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

Navigating the trough of sorrow

While I was reading through most of the success stories that were published on IndieHackers.com, it occurred to me that my project GetData.IO really took longer than most others to gain significant traction, a full 5 years actually.

The beginning

I first stumbled upon this project back in December 2012 when I was trying to solve two other problems of my own.

In my first problem, I was trying to identify the best stocks to buy on the Singapore Stock Exchange. While browsing through the stocks listed on their website, I soon realize that most stock exchanges as well as other financial websites gear their data presentation towards quick buy and sell behaviors. If you were looking to get data for granular analysis based on historical company performance as opposed to stock price movements, its like pulling teeth. Even then, important financial data I needed for decision making purposes were spread across multiple websites. This first problem lead me to write 2 web-scrappers, one for SGX.com and the other for Yahoo Finance, to extract data-sets which I later combined to help me with my investment decision-making process.

Once I happily parked my cash, I went back to working on my side project then. It was a travel portal which aggregates all the travel packages from tour agencies located in Southeast Asia. It was not long before I encountered my second problem… I had to write a bunch of web-scrapers again to pull data from vendor sites which do not have the APIs! Being forced to write my 3rd, 4th and maybe 5th web-scraper within a single week lead me to put on hold all work and step back to look at the bigger picture.

The insight

Being a web developer, and understanding how other web developers think, it quickly occurred to me the patterns that repeat themselves across webpage listings as well as nested webpages. This is especially true for naming conventions when it came to CSS styling. Developers tend to name their CSS classes the way they would actual physical objects in the world.

I figured if there existed a Semantic Query Language that is program independent, it would provide the benefit of querying webpages as if they were database tables while providing for clean abstraction of schema from the underlying technology. These two insights still prove true today after 6 years into the project.

The trough of sorrow

While the first 5 years depicted in the trend line above seem peaceful due to a lack of activity, it felt anything but peaceful. During this time, I was privately struggling with a bunch of challenges.

Team management mistakes and pre-mature scaling

First and foremost was team management. During the inception of the project my ex-schoolmate from years ago approached me to ask if there was any project that he could get involved in. Since I was working on this project, it was a natural that I would invited him to join the project. We soon got ourselves into an incubator in Singapore called JFDI.

In hindsight, while the experience provided us with general knowledge and friends, it really felt like going through a whirlwind. The most important piece of knowledge I came across during the incubation period was this book recommendation?—?The Founder’s dilemma. I wished I read the book before I made all of the mistakes I did.

There was a lot of hype (see the blip in mid-2013), tension and stress during the period between me and my ex-schoolmate. We went our separate ways due to differences in vision of how the project should proceed shortly after JDFI Demo Day. It was not long before I grew the team to a size of 6 and had it disbanded, realizing it was naive to scale in size before figuring out the monetization model.

Investor management mistakes

During this period of time, I also managed to commit a bunch of grave mistakes which I vow never to repeat again.

Mistake #1 was being too liberal with the stock allocation. When we incorporated the company, I was naive to believe the team would stay intact in its then configuration all the way through to the end. The cliff before vesting were to begin was only 3 months with full vesting occurring in 2 years. When my ex-schoolmate departed, the cap table was in a total mess with a huge chunk owned by a non-operator and none left for future employees without significant dilution of existing folks. This was the first serious red-flag when it came to fund raising.

Mistake #2 was giving away too much of the company for too little, too early in the project before achieving critical milestones. This was the second serious red-flag that really turned off follow up would-be investors.

Mistake #3 was not realizing the mindset difference of investors in Asia versus Silicon Valley, and thereafter picking the wrong geographical location (a.k.a network) to incubate the project. Incubating the project in the wrong network can be really detrimental to its future growth. Asian investors are inclined towards investing in applications that have a clear path to monetization while Silicon Valley investors are open towards investing in deep technology of which the path to monetization is yet apparent. During the subsequent period, I saw two similar projects incubated and successfully launched via Ycombinator.

The way I managed to fix the three problems above was to acquire funds I didn’t yet have by taking up a day job while relocating the project to back to the Valley’s network. I count my blessings for having friends who lend a helping hand when I was in a crunch.

