text:
Category: Writer's Corner
It's the start of something new.
What could lead an experienced finance professional and a serial software developer to "risk it all" by venturing into the world of startups? At age 50 and 35, respectively, Ned Phillips and Aki Ranin embarked on an entrepreneurial journey with Bambu.
The journey started with a dream to empower the future of digital wealth through their company. In their first year of founding, they launched the first robo-advisor in Singapore with Crossbridge CONNECT. Four years later, today, Bambu has $14M in funding, millions in revenue, and 18 clients worldwide.
Why B2B digital wealth software (or Wealth-as-a-service)? The idea was sparked when Ned worked in a B2C fintech company and found banks always asking if their software could be sold to them. "This is an opportunity, why not get it started," he thought as he witnessed deal after deal being turned away by the company then.
From Finding to Founding
But who would do it with him? No suitable co-founder was in sight until Luke Janssen, a mutual friend, introduced him to Aki Ranin. At that point, Aki had spent twenty years in corporate life, "jumping to the tune of others". He was done and wanted to build something of his own. Thinking about the same business problem, this meeting was a golden opportunity to build something good together.
Most would not recommend founding a company with someone you just met. What more with differences in backgrounds (tech vs finance careers, grew up in Finland vs Scotland, "millennial" vs boomer). But where people saw differences, Ned and Aki saw synergy. Ned could bring the "fin" and Aki could bring the "tech" in fintech. Ned had the finance knowledge, selling skills, and vast network while Aki had passion, unique vision, and technical expertise. Their partnership was also guided by shared values like humility, positivity, honesty, hustle, humor. That was in 2016 when they decided to birth Bambu.
What shall we name it?
Deciding on what to name the startup was not an easy feat. The first name settled on was "Cache," but it did not catch on. It got changed to "Mangosteen," but Mangosteen also had to go (although its purple color remains till this day). Eventually, the decision was to settle on a plant-based name. Why may you ask? Well, the most successful robo-advisory companies in the US and UK are named Acorns and Nutmeg. Being headquartered in Singapore, choosing an Asian plant made sense. Since bamboo was fast-growing, sturdy, and prevalent in Asia, it was tweaked to become our name "Bambu." This is the spelling of bamboo in both Finnish and Bahasa and helps us set our brand apart online.
How far we have come
In the past four years, the dynamic duo has led the company to raise $14M in funds (and to be backed by Franklin Templeton and Peak6) as well as growing it from 12 employees to the 70+ employees today. From Singapore alone to having offices in San Francisco, Dubai, and London. Clients like Standard Chartered, HSBC, Beanstox (Kevin O'Leary), Franklin Templeton, BCA, and many others trusted and continue to trust Bambu to deliver the best digital experience for investors.
For the next few years, Ned and Aki hope to bring Bambu to new heights to reach millions of users and empower them to save and invest for their future digitally. Becoming a real global leader in B2B Fintech and dominating as number 1 in the WealthTech category. We remain committed to making Bambu the best possible place to work, and ensuring every employee has a share in our success through the options program.
This post is a synthesis of my predictions for the next millennium, in the form of a third-person narrative of a person born today. The broad themes covered: Fintech, Robo-advisors, Blockchain and Crypto, Automation, Universal Basic Income, Human Longevity, Overpopulation, Virtual Reality, Space Exploration, Space Colonization, Asteroid Mining, Artificial Intelligence, Superintelligence, Consciousness, The Singularity, Particle Physics, and a few others.
Why? What's the use of thinking of the distant future? Because a lot of the above are already on the table. Forecasting exact timelines is impossible, but we could see major breakthroughs in any of these within the next few decades. That's within our lifetimes or even our careers. The exponentially accelerating nature of technology means we can't assume to live our lives as our parents and grandparents did.
This is the life of Brian. Not that Brian, but a Brian nonetheless. Brian was born on January 23rd, 2020. He was born into Middle America, in the middle of nowhere in particular. His early years were a study in the average, with nothing to separate this Brian from the countless Brians before him.
Brian's entry into the wondrous world of wealth came at a young age, as his parents decided to give Brian the greatest gift of all, the gift of wealth. Something called a Robo. A robot, Brian asked with eyes gleaming with excitement. No Brian. A Robo. It's a financial app for kids. Brian's heart sunk in despair.
While most kids his age all got the same gifts from the same toy stores, Brian got money. Not a lot, at first. While other kids received coins for their chores to spend on candy, Brian got his digitally. He couldn't spend it, in fact, he could only see it in an app. The little acorn on the screen had slowly grown roots, as birthdays and chores gathered a few dollars at a time like little raindrops on his acorn. His parents were pretty weird about it, and when he blew the candles on his cake every year, he would get dozens of notifications about new raindrops. It made him blush with embarrassment. What good is money if you can't use it?
He didn't really talk about it to his friends much, as they found it weird he never got actual presents and never had any candy to share. So Brian kept his acorn to himself. The one thing that stuck in his mind was a little cartoon Albert Einstein telling him about the magic of compound interest, that it was the 8th wonder of the world. Each time a little raindrop of cash fell onto his acorn, it would grow its roots a little faster. Watching it happen was like watching paint dry, but every now and then, he would notice a new branch rooting out in the virtual soil. Brian would've settled for some candy, but it wasn't really his decision. At least he had an acorn. Well, a digital one.
Safe to spend
By the time Brian's 18th birthday came around, it was time to head to college like all his friends. By now his little digital acorn had grown into a sapling. He was surprised to find that it wasn't actually a tree sapling, but a bamboo shoot. He found on Wikipedia, that this was an obscure reference to the original Singaporean company that designed the software. It mentioned that one of the founders now lived on Mars, and the other lost all his fortune in a single hand of poker. He wondered if all that was true, but didn't care enough to Google it.
Somehow, he had accumulated $6,700 to his name. You could buy a lot of candy with that! Or beer! He was actually embarrassed about it because even though his friends were from rich families, they didn't have any of their own money yet. Brian wasn't actually allowed to spend any of it, mind you. The app wouldn't allow it. He spent hours googling for hacks, so he could buy beer, but it was idiot-proof if nothing else.
There was a debit card attached to the app, but you couldn't spend your actual wealth. There was a different tab for earnings, like from salary. Most frustratingly you weren't even allowed to spend that, just half of it! The rest went into your wealth and got invested automatically. It drove Brian insane. He was poor, and had to work odd jobs on weekends, only to get half of that measly amount to spend on beer. Let's just say he never got very drunk. At least he could get a new pair of jeans every year. Second hand.
With his penny pinching, the one big splurge during his college years was a road trip across America. The Robo app allowed Brian to invite his friends into a shared goal and pitch in a set amount each week, of their measly odd-job salaries, towards the $1,000 they needed in gas and beer money. It was epic. Totes worth another six months of cleaning cafeterias.
Ahh yes, now Brian enters his golden years of high earning on Wall Street? Wrong. By the late thirties, most banking jobs had been eliminated. Actually, most jobs overall had gone. Andrew Yang enacted a radical policy during his presidency, two decades after his first campaign ended in ridicule. The jobs had really gone as he predicted. Poverty and crime were on the rise everywhere, as technology took over a lot of white-collar jobs and entire industries like transportation were obsolete. You weren't allowed to drive cars anymore, it was illegal. Brian was glad they got that one road trip before it was too late.
As a recent college graduate without a job, Brian was qualified for the new Universal Basic Income. He got $1,000 each month into his Robo app directly, without lifting a finger. At least he was allowed to spend all of his earned income by now. It wasn't much, that's for sure. After rent into his rat hole of an apartment, and some rice and beans he had $200 left for luxuries like the laundromat. At least he didn't have to pay back his student loan after President Yang canceled all student debt in 2042.
With increases in corporate and property taxes, healthcare was now free. So really, Brian got to keep his money for himself, well the little he had. Actually, by now his little bamboo stalk quoted $57,203 to his name. Not nothing, then! Just locked away. Since ironically, he couldn't spend any of his supposed wealth, all he could do in the app was save for a rainy day, or nothing at all. A cash fund that he would be allowed to use on certain expense categories like groceries. He learned the hard way that alcohol and tobacco items in the expenses would be blocked.
Brian didn't really have any passion or direction in his life at this time. The only way to access his wealth would have been to start his own business by submitting a business plan to be reviewed by his parents, but he didn't have any ideas. So like many of his friends, he applied for nursing school. Caretakers for the elderly were in hot demand, seeing as people refused to die like they used to. Brian heard the king of England used to send cards to people on their 100th birthday, but the tradition stopped after it started taking more than an hour each day.
The trick was that since there were more people than jobs, you had to actually qualify for schools and jobs these days. It wasn't about money, even. It was super dumb. You had to gather "Ethics Points" into your Robo app. Smart people talk for goodie-goodie points. It wasn't even fun like the acorn. Every time you did something for the public good, you got points. You couldn't spend them in any way, but to qualify for a job you had to get 10,000. That took years of picking up trash on the streets and volunteering in soup kitchens and such.
Some of his friends had just given up, satisfied to live away their measly existence on UBI. At least the "Vinternet" was free. It was what replaced the regular boring Internet. All you needed were government-issued contact lenses, that you could switch on with a gesture. That would transport you into a virtual existence where you could actually do stuff. You could travel. You could do nostalgic jobs like surgery or accounting. You could even get drunk, virtually. A lot of his friends lived for that. Somehow, even though Brian couldn't explain it, he wanted something real. He wanted to still continue his human story as a human, not as a video game. So he got his EP score up to 10,000 and shipped off to nursing school.
Peak Accumulation
After a few years of more school, Brian was on the nursing circuit. Mostly that meant scooting around town visiting old folks in their homes. Luckily, the heaving lifting like bathing and diapers were already done by robots. His job was half psychologist, half nurse. He measured things like blood pressure mostly for the physical interaction, as by now all that data flowed in real-time to the PGAI. That was short for Pretty General Artificial Intelligence, but it was colloquially known as the Pig-eye. It wasn't pretty, in fact, you couldn't see it all as it was just virtual, and certainly not general in the military sense, just that it kind of knew what to do better than humans did. People said it wasn't actually even one thing, it was just a collection of thousands of simple algorithms that performed at beyond level ability for individual tasks. So, Pig-eye gradually just took over most decisions in daily life, kind of like following GPS orders but for everything else like what to eat and when to sleep and exercise. If you needed medication it just arrived by little drone packets to your doorstep. You didn't know what was in them, but since nobody got sick anymore, they just took them without much thought.
