Friday, February 10, 2017

Don't Ask For Too Much Money

A common mistake that founders make when raising a venture round is to anchor high and ask for too much money, at too high a valuation, with the hope the VC will bid them down. This is a common failure mode that prevents people from raising money successfully when they otherwise could. Asking for too much money is driven by misunderstanding the nature of a fundraise negotiation. When fundraising, you are trying to create an auction dynamic - not a 1:1 negotiation.

In a traditional negotiation, you want to anchor high and then have people bid you down. In a venture round, you actually want to do the opposite - you want to anchor low and pull multiple VCs into an auction around the company. Once a VC is emotionally engaged in the auction they will want to win against their peers. This will drive up the dollar amount you are raising and along with it your valuation (as VCs tend to want to buy a certain % ownership). In other words, the VCs will start to bid against each other and drive up your value.

A tangible example of this - suppose you want to raise a $10M series A. Rather then telling VCs you want to raise $15M (and an implied valuation of $60M to $75M post-money for 20-25% of the company), you should tell VCs you want to raise $6-7M (and thus a $24M to $35M valuation)[1]. VCs will view this as a potentially cheap company and kick off diligence, multiple meetings, and will get emotionally invested in the business and excited about the team and their prospects. Once the VC is emotionally engaged and excited, they are more likely to drive up your valuation so they can win the deal and you will get to $10M. In contrast, if you ask for $15M they will never put in the effort to get to know you and will just pass up front.

It Is Hard (Close To Impossible) To Go Back Once A VC Passes
Once an investor passes on your round it is almost impossible to go back at a lower price / dollar amount. You have already burnt that bridge. The VC has moved on to other potential investments and your company is seen as a "stale" or unattractive deal. After all, you could not convince anyone to give you money before, so that means the herd of other VCs is not interested (and therefore your company must not be great).[2]

What To Do If Everyone Passes On Valuation
If you get a consistent message that VCs are passing on you due to valuation you should:
1. Ask for additional feedback on what your company is doing and your story. Sometimes it is purely valuation, but sometimes VCs also use valuation as an excuse to pass if they don't like something else. Incorporate this feedback into your pitch and iterate on it. If at all possible, ask for this feedback over the phone. VCs will be less willing to be up front with you over email then on a call[3].

2. Add more people into the pipeline for your fundraise and go to them with a lower valuation then before. Even a $3M-4M drop in requested dollars raise can make all the difference (e.g. raising $6-7M versus $10M).

3. Iterate on (1) and (2).

If you can not raise money even after dropping your up front ask, there may be something more fundamentally at issue. Dig in and see what is turning investors off about your company.

Notes
[1] VCs tend to try to buy between 18-30% of the company in a series A with most falling between 20-25%. So, a rough rule of thumb for valuation is to multiple your capital raise by 4-5X to get your post money valuation.

[2] You can always engage with the VC at a later round e.g. 6-12 months later. You just can not go back to them for the same round.

[3] You can of course, ask a VC for 5 minutes by phone to get the feedback. Tell them up front on the call that your company culture is one of continuous improvement and getting feedback on your pitch is part of it.

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Monday, February 6, 2017

Market Caps & The 2% Rule

One way to assess whether a startup idea is in a good market is to ask what are the market capitalizations of the biggest companies in that sector. For example in consumer internet, Google ($560 billion) and Facebook ($370 billion), and in enterprise software Microsoft ($460 billion), and Oracle, ($167 billion) are all large, high margin businesses.

Market caps in a pre-existing industry[1] tend to be proxies for the potential of the idea you are working on. There are three reasons for this:
1. The market capitalization of a set of companies reflects revenue in the market, growth rate of revenue and earnings, and the margins of the companies.
These core metrics used by wall street to value a stock are all metrics that help you understand whether a market is overall large, growing and profitable - all signs of a good market to enter.

2. Often, potential competitors are also potential acquirers. 
Having a large number of high valuation potential buyers in a market creates strong exit opportunities for a startup. For example, for Google to pay $1 billion for a company, it is only 0.2% of its overall stock or market cap. In other words, Google can afford a lot of acquisitions in the $100 million to $1 billion range.

