The best of Jitha.me – A Compilation

Today I send out the 100th issue of Sunday Reads. It’s a good time to look back.

So I’m compiling some of the most well-received articles I’ve written over the last few years. On startups, business and management, and on mental models that make us more effective at what we do.

Hope you find the articles useful! Don’t read them all at once. Read whatever catches your fancy. You can always come back later 😊.

[PS. It’s also a good time to subscribe if you haven’t. You’ll get issue #101 next Sunday. I promise you won’t regret it.]

The World of Startups

How to save yourself from a bad startup idea that looks good.

(Go to article).

This is an article I wrote in late 2015, a couple of years into my startup and when I was just starting OperatorVC, the angel fund I invest through.

It struck a chord with readers. It still gets 100+ views a week (and ranks in top 3 on Google for “bad startup idea”) despite being not very optimized for search.

We have plenty of startup ideas. Many of them are bad, and we dismiss them right away (or our friends warn us off the idea).

They’re the easy ones.

The dangerous ones are the ideas that look quite good. The ones that give you goosebumps, and then three wasted years.

In this article, I list some of the common patterns that such plausible (but actually bad) ideas have, so that you can spot them early and save your time.

Read on here.

On a related note: Why describing your startup as the “Uber of X” is a bad idea. Yes, despite what Y-Combinator says.

How Uber solved its Chicken and Egg problem (and you can too!).

(Go to article).

Some of the most exciting companies of the 2000s are multi-sided networks. Think Uber, or Airbnb, or even ecommerce marketplaces. They’re massive, and they have immense defensibility.

Anyone who wants to compete needs to get both suppliers and consumers, at the same time.

That’s the proverbial chicken and egg problem. How do you get consumers when you don’t have suppliers, and vice versa?

Turns out there are four specific ways you can solve the chicken and egg problem.

Read on here for examples of each of these solutions.

I’ve also captured it as a framework on Slideshare, that you can download.

Your Minimum Viable Product can be more minimum than you think.

(Go to article).

Most of us in the startup community understand the concept of a Minimum Viable Product, or MVP. It’s the most basic version of your product that still delivers your core offering.

Aiming for an MVP helps entrepreneurs (especially first-timers) avoid the rookie mistake – building too much product before validating market need. We all want the ten revolutionary features in our first version. But not only will these features take five extra months to build, most users will also not see them.

So that’s the concept of an MVP. Sounds simple, right?

And yet, we slog for 3 months to build the MVP. And congratulate ourselves on finding out it didn’t work, and then spend another 3 months on a pivot.

Three months is way too long! Why does the MVP take so long?

The reason is that we’ve got the notion of an MVP all wrong.

Read on here.


The World of Business and Management

What I learnt from talking toilets in rural Bihar.

(Go to article).

My last project in consulting (back in 2012) was to develop a market-based solution to the problem of sanitation in rural Bihar (one of India’s poorest states).

At that time, less than 20% of households in rural Bihar had toilets. And many of those who did have toilets, didn’t use them – they would defecate in the open instead.

Against this intimidating backdrop, we set out to build a private-sector led solution to the problem.

And we were fairly successful. The project helped over 500K rural households construct toilets in their homes. It increased the number of toilets in our focus districts by 10 percentage points.

This article talks about the timeless lessons I learned through the project, on markets, consumers, and how to sell.

On a related note, the job to be done framework. Or, as they say, “You don’t sell saddles. You sell a better way to ride.”

What doesn’t get measured… doesn’t exist?

(Go to article).

We’ve all heard the saying “What gets measured, gets managed”.

A simple, yet powerful thought. With a simple corollary – what doesn’t get measured, doesn’t get managed.

But in reality, the corollary is far more extreme.

In the eyes of the person responsible, what doesn’t get measured… doesn’t really exist!

Read on here, to see the dark flipside of this common management adage.

On a related note, the Availability heuristic. Or “what you see is all there is”.

How to manage your team LIKE A BOSS (even while working remote).

(Go to article).

This is a more recent, and more topical article.

Effective team management (whether in-person or remote) can be distilled into five key axioms.

Call them the Minimum Effective Dose, or the 80:20 of team management.

Team management 101

Read on here .

Hiring Great People.

(Go to article).

This links back to the previous article. You can only work with people you end up hiring. So, hiring well has an inordinate influence on your team’s future output.

Hire well, and you have an NFL Dream Team. Hire badly, and at best you get a squabbling dysfunctional family. Not much effective team management you can do there.

In the same vein as the previous article, here are 7 key learnings on hiring.

1. Hire only when you absolutely need to.

2. Don’t be too hard on yourself. 1 in 3 hires don’t work out – if you do it right.

3. False Positives are OK. False Negatives are not.

4. What to look for in candidates: drive and self-motivation, innate curiosity, and ethics.

5. A few tips for running an interview process. Most important one – do reference checks.

6. How to let people go. Decisively, but with sensitivity. It’s your fault – not theirs – that you hired them into a role where they can’t succeed.

7. Diversity will not happen on its own. You’ve got to make it happen.

Read on here.


The World of Mental Models

What are “mental models”?

They are tools that help us understand the world faster and better. Instead of approaching every new problem from scratch.

Simple but powerful concepts, that help us understand situations more clearly, and make quicker yet better decisions.

For example, take this core principle from economics: “There ain’t no such thing as a free lunch“. It reminds us to look at every wonderful business deal with care. What’s the catch? There’s always a catch.

In a way, mental models help us think in a more “modular” fashion.

Modular programming makes software much faster. In the same way, mental models are the modules that soup up your decision-making engine.

Mental models are the modules that soup up your decision-making engine.

Over the years, I’ve written about a few powerful mental models, that have helped me think faster (and better) about business problems.

Listing a few of them below.


Hope you like some of these articles!

Do write back or comment with the articles you liked best, and I’ll share more on those topics in the coming weeks.

And don’t forget to subscribe, so you get issue #101 of Sunday Reads!

They will never take… our FREEDOM!

psychological reactance
Mel Gibson in Braveheart, feeling a little blue.

Last Friday, the lockdown in Singapore was lifted. It was a glorious, sunny day. As I looked out of my window and saw a few people swimming in the pool (it was closed through the lockdown), my first thought was, “I’ll go for a swim this evening. It will be amazing.”

