Key insights from mooncake festival at house of Jerry and Liza

Technology trends

  • Companies are increasing shifting their service from one-off on premise licensing deployment monetization to cloud based SaaS recurring subscription models
    • revenue hit in the short run
    • increased customer LTV in the long run
    • affected publicly traded companies will experience short term discounts to their shares
  • Artificial intelligence versus Augmented intelligence
    • companies are increasingly shifting away from automatic insight generation to systems that help decision makers simulate and model potential outcomes when specific policies are executed
    • demand is shifting from insight generation to data cleaning services
  • Corporate adoption of artificial intelligence
    • CEOs are increasingly considering how to leverage AI as a tool for their trade
    • primary use case is figuring out how to increase their sales volume
    • experiencing challenge on how to apply AI on in-house data to achieve monetization goals
  • Rise of deep vertical data networks
    • EverString – provides sales lead refresh for all client companies ends up becoming a large database for decision-making executives information, approximately 6 million records
    • – cleans up real estate data to help agents better price houses for sale by utilizing in-house agency ends up becoming a large database of high quality real estate data
  • Crypto-currency
    • Bit coin is still the main poster child
    • general population still skeptical about libra
    • main argument is still to remove central bank controls
    • main adoption hurdles
      • writing throughput volume
      • a stable store of wealth
      • starting to be using as a means to facilitate transaction in China
      • Inability to increase or decrease currency supply in times of need is going to be hard as a means to provide much needed stimulation during economic recessions and inflations

US/China trade war

  • sources of conflict
    • technology theft
    • forced technology transfer
    • unfair trade practices like subsidized state owned Chinese companies operating in the export markets
  •  economy
    • China is experiencing inflationary deleveraging
      • local farmers are not growing critical food sources
      • critical food supplies are imported
      • price of imported goods are denominated in US reserve currency
      • shifting of supply chain out of China to
        • Vietnam
        • India
        • Taiwan
      • capital flight
        • Li Ka Shing moved funds out from Hong Kong in 2013 to Europe
        • raising funds for US Venture capital from China was easy prior to Chinese and US government shut down
    • US is experiencing deflationary deleveraging
      • businesses are concerned about macro environment and are reducing fixed investments
      • manufacturing is slowing due to decreased demand both locally and overseas
      • consumer spending and confidence is still strong
  • Chinese domestic concerns
    • Potential US meddling in Chinese domestic affairs – Hong Kong’s demonstration and demands
      • Revoking of National Education
      • Revoking of extradition bill
      • Resignations of HK Chief executive
      • universal suffrage: freedom to elect their own leaders
    • destabilized situation presents a challenge for Xi JinPing’s party to retain control of power over former Jian Zemin’s faction
  • value system
    • US is a highly rule based system
    • China’s system of control is highly subjective to the individual in power.  Direct government intervention in the distribution of wealth is a major source of concern

US/Mexico and world issues

  • NAFTA agreement was too one side and failed to take into account large  imbalance between the two economies
  • US’s arrangement of allowing Mexican tax payers the right to claim dependents ultimately resulted into tax claims and refunds for entire extended families in Mexico. This has the effect of subsidizing Mexican’s at the expense of Americans living along the rust belts
  • Its observed income inequality is becoming prevalent across the entire world not just within US and China.

Related readings

Key insights from weekend with Jerry, Liza, Ada and Dan

On US/China trade war

  • 25% tariff basically wiped out whatever profit margin importing from China could bring
  • China’s labor cost have been increasing over the past few years that it is no longer a competitive advantage
  • China’s main advantage is the expertise they built up over the years. A company can easily spin up 25 manufacturing lines in China very quickly
  • US companies are all rapidly shifting their manufacturing activities out of China
  • New locations are Taiwan, India and Vietnam
  • Chinese staff that were retained by Google are now flying to new manufacturing facilities spun up in these countries to oversee the spin up process

On alternate data

  • Real estate
    • the rise  of platforms like AirBnB has lead investors to seek out alternate data that can help predict short term rental yield as opposed to long term rental yield in a neighborhood
    • government agencies are seeking such data to detect neighborhoods where they should focus their efforts to crack down illegal subletting on AirBnB
  • Sports
    • being able to predict strategy coaches of football teams will employ in real time will help support strategies
    • being able to predict starting line in close to real time and the corresponding outcomes will be valuable for coaches in making play decisions

On HongKong/China protest

  • Public opinion is the agenda for the ongoing protest is now getting really murky
  • Airport has stopped operations, it’s hard to even get in and out of the country
  • Foreign Chinese nationals are supportive of protest in HongKong
  • Topics pertaining to Taiwan, Tibet and TianAnMen massacre are sensitive topics amongst mainland Chinese
  • Funds of funds from Hong Kong are still very liquid

Thoughts on avoiding the greater fool theory

Once a project’s mission statement is defined, it becomes easy to determine when to stop further iterations. 

