Insights from dinner with Brian and Jason

Engineering compensation

  • 1% equity max for first 2 engineers.
    • 1% equity and low salary
    • 0.5% equity and medium salary
    • 0.25% equity and full salary
  • 0.5% equity max for second round of engineers
    • 0.5% equity and low salary
    • 0.25% equity and medium salary
    • 0.1% equity and full salary

Advisers compensation

  • Typically 0.5% to 0.25%

Fund raising

  • can raise money from angels before there is even a product and distribution
  • Jason Calacanis invest in people who he feels are winners
  • Frame the opportunity and let investors fill in the details with their own models

Investors

  • Really open doors to help with follow on funding rounds
  • Brad slowly stepping away from USV to pursue his investment thesis in the crypto-currency space

Building and exiting

  • seek out other operators within the space seeking to bolt on to their business model
  • hire people to fill the positions and eventually replace yourself
  • practice discipline do not step across boundaries and get in the way of specialist. Focus on framing the problem and let the specialist define the solution.
  • always focus your time on the highest leverage activity
  • company making 20K to pay their staff sufficient wage to continue working on the project
  • eventually find a new home of the team by selling off the company

Related references

  • https://medium.com/@SiliconValleyGC/how-much-is-my-startup-advisor-worth-d97d825a6742
  • JoinMassive.com
  • Begin.com

Summary of readings and conversations for the week

Trends observed

  • Worsening income inequality
    • driven by increased globalization and automation with failure in re-education as the primary cause
    • Continued low worldwide interest rates as central banks the world over struggles to prop up inflation rate at 2%
  • Rise in protectionism around the world in response to income inequality
    • Slowing trade volumes around the world
  • Demand saturation at the upper income segments
    • Slowing demand for housing in South Bay
    • Too much money chasing after too little deals

Related sources

Book summary AI super-powers, China, silicon valley and the new world order by Kai Fu Lee

The difference waves of AI

  • Internet AI – Facebook, Netflix, Google search
  • Business AI – Palantir
  • Perception AI – Tesla cars
  • Autonomous AI – Tesla self driving, Google self driving

Key locations

  • Silicon Valley
  • Zhong Guan Cun – Beijing

State of the Union

  • We are in the stage of implementation/application as opposed to RnD
    • having access to more data is more important than have expertise to do more RnD
    • having solid AI engineers is more important than AI researchers
  • We are still far from general AI
  • Key ingredients
    • data
    • computing
    • maybe work of strong AI algorithms engineers

Key differences between eco-systems

  • Silicon Valley businesses are mission and core values driven while Chinese businesses are pragmatically focused on profitability.
  • Silicon Valley businesses stay in bits and binaries offloading the brick and mortar to external vendors vendors while Chinese businesses extend their business model into the brick and mortar (online to offline)
  • Silicon valley prefers one size fit all strategy, Chinese businesses utilized localized solutions often investing/acquiring in local startups
  • Americans treat search engines like Yellow Pages (come and leave fast) while Chinese treat search engines like shopping mall (come to linger around long)
  • Silicon Valley is adversed to copying preferring to be unique Chinese business copy the heck out of each other

Chinese Advantage

  • Abundant data – quality and quantity aided by their online to offline initiatives
  • hungry entrepreneurs
  • AI scientist
  • AI friendly policy environment – strong emphasis by Chinese government
  • Hardware manufacturing know how – Shen Zhen
    • unparalleled supply chain flexibility – XiaoMi

Silicon Valley Advantage

  • Microchip manufacturing know-how

Trends within the Chinese eco-system

  • Darwinian eco-system has lead to extreme levels of competition
  • Chinese companies have already moved past the stage of clone Silicon Valley business models
  • Businesses innovate to build a defensive moat around themselves. Local businesses have advantage, with no timezone differences to deal with, decision making is relatively faster.
  • Online to offline
    • an essential ingredient to building strategic moats
    • caused the decline of cash use
  • Chinese government information systems will be able to leap frog US government information systems

Policy approaches

  • Google – impeccable safety
  • Tesla / China – trial by fire
  • key to winning the Autonomous AI race
    • is the bottleneck technology (Silicon Valley) or policy (China)?

