Is it over?

2026-10-25

Under the heading:

In 88 hours, AI completely crushed thousand years of science. “It is a huge bomb and people are completely in shock.”

mathematician Nikolaj Thams explains in an article in Berlingske [Dansih newspaper] today, 24/10-26, just how shaken mathematicians around the world are these days.

The reason: About two weeks ago, OpenAI published around 700 papers in which AI agents solved mathematical problems at an unprecedented high level.

It is yet another "Deep Blue moment", just far more extensive than when a dedicated chess computer first beat a human, back in 1997.

He continues:

"If we were to translate that to journalism, it would be like every article containing a Watergate-level revelation, the kind that would lead to major careers and awards for whoever published it. And they just toss them out, more or less as a zip file.” [Nikolaj Thams]

And it has happened quickly: A year ago, AI agents were far behind humans. Now any doubt about whether agents are better than humans at solving these tasks has been completely swept off the board.

I'm sitting with much the same experience when it comes to writing code, which has been a big part of my job as a systems developer. There are some differences - and some hope!

Systems development is more than writing code

A year ago I was skeptical about whether coding agents could write code of the same quality as the best developers. That skepticism is gone. Just like the mathematicians above, I no longer have any doubt: The agents produce code that is at least on par with the best developers I have met.

If they are guided correctly.

And guiding agents itself has become markedly easier than it was a year ago. Tools like Claude Code and OpenAI Codex are far better now, and the days when we sent people on courses to learn "prompt engineering" are over.

However, when I look back, one thing really stands out: Only about half of my time has gone into actually writing the code. The other half has consisted of:

  • Communication with users, customers or other key starkeholders.
  • Clarifying what is actually needed (not necessarily what the user/customer says they want).
  • Prioritising and "bundling" tasks - what can we deliver in the next sprint, for example.
  • Assessing and choosing between solution options.
  • Design and architecture. Devising and sketching proposals that are shared at meetings or in other ways with the users/system owner.
  • Monitoring running solutions, debugging errors and communicating about these.
  • Writing and maintaining the necessary documentation.

Some of these points are tasks AI agents can also assist with - for example, producing and keeping the right documentation up to date.

But I think we will benefit from and keep using human intelligence primarily on the points above.

A modern AI agent can generate output almost endlessly, never gets tired, and can deliver 10 different solutions to any challenge - but it is still us who must assess, analyse, prioritise and communicate with the business, users or customers about the solutions.

We still need to build solutions and we still need to make workflows smarter. We need to choose the right things to build.

So we are not quite as challenged as a theoretical mathematician, I would argue. Because we are also practitioners as systems developers. Something needs to be built - it is not about finding correct answers to complicated theoretical problems.

New possibilities

In addition, AI agent-driven development has given me the opportunity to take on things that were not previously possible.

A few examples of solutions I have made this year that were not feasible a year ago:

  • We dropped a large American SaaS product for project/task management in the IT department and built our own solution. The first version was live within 21 hours, and we now have exactly the functionality we need - no more, no less. Plus, 7 x $21 is saved every month.
  • I have made my own news agent that scans RSS feeds for interesting AI news and emails me every morning at 06:00. The first version of this solution was made in 2 hours, lying in a half-sick state on my sofa, under a blanket, 100% from my iPhone (/remote-control in Claude Code). Including deployment of an Azure Function and Table Storage, integration of Resend for sending the email, and calls to the Haiku 5.5 model for summaries, de-duplication, translation, etc.
  • This blog - and the site behind Biilmann.dk. Something I have long wanted to build but have not had time for. Now I have a blog that uses Tina CMS to manage all posts as markdown files in a GitHub repo, uses Haiku 5.5 for automatic translation (draft) into English, and automated GitHub workflows to publish new articles (without me having written the underlying YAML files for this).

These are all examples of things that would have been projects lasting several weeks or months before I had AI agents produce code.

Is it over?

So is it over for writing code?

I will not rule out that there is some code that still needs to be written by humans - but I am, right now, very quickly moving away from it in my daily work, I can observe.

But being a systems developer is not over. There is still a huge demand for good solutions - in fact, I would argue that demand has increased, because AI is being hyped enormously in the media, online and in the corridors of management. The promise of enormously increased productivity itself causes expectations and requirements to rise.

So I still don't think we will reach the bottom of the stack and run out of tasks anytime in the near future.

It is not over - but our way of working is changing.