Vision¶

August 27, 2026
in Vision
10 min read

AI-Native Systems, Six Months In: What We've Learned Building the Loop

Tamar Eilam, Michael Factor, Shila Ofek-Koifman, Fabio Oliveira

In April, we introduced AI-Native Systems as a bet: that a system's evolution, the whole loop from observing a problem to deploying a fix, could be driven primarily by AI, continuously, at machine speed, rather than mediated step-by-step by humans. We described that loop in terms of a Reasoner that observes and hypothesizes, and a Changer that plans and implements, operating over a System Under Control.

Since then we've built and run pieces of that loop against real systems: a distributed inference platform, a hyperspecialized storage engine, and compute kernels for accelerators. Based on that experience, we now have a better understanding of the principles behind building an AI-native system and what's actually required in practice to build such a system. This post is an update, not a reboot: we'll walk through a sharper, more concrete architecture, show with a concrete example how its pieces fit together, and share what we've learned over the past several months of work, including the parts that didn't work the first time.

If you read the April post, this picks up where it left off. If you didn't, this should stand on its own.

June 24, 2026
in Deep Dives, Vision
19 min read

The Scientific Method on Code: How a Hypothesis-Driven AI Learned What Evolution Couldn't

Part 2 of a series on AI-driven algorithm discovery. Part 1 is here.

The first post ended on a cliffhanger.

I had spent weeks running an evolutionary AI framework — OpenEvolve — against a classic graph theory problem, watching it rediscover a 1979 algorithm called DSatur and then spin its wheels. It kept proposing variations on the same idea. It couldn't reason about why things worked or didn't. When I explicitly asked it to implement Kempe chains — an elegant post-processing technique — it produced buggy code that never ran a single useful operation.

The root cause, I argued, was structural: mutation-based evolution finds better code without understanding it. There's no mechanism to ask why something works, no way to rule out dead ends, no compounding of knowledge across iterations. The AI was playing a slot machine, not doing science.

So I decided to try the other philosophy. Instead of evolution, I used a framework built around the scientific method: the Nous open-source project. Same benchmark. Same problem. Same starting algorithm. Completely different approach.

What followed was one of the more instructive experiments I've run — not because the AI succeeded spectacularly, but because of what the structure of failure and success revealed about where this technology actually is.

April 21, 2026
in Vision
13 min read

AI Native Systems: Autonomous Evolution at Machine Speed

Tamar Eilam, Fabio Oliveira, Michael Factor

1. Introduction: The Bottleneck in System Evolution and AI Native Systems

Modern software systems, especially those that serve AI workloads, are extraordinarily complex and must evolve continuously under pressure from new models, new hardware, changing usage patterns, and shifting business objectives. These pressures drive constant change, both in configuration and in code. Yet, even with increasingly powerful AI tools, improvement of such systems remains fundamentally human-driven. Engineers inspect logs and metrics, diagnose problems, open tickets, draft and review pull requests, extend tests, and orchestrate deployments through fragmented workflows. AI assists at each step, but progress is mediated by people, one decision at a time.