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.
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.
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
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.”
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)
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.
Amazon’s Lightsail service has made it remarkably cheap and simple for people hosting webpages (including this blog) and simple web applications to get access to high quality virtual private servers (VPS). Beyond the ability to do really stupid things with your VPS powers, my only real complaint with the experience thus far has been that getting HTTPS working with Let’s Encrypt’s free SSL certificates has been a a hassle, requiring a lot more DNS configuration and manual command-line tweaking than is really necessary.
Manually adjusting your DNS configuration with TXT records to confirm domain ownership (each renewal)
Manually copying of certificate files to the right directory
Requires you to manually setup URL redirection (i.e. moving your HTTP URLs to HTTPS, figuring out how to handle “www.” subdomain, etc)
Manual renewal every cycle (Let’s Encrypt certificates expire every 90 days)
Bitnami’s new command line tool (bncert) is vastly simpler to use:
Assuming you’ve already linked the domain you wish to enable HTTPS for to the public IP address for your server, the domain certification is handled automatically without any effort (if it’s not, you may need to add TXT records)
Automatically configures URL redirection for you based on what you enter when prompted
Automatically puts the certificates where they need to be
Automatically renews your certificates in 80 days (right before they expire)
The only downside I can see from the new bncert tool is that it does not support a wildcard certificate (which would work with any subdomain the way that certbot did), so if you’re planning on using multiple subdomains, it may be worth first planning out which domains you intend to use prior to using bncert (or to continue with certbot)
If you’re using a recent Bitnami WordPress image, bncert should already be present (and you can simply follow the instructions on the Lightsail documentation page and skip Step 4) — if not, you can either install the tool (Step 4 of the instructions) or (and this is the path I’d recommend) create a new Lightsail image and migrate your WordPress blog over. It’s a bit of a pain but it’s worth the effort as if you’ve been using an older version of Bitnami WordPress, there’s a good chance you’re using an out of date PHP installation. For help, here is a guide I wrote to how to move an old WordPress blog to a new WordPress setup.
Then the only remaining step is to change your WordPress Domain name in the wp-config.php file (so that your WordPress install thinks of itself as an https rather than an http domain). To do this, use the Lightsail command line interface and enter
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.
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.
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.
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.
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.
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
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).
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.
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 âbacktestingâ). While itâs 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. Itâs 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 itâs significant, imagine that youâve 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 youâre 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 â 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-YearReturns 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 âoutwardâ 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 yearâs stock market returns. From 1928 â 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 â in line with the historical arithmetic mean return of the market â but dropping slowly to ~10% by year 10 and to ~9.8% starting in year 30), the âspreadâ 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 âneedâ. 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. Letâs 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-YearReturns 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 investmentâs 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 isnât 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 itâs âspreadâ. However, I was surprised to find that adjusting the volatility didnât just impact the âspreadâ 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-YearReturns 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 âspreadâ 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!
Confidence Interval Map of 40-Year Return for Low Volatility Investment (Red) vs. S&P500 (Blue) Left: (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 mean but 2% lower variance (red lines). Right: Zoomed-in look just at the median lines and 50% confidence interval bounds
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 â 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, itâs not easy to find a âS&P500 but less volatileâ or a âS&P500 but higher returnâ. 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:
Confidence Interval Map for Low Volatility/Low Return (Purple) vs. High Volatility/High Return (Gray) Left: (from outside to middle/darkest to lightest) 90%, 50% confidence interval, and median investment returns 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). Both returns assume normal distribution clipped from -90% to +100% with mean/variance based on 1928-2019 annual returns for S&P500. Right: Zoomed-in look just at the median lines and 50% confidence interval bounds
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 âvolatility is the same thing as riskâ:
Time horizon matters as uncertainty in returns decreases with time: As the charts above showed, âreversion to the meanâ reduces the uncertainty (or âspreadâ) 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 âneededâ 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. Itâs 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 â 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/ârisklessâ things like cash and government bonds.