In contrast, the US car rental business is a tougher one. There are 4-5 major players. The largest by far is Enterprise, with $20 billion in global revenue and a $20 billion market cap. The remaining players are much smaller ranging between $1 to $5 billion in market cap. The key characteristic of these companies is that they trade at a low multiple of revenue (e.g. Enterprise trades at 1X revenue) suggesting low growth and a competitive, low margin, market.  Starting a traditional car rental company therefore may be a tough endeavor. Starting a software company that only sells its product to car rental companies also seems like a bad idea - you only have a few potential customers who will each have lot of bargaining power.

3. Strategic investors.
Large, high market cap, cash rich companies tend to be great strategic investors at the later stages for a company. This valuation-insensitive capital can help accelerate a company by giving it a large war chest to act on. For example, GRAIL, an early cancer detection company is raising over $1 billion for its series B. I would not be surprised to see a number of pharmaceutical companies, payors, and health networks participating in this investment round and investing e.g. $100M+ each.

Early Markets Can Be Misleading
The hard part about this approach is defining the real market you are in and the companies that make it up. You can also be fooled by smaller, nascent markets that grow really fast. These are the best types of markets to be in. For example, the consumer cloud storage market when Dropbox started was itself not a large market yet, but clear potential buyers like Google (who had a cloud storage product internally for many years that never quite could launch for some reason), Microsoft and Apple were clearly relevant and in the same general market area. Dropbox recently claimed $1 billion in revenue.

At founding, was Uber a black car company? A taxi company? Or a replacement for transportation and buying a car? If you thought Uber was just a black car company (which in some sense it was at founding) the overall market size seemed middling. Even as a rental car replacement the market size is small and the potential buyers for it like Avis, Dollar, and Hertz range in market cap from $1.5 to $5 billion. This means, with the exception of Enterprise, it would have been tough for any of these other companies to pay $1 billion for Uber. In contrast, thinking of Uber as a replacement for cars (GM alone has a market cap of $50 billion and car makers in general are worth hundreds of billions in aggregate) means Uber has enormous potential especially given its lower fixed costs and higher margins. If reframed as a technology company, Google et al. become potential acquirers and potential market value is even higher. It is therefore no surprise Cruise was bought by GM for around $1 billion - this is 2% of GM market cap and therefore worth the dilution by GM relative to the potential upside (and cataclysmic downside if self-driving cars happen and GM is not a player).

New, high growth markets are also hard to assess. For example, when Google was founded the internet was a much smaller place. So looking at the market capitalizations of search engine companies would be a bad proxy overall. However, if you viewed Google as an ads business, or as a technology business, it became more attractive due to the market caps of companies back in 1998 such as Microsoft, IBM, Time Warner, and others.

M&A: The 2% And $1 Billion Rule
In general, you want to be in markets where multiple companies could afford to buy you for $1 billion, or where 2% of their market cap is at least in the hundreds of millions of dollars.

For example, Walmart's acquisition of Jet.com for $3.3 billion was around 1.5% of its market cap, Cruise's $1 billion acquisition was 2% of GMs, and Unilever's acquisition of Dollar Shave Club was slightly under 1% of market cap. Above a few % of market cap, the nature of an acquisition and its approval by the company's board becomes a dramatically different conversation.

Thanks to Ali Rowghani for comments on a draft of this post.

NOTES
[1] Versus a new industry, which is addressed above.

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Saturday, February 4, 2017

Building VC Relationships

A common mistake founders make is to try to meet VCs to "build relationships" a month or two before going out for a series A or series B fundraise[1] . I explain why this is a mistake below. If you do not have strong VCs relationships and plan to fundraise in 2-3 months, wait to talk to VCs until you go out to raise. Do not do a separate "get to know you" tour. If you plan to go fundraise in 12 months, you can start to build select VC relationships early with a handful of firms.

