Your SlideShare is downloading. ×
  • Like
CVPR2010: Learnings from founding a computer vision startup: Chapter 5: Funding: who and how to ask for money
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×

Now you can save presentations on your phone or tablet

Available for both IPhone and Android

Text the download link to your phone

Standard text messaging rates apply

CVPR2010: Learnings from founding a computer vision startup: Chapter 5: Funding: who and how to ask for money

  • 1,279 views
Published

 

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
No Downloads

Views

Total Views
1,279
On SlideShare
0
From Embeds
0
Number of Embeds
1

Actions

Shares
Downloads
32
Comments
0
Likes
1

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 1. 5. Funding
  • 2. Funding alternatives Angel vs VC Pitching investors How much?Flickr: mmoorr
  • 3. Learnings from founding a Computer Vision Startuphttp://en.wikipedia.org/wiki/Seed_money
  • 4. Learnings from founding a Computer Vision Startup FFF (friends, family & fools) A small investment can take a small fresh team far E.g. early stage Web product <$10k/year for computing and tools Bootstrapping: Consulting work on the side (that hopefully also builds your product) Hire young, cheap, and hungry Minimum viable product (don’t build more than an absolute minimum)
  • 5. Learnings from founding a Computer Vision Startup Sanity check! Do you really need to raise external funding? can you bootstrap of own investment, consultancy work, part time... What is the money for? after “how much?”, this is the next question investors will ask
  • 6. External funding alternatives
  • 7. Learnings from founding a Computer Vision Startup Grants & Research Funding Soft loans Regional & National grants EU research funding (FP7 etc) Beware of business plan competitions! “No one wins in business plan competitions“ -Steve Blank Flickr:denniswong
  • 8. Learnings from founding a Computer Vision Startup Angels What is a business angel? someone with experience, energy and cash invests own money typical range 25-50k Angel networks groups of angels that co-invest needed since each angel has limited funds/investment makes passive investments possible Flickr: onkel_wart
  • 9. Learnings from founding a Computer Vision Startup Venture Capital Fixed life funds (~8-10 years) Money comes from institutional investors pensions, universities, foundations, individuals Types regional vs. (inter)national, early vs. late, branch specific Flickr: markcoggins
  • 10. Learnings from founding a Computer Vision StartupFlickr: courtneybolton How VCs work
  • 11. Learnings from founding a Computer Vision Startup Organization partners & associates End-goal is an EXIT Trade sale IPO The VC business model (2/20) Expectations management fees - partners get ~2% of invested capital carried interest - partners’ management company get ~20% of profits some form of control most investments fail the few that succeed should give high multiples >>> High risk is a part if the game but risk level depends on timing, spread of portfolio and financial climate. Flickr: sophistechate
  • 12. Learnings from founding a Computer Vision Startup Pitching investors Checklist: Storytelling + elevator pitch + slide deck (doubles as presentation and business plan) Problem & solution + exec summary (optional, very short!) (technology is secondary) - forget about NDAs - detailed plan comes later Show a market Flickr: akrobat77
  • 13. Learnings from founding a Computer Vision Startup Pitching tips Pitching angels Pitching VCs chemistry: it’s about relationship and trust prepare: look at their portfolio & profiles often a group is needed (don’t worry, they find a “champion” on the inside bring in “friends”) partners decide together - convince them all! if decisions don’t come quickly, it is a “no”, - pitch at partner meeting will be short, be move on. flexible Be careful with revenue projections! (skip unless really good data exists) If you send material or a deck, make it .pdf with recipients name and date. If multiple investors, work the “lead” investor into agreement, then offer others same terms.
  • 14. Learnings from founding a Computer Vision Startup David S. Rose on pitching to VCs http://www.ted.com/talks/david_s_rose_on_pitching_to_vcs.html
  • 15. Learnings from founding a Computer Vision Startup Angels or VC? if possible: look for integrity, Angels VC “you can’t fire your investors” less money more money quick exit vs. big exit for fun professionally quick slow beware that VC irreversibly sets company on path to EXIT no control lots of control seek investors who agree with your definition of success
  • 16. Learnings from founding a Computer Vision Startup Negotiation Know your BATNA* “to get good terms you need multiple offers, to get multiple offers you need to tell a good story to several investors at the same time” There is no formula for valuation, it is decided on comfort levels. Actual terms are more important than valuation and dilution. * Best Alternative To a Negotiated Agreement
  • 17. Learnings from founding a Computer Vision Startup How much to raise? “It depends”, 3 different views: 1) As little as possible 2) As much as you can (against reasonable equity, of course) 3) Whatever you can get and get on with building stuff 1) Sound in principle but risks spending time in “constant fundraising mode” which takes lots of energy from building stuff 2) “you are always going to need more than you think” 3) Fundraising is a distraction, time spent is distraction and reduces velocity Tip: If possible raise all at once, avoid tranche investments
  • 18. What is special about Vision? In terms of raising capital
  • 19. Learnings from founding a Computer Vision Startup What’s special about Vision? Research grants can be a serious option Visual demos can help pitching Currently lots of traction in consumer markets (people and businesses asking for vision) but few strong success (exit) cases. Vision is technology heavy - you will encounter the “here’s the solution where’s the problem” comments
  • 20. How we did it
  • 21. Learnings from founding a Computer Vision Startup Polar Rose: How we did it Grants, Friends & Seed $300k over 2004-2006 Series A $5.4M in July 2006 from Nordic Venture Partners -> smaller follow up in 2009-2010 from Nordic Venture Partners
  • 22. Learnings from founding a Computer Vision Startup Kooaba: How we did it Grants, work besides PhD ~ CHF 100k over 2006-2007 Loans seed round (bank + FFF) & Grants ~ CHF 700k seed (convertible loans), CHF 300k Grants (2008) Pre series A round & Grants ~ CHF 1.5 Million total (2009) Series A now
  • 23. Learnings from founding a Computer Vision Startup Resources Twitter: @venturehacks (Startup advice on VC and entrepreneurship) @msuster (Mark Suster, entrepreneur gone VC, also at http://bothsid.es) @fdestin (Fred Destin, VC at Atlas Ventures) Seed funding: Ycombinator (North America) http://ycombinator.com/ Seedcamp (Europe) http://www.seedcamp.com/ Angels: http://venturehacks.com/angellist (list of angels on Twitter) Bootstrapping: http://blog.guykawasaki.com/2006/01/the_art_of_boot.html (Guy Kawasaki on bootstrapping)