Below are the listed of statements I periodically revisit when pursuing GetData.IO’s mission to help people make good decisions by making data gathering simple and affordable. 

Hypothesis 1: People no longer need make good decisions.

Hypothesis 2: People no longer need data to make good decisions.

Hypothesis 3: People no longer find it hard to get data.

Hypothesis 4: We have exhausted all known approaches to lower the cost of data gathering to an affordable range. 

Hypothesis 5: We have exhausted all viable approaches to reach people who need to make good decisions.

Hypothesis 1 and 2 are existential questions, while hypothesis 3 focuses on substitute availability. These are out of our control. The only thing we could do is monitor for changes.

Hypothesis 4 and 5 focuses on economic feasibility. These we will fully focus our efforts on. Once we eliminate all none viable options, whatever remains will be the limitations we must accept and live with. 

It is useful to note the lack of any mention on funding. The underlying assumption is that every successful iteration necessarily unlocks resources from the environment which is then fed back to further the compounding process. The discipline is to minimize wastage. 

Relying on external funding is like utilizing margins during day trading. While earnings get amplified, failures tend to be really spectacular. One additional drawback is that they tend to mask critical flaws in the short run leading to the commonly observed greater fool phenomena in the financial markets. 

Learnings on enterprise sales – SVB and SalesForce workshop

General observation on networking

  • There were a total of 40 attendees and we received a total of 2 name cards – net conversion rate 5%
  • Social expectation during a networking session is that you can approach people to talk
  • Start with a clearly one liner if asked about your project
  • actively direct the conversation to them and spend more time listening
  • make sure to bring name cards

Compare and contrast small businesses and medium sized businesses

  • Small businesses
    • acquiring new customers
    • accessing to investment capital
    • not enough time
  • Medium sized business
    • acquiring new customers
    • achieving work life balance
    • not enough time

Sales best practices for founders n SMB sales leaders

managing leads process

  • Develop a concrete definition of a lead and make sure all employees understand it.
  • Install an effective Customer Relationship Management (CRM) Tool.
  • Track the source.
  • Distribute your leads quickly.
  • Nurture your leads and get your Sales team excited about every prospect.
  • Treat your prospects like customers.
  • Measure everything you do.
  • Hold regular meetings with your sales staff and anyone else involved in the sales process.

build out sales stages n codifying it

standardized sales process see up to 28% increase revenue

  • A consistent schedule: You should know when and how often you are going to be
    performing your sales activities.
  • A strong message. You should know what you are going to say and at what point in the
    process you are going to say it.
  • Mixed media plan. Use multiple channels to convey your message and mix it up – emails and phone calls are the most common, but perhaps it’s appropriate to reach your potential customers on a favorite social channel.

focus on boosting sales rep productivity

  • Make ongoing sales coaching a priority.
  • Advance prospects faster with Value.
  • Evaluate & re-evaluate sales processes.
  • Embrace Automation and technology.
  • Use Analytics to always be improving.

Observations on Sales force

  • UVP helps users get data from spreadsheet into systems that allows easy sharing within the sales team
  • Sales force comes with Gmail integration
    • disrupted CollaSpot’s business model
  • Social proofing: Video where users talk about the benefits they get using a tool
  • Extending existing business lines
    • current base – market segment with higher margins using enterprise sales acquisition strategy
    • new business line market segment with lower margins using self service model
      • USD25/user/mth package self service tier
      • acquisition strategy is not well defined yet
  • partnership with companies to value add for their customers
    • sales force partners with SVB to throw event to teach SVB clients how to better do sales.
    • Helps with SVB retention.
    • tap into Silicon Valley Bank’s extensive distribution network

Alessandro Chesser, VP of Sales Carta

Key regrets

  • Not investing early enough in sales operations.
    • CRM system very important. Cleaning up the mess later is going to be a headache
    • Dealing with duplicate accounts are pretty painful
  • Don’t over engineer sales process up front.

