San Francisco has a problem.
The city that birthed the tech revolution is now allergic to real problems.
Walk into any VC pitch meeting on Sand Hill Road and you'll hear about:
- "AI-powered note-taking apps"
- "ChatGPT wrapper for [insert mundane task]"
- "Consumer social app but with AI"
Meanwhile, in Oklahoma City, energy companies are losing $2M annually on unplanned downtime because predictive maintenance relies on spreadsheets and gut feel.
In Dallas, manufacturing plants run 40% below optimal efficiency because their quality control systems can't keep up with production demands.
In Des Moines, family farms are going bankrupt because they can't afford agronomists to optimize yield.
These are billion-dollar problems. Real problems. Problems that affect how America actually works.
And coastal VCs won't touch them because they're "not sexy" and "too far from Silicon Valley."
That's exactly why we're building there.
The Forgotten Industries That Built America
Let's be clear about what we mean by "forgotten industries":
Manufacturing
- 12.8 million US jobs
- $2.3 trillion annual contribution to GDP
- Struggling with skilled labor shortages, quality control, supply chain optimization
Agriculture
- 2.6 million farms in the US
- $1.1 trillion economic impact
- Facing consolidation, climate uncertainty, labor shortages
Construction & Infrastructure
- 11 million workers
- $1.8 trillion industry
- Dealing with cost overruns, safety issues, inefficient project management
Logistics & Supply Chain
- 5.8 million trucking industry workers
- $875 billion freight industry
- Plagued by inefficiency, driver shortages, optimization challenges
Energy & Utilities
- 2.8 million workers
- Critical infrastructure
- Aging systems, grid reliability, transition challenges
Combined economic impact: $6+ trillion annually.
But when was the last time you saw a headline about AI revolutionizing any of these?
Why Coastal VCs Ignore These Industries
Let's be honest about why Sand Hill Road doesn't fund these companies:
1. They Don't Understand the Problems
A VC who's never been in a manufacturing plant can't grasp why predictive maintenance is hard. They don't understand the difference between a combine harvester and a tractor. They think "construction tech" means Zillow for contractors.
Domain expertise matters. You can't invest in industries you don't understand.
2. They're Scared of "Slow" Sales Cycles
Consumer apps can acquire users overnight. Enterprise SaaS can close deals in 3-6 months.
Manufacturing, agriculture, construction? 12-18 month sales cycles are normal.
But here's what VCs miss: Once you land these customers, they never leave. LTV is massive. Churn is near zero. Switching costs are enormous.
We'd rather have 10 customers paying $500K/year for a decade than 10,000 users paying $10/month for 6 months.
3. They Think These Industries Are "Unsexy"
There's no TechCrunch headline for "AI Reduces Defect Rate in Steel Manufacturing by 23%."
But you know what's actually sexy? Profit margins.
These industries have:
- Real budgets (they pay for value)
- Desperate need (ROI is obvious)
- Low competition (few startups bother)
- High switching costs (once you're in, you're in)
4. They Can't Picture the Founder
Sand Hill Road has a founder archetype: Stanford CS grad, worked at Google, knows the right people.
But the best manufacturing AI founder? They spent 10 years on factory floors. They understand Six Sigma. They've lived the pain.
Same for agriculture. The best agtech founders grew up on farms or worked with co-ops for years.
Domain expertise > CS degree.
The Real Opportunity: Where AI Actually Matters
Here's what most people miss about AI: The technology doesn't matter if you're solving fake problems.
ChatGPT for writing emails? Sure, it's useful. But it doesn't fundamentally change anything.
AI for predictive maintenance in a manufacturing plant? That's transformative.
Let me show you what we mean with examples of the types of problems we see.
Case Study 1: Manufacturing Quality Control
The Problem:
- Mid-sized manufacturer making aerospace components
- Human inspectors checking parts manually
- 12-hour shifts, fatigue-induced errors
- 2.1% defect rate escaping to customers
- $1.8M annual cost in returns, rework, reputation damage
The Old "Solution": Hire more inspectors. Costs rise. Defect rate stays the same (humans get tired).
