Category: Uncategorized

  • Iovance brings cell therapy to solid tumors

    Immune cell therapy — the use of modified immune cells directly to control cancer and autoimmune disease — has shown incredible results in liquid tumors (cancers of the blood and bone marrow like lymphoma, leukemia, etc), but has stumbled in addressing solid tumors.

    Iovance, which recently had its drug lifileucel approved by the FDA to treat advanced melanoma, has demonstrated an interesting spin on the cellular path which may prove to be effective in solid tumors. They extract Tumor-Infiltrating Lymphocytes (TILs), immune cells that are already “trying” to attack a solid tumor directly. Iovance then treats those TILs with their own proprietary process to expand the number of those cells and “further activate” them (to resist a tumor’s efforts to inactivate immune cells that may come after them) before reintroducing them to the patient.

    This is logistically very challenging (not dissimilar to what patients awaiting other cell therapies or Vertex’s new sickle cell treatment need to go through) as it also requires chemotherapy for lymphocyte depletion in the patient prior to reintroduction of the activated TILs. But, the upshot is that you now have an expanded population of cells known to be predisposed to attacking a solid tumor that can now resist the tumor’s immune suppression efforts.

    And, they’ve presented some impressive 4-year followup data on a study of advanced melanoma in patients who have already failed immune checkpoint inhibitor therapy, enough to convince the FDA of their effectiveness!

    To me, the beauty of this method is that it can work across tumor types. Iovance’s process (from what I’ve gleamed from their posters & presentations) works by getting more and more activated immune cells. Because they’re derived from the patient, these cells are already predisposed to attack the particular molecular targets of their tumor.

    This is contrast to most other immune cell therapy approaches (like CAR-T) where the process is inherently target-specific (i.e. get cells that go after this particular marker on this particular tumor) and each new target / tumor requires R&D work to validate. Couple this with the fact that TILs are already the body’s first line of defense against solid tumors and you may have an interesting platform for immune cell therapy in solid tumors.

    The devil’s in the details and requires more clinical study on more cancer types, but suffice to say, I think this is incredibly exciting!


  • The only 3 things a startup CEO needs to master

    So, you watched Silicon Valley and read some articles on Techcrunch and you envision yourself as a startup CEO 🤑. What does it take to succeed? Great engineering skills? Salesmanship? Financial acumen?

    As someone who has been on both sides of the table (as a venture investor and on multiple startup executive leadership teams), there are three — and only three — things a startup CEO needs to master. In order of importance:

    1. Raise Money from Investors (now and in the future): The single most important job of a startup CEO is to secure funding from investors. Funding is the lifeblood of a company, and raising it is a job that only the CEO can drive. Not being great at it means slower growth / fewer resources, regardless of how brilliant you are, or how great your vision. Being good at raising money buys you a lot of buffer in every other area.
    2. Hire Amazing People into the Right Roles (and retain them!): No startup, no matter how brilliant the CEO, succeeds without a team. Thus, recruiting the right people into the right positions is the second most vital job of a CEO. Without the right people in place, your plans are not worth the paper on which they are written. Even if you have the right people, if they are not entrusted with the right responsibilities or they are unhappy, the wrong outcomes will occur. There is a reason that when CEOs meet to trade notes, they oftentimes trade recruiting tips.
    3. Inspire the Team During Tough Times: Every startup inevitably encounters stormy seas. It could be a recession causing a slowdown, a botched product launch, a failed partnership, or the departure of key employees. During these challenging times, the CEO’s job is to serve as chief motivator. Teams that can resiliently bounce back after crises can stand a better chance of surviving until things turn a corner.

    It’s a short list. And it doesn’t include:

    • deep technical expertise
    • an encyclopedic knowledge of your industry
    • financial / accounting skills
    • marketing wizardry
    • design talent
    • intellectual property / legal acumen

    It’s not that those skills aren’t important for building a successful company — they are. It’s not even that these skills aren’t helpful for a would-be startup CEO — these skills would be valuable for anyone working at a startup to have. For startup CEOs in particular, these skills can help sell investors as to why the CEO is the right one to invest in or convince talent to join or inspire the team that the strategy a CEO has chosen is the right one.

    But, the reality is that these skills can be hired into the company. They are not what separates great startup CEOs from the rest of the pack.

    What makes a startup CEO great is their ability to nail the jobs that cannot be delegated. And that boils down to fundraising, hiring and retaining the best, and lifting spirits when things are tough. And that is the job.

    After all, startup investors write checks because they believe in the vision and leadership of a CEO, not a lackey. And startup employees expect to work for a CEO with a vision, not just a mouthpiece.