Enterprise sales versus organic adoption

Organic signup forces u to put your whole pitch online for easy copying. A sales rep can sell the vision to extract higher margins. Deliberate back and forth. Use the demo form to generate leads for sales rep

Getting the enterprise sales engine started

  • The more data you have the better your decisions. Create baseline and iterate over it over time.
  • Marc Benoff on Closing. When he joined eshare there was no product. His job was to get potential customers excited about the future of their own company. The vision and the pitch is so important. Get their feedbacks.
  • Don’t sell to far ahead and can’t deliver. Need to spend time with engineers to know what can be delivered. Always keep in mind a 1 to 2 months implementation cycle. Only sell ahead when is early stage company but don’t over promise. If failed to deliver, will create bad PR
  • Always iterate on the vision of the company and the pitch to figure out what resonates with your potential buyers
  • Early stage startups can utilized VC to generate inbound leads. The more money raised the more inbound happens with PR that follow each fund raising events

Scaling the sales organization

  • Make sure one person can bring in 100K ARR before replicate and scale up sales process
  • When is Sales cycle replicable? Use revenue as signal. Driving 100K ARR per month is a good signal. Gut feeling. When deals leads are starting to slip through the cracks
  • Charge via ACH instead of credit cards. ACH is more scalable, since the latter tends to expire.

Structuring and managing the sales team

  • For smooth transition from inbound sales to outbound sales first create demand from SDR first before hire sales team.
    • Not all sales people are comfortable with generating sales leads
  • Extremely important to make that SDR hire early. Utilize tools like OutReach.IO
  • SDR – handle email marketing and phone calls. Uses pitchbook. Scrape Startup names and email addresses. Thousands of emails a day to generate leads.
  • Ensure at least sales development representatives hit at least 60% their quota. If hitting below, it means u have over hired. It’ll create very bad culture like sales leads stealing if not hitting above quota.
  • Have sales people prioritize and focus on closing and not be the jack of all trades.
  • Promote Successful SDR  to become sales reps. They will be well positioned for success.

Mapping the hiring process for the sales leader.

The mistake is hiring really experienced and expensive people who are not willing to roll out his sleeves. You need someone who is really willing to get his hands dirty to go out to close sales. Industry experience is important, will ensure sales leader motivation level. Since he knows what is broken

Balancing the functions of marketing and sales

  • Early in startup marketing and sales goes hand in hand. How to ensure no stepping on each other’s toes?
  • Marketing organization should have good process too
  • Need to balance load of marketing and sales organization. Make sure invest more heavily in marketing to generate more leads than sales can handle to ensure good culture.

On Pricing

Pricing is important. Need to make sure not too cheap. Perceived value is very important. Need to be more expensive than competition and explain the value clearly to targeted subset of customers.

Learnings from Loominance

 UVP to attract customers via word of mouth: They need us when they are drowning in data.
Account based marketing
  • Scrap company websites, identify and recommend similar companies as sales leads to clients

Related readings

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%

On managing the sales team

  • important to clearly defined guiding principles and policies up front
  • This will prevent sales representative from selling out of line and clashing with the Ops team leading to all round frustration.

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


  • 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
  • SiliconValley Banks has established fund for investment purposes


  • figure out what your super power is and quadruple down on it
  • always focus your time on the highest leverage activity
  • 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
  • Use Slack – setup process to ensure information about sales process is disseminated to engineering team as well

On exiting

  • important to grow your personal network so that you can tap on it for key resources
  • seek out other operators within the space seeking to bolt on to their business model
  • 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
  • figure out the key strengths of the CEO you hired and structure operations around them.

Related references


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
  • – an online repository of scientific papers
  • Folding Beijing – Hao JingFang

ETFs disrupts hedge funds and democratizes playing field for individuals while driving demand for alternate data


We observe the following impact to be true for ETFs

  • democratizing access for individuals to positions traditionally accessible only to  hedge fund managers
  • disrupting hedge fund business models
  • increasing population of investors with passively managed portfolios
  • giving tech savvy individuals with finance background an edge by reducing active professional competition

Related references

At the surface level we observe hedge fund managers flocking to alternative data to seek alpha

A deeper dive shows this trend to be driven by the deterioration of the sector over the past few years as more investors shift into ETFs.

Survivors are struggling with transition to increased data intensity.

Even Goldman Sachs embraces the trend by going on a hiring spree to recruit coders as opposed to traditional traders.

As this trend of disruption forces more hedge funds to shutdown, a unexpected gap opens up in the field.

It more than levels the playing field for individuals that are savvy in both tech and finance

On the flip side, it can be interpreted that a bubble is forming within ETFs sector similar to the subprime CDO bubble that popped back in 2008


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

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