The AI Solution:
- Computer vision system with custom-trained models
- Catches defects human eyes miss
- Runs 24/7 without fatigue
- 0.3% defect rate (86% reduction)
Results After 18 Months:
- $1.2M annual savings
- Customer complaints dropped 91%
- Won two new contracts (competitors trusted quality)
- ROI: 4.2x in first year
- System now being deployed to 3 additional plants
Why This Works:
- Obvious ROI (company breaks even in ~4 months)
- Doesn't require "AI expertise" from customer
- Solves a painful, quantifiable problem
- Defensible (trained on proprietary part designs)
Case Study 2: Precision Agriculture
The Problem:
- Family farm operation, 5,000 acres
- Variable soil quality across fields
- Over-fertilizing some areas (waste), under-fertilizing others (yield loss)
- Agronomist consulting costs $18K/year but only visits quarterly
The AI Solution:
- Satellite imagery + soil sensors + yield data
- ML models predict optimal fertilizer application by sub-acre zones
- Prescription maps for variable rate application
- Weekly recommendations via simple mobile app
Results After One Growing Season:
- 18% increase in corn yield
- 31% reduction in fertilizer costs
- Additional revenue: $147K
- Cost savings: $52K
- ROI: 8.9x in first year
Why This Works:
- Farmer sees results in dollars per acre (language they speak)
- Fits existing equipment (variable rate spreaders)
- Pays for itself in one season
- Competitive advantage (higher yields = more profit)
Case Study 3: Construction Project Management
The Problem:
- Commercial construction firm, $200M annual projects
- 35% average cost overrun
- Material delivery delays cause $400K monthly in labor waste
- No way to predict bottlenecks accurately
The AI Solution:
- Historical project data + real-time progress tracking
- ML models predict material needs 6 weeks in advance
- Flags likely delays before they happen
- Optimizes subcontractor scheduling
Results After 12 Months (4 Major Projects):
- Cost overruns reduced to 12% (from 35%)
- Material waste down 41%
- Projects completed 3-4 weeks faster on average
- $2.8M savings across 4 projects
- ROI: 7.1x
Why This Works:
- Addresses the #1 pain point (cost overruns kill margins)
- Integrates with existing systems (Procore, etc.)
- Provides daily value (not a one-time analysis)
- Scales to additional projects automatically
Why the Heartland? Why Now?
Geography matters more than Silicon Valley thinks.
Talent Exists Outside Coastal Hubs
Oklahoma City has University of Oklahoma engineering grads who don't want to move to SF.
Kansas City has a thriving startup scene and lower cost of living.
Nashville has Vanderbilt and a growing tech ecosystem.
Austin (obviously) is booming.
Phoenix is growing faster than any coastal city.
Dallas-Fort Worth is America's 4th largest metro with deep industry roots.
These cities have:
- Engineering talent that's undervalued and overlooked
- People who understand the local industries (grew up in them)
- Lower operational costs (office, living expenses)
- Proximity to customers (manufacturers, farms, construction firms)
Customers Are Local
You can't sell manufacturing AI from San Francisco to a plant in Tulsa via Zoom.
You need to:
- Visit the factory floor
- Understand their specific processes
- Build relationships over months
- Provide on-site support during implementation
Being local is a competitive advantage.
Economic Development Incentives
States like Oklahoma, Kansas, Tennessee are desperate for high-tech job creation.
They offer:
- Tax incentives
- R&D credits
- Workforce training programs
- Office space subsidies
Meanwhile, SF and NYC are trying to tax tech companies out of existence.
Lower Burn Rates = More Runway
San Francisco startup burn rate: $150K/month (office, salaries, living costs) Oklahoma City startup burn rate: $45K/month
Same team, same product, 70% less monthly cash burn.
That difference is survival vs death.
The Industries Silicon Valley Will Never Understand
Let me be blunt: Coastal VCs don't want to understand these problems.
They want:
- Consumer social apps (they understand Instagram)
- Enterprise SaaS (they understand Salesforce)
- Fintech (they understand money)
They don't want:
- Manufacturing (too "old economy")
- Agriculture (too "rural")
- Construction (too "blue collar")
This is their blind spot. And it's our opportunity.
The real AI revolution isn't happening in:
- Another task management app
- Another AI writing assistant
- Another ChatGPT wrapper
It's happening in:
- Factories that make the parts that build America
- Farms that feed America
- Construction sites building America's infrastructure
What We Look For
We're not interested in "AI for X" where X is something that doesn't matter.
We invest in companies that:
1. Solve Expensive, Obvious Problems
If we can't calculate ROI in 10 minutes, it's not interesting.