Key concerns

  • having cheap labor is no longer going to be a source of advantage in a world heavily powered by automaton.  Developing countries hoping to employ this well tested strategy to progress will not be able to do so anymore
  • Estimated 60% potential job loss worldwide barring policy interventions
  • Job loss probability assessment
    • physical labor
      • environment – unstructured versus structure
      • tasks nature – level of dexterity versus high dexterity
    • cognitive labor
      • social – high versus low
      • cognitive – optimization based versus creativity/strategy based
  • AI replacement approach
    • single tasks approach
    • ground up rethink re-imagination
  • A population of irrelevant (no longer employable) as opposed to unemployed

Tackling Key concerns

  • Silicon valley – reduce, retrain and redistribute
  • Kai Fu Lee – stipends for care, service, education

New promise

  • Humans freed up from repetitive tasks can now focus on becoming more human oriented

Related readings

  • Disruptor, Zhou
  • www.Arvix.org – an online repository of scientific papers
  • Folding Beijing – Hao JingFang

Hedge fund sector challenges and flock to alternative data seeking alpha

When taking into account different signals about a sector you get a very different perspective altogether.

This signals says that hedge fund managers are all flocking to alternative data to seek alpha

https://www.businessinsider.com/hedge-fund-data-buyers-at-alternative-data-conference-battlefin-2019-6

Taking into account the signal from these two articles, we can infer that the above trend has been mainly driven by the deterioration of the sector over the past few years.

https://www.highlandfunds.com/business-insider-hedge-fund-apocalypse-coming/

https://www.bloomberg.com/news/articles/2019-05-31/quant-hedge-fund-amplitude-returning-client-cash-after-outflows

When further taking into account this signal, we see a subset of the survivors are struggling to transit

https://www.businessinsider.com/hedge-funds-open-source-platforms-2019-5

Insights from dinner with Yuan

Conversations with Wang Ji Lei (Google)

China’s 996 practice has allowed China to close the 30 years technology gap between China and the US within the past 5 years. Some segments like cashless commerce have already surpassed US. The norm is some labor regulations are considered formalities and adherence is not necessary.

China’s distribution path starts from Beijing, 1st tier cities, 2nd tier cities, 3rd tier cities and finally remote villages. While double digits growth has been the norm over the past five years, market saturation has been achieved within the Chinese digital space and most apps are having problems growing.

Baidu’s Ads revenue model via search is increasingly being disrupted by Tic Toc. They have lost 50% ofvtheir search revenue last year. Similar trend is being observed with Google’s Ad revenue model which is bring increasingly disrupted by Amazon in the below USD75 consumer goods segment, Facebook between the USD75 and USD2000 consumer goods segment.

Tic Toc and WeChat dominate the bulk of the mobile usage bandwidth in China. Tic Toc with string positions within the short video entertainment segment while WeChat in the social conversations segment. Both companies face significant challenges encroaching into the other’s territory.

Tic Toc is the only Chinese mobile entertainment app that has made significant headway out of China. They have a well defined playbook which specialized tiger teams follow to gain traction in new territories. Maintenance is transferred to local team once penetration threshold is achieved. Due to fickle nature of users, retention remains the number 1 challenge. Position can be easily displaced by new entrants. Thebunderlyimg thesis is that city dwellers have lots of fragmented time spread across a day. Each video is restricted to 45secs to reduce anxiety that user has to stop watching between engagements. This also helps induce the habit of chipping in between engagements.

Typical engineer’s salary in Beijing is USD50,000 per year while a house cost around USD2million. While typical engineering salary in Silicon Valley is at least USD100,000 per year while house cost at least USD1 million. Wealth is mainly retained at the upper levels in China. Given the ratio of salary to house price as well as the comparative working hours, there is significant pull of talents to Silicon Valley.

Average green card waiting time for EB1 is now five since it’s been expended to include executives.

Major housing development is occurring in South Bay given huge influx of population and hiring by google (50,000 people) and other giants. Heavy recruitment is happening as Google continues to build out its cloud team.

Google cloud’s market share continues to lag with Amazon leading, Microsoft Azure in Second and AliCloud 3rd. Google cloud organization is facing an inflection point as upper management faces difficulties with zeroing in on a clear strategy. The symptoms are the dilemma between engineering culture (Google Default) versus customer culture (Amazon) and SMB versu Enterprise. Google’s traditional engineering culture has the effect of causing company to drift towards heavy engineering solutions which might be an overkill for SMB needs coupled with a lot of wastage which no one needs. This opens up a gap for customer focused companies.

