Category: thoughts

With yesterday’s death of a pedestrian by one of Uber’s self-driving cars, I’m writing this post. I’ve been thinking about these issues for a while but ended up putting the post up quickly. I’ll probably edit in the near future as I learn more.

When it comes to upcoming big changes to a big part of our lives, I naturally have big questions. This is a list of questions related to autonomous vehicles. If there are answers then I hope you can help me answer them. Note: I’m not accepting “positions” or “opinions” as answers for these questions. If answers exist today, then there should also be an understanding of why it’s so and who gains or loses because of it.

Also note: Many of these questions are about second-order effects that could result from such big changes. I haven’t seen much thoughtful writing about this yet. I’m going to take the claim that it shows a lack of thought to simply say that autonomous vehicles will automatically always be safer. If you think some of my questions are extreme edge cases, well, edge cases turn out to be more common than we think. Also, when self-driving vehicles operate at scale edge cases can be big issues. Without a measure of the risks that will be generated as things also get better most of the time, we expose ourselves to shocks when the new system breaks down. I’ll explain why below.

Here are my questions so far…

1. Yes, the pedestrian who was killed (mentioned above) was apparently “jaywalking,” which is prohibited in Tempe, Arizona and many other places. That jaywalking is prohibited still comes as a shock to me. Having spent so much time living and working in New York City, crossing at odd points within the block is a common occurrence. I believe that the city would stop working if all pedestrians had to wait at the crosswalk for the light to change. That’s no reason to push that model everywhere, but I generally believe that cities are best built as places for people, not cars. How much of the responsibility should be put on the pedestrian and how much on the autonomous vehicle company? I believe that the model for human driven cars is not necessarily a guide here.

2. How does the human reaction to liability change with self-driving cars? As in, if one casualty is caused by an autonomous vehicle (AV), what is multiple of liability from one casualty caused by a human-driven car? I believe that this multiple is large, at least today.

3. In the US, road fatalities killed over 37,000 people in 2016. That’s a little over 3,000 per month, or 100 a day. This great chart also describes the change in the US as more cars came on the road. If AVs work as they are described and there is widespread adoption, then those fatalities could indeed be driven down to something close to zero. But nothing ever works perfectly. What happens with cybersecurity breaches?

The above chart is where the US is today. Around 3,000 fatalities per month. For the last 40 years there have been more years of fatality reduction than increase (multiple reasons).

If all goes smoothly with widespread adoption (or even a mandate) for AVs, the red line in this graph could be shifted down to something close to zero, as below.

That’s great. Now what could happen if a single large cybersecurity breach takes place (intentionally not trying to estimate the upper bound).

That’s with a quick resolution. We’ll get to the financial and legal impact later. But for now, let’s just consider how a hacked national fleet, or national grid of AVs could possibly impact road fatalities. Here’s another example.

That lingering impact could be generated by anything from lost trust in AVs, leading to people bringing their human-driven vehicles back (even if illegal), inexperienced people driving cars (more accidents), sabotage of autonomous cars, or lingering effects of the hack that may or may not be not apparent, such as infrastructure damage created by the hack itself.

Here’s one more.

In this example, it takes a while for the hack to be resolved. Travel may decrease temporarily, but it is more dangerous than it was previously.

4. Life savings as a percentage of overall deaths is different depending on where you are in the world. The World Health Organization organizes the Top 10 causes of death globally and by country income level. While road deaths aren’t on the top 10 for high-income countries, these deaths are number 10 for both low-income and lower-middle income economies. That means that the last places likely to see autonomous cars (the poorer countries) are also the ones most likely to benefit from the decreased road deaths (assuming that road deaths indeed do decrease). The top of the list for Road fatalities per 100,000 inhabitants per year is Libya, Thailand, Malawi, Liberia, and Democratic Republic of the Congo. The top of the list for Road fatalities per 100,000 motor vehicles is Guinea, Benin, Democratic Republic of the Congo, Sao Tome and Principe, and Ethiopia. I’m not counting data for Road fatalities per 1 billion vehicle-km since there is data for only around 20 countries.