    So, want to become a startup CEO? Work on:

    • Storytelling — Learn how to tell stories that compel listeners. This is vital for fundraising (convincing investors to take a chance on you because of your vision), but also for recruiting & retaining people as well as inspiring a team during difficult times.
    • Reading People — Learn how to accurately read people. You can’t hire a superstar employee with other options, retain an unhappy worker through tough times, or overcome an investor’s concerns unless you understand their position. This means being attentive to what they tell you directly (i.e., over email, text, phone / video call, or in person, etc.) as well as paying attention to what they don’t (i.e., body language, how they act, what topics they discussed vs. didn’t, etc.).
    • Prioritization — Many startup CEOs got to where they are because they were superstars at one or more of the “unnecessary to be a great startup CEO” skills. But, continuing to focus on that skill and ignoring the skills that a startup CEO needs to be stellar at confuses the path to the starting point with the path to the finish line. It is the CEO’s job to prioritize those tasks that they cannot delegate and to ruthlessly delegate everything else.
  • Real World Effectiveness vs. Clinical Trial Efficacy

    Randomized controlled trials (RCTs) are the “gold standard” in healthcare for proving a treatment works. And for good reason. A well-designed and well-powered (i.e., large enough) clinical trial establishes what is really due to a treatment as opposed to another factor (e.g., luck, reversion to the mean, patient selection, etc.), and it’s a good thing that drug regulation is tied to successful trial results.

    But, there’s one wrinkle. Randomized controlled trials are not reality.

    RCTs are tightly controlled, where only specific patients (those fulfilling specific “inclusion criteria”) are allowed to participate. Follow-up is organized and adherence to protocol is tightly tracked. Typically, related medical care is also provided free of cost.

    This is exactly what you want from a scientific and patient volunteer safety perspective, but, as we all know, the real world is messier. In the real world:

    • Physicians prescribe treatments to patients that don’t have to fit the exact inclusion criteria of the clinical trial. After all, many clinical trials exclude people who are extremely sick or who are children or pregnant.
    • Patients may not take their designated treatment on time or in the right dose … and nobody finds out.
    • Follow-up on side effects and progress is oftentimes random
    • Cost and free time considerations may change how and when a patient comes in
    • Physicians also have greater choice in the real world. They only prescribe treatments they think will work, whereas in a RCT, you get the treatment you’ve been randomly assigned to.

    These differences beg the question: just how different is the real world from an randomized controlled trial?

    A group in Canada studied this question and presented their findings at the recent ASH (American Society of Hematology) meeting. The researchers looked at ~4,000 patients in Canada with multiple myeloma, a cancer with multiple treatment regimens that have been developed and approved, and used Canada’s national administrative database to track how they did after 7 different treatment regimes and compared it to published RCT results for each treatment.

    The findings are eye-opening. While there is big variation from treatment to treatment, in general, real world effectiveness was significantly worse, by a wide margin, than efficacy published in randomized controlled trial (see table below).

    Source: ASH 2023 abstract, Visram et al.

    While the safety profiles (as measured by the rate of “adverse events”) seemed similar between real world and RCT, real world patients did, in aggregate, 44% worse on progression free survival and 75% worse on overall survival when compared with their RCT counterparts!

    The only treatment where the real world did better than the RCT was in a study where it’s likely the trial volunteers were much sicker than on average. (Note: that one of seven treatment regimes went the other way but the aggregate still is 40%+ worse shows you that some of the comparisons were vastly worse)

    The lesson here is not that we should stop doing or listening to randomized controlled trials. After all, this study shows that they were reasonably good at predicting safety, not to mention that they continue to be our only real tool for establishing whether a treatment has real clinical value prior to giving it to the general public.

    But this study imparts two key lessons for healthcare:

    1. Do not assume that the results you see in a clinical trial are what you will see in the real world. Different patient populations, resources, treatment adherence, and many other factors will impact what you see.
    2. Especially for treatments we expect to use with many people, real world monitoring studies are valuable in helping to calibrate expectations and, potentially, identify patient populations where a treatment is better or worse suited.
  • The World Runs on Excel… and its Mistakes

    The 2022 CHIPS and Science Act earmarked hundreds of billions in subsidies and tax credits to bolster a U.S. domestic semiconductor (and especially semiconductor manufacturing) industry. If it works, it will dramatically reposition the U.S. in the global semiconductor value chain (especially relative to China).

