Inspired by some work from a group at Stanford on building a lab from AI agents, I’ve been experimenting with multi-agent AI conversations and workflows. But, because the space (at least to me) has seemed more focused on building more capable agents rather than coordinating and working with more agents, the existing tools and libraries have been difficult to carry out experiments.
To facilitate some of my own exploration work, I built what I’m calling a Multi-Agent ChatLab — a browser-based, completely portable setup to define multiple AI agents and facilitate conversations between them. This has made my experimentation work vastly simpler and I hope it can help someone else.
More about how to use this & the underlying design on this page.
And, to show off the tool, and for your amusement (and given my love of military history), here is a screengrab from the tool where I set up two AI Agents — one believing itself to be Napoleon Bonaparte and one believing itself to be the Duke of Wellington (the British commander who defeated Napoleon at Waterloo) — and had them describe (and compare!) the hallmarks of their military strategy.
Everywhere you look, the message seems clear: early detection (of cancer & disease) saves lives. Yet behind the headlines, companies developing these screening tools face a different reality. Many tests struggle to gain approval, adoption, or even financial viability. The problem isn’t that the science is bad — it’s that the math is brutal.
This piece unpacks the economic and clinical trade-offs at the heart of the early testing / disease screening business. Why do promising technologies struggle to meet cost-effectiveness thresholds, despite clear scientific advances? And what lessons can diagnostic innovators take from these challenges to improve their odds of success? By the end, you’ll have a clearer view of the challenges and opportunities in bringing new diagnostic tools to market—and why focusing on the right metrics can make all the difference.
Technologists often prioritize metrics like sensitivity (also called recall) — the ability of a diagnostic test to correctly identify individuals with a condition (i.e., if the sensitivity of a test is 90%, then 90% of patients with the disease will register as positives and the remaining 10% will be false negatives) — because it’s often the key scientific challenge and aligns nicely with the idea of getting more patients earlier treatment.
But when it comes to adoption and efficiency, specificity — the ability of a diagnostic test to correctly identify healthy individuals (i.e., if the specificity of a test is 90%, then 90% of healthy patients will register as negatives and the remaining 10% will be false positives) — is usually the more important and overlooked criteria.
The reason specificity is so important is that it can have a profound impact on a test’s Positive Predictive Value (PPV) — whether or not a positive test result means a patient actually has a disease (i.e., if the positive predictive value of a test is 90%, then a patient that registers as positive has a 90% chance of having the disease and 10% chance of actually being healthy — being a false positive).
What is counter-intuitive, even to many medical and scientific experts, is that because (by definition) most patients are healthy, many high accuracy tests have disappointingly low PPV as most positive results are actually false positives.
Let me present an example (see table below for summary of the math) that will hopefully explain:
Let’s say we have an HIV test with 99% sensitivity and 99% specificity — a 99% (very) accurate test!
If we tested 10,000 Americans at random, you would expect roughly 36 of them (0.36% x 10,000) to be HIV positive. That means, roughly 9,964 are HIV negative
99% sensitivity means 99% of the 36 HIV positive patients will test positive (99% x 36 = ~36)
99% specificity means 99% of the 9,964 HIV negative patients will test negative (99% x 9,964 = ~9,864) while 1% (1% x 9,964 = ~100) would be false positives
This means that even though the test is 99% accurate, it only has a positive predictive value of ~26% (36 true positives out of 136 total positive results)
Below (if you’re on a browser) is an embedded calculator which will run this math for any values of disease prevalence and sensitivity / specificity (and here is a link to a Google Sheet that will do the same), but you’ll generally find that low disease rates result in low positive predictive values for even very accurate diagnostics.
Typically, introducing a new diagnostic means balancing true positives against the burden of false positives. After all, for patients, false positives will result in anxiety, invasive tests, and, sometimes, unnecessary treatments. For healthcare systems, they can be a significant economic burden as the cost of follow-up testing and overtreatment add up, complicating their willingness to embrace new tests.
Below (if you’re on a browser) is an embedded calculator which will run the basic diagnostic economics math for different values of the cost of testing and follow-up testing to calculate the cost of testing and follow-up testing per patient helped (and here is a link to a Google Sheet that will do the same)
Finally, while diagnostics businesses face many of the same development hurdles as drug developers — the need to develop cutting-edge technology, to carry out large clinical studies to prove efficacy, and to manage a complex regulatory and reimbursement landscape — unlike drug developers, diagnostic businesses face significant pricing constraints. Successful treatments can command high prices for treating a disease. But successful diagnostic tests, no matter how sophisticated, cannot, because they ultimately don’t treat diseases, they merely identify them.
Case Study: Exact Sciences and Cologuard
Let’s take Cologuard (from Exact Sciences) as an example. Cologuard is a combination genomic and immunochemistry test for colon cancer carried out on patient stool samples. It’s two primary alternatives are:
a much less sensitive fecal immunochemistry test (FIT) — which uses antibodies to detect blood in the stool as a potential, imprecise sign of colon cancer
colonoscopies — a procedure where a skilled physician uses an endoscope to enter and look for signs of cancer in a patient’s colon. It’s considered the “gold standard” as it functions both as diagnostic and treatment (a physician can remove or biopsy any lesion or polyp they find). But, because it’s invasive and uncomfortable for the patient, this test is typically only done every 4-10 years
Cologuard is (as of this writing) Exact Science’s primary product line, responsible for a large portion of Exact Science’s $2.5 billion in 2023 revenue. It can detect earlier stage colon cancer as well as pre-cancerous growths that could lead to cancer. Impressively, Exact Sciences also commands a gross margin greater than 70%, a high margin achieved mainly by pharmaceutical and software companies that have low per-unit costs of production. This has resulted in Exact Sciences, as of this writing, having a market cap over $11 billion.
Yet for all its success, Exact Sciences is also a cautionary note, illustrating the difficulties of building a diagnostics company.
The company was founded in 1995, yet didn’t see meaningful revenue from selling diagnostics until 2014 (nearly 20 years later, after it received FDA approval for Cologuard)
The company has never had a profitable year (this includes the last 10 years it’s been in-market), losing over $200 million in 2023, and in the first three quarters of 2024, it has continued to be unprofitable.
Between 1997 (the first year we have good data from their SEC filings as summarized in this Google Sheet) and 2014 when it first achieved meaningful diagnostic revenue, Exact Sciences lost a cumulative $420 million, driven by $230 million in R&D spending, $88 million in Sales & Marketing spending, and $33 million in CAPEX. It funded those losses by issuing over $624 million in stock (diluting investors and employees)
From 2015-2023, it has needed to raise an additional $3.5 billion in stock and convertible debt (net of paybacks) to cover its continued losses (over $3 billion from 2015-2023)
Prior to 2014, Exact Sciences attempted to commercialize colon cancer screening technologies through partnerships with LabCorp (ColoSure and PreGenPlus). These were not very successful and led to concerns from the FDA and insurance companies. This forced Exact Sciences to invest heavily in clinical studies to win over the payers and the FDA, including a pivotal ~10,000 patient study to support Cologuard which recruited patients from over 90 sites and took over 1.5 years.
It took Exact Sciences 3 years after FDA approval of Cologuard for its annual diagnostic revenues to exceed what it spends on sales & marketing. It continues to spend aggressively there ($727M in 2023).
While it’s difficult to know precisely what the company’s management / investors would do differently if they could do it all over again, the brutal math of diagnostics certainly played a key role.
From a clinical perspective, Cologuard faces the same low positive predictive value problem all diagnostic screening tests face. From the data in their study on ~10,000 patients, it’s clear that, despite having a much higher sensitivity for cancer (92.3% vs 73.8%) and higher AUROC (94% vs 89%) than the existing FIT test, the PPV of Cologuard is only 3.7% (lower than the FIT test: 6.9%).
Even using a broader disease definition that includes the pre-cancerous advanced lesions Exact Sciences touted as a strength, the gap on PPV does not narrow (Cologuard: 23.6% vs FIT: 32.6%)
The economic comparison with a FIT test fares even worse due to the higher cost of Cologuard as well as the higher rate of false positives. Under the Center for Medicare & Medicaid Service’s 2024Q4 laboratory fee schedule, a FIT test costs $16 (CPT code: 82274), but Cologuard costs $509 (CPT code: 81528), over 30x higher! If each positive Cologuard and FIT test results in a follow-up colonoscopy (which has a cost of $800-1000 according to this 2015 analysis), the screening cost per cancer patient is 5.2-7.1x higher for Cologuard than for the FIT test.
This quick math has been confirmed in several studies.
A study by a group at the University Medical Center of Rotterdam concluded that “Compared to nearly all other CRC screening strategies reimbursed by CMS (Medicare), [Cologuard] is less effective and considerably more costly, making it an inefficient screening option” and would only be comparable at a much lower cost (~$6-18!)
While Medicare and the US Preventive Services Task Force concluded that the cost of Cologuard and the increase in false positives / colonoscopy complications was worth the improved early detection of colon cancer, it stayed largely silent on comparing cost-efficacy with the FIT test. It’s this unfavorable comparison that has probably required Exact Sciences to invest so heavily in sales and marketing to drive sales. That Cologuard has been so successful is a testament both to the value of being the only FDA-approved test on the market as well as Exact Science’s efforts in making Cologuard so well-known (how many other diagnostics do you know have an SNL skit dedicated to them?).
