5-6pages 

CSE170
Winter 2020
02/06/2020

CSE 170 Term Paper A: Instructions for creating “Term Paper, Part A”
(due in canvas on 02/13/2020)

First, select one topic from the first four (including today’s) presentations in Winter
2020:
​1. Water Resource Management and Information Technology

Professor Brent Haddad, 01/16/20.

2. What Drives Venture Capital
Paul Vroomen, VC Partner & UCSC PhD
Alumnus, 01/23/20

3. A Customer-Centric approach to Product Development
Tanguy Leborgne, Vice-President, Plantronics
01/30/20

4. Technology Management: A CIO Perspective
Tom Gill, former CIO, Plantronics
02/06/20

For the term-paper topic you have selected write a ​1200-1500 word draft (5-7 double-spaced
pages) ​using the process and structure described below:

Outline: ​Before writing the paper ​create an outline ​for the paper based on the structure for the
term-paper described in the next four bullets. ​You must attach your outline at the end of your
paper. Carefully ​read the next four bullets before creating your outline.

Divide ​your paper into sections and subsections, each with an ​appropriate heading​. Each
paragraph should only contain one idea, which is clearly stated at the beginning of the paragraph,
and then developed in the paragraph. The next three bullets describe how the sections are
organized.

Set-up of a theme for your paper (1-2 paragraphs)​: Develop a creative, thought- provoking
theme ​or ​hypothesis ​for your paper. This theme should be within the context of the (clearly
stated) key product/technology, and/or business/management, and/or commercialization issues
addressed in the speaker’s presentation.

Develop the theme (8-10 paragraphs). ​Develop your theme or hypothesis with suitable
arguments, supported by ​evidence ​and ​examples​. Be sure that the technology issues (e.g., a
company’s technology and product line) and business issues (e.g., marketing and sales strategy)
that you discuss are clearly related to the specific context of the speaker’s presentation (e.g., how
distribution channels are used to market products, etc.). Provide several concrete examples (e.g.,
examples of successful research projects at Microsoft, Seagate’s markets and products, etc.).

End the theme (1-2 paragraphs)​: Re-examine your theme or hypothesis. Does it still stand; does
it need to be refined, or changed? What conclusions can you draw from this seminar presentation
relative to the development, management, and commercialization of technologies and products?
What are the technology, business, and management challenges and opportunities going forward?

Use the following ​sources of information ​in writing your term paper: your own class notes the
speaker presentations on the CSE170 class website: ​https://canvas.ucsc.edu/courses/30042
internet and library research on relevant companies and topics. Hand in the completed term-paper,
Part A, in canvas on Thursday, 02/13/20.

https://canvas.ucsc.edu/courses/30042

What Drives Venture Capital?
A Perspective From Both Sides of the Table
Paul Vroomen
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Agenda
Introduction
How Venture Capital Works
The Impact of Internal Rate of Return Expectations
Case Study: Sandbridge Technologies, Inc.
A $15M Powerpoint Presentation
The Future of Venture Capital
Big Changes Coming….
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Thought Experiment
You are an entrepreneur….
You have worked for 10 to 12 hours per day, often 7 days a week for the past 3 years,
You have risked your entire personal savings,
You have endangered your marriage,
You see your kids mostly just before they fall asleep,
You have questioned your own sanity,
But, you finally have a working prototype….
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The Entrepreneur’s View
What??

They want 10X return on their money?
They want 65% ownership of my company?
They want a controlling vote on the Board of Directors?
They want to be paid their money first before anyone else gets anything, even me, the founder, if we sell the company?