Tesla autonomy

https://www.youtube.com/watch?v=tbgtGQIygZQ

The Mission

Building and optimizing the entire infrastructure (hardware and software) from ground up with autonomous self driving as the mission

Mission and decision making

Design decisions are made with trade off between functionality and cost to achieve the mission while keeping cost in control

  • Lidar is not useful when cameras are available
  • driving cars with HD mapping makes the entire operation brittle since actual road conditions can change

Operating structure

  • Data Team
  • Hardware Team
  • Software Team

The Data model

  • Cars on roads are constantly collecting new data
  • New data is being utilized to train and improve neural network model
  • New improved model is constantly being deployed back to the car to improve self driving
  • Real world data provides visibility into long tail scenarios that simulated data cannot. Simulating long tail scenario is an intractable problem
  • Balancing between data model and software
    • Neural network is suitable for problems that are hard to solve by defining functions / heuristics
    • Simple heuristics are better handled through coding in software

Future revenue model

Robo-taxi that will disrupt the ride-sharing space.

  • Consumer car – USD0.60 / mile
  • Ride sharing – USD 2-3 / mile
  • Telsa Network – USD 0.18 / mile

Main challenges:

  • Legal – need more data and processing time to get approved
  • Battery capacity
  • Social norms around robo taxi

Insights on managing Big Data from meet up with Dean and Ved

From Dean (Reputation.com)

  • Enterprise sales as an acquisition strategy is feasible because revenue per account ranges in the USD millions – e.g. 70 million USD
  • Once an auto company like Ford or GM signs up, they will start bringing their dealerships in
  • The infrastructure needs to be able to support the size of the data which can be up to billions of rows
  • Scaling of infrastructure to handle load ever increasing data becomes critical for the continued growth of the data company
  • Data Product will appear broken when user attempts generate report while the data is still being written into the database
  • The key challenge is that different solution is suitable for different operation
  • Types of data operation include
    • writing into the database
    • reading from the database
    • map reduce to generate custom view for data in the database to support different types of reporting for different departments in the client companies.
  • Successful data companies will create different layers of data management solutions to cater to the different data needs
    • MongoDB
      • good for storing relatively unstructured data
      • querying is slow
      • writing is slow
      • good for performing map reduce
    • Elastic Search
      • good for custom querying for data
  • Dev ops become a very important role
    • migration of data between different systems can extend up to weeks before completion
    • bad map-reduce query in codes while start causing bottlenecks in reading and writing causing the data product to fail
    • dev ops familiar with infrastructure might on occasion have to flush out all queries to reset
    • The key challenge is the inability to find bandwidth for flushing out bad queries within the codebase
  • Mistakes in hindsight
    • In hindsight lumping all the data from different companies into the same index on MongoDB does not scale very well
    • Might make better sense to create separate database clusters for different clients
  • Day to day operations
    • Hired a very large 100 strong Web Scraping company in India to make sure web-scrapers for customer reviews are constantly up
    • Clients occasionally will provide data which internal engineer (Austin) will need to look through before importing into relevant database
  • Need to increase revenue volume to gear up for IPO
  • The Catholic church has 10 times more money than Apple and owns a lot of health care companies.

From Dan (Dharma.AI), the classmate of Ved

  • Currently has 15 customers for their company
  • Customers prefer using their solution versus open source software because they can scale the volume of data to be digested and solution comes with SLA
  • Company provides web, mobile and table solutions which client companies’ staff can use in the field to collect demographic and research data in developing countries
  • The key challenge is balancing between building features for the platform and building features specific verticals:
    • Fields differ between industry: fields in the survey document for healthcare company will be very different for fields in the survey document for an auto company
    • Fields differ between across company size: survey format for one company might be different as compared to another in the same industry but of different size
    • Interface required is differs between companies
  • Original CEO has been forced to leave the company, new CEO was hired by PE firm to increase revenue volume to gear up for IPO

From Ved

  • As number of layers increase in the hierarchy, it becomes increasingly challenging for management to keep up to date on the actual situation in the market
  • New entrant of large establish competitor might sometime serve as an opportunity to ride the wave
  • when Google decided to repackage Google Docs for Education, it was a perfect opportunity for Edmodo to more tightly integrate into Google and ride that trend rather than being left behind
  • Failure to ride the wave will result in significant loss of market shares
  • It takes a lot of discipline to decide on just focusing on the core use case and constantly double down on it.
  • Knowing that a critical problem, which could potentially kill the company, exists versus successfully convincing everyone in the company that it is important to address it are two different things.