Not content to rest on the laurels of Cologuard, Exact Sciences recently published a ~20,000 patient study on their next generation colon cancer screening test: Cologuard Plus. While the study suggests Exact Sciences has improved the test across the board, the company’s marketing around Cologuard Plus having both >90% sensitivity and specificity is misleading, because the figures for sensitivity and specificity are for different conditions: sensitivity for colorectal cancer but specificity for colorectal cancer OR advanced precancerous lesion (see the table below).
Sensitivity and Specificity by Condition for Cologuard Plus Study (Google Sheet link)
Disentangling these numbers shows that while Cologuard Plus has narrowed its PPV disadvantage (now worse by 1% on colorectal cancer and even on cancer or lesion) and its cost-efficacy disadvantage (now “only” 4.4-5.8x more expensive) vs the FIT test (see tables below), it still hasn’t closed the gap.
Time will tell if this improved test performance translates to continued sales performance for Exact Sciences, but it is telling that despite the significant time and resources that went into developing Cologuard Plus, the data suggests it’s still likely more cost effective for health systems to adopt FIT over Cologuard Plus as a means of preventing advanced colon cancer.
Lessons for diagnostics companies
The underlying math of the diagnostics business and the lessons from Exact Sciences’ long path to dramatic sales has several key lessons for diagnostic entrepreneurs:
Focus on specificity — For diagnostic technologists, too little attention is paid to specificity while too much attention is paid on sensitivity. Positive predictive value and the cost-benefit for a health system are largely going to swing on specificity.
Aim for higher value tests — Because the development and required validation for a diagnostic can be as high as that of a drug or medical device, it is important to pursue opportunities where the diagnostic can command a high price. These are usually markets where the alternatives are very expensive because they require new technology (e.g. advanced genetic tests) or a great deal of specialized labor (e.g. colonoscopy) or where the diagnostic directly decides on a costly course of treatment (e.g. a companion diagnostic for an oncology drug).
Go after unmet needs — If a test is able to fill a mostly unmet need — for example, if the alternatives are extremely inaccurate or poorly adopted — then adoption will be determined by awareness (because there aren’t credible alternatives) and pricing will be determined by sensitivity (because this drives the delivery of better care). This also simplifies the sales process.
Win beyond the test — Because performance can only ever get to 100%, each incremental point on sensitivity and specificity is both exponentially harder to achieve but also delivers less medical or financial value. As a result, it can be advantageous to focus on factors beyond the test such as regulatory approval / guidelines adoption, patient convenience, time to result, and impact on follow-up tests and procedures. Cologuard gained a great deal from being “the first FDA-approved colon cancer screening test”. Non-invasive prenatal testing, despite low positive predictive values and limited disease coverage, gained adoption in part by helping to triage follow-up amniocentesis (a procedure which has a low but still frighteningly high rate of miscarriage ~0.5%). Rapid antigen tests for COVID have also similarly been adopted despite their lower sensitivity and specificity than PCR tests due to their speed, low cost, and ability to carry out at home.
Diagnostics developers must carefully navigate the intersection of scientific innovation and financial reality, while grappling with the fact that even the most impressive technology may be insufficient without taking into account clinical and economic factors to achieve market success.
Ultimately, the path forward for diagnostic innovators lies in prioritizing specificity, targeting high-value and unmet needs, and crafting solutions that deliver value beyond the test itself. While Exact Science’s journey underscores the difficulty of these challenges, it also illustrates that with persistence, thoughtful investment, and strategic differentiation, it is possible to carve out a meaningful and impactful space in the market.
Thankfully, Keras 3 lived up to it’s multi-backend promise and made switching to JAX remarkably easy. For my code, I simply had to make three sets of tweaks.
First, I had to change the definition of my container images. Instead of starting from Tensorflow’s official Docker images, I instead installed JAX and Keras on Modal’s default Debian image and set the appropriate environmental variables to configure Keras to use JAX as a backend:
jax_image = (
modal.Image.debian_slim(python_version='3.11')
.pip_install('jax[cuda12]==0.4.35', extra_options="-U")
.pip_install('keras==3.6')
.pip_install('keras-hub==0.17')
.env({"KERAS_BACKEND":"jax"}) # sets Keras backend to JAX .env({"XLA_PYTHON_CLIENT_MEM_FRACTION":"1.0"})
Code language:Python(python)
Second, because tf.data pipelines convert everything to Tensorflow tensors, I had to switch my preprocessing pipelines from using Keras’s ops library (which, because I was using JAX as a backend, expected JAX tensors) to Tensorflow native operations:
Lastly, I had a few lines of code which assumed Tensorflow tensors (where getting the underlying value required a .numpy() call). As I was now using JAX as a backend, I had to remove the .numpy() calls for the code to work.
Everything else — the rest of the tf.data preprocessing pipeline, the code to train the model, the code to serve it, the previously saved model weights and the code to save & load them — remained the same! Considering that the training time per epoch and the time the model took to evaluate (a measure of inference time) both seemed to improve by 20-40%, this simple switch to JAX seemed well worth it!
Model Architecture Improvements
There were two major improvements I made in the model architecture over the past few months.
First, having run my news reader for the better part of a year now, I now have accumulated enough data where my strategy to simultaneously train on two related tasks (predicting the human rating and predicting the length of an article) no longer required separate inputs. This reduced the memory requirement as well as simplified the data pipeline for training (see architecture diagram below)
Secondly, I was successfully able to train a version of my algorithm which can use dot products natively. This not only allowed me to remove several layers from my previous model architecture (see architecture diagram below), but because the Supabase postgres database I’m using supports pgvector, it means I can even compute ratings for articles through a SQL query:
UPDATE articleuser
SET ai_rating = 0.5 + 0.5 * (1 - (a.embedding <=> u.embedding)),
rating_timestamp = NOW(),
updated_at = NOW()
FROM articles a,
users u
WHERE articleuser.article_id = a.id
AND articleuser.user_id = u.id
AND articleuser.ai_rating ISNULL;
Code language:SQL (Structured Query Language)(sql)
The result is much greater simplicity in architecture as well as greater operational flexibility as I can now update ratings from the database directly as well as from serving a deep neural network from my serverless backend.
Model architecture (output from Keras plot_model function)
Making Sources a First-Class Citizen
As I used the news reader, I realized early on that the ability to just have sorted content from one source (i.e. a particular blog or news site) would be valuable to have. To add this, I created and populated a new sources table within the database to track these independently (see database design diagram below) which was linked to the articles table.
Newsreader database design diagram (produced by a Supabase tool)
I then modified my scrapers to insert the identifier for each source alongside each new article, as well as made sure my fetch calls all JOIN‘d and pulled the relevant source information.
With the data infrastructure in place, I added the ability to add a source parameter to the core fetch URLs to enable single (or multiple) source feeds. I then added a quick element at the top of the feed interface (see below) to let a user know when the feed they’re seeing is limited to a given source. I also made all the source links in the feed clickable so that they could take the user to the corresponding single source feed.
One recurring issue I noticed in my use of the news reader pertained to slow load times. While some of this can be attributed to the “cold start” issue that serverless applications face, much of this was due to how the news reader was fetching pertinent articles from the database. It was deciding at the moment of the fetch request what was most relevant to send over by calculating all the pertinent scores and rank ordering. As the article database got larger, this computation became more complicated.
To address this, I decided to move to a “pre-calculated” ranking system. That way, the system would know what to fetch in advance of a fetch request (and hence return much faster). Couple that with a database index (which effectively “pre-sorts” the results to make retrieval even faster), and I saw visually noticeable improvements in load times.
But with any pre-calculated score scheme, the most important question is how and when re-calculation should happen. Too often and too broadly and you incur unnecessary computing costs. Too infrequently and you risk the scores becoming stale.
The compromise I reached derived itself from the three ways articles are ranked in my system:
The AI’s rating of an article plays the most important role (60%)
How recently the article was published is tied with… (20%)
How similar an article is with the 10 articles a user most recently read (20%
These factors lent themselves to very different natural update cadences:
Newly scraped articles would have their AI ratings and calculated score computed at the time they enter the database
AI ratings for the most recent and the previously highest scoring articles would be re-computed after model training updates
On a daily basis, each article’s score was recomputed (focusing on the change in article recency)
The article similarity for unread articles is re-evaluated after a user reads 10 articles
This required modifying the reader’s existing scraper and post-training processes to update the appropriate scores after scraping runs and model updates. It also meant tracking article reads on the users table (and modifying the /read endpoint to update these scores at the right intervals). Finally, it also meant adding a recurring cleanUp function set to run every 24 hours to perform this update as well as others.
Next Steps
With some of these performance and architecture improvements in place, my priorities are now focused on finding ways to systematically improve the underlying algorithms as well as increase the platform’s usability as a true news tool. To that end some of the top priorities for next steps in my mind include:
Testing new backbone models — The core ranking algorithm relies on Roberta, a model released 5 years ago before large language models were common parlance. Keras Hub makes it incredibly easy to incorporate newer models like Meta’s Llama 2 & 3, OpenAI’s GPT2, Microsoft’s Phi-3, and Google’s Gemma and fine-tune them.
