AI feels like saving your time until you realise it isn’t: the productivity paradox explained
A thoughtful post on Reddit argues that ChatGPT can make you feel faster while actually slowing you down. The author shares real examples – coding a small tool for an industrial machine firm and learning time-lapse software – where chat-based help looked efficient but created hidden rework, errors and delays.
Original post: AI feels like saving your time until you realise it isn’t by /u/New_Cod6544.
It makes you feel faster and more productive but actually makes you slower.
It’s a familiar pattern: AI generates an almost-right answer; you spend ages fixing edge cases and checking accuracy; the net result isn’t quicker, and you’ve learned less than if you’d read the manual or hired a specialist.
Why chat-based AI can feel fast but be slow
There are predictable reasons why a chat-first workflow can miss the mark, especially for rigid, well-defined tasks:
- The accuracy tax – Large language models (LLMs) are probabilistic. They can “hallucinate” (confidently state wrong facts) and omit caveats. You pay in verification time.
- Compounded small errors – Tiny mistakes in shortcuts, paths or parameters can snowball into hours of debugging.
- Context switching – Jumping between chat, docs and the tool breaks flow and adds cognitive load.
- Shallow understanding – You get answers, not mental models. That makes future work slower.
- Build-vs-buy misjudgements – DIY with AI can outstrip the cost of hiring a developer who uses the same tools but already knows the domain.
If I had just read that 100 page manual, I would have been faster.
The poster also cites an HBR piece claiming AI-generated “workslop” harms productivity. The link is here: HBR. Evidence quality and methods are not disclosed in the Reddit post; treat the claim cautiously but the intuition resonates with many teams.
Case studies from the post: what went wrong
Small internal tool for image categorisation
ChatGPT wrote the first version quickly. But the “last 20%” – edge cases, fixing flaws, and getting it production-ready – took so long that a professional developer would likely have been cheaper and faster overall.
Learning a time-lapse tool via chat
ChatGPT offered helpful tips sprinkled with wrong shortcuts and partial truths. Post-hoc, reading the official manual end-to-end would have been faster and taught more.
When ChatGPT helps vs hurts: a practical matrix
| Task type | Good fit for ChatGPT? | Common trip-up | What to do instead/plus |
|---|---|---|---|
| Brainstorming, outlining, variant generation | Yes | Generic outputs | Give examples and constraints; iterate quickly, then edit hard. |
| Rigid, well-specified workflows | Often no | Subtle inaccuracies | Use official docs and tutorials; validate with checklists. |
| Unknown software features/shortcuts | Maybe | Hallucinated commands | Search official manuals or in-app help; ask AI to point to page numbers. |
| Coding small tools | Maybe | Hidden edge cases | Define acceptance tests; timebox; escalate to a developer if exceeded. |
| Data wrangling in spreadsheets | Yes, with care | Formula errors | Request step-by-step with sample data; verify on a copy. |
A quick ROI check before you start
- Define “done” – Clear acceptance criteria, test cases, and performance thresholds.
- Timebox the AI attempt – e.g. 60-120 minutes. If you can’t hit “done”, stop.
- Compare options – Manual, AI-assisted, or hire an expert. Include your own hourly cost, not just subscription fees.
- Decide deliberately – For critical tools and anything customer-facing, default to expertise.
Make AI actually faster: a practical playbook
- Docs-first workflow – Start with the vendor manual or official docs, then use ChatGPT to clarify. Ask it to cite specific sections.
- Use RAG where possible – Retrieval-augmented generation (RAG) lets an AI answer using your selected sources. Upload the manual and have the model quote relevant pages.
- Insist on verifiability – For code, ask for runnable examples and unit tests. For steps, ask for a checklist and expected outputs.
- Work in the tool, not just the chat – IDE copilots and in-product assistants reduce context switching and hallucinations by using tool context.
- Structure your prompts – State role, constraints, inputs, exact output format, and acceptance criteria. Avoid vague “help me” requests.
- Leave an audit trail – Keep prompts, outputs, and decisions in your repo or knowledge base.
- Use AI to teach, not just tell – Ask for mini-quizzes, “explain like I’m new to the tool”, and spaced-repetition flashcards. You’ll actually learn.
Example: keep data work grounded
If you’re using ChatGPT for spreadsheet tasks, tether it to your sheet, run small tests, and verify outputs on a copy. I’ve written about a practical setup here: How to connect ChatGPT and Google Sheets (Custom GPT).
UK-specific considerations: data protection, compliance and cost
- Data protection – If you’re pasting anything sensitive (customer data, CAD drawings, contracts), you need a lawful basis and safeguards. See the ICO’s guidance on AI and data protection: ICO AI guidance.
- Vendor controls – Prefer enterprise plans with data retention controls, private connectors and audit logging. Check where data is processed and stored.
- Policy – Set a clear internal policy: what can be shared with AI tools, approval for code generation, and verification requirements.
- Procurement – For anything customer-facing or safety-related, prioritise vendors with certifications and strong support SLAs. DIY can be false economy.
- Skills – Invest in training. A little time on “prompting plus verification” often pays back quickly in quality and speed.
How to avoid the “nearly perfect” trap
It presents you with the nearly perfect result with just enough errors.
That’s the trap. You beat it by making correctness visible and required.
- Always ask for sources, page references, or links to the official docs.
- Require tests or checklists that you can run independently.
- Timebox; escalate if you blow the budget.
- Use AI to build your learning plan and quiz you, not to replace reading the manual.
Bottom line: be deliberate about where AI belongs
The Redditor is right that AI can make us feel busy while learning less and shipping slower. But that’s not inevitable. Used deliberately – with verifiability, timeboxing, and a docs-first approach – AI can speed up the right parts of the job: ideation, drafting, scaffolding and tedious transformations.
For rigid, high-stakes or tightly specified work, lean on official documentation and experienced people, with AI as a helper at the edges. That’s how you keep the benefits while avoiding the productivity paradox.
