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Visualizing How Market Volatility Impacts Risk and Returns

S&P500 Performance for 2020 (Yahoo Finance), pulled Aug 9, 2020

2020 has seen the greatest market volatility in history for American stocks. The roller-coaster ride investors have experienced over the last 6 months included a steep ~33% single-month drop followed by a four-month bull market run taking the S&P500 back roughly to where it started.

While not usually so dramatic, volatility is a fact of life for investors. In researching how to create a long-term investment strategy that can cope with volatility, I found a lot of the writing on the subject unsatisfying for two reasons:

First, much of the writing on investment approaches leans heavily on historical comparisons (or “backtesting”). While it’s important to understand how a particular approach would play out in the past, it is dangerous to assume that volatility will always play out in the same way. For example, take a series of coin tosses. It’s possible that during the most recent 100 flips, the coin came up heads 10 times in a row. Relying mainly on backtesting this particular sequence of coin tosses could lead to conclusions that rely on a long sequences of heads always coming up. In a similar way, investment strategies that lean heavily on backtesting recent history may be well-situated for handling the 2008 crash and the 2010-2019 bull market but fall apart if the next boom or bust happens in a different way.

Second, much of the analysis on investment allocation is overly focused on arithmetic mean returns rather than geometric means. This sounds like a minor technical distinction, but to illustrate why it’s significant, imagine that you’ve invested $1,000 in a stock that doubled in the first year (annual return: 100%) and then halved the following year (annual return: -50%). Simple math shows that, since you’re back where you started, you experienced a return over those two years (in this case, the geometric mean return) of 0%. The arithmetic mean, on the other hand, comes back with a market-beating 25% return [1/2 x (100% + -50%)]! One of these numbers suggests this is an amazing investment and the other correctly calls it as a terrible one! Yet despite the fact that the arithmetic mean always overestimates the (geometric mean) return that an investor experiences, much of the practice of asset allocation and portfolio theory is still focused on arithmetic mean returns because they are easier to calculate and build precise analytical solutions around.

Visualizing a 40-Year Investment in the S&P500

To overcome these limitations, I used Monte Carlo simulations to visualize what volatility means for investment returns and risk. For simplicity, I assumed an investment in the S&P500 would see annual returns that look like a normal distribution based on how the S&P500 has performed from 1928 – 2019. I ran 100,000 simulations of 40 years of returns and looked at what sorts of (geometric mean) returns an investor would see.

This first chart below is a heatmap showing the likelihood that an investor will earn a certain return in each year (the darker the shade of blue, the more simulations wound up with that geometric return in that year).

Density Map of 40-Year Returns for Investment in S&P500
Densities are log (base 10)-adjusted; Assumes S&P500 returns are normally distributed (clipped from -90% to +100%) based on 1928-2019 annual returns; Years go from 0-39 (rather than 1-40)

This second chart below is a different view of the same data, calling out what the median return (the light blue-green line in the middle; where you have a 50-50 shot at doing better or worse) looks like. Going “outward” from the median line are lines representing the lower and upper bounds of the middle 50%, 70%, and 90% of returns.

Confidence Interval Map of 40-Year Return for Investment in S&P500
(from outside to middle) 90%, 70%, and 50% confidence interval + median investment returns. Assumes S&P500 returns are normally distributed (clipped from -90% to +100%) based on 1928-2019 annual returns

Finally, the third chart below captures the probability that an investment in the S&P500 over 40 years will result not in a loss (the darkest blue line at the top), will beat 5% (the second line), will beat 10% (the third line), and will beat 15% (the lightest blue line at the bottom) returns.

Probability 40-Year Investment in S&P500 will Exceed 0%, 5%, 10%, and 15% Returns
(from top to bottom/darkest to lightest) Probability that 40-year S&P500 returns simulation beat 0%, 5%, 10%, and 15% geometric mean return. Assumes S&P500 returns are normally distributed (clipped from -90% to +100%) based on 1928-2019 annual returns

The charts are a nice visual representation of what uncertainty / volatility mean for an investor and show two things.

First, the level of uncertainty around what an investor will earn declines the longer they can go without touching the investment. In the early years, there is a much greater spread in returns because of the high level of volatility in any given year’s stock market returns. From 1928 – 2019, stock markets saw returns ranging from a 53% increase to a 44% drop. Over time, however, reversion to the mean (a fancy way of saying a good or bad year is more likely to be followed by more normal looking years) narrows the variation an investor is likely to see. As a result, while the median return stays fairly constant over time (starting at ~11.6% in year 1 — in line with the historical arithmetic mean return of the market — but dropping slowly to ~10% by year 10 and to ~9.8% starting in year 30), the “spread” of returns narrows. In year 1, you would expect a return between -21% and 44% around 90% of the time. But by year 5, this narrows to -5% to 25%. By year 12, this narrows further to just above 0% to 19.4% (put another way, the middle 90% of returns does not include a loss). And at year 40, this narrows to 4.6% to 15%.

Secondly, the risk an investor faces depends on the return threshold they “need”. As the probability chart shows, if the main concern is about losing money over the long haul, then the risk of that happening starts relatively low (~28% in year 1) and drops rapidly (~10% in year 7, ~1% in year 23). If the main concern is about getting at least a 5% return, this too drops from ~37% in year 1 to ~10% by year 28. However, if one needs to achieve a return greater than the median (~9.8%), then the probability gets worse over time and gets worse the greater the return threshold needed. To beat a 15% return, in year 1, there is a ~43% chance that this will happen. But this rapidly shrinks to ~20% by year 11, ~10% by year 24, and ~5% by year 40.

The Impact of Increasing Average Annual Return

These simulations are a useful way to explore how long-term returns vary. Let’s see what happens if we increase the (arithmetic) average annual return by 1% from the S&P500 historical average.

As one might expect, the heatmap for returns (below) generally looks about the same:

Density Map of 40-Year Returns for Higher Average Annual Return Investment
Densities are log (base 10)-adjusted; Assumes an asset with normally distributed annual returns (clipped from -90% to +100%) based on 1928-2019 S&P500 annual returns but with 1% higher mean. Years go from 0-39 (rather than 1-40)

Looking more closely at the contour lines and overlaying them with the contour lines of the original S&P500 distribution (below, green is the new, blue the old), it looks like all the lines have roughly the same shape and spread, but have just been shifted upward by ~1%.

Confidence Interval Map of 40-Year Return for Higher Average Return Investment (Green) vs. S&P500 (Blue)
(from outside to middle/darkest to lightest) 90%, 50% confidence interval, and median investment returns for S&P500 (blue lines; assuming normal distribution clipped from -90% to +100% based on 1928-2019 annual returns) and hypothetical investment with identical variance but 1% higher mean (green lines)

This is reflected in the shifts in the probability chart (below). The different levels of movement correspond to the impact an incremental 1% in returns makes to each scenario. For fairly low returns (i.e. the probability of a loss), the probability will not change much as it was low to begin with. Similarly, for fairly high returns (i.e., 15%), adding an extra 1% is unlikely to make you earn vastly above the median. On the other hand, for returns that are much closer to the median return, the extra 1% will have a much larger relative impact on an investment’s ability to beat those moderate return thresholds.

Probability Higher Average Return Investment (Green) and S&P500 (Blue) will Exceed 0%, 5%, 10%, and 15% Returns
(from top to bottom/darkest to lightest) Probability that 40-year S&P500 returns simulation beat 0%, 5%, 10%, and 15% geometric mean return. Assumes S&P500 returns are normally distributed (clipped from -90% to +100%) based on 1928-2019 annual returns. Higher average return investment is a hypothetical asset with identical variance but 1% higher mean

Overall, there isn’t much of a surprise from increasing the mean: returns go up roughly in line with the change and the probability that you beat different thresholds goes up overall but more so for moderate returns closer to the median than the extremes.

What about volatility?

The Impact of Decreasing Volatility

Having completed the prior analysis, I expected that tweaking volatility (in the form of adjusting the variance of the distribution) would result in preserving the basic distribution shape and position but narrowing or expanding it’s “spread”. However, I was surprised to find that adjusting the volatility didn’t just impact the “spread” of the distribution, it impacted the median returns as well!

Below is the returns heatmap for an investment that has the same mean as the S&P500 from 1928-2019 but 2% lower variance. A quick comparison with the first heat/density map shows that, as expected, the overall shape looks similar but is clearly narrower.

Density Map of 40-Year Returns for Low Volatility Investment
Densities are log (base 10)-adjusted; Assumes S&P500 returns are normally distributed (clipped from -90% to +100%) based on 1928-2019 annual returns but with 2% lower variance. Years go from 0-39 (rather than 1-40)

Looking more closely at the contour lines (below) of the new distribution (in red) and comparing with the original S&P500 distribution (in blue) reveals, however, that the difference is more than just in the “spread” of returns, but in their relative position as well! The red lines are all shifted upward and the upward shift seems to increase over time. It turns out a ~2% decrease in variance appears to buy a 1% increase in the median return and a 1.5% increase in the lower bound of the 50% confidence interval at year 40!

The probability comparison (below) makes the impact of this clear. With lower volatility, not only is an investor better able to avoid a loss / beat a moderate 5% return (the first two red lines having been meaningfully shifted upwards from the first two blue lines), but by raising the median return, the probability of beating a median-like return (10%) gets better over time as well! The one area the lower volatility distribution under-performs the original is in the probability of beating a high return (15%). This too makes sense — because the hypothetical investment experiences lower volatility, it becomes less likely to get the string of high returns needed to consistently beat the median over the long term.

Probability Low Volatility Investment (Red) and S&P500 (Blue) will Exceed 0%, 5%, 10%, and 15% Returns
(from top to bottom/darkest to lightest) Probability that 40-year S&P500 returns simulation beat 0%, 5%, 10%, and 15% geometric mean return. Assumes S&P500 returns are normally distributed (clipped from -90% to +100%) based on 1928-2019 annual returns. Low volatility investment is a hypothetical asset with identical mean but 2% lower variance

The Risk-Reward Tradeoff

Unfortunately, it’s not easy to find a “S&P500 but less volatile” or a “S&P500 but higher return”. In general, higher returns tend to go with greater volatility and vice versa.

While the exact nature of the tradeoff will depend on the specific numbers, to see what happens when you combine the two effects, I charted out the contours and probability curves for two distributions with roughly the same median return (below): one investment with a higher return (+1%) and higher volatility (+2% variance) than the S&P500 and another with a lower return (-1%) and lower volatility (-2% variance) than the S&P500:

Probability Low Volatility/Low Return (Purple) vs. High Volatility/High Return (Gray) Exceed 0%, 5%, 10%, and 15% Returns
(from top to bottom/darkest to lightest) Probability that 40-year returns simulation for hypothetical investment with 1% higher mean and 2% higher variance than S&P500 (gray) and one with 1% lower mean and 2% lower variance than S&P500 (purple) beat 0%, 5%, 10%, and 15% geometric mean return. Both returns assume normal distribution clipped from -90% to +100% with mean/variance based on 1928-2019 annual returns for S&P500.

The results show how two different ways of targeting the same long-run median return compare. The lower volatility investment, despite the lower (arithmetic) average annual return, still sees a much improved chance of avoiding loss and clearing the 5% return threshold. On the other hand, the higher return investment has a distinct advantage at outperforming the median over the long term and even provides a consistent advantage in beating the 10% return threshold close to the median.


The simulations above made it easy to profile unconventional metrics (geometric mean returns and the probability to beat different threshold returns) across time without doing a massive amount of hairy, symbolic math. By charting out the results, they also helped provide a richer, visual understanding of investment risk that goes beyond the overly simple and widely held belief that “volatility is the same thing as risk”:

  • Time horizon matters as uncertainty in returns decreases with time: As the charts above showed, “reversion to the mean” reduces the uncertainty (or “spread”) in returns over time. What this means is that the same level of volatility can be viewed wildly differently by two different investors with two different time horizons. An investor who needs the money in 2 years could find one level of variance unbearably bumpy while the investor saving for a goal 20 years away may see it very differently.
  • The investment return “needed” is key to assessing risk: An investor who needs to avoid a loss at all costs should have very different preferences and assessments of risk level than an investor who must generate higher returns in order to retire comfortably, even at the same time. The first investor should prioritize lower volatility investments and longer holding periods, while the latter should prioritize higher volatility investments and shorter holding periods. It’s not just a question of personal preferences about gambling & risk, as much of the discussion on risk tolerance seems to suggest, because the same level of volatility should rationally be viewed differently by different investors with different financial needs.
  • Volatility impacts long-run returns: Higher volatility decreases long-term median returns, and lower volatility increases long-term returns. From some of my own testing, this seems to happen at roughly a 2:1 ratio (where a 2% increase in variance decreases median returns by 1% and vice versa — at least for values of return / variance near the historical values for S&P500). The result is that understanding volatility is key to formulating the right investment approach, and it creates an interesting framework with which to evaluate how much to hold of lower risk/”riskless” things like cash and government bonds.