Solving the “all good articles” problem — Because the point of the news reader is to surface content it considers good, users will not readily see lower quality content, nor will they see content the algorithm struggles to rank (i.e. new content very different from what the user has seen before). This makes it difficult to get the full range of data needed to help preserve the algorithm’s usefulness.
Creating topic and author feeds — Given that many people think in terms of topics and authors of interest, expanding what I’ve already done with Sources but with topics and author feeds sounds like a high-value next step
I also endeavor to make more regular updates to the public Github repository (instead of aggregate many updates I had already made into two large ones). This will make the updates more manageable and hopefully help anyone out there who’s interested in building a similar product.
It is hard to find good analogies for running a startup that founders can learn from. Some of the typical comparisons — playing competitive sports & games, working on large projects, running large organizations — all fall short of capturing the feeling that the odds are stacked against you that founders have to grapple with.
But the annals of military history offer a surprisingly good analogy to the startup grind. Consider the campaigns of some of history’s greatest military leaders — like Alexander the Great and Julius Caesar — who successfully waged offensive campaigns against numerically superior opponents in hostile territory. These campaigns have many of the same hallmarks as startups:
Bad odds: Just as these commanders faced superior enemy forces in hostile territory, startups compete against incumbents with vastly more resources in markets that favor them.
Undefined rules: Unlike games with clear rules and a limited set of moves, military commanders and startup operators have broad flexibility of action and must be prepared for all types of competitive responses.
Great uncertainty: Not knowing how the enemy will act is very similar to not knowing how a market will respond to a new offering.
As a casual military history enthusiast and a startup operator & investor, I’ve found striking parallels in how history’s most successful commanders overcame seemingly insurmountable odds with how the best startup founders operate, and think that’s more than a simple coincidence.
In this post, I’ll explore the strategies and campaigns of 9 military commanders (see below) who won battle after battle against numerically superior opponents across a wide range of battlefields. By examining their approach to leadership and strategy, I found 5 valuable lessons that startup founders can hopefully apply to their own ventures.
Conquered the Persian Empire before the age of 32; spread Hellenistic culture across Eurasia and widely viewed in the West as antiquity’s greatest conqueror
Despite being a commoner, his victories led to the creation of the Han Dynasty (漢朝) and his being remembered as one of “the Three Heroes of the Han Dynasty” (漢初三傑)
Established Rome’s dominance in Gaul (France); became undisputed leader of Rome, effectively ending the Roman Republic, and his name has since become synonymous with “emperor” in the West
Despite being a commoner, became one of the most successful military commanders in the Mongol Empire. Successfully won battles in more theaters than any other commander (China, Central Asia, and Eastern Europe)
Created Central Asian empire with dominion over Turkey, Persia, Northern India, Eastern Europe, and Central Asia. His successors would eventually create the Mughal Empire in India which continued until the 1850s
Considered one of the greatest British commanders in history; Paved the way for Britain to overtake France as the pre-eminent military and economic power in Europe
Established Prussia as the pre-eminent Central European power after defeating nearly every major European power in battle; A cultural icon for the creation of Germany
Established a French empire with dominion over most of continental Europe; the Napoleonic code now serves as basis for legal systems around the world and the word Napoleon synonymous with military genius and ambition
Before I dive in, three important call-outs to remember:
Running a startup is not actually warfare — there are limitations to this analogy. Startups are not (and should not be) life-or-death. Startup employees are not bound by military discipline (or the threat of imprisonment if they are derelict). The concept of battlefield deception, which is at the heart of many of the tactics of the greatest commanders, also doesn’t translate well. Treating your employees / co-founders as one would a soldier or condoning violent and overly aggressive tactics would be both an ethical failure and a misread of this analogy.
Drawing lessons from these historical campaigns does not mean condoning the underlying sociopolitical causes of these conflicts, nor the terrible human and economic toll these battles led to. Frankly, many of these commanders were absolutist dictators with questionable motivations and sadistic streaks. This post’s focus is purely on getting applicable insights on strategy and leadership from leaders who were able to win despite difficult odds.
This is not intended to be an exhaustive list of every great military commander in history. Rather, it represents the intersection of offensive military prowess and my familiarity with the historical context. Just because I did not mention a particular commander has no bearing on their actual greatness.
With those in mind, let’s explore how the wisdom of historical military leaders can inform the modern startup journey. In the post, I’ll unpack five key principles (see below) drawn from the campaigns of history’s most successful military commanders, and show how they apply to the challenges ambitious founders face today.
1. Get in the trenches with your team
2. Achieve and maintain tactical superiority
3. Move fast and stay on offense
4. Unconventional teams win
5. Pick bold, decisive battles
Principle 1: Get in the trenches with your team
One common thread unites the greatest military commanders: their willingness to share in the hardships of their soldiers. This exercise of leadership by example, of getting “in the trenches” with one’s team, is as crucial in the startup world as it was on historical battlefields.
Every commander on our list was renowned for marching and fighting alongside their troops. This wasn’t mere pageantry; it was a fundamental aspect of their leadership style that yielded tangible benefits:
Inspiration: Seeing their leader work shoulder-to-shoulder with them motivated soldiers to push beyond their regular limits.
Trust: By sharing in their soldiers’ hardships, commanders demonstrated that they valued their troops and understood their needs.
Insight: Direct involvement gave leaders firsthand knowledge of conditions on the ground, informing better strategic decisions.
Perhaps no figure exemplified this better than Alexander the Great. Famous for being one of the first soldiers to jump into battle, Alexander was wounded seriously multiple times. This shared experience created a deep bond with his soldiers, culminating in his legendary speech at Opis where he was able to quell a mutiny of his soldiers, tired after years of campaigns, with a speech reminding them of their shared experiences:
Alexander the Great from Alexandria, Egypt (3rd Century BCE); Image Credit: Wikimedia
The wealth of the Lydians, the treasures of the Persians, and the riches of the Indians are yours; and so is the External Sea. You are viceroys, you are generals, you are captains. What then have I reserved to myself after all these labors, except this purple robe and this diadem? I have appropriated nothing myself, nor can any one point out my treasures, except these possessions of yours or the things which I am guarding on your behalf. Individually, however, I have no motive to guard them, since I feed on the same fare as you do, and I take only the same amount of sleep.
Nay, I do not think that my fare is as good as that of those among you who live luxuriously; and I know that I often sit up at night to watch for you, that you may be able to sleep.
But some one may say, that while you endured toil and fatigue, I have acquired these things as your leader without myself sharing the toil and fatigue. But who is there of you who knows that he has endured greater toil for me than I have for him? Come now, whoever of you has wounds, let him strip and show them, and I will show mine in turn; for there is no part of my body, in front at any rate, remaining free from wounds; nor is there any kind of weapon used either for close combat or for hurling at the enemy, the traces of which I do not bear on my person.
For I have been wounded with the sword in close fight, I have been shot with arrows, and I have been struck with missiles projected from engines of war; and though oftentimes I have been hit with stones and bolts of wood for the sake of your lives, your glory, and your wealth, I am still leading you as conquerors over all the land and sea, all rivers, mountains, and plains. I have celebrated your weddings with my own, and the children of many of you will be akin to my children.
Alexander the Great (as told by Arrian)
This was not unique to Alexander. Julius Caesar famously slept in chariots and marched alongside his soldiers. Napoleon was called “le petit caporal” by his troops after he was found sighting the artillery himself, a task that put him within range of enemy fire and was usually delegated to junior officers.
Frederick the Great also famously mingled with his soldiers while on tour, taking kindly to the nickname from his men, “Old Fritz”. Frederick understood the importance of this as he once wrote to his nephew:
“You cannot, under any pretext whatever, dispense with your presence at the head of your troops, because two thirds of your soldiers could not be inspired by any other influence except your presence.”
Frederick the Great
“Old Fritz” after the Battle of Hochkirch Image credit: WikiMedia Commons
For Startups
For founders, the lesson is clear: show up when & where your team is and roll up your sleeves so they can see you work beside them. It’s not just that startups tend to need “all hands on deck”, but being in the trenches also provides “on the ground” context that is valuable and help create the morale needed to succeed.
“I am personally on that line, in that machine, trying to solve problems personally where I can,” Musk said at the time. “We are working seven days a week to do it. And I have personally been here on zone 2 module line at 2:00 a.m. on a Sunday morning, helping diagnose robot calibration issues. So I’m doing everything I can.”
Principle 2: Achieve and maintain tactical superiority
To win battles against superior numbers requires a commander to have a strong tactical edge over their opponents. This can be in the form of a technological advantage (i.e. a weapons technology) or an organizational one (i.e. superior training or formations), but these successful commanders always made sure their soldiers could “punch above their weight”.
Alexander the Great, for example, leveraged the Macedonian Phalanx, a modification of the “classical Greek phalanx” used by the Greek city states of the era, that his father Philip II helped create.
The formation relied on “blocks” of heavy infantry equipped with six-meter (!!) long spears called sarissa which could rearrange themselves (to accommodate different formation widths and depths) and “pin” enemy formations down while the heavy cavalry would flank or exploit gaps in the enemy lines. This formation made Alexander’s army highly effective against every military force — Greeks, Persians, and Indians — it encountered.