5. Interesting, the global average (2002 data) for road deaths per 100,000 people is 4x higher for men than it is for women. Does anything change about this with  driverless cars? Remember that when Uber driver data was analyzed it revealed that women are paid 7% less than men (for factors mostly related to length of time as drivers, time and location of work, and speed).

6. What about hacks that cause no loss of life but have a different type of cost? For example, a hack that caused less fuel efficiency (less efficient routing, driving speed, or other actions) for a national fleet or network may not be noticeable to passengers. However, this could have a big impact nationally. Like bank robbery, it is not worth it for the autonomous car company to disclose it.

7. What about hacking that does not cause loss of life, but diminishes the value or trust of AVs? E.g. driving someone to the wrong location? Eavesdropping or other activities?

8. Hacking, whether by outside criminal forces or internal company forces where people are driven somewhere they don’t want to go, or where people are intentionally targeted for surveillance for voyeurism? If that targeting already happens within video surveillance systems, how do we prevent it in AVs?

9. Even if self-driving cars are less susceptible to hacking than vehicles driven by humans, remember that the value of hacking a fleet, company, brand, etc is much higher than hacking individuals. Large entities will pay to get control back or keep things quiet. Individuals much less so, or not at all. How does that change the intent of groups wanting to break into AV networks or specific vehicles?

10. Are there some issues where the autonomous vehicle will refuse to accept passengers, some where it will accept but refuse to drive, some where it will drive but impose a fine? For example, while a human driven car (rideshare or taxi) may refuse to serve a drunk, dangerous, or offensive person, what about the AV (autonomous vehicle)? Why not serve them but impose a fine, whether monetary or to reputation/ranking, which could impact future pricing and rides.

11. Business model. Today, there are automotive transportation companies that require human drivers to own or rent the vehicles (most taxi companies and rideshare companies today). One of the future models is for fleet operators (could be Uber, Google, or “even” Ford) to offer services using AVs. How does that change the business models for these driverless car companies? Today, it is typically the drivers who cover vehicle costs, including purchase or lease, regulatory licensing, fuel, maintenance and cleaning. That’s a large upfront cost of buying the vehicle plus the ongoing operational costs. In a transition to AVs providing transportation services, the service provider must cover those costs. How much does that change the presumed savings and the pricing extended to customers?

12. Savings: human labor, extra fuel cost of carrying human driver, extra cost of non-essential car parts (driver’s seat, trunk), ability to run more efficient vehicles by changing the fleet more frequently (more usage in shorter time periods), extra waiting time required since human drivers must see and accept potential customer rides, inefficiencies related to human drivers (need to take food, bathroom, and rest breaks).

13. What happens during shocks? For example, oil (if AVs are still gas powered), or shocks impacting electric vehicles, for example cost increases caused by access to components of battery technology or new costs put on machinery using batteries to account for future disposal and pollution caused?

14. Cleanliness: When many people ride in a car during the day, by the time you get into the vehicle, how clean is it? Problems include both refuse left behind by previous passengers, but also smells… Will there be businesses established just to clean the inside of autonomous vehicles? Or, we could imagine a coating on the inside of the vehicle which can be peeled off and disposed of when needed…

15. What about surveilling riders themselves for no reason? Is there anything prohibiting open and intentional surveillance of passengers, done with the claim of public safety? What about security back-doors for government access to monitor movements of individuals?

16. What about passive surveillance for the purpose of data collection. E.g. compiling a list of conversation topics to improve customer satisfaction or develop new products?

17. Can children ride in an AV by themselves? What about babies? Before you say “no way!,” what’s your reasoning? What if the vehicle included child care options?

18. Could people just put their kids in an AV when they need to get some work done? Baby-sitter on the go when none is available.

19. Will an AV prevent an unsafe number of people from squeezing inside? Who decides what is unsafe? Or will this be handled by pushing a fine to the passengers, rather than preventing them from entering?

20. Will an AV prevent someone from unbuckling their seat belt? Will the car refuse to drive or pull over if the seat belt is unbuckled? If so, what about unbuckling the seat belt when needing to take off a jacket? Does a small adjustment like that trigger a larger response? What kind of AI is needed to understand the associated human intent?

