Your 'Efficient' AI is Useless. Here's What's Next

AI-powered efficiency is now free.

That sentence should terrify most founders. If it doesn't, you haven't been paying attention to the signals. The venture-backed world has been obsessed with this game for years. We've all seen the push from major players like Y Combinator for "lean unicorns" powered by AI, where the ultimate metric is "Revenue Per Employee". This glorification of efficiency has created a generation of startups that are brilliant at automating tasks but often lack a durable, defensible core.

The conversation is finally starting to shift. The problem is, when your entire business is built on efficiency, you’re playing a game of diminishing returns. You’re caught in what I call the

Complexity Bias: the invisible rule that pressures founders to build complex, high-tech solutions when simple, elegant ones are what customers actually need. You start obsessing over technology for its own sake rather than the actual outcome.

The uncomfortable truth is that the race to be the most efficient is over. The race to build the most credible, opinionated AI has just begun.

The next generation of breakout companies won't be built by founders who simply "use AI." They will be built by those who successfully imbue their technology with the human principles that create real value and credibility.

1. Building AI for Judgment

Commodity AI gives you answers. An AI built for judgment gives you context.

The market is saturated with tools that can "optimize" workflows. But optimization alone doesn't create value; it just creates a faster path to a potentially wrong answer. Investors are growing wary of founders who talk only about features and automation. They want to see a founder who can answer a more critical question: “Who’s the most credible person/entity in your industry, and what will it take for you to be in the same conversation?”. That answer has nothing to do with efficiency and everything to do with strategic judgment.

• What it looks like in practice: An efficiency-based AI tells you the cheapest supply chain option. An AI built for judgment flags that the cheapest option relies on a politically unstable region, has a high carbon footprint, and would violate the unspoken ethical expectations of your customer base, presenting a nuanced risk-vs-cost scenario. It doesn't just give you an answer; it helps you make a better decision.

• How to build it: Design your systems to surface trade-offs, model second-order consequences, and flag when a purely data-driven answer might be socially or reputationally tone-deaf. Your AI stops being a calculator and starts becoming a strategic sparring partner.

2. Building AI for Taste

Commodity AI is generic. An AI built for taste is curated and opinionated.

Taste is the specific worldview that makes your solution feel distinct. In a world drowning in generic, AI-generated content, a strong, curated point of view is the only thing that stands out. Look at the entertainment industry: a recent article in The Verge highlighted a new tool that's the talk of Hollywood. It doesn't write scripts; it acts as a "Taste-Maker," analyzing cultural trends and audience subcultures to predict what kind of story will resonate. It's a perfect example of an AI built not for efficiency, but for taste.

• What it looks like in practice: An efficient AI recommends songs based on listening history. An AI with taste acts like a trusted music critic who understands your specific niche, introduces you to an obscure band you'll love, and explains why it fits your unique style. It has personality.

• How to build it: Your AI should learn and reflect your brand's unique aesthetic and values. It should deliver results that feel hand-picked, not just algorithmically generated. This is how you build a product that feels like it belongs to a specific tribe—your tribe.

3. Building AI for Cultural Fluency

This is the holy grail. Commodity AI understands keywords. An AI with cultural fluency understands context.

This is where my work in anthropology becomes a secret weapon. The real magic happens when you move past what people say and start understanding the invisible rules that govern their world. The VC pivot away from "wrapper" apps isn't just about tech; it's a search for companies with a deep, defensible understanding of a niche market—a moat built on cultural fluency.

• What it looks like in practice: An efficient CRM analyzes keywords from a sales call transcript. A culturally fluent CRM analyzes the conversation’s power dynamics, identifies the real decision-maker based on who others defer to, and flags that the prospect is using specific insider jargon that signals they need to see a certain kind of Credibility Trigger to build trust.

• How to build it: You must decode your customers' unspoken expectations and hidden pressures first. Only then can you design your AI to interpret the human dimension of the data. Your technology must become a reflection of your deep understanding of the game your customers are playing.

Founders who keep flexing efficiency are now competing with free, open-source tools. It’s a game you can’t win.

Founders who figure out how to embed their unique judgment, taste, and cultural understanding into their technology will build the next generation of defensible, credible, and truly valuable businesses. They are the ones who will get funded in this new era.

Because growth used to come from efficiency.

But enduring leadership? That will come from credibility, scaled through technology that has a soul.

My Question For You:

What's one core belief or 'taste' of your company that, if you could bake it into your technology, would make you impossible to copy?