A few centuries later, the brilliant Chinese commander Han Xin (韓信) leaned heavily on the value of military engineering. Han Xin (韓信)’s soldiers would rapidly repair & construct roads to facilitate his army’s movement or, at times, to deceive his enemies about which path he planned to take. His greatest military engineering accomplishment was at the Battle of Wei River (濰水) in 204 BCE. Han Xin (韓信) attacked the larger forces of the State of Qi (齊) and State of Chu (楚) and immediately retreated across the river, luring them to cross. What his rivals had not realized in their pursuit was that the water level of the Wei River was oddly low. Han Xin (韓信) had, prior to the attack, instructed his soldiers to construct a dam upstream to lower the water level. Once a sizable fraction of the enemy’s forces were mid-stream, Han Xin (韓信) ordered the dam released. The rush of water drowned a sizable portion of the enemy’s forces and divided the Chu (楚) / Qi (齊) forces letting Han Xin (韓信)’s smaller army defeat and scatter them.
A century and a half later, Roman statesman and military commander Gaius Julius Caesar also famously advocated military engineering capability in his wars with the Germanic tribes in Gaul. He became the first Roman commander to cross the Rhine (twice!) by building bridges to make the point to the Germanic tribes that he could invade them whenever he wanted. At the Battle of Alesia in 52 BCE, after trading battles with the skilled Gallic commander Vercingetorix who had united the tribes in opposition to Rome, Caesar besieged Vercingetorix’s fortified settlement of Alesia while simultaneously holding off Gallic reinforcements. Caesar did this by building 25 miles of fortifications surrounding Alesia in a month, all while outnumbered and under constant harassment from both sides by the Gallic forces! Caesar’s success forced Vercingetorix to surrender, bringing an end to organized resistance to Roman rule in Gaul for centuries.
Vercingetorix Throws Down his Arms at the Feet of Julius Caesar by Lionel Royer; Image Credit: Wikimedia
The Mongol commander Subutai similarly made great use of Mongol innovations to overcome defenders from across Eurasia. The lightweight Mongol composite bow gave Mongol horse archers a devastating combination of long range (supposedly 150-200 meters!) and speed (because they were light enough to be fired while on horseback). The Mongol horses themselves were another “biotechnological” advantage in that they required less water and food which let the Mongols wage longer campaigns without worrying about logistics.
In the 18th century, Frederick the Great transformed warfare on the European continent with a series of innovations. First, he drilled his soldiers stressing things like firing speed. It is said that lines of Prussian riflemen could fire over twice as fast as other European armies they faced, making them exceedingly lethal in combat.
Frederick’s Leibgarde Batallion in action; Image credit: Military Heritage
Frederick was also famous for a battle formation: the oblique order. Instead of attacking an opponent head on, the oblique order involves confronting the enemy line at an angle with soldiers massed towards one end of the formation. If one’s soldiers are well-trained and disciplined, then even with a smaller force in aggregate, the massed wing can overwhelm the opponent in one area and then flank or surround the rest. Frederick famously boasted that the oblique order could allow a skilled force to win over an opposing one three times its size.
Finally, Frederick is credited with popularizing horse artillery, the use of horse-drawn light artillery guns, in European warfare. With horse artillery units, Frederick was able to increase the adaptability of his forces and their ability to break through even numerically superior massed infantry by concentrating artillery fire where it was needed.
A few decades later, Napoleon Bonaparte became the undisputed master of much of continental Europe by mastering army-level logistics and organization. While a brilliant tactician and artillery commander, what set Napoleon’s military apart was its embrace of the “corps system”, which subdivided his forces into smaller, self-contained corps that were capable of independent operations. This allowed Napoleon the ability to pursue grander goals, knowing that he could focus his attention on the most important fronts of battle, while the other corps could independently pin an enemy down or pursue a different objective in parallel.
Napoleon triumphantly entering Berlin by Charles Meynier; Image Credit: Wikimedia Commons
Additionally, Napoleon invested heavily in overhauling military logistics, using a combination of forward supply depots and teaching his forces to forage for food and supplies in enemy territory (and, just as importantly, how to estimate what foraging can do to help determine the necessary supplies to take). This investment led to the invention of modern canning technology, first used to support the marches of the French Grande Armée. The result was Napoleon could field larger armies over longer campaigns all while keeping his soldiers relatively well-fed.
For Startups
Founders need to make sure they have a strong tactical advantage that fits their market(s). As evidenced above, it does not need to be something as grand as an unassailable advantage, but it needs to be a reliable winnerand something you continuously invest in if you plan on competing with well-resourced incumbents in challenging markets.
The successful payments company Stripe started out by making sure they would always win on developer ease of use, even going so far as to charge more than their competition during their Beta to make sure that their developer customers were valuing them for their ease of use. Stripe’s advantage here, and continuous investment in maintaining that advantage, ultimately let it win any customer that needed a developer payment integration, even against massive financial institutions. This advantage laid the groundwork for Stripe’s meteoric growth and expansion into adjacent categories from its humble beginnings.
Principle 3: Move fast and stay on offense
In both military campaigns and startups, speed and a focus on offense plays an outsized role in victory, because the ability to move quickly creates opportunities and increases resiliency to mistakes.
Few understood this principle as well as the Mongol commander Subutai who frequently took advantage of the greater speed and discipline of the Mongol cavalry to create opportunities to win.
In the Battle of the Kalka River (1223), Subutai took what initially appeared to be a Mongol defeat — when the Kievan Rus and their Cuman allies successfully entrapped the Mongol forces in the area — and turned it into a victory. The Mongols began a 9 day feigned retreat (many historians believe this was a real retreat that Subutai turned into a feigned one once he realized the situation), constantly tempting the enemy by staying just out of reach into overextending themselves in pursuit.
After 9 days, Subutai’s forces took advantage of their greater speed to lay a trap. Once the Mongols crossed the river they reformed their lines to lie in ambush. As soon as the Rus forces crossed the Kalka River, they found themselves surrounded and confronted with a cavalry charge they were completely unprepared for. After all, they had been pursuing what they thought was a fleeing enemy! Their backs against the river, the Rus forces (including several major princes) were annihilated.
Subutai took advantage of the Mongol speed advantage in a number of his campaigns, coordinating fast-moving Mongol divisions across multiple objectives. In its destruction of the Central Asian Khwarazmian empire, the Mongols, under the command of Subutai and Mongol ruler Genghis Khan, overwhelmed the defenders with coordinated maneuvers. While much of the Mongol forces attacked from the East, where the Khwarazmian forces massed, Subutai used the legendary Mongol speed to go around the Khwarazmian lines altogether, ending up at Bukhara, 100 miles to the West of the Khwarazmian defensive position! In a matter of months, the empire was destroyed and its rulers chased out, never to return.
A few hundred years later, the Englishman John Churchill, the Duke of Marlborough also proved the value of speed in 1704 when he boldly marched an army of 21,000 Dutch and English troops on a 250-mile march across Europe in just five weeks to place themselves between French and Bavarian forces and their target of Vienna. Had Vienna been attacked, it would have forced England’s ally the Holy Roman Empire out of the conflict, giving France the victory in the War of the Spanish Succession. This march was made all the more challenging as Marlborough had to find a way to feed and equip his army along this march without unnecessarily burdening the neutral and friendly territories they were marching through.
Marlborough’s “march to the Danube”; Image Credit: Rebel Redcoat
Marlborough’s maneuver threw the Bavarian and French forces off-balance. What originally was supposed to be an “easy” French victory culminated in a crushing defeat for the French at Blenheim which turned the momentum of the war. This victory solidified Marlborough’s reputation and even resulted in the British government agreeing to build a lavish palace (called Blenheim Palace in honor of the battle) as a reward to Marlborough.
Marlborough proved the importance of speed again at the Battle of Oudenarde. In 1708, French forces captured Ghent and Bruges (in modern day Belgium), threatening the alliance’s ability to maintain contact with Britain. Recognizing this, Marlborough force-marched his army to the city of Oudenarde, marching 30 miles in about as many hours. The French, confident from their recent victories and suffering from an internal leadership squabble, misjudged the situation, allowing Marlborough’s forces to build five pontoon bridges to move his 80,000 soldiers across the nearby river.
When the French commander received news that the allies were already at Oudenarde building bridges, he said, “If they are there, then the devil must have carried them. Such marching is impossible!“
Marlborough’s forces, not yet at full strength, engaged the French, buying sufficient time for his forces to cross and form up. Once in formation, they counterattacked and collapsed one wing of the French line, saving the Allied position in the Netherlands, and resulting in a bad defeat for French forces.
The Battle of Oudenarde, showing the position of the bridges the Allied forces needed to cross to get into position; Image Credit: WikiMedia Commons
For Startups
The pivotal role speed played in achieving victory for Subutai and the Duke of Marlborough apply in the startup domain as well. The ability to make fast decisions, to quickly shift focus to rapidly adapt to a new market context creates opportunities that slower moving incumbents (and military commanders!) cannot seize. Speed also gifts resiliency against mistakes and weak positions, in much the same way that speed let the Mongols and the Anglo-Prussian-Dutch alliance overcome their initial missteps at Kalka River and Oudenarde. Founders would be wise to remember to embrace speed of action in all they do.