VCs Remember Most Early Interactions As Pitches
Investors at top tier VCs are constantly deluged with a stream of companies wanting to pitch them. If an investor meets *only* 3 to 5 new companies a week, she is meeting with literally 150 to 250 companies a year. As such, it is unlikely she will remember every nuance of why and how you are meeting - and will default to remembering your meeting as a pitch. Additionally, relationship-building meetings a month or two before a real fundraise in reality often turn into a half-cocked fundraise. You are not really fundraising, but you really sorta are, even if you tell yourself otherwise.

For a VC round you need to have a well-rehearsed, pressure-tested pitch ready to go. You not only need to wow the investor in the first 5 minutes, but create momentum around an active fundraise. Going in half-baked will only backfire.

You also want the timing dynamics of a fundraise properly in place - e.g. if the VC is super excited about you, she will press for you to come meet with the partnership and your company will not have a competitive process in place. If she is not super-excited, she will think of your company as a "pass" and will decline to engage 2 months later when you have your materials and pitch honed. Either outcome is a loser from a fundraise perspective.

If You Want To Build Relationships, Do It 6-12 Months Before A Fundraise

If you want to build VC relationships early, choose a small number of select firms you want to get to know. If you talk to them 6-12 months before a fundraise, enough will have changed with the company since you last spoke that they will want to engage for your actual raise. Some general rules of thumb:

  • Choose which partner you would want to work with eventually - and get introduced only to her up front. VC firms have a relationship management system where the first person who meets a company becomes that company's lead (in some cases in perpetuity). This is crucial, as you are largely stuck with that person as point of contact going forward. If she ends up being a bad advocate for you, you will not get funded by that firm. Ask for intros only to the partner you would want to work with at a given firm.
  • Don't meet with too many VCs. Meeting VCs can become a full time job. Between the travel time to Sandhill / SOMA and the meeting prep VC meetings can take a lot of time. Choose the 4-5 people you really want to stay in touch with, and then meet with them every 6-12 months.
  • Learn to say no. Once you make the VC relationship, that investor may want to meet more frequently then you do, to introduce you to their portfolio companies[2], or otherwise engage. It is OK to reply with "I am heads down on product right now but happy to engage later when I come back up for air".

VCs (Usually) Won't Invest Preferentially In Friends
I have seen entrepreneurs build incredibly deep relationships to firms - who then never invest in the founder. A common message from a VC to an entrepreneur who is not fundraising is "my partnership loves you, and loves what you are doing - we want to fund you anytime". Unfortunately, this message often ends in tears when you go out for an actual fundraise if your startup does not have the traction to get funded. VCs make business, not personal, decisions around investing. They also need to convince all their other partners to invest in you and do not have unilateral decision making. It costs them nothing to emphasize how much you should really talk to them when you decide to raise money. Would you give any friend of yours $10 million just for being a friend? If not, why would a VC do that? Don't be deluded by the VC friendship.

NOTES
[1] I truly mean series A and later venture funds here. Angel fundraises are different and you can talk to them early if you want to suss out the landscape. Even there, I would limit conversations to a handful of folks. You do not spend all your time in meetings instead of building a product and team.
[2] VCs may sometimes do "blind intros" to other companies in their portfolio for you and that company to partner or work together. Most startup to startup intros are a total waste of time from a partnership perspective.

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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.

NOTES
[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.

Friday, November 18, 2016

Facebook Must Really Suck At Machine Learning

Facebook recently claimed it is hard[1] to differentiate between fake news[2] and real news. Given how similar fake news detection is to related problems such as search index spam, ads landing page spam, social networking bots, and porn detection this suggests one of two things: (1) Facebook really sucks at machine learning or (2) Facebook does not want to address the problem. Lets look at each of these:

1. Facebook Sucks At Machine Learning?
Over the course of my career I worked on, amongst other things, Google mobile products (including mobile search index and looking at items like porting Google News to mobile), Google ads targeting to pages across the web, and Twitter Search (I was Director of Search Product for a time). At both Google and Twitter, the companies had to deal with large number of ambiguous signals including:

  • Ambiguous content on web pages. Google classifies these pages for search results, but also to determine the right set of ads to target to the pages. This included semantic analysis of the pages as well as a look at page "quality" scores.
  • Fake landing pages for ads. Google needs to make sure ads and the web pages the ads pointed too were legitimate.
  • Spam tweets. There were (and are) a lot of bots and spam tweets on Twitter. The service was continuously removing poorly ranked tweets from search results. 

