Sales best practices for founders n SMB sales leaders
- 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
- 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.
- 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
- 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
- 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.
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
- 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
- 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
- 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
- Google – impeccable safety
- Tesla / China – trial by fire
- key to winning the Autonomous AI race
- is the bottleneck technology (Silicon Valley) or policy (China)?
- 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
- physical labor
- 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
- Humans freed up from repetitive tasks can now focus on becoming more human oriented
- Disruptor, Zhou
- www.Arvix.org – an online repository of scientific papers
- Folding Beijing – Hao JingFang
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
- 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
- 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
Reasons for non-purchase
- Macro economic environment is still uncertain given US/China Trade war where leaders are set to meet during the 28th-29th June 2019 G20 Osaka summit.
- US Treasury yield curve is currently inverted signaling a forthcoming recession
- DOMO share price has been on a steady trend for past 3 months.
- Competition purchases
- Google has recently purchased Looker
- SalesForce has recently purchased Tableau
- Cash and cash equivalent is down by more than 50%
- Total assets is down by more than 10% while total liabilities is down by only 3%
Management: make people capable of working together to respond to change in the environment through:
- common goals
- common values
- the right structure
- proper training and development
Reasons for failure
- Not innovating
- inability to manage innovation
The goal of any business is to create a customer. A business does so by producing generate products and services the community wants in exchange for profits to sustain continued operation.
The goal of marketing is to know understand and know the customer so well the product sells itself
The Purpose of a business is the change it wants to effect in the community
The Mission is what it wants to do to effect the change
The Objectives are key tasks it will execute upon to achieve the mission.
Types of innovation
- Product innovation
- Social innovation
- Management innovation
Waste as little effort as possible on areas of low competence.
First figure out how you learn to figure out how you perform.
Number two person often fails in number one position because top spot requires a decision maker.
Effective people are perpetually working on time management.
The first rule of decision is one does not make a decision unless there is a disagreement.
Determine the right organization size to fit the requirements of the mission.
Conversations with Yi (EverString)
The forthcoming trend for engineering
Machine learning is increasingly becoming commoditized. DevOps becomes more important. Demand for specialized service where DevOps is encapsulated will further increase as demand for engineering tasks further outstrips engineering supplies.
On lead generation market
Companies in the lead generation space have need for scalable web crawlers. This helps offset the cost of retaining three in-house engineers.
Lead generation space has consolidated. There were priorly 120k such companies. There is 7k companies in operation. Majority of players are generating leads by scraping LinkedIn.
Consumer space require constant development of new features. Enterprise space requires service heavy. Enterprise space requires not just lead generation but entire channel marketing service suit (physical mail, online advertising, email marketing)
Lead gen hard to retain. The list becomes less valuable once it’s been used. 80% yearly churn is normal. One company reduces yearly churn to just 10% this by reducing second year subscription from USD800/yr to USD200/yr. further discount to USD100/yr if they don’t like. Recurring service is for grabbing fresh leads from same data source.
On Tele conference
Zoom’s product team compared with UberConference has developed a better understanding of the true conference needs of their users in various context. They have worked harder to ensure their product work seamlessly in identified scenarios. A typical example is the ability to join s conference bybthe press of a button on their mobile phone while driving instead of having to type the typical 4 pin digits.
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.
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
- 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
- Legal – need more data and processing time to get approved
- Battery capacity
- Social norms around robo taxi