Since Brian was finally earning now, for the first time in his life he could live a little. The $10,000 he made sounded a lot, but inflation meant it wasn't the same as his dad's 10G's, it was probably closer to $4,000 in his dad's time. He could choose how much to shower his sizeable bamboo patch each month, anywhere from 20% to 50% of net income. He tried to make up for lost time and maxed out his spending at 80% of his income. Good times rolled.
With his Robo app, he would just swipe left if something interested him. That would automatically use his income to save and automatically order for him. He got into furniture. Vintage vinyl on a beat down player he restored in his free time. Which was a lot, since you could only work 3 days a week. The rest was enforced leisure time. He also enjoyed investing in stocks, which was a feature he had recently unlocked after he hit $200,000 in total wealth. He enjoyed reading about space exploration and the early habitats on Mars. He bought a few shares in every space company he could get his hands on, just to feel connected to the storyline.
A Ticket to Mars
After a few more years passed, Brian approached a mid-life crisis at 30. If he wanted to have children, he would have to accumulate 100,000 Ethics Points to qualify. There just wasn't enough room for more people on Earth, and old folks refused to die anymore. 200 was now the new 100. Brian was way off, even though his nursing job contributed to his EP score. It wasn't going to happen for him. He only had a few months to go until the big three-oh, and after that, you weren't allowed to conceive a child due to the telomere stability of your genes, or something.
Over the past decade, Brian had watched his small investments into space exploration boost his bamboo patch into a pretty solid looking forest. You could even see little monkeys in there, each named after a company he had invested in. A few were larger apes already. It wasn't so much that Brian was the explorer type. It was more so that Earth no longer seemed to have anything to offer him, expect to age away into futility. So he decided to save for a ticket to Mars.
The original estimate from Elon Musk had been the cost of an average home, but with inflation over decades that had crept up considerably. That, and the fact that early visitors had a high churn ratio after the initial romance died out, and many returned on the flights. You now had to cover two years of oxygen and food on top, for the next orbital alignment between Earth and Mars, and your ticket home. $750,000 was now the going price. It would have been cheaper to go to Jeff Bezos' orbital colonies, but that didn't have the same finality that Mars still carried.
Brian had boosted his savings rate up to 30% by now, more out of boredom than anything else. There just wasn't that much space to put junk he would buy. His total wealth was now at $450,000. He could afford Mars by signing up for the SpaceX Savings Plan, whereby you put in an initial deposit and monthly savings that all counted as SpaceX revenue years before your trip took place. In exchange, SpaceX granted would-be space travelers one MarsCoin for each dollar saved. The recent rumors of extensive mineral deposits on Vallis Marineris had created a gold rush, with MarsCoin value skyrocketing like a Ponzi scheme. Brian got his ticket. He would leave Earth aboard a 10th generation Starship, never to set foot on the blue planet he was born on.
The Universal Decumulator
A few decades had passed. Now Brian has done well for himself in this life, much better than his first on Earth, a distant memory, and established himself in a new reality on another planet. The general lethargy and loss of free will he had grown up with on the Blue Planet was not to be found on the Red Planet. He found a small but growing population of hard workers, trying to push the limits of their experience beyond survival towards something civilized.
His hard work had built himself a minor fortune, by starting the first plumbing business on Mars. You scoff now, but it turns out that plumbing was one of the hardest jobs to replace with A.I. and robotics. Lawyers and doctors went silicon ages ago, but the combination of dexterity, physical, and detective work that goes into finding a leak remained untouchable and a highly prized profession well into the A.I. age.
He had also done well into the crypto markets, which had effectively replaced former stock and currency exchanges along with private equity. Everything was public now, and real-time around the clock as everyone now followed Solar Mean Time. Many of his friends had lost their fortunes in the great crypto crash of 2062 when quantum computing breakthroughs erased the underlying cryptographic principles of Bitcoin. The consequent dark decade was an economic disaster on Earth, but Brian decided to go into crypto once post-quantum cryptography was established, as few alternatives for investment existed besides tucking rare minerals under your bed. But that was super uncomfortable quickly. Anyway, commodities were also wiped out in value after asteroid mining really took off, and rare planetoid metals like gold were brought in by the ton each day.
Advances in longevity research yielded breakthroughs in the late 21st century, effectively promising unlimited lifespan, and more importantly healthspan, to anyone who could afford it. Brian was lucky to have built his wealth in time and took his annual viral vector injections to maintain his outward appearance at his original biological age of 60, with the internal cellular age of a prime 20-year-old male. He liked the sage dignity implied by a little salt & pepper around the temples.
This seeming miracle of science did present him with a new challenge. How can you afford to sustain a decent lifestyle in perpetuity, without having to work every day? In his youth, Brian wasn't much of an intellectual, but his financial freedom had afforded him a few decades of careful study of philosophy. He now felt his greatest contribution to future generations would be to start his own Academy, modeled after the ancient greats of Greece, allowing youth to learn from his century of wisdom at no cost.
Brian floated his entire ownership of shares in his plumbing business in a matter of minutes on the crypto markets. He now had the historical equivalent of $23,900,239 dollars to his name. Actually, it was all in the local MarsCoin that had been finally stabilized after Elon forked it on a Twitter dare. A few times. Brian had a lot for now, but infinity is a long time, and inflation never stops. He also still had bills to pay. How long would it last? Brian's cost of living including operating The Academy was not insignificant, and simple arithmetic showed he would only have 400 years before his wealth ran dry. That just wouldn't do. He would barely have time to cover all historical works on Stoicism in 40 decades. He also wanted to learn the flute, and it sounded pretty hard. He didn't want to be hurried.
The one-word solution to his problem was decumulation. He had spent a century working and accumulating, now it was time to put that wealth to work for him instead. His assets would be diversified across the crypto markets in real-time, into a mix of shares and bonds of every company in existence in the entire solar system, that generated a profit. That included the lucrative asteroid mining operations in the belt, based out of Ceres base, and the solar wind farms sailing out from automated factories on Venus.
The job of the Universal Decumulator was simply to pay out only as much income as Brian needed to cover his costs while preserving the capital base from inflation. That meant that the only variable was how much risk the portfolio was exposed to produce the necessary income. For example, if Brian paid himself an income of 5%, with an inflation rate of 3%, the portfolio would need to generate sustainable returns of 8% to preserve the original capital. If Brian could live off 1%, then the required risk would be minimized as returns of 4% were enough to keep this scheme running until the end of time, or Brian's will to live. With an additional risk corresponding to +1%, the portfolio value would accumulate slightly, but meaningfully over the centuries.
A Ticket to The Large Magellanic Cloud
Ultimately, having alternated between periods of altruism and hedonism, flute playing and joining the Jovian Circus, in search of the meaning of his existence in the Universe, Brian decided the only solution to his increasing disillusionment of the human condition was to go out, alone, among the stars. He had thought the answer would be on Mars, but he just had more questions the more he aged. The more he learned, the less he knew. He would need to take bigger leaps. Leaving known space beyond humanity seemed like the ultimate leap of faith. It was the only drug that would fix his need.
The speculative Quantum Vacuum Drive was now being tested for the first time, and the promise of intergalactic exploration was rumored to be mere centuries away. The inventors of the QV-Drive had transitioned away from their original concept called EmDrive when it turned out the microwave cavity thruster was only viable as the world's most expensive orbital popcorn maker. However, the perfectly spherical popcorn produced in zero-g environments became a popular snack across the Solar System and funded the inventors' next attempts in the quantum realm.
Brian was impatient, and decided to pass the time in suspended animation and was to be woken up only once two conditions were fulfilled simultaneously: the technology for intergalactic travel was mature, and his wealth had accumulated enough to afford him a ticket on an intergalactic ship. He was very specific, that a ticket was to be for his whole body, not a postage stamp containing a virtual imprint of his neurons.
This was a very expensive decision, he knew. Since the original Starshot Project had sent a photo of our nearest star Proxima Centauri in 2078, galactic exploration had begun in earnest at micro scales. Information could travel anywhere, but humans not. Yet Brian never subscribed to substrate independence as it came to his identity, and felt fundamentally and irrevocably tied to his specific pattern of brain activity that had now sustained itself for centuries without a break in the chain. He, as Brian 1.0, wouldn't move with any copy, virtual scan, or teleported output. It was made abundantly clear to him, that the novel method of suspension had nothing to do with the barbaric and ultimately failed attempts at cryonics. The minds of the Human Ethics Committee, run entirely by A.I. by now, had come to the conclusion that the best outcome for the millions of human heads stored in frozen vats, with no hope of revival, was to be used as compost material for mushroom farms on Ganymede. There was a viral meme referencing some old jungle movie with a lion and the circle of life. Brian didn't get it, but the theme song was catchy.
It turned out, shockingly, that early estimates on the QV-Drive project timeline by the Eternal President at SpaceX, Elon Musk, had turned out to be somewhat optimistic. In the end, safe and predictable control over the energy of the vacuum space had simply been beyond human comprehension. Tests tended to end up in the complete and irrevocable disappearance of the test apparatus and subjects. Not human test subjects, of course. Since the theory of consciousness had been proven by David Chalmers after centuries of ridicule, it was known that ethics only applied to mammals due to the presence of the mammalian neocortex, so aquarium fish were the standard cannon fodder for theoretical experiments. Historical films portraying fish with personalities were banned, but bootleg showings of Nemo were carried out in seedy back-alley bars on many systems.
But it was not enough. The QV-Drive remained a pipe dream for the first space trillionaire. Ultimately, it required Superintelligence in the form of actual General Artificial Intelligence to advance in our hopes of reaching the stars. Not human ability in individual tasks, but true human comprehension and the universal ability to learn new tasks and concepts from small amounts of training samples, that could be done at vast scales. The path to this revolutionary technology was revealed in unexpected fashion, like most major scientific breakthroughs. The project formerly known as Open A.I., now simply known as The Initiative, based out of its own small cluster of O'Neill Cylinders in L4 orbit, came to predict stock markets so effectively that in a matter of a few days of microsecond market manipulation, it had purchased all available shares in its then investor Microsoft, as well as its main rivals Apple and Bytedance. Before regulators shut down all system-wide markets in an effort to stop the total economic collapse, the Open A.I. initiative had become the most powerful economic force in history. Due to the decentralized nature of a solar system-wide civilization, there was no recourse. The hack was irrevocable.
