Cross-functional operations leader with a Harvard Economics degree and a PhD in Economics from the University of Chicago. I help organizations scale efficiently by combining commercial strategy, advanced systems, and team alignment.

I’ve led companies through rapid growth, international expansion, and large-scale transformation, delivering measurable financial and operational results. My focus is on building adaptable systems, integrating AI-native solutions, and aligning stakeholders around data-driven decision frameworks.

Key achievements:
→ Scaled a B2B inventory management firm from $25M to $75M while expanding into international markets
→ Negotiated and structured 7–8 figure Fortune 500 contracts
→ Implemented enterprise systems and automated workflows, cutting cycle times and driving operational efficiency
→ Designed AI-enhanced workflows (pricing intelligence, candidate evaluation, productivity automation) that delivered measurable business outcomes

I bring both strategic vision and operational rigor, combining analytical depth with relationship-building to lead distributed teams across regions and time zones.

Core strengths:
→ Competitive operating & go-to-market strategy
→ Cross-functional team orchestration
→ Enterprise system & workflow automation
→ AI-driven operational scaling
→ International expansion & P&L growth

I share insights on these themes in my newsletter Operating by John Brewton, exploring how companies can evolve and thrive in the AI-enabled future of work.


The most powerful organizations in the world don’t just have one operating system. 

They run two. 

Behavioral economists call this the “Dual Operating System” of the human mind: 

1️⃣ System 1—fast, automatic, intuitive
2️⃣ System 2—slow, deliberate, analytical

The Kahneman-Tversky Revolution

In 1974, Daniel Kahneman and Amos Tversky published “Judgment Under Uncertainty: Heuristics and Biases” in Science. 

They documented how people systematically rely on mental shortcuts that lead to predictable errors.

This research culminated in their 1979 Prospect Theory. For this work, Kahneman was awarded the Nobel Prize in Economics in 2002, the first psychologist ever to receive the honor.

Two Systems, One Mind

System 1 is the brain’s autopilot, quick, associative, effortless. It lets us read a room, sense danger, or make expert judgments. It thrives in stable environments where experience has taught us the patterns. But it’s also the source of cognitive bias. It prefers stories that make sense over truths that are complex.

System 2 is slow, energy-demanding, reflective. It steps in when we face problems that are novel, ambiguous, or high-stakes. It’s capable of strategic reasoning and long-term planning. 

But it’s also lazy. It tends to rubber-stamp System 1’s conclusions unless explicitly provoked.

Organizational Implications
Teams rely on fast, intuitive System 1 thinking. It works until the environment changes or the stakes rise. Then leaders scramble to switch to 
System 2, rigorous analysis, scenario planning, deep reflection.

The best companies don’t wait for a crisis to make that switch. They design decision triggers—clear moments when deliberate thinking must be activated: when decisions are irreversible, when uncertainty is high, when novelty overwhelms precedent, when the cost of error is existential.

The goal isn’t to suppress intuition. It’s to build an organization fluent in both modes—instinctive enough to act fast, disciplined enough to know when to slow down.

Which system runs your organization right now—and is it the right one for the decision in front of you?

I write about the history and future of operating companies every day.

Subscribe to Operating by John Brewton, the only newsletter you need to become an elite operator (📬🔗 in comments).
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Further Reading: 

📖 Thinking, Fast and Slow by Daniel Kahneman (2011)  
The definitive exploration of dual-process theory: 
https://lnkd.in/exw33ymn
📄 “Judgment under Uncertainty: Heuristics and Biases” — Tversky & Kahneman, Science (1974)  
https://lnkd.in/eK9ZFG-E
📄 “Prospect Theory: An Analysis of Decision under Risk” — Kahneman & Tversky, Econometrica (1979)   
https://lnkd.in/eArC58aC

John Brewton

Operating Strategist 📝Writer @ Operating by John Brewton 🤓Founder @ 6A East Partners ❤️🙏🏼 Husband & Father

A bubble is when everyone can see the value creation, 
but no one is thinking about the value capture.

Andy Wu nailed the entire AI boom in one sentence.

Pets.com proved pet e-commerce worked. 
Then went bankrupt in 2000. Investors lost everything.

Chewy(dot)com does the same thing today, but with a $10B market cap. 

Same value creation. 
Different value capture.

This is the difference between FINANCIAL bubbles and INDUSTRIAL bubbles.

