Thursday, December 22, 2016

Founder Roles

As the founder of a company, you will likely play many roles across the life of the company. The core tenet of being a founder is that you should do whatever it takes to make your company successful.

Although my title at Color for the last ~4 years was CEO, at different times I played the role of recruiter, supply chain lead, product manager, office manager, and head of sales. Similarly, my 3 other co-founders played a variety of roles across engineering, design, PR, genetics, and other areas. As Color has grown we have hired smart experienced people to take over these areas. It is always a magical moment (and a relief) when you find a smart, experienced person to take over a function from you who does the job much better then you ever will. The role of the CEO is, in part, to find amazing talent so that the company as a team is as strong as possible. Many startup CEOs think they themselves need to be great at everything, which leads to all sorts of bad dynamics. Startups are fundamentally a team sport.

My co-founder Othman Laraki and I have been working together for 9 years. We co-founded MixerLabs together, were acquired by Twitter, and then co-founded Color together (along with Taylor Sittler and Nish Bhat). Traditionally over the last 9 years of working together, he and I have divided up roles with me as CEO and he as President. While we ran different areas of the company between us (I ran most business functions, the lab, and science; Othman ran product, engineering, design, PR) in practice the lines were more blurry and there was redundancy between us. Since we made major decisions together, this created both inefficiency in where time was spent, but it also slowed us down. Teams needed to be able to schedule both of us for every key meeting, and people bounced back and forth between us to get to a decision.

As Color scaled to 80+ people, Othman and I decided this structure need to change. We felt that the company needed a single decision maker and owner of the day to day operations. We spent some time talking through scenarios between the two of us and then discussed it with our amazing board of directors (Hemant Taneja and Sue Wagner).

We wanted to create a structure with the following characteristics:
A. Clear decision making and ownership of areas
B. Something that would keep us both involved full time with the company
C. Allocation of roles based on where we thought the future of the company lay to maximize the impact and value of the company and its products. As a software company dealing with important health information, we wanted to re-emphasize both the technology roots of Color, but also the patient advocacy side of what we are doing [1]

In the end we were most excited by a structure where Othman would move to CEO, and I would move to full time chairman. My reports would move to Othman, freeing time up on my end and giving him direct operational control. This means Othman could make decisions unencumbered, and I could focus on four areas we both felt were key to the future of Color[2]. As part of this process it was important to ensure there was a clear breakout of roles so that people internal and external to Color would know who to go to for what.

The next steps were to communicate this decision. We started with our major shareholders[3], then my direct reports, then the company, and then our smaller investors. The last step was to let people know externally what had changed. We thought about skipping a blog post about it as not much is really going to change at Color day-to-day, but in the end our spectacular CMO Katie Stanton convinced us that this could be good information to share as almost a "how to" for other entrepreneurs facing similar decision points. As a sucker for entrepreneurs blogging experientially to help each other - I fell for Katie's reasoning. Hence this post :)

Our goal for Color is to empower people, their families, and their healthcare providers to make more informed health decisions. We think this new structure is the best way to achieve the world changing goal.

[1] Othman is a BRCA2 carrier and has breast cancer in his family. The lack of affordable, high-quality, streamlined, available genomics and genetic testing is part of what drove the four of us to found the company.
[2] The four areas I will focus on include large deals, engineering hiring (I am going to focus on female and diversity hiring in particular), longer term product roadmaps, and short 3-6 week projects that need executive bandwidth but lack an executive sponsor.
[3] Our first two calls were to Vinod Khosla from Khosla Ventures and Joe Lonsdale from 8VC. Their insights, wisdom, and feedback are always spot-on and helpful.

Monday, August 29, 2016

Startups in Machine Learning & AI

Artificial intelligence is going to have a massive impact on multiple business verticals over time. The displacement of both blue collar and white collar work by machine learning is going to cause major societal displacements in the next 10-20 years[0].

While there is a lot of discussion in the popular press about general purpose AI (aka AGI - which is defined as a machine that can perform any intellectual task a person can), much less emphasis has been placed on near-term specific vertical markets or areas that AI and machine learning (ML) are likely to transform in the coming 5 years. In short, I think AGI is still 10+ years away, but vertical products driven by AI will be transformative in the coming years.

