We’re Not Solving Problems Anymore
Source: Dev.to
My Journey as a Developer
I’ve been developing for two decades: web things, applications, games, scripts, plugins… whatever my curiosity led me to. What always drove me was creating – turning “nothing” into something I can use.
I also like puzzles (as, I guess, most developers do). How to architect code? How to put things together? How to do X when Y happens? These are things we (used to?) do every day.
That’s what I liked about development: figuring things out. Lately, I feel like that part is disappearing.
How I Work
I tend to think about how things will work together before writing any line of code. Earlier in my career, planning could get me lost in a maze of notebook pages and poorly‑drawn UML diagrams.
This mindset also pushed me to keep learning: new skills, new languages, staying up‑to‑date with what’s going on.
- Need to send messages between two apps? Check out that RabbitMQ clone some random guy made – it’s great.
- Need to compute complex coordinate distances? Here’s an NPM package that I promise hasn’t been hacked… yet.
- Need a one‑page website? Use Rust: you won’t have memory leaks.
I’m being cheeky, but learning and staying in the loop is a big part of the job.
A Good Environment
I’ve been fortunate to work at a company that encourages writing technical specifications before implementing features, with validation before touching code.
That gave me space to:
- Think things through.
- Discuss design with colleagues.
- Improve projects together.
It also taught me not to over‑do it: clients have budgets, and at some point, things need to ship.
Nowadays, fast iteration is standard. It wasn’t always like that.
My Current Role
As I became more senior, I spent a lot of time doing code reviews. Even a few months ago, it was a third of my workload. I’m also given time to stay up‑to‑date and share what I learn through an internal newsletter (for others to enjoy, or to trash the mail).
AI: A Tool, Not a Replacement
I’ll say it right away: I’m not anti‑AI.
AI is a great tool, even if the crypto‑consortium‑turned‑AI‑bro‑lobby is trying to turn it into something it’s not.
- Like everyone else, I ask ChatGPT things I’m too lazy to Google.
- I’ve even asked it to put googly eyes on pictures of friends and family (Google can do that, that’s a missed opportunity), while feeling slightly guilty about the environmental cost.
As someone who enjoys building systems, AI is great for writing code I’ve already solved dozens of times. I don’t care about CRUD, date handling, or email validation. Hell, I don’t even want to open a Swagger file just to find an API route. AI can do that.
What I care about is how the pieces fit together.
For a while, I used AI as a rubber duck: I would iterate on ideas, get a clear view of the system, then use an agent to generate code step‑by‑step. In that setup, AI is a tool. I’m still the one using it.
The New “Agentic AI” Workflow
Recently, my company adopted a full‑agentic AI workflow. Everything from business analysis to code review is handled through internal tools: AI agents, skills, commands, etc.
Goal
- Take a client need and split it into detailed use cases.
- Turn use cases into technical specifications.
- Turn specifications into implementation steps.
- Execute those steps.
- Review the generated code.
What a Developer Is Expected to Do
| Role | Expected Tasks |
|---|---|
| Lead dev | • Read a 300‑line use‑case markdown file. • Re‑prompt and go AFK for a coffee, until validation. |
| Lead dev | • Read a 500‑line specification. • Re‑prompt and go AFK again, until validation. |
| Dev | • Ask AI to split the spec into tasks. AFK. • Ask AI to code each task. Type “next” until it’s done. AFK. • Ask AI to review, fix, and push. AFK. |
Wife: “What did you do today?”
Me: “I spent my day writing nothing but ‘next’ in a conversation with a Turing‑test‑passing LLM.”
What’s Missing?
There is no figuring things out. No puzzle solving. One could even say there is not much thinking involved at all—just babysitting the AI.
Sure, you might step in if something looks wrong. In the first weeks of using only this workflow you will course‑correct (adjust prompts, fix output). But you don’t really engage with the problem anymore.
Consequences
- You become out‑of‑touch with the code.
- Your brain becomes lazy; you start missing issues (e.g., a random color the client chose has been forgotten).
- Shipping faster while touching less code means the whole team adds code without truly understanding the system.
- When you join a project you’ve never worked on, you read specifications but you don’t know how the system behaves globally.
- Over time you trust the AI’s word more than you should.
Even if you have great prompting skills, you can’t fit everything into a prompt or a context window. That feature mentioned casually by the client in a meeting that will come with v2? AI wasn’t there. You were. You’re the one who can shape the system so future changes don’t break everything—at least, you used to.
The Nail in the Coffin
Two comments from my team lead and the universally‑beloved‑scrum‑… summed up the problem perfectly.
“We’re no longer solving problems; we’re just validating outputs.”
“If we keep this up, we’ll lose the ability to think critically about our own code.”
Closing Thoughts
AI is an incredible assistant, but it shouldn’t replace the core activity that made us fall in love with development: the joy of figuring things out.
We need to strike a balance—use AI to augment our work, not to erase the very puzzles that keep us sharp.
Summary
Your manager’s recent statements have left you frustrated:
- “You don’t need to learn a frontend framework; AI will write the code for you.”
- “Human code reviews are obsolete; AI can auto‑merge.”
Both claims are fundamentally wrong, but the broader workflow they’re promoting isn’t entirely without merit.
Why Learning Still Matters
- Skill growth: Learning sharpens your problem‑solving abilities.
- Quality control: Understanding the code lets you spot issues in AI‑generated output.
- Learning through reviews: Code reviews are one of the best ways to improve.
The Business Perspective
A company’s primary goal is revenue. A tool that ships faster— even if it introduces bugs— can be attractive as long as fixing those bugs remains quicker than doing everything manually. This is why your manager’s stance makes sense from a business standpoint.
Questions for the Future
- Will we still read the code we ship?
- Will large AI‑generated codebases be maintainable?
- Will adding features be smooth or a constant fight?
- Should I keep learning? Yes.
- Do I still bring value?
Personal Reflection
- Energy boost: With the new workflow, you have more energy for personal projects because you’re not thinking about code all day.
- Uncertainty: You’re not sure whether this is ultimately beneficial.
Takeaway
- Keep learning. It remains essential for spotting AI mistakes and for personal growth.
- Balance efficiency with quality. Faster shipping is valuable, but maintainability and continuous learning are equally important.