Whatâs 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. Iâd love to hear requests for other questions of interest, but for now, I plan to look into:
Iâve been reading a lot of year-end/decade-end reflections (as one does this time of year) â and while a part of me wanted to #humblebrag about how I got a đ /đ/đśđť this decade đ â 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.
Sales is an unimportant skill relative to hard work / being smart As a stereotypical âgood Asian kidâ đ¤, I was taught to focus on nailing the task. I still think that focus is important early in oneâs life & career, but this decade has made me realize that everyone, whether they know it or not, has to sell â 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).
Marriage is about finding your soul-mate and living happily ever after Having been married for slightly over half the decade, Iâve now come to believe that marriage is less about finding the perfect soul-mate (the âHollywood versionâ) 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 otherâs sentences, etc.
Industrial policy doesnât work I tend to be a big skeptic of big government policy â 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 donât 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 Studwellâs book How Asia Works
Obesity & weight loss are simple â its just calories in & calories out From a pure physics perspective, weight gain is a âsimpleâ thermodynamic equation of âcalories in minus calories outâ. But in working with companies focused on dealing with prediabetes/obesity, Iâve come to appreciate that this âlogicâ 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
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, Iâve 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.
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 thereâs 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, Iâve 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 âstartups are great for learningâ 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 startupâs mission), but I frankly think most people donât fit the bill.
Microaggressions are just people being overly sensitive Iâve been blessed at having only rarely faced overt racism (telling me to go back to China đ / or that I donât belong in this country). Itâs a product of both where Iâve 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 youâre trying to raise money from). But, having spent some dedicated time outside of those coastal areas this past decade and speaking with minorities whoâve lived there, Iâve become exposed to and more aware of âmicroaggressionsâ, forms of non-overt prejudice that are generally perpetrated without ill intent: questions like âso where are you really from?â or comments like âyou speak English really well!â. I once believed people complaining about these were simply being overly sensitive, but Iâve 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.
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 wouldnât have gone as far as Fukuyama did in proclaiming âthe end of historyâ, 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, âanti-globalismâ, 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.
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 shouldnât 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 âstrategic positioningâ (Nokia, Blackberry, Walmart, big media), Iâve come around to a different understanding of how the two balance each other.
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 â especially since I net out here very differently from Bill Gates â 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 â Iâd also like to see more progress made on long-duration energy storage, carbon capture, and industrial â but whereas I once believed that weâd 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.
Hereâs to more worldview shifts in the coming decade!
While itâs 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 investmentâs 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 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 Bachelorâs 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%.
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 yearâs 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 graduateâs wages growrelative to a high school diploma holderâs also has a significant impact on the returns to a college education:
Census/BLS data suggests that, between 1997 and 2017, wages of bachelorâs 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 Sallyâs 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 Sallyâs 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 itâs 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 youâd 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 donât factor in any particular individualâs 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 âMake a Copyâ 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 doesnât 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 shouldnât 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!
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 Lyftâs and Uberâs respective S-1âs become public within mere weeks of each other.
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-1âs donât make it easy to get much on (2) or (3) â 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
Uberâs Users Spend More, Despite Cheaper Rides
As someone who first knew of Uber as the UberCab âblack-carâ service, and who first heard of Lyft as the Zimride ridesharing platform, I was surprised to discover that Lyftâs average ride price is significantly more expensive than Uberâs and the gap is growing! In Q1 2017, Lyftâs average bookings per ride was $11.74 and Uberâs was $8.41, a difference of $3.33. But, in Q4 2018, Lyftâs average bookings per ride had gone up to $13.09 while Uberâs had declined to $7.69, increasing the gap to $5.40.
This is especially striking considering the different definitions that Lyft and Uber have for âbookingsâ â Lyft excludes â 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 feesâ whereas Uberâs includes â applicable taxes, tolls, and fees â. This gap is likely also due to Uberâs 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 Lyftâs Line product (where multiple users can share a ride), or its general pricing is encouraging greater use.
Note: the â~monthlyâ that youâll see used throughout the charts in this post are because the aggregate data â rides, bookings, revenue, etc â 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, Lyftâs average rider spend has been growing and has almost caught up to Uberâs which is slightly down.