What’s Next

Having demonstrated how simulations can be applied to get a visual understanding of investment decisions and returns, I want to apply this analysis to other problems. I’d love to hear requests for other questions of interest, but for now, I plan to look into:

  • Diversification
  • Rebalancing
  • Withdrawal levels
  • Dollar cost averaging
  • Asset allocation
  • Alternative investment return distributions
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Using AI to Predict if a Paper Will Be In a Top-Tier Journal

I have been doing some work in recent months with Dr. Sophia Wang (who also happens to be my wife) at Stanford applying deep learning/AI techniques to make predictions using notes written by doctors in electronic medical records (EMR). 

Because of the sensitivity of the information, a lot of what we’re working on can be difficult to share. So, I put together a fun project based on public data and some of the lessons I’ve picked up from working on these projects (lessons on working with Tensorflow, Keras, BeautifulSoup,, etc.) to see if, given a paper abstract and title, you can predict if a paper is going to make it into a top-tier ophthalmology journal 😇. Surprisingly it did pretty well (87% accuracy, 91% AUROC — higher than I expected when I set out to do this!) and so I’m releasing the code on Github as well as a tutorial explaining the code to help people out there who want to try experimenting with these new powerful AI tools on something similar (but are having trouble starting due to a lack of simple documentation and tutorials for some of these features).

If you’re curious, check it out.


What Can We Learn from Covid-19

(Source: Duke Health)

I’ve been reflecting a bit on what government officials & policymakers should learn from the Covid-19 crisis. While history, with the benefit of hindsight and data, will be the ultimate judge, a few things jump out to me as obvious:

  1. A few decades lucky streak of no major pandemic should not make anyone complacent on the importance of public health. Many Americans, at the onset of the crisis, simply assumed that pandemics were like the bubonic plague or ebola: concerns for a faraway time and place. But the unpleasant reality is infections have been with us since before civilization and will continue long after the current crisis ends. All our modern conveniences and sophistication don’t mean a damn to a microbe. As a result, making sure public health leadership and infrastructure is in place and has the emergency powers and resources it needs when crisis hits is absolutely vital.
  2. I think there are very good reasons to want a single payer healthcare system, and plenty of reasons to be wary of one. But, despite the efforts of pundits, I think Covid-19 is largely irrelevant to this debate. The practical reality is many countries with single payer systems (like those in Europe) appear to have been completely incapable of managing this while some without it (ie China) have. This is not to say China did everything perfectly (obviously they didn’t and I pray they’ve learned this time to permanently shut down wildlife wet markets) nor that single payer systems are to blame for whats happening in places like Italy (it’s not). But even holding aside how the term “single payer” papers over important nuances about how different “single payer” systems mix different levels of private coverage in, the truth of the matter is that countries that have been able to control and contain the disease share more around bold actions by public health officials than they do around how exactly healthcare is provisioned. The “Medicare for all” debate needs to be had and resolved — I’m not cheapening that — but I just don’t think Covid-19 is particularly relevant for either side of it.
  3. Sick leave is critical for public health. People who are sick need to feel like they can stay home and not jeopardize their financial well being. Otherwise economic activity (which we can’t all avoid: everyone eventually needs food even when locked down on quarantine) becomes increasingly the domain of those who are sick but forced to be working from economic desperation.
  4. Confused and contradictory messaging from government officials is NOT helpful. When different officials give wildly different responses (see Devin Nunes encouraging people to go out for dinner earlier Sunday vs. the Director of NIAID advocating for a national lockdown), is it any wonder that the public can’t tell how seriously to take this? On the one hand, we have toilet paper shortages at stores and on the other hand people in this club in Nashville last night are partying as if nothing was happening).
  5. I’ve had a number of conversations with smart people I respect who have commented on the difference in reaction to the crisis in “the West” (US, UK, Europe) vs “the East” (China, Japan, Korea, Taiwan, Singapore). Its hard to have this sort of conversation without racial undertones creeping in, but its also hard to ignore the chorus of commentators who believe that East Asian countries were able to more quickly implement systems and policies many in the West initially thought were harsh and excessive because they have the type of governance systems & culture to support it — individual rights and preferences be damned. I’m sure a big part of this is that these governments had “practice” with SARS at the turn of the century, but I think we’ll need to think long and hard about some of the tradeoffs a highly federated governance system oriented around the rights of the individual have.
  6. The internet may not have been this before but its certainly now a utility that is necessary for education and the modern workforce. We should act accordingly as it pertains to increasing access and maintaining it as an open platform for all.

Stay safe everyone — here’s to being able to pontificate more with you all (online or in-person when the crisis is over)! 🍸

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What I've Changed My Mind on Over the 2010s

I’ve been reading a lot of year-end/decade-end reflections (as one does this time of year) — and while a part of me wanted to #humblebrag about how I got a 🏠/💍/👶 this decade 😇 — I thought it would be more interesting & profound to instead call out 10 worldviews & beliefs I had going into the 2010s that I no longer hold.

  1. Sales is an unimportant skill relative to hard work / being smart
    As a stereotypical “good Asian kid” 🤓, I was taught to focus on nailing the task. I still think that focus is important early in one’s life & career, but this decade has made me realize that everyone, whether they know it or not, has to sell — you sell to employers to hire you, academics/nonprofits sell to attract donors and grant funding, even institutional investors have to sell to their investors/limited partners. Its a skill at least as important (if not more so).
  2. Marriage is about finding your soul-mate and living happily ever after
    Having been married for slightly over half the decade, I’ve now come to believe that marriage is less about finding the perfect soul-mate (the “Hollywood version”) as it is about finding a life partner who you can actively choose to celebrate (despite and including their flaws, mistakes, and baggage). Its not that passionate love is unimportant, but its hard to rely on that alone to make a lifelong partnership work. I now believe that really boring-sounding things like how you make #adulting decisions and compatibility of communication style matter a lot more than things usually celebrated in fiction like the wedding planning, first dates, how nice your vacations together are, whether you can finish each other’s sentences, etc.
  3. Industrial policy doesn’t work
    I tend to be a big skeptic of big government policy — both because of unintended consequences and the risks of politicians picking winners. But, a decade of studying (and working with companies who operate in) East Asian economies and watching how subsidies and economies of scale have made Asia the heart of much of advanced manufacturing have forced me to reconsider. Its not that the negatives don’t happen (there are many examples of China screwing things up with heavy-handed policy) but its hard to seriously think about how the world works without recognizing the role that industrial policy played. For more on how land management and industrial policies impacted economic development in different Asian countries, check out Joe Studwell’s book How Asia Works
  4. Obesity & weight loss are simple — its just calories in & calories out
    From a pure physics perspective, weight gain is a “simple” thermodynamic equation of “calories in minus calories out”. But in working with companies focused on dealing with prediabetes/obesity, I’ve come to appreciate that this “logic” not only ignores the economic and social factors that make obesity a public health problem, it also overlooks that different kinds of foods drive different physiological responses. As an example that just begins to scratch the surface, one very well-controlled study (sadly, a rarity in the field) published in July showed that, even after controlling for exercise and calories, carbs, fat, fiber, and other nutrients present in a meal, diets consisting of processed foods resulted in greater weight-gain than a diet consisting of unprocessed foods
  5. Revering luminaries & leaders is a good thing
    Its very natural to be so compelled by an idea / movement that you find yourself idolizing the people spearheading it. The media feeds into this with popular memoirs & biographies and numerous articles about how you can think/be/act more like [Steve Jobs/Jeff Bezos/Warren Buffett/Barack Obama/etc]. But, over the past decade, I’ve come to feel that this sort of reverence leads to a pernicious laziness of thought. I can admire Steve Jobs for his brilliance in product design but do I want to copy his approach to management or his use of alternative medicine to treat his cancer or condoning how he treated his illegitimate daughter. I think its far better to appreciate an idea and the work of the key people behind it than to equate the piece of work with the person and get sucked in to that cult of personality.
  6. Startups are great place for everyone
    Call it being sucked into the Silicon valley ethos but for a long time I believed that startups were a great place for everyone to build a career: high speed path to learning & responsibility, ability to network with other folks, favorable venture funding, one of the only paths to getting stock in rapidly growing companies, low job seeking risk (since there’s an expectation that startups often fail or pivot). Several years spent working in VC and startups later, and, while I still agree with my list above, I’ve come to believe that startups are really not a great place for most people. The risk-reward is generally not great for all but the earliest of employees and the most successful of companies, and the “startups are great for learning” Kool-aid is oftentimes used to justify poor management and work practices. I still think its a great place for some (i.e. people who can tolerate more risk [b/c of personal wealth or a spouse with a stable high-paying job], who are knowingly optimizing for learning & responsibility, or who are true believers in a startup’s mission), but I frankly think most people don’t fit the bill.
  7. Microaggressions are just people being overly sensitive
    I’ve been blessed at having only rarely faced overt racism (telling me to go back to China 🙄 / or that I don’t belong in this country). It’s a product of both where I’ve spent most of my life (in urban areas on the coasts) and my career/socioeconomic status (its not great to be racist to a VC you’re trying to raise money from 🤑). But, having spent some dedicated time outside of those coastal areas this past decade and speaking with minorities who’ve lived there, I’ve become exposed to and more aware of “microaggressions”, forms of non-overt prejudice that are generally perpetrated without ill intent: questions like ‘so where are you really from?’ or comments like ‘you speak English really well!’. I once believed people complaining about these were simply being overly sensitive, but I’ve since become an active convert to the idea that, while these are certainly nowhere near as awful as overt hate crimes / racism, they are their own form of systematic prejudice which can, over time, grate and eat away at your sense of self-worth.
  8. The Western model (liberal democracy, free markets, global institutions) will reign unchallenged as a model for prosperity
    I once believed that the Western model of (relatively) liberal democracy, (relatively) free markets, and US/Europe-led global institutions was the only model of prosperity that would reign falling the collapse of the Soviet Union. While I probably wouldn’t have gone as far as Fukuyama did in proclaiming “the end of history”, I believed that the world was going to see authoritarian regimes increasingly globalize and embrace Western institutions. What I did not expect was the simultaneous rise of different models of success by countries like China and Saudi Arabia (who, frighteningly, now serve as models for still other countries to embrace), as well as a lasting backlash within the Western countries themselves (i.e. the rise of Trump, Brexit, “anti-globalism”, etc). This has fractured traditional political divides (hence the soul-searching that both major parties are undergoing in the US and the UK) and the election of illiberal populists in places like Mexico, Brazil, and Europe.
  9. Strategy trumps execution
    As a cerebral guy who spent the first years of his career in the last part of the 2000s as a strategy consultant, it shouldn’t be a surprise that much of my focus was on formulating smart business strategy. But having spent much of this decade focused on startups as well as having seen large companies like Apple, Amazon, and Netflix brilliantly out-execute companies with better ‘strategic positioning’ (Nokia, Blackberry, Walmart, big media), I’ve come around to a different understanding of how the two balance each other.
  10. We need to invent radically new solutions to solve the climate crisis
    Its going to be hard to do this one justice in this limited space — especially since I net out here very differently from Bill Gates — but going into this decade, I never would have expected that the cost of new solar or wind energy facilities could be cheaper than the cost of operating an existing coal plant. I never thought that lithium batteries or LEDs would get as cheap or as good as they are today (with signs that this progress will continue) or that the hottest IPO of the year would be an alternative food technology company (Beyond Meat) which will play a key role in helping us mitigate food/animal-related emissions. Despite the challenges of being a cleantech investor for much of the decade, its been a surprising bright spot to see how much pure smart capital and market forces have pushed many of the technologies we need. I still think we will need new policies and a huge amount of political willpower — I’d also like to see more progress made on long-duration energy storage, carbon capture, and industrial — but whereas I once believed that we’d need radically new energy technologies to thwart the worst of climate change, I am now much more of an optimist here than I was when the decade started.

Here’s to more worldview shifts in the coming decade!

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How Tesla is like a Startup in a Bad Way

TSLAs 2019 Stock Price (YTD) (Source: Yahoo Finance)

The word “startup” is usually associated with innovation and speed. But, from a financial perspective, the thing that most distinguishes a startup from other types of businesses is that startups are dependent on investors for cash to fund growth.