The areas listed below are underinvested by entrepreneurs and VCs. I believe some of the largest AI companies in the near term will emerge from these areas[1]. The two keys to success in all machine-learning driven areas will be (i) the ability to construct useful data sets with which to train models and having the staging ground to test these models recursively and with closed feedback loops and (ii) choosing a market where machine learning helps to create a product that the market needs. AI for AIs-sake alone is simply a technology looking for a problem - and those sorts of startups tend to fail[2].

Here are some areas where I think AI can create big companies in the next 5 years:

1. Hardware & ASICs. 
One likely outgrowth of the inevitable rise of self driving cars, and other markets using lots of machine learning, will be the need for more efficient hardware optimized to run ML models. There has been little investment by startups or venture capitalists in the underlying chip architectures to allow for running faster models or ML systems. Many companies that use specialized clusters for ML tend to use NVIDIA GPUs (graphic processing units), which have not been optimized specifically for machine learning. There is room for hardware innovation (ASICs or other approaches) in this area and for the equivalent of an ARM or Qualcomm to emerge. This will be driven not only by the overall growth in ML applications across various verticals, but also by the need for processors in self-driving cars and other hardware at scale. Perhaps the first $10 billion dollar plus AI company will be a chip company.[3]

Companies like Cerebras[4] and Nervana are currently working in this area.

2. Fin Tech. 
The rise of the robo-advisors like WealthFront and FutureAdvisor (acquired by BlackRock) shows that financial services companies are aware of machine driven portfolio management and trading. Machine learning will have a growing impact on how portfolio are constructed and traded, as well as insights extracted from various types of financial data.

Fin Tech applications of ML and big data will have at least 3 components:
a. Tools to make traders more efficient or glean unique insights. Companies like OmegaPoint are focused on making a “Next-generation Bloomberg” where machine learning models can be built on top of data feeds used for trading.
b. ML based portfolio management and trading. 
c. Applying machine learning models to understand and properly price financial products. 

Areas like insurance, mortgages, and derivatives may all benefit dramatically from the applications of artificial intelligence.  A startup or incumbent that will use new statistical approaches and ML to generate better priced mortgages will end up being a massive company.

3. Self-driving cars and trucks. 
Self driving cars are going to disrupt the multi-billion dollar transportation market. Major car and truck manufacturers already realize an existential crisis is coming their way. The pressure in this area on incumbents is exacerbated by Tesla, who may finally have a mass market car and is pushing hard on the self-driving side. Matters are made worse for incumbents by tech companies like Google and Baidu working on self-driving technology. Many car companies would prefer to go it alone and avoid dependence on these tech companies – hence the $1 billion acquisition of Cruise by GM and Uber paying 1% of its company for Otto. Acquisitions in self-driving cars and trucks will continue over the next 12-36 months.

 Self-driving technology will lead to the loss of millions of jobs and the transformation of society (should be largely deflationary force economically). While the industrial revolution occurred over a period of 150 years, I would not be surprised if there is massive job displacement over a 10-20 year period as multiple professions disappear and people are displaced. If opportunities are not created for this segment of society, we will see political upheaval.

4. Medicine. 
While most examples people tend to use talks about AI & robotics displacing blue-collar workers, I think AI will actually displace a much larger segment of white collar workers[5].

One area of big disruption will be medicine. ML will radically change how diseases are defined, diagnosed and treated. Vinod Khosla has some great thoughts in this area.

Machine learning has enormous potential to augment or replace major components of medical care. Imagine if literally anyone with a smart phone had access to the equivalent of the world's best specialist doctors, at low cost, from anywhere.

a. Diagnosis. 
Ever have the terrible experience of waiting for a doctor for 45 minutes, being seen for 5 minutes, and then told a simple solution to your medical problem? The poor customer service and high cost of medicine would not be tolerated in any truly competitive industry.

Moving diagnosis from people to machines will likely start on a per-disease vertical basis - for example IBM Watson has claimed progress in oncology. Similarly, there has been some fascinating work on diagnosis of depression and other mental health disorders using deep learning. The limiter is often the data sets available, and the ability to have a closed feedback loop on diagnosis and outcome.