Vulture Capitalists!!
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The Venture Capitalists View
This guy has a good idea , BUT:
He has no CEO track record and has never run a company before
His executive team has significant holes (especially in marketing)
The company’s business plan is way too optimistic, especially given that it has missed critical milestones, twice
They have one significant customer, but that customer is known for collaborating with innovative start-ups and then doing their own thing

I will ensure that our term sheet enables me to protect my capital and is structured so that I can direct the CEO to correct the issues with the company or replace him with someone that can if he does not!
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The Primary Reason
The Entrepreneur, by definition, is an optimist
The Venture Capitalist, by experience, is a pessimist
The partnership of the two can work, sometimes spectacularly,
if each understands what is driving the other.
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How Venture Capital Firms Work
Venture
Capital
Firm
Managed by
General Partners
Limited Partners
All Accredited
Investors
Investor 1
Investor 2
Investor n
$xM
$yM
$zM
Company 1
Company 2
Company k
Venture Fund
Committed Capital
$(x+y+…+z)
Private Equity
Portfolio
$aM
$bM
$uM
Base+(1-c).Surplus
c.Surplus
Liquidity Event
C = “Carry” = 20% – 35%
Mgt. Fee ~2%p.a.
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EBO and VC Historical Performance
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Source: US Venture Capital Index and Selected Benchmark Statistics, June 2014, Cambridge Associates, LLC
IRR: Net cash on cash returns to Limited Partners (after deduction of management fees and carry percentages)
AVERAGE IRR (1999-2009)
Electronics: -0.54%
Financial Services: 14.42%
BioTech: 16.04%
AVERAGE IRR (1999 -2009)
Information Technology: 24.23%
– Internet-Business: 23.2%
– Internet-Commerce: 37.8%

Implications of IRR Expectations
Required capital growth to achieve IRR:
To achieve 33% IRR, a $1 investment needs to grow to $7.40 in 7 years.
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Time from investment: 5 Years 7 Years 10 Years
IRR: 25% 3.0X 4.8X 9.3X
IRR: 33% 4.2X 7.4X 17.3X
IRR: 50% 7.6X 17.1X 57.7X

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Typical VC Fund Performance
Ten year VC fund that returned 3X net to limited partners from a portfolio of 20 companies:

Received proposals from >1,000 companies per year
Agreed to view presentations from 300+ companies per year
Performed Due Diligence on 30 companies per year
Invested in 3 – 4 companies per year (during first 5 years of fund)
10 companies were shut down within 3 years of initial investment
6 companies were acquired within 3-7 years and returned sufficient to recover capital
3 companies were acquired within 3-7 years and returned 1.5X to 5X
1 company returned >38X in 8 years (IPO – Home Run!)
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Sandbridge Technologies, Inc.
Company/Team:
– Fabless semiconductor company based in White Plains, NY
– Founded in 2002 by 2 veteran IBM TJ Watson Research Center engineers
– Team of 55 experienced semiconductor and software engineers

Product:
– Multi-threaded, multi-CPU DSP chip for mobile phone baseband applications
– Automatically adapts to the protocol in which the phone was operating.

Customers:
– Multi-million dollar contractual partnership with one of the world’s top three cell
phone makers.

Intellectual Property:
– 25 granted patents and 20 pending or provisional patents
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By late 2008, Sandbridge had consumed $53M in 3 VC funding rounds and needed a further $15M for the commercialization phase
The $15M Presentation…
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Sandbridge Technologies, Inc.
But things change quickly! By late 2009:
The company’s primary customer and software partner had withdrawn from the partnership – it had quietly built its own chip in parallel with the partnership
The software team was behind schedule in delivering the broadband communications software stack; PC dongle and femtocell markets were well below forecast
The company was scrambling to identify and negotiate partnership agreements with software partners for cellular protocol software development
To generate short term revenue the company was negotiating license agreements with several interested parties.
The Board of Directors had decided that the company should focus on identifying potential M&A transactions