A common misconception here is that startups need investors because they are unprofitable. While many startups are indeed unprofitable (in many cases, rationally so), profitability does not shield a business from the need to invest capital (to build a factory, to build up inventory, to order raw materials for production in advance of sales, etc.) to grow. In the case of a rapidly growing, cash strapped startup, this problem is particularly acute as there are no certain past or future cash flows with which to finance growth and so startups have to turn to pitching investors.

By that definition, electric vehicle maker Tesla is a startup operating on an unprecedented scale. While it may have a valuation (over $40 billion market cap as of this writing) and revenues (over $20 billion in 2018) that look like a “grown up” company, as with virtually all startups, it is completely dependent on investors to finance its growth. Since Tesla went public in 2010, the company has raised over $15 billion of debt and equity (net of paying out dividends and repaying loans), over 2/3 of which has gone into funding the extensive capital expenditures (CAPEX: investments in tooling, equipment, factories, land, etc) they’ve needed to grow.

Note: the numbers / figures presented in this post are based on publicly available data provided by Tesla on its deliveries and financials. As Tesla has a penchant for revising old figures, some of these may be based on slightly outdated figures, but I have tried to use the most recent versions I could find. Tesla does not break out much detail by segment or, for automotive, by car model, and as a result most of the figures here are aggregate level. For revenues and gross profitability, I’ve used GAAP numbers from their automotive segment (inclusive of leasing) but for capital expenditures, operating expenditures, depreciation, and cash flows I’m using the entire entity. This is done both because Tesla does not provide breakouts by segment but also because burdening these costs on Tesla’s automotive business is likely both realistic (due to the fact that Tesla’s automotive segment is responsible for the vast majority of revenue and expenditure both today and in the past) and presents a more favorable view of the business (due to Tesla’s automotive segment consistently being more profitable than the others). Refer to this Google Sheet for additional information.

This has fueled an astonishing 76.4% compounded annual growth in revenue from 2009-2018, which is especially impressive considering that Tesla vehicles sell at a premium relative to the rest of the market.

However, because the company continues to require injections of investor cash (having raised $1.5B in the first two quarters of 2019, after burning $323M of cash in that same time), the key question for any current or prospective investor into Tesla is will all of this cash burn ever pay off?

This is a question that VCs are used to asking with the startups they pour money into, but it’s one that is a lot trickier for Tesla shareholders to answer. A small software startup looking for $10M in venture capital can find many patient sources of capital who are willing to bet that the company either turns profitable (because most of the cost lies in initial development and sales) or gets sold at an attractive valuation.

But, Tesla, with a valuation in the $10’s of billions (pricing out most buyers) and needing to raise $100’s of millions (if not more) each year from investors demanding near-term results (i.e. public market investors, large corporate debt holders and their rating agencies), will likely have to prove that it can generate real profits.

But, that isn’t happening today. While Tesla proudly boasts about record deliveries as a sign of healthy demand, the numbers show this is a direct result of Tesla’s choice to shift away from selling more profitable Model S/X vehicles to selling lower price, less profitable Model 3s. This has exacerbated a multi-year trend of declining per vehicle profitability:

Lower gross profits per vehicle are not the end of the world, provided that Tesla can sell enough Model 3s to make up for the lower unit profit and start covering their other costs. But that also isn’t happening. At a fundamental level, Tesla is just not getting any real operating leverage. While booming sales volumes have boosted Tesla’s gross profits, the company’s operating expenditures (OPEX; or spending on sales, administrative overhead, and research & development) have more than kept pace. Rational watchers can choose to interpret this as either an inability to maintain growth without spending huge amounts on R&D and SG&A or as smart, long-term bets on future technologies, but the data is clear that Tesla has a long way to go before proving it can fund its own growth just by selling more cars.

The chart below shows another way of looking at this — it graphs the number of vehicles Tesla needed to deliver to cover its OPEX in a given year against the number of vehicles Tesla actually delivered that year. What is astonishing is that the number of vehicles needed to cover OPEX has gone up dramatically each year. Only in one year since 2014 did Tesla close that gap — 2018 after two amazing quarters — and from the available data for the first half of 2019, it looks like, barring a dramatic shift in pricing or profitability, Tesla will need to hit its guidance of 360,000-400,000 cars to just breakeven.

*Multiplied 2019 H1 vehicles deliveries & deliveries to break-even by 2 to compare directly with data from past years

If Tesla is not clearly demonstrating improving profitability, then for the startup investment story to work, it needs to at least demonstrate improved capital efficiency (how effectively it spends investor cash on production). While one can point to Tesla’s more moderate CAPEX spend since 2017 as evidence for this, it is more relevant to understand how Tesla is progressing in its ability to turn CAPEX investments into profit.

While its difficult to calculate precise figures around capital efficiency in the absence of specific data on the cost to build a factory and how the factories are utilized, a ratio of Tesla’s annual automotive gross profits (adjusted to remove depreciation) to its annual depreciation (a way of measuring how current and past capital expenditures are utilized in a given year, albeit one which also factors in CAPEX from Tesla’s non-automotive businesses because Tesla does not break those out separately) can be instructive. The chart below shows that, where Tesla once generated nearly $5 in profit per $1 of depreciation in 2015, it generated only $2.69 in the first half of 2019 (over 40% less). In other words, if Tesla is improving its capital efficiency and utilization as it ramps production and learns from its past mistakes, its not apparent in the numbers.

Adj. Automotive Gross Profits are GAAP Automotive Gross Profits, Less Total TSLA Depreciation

All of this is not to say that Tesla is doomed — the company’s sales, despite missteps (happy one year anniversary of “funding secured”), continues to grow, and the company has clearly captured the American public’s imagination and mind-share as it pertains to electric vehicles, and equity/debt investors continue to extend Tesla more capital even at its current valuation and debt load.

But, in terms of capital requirements, Tesla is running the largest startup experiment of all time. Earlier this year, Bird raised $300M to invest in (what are currently) money-losing electric scooters. In a sense, Tesla is doing the same thing with the Model 3 but at a far greater scale, all the while trying to develop autonomous driving technology and financing the massive liabilities of its SolarCity business. As a result, Tesla needs to continue to sell the dream both to the public and to investors, and to continue to maintain the vision of future profitability and capital efficiency as a misstep here could cause things to rapidly unravel.

Special thanks Andrew Garvin and Derek Yang for reading an earlier version of this and sharing helpful comments!

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Calculating the Financial Returns to College

Despite the recent spotlight on the staggering $1.5 trillion in student debt that 44 million Americans owe in 2019, there has been surprisingly little discussion on how to measure the value of a college education relative to its rapidly growing price tag (which is the reason so many take on debt to pay for it).

(Image Credit: Dowling & Yahnke Wealth Advisors)

While it’s impossible to quantify all the intangibles of a college education, the tools of finance offers a practical, quantitative way to look at the tangible costs and benefits which can shed light on (1) whether to go to college / which college to go to, (2) whether taking on debt to pay for college is a wise choice, and (3) how best to design policies around student debt.

The below briefly walks through how finance would view the value of a college education and the soundness of taking on debt to pay for it and how it can help guide students / families thinking about applying and paying for colleges and, surprisingly, how there might actually be too little college debt and where policy should focus to address some of the issues around the burden of student debt.

The Finance View: College as an Investment

Through the lens of finance, the choice to go to college looks like an investment decision and can be evaluated in the same way that a company might evaluate investing in a new factory. Whereas a factory turns an upfront investment of construction and equipment into profits on production from the factory, the choice to go to college turns an upfront investment of cash tuition and missed salary while attending college into higher after-tax wages.

Finance has come up with different ways to measure returns for an investment, but one that is well-suited here is the internal rate of return (IRR). The IRR boils down all the aspects of an investment (i.e., timing and amount of costs vs. profits) into a single percentage that can be compared with the rates of return on another investment or with the interest rate on a loan. If an investment’s IRR is higher than the interest rate on a loan, then it makes sense to use the loan to finance the investment (i.e., borrowing at 5% to make 8%), as it suggests that, even if the debt payments are relatively onerous in the beginning, the gains from the investment will more than compensate for it.

To gauge what these returns look like, I put together a Google spreadsheet which generated the figures and charts below (this article in Investopedia explains the math in greater detail). I used publicly available data around wages (from the 2017 Current Population Survey, GoBankingRate’s starting salaries by school, and National Association of Colleges and Employer’s starting salaries by major), tax brackets (using the 2018 income tax), and costs associated with college (from College Board’s statistics [PDF] and the Harvard admissions website). To simplify the comparisons, I assumed a retirement age of 65, and that nobody gets a degree more advanced than a Bachelor’s.

To give an example: if Sally Student can get a starting salary after college in line with the average salary of an 18-24 year old Bachelor’s degree-only holder ($47,551), would have earned the average salary of an 18-24 year old high school diploma-only holder had she not gone to college ($30,696), and expects wage growth similar to what age-matched cohorts saw from 1997-2017, then the IRR of a 4-year degree at a non-profit private school if Sally pays the average net (meaning after subtracting grants and tax credits) tuition, fees, room & board ($26,740/yr in 2017, or a 4-year cost of ~$106,960), the IRR of that investment in college would be 8.1%.

How to Benchmark Rates of Return

Is that a good or a bad return? Well, in my opinion, 8.1% is pretty good. Its much higher than what you’d expect from a typical savings account (~0.1%) or a CD or a Treasury Bond (as of this writing), and is also meaningfully higher than the 5.05% rate charged for federal subsidized loans for 2018-2019 school year — this means borrowing to pay for college would be a sensible choice. That being said, its not higher than the stock market (the S&P500 90-year total return is ~9.8%) or the 20% that you’d need to get into the top quartile of Venture Capital/Private Equity funds [PDF].

What Drives Better / Worse Rates of Return

Playing out different scenarios shows which factors are important in determining returns. An obvious factor is the cost of college:

T&F: Tuition & Fees; TFR&B: Tuition, Fees, Room & Board
List: Average List Price; Net: Average List Price Less Grants and Tax Benefits
Blue: In-State Public; Green: Private Non-Profit; Red: Harvard

As evident from the chart, there is huge difference between the rate of return Sally would get if she landed the same job but instead attended an in-state public school, did not have to pay for room & board, and got a typical level of financial aid (a stock-market-beating IRR of 11.1%) versus the world where she had to pay full list price at Harvard (IRR of 5.3%). In one case, attending college is a fantastic investment and Sally borrowing money to pay for it makes great sense (investors everywhere would love to borrow at ~5% and get ~11%). In the other, the decision to attend college is less straightforward (financially), and it would be very risky for Sally to borrow money at anything near subsidized rates to pay for it.

Some other trends jump out from the chart. Attending an in-state public university improves returns for the average college wage-earner by 1-2% compared with attending private universities (comparing the blue and green bars). Getting an average amount of financial aid (paying net vs list) also seems to improve returns by 0.7-1% for public schools and 2% for private.

As with college costs, the returns also understandably vary by starting salary:

There is a night and day difference between the returns Sally would see making $40K per year (~$10K more than an average high school diploma holder) versus if she made what the average Caltech graduate does post-graduation (4.6% vs 17.9%), let alone if she were to start with a six-figure salary (IRR of over 21%). If Sally is making six figures, she would be making better returns than the vast majority of venture capital firms, but if she were starting at $40K/yr, her rate of return would be lower than the interest rate on subsidized student loans, making borrowing for school financially unsound.

Time spent in college also has a big impact on returns:

Graduating sooner not only reduces the amount of foregone wages, it also means earning higher wages sooner and for more years. As a result, if Sally graduates in two years while still paying for four years worth of education costs, she would experience a higher return (12.6%) than if she were to graduate in three years and save one year worth of costs (11.1%)! Similarly, if Sally were to finish school in five years instead of four, this would lower her returns (6.3% if still only paying for four years, 5.8% if adding an extra year’s worth of costs). The result is that an extra / less year spent in college is a ~2% hit / boost to returns!

Finally, how quickly a college graduate’s wages grow relative to a high school diploma holder’s also has a significant impact on the returns to a college education:

Census/BLS data suggests that, between 1997 and 2017, wages of bachelor’s degree holders grew faster on an annualized basis by ~0.7% per year than for those with only a high school diploma (6.7% vs 5.8% until age 35, 4.0% vs 3.3% for ages 35-55, both sets of wage growth appear to taper off after 55).

The numbers show that if Sally’s future wages grew at the same rate as the wages of those with only a high school diploma, her rate of return drops to 5.3% (just barely above the subsidized loan rate). On the other hand, if Sally’s wages end up growing 1% faster until age 55 than they did for similar aged cohorts from 1997-2017, her rate of return jumps to a stock-market-beating 10.3%.

Lessons for Students / Families

What do all the charts and formulas tell a student / family considering college and the options for paying for it?