One approach to accelerating machine learning application in medicine would be to buy out an existing radiology center or clinic. This center could be wired up to optimize for data generation, which would be used to train a machine learning model to diagnose and treat a patient. By launching the model side-by-side with standard care in this clinic, you would have both a feedback loop for the model as well as ease regulatory and patient care concerns.

In parallel, diagnostic tests themselves will improve with the use of ML models. For example, using ML to determine if a variant call on a DNA sequencer is correct, or what cell type you see on a FACS sorter. At my company, Color Genomics, we have started to apply machine learning to genomics in multiple ways.

b. Treatment. 
Similar to diagnosis, machine learning should be able to help select the right treatment options for patients. The biggest limiter here is probably (again) access to data).

c. Continuous monitoring and analysis. 
One way to increase available data for machine learning models is through new consumer-centric technologies for ongoing monitoring. Self-monitoring of health data has a niche, passionate community behind it[6]. In parallel, companies like Cardiogram are putting more power in consumers hands by continuously monitoring pulse and other data. The Freestyle Libre is being adopted by a number of Silicon Valley denizen to self-monitor glucose levels. This may change medical practice by empowering “citizen science” and having more people be proactive about their own healthcare and outcomes.

5. Education. 
I have never invested in an education startup as the education as a market in the US is pretty terrible from a technology perspective. That said I am pessimistically hopefully that an adaptive learning company will emerge that tailors online content and coursework based on the student. This should accelerate learning abilities in both the developed and developing world.

6. Other areas.
A number of areas are not mentioned in this post (manufacturing, advertising etc.) that have been, or will be dramatically changed by machine learning. As an entrepreneur and investor, I am personally most interested in the above right now but many other opportunities abound. Entrepreneurs need to ask themselves how machine learning makes a product 10X better in a market, rather then just think ML itself has value in its own right. That is the key to building a big company around AI today.

Data limitations.
Fundamentally, many of the limitations to vertical AI are data set dependent. Many advanced ML applications in finance, medicine, and other areas would exist today if the data sets for these areas were broadly available. Large companies (e.g. Google, IBM) as well as startups will generate useful data sets by either buying access or partnering on solutions. Data is going to transform multiple industries. This will be both a deflationary and democratizing force (same standard of medical care in poor as well as rich countries), but will also displace massive portions of the developed world workforce. The ultimate impact of machine learning in the next 5-10 years will be both to broaden access to key information (e.g. medical diagnosis) as well as to displace millions of people. More on this in another post soon.

[0] More on this in another post I am working on.

[1] Once, of course, we have true AGI, the world will change rapidly, but that is still enough time away that exact timeframes are mere speculation.

[2] Or get acquired by one of Google, Facebook, Uber, etc. There are going to be tons of acquisitions in this market in the coming years as companies try to beef up their talent in this area.

[3] One could argue that Google search / ads is really the world most successful vertical application of ML and it is already well above $10 billion in both market cap and revenue. So, I mean the next startup to succeed :)

[4] Thanks to Andrew Feldman for quick read of this blog.

[5] More on this in a future post.

[6] Most world-changing products start off looking like a toy, and self-monitoring of health information will likely fit this trend.

End of Cycle?
3 Types Of Platform Companies
Defensibility and Lock-In: Uber and Lyft
Uber And Disruption
Who Cares If Its Been Tried Before?
The Road To $5 Billion Is A Long One
How To Win As Second Mover
End Of Silicon Valley
Social Products
Hot Markets For 2015

Monday, July 25, 2016

It’s M&A Time! (IPOs Return In 2018)

The first half of 2016 saw an initial set of acquisitions that will only accelerate in the next 12-18 months. From now through the end of 2017, we will see an increased wave of large M&A sweeping through the technology industry. This will be following in late 2017 through 2018 with a wave of IPOs.

The driver for the 2016-2017 M&A cycle is a few fold:

1.  Valuations have been coming down, and raising money has gotten harder.
Companies can no longer rely on new investors coming in with ever-larger amounts of capital and ever higher valuations. With 6-12 months of cash left, and the inability to raise an up round, companies will exit.

