Stop Paying for AI Snake Oil: 99% of Your Stack Doesn’t Need an LLM
Let’s talk about the real summer scam: agencies and SaaS vendors upselling AI like it’s magic fairy dust, when all you needed was a damn IF statement. This week, I watched a midtown marketing team get pitched a “hyper-personalized, agentic AI” for automating email subject lines. The price tag? $32,000 a month. The actual value? Less than what a junior copywriter with a can of Red Bull and a decent regex script could deliver.
Here’s the unvarnished truth: most of your workflows don’t need generative AI, and anyone peddling it for basic sorting, tagging, or analysis is running a margin play—on your budget. Rule-based logic has existed since before half these LinkedIn AI influencers were born. Predictive systems, fine. But if you’re buying LLMs for anything short of actual content generation or unstructured query understanding, you’re just burning cash for the privilege of reading about yourself in a vendor case study.
The MarTech crowd is pushing this narrative because it’s profitable. They want you to believe more complexity equals sophistication. But the more layers you add, the harder it is to untangle when (not if) things break—especially on a humid August Friday when your lead dev is at the beach and the only thing available is a 60-page prompt engineering PDF that reads like a parody.
Here’s your uncomfortable recommendation: Demand an exact breakdown of AI spend by system type before you sign anything. If your vendor can’t show you why a ruleset or a simple Bayesian model won’t suffice, fire them. Save your LLM budget for where it actually matters—content, clustering, true NLP tasks. Everything else? Write the logic yourself or pay an engineer who still knows what a switch statement is. Don’t let the industry gaslight you into thinking complexity is value.
Frequently Asked Questions
Why doesn’t most of my business stack need an LLM?
Most business workflows like sorting, tagging, or analysis can be handled by simple rule-based logic or basic models, making expensive LLMs unnecessary.
What are examples of tasks that don’t require generative AI?
Basic tasks such as sorting, tagging, and routine analysis can be accomplished with IF statements, regex, or Bayesian models instead of generative AI.
How are vendors upselling unnecessary AI solutions?
Vendors often push AI solutions for profit by convincing businesses to use complex and costly generative AI for tasks that don’t require it, especially in MarTech.
What should I ask vendors before buying an AI solution?
Demand a detailed breakdown of AI spend by system type and ask vendors to justify why simple rules or models won’t suffice for your needs.
When is it actually appropriate to use LLMs in my stack?
LLMs should be reserved for genuine content generation, clustering, or true NLP tasks involving unstructured data or complex language understanding.