Book Summary: Lost and Founder

Radical Candor/Transparency

It is hard but it works – needs to be tampered with empathy

On being product focused

  • Consulting is limited by time and people – not scalable
  • Effective Product-focused business
    • reach
    • scalability
  • Start with a product informed by your consulting – real life problems others face

Impediment to shifting focus

  • Too comfortable
  • not enough time
  • difficulty finding the right customers for the product

On being a founder

  • Great founders enable a vision
  • forget about being hands on most of the time
  • job scope changes every six months – for any road block encountered focus on sufficing the requirements instead of perfecting it
  • you rarely get to do what you love to do
  • be cognizant on when to lead and when not to – have the specialist do the job
  • Cultivate self awareness in strength and weakness – structure company to work around them
  • Attribute of founder is instilled with near-permanence in the organization while those of supporting team fluctuates
  • the hardest parts of the business is less a reflection about the business than about the person experiencing them
  • Build expertise before building network, build network before building company
  • Focus on and reward the behavior, let the outcome take care of itself

On Values

  • Authentic values force hard decisions – held to be more important than money
  • have real costs: Impede certain behavior and strategy
  • Values are discovered instead of set
  • Used as a yard stick for recruiting new members to the cause. Helps get pass the competence versus cultural fit dilemma

On recruiting

  • CTO should be those that should be oriented towards education instead of shielding you from the nitty gritty details (black box)
  • Use your value system as a yard stick
  • Hiring for diversity will make the mental model of the organization more holistic
  • Great managers / coaches might not be great individual contributors

On markets and pivots

  • Pivots Are expensive don’t make it a habit – only resort to this tactic when the original hypothesis is not longer valid
  • focus on the market and then find a field ignored by others because it appears unsexy. From there craft a solution
  • Err on the side of execution

On investors

  • Need to take money for the right reason
  • Investors interest will tend to get out of alignment overtime (return multiples and investment horizons)
  • 80 percent of returns are by 20 percent of investments
  • They need at least a 10X to break even in a position for all the other losing positions they took
  • They don’t bring much value to the table
  • follow up with CEOs they invested in to understand how they react in a shit storm
  • Can help provide information on salary ranges

Choosing a market

  • If you can keep your ego in check you can chase after smaller markets and don’t need VC money
  • Great ideas are born of mediocre ideas that become better by
    • time spent iterating
    • humility learning
    • surviving
  • look for searches that indicate problems
    • Google Adwords
    • Moz’s keyword Explorer

Knowing your customers

Defining your user base

  • Call 3 different types of users
  • Find out why they subscribed and stayed
  • Craft messaging toward this group of people

Discounts are a doubled edged sword – while they might attract signups, these folks tend to have a higher churn rate

Schedule regular interactions with your user so that you can understand their habits. It helps you get to an empathetic position with them.

On Products

  • Feature set needs to be coherent enough to be able to deliver value
  • Early adopters
    • have very different expectation as compared to early majority –
    • hence more forgiving
    • ok accepting MVP
  • Retention triumphs acquisition any day

Marketing

  • Optimize for acquisition loops that reinforces the UVP instead of linear acquisition channels

Focused Execution

  • Practice the discipline of focus.
  • Important to Focus and not waver around unnecessarily. Its a waste of resources
  • A very focused and simplified product offering will help users to more easily understand and adopt it
  • Helps keep teams lean as a by-product
    • ROA improves dramatically
    • helps avoid future layoffs
  • Focus on what will not change in the next 10 years