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Sandbridge Technologies, Inc.
Conclusion
Company was sold for an aggregate of $55M in several transactions in late 2009 and early 2010.
All employees were hired by two licensees of the company’s technology
One licensee also acquired the right to deploy the original chip in electronic systems in China and is using it in “home gateway” applications
Management received a “carve out” from the proceeds of the sale
C Round Investors received a 1.6X return on their investment of $35M in 3 years, for an IRR of 16%; Venture Funds in the Electronics industry returned (4.9%) in 2010.
Everyone was happy.
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Big Changes Are Coming to the Private Equity Investment World….
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Security Token Offerings (STO) create a new asset class in private equity:
Crowd Funding (CF)
Why?
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Crowd Funding Growing Rapidly
Source: coinschedule.com/stats
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Crypto-token (ICO + STO) Crowd Funding

2015 2016 2017 2018 (to 9/21)
No. of ICOs/STOs 0 50 371 785
Amount Raised ($B) – 0.098 6.24 20.033

Why ECF/ICO Growth?
REACH:
Online – investors and issuers worldwide can participate
Not limited to accredited investors – access to larger amount of capital
LIQUIDITY:
ECF smart contracts/ICO tokens can be bought & sold at will (for now)
JOBS Act mandates 1 year holding period
TRANSACTION COST
Standardized terms – reduces legal expense
Smart contracts – reduces investor management expense
Transaction fees lower than traditional financing cost
TIME TO MONEY
Financing campaign can be completed in days, even hours.
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Private Equity Model Changing
Private
Equity
Firm
Managed by
General Partners
Limited Partners
All Accredited
Investors
Investor 1
Investor 2
Investor n
$xM
$yM
$zM
Company 1
Company 2
Company k
Venture Fund
Committed Capital
$(x+y+…+z)
Private Equity
Portfolio
$aM
$bM
$uM
Base+(1-c).Surplus
c.Surplus
Liquidity Event
C = “Carry” = 20% – 35%
Mgt. Fee ~2%p.a.
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Dividends
ICO/ECF
Web
Platform
Investors
Accredited and
Non Accredited
Investors
Investor
Group 1
Investor
Group 2
Investor
Group n
$xM
$yM
$zM
Issuer 1
Issuer 2
Issuer k
Online Intermediary
CF
Portfolio
$xM – TF
$yM – TF
$zM – TF
Base + Surplus
c.Surplus
Liquidity Event
c = “Intermediary Commission %”
Intelligent
Decision
Support
System
Transaction Fee
The New Private Equity Investment Model
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The Problem
How to enable a large, diverse set of non-expert investors to identify investment opportunities that are likely to succeed, from a large, diverse set of privately held companies of different size, stage and quality?
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A Solution
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Create an Intelligent Decision Support System (IDSS) drawing on:
Private Equity Investment Practice
Finance Theory
Statistical Learning Algorithms & Processes
Decision Support Systems Engineering

Deploy the IDSS as a tool to support investors and issuers on a web based Crowd Funding Platform (CFP)

IDSS Overview
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Can the enterprise yield an acceptable rate of return?
Does the enterprise have the team, market and products to credibly achieve the rate of return?
Revise/Reject
Revise/Reject
Accept
Yes
No
Yes
No
Stage 1
Evaluate
Stage 2
Verify

What is an “Acceptable Rate of Return”?
Apply:
Finance Theories
Modern portfolio theory
The theory of efficient markets
Statistics:
The central limit theorem
Historical IRR data for large datasets of: Equity Funds, VC Funds (Cambridge Associates), Angel Investments (AIPP)
To Determine:
The optimum risk-reward “frontier” for the Private Equity market
The efficient frontier
The target IRR for an efficient CF portfolio and consequently, individual CFF investments
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Efficient Frontier: Equity Market
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At 10% higher risk for ECF, the expected IRR target for an efficient portfolio of ECF assets is >= 28% with 99% confidence

CF Investment Risk
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Angel Investments
are Risky….
1,137 AIPP exited angel investments:
IRR = -100%: 46.7%
-100% target investment IRR = 58%
Valuation: 12x rolling average of future 4 quarter cash flow
Demonstration: Stage 1