21. Will an AV prevent someone from smoking (if that’s prohibited)? Or just generate a fine for the passenger?

22. Do driverless cars lead to the development of something called the “60-mile per hour club”? I’ll leave you to draw your own conclusions of what that is (hint: think of the “mile-high club”). If so, what do AV companies do about that, if anything?

23. What human vs machine competitions will self-driving cars spawn? For example, competitions where humans try to maneuver AVs (think Kasparov vs Deep Blue, or even John Henry vs the machine). I assume the AVs are less interested in the competitions than the humans.

24. Who owns your travel data? Who can obtain the data and under what conditions? What compensation is made in the event of a data breach?

25. What other population data of interest could be collected? For example, animal populations seen while passing, insect populations (hitting the windscreen etc), roadkill species identification. A lot could be done to automate these counts.

26. There are road conditions which are dangerous at outset — severe storms for example. But could passengers pay a premium to travel during incredibly dangerous conditions? Why or why not?

27. How does parking change when most or all cars are AVs? On the one hand, passengers can have their cars (in this case assuming ownership and not rideshare) drive to a parking location and return later. On the other hand, what impact does this bring to traffic and parking availability? Do human-driven cars take priority when it comes to parking?

28. Will many vehicles have no one in them? The reasons could include that they are traveling elsewhere where there is demand, that the vehicle is not primarily used for human transport but instead is a traveling store, entertainment center, or service provider (for example, cleaning services as described above).

29. If it is cheaper to hire an AV truck to move your items when moving houses, how does that impact moving services and truck rentals?

30. Could you sleep in the car overnight for purposes of long-distance travel? Could the AVs include a shower, bathroom, and proper bed?

31. What about including a gym, cafe, restaurant, office, or other specialized spaces? How does AV development impact these businesses?

32. How do AV companies deal with antisocial behavior from humans? For example, humans who harass AVs either as pedestrians or while driving their non-AV vehicles.

33. Should weight be a factor in pricing? E.g. heavier people, or people bringing luggage charged a premium? Could an extra charge to people above a certain body mass index be used in pricing — basically taxing individuals who don’t fit current standards of healthiness?

34. Could you ride while high? How does the ability to monitor likelihood of substance abuse factor in to legal action?

35. Could you ride naked? Is the AV a public space or a private space?

36. Could you pay a premium to have your identity and the identity of your co-passengers kept secret? This question somehow reminded me of this Monty Python skit.

37. What type of advertisements are passengers exposed to? In many taxis, there is some type of ads, from visual print ads to videos that play with sound. Can you opt in or out? Is there a cost or benefit associated with that choice?

38. How do passenger travel patterns change with AVs? Does anyone ride mass transit anymore?

39. What types of entertainment grow or decline? Does video grow in relation to audio (which is the only entertainment you can get if you’re driving). So, does anyone do podcasts anymore if most travel is via AV (that is, there are only passengers and no drivers)?

40. What other types of businesses change or stop working? Billboards, rest stops, fueling stations, parking lots, retail are all up for big changes.

41. Do AV ride completion success rates (no accidents) vary by location because of differences in locale road and weather conditions, including differing human attitudes toward to vehicles, their passengers, variations in comfort with ambiguity globally…?

42, Tesla now has a second fatality (occurred after I originally wrote this list). How far in the other direction will acceptance of AVs swing now?

43. New addition after this post by Jason Calacanis on April 6, 2018. Writing that “[t]he most disturbing and frustrating trend, in all four deaths, is that the human drivers played a significant role in them,” misses the point. It will be easier to expect that people will continue to act as people when building autonomous vehicles. He further wrote “[w]e won’t know for a while, but there is a chance that if the driver — who was being paid to drive the car — had not been blatantly and knowingly breaking the law, they might have been able to apply the brakes in time.” It’s as though we should also blame the killed pedestrian for being so inconvenient as to allow herself to be hit. Edge cases are plentiful in driving. I believe that thinking on this issue will evolve over time.

44. Should a small fee per driverless mile be set aside in a fund to pay for future costs related to the potential system risks? Otherwise, who pays if the system breaks down?

