Why the Best Engineers Are Actually Lazy

There’s a popular archetype in web scraping circles: the heroic engineer who fights CAPTCHAs at 3 a.m., hand-tunes a proxy farm before breakfast, then rewrites four spiders after lunch because the target sites pushed new JavaScript.

For him, the codebase is a badge of honour, the scars of bans proof of craft. It’s an inspiring image—and now increasingly out of date. The smartest data engineers I meet are shockingly, almost offensively, lazy.

Lazy, you see, is a principled stance. It means asking one brutal question before every line of Python: Could someone—or something—already be doing this for me? If the answer is yes, smart developers delete the todo, close the editor and integrate it. By refusing to code, they out-ship the virtuoso who insists on reinventing the stack.

The hero’s journey is simplicity

In 2025, the refusal is easier than ever, because most of the “hard parts” have already ossified into APIs, managed platforms and AI auto-extractors.

Consider the chores a traditional scraper inherits. A single project can entail proxy rotation, TLS fingerprint spoofing, CAPTCHAs solving, session handling, dynamic rendering, infinite-scroll interaction, pagination logic, schema design, data validation, error monitoring, and a recurring maintenance slot every time the site owner tweaks a class name.

Extensive traditional scraping workflows list as many as 10 job-to-be-done buckets, the majority before a single CSV lands in storage. Each is failure-prone: modern anti-bot services escalate from rate-limiting to JavaScript puzzles at speed, while CAPTCHAs remain the most cited break-point for in-house teams. All of that labour produces zero new insight—it merely preserves the ability to fetch HTML.

Software history teaches us that this sort of toil ebbs away. Assembler yielded to C, raw sockets made way for HTTP libraries, hand-rolled auth gave way to OAuth. Abstraction always wins because the economic pressure for simplification is relentless.

The scraping market is feeling the same squeeze: low-code and LLM-powered tools now top developer wish-lists, and the “rise of APIs” is a headline finding in Zyte’s 2025 Web Scraping Industry Report. Global spend on dedicated scraping software crossed $1 billion last year and is projected to more than double by 2032, driven largely by managed-service adoption, according to Market Research Future. But markets don’t pay for drudgery; they pay for leverage.

Holding on to control

Leverage is exactly what the lazy engineer buys, because lazy engineers will always seek the most efficient route to achieve the same goal at the same expected quality. A modern, all-in-one web scraping API already bundles automatic proxy management, browser emulation, geographical routing, ban detection and self-healing extractors. Plug it in and 80 percent of the legacy task list dissolves.

The same applies at the data layer: pre-built schemas for products, articles, jobs, and search results spare you the tedium of mapping every selector. AI-driven extractors now boast high accuracy rates on complex, previously unstructured pages. The “hero” who still hand-parses DOM nodes is skilled but not necessarily noble; rather, he may be wasting time.

“But I lose control,” the skeptic argues. Ten years ago, such a granular goal mattered; today, that need is being adequately serviced by API services that allow developers to retain control while absolving them of the burdens such a privilege usually comes with. Engineers who crave fine-grained control still tinker, while those optimizing for output gravitate to turnkey APIs. Choosing control for its own sake can be ego wearing a DevOps hoodie.

Ego isn’t the only culprit; habit plays a role, too. Many engineers cut their teeth when rolling their own scraping stack was the only option. Patterns learned in that era feel foundational, so they linger on.

But the landscape is shifting under our feet. Real-time retrieval bots now scour the open web for AI models at volumes that dwarf classic crawlers. If you’re still soldering proxies by hand, you may be optimizing for a world that’s disappearing.

The up sides of laziness

There is, of course, creative code left to write. Lazy data engineers simply reserve their keystrokes for the bits that differentiate—data modelling, feature pipelines, anomaly detection, domain-specific insight.

Freed from the trench warfare of scraping logistics, they iterate faster on the algorithms that move revenue or research forward. When something breaks, they open a status page, not a headless browser session. Their Saturday mornings remain untroubled by sudden ban waves.

The payoff shows up on the balance sheet as uptime and on the roadmap as velocity, but those are side effects. The real win is psychological: disciplined laziness restores curiosity. When data extraction is cheap and boring, you can afford to ask better questions of the data—and to discard whole projects that no longer matter.

It’s a mindset the late computer-science pioneer Larry Wall once celebrated: “The three great virtues of a programmer are laziness, impatience, and hubris.” Modern web data engineering just rediscovered the first two.

So, if you’re still proudly wrangling Selenium scripts and home-rolled rotation pools, pause. Write a final, glorious rm -rf in your crawler directory and outsource the grunt work to the machines that were built for it.

Your future self will thank you for the free evenings; your team will thank you for the cleaner backlog; and your stakeholders will thank you for caring about insights rather than infrastructure. 

Heroic engineering is overrated. Be lazy—on purpose.