My second thought was, “Wait, that doesn’t make sense!”

  1. I hate swimming.
  2. I’m not a good swimmer.
  3. In the two years I’ve lived in this condo, I’ve never used the pool. Not once.

So what the hell happened there?

What happened was, I got some freedom back, and I loved it. Even if I’ve never actually used that freedom, and therefore, its value to me is precisely zero.


This was a benign example. But this yearning for freedom, even when we don’t actually need it, is an intense force driving our behavior.

The term for this is Psychological reactance. Here’s Wikipedia on the subject:

Reactance is an unpleasant motivational arousal (reaction) to offers, persons, rules, or regulations that threaten or eliminate specific behavioral freedoms. Reactance occurs when a person feels that someone or something is taking away their choices or limiting the range of alternatives.

As Dr. Robert Cialdini says in Influence: The Psychology of Persuasion, this is a powerful impulse.

As opportunities become less available, we lose freedoms. And we hate to lose freedoms we already have.

This desire to preserve our established prerogatives is the centerpiece of psychological reactance theory.

According to the theory, whenever free choice is limited or threatened, the need to retain our freedoms makes us desire them (as well as the goods and services associated with them) significantly more than previously. So when increasing scarcity—or anything else—interferes with our prior access to some item, we will react against the interference by wanting and trying to possess the item more than before.

That’s why we have these videos of people rejecting masks in different parts of the US.

In this one, a protester thunders, “I will not be muzzled like a mad dog!”.

And the video in this twitter post has a few strange quotes:

  • “They want to throw God’s wonderful breathing system out the door.” Umm, no.
  • “You, doctor, are going to be arrested for crimes against humanity!” (for saying that people should wear masks).
  • “The mask is literally killing people”.

That’s why young parents experience the “terrible twos”.

Around the age of two, children come to a full recognition of themselves as individuals. This newfound sense of autonomy also brings along the concept of freedom. And the child wants to explore and test (again and again) the boundaries of this freedom.

Much to the chagrin and frustration of the parents.

There’s this hilarious example in Cialdini’s book, about banned detergents in Florida.

Dade County (containing Miami), Florida, imposed an antiphosphate ordinance prohibiting the use—and possession!—of laundry or cleaning products containing phosphates.

A study done to determine the social impact of the law discovered two parallel reactions on the part of Miami residents.

First, in what seems a Florida tradition, many Miamians turned to smuggling. Sometimes with neighbors and friends in large “soap caravans,” they drove to nearby counties to load up on phosphate detergents. Hoarding quickly developed; and in the rush of obsession that frequently characterizes hoarders, families were reported to boast of twenty-year supplies of phosphate cleaners.

The second reaction to the law was more subtle and more general than the deliberate defiance of the smugglers and hoarders. Spurred by the tendency to want what they could no longer have, the majority of Miami consumers came to see phosphate cleaners as better products than before.

That’s also why book censoring doesn’t work.

Or rather, it works too well. It’s every new writer’s dream for their first book to be banned.

Readers not only want the book even more than before, the book also gets a halo effect of “truths they don’t want us to hear”.


Have you noticed other examples of psychological reactance? Of how we overvalue unimportant freedoms we’re about to lose? Hit reply or comment, and let me know!

COVID-19 and Taboo Tradeoffs

COVID-19 Quarantine

Scott Alexander has written a great “where are we now” primer on COVID-19: When all you have is a hammer, everything starts looking like a dance.

Apart from his updates on how we’re doing in our battle against the virus, there were two pieces I wanted to call out. One interesting, and one insightful.


Why are some countries containing COVID-19 better than others?

Scott evaluates the different theories for why some countries are doing better than others.

  • Stay at home orders: Don’t seem to have mattered at all.
  • General government policy: Also seem to matter much less than we’d imagine. We thought Korea and Taiwan are doing well because of their brilliant governments. Japan, on the other hand, denied the problem for a long time so they could still stage the Olympics. Yet, they’re not doing too shabbily.
  • Testing policy: Yes, this matters, as I’ve mentioned before in Sunday Reads #85: Black Swans, Honesty, and Dishonest Statistics. But most (developed) countries are now testing properly, so this doesn’t explain the differences either.

Clearly, there’s still a lot to discover about this virus.


Lockdowns and taboo tradeoffs.

Second, and I found this far more insightful: He also talks about the importance of framing.

Coronavirus has killed about 100,000 Americans so far. How bad is that compared to other things?

Well, on the one hand, it’s about 15% as many Americans as die from heart attacks each year. If 15% more people died from heart attacks in the US next year, that would suck, but most people wouldn’t care that much. If some scientist has a plan to make heart attacks 15% less deadly, then sure, fund the scientist, but you probably wouldn’t want to shut down the entire US economy to fund them. It would just be a marginally good thing.

On the other hand, it’s also about the same number of Americans who died in the Vietnam War plus the Korean War plus 9/11 plus every school shooting ever. How much effort would you exert to prevent the Vietnam War plus the Korean War plus 9/11 plus every school shooting ever? Probably quite a lot!

Sure, you say, “This is a good example. But I already know the importance of framing, and anchoring.”

Great. Then let’s try another one for size.

Suppose you reopened the economy tomorrow. You tried as hard as you could to put profits above people, squeezed every extra dollar out of the world regardless of human cost. And then you put a 1% tax on all that economic activity, and donated it to effective charity. Would that save more people than a strict lockdown?

If a lockdown costs $5 trillion, then the 1% tax would make $50 billion. That’s about how much the Gates Foundation has spent, and they’ve saved about ten million lives.

Ten million is higher than anyone expects US coronavirus deaths to be, so as far as I can tell this is a good deal.

This reminds me of the discussion on Taboo Tradeoffs in the Rationality fan-fic, Harry Potter and the Methods of Rationality. Won’t share any spoilers, but the gist (I paraphrase from Chapters 78-85):

When you compare the value of sacred vs. secular objects (e.g., paying $5M for a liver replacement so a person can live, vs. for improving medical equipment), you make a taboo tradeoff.

Whenever you refuse to pay a certain amount (“I will not donate $2M for upgrading medical equipment”), you set an upper bound on a life.

Whenever you agree to pay a certain amount (“I will pay $5M to get this poor person a liver”), you set a lower bound on a life.

