A 51-year-old man walked into the emergency room with chest pain. The symptoms seemed clear enough: elevated blood pressure, chest discomfort, some cardiac irregularities. The emergency physician, attending doctor, and cardiologist all converged on the same diagnosis—acute coronary syndrome or accelerated hypertension. The classic signs of anything more serious simply weren’t there.

But one hospitalist wasn’t satisfied. Despite multiple colleagues ruling out aortic dissection as unlikely, something felt incomplete. The pieces fit the common diagnosis, but not perfectly. A CT scan revealed what everyone had nearly missed: a life-threatening Stanford Type A aortic dissection that could have been fatal if left untreated.

This medical case study illustrates “premature closure”—a cognitive error where physicians latch onto an initial diagnosis and fail to consider reasonable alternatives. In this case, the atypical presentation led multiple experienced doctors to anchor on the most probable explanation while nearly missing a critical, less common condition.

The same pattern is emerging in software development as AI coding assistants become more sophisticated. The models are so good now that their first suggestion often looks not just plausible, but convincing. Professional formatting, clean code, proper naming conventions—everything appears right. And therein lies the trap.

The allure of the perfect-looking solution

Consider this scenario: You’re debugging a performance issue, and your AI assistant immediately suggests adding an index on a specific column. The suggestion comes with clean SQL, a proper migration script, and even considerations for naming conventions. It looks professional, addresses the immediate symptoms, and would probably work.

CREATE INDEX CONCURRENTLY idx_orders_customer_id
ON orders (customer_id);

But what if the real issue is an N+1 query pattern that the index would merely mask? What if the performance problem stems from inefficient data modeling that a different approach might solve more elegantly? By accepting that first suggestion, you might miss opportunities for more fundamental improvements.

The AI’s suggestion isn’t wrong—it might be perfectly adequate. The issue is that we’re increasingly likely to stop exploring once we have something that looks right.

When speed becomes the enemy

There’s an undeniable ROI argument for AI-assisted development. If an AI can suggest the correct fix for a flaky spec, identify the PR that caused an exception spike, or pinpoint a workflow causing availability issues—all for minimal cost and time investment—the engineering time saved is enormous.

But here’s where it gets tricky: by incentivizing speed and ROI, we risk creating a perception where the first plausible solution becomes the default choice. This manifests as technical docs and PRs (code changes) that feel “one-shotted,” where deeper probing reveals that the author hasn’t fully explored the problem space.

The paradox is that the better AI gets, the more this becomes a problem (debatable?). When suggestions are obviously flawed, we naturally dig deeper. When they’re 85% correct and professionally formatted, our critical thinking muscles don’t get the same workout.

The spectrum of AI-appropriate tasks

Not all development tasks carry the same risk of premature closure. There’s a meaningful distinction between predictable, consistent work and complex problems with multiple valid approaches.

Menial tasks where AI excels

For routine tasks, AI shines. These are scenarios with well-established patterns and minimal variation:

class AddIndexToOrders < ActiveRecord::Migration[7.0]
  def change
    add_index :orders, :customer_id, algorithm: :concurrently
  end
end

app.get("/api/users/:id", async (req, res) => {
  try {
    const user = await User.findById(req.params.id);
    if (!user) {
      return res.status(404).json({ error: "User not found" });
    }
    res.json(user);
  } catch (error) {
    res.status(500).json({ error: "Server error" });
  }
});

The risk of missing a better approach is low because the problem space is well-defined.

Complex tasks and high premature closure risk

But for complex architectural decisions, performance optimizations, or feature implementations with multiple viable paths, AI’s tendency to present a single solution can bias us away from exploring alternatives.

Consider these examples where AI-generated code looks clean but misses critical issues:

Example 1:

def send_notifications(user_ids)
  user_ids.each do |user_id|
    user = User.find(user_id)
    NotificationService.deliver(user.email, build_message(user))
  end
end

Looks straightforward, but if you look closely you should spot the N+1 query problem and missing error handling for external service calls. Additionally, what mitigations do we have in place if the NotificationService were to get overwhelmed and how do we recover from such conditions?

Example 2:

def update_user_stats(user_id, new_stats)
  user = User.find(user_id)
  user.update!(stats: new_stats)
  Rails.cache.delete("user_stats_#{user_id}")
end

This introduces a race condition in distributed environments where another process might repopulate the cache with stale data between the database update and cache deletion.