However, Uberâs 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 âridersâ, but Uber calls them âActive Platform Consumersâ.
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 companyâs 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) â ~13% in Q4 2018 compared with ~9% for Uber. While Uberâs has bounced up and down, Lyftâs has steadily increased (up nearly 2x from Q1 2017). I would hazard a guess that Uberâs 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.
Note: the gross margin Iâm 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 âContribution Marginâ 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) â nearly 30% in the case of Lyft in Q4 2018 but only 20% for Uber (and under 10% for UberEats)
Note: Uber uses a different definition of take rate in their filings based on a separate cut of âCore Platform Revenueâ which excludes certain items around referral fees and driver incentives. Iâve 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.
All of this data begs the question, given Lyftâs 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 Lyftâs other costs per user declining as it grows? Sadly, the data does not seem to pan out that way
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 Lyftâs 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
The story is similar for profit per ride. Uber has consistently been more profitable since 2017, and theyâve only increased that lead since. This is despite the fact that Iâve included the costs of Uberâs other businesses in their cost per ride.
One possible interpretation of Lyftâs 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 Uberâs respective user growth during this period of time.
The data shows that Lyftâs compounded quarterly growth rate (CQGR) from Q1 2016 to Q4 2018 of 16.4% is only barely higher than Uberâs 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 doesnâst 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-1âs on the number of drivers on each platform in Q4 2018 which are illuminating.
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 âutilizedâ given that they handled 136% more rides than the average Lyft driver and, despite Uberâs 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
Iâd 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 Khosrowshahiâs assumption of CEO role) which led to better spending discipline and, as a result, better unit economics despite falling gross profits per user
Uberâs new businesses (in particular UberEats) have had a significant impact on Uberâs 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!
Thereâs been a fair amount of talk lately about proactively regulating â and maybe even breaking up â the âBig Techâ companies.
Full disclosure: this post discusses regulating large tech companies. I own shares in several of these both directly (in the case of Facebook and Microsoft) and indirectly (through ETFs that own stakes in large companies)
Like many, I have become increasingly uneasy over the fact that a small handful of companies, with few credible competitors, have amassed so much power over our personal data and what information we see. As a startup investor and former product executive at a social media startup, I can especially sympathize with concerns that these large tech companies have created an unfair playing field for smaller companies.
At the same time, though, Iâm mindful of all the benefits that the tech industry â including the âtech giantsâ â 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 âbig techââs growing power, I am wary of reaching for âquick fixesâ that might change that.
Another factor which complicates tech policy is that the traditional âbig is badâ mentality ignores the benefits to having large platforms. While Amazonâs 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 wouldâve been virtually impossible before. While the dominance of Googleâs 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 âBig Techâ 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 âBig Techâ, 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 âBig Techâ problems affect smaller companies and new concepts as well.
To be 100% clear, Iâm 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 âBig Techâ as just a problem to kill, I think weâd 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 itâs 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 userâs 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.
I believe that is an overreaction. Platform owners offering attractive products and services (i.e., Google offering turn-by-turn navigation on Android phones) can be a great thing for users (after all, most prominent platforms started by providing compelling first-party offerings) and for 3rd party participants if these offerings improve the attractiveness of the platform overall.
What is hard to justify is when platform owners stack the deck in their favor using anti-competitive moves such as banning or reducing the visibility of competitors,crippling third party offerings, making excessive demands on 3rd parties, etc. Its these sorts of actions by the largest tech platforms that pose a risk to consumer choice and competition and should face regulatory scrutiny. Not just the fact that a large platform exists or that the platform owner chooses to participate in it.
IV. Modernizing how anti-trust thinks about defensive acquisitions
The rise of the tech giants has led to many calls to unwind some of the pivotal mergers and acquisitions in the space. As much as I believe that anti-trust regulators made the wrong calls on some of these transactions, I am not convinced, beyond just wanting to punish âBig Techâ for being big, that the Pandoraâs 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., Googleâs acquisition of Waze and Facebookâs acquisition of WhatsApp are two examples of landmark acquisitions which probably should have been evaluated more closely).