And if these two bounds are inconsistent, it’s an opportunity to move money to achieve a greater good.

What the hell is going on with the stock markets?

The world is tipping into the mother of all recessions. And yet the stock markets are on a tear like nothing’s happened.

As I mentioned in COVID-19 is a Black Swan, but not for the reason you think, this is the state of the real economy in the US:

And here’s the S&P 500, halfway back to its previous highs.

Why is the stock market going up?

The stock market back home in India is not as exuberant. But it has also risen, after a dip in March. Although the situation has only gotten worse.

What the hell is going on with the stock markets?

Over the last weeks, I’ve come across three hypotheses for this strange behavior. I don’t know which is right – I’m not a stock market expert. Truth be told, I’d guess it’s a combination of all three.

Hypothesis #1: What kills you makes me stronger.

Maybe the stock market is still efficient. We actually expect large companies to do well. And we expect a real divergence between equities and the broader economy.

As Bryne Hobart says in V-Shaped Recovery for Me, L-Shaped Recovery for Thee:

Large companies are unusually well-equipped to survive, and they’re better able to benefit from monetary interventions—which have been far faster and more effective than fiscal ones.

Meanwhile, small companies, individuals, and municipalities just don’t have the cash reserves or flexibility to react.

There are two ways in which this could play out:

The pessimistic scenario is front-end corporatization: small businesses just evaporate, their real estate is taken over by big companies.

Amazon and Walmart (and Jiomart in India) are the villains of this story.

The optimistic view is back-end corporatization: that software companies and lenders launch an all-out sprint to modernize and recapitalize small businesses, applying the scale advantage of big companies to solving the problems of local ones.

In that happier outcome, small companies hang on for dear life, and come back leaner and ready to fight. Unlike big companies, they won’t necessarily respond to efficiency growth with layoffs.

Shopify, Square, and fintech lenders are the heroes of this story. As are the SMEs that struggle through and survive.

Hypothesis #2: Everyone’s buying ETFs.

The Relentless Bid, Explained posits a different model. It’s an article from 2014, but it’s rung true throughout the crazy bull run of the last decade.

The [stock market] dips have become shallower and the buyers have rushed in more quickly each time. Sell-offs took months to play out during 2011 – think of the April-October peak-to-trough 21% decline for the S&P. In 2012, these bouts of selling ran their course in just a few weeks, in 2013 a few days and, thus far in 2014, just a few hours.

Why is that?

75% of the wealth business in this country [US] is largely driven toward fee-based strategies and accounts.

The vast majority of this snowballing asset base being reported by both wirehouse firms and RIAs is being put to work in a calm and methodical fashion: long-term mutual funds, tax-sensitive separately managed accounts (SMAs) and, of course, index ETFs.

What does this mean for the character of the stock market?

It means that, almost no matter what happens, each week advisors of every stripe have money to put to work and they’re increasingly agnostic about the news of the day. They’re well aware that their clients are living longer than ever – hence, a gently increased proportion of their managed accounts are being allocated toward equities. And so they invariably buy and then buy more.

In short, it means a relentless bid as the torrent of assets comes flowing in every day, week and month of the year.

The lighter volume on the NYSE in recent years also suggests this. Trades are only taking place at the margin and about half of it is ETF creation-redemption related.

Hypothesis #3: Zero Interest Rate Policy (ZIRP for the hip crowd).

Money is always swimming towards yield. All of Capitalism rests on this constant flow – of investments in search of return.

As Ranjan Roy says in ZIRP explains the world, strange things happen when the risk-free rate nears zero.

At an individual level, most of us have become accustomed to bank savings accounts effectively returning zero. That wasn’t enough for us though. Our money felt antsy, so it found index funds and other passive funds, to once again, find a bit of yield.

That same, tiny behavioral shift takes place at every level of the risk curve, from your savings account to the trillions of dollars managed by large pension funds.

So all these dollar-organisms all start swimming towards riskier waters. Treasury investors shift to corporate debt. Public equity hedge funds shift to late-stage private equity. Late-stage private equity shifts to mid-stage, mid-stage to early stage. Seed rounds become bigger. Angel investors become a thing. Unicorns, unicorns, and more unicorns. Ashton Kutcher.

Blackrock gets jealous of KKR who gets jealous of a16z who gets jealous of YC. There is just so much money looking to do so many new, riskier things.

Where does this take us? It takes us to bike graveyards.

[Source: All Tech Asia]

As Howard Marks explains in The Most Important Thing: when people are less risk averse, risk premiums reduce.

When interest rates are near zero, even a slight increase in yield feels like an immense reward for taking risk.

And that’s what has been happening since 2009, as KKR says in their 2018 paper, Rethinking Asset Allocation:

This is the historical risk-reward ratio.

And this is what is happening now.

Investors are ready to take on higher amounts of risk (x-axis), for much lower return (y-axis).

The yield curve is flattening. (Unfortunately, it’s not the curve we’re trying to flatten).

This is why, even now while the economy seems headed for a never-before seen recession, investors are piling into the stock market. Desperate for a little yield. Hungry for a little return.


Which of these hypotheses ring true to you? I’m leaning towards Hypothesis #3: ZIRP.

The Fat Startup Experiment

Is the Lean Startup dead?

If there’s any ideology that’s gone from radical idea to article of faith in less than a decade, it’s “Lean Startup”.

When the idea was first proffered by Steve Blank in the early 2000s, it took the world by storm. A simple idea. That generated so much momentum for a startup.

And by 2007, everyone could point to Facebook – or even better, Twitter – as proof that Lean Startup works. Facebook at least scaled with a consistent vision. Harvard → other universities → the world. Twitter was the original clown car that pivoted its way into a gold mine .

I’m a strong believer in starting up lean.

It’s how, in a past life, I iterated my consumer loyalty app to 200K users… with zero marketing dollars. Or how we launched a new lending segment at Indifi, and made it our top segment in 6 months.

I’ve also written about it scores of times.

So yes, Lean Startup works. And it is an article of faith. The word “lean” is almost redundant today. Of course a Startup has to be Lean. Of course you first build an MVP and test it with customers. Raise serious money only when you find product/market fit.

And then there’s Quibi.

Quibi raised USD 1.75 billion two years before launching its first product.

