Why Generative AI is More Hype than Help
Generative AI is everywhere right now. Headlines promise machines that write scripts, design logos, generate entire strategies etc. Despite all of the buzz, when it comes to high stakes decisions, the kind where failure carries serious consequences, generative models fall short.
The Hype and the Bubble
A lot of the excitement around AI is built on a misunderstanding. The industry is chasing huge visions and even larger profit margins but lacking deep technical backing. Many startups and vendors claim they’ve built the next AI solution, but what they really have is an off-the-shelf model with a fresh UI or under-paid analysts propping up the illusion of intelligence. The result? A bubble of expectation that our industry is only beginning to burst.
The Technical Gap
What many investors and adopters overlook is that AI models don’t magincally understand context, nuance or consequence. For example, researchers at the University of California, Santa Crus found that a bleeding edge generative model both replicated and amplified human biases when tested with careful prompts. This isn’t just an ethics issue. It’s a signal that the model’s “understanding” is surface level, not strategic.
The Power of Specialized Models
Where AI is genuinely effective is less about creative generation, and more about purpose-build models trained for a single task and constrained to a known domain. For example, in aviation, AI-based detection systems are deployed to identify foreign object debris (FOD) on runways. A comprehensive review found that systems combining computer vision and sensor fusion could detect debris in real time with high accuracy, but only because the task was limited, the environment structured, and the stakes clearly defined.
The Real Cost of Misalignment
When teams chase the generation hype, they risk relying on fragile infrastructures: proprietary frameworks no one else can maintain, or vendor partnerships that create dependency rather than capability. This misalignment, between model capabilities and business consequences, is exactly where firms like ours get involved.
Bottom Line
It’s not in anyone’s best interest to rely on generative AI to independently reason through complex problems. There are a myriad tested AI tools that are cheaper and far more effective than generative AI. They just require a little more effort to set up. Don’t succumb to the hype and partner with folks that will help you help yourself.
