Better, Not Bigger
David Cohen on Venture, AI, and the Future of Founders
Interview Summary
Guest: David Cohen
Role: CEO & Co‑Founder, Techstars
Location: Boulder, Colorado
Years Running Techstars: ~20 years
Investments: 5,000+ companies
1. What David Is Focused on (2026)
Main Priorities
Helping portfolio founders succeed
Managing billions in venture capital
Supporting global team (hundreds of employees)
Maintaining LP (investor) relationships
Organization Structure
Offices: Colorado, New York, London
Mostly globally distributed team
2. His Return as CEO – What He Changed
Three Recommitments
Help Founders Succeed
Founders are the core customer
Embrace Startup Communities
Long‑term partnerships with ecosystems (e.g., Istanbul)
“Better, Not Bigger”
Focus on quality over volume
Real Actions Taken
Increased capital per startup
Improved founder terms
Reduced annual investments (from ~500 to ~250)
Focused on higher quality companies
3. What Good Mentorship Looks Like
Good Mentors
Ask questions (Socratic style)
Share experience, not orders
Don’t say “I told you so”
Learn together with founders
Bad Mentors
Give rigid instructions
Say “this is always right”
Act like they know everything
How Techstars Filters Mentors
Feedback system
Ratings from founders
All-Star Mentor program
Remove low-quality mentors
4. AI in Techstars
How They Use AI
AI member in investment committee
Trained on historical data (unicorns, failures, etc.)
Helps challenge human decisions
Application summarization & enrichment
Internal search (mentors, network matching)
~50% of code written with AI tools
Philosophy on AI
AI = smarter software
Must use AI to stay competitive
AI should augment, not replace humans
Human relationships cannot be automated
5. Investment Committee & Decision Making
How It Works
Local managing directors bring deals
Committee challenges and improves decisions
Not just “yes/no” voting
Focus on asking better questions
Humans vs AI
AI helps with data
Humans better at nuance:
Storytelling
Relationships
Charisma
Market intuition
Team + AI = Best Decisions
6. What Makes a Startup Fundable (Pre‑Seed)
Key Green Flags
Big idea
Good storyteller
Early momentum
Unique insight or relationships
Strong intrinsic motivation
What Investors Overvalue
Too much focus on traction
What Investors Undervalue
Founder drive and motivation
Founder-market fit
7. Lessons from Uber & Lyft
Uber
Clear big vision
Felt inevitable
Early momentum
Lyft (Missed Opportunity)
Initially didn’t believe in idea
Team later pivoted successfully
Lesson: Focus more on team than idea
8. Founder Evaluation
What Matters
Intrinsic motivation
Market insight
Relationships
Real progress (not just a prototype)
Founder-market fit
Global Evaluation
Same core traits everywhere
Cultural & legal differences matter
Talent is equally distributed
Opportunity is not
9. Venture & Diversification
Power Law Reality
1 in 100–150 becomes a unicorn
One company can return entire portfolio
Advice for Angels
5–10 investments = not enough
Need 100+ investments to diversify
Diversification reduces risk
Spray & Pray?
No.
Invest selectively (~1%)
But at scale and early
10. Accelerators in 2026
Why They Still Matter
Human network
Mentor network
Alumni support
Signal & validation
Built-in customers
Who Should NOT Join
Founders who can easily raise $10–50M
Founders with huge global networks
Very experienced operators
11. Advice for Founders (2026)
Weekly Question
What can I stop doing?
What can I automate?
Reality of AI
Easy to fake building ability
Harder to fake real business understanding
AI bubble likely coming
Overinvestment in AI
12. Advice for Investors
Stop Doing
Over‑analyzing pre-seed startups
Treating them like late-stage companies
Must Do
Background checks on founders
Believe in big market
Trust intuition early
Invest in people you enjoy working with
13. Startup Ecosystems
How to Build One
20-year mindset
Open and inclusive
No “president”
Encourage giving first
Build events and activity
Leverage local strengths (e.g., Berlin deep tech, London fintech)
14. Personal Lessons & Failures
Early Startup Failure
Good idea, bad distribution
Wrong founder-market fit
Tried doing something outside expertise
Lesson
Leverage what you know
Careers built on experience
15. Techstars Mistakes
Market Exits (e.g., Seattle, Chicago)
Viewed as mistakes
Now reopening
Must stay long-term in communities
16. Big Themes from the Interview
Relationships matter most
Team matters more than idea
Diversification is critical in venture
AI augments but doesn’t replace humans
Motivation + big market = strongest signal
Think long-term (20 years)
17. What Keeps Him Excited
Growing global startup communities
Backing more successful companies
Helping founders succeed
No retirement plan — just doing less over time
Core Philosophy Summary
Help founders first
Focus on quality, not scale
Diversify investments
Trust intuition early
Build long-term communities
Use AI smartly, but protect human relationships
TechStars.com 💙