Subscribe • Previous Issues How Leaders Are Using RL to Build a Competitive AI Advantage I have long been fascinated by reinforcement learning (RL), but have always viewed it as complex and beyond the reach of most enterprise AI teams. That perception began to shift slightly earlier this year after a conversation with Travis Addair, co-founder ofContinue reading “The data flywheel effect in AI model improvement”
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12 hard-won AI product lessons
Subscribe • Previous Issues Signal Through the Noise: An AI Product Builder’s Guide As AI capabilities rapidly advance, the challenge for product teams has shifted from “what can we build?” to “what should we build?” The following insights, drawn from recent presentations and conversations with AI founders, successful product launches, and emerging security research, offer practical guidanceContinue reading “12 hard-won AI product lessons”
AI’s Design Constraints You Can’t Abstract Away
Subscribe • Previous Issues The End of Limitless Compute: AI’s Physical Reality For developers, compute has long been an abstraction—a limitless resource summoned with an API call. That illusion is now shattering against hard physical realities. The voracious appetite of AI means the success of your next application may depend less on the elegance of your algorithmContinue reading “AI’s Design Constraints You Can’t Abstract Away”
Rogue AI Agents & Productivity Paradoxes
Subscribe • Previous Issues The Two-Sided Coin of AI-Assisted Coding SoftBank’s recent declaration that the era of human programmers is ending caught my attention, especially the audacious estimate that one thousand AI agents would be needed to replicate the capabilities of a single human developer. As readers of this newsletter and listeners of my podcast will attest,Continue reading “Rogue AI Agents & Productivity Paradoxes”
New Report: The Architectural Patterns of Financial AI
At Bankwell Bank, a new employee named Sarah works around the clock. She responds to loan applicants via email and SMS in under three minutes, gathers missing documents, and hands a perfectly structured file to her human colleagues. She has re-activated roughly half of the bank’s otherwise lost applicants and saved loan officers 90% ofContinue reading “New Report: The Architectural Patterns of Financial AI”
From tool-chaining to true agentic systems
Subscribe • Previous Issues The Next Generation of AI Agents: Large Action Models Explained As AI agents become commonplace in enterprise workflows, teams are discovering the limitations of building task-specific automated systems from scratch. Large Action Models (LAMs) represent the foundational layer that transforms how we build agents—providing the general-purpose perception, planning, and execution capabilities that individualContinue reading “From tool-chaining to true agentic systems”
AI Is Quietly Rewriting Work—Here’s What You Need to Know
Compound Interest: AI’s Invisible Impact on Productivity and Jobs I’ve learned to tune out the “Are we there yet?” chorus that follows every AI model release. While Twitter debates rage about AGI timelines, something more interesting is happening in the trenches: current foundation models are quietly revolutionizing how knowledge work gets done. My own workflowContinue reading “AI Is Quietly Rewriting Work—Here’s What You Need to Know”
Before you scale your AI, read this
Subscribe • Previous Issues Beyond the Lab: Performance Engineering for Production AI Systems The conversation around AI has shifted from whether to adopt the technology to how to make it economically viable at production scale. In previous articles, I’ve covered the strategic playbooks for AI adoption and evaluation frameworks that define success. However, a critical gap persistsContinue reading “Before you scale your AI, read this”
Superposition Meets Production—A Guide for AI Engineers
Subscribe • Previous Issues A DeepMind veteran on the future of AI and quantum Quantum computing has always felt just over the horizon, so I’ve only tracked its progress from a distance. But that horizon is suddenly much closer: prototype machines with around 100 logical qubits are already tackling niche but valuable AI workloads, and startups areContinue reading “Superposition Meets Production—A Guide for AI Engineers”
Quick Wins for your AI eval strategy
Subscribe • Previous Issues The Complete Guide to AI Evaluation In the context of AI applications, “eval” means systematically assessing the quality, reliability, and business impact of AI-generated outputs—from text and code to complex agent decisions. In my recent AI playbook, I argued that a robust evaluation framework is not just a best practice but proprietary intellectualContinue reading “Quick Wins for your AI eval strategy”