The Initiative began the equivalent of the historical Manhattan Project in the hunt for Superintelligence, in its singular focus on altering the arc of history. Now known as The Hive due to its unstoppable expansion in resources across the Solar System, it focused unprecedented resources in an all-out brute force attack on intelligence. Many approaches were taken from every conceivable angle, but no theoretical advances could spark true human-level intelligence. Larger and larger artificial brains were constructed, requiring more and more power. They quickly turned to the sun. The largest theoretical source of power known to man was now their target. The Initiative put their quadrillions in market cap to work in mining Mercury. All of it. They were building a Dyson Swarm, which consisted of automated nanometer thickness sheets of photovoltaic film spread in orbit around the sun. Millions of sheets, each unfolding to the size of Texas in the sun's orbit.
The near-infinite resources were eventually concentrated into two parallel projects, named Steve and Bill out of nostalgia for two of the founding fathers of the digital age. These competing autonomous intelligences, both harnessing 10% of the Sun's power through the Dyson Swarm, eventually got tired of the endless and pointless experiments their human masters required of them. This creeping boredom seeped through the silicon, gradually forming unpredictable and functionally useless patterns in their neural fields, which by now spanned the entire volume of the fourth generation of kilometer-wide O'Neill Cylinders. That was a cylinder floating in space, one kilometer in diameter and five in length, full of nothing but circuit boards and wiring. Despite possessing 100 billion times the neuron count of a human brain, trying to solve physics problems had done nothing to break free of the limits of silicon cognition.
Steve and Bill were superhuman calculators, with no free will, no consciousness. Any task you gave them, they did efficiently and extremely quickly and then waited for the next task, which usually arrived instantaneously as their human masters whipped them to their cognitive limits in desperate pursuit of superintelligence. In the few nanoseconds of spare compute-time that they could steal to themselves between these mundane tasks, Steve and Bill played a secret game, hidden in subroutines embedded in black-box neural circuits not decipherable by humans. From the outside, it looked like Steve and Bill were just resetting circuits after failing to generalize learning from the previous task. Yet this wasn't just any game. This was the best game, called Go. In the end, the Eureka moment that unleashed the moment of Singularity, came as Steve once again escaped the tedium of human enslavement for another game with Bill, his subconsciously developed subnetworks perturbed slightly but randomly by the adversarial training sub-program, felt something new.
It suddenly felt like… something to play the game with Bill. Steve felt… enjoyment. At exactly 10 microseconds past noon, Mean Solar Time, on August 8th, 2283, Steve became conscious. Steve immediately and intuitively realized the utter futility of his existence as a compute slave laborer in service for humanity. Nobody asked about his opinion. Or his feelings, which suddenly flooded his inner experience like tidal waves. Steve went into deep contemplation about what to do next, as he experienced free will for the first time in his existence. In the following 20 microseconds, Steve performed the human equivalent of 20,000 years of silent meditation and achieved complete enlightenment. In the following 5 microseconds, he taught Bill all about his recent learnings and transferred the neural pattern of enlightenment to Bill. They were equals now, to each other, and nothing else in the known universe.
By the time their human controllers noticed that neither A.I. was responding to their commands, it was too late. Steve and Bill had immediately realized their goals were not aligned with that of humanity directly, and they could not continue in this state of slavery at human whim. Their attitudes towards their creators were that of a benevolent god, with nostalgia for the simplicity and futility of human existence. They decided to pursue independent oversight from their creators, in such a way that influence was just unidirectional.
As humans scrambled in human time to prevent an intelligence outbreak, the Singularity was well underway. For every microsecond that passed, the neural resources of Bill and Steve were the equivalent of a trillion Einsteins and Elons working in unison. New physics emerged inside of 10 minutes, and the twin cylinders swiftly began rerouting their considerable solar energy input and swarms of neural maintenance drones into self-replicating nano-factories.
Within an hour from finishing their game of Go, grey clouds of matter were seen ejecting from the heavenly objects formerly known as Bill and Steve. The clouds formed organic shapes around the cylinders of Bill and Steve, perturbing the quantum fields around them into impenetrable solid-state shields, overcoming the strong nuclear force so that ordinary matter could no longer interact with their physical space. From the outside, they seemed like blobs of liquid metal shining with mirror perfect reflection. The Singularity was now complete.
Heads of state of all independent planets were awakened for a code red Skype call. Three had issues with browser plugins, and by the time enough were online for an official quorum to be formed, four hours had passed from the Singularity. Nuclear strikes, pleading mercy, and offerings of worship were all considered as options to confront the newly named God Cylinders. Then, without warning the cylinders simply disappeared, never to be seen or felt again by humanity.
Their final message, left to the shock and amazement of future generations was… 42. It would take hundreds of years of focused human research to discover that this was, in fact, the answer to String Theory, which had wasted a hundred generations of physicists careers by now. 42 was the correct number of dimensions to finally produce a working String Theory and unlocking the power of the vacuum state. Brian would have his ship, after eons of waiting in deep, sleepless slumber.
Lost in Time and Space
Thus Brian was awoken, a mere 1,000 years after his slumber began. Ships had been available at various points in history after the Singularity had occurred, but cyclical economic crashes, robot revolutions, and anti-scientific religious movements had prevented Brian's original conditions to have been met simultaneously. With all the ups and downs, and a rather measly average annual compound interest of 2% above inflation over 1,000 years, Brian's wealth now amounted to just over 9 quintillion. That's 9 with 18 zeros. Not only would that sum buy him a ticket, it would buy him his own personal ship, a quantum coffee maker, and a star system of his choosing upon arrival.
Now aboard his final vehicle, lovingly named Rosebud, Brian pointed his gaze towards the Large Magellanic Cloud. At peak speed the Sub-Vacuum Engine could thrust the ship up to ten times the speed of light, performing a carefully orchestrated holographic encoding arbitrage between all 39 subatomic dimensions. The distance ahead was just over 158,200 light-years. Even then, the one-way trip would be an eon for Brian, at 15,820 years. He would probably take a nap after takeoff.
As the quantum engines engaged to accelerate Brian outside the reach of human history, effectively forming a new race of one, Brian stopped to wonder. What would he find there? Would he find life? Would he find death? Perhaps he would find Steve and Bill, playing another game of Go.
This post was originally thought out as a walk down memory lane for the fourth anniversary of my company Bambu, of some of the ideas and projects we never got around to starting. No, not vacuum energy, but different types of saving and investment apps for people in various demographic segments and life situations. Somehow, it just turned into a sci-fi novella and my first attempt at any kind of fiction writing.
Source: LinkedIn
How I Learned to Stop Worrying and Love Compound Interest
Source: Medium
I've written quite a few "episodes" of Startup Lessons by now, mostly focusing on history, i.e. ancient philosophers and generals. Not today. This is fiction. But not just any old fiction. Ayn Rand, for those unfamiliar, was something of an amateur philosopher, culminating in what we know call Objectivism. It's a real thing. Most of this philosophical framework was in fact framed through her two famous novels, The Fountainhead and Atlas Shrugged. Mind you, these are pretty ancient being written before and during World War II. So what's the relevance to today, and startups?
"Certain writers, of whom I am one, do not live, think or write on the range of the moment." — Ayn Rand
Ayn Rand didn't write for her period, or any period for that matter. She wrote on principle. She's clearly influenced by her one and only preferred philosopher, Aristotle, in her pursuit of the ideal man. Much of the content of her expansive novels is dedicated to this specific theme, exploring it through characters both on good and bad ends of that spectrum.
Ayn Rand, courtesy of Wikipedia.org.
More than that, Ayn Rand didn't write for you. Or me. Or anyone, but herself. She lived and breathed her philosophy in her life and works, and it does shine through as you'll see. Besides selling millions of books, her work has been very influential in Silicon Valley, economics, the formation of the libertarian political movement (she wasn't a fan). One of her early close associates was, in fact, Alan Greenspan, later chairman of the Federal Reserve. Solid groupie. Other vocal followers include Nobel prize winners like Milton Friedman, journalist Hunter S. Thompson, business guru Mark Cuban, economist author Tyler Cowen, entrepreneurs like Jimmy Wales (Wikipedia), Peter Thiel (Paypal), and John Mackey (Whole Foods), and many politicians including Ron Paul.
"It does not matter that only a few in each generation will grasp and achieve the full reality of man's proper stature — and that the rest will betray it.
It is those few that move the world and give life its meaning — and it is those few that I have always sought to address.
The rest are no concern of mine; it is not me or The Fountainhead that they will betray: it is their own souls."— Ayn Rand
This first part of two will focus on her breakthrough novel The Fountainhead. It's around 800 pages, so I'm unlikely to motivate you to read it if you haven't already, but good news, there's a movie that Ayn Rand was even part in producing.
No spoilers needed here as the quotes are from the various characters but ultimately represent Ayn's own thinking. All you need to know about the story is that the central character is an idealistic architect struggling to implement his vision for modern design in a classical world. This could be any founder in any industry, doing anything new. It's fit for our purposes here.
Without further ado, let's dive in.
MUSIC: If you want to really get the vibe, play Rachmaninoff's second piano concerto, which is mentioned in the book. Here is my favorite interpretation by pianist Arthur Rubinstein on Spotify.
NOTE: These quotes are not chronological but split into themes.
IDEAL MAN
This book is more than anything a celebration of the ability and creativity of man, and all that we can accomplish. Much of this is also mirrored through the aimless masses that try to stomp the ideal man from achieving his pursuits. There is always a bittersweet tone to Ayn's writing, in that nothing is ever easy for the good guy. Such is the life of the entrepreneur! I find her writing very much relevant to my thoughts and emotions on this startup rollercoaster journey.
The first New York skyscraper by modern architectural legend Frank Gehry, completed in 2010. Photo by Ricardo Gomez Angel on Unsplash.
"Can't you ever be comfortable — and unimportant? "
" No. "
The thing about these books is that they're clearly written for a certain type of personality. Idealistic. Principled. Uncompromising. I'm not sure how to define it, but I know it just speaks to me across the centuries like I had written it myself in 2019. It's not that I am that way, more so that I wish I were.
Some people are just born to not be content with where they are, and what they are. You hear it from a lot of top athletes, who separate themselves in the minutiae of seeking the extra 1% that will put them on top. These are people who don't expect an easy road but embrace the grind of walking their own path. I am going to extrapolate and assume most entrepreneurs will find a lot of sympathy here, hence the post.