First, college can be an amazing investment, well worth taking on student debt and the effort to earn grants and scholarships. While there is well-founded concern about the impact that debt load and debt payments can have on new graduates, in many cases, the financial decision to borrow is a good one. Below is a sensitivity table laying out the rates of return across a wide range of starting salaries (the rows in the table) and costs of college (the columns in the table) and color codes how the resulting rates of return compare with the cost of borrowing and with returns in the stock market (red: risky to borrow at subsidized rates; white: does make sense to borrow at subsidized rates but it’s sensible to be mindful of the amount of debt / rates; green: returns are better than the stock market).

Except for graduates with well below average starting salaries (less than or equal to $40,000/yr), most of the cells are white or green. At the average starting salary, except for those without financial aid attending a private school, the returns are generally better than subsidized student loan rates. For those attending public schools with financial aid, the returns are better than what you’d expect from the stock market.

Secondly, there are ways to push returns to a college education higher. They involve effort and sometimes painful tradeoffs but, financially, they are well worth considering. Students / families choosing where to apply or where to go should keep in mind costs, average starting salaries, quality of career services, and availability of financial aid / scholarships / grants, as all of these factors will have a sizable impact on returns. After enrollment, student choices / actions can also have a meaningful impact: graduating in fewer semesters/quarters, taking advantage of career resources to research and network into higher starting salary jobs, applying for scholarships and grants, and, where possible, going for a 4th/5th year masters degree can all help students earn higher returns to help pay off any debt they take on.

Lastly, use the spreadsheet*! The figures and charts above are for a very specific set of scenarios and don’t factor in any particular individual’s circumstances or career trajectory, nor is it very intelligent about selecting what the most likely alternative to a college degree would be. These are all factors that are important to consider and may dramatically change the answer.

*To use the Google Sheet, you must be logged into a Google account; use the “Make a Copy” command in the File menu to save a version to your Google Drive and edit the tan cells with red numbers in them to whatever best matches your situation and see the impact on the yellow highlighted cells for IRR and the age when investment pays off

Implications for Policy on Student Debt

Given the growing concerns around student debt and rising tuitions, I went into this exercise expecting to find that the rates of return across the board would be mediocre for all but the highest earners. I was (pleasantly) surprised to discover that a college graduate earning an average starting salary would be able to achieve a rate of return well above federal loan rates even at a private (non-profit) university.

While the rate of return is not a perfect indicator of loan affordability (as it doesn’t account for how onerous the payments are compared to early salaries), the fact that the rates of return are so high is a sign that, contrary to popular opinion, there may actually be too little student debt rather than too much, and that the right policy goal may actually be to find ways to encourage the public and private sector to make more loans to more prospective students.

As for concerns around affordability, while proposals to cancel all student debt plays well to younger voters, the fact that many graduates are enjoying very high returns suggests that such a blanket policy is likely unnecessary, anti-progressive (after all, why should the government zero out the costs on high-return investments for the soon-to-be upper and upper-middle-classes), and fails to address the root cause of the issue (mainly that there shouldn’t be institutions granting degrees that fail to be good financial investments). Instead, a more effective approach might be:

  • Require all institutions to publish basic statistics (i.e. on costs, availability of scholarships/grants, starting salaries by degree/major, time to graduation, etc.) to help students better understand their own financial equation
  • Hold educational institutions accountable when too many students graduate with unaffordable loan burdens/payments (i.e. as a fraction of salary they earn and/or fraction of students who default on loans) and require them to make improvements to continue to qualify for federally subsidized loans
  • Making it easier for students to discharge student debt upon bankruptcy and increasing government oversight of collectors / borrower rights to prevent abuse
  • Government-supported loan modifications (deferrals, term changes, rate modifications, etc.) where short-term affordability is an issue (but long-term returns story looks good); loan cancellation in cases where debt load is unsustainable in the long-term (where long-term returns are not keeping up) or where debt was used for an institution that is now being denied new loans due to unaffordability
  • Making the path to public service loan forgiveness (where graduates who spend 10 years working for non-profits and who have never missed an interest payment get their student loans forgiven) clearer and addressing some of the issues which have led to 99% of applications to date being rejected

Special thanks Sophia Wang, Kathy Chen, and Dennis Coyle for reading an earlier version of this and sharing helpful comments!

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Lyft vs Uber: A Tale of Two S-1’s

You can learn a great deal from reading and comparing the financial filings of two close competitors. Tech-finance nerd that I am, you can imagine how excited I was to see Lyft’s and Uber’s respective S-1’s become public within mere weeks of each other.

While the general financial press has covered a lot of the top-level figures on profitability (or lack thereof) and revenue growth, I was more interested in understanding the unit economics — what is the individual “unit” (i.e. a user, a sale, a machine, etc.) of the business and what does the history of associated costs and revenues say about how the business will (or will not) create durable value over time.

For two-sided regional marketplaces like Lyft and Uber, an investor should understand the full economic picture for (1) the users/riders, (2) the drivers, and (3) the regional markets. Sadly, their S-1’s don’t make it easy to get much on (2) or (3) — probably because the companies consider the pertinent data to be highly sensitive information. They did, however, provide a fair amount of information on users/riders and rides and, after doing some simple calculations, a couple of interesting things emerged

Uber’s Users Spend More, Despite Cheaper Rides

As someone who first knew of Uber as the UberCab “black-car” service, and who first heard of Lyft as the Zimride ridesharing platform, I was surprised to discover that Lyft’s average ride price is significantly more expensive than Uber’s and the gap is growing! In Q1 2017, Lyft’s average bookings per ride was $11.74 and Uber’s was $8.41, a difference of $3.33. But, in Q4 2018, Lyft’s average bookings per ride had gone up to $13.09 while Uber’s had declined to $7.69, increasing the gap to $5.40.

Average Bookings $/Ride
Sources: Lyft S-1; Uber S-1

Note: the numbers presented above for Uber are for Uber ridesharing bookings divided by total Uber rides, which includes rides for Uber Eats — this was done because we don’t have great data on rides for Uber Eats and because I suspected that Uber Eats trips represent a small minority of trips — something borne out by the fact that the trend / numbers I arrived at roughly matches the Ridesharing Gross Bookings per Trip chart in the Uber S-1

This is especially striking considering the different definitions that Lyft and Uber have for “bookings” — Lyft excludes “pass-through amounts paid to drivers and regulatory agencies, including sales tax and other fees such as airport and city fees, as well as tips, tolls, cancellation, and additional fees” whereas Uber’s includes “applicable taxes, tolls, and fees“. This gap is likely also due to Uber’s heavier international presence (where they now generate 52% of their bookings). It would be interesting to see this data on a country-by-country basis (or, more importantly, a market-by-market one as well).

Interestingly, an average Uber rider appears to also take ~2.3 more rides per month than an average Lyft rider, a gap which has persisted fairly stably over the past 3 years even as both platforms have boosted the number of rides an average rider takes. While its hard to say for sure, this suggests Uber is either having more luck in markets that favor frequent use (like dense cities), with its lower priced Pool product vs Lyft’s Line product (where multiple users can share a ride), or its general pricing is encouraging greater use.

Monthly Rides / Monthly Active Rider

Sources: Lyft S-1; Uber S-1

Note: the “~monthly” that you’ll see used throughout the charts in this post are because the aggregate data — rides, bookings, revenue, etc — given in the regulatory filings is quarterly, but the rider/user count provided is monthly. As a result, the figures here are approximations based on available data, i.e. by dividing quarterly data by 3

What does that translate to in terms of how much an average rider is spending on each platform? Perhaps not surprisingly, Lyft’s average rider spend has been growing and has almost caught up to Uber’s which is slightly down.

Monthly Bookings $ / Monthly Active User

Sources: Lyft S-1; Uber S-1

However, Uber’s new businesses like UberEats are meaningfully growing its share of wallet with users (and nearly perfectly dollar for dollar re-opens the gap on spend per user that Lyft narrowed over the past few years). In 2018 Q4, the gap between the yellow line (total bookings per user, including new businesses) and the red line (total bookings per user just for rides) is almost $10 / user / month! Its no wonder that in its filings, Lyft calls its users “riders”, but Uber calls them “Active Platform Consumers”.

Despite Pocketing More per Ride, Lyft Loses More per User

Long-term unit profitability is more than just how much an average user is spending, its also how much of that spend hits a company’s bottom line. Perhaps not surprisingly, because they have more expensive rides, a larger percent of Lyft bookings ends up as gross profit (revenue less direct costs to serve it, like insurance costs) — ~13% in Q4 2018 compared with ~9% for Uber. While Uber’s has bounced up and down, Lyft’s has steadily increased (up nearly 2x from Q1 2017). I would hazard a guess that Uber’s has also increased in its more established markets but that their expansion efforts into new markets (here and abroad) and new service categories (UberEats, etc) has kept the overall level lower.

Gross Margin as % of Bookings
Sources: Lyft S-1; Uber S-1

Note: the gross margin I’m using for Uber adds back a depreciation and amortization line which were separated to keep the Lyft and Uber numbers more directly comparable. There may be other variations in definitions at work here, including the fact that Uber includes taxes, tolls, and fees in bookings that Lyft does not. In its filings, Lyft also calls out an analogous “Contribution Margin” which is useful but I chose to use this gross margin definition to try to make the numbers more directly comparable.

The main driver of this seems to be higher take rate (% of bookings that a company keeps as revenue) — nearly 30% in the case of Lyft in Q4 2018 but only 20% for Uber (and under 10% for UberEats)

Revenue as % of Bookings
Sources: Lyft S-1; Uber S-1

Note: Uber uses a different definition of take rate in their filings based on a separate cut of “Core Platform Revenue” which excludes certain items around referral fees and driver incentives. I’ve chosen to use the full revenue to be more directly comparable

The higher take rate and higher bookings per user has translated into an impressive increase in gross profit per user. Whereas Lyft once lagged Uber by almost 50% on gross profit per user at the beginning of 2017, Lyft has now surpassed Uber even after adding UberEats and other new business revenue to the mix.

Monthly Gross Profit $ per Monthly Active User
Sources: Lyft S-1; Uber S-1

All of this data begs the question, given Lyft’s growth and lead on gross profit per user, can it grow its way into greater profitability than Uber? Or, to put it more precisely, are Lyft’s other costs per user declining as it grows? Sadly, the data does not seem to pan out that way

Monthly OPEX $ per Monthly Active User
Sources: Lyft S-1; Uber S-1

While Uber had significantly higher OPEX (expenditures on sales & marketing, engineering, overhead, and operations) per user at the start of 2017, the two companies have since reversed positions, with Uber making significant changes in 2018 which lowered its OPEX per user spend to under $9 whereas Lyft’s has been above $10 for the past two quarters. The result is Uber has lost less money per user than Lyft since the end of 2017

Monthly Profit $ per Monthly Active User
Sources: Lyft S-1; Uber S-1

The story is similar for profit per ride. Uber has consistently been more profitable since 2017, and they’ve only increased that lead since. This is despite the fact that I’ve included the costs of Uber’s other businesses in their cost per ride.

Profit $ per Ride
Sources: Lyft S-1; Uber S-1

Does Lyft’s Growth Justify Its Higher Spend?

One possible interpretation of Lyft’s higher OPEX spend per user is that Lyft is simply investing in operations and sales and engineering to open up new markets and create new products for growth. To see if this strategy has paid off, I took a look at the Lyft and Uber’s respective user growth during this period of time.

Sources: Lyft S-1; Uber S-1

The data shows that Lyft’s compounded quarterly growth rate (CQGR) from Q1 2016 to Q4 2018 of 16.4% is only barely higher than Uber’s at 15.3% which makes it hard to justify spending nearly $2 more per user on OPEX in the last two quarters.

Interestingly, despite all the press and commentary about #deleteUber, it doesn’st seem to have really made a difference in their overall user growth (its actually pretty hard to tell from the chart above that the whole thing happened around mid-Q1 2017).

How are Drivers Doing?

While there is much less data available on driver economics in the filings, this is a vital piece of the unit economics story for a two-sided marketplace. Luckily, Uber and Lyft both provide some information in their S-1’s on the number of drivers on each platform in Q4 2018 which are illuminating.

Q4 2018LyftUberComparison
Drivers1.1 million 3.9 million
Rides / Driver162.18382.82Uber is higher by 136%
Rides Bookings
$ / Driver
$2,123$2,943Uber higher by 39%
because Uber bookings
per ride lower by 41%
Total Bookings
$ / Driver
$2,123$3,63319% of Uber bookings
are non-ride
Take Home
$ / Driver
$1,514$2,982 (total)
$2,350 (rides)
Uber higher by 97%
because drivers take
home 15% more per $
If only rides, Uber
higher by 55%

Sources: Lyft S-1; Uber S-1

The average Uber driver on the platform in Q4 2018 took home nearly double what the average Lyft driver did! They were also more likely to be “utilized” given that they handled 136% more rides than the average Lyft driver and, despite Uber’s lower price per ride, saw more total bookings.