Founders realize a $100M exit is a big deal and a $1 billion exit is a huge deal. With an ever-inflating valuation it is easy to think that the company and team is unstoppable and that a $10 billion valuation is the new normal. Expectations are getting re-set as people realize it takes many years and a lot of luck to reach a sustainable valuation in the hundreds of millions or low billions of dollars.

Many founders will be tempted to exit when faced with a tougher fundraising environment or down round. People forget that even great companies like Facebook ended up doing down rounds at some point (Facebook did one with DST right after their $15 billion valuation with Microsoft). I know a number of companies who are not closing financings due to ego around valuation. Unfortunately this only causes risk to the company and may not end well.

2. Big non-tech companies are realizing that they need to buy technology driven companies, or companies using new distribution platforms (like Dollar Shave Club).
The acquisition of Cruise by GM shows how a set of traditional companies are seeing their business change dramatically due to the latest waves of mobile, cloud, and machine learning. Examples include BlackRock's acquisition of FutureAdvisor, and Visa buying TrialPay. Similarly, new ways of distributing product via online platforms is continuing to change how commerce works, leading to the Dollar Shave Club buy by Unilever. Between these two trends, companies in the automotive, food, healthcare, and other segments are realizing they need to participate in the latest technological innovations. This will drive a new set of acquisitions in the next year and a half.

3. Large, old-school technology companies want to participate in the latest wave of technology.
The recent purchase of LinkedIn by Microsoft demonstrates the value of the latest wave of social products to large incumbents like Microsoft. Similarly, older enterprise companies will want to participate in the massive shift to the cloud and SaaS, as well as the rise of AI/machine learning technologies. This will lead to a flurry of deals in the next year as Microsoft, IBM, Oracle, HP, Salesforce, and others will want to accelerate their businesses or shift more to the cloud. Similarly, Google, Apple, Baidu, Facebook, Tencent, Alibaba, Softbank, Samsung, and others will be battling across mobile, cloud, commerce, ads, consumer and other major platform wars leading to additional major acquisitions. 

Small Acquisitions in 2016-2017

In 2016-2017, we will also see a shift in both who the most active acquirers are, as well as the acceleration of machine learning / AI as a major talent acquisition category.  

1. The companies doing acquihires / small M&A will shift.
In recent years, Twitter, Facebook, and Yahoo! had been amongst the most acquisitive buyers of teams, the mantle has been passed as these companies matured. Uber, Lyft, Dropbox, Pinterest, and AirBnB are all likely to become more acquisitive[1]. As markets cap rise and companies grow their engineering and design teams rapidly, the use of M&A as a recruiting function tends to scale. Given that funding is becoming ever harder to obtain, now is a good time for breakout companies to double down on M&A. If your breakout company does not have an M&A person, you should hire one.

Depending on how it strategy evolves under new leadership, Microsoft is one to watch in terms of M&A volume and directions.

2. AI & machine learning M&A will accelerate.
During the social era when smart phones were still a new phenomenon, a company could get acquired by Twitter or Facebook solely for having e.g. strong mobile talent. The next 18 months will be the best time to have a machine learning / AI company from an M&A perspective as both new breakout companies (Uber, Pinterest) as well as older incumbents (Apple, Google, Facebook) will continue to buy great machine learning and data centric talent. Large non-tech companies will also buy more machine learning talent to augment their engineering or commerce divisions. I would not be surprised if companies like WalMart of Visa go in this direction.This machine learning shift is ongoing and fundamental.

If you want to build a startup for a fast small flip, machine learning targetted to a specific vertical is your best bet over the next 2 years.

2018 As The Year of IPO

As major M&A unfolds in 2016 and 2017, we will finally start to see major IPOs occur for technology companies in (perhaps) late 2017 and (for sure) in 2018. Big breakouts like Uber have both near exhausted many private sources of capital, but will also see increasing demands to provide liquidity to investors and employees. Additionally, they will realize the additional benefits of liquidity for M&A, equity & debt raises, and hiring.  An interesting side effect of these IPOs will be the creation of new classes of angels and entrepreneurs due to more people having liquid cash. 2018 and 2019 will be interesting years indeed.

[1] I am making this prediction with no inside knowledge. Rather, once you hit a certain market cap and growth rate as a company, you tend to buy more stuff.