IDSS Overview
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Can the enterprise yield an acceptable rate of return?
Does the enterprise have the team, market and products to credibly achieve the rate of return?
Revise/Reject
Revise/Reject
Accept
Yes
No
Yes
No
Stage 1
Evaluate
Stage 2
Verify

Classifier
Database
IDSS Functional overview
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Issuer Business Plan
Calculate IRR
Industry
Database
Issuer Database:
4 +year revenue, cash flow forecast
20 attributes
Adjust Plan?
NO
NO
YES
Modified Business Plan
Classifier Attributes
Classify
Optimal Classifier
NO, Yellow
Modified Attributes
YES
Issuer
Database
YES
Industry Metrics
Adjust
Attributes?
Stage 1
Evaluate
Stage 2
Verify
Issuer
Database
Industry Database:
Public company valuation data
Industry TAM, ratios
Classifier Database:
Training and test set
Reject
Review
Accept
Feasible?
Reject
Green
Red,
Yellow
NO, Red

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Statistical Learning Process
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Attribute Selection
Create preliminary set based on domain knowledge (15 attributes)
Interview domain experts (3 VC General Partners, 3 CFOs (1 large, 2 small companies) and add if not identified in 1. (18 attributes)
Extract attributes from the research literature and add if not identified in 1, 2. (31 attributes)
Identify research papers from peer reviewed journals containing statistically significant correlation between attributes and success
Rank research papers by citation rate (citations/year)
Starting at highest ranked article extract attributes if not already identified in 1,2
Repeat until no new attributes are found
Analyze attributes to eliminate redundancy or correlation (21 attributes)
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Attribute set
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Attribute Quantification
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Performance
Error Rate
Number of Attributes
Sample Size
Room for performance improvement with more data
At ~ 60 instances, synthetic model error rate approaches Bayes error within 5% (current dataset has 17 balanced instances)
Learning curves for kNN algorithm
(synthetic model, uncorrelated attributes)
Accuracy (true positives plus false yellows): 64%
Vroomen Capital, Inc. Proprietary & Confidential
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(17,20)
Vroomen Capital, Inc. Proprietary & Confidential

Key Outcomes
Accuracy (true positives plus false yellows): 64%
Accuracy will improve with a larger training set – learning curve
94.5% confidence that IDSS performs better than random selection
Stage 1: 99% confidence that IRR target for CF assets is >= 28% at 10% higher risk
Stage 2: >98% confidence kNN (k=1) performs best for current data set
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Conclusion
The private equity industry is on the verge of facing the same disruption that the Internet has brought to the music industry, the airline industry, the hotel industry, the car rental industry,……
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Relate to Hypothesis; describe how this helps both expert and non-expert decisions – removing emotion. Establish the need for the KE system.
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Appendix
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Initial Coin Offerings: Background
Issuer offers a pre-determined quantity of its own crypto-currency “tokens” (mostly on Ethereum) – these are NOT share certificates
Investors offer to buy a fixed number of tokens at a fixed price in a Dutch auction
Transaction is recorded on a blockchain (again, mostly the Ethereum blockchain) – significantly reduces transaction cost
The tokens are liquid and so can be bought and sold directly between sellers and buyers
So far ICOs not regulated by JOBS Act or SEC in the US (but see: https
://www.nytimes.com/2017/07/25/business/sec-issues-warning-on-initial-coin-
offerings.html)
ICOs experiencing rapid growth: >$1.2B raised so far in 2017 (>10X entire 2016)
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JOBS Act, Title III: Background
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Passed in both Houses of Congress in early 2012 with large bipartisan and private equity industry support
Signed into law by President Obama on April 5, 2012
Recorded in Federal Register on January 30, 2016
Enables privately held companies to publicly solicit up to $1M per year in equity financing by issuing securities to an unlimited number of investors.
Opens up private equity investment to non-accredited investors
Accredited investor: Net worth >$1M; Annual Income >$300K
Only 2.6% of the US population qualify as accredited investors
Caps the amounts that non-accredited investors can invest
$5,000 per year if Net Worth <$100K; Annual Income <$100K $10,000 per year if Net Worth >$100K per year; Annual Income >$100K per year
Requires ECF transactions to be conducted by a web based Intermediary that ensures transactions conform to the requirements of the Act and the SEC regulations governing such transactions.