It should be said that this is only a point in time comparison (and its hard to know if Q4 2018 was an odd quarter or if there is odd seasonality here) and it papers over many other important factors (what taxes / fees / tolls are reflected, none of these numbers reflect tips, are some drivers doing shorter shifts, what does this look like specifically in US/Canada vs elsewhere, are all Uber drivers benefiting from doing both UberEats and Uber rideshare, etc). But the comparison is striking and should be alarming for Lyft.

Closing Thoughts

I’d encourage investors thinking about investing in either to do their own deeper research (especially as the competitive dynamic is not over one large market but over many regional ones that each have their own attributes). That being said, there are some interesting takeaways from this initial analysis

  • Lyft has made impressive progress at increasing the value of rides on its platform and increasing the share of transactions it gets. One would guess that, Uber, within established markets in the US has probably made similar progress.
  • Despite the fact that Uber is rapidly expanding overseas into markets that face more price constraints than in the US, it continues to generate significantly better user economics and driver economics (if Q4 2018 is any indication) than Lyft.
  • Something happened at Uber at the end of 2017/start of 2018 (which looks like it coincides nicely with Dara Khosrowshahi’s assumption of CEO role) which led to better spending discipline and, as a result, better unit economics despite falling gross profits per user
  • Uber’s new businesses (in particular UberEats) have had a significant impact on Uber’s share of wallet.
  • Lyft will need to find more cost-effective ways of growing its business and servicing its existing users & drivers if it wishes to achieve long-term sustainability as its current spend is hard to justify relative to its user growth.

Special thanks to Eric Suh for reading and editing an earlier version!


How to Regulate Big Tech

There’s been a fair amount of talk lately about proactively regulating — and maybe even breaking up — the “Big Tech” companies.

Full disclosure: this post discusses regulating large tech companies. I own shares in several of these both directly (in the case of Facebook and Microsoft) and indirectly (through ETFs that own stakes in large companies)

†Like many, I have become increasingly uneasy over the fact that a small handful of companies, with few credible competitors, have amassed so much power over our personal data and what information we see. As a startup investor and former product executive at a social media startup, I can especially sympathize with concerns that these large tech companies have created an unfair playing field for smaller companies.

At the same time, though, I’m mindful of all the benefits that the tech industry — including the “tech giants” — have brought: amazing products and services, broader and cheaper access to markets and information, and a tremendous wave of job and wealth creation vital to may local economies. For that reason, despite my concerns of “big tech”‘s growing power, I am wary of reaching for “quick fixes” that might change that.

As a result, I’ve been disappointed that much of the discussion has centered on knee-jerk proposals like imposing blanket stringent privacy regulations and forcefully breaking up large tech companies. These are policies which I fear are not only self-defeating but will potentially put into jeopardy the benefits of having a flourishing tech industry.

The Challenges with Regulating Tech

Technology is hard to regulate. The ability of software developers to collaborate and build on each other’s innovations means the tech industry moves far faster than standard regulatory / legislative cycles. As a result, many of the key laws on the books today that apply to tech date back decades — before Facebook or the iPhone even existed, making it important to remember that even well-intentioned laws and regulations governing tech can cement in place rules which don’t keep up when the companies and the social & technological forces involved change.

Another factor which complicates tech policy is that the traditional “big is bad” mentality ignores the benefits to having large platforms. While Amazon’s growth has hurt many brick & mortar retailers and eCommerce competitors, its extensive reach and infrastructure enabled businesses like Anker and Instant Pot to get to market in a way which would’ve been virtually impossible before. While the dominance of Google’s Android platform in smartphones raised concerns from European regulators, its hard to argue that the companies which built millions of mobile apps and tens of thousands of different types of devices running on Android would have found it much more difficult to build their businesses without such a unified software platform. Policy aimed at “Big Tech” should be wary of dismantling the platforms that so many current and future businesses rely on.

Its also important to remember that poorly crafted regulation in tech can be self-defeating. The most effective way to deal with the excesses of “Big Tech”, historically, has been creating opportunities for new market entrants. After all, many tech companies previously thought to be dominant (like Nokia, IBM, and Microsoft) lost their positions, not because of regulation or antitrust, but because new technology paradigms (i.e. smartphones, cloud), business models (i.e. subscription software, ad-sponsored), and market entrants (i.e. Google, Amazon) had the opportunity to flourish. Because rules (i.e. Article 13/GDPR) aimed at big tech companies generally fall hardest on small companies (who are least able to afford the infrastructure / people to manage it), its important to keep in mind how solutions for “Big Tech” problems affect smaller companies and new concepts as well.

Framework for Regulating “Big Tech”

If only it were so easy… (Image credit: XKCD)

To be 100% clear, I’m not saying that the tech industry and big platforms should be given a pass on rules and regulation. If anything, I believe that laws and regulation play a vital role in creating flourishing markets.

But, instead of treating “Big Tech” as just a problem to kill, I think we’d be better served by laws / regulations that recognize the limits of regulation on tech and, instead, focus on making sure emerging companies / technologies can compete with the tech giants on a level playing field. To that end, I hope to see more ideas that embrace the following four pillars:

I. Tiering regulation based on size of the company

Regulations on tech companies should be tiered based on size with the most stringent rules falling on the largest companies. Size should include traditional metrics like revenue but also, in this age of marketplace platforms and freemium/ad-sponsored business models, account for the number of users (i.e. Monthly Active Users) and third party partners.

In this way, the companies with the greatest potential for harm and the greatest ability to bear the costs face the brunt of regulation, leaving smaller companies & startups with greater flexibility to innovate and iterate.

II. Championing data portability

One of the reasons it’s so difficult for competitors to challenge the tech giants is the user lock-in that comes from their massive data advantage. After all, how does a rival social network compete when a user’s photos and contacts are locked away inside Facebook?

While Facebook (and, to their credit, some of the other tech giants) does offer ways to export user data and to delete user data from their systems, these tend to be unwieldy, manual processes that make it difficult for a user to bring their data to a competing service. Requiring the largest tech platforms to make this functionality easier to use (i.e., letting others import your contact list and photos with the ease in which you can login to many apps today using Facebook) would give users the ability to hold tech companies accountable for bad behavior or not innovating (by being able to walk away) and fosters competition by letting new companies compete not on data lock-in but on features and business model.

III. Preventing platforms from playing unfairly

3rd party platform participants (i.e., websites listed on Google, Android/iOS apps like Spotify, sellers on Amazon) are understandably nervous when the platform owners compete with their own offerings (i.e., Google Places, Apple Music, Amazon first party sales). As a result, some have even called for banning platform owners from offering their own products and services.

I believe that is an overreaction. Platform owners offering attractive products and services (i.e., Google offering turn-by-turn navigation on Android phones) can be a great thing for users (after all, most prominent platforms started by providing compelling first-party offerings) and for 3rd party participants if these offerings improve the attractiveness of the platform overall.

What is hard to justify is when platform owners stack the deck in their favor using anti-competitive moves such as banning or reducing the visibility of competitors, crippling third party offerings, making excessive demands on 3rd parties, etc. Its these sorts of actions by the largest tech platforms that pose a risk to consumer choice and competition and should face regulatory scrutiny. Not just the fact that a large platform exists or that the platform owner chooses to participate in it.

IV. Modernizing how anti-trust thinks about defensive acquisitions

The rise of the tech giants has led to many calls to unwind some of the pivotal mergers and acquisitions in the space. As much as I believe that anti-trust regulators made the wrong calls on some of these transactions, I am not convinced, beyond just wanting to punish “Big Tech” for being big, that the Pandora’s Box of legal and financial issues (for the participants, employees, users, and for the tech industry more broadly) that would be opened would be worthwhile relative to pursuing other paths to regulate bad behavior directly.

That being said, its become clear that anti-trust needs to move beyond narrow revenue share and pricing-based definitions of anti-competitiveness (which do not always apply to freemium/ad-sponsored business models). Anti-trust prosecutors and regulators need to become much more thoughtful and assertive around how some acquisitions are done simply to avoid competition (i.e., Google’s acquisition of Waze and Facebook’s acquisition of WhatsApp are two examples of landmark acquisitions which probably should have been evaluated more closely).


This is hardly a complete set of rules and policies needed to approach growing concerns about “Big Tech”. Even within this framework, there are many details (i.e., who the specific regulators are, what specific auditing powers they have, the details of their mandate, the specific thresholds and number of tiers to be set, whether pre-installing an app counts as unfair, etc.) that need to be defined which could make or break the effort. But, I believe this is a good set of principles that balances both the need to foster a tech industry that will continue to grow and drive innovation as well as the need to respond to growing concerns about “Big Tech”.

Special thanks to Derek Yang and Anthony Phan for reading earlier versions and giving me helpful feedback!

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Different Paths to Success for Tech vs Hardtech Startups

Having been lucky enough to invest in both tech (cloud, mobile, software) and “hardtech” (materials, cleantech, energy, life science) startups (and having also ran product at a mobile app startup), it has been striking to see how fundamentally different the paradigms that drive success in each are.

Whether knowingly or not, most successful tech startups over the last decade have followed a basic playbook:

  1. Take advantage of rising smartphone penetration and improvements in cloud technology to build digital products that solve challenges in big markets pertaining to access (e.g., to suppliers, to customers, to friends, to content, to information, etc.)
  2. Build a solid team of engineers, designers, growth, sales, marketing, and product people to execute on lean software development and growth methodologies
  3. Hire the right executives to carry out the right mix of tried-and-true as well as “out of the box” channel and business development strategies to scale bigger and faster

This playbook appears deceptively simple but is very difficult to execute well. It works because for markets where “software is eating the world”:

  • There is relatively little technology risk: With the exception of some of the most challenging AI, infrastructure, and security challenges, most tech startups are primarily dealing with engineering and product execution challenges — what is the right thing to build and how do I build it on time, under budget? — rather than fundamental technology discovery and feasibility challenges
  • Skills & knowledge are broadly transferable: Modern software development and growth methodologies work across a wide range of tech products and markets. This means that effective engineers, salespeople, marketers, product people, designers, etc. at one company will generally be effective at another. As a result, its a lot easier for investors/executives to both gauge the caliber of a team (by looking at their experience) and augment a team when problems arise (by recruiting the right people with the right backgrounds).
  • Distribution is cheap and fast: Cloud/mobile technology means that a new product/update is a server upgrade/browser refresh/app store download away. This has three important effects:
    1. The first is that startups can launch with incomplete or buggy solutions because they can readily provide hotfixes and upgrades.
    2. The second is that startups can quickly release new product features and designs to respond to new information and changing market conditions.
    3. The third is that adoption is relatively straightforward. While there may be some integration and qualification challenges, in general, the product is accessible via a quick download/browser refresh, and the core challenge is in getting enough people to use a product in the right way.

In contrast, if you look at hardtech companies, a very different set of rules apply:

  • Technology risk/uncertainty is inherent: One of the defining hallmarks of a hardtech company is dealing with uncertainty from constraints imposed by reality (i.e. the laws of physics, the underlying biology, the limits of current technology, etc.). As a result, hardtech startups regularly face feasibility challenges — what is even possible to build? — and uncertainty around the R&D cycles to get to a good outcome — how long will it take / how much will it cost to figure this all out?
  • Skills & knowledge are not easily transferable: Because the technical and business talent needed in hardtech is usually specific to the field, talent and skills are not necessarily transferable from sector to sector or even company to company. The result is that it is much harder for investors/executives to evaluate team caliber (whether on technical merits or judging past experience) or to simply put the right people into place if there are problems that come up.
  • Product iteration is slow and costly: The tech startup ethos of “move fast and break things” is just harder to do with hardtech.
    1. At the most basic level, it just costs a lot more and takes a lot more time to iterate on a physical product than a software one. It’s not just that physical products require physical materials and processing, but the availability of low cost technology platforms like Amazon Web Services and open source software dramatically lower the amount of time / cash needed to make something testable in tech than in hardtech.
    2. Furthermore, because hardtech innovations tend to have real-world physical impacts (to health, to safety, to a supply chain/manufacturing line, etc.), hardtech companies generally face far more regulatory and commercial scrutiny. These groups are generally less forgiving of incomplete/buggy offerings and their assessments can lengthen development cycles. Hardtech companies generally can’t take the “ask for forgiveness later” approaches that some tech companies (i.e. Uber and AirBnb) have been able to get away with (exhibit 1: Theranos).