End of Cycle?
Waiting Too Long To Go Public
What Is Your Startup Acquisition Really Worth?
5 Reasons To Sell Your Startup
M&A Ladder
How To Sell Secondary Stock
The Road to $5 Billion

Wednesday, July 6, 2016

End of Cycle?

One sign that technology markets often exhibit at the tail end of a cycle is a fast diversification of the types of startups getting funded. For example, following the core internet boom of the late 90s (Google, Yahoo!, eBay, PayPal), in early 2000 and 2001 there was a sudden diversification and investment into P2P and mobile (before mobile was ready) and then in 2002-2003 people started looking at CleanTech, Nanotech etc - industries that obviously all eventually failed from an entrepreneurial and investment return perspective.

It turned out the real wave was just around the corner with the rise of social products (LinkedIn, Facebook, Twitter, Instagram, Whatsapp, Pinterest) and consumer enabled marketplaces (aka sharing economy - e.g. AirBnB, Uber, Lyft). The heavy investments in cleantech and other areas was a sign that one economic cycle had ended and there was a gap in identifying the next one.

Similarly, today we are seeing a shift to a boom in the variety and type of companies being funded as tech investors pursue other areas that I would characterize as "software aware" versus "software driven"[0]. There are two ways to interpret this trend[1]:
1. There are lots of industries suddenly available for transformation.
While I think the range of markets about to be transformed by software is large, the interpretation of what is truly a tech business is being misapplied. Software, the Internet, and AI are transforming a variety of industries on an ongoing basis and I am a huge fan of software is eating the world pmarca statement. However, people are starting to apply software valuations to low gross margin, physical good businesses that are not software businesses. In other words, lots of tech investors are now investing in areas they do not understand, at valuation multiples that do not make sense for these alternate businesses. This is similar to the 2001-2003 bad period of cleantech and nanotech.

2. We are at the end of an economic cycle for tech, and tech investors are desperate for the next new thing.
It is always hard to call the end of an economic or innovation cycle[2]. Technology-driven shifts will continue to be incredibly resilient and transformative. However, the rate of creation of truly fundamental massive businesses accelerated for a few years, and may decelerate for a few years before the next wave hits. During this period of deceleration, entrepreneurs and investors will go into a search pattern to try to find the next wave.

The reasons people shift startup founding and investing patterns at the end of the cycle include:

Everyone is searching for the next thing.
The period of 2004 to the 20-teens will be viewed as the era of network driven business, developer & B2B SaaS infrastructure, and the lean startup. This rich vein of innovation is not over, but appears to be slowing. As this happens, entrepreneurs and VCs go into search mode, trying to seek out other markets that have not been mined as deeply. This explosion in startup investment diversity by technology investors in my opinion is a sign of weakness versus strength in the entrepreneurial ecosystem. Tech investors are investing in food, hardware, traditional biotech, oil and gas, and other industries they know nothing about. Is this a sign of software transforming these areas, or unstated (and perhaps, not even self-aware) desperation?

Investors have fewer great organic opportunities and shift from reactive to thesis driven. Further, past success investing in one area gives false confidence to invest in unrelated areas.
Investors tend to get confident about success irrespective of whether the success was deserved or merely being at the right place at the right time. If you are an investor in great companies like Uber or AirBnB you may start to believe you are smarter then you are about non-tech driven areas and begin to invest more broadly than you should.

The warning sign is often when a large portion of venture firms shift from entrepreneur-driven to thesis-driven. There are a handful of venture firms that are always thesis-driven, for example Union Square investing in network driven businesses. However, most venture firms are admittedly reactive - they do not have a specific theme they are driving themselves, but rather respond to where the best entrepreneurs are creating the most high growth, high margin, companies fastest.

When lots of VC firms shift into a thesis driven mode, it is usually a sign that organic entrepreneurial activity is no longer sufficient to drive that firms investments. As a result, lots of capital gets invested in areas that do not merit the investment, there is a flurry of activity that looks important (Cleantech), but ultimately this activity does not yield great returns. Typically these areas are ones where the investors lack real expertise.

