Software Engineering Nov 5 2025 • 4 min read

The Human Learning Loop: Why AI Sidekicks Can’t Replace Struggle and Curiosity

Software engineering is still grounded by the fundamental truths and paradoxes of the trade—no matter how many AIs or frameworks appear promising to automate, organize, or accelerate it all away. This week’s blog bounty provided a panoramic view across some classic roadblocks, persistent debates, and the ways the latest AI-infused tools are quietly (and sometimes noisily) shifting how code is written, secured, and learned. From debugging Monday-morning BI Meltdowns to skeptical optimism about LLM-powered code generation, and fresh musings on modular monoliths and data lakes, a running theme emerges: the human learning loop is unskippable, and the best tools amplify the engineer’s curiosity, not just their output.

Coding Is Not an Assembly Line (No Matter What Your LLM Says)

The dream of turning software creation into an assembly line has haunted the industry for decades. Unmesh Joshi’s piece on LLMs and the learning loop offers a gentle but firm reminder: code is still not a factory product. Even as LLMs make that initial project setup breezier than ever (goodbye, hours lost to build system quirks), the real work—the continuous, experimental, hands-on process of learning—remains as essential as ever. Automation gives us powerful shortcuts for the rote and the routine, but the act of learning a system’s context, structure, and subtleties is not something you can delegate to an LLM. The code that lasts, and the expertise that matters, are forged on the anvil of trial and error, not just by following prompts.

This dynamic showed up again in Jessica Wachtel’s approachable guide to building an HTTP server in Python, which affectionately guides even new programmers through the slow, concrete act of making things work and then understanding their implications. Sure, you can get a server up with a couple of ChatGPT prompts, but the debugging, refactoring, and design decisions are still stubbornly old-fashioned in their complexity.

AI in the Editor: Friend, Foe, or Charming Sidekick?

The state-of-the-art IDE is becoming distinctly less lonely, as tools like Cursor 2.0 (and its multi-agent code editors) position themselves to sit right beside you—or maybe a little bit ahead—offering suggestions, reviewing code, automating tests, and keeping context of your entire project. There’s an eerie duality here: AI agents can remember every code change, propose fixes speedily, and turn “vibe coding” into an art form. But they’re also, as explored in the Stack Overflow interview with Greg Foster of Graphite, exceptionally gullible—willing to execute the sort of operations no cautious human reviewer would. Security, trust, and context are still very much human responsibilities, and the onus is on us to keep the review process (and our AI companions) honest, readable, and chunked into small, manageable changes.

AI’s real power in practice seems to be as a fearless, perfectly patient interlocutor—happy to take on boilerplate, propose ideas for your stubborn bug, or help you refactor endlessly. But entrusting it blindly is a recipe for both subtle bugs and spectacular breaches. As Greg reminds us: “To write secure code, be less gullible than your AI.” These new superpowers are best wielded by those who maintain a skeptical, attentive eye.

Team Habits, AI Adoption, and the Human Beachhead

Laura Burkhauser’s framework for teams rewiring for AI offers insight into what’s required for actual, lasting change in organizations. Her “hostile → skeptical → converted → rewired” curve puts words to what a lot of practitioners feel: excitement is important, but it’s structured habits (like simulating, automating, and then thoughtfully delegating to AI) that create enduring productivity. Employees move fastest not when motivated by fear (that burning platform), but by the beach—tangible, energizing wins from experimenting with new tools. But, as Laura notes, the scaffolding you need—clear values about responsible use, channels for experimentation, and always keeping an eye on context—matters as much as sheer technical power.

Monoliths, Orms, and the Many Faces of Scale

Tucked between all the LLM discourse, we get old-fashioned wisdom from the trenches of system design and scale. The pg_lake open source project bridges PostgreSQL, Iceberg tables, and DuckDB to turn your favorite database into a data lake workhorse, exemplifying a modular ethos that echoes the week’s musings on maintainable, composable systems. Meanwhile, the debate between Dapper and Entity Framework persists—do you want speed and hand-tuned SQL, or comfy abstractions? Much like choosing between microservices and monoliths, it’s less a matter of fashion and more a question of context, team experience, and long-term maintenance legacies.

BI, Governance, and the Perils of the Monday Morning Stampede

Finally, there’s the very human story of a compute spike that nearly crashed a BI platform as a flock of stores hit "print" around the same time. The diagnosis? Not a technical edge-case, but a familiar governance trap: uncached, concurrent reads by hundreds of users on a complex semantic model. The solution was not to further scale or automate, but to thoughtfully segment the workloads, add visibility, and keep models as simple as practical. As Rupesh Ghosh writes, “Analytics leaders outperform others not by producing more reports, but by governing how data is consumed.”

Conclusion: Don’t Outsource Your Curiosity

The more software engineering “accelerates” under the twin banners of cloud scale and AI tooling, the more it seems to circle back to its roots: learning built by doing, skepticism that keeps us safe, and habits that value the messy, context-laden reality of building things that last. There are no shortcuts to learning, but we are living in an era rich with tools for amplifying our curiosity—not outsourcing it. The assembly line dream, it seems, will always play second fiddle to the workshop.