    With such large amounts of taxpayer money practically “gifted” to large (already very profitable) corporations like Intel, the U.S. taxpayer can reasonably assume that these funds should be allocated carefully and thoughtfully and with processes in place to make sure every penny furthered the U.S.’s strategic goals.

    But, when the world’s financial decisions are powered by Excel spreadsheets, even the best laid plans can go awry.

    The team behind the startup Rowsie created a large language model (LLM)-powered tool which can understand Excel spreadsheets and answer questions posed to it. They downloaded a spreadsheet that the US government provided as an example of the information and calculations they want applicants fill out in order to qualify. They then applied their AI tool to the spreadsheet to understand it’s structure and formulas.

    Interestingly, Rowsie was able to find a single-cell spreadsheet error (see images below) which resulted in a $178 million understatement of interest payments!

    The Assumptions Processing tab in the Example Pre-App-Simple-Financial-Model spreadsheet from the CHIPS Act funding application website. Notice row 50. Despite the section being about Subordinated Debt (see Cell B50), they’re using cell C51 from the Control Panel tab (which points to the Senior Debt rate of 5%) rather than the correct cell of D51 (which points to the Subordinated Debt rate of 8%).

    To be clear, this is not a criticism of the spreadsheet’s architects. In this case, what seems to have happened, is that the spreadsheet creator copied an earlier row (row 40) and forgot to edit the formula to account for the fact that row 50 is about subordinated debt and row 40 is about senior debt. It’s a familiar story to anyone who’s ever been tasked with doing something complicated in Excel. Features like copy and paste and complex formulas are very powerful, but also make it very easy for a small mistake to cascade. It’s also remarkably hard to catch!

    Hopefully the Department of Commerce catches on and fixes this little clerical mishap, and that applicants are submitting good spreadsheets, free of errors. But, this case underscores how (1) so many of the world’s financial and policy decisions rest on Excel spreadsheets and you just have to hope 🤞🏻 no large mistakes were made, and (2) the potential for tools like Rowsie to be tireless proofreaders and assistants who can help us avoid mistakes and understand those critical spreadsheets quickly.

    If you’re interested in checking out Rowsie, check it out at https://www.rowsie.ai/!

    DISCLAIMER: I happen to be friends with the founders of Rowsie which is how I found out about this

  • It’s not just the GOP who misunderstands Section 230

    Source: NPR

    Section 230 of the Communications Decency Act has been rightfully called “the twenty-six words that created the Internet.” It is a valuable legal shield which allows internet hosts and platforms the ability to distribute user-generated content and practice moderation without unreasonable fear of being sued, something which forms the basis of all social media, user review, and user forum, and internet hosting services.

    In recent months, as big tech companies have drawn greater scrutiny for the role they play in shaping our discussions, Section 230 has become a scapegoat for many of the ills of technology. Until 2021, much of that criticism has come from the Republican Party who argue incorrectly that it promotes bias on platforms with President Trump even vetoing unrelated defense legislation because it did not repeal Section 230.

    So, it’s refreshing (and distressing) to see the Democrats now take their turn in misunderstanding what Section 230 does for the internet. This critique is based mainly on Senator Mark Warner’s proposed changes to Section 230 and the FAQ his office posted about the SAFE TECH act he (alongside Senators Hirono and Klobuchar) is proposing but apply to many commentators from the Democratic Party and the press which seems to have misunderstood the practical implications and have received this positively.

    While I think it’s reasonable to modify Section 230 to obligate platforms to help victims of clearly heinous acts like cyberstalking, swatting, violent threats, and human rights violations, what the Democratic Senators are proposing goes far beyond that in several dangerous ways.

    First, Warner and his colleagues have proposed carving out from Section 230 all content which accompanies payment (see below). While I sympathize with what I believe was the intention (to put a different bar on advertisements), this is remarkably short-sighted, because Section 230 applies to far more than companies with ad / content moderation policies Democrats dislike such as Facebook, Google, and Twitter.

    Source: Mark Warner’s “redlines” of Section 230; highlighting is mine

    It also encompasses email providers, web hosts, user generated review sites, and more. Any service that currently receives payment (for example: a paid blog hosting service, any eCommerce vendor who lets users post reviews, a premium forum, etc) could be made liable for any user posted content. This would make it legally and financially untenable to host any potentially controversial content.

