The AI Quality vs. Quantity: Why More Isn't Always Better

Something I talk about a lot with founders, particularly those building AI products and companies, is this idea of quality over quantity. What most founders see when they look at AI is "look how fast you can do something" rather than "look how well you can automate something." And there's a crucial difference here.

The Quantity Trap: Why We're Getting AI Wrong

The point of automation isn't just to create more of something. It's to create consistency in quality at scale. Same with processes. We don't implement these things just to do more stuff faster - we do it to maintain high standards while scaling up operations.

When I was a kid, I remember visiting a car factory. What struck me wasn't just the speed of the assembly line, but the precision and consistency of each weld, each part placement. That's the kind of automation we should be aiming for with AI – not just faster production, but better, more consistent production.

Now, here's the thing: quantity is rarely the core problem companies are trying to solve. It's the quality of the quantity that's the hard nut to crack. And that's where AI should be focusing its superpowers.

The Sales Email Dilemma: 1000 Mediocre vs. 10 Exceptional

Let's take the sales example. A lot of AI companies today are building tools to write sales emails, do SDR work, research prospects, and personalize outreach. But the way they position and price their product is all about sending more personalized emails out. They're proud of their ability to churn out thousands of messages a day, each with a veneer of customization.

But here's where they're getting it wrong: Instead of sending 1,000 mediocre emails a day, what if we used AI to send just 10 emails, but each one was so deeply personalized, so meticulously crafted, that it would be practically impossible for a human to replicate without an enormous investment of time and resources?

I'm talking about emails that demonstrate a profound understanding of the recipient's business, their challenges, their industry landscape, even going so far as to actually listen or read their content. Emails that don't just name-drop a few facts from their LinkedIn profile, but synthesize insights from multiple sources to offer genuinely valuable perspectives.

AI's True Promise: Less is More (But Better)

The success of AI shouldn't be measured by how much more bullshit we can put on the internet, how much more spam we can generate, or how many more robocalls we can make. If that's what AI delivers, we've failed miserably in harnessing its potential.

Instead, the true promise of AI is to enable us to do less, but with dramatically higher quality. It's about using AI's analytical power to do the deep diligence and work required to answer crucial questions, leading to lower volume but exponentially higher quality output.

Imagine an AI system that could analyze a company's entire customer base, identify the 20 accounts with the highest potential lifetime value, and then craft bespoke engagement strategies for each one. That's the kind of quality-at-quantity approach that AI should be enabling.

The Quality-First Paradigm Shift

This paradigm shift – from quantity-first to quality-first thinking – is something that many AI founders get wrong. They're still stuck in the old mindset of "more is better." But the market is evolving, and so are customer expectations.

People are increasingly willing to pay a premium for quality over quantity. We're drowning in content, in emails, in notifications. What we crave is signal amidst the noise, substance amidst the fluff.

When, in reality, AI should mean we're getting fewer spam calls and emails.

Example: Email Management, why write more?

As I write this, I'm reminded of a conversation I had with a founder working on an AI-powered email tool. They were proud of how their system could help users churn out dozens of emails in minutes. But when I asked about how it improved overall email management and productivity, things got a bit fuzzy.

I challenged them to flip their approach: What if, instead of helping people write more emails faster, their AI could act as a true personal email assistant? One that manages your inbox intelligently, prioritizes messages like a seasoned executive assistant, and helps you focus on what truly matters?

Just as a revealed preference, people spend thousands of dollars a month on assistants that manage their emails and calendars, and but very rarely outsourcing just the email writing part.

Think about it. Why is the first thing every AI email app builds is a feature to write more emails? Even the best ones rarely sound like you and often just contribute to the ever-growing mountain of digital noise. Sure, AI-powered summarization was a great first step, but we're barely scratching the surface of what's possible.

The Road Ahead: Quality at Quantity

As we stand on the brink of this AI revolution, we have a choice to make. We can use AI to flood the world with more mediocrity, or we can harness its power to elevate the quality of our work, our communications, and our decisions.

To the founders and developers out there working on AI applications: I challenge you to think beyond quantity. Don't just ask "How much can we produce?" but "How good can we make it?" Use AI not to do more of the same, but to do what was previously impossible.

And to the businesses considering AI solutions: Demand more than just speed and volume. Look for AI tools that promise genuine improvements in quality, that offer insights and outputs that truly move the needle for your business.

The future of AI isn't about replacing humans or overwhelming us with more of everything. It's about augmenting our capabilities, about enabling us to work smarter, not just faster. It's about quality at quantity – and that's a future worth building towards.

(Side note: If a human can auto-pilot a task today, it's likely going to be done by AI in the near future. But the real value will come from AI that can do what humans can't – like processing and synthesizing vast amounts of data to produce truly novel insights.)

The question remains then: How do we build AI systems that prioritize quality over quantity? How do we shift the focus from speed to depth, from volume to value?

These are the challenges that will define the next phase of AI development, and I'm excited to see how we tackle them.