I don't think AI will make your processes go faster

Scope of AI Speedups

• Many agree AI can make coding much faster: boilerplate, CRUD, simple services, tests, and small tools often get 2–10x speedups.
• Several report 10–20% net gain on serious projects once debugging, refactoring, and understanding AI-written code are included.
• A recurring theme: development time is a minority slice of the full lifecycle (requirements, coordination, legal, deployment), so overall project speed barely moves unless processes change.

Impact on Teams and Organizations

• Solo devs and small, aligned teams report “lightning fast” progress and the ability to build things previously out of reach (frontends, tools, niche apps).
• In large orgs, benefits are blunted by bureaucracy, slow approvals, “frozen middle management,” and deployment gates; coding was rarely the bottleneck.
• Some big-company engineers claim 3–10x faster delivery in AI-forward orgs; others at similarly large orgs see minimal or even negative net gains.

Code Quality, Review, and Maintenance

• Strong concern that AI encourages “vibe coding”: huge, messy diffs, lots of dead or redundant code, subtle bugs, and security issues.
• Review and comprehension become the new bottlenecks; reading and validating AI output is often harder than writing focused code.
• Good results seem to require: precise prompts, small scoped edits, strict review, strong tests, and architectural guardrails.

Requirements, Product, and “Spec Bottleneck”

• Many argue requirements and understanding the problem are the true bottlenecks; vague asks in → vague or wrong outputs out.
• LLMs can help structure and elaborate specs, but also generate plausible-sounding nonsense that PMs may not validate.
• Faster prototyping shifts pressure onto product and users: more throwaway iterations, potential “Ikea era” disposable software, and unstable UX for users.

Management, Hype, and Process Change

• Reports of top-down AI mandates, token quotas, and leaderboards; skepticism is sometimes penalized.
• Some see AI mainly exposing existing dysfunction: misaligned incentives, cargo-cult innovation, overstaffing, and process theater.
• Several suggest real gains require rethinking workflows (e.g., using agents across ideation, exploration, coordination, and deployment), not just bolting AI onto old processes.

Non-coding Uses and Long-term Views

• High value reported in debugging, log/trace analysis, docs, onboarding, search, UI mockups, inbox summarization, and non-dev staff building small tools.
• Debate over economics: modest productivity gains may not justify massive investment, especially if token prices rise.
• Long-term outlook splits: some see rapid progress toward agentic systems that can own whole features; others point to failed experiments (e.g., AI-written compilers) as evidence we’re still far from replacing human understanding.