AI Engineers Aren't Safe From Being Replaced by AI

The popular narrative says AI engineers are safe from automation because they build the AI. But this article argues the opposite: AI engineers will likely be replaced sooner than most other developer roles. The reason? General-purpose foundation models are cannibalizing the need for specialized AI engineering.

What is an AI engineer?

The term "AI engineer" is an umbrella that covers vastly different domains: LLMs (transformer-based, billions of parameters), computer vision (convolutional neural networks), recommender systems, and even classical algorithms like A* for NPC pathfinding. The underlying knowledge differs as much as a car mechanic vs. a rocket engine mechanic. Yet marketing lumps it all under “AI”.

Why AI engineers are at risk

LLMs and foundation models are becoming so general that they absorb adjacent domains. The article cites Meta's recent DINO release: a vision model that is versatile, powerful, efficient, and requires little to no annotations. It's a plug-and-play solution that works for many tasks. As these general models improve, the need for custom-tailored AI solutions vanishes.

“We’ll eventually reach a point where having AI engineers and researchers will no longer be convenient for most companies. The best AI researchers will be concentrated in big tech, and the rest of the market will be highly saturated. Tailored AI solutions will become a luxury that most companies will happily avoid.”

The author's key takeaways:

• Foundation models are cannibalizing specialized subfields of AI (vision, NLP, etc.).
• Most companies will default to a single general model rather than maintain custom pipelines.
• Only the top AI researchers in big tech will remain; the rest face a saturated market.
• Job titles like "AI engineer" are so broad they're meaningless — hiring for “AI” often means renting ChatGPT API skills.

Practical implications

If you're a specialized AI engineer (e.g., custom vision model builder), expect pressure to pivot toward integration and deployment of existing foundation models. The era of building bespoke neural networks for every problem is closing — unless you're in a research lab with deep pockets.