Coding with AI in 2026: GitHub Copilot vs Cursor, agents, costs and control
For years, programming was a specialized skill. Today, in 2026, it is increasingly clear that writing code is no longer the real bottleneck.
The conversation is no longer whether AI writes code better or worse than a human. The real conversation is another one:
Who is able to control, govern and orchestrate AI systems working at scale?
This post is born from that place, but without theory. Only real usage, real numbers, real errors, frustrations and learnings.
Real usage context (because without this everything is misunderstood)
Before talking about tools, I need to clarify something important.
I am not a programmer, and my main job is not to code all day. My role is closer to:
- project manager
- functional architect
- product tech lead
- delivery orchestrator
And even so, in my free time I spend 20+ hours a week coding with AI on personal projects that excite me, across:
- multiple projects
- multiple repos
- backend, UI, tests, documentation
- several agents working in parallel
I do not “try” tools. I depend on them.
When that is the scenario, pricing models, stability and control stop being a detail.
In this post I will share, in this order:
- how Cursor behaved when it became my main tool – and why cost broke trust
- why I moved to Copilot Pro+ and what I gained and lost
- how I actually work day to day with agents instead of “traditional” coding
- what I see in the ecosystem: popularity, pricing and where AI coding assistants really deliver
Cursor: technical power… until cost breaks trust
For almost a year I used Cursor as my main tool.
Real usage
- 20+ hours per week
- Multiple active projects
- Intensive use of agent mode
- Large refactors
- Integration between repos
For months, the setup was simple:
- Pro plan
- ~€20 per month
- No friction
- No surprises
Cursor is, without discussion, one of the most intelligent tools I have used. Its systemic reasoning and autonomy are impressive.
The breaking point: December and a €300 invoice
Without changing the way I work:
- same hours
- same projects
- same flows
Result:
- Invoice close to €300
- Justification: over-usage
- No clear alerts
- No hard limits
- No prior simulation
This is not a small overage. It is a 15× jump in the monthly cost.
And here is the key point:
In professional tools, unpredictable cost is unacceptable.
It does not matter how smart the AI is if you cannot trust what you will see on the invoice at the end of the month.
Technical friction that only appears under heavy use
On top of that, issues appeared that you barely notice with light usage:
- connection drops
- VPN problems
- lost chat history
- agents losing context
- expensive re-analysis of large repos
None of this is catastrophic in isolation. But in continuous use, it wears you down.
Result: Cursor stopped being a tool I could safely depend on every single day.
Copilot Pro+: less dazzling, much more governable
The change was deliberate: I moved to GitHub Copilot with Copilot Pro+.
Conditions
- 20+ hours per week
- Multiple repos
- ASK + Agent
- Full integration with GitHub
Result so far
- Fixed cost ~€40
- Zero over-consumption
- Zero surprises
- Full stability
Copilot does not “dazzle” like Cursor. It is not as creative. It improvises less.
But:
- it is predictable
- it is stable
- it integrates better
- it is easier to govern
In real work, that weighs more than the magic.
ASK vs Agent: where theory separates from real usage
In Copilot, the difference between ASK and Agent is essential.
ASK
- Low consumption
- Ideal for:
- validations
- explanations
- quick reviews
- Very stable in Copilot
Agent (the critical point)
Real example:
- cross-cutting refactor
- 20+ files
- tests + models + services
- multiple iterations
Cursor
- more autonomous
- more powerful
- but with unbounded consumption
Copilot
- more conservative
- requires clearer prompts
- but consumption is bounded and predictable
With heavy use, control beats raw power.
Git integration: Copilot plays in another league
Here there is no real debate.
Copilot:
- understands PRs
- understands diffs
- understands issues
- understands history
For:
- teams
- long-term projects
- traceability
- cross-review
Copilot is clearly superior.
Cursor is excellent for deep individual work. But Git is not at the center of its universe.
Other alternatives (for real-world context)
Codeium
- very low cost or free
- good autocomplete
- popular in startups
- limited for complex agents
Amazon CodeWhisperer
- strong in AWS
- ideal for infrastructure
- not very flexible outside that ecosystem
These tools help to understand the landscape, but for my daily work the real comparison has been Cursor vs Copilot.
How I actually code with AI today
So what does “coding with AI” actually look like for me in practice? It is not sitting down to type every single line of code. It is more like having a team of AI agents that can execute what I imagine.
In those hours I:
- work with agents specialized in backend, UI, tests and documentation
- do integration work across different repos
- iterate on prompts and plans using Copilot ASK and Agent
- ship small products, pages and tools that I would not be able to build alone
Practical playbook: what I actually do with agents
I am not a professional developer and I do not manage a human engineering team. What I have is a team of coding agents that write code based on what I ask for.