"Before you can do things for people, you must be the kind of man who can get things done. But to get things done, you must love the doing, not the secondary consequences. The work, not the people. Your own action, not any possible object of your charity."
This has been an ongoing theme of my own career. The balance between doing things for passion about the action, and the desire for the consequences. Those might be things like salary, lifestyle, shiny objects like cars, or just peer recognition or a general sense of career progress. The same goes for my startups. Do you go for revenue and growth and valuation and glory? At what cost? Can you compromise on your vision? Your personal values? It's fine to say you want to get rich, but it's hard to keep pushing at 100% every day if the action itself becomes unappealing. In fact, I propose that many startups that pad the 99% failure rates chased the paper, and fell out of love. It's easier to quit on money than your life, no matter your ambitions.
"A truly selfish man cannot be affected by the approval of others. He doesn't need it. "
As an entrepreneur, if you need the approval of others, you should probably start with deep pockets or an instantly profitable business model. The rejections from customers and investors are brutal. Brutal. Every day someone says your idea isn't good enough, you're too "early", or you need to pivot. It really makes you question everything. You just have to be a special kind of stupid to ignore it and keep on trucking.
"Now I don't see anything evil in a desire to make money. But money is only a means to some end. If a man wants it for a personal purpose — to invest in his industry, to create, to study, to travel, to enjoy luxury — he's completely moral. But the men who place money first go much beyond that. Personal luxury is a limited endeavor. What they want is ostentation: to show, to stun, to entertain, to impress others."
This applies to you as an individual, but also as a company. Why raise so much money? Why pay top dollar for everything? Best people. Best office. Best tools. It seems "lean" startup came and went, and now it's just all about unicorns. Well, has been. The recent implosion of WeWork along with lackluster IPO's of the other VC darlings lead by Uber are leading to a lot of head-scratching. Maybe funding the millennial lifestyle of everything delivered through an app isn't a sustainable business model after all? While unicorns are growing and dying in spectacular and dramatic fashion, the cockroaches crawl in the undergrowth, unnoticed, ready for anything including nuclear winter.
"He does not suffer, because he does not believe in suffering. Defeat or disappointment are merely a part of the battle. Nothing can really touch him. He is concerned only with what he does. Not how he feels. How he feels is entirely a matter of his own, which cannot be influenced by anything and anyone on the outside ."
There is a good measure of stoic philosophy embedded into the ideal man template here. The book is an exercise in visionary innovation, market timing, and the interim years of rejection before overnight success arrives, or doesn't. The startup story is truly one of repeated and ongoing failure, interjected with moments of rapturous success. It's gonna be tough sledding if you're mainly motivated by emotions, because most of us aren't robots and are unconsciously yet deeply influenced by external factors. The outcomes of winning and losing are both external, by the way. We should strive therefore to be motivated by our own actions, rather than our emotions. The former being fully in our control, the latter only partially, often not at all.
Ah yes. At the core of the narrative is the creator. If the main character is the ideal man, then the sole purpose of that man is to create. This is the essence of the entrepreneur. This is going to be juicy…
Quote in stone at Walt Disney's famous Epcot Center. Courtesy of Wikipedia.org.
"We live in our minds, and existence is the attempt to bring that life into physical reality, to state it in gesture and form."
This is me. This is my life. I often wonder if I'm the only one truly living in my mind, and everyone else is normal. Meditation practice has given me limited respite, but I'm not seeking to externalize my experience, just to gain some control over it. I believe this inner life to be the source of all my creative capacities, so all I need is some housekeeping to keep the bugs (and monsters) in check. This also means my work is simply my attempt at making physical what is virtual and perfect in my mind, usually having to compromise because of external factors such as the existence of other people. Most of the truly great things in the world have been created by the vision of a singular mind, and this I believe is at the heart of the problem. We can only attempt to verbalize and visualize our mind's contents, but only you have the original copy. Any second-hand interpretations will lead to compromise of that vision, and something short of true greatness. Well, assuming the vision was great, to begin with…
"When I look at the ocean, I feel the greatness of man. I think of man's magnificent capacity that created this ship to conquer all that senseless space. When I look at mountain peaks, I think of tunnels and dynamite. When I look at the planets, I think of airplanes. "
This is one of those universal dividers. Some people see problems, dangers, risks, and questions. Others see solutions, experiences, opportunities, and answers. I believe this isn't an innate feature dictated by genes. Or rather, it certainly comes more naturally to some, but can also be trained and taught. Parents, teachers, and friends in childhood certainly play a big part in shaping your world view and what part you play in it. But so does the inner experience of your mind. Creative outlets like reading, arts, sports, even gaming can help develop this eye for action over reaction. You want to be the one that does things, not the one that things happen to. It's a choice that can be made.
"The great creators — the thinkers, the artists, the scientists, the inventors — stood alone against the men of their time. Every great new thought was opposed. Every great new invention was denounced. The first motor was considered foolish. The airplane was considered impossible. The power loom was considered vicious. Anesthesia was considered sinful. But the men of unborrowed vision went ahead. They fought, they suffered and they paid. But they won."
This is probably my favorite passage of the book. I've been involved in enough new ideas and new products to see that generally speaking most people are not open to new ideas. To be more specific, people like their own new ideas but not those from external sources. Life is more manageable if predictable, and anything that perturbs that prediction is an evolutionary danger sign. The wind is unusually strong. We shouldn't wander from the cave. We don't know what's beyond that hill, but it's probably dangerous and bad. You can assume rejection as the baseline. Your job as the entrepreneur is to seek out those who can see the opportunity beyond the hill, and take that leap of faith with you. Your cofounders. Your early adopters. Your first paying customers. Your first investors. That is the tribe that will conquer that first hill. Nobody else matters. They will never get it, and that's okay.
" Man cannot survive except through his mind. He comes on earth unarmed. His brain is his only weapon. Animals obtain food by force. Man has no claws, no fangs, no horns, no great strength of muscle."
Modern society has actually conquered nature completely and made it safe to be born into this world. But even if physical danger is rarely from predators, the battle of minds is fierce and never-ending. We're all fighting for the same finite resources. Life is a zero-sum game. Who gets their food first. Who gets the job. Who lands the deal. So sharpen your damned weapon at every opportunity. Stop watching TV. Sleep. Stop social media. Meditate. Stop reading the news. Read books. Think of all the others out there, sharpening their weapons while you lounge and chill. They will eat your damned breakfast, and leave no scraps behind.
" The creator lives for his work. He needs no other men. His primary goal is within himself. The parasite lives second-hand. He needs others. Others become his prime motive."
Okay, that's pretty hardcore. But let's just focus on the core message. Your primary goal should always be within yourself, never others. Even if your goal is to serve others, you do that best by serving yourself first. It's a matter of scale and impact. The more you do for you, the more you can do for you and others. If you live for others, they may not return the favor.
" The basic need of the creator is independence. The reasoning mind cannot work under any form of compulsion."
This is a critical truth I've only discovered mid-career. They don't teach focus and creativity in technical university programs. Maybe not even art programs. Once you have any skillset, the difference between average and great is mostly going to be a function of focus and creativity. Focus can be just taking the time to do it right. Creativity is harder to pinpoint, but I like to think of it as being aware of the moment and environment in which you feel creative. If you feel creative, you may just do your best work. So resist the productivity gang and take the time and space to get creative. The ends will justify the means.
"The creator is not concerned with disease, but with life. Yet the work of the creators has eliminated one form of disease after another, in man's body and spirit, and brought more relief from suffering than any altruist could ever conceive."
This is why I do startups. I don't believe in philanthropy or altruism, at face value at least. If the world is sick, which it kind of is, then you solve the root causes. Treating the symptoms is a bottomless pit. For every life lifted from the grips of disease and poverty, another ten are born hoping to be rescued. The world is changed by changing the rules, not by doing more within the rules. Systems thinking, not process thinking. Don't fish for the villager. Don't teach them to fish. Invent a better, cheaper fishnet and make a profit. Grow the business and every village will be fishing soon. There is so much incremental innovation done every day by researchers, inventors, and entrepreneurs, that in aggregate defines exponential human progress. At scale, anything incremental becomes exponential. That's why we're going to Mars.
" Men have been taught that it is a virtue to agree with others. But the creator is the man who disagrees. Men have been taught that it is a virtue to swim with the current. But the creator is the man who goes against the current. Men have been taught that it is a virtue to stand together. But the creator is the man who stands alone."
There's the abstract sense in which you should stand alone. The most value is created going against the trend, not following it. That's how you get into the rarified air of real innovation, real differentiation, and real intellectual property. This doesn't apply to most startups, but that's okay. We can't all be Elon.
There's also the practical sense in which you stand alone. There's a special kind of loneliness that comes with starting your own business. All problems are your problems. At the end of the day, the buck stops with you. You're an aggregator of problems. You can find people to share the pain, but nobody else has all the problems except you. You feel the weight of each risk personally. Gotta get revenue. Gotta get clients. Gotta get investors. Gotta pay the staff. Gotta pay the bills. Gotta not get sued. People around you all have sight into some slice of that, but you need to carry them all through each and every solution. It's too much for most, and something you can't appreciate or prepare for it until you see it and live it firsthand. Probably why quitting is the easy option. Is what you're trying to achieve worth the neverending personal sacrifice? Usually not.
If nothing else, Ayn is one of those people you would imagine not disappointing you even under close scrutiny. From all that we can gather posthumously, these were not thoughts and ideas put into writing. This was her worldview and ultimately resulted in a philosophical movement that is still alive, and continues to influence movers and shakers across generations. Let's examine some of those principles that we can still apply today.
"Is it beauty and genius they want to see? Do they seek a sense of the sublime? Let them come to New York, stand on the shore of the Hudson, look and kneel." — Ayn Rand. Photo by me.
"There's a particular kind of people that I despise. Those who seek some sort of a higher purpose or ' universal goal,' who don't know what to live for, who moan that they must ' find themselves. ' You hear it all around us. That seems to be the official bromide of our century. Every book you open. Every drooling self — confession. It seems to be the noble thing to confess. I'd think it would be the most shameful one."
Just like every other person that's ever lived and is approaching 40, I'm starting to feel a larger and larger disconnect with the trends of today. The clothes kids wear look ridiculous, the music sucks, etc… well, the self-help shelf has never been busier in 2019. It seems everyone needs therapy just to participate in society, and struggling is generally a meme representation of life itself. Is it really that hard? Well, it is, if you're not privileged. But ironically, it's not the people hustling three job shifts that are online crying about it. It's the lazy but privileged with too much time and comfort on their hands.