As a result, while there is no single playbook that works across all hardtech categories, the most successful hardtech startups tend to embody a few basic principles:

  1. Go after markets where there is a very clear, unmet need: The best hardtech entrepreneurs tend to take very few chances with market risk and only pursue challenges where a very well-defined unmet need (i.e., there are no treatments for Alzheimer’s, this industry needs a battery that can last at least 1000 cycles, etc) blocks a significant market opportunity. This reduces the risk that a (likely long and costly) development effort achieves technical/scientific success without also achieving business success. This is in contrast with tech where creating or iterating on poorly defined markets (i.e., Uber and Airbnb) is oftentimes at the heart of what makes a company successful.
  2. Focus on “one miracle” problems: Its tempting to fantasize about what could happen if you could completely re-write every aspect of an industry or problem but the best hardtech startups focus on innovating where they won’t need the rest of the world to change dramatically in order to have an impact (e.g., compatible with existing channels, business models, standard interfaces, manufacturing equipment, etc). Its challenging enough to advance the state of the art of technology — why make it even harder?
  3. Pursue technologies that can significantly over-deliver on what the market needs: Because of the risks involved with developing advanced technologies, the best hardtech entrepreneurs work in technologies where even a partial success can clear the bar for what is needed to go to market. At the minimum, this reduces the risk of failure. But, hopefully, it gives the company the chance to fundamentally transform the market it plays in by being 10x better than the alternatives. This is in contrast to many tech markets where market success often comes less from technical performance and more from identifying the right growth channels and product features to serve market needs (i.e., Facebook, Twitter, and Snapchat vs. MySpace, Orkut, and Friendster; Amazon vs. brick & mortar bookstores and electronics stores)

All of this isn’t to say that there aren’t similarities between successful startups in both categories — strong vision, thoughtful leadership, and success-oriented cultures are just some examples of common traits in both. Nor is it to denigrate one versus the other. But, practically speaking, investing or operating successfully in both requires very different guiding principles and speaks to the heart of why its relatively rare to see individuals and organizations who can cross over to do both.

Special thanks to Sophia Wang, Ryan Gilliam, and Kevin Lin Lee for reading an earlier draft and making this better!

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Migrating WordPress to AWS Lightsail and Going with Let’s Encrypt!

I recently made two big changes to the backend of this website to keep up with the times as internet technology continues to evolve.

First, I migrated from my previous web hosting arrangements at WebFaction to Amazon Web Services’s new Lightsail offering. I have greatly enjoyed WebFaction’s super simple interface and fantastic documentation which seemed tailored to amateur coders like myself (having enough coding and customization chops to do some cool projects but not a lot of confidence or experience in dealing with the innards of a server). But, the value for money that AWS Lightsail offers ($3.50/month for Linux VPS including static IP vs. the $10/month I would need to pay to eventually renew my current setup) ultimately proved too compelling to ignore (and for a simple personal site, I didn’t need the extra storage or memory). This coupled with the deterioration in service quality I have been experiencing with WebFaction (many more downtime email alerts from WordPress’s Jetpack plugin and the general lagginess in the WordPress administrative panel) and the chance to learn more about the world’s pre-eminent cloud services provider made this an easy decision.

Given how Google Chrome now (correctly) marks all websites which don’t use HTTPS/SSL as insecure and Let’s Encrypt has been offering SSL certificates for free for several years, the second big change I made was to embrace HTTPS to partially modernize my website and make it at least not completely insecure 😅🤓. Along the way, I also tweaked my URLs so that all my respective subdomains and domain variants would ultimately point to

For anyone who is also interested in migrating an existing WordPress deployment on another host to AWS Lightsail and turning on HTTPS/SSL, here are the steps I followed (gleamed from some online research and a bit of trial & error). Its not as straightforward as some other setups, but its very do-able if you are willing to do a little bit of work in the AWS console:

  • Follow the (fairly straightforward) instructions in the AWS Lightsail tutorial around setting up a clean WordPress deployment. I would skip sub-step 3 of step 6 (directing your DNS records to point to the Lightsail nameservers) until later (when you’re sure the transfer has worked so your domain continues to point to a functioning WordPress deployment).
  • Unless you are currently not hosting any custom content (no images, no videos, no Javascript files, etc) on your WordPress deployment, I would ignore the WordPress migration tutorial at the AWS Lightsail website (which won’t show you how to transfer this custom content over) in favor of this Bitnami how-to-guide (Bitnami provides the WordPress server image that Lightsail uses for its WordPress instance) which takes advantage of the fact that the Bitnami WordPress includes the All-in-One WP Migration plugin which, for free, can do single file backups of your WordPress site up to 512 MB (larger sites will need to pay for the premium version of the plugin).
    • If, like me, you have other content statically hosted on your site outside of WordPress, I’d recommend storing it in WordPress as part of the Media Library which has gotten a lot more sophisticated over the past few years. Its where I now store the files associated with my Projects
    • Note: if, like me, you are using Jetpack’s site accelerator to cache your images/static file assets, don’t worry if upon visiting your site some of the images appear broken. Jetpack relies on the URL of the asset to load correctly. This should get resolved once you point your DNS records accordingly (literally the next step) and any other issues should go away after you mop up any remaining references to the wrong URLs in your database (see the bullet below where I reference the Better Search Replace plugin).
  • If you followed my advice above, now would be the time to change your DNS records to point to the Lightsail nameservers (sub-step 3 of step 6 of the AWS Lightsail WordPress tutorial) — wait a few hours to make sure the DNS settings have propagated and then test out your domain and make sure it points to a page with the Bitnami banner in the lower right (sign that you’re using the Bitnami server image, see below)
Bitnami banner
The Bitnami banner in the lower-right corner of the page you should see if your DNS propagated correctly and your Lightsail instance is up and running
  • To remove that ugly banner, follow the instructions in this tutorial (use the AWS Lightsail panel to get to the SSH server console for your instance and, assuming you followed the above instructions, follow the instructions for Apache)
  • Assuming your webpage and domain all work (preferably without any weird uptime or downtime issues), you can proceed with this tutorial to provision a Let’s Encrypt SSL certificate for your instance. It can be a bit tricky as it entails spending a lot of time in the SSH server console (which you can get to from the AWS Lightsail panel) and tweaking settings in the AWS Lightsail DNS Zone manager, but the tutorial does a good job of walking you through all of it.
    • I would strongly encourage you to wait to make sure all the DNS settings have propagated and that your instance is not having any strange downtime (as mine did when I first tried this) as if you have trouble connecting to your page, it won’t be immediately clear what is to blame and you won’t be able to take reactive measures.
  • I used the plugin Better Search Replace to replace all references to intermediate domains (i.e. the IP addresses for your Lightsail instance that may have stuck around after the initial step in Step 1) or the non-HTTPS domains (i.e. or with your new HTTPS domain in the MySQL databases that power your WordPress deployment (if in doubt, just select the wp_posts table). You can also take this opportunity to direct all your traffic to (or vice versa). You can also do this directly in MySQL but the plugin allows you to do this across multiple tables very easily and allows you to do a “dry run” first where it finds and counts all the times it will make a change before you actually execute it.
  • If you want to redirect all the traffic to to, you have two options. If your domain registrar is forward thinking and does simple redirects for you like Namecheap does, that is probably the easiest path. That is sadly not the path I took because I transferred my domain over to AWS’s Route 53 which is not so enlightened. If you also did the same thing / have a domain registrar that is not so forward thinking, you can tweak the Apache server settings to achieve the same effect. To do this, go into the SSH server console for your Lightsail instance and:
    • Run cd ~/apps/wordpress/conf
    • To make a backup which you can restore (if you screw things up) run mv httpd-app.conf httpd-app.conf.old
    • I’m going to use the Nano editor because its the easiest for a beginner (but feel free to use vi or emacs if you prefer), but run nano httpd-app.conf
    • Use your cursor and find the line that says RewriteEngine On that is just above the line that says #RewriteBase /wordpress/
    • Enter the following lines
      • # begin www to non-www
      • RewriteCond %{HTTP_HOST} ^www\.(.*)$ [NC]
      • RewriteRule ^(.*)$ https://%1/$1 [R=permanent,L]
      • # end www to non-www
      • The first and last line are just comments so that you can go back and remind yourself of what you did and where. The middle two lines are where the server recognizes incoming URL requests and redirects them accordingly
      • With any luck, your file will look like the image below — hit ctrl+X to exit, and hit ‘Y’ when prompted (“to save modified buffer”) to save your work
    • Run sudo /opt/bitnami/ restart to restart your server and test out the domain in a browser to make sure everything works
      • If things go bad, run mv httpd-app.conf.old httpd-app.conf and then restart everything by running sudo /opt/bitnami/ restart
What httpd-app.conf should look like in your Lightsail instance SSH console after the edits

I’ve only been using AWS Lightsail for a few days, but my server already feels much more responsive. It’s also nice to go to my website and not see “not secure” in my browser address bar (its also apparently an SEO bump for most search engines). Its also great to know that Lightsail is integrated deeply into AWS which makes the additional features and capabilities that have made AWS the industry leader (i.e. load balancers, CloudFront as CDN, scaling up instance resources, using S3 as a datastore, or even ultimately upgrading to full-fledged EC2 instances) are readily available.


Advice VCs Want to Give but Rarely Do to Entrepreneurs Pitching Their Startups

I thought I’d re-post a response I wrote a while ago to a question on Quora as someone recently asked me the question: “What advice do you wish you could give but usually don’t to a startup pitching you?”

  • Person X on your team reflects poorly on your company – This is tough advice to give as its virtually impossible during the course of a pitch to build enough rapport and get a deep enough understanding of the inter-personal dynamics of the team to give that advice without it unnecessarily hurting feelings or sounding incredibly arrogant / meddlesome.
  • Your slides look awful – This is difficult to say in a pitch because it just sounds petty for an investor to complain about the packaging rather than the substance.
  • Be careful when using my portfolio companies as examples – While its good to build rapport / common ground with your VC audience, using their portfolio companies as examples has an unnecessarily high chance of backfiring. It is highly unlikely that you will know more than an inside investor who is attending board meetings and in direct contact with management, so any errors you make (i.e., assuming a company is doing well when it isn’t or assuming a company is doing poorly when it is doing well / is about to turn the corner) are readily caught and immediately make you seem foolish.
  • You should pitch someone who’s more passionate about what you’re doing – Because VCs have to risk their reputation within their firms / to the outside world for the deals they sign up to do, they have to be very selective about which companies they choose to get involved with. As a result, even if there’s nothing wrong with a business model / idea, some VCs will choose not to invest due simply to lack of passion. As the entrepreneur is probably deeply passionate about and personally invested in the market / problem, giving this advice can feel tantamount to insulting the entrepreneur’s child or spouse.

Hopefully this gives some of the hard-working entrepreneurs out there some context on why a pitch didn’t go as well as they had hoped and maybe some pointers on who and how to approach an investor for their next pitch.

(Image credit – Someecard)

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The Four Types of M&A

I’m oftentimes asked what determines the prices that companies get bought for: after all, why does one app company get bought for $19 billion and a similar app get bought at a discount to the amount of investor capital that was raised?

While specific transaction values depend a lot on the specific acquirer (i.e. how much cash on hand they have, how big they are, etc.), I’m going to share a framework that has been very helpful to me in thinking about acquisition valuations and how startups can position themselves to get more attractive offers. The key is understanding that, all things being equal, why you’re being acquired determines the buyer’s willingness to pay. These motivations fall on a spectrum dividing acquisitions into four types:


  • Talent Acquisitions: These are commonly referred to in the tech press as “acquihires”. In these acquisitions, the buyer has determined that it makes more sense to buy a team than to spend the money, time, and effort needed to recruit a comparable one. In these acquisitions, the size and caliber of the team determine the purchase price.
  • Asset / Capability Acquisitions: In these acquisitions, the buyer is in need of a particular asset or capability of the target: it could be a portfolio of patents, a particular customer relationship, a particular facility, or even a particular product or technology that helps complete the buyer’s product portfolio. In these acquisitions, the uniqueness and potential business value of the assets determine the purchase price.
  • Business Acquisitions: These are acquisitions where the buyer values the target for the success of its business and for the possible synergies that could come about from merging the two. In these acquisitions, the financials of the target (revenues, profitability, growth rate) as well as the benefits that the investment bankers and buyer’s corporate development teams estimate from combining the two businesses (cost savings, ability to easily cross-sell, new business won because of a more complete offering, etc) determine the purchase price.
  • Strategic Gamechangers: These are acquisitions where the buyer believes the target gives them an ability to transform their business and is also a critical threat if acquired by a competitor. These tend to be acquisitions which are priced by the buyer’s full ability to pay as they represent bets on a future.