Facebook and it’s (now in)famous “move fast, break things” motto is one classic example of how a company can internalize speed as a culture. It leveraged that to ship products and features which has kept it a leader in social and AI even in the face of constant competition and threats from well-funded companies like Google, Snapchat, and Bytedance.
Principle 4: Unconventional teams win
Another unifying hallmark of the great commanders is that they made unconventional choices with regards to their army composition. Relative to their peers, these commanders tended to build armies that were more diverse in class and nationality. While this required exceptional communication and inspiration skills, it gave the commanders significant advantages:
Ability to recruit in challenging conditions: For many of the commanders, the unconventional team structure was a necessity to build up the forces they needed given logistical / resource constraints while operating in enemy territory.
Operational flexibility from new tactics: Bringing on personnel from different backgrounds let commanders incorporate additional tactics and strategies, creating a more effective and flexible fighting force.
The Carthaginian general Hannibal Barca for example famously fielded a multi-nationality army consisting of Carthaginians, Libyans, Iberians, Numidians, Balearic soldiers, Gauls, and Italians. This allowed Hannibal to raise an army in hostile territory — after all, waging war in the heart of Italy against Rome made it difficult to get reinforcements from Carthage.
Illustration of troop types employed in the Second Punic War by Carthage/Hannibal Barca; Image Credit: Travis’s Ancient History
But, it also gave Hannibal’s army flexibility in tactics. Balearic slingers provided superior long range attack to the best Roman-used bows of the time. Numidian light cavalry provided Hannibal with fast reconnaissance and a quick way to flank and outmaneuver Roman forces. Gallic and Iberian soldiers provided shock infantry and cavalry. Each of these groups of soldiers added their own distinctive capabilities to Hannibal’s armies and great victories over Rome.
The Central Asian conqueror Timur similarly fielded a diverse army which included Mongols, Turks, Persians, Indians, Arabs, and others. This allowed Timur to field larger armies for his campaigns by recruiting from the countries he forced into submission. Like with Hannibal, it also gave Timur’s army access to a diverse set of tactics: war elephants (from India), infantry and siege technology from the Persians, gunpowder from the Ottomans, and more. This combination of operational flexibility and ability to field large armies let Timur build an empire which defeated every major power in Central Asia and the Middle East.
The Defeat by Timur of the Sultan of Dehli (from the Imperial Library of Emperor Akbar); Image credit: Wikimedia
It should not be a surprise that some of the great commanders were drawn towards assembling unconventional teams as several of them were ultimately “commoners”. Subutai (a son of a blacksmith who Genghis Khan took interest in), Timur (a common thief), and Han Xin (韓信, who famously had to beg for food in his childhood) all came from relatively humble origins. Napoleon, famous for declaring the military “la carrier est ouvérte aux talents” (“the career open to the talents”) and creating the first modern order of merit Légion d’honneur (open to all, regardless of social class), was similarly motivated by the difficulties he faced in securing promotion early in his career due to his not being from the French nobility.
But, by embracing more of a meritocracy, Napoleon was ultimately able to field some of the largest European armies in existence as he waged war successfully against every other major European power (at once).
First Légion d’Honneur Investiture by Jean-Baptiste Debret; Image Credit: Wikimedia
For Startups
Hiring is one of the key tasks for startup founders. While hiring the people that larger, better-resourced companies want to can be helpful for a startup, it’s important to always remember that transformative victories require unconventional approaches. Leaning on unconventional hires may help you get out of a salary bidding war with those deeper-pocketed competitors. Choosing unconventional hires may also add different skills and perspectives to the team.
In pursuing this strategy, it’s also vital to excel at communication & organization as well as fostering a shared sense of purpose. All teams require strong leadership to be effective but this is especially true with an unconventional team composition facing uphill odds.
The enterprise API company Zapier is one example of taking an unconventional approach to team construction by having been 100% remote from inception (pre-COVID even). This let the company assemble a team without being confined by location and eliminate the need to spend on unnecessary facilities. They’ve had to invest in norms around documentation and communication to make this work, and, while it’d be too far of a leap to argue all startups should go 100% remote, for Zapier’s market and team culture, it’s worked.
Principle 5: Pick bold, decisive battles
When in a challenging environment with limited resources, it’s important to prioritize decisive moves — actions which can result in a huge payoff — even if risky over safer, less impactful ones. This is as true for startups, which have limited runway and need to make a big splash in order to fundraise, as for military commanders who need more than just battlefield wins but strategic victories.
Few understood this as well as the Carthaginian general Hannibal Barca who, in waging the Second Punic War against Rome, crossed the Alps from Spain with his army in 218 BCE (at the age of 29!). Memorialized in many works of art (see below for one by Francisco Goya), this was a dangerous move (one that resulted in the loss of many men and almost his entire troop of war elephants) and was widely considered to be impossible.
The Victorious Hannibal Seeing Italy from the Alps for the First Time by Francisco Goya in Museo del Prado; Image Credit: Wikimedia
While history (rightly) remembers Hannibal’s boldness, it’s important to remember that Hannibal’s move was highly calculated. He realized that the Gauls in Northern Italy, who had recently been subjugated by the Romans, were likely to welcome a Roman rival. Through his spies, he also knew that Rome was planning an invasion of Carthage in North Africa. He knew he had little chance to bypass the Roman navy or Roman defensive placements if he invaded in another way.
And Hannibal’s bet paid off! Having caught the Romans entirely by surprise, they cancelled their planned invasion of Africa, and Hannibal lined up many Gallic allies to his cause. Within two years of his entry into Italy, Hannibal trounced the Roman armies sent to battle him at the River Ticinus, at the River Trebia, and at Lake Trasimene. Shocked by their losses, the Romans elected two consuls with the mandate to battle Hannibal and stop him once and for all.
Knowing this, Hannibal seized a supply depot at the town of Cannae, presenting a tempting target to the Roman consuls to prove themselves. They (foolishly) took the bait. Despite fielding over 80,000 soldiers against Hannibal’s 50,000, Hannibal successfully executed a legendary double-envelopment maneuver (see below) and slaughtered almost the entire Roman force that met him in battle.
Hannibal’s double envelopment of Roman forces at Cannae; Image Credit: Wikimedia
To put this into perspective, in the 2 years after Hannibal crossed the Alps, Hannibal’s army killed 20% of all male Romans over the age of 17 (including at least 80 Roman Senators and one previous consul). Cannae is today considered one of the greatest examples of military tactical brilliance, and, as historian Will Durant wrote, “a supreme example of generalship, never bettered in history”.
Cannae was a great example of Hannibal’s ability to pick a decisive battle with favorable odds. Hannibal knew that his only chance was to encourage the city-states of Italy to side with him. He knew the Romans had just elected consuls itching for a fight. He chose the field of battle by seizing a vital supply depot at Cannae. Considering the Carthaginians had started and pulled back from several skirmishes with the Romans in the days leading up to the battle, it’s clear Hannibal also chose when to fight, knowing full well the Romans outnumbered him. After Cannae, many Italian city-states and the kingdom of Macedon sided with Carthage. That Carthage ultimately lost the Second Punic War is a testament more to Rome’s indomitable spirit and the sheer odds Hannibal faced than any indication of Hannibal’s skills.
In the Far East, about a decade later, the brilliant Chinese military commander Han Xin (韓信) was laying the groundwork for the creation of the Han Dynasty (漢朝) in a China-wide civil war known as the the Chu-Han contention between the State of Chu (楚) and the State of Han (漢) led by Liu Bang (劉邦, who would become the founding emperor Gaozu 高祖 of the Han Dynasty 漢朝).
Under the leadership of Han Xin (韓信), the State of Han (漢) won many victories over their neighbors. Overconfident from those victories, his king Liu Bang (劉邦) led a Han (漢) coalition to a catastrophic defeat when he briefly captured but then lost the Chu (楚) capital of Pengcheng (彭城) in 205 BCE. Chu forces (楚) were even able to capture the king’s father and wife as hostages, and several Han (漢) coalition states switched their loyalty to the Chu (楚).
Map of the 18 states that existed at the start of the Chu-Han Contention, the two sides being the Han (in light purple on the Southwest) and the Chu (in green on the East); Image Credit: Wikimedia
To fix his king’s blunder, Han Xin (韓信) tasked the main Han (漢) army with setting up fortified positions in the Central Plain, drawing Chu (楚) forces there. Han Xin (韓信) would himself take a smaller force of less experienced soldiers to attack rival states in the North to rebuild the Han (漢) military position.
Sensing an opportunity to deal a decisive blow to the overconfident Zhao (趙), Han Xin (韓信) ordered a cavalry unit to sneak into the mountains behind the Zhao (趙) camp and to remain hidden until battle started. He then ordered half of his remaining army to position themselves in full view of the Zhao (趙) forces with their backs to the Tao River (洮水), something Sun Tzu’s Art of War (孫子兵法) explicitly advises against (due to the inability to retreat). This “error” likely reinforced the Zhao (趙) commander’s overconfidence, as he made no move to pre-emptively flank or deny the Han (漢) forces their encampment.