When I work with them, my real routine looks more like this:
- I define the final objective and explain it with concrete examples
- I choose which agent will handle backend, which one UI, which one tests or docs
- I craft prompts, tweak them and compare the result with what I had in mind
- I ask them to integrate across different repos and product pieces
- I review, fix and iterate until the system behaves the way I need
I feel I never stop learning, and that these tools adapt better and better when I:
- explain the final objective clearly
- give enough context about the system
- and treat agents as a team that needs direction, not as automatic magic
A real bug that taught me respect for tests
All this sounds very clean in theory, but real code hurts.
While working on Neurapro I spent almost a week chasing a critical bug: user databases kept being reset. It happened right when I was refactoring, testing and integrating, so I did not know what was actually breaking production data.
I added safety checks in the database layer, reviewed migrations, verified agents’ changes. Nothing. The problem kept coming back.
The root cause was brutal and simple:
- I had a set of critical tests that were using the same database folder as real customers
- those tests created tables they needed… and deleted the whole folder when they finished
- every test run wiped not only the test data, but also the real databases
The fix and the lesson were clear:
- tests would never again use real database paths
- they now always create and destroy data in a separate temporary folder
Since then, that bug never came back. And it became one more example of what this journey really is: trial, error, and lessons learned, side by side with my AI agents.
The real shift: I no longer “code”, I orchestrate
By now the pattern is clear: I am not the one typing every line. My real work is to define objectives, break down problems, create prompts and coordinate agents across backend, UI, tests, refactors and docs.
In practice, it feels like having an almost infinite technical team executing on my ideas — and my value is in how well I orchestrate them.
The new bottleneck: me
The limitation is no longer technical. It is human.
- time
- focus
- mental energy
- ability to maintain context
With many agents and chats:
- you get lost
- you duplicate work
- you get frustrated
- you change direction without noticing
This is the new knowledge problem in 2026.
The key skill I have learned: “feeling” the agent
It is not programming. It is judgment.
- knowing when to let it continue
- knowing when to stop it
- noticing when it is improvising
- detecting when it has lost the objective
- understanding that many times the error is the prompt
It is the same instinct that a good project manager develops with human teams.
The paradox: I understand more technology than before
Paradoxically, even without being a traditional programmer, I now understand architecture better, spot technical debt faster and value documentation much more. AI has forced me to think better. Documentation stopped being bureaucracy and became fuel for me and for the agents.
What I see in the ecosystem: popularity, pricing and real impact
Stepping outside my own use, a few patterns are becoming clear in the market:
- Popularity: GitHub Copilot is, by far, the most widely adopted coding assistant today simply because it lives where most code already is: on GitHub and in mainstream IDEs. Cursor is smaller but has a very active base among power users, indie hackers and startups that want more control and agent features.
- Pricing models: Copilot usually follows a flat monthly subscription, which makes cost easy to predict. Cursor tends to combine a base plan with usage-based limits, which is what can generate surprises if you run heavy agents for long periods.
- Where it really works: the most convincing stories I see are not “an app built only with Copilot”, but teams that:
- ship internal tools or dashboards much faster
- keep legacy code alive without burning developers out
- automate repetitive glue work between services and tests
In other words, the big win is not one emblematic app that “Copilot built alone”, but hundreds of small wins that compound: features shipped sooner, refactors that finally happen, and fewer hours lost in boilerplate.
Looking towards 2030: what changes for people
This post is about my experience today, but the wave is much bigger than me.
Research from McKinsey estimates that, combining generative AI with other automation technologies:
- between 25% and 33% of current work activities could change in the coming decade
- up to 60–70% of the time people spend at work is technically automatable with existing tools
- global productivity could grow 0.5 to 3.4 percentage points per year until 2040 if those hours are successfully redeployed, with generative AI contributing 0.1 to 0.6 points of that
Translated: by 2030 and the early 2030s, millions of people will not just “use” AI tools — they will have to redefine their roles around orchestration, judgment and learning new skills at speed.
What I am living today as an individual will very likely become a much more common reality for many teams and professions.
Conclusion: the game has changed, but not in the way many think
This is not about coding faster. It is not about AI “replacing” developers.
It is about this:
Whoever learns to orchestrate agents, maintain focus, control costs, and think with clarity, will play in a different league.
Cursor showed me the future. Copilot allowed me to make it sustainable.
And the most important learning was not technical. It was cognitive and organizational.
📚 Recommended reading
-
What is GitHub Copilot? (GitHub Docs)
High-level overview of GitHub Copilot, its features across IDE, CLI and GitHub, and how the different plans work.
https://docs.github.com/en/copilot/get-started/what-is-github-copilot -
Visual Studio Code and GitHub Copilot
Official Visual Studio Code blog explaining how Copilot integrates into the editor, with inline suggestions, chat and practical workflows.
https://code.visualstudio.com/blogs/2023/03/30/vscode-copilot -
Research: quantifying GitHub Copilot’s impact on developer productivity and happiness
GitHub’s own research on how Copilot affects speed, focus and satisfaction for professional developers.
https://github.blog/2022-09-07-research-quantifying-github-copilots-impact-on-developer-productivity-and-happiness/
✍️ Claudio from ViaMind
Dare to imagine, create, and transform.
