Summary: Reflection, a startup founded by former Google AI minds, is rewriting how artificial intelligence learns—by teaching it to build software like a real team member. By letting the AI read through internal documents, code, conversations, and tech specs, Reflection’s new AI agent, Asimov, is being trained to think like a software engineer. This isn’t about chasing hype. It’s about building deep, practical intelligence, a machine that understands not only lines of code but the logic behind the entire organization’s software stack.
Why Coding Is the Smartest Way to Train an AI
Reflection’s co-founder and CEO, Misha Laskin, argues that coding is the best context for AI to develop real intelligence. He sees code not just as a set of instructions but as the most direct, logical path for AI to interact with the world. Unlike systems trying to mimic human actions through clunky interfaces and browser APIs, Asimov doesn’t pretend to be a person. It learns from the source—the company itself.
That means feeding it everything a developer sees in the real world. Not isolated code snippets. Not curated training sets. Everything. Emails, Slack pings, project tickets, architecture diagrams, Git histories. It’s the full stream of consciousness from a software team, made readable to a machine that can learn from it all.
How Reflection Is Structuring the Learning
Asimov isn’t one monolithic model. It’s a layered setup. There are multiple specialized agents—some fetch relevant data, others analyze dependencies, and a central agent composes high-level answers. This mirrors how real dev teams operate: small roles, working together, flowing toward a deeper outcome.
The technique they’re using to train Asimov—reinforcement learning—isn’t new. But how they’re applying it is. Instead of teaching the system to win games like Chess or Go, Reflection’s model learns to succeed at software delivery. It writes, rewrites, fixes, documents, and eventually learns what makes a clean pull request versus a brittle workaround.
Building Smarter Tools from the Ground Up
Laskin suggests most coding assistants right now suffer from the same weakness: they’ve been trained on finished code, not the process that generated it. That’s like teaching someone to become a mechanic by letting them stare at a parked car. Asimov is learning inside the garage, grease and all.
Once trained this way, the AI doesn’t just autocomplete functions. It helps understand system design, why certain tickets get deprioritized, and how a bug fix will ripple downstream. This gives Asimov context—exactly what today’s AI assistants lack. And context is king if you want decisions, not just predictions.
The Real Endgame: Superintelligence Through Software
It’s no secret that Reflection’s ambitions go far beyond product-market fit. This is about superintelligence. Not a loose metaphor, but the actual engineering of an AI that can routinely outperform humans in cognitive complexity. Meta’s Superintelligence Lab is one big signal. Reflection’s approach is another. Software is just the training ground.
The long-term idea is a system capable of not only patching bugs or answering support tickets but conceiving entirely new software architectures. Eventually, Asimov could design new kinds of algorithms, propose novel products, or even generate hardware specs to match future applications. This turns AI into not just a code assistant, but a collaborator—or even inventor.
That potential, however, rests on solving some practical problems now. Can we get these agents to handle real software environments, know when they’re missing context, stay secure, and avoid unwanted side effects? That’s where today’s work is squarely focused.
Starting Small: Real-World Applications in Tech Sales and Support
Reflection isn’t pitching vaporware. Their current customer targets are pragmatic: technical sales and support teams. These are often bogged down by repetitive queries, slow access to institutional knowledge, and complicated product landscapes. Asimov can step in to shorten onboarding, clarify answers, and help specialists stay in flow by fetching info on demand, not through endless digging.
The ability for Asimov to function as an always-on, context-aware teammate means faster responses, fewer escalated tickets, and tighter sales decks. But deeper than that—every interaction trains the AI further. It creates a positive feedback loop: the more it helps, the more it learns how to help better next time.
The Bigger Question: Should Companies Trust an AI as a Co-Developer?
That’s the psychological hurdle. We know humans resist change. Delegating full software development to a machine? That sounds crazy to seasoned engineers and CTOs alike. It confirms their suspicion that the tech world is sprinting toward something none of us fully controls.
And yet… how long did it take for GitHub Copilot to find its way into serious workflows? If the AI actually makes the day easier, trust isn’t won—it’s earned.
The real ask here isn’t perfection. It’s whether the tool helps or hinders. Does it reduce toil? Does it give back time? Maybe it’s not about replacing team members. Maybe it’s about finally making sense of the chaos buried in corporate folders, email threads, and deprecated wikis. Does that sound like something your team could use?
What’s Next? Reflection is laying intellectual groundwork for something big, but they’re entering the market with humility. Opening doors through technical sales teams is smart—low friction, high payoff, and measurable results. The best way to teach an AI to build the future is to immerse it in the present problems companies are suffering through every day.
Whether Asimov becomes the next big leap or just another clever tool will depend on whether it can keep learning without needing babysitting. Either way, the thesis is clear: if you want to build general intelligence, make it earn its stripes writing code, not just summarizing search results.
So… would your team feel comfortable putting an AI in the loop? Why or why not?
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Featured Image courtesy of Unsplash and Boitumelo (5qvBHCflHyM)