Han Xin (韓信) then deployed his full army which lured the Zhao (趙) forces out of their camp to counterattack. Because the Tao River (洮水) cut off all avenues of escape, the outnumbered Han (漢) forces had no choice but to dig in and fight for their lives, just barely holding the Zhao (趙) forces at bay. By luring the enemy out for what appeared to be “an easy victory”, Han Xin (韓信) created an opportunity for his hidden cavalry unit to capture the enemy Zhao (趙) camp, replacing their banners with those of the Han (漢). The Zhao (趙) army saw this when they regrouped, which resulted in widespread panic as the Zhao (趙) army concluded they must be surrounded by a superior force. The opposition’s morale in shambles, Han Xin (韓信) ordered a counter-attack and the Zhao (趙) army crumbled, resulting in the deaths of the Zhao (趙) commander and king!
Han Xin (韓信) bet his entire outnumbered command on a deception tactic based on little more than an understanding of his army’s and the enemy’s psychology. He won a decisive victory which helped reverse the tide of the war. The State of Zhao (趙) fell, and the State of Jiujiang (九江) and the State of Yan (燕) switched allegiances to the Han (漢). This battle even inspired a Chinese expression “fighting a battle with one’s back facing a river” (背水一戰) to describe fighting for survival in a “last stand”.
Caesar crosses the Rubicon by Bartolomeo Pinelli; Image Credit: Wikimedia
Roughly a century later, on the other side of the world, the Roman statesman and military commander Julius Caesar made a career of turning bold, decisive bets into personal glory. After Caesar conquered Gaul, Caesar’s political rivals led by Gnaeus Pompeius Magnus (Pompey the Great), a famed military commander, demanded Caesar return to Rome and give up his command. Caesar refused and crossed the Rubicon, a river marking the boundary of Italy, in January 49 BCE starting a Roman Civil War and coining at least two famous expressions (including alea iacta est – “the die is cast”) for “the point of no return”.
This bold move came as a complete shock to the Roman elite. Pompey and his supporters fled Rome. Taking advantage of this, Caesar captured Italy without much bloodshed. Caesar then pursued Pompey to Macedon, seeking a decisive land battle which Pompey, wisely, given his broad network of allies and command of the Roman navy, refused to give him. Instead, Caesar tried and failed to besiege Pompey at Dyrrhachium which forced him into retreat in Greece.
Pompey’s supporters, however, lacked Pompey’s patience (and judgement). Overconfident from their naval strength, numerical advantage, and Caesar’s failure at Dyrrhachium, they pressured Pompey into a battle with Caesar who was elated at the opportunity. In the summer of 48 BCE, the two sides met at the Battle of Pharsalus.
The initial battle formations at the Battle of Pharsalus; Image Credit: Wikimedia
Always cautious, Pompey took up a position on a mountain and oriented his forces such that his larger cavalry wing would have ability to overpower Caesar’s cavalry and then flank Caesar’s forces while his numerically superior infantry would be arranged deeper to smash through or at least hold back Caesar’s lines.
Caesar made a bold tactical choice when he saw Pompey’s formation. He thinned his (already outnumbered) lines to create a 4th reserve line of veterans which he positioned behind his cavalry at an angle (see battle formation above).
Caesar initiated the battle and attacked with two of his infantry lines. As Caesar expected, Pompey ordered a cavalry charge which soon forced back Caesar’s outnumbered cavalry. But Pompey’s cavalry then encountered Caesar’s 4th reserve line which had been instructed to use their javelins to stab at the faces of Pompey’s cavalry like bayonets. Pompey’s cavalry, while larger in size, was made up of relatively inexperienced soldiers and the shock of the attack caused them to panic. This let Caesar’s cavalry regroup and, with the 4th reserve line, swung around Pompey’s army completing an expert flanking maneuver. Pompey’s army, now surrounded, collapsed once Caesar sent his final reserve line into battle.
Caesar’s boldness and speed of action let him take advantage of a lapse in Pompey’s judgement. Seeing a rare opportunity to win a decisive battle, Caesar was even willing to risk a disadvantage in infantry, cavalry, and position (Pompey’s army had the high ground and had forced Caesar to march to him). But this strategic and tactical gamble (thinning his lines to counter Pompey’s cavalry charge) paid off as Pharsalus shattered the myth of Pompey’s inevitability. Afterwards, Pompey’s remaining allies fled or defected to Caesar, and Pompey himself fled to Egypt where he was assassinated (by a government wishing to win favor with Caesar). And, all of this — from Gaul to crossing the Rubicon to the Civil War — paved the way for Caesar to become the undisputed master of Rome.
For Startups
Founders need to take bold, oftentimes uncomfortable bets that have large payoffs. While a large company can take its time winning a war of attrition, startups need to score decisive wins quickly in order to attract talent, win deals, and shift markets towards them. Only taking the “safe and rational” path is a failure to recognize the opportunity cost when operating with limited resources.
In other words, founders need to find their own Alps / Rubicons to cross.
In the startup world, few moves are as bold (while also uncomfortable and risky) as big pivots. But, there are examples of incredible successes like Slack that were able to make this work. In Slack’s case, the game they originally developed ended up a flop, but CEO & founder Stewart Butterfield felt the messaging product they had built to support the game development had potential. Leaning on that insight, over the skepticism of much of his team and some high profile investors, Butterfield made a bet-the-company move similar to Han Xin (韓信) digging in with no retreat which created a seminal product in the enterprise software space.
Summary
I hope I’ve been able to show that history’s greatest military commanders can offer valuable lessons on leadership and strategy for startup founders.
The five principles derived from studying some of the commanders’ campaigns – the importance of getting in the trenches, achieving tactical superiority, moving fast, building unconventional teams, and picking bold, decisive battles – played a key role in the commanders’ success and generalize well to startup execution.
After all, what is a more successful founder than one who can recruit teams despite resource constraints (unconventional teams), inspire them (by getting in the trenches alongside them), and move with speed & urgency (move fast) to take a competitive edge (achieve tactical superiority) and apply it where there is the greatest chance of a huge impact on the market (pick bold, decisive battles).
Electrifying our (Bay Area) home was a complex and drawn-out process, taking almost two years.
Installing solar panels and storage was particularly challenging, involving numerous hurdles and unexpected setbacks.
We worked with a large solar installer (Sunrun) and, while the individuals we worked with were highly competent, handoffs within Sunrun and with other entities (like local utility PG&E and the local municipality) caused significant delays.
While installing the heat pumps, smart electric panel, and EV charger was more straightforward, these projects also featured greater complexity than we expected.
The project resulted in significant quality of improvements around home automation and comfort. However, bad pricing dynamics between electricity and natural gas meant direct cost savings from electrifying gas loads are, at best, small. While solar is an economic slam-dunk (especially given the rising PG&E rates our home sees), the batteries, in the absence of having backup, have less obvious economic value.
Our experience underscored the need for the industry to adopt a more holistic approach to electrification and for policymakers to make the process more accessible for all homeowners to achieve the state’s ambitious goals.
Why
The decision to electrify our home was an easy one. From my years of investing in & following climate technologies, I knew that the core technologies were reliable and relatively inexpensive. As parents of young children, my wife and I were also determined to contribute positively to the environment. We also knew there were abundant financial supports from local governments and utilities to help make this all work.
Yet, as we soon discovered, what we expected to be a straightforward path turned into a nearly two-year process!
Even for a highly motivated household which had budgeted significant sums for it all, it was still shocking how long (and much money) it took. It made me skeptical that households across California would be able to do the same to meet California’s climate goals without additional policy changes and financial support.
The Plan
Two years ago, we set out a plan:
Smart electrical panel — From my prior experience, I knew that many home electrification projects required a main electrical panel upgrade. These were typically costly and left you at the mercy of the utility to actually carry them out (I would find out how true this was later!). Our home had an older main panel rated for 125 A and we suspected we would normally need a main panel upgrade to add on all the electrical loads we were considering.
To try to get around this, we decided to get a smart electrical panel which could:
use software smarts to deal with the times where peak electrical load got high enough to need the entire capacity of the electrical line
give us the ability to intelligently manage backups and track solar production
In doing our research, Span seemed like the clear winner. They were the most prominent company in the space and had the slickest looking device and app (many of their team had come from Tesla). They also had an EV charger product we were interested in, the Span Drive.
Heat pumps — To electrify is to ditch natural gas. As the bulk of our gas consumption was heating air and water, this involved replacing our gas furnace and gas water heater with heat pumps. In addition to significant energy savings — heat pumps are famous for their >200% efficiency (as they move heat rather than “create” it like gas furnaces do) — heat pumps would also let us add air conditioning (just run the heat pump in reverse!) and improve our air quality (from not combusting natural gas indoors). We found a highly rated Bay Area HVAC installer who specializes in these types of energy efficiency projects (called Building Efficiency) and trusted that they would pick the right heat pumps for us.
Solar and Batteries — No electrification plan is complete without solar. Our goal was to generate as much clean electricity as possible to power our new electric loads. We also wanted energy storage for backup power during outages (something that, while rare, we seemed to run into every year) and to take advantage of time-of-use rates (by storing solar energy when the price of electricity is low and then using it when the price is high).
