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In today's newsletter:
Latest Podcasts: What You Missed
The Book That Replaced the Sales Team: Interview with Gia Laudi, Customer-Led - A 4-person team with no sales department counts Bitly, Sprout Social, and dbt Labs as clients, because a book replaced the sales motion entirely.
No-Code as Leverage - Interview with Emmanuel Straschnov, CEO & Co-Founder of Bubble. - Emmanuel bootstrapped Bubble for seven years without a dollar of outside capital. Today, companies built on Bubble have generated over $1 billion in revenue in 2025 alone.
Stop Broadcasting, Start Focusing - Interview with Brennan Dunn, CEO & Founder, RightMessage - Brennan Dunn built something different: a behavioral system that segments, routes, and converts without a marketing team behind it. ~$1M ARR business with just himself and some contract help.
Community as the Unfair Advantage: Interview with Gina Bianchini, Founder & CEO of Mighty Networks - Learn why community works as a retention engine, what founders get wrong about building one, and how Mighty Networks powers over $500M in revenue for its customers.
Scaling the Creator Economy: Interview with Tyler Denk, CEO & Founder, Beehiiv - Tyler Denk joined Morning Brew as employee #2. A single newsletter send earned $4,000. By 3 million subscribers, that same send earned $80,000. Learn how that leverage sparked the creation of Beehiiv.
AI Can Tell the Future: Forecasting with AI
Your CFO asks: "What's our revenue going to be next quarter?"
You guess. "Uh... $150K? Maybe?"
They ask: "Based on what?"
You have no answer. You're just... hoping.
This is the forecasting blindness problem. You're making decisions with no idea what's coming.
The fix? AI-powered forecasting: using historical data and machine learning to predict revenue, churn, demand, and cash flow.
The AI Model That Predicted a $50K Shortfall
Let me tell you about “Olivia”, founder of a 7-person SaaS company.
Olivia's forecasting method: Gut feel + hope.
Example conversation with her CFO:
CFO: "What's Q3 revenue going to be?"
Olivia: "We're at $40K MRR now. Growing 10% per month. So... $55K MRR by Q3? That's $165K for the quarter."
CFO: "Are you accounting for churn?"
Olivia: "Uh... sure?"
What actually happened in Q3:
Revenue: $120K (not $165K)
$45K shortfall
Reason: Higher-than-expected churn + slower growth
Olivia almost ran out of cash.
Then Olivia implemented an AI forecasting model.
What she did:
1. Gathered 12 months of historical data:
Monthly revenue
New customers
Churned customers
Customer lifetime value (LTV)
Marketing spend
2. Fed it into an AI forecasting tool (ChatGPT + Google Sheets script)
3. Asked the AI:
"Based on the last 12 months, predict revenue for the next 3 months. Account for churn and seasonal trends."
AI output:
Month 1: $42K (±$3K)
Month 2: $44K (±$3K)
Month 3: $46K (±$4K)
Q3 total: $132K
Actual results:
Month 1: $43K
Month 2: $45K
Month 3: $44K
Q3 total: $132K
AI forecast accuracy: 99%
Key insight: "I used to think forecasting was guesswork. But AI turned my historical data into a crystal ball. Now I make decisions based on predictions, not hope."
Why Founders Don't Forecast (And Why That's Dangerous)
Here's why most microteam founders avoid forecasting:
1. "I don't have enough data"
You think you need years of data
Reality: 6-12 months is enough for basic forecasting
2. "Forecasting is for big companies"
Wrong. Small companies need forecasting more (less margin for error)
3. "I don't know how to build models"
You don't need to. AI does it for you.
4. "My business is too unpredictable"
Even volatile businesses have patterns
AI finds them
Think of forecasting like weather prediction.
Without forecasting:
You don't know if it's going to rain
You leave your umbrella at home
You get soaked
With forecasting:
You see rain is 80% likely
You bring an umbrella
You stay dry
AI forecasting is your business weather report.
Why This Matters for Microteams
Big companies have data science teams building forecast models.
You? You're flying blind.
Here's why AI forecasting is critical:
Prevent cash crunches. Know when revenue will dip before it happens.
Hire confidently. Only add headcount if the forecast supports it.
Plan inventory. Predict demand spikes (e-commerce, physical products).
Set realistic goals. Stop guessing, start predicting.
Spot problems early. If AI predicts a revenue drop, you can act now.
Stop hoping, start forecasting.
The AI Forecasting Framework
Here's how to use AI to predict revenue, churn, demand, and cash flow.