45. Will AVs not even bother to park? Or will they only park when the economics work out? See this tweet about how AV demand for parking may be zero, depending on cost to park and AV operating cost.

46. Are AVs exposed to the potential to issues with GPS network?

47. Along with operational effectiveness, AVs bring systemic risk.

I will add to this list, especially as I receive feedback and learn more. Thanks!

After a couple years of talking about Blue Apron in my classes and accelerators I thought I’d write of those thoughts down.

Blue Apron is the meal delivery company that everyone loves to hate. It’s not without good reasons. Their stock price is currently $2.15 (it was $10 on opening day). We’ve seen their high CAC (customer acquisition cost) and customer retention problems. But what are the root causes of some of these issues? And why might Blue Apron have been forced into a position that looks, at least temporarily, as impossible to improve?

A problem faced by many mass market subscription services that deliver physical products (as opposed to digital products) is that costs vary depending on customer location and yet the company cannot easily assign different pricing based on location. Even if costs did not vary, local market desire to pay different prices should be something that a national company could benefit from. In the past, a price difference might have been accommodated by charging more or less in delivery fees, but that is something that consumers have been socialized (by Amazon) to disdain. Further, when the business’s storefront is online (as opposed to a brick and mortar storefront that customers walk into), price comparison is easy. To the consumer, online feels like it should be the same no matter where they are, at least if they are within the same country. But that attitude changes when it comes to physical storefronts. When traveling, customers don’t expect to pay the same in every Starbucks they enter (the Starbucks model is a subject for another post). Different pricing in Blue Apron’s online storefront, even if justified, just feels unfair and it is understandable that the company has avoided changing pricing based on location. The two states reported where Blue Apron customers are a larger proportion of their population are California (15% of customers) and New York (8% of customers). But due to inability to vary prices by location, Blue Apron can’t charge higher prices to this 23% of customers who live in the higher income parts of the US.

Blue Apron’s Net Revenue per Customer in their S-1 deals with paid customers only, not people who tried the service for free and then never converted. Those free trials include real food costs and so are quite different than digital product SaaS companies doing something similar.

Orders per customer per quarter is another issue for the company. Quarterly orders declined from 4.5 Q1 2016 to 4.1 Q1 2017. It would be interesting to know the variation in orders per customer by looking at the raw data, or at least knowing the standard deviation of the repeat orders. That is, does the average of 4.1 orders per quarter mean that most customers center around that number, or is there drastic variation, with some customers ordering 36 times (three times per week) but many ordering just once.

CAC (customer acquisition cost) as reported in the S-1 is $94. That’s pretty close to the the 31% gross margin on the $939 in revenue for the 36-month cohort (which is $291 total or $97 per year straight-lined). Best case and not discounting future cash flows, Blue Apron breaks even on their CAC after one year (not counting any of their other operating costs). More specifically, and again because of coupons and free trials this is the best case since we don’t know how long it took Blue Apron to get the acquired customer to pay. Otherwise, according to the S-1 for the first six months after signup, customers generate $402 in revenue (at 31% gross margin that’s $124) and therefore actually pay off the CAC in nine months. Longer if you factor in the free period. But again, we’re not accounting for all the other costs of running the business when we typically calculate these CAC/LTV ratios and payoff periods. That CAC is also an average. What are the segments that are cheaper? What are the channels that are cheaper? For both, which provide a better LTV to CAC ratio? 

Where customer payback period (how long until customer revenue pays back to the company the cost of acquiring the customer) would be a relevant metric to a company without a war chest, to Blue Apron this matters less, at least for a while. Blue Apron can make customer acquisition expensive for everyone else while having the funds to outlast the other companies that can’t afford to lose money for as long. For smaller startups that went into the meal kit business, or for startups thinking about getting into it now (is anyone thinking about getting into meal kits now?) their economics are very different. That is, they have a much smaller capability to last through tough times unless they don’t need to compete on price or if they can focus on a tight customer niche. In other words, the company would need to put tight limits on the overall size that they could grow to but they would also make their internal economics better while operating in that niche. Companies like Blue Apron that have already raised hundreds of millions followed by going public no longer have the option to stay small.