It then went on to raise another USD 750 million a year after that. Still a relaxed one year before launch.

If you haven’t heard of Quibi, you’d be forgiven to think it was a rocket company to rival SpaceX. But no, Quibi is a mobile content platform (short for Quick Bites), which launched a month ago.

The antithesis of Lean Startup. Will it work? Is this how we’ve got to do it now?

Get Big Fast Baby[1]

The first insight about Lean Startup is that it bootstraps leverage from scratch.

As Ben Horowitz says in The Case for the Fat startup:

There are only two priorities for a start-up: Winning the market and not running out of cash

Every start-up is in a furious race against time. The start-up must find the product-market fit that leads to a great business and substantially take the market before running out of cash.

That’s where leverage helps. Maximize the ROI on the cash, people, or other resources you bring to the business. Win the market as fast as you can, before you run out of cash.

Now, there are many types of leverage a startup can have.

  • Proprietary IP / best-in-class technology
  • Regulatory capture: Microsoft scaled only because of their hilariously one-sided deal with IBM[2].
  • The product itself: Instagram has in-built virality. In its early days, Instagram’s core use-case was to touch up photos, and post them on Facebook or Twitter. So, by simply using the product, you were marketing it to your friends.
  • Network effects: Uber

The revolutionary suggestion of the Lean movement was that the process itself could generate leverage.

Create hypothesis → Test → Observe results → Refine hypothesis → Repeat.

That’s how Lean Startup works. Start with a hypothesis: product X solves problem Y for consumer Z. Test it in the most basic way possible, and iterate and refine.

Such a simple process, but generates such strong momentum.

It’s like you try to lift yourself by your shoelaces. You wouldn’t expect it to work, but magic, it does!

The first insight about #leanstartup is that it bootstraps leverage from scratch. The process itself generates momentum.

Now here’s the thing – the leverage could simply be intuition.

The founder of Quibi is Jeff Katzenberg.

His catalogue of accomplishments is stunning. He oversaw The Lion King (the original), Beauty & the Beast, Aladdin, etc. at Walt Disney Studios. Then he co-founded DreamWorks, and produced Shrek, Kung Fu Panda, Madagascar, and several other massive hits.

Jeff knows more about film-making than 99.9% of the world’s population. Combined.

And the CEO of Quibi is Meg Whitman, ex-CEO of HP and eBay. A tech veteran.

Katzenberg and Whitman have boatloads of intuition, sharpened by decades building large media and tech companies.

Katzenberg recognizes this. In an early interview, back in 2019,

“I said to Meg that, until day one, every decision that we make around content will be driven by instinct,” Katzenberg said. “Minutes after we launch, everything will be driven by data.”

And it’s true. They’ve doubled down, extracting every ounce of instinct from their extensive experience.

Everything was so meticulously planned. From the same interview:

  • A launch date a year in the future (and they were bang on time)
  • Crystal clear financial model for funding content production
  • Firm view on pricing tiers for the app

No testing hypotheses with an MVP. Just dive right in. With a few billion dollars in the bank.

What do you do when there’s no leverage?

The second insight about Lean Startup is that it was an answer to a specific question, posed at a specific point in time.

As Steve Blank says, the idea of the Lean startup was built on top of the rubble of the 2000 Dot-Com crash.

Most entrepreneurs today don’t remember the Dot-Com bubble of 1995 or the Dot-Com crash that followed in 2000. As a reminder, the Dot Com bubble was a five-year period from August 1995 (the Netscape IPO) when there was a massive wave of experiments on the then-new internet, in commerce, entertainment, nascent social media, and search.

After the crash, venture capital was scarce to non-existent. (Most of the funds that started in the late part of the boom would be underwater). Angel investment, which was small to start with, disappeared, and most corporate VCs shut down. VCs were no longer insisting that startups spend faster, and “swing for the fences”. In fact, they were screaming at them to dramatically reduce their burn rates. It was a nuclear winter for startup capital.

The Lean movement started during a nuclear winter for venture capital.

The idea of #leanstartup was built on top of the rubble of the 2000 Dot-Com crash.

When capital is scarce, you have no choice but to go Lean.

When capital is not scarce, it’s worth considering whether other forms of leverage can help you win the market faster.

Well, today, capital is not so scarce. And it’s chasing fewer good deals.

If you can raise the capital, it makes sense to go big, and go fast. Correct mistakes along the way. Figure out product/market fit as you go.

Thin was in, but fat is where it’s at.

Will Lean Startup have a resurgence, post #COVID19?

It’s tempting to say that Lean will have a resurgence post COVID-19, as the world tips into recession.

But many funders have raised large funds at the top of the market. 10 of the largest 15 VC funds ever raised, have been since 2016.

And tech startups will be especially hot, seeing the resilience of tech in this downturn (NASDAQ is inching back towards its peak!).

So capital is available, searching for “fat” tech startups that can absorb a lot of their capital. The number of seed deals will continue to fall, as they have since 2015.

Let’s talk about Quibi.

OK, we’ve discussed why fat startups like Quibi will be the norm going forward. Now it’s time for the punchline.

I don’t think Quibi will work. Despite all the leverage (or “fat”) it has.

It won’t go bust. But the value of the company will trend towards the value of the content it has bought / licensed. The platform itself will have limited value at best.

Let’s get the obvious first-level problems out of the way.

Quibi launched at the wrong time

At one level, this is correct. It’s also a massive understatement. The launch date of April 6, decided a year ago in true fat-startup style, ended up smack in the middle of a worldwide quarantine.

People aren’t traveling to work. They don’t need Quick Bites on their mobiles. Which is a bummer, because Quibi’s first product works only on mobile.

But this is a problem Quibi can surmount. Remember, Katzenberg and Whitman have plenty of capital. They can wait till people start traveling again.

Quibi isn’t as viral as TikTok

You’re right, it’s not. And yes, the product needs to be viral to succeed.

But it will improve. Again, with so much cash in the bank, Quibi can afford to iterate and improve the product.

In fact, Tiktok is not the point.

Quibi isn’t about user-generated content. It’s about quality, Hollywood content.

It’s like Netflix, but for mobile.

The Netflix of mobile is ____

Unfortunately for Quibi, the Netflix of mobile is… Netflix.