Example 3:

class DataProcessor
  def initialize
    @results = []
  end

  def process_batch(records)
    records.each do |record|
      @results << expensive_calculation(record)
    end
  end
end

The instance variable creates a memory leak when processing large datasets, since results accumulate across job executions in long-running worker processes.

What happens when we give in

When you accept AI suggestions without deeper exploration, there is a risk of few things happening:

Missed learning opportunities: You don’t build the mental models that help you recognize similar patterns in the future. The struggle that comes before the solution has real value—it develops intuition about what might go wrong and why certain approaches are preferable.

Technical debt accumulation: Quick fixes that work for the immediate case often create maintenance burdens later. What looks like efficiency becomes a liability when requirements change or scale increases.

Reduced problem-solving skills: If every complex problem becomes a quick “ask AI → copy → paste” routine, the muscle memory for deep technical thinking atrophies.

Software engineering in the age of AI

I am finding that being a software engineer in this environment means developing new practices around AI collaboration and here are some nuggets that I have to share:

Review your own AI-generated code like a peer’s

Treat AI suggestions with the same lens you’d apply to another developer’s pull request. Look for the nits, challenge assumptions, ask about edge cases. The fact that it’s generated doesn’t make it automatically correct even if all the test cases pass.

Always ask for alternatives

Don’t accept the first solution. Ask the AI: “What are three different ways to solve this problem?” or “What are the trade-offs of this approach versus using X?” This forces exploration of the solution space. Try to come up with some alternatives of your own and have a conversation like you would with a peer. Either you’d be pleasantly humbled or might end up teaching the AI model something new (jk)

Assess the speed vs learning trade-offs wisely

If you’re building a simple CRUD application with well-understood patterns, optimize for speed. If you’re designing a complex piece of distributed software or solving novel performance challenges, take the time to understand what’s being generated and why.

Trust but verify

Put in the work to understand the code. Imagine you are giving a talk to your teammates - Can you explain why this approach was chosen? What would break if the requirements changed slightly? The goal isn’t to reject AI assistance, but to remain an active participant in the thinking process.

Preserving the art of exploration

I’ve started experimenting with a different approach: do the exploration the “old-fashioned way” first, before consulting AI. This means:

• Spending time understanding the problem space thoroughly
• Sketching out multiple potential approaches
• Considering trade-offs and edge cases
• Only then using AI to help implement or refine the chosen solution

Sometimes the AI surprises me with insights I missed; other times, my human reasoning uncovers nuances the model overlooked. It becomes a bidirectional learning process rather than a one-way dependency.

This approach takes longer upfront—maybe a few hours instead of say 30 minutes—but it preserves something crucial: the deep understanding that comes from wrestling with a problem yourself.

Become good at reviewing others’ code

One of the most effective ways to develop the critical thinking skills needed for AI collaboration is to become excellent at reviewing human-written code. The patterns you learn to spot in code reviews—performance bottlenecks, race conditions, error handling gaps, scalability issues—are exactly the same patterns that AI-generated code often misses.

When you review a colleague’s pull request, you’re not just looking for syntax errors or style violations. You’re asking deeper questions: Does this handle edge cases? How will this behave under load? What happens when this external service is down? Will this scale? These are the same questions you need to ask when reviewing AI suggestions and perhaps more.

The more you practice spotting subtle issues in code that you haven’t written, the better your pattern recognition becomes. You develop an intuition for what looks right but might break in production. This skill transfers directly to evaluating AI-generated solutions, where the code often looks clean and professional but may lack the nuanced understanding of production realities.

Finding the balance

I’m not advocating for abandoning AI tools—they’re incredibly powerful and genuinely improve productivity. The key is being intentional about when and how we use them.

For predictable, well-scoped tasks, let AI handle the heavy lifting. But for complex problems where multiple solutions exist, consider using AI as a collaborator rather than an oracle. Let it help you implement and refine your ideas, but don’t let it do the thinking for you.

The goal isn’t to slow down everything, but to recognize when thoroughness matters more than speed. As these tools continue to improve, our ability to discern between routine automation and complex problem-solving will become an increasingly valuable skill.