One way you can tell that Blue Apron faces stiff competition is from Google search results. Google Adwords displays the maximum number of paid ads at the top of the search page. When I did a search on the name “Blue Apron,” the first ad was from HelloFresh, then came Blue Apron itself, followed by Plated (acquired by grocery chain Albertsons), and Home Chef. Searching “Blue Apron Alternatives” results in ads from Sunbasket, HelloFresh, Plated, and Green Chef. There’s even a war over who can deliver the biggest discount. Searching “Blue Apron Coupon” shows offers from Blue Apron for $40 off, followed by an offer from Sun Basket for $50 off, HelloFresh for 50% off, and Green Chef for $50 off. You might be surprised to see me spend so much time writing about Google search results, but for this market online search is an important channel both because it leads to new customer acquisition and also because customers churn away when they find other better offers from competitors.

Another way we can see that competition if from recent large entrants to the meal kit business, notably Amazon, Walmart, HelloFresh (entering the US market from Germany), and Weight Watchers (which had previously partnered with Chef’d). Amazon and Walmart already have storefronts scattered throughout the US in different markets, making food preparation and local delivery more effective. Weight Watchers already has the brand and customer base.

Market Share Leader Without Loyalty Is a Position of Weakness

Blue Apron’s position as market share leader in the meal kit delivery market is that since there is low customer loyalty, market share leadership is actually a position of weakness, not of strength. Since meal kit subscription services can generally be thought of as interchangeable (there’s nothing that prevents one from emulating the meals of others), what keeps customers loyal is inertia, laziness, and only occasional price comparisons. The weakness comes from this fact. As market leader without loyalty, when a small rival offers a discount, say based on a “Blue Apron alternatives” search result, Blue Apron needs to make some sort of response. That response costs Blue Apron more than it costs the smaller companies since it is multiplied by a greater number of people. If Blue Apron still commands most of the new account requests and it must match or come close on its smaller competitors’ offers, then Blue Apron must give away discounts to more people than its competitors.

Well, it’s much cheaper than drinking nothing but soylent all week (estimated at $100 – $120).

It’s a common phrase, “you must crawl before you walk.” We say it because that’s what we see babies do as they mature. We then apply the same logic to businesses and founders. But is the saying true?

Turns out, many babies go from some other type of movement (sliding, rolling etc) and straight into walking. This skipping of the walk phase has become associated in common knowledge with warnings of learning disabilities and undeveloped motor skills. But I cannot find any reasonable research study that shows that babies should crawl before they walk — that is, that they are actually at a disadvantage is they skip crawling.

Turns out in many parts of the world babies are encouraged not to crawl. The ground is not clean, so there are disadvantages to putting your face close by. These babies learn to walk just the same. Is this saying an example of common knowledge which misleads us when we think about development and growth? Or if the babies just don’t want to crawl, how hard should we try to encourage them to do so?

I Recreate Every Time

We tell startups and businesses developing new products to pass through a series of preset steps in their path toward development and growth. Again, largely this works and the intentions are good. But sometimes that preset process is better for the advisor than it is for the business people. Better for the person guiding the work than the person actually doing the work. Or, there are other factors (which all make sense) that prevent the business person from following the preset steps. Sometimes there is just resistance to doing something different. And that is totally normal. Why expect complete trust when past advisors led them astray?

With every business I work with — new startup or established corporation — I recreate a process rather than reapply the exact same things that worked (or were just the “common knowledge”) elsewhere. Every situation is a little different. The timing and place and people are different. So I recreate. This is inherently unscalable and that is fine with me.

Otherwise, the risk is that we fall into what I call the public health policy approach to innovation. Public health policy does make sense in the aggregate but there are also reasons for why you may not want to follow policy if you know your specific situation within the population.