What’s useful about this framework is that it gives guidance to companies who are contemplating acquisitions as exit opportunities:

  • If your company is being considered for a talent acquisition, then it is your job to convince the acquirer that you have built assets and capabilities above and beyond what your team alone is worth. Emphasize patents, communities, developer ecosystems, corporate relationships, how your product fills a distinct gap in their product portfolio, a sexy domain name, anything that might be valuable beyond just the team that has attracted their interest.
  • If a company is being considered for an asset / capability acquisition, then the key is to emphasize the potential financial trajectory of the business and the synergies that can be realized after a merger. Emphasize how current revenues and contracts will grow and develop, how a combined sales and marketing effort will be more effective than the sum of the parts, and how the current businesses are complementary in a real way that impacts the bottom line, and not just as an interesting “thing” to buy.
  • If a company is being evaluated as a business acquisition, then the key is to emphasize how pivotal a role it can play in defining the future of the acquirer in a way that goes beyond just what the numbers say about the business. This is what drives valuations like GM’s acquisition of Cruise (which was a leader in driverless vehicle technology) for up to $1B, or Facebook’s acquisition of WhatsApp (messenger app with over 600 million users when it was acquired, many in strategic regions for Facebook) for $19B, or Walmart’s acquisition of (an innovator in eCommerce that Walmart needs to help in its war for retail marketshare with

The framework works for two reasons: (1) companies are bought, not sold, and the price is usually determined by the party that is most willing to walk away from a deal (that’s usually the buyer) and (2) it generally reflects how most startups tend to create value over time: they start by hiring a great team, who proceed to build compelling capabilities / assets, which materialize as interesting businesses, which can represent the future direction of an industry.

Hopefully, this framework helps any tech industry onlooker wondering why acquisition valuations end up at a certain level or any startup evaluating how best to court an acquisition offer.

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Henry Ford

This weekend, I paid a visit to The Henry Ford. Its a combination of multiple venues — a museum, an outdoor “innovation village”, a Ford Motors factory tour — which collectively celebrate America’s rich history of innovation and manufacturing and, in particular, the legacy of Henry Ford and the Ford Motors company he built.

While ambitious super-CEOs like Larry Page (Google), Elon Musk (Tesla), and Jeff Bezos (Amazon) with their tentacles in everything sometimes seem like a modern phenomena, The Henry Ford shows that they are just a modern-day reincarnations of the super-CEOs of yesteryear. Except, instead of pioneering software at scale, electric vehicles, and AI assistants, Ford was instrumental in the creation of assembly line mass production, the automotive industry (Ford developed the first car that the middle class could actually afford), the aerospace industry (Ford helped develop some of America’s first successful passenger planes), the forty hour workweek, and even the charcoal briquet (part of a drive to figure out what to do with the lumber waste that came from procuring the wood needed to build Model T’s).

In the same way that the tech giants of today pursue “moonshots” like drone delivery and self-driving cars, Ford pushed the frontier with its own moonshots: creating cars out of bioplastic, developing biofuels, and even an early collaboration with Thomas Edison to build an electric car.

It was a striking parallel, and also an instructional one for any company that believes they can stay on top forever: despite the moonshots and the technology advantages, new technologies, market forces, and global shifts come one after the other and yesterday’s Ford (eventually) gets supplanted by tomorrow’s Tesla.

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Snap Inc by the Numbers

If you follow the tech industry at all, you will have heard that consumer app darling Snap Inc. (makers of the app Snapchat) has filed to go public. The ensuing Form S-1 that has recently been made available has left tech-finance nerds like yours truly drooling over the until-recently-super-secretive numbers behind their business.

Much of the commentary in the press to date has been about how unprofitable the company is (having lost over $500M in 2016 alone). I have been unimpressed with that line of thinking — as what the bottom line is in a given year is hardly the right measure for assessing a young, high-growth company.

While full-time Wall Street analysts will pour over the figures and comparables in much greater detail than I can, I decided to take a quick peek at the numbers to gauge for myself how the business is doing as a growth investment, looking at:

  • What does the growth story look like for the business?
  • Do the unit economics allow for a path to profitability?

What does the growth story look like for the business?

As I noted before, consumer media businesses like Snap have two options available to grow: (1) increase the number of users / amount of time spent and/or (2) better monetize users over time

A quick peek at the DAU (Daily Active Users) counts of Snap reveal that path (1) is troubled for them. Using Facebook as a comparable (and using the midpoint of Facebook’s quarter-end DAU counts to line up with Snap’s average DAU over a quarter) reveals not only that Snap’s DAU numbers aren’t growing so much, their growth outside of North America (where they should have more room to grow) isn’t doing that great either (which is especially alarming as the S-1 admits Q4 is usually seasonally high for them).

Last 3 Quarters of DAU growth, by region

A quick look at the data also reveals why Facebook prioritizes Android development and low-bandwidth-friendly experiences — international remains an area of rapid growth which is especially astonishing considering how over 1 billion Facebook users are from outside of North America. This contrasts with Snap which, in addition to needing a huge amount of bandwidth (as a photo and video intensive platform) also (as they admitted in their S-1) de-emphasizes Android development. Couple that with Snap’s core demographic (read: old people can’t figure out how to use the app), reveals a challenge to where quick short-term user growth can come from.

As a result, Snap’s growth in the near term will have to be driven more by path (2). Here, there is a lot more good news. Snap’s quarterly revenue per user more than doubled over the last 3 quarters to $1.029/DAU. While its a long way off from Facebook’s whopping $7.323/DAU (and over $25 if you’re just looking at North American users), it suggests that there is plenty of opportunity for Snap to increase monetization, especially overseas where its currently able to only monetize about 1/10 as effectively as they are in North America (compared to Facebook which is able to do so 1/5 to 1/6 of North America depending on the quarter).

2016 and 2015 Q2-Q4 Quarterly Revenue per DAU, by region

Considering Snap has just started with its advertising business and has already convinced major advertisers to build custom content that isn’t readily reusable on other platforms and Snap’s low revenue per user compared even to Facebook’s overseas numbers, I think its a relatively safe bet that there is a lot of potential for the number to go up.

Do the unit economics allow for a path to profitability?

While most folks have been (rightfully) stunned by the (staggering) amount of money Snap lost in 2016, to me the more pertinent question (considering the over $1 billion Snap still has in its coffers to weather losses) is whether or not there is a path to sustainable unit economics. Or, put more simply, can Snap grow its way out of unprofitability?

Because neither Facebook nor Snap provide regional breakdowns of their cost structure, I’ve focused on global unit economics, summarized below:

2016 and 2015 Q2-Q4 Quarterly Financials per DAU

What’s astonishing here is that neither Snap nor Facebook seem to be gaining much from scale. Not only are their costs of sales per user (cost of hosting infrastructure and advertising infrastructure) increasing each quarter, but the operating expenses per user (what they spend on R&D, sales & marketing, and overhead — so not directly tied to any particular user or dollar of revenue) don’t seem to be shrinking either. In fact, Facebook’s is over twice as large as Snap’s — suggesting that its not just a simple question of Snap growing a bit further to begin to experience returns to scale here.

What makes the Facebook economic machine go, though, is despite the increase in costs per user, their revenue per user grows even faster. The result is profit per user is growing quarter to quarter! In fact, on a per user basis, Q4 2016 operating profit exceeded Q2 2015 gross profit (revenue less cost of sales, so not counting operating expenses)! No wonder Facebook’s stock price has been on a tear!

While Snap has also been growing its revenue per user faster than its cost of sales (turning a gross profit per user in Q4 2016 for the first time), the overall trendlines aren’t great, as illustrated by the fact that its operating profit per user has gotten steadily worse over the last 3 quarters. The rapid growth in Snap’s costs per user and the fact that Facebook’s costs are larger and still growing suggests that there are no simple scale-based reasons that Snap will achieve profitability on a per user basis. As a result, the only path for Snap to achieve sustainability on unit economics will be to pursue huge growth in user monetization.

Tying it Together

The case for Snap as a good investment really boils down to how quickly and to what extent one believes that the company can increase their monetization per user. While the potential is certainly there (as is being realized as the rapid growth in revenue per user numbers show), what’s less clear is whether or not the company has the technology or the talent (none of the key executives named in the S-1 have a particular background building advertising infrastructure or ecosystems that Google, Facebook, and even Twitter did to dominate the online advertising businesses) to do it quickly enough to justify the rumored $25 billion valuation they are striving for (a whopping 38x sales multiple using 2016 Q4 revenue as a run-rate [which the S-1 admits is a seasonally high quarter]).

What is striking to me, though, is that Snap would even attempt an IPO at this stage. In my mind, Snap has a very real shot at being a great digital media company of the same importance as Google and Facebook and, while I can appreciate the hunger from Wall Street to invest in a high-growth consumer tech company, not having a great deal of visibility / certainty around unit economics and having only barely begun monetization (with your first quarter where revenue exceeds cost of sales is a holiday quarter) poses challenges for a management team that will need to manage public market expectations around forecasts and capitalization.

In any event, I’ll be looking forward to digging in more when Snap reveals future figures around monetization and advertising strategy — and, to be honest, Facebook’s numbers going forward now that I have a better appreciation for their impressive economic model.

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Dr. Machine Learning

Not going to happen anytime soon, sadly: the Doctor from Star Trek: Voyager; Image Credit: TrekCore

Despite the hype, it’ll likely be quite some time before human physicians will be replaced with machines (sorry, Star Trek: Voyager fans).

While “smart” technology like IBM’s Watson and Alphabet’s AlphaGo can solve incredibly complex problems, they are probably not quite ready to handle the messiness of qualitative unstructured information from patients and caretakers (“it kind of hurts sometimes”) that sometimes lie (“I swear I’m still a virgin!”) or withhold information (“what does me smoking pot have to do with this?”) or have their own agendas and concerns (“I just need some painkillers and this will all go away”).

Instead, machine learning startups and entrepreneurs interested in medicine should focus on areas where they can augment the efforts of physicians rather than replace them.

One great example of this is in diagnostic interpretation. Today, doctors manually process countless X-rays, pathology slides, drug adherence records, and other feeds of data (EKGs, blood chemistries, etc) to find clues as to what ails their patients. What gets me excited is that these tasks are exactly the type of well-defined “pattern recognition” problems that are tractable for an AI / machine learning approach.

If done right, software can not only handle basic diagnostic tasks, but to dramatically improve accuracy and speed. This would let healthcare systems see more patients, make more money, improve the quality of care, and let medical professionals focus on managing other messier data and on treating patients.

As an investor, I’m very excited about the new businesses that can be built here and put together the following “wish list” of what companies setting out to apply machine learning to healthcare should strive for:

  • Excellent training data and data pipeline: Having access to large, well-annotated datasets today and the infrastructure and processes in place to build and annotate larger datasets tomorrow is probably the main defining . While its tempting for startups to cut corners here, that would be short-sighted as the long-term success of any machine learning company ultimately depends on this being a core competency.
  • Low (ideally zero) clinical tradeoffs: Medical professionals tend to be very skeptical of new technologies. While its possible to have great product-market fit with a technology being much better on just one dimension, in practice, to get over the innate skepticism of the field, the best companies will be able to show great data that makes few clinical compromises (if any). For a diagnostic company, that means having better sensitivty and selectivity at the same stage in disease progression (ideally prospectively and not just retrospectively).
  • Not a pure black box: AI-based approaches too often work like a black box: you have no idea why it gave a certain answer. While this is perfectly acceptable when it comes to recommending a book to buy or a video to watch, it is less so in medicine where expensive, potentially life-altering decisions are being made. The best companies will figure out how to make aspects of their algorithms more transparent to practitioners, calling out, for example, the critical features or data points that led the algorithm to make its call. This will let physicians build confidence in their ability to weigh the algorithm against other messier factors and diagnostic explanations.
  • Solve a burning need for the market as it is today: Companies don’t earn the right to change or disrupt anything until they’ve established a foothold into an existing market. This can be extremely frustrating, especially in medicine given how conservative the field is and the drive in many entrepreneurs to shake up a healthcare system that has many flaws. But, the practical reality is that all the participants in the system (payers, physicians, administrators, etc) are too busy with their own issues (i.e. patient care, finding a way to get everything paid for) to just embrace a new technology, no matter how awesome it is. To succeed, machine diagnostic technologies should start, not by upending everything with a radical solution, but by solving a clear pain point (that hopefully has a lot of big dollar signs attached to it!) for a clear customer in mind.