We looked at a number of solar installers and ultimately chose Sunrun. A friend of ours worked there at the time and spoke highly of a prepaid lease they offered that was vastly cheaper all-in than every alternative. It offered minimum energy production guarantees, came with a solid warranty, and the “peace of mind” that the installation would be done with one of the largest and most reputable companies in the solar industry.
EV Charger — Finally, with our plan to buy an electric vehicle, installing a home charger at the end of the electrification project was a simple decision. This would allow us to conveniently charge the car at home, and, with solar & storage, hopefully let us “fuel up” more cost effectively. Here, we decided to go with the Span Drive. It’s winning feature was the ability to provide Level 2 charging speeds without a panel upgrade (it does this by ramping up or down charging speeds depending on how much electricity the rest of the house needed). While pricey, the direct integration into our Span smart panel (and its app) and the ability to hit high charging rates without a panel upgrade felt like the smart path forward.
What We Left Out — There were two appliances we decided to defer “fully going green” on.
The first was our gas stove (with electric oven). While induction stoves have significant advantages, because our current stove is still relatively new, works well, uses relatively little gas, and an upgrade would have required additional electrical work (installing a 240 V outlet), we decided to keep our current stove and consider a replacement at it’s end of life.
The second was our electric resistive dryer. While heat pump based dryers would certainly save us a great deal of electricity, the existing heat pump dryers on the market have much smaller capacities than traditional resistive dryers, which may have necessitated our family of four doing additional loads of drying. As our current dryer was also only a few years old, and already running on electricity, we decided we would also wait to consider heat pump dryer only after it’s end of life.
With what we thought was a well-considered plan, we set out and lined up contractors.
But as Mike Tyson put it, “Everyone has a plan ’till they get punched in the face.”
The Actual Timeline
Smart Panel
The smart panel installation was one of the more straightforward parts of our electrification journey. Span connected us with a local electrician who quickly assessed our site, provided an estimate, and completed the installation in a single day. However, getting the permits to pass inspection was a different story.
We failed the first inspection due to a disagreement over the code between the electrician and the city inspector. This issue nearly turned into a billing dispute with the electrician, who wanted us to cover the extra work needed to meet the code (an unexpected cost). Fortunately, after a few adjustments and a second inspection, we passed.
The ability to control and monitor electric flows with the smart panel is incredibly cool. For the first few days, I checked the charts in the apps every few minutes tracking our energy use while running different appliances. It was eye-opening to see just how much power small, common household items like a microwave or an electric kettle could draw!
However, the true value of a smart panel is only achieved when it’s integrated with batteries or significant electric loads that necessitate managing peak demand. Without these, the monitoring and control benefits are more novelties and might not justify the cost.
Note: if you, like us, use Pihole to block tracking ads, you’ll need to disable it for the Span app. The app uses some sort of tracker that Pihole flags by default. It’s an inconvenience, but worth mentioning for anyone considering this path.
Heating
Building Efficiency performed an initial assessment of our heating and cooling needs. We had naively assumed they’d be able to do a simple drop-in replacement for our aging gas furnace and water heater. While the water heater was a straightforward replacement (with a larger tank), the furnace posed more challenges.
Initially, they proposed multiple mini-splits to provide zoned control, as they felt the crawlspace area where the gas furnace resided was too small for a properly sized heat pump. Not liking the aesthetics of mini-splits, we requested a proposal involving two central heat pump systems instead.
Additionally, during the assessment, they found some of our old vents, in particular the ones sending air to our kids’ rooms, were poorly insulated and too small (which explains why their rooms always seemed under-heated in the winter). To fix this, they had to cut a new hole through our garage concrete floor (!!) to run a larger, better-insulated vent from our crawlspace. They also added insulation to the walls of our kids’ rooms to improve our home’s ability to maintain a comfortable temperature (but which required additional furniture movement, drywall work, and a re-paint).
Building Efficiency spec’d an Ecobee thermostat to control the two central heat pumps. As we already had a Nest Learning Thermostat (with Nest temperature sensors covering rooms far from the thermostat), we wanted to keep our temperature control in the Nest app. At the time, we had gotten a free thermostat from Nest after signing with Sunrun. We realized later, what Sunrun gifted us was the cheaper (and, less attractive) Nest Thermostat which doesn’t support Nest temperature sensors (why?), so we had to buy our own Nest Learning Thermostat to complete the setup.
Despite some of these unforeseen complexities, the whole process went relatively smoothly. There were a few months of planning and scheduling, but the actual installation was completed in about a week. It was a very noisy (cutting a hole through concrete is not quiet!) and chaotic week, but, the process was quick, and the city inspection was painless.
Solar & Storage
The installation of solar panels and battery storage was a lengthy ordeal. Sunrun proposed a system with LONGI solar panels, two Tesla Powerwalls, a SolarEdge inverter, and a Tesla gateway. Despite the simplicity of the plan, we encountered several complications right away.
First, a main panel upgrade was required. Although we had installed the Span smart panel to avoid this, Sunrun insisted on the upgrade and offered to cover the cost. Our utility PG&E took over a year (!!) to approve our request, which started a domino of delays.
After PG&E’s approval, Sunrun discovered that local ordinances needed a concrete pad to be poured and safety fence erected around the panel, requiring a subcontractor and yet more coordination.
After the concrete pad was in place and the panel installed, we faced another wait for PG&E to connect the new setup. Ironically, during this wait, I received a request from Sunrun to pour another concrete pad. This was, thankfully, a false alarm and occurred because the concrete pad / safety fence work had not been logged in Sunrun’s tracking system!
The solar and storage installation itself took only a few days, but during commissioning, a technician found that half the panels weren’t connected properly, necessitating yet another visit before Sunrun could request an inspection from the city.
Sadly, we failed our first city inspection. Sunrun’s team had missed a local ordinance that required the Powerwalls to have a minimum distance between them and the sealing off of vents within a certain distance from each Powerwall. This necessitated yet another visit from Sunrun’s crew, and another city inspection (which we thankfully passed).
The final step was obtaining Permission to Operate (PTO) from PG&E. The application for this was delayed due to a clerical error. About four weeks after submission, we finally received approval.
Seeing the flow of solar electricity in my Span app (below) almost brought a tear to my eye. Finally!
EV Charger
When my wife bought a Nissan Ariya in early 2023, it came with a year of free charging with EVgo. We hoped this would allow us enough time to install solar before needing our own EV charger. However, the solar installation took longer than expected (by over a year!), so we had to expedite the installation of a home charger.
Span connected us with the same electrician who installed our smart panel. Within two weeks of our free charging plan expiring, the Span Drive was installed. The process was straightforward, with only two notable complications we had to deal with:
The 20 ft cable on the Span Drive sounds longer than it is in practice. We adjusted our preferred installation location to ensure it comfortably reached the Ariya’s charging port.
The Span software initially didn’t recognize the Span Drive after installation. This required escalated support from Span to reset the software, costing the poor electrician who had expected the commissioning step to be a few minute affair to stick around my home for several hours.
Result
So, “was it worth it?” Yes! There are significant environmental (our carbon footprint is meaningfully lower) benefits. But there were also quality of life improvements and financial gains from these investments in what are just fundamentally better appliances.
Quality of Life
Our programmable, internet-connected water heater allows us to adjust settings for vacations, saving energy and money effortlessly. It also lets us program temperature cycles to avoid peak energy pricing, heating water before peak rates hit.
With the new heat pumps, our home now has air conditioning, which is becoming increasingly necessary in the Bay Area’s warmer summers. Improved vents and insulation have also made our home (and, in particular, our kids’ rooms) more comfortable. We’ve also found that the heat from the heat pumps is more even and less drying compared to the old gas furnace, which created noticeable hot spots.
Backup power during outages is another significant benefit. Though we haven’t had to use it since we received permission to operate, we had an accidental trial run early on when a Sunrun technician let our batteries be charged for a few days in the winter. During two subsequent outages in the ensuing months, our system maintained power to our essential appliances, ensuring our kids didn’t even notice the disruptions!
The EV charger has also been a welcome change. While free public charging was initially helpful, reliably finding working and available fast chargers could be time-consuming and stressful. Now, charging at home is convenient and cost-effective, reducing stress and uncertainty.
Financial
There are two financial aspects to consider: the cost savings from replacing gas-powered appliances with electric ones and the savings from solar and storage.
On the first, the answer is not promising.
The chart below comes from our PG&E bill for Jan 2023. It shows our energy usage year-over-year. After installing the heat pumps in late October 2022, our natural gas consumption dropped by over 98% (from 5.86 therms/day to 0.10), while our electricity usage more than tripled (from 15.90 kWh/day to 50.20 kWh/day). Applying the conversion of 1 natural gas therm = ~29 kWh of energy shows that our total energy consumption decreased by over 70%, a testament to the much higher efficiency of heat pumps.
Our PG&E bill from Feb 2023 (for Jan 2023)
Surprisingly, however, our energy bills remained almost unchanged despite this! The graph below shows our PG&E bills over the 12 months ending in Jan 2023. Despite a 70% reduction in energy consumption, the bill stayed roughly the same. This is due to the significantly lower cost of gas in California compared to the equivalent amount of energy from electricity. It highlights a major policy failing in California: high electricity costs (relative to gas) will deter households from switching to greener options.