1. Policy can set actions that suit the average case within the population. In the health example, this includes when to recommend vaccinations, diet recommendations, intervention for developmental milestones, and more. These broad recommendations are not for you individually, but are generalized for the whole population. In other words, in order to generally satisfy everyone, simplify the recommendations. The result is that everyone will be a little unsatisfied but few should be misled to harmful actions.
2. Focus on the high-risk cases within the population and make general policy that reduces their risk. The idea is to prevent the most common big problems through actions. These actions should have little downside for the rest of the population.

Related to business, this would be the recommendation that everyone do customer interviews for example. There is something to be gained, little to be lost, and it might help prevent someone who has spent years in an R&D lab from pursuing the unlikely commercialization of interesting but useless technology.

In other situations I see better results coming out of rapid experimentation — running 100 small tests simultaneously — in order to be open to surprises and to be given the opportunity to double down on what shows early promise. Broadly guided by metrics but also somewhat chaotic in the beginning.

If we have all of this knowledge about how to learn from potential customers, but few people apply it, that’s a problem. Rather than continue to make the same recommendations and be frustrated when there is push-back, time for the advisors to see the world through the eyes of the business.

A few years ago I wrote a piece called The Disposable Startup. The piece was an appeal to let young startup founders explore and have fun building new things (like the college bands I remember), rather than see a need to provide adult supervision and oversight into every aspect (like college today). This is a continuation of some of those thoughts, with another new type of “disposability” that I noticed before but didn’t want to write about until now…

Mutually Destructive Creation

I’ve long ranted against startup events and the way investments are typically made. Short explanation: events benefit their organizers more than the attendees and investors are often still traditional and herd-focused in their process. But there is a byproduct of events and investor attitudes that changes the behavior of startup founders themselves.

Unless a competition has a financial prize I discourage startups from applying and spending time to pitch. Truly competing requires a lot of time away from building a business. But where there is a fit, I love it when a startup I advise takes prize money from these competitions. (“My” advised startup win rate is currently 80%, which is a recent decrease.) The other impact that these competitions have is in multiplying the willful creation of startups purely for the purposes of shutting them down. This is how the process goes.

• The startup community event. Startup founders attend. They listen to a speaker (often someone with more or less celebrity status in the community). Nothing will be different about the way they operate after the event, but they will meet two people over beers while there. Those two people build their network.
• The competition. Startup founders apply. Some are chosen to pitch. Some then win. Those who win (and often all) get some exposure from their participation, even if it only means that they have shaken hands with a few judges and investors. They have something interesting to say — they’re working on a startup, after all. Those judges and investors build the founder’s network a little more.
• Winning the competition / Awarding the prizes. The cycle requires that there are one or more winners in an event. When there are prizes, some prizes fall into the type that sound good on but which are actually of little to zero value (a 30-minute follow-up meeting with one of the judges, free hosting etc). Some prizes are cash, or the appearance of it. Especially at universities, cash prizes are dispensed carefully. As one administrator said to me, “we require invoices to reimburse them for specific approved cost items. We can’t have them spend the prize money on beer…” But chances are if you can’t tell if your award-winner is serious enough to be thoughtful on how they spend money (or if you care that they will spend it on beer) you’re not running competitions as well as you could. The other reason of course is that not all awarded prize money gets paid out, which helps keep down the costs of the competitions while not diminishing their status.
• The founders’ bigger networks. After, say, half a year on the startup circuit and spending a lot of time meeting people, founders can build up networks that they never could have otherwise. There can be a real benefit to spending your time networking rather than with customers and working on the business itself. You know where to go, who to ask, how to get connected further, how to talk about their work. The goal is to become known.

Dispose of the Startup to Create the Next One

The process above can be good for founders who really want to give it a second shot. They have a network now. They know how things work. They’ve had the experience of pitching 100 times and hearing no each (or almost each) time. They have gained some respect. Going at it the second time will only be easier. If their failure were costly (money, respect, trust), then their actions would be different.

I’ve seen this type of disposable startup, done intentionally  or unintentionally, many times now. Seen up close it bothers me a little when I catch myself caring more about the success of the business than the founders. I’ll always try to help founders, but disposable startups are one of the reasons that when I do help them, I focus my efforts on the ones that are past the disposable stage.

or Why I Recreate Before Every Startup Program I Run and Company I Advise

It’s easy to spend money when it’s not your own.

