Note: We’re continuing Part 2 of Growth Isn’t One Sided: The Right People for the Right Problems. Last week we explored how managerial thinking optimizes what exists while entrepreneurial thinking discovers what’s possible. This week: why companies that only optimize eventually stall—and how growth requires balancing efficiency with investments that can’t be immediately measured.

One Takeaway: You build businesses, you don’t optimize them into existence. Growth comes from smart investments, adapting to change, and making good judgment calls. It doesn’t come from simply finding the most efficient way to spend money.

Markets Evolve

Markets aren’t math problems. Your business doesn’t work in a perfect world where formulas solve every decision. Numbers and sophisticated models can, without question, help, but they can also hurt you by making you miss growth opportunities.

Making everything efficient has limits. Many sophisticated models are based on mostly static scenarios where not much changes. As we’ve discussed, these models can be helpful in specific situations. However, customer needs and competition cause markets to shift constantly. These changes need to be acted on before they become historical trends everyone else can see. Real growth means balancing efficiency with the flexibility to chase opportunities that don’t fit neat formulas.

The “Throw More Money At It” Trap

Here’s a common scenario: Your company has $100,000 to spend between two markets. Market A shows 15% return based on past data. Market B shows 12% return. Simple math says: “Obviously spend it on Market A.”

But this misses important details:

Market A might be maxed out

Market B might have new competitors you could beat

Things in Market B could be changing in your favor

You’re already strong in Market A, while Market B is untapped

The “only math” approach works great short-term but can create long-term blind spots.

Why Too Much Efficiency Kills Growth

Markets change constantly. Models assume nothing changes, but customer needs, competitors, and rules shift. Yesterday’s best choice may be tomorrow’s missed chance.

Growth isn’t always efficient. Netflix moving into making their own shows looked wasteful at first. High costs, uncertain returns, unproven approach. But it created advantages that pure cost-cutting never could.

Side effects matter. Cutting spending in a “weak” region might save money now but could hand that market to competitors, create gaps in your customer network, or hurt key relationships.

Real Examples

Amazon’s long-term approach: For years, Amazon put growth over profits, putting revenue back into infrastructure, new markets, and new tech. Wall Street criticized this, but it created advantages that pure efficiency couldn’t match.

Starbucks’ “unprofitable” stores: Early on, Starbucks opened stores that didn’t meet their profit targets but served bigger purposes. This built brand recognition, let them test new ideas, and supported existing stores. Many became profitable as neighborhoods grew.

Southwest Airlines’ different approach: When other airlines made efficient hub systems, Southwest built what looked like a less efficient point-to-point network. This “worse” approach created advantages in convenience and costs.

When to Make Things Better vs. When to Try New Things

Making things better works for established processes:

Supply chains: Clear inputs, outputs, and costs
Pricing: Past data shows reliable patterns
Operations: You can measure improvements in existing systems

Growth needs creative thinking for uncertain opportunities:

New markets: Local conditions need judgment over data
New products: Customer needs might not show up in existing numbers
Strategic moves: You can’t perfectly predict competitor responses

The Do-Both Approach

Successful companies don’t pick between efficiency and growth. They do both strategically.

Netflix makes content delivery efficient (algorithms, streaming systems, user interface) while simultaneously creating original content that can’t be made efficient in advance.

Amazon makes logistics and pricing efficient while investing in experimental projects like AWS and grocery delivery.

Retail chains make existing stores efficient while trying new formats, product types, and customer experiences.

How to Actually Do This

If you want to put these ideas into action, start by keeping these ideas in mind:

Save some resources for growth: Put 5-10% of resources toward experiments and opportunities that don’t meet traditional return requirements.
Think like a portfolio: Some investments should make existing operations better. Others should explore new possibilities. Both help long-term success.
Measure different things: Track efficiency numbers for optimization work and learning numbers for growth work. Not everything needs immediate returns.
Challenge your assumptions: Regularly ask whether your “best” decisions are missing new opportunities or changing conditions.

The Bottom Line

Solutions that scale everywhere should get most of your resources. They provide the foundation that keeps your business running efficiently. But growth requires chasing opportunities that can’t be immediately scaled or optimized.

As Paul Graham, founder of Y Combinator, noted, the biggest opportunities often start in places that can’t be immediately scaled.1

The key is knowing when to optimize what you have and when to build what you need for the future.

Companies that only work on scalable ideas risk missing valuable opportunities. Real growth comes from combining the precision of optimization with the adaptability of good judgment.

This raises an important question: if growth requires both scalable and non-scalable approaches, how do you decide which approach to use for specific challenges? The answer lies in understanding that markets aren’t the same everywhere. They’re collections of unique, local conditions...

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