The terminal spits fire—files duplicating endlessly, S3 bucket bloating like a bad balloon animal, recursion gone rogue.
That’s the scene Krasimir Tsvetanov, a battle-hardened senior engineer, walked into when he handed GitHub Copilot the keys to a simple task: upload a directory to S3.
He’d been tinkering with the AI sidekick for months, expecting it to shine. Instead? A comedy of errors that proved his point in neon lights.
“Just recently I wanted to write a script that uploads a directory to S3. I decided to use Copilot. I have been using it for a while. This article is an attempt to prove two things: (a) that AI can’t (still) replace me as a senior software engineer and (b) that it still makes sense to learn programming and focus on the fundamentals.”
Krasimir’s blog post—straight fire, no fluff—lays it bare. Copilot churned out code that looked slick: recursive directory traversal, AWS SDK calls, the works. But production? Nah. It looped infinitely on subdirs, ignored symlinks (hello, infinite recursion), botched multipart uploads for big files, and skipped basics like progress bars or dry runs. Run it on a real project? Disaster.
Look.
This isn’t just one guy’s gripe. It’s a snapshot of where AI coding assistants stand today—prototyping wizards, production fumblers.
Can Copilot Handle Real Production Code?
Short answer: Not yet. Not like a senior dev who smells trouble before it brews.
Krasimir prods the code: Copilot suggests os.walk(), fine for toy dirs, but production dirs? Nested symlinks, millions of files, terabytes—boom, stack overflow or eternal hang. No safeguards. No --dry-run flag to preview. Authentication? Hardcoded creds in examples (red flag city). And error handling? Laughable. One network blip, and it craters silently.
But here’s my twist, the one Krasimir doesn’t drop: this mirrors the dawn of spreadsheets in the ’80s. VisiCalc wowed accountants—zapped hours of ledgers into minutes. Did it kill accounting jobs? Nope. It birthed financial modeling, FP&A teams exploded. AI’s doing the same for code: not replacement, but rocket fuel. Imagine Copilot 2.0 as that spreadsheet on steroids—agents that iterate, test, deploy. We’re months from that shift.
Energy surges here. Picture code as a bustling city. Copilot builds shiny highways fast, but forgets sewers, traffic lights, emergency exits. Humans? We engineer the resilient grid.
And yet—
Thrill of it: AI’s learning. GitHub’s feeding it billions of repos. Next iteration? It’ll preempt recursion traps, auto-configure IAM roles, even suggest CI/CD hooks.
Krasimir iterates with Copilot, tweaking prompts. It improves—slightly. Adds logging after nudges. But still misses the production mindset: idempotency, retries, monitoring hooks. That’s senior sauce—years of scars from outages at 3 AM.
One punchy para: Fundamentals win.
Why Fundamentals Trump AI Hype Right Now?
Strip it down. Programming’s chess, not tic-tac-toe. Copilot plays openings brilliantly—boilerplate, syntax sugar. But mid-game? When deps clash, edge cases lurk, scale bites? It hallucinates.
Krasimir’s gem: he rewrote it manually. Ten minutes. Bulletproof. Handles symlinks via os.path.islink(), multipart with thresholds, configurable concurrency. AI couldn’t grok the why—production demands paranoia.
(Yeah, GitHub spins Copilot as ‘your pair programmer.’ Cute. But pairs don’t ghost on context.)
So, bold call: by 2026, agentic AI—like Devin on crack—will own 80% of boilerplate deploys. S3 scripts? One prompt, auto-tested, prod-ready. But seniors? We’ll architect systems AI dreams of, orchestrate fleets of agents. Platform shift, baby—like electricity freed us from hand-cranking engines.
Vivid? Code gen today: a teenager with a textbook, acing theory, flunking lab. Tomorrow: PhD candidate, lab-coated, experiments humming.
Krasimir’s skeptical—fair. He’s shipping daily, dodging AI pitfalls. But wonder this: what if we taught it production lore? Fine-tune on war stories, not just GitHub stars.
His script evolved. Copilot helped snippets—auth helpers, async uploads. Glue? Human.
Pace picks up.
What This Means for Devs Chasing the AI Wave
Don’t ditch the keyboard. Double down on fundamentals: algorithms (recursion traps), systems (distributed file ops), ops (observability). AI amplifies pros, exposes posers.
Corporate hype check: Microsoft/GitHub touts Copilot boosting productivity 55%. True for juniors prototyping. Seniors? We’re bored by autocomplete, crave architecture debates.
Unique edge: parallel to Unix shell’s birth. Early ’70s, scripting was manual drudgery. Shell + pipes? Augmented humans, birthed DevOps. AI’s the new pipe—composable, wild.
Krasimir wraps: learn fundamentals. Spot on. But add: wield AI like a scalpel. Prompt engineering’s the new black magic—craft queries like “production-grade S3 sync, handle symlinks, multipart, retries, dry-run.”
It gets closer.
Excitement builds. This stumble? Catalyst. Forces AI labs to tackle production head-on. OpenAI, Anthropic—watch your backs. GitHub’s repo goldmine turns the tide.
🧬 Related Insights
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Frequently Asked Questions
Will AI like Copilot replace senior software engineers?
Not soon. It prototypes fast but fumbles production realities like edge cases and scale. Seniors architect what AI executes.
Is it worth learning programming fundamentals in the AI era?
Absolutely. AI hallucinates without them—you’ll debug, optimize, and innovate where it stalls.
Can Copilot write a reliable S3 directory upload script?
Today? Risky—needs heavy human fixes. Tomorrow? With better prompts and models, yeah, it’ll shine.