When business people plan and spend a budget (not their own money) I rarely see evaluations of the efficacy of the activities the budget will support. In new product development, innovation management, startup programs, or whatever other types of programs you are involved in centered around creation, here’s an approach that’s a little different. It’s based on the last six years I’ve worked with companies around the world.

First, let’s introduce a concept from medicine called Number Needed to Treat (NNT) and Number Needed to Harm (NNH). NNT is a statistical measure of how many people must take a treatment in order for one person to see the benefit. Those medications and procedures don’t help 100% of the people who take them — that’s something easily forgotten.

Let’s see how NNT is applied in medical treatments.

Example 1: Antibiotics for Hand Lacerations.

What do you guess is the NNT? Seems reasonable to believe that using an antibiotic in this situation could only help the patient? That’s what most people say when I present this case. But actually, there is no benefit found.

Here’s another one.

Example 2: Aspirin to prevent a first heart attack or stroke.

What do you guess is the NNT? We’ve heard about aspirin used in this way for a long time. Seems reasonable to believe that using a drug that has been on the market for 100 years would help the patient and have no downside? Again, that’s what most people say when I present this case. But again, there is no benefit found. For the one person helped in this case, another 1,666 took aspirin daily with no impact and some now suffered side effects as well.

I bet you’re getting more skeptical, but I’ll keep asking you to guess.

Example 3: CT Scanning for lung cancer screening in high-risk smokers.

What’s the NNT? It’s beneficial to preemptively identify and treat cancer in a high-risk individual. But how is the cancer to be identified? Here again, the procedure offers some benefit but also side effects. For the one person helped out of 217, we find that one in four (the NNH) suffer from the false positive (everything from the stress of thinking you have cancer when you don’t to taking treatments for it unnecessarily).

Last one.

Example 4: Mediterranean Diet for 5 Years for Heart Disease Prevention (Without Known Heart Disease).

I’m all for the Mediterranean diet. But what’s NNT and NNH? Finally, some good news. The NNT is one in 61 and there is no NNH (no side effects from eating olive oil, fish, pasta and other good food).

How does this affect decision-making?

I was thinking about why I do certain activities and avoid others.

For example, one point of contention is why I don’t do startup demo days in the program I currently run at USC. This is an Incubator that has a 10% acceptance rate, has taken in 70+ companies over three years, sees about 40% raise capital, and other good results. Why not do a demo day when everyone else does? It’s easy. The NNT and NNH don’t warrant the demo day.

My Estimates: Demo Day for a University Incubator to Solve Funding Problem

Benefits in NNT: 1 in 10? / 1 in 50? Meaning, that number of people would raise after the demo day but would not be able to raise without the demo day.

Harms in NNH: 1 in 5? Why is the NNH so high? To prepare for a demo day, founders will do little work on their business while preparing for the event. I’ll also do less to help them on their business because I’ll be so busy organizing all the visitors and the whole production.
There’s also a further NNH of 1 in 5? because those who do not immediately raise capital therefore send a signal that they are a bad investment.

Throughout all of this, I however would look like the hero. In other words, the demo day is bad for the startups but good for me (the person running the Incubator). So I don’t do demo days. Here’s another one.

My Estimates: Many Group Meetings to Teach New Knowledge and Best Practices

Another question I get a lot is why I no longer to lots of group meetings with the startups in the Incubator throughout the week. Instead, the program has evolved to be mostly founder-driven with one-on-one office hours with me and other people from industry who I bring in. This change happened as the founder mix became half USC alumni (e.g. older, more experienced, and not living on campus) and broader (I have a wide range of company industries and tech focus areas from software, hardware, food, apparel, consumer products…).

Many group meetings benefits in NNT: 1 in 10? were helped mostly from networking with others in attendance.

Harms in NNH: 1 in 10? did little work on their business since they’re in so many meetings. Actual behavior change and application of new knowledge is limited.

If I were to just copy what worked in other programs, I’d risk harming people with side-effects. If you don’t yet know the effect, be biased toward non-interventionism.

I’m exploring these concepts in a new project on unintended consequences.