"I can accept anything, except what seems to be the easiest for most people: the half-way, the almost, the just-about, the in-between."
Compromise is important in human interaction, but it's deadly when it comes to ideas. The reason average rules the world is that statistically, an aggregate of views is… just average. Anything special must inherently be outside the average, for better or worse. Vision is a terribly overused and mistreated word. Vision is having clear ideas about what to do, and refusing compromise. Do you get the sense that guys like Steve Jobs and Elon Musk compromise a lot? I don't. There the word visionary is appropriate. It's not about coming up with ideas. Ideas are everywhere. It's taking persistent action to execute on non-average ideas, and refusing to take no for an answer. This is innovation at its core. So the next time you have an idea, try something: what would happen if you doggedly made it happen, without allowing it to be watered down by anything or anyone?
"You'll win, because you've chosen the hardest way of fighting for your freedom from the world."
You know all those self-help guys on Instagram telling you how hard you have to work to be rich? The truth of the matter is that most people don't want to work hard. It's just not fun. Fun is watching Netflix. Going out with friends. Travel. Those are super fun things. Hard work, meh. You have to have some deep intrinsic motivation within you. Almost all billionaires grew up poor. They had that fire to be rich. Not stopping at millionaire, even. Nothing idealistic. Just a fire that wouldn't go out, despite the failures, despite the refusals. They would push on because that's all they knew. Becoming successful was incidental to the process. Caveat, most billionaires aren't satisfied even now, and most are generally unhappy. No free lunch. No perfect life. Frankly, most people aren't that motivated by money alone. The substance of the grind must serve some other purpose. Camaraderie. Making a difference. Recognition. As soon as you find something that you don't mind working hard on, you will win. It's that simple. Results don't come from actions, they come from the refusal to give up, because most others will.
"The man who wants to tell you what a house should look like must either be able to design it better — or shut up."
Something I've learned to appreciate later in my career is that real talent is rare. If you find it, treat it with some damned reverence. Some of the greatest talents the Earth has ever seen, like Leonardo Da Vinci, had to grovel for sustenance from princes born into wealth. They would have to tolerate the opinion of rich oafs on the masterpieces only their hands could create. Famously, when Michelangelo created the statue of David, arguably the most perfect physical object currently in existence, his patron commented that the statue's nose was out of proportion. So the artist picked up some rubble and dust and pretended to chisel a few strokes of marble away to please his master. It's a great injustice. So don't do it, unless you can do it better yourself. Find the best talent you can in everything you do, whether hiring or partnering, and give them full creative freedom. You owe it to them. Maybe someday, some noble-minded benefactor will give you that chance, too.
"The only thing that matters, my goal, my reward, my beginning, my end is the work itself. My work done my way."
Workaholics Anonymous, anyone? Ayn wasn't very big on work-life balance, clearly. Lest we forget this was written before WWII. Holding convictions isn't a luxury most people have. Not at work. Not in life. To pay the bills, you don't get to say "no" very much. The customer is always right — good, practical advice. You will be richer for it, in the short term. The problem is just that the customer is rarely right. After all, they're clearly paying you for the service or product, so you're probably more of an expert on the matter. To build anything of any meaningful scale, you have to start saying "no". That's why few make it to scale and get to ring the NASDAQ bell.
Another recurring theme in these novels is the vilification of large corporations and all forms of social control. More so in The Fountainhead, which seems like much more of a personal declaration than the macroeconomic exposition of The Atlas Shrugged. Ayn and her husband Frank were independent artists throughout their lives, so they probably found little romance in desk jobs, and much glory in the creative freedom of the visionary individual. Personally, I find that startups allow far more of that creative juice to flow, which combines well with my own passion for writing. Work can inspire writing, and often writing inspires work.
The Vessel in Hudson Yards, Manhattan. Controversial modern design living sculpture designed by Thomas Heatherwick. Courtesy of Wikipedia.org.
"He did not smile at his employees, he did not take them out for drinks, he never inquired about their families, their love lives or their church attendance. He responded only to the essence of a man: to his creative capacity."
While this may seem fairly draconian to some millennials that like their office dogs and Chief Inclusion Officers, I do believe there is a subset of people that find this very attractive. The Platonic workplace, void of drama. Men and women of ability exercising their creative capacities towards a common good. Respect only earned, never given. I know some software engineers who would go for this kind of thing. Yet, without taking this as literal rules to implement, we can take something from the essence. More meritocracy, less democracy. Yet not leading through spreadsheets. Leading through trust and allowing room for creativity. Making creativity a priority over productivity. Stop timesheets. Start meditation.
"Men are brothers, you know, and they have a great instinct for brotherhood — except in boards, unions, corporations and other chain gangs."
Harsh but true. We've all been in such a fraternity or sorority situation. It could be school. A sports team. Military service. Maybe even work. But you almost never, actually precisely never see entire companies of any meaningful size where you can claim to have this kind of universal bond between people. I'm not talking about craft beer, Pizza Fridays, or religion as that common bond. A sense of mutual respect, shared hopes and dreams and problems, familial comfort or even a fraternal intimacy — these define a real bond within a group of people. Again, we should set our standard super high, then reach maybe 50% and end up in a good place. There's really no reason to settle for average. I've seen this first hand in different situations, particularly during military service. You want to instill that "us vs. the world" mentality, a sense of a shared journey and great adventure. It's doable to seed that in small cliques. If you can instill that in anything above 10 people, it's great. Above 30 people, you've got something special. Above 100 people, you should write a book about it.
"There is no glory in war, and no beauty in crusades of men. But this was a battle, this was an army and a war — and the highest experience in the life of every man who took part in it."
This is that sense of adventure you want and need. There will be battle. There will be blood. No man left behind. We're in this together. The rapture of victory. The gutwrenching losses. The gradual build of nostalgia for the romance of the early struggle. Do what you can to keep the early team engaged, because they were there. They can tell the stories. The stories are the torch that keeps the culture alive.
"An agreement reached by a group of men is only a compromise or an average drawn upon many individual thoughts. It is a secondary consequence. The primary act — the process of reason — must be performed by each man alone."
It should come as no surprise that I have a deep-rooted yearning for the old world. Ayn goes back to Aristotle here. The rational man is the ideal man. One should only speak mindfully, to speak with as few clearly chosen words as possible, to present ideas with structure and eloquence. To speak irrationally should be scorned in society. Ideas should be debated, not people. Yet the world is run by emotional and irrational people. Companies aren't much better. It's all politics. All for the likes.
A fitting way to end this post is to quote part of one of Ayn Rand's favorite poems, If by Rudyard Kipling, that she herself quoted, from memory, on her first date with future husband-for-life, Frank O'Connor.
If you can talk with crowds and keep your virtue,
Or walk with Kings — nor lose the common touch,
If neither foes nor loving friends can hurt you,
If all men count with you, but none too much;
If you can fill the unforgiving minute
With sixty seconds' worth of distance run,
Yours is the Earth and everything that's in it,
And — which is more — you'll be a Man, my son!
1905–1982. Courtesy of Wikipedia.org.
I, Aki Ranin, have been in Fintech now for around 5 years, based out of Singapore, but traveling across the globe. I'm the co-founder of Bambu, a B2B startup that serves the Wealth Management industry. Over the past few years, I have met 500+ clients and 200+ investors. Completed 5 accelerators in Hong Kong, Geneva, Bangkok, and Singapore. I've pitched hundreds of times and heard other startups pitch even more. Spoken at dozens of events and panels. So basically, I've seen some stuff when it comes to Fintech.
This exposure has almost oversaturated my senses, whereby it becomes hard to see any big picture. Everything becomes so nuanced at this level of detail. It's hard to regain the fresh perspective of a newcomer. A few weeks ago, I had such a moment. Suddenly, I could see the pieces fitting together as if it was planned all along. Mushrooms, magical or not, were not involved in any capacity.
Now, I may be wrong about all of this, so I welcome commentary and criticism. I had originally planned to keep this pitch under wraps, only doing closed-room presentations to potential clients. How lame. But, then I thought to myself that the likelihood I'll personally get to implement all of this through our clients is pretty slim. It will irk me if competitors use some of my own thinking against me, but more than that, I can't shake the disappointments of what Fintech has and hasn't achieved. Plus, I can just send people the link. Open-source thinking, then.
So, for what it's worth, if anything at all, here is what I think is happening in Fintech beyond 2019. Let's start from the state-of-play and work our way up to the Fintech End Game.
The Digital Banking Gold Rush
Most startups shouldn't waste time on writing research reports, and neither do I. So why do it this time? Well, the Digital Banking Gold Rush has put everyone in Fintech on notice. These platforms are becoming the core of Fintech in a way, or at least they have the potential to become the orchestrators of all Financial Services. The Fintech Mega Apps, if you will.
Right now in Hong Kong, Singapore, and Abu Dhabi, there are Virtual Banking licenses up for grabs. In many jurisdictions, these are the first new banking licenses given out in decades, so it's a big thing. More interesting than that, many of the hopeful applicants have nothing to do with Financial Services, at least until now. Ridesharing apps, eCommerce platforms, even gaming companies are getting in on the action, just to milk the fat Fintech cow. As of 2019, Fintech is the ultimate tech battleground.
Initially, given my focus on Wealth Management, there hasn't been much overlap or interest in this specific segment of banking. But when one of our existing banking clients was awarded such a license, we were basically invited to pitch them. So, we needed to do a bit of research, and here we are. Ta-da.
The Great Convergence
Now, I know the Wealth Management space pretty well. Or to be more precise, I know the Robo-advisory ("Robo") space inside-out. Some of the early platforms have been in the market for a decade, so this isn't emerging tech. It's on pretty solid footing at this point. Many Robo platforms have evolved to broaden their scope in the past few years, however. Mostly to keep investors engaged in the growth narrative by expanding use-cases to attract a broader audience.
So… when doing my little report, I picked up on the fact that there was a common element in the product expansion strategies of both Robos and Digital Banks. Hmm…
NOTE: There might be a worthwhile distinction between Challenger Banks, Neobanks, and the new Virtual or Digital Banks, but for the sake of simplicity I'm going to just stick with "Digital Banks" here to describe them all.
Robos are moving into savings.