Financial bubbles (2008 subprime) destroy wealth and leave nothing behind.

Industrial bubbles (dot-com, railroads, AI) destroy investor capital but BUILD infrastructure that outlasts the speculation.

The AI boom is creating $4.4 trillion in real assets: 
chips, data centers, power generation, fiber networks.

MIT research shows 14% productivity gains already. 

These aren't promises. 

They're operational improvements happening in the immediate. 

Will AI stocks crash 50-70% like dot-com? Maybe.

Will the infrastructure disappear? No.

Will society benefit even as investors lose? 

Absolutely.

As operators, we face a different question than Wall Street:

Not "Will the bubble pop?" but "Where does my capital create most value?"

$10,000 in Nvidia stock? Or $10,000 in AI tools giving you 14% productivity gains you can measure, control, and compound?

For most operators, the answer is clear: invest where you control the asset.
You can be RIGHT about value creation (AI will transform everything) and still go BANKRUPT being early on value capture (wrong companies, wrong prices).

Industrial bubbles punish individual timing. But they reward patient builders who inherit the infrastructure at fire-sale prices after the speculation burns out.

Are you positioning for the Pets(dot)com pain, 
or the Chewy infrastructure opportunity?

For more thoughts on bubbles, check out my new article at Operating by John Brewton on Substack: https://lnkd.in/e8t-7P85

I write about the history and future of operating companies every day.

Subscribe to Operating by John Brewton, the only newsletter you need to become an elite operator (📬🔗 in comments).

Found this valuable? ♻️Repost to help others

Follow me, John Brewton, for more content about operating.

AI’s appetite for power is reshaping markets in ways most operators aren’t tracking yet.

A fascinating new analysis from The Wall Street Journal  reveals an unexpected surge: compact turbine and fuel cell manufacturers are seeing unprecedented demand and investor interest. 

(🔗 to article: https://lnkd.in/eFvjkhnc)

Tech giants are scrambling to source electricity for AI infrastructure, and “readily available” equipment from smaller players is suddenly commanding premium prices.

This is a masterclass in second-order effects.

When dominant production units face bottlenecks, entire supply chains reconfigure. Price signals spike. 

New winners emerge. Nimble players seize advantages that scale alone can’t deliver.

Smaller, agile manufacturers are gaining ground because they can deliver now. Speed and availability matter more than size when constraints bite hard.

This week’s Operating by John Brewton will dig into how these ripple effects are changing capital allocation, procurement, and competitive dynamics across tech-facing industries.

The critical question: In a world of acute AI power demands, who profits most, the scale giants or the nimble suppliers providing the picks and shovels?

Or both?

Where is your company seeing unexpected constraints or opportunities as the AI power rush accelerates?

I write about the history and economics of operating companies every day.

Subscribe to Operating by John Brewton, the only newsletter you need to become an elite operator (📬🔗 in comments).

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The Fed is forecasting the singularity. 
What a time to be alive. 

The Federal Reserve Bank of Dallas recently charted four scenarios for how AI could reshape U.S. economic growth. 

The divergence is staggering.

The Baseline (1.9% annual growth)

Conventional innovation continues. Steady, predictable, uninspiring. This is what we’ve seen for decades.

Modest AI Boost (2.1% annual growth)

A small nudge upward. Incremental productivity gains compound over time. For operators, this means gradual margin expansion and process optimization—familiar territory with better tools.

The Singularity Scenario (Explosive growth)

Superintelligent AI transforms everything. Economic growth reaches levels never experienced in human history. What does “operating a company” even mean when AGI can outthink entire organizations?

The Catastrophic Scenario (Economic collapse)

Mismanaged transformative AI triggers existential risks. Growth inverts. This isn’t science fiction—it’s a credible risk scenario mapped by central bank economists.

The gap between these futures isn’t academic. It’s operational. Your strategic planning horizon just expanded from quarters to decades, but your decision-making window compressed to months.

Which future you’re building toward matters more than your current competitive position.

Are you positioning your company for gradual AI-enabled improvement, or are you preparing for a world where the fundamental rules of business get rewritten?

I write about the history, economics of operating companies every day.

Subscribe to Operating by John Brewton, the only newsletter you need to become an elite operator (📬🔗 in comments).

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The part the headlines, conversation on the socials and amongst friends will miss. 

- j -

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