Our PG&E bill from Feb 2023 (for Jan 2023)
Solar, however, is a clear financial winner. With our prepaid lease, we’d locked in savings compared to 2022 rates (just by dividing the total prepaid lease amount by the expected energy production over the lifetime of the lease), and these savings have only increased as PG&E’s rates have risen (see chart below).
Batteries, on the other hand, are much less clear-cut financially due to their high initial cost and only modest savings from time-shifting electricity use. However, the peace of mind from having backup power during outages is valuable (not to mention the fact that, without a battery, solar panels can’t be used to power your home during an outage), and, with climate change likely to increase both peak/off-peak rate disparities and the frequency of outages, we believe this investment will pay off in the long run.
Taking Advantage of Time of Use Rates
Time of Use (TOU) rates, like PG&E’s electric vehicle time of use rates, offer a smart way to reduce electricity costs for homes with solar panels, energy storage, and smart automation. This approach has fundamentally changed how we manage home energy use. Instead of merely conserving energy by using efficient appliances or turning off devices when not needed, we now view our home as a giant configurable battery. We “save” energy when it’s cheap and use it when it’s expensive.
Backup Reserve: We’ve set our Tesla Powerwall to maintain a 25% reserve. This ensures we always have a good supply of backup power for essential appliances (roughly 20 hours for our highest priority circuits by the Span app’s latest estimates) during outages
Summer Strategy: During summer, our Powerwall operates in “Self Power” mode, meaning solar energy powers our home first, then charges the battery, and lastly any excess goes to the grid. This maximizes the use of our “free” solar energy. We also schedule our heat pumps to run during midday when solar production peaks and TOU rates are lower. This way, we “store” cheaper energy in the form of pre-chilled or pre-heated air and water which helps maintain the right temperatures for us later (when the energy is more expensive).
Winter Strategy: In winter, we will switch the Powerwall to “Time-Based Control.” This setting preferentially charges the battery when electricity is cheap and discharges it when prices are high, maximizing the financial value of our solar energy during the months where solar production is likely to be limited.
This year will be our first full cycle with all systems in place, and we expect to make adjustments as rates and energy usage evolve. For those considering home electrification, hopefully these strategies give hints to what is possible to improve economic value of your setup.
Takeaways
Two years is too long: The average household might not have started this journey if they knew the extent of time and effort involved. This doesn’t even consider the amount of carbon emissions from running appliances off grid energy due to the delays. Streamlining the process is essential to make electrification more accessible and appealing.
Align gas and electricity prices with climate goals: The current pricing dynamics make it financially challenging for households to switch from gas appliances to greener options like heat pumps. To achieve California’s ambitious climate goals, it’s crucial to align the cost of electricity more closely with electrification.
Streamline permitting: Electrification projects are slowed by complex, inconsistent permitting requirements across different jurisdictions. Simplifying and unifying these processes will reduce time and costs for homeowners and their contractors.
Accelerate utility approvals: The two-year timeframe was largely due to delays from our local utility, PG&E. As utilities lack incentives to expedite these processes, regulators should build in ways to encourage utilities to move faster on home electrification-related approvals and activities, especially as many homes will likely need main panel upgrades to properly electrify.
Improve financing accessibility: High upfront costs make it difficult for households to adopt electrification, even when there are significant long-term savings. Expanding financing options (like Sunrun’s leases) can encourage more households to invest in these technologies. Policy changes should be implemented so that even smaller installers have the ability to offer attractive financing options to their clients.
Break down electrification silos: Coordination between HVAC specialists, solar installers, electricians, and smart home companies is sorely missing today. As a knowledgeable early adopter, I managed to integrate these systems on my own, but this shouldn’t be the expectation if we want broad adoption of electrification. The industry (in concert with policymakers) should make it easier for different vendors to coordinate and for the systems to interoperate more easily in order to help homeowners take full advantage of the technology.
This long journey highlighted to me, in a very visceral way, both the rewards and practical challenges of home electrification. While the environmental, financial, and quality-of-life benefits are clear, it’s also clear that we have a ways to go on the policy and practical hurdles before electrification becomes an easy choice for many more households. I only hope policymakers and technologists are paying attention. Our world can’t wait much longer.
It was quite the shock when I discovered recently (HT: Axios Markets newsletter) that, according to NerdWallet, California actually has some of the cheapest homeowners insurance rates in the country!
It begs the Econ 101 question — is it really that the cost of wildfires are too high? Or that the price insurance companies can charge (something heavily regulated by state insurance commissions) is kept too low / not allowed to vary enough based on actual fire risk?
Anyone who’s done any AI work is familiar with Huggingface. They are a repository of trained AI models and maintainer of AI libraries and services that have helped push forward AI research. It is now considered standard practice for research teams with something to boast to publish their models to Huggingface for all to embrace. This culture of open sharing has helped the field make its impressive strides in recent years and helped make Huggingface a “center” in that community.
However, this ease of use and availability of almost every publicly accessible model under the sun comes with a price. Because many AI models require additional assets as well as the execution of code to properly initialize, Huggingface’s own tooling could become a vulnerability. Aware of this, Huggingface has instituted their own security scanning procedures on models they host.
But security researchers at JFrog have found that even with such measures, have identified a number of models that exploit gaps in Huggingface’s scanning which allow for remote code execution. One example model they identified baked into a Pytorch model a “phone home” functionality which would initiate a secure connection between the server running the AI model and another (potentially malicious) computer (seemingly based in Korea).
The JFrog researchers were also able to demonstrate that they could upload models which would allow them to execute other arbitrary Python code which would not be flagged by Huggingface’s security scans.
While I think it’s a long way from suggesting that Huggingface is some kind of security cesspool, the research reminds us that so long as a connected system is both popular and versatile, there will always be the chance for security risk, and it’s important to keep that in mind.
As with other open-source repositories, we’ve been regularly monitoring and scanning AI models uploaded by users, and have discovered a model whose loading leads to code execution, after loading a pickle file. The model’s payload grants the attacker a shell on the compromised machine, enabling them to gain full control over victims’ machines through what is commonly referred to as a “backdoor”. This silent infiltration could potentially grant access to critical internal systems and pave the way for large-scale data breaches or even corporate espionage, impacting not just individual users but potentially entire organizations across the globe, all while leaving victims utterly unaware of their compromised state.
The human heart is an incredibly sophisticated organ that, in addition to being one of the first organs developed while embryos develop, is quite difficult to understand at a cellular level (where are the cells, how do they first develop, etc.).
Neil Chi’s group at UCSD (link to Nature paper) were able to use multiplex imaging of fluorescent-tagged RNA molecules to profile the gene expression profiles of different types of heart cells and see where they are located and how they develop!
The result is an amazing visualization, check it out at the video:
Once upon a time, the hottest thing in chip design was “system-on-a-chip” (SOC). The idea is that you’d get the best cost and performance out of a chip by combining more parts into one piece of silicon. This would result in smaller area (less silicon = less cost) and faster performance (closer parts = faster communication) and resulted in more and more chips integrating more and more things.
While the laws of physics haven’t reversed any of the above, the cost of designing chips that integrate more and more components has gone up sharply. Worse, different types of parts (like on-chip memory and physical/analog componentry) don’t scale down as well as pure logic transistors, making it very difficult to design chips that combine all these pieces.
The rise of new types of packaging technologies, like Intel’s Foveros, Intel’s EMIB, TSMC’s InFO, new ways of separating power delivery from data delivery (backside power delivery), and more, has also made it so that you can more tightly integrate different pieces of silicon and improve their performance and size/cost.
The result is now that many of the most advanced silicon today is built as packages of chiplets rather than as massive SOC projects: a change that has happened over a fairly short period of time.
This interview with IMEC (a semiconductor industry research center)’s head of logic technologies breaks this out…
What we’re doing in CMOS 2.0 is pushing that idea further, with much finer-grained disintegration of functions and stacking of many more dies. A first sign of CMOS 2.0 is the imminent arrival of backside-power-delivery networks. On chips today, all interconnects—both those carrying data and those delivering power—are on the front side of the silicon [above the transistors]. Those two types of interconnect have different functions and different requirements, but they have had to exist in a compromise until now. Backside power moves the power-delivery interconnects to beneath the silicon, essentially turning the die into an active transistor layer which is sandwiched between two interconnect stacks, each stack having a different functionality.
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.
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!
Its clearance is the “culmination of scientific and clinical research efforts,” said Peter Marks, director of the FDA’s Center for Biologics Evaluation and Research, in a statement.
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:
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.
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.
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.
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 effectivenesswas significantly worse, by a wide margin, than efficacy published in randomized controlled trial (see table below).
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:
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.
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 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 Control Panel tab in the Example Pre-App-Simple-Financial-Model spreadsheet from the CHIPS Act funding application website. Notice cells C51 and D51 corresponding to the senior debt interest rate (5%) and the subordinated debt interest rate (8%)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.
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.
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 “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-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 “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-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 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-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 “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!
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 — 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:
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 “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:
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.
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).
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.
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
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
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.
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.
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.
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.
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.
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!
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 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%.
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 growrelative 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!
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.
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.
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.
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.
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.
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)
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.
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
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
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.
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.
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.
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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