People move from bits to atoms without realizing the change in underlying fundamentals.
Many tech investors are shifting from investing in bits-driven business (software) into atoms driven businesses (anything you need to manufacture). I know tech investors now looking heavily at food, traditional biotech, hardware, pre-fabricated housing, and numerous other areas. These businesses all have fundamentally different development and ship cycles, distribution models, and margin structures than software. However, investors are applying tech multiples expectations to these radically different types of businesses. This is unlikely to end well.

I think it is important on an ongoing basis to ask "how important is software to this business" and "why now?". Software is truly eating the world, but you need what is fundamentally a software business in these traditional industries to make a real difference. Too many people are saying "oh this biotech is using algorithms so it is a tech company" even though it is really still a drug company with all the standard drug business timelines and fundamentals. They are merely using software for one part of their approach, but it is not a software driven business.

Another way to put it - is software truly transformative/the basis for competition for the startup? If so, you may end up with a tech model of innovation and disruption which is great. If you are merely using software but the business fundamentals have not shifted - than the startup is probably not that differentiated and will not merit tech multiples. A software-enabled, network connected, crowd funded, smart toaster is, when all is said and done, still just a toaster.

There are still lots of strong opportunities today. 

I am a huge optimist about the future of technology and its ability to transform large markets. There is still a lot of transformation happening in the world due to software driven businesses. Self driving vehicles, AI, the ongoing FinTech transformation, and digital health are all examples of rich entrepreneurial veins. Similarly, there are still a few great network driven businesses to be founded and funded. However, we are seeing an explosion in a lot of other businesses areas concurrently with tech VCs investing in areas they know nothing about. I believe this to be a sign that we are entering a period where everyone is looking for the next truly deep vein to explore. It may already be here - just as social products co-existing with cleantech and nanotech - but my sense is the tech community is in a period of searching for the next big thing.

[0] By "software aware" I mean some software is used by the startup. However, the true basis for value for the startup has little to do with software despite claims by the founders. At least one prominent food tech company is like this. E.g. a food company masquerading as a tech company.
[1] Obviously there are many more ways to interpret this. But here are the two that stand out most to me.
[2] Maybe calling the end of a cycle is overly dramatic. Rather, we are likely to see a slow down in the rate at which huge companies in one market segment are funded and a gap in activity as the next trend is identified and accelerated.

Wednesday, January 20, 2016

Experience, Instincts, and Maturity

There are three interrelated, but often independent traits that are valuable in any employee (and, in your personal life as well[1]): (i) experience, (ii) instincts, and (iii) maturity. I think all three can be gained with time, but two of them may never come for some people. When hiring managers and executives, I would weigh instincts and maturity higher for non-specialist roles, and experience higher for a specialist role (e.g. leading a data center build out).

This is what you have done in the past and the knowledge base you have acquired. Maybe you are really good at picking up new programming languages because you have used so many over the years. Or maybe you immediately know how to solve a problem that a less experienced engineer or manager can solve because you have seen it before (and maybe even seen seven different ways of solving this issue and know which two really work and which three are awful ideas in the long run.). The only way to gain experience is to do stuff. For most people, the benefits of experience eventually starts to run towards an asymptote unless you do new things or new roles every few years.

"Experience" may also mean organizational experience. For example, if you ran Google Ads and then switched to run YouTube, you have the knowledge of who at Google it is important to get on board for your decisions, how to get resources and headcount, and how processes at the company works. Even if you are not an expert on consumer video, you are an expert on getting things done at Google, which can make you a better executive and leader of the area then someone with ten years of consumer video experience who has never met Larry Page[2].

This is your gut reaction on how to act, often in the absence of information. There are some things experience has taught you that is wrong and sometimes your gut overrides your experience and tells you to do something new in this specific context. Alternatively, there may be a problem that you or someone on your team has never faced before.  Like experience, instincts can be gained with time for most people. It is the background process or pattern matching that causes you to make the right call or say the right thing on the spot. Or it is the "muscle memory" of management that allows you to act the right way in a situation you have never seen before.

Unfortunately, some people just have bad instincts. They try hard to do good but they just keep screwing up the same types of items. These may be very smart and well intentioned people, but sometimes a person doesn't have great instincts. They can be taught almost by rote situational memorization, but it feels like you literally need to rewire some people's brains via a painful process for them to change. In some cases they can never pick up the right instincts and will hit a natural limit on what types of work they can do.

