From Coders to Orchestrators: Why We’ve Re-Engineered Engineering at Opply

The industry consensus is shifting fast: the days of developers spending 80% of their time manually typing syntax are numbered. Even Ryan Dahl, the creator of Node.js, recently said “the era of humans writing code is over”. At Opply, we aren’t just watching this happen - we’ve rewritten our playbook.

We believe that a "first-mover advantage" in AI-native engineering isn't just about speed; it's about a compounding advantage in how you solve business problems, and in fact it goes way beyond engineering. If you ignore these tools, you’ll be left behind - not in years, but in months.

The Three Phases of Evolution

Looking back, our (and it will be very similar for many other companies) journey with AI tools has moved through three distinct eras:

• Phase 1 (2023): The Autocomplete Era. Using IDEs as co-pilots for inline tab-completion.
• Phase 2 (2024): The Chat Era. Using IDE plugins to prompt for snippets and small functions.
• Phase 3 (2025/2026): The Agentic Era. This is where we are now. Using agentic terminals like Claude Code to orchestrate multi-file changes and autonomous test loops.

In Phase 3, manual coding is no longer the bottleneck. As Andrej Karpathy (founding member of OpenAI) recently noted, many engineers have rapidly flipped from 80% manual coding to 80% agent-driven orchestration. We are now essentially "programming in English," operating over software in large "code actions" rather than line-by-line edits.

Hiring for "Phase 3" Developers

Because the work has changed, our hiring process had to change. We’ve replaced the traditional coding assessment with the Opply Product Engineering Challenge. We explicitly look for engineers who use AI tools like Claude Code or Cursor not just occasionally, but as their core workflow. We want "Product Engineers" - a shift from siloed roles to builders who can ideate, design, and execute simultaneously.

Our live challenge focuses on:

• The Stakeholder Interview: Candidates must interview us to understand the "why" before they touch the "how".
• Architectural Reasoning: We watch them build a working prototype live. We don't care how fast they type; we care how they articulate tradeoffs and UX decisions while letting AI handle the implementation. 
• Verification Loops: Spotting when LLMs misunderstand intent is extremely crucial. We look for the discipline to read the diffs and ensure business logic remains intact while the mechanical work is automated.

The "Allocation Economy" and Agentic Realism

Guillermo Rauch (CEO of Vercel) describes this as the "Allocation Economy." The manual labor is automated; the developer’s job is now the allocation of "compute". At Opply, we hire people who are "agentic by nature," mastering Systems Thinking and Parallel Orchestration.

However, we are well aware of the tradeoffs that come with the "Agentic Era." As Karpathy warns, these agents are essentially "slightly sloppy, hasty junior devs". They have incredible stamina - they never get tired or demoralized - but they can overcomplicate abstractions, fail to push back on bad ideas and can easily misunderstand intent if not properly instructed. 

Navigating the "Slopacolypse"

A common concern is that high-speed AI generation will lead to "Slopacolypse"- a world of bloated, brittle code that "looks" right but is conceptually hollow. We mitigate this through:

• Observability-Driven Development (ODD): AI-generated code must be validated in production contexts. 
• The Boris Cherny Model: Running multiple agents simultaneously - one for testing, one for debugging, one for documentation - to create a system of checks and balances.
• Verification Skills: We recognize that while manual coding skills may "atrophy," our ability to discriminate (read and review code) must become our strongest asset.

The Cost of Velocity

Yes, it’s an investment. AI tools increase the total cost of an engineer by roughly 15%. But when you realize your team can build certain features with higher quality at 5–10x the velocity compared to a year ago, that cost becomes negligible. The "cost of refactoring" has been slashed by agentic tools; we can now restructure entire modules in minutes, not weeks.

Conclusion: Engineering as a Creative Space

We are moving away from engineering as a "laborious IT task" and toward it being a purely creative space.

At our stage, the risk isn't making mistakes while experimenting with autonomous agents. The real risk is "process debt" - slow processes and not being able to move and iterate quick enough. For the "code artist," AI may be a struggle, but for the empathic and well-versed builder, it is the ultimate partner. And the latter, people with a profound business understanding who immediately spot when it goes in the wrong direction, are the people we search for.