Related references

  • Lean Startup, Eric Ries
  • Sprint, Jake Knapp
  • Venture Deals, Brad Feld

Key take aways from Exit Strategies for Entrepreneurs and Angel Investors

Early Exits
Early Exits

Key advice for Startups and emerging companies

  • Start small
  • Stay lean
  • Raise only the funding you really need and grow judiciously.
  • Alignment from all parties on exit strategy is extremely important
  • Best time to sell a company is when the future has never looked brighter

On VCs

  • Interest of VCs might not be aligned with interest of founders and angel investors
  • VCs need to satisfy the needs of their LPs
    • Need their successful companies to generate a minimum of 10-30X return for their fund to perform respectably, taking into account overall failure rates
    • They thus need to wait longer to exit and work their investments harder.
    • They are ok to accelerate the growth of their investments with their capital or blow it up quick for a capital right off. The latter helps minimize management overheads.
    • They will block a sale if the return multiples do not meet their expectation
  • VC return multiples of term sheet valuation
    • Series A – 10X return
    • Series B – 4-7X returns
    • SEries C – 2-4X returns
  • VC funds have been getting bigger overtime. The need to deploy their capital forces them to seek for opportunities where likelihoods are slim.
  • Companies with VC money tend to exit at year 16 on the average

On Angels

  • Invest much less money than VCs
    • USD10,000 to USD250,000
  • Happy to exit in a few years with a 3-5X return
  • In the 50s and 60s
  • prior successful entrepreneurs or senior executives
  • allocate around 5-10% for angel investing
  • has experience and inclination to be great mentors and valuable directors
  • Companies with angel only money tend to exit at year 4 on the average

Drivers of acquisition

  • trend has been dramatic shift towards earlier exits
  • huge amounts of cash on balance sheets of large corporation
  • growth in Private equity and buy out funds

Insights on Growth

  • The first USD10 to USD20 million valuation are the easiest and less challenge on the skills of the CEO
    • It is easy for young companies to maintain year on year compound annual growth rates of 100% or even 200%
  • Knowledge of how hard it is to be a CEO and lots of money in the bank is usually a huge deterrent for serial entrepreneurship.
  • VCs replace CEOs of 75% of companies within 18 months of their initial investments
    • Founder’s shares get trapped in an illiquid private company for another 5-10 years
  • Use a 2 year time horizon
    • year 1 develop technology
    • year 2 develop distribution

On valuation

  • A lot of factors that have the biggest impact on a company’s short term value fluctuation will be out of management’s control
  • The factors will also be unforeseen
  • General valuation multiples
    • SAAS companies are typically valued at 3-4 RR
    • Service body shops 0.5 of per staff revenue or PE ratio of 3-4

On sales process

  • Typically 4-5 months
  • CEOs must focus on the business to ensure metrics are at their best during the sales to maximize valuation
    • can add up to 10-20% more valuation
  • Until the very last phase of the sales, it is best to delegate the sales process to a professional
    • Business broker or M&A advisor – use them as the bad guy
      • big firms shoot for exit above USD100million
        • 2-3% of final value
      • boutique firms shoot for USD20-70 million
        • 4-6% of final exit value

Related references

  • Evolution and revolution as organizations grow, Larry Greiner Harvard Business School
  • Raising money: The canadian guide to successful business financing, Douglas Gray and Brian Nattrass
  • High Anxiety or Great Expectations, Bart Schachter and George Hoyem, Venture Capital Journal

Insights from party at Ilya’s place

  • The successful investor is not very different from an investigative journalist or a crime detective
  • Most useful data are public.
  • The only difference between the successful investor and a mediocre one is the amount of work he is willing to dedicate towards validating all the key assumptions.
  • Investment relationships team of all public companies are very willing and helpful with providing information.
  • More qualitative data can be obtained by calling up customers or ex-employees of competitors
  • Once you are able to reconstruct a company’s business model, you will be able to predict generally whether a company will make or miss earnings
  • Legacy technology companies tend to have a longer half life than expected. The key is to determine how much longer the half life is and if there are legal protections that will extend it.
  • Beyond the core functionality, it is important to go into the realms of human psychology (adrenaline and dopamine) to figure out the defensible strategy
  • Smaller funds are structured to incentivize playing to win (1%-2% carry) while bigger funds are structured to incentivize playing not to lose (expecting only returns matching LIBOR rate of 2.5%) . The difference in mind set results in very different strategies.

Related References