The new engineering superpower isn't coding, it's curation

Innovation isn't just about building great products—it's about rethinking how we build them. 

Engineering is undergoing one of its most rapid transformations in decades, and the teams that thrive will be the ones who master a new core skill: curating excellent code, not just writing it.

Most developers have experienced the "first wave" of AI assistance: autocompletion, rapid prototyping, and the occasional MVP accelerator. Useful, but surface-level. Now, we’re reaching an inflection point as tools evolve from helpful assistants into sophisticated systems that fundamentally change what one developer can accomplish.

Beyond the basics

At Point, we're pushing past the most rudimentary use cases and into territory that actually moves the needle:

Claude Code with skills lets us optimize context windows and share useful functionality across the entire team. No more reinventing solutions someone else already built. MCP servers unlock powerful integrations, enabling our AI agents to pull together data from multiple systems seamlessly. Deep configuration tuning (adjusting parameters, context windows, and tool configs) turns inconsistent results into reliable ones.

These aren't abstract improvements. They're the difference between using AI tools and extracting value from them. Without proper configuration, AI assistance is hit-or-miss: sometimes brilliant, sometimes baffling, always unpredictable. With it, you get consistent, production-ready output that you can actually build on.

Think of it this way: anyone can install an AI coding assistant. But getting reliable results requires understanding how to structure context, which models excel at which tasks, and how to configure tools so they integrate with your actual workflow instead of fighting it. The developers who invest time in this setup work aren't just being thorough—they're unlocking capabilities that look like magic to everyone else.

This is where the real productivity gains live. Not in the demo—but in the 50th use case, the complex refactoring, the gnarly bug that requires understanding six different systems. When tools are properly configured and the team knows how to use them well, one developer can genuinely accomplish what used to take three.

Curation is critical

Here's what we've discovered: the years you spent debugging obscure errors at 2 AM, the countless hours wrestling with edge cases, the hard-won intuition about what makes code maintainable? None of that is obsolete. It's actually more valuable than ever, just deployed differently.

When AI generates code, the bottleneck shifts from writing to evaluation. Can you spot the subtle bug in a 200-line function in under a minute? Do you immediately recognize when an algorithm choice will cause performance issues at scale? Can you tell whether error handling is robust or just superficially correct? These aren't new skills—they're the same instincts you've honed over years of writing code yourself. The difference is that now you're applying them at 10x the speed.

The engineers who excel in this new paradigm aren't the ones who can prompt the AI most cleverly. They're the ones who can read generated code with the same critical eye they'd apply to a critical pull request, rapidly identifying what works, what's risky, and what needs to change. That depth of understanding comes only from having written thousands of lines of code yourself, lived through the consequences of poor decisions, and developed an instinct for quality.

And here's the crucial part: the code is still your responsibility. When it ships, your name is on it. When it breaks at 3 AM, you're the one who needs to fix it. When another engineer needs to modify it six months from now, you're accountable for its clarity. AI doesn't change that accountability—it just means you can produce more, faster. But "more, faster" only works if you can ensure that your code works and won’t cause maintenance nightmares months from now.

AI can accelerate your work by leveraging your deep code knowledge, and can make you more valuable than ever. Junior engineers who rely entirely on AI without building foundational skills will struggle to distinguish good suggestions from plausible-looking disasters. But experienced engineers who understand what they're looking at? They're unstoppable.

The wrong kind of pressure

We've heard the rumors, some companies are tying AI tool usage to performance reviews or compensation. Use the AI assistant enough times this quarter, get a better rating. Hit your co-pilot metrics, unlock your bonus.

This is exactly the wrong approach. It transforms powerful tool usage into mere performative theater. It replaces judgment with box-checking. And it fundamentally misunderstands what makes a great engineer great.

AI tooling should empower developers, not become another metric that overshadows what actually matters: creativity, problem-solving, knowing when to push back on a bad idea, and understanding the second-order effects of your decisions. At Point, we see AI as an amplifier of human talent; it’s a way to remove friction so that talent can shine through. Not a number to hit in a dashboard.

Becoming the engineer who shapes what's next

That said, investing in AI tools isn't optional anymore. It's strategic. The engineers who lean into AI-assisted development aren't just keeping pace; they're pulling ahead. Here's what that looks like in practice:

You stay competitive with peers who are already leveraging AI to deliver more, faster, and with higher quality. You achieve levels of ownership that weren't feasible before, taking an idea from concept to shipped product, end-to-end, without waiting for five other calendars to open up. And you increase your impact on the business and on customers, because you’re no longer bottlenecked by the most mechanical parts of development.

This is what the “10x developer” looks like now. Not someone who types faster or memorizes more syntax. Someone who knows how to wield the best tools, review critically, experiment rigorously, and own outcomes completely.

Why this matters at Point

At Point, we're committed to pushing the limits of what our engineering team can do. Not because we're chasing some abstract notion of productivity, but because when our developers become better, our customers feel it. They feel it in products that ship faster, in features that actually work, and in experiences that solve real problems instead of creating new ones.

That's the future we're building. And if you're the kind of engineer who sees these shifts not as threats but as opportunities, who wants to shape what software development becomes rather than just adapt to it, we should talk!

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