For twenty years, enterprise software that processed decisions at scale had a clean three-layer separation. The CRM layer owned the customer touchpoint — above the glass, the intake, the first interaction. Behind it sat the orchestration layer — workflow engines, business rules, approval chains, human queues. Behind that sat the back-office actions: disbursement, fulfillment, settlement, reconciliation. Below the glass.

A loan application entered through the CRM. A workflow engine routed it through underwriting queues, compliance checks, and approval chains. When the process completed, a back-office system disbursed the funds. Three systems. Three vendor contracts. Three integration projects. An entire consulting ecosystem existed to wire them together, and an entire certification industry existed to staff the wiring. — Read More

On March 31, 2026, thousands of developers worldwide did the same thing: they fed Claude Code’s own source code back into Claude and asked it to explain itself.

Anthropic’s flagship CLI tool had just leaked its entire 512,000-line TypeScript codebase through a source map file accidentally bundled into an npm package. Within hours, the internet had cataloged 44 feature flags, a Tamagotchi pet system with 18 species and gacha mechanics, and internal codenames like “Tengu,” “Fennec,” and “Penguin Mode.”

But the feature list is not the story. Everyone wrote that article already. The real value of this leak is not what Claude Code can do. It is how Claude Code thinks. And the fact that developers paid Anthropic, per token, to understand Anthropic’s own product? That is not irony. That is the thesis. — Read More

In this article, I want to make the case for a structured way to think about Large Language Model (LLM)-based agentic systems (mostly for coding, but also for knowledge work in general) that fixes some of the greatest pains I (and I sure most of you) have been facing when trying to scale AI-assisted workflows to professional levels.

It’s a system that puts the right constraints in the right places and leaves just enough space for creative exploration (or however you want to call what LLMs do when they hallucinate in your favor). It’s also a system that makes it clear you are in charge. — Read More

GenAI has slashed the effort required to produce code, and rapid prototyping is increasingly common. As a result, the software development lifecycle is now constrained by an organization’s ability to bring ideas into alignment and maintain cohesion across the system.

Historically, organizations have relied on manual processes and human oversight to achieve architectural cohesion. Startups rely on key individuals to catch misalignment between architectural intent and implementation. Enterprise-level organizations attempt to maintain cohesion through change boards and proliferating ADRs and documentation. In both contexts, identifying misalignment is slow because it requires synchronous dependence on a central authority. In the startup case, development teams are stuck waiting for busy experts. In the enterprise case, they have to wait on review boards and sift through documented guidance with the hope that what they find has not become obsolete. GenAI exacerbates this by accelerating the production of work that’s subject to review. Where previously only developers were producing code over days or weeks, executives and product managers can now vibe-code functional prototypes in minutes or hours. As a result, development teams are left with an impossible choice: be beholden to the pace of manual oversight at the cost of velocity, or push forward without knowing whether they are aligned.

Over time, these small pushes compound into architectural fragmentation, which the organization responds to with more process and stricter guidelines, which further increase the difficulty of releasing software in alignment. This is a vicious cycle that slows delivery and blunts innovation. — Read More

At a high level, you can think about an AI product that achieves outcomes as having three layers:

1. At the bottom, the model
2. In the middle, the application or agent which includes the data/context, etc
3. At the top, the human or service layer needed to review/prompt/do the last mile to actually get to an outcome

… Traditional application layer companies would sit just in the middle layer. But these companies are increasingly beginning to (or starting off) vertically integrate in one of two directions. Some move down into the model layer. Others start or move up into the human or service layer. Both end up looking “full-stack1,” just in very different ways. — Read More

The first generation of AI was built for a world where the model was the product, and progress meant bigger weights, more data, and stellar benchmarks. AI infrastructure mirrored this reality, fueling the rise of giants in foundation models, compute capacity, training techniques, and data ops. This was the focus of our 2024 AI Infrastructure Roadmap, which drove our investments in companies such as Anthropic, Fal AI, Supermaven (acquired by Cursor), and VAPI as the AI infrastructure revolution unfolded.

But the landscape has changed. Big labs are moving beyond chasing benchmark gains to designing AI that interfaces with the real world, and enterprises are graduating from POCs to production. The infrastructure that got us here — which was optimized for scale and efficiency — won’t get us to the next phase. What’s needed now is infrastructure for grounding AI in operational contexts, real-world experience, and continuous learning.

The stage is being set for a new wave of AI infrastructure tools to enable AI to operate in the real world. — Read More

AI‑native development isn’t about sprinkling LLM calls on top of an old app. It’s about designing software from the ground up around intelligence, context, reasoning, and autonomy.

I’ve spent the last six months watching teams try to “force” LLMs into legacy architectures. The result is almost always the same: high latency, fragile prompts, and low reliability. We’ve hit a wall where simply adding a chatbot to a side panel no longer counts as innovation.

In the last two years, a clear architectural blueprint has emerged across AI products — from nimble startups to Fortune 500 platforms. If you’re building anything with AI today, these four patterns define how systems are structured to actually survive in production. — Read More

After writing an article a few years ago called “Big Data is Dead,” it feels a bit clichéd to call things “dead.” So I won’t say any such thing about the Modern Data Stack. It does, however, appear very, very sleepy. Someone should go and poke it with a stick.

The Modern Data Stack – deceased or just drowsy?

While we’re all dead in the long run, one thing that is different now is that AI is bringing the “long run” a lot closer than it has ever been. In the last couple of years, AI has forever changed a number of professions that were once thought to be safe from disruption. From art to software engineering, AI is changing how people get things done, and changing things much faster than you’d expect.

… The interesting question to me is, “What comes next?” If we assume models continue to get better, companies capitalize on the opportunities, things get tied together in a nice bow, what does the world look like? What could it look like? Let’s start with what we know. — Read More