systems,Continue reading “AI agents just made your data pipeline obsolete”
You Don’t Need a Massive ML Team to Scale AI Affordably
As generative AI applications mature, engineering teams are finding that standard API endpoints often fall short on cost and performance. Companies increasingly need to customize and scale their own AI workloads to remain efficient. A recent engineering blog post from Notion illustrates this shift perfectly. To handle billions of vector embeddings, Notion overhauled its infrastructureContinue reading “You Don’t Need a Massive ML Team to Scale AI Affordably”
The AI Bubble Is Real. Enterprise Usage Is Even More Telling.
The existence of an AI bubble is beyond dispute. What remains unclear is when or how it deflates. As investors know all too well, the most costly mistake in business is often being correct prematurely. The infrastructure layer has already booked revenues. This includes sectors like semiconductors, data centers, and power grids. The application sideContinue reading “The AI Bubble Is Real. Enterprise Usage Is Even More Telling.”