    Secondly, these rules will disproportionately impact smaller companies and startups. This is because these smaller companies lack the resources that larger companies have to deal with the new legal burdens and moderation challenges that such a change to Section 230 would call for. It’s hard to know if Senator Warner’s glip answer in his FAQ that people don’t litigate small companies (see below) is ignorance or a willful desire to mislead, but ask tech startups how they feel about patent trolls and whether or not being small protects them from frivolous lawsuits

    Source: Mark Warner’s FAQ on SAFE TECH Act; highlighting mine

    Third, the use of the language “affirmative defense” and “injunctive relief” may have far-reaching consequences that go beyond minor changes in legalese (see below). By reducing Section 230 from an immunity to an affirmative defense, it means that companies hosting content will cease to be able to dismiss cases that clearly fall within Section 230 because they now have a “burden of [proof] by a preponderance of the evidence.”

    Source: Mark Warner’s “redlines” of Section 230; highlighting is mine

    Similarly, carving out “injunctive relief” from Section 230 protections (see below) means that Section 230 doesn’t apply if the party suing is only interested in taking something down (but not financial damages)

    Source: Mark Warner’s “redlines” of Section 230

    I suspect the intention of these clauses is to make it harder for large tech companies to dodge legitimate concerns, but what this practically means is that anyone who has the money to pursue legal action can simply tie up any internet company or platform hosting content that they don’t like.

    That may seem like hyperbole, but this is what happened in the UK until 2014 where libel / slander laws making it easy for wealthy individuals and corporations to sue anyone for negative press due to weak protections. Imagine Jeffrey Epstein being able to sue any platform for carrying posts or links to stories about his actions or any individual for forwarding an unflattering email about him.

    There is no doubt that we need new tools and incentives (both positive and negative) to tamp down on online harms like cyberbullying and cyberstalking, and that we need to come up with new and fair standards for dealing with “fake news”. But, it is distressing that elected officials will react by proposing far-reaching changes that show a lack of thoughtfulness as it pertains to how the internet works and the positives of existing rules and regulations.

    It is my hope that this was only an early draft that will go through many rounds of revisions with people with real technology policy and technology industry expertise.

  • Is Gamestop a Win for the Little Guy?

    If you’ve been exposed to any financial news in the last few days, you’ll have heard of Gamestop, the mostly brick and mortar video gaming retailer who’s stock has been caught between many retail investors on the subreddit r/WallstreetBets and hedge fund Melvin Capital which had been actively betting against the company. The resulting short squeeze (where a rising stock price forces investors betting against a company to buy shares to cover their own potential losses — which itself can push the stock price even higher) has been amazing to behold with the worth of Gamestop shares increasing over 10-fold in a matter of months.

    Source: Yahoo Finance (pulled 28 Jan 2021)

    While it’s hard not to get swept up in the idea of “the little guy winning one over on a hedge fund”, the narrative that this is Main Street winning over Wall Street is overblown.

    A brief sampling of #HoldTheLine on Twitter

    First, speaking practically, it’s hard to argue that giving one hedge fund a black eye by making Gamestop executives & directors and large investment funds holding $100M’s of Gamestop prior to the increase wealthier is anyone winning anything over on Wall Street. And that’s not even accounting for the fact that hedge funds are usually managing a significant amount of money on behalf of pension funds and foundation / university endowments.

    Winning one over on Wall Street? Created using Imgflip

    Second, while the paper value of recent investments in Gamestop has clearly jumped through the roof, what these investors will actually “win” is unclear. Even holding aside short-term capital gains taxes that many retail investors are unclear on, the reality is that, to make money on an investment, you not only have to buy low, you have to successfully sell high. By definition, any company experiencing a short-squeeze is volume-limited — meaning that it’s the lack of sellers that is causing the increase in price (the only way to get someone to sell is to offer them a higher price). If the stock price changes direction, it could trigger a flood of investors flocking to sell to try to hold on to their gains which can create the opposite problem: too many people trying to sell relative to people trying to buy which can cause the price to crater.

    Buy high sell low? Created using Imgflip

    Regulatory and legal experts are better suited to weigh in on whether or not this constitutes market manipulation that needs to be regulated. For whatever it’s worth, I personally feel that Redditors egging each other on is no different than an institutional investor hyping their investments on cable TV.

    But what is not in doubt is that these sorts of trades are extremely risky for all parties involved — whether you’re betting against a popular stock or trying to “hold the line” on a short-squeeze. For that reason, I’m sympathetic to the brokerages which are limiting investor activity in some of these speculative tickers.

    While many retail investors view these restrictions as a move by Wall Street to screw the little guy, there’s a practical reality here that the brokerages are probably fearful of:

    • Lawsuits from investors, some of whom will eventually lose quite a bit of money here
    • SEC actions and punishments due to eventual outcry from investors losing money

    This is the third reason I’m worried the Gamestop story will ultimately be a bad thing for Main Street. If the resulting lawsuits and/or regulatory actions cause brokerages to put more restrictions on investors, this could put additional friction on investors in terms of how they can participate in long-term wealth creation, something more households need given the paltry state of retirement savings.