Or Disney+ or Amazon Prime or Hulu or HBO Max.

For one, Netflix does have some short-form content that you can watch on mobile.

And if Quibi unearths a crazy-large latent need for “quick bites”, Netflix will copy it. Without mercy. Just like Instagram cloned Snapchat and starved it of oxygen.

Remember the TiVo Problem:

The battle between every startup and incumbent comes down to whether the startup gets distribution before the incumbent gets innovation.

The Netflix of mobile is… Netflix. Or Disney+ or Amazon Prime or Hulu or HBO Max.

But the more important reason Quibi will fail is…

This tweet:

What is Quibi’s Seinfeld?

Content follows a Power Law distribution. You can have a million shows, but viewership will concentrate around a few.

I couldn’t find a chart on TV shows, but here’s a chart about movie viewership from Michael Tauberg.

Movie box office collections follow a power law.

Netflix paid 6 years (!) of its content budget for Seinfeld, because Seinfeld is a “whale”. Like The Office and Friends, this is what people will subscribe to Netflix for. Not for Altered Carbon or Too Hot to Handle or whatever else.

That’s the math of the streaming video industry.

Consumers will pay for 2-3 subscriptions, and you do what you can to be one of them. You need proven hits like Seinfeld, not hit-or-miss new shows.

It’s like the SEO Red Queen Effect – if you aren’t in the first three results on Google, you don’t exist.

Quibi has some solid content. But unless it unearths a Seinfeld or Sopranos or Big Bang Theory in its first try (possible, it’s Katzenberg after all), it’s down for the count.

Well, that’s how the Quibi crumbles.


Related: Teledesic was the original fat startup. Raised two billion dollars for a satellite network… and then didn’t even launch.

As Tren Griffin writes in this short memoir, some investors received many times their original investment. Even though the company never provided service!


[1] Referencing Jeff Bezos, via Eugene Wei

[2] More in The agreement that catapulted Microsoft over IBM

Thanks to Srinivas KC, Jinesh Bagadia, Aditi Gupta, Bharat Ram, and Anupam Agarwal for reading previous drafts of this.

[UPDATE 5/31/2020]: Seems like some of my predictions are starting to come true, ahead of schedule.

How dumb are these Nigerian princes! Or are they?

Have you ever received an email from a Nigerian prince, going somewhat as follows?

Nigerian prince email

I’ve received a bunch of these over the years.

It’s a standard template. Someone in Nigeria or Congo or Dubai, is dying or is dead. They have several million dollars that they want you to help safekeep. They need you to make a small payment first for some ridiculous reason.

Would you fall for an email like this? Of course not. Come on, this is 2020! No one would fall for it.

But “Nigerian prince” email scams still rake in over USD 700,000 a year – and that’s from the US alone.

Well, you say, you didn’t mean no one. Of course there are some clueless people around. And 700K is not an astronomical sum.

In fact, if the scamsters could make their email even a little more plausible (a small business owner in the Mid West instead of a West African prince, for example), more people might fall for it?

And while we’re on the topic – we should also correct the spelling mistakes. Seriously, why do scamsters always make so many spelling mistakes! Even in subject lines!

Yes, these “Nigerian prince” emails could be more polished and plausible. But making them less plausible is precisely the point.

Hold that thought.

Yes, these “Nigerian prince” emails could be more polished and plausible. But making them less plausible is precisely the point.

Marketers & Hungry Crowds

The #1 principle of Direct Marketing is – Qualify the funnel.

As Gary Halbert (“history’s greatest copywriter”) says in The Boron Letters:

One of the questions I like to ask my students is, “If you and I both owned a hamburger stand and we were in a contest to see who would sell the most hamburgers, what advantages would you most like to have on your side?”

Some people say they would like to have the advantage of having superior meat from which to make their hamburgers. Others say they want sesame seed buns. Others mention location. Someone usually wants to be able to offer the lowest prices. And so on.

After my students are finished telling what advantages they would most like to have, I say to them: “OK, I’ll give you every single advantage you asked for. I, myself, only want one advantage and, if you will give it to me, I will whip the pants off of all of you when it comes to selling burgers!”

“What advantage do you want?”, they ask.

“The only advantage I want, ” I reply, “is a STARVING CROWD!”

If you’ve found a starving customer, you don’t need much else to close the sale.

Find rotten eggs early

One of the key lessons from High Output Management is this:

Material becomes more valuable as it moves through the production process. So, fix any problems at the lowest value stage.

To quote from the book:

All production flows have a basic characteristic: material becomes more valuable as it moves through the process. A boiled egg is more valuable than a raw one… A college graduate to whom we are ready to extend an employment offer is more valuable to us than the college student we meet on campus for the first time.

A common rule we should always try to heed is to detect and fix any problem in a production process at the lowest value stage possible.

Thus, we should find and reject the rotten egg as it’s being delivered from our supplier, rather than permitting the customer to find it. Likewise, if we can decide that we don’t want a college candidate at the time of the campus interview rather than during a plant visit, we save the cost of the trip and the time of both the candidate and the interviewers.

Let’s say you run an apparel factory. If the input cloth you received has quality defects, when would you rather find out? When the shirt is ready, or before the shirt goes into production?

Or you run a SaaS business. If your prospect is going to drop off the funnel next week, wouldn’t you rather find out today? Instead of after inviting them to an online workshop, doing a 1-1 free consulting call, and mailing them three times?

Or let’s say you have your team working on a complicated analysis. If they are making a basic assumption that’s wrong, would you like to find out once the analysis is complete? Or would you rather align at the start, and save a lot of time?

Catch errors early. If an egg is rotten, find out before you scramble it.

That’s why it’s always a Nigerian prince.

It’s easy to send that first email to thousands of people.

The next steps are more labor-intensive. A person has to talk to the target, persuade them to wire money, and cajole them to jump through other assorted hoops.

Labor = costly.

Wouldn’t it be nice if there was a way to spend time only on the most qualified customers (i.e., the most gullible targets)?

In fact, as the original research paper from Microsoft says:

Far-fetched tales of West African riches strike most as comical. Our analysis suggests that is an advantage to the attacker, not a disadvantage. Since his attack has a low density of victims the Nigerian scammer has an over-riding need to reduce false positives. By sending an email that repels all but the most gullible the scammer gets the most promising marks to self-select, and tilts the true to false positive ratio in his favor.