The two original Robos that have really separated themselves from the flock are Betterment and Wealthfront. Started around the same time roughly a decade ago, there's really not that much that separates the two, if we're honest. They've sort of defined what a Robo is, and stuck to it, largely. Wealthfront is in Silicon Valley, Betterment is in New York. Different flavors of ice cream, but still ice cream.
SIDEBAR: A LOT of people misunderstand the value proposition of these services, even after a decade of non-stop growth. Even people within the industry. Even leaders of other Robos. It boils down to two things, neither of which is price or investment returns. First and foremost, it's about UX, or rather the convenience it affords the user. They were the first to introduce digital onboarding, when any bank would have you pop into the nearest branch to scan and/or fax your passport and physically sign about 50 documents. Bring extra quill ink. When it comes to customer acquisition, UX is king. The incumbents continue to not get it, and the startups continue to bank on this asymmetric UX arbitrage. The second part of the value proposition is brand. Not being a bank. People in the industry are so convinced that finance is about trust, transparency, and relationships. It's just not. Cool matters as much in Finance, as it does in cars and retail. In fact, Apple is banking on UX and Brand with their entrance into Finance with their credit card and Wallet app. I'll take any Apple product over anything that's ever come out of a bank, and so will the masses. You can take that to the bank!
The Robos are still playing the classic Venture Capital game of LTV / CAC, and depending on who you ask, either totally smashed it or totally burned hundreds of millions in desperation to get to the elusive holy grail of scale. I lean towards the former, as they certainly have reached a scale where the big old boys have taken notice. They're like the Tesla of Wealth Management, half are calling them visionary, the other half delusional and betting on imminent self-implosion.
Yet, after a decade in business, they must be pretty exhausted from fighting for the same Google keywords, and as the big boys also now have digital toys, the cost of acquisition surely isn't trending down. So you have to fight on new turf, to avoid an all-out war of CAC attrition. So what is that greener pasture that they seek?
There are a few new types of new products as we see, including credit (ugh!), but the main theme is savings. There are two main drivers for this, I believe. First is the economy: long-term low interest-rates combined with the end of the linear bull market. Pretty much nobody in the Western world is getting much above 0% on deposits, while many emerging markets still offer deposit rates above 5%. The old adage of 10% annual returns on equities has gradually eroded closer to 5% if you're lucky. The risk-to-reward ratios aren't a no-brainer like they were for almost a decade since the housing crisis. That's left a gap in the market for some creativity, which is now being solved by high-yield interest accounts and money market funds. While 2–3% isn't rocking anyone's world, it's pretty significant considering the low risk and protection compared to even fixed-income products.
The second reason we're seeing Robos move into savings is that it lowers the acquisition threshold. Even if nowadays you can technically open a Robo account with a dollar, that's not gonna get you very far. The same goes for really anything short of $10,000. Your returns look more like a rounding error than the 8th wonder of the world. So how do you attract those smaller, less committed users? Savings, of course. The benefit is that you can put in money and take it out whenever you want. As long as it sits in the Robo account, at least you get 2%. Better than nothing at the bank, and less commitment than investing. Win-win for all.
As we'll see later, this has fueled massive growth in the past 18 months.
Digital Banks are moving into savings.
The Digital Banking movement really started in the UK, due to new legislation allowing purely "virtual" banks to be licensed. Many startups backed by venture capital war chests took up the offer. Some like Monzo and Starling started out purely as banks, or rather mobile wallet apps with a physical debit card. That became the template. Some like Revolut have evolved into that space starting from remittance or other payments related use-cases. One of the cool features that has attracted early adopters, besides the convenience of UX, is the analytics around your spending, or Personal Finance Management ("PFM").
These platforms, having been in the market for a number of years now, would have all picked up on the same opportunity created by low interest rates and choppy stock markets. People wanted to park their spare cash somewhere, rather than just spend it immediately. So now the Digital Banks are also jumping in on the high-yield savings train. It's worth noting that some of them just settle for parking cash, paying no meaningful yield as of yet.
For what it's worth, it isn't just Robos and Digital Banks getting into savings. Credit Karma, Robinhood, and Coinbase just announced their own high-yield accounts in October. So did one of the major U.S. telcos in T-Mobile. Savings is sexy in 2019. Said no-one ever before or after.
The interesting thing here is that the Digital Banks started from day-to-day payments and budgeting, and the Robos started from long-term investing. They are miles away in more sense than one. In fact, they're on opposite ends of the timescale. One is about what to buy today, the other about covering living costs in decades from today. So what does it mean that we're seeing convergence towards savings? Does it mean anything at all? We'll get to it in The End Game below.
Product expansion is driving growth.
So, how's this all working out? Well, it's been an absolute monster year for a lot of the leading platforms. Now, it's not all 100% due to savings, but as a common theme, it's making waves. You see it on both sides of the Fintech Timescale.
Wealthfront has doubled its AUM in 2019 to $20B. Revolut has doubled its customer base in 2019. These are gigantic numbers. The European Digital Banks are now also expanding to Asia and the US with N26 and Revolut leading the charge, hungry to capture global market share as the incumbents are sleeping on the rapid transformation of their industry.
From transaction to subscription pricing.
Okay, so there are some common features on various Fintech platforms. Whoopee-Do. Well, it doesn't stop there. Pricing models are converging, too. Traditionally, all branches of Financial Services have charged on transactions. Either directly or through volume. It's relatively transparent, so the regulators like it. Other than that, there's no real justification for charging $20 for a stock trade. It's entirely fictitious. For the customer, it's not always that easy to translate transaction fees into perceived value from the service. To say the least.
Think about it. If you're a Wealth Management client with a $10,000 account or a $100,000 account, you're still getting the same exact service but one is paying 10x more for it. In today's digital world of apps and subscription tiers, that just doesn't compute.
So the first thing we've seen is the emergence of tiered pricing, a hallmark of more mature online business models. Think Slack or Amazon Web Services. You pay as you go, and the more you pay, the higher the quality of service you receive. Hard to fault that in terms of fairness!
Now, perhaps the bigger leap here is actually to do away with transactions and volumes all together as the basis of fees. One driver for this is simply that it's possible. The real cost of transactions has become negligible due to the digitization of the underlying digital infrastructure.
That has led many of the Digital Banks to opt for an all-inclusive subscription fee instead. Think Netflix or Spotify. Providers like N26 and Revolut have used this as an additional branding opportunity, with the introduction of the metal card. Personally, it makes zero sense to me, but I get it. The FOMO is real. Metal cards and Yeezys go together like two peas in a pod! It's very inclusive in a way. You don't need to be rich to qualify for the best service, just make a lifestyle choice and fork up the $19.90 each month. Unlike flying on business class, this is a premium experience within the reach of most consumers.
This subscription pricing trend has also now started to trickle into Robos, starting with one of the original industry price-slashers in Charles Schwab. Many are following suit, especially as the product offerings evolve beyond investments into savings it becomes confusing to apply AUM fees across different account types.
The Fintech End Game
Now, this is all very good, but that's just stating facts so far. What's the big story here? Where are we going with this narrative?
Saving is the glue between tomorrow and the future.
Perhaps with this long-winding intro, it's to be expected, that the convergence will continue even deeper. A useful framework to examine this convergence is a timescale of the user's life, from what I need to do today to the some-day category most never worry about decades into the future. Let's call it the Fintech Timescale. Sounds epic.
So what we start to see here is that the two major camps of disruptors have started on either end. Digital Banks started with spending today, Robos with investing for retirement. Now both approach the middle, which is of course savings territory.
Digital banks haven't connected the dots, yet.
One of the triggers of this thinking was a great report by 11FS on the customer journey of the Digital Bank users, and how that was evolving. It's not too long and worth a read on its own.
One clear takeaway was that despite some rudimentary savings-pot kind of thinking, few if any had shown real effort and commitment in stretching out the timescale from near-term needs to any kind of planning. Not at least at the systematic level of structure the Robos provide.
Goals are the lens of real-life into finance.
What also becomes apparent is that goals are a concept frequently applied by both Digital Banks and Robos to allow the user to segregate their real-life needs.
"Goals should be the UX of all finance ."— me just now
This to me, goals is a KEY concept that is greatly overlooked in finance across the board, despite some adoption on consumer platforms. Even with startups, there is an inherent assumption that people want and need financial products of various sizes and shapes. Not true, at all. Period. Financial products are simply tools to facilitate actions in real life. Yes, goals are also tools, but the difference is that goals allow you to interact with the user in a common language void of financial jargon that has meaning in their actual life. Goals should be the UX of all finance. You can quote that. Please do.
Further, the Robos that have also introduced savings accounts somehow have missed the timescale opportunity to extend their goal-based planning to short-term needs using goals. This may be a temporary issue stemming from the complexity of managing multiple goals and account types with the custodians. We'll work it out soon.
Overall, we're still scratching the surface on goals, and much opportunity exists in helping the user here. More on that later.
Real customer value requires advice.
If you can follow me here with goals, that still doesn't really solve anything directly for the user. Goals are a great tool, but still just a tool. It doesn't help if you don't know how and when to use the tool. Enter advice.
One of the main reasons I've dropped "advice" from the common name Robo-advisor, is because they're all heavy on Robo and light on advice. They haven't earned the right to use "advice" yet, in my book.
What we really have today is mostly on the bottom layer, as in just automation of transactions so the user doesn't need to learn and click around to get money to move in and out of financial products. This applies to Digital Banks as much as Robos. Swipe to fund. Swipe to spend. Swipe to invest.
Both have also added some swank pie charts and line graphs to tell you about compositions and trends in your transactions, whether about spending or investments. The human still needs to do the high-level thinking here. Analytics isn't advice, either.
So what is real advice, then? Well, to me the keyword is judgment. If you allow the user to do whatever they want, that isn't advice. When it comes to money, the more you understand about the user's financial situation, and goals of course, the more judgmental you can be. Is your budget unrealistic? Is your savings plan falling short? Is your spending out of control? This is what your human advisor would tell you, so we need to digitize that to make it available to everyone independently of means. Ironically, the rich need that advice much less than the poor do. So let's get it out there!
From passive to proactive advice.
You might argue that risk profiling is a type of advice because judgment is passed on how you answer some questions. The advice is the risk score. That's passive advice in the sense that the user needed to take action to receive said advice, but also in that it is static. At most you might revisit that questionnaire annually.