A friend of mine put it about one of her director-level reports, who had 15 years experience but bad instincts, as "He is like that really cute puppy that keeps peeing on your bed. He tries really hard, but doesn't understand that what he is doing is fundamentally wrong until it is too late."

Maturity is understanding what is worth fighting for and what is worth letting go. It is properly allocating credit to others because you do not feel threatened or competitive with members of your team. It is realizing when someone on the team needs your help and helping them in whatever way makes sense. It also means realizing when someone is beyond your help. Maturity also includes things like being open and willing to admit that you are wrong on something.

Some people never really mature. They may be scared to surface issues on their team as managers because they want to show they are in control. They don't ask for help or keep saying "I got this" even if they don't, which can be disastrous if they are managing a team. They may feel easily threatened or confronted when someone tries to ask questions about their ideas or approaches. Some immature employees can be recognized as they always have a "bone to pick with management" irrespective of who is doing the managing. Or, another sign is someone who fights their manager or team members needlessly or on items that don't really matter.

Sometimes a bad company culture encourages and promotes immaturity. Other times the person is feeling threatened or insecure due to having a bad manager, and therefore acts out in immature ways - which is a call for help. And then there are people who never really grow up.

[1] Obviously, there are a lot of other traits that are valuable. I am focusing on these three here given how intermixed they are.
[2] Although in Susan W's case she did indeed have experience with consumer products (for example she launched Google image search) and video products (a part of the original Google Video team early on worked for her).


Friday, January 8, 2016

Waiting Too Long To Go Public

A meme in the tech startup world over the last few years is that you should wait as long as possible to go public. While holding off on an IPO may be beneficial for a small number of startups (e.g. Uber, and Facebook before it) it may be harmful for a number of startups who are not, well, Uber or Facebook. In particular, as public market conditions worsen and tech IPOs are scarce, a number of companies may regret not having gone public in late 2015 when they had the chance to do so. Public markets are sources of ongoing capital, provide a liquid stock with which to both reward and compensate employees as well as to make acquisitions.

Square was smart to go public while it was able to do so, just as PayPal did back in 2001. Once the IPO window shuts it becomes harder for many companies to raise money from public markets. However, the big names will always be able to go public irrespective of market conditions (e.g. Uber and AirBnB).

In general, you want to go public while you are still in the high growth part of the S-Curve (AKA the logistic function). The S-Curve is an old concept that describes the maturation of a market or company. Early in the life of a market (or product) there is a slow growth phase as early adoption happens. This is followed by accelerated growth / mass adoption. Then the market or company matures, and growth tends to slow down. In the mature phase competition may also be heightened and growth or margin may decrease due to competitive pressures.
In general, investors reward fast growth and high margin in defensible businesses. If you go public while still in the high growth, less competitive phase of your business, you will be awarded a larger multiple on your stock. This more valuable stock allows you to hire great people and buy other companies, which hopefully helps you catch the next S-Curve and continue to scale the company and opportunity.

If you decide to continue to stay private instead, your ever increasing valuation only continues to work if you show rapid user/revenue growth and positive margin expansion or increases in net cash flows. In addition, in order to sustain a large, late stage private company (e.g. multi-billion dollar market capitalization plus) you need the following:
1. Ongoing secondary tenders & demand for your stock.
At some point your employees and investors will expect liquidity. After a few years with your company, employees will need to be able to trade stock for cash in a secondary transaction in order to fulfill their ongoing life needs (school for kids, buying a house, medical emergencies, etc.).

In order to provide liquidity for employees your company will effectively need to run a tender process[1] or have company selected "preferred buyers" every ~12 months or so after your company is old enough (e.g. 5-7 years) and you have not gone public. If there is no ongoing demand for your stock, or demand begins to slide, employees will start to seek employers who can either pay them more cash, or have a liquid stock. This may be exacerbated if you switch to RSUs and then delay going public for too long a time. Since RSUs are typically tied to a liquid stock / IPO and are harder to liquidate from a secondary perspective, you end up with an inability for your employees to trade stock (which for early employees is likely the majority of their compensation at this point) for cash.