That’s why phishing emails have spelling mistakes.

To self-qualify the funnel.

It’s not that the phishers struggle with English. That would be funny if it were true. Masterful confidence tricksters, but struggle to put together rudimentary sentences.

No, they speak English fine. It’s just that they don’t want people with high attention to detail to click on the link. If you notice such minutiae as spelling errors, then you’d notice other more suspicious details later and stop responding anyway.

They want only gullible prospects, with the least attention to detail.

They want to have a high percentage of such people pass through the next steps of the funnel. Share their passwords in a mindless fog. Click on executable links as an afterthought. Download Trojans in utter oblivion.

Phishing emails deliberately have spelling mistakes. So that only less-attentive people click through.

Fascinating. So is there a lesson in all this?

Yes, three in fact.

Lesson 1 – qualify your funnel as early as you can. And if possible, create a way for your audience to self-qualify. Don’t do sales calls with every visitor who stumbles across your website and shares their email. Instead, make them do the work of qualifying themselves. Have them join a webinar or download two white papers (both have zero marginal cost to you), before you do the hard pitch.

Lesson 2: Catch errors early. If your team is working on a complex analysis, first agree on the basic assumptions and logical flow. If it’s an investor presentation for next week, agree on the key messages and storyline today.

Lesson 3: Don’t click on emails from Nigerian princes.

Yes, COVID-19 is a Black Swan. But not for the reason you think.

COVID-19 is a Black Swan.

Many pundits call COVID-19 a Black Swan event. And I have done the same. In countless conversations with clients, suppliers and others, ruminating wisely, “yes, it’s a black swan event. All bets are off.”

But is it really?

Black swans are supposed to take us by complete and utter surprise. Unpredictable before the fact, inevitable after.

But, a lot of people have predicted this pandemic. Not only predicted it, but also highlighted how unprepared we are. Among the famous examples, here’s Bill Gates, warning of a pandemic that could kill 33 million. And the resemblance of the current situation to the 2011 movie Contagion is eerie.

So, no – the epidemic was foretold.

Black swans are supposed to take us by complete and utter surprise. Unpredictable before the fact, inevitable after. But this pandemic was predicted.

What wasn’t foretold though, was the immediate and devastating economic armageddon. That was the Black Swan.

image

That spike at the far right of the graph – from 300K jobless claims to 3.3 millionThat was the Black Swan.

The video in the tweet below illustrates this vividly.

COVID-19 was foretold. But the economic impact wasn’t.

Experts (and movies) predicted, sometimes in frightening detail, how medical capacity would run out. How we’d wait, interminably, for a vaccine.

But they didn’t think – what will happen if everyone has to sit at home for two months? What will happen if every business closes, all at once?

Like all Black Swans, seems blindingly obvious after the fact. Hindsight, especially in the case of Black Swans, is 20:20.

Alex Danco talks about this in his article on Black Swan Events.

Why did our governments react so slowly to the pandemic?

Separately, the chart above also explains why so many democratic heads of state reacted slowly at the start.

In The Dictator’s Handbook (an excellent book on Politics and Game Theory), Bruce Bueno de Mesquita says,

Politics is about getting and keeping political power, not about general welfare. Leaders do what they can do to come to power, and stay in power.

That’s what happened.

In the early days of the pandemic, COVID-19 was an unknown quantity. Many hoped prayed fervently that it was “just another flu”. On the other hand, every head of state knew what a lockdown meant. Unemployment.

And in any democracy, unemployment means one thing for sure – the leader in power crashes out in the next election.

Like Upton Sinclair said:

“It is difficult to get a man to understand something when his salary depends upon him not understanding it.”

Amen.

[Note: This article appeared in my newsletter Sunday Reads #85: Black Swans, Honesty, and Dishonest Statistics]

Digital advertising doesn’t work. And marketers don’t care.

Digital advertising doesn't work

Digital advertising is one of the biggest new industries of the Internet era. In 2018, USD 273Bn was spent on digital ads globally.

What if I told you it was all fake?

Or more accurately, what if this article told you it was all fake?

The article has several examples of real-life experiments – e.g., when eBay stopped all its ads on Google for 3 months, the drop in online sales was 0%. Yes, you read that right – zero, nada.

But how? Aren’t people clicking on those ads? How can the impact of removal be zero?

Two factors drive this surprising outcome.

  1. Do you remember the last time you searched for an app on Google? For example, I searched for “Spotify” last week. The first link was an ad, for Spotify.com. So I just clicked on that. And an ad-click counter somewhere in Google went up. This is called Selection Bias. Of the people who clicked on any given ad, you don’t know how many were looking for that item anyway.
  2. Search engine algorithms are trained to show a given ad to people who are most likely to click on it. This Algorithmic Bias magnifies the selection bias. As the algorithm gets better (and Google’s algorithm is pretty mature now), increasingly, the people who are shown an Amazon ad are the ones who were planning to go to Amazon anyway.

In fact, some skeptics believe that ads don’t work at all. That no one is convinced to change behavior based on ads. Could it be true?

Well, do you remember the last time you clicked on an ad while not looking for that exact item? Me neither. (except for that banner selling N95 masks last week, but it was sadly out of stock).

But surely, you say – if digital ads didn’t work, why are marketers still spending hundreds of millions on them?

As the article says, quoting Steve Tadelis, “marketers actually believe that their marketing works, even if it doesn’t.” Good old Cognitive dissonance.

Yes, cognitive dissonance does play a role. But I think there’s a second element too. You know the saying, “what gets measured gets managed“. Google and Facebook report clicks and impressions, not actual incremental purchases. That’s what is available, so that’s what marketers track and optimize. They don’t track incremental purchases, because there’s no way to monitor that.

Digital advertising doesn’t work. And marketers don’t care.

In other words, what doesn’t get measured, doesn’t exist.

Related articles:

[Note: this article appeared in my newsletter, Sunday Reads #84. Read the rest of the newsletter here]

What doesn’t get measured… doesn’t exist?

Many of us have heard the saying “What gets measured, gets managed”. A simple, yet powerful thought. With a simple corollary – what doesn’t get measured, doesn’t get managed.