Biography – Paul Vroomen
MSEE, MBA, PhD (Technology & Information Management, UCSC)
Chip Designer – first 7 years out of college
Business Unit VP/GM – 3 Public Companies
Zilog, Inc. – Microprocessors; VLSI Technology, Inc. – Satellite TV.; Oak Technology, Inc. – HDTV/DVD.
President/CEO – 3 Venture funded Start-ups
SandCraft, Inc. – Datacom; Connex Inc. – HDTV; Sandbridge Inc. – Mobile Phone
Executive-In-Residence
Tallwood Venture Capital, LLP
Raised $73M in Venture Capital funding
Co-Board and Board member at 7 companies
Currently working on next start-up – Fintech (ICO/Equity Crowdfunding Platform); Member of Sand Hill Angels
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Relate to Hypothesis; describe how this helps both expert and non-expert decisions – removing emotion. Establish the need for the KE system.
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Cumulative Cash Generated/Consumed
-15
-10
-5
0
5
10
15
012345678910
Years from Initial investment
$Million
Entrepreneur’s ViewVenture Capitalist’s View

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

IRR
<= 0% 0% on Pareto (Calc)

0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
I
R
R
< = 0 % 0 % < I R R < = 2 5 % 2 5 % < I R R < = 5 0 % 5 0 % < I R R < = 7 5 % 7 5 % < I R R < = 1 0 0 % 1 0 0 % < I R R < = 1 2 5 % 1 2 5 % < I R R < = 1 5 0 % 1 7 5 % < I R R < = 2 0 0 % 2 0 0 % < I R R < = 2 2 5 % 2 2 5 % < I R R < = 2 5 0 % 2 5 0 % < I R R < = 2 7 5 % 2 7 5 % < I R R < = 3 0 0 % ( n o t e 1 ) 3 0 0 % < I R R < = 3 2 5 % 3 2 5 % < I R R < = 3 5 0 % 3 5 0 % < I R R < = 3 7 5 % 3 7 5 % < I R R < = 4 0 0 % % o f I n v e s t m e n t s Distribuon of Returns: AIPP Raw Data Distribuon Pareto (Calc) -60% -40% -20% 0% 20% 40% 60% 80% 100% 10 11 12 13 14 15 16 17 18 19 20 IR R (% ) Quarters from First Investment 10% Equity 15% Equity 20% Equity 25% Equity -60% -40% -20% 0% 20% 40% 60% 80% 100% 10 11 12 13 14 15 16 17 18 19 20 I R R ( % ) Quarters from First Investment 10% Equity 15% Equity 20% Equity 25% Equity -60% -40% -20% 0% 20% 40% 60% 80% 100% 10 11 12 13 14 15 16 17 18 19 20 IR R (% ) Quarters from First Investment Baseline 90% of Baseline QoQ Revenue Growth -60% -40% -20% 0% 20% 40% 60% 80% 100% 10 11 12 13 14 15 16 17 18 19 20 I R R ( % ) Quarters from First Investment Baseline 90% of Baseline QoQ Revenue Growth -10 -5 0 5 10 15 20 25 30 0 2 4 6 8 10 12 14 16 18 20 $M Quarters Quarterly Revenue Cumulative Investment Cumulative Cash Flow 90% Baseline Revenue -10 -5 0 5 10 15 20 25 30 0 2 4 6 8 10 12 14 16 18 20 $ M Quarters Quarterly Revenue Cumulative Investment Cumulative Cash Flow 90% Baseline Revenue