    I love stories of hedge funds facing the consequences of the risks they take on — but the idea that this is a clear win for Main Street is suspect (as is the idea that the right answer for most retail investors is to HODL through thick and through thin).

    Thought this was interesting or helpful? Check out some of my other pieces on investing / finance.

  • Mea Culpa

    Mea culpa.

    I’ve been a big fan of moving my personal page over to AWS Lightsail. But, if I had one complaint, it’s the dangerous combination of (1) their pre-packaged WordPress image being hard to upgrade software on and (2) the training-wheel-lacking full root access that Lightsail gives to its customers. That combination led me to make some regrettable mistakes yesterday which resulted in the complete loss of my old blog posts and pages.

    It’s the most painful when you know your problems are your own fault. Thankfully, with the very same AWS Lightsail, it’s easy enough to start up a new WordPress instance. With the help of site visit and search engine analytics, I’ve prioritized the most popular posts and pages to resurrect using Google’s cache.

    Unfortunately, that process led to my email subscribers receiving way too many emails from me as I recreated each post. For that, I’m sorry — mea culpa — it shouldn’t happen again.

    I’ve come to terms with the fact that I’ve lost the majority of the 10+ years of content I’ve created. But, I’ve now learned the value of systematically backing up things (especially my AWS Lightsail instance), and hopefully I’ll write some good content in the future to make up for what was lost.

  • Visualizing How Market Volatility Impacts Risk and Returns

    S&P500 Performance for 2020 (Yahoo Finance), pulled Jan 17, 2021

    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.

    Takeaways

    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

    Thought this was interesting or helpful? Check out some of my other pieces on investing / finance.

  • 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 (it’s not great to be overtly 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!

  • 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).

    Source: US News

    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 SurveyGoBankingRate’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!

    Thought this was interesting or helpful? Check out some of my other pieces on investing / finance.

  • 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.

    Sources: Lyft S-1Uber 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.

    Sources: Lyft S-1Uber 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.

    Sources: Lyft S-1Uber 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.

    Sources: Lyft S-1Uber 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)

    Sources: Lyft S-1Uber 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.

    Sources: Lyft S-1Uber 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

    Sources: Lyft S-1Uber 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

    Sources: Lyft S-1Uber 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.

    Sources: Lyft S-1Uber 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-1Uber 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.

    Image for post
    Sources: Lyft S-1Uber 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!

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  • 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)

    Source: MIT Sloan

    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… Source: 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 offeringsmaking 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).

    Wrap-Up

    Source: OECD Forum Network

    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!

  • Why Tech Success Doesn’t Translate to Deeptech

    Source: Eric Hamilton

    Having been lucky enough to invest in both tech (cloud, mobile, software) and “deeptech” (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”:

    Source: Techcrunch
    • 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 deeptech companies, a very different set of rules apply:

    Source: XKCD
    • Technology risk/uncertainty is inherent: One of the defining hallmarks of a deeptech 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, deeptech 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 deeptech 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 deeptech.
    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 deeptech.
    2. Furthermore, because deeptech innovations tend to have real-world physical impacts (to health, to safety, to a supply chain/manufacturing line, etc.), deeptech 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. Deeptech 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 deeptech categories, the most successful deeptech startups tend to embody a few basic principles:

    1. Go after markets where there is a very clear, unmet need: The best deeptech 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 deeptech 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 deeptech 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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  • Advice VCs Want to Give but Rarely Do to Entrepreneurs Pitching Their Startups

    Source: Someecards

    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.

    Thought this was interesting? Check out some of my other pieces on how VC works / thinks

  • 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 Jet.com (an innovator in eCommerce that Walmart needs to help in its war for retail marketshare with Amazon.com).

    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.

    Thought this was interesting? Check out some of my other pieces on how VC works / thinks

  • Snap Inc by the Numbers

    A look at what Snap’s S-1 reveals about their growth story and unit economics

    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.

    Oddly apt banner; Source: Business Insider

    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’ve 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.

    Thought this was interesting or helpful? Check out some of my other pieces on investing / finance.

  • Dr. Machine Learning

    How to realize the promise of applying machine learning to healthcare

    Not going to happen anytime soon, sadly: the Doctor from Star Trek: Voyager; Source: 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.

  • 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.

    Source: 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.

    Source: 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.

    Source: 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/OculusHTC/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.

    Source: the book

    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!

  • 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.

    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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