But last week I heard an interesting anecdote that drove home the power of measurement. In reality, the corollary is far more extreme. In the eyes of the person responsible, what doesn’t get measured… doesn’t really exist.

But before we get into that, let’s take a second look at Peter Drucker’s statement.

What gets measured gets managed - Peter Drucker

In just five words, he captures an overwhelming amount of insight. On human behavior, cognitive biases, the power of incentives, and the strength of a goal-driven approach.

If you want something done, measure it. If there’s an objective number representing the outcome of your actions, you (or your team or partner) will automatically work towards improving it. If you’re assessing something subjectively (or not at all), then progress will never happen.

We see scores of examples of this, across our personal and professional lives:

A. Choose an objective metric

  1. If you need to exercise more, then don’t just tell yourself to do that every day. Track your activities. A guy I know walks up and down his office building five times every day, just so he can hit the step goal on his Fitbit.
  2. If you want to increase user engagement on your app, look at your Daily Active Users or Monthly Active Users metrics. Run experiments to increase them. Don’t directly start integrating fun-and-games mechanics without an objective goal.

B. Choose the right metric

  1. If you want to lose weight, don’t just count calories. Instead, count the amount of simple carbs you’re eating. [Aside: this is an excellent layman’s book on the subject].
  2. If you want your salespeople to increase sales, don’t measure the daily hours they clock. Count the amount of sales they make. Else, you’ll have diligent workers… who use Facebook 8 hours a day (9-5, on the clock).

As they say, “track it till you crack it” (it rhymes, so it must be true).

What doesn't get measured

Photo by Russ Hendricks on Flickr (https://bit.ly/32vlBp8)

But I heard a surprising story last week about Indian broadcast media. It underlined the power of the measure, or in business jargon, the Key Performance Indicator (KPI). It showed how, when marketers can’t measure their impact on a market, they all pretend it doesn’t exist. Even though they know full well that the market is substantial.

First, some quick background on the Indian broadcast market.

Traditional audience measurement was flawed

Till recently, TV broadcasters and advertisers in India measured impact of TV programming using an agency called TAM. TAM had special set-top boxes installed in 20K households across India, which tracked data on TV watching habits.

Using this, TAM could tell broadcasters how many people, in which cities, were watching which show. Broadcasters were incentivized to produce content that generated high TAM scores, so they could show this data to advertisers and demand hefty ad rates.

It was a useful KPI, aligning incentives all round. Except for a tiny problem – TAM’s sample was urban-centric. In a market where the rural populace forms a sizeable proportion of TV watchers.

Broadcasters recognized this problem, and fixed it

Since TAM placed disproportionate importance on urban TV viewing, it was clearly unrepresentative. And the powers-that-be knew that. So, in the last two years, the industry created an alternative – the Broadcast Audience Research Council, or BARC.

BARC looks at a much larger sample, including a sizeable rural proportion. The system rolled out just recently, with an objective of giving a clearer picture to broadcasters and advertisers.

Instantly, broadcasters started generating new, “rural-focused”, content

Here’s where it gets interesting.

As BARC launched, more and more shows with supernatural elements started appearing on TV. Even existing shows, whether staid family dramas or comedies, started having occult “tracks”. This is what happened in 2015:

  1. New shows were launched, like Darr Sabko Lagta Hai (“Everyone gets scared”), Naagin (rough translation: “serpent woman”), etc. In fact, Naagin was one of FOUR new shows about serpent-women!
  2. Many ongoing shows started introducing supernatural elements. Not only daily soaps (or saas-bahu shows as we call them in India), but also comedies.

Why did this happen? The reasoning goes – ghosts, serpent-people and others are integral parts of age-old Indian folklore. These beliefs are still a major part of rural lives. Hence we’ve introduced them to create a better connect with the new rural audience.

Disregard the blatant stereotyping for now (we’ll come back to it later). Assuming that this is what the rural audience wants, the logic makes eminent sense.

Except for one trivial detail.

The rural market is not new!

It has existed for some time. The 2011 Census showed that the no. of rural households with TVs were as many as urban. A major satellite TV company, Dish TV, said in 2013 that 50% of its connections were in rural areas.

Why then was television content urban-focused? Why were there no shows targeting the rural populace?

Because rural wasn’t measured.

The only number available was the urban-focused TAM, which was out of step with reality. And producers of TV shows, in full knowledge of this fact, nevertheless used it. There was no other number, so they followed this one. Single-minded and unswerving, like mice following the Pied Piper.

A classic example of availability bias.

[Tweet “If you can’t measure it, it doesn’t exist.”]

What does this mean for us? We need to be more careful than ever in choosing our KPI – the True North that we set our sails by.

If we don’t choose the right metric (or worse, choose the wrong one), the outcomes will be the antithesis of our objectives. No matter how well-meaning we or our colleagues are.

Coming back to our rural stereotype, we’ll find out how true it is in the coming months. If it isn’t, expect the broadcasters to nimbly eliminate the black magic tracks from their shows. It will be managed adeptly. After all, it’s getting measured now.


PS. I couldn’t help but see the link to quantum mechanics. At sub-atomic scales, things don’t exist until they are measured.

PPS. And in this article that first seems to be about politics, Scott Adams explains why this means our reality is probably a computer simulation.

Does a great market really pull product out of a startup?

Large market

Over the last few years, I’ve been quite interested in the startup investing process.

At the trivial level, understanding the investing process could help struggling entrepreneurs (like me) raise funding faster. And, assuming that this investing philosophy does pick winners, this could also teach us what kinds of businesses tend to make it big. And we could then apply those patterns to our own businesses.

Marc Andreessen wrote a landmark article in 2007, on the only thing that matters. If you haven’t read it, go do so now. I’ll wait.


I re-read this article every few months. One line stood out to me the first time itself (and every time since).

A great market pulls product out of a startup.

He channels Andy Rachleff (Co-founder of Benchmark Fund, one of the most successful VCs) in his article, saying:

When a great team meets a lousy market, market wins.
When a lousy team meets a great market, market wins.
When a great team meets a great market, something special happens.

Thus, of the three key dimensions of a startup opportunity – market, product and team – market is far and away the most important aspect.

What’s the takeaway for an entrepreneur? Take aim at a humongous market, and put your head down and execute.