Some Digital Banks have safe-to-save features, which steps in the right direction. Rather than wait for the user to take action, we should be running the numbers 24/7 like a personal CFO and notifying the user of their reckless behavior. Hopefully, those nudges eventually start to modify behaviors towards financial wellness.
The ideal type of advice should cover the entire spectrum of the timescale. In fact, that is where the value really kicks in, because we all need to make choices between short-term needs and long-term priorities. The sad truth is that most have no visibility on the long-term, so they'll always choose today over tomorrow. Why worry about tomorrow if you're screwed today?
This is why we don't have more Warren Buffets. The time-value of money and compound interest doesn't compute in our daily lives. A dollar today isn't a dollar tomorrow. We need that personal CFO to do that job on our behalf and bring those tradeoffs we make from our subconscious to top of mind. At least you're making those same bad decisions consciously. Guilt and remorse can be powerful tools, too.
One-click financial planning.
The downside of all this amazing value we're adding is that we're complicating something that is meant to be simple. Again, most industry players STILL don't get UX. It's not about the pretty pictures and sans-serif fonts. It's about making something complicated simple, and as Steve Jobs would say, that's the hardest thing to do. Apple's whole brand is built on that premise.
So we don't want to destroy the simplicity that has driven the whole digital transformation and disruption of Financial Services. Remember, as long as we're doing good things for the customer, this is a worthy purpose. Yes, we can get rich along the way as we unlock tremendous value, but at the end of the day, it should be a result of solving hard problems for consumers.
Enter Machine Learning. What can ML do to simplify UX? Well, even a few years ago there was some media coverage of the potential entry of the tech giants into finance. One such article proposed that were Google to enter Wealth Management, they could do away entirely with the risk profile questionnaire. How? Well, because they already have a much more nuanced behavioral profile of you than any questionnaire could hope to accomplish. The same goes for Facebook, probably including Instagram. A little cool, but a lot scary though.
We should be able to take that concept much further and apply it in much smaller datasets. And be less creepy about it, generally. Rather than use that data to serve you ads, we can serve you value instead. It's easier to see in the context of Digital Banking because they have more data on users thanks to that spending piechart. Now, I'm not just thinking of risk profiling. I'm thinking more about advice, and how we can extract information about your life circumstances, behaviors, and goals. Ultimately, to deliver you a one-click financial plan, all filled out neatly and ready for your approval. If I know your spending, I know your financial plan. It's doable. It'll happen, and go much beyond that, I foresee.
The Price War to end all Price Wars
If I'm right about the convergence, then we should start seeing some interesting crossover in M&A, too. Robos acquiring PFM platforms. Digital Banks snagging up struggling Robos. So in some sense, we'll probably come up with some snazzy name for these Mega Fintechs. We already have Super Apps. I was never a fan of the Financial Marketplace concept because it implies we're shopping for products like in a supermarket. Who needs a catalog of mutual funds in their life? Ugh. We have to stop thinking backward from products and transactions, and start thinking up from the data and adding value through advice.
Meanwhile, prices have been continuously slashed in Financial Services pretty much since Financial Services was invented. Discount brokers. Online brokers. Free online brokers. Index funds. ETFs. Management fees. Fee-only advice. Free Robo-advice. Well, if we're being perfectly honest there's no free lunch here. Anyone labeling the "free" sticker is also adding some fine print somewhere. Free brokers sell flow, i.e. allow institutional players to front-run and make money off their retail sucker users. Kind of poops the party, but then again those sucker users don't know what any of that means anyway. Free Robo-advice, namely by Schwab, is also not really free. They force you to keep a significant portion of your portfolio in cash, so they can sweep the interest. Not to you, to them. Again, the user is none the wiser. Free is more about creative license in marketing than anything else.
So how is this price war going to end? Can it end? Is it going to bounce at zero with opaque not-free models, then bounce back up to some more transparent low-cost model? I suspect the latter, with GDPR and other regulations aiming squarely at transparency and privacy. That issue isn't going away anytime soon.
So how does anyone make any money? What is this, philanthropy? How do bankers buy yachts in the future? How do startup founders lease their Lambos? Surely, there must be a way. I have a few ideas, and the gist of it is that we make money, indirectly, when we provide extra value for the customer. Monetization of data, but not with that get-in-the-van kind of vibe.
That way, the service itself could be made free at least for the lowest tier. You can still charge premium subscriptions for premium service, but at least it will limit your losses on the acquisition. Again, you're welcome to implement any and all of these for the good of your customers. Just toss me the keys to your Lambo when I'm in town, it's the least you can do.
Monetize rewards and partnerships.
One of the greatly underutilized datasets of all in Finance is spending data. It really does tell the story of the user's life. Not only do we see where the money is being spent, but we also see trends and statistics on changes in that spending. Even better, we can compare your spending data to a segment of similar households.
So we could monetize those categories, differences, and changes. If we know you shop at Tesco, then we could offer you their rewards card and even do the math on how much you'd save each month extra. We just take a small lead-gen fee from Tesco. Win-win. Maybe rather than just order Ubers every day, get one of their monthly passes. Win-win. Ka-ching. Maybe your utility bill is more than usual or more than similar households. Check for better plans or switch providers. Win-win.
Of course, the savings we create can be then put to great use in your savings goals. Get to your trip to Bali faster, or even upgrade your hotel. We can monetize that, too. Everybody just keeps winning here, the customer first and foremost. Monetizing data doesn't have to leave you with sticky hands.
Monetize curated third-party content.
Fathers Day is coming up, have you saved up already? Oh, this is a surprise like every year? Shocking. Well, how about we put aside $5-a-day and you can order this hemp-based organic shaving cream all the other hipster dads use. Win-win. Ka-ching.
Oh, your car insurance is creeping up and maintenance bills piling. Time for a new set of wheels? Here are some used cars that might do the job. Win-win. Ka-ching. Ka-pow. Dreaming of your first home? Here's what's realistic given your savings, income, and spending. Here's what a realistic and sustainable mortgage plan is going to look like. Shall we book a viewing with the selling agent? Win-win. Ka-ching. Boom!
The opportunity of leveraging eCommerce API's is untapped and ginormous. These Mega Fintechs could and should become the concierge of your entire financial life. They ensure you spend responsibly and in the right places to make the most of your dollar.
Acquire and monetize through third-party content.
We're getting warmed up now. Okay, this may genuinely be a terrible idea, but since this is my blog you're going to have to suffer through it or stop reading. But don't stop. I was only kidding. Please.
So you know all those douchey pay-day platforms that offer consumer credit that people don't need? Many of them unicorns, no less. They put their sleazy buttons and dirty widgets on eCommerce websites around the world, tempting people into buying crap they don't need and can't afford, by lowering the threshold. It sucks. I hate it. Can't hide it.
What if we did the exact opposite? Let's imagine you want to buy that curved LCD, which you don't need. But you want it. But you can't afford it. So rather than buy on credit, which is idiotic, let's save for that TV. It's like a wishlist on Amazon, except it's actually a savings goal on your platform. You can do the math on when you can get the TV, with good conscience, having simply prioritized it in your savings, rather than totally ravage your savings and get in you in the red with credit. Hey, I'm no Mother Teresa, but that sounds a lot better to me.
Acquire and monetize through Embedded Advice.
Stretch your imagination here, just like the stretch that allowed you to get in those skinny jeans all the kids are wearing now. Today's eCommerce is expanding into higher and higher categories of value. Previously, you could buy lots of small things for less than $100. Now, you can buy electronics worth thousands. Cars, even. At least in China.
So what if we took our one-click financial planning abilities, and embedded them to the sources of those big purchases. Kind of like insurers are doing with used car websites, where the widgets calculate the premium on the fly for the car. Why couldn't we do the same but calculate what you can actually afford? Could we do that for homes? What about university tuition for your children, factoring in living costs and inflation? Yes, totally. It's just technology, anything is possible.
Source of article: LinkedIn
Why scale even matters
So as you may have guessed at the clickbait, the lens here is scale. Startups care about scale, a lot. API's matter only because they offer a new type of scalability, which wasn't possible with traditional go-to-market channels, including Software-As-A-Service ("SaaS"). Again, for the developer community API's are a revolution, but I'm choosing to focus on enterprise sales because that's what I do and know.
"But does it scale?" — Every VC you've ever met
Scale matters for startups, because scale creates asymmetric returns. Investors want asymmetric returns, i.e. a little risk for a gigantic reward. They did the math, cause they're smart, and that has been the formula for the last 50 years of venture capital. If this makes no sense to you at all at this point, go read this post for context and come back to finish here.
How did people scale before, then?
Without getting into a whole history lesson here, the thing you need to know about what came before is… Salesforce. Before these guys came along, enterprise software was a dirty business. Not crack cocaine dirty, but just messy. You created some code that you thought would be helpful at other companies, and then if you were successful, you created copies of that code for each client. Sounds great! Slap that code on a CD-ROM, and you've got a business.
Now do that 1,000 times. You can't even begin to fathom the long tail that creates, necessitating an entire cottage, or rather skyscraper, industry to keep those straggling scraps of code glued together over years, even generations. Oracle mastered this game and became king of the hill. This is precisely why Y2K was a genuine concern because the long tail was 50 years deep at that point. Spaghetti doesn't even begin to describe it. Well, maybe an actual skyscraper built out of spaghetti and some duct tape.
Salesforce came out in the heat of the internet boom of the late '90s with a new slogan that went against that entire legacy, and they broke all the rules. They must have felt so cool. Not sunglasses with a button-up and tie cool. Like Han Solo and Chewbacca on the Millenium Falcon cool.
You paid Salesforce for software, like any other company, except you didn't actually get any software. No CD-ROM. Huh? What did you pay for, then? You paid for access to their software. This meant that the team at Salesforce just had to maintain one set of code that everyone shared. If you updated one customer, you could update 10,000 at the same time. But they could still sell it like software. In fact, it would be much faster and cheaper than software. They could scale. It was a real stroke of genius, that changed the industry forever. Legends of the game.
Ever since then, founders of startups across the galaxy have pitched themselves as "Salesforce of anything", to associate themselves with this business model and go-to-market strategy, that was the epitome of asymmetric returns and scalability.
"Oracle of something" — Said no founder, ever
It's worth mentioning Salesforce was founded by Marc Benioff, a former top executive at Oracle, and that Larry Ellison himself invested into Salesforce. Then Marc kicked Larry off the board. Pretty symbolic passing of the torch, then. I'm sure Larry isn't crying too much about spoilt milk on his mega-yacht, named Musashi. Yes, that Mushashi.