This is hardly a complete set of rules and policies needed to approach growing concerns about âBig Techâ. 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 âBig Techâ.
Special thanks to Derek Yang and Anthony Phan for reading earlier versions and giving me helpful feedback!
Having been lucky enough to invest in both tech (cloud, mobile, software) and âhardtechâ (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:
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.)
Build a solid team of engineers, designers, growth, sales, marketing, and product people to execute on lean software development and growth methodologies
Hire the right executives to carry out the right mix of tried-and-true as well as âout of the boxâ channel and business developmentstrategies to scale bigger and faster
This playbook appears deceptively simple but is very difficult to execute well. It works because for markets where âsoftware is eating the worldâ:
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 â what is the right thing to build and how do I build it on time, under budget? â 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:
The first is that startups can launch with incomplete or buggy solutions because they can readily provide hotfixes and upgrades.
The second is that startups can quickly release new product features and designs to respond to new information and changing market conditions.
The third is that adoption is relatively straightforward. While there may be some integration and qualification challenges, in general, the product is accessible via a quick download/browser refresh, and the core challenge is in getting enough people to use a product in the right way.
In contrast, if you look at hardtech companies, a very different set of rules apply:
Technology risk/uncertainty is inherent: One of the defining hallmarks of a hardtech company is dealing with uncertainty from constraints imposed by reality (i.e. the laws of physics, the underlying biology, the limits of current technology, etc.). As a result, hardtech startups regularly face feasibility challenges â what is even possible to build? â and uncertainty around the R&D cycles to get to a good outcome â how long will it take / how much will it cost to figure this all out?
Skills & knowledge are not easily transferable: Because the technical and business talent needed in hardtech is usually specific to the field, talent and skills are not necessarily transferable from sector to sector or even company to company. The result is that it is much harder for investors/executives to evaluate team caliber (whether on technical merits or judging past experience) or to simply put the right people into place if there are problems that come up.
Product iteration is slow and costly: The tech startup ethos of âmove fast and break thingsâ is just harder to do with hardtech.
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. Itâs not just that physical products require physical materials and processing, but the availability of low cost technology platforms like Amazon Web Services and open source software dramatically lower the amount of time / cash needed to make something testable in tech than in hardtech.
Furthermore, because hardtech innovations tend to have real-world physical impacts (to health, to safety, to a supply chain/manufacturing line, etc.), hardtech companies generally face far more regulatory and commercial scrutiny. These groups are generally less forgiving of incomplete/buggy offerings and their assessments can lengthen development cycles. Hardtech companies generally canât take the âask for forgiveness laterâ approaches that some tech companies (i.e. Uber and AirBnb) have been able to get away with (exhibit 1: Theranos).
As a result, while there is no single playbook that works across all hardtech categories, the most successful hardtech startups tend to embody a few basic principles:
Go after markets where there is a very clear, unmet need: The best hardtech entrepreneurs tend to take very few chances with market risk and only pursue challenges where a very well-defined unmet need (i.e., there are no treatments for Alzheimerâs, 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.
Focus on âone miracleâ problems: Its tempting to fantasize about what could happen if you could completely re-write every aspect of an industry or problem but the best hardtech startups focus on innovating where they wonât 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 â why make it even harder?
Pursue technologies that can significantly over-deliver on what the market needs: Because of the risks involved with developing advanced technologies, the best hardtech entrepreneurs work in technologies where even a partial success can clear the bar for what is needed to go to market. At the minimum, this reduces the risk of failure. But, hopefully, it gives the company the chance to fundamentally transform the market it plays in by being 10x better than the alternatives. This is in contrast to many tech markets where market success often comes less from technical performance and more from identifying the right growth channels and product features to serve market needs (i.e., Facebook, Twitter, and Snapchat vs. MySpace, Orkut, and Friendster; Amazon vs. brick & mortar bookstores and electronics stores)
All of this isnât to say that there arenât similarities between successful startups in both categories â 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!