Self-doubt

I remembered having the conversation with the head of the incubator two years into the project during my visit back to Singapore when he tried to convince me the project was going nowhere and I should just throw in the towel. I managed to convince him and more importantly myself to give it go for another 6 months till the end of the year.

I remember the evenings and weekends alone in my room while not working on my day job. In between spurts of coding, I would browse through the web or sit staring at the wall trying to envision how product market fit would look like. As what Steve Jobs mentioned once in his lecture, it felt like pushing against a wall with no signs of progress or movement whatever so. If anything, it was a lot of frustration, self-doubt and dejection. A few times, I felt like throwing in the towel and just giving up. For a period of 6 months in 2014, I actually stopped touching the code in total exasperation and just left the project running on auto-pilot, swearing to never look at it again.

The hiatus was not to last long though. A calling is just like the siren, even if somewhat faint sometimes, it calls out to you in the depths of night or when just strolling along on the serene beaches of California. It was not long before I was back on my MacBook plowing through the project again with renewed vigor.

First signs of life

It was mid-2015, the project was still not showing signs of any form of traction. I had by then stockpiled some cash from my day job and was starting to get interested in acquiring a piece of real estate with the hope of generating some cashflow to bootstrap the project while freeing up my own time. It was during this period of time that I got introduced to my friend’s room mate who also happened to be interested in real estate.

We started meeting on weekends and utilizing GetData.IO to gather real estate data for our real estate investment purposes. We were gonna perform machine learning for real estate. The scope of the project was really demanding. It was during this period of dog fooding that I started understanding how users would use GetData.IO. It was also then when I realized how shitty and unsuited the infrastructure was for the kind and scale of data harvesting required for projects like ours. It catalyzed a full rewrite of the infrastructure over the course of the next two years as well as brought the semantic query language to maturity.

Technical challenges

Similar to what Max Levchin mentioned in the book Founder’s at work, during this period of time there was always this fear in the back of my mind that I would encounter technical challenges which would be unsolvable.

The site would occasionally go down as we started scaling the volume of daily crawls. I would spend hours on the weekends digging through the logs to attempt at reproducing the error so as to understand the root cause. The operations was like a (data) pipeline, scaling one section of the pipeline without addressing further down sections would inevitably cause fissures and breakage. Some form of manual calculus in the head would always need to be performed to figure out the best configuration to balance the volume and the costs.

The number 1 hardest problem I had to tackle during this period of time was the problem of caching and storage. As the volume of data increase, storage cost increase and so did wait time required before data could be downloaded. This problem brought down the central database a few times.

After procrastinating for a while as the problem festered in mid-2016, I decided that it was to be the number 1 priority to be solved. I spend a good 4 months going to big-data and artificial intelligence MeetUps in the Bay Area to check out the types of solutions available for the problem faced. While no suitable solutions were found, the 4 months helped elicit corner cases to the problem which I did not previously thought of. I ended up building my own in-house solution.

Traction and Growth

An unforeseen side effect of solving the storage and caching problem was its effect on SEO. The effects on SEO would not be visible until mid-2017 when I started seeing increased volume of organic traffic to the site. As load times got reduced from more than a minute in some cases to less than 400 milliseconds seconds, the volume of pages indexed by bots would increase, accompanied by increase in volume of visitors and reduction in bounce rates.

Continued education

It was in early-2016 that I came across an article expounding the benefits of reading widely and deeply by Paul Graham which prompted me to pick up my hobby of reading again. A self-hack demonstrated to me by the same friend, who helped relocated me here to the Bay Area, which I pursued vehemently got me reading up to 1.5 books a week. These are books which I summarized on my personal blog for later reference. All the learnings developed my mental model of the world and greatly aided in the way I tackled the project.

Edmodo’s VP of engineering hammered in the importance of not boiling the ocean when attempting to solve a technical problem, of always being judicious with the use of resource during my time working as a tech-lead under his wing.  Another key lesson learned from him is that in some circumstances being liked and being effective do not go hand in hand. As the key decision maker, it is important to steadfastly practice the discipline of being effective.

Head of Design, Tim and Lukas helped me appreciate the significance of UX during my time working with them and how it ties to user psychology.

