Why Understanding Code, Not Just Writing It, Might Be the True Path to Superintelligence

What Reflection’s Asimov Agent Means for the Future of AI (and for Teams Like STEKA)

In the race to build the smartest AI agents in the world, everyone’s talking about writing better code. But what if the real key isn’t writing code—it’s understanding it?

That’s the bet being made by Reflection, a Brooklyn-based startup co-founded by former Google DeepMind researchers. Their new agent, called Asimov, aims to become the first truly intelligent software collaborator—not by generating code alone, but by reading, reasoning through, and learning from the entire lifecycle of software development.

At first glance, Asimov might sound like just another AI coding tool. But there’s one major difference: Asimov learns from everything—Slack threads, emails, JIRA tickets, engineering docs—ingesting the full bloodstream of a company’s software process.

And this, Reflection believes, is how we reach something bigger: superintelligence.

Why Understanding Trumps Generation (For Now)

Reflection CEO Misha Laskin—formerly of Google’s DeepMind—says most companies are still chasing flashy code generation demos. But real-world software development requires teamwork, context, and iteration.

“Everyone’s focused on generation,” Laskin says. “But agents that work in a team are still unsolved.”

Asimov is designed around this problem.

Its architecture includes smaller agents that retrieve relevant data and a larger reasoning agent that synthesizes this information to answer developer questions. According to internal surveys, developers preferred Asimov’s answers 82% of the time over Claude’s Sonnet 4.

All this happens inside virtual private cloud environments, ensuring customer data stays secure—a smart move given growing LLM privacy concerns.

Reinforcement Learning Reimagined

Behind the scenes, Asimov is built on open-source models post-trained using reinforcement learning (RL). It’s the same concept that powered AlphaGo, and one of Asimov’s creators, Ioannis Antonoglou, was behind that project too.

This time, the goal isn’t to win Go… it’s to build software.

By training agents on realistic engineering workflows (with both human and synthetic data), Asimov learns to reason like a senior dev: how to navigate legacy code, interpret support tickets, and connect requirements to final delivery.

It’s not just about syntax. It’s about systems thinking.

The Shadow Side of Superintelligence

We also need to face a harder truth: what happens if the wrong people get there first?

AI that understands your company’s full technical DNA could be a dream or a nightmare. The same agent that assists with product roadmaps could become an attack vector—exploiting gaps in compliance flows, cloning architecture, or silently disrupting entire pipelines.

Without strict ethical guardrails and secure-by-default deployment, superintelligence becomes super-surveillance.

Reflection mitigates some of this by deploying Asimov in private cloud environments, but the industry needs more than good intentions. Regulation, transparency, and architecture must evolve together.

Because the real danger isn’t just what these agents can do. It’s who commands them—and why.

Why This Matters to STEKA (and You)

Let’s look at things the way they really are: the next generation of agents won’t win based on speed or token limits. They’ll win by understanding context. That’s the same path we’ve been walking with Kathy’s STT™ (StockTradeTracker™) and AssetAlgo™.

We’re training agents to interpret financial and operational flows before automating anything.

Like Asimov, our approach prioritizes clarity, situational awareness, and purpose-built value over raw output. One agent powers dev workflows. Another optimizes portfolios. But both aim for a future where intelligent systems become trusted partners.

Reflection’s work confirms we’re not alone in that belief.