But is that true? Is targeting a large market the only important factor? Are the team, technology, etc. not as important?[1]

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Is large market the most important factor?

It certainly is, according to the conventional wisdom. According to Andy Rachleff, again:

The best investments have high technical risk and low market risk. Market risk causes companies to fail. In other words, you want companies that are highly likely to succeed if they can really deliver what they say they will.

Don’t take market risk – i.e., aim for markets that are already large. Instead, take tech risk – where the product itself is hard to create.

This sounds great, and is a commonly accepted truism. And it also seems to be common sense.

But, again, is it true?

One way to settle this is to look at the performance of venture capital over time. As they say, nothing talks like money. But a quick look at VC returns can be quite sobering.

The Kauffman Foundation reports that VC hasn’t outperformed public markets since the late 1990s. In fact, since 1997, VCs have returned less cash to investors than they invested!

Could it be that this VC approach of taking high tech risk but low market risk isn’t working? 

Tech matters (more)

I’ve just finished reading Crossing the Chasm, Geoffrey Moore’s landmark book. He presents technology adoption as a bell curve, with a few “gaps” between segments.

Chasm Model

It’s easy to get the innovators and the early adopters. They want to be the first to try new technologies, so they’re primed to be convinced. You start hitting the main market only with the next group, the early majority.

Moore’s key insight is that it’s not natural to move from the innovators and the early adopters to the early majority. That’s why there’s such a huge chasm between these segments in the image above. A graveyard of companies that show great early traction, but suddenly hit a wall and collapse into the chasm.

His model suggests two pointers for technology companies:

  1. Building a version of the tech, and serving innovators and early adopters, comes first.
  2. The real challenge is crossing the chasm. You need to find a specific application to solve the early majority’s existing problems. This market isn’t visible or obvious at first – you need to create / discover it.

Thus, tech companies don’t take tech risk. They take market risk. If they find a big market, they succeed big. If they don’t, they fail. 

Don’t take tech risk. Take market risk. If you find a big market, you succeed big. Else, you fail.

Jerry Neumann has written an excellent history of venture capital in the 80s. He makes a few similar observations (I paraphrase):

  • Whenever VC returns peaked, the driver was high market risk. Would there be a big market for computers (60s, Intel)? Would there be a big market for PCs (70s, Apple, Microsoft)? Would biotech become big (Genentech)? Would the Internet reach the masses, or would it remain a plaything of the elite (90s)?
  • These markets may seem inevitable today, but that’s just hindsight bias. Ask Ken Olsen. Or Thomas Watson. Or anyone in this article.
  • In most cases, investors didn’t take tech risk. Often, they found already-working products. Apple’s technology was already working when it raised funding.
  • Whenever VCs tried to reduce market risk to stabilize returns, they failed. For example, in the 80s, they entered more traditional, massive industries like retail. Result: returns were consistent and stable. But bad.

Thus, VCs didn’t often take tech risk. They preferred technologies that were already proven, and showed promise. And whenever they tried to reduce market risk by entering existing large markets, they failed.

At the end, Jerry summarizes:

The only thing VCs can control that will improve their outcomes is having enough guts to bet on markets that don’t yet exist. Everything else is noise.

There is no reason anyone would want a computer in their home – Ken Olsen, Founder, DEC
There is no reason anyone would want a computer in their home – Ken Olsen, Founder, DEC

Peter Thiel’s Founders Fund adds its own voice to the argument. It highlights how, from the 60s to the 90s, VC was a predictor of the future. Today, though,

VC has ceased to be the funder of the future, and instead has become a funder of features, widgets, irrelevances. In large part, it also ceased making money, as the bottom half of venture produced flat to negative return for the past decade.

When you focus on incremental innovation, for a market that’s here and now, returns fall.

And last, Paul Graham makes a similar point, even more indirectly:

When something is described as a toy, that means it has everything an idea needs except being important. It’s cool; users love it; it just doesn’t matter. But if you’re living in the future and you build something cool that users love, it may matter more than outsiders think. Microcomputers seemed like toys when Apple and Microsoft started working on them… The Facebook was just a way for undergrads to stalk one another.

Build a product users love. Even if the market’s small today, it could become massive in the future.

I alluded to a similar point in a previous article, where I said that you must target a deep need for a narrow population, rather than a shallow need for a broad one.

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What about the team, then?

As a VC friend of mine was quick to remind me when we discussed this, the quality of the team is incredibly important!

Large Market or Strong Team

But this quality is not theoretical or bookish. It’s not about which Ivy League school you graduated from. Or even whether you have a string of successes under your belt (at least in consumer).

Instead, it’s about three things:

  1. How driven you are. Will you overturn that 99th stone to find the gold mine? Or will the first 2-3 pivots fatigue you? Your initial ideas for tackling a problem will rarely be right. You’ll need to persist: find a new beachhead, and wade in again.
  2. Are you willing to learn? Again, you won’t be right the first time. They say industry knowledge is a great unfair advantage. True, but it’s also a double-edged sword.
  3. Can you execute?

So what’s the conclusion?

Which of these three is the most important?

The ex-consultant in me would answer, “all three”. And he’d throw in an “it depends” for good measure.

But it appears the conventional VC wisdom, of taking tech risk but not market risk, is wrong. As the Founders’ Fund article above says, the current trend of funding incremental innovations and more efficient solutions for existing markets is what has pushed VC returns downwards.

And what does this mean for entrepreneurs? Instead of trying to build something for large markets that VCs seem to be interested in, “swing for the fences”. But not in the conventional sense of aiming for large markets. Instead, try and piggy back on emerging trends that could become waves.

Sure, you’ll probably strike out. But should the market materialize, you will laugh all the way to buying the bank.


I’d love to hear your opinions. If you’re an entrepreneur or startup investor – what’s your stand on market risk vs. tech risk? Do email me at mail@jitha.me, tweet at @jithamithra, or comment here. I’d love to publish a follow-up sharing your opinions.

Thanks to Aditi Gupta and Abhishek Agarwal for commenting on drafts of this post.

[1] This article is about VC backable startup, and not a small business in general. Many great cashflow businesses (e.g., auto dealerships, general manufacturing) are often not high-growth businesses that can return 20x on invested capital, and are therefore not VC backable. See this article for a great description of such businesses.