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

Insights from Josh

Product

  • Starts with search
  • once query and corresponding is good visualize using graph which users can easily zoom in and out

Key APIs

Key data pipeline technology

  • Apache Spark
  • Apache Kafka
  • Elastic Search
  • GPL visualization

Acquisition strategy

  • Enterprise sales
  • Hire users from customers to act as salesman to other users who are in their network

Key challenge

  • Business is stuck at USD30 million ceiling per year right now
  • Trying to build more granular apps to target verticals to generate more profits

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

How banks and the Federal reserve / central bank works

On US banks

  • they pay interest on deposits from customers and either borrow out the money to lenses or purchase short term US treasury . The spread between deposit interest rate paid to customers and US treasury yield/loan interest rate charged to lender is their profit
  • they charge lenders interest above long term treasury yield rate and finance the loan through either their own deposits or from borrowing
  • US Banks with deposits above USD122.3million needs to meet minimum reserve requirements of 10% imposed by the Federal reserve as of 2018

Meeting minimum reserve requirements

  • Borrow from Federal Fund Rate based on central bank interest rates
    • Used within US economy
    • rates are higher
    • hassle free
    • The federal funds rate is set in U.S. dollars and
    • charged on overnight loans.
    • The fed funds rate is the interest rate at which commercial banks in the US lend reserves to one another on an overnight basis
  • London Interbank Offered Rate (LIBOR) –
    • Used internationally
    • Borrow from other banks
    • rates are lower based on global supply and demand equilibrium
    • based on USD, EURO, Sterling, Swiss Franc, Yen
    • Quotations:
      • overnight, one week, and
      • one, two, three, six, and 12 months.

Federal reserve debt structure

  • US treasury bills:
    • short term maturity at one year or less.
    • Sold at discount
    • paid fully at maturity
  • US treasury notes:
    • 1 year to 9 years maturity
    • Sold at face value
    • pays fixed interest rates every six months.
    • Sold auction style.
  • US treasury bonds:
    • 10 years to 30 years maturity.
    • Sold at face value and
    • pays fixed interest rates every six months .
    • The original vehicle.
    • Registered to single owner and cannot be resold.

Federal Interest rate hike

  • Long term interest rate tend to react faster to hikes then short term interest rates
  • Long term Federal interest rates are used as benchmarks by banks to determine interest to charge lenders.
  • To prevent hyper inflation (price stability) after all employable people within the country have been employed into the economy.

Mark Zuckerberg chats with Yuval Noah Harrai on the Future of AI

Key take aways

  • Spread of inequality where some countries have the ability to harness AI while others don’t
  • AI based recommendation systems moving from being just an oracle to becoming a sovereign
  • AI as a tool is an amplifier
    • concerns that it will benefit totalitarianism more than democracy leading to totalitarianism becoming a more favorable governance model worldwide
    • surveillance
    • psychological manipulation – the inability to know your true self through your thoughts
    • what happens if morality and expediency diverge when it comes to governance
  • Effectiveness of curbing the negative effects of AI by encoding values within policy frameworks governing these AI based systems
    • Companies based in Democratic countries will encode democratic values within their systems vice versa for Totalitarian countries
  • Personalization versus Fragmentation
    • when everyone in a country chooses his own community that is mainly online there is no longer a glue holding the local community together
  • Long term versus short term
    • The long term benefits might come sooner than expected when taking a short term trade off

 

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

Trends associated with GetData.IO

Alternative Data

Robotic Process Automation

Analysis of the Facebook Libra Token

High level

  • The launching of Libra Token will allow large swath of people access to banking
  • It will also allow corporations with a huge stock pile of cash the access to alternate forms of investment

Libra currency liquidity

Every Libra token that gets created is backed by a reserve of real assets. Close examination of partner balance sheet figures shows approximately USD148 billion dollars of cash and equivalent available for deployment right out the gates.

Libra social impact

One of Libra’s goals is to provide banking access to segments of the world’s population that don’t. Close examination of partners’ reach to this segment of the world shows 7.9million people. This is not including the 204 million African Internet Users on Facebook.

Related References

Book summary – Range

  • specialists by instinct will grip tighter to their core tools when the environment becomes more chaotic
  • Repetition to optimize for efficiency is only suitable for a predictable environment
  • in a chaotic environment being able to draw solutions from a wider range of domains will lead to qualitatively better and more innovative break through solutions
  • build culture that encourage dissent rather than blind adherence
  • build teams that are deeply networked rather than hierarchical

Book summary: Everybody lies by Seth Stephens-Davidowitz

Signals from Search

  • What people search for is in itself a signal
  • The order of keywords in which they search is also a signal
  • Quality of google search data is better than in Facebook because
    • You are alone with no fear of being judged
    • You have an incentive to be honest

On Big data

  • the needle is still the same size but the haystack has been getting bigger
  • Be judicious by cutting down the sample size of the data to be used

Data science

  • Trust your intuition as the initial signal but verify quantitatively to avoid narrative bias
  • Correlation is most often sufficient for utilization purposes – often the explanation of why the model works comes after the fact
  • critically assess the actual data underlying the narrative. At times it might tell a very different story than narrative presented
  • Clustering of groups of people helps predicts behavior – Netflix and baseball
  • AB testing to discover causations

Social Impact

  • great business are found on:
    • secrets about nature
    • secrets about people
  • Modeling
    • Physics – utilize neat equation
    • Human behavior – probabilistically via Naive Bayes classification

Managing angry people

  • Lecturing them will provoke their anger
  • Provoking their curiousity will cause their attention to be diverted causing anger to subside

Related readings

  • Zero to one, Peter Thiel