Edmodo’s CEO introduced us to mindfulness meditation late-2016 to help us weather through the turbulent times that was happening within the company then. It was rough. The practice which I have adopted till to date has helped keep my mind balance while navigating the uncertainties of the path I am treading.

Edmodo’s VP of product sent me for a course late-2017 which helped consolidate all the knowledge I have acquired till then into a coherent whole. The knowledge gained has helped greatly accelerated the progress of GetData.IO. During the same period, I was also introduced by him the Vipasanna mediation practice which coincidentally a large percentage of the management team practices.

One very significant paradigm shift I observed in myself during this period of continued education is the observed relationship between myself and the project. It has changed from an attitude of urgently needing to succeed at all cost to an attitude of open curiosity and fascination as one would an open ended science project.

Moving forward

To date, I have started working full time on the project again. GetData.IO has the support of more than 1,500 community members worldwide. Our mission is to turn the Web into the fully functional Giant Graph Database of Human Knowledge. Financially, with the help of our community members, the project is now self-sustaining. I feel grateful for all the support and lessons gained during this 6 year journey. I look forward to the journey ahead as I continue along my path.

Key insights for Jerry’s birthday party

On web scraping

  • There is only a total of 180 million registered domains in the world
  • There is a total of 120 billion web pages in the world
  • .IO is the most popular top level domain right now in the world
  • make sure to buy up “CompanyEntity”Suck.com domain as they tend to get a lot of traffic
  • Similarities comparison
    • pull out words on website’s about us page and comparing with other website’s about us page
    • reduce total number of dimensional space to compute similarities – sample space 500,000 word
    • K-Means as opposed to Cosine similarities is a cheaper approach
    • still need humans to tag specific data sets
  • Detecting structure in page
    • find the tables and row
    • extract the values within the column, check if is
      • place
      • person
      • address
    • tag all the rest of the content on the page as the same entity
  • Approaches
    • pure machine – inaccurate
    • pure human – not-scalable
    • set process to mix of machine and human for optimal configuration
  • On Distill Networks/Bots blocking
    • this company utilizes machine learning to detect for bots
    • currently only 10 of 1000 fortune 1000 companies are using their service
    • fat tail companies will have the resource and motivation to protect their publicly available data
    • long tail companies will have neither the resource nor motivation to protect their publicly available data

On enterprise sales

  • It takes 5 years to pass through the trough of sorrows after the initial hype. Enterprise companies come to trust you after you have been around long enough
  • Most enterprise companies do not have the capacity to plough through the volume of automated sales leads generated even if they want to. The main bottleneck is their sales team
  • Enterprise companies are willing to pay really high margins
  • Sample concepts
    • BuiltWith.com – used by hedge fund managers to track how well a software has gain traction amongst users based on javascript snippet

On Social networks

  • Facebook uses collaborative filtering
  • Most lucrative advertising audience are still North America Whites
  • African demographic don’t spend much which makes them really bad advertising targets but are really loyal users once acquired
  • African demographic drive much of the music and culture
  • To ensure optimize monetization spend as well as server resource, could use Facebook page like condition to filter for more lucrative demographics
  • Short video is the trend now
    • SnapChat is considered messaging than video
    • Instagram is in the space
    • Tic Tak is in the space
    • Differentiation is a challenge in this space
  • iMessage is the largest competitor of Facebook Messenger. The former spans across East and West.

On Venture Capital

  • Founders might potentially get blocked from selling company by investors past trough of sorrow stage (typically 5 years in)
  • Founders might want to exit while investors need to get their return multiples (4x minimal)
  • Investors might seek to replace CEO to bring in a growth/scaling CEO as opposed to a product centric CEO

On Mobile gaming

  • Each games has a life span of 5-6years
  • In app purchases is the main driver of revenue
  • Failure rate is very high
  • Assuming 4 experimental teams, the operations typically generates one successful mobile game per year.

Contributors

  • Jerry
  • Perry
  • Yi

Insights from Hannes

Three critical conditions for any projects to work

  1. If the technology will work
  2. If there is an actual applicable use case
  3. If we can build a viable business model

Once these three conditions are met, it then makes sense to double down on investment and scale the projects rapidly.