Benoit Schillings has spent more than three decades in Silicon Valley, working in senior engineering roles at Be Inc., Nokia, Yahoo, and Google X, where he served as CTO.
Today, at Google DeepMind, he leads research into generative AI for code, deep reasoning algorithms, and the future of pre-training and transformer models. This September, he’ll be a** keynote speaker at Infobip Shift Zadar 2026** – and ShiftMag readers can get a
special discount. I met him at the AI Engineer World’s Fair conference in San Francisco and one line from him stuck with me:
I no longer see manually writing (or even reading) every line of code as the core engineering task.
AI writes fast, engineers still own the mess #
Benoit has been writing code for decades, but these days, he admits, he no longer enjoys it:
I have an astronomy project in the Sierra Nevada: a half-meter telescope, a small Linux computer, and photon-counting cameras working together to track the sky and process data in real time. For it,
I don’t even read the generated code anymore; I just check that it works, while AI helps write the control software, driver bindings, and parts of the wavefront reconstruction.
To him, this is just the next step in software development. Programmers used to distrust compilers, but now most engineers don’t even look at compiler output unless they’re doing extreme low-latency work. AI-generated code is going through the same change, only now it’s happening inside the tools programmers use every day.
That raises another question: if generating code becomes increasingly cheap, what does an engineer need to be good at?
Being a software engineer is so much more than writing 100 lines of Python. It means walking into an enterprise, discovering a codebase with 150 million lines of legacy Python, and being tasked with fixing a race condition on day one.
Engineers inherit systems they didn’t design and constraints they didn’t choose. AI can write code fast, but someone still has to know where it belongs, how to test it, and what happens when it fails.
Security will become a bigger part of the engineer’s job #
That last point becomes more complicated when the models generating code are trained on the code already available online:
Models have been trained based on the GitHub data, and 90% of GitHub is crap. Even worse, people are now up things to GitHub to create vulnerabilities.
That creates a problem beyond the familiar concern that AI might generate buggy code: if the training data contains poor practices, vulnerabilities or deliberately malicious examples, the model can become part of the security problem rather than simply a tool for solving it.
This is why Schillings expects security to become a larger part of the engineering job rather than something that can be delegated to the model producing the code:
You also need to use models to monitor models. You need to fight fire with fire.
The point isn’t to have one AI police another, but to recognize that AI-generated software needs another layer of scrutiny. As more code is automated, humans can’t realistically review every line.
That doesn’t eliminate human responsibility. Someone still needs to define what software should do, verify that it does it, and decide what risks are acceptable.
For engineers entering the field, that means developing a different set of skills: The more mundane aspect of just spewing out the code is going away. Understanding the problem, understanding if that meets some requirements, understanding how to validate if that code will do what you expect, actually being paranoid about security, probably thinking about performance, I think that’s where the job is going to move in the future.
That is a considerably different proposition from simply becoming faster at programming. It puts more weight on requirements, architecture, testing, security and judgment, the parts of software development that remain difficult even when the implementation itself can be generated in seconds.
AI agents create an organizational problem #
Schillings also sees AI changing how engineering teams are organized:
Management is an incredibly archaic skill. You look at the way a large company takes decisions and is managed, you could probably show that to the Babylonians.
That point matters even more as companies move from using one AI assistant to using multiple agents on different parts of the problem.
Benoit doesn’t think the answer is to just make hundreds of copies of the same agent:
You don’t want one brain cloned a hundred times. You want diverse, specialized agents colliding and collaborating.
This feels less like a software design issue and more like a team issue. If you use the same kind of agent everywhere, you just scale the same mistakes. Different agents with different roles can push back on each other, like people with different expertise on a real team.
But that also means someone still has to decide who does what, judge the results, and take responsibility when things go wrong.
That may be the real change AI brings to engineering: less about writing code, more about directing, checking, and owning systems that act more on their own.
Want to hear more from Benoit Schillings, VP of Research at Google DeepMind? Get your ticket for Shift conference with a special discount for ShiftMag readers!