- Thinking companies rarely use algorithms: many companies use well-known algorithms, but rarely textbook problems end‑to‑end — they adapt patterns (caching, batching, map‑reduce, heuristics) to product constraints.
- Believing interviews equal day‑to‑day work: interviews test problem solving and fundamentals; production work emphasizes design, reliability, scale, and tradeoffs.
- Assuming one perfect “industry algorithm” exists: teams combine multiple techniques (indexing, probabilistic data structures, approximations, domain models) rather than swapping a single canonical algorithm.
- Overvaluing asymptotic complexity only: constant factors, memory layout, IO, latency, and maintainability matter far more in real systems.
- Neglecting system engineering: monitoring, deployment, testing, observability, and incident response are core skills but get little textbook coverage.
- Underestimating domain knowledge: many solutions are domain‑specific (search relevance, recommendation scoring, ad auctions, compression formats) and require product context.
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### Things students rarely know that matter in real jobs
- Production constraints change solutions: service‑level objectives, throughput, cost, and privacy rules often force approximate or hybrid approaches.
- Engineering tradeoffs beat pure optimality: simpler, debuggable code that meets requirements is preferred over a theoretically optimal but brittle solution.
- Data matters more than algorithm choice: garbage-in → garbage-out; feature quality, sampling bias, and data pipelines drive results.
- Maintenance cost is critical: code that’s easy to monitor, test, and extend reduces long‑term cost more than micro‑optimizations.
- Team and process skills scale impact: code reviews, design documents, PR triage, and communication often unlock more value than lone clever hacks.
- Hidden performance killers: serialization format, cache misses, GC pauses, network retries, and lock contention are common real bottlenecks.
- Security and compliance are not optional: authentication, authorization, rate limiting, and audit trails are integral to production work.
- Tooling and infra fluency matters: CI/CD, containerization, observability stacks, cloud providers, and infra as code are daily tools.
- Legacy and integration work dominate: integrating with existing systems and migrating safely often occupies most engineering time.
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### Is an inexpensive AI subscription making software development “easy”?
Short answer: no — it helps, but it doesn’t replace the job.
- What AI helps with: boilerplate generation, code snippets, quick prototypes, documentation, debugging hints, and improving productivity for repeated or well‑defined tasks.
- What AI cannot replace reliably: system design, architectural tradeoffs, ownership, debugging production incidents, understanding domain constraints, and the social processes of shipping software.
- Human skills remain essential: asking the right questions, designing resilient systems, reasoning about tradeoffs, reviewing and testing AI output, and taking responsibility for production code.
- Risks of overreliance: subtle bugs, security holes, incorrect assumptions, and duplicated technical debt if AI output isn’t critically audited.
- Economic effect: AI can raise productivity and shift what junior and mid engineers work on (more integration, testing, and higher‑level tasks), but companies still need experienced engineers to validate and operate systems.
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### Practical advice to stay valuable as a software engineer
- Deepen fundamentals: algorithms, complexity intuition, systems (OS, networks), and data modeling remain foundational.
- Build production literacy: learn monitoring, deployments, incident response, testing strategies, and reliability engineering.
- Learn to evaluate AI output: always review, test, and adapt generated code; understand failure modes.
- Focus on domain expertise: become the person who understands the data and user needs for a product area.
- Practice communication and design: clear design docs, tradeoff discussion, and stakeholder alignment scale your impact.
- Automate safely: use AI to automate repetitive tasks but invest time to make the automation reliable, auditable, and maintainable.
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### Quick career perspective
- Short term: AI tools accelerate some tasks and can boost productivity for individuals who use them well.
- Medium/long term: job complexity shifts — demand grows for engineers who combine technical depth, system thinking, and the ability to supervise and integrate AI outputs.
- Conclusion: AI makes parts of the job easier, but the core of software engineering — designing, owning, and operating real systems — remains challenging and in demand.