In all cases, the important thing to do was to understand the content of a tweet, web page, or other content unit, and then to rank the relative quality and importance of that content. Similar problems also exist in areas like Google web index spam and porn detection. In all cases, there are a lot of shades of grey - i.e. there is a fine line between porn and not-porn, or a spammy tweet and a silly or satiric tweet.

Facebook has developed a number of technologies to rank its news feed, to target ads, and to classify its users. However, the claim from Facebook has been that fake news is a complex area, and this complexity makes it difficult to address.

Intriguingly, a group of undergrads at Princeton were able to build a quick and dirty fake news classifier during a 36 hour hackathon. It is possible these Princeton students a set of once-in-a-generation geniuses. Or, perhaps, fake news is actually tractable as a problem using existing techniques Facebook already has in house.

2. Facebook Does Not Want To Address The Problem?
Facebook's CEO recently posted that 99% of news post on Facebook are not fake (see below for chart of Facebook user engagement with fake versus non-fake news). Facebook has also been under fire by the right for its "liberal bias". This prompted Facebook to hold a meeting with leading conservative members of the GOP to discuss its newsfeed and how to play a non-partisan role in content.

Fake news is not a partisan issue. It is about ensuring that people are helped to understand what is real and what are lies. A lack of willingness to tackle the issue of fake news is a willingness to accept a lack of truth in our society at mass scale.



Other Companies You Can Work At Instead Of Facebook
Great engineers want to work with other great engineers. If Facebook lacks the talent to address the fake news problem, do you really want to join an organization so poor at machine learning? Alternatively, if Facebook simply lacks the will to address this issue, it might be something worth taking into account as well. A number of talented engineers are also immigrants - a group much maligned in fake news posts. If you are a talented machine learning or AI engineer, there are a number of companies you can work at instead of Facebook. Some potential ideas:
  • Google. Lots of amazing Machine learning problems across search, ads, AGI, virtual assistants, etc.
  • Uber. Work on intelligent routing and optimization, self driving cars, and other technologies.
  • Microsoft. Microsoft is working on applying machine learning to health care problems like cancer.
  • Tesla. Self-driving cars.
  • Wish. Large scale commerce platform powered by data analytics and ML.
  • Stripe. Payment fraud and other areas that power our global payments infrastructure.
  • Netflix. Media recommendations.
  • Apple. While less known for machine learning, Apple has been applying it to areas around privacy as well as apps like Siri.
  • Amazon. Amazon has been doing cool things in voice recognition technology with Alexa/Echo. In addition, I would not be surprised if AWS extended its efforts around GPU clusters as well.
  • Dozens of AI startups. There are lots of Deep Learning, AI, and ML companies that have been funded recently. There are lots of cool things for you to work on instead.
If you work on machine learning or data science and want to work somewhere other then Facebook - feel free to drop me a line. I am happy to refer you to a few dozen companies as alternatives.

Notes
[1] Exact quote from Zuckerberg is:
"This is an area where I believe we must proceed very carefully though. Identifying the "truth" is complicated. While some hoaxes can be completely debunked, a greater amount of content, including from mainstream sources, often gets the basic idea right but some details wrong or omitted. An even greater volume of stories express an opinion that many will disagree with and flag as incorrect even when factual. I am confident we can find ways for our community to tell us what content is most meaningful, but I believe we must be extremely cautious about becoming arbiters of truth ourselves."

This "grey area" argument is made all the time. Yet machine learning classifiers work incredibly well for porn and other areas that have lots of grey. Similarly, getting rid of the 80% easy to spot, most egregious stuff is a good starting point. This argument strikes me as a red herring.

[1] "Fake news" is a nice way to say lies and propaganda. 

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.

NOTES
[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.

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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.

NOTES
[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.

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