The TL; DR is you lose employees due to a lack of liquidity.

2. Ever rising stock price.
If the private market environment shifts and you can not raise money at ever higher valuations, your employees will start to view the company as sliding sideways and may consider alternative employers. This can happen equally with a public stock that is going nowhere, but in that case the employee has a greater opportunity to easily sell the stock on the public market and therefore less stress on the "true" value of the company. Also, if your company stock moves in concert with the rest of the public market, other risk-adjusted opportunities will appear similar to your employees - e.g. if the market tanks overall no one blames just your company.

Note that (1) and (2) may be at odds - you may eventually raise at such high valuations that fewer secondary buyers are willing to buy your stock. Or, you may have tons of interest in secondary purchases of your stock since you have not reset your valuation with a primary financing.

3. Private stock that other companies will treat as liquid.
In order to use your stock to buy other startups, you need people to think your stock is either fairly priced our cheap. This actually cuts both ways - if your stock is believed to still have a lot of upside, founders whose company you buy will view your stock as more attractive then public market companies with little likely upside. E.g. if you sell your company to Slack in exchange for stock and the stock appreciates 5X it might be a better outcome than receiving an acquisition offer with 50% more up front from a public company that is unlikely to move much stock price wise (e.g. eBay).

However, a number of late stage companies may be perceived as overvalued. Since private market valuations are often opaque and illiquid, it might be harder to acquire a company than if you had a public company with the same valuation.

Benefits of going public:

  • Liquid stock you can use for compensation, acquisitions, etc. The market has priced your stock and at any moment you can find someone to buy it at that price. 
  • Customers may consider you more "safe" as a supplier or partner. Large enterprise companies may feel more comfortable buying things from you.
  • Access to capital. Ultimately public markets provide you with the ability to raise capital and debt from a variety of sources.
  • Financial discipline. You will focus more on revenue, margin, and profitability and (as long as you keep a longer term view) build a company that is hopefully more self-sustaining and able to subsidize new businesses. A friend of mine at Facebook mentioned when Facebook got hammered by Wall Street for the first time it forced the company to truly invest in ads, which has led to a higher market cap and increased the ability to buy WhatsApp, Instagram, Oculus, etc.

Downsides of going public:

  • People will work less hard once the lockup expires. I saw this happen first hand at a number of companies.
  • Early employees will get distracted by their newfound wealth. Many will quit.
  • You will attract more risk averse people. The hiring profile of the people who apply to Google or Facebook today is more similar to the people who would join McKinsey or Goldman Sachs than the people who would join a raw startup. This means your company will still hire really smart, driven people, but you will likely have fewer people willing to experiment or take risks. I should say one surprising trend I have seen is former serial entrepreneurs start to take "retirement" jobs at Google and Facebook. I.e. after a few rounds of tilting at entrepreneurial windmills they join a company like Google or Facebook for the good pay, more reasonable hours, and potential to make an impact. They bring their entrepreneurial energy to these companies, but also get to see their kids after work and not have the weight of the entire company on their shoulders.
  • Lack of long term focus. Many public companies start to care too much about Wall Street's wishes, and loose focus on building long term sustainable value. Executives and employees may spend too much time watching the stock price and reacting emotionally to it. Turn arounds (e.g. Yahoo! or Dell) or large changes in direction become much more difficult as the public markets tend to punish truly innovative thinking if it comes at a short term cost.
  • Extra overhead associated with public market compliance.
  • Extra transparency in quarterly earnings reports and other SEC filings you are required to complete - competitors can understand your business in detail.
  • Public markets are reactive and frequently irrational. I left Twitter about a year before it went public. Every time the company announced news I viewed as a net negative, the stock would move up. When the company announced news I thought was positive, the stock dropped. In general, public market investors may have keen insights on macro tends and financial aspects of a business, but they can often get things wrong too. This can create whiplash in your stock.
[1] A "Tender" is a company arranged program in which where a large buyer comes in and agrees to buy a bunch of common stock or early preferred stock from employees and investors in a single large transaction. People who own stock in the company typically have the ability to sell up to a certain dollar or percentage amount of their stock.

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