Its reasons like this that I eagerly follow the development of companies with initiatives in applying machine learning to healthcare like Google’s DeepMind, Zebra Medical, and many more.

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Why VR Could be as Big as the Smartphone Revolution

Technology in the 1990s and early 2000s marched to the beat of an Intel-and-Microsoft-led drum.

via IT Portal

Intel would release new chips at a regular cadence: each cheaper, faster, and more energy efficient than the last. This would let Microsoft push out new, more performance-hungry software, which would, in turn, get customers to want Intel’s next, more awesome chip. Couple that virtuous cycle with the fact that millions of households were buying their first PCs and getting onto the Internet for the first time – and great opportunities were created to build businesses and products across software and hardware.

But, over time, that cycle broke down. By the mid-2000s, Intel’s technological progress bumped into the limits of what physics would allow with regards to chip performance and cost. Complacency from its enviable market share coupled with software bloat from its Windows and Office franchises had a similar effect on Microsoft. The result was that the Intel and Microsoft drum stopped beating as they became unable to give the mass market a compelling reason to upgrade to each subsequent generation of devices.

The result was a hollowing out of the hardware and semiconductor industries tied to the PC market that was only masked by the innovation stemming from the rise of the Internet and the dawn of a new technology cycle in the late 2000s in the form of Apple’s iPhone and its Android competitors: the smartphone.

via Mashable

A new, but eerily familiar cycle began: like clockwork, Qualcomm, Samsung, and Apple (playing the part of Intel) would devise new, more awesome chips which would feed the creation of new performance-hungry software from Google and Apple (playing the part of Microsoft) which led to demand for the next generation of hardware. Just as with the PC cycle, new and lucrative software, hardware, and service businesses flourished.


But, just as with the PC cycle, the smartphone cycle is starting to show signs of maturity. Apple’s recent slower than expected growth has already been blamed on smartphone market saturation. Users are beginning to see each new generation of smartphone as marginal improvements. There are also eery parallels between the growing complaints over Apple software quality from even Apple fans and the position Microsoft was in near the end of the PC cycle.

While its too early to call the end for Apple and Google, history suggests that we will eventually enter a similar phase with smartphones that the PC industry experienced. This begs the question: what’s next? Many of the traditional answers to this question – connected cars, the “Internet of Things”, Wearables, Digital TVs – have not yet proven themselves to be truly mass market, nor have they shown the virtuous technology upgrade cycle that characterized the PC and smartphone industries.

This brings us to Virtual Reality. With VR, we have a new technology paradigm that can (potentially) appeal to the mass market (new types of games, new ways of doing work, new ways of experiencing the world, etc.). It also has a high bar for hardware performance that will benefit dramatically from advances in technology, not dissimilar from what we saw with the PC and smartphone.

via Forbes

The ultimate proof will be whether or not a compelling ecosystem of VR software and services emerges to make this technology more of a mainstream “must-have” (something that, admittedly, the high price of the first generation Facebook/Oculus, HTC/Valve, and Microsoft products may hinder).

As a tech enthusiast, its easy to get excited. Not only is VR just frickin’ cool (it is!), its probably the first thing since the smartphone with the mass appeal and virtuous upgrade cycle that can bring about the huge flourishing of products and companies that makes tech so dynamic to be involved with.

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Laszlo Bock on Building Google’s Culture

Much has been written about what makes Google work so well: their ridiculously profitable advertising business model, the technology behind their search engine and data centers, and the amazing pay and perks they offer.

My experiences investing in and working with startups, however, has taught me that building a great company is usually less about a specific technical or business model innovation than about building a culture of continuous improvement and innovation. To try to get some insight into how Google does things, I picked up Google SVP of People Operations Laszlo Bock’s book Work Rules!

Bock describes a Google culture rooted in principles that came from founders Larry Page and Sergey Brin when they started the company: get the best people to work for you, make them want to stay and contribute, and remove barriers to their creativity. What’s great (to those interested in company building) is that Bock goes on to detail the practices Google has put in place to try to live up to these principles even as their headcount has expanded.

The core of Google’s culture boils down to four basic principles and much of the book is focused on how companies should act if they want to live up to them:

  1. Presume trust: Many of Google’s cultural norms stem from a view that people are well-intentioned and trustworthy. While that may not seem so radical, this manifested at Google as a level of transparency with employees and a bias to say yes to employee suggestions that most companies are uncomfortable with. It raises interesting questions about why companies that say their talent is the most important thing treat them in ways that suggest a lack of trust.
  2. Recruit the best: Many an exec pays lip service to this, but what Google has done is institute policies that run counter to standard recruiting practices to try to actually achieve this at scale: templatized interviews / forms (to make the review process more objective and standardized), hiring decisions made by cross-org committees (to insure a consistently high bar is set), and heavy use of data to track the effectiveness of different interviewers and interview tactics. While there’s room to disagree if these are the best policies (I can imagine hating this as a hiring manager trying to staff up a team quickly), what I admired is that they set a goal (to hire the best at scale) and have actually thought through the recruiting practices they need to do so.
  3. Pay fairly [means pay unequally]: While many executives would agree with the notion that superstar employees can be 2-10x more productive, few companies actually compensate their superstars 2-10x more. While its unclear to me how effective Google is at rewarding superstars, the fact that they’ve tried to align their pay policies with their beliefs on how people perform is another great example of deviating from the norm (this time in terms of compensation) to follow through on their desire to pay fairly.
  4. Be data-driven: Another “in vogue” platitude amongst executives, but one that very few companies live up to, is around being data-driven. In reading Bock’s book, I was constantly drawing parallels between the experimentation, data collection, and analyses his People Operations team carried out and the types of experiments, data collection, and analyses you would expect a consumer internet/mobile company to do with their users. Case in point: Bock’s team experimented with different performance review approaches and even cafeteria food offerings in the same way you would expect Facebook to experiment with different news feed algorithms and notification strategies. It underscores the principle that, if you’re truly data-driven, you don’t just selectively apply it to how you conduct business, you apply it everywhere.

Of course, not every company is Google, and not every company should have the same set of guiding principles or will come to same conclusions. Some of the processes that Google practices are impractical (i.e., experimentation is harder to set up / draw conclusions from with much smaller companies, not all professions have such wide variations in output as to drive such wide variations in pay, etc).

What Bock’s book highlights, though, is that companies should be thoughtful about what sort of cultural principles they want to follow and what policies and actions that translates into if they truly believe them. I’d highly recommend the book!

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Trust but Verify

Back in my consulting days, I learned a lot of management aphorisms. One that always felt oxymoronic to me was “trust, but verify” — apparently Russian in origin — I came to understand it as a two-part idea:

  1. You should have a trusting and open attitude regarding your colleagues’ conclusions but still spend the time and effort to validate them
  2. That you should not view a colleague checking the assumptions / data behind your work as a sign of lack of trust

Of course, when it stops being a two-sided thing, you get something made for Dilbert:

Trust but Verify

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How IPOs are Doing in the Public Markets

After reading my last post on what the decline in recently IPO’d startups means for the broader tech industry, a friend of mine encouraged me to look closer at how IPO’s in general have been performing. The answer: badly

Recent IPO performance vs S&P 500 over last year

The chart above shows how Renaissance Capital’s US IPO index (prospectus), which tracks major IPOs in US markets, has performed versus the broader market (represented by the S&P500) over the past year. While the S&P500 hasn’t had a great year (down just over 10%), IPOs have done even worse (down over 30%).

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What Happens After the Tech Bubble Pops

In recent years, it’s been the opposite of controversial to say that the tech industry is in a bubble. The terrible recent stock market performance of once high-flying startups across virtually every industry (see table below) and the turmoil in the stock market stemming from low oil prices and concerns about the economies of countries like China and Brazil have raised fears that the bubble is beginning to pop.

Company Ticker Industry Stock Price Change Since IPO (Feb 5)
GoPro NASDAQ:GPRO Consumer Hardware -72%
FitBit NYSE:FIT Wearable -47%
Hortonworks NASDAQ:HDP Big Data -68%
Teladoc NYSE:TDOC Telemedicine -50%
Evolent Health NYSE:EVH Healthcare -46%
Square NYSE:SQ Payment & POS -34%
Box NYSE:BOX Cloud Storage -42%
Etsy NASDAQ:ETSY eCommerce -77%
Lending Club NYSE:LC Lending Platform -72%

While history will judge when this bubble “officially” bursts, the purpose of this post is to try to make some predictions about what will happen during/after this “correction” and pull together some advice for people in / wanting to get into the tech industry. Starting with the immediate consequences, one can reasonably expect that:

  • Exit pipeline will dry up: When startup valuations are higher than what the company could reasonably get in the stock market, management teams (who need to keep their investors and employees happy) become less willing to go public. And, if public markets are less excited about startups, the price acquirers need to pay to convince a management team to sell goes down. The result is fewer exits and less cash back to investors and employees for the exits that do happen.
  • VCs become less willing to invest: VCs invest in startups on the promise that future IPOs and acquisitions will make them even more money. When the exit pipeline dries up, VCs get cold feet because the ability to get a nice exit seems to fade away. The result is that VCs become a lot more price-sensitive when it comes to investing in later stage companies (where the dried up exit pipeline hurts the most).
  • Later stage companies start cutting costs: Companies in an environment where they can’t sell themselves or easily raise money have no choice but to cut costs. Since the vast majority of later-stage startups run at a loss to increase growth, they will find themselves in the uncomfortable position of slowing down hiring and potentially laying employees off, cutting back on perks, and focusing a lot more on getting their financials in order.

The result of all of this will be interesting for folks used to a tech industry (and a Bay Area) flush with cash and boundlessly optimistic:

  1. Job hopping should slow: “Easy money” to help companies figure out what works or to get an “acquihire” as a soft landing will be harder to get in a challenged financing and exit environment. The result is that the rapid job hopping endemic in the tech industry should slow as potential founders find it harder to raise money for their ideas and as it becomes harder for new startups to get the capital they need to pay top dollar.
  2. Strong companies are here to stay: While there is broad agreement that there are too many startups with higher valuations than reasonable, what’s also become clear is there are a number of mature tech companies that are doing exceptionally well (i.e. Facebook, Amazon, Netflix, and Google) and a number of “hotshots” which have demonstrated enough growth and strong enough unit economics and market position to survive a challenged environment (i.e. Uber, Airbnb). This will let them continue to hire and invest in ways that weaker peers will be unable to match.
  3. Tech “luxury money” will slow but not disappear: Anyone who lives in the Bay Area has a story of the ridiculousness of “tech money” (sky-high rents, gourmet toast, “its like Uber but for X”, etc). This has been fueled by cash from the startup world as well as free flowing VC money subsidizing many of these new services . However, in a world where companies need to cut costs, where exits are harder to come by, and where VCs are less willing to subsidize random on-demand services, a lot of this will diminish. That some of these services are fundamentally better than what came before (i.e. Uber) and that stronger companies will continue to pay top dollar for top talent will prevent all of this from collapsing (and lets not forget San Francisco’s irrational housing supply policies). As a result, people expecting a reversal of gentrification and the excesses of tech wealth will likely be disappointed, but its reasonable to expect a dramatic rationalization of the price and quantity of many “luxuries” that Bay Area inhabitants have become accustomed to soon.

So, what to do if you’re in / trying to get in to / wanting to invest in the tech industry?

  • Understand the business before you get in: Its a shame that market sentiment drives fundraising and exits, because good financial performance is generally a pretty good indicator of the long-term prospects of a business. In an environment where its harder to exit and raise cash, its absolutely critical to make sure there is a solid business footing so the company can keep going or raise money / exit on good terms.
  • Be concerned about companies which have a lot of startup exposure: Even if a company has solid financial performance, if much of that comes from selling to startups (especially services around accounting, recruiting, or sales), then they’re dependent on VCs opening up their own wallets to make money.
  • Have a much higher bar for large, later-stage companies: The companies that will feel the most “pain” the earliest will be those with with high valuations and high costs. Raising money at unicorn valuations can make a sexy press release but it doesn’t amount to anything if you can’t exit or raise money at an even higher valuation.
  • Rationalize exposure to “luxury”: Don’t expect that “Uber but for X” service that you love to stick around (at least not at current prices)…
  • Early stage companies can still be attractive: Companies that are several years from an exit & raising large amounts of cash will be insulated in the near-term from the pain in the later stage, especially if they are committed to staying frugal and building a disruptive business. Since they are already relatively low in valuation and since investors know they are discounting off a valuation in the future (potentially after any current market softness), the downward pressures on valuation are potentially lighter as well.
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