- Eric D. Brown, D.Sc.
- Posts
- The Simplification Problem
The Simplification Problem
Why "AI Sucks" (or..."AI is great!") Won't Help You
Eric Brown
June 17, 2025
In partnership with
When experts say 'this sucks,' they're dismissing all of this complexity. The messy reality deserves better explanation than two-word judgments."
A few weeks ago, a well-known AI architect posted on LinkedIn: "Obviously, Naive RAG sucks."
No explanation. No context. Just a dismissive statement thrown into the void.
This drives me crazy.
Here's someone with real expertise making a point that could help people make better decisions. Instead, he signals to his peer group with insider shorthand and leaves everyone else guessing.
Note: It should be noted that this particular expert then went on to tout his framework’s approach to RAG and why it’s better…which hits the point of this story home.
The Expert's Curse
I get it.
When you've spent years understanding why certain approaches fail, explaining the basics feels tedious. You see a pattern, you know the outcome, and you want to save people the trouble.
But "this sucks" isn't analysis. It's frustration dressed up as wisdom.
The architect who dismissed "Naive RAG" probably meant something specific: basic retrieval systems return documents based on keyword matching rather than understanding the user's needs. In practice, this means that your customer service chatbot gives people generic policy pages instead of answers to their real questions.
That distinction matters. The first version signals expertise to other AI architects. The second helps a COO decide whether to invest in a customer service AI project.
What Happens When Experts Stop Explaining
I've watched this pattern play out in boardrooms across the country. Technical leaders make proclamations without providing reasoning. Business leaders, locked out of the conversation, make one of three moves:
They blindly trust the expert and approve projects they don't understand. Six months later, they're staring at expensive systems that don't deliver the promised results.
They get paralyzed by conflicting expert opinions and delay decisions while competitors advance.
They turn to vendors to fill the knowledge gap. Sales teams are happy to explain why their particular solution solves everything.
None of these outcomes serves the business well.
The Real Cost
A manufacturing CEO recently told me his CTO announced that "traditional dashboards are dead" and pushed for a complete migration to AI-powered analytics. The CEO, trusting the technical expertise, approved a $2 million platform change.
Eighteen months later, plant managers still can't get the needed production reports. The new system is more sophisticated but more complex and less reliable than what it replaced.
The CTO wasn't wrong about traditional dashboards' limitations. But he never explained why the alternative would work better for their use case. The CEO made a million-dollar decision based on a technical opinion, not technical analysis.
What Good Analysis Looks Like
The difference between opinion and analysis is specificity.
Good technical communication doesn't require dumbing things down…it requires being complete about trade-offs and context.
Instead of "Naive RAG sucks," try: "Basic RAG systems match keywords rather than understanding intent. For customer service, this means users get policy documents instead of answers. If your goal is reducing call volume, this approach will frustrate customers and increase escalations."
That explanation works for everyone. Engineers understand the technical limitations. Business leaders see the customer impact. Finance understands the cost implications. Everyone can make informed decisions.
Why This Matters Now
AI has moved from IT experiments to business strategy. CEOs, not just CTOs, are making decisions about AI investments, which means technical leaders need to communicate differently.
When you say "this approach sucks," you might be right. But rightness without reasoning doesn't help anyone build better systems or make smarter investments.
The field needs technical leaders who can bridge the gap between deep expertise and practical decision-making. Not simpler explanations…more complete ones.
A Different Standard
Next time someone dismisses an approach with a sweeping statement, ask for specifics. What failure modes are they worried about? What context makes the alternative better? What would success look like?
Push for the analysis that helps teams make better decisions rather than just confirming who knows what.
Technical expertise deserves better expression than "this sucks." Business decisions deserve better input than expert opinions without reasoning.
This kind of clear technical analysis separates successful AI implementations from expensive experiments. I help executives cut through technical noise to make informed decisions about AI strategy. If you're wrestling with these kinds of decisions, let's talk.
If you found this post helpful, consider sharing it with another executive grappling with AI, technology, and data. If you want to explore AI and other Technology strategies, grab some time on my calendar, and let's chat.
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