- "This AI reduces defect rates" → Calculate savings immediately
- "This AI makes workflows more efficient" → Too vague
2. Have Founders With Domain Expertise
We'd rather back:
- An ex-manufacturing engineer learning to code than a Stanford CS grad learning manufacturing
- A 4th-generation farmer who taught themselves ML than a data scientist who's never been on a farm
Domain knowledge is harder to learn than coding.
3. Target Multi-Billion Dollar Industries
We don't care about TAM slides with "if we capture 1% of X."
We care about:
- Is the industry large? (Yes, $6 trillion)
- Is the problem widespread? (Yes, every manufacturer faces this)
- Will customers pay? (Yes, ROI is obvious)
4. Can Build Defensible Advantages
What prevents competitors from copying you in 6 months?
- Proprietary training data (customer-specific)
- Integrations with industry-specific systems
- Deep customer relationships
- Switching costs
If your only edge is "we were first," you do not have an edge.
5. Are Building in the Heartland
Location isn't everything, but it matters.
We prefer teams that:
- Live near their customers
- Understand local industries
- Can recruit local talent
- Benefit from lower costs
The Cities We're Watching
If you're building AI for real industries in these cities, we want to talk:
Tier 1: Proven Ecosystems
- Austin, TX - Tech talent, manufacturing, energy
- Dallas-Fort Worth, TX - Manufacturing, logistics, construction
- Phoenix, AZ - Manufacturing, aerospace, rapid growth
- Nashville, TN - Healthcare, logistics, emerging tech scene
- Charlotte, NC - Banking, manufacturing, infrastructure
Tier 2: Emerging Hubs
- Kansas City, MO - Agriculture, logistics, growing startup scene
- Oklahoma City, OK - Energy, aerospace, manufacturing
- Tulsa, OK - Energy, manufacturing, incentive programs
- St. Louis, MO - Manufacturing, agriculture, life sciences
Tier 3: Underrated & Growing
- Little Rock, AR - Agriculture, logistics, low costs
- Boise, ID - Agriculture, manufacturing, tech migration
- Des Moines, IA - Agriculture, insurance, financial services
- Omaha, NE - Agriculture, logistics, finance
Note: We're not saying Silicon Valley is bad. We're saying it's overpriced and oversaturated for the problems we care about.
What Success Looks Like
In 10 years, we believe:
The most valuable AI companies won't be consumer apps.
They'll be companies that:
- Automated quality control in manufacturing (saving billions)
- Optimized agricultural yields (feeding more people with less resources)
- Made construction predictable (building infrastructure on time and budget)
- Improved energy grid reliability (keeping lights on)
These companies will have been built in:
- Oklahoma City (not San Francisco)
- Kansas City (not New York)
- Phoenix (not Boston)
- Dallas (not Seattle)
By founders who:
- Grew up in these industries
- Understand real problems
- Built sustainable, profitable companies
- Created thousands of high-paying jobs
That's the future we're building.
Our Call to Founders
If you're building AI for manufacturing, agriculture, construction, energy, or logistics:
We want to hear from you.
Especially if:
- You're based in the heartland
- You've spent years in the industry you're disrupting
- You can articulate ROI in the first conversation
- You're solving a problem coastal VCs won't understand
We don't care if:
- You went to Stanford (or any school)
- You worked at FAANG
- You know the "right people" in Silicon Valley
- Your pitch deck has fancy graphics
We care that:
- The problem is real and expensive
- You understand it deeply
- Customers will pay
- You can execute
The Opportunity Coastal VCs Are Missing
Let me end with a prediction:
In 10 years, the most valuable AI companies will be ones solving problems in industries that Silicon Valley ignored.
Not because these problems are harder (they're not).
Not because the technology is more sophisticated (it's often simpler).
But because:
- The TAM is enormous
- The ROI is obvious
- The competition is minimal
- The advantages are durable
While everyone else chases the next consumer social app, we'll be helping manufacturers save millions, farmers increase yields, and construction firms deliver on time.
The future of AI isn't in San Francisco.
It's in the factories, farms, and construction sites where America actually works.
And we're building it in Oklahoma City, Kansas City, Dallas, Phoenix, Nashville, and everywhere else Silicon Valley forgot.
Building AI for manufacturing, agriculture, construction, or other overlooked industries? Based in the heartland? We'd love to talk. Reach out.