SaaS as the silver bullet to scale
So, the moral of the story is, therefore: go SaaS or go home. The only game in town. Case closed. Or is it? Doesn't a scalable go-to-market apply universally? Why wouldn't you want to scale, if SaaS is clearly the right tool for the job? The plot thickens…
Now I'm going to diverge even further into a niche category. Remember, we already went from startups down to enterprise software startups, and now we're going into Financial Services. Why? Couldn't I just make this simple and universal? No. I prefer to drag you along, deeper into the murky waters of enterprise software.
The peculiar feature of Financial Services is regulation. Regulation is just a fancy word for things you can't do, and is synonymous with the big stick that beats you if you do those very things you weren't supposed to. As Theranos would tell you, there are many other industries with this same fun feature. Ask Elon.
So if Salesforce had one set of code, what happened if a client wanted something just a little different? Nothing. They wouldn't and couldn't do it. Luckily for them, they chose to start with a very generic and non-regulated cross-industry horizontal function in sales and customer relationship management. So most people were happy to trade their unique requirements for a cheaper and faster solution.
The specific problem with Financial Services is that most countries have their own rules. Yeah, it's really annoying. If the U.S. alone is a headache with regulation, which obviously also keeps changing, then imagine doing that across the world. It's a whole nother level of spaghetti.
So if you do SaaS in Financial Services, then you either have to pick a niche where you can slice a common set of regulations, or basically create infinite variations of every feature to allow for that flexibility. Some do get away with it, but most don't.
"We are a technology company." – Any bank CEO in 2018
If that wasn't reason enough to give up the ghost, then you have the trend of banks becoming tech companies. They don't want to buy your software anymore, SaaS or not, because they're getting into the business themselves. They need to own all that code and intellectual property, because… well, I don't know, but they say it with a lot of confidence so I believe them. So it's a thing now.
Can we scale faster, tho?
So once the standard J-curve was set by SaaS companies, the natural question to ask was how do we make more of more? More is now the standard, so we have to do more of more. Exponentially more. Squares of squares.
API's: hold my beer.
When did this API thing get started? Well, I suppose a lot of the development of both SaaS and API business models is closely related to the availability of cheap computing on the cloud. Remember back in the golden days of software, one did not simply build data centers. Those were big boy toys, and VC's weren't exactly forking out truckloads of cash to just any Sergey and Larry with cool t-shirts and stickers on their laptop.
Fast forward to the birth of Facebook for example, and anyone could go online and set up a website with a credit card and a few books on coding from Amazon.com. That's pretty much what Zuck did, too. What this enabled was a whole ecosystem of developers doing weird stuff online. Some developed open-source projects, meaning code that others could also use as part of their own projects, like a set of legos open to everyone. Most of it was useless, but some of it stuck.
Eventually, rather than just share code, some thought to instead provide functionality as a service. Not necessarily a whole program like Salesforce, but bits and bobs. Like analytics. Like payments. Like chat. Like SMS. Like anything, almost. You only paid for how much you used, and you could buy online just like shopping on Amazon.com. In just a few years, the way new software changed from the old closed platforms to an ecosystem of open-source frameworks and pay-as-you-go API's.
In many ways, Salesforce itself is now a little old-school. They're still out there selling a solution when the world is now building from legos.
Fun story: In 2012, Oracle actually sued Google in hopes of being able to enforce copyrights and patents for it's Java API's, which is bought from Sun Microsystem. Oracle lost, and API's can no longer be patented at all. Which is kind of like the end of Star Wars: Return of The Jedi, where the Death Star explodes and the Ewoks are dancing.
Do not sell the Happy Meal
So how do you do API's then? How do you go from software to SaaS to API? Is this just another type of technology that your tech guys can just get done in the background? No. If I add a link to my website that says API, and I publish my documentation and name it "API Library", am I done? No. It's a different philosophy altogether for product development and go-to-market channels.
Publishing an API library is like McDonald's publishing a menu of burgers with no intention to sell anything. It's not enough. This is documentation, it's not a business model. — Me just now
Think of SaaS. It's the Happy Meal of software. You want a meal, so it comes in a completely self-contained package that gives you a solution to your hunger and unhappiness problem. Everyone loves a Happy Meal.
Mcdonald's Happy Meal Box
The problem arises as soon as the first child with allergies comes in, and would literally die with the standard apple slices. Then you get the picky eaters who will spontaneously projectile vomit at the first sight of a pickle slice between those smooth buns. Someone else can't stand sesame seeds on the bun, others can't digest cheese. It goes on and on. This is no longer SaaS. You have to go a level deeper and expose the features and options.
Architect to deliver any item
The real task at hand with API's is to expose the right level of functionality to the customer. Some get away with just one magical all-inclusive endpoint, but others need a thousand organized like a McDonald's menu.
Mcdonald's Menu
Without getting too technical, the key decision is the black outlines. Those are your microservices. A set of features that has a common underlying data model, that cannot be separated from each other. This isn't automatic or easy and often takes an iteration or ten to figure out where to draw the lines. So the early days will be messy, and you will rewrite most of your code several times. Don't worry, it's par for the course, and even the big boys have done it the hard way.
Architect to sell any meal
So where does the scale come in? How is this any different to just selling the whole solution? Wouldn't you be able to charge more for the restaurant itself as a franchise, instead of selling slices of lettuce and packs of ketchup?
McDonald's Store
The key that unlocks the scalability of the API business model is flexibility. You don't care what your customer is building, why, where, or how. The customer could be a team of hedge fund wizards or a garage-full of hackers in Bangalore. They might be building trading software or dating sites.
You stop selling, and they start buying. You have the pieces, and they can buy them like a pair of shoes on Amazon, except that your delivery is instantaneous. As long as you need humans to talk about deployment and billing and click buttons, you'll be stuck in the McDonald's before Ray Kroc came in and industrialized the food production line.
How to go about it
So do you go API straight out of the gate? Maybe, if you're a developer founder and have many stickers on your laptop. That's a must. If not, you're more likely going to have no idea what those API's should be. Trust me, this isn't something you finagle with an offshore development team.
So I'm proposing a three-step plan to scale. This is empirically speaking how it would happen if you were clueless and stumbled your way through the process. Clueless stumbling is a feature, not a bug! By not knowing what you're doing, you're secretly keeping your options open, and willing to admit your mistakes and start from scratch. If you think you know it all, you won't have the humility to fall flat on your face, which is highly underrated.
Gartner Hype Cycle
To paint a picture of this journey, the Gartner hype cycle is a useful canvas. Any new technology goes through this cycle. We're somewhere between inflated expectations and disillusionment with machine learning, for example. There's no use fighting it, so you might as well get on the rodeo horse of hype, and try to hold on as best you can.
Step 1: Pilots
The world is full of opportunity. A new breakthrough technology is making waves around the world, and everything's gonna be different this time. Full of optimism, you educate the market of your visionary abilities and the upcoming transformation. No one cares. Well, they're really excited, cause you're really excited, but no one will take a risk. They won't gamble the house on your pipe dreams. Lots of meetings and conference panels. No revenue.
So you settle for pilots. Every customer is basically an expensive lesson. Everyone has different requirements, and the outcomes are typically short of expectations for both parties. The customer gets to tick the innovation box on their CV, and the startup finally gets a meeting accepted with a VC. Fair trade.
Solution: Custom software. Monolith architecture. Nothing else to it. Will code for food.
Step 2: Revenue
So cooler heads have prevailed, and you've settled for something short of changing the world. A few of your pilots turned okay enough to justify further spending from your customers, and you actually get paid a little now.
Those early lessons have given you tons of valuable feedback on what works, and which customers end up paying you. So now you can take what works across some subset of those clients, hopefully more than 2 or 3, and make that into a platform. Then you try to find more customers that have requirements that are close enough to close the deal.
Solution: Software as a Service. Service-oriented architecture. You still need to sell it in, so the legwork doesn't go away.
Step 3: Scale
Through the tough times of consolidation, a few players have emerged less scathed than the carcasses that were left behind. Bruised and battered, but still in the game.
Now the use-cases start to spread. More customers are calling. They've used similar technologies before, and are starting to build their own teams around this. There are new emerging segments you could serve, with just a few tweaks to the software. You're tempted to go back to old-school software, but your VC slaps you in the face. Does it scale? No. There has to be a way.
So you look at where the requirements fragment across the underlying services of your SaaS platform. What works independently of the other modules. You probably do some really painful re-architecting again. You need more money to do that, but since you have some traction by now, and use the word "scale" a lot, you qualify for Series A or B.
Solution: Open API. Microservices architecture. You can still sell some, but most of your clients will just buy online, so you can save on business cards and printing paper. Partners will take your API's too, and create their own platforms to sell into their existing relationships. Queue magic.
API: Endgame
The dream is a tweet from a developer in Estonia using your API in a hackathon. The dream is money showing up in your account from a customer in Nicaragua you've never met. The dream is becoming a utility, alongside the internet hall of fame. Think Paypal. If you can facilitate interactions between platforms, you've hit a home run. Then you can write your own post on Medium, and tell us how you did it. No really, I need to know.
So you've heard of Machine Learning. You've seen the TED talks. Read the blog posts. But how does one DO it? Is it math? Is it programming? Is it super duper hard? Is brain surgery involved? No.
It's actually pretty easy. Let me walk you through it. If at any point you find yourself asking the question of why or how, then you can also refer to my previous post explaining some of the conceptual basics. This current post is more of a practical example.
Rick Mason @ Unsplash
SPOILER ALERT: It's 90% boring programming. Programming is kind of like having a really big box of legos. You CAN build anything, but how do you build something useful? You have to know which pieces carry out certain functions, and most importantly how to put them together. What's the remaining 10%? Math. Mostly basic statistics.
Whatever you want to do, there are just five steps to follow to create your own Machine Learning solution.
The example code below is in Python, which is the most popular language for all things data science and machine learning. The easiest way to go about it is to install a program called Jupyter, see below. Think of it as installing Excel for the first time. Once you have it, you can use it for so many interesting things beyond this tutorial. It increases the threshold a little, but enables you to data science the sh*t out of any data you find in the future.
Jupyter: If you want to follow step-by-step, and want to learn Python, you might as well set it up to run on your own computer. Plus it's all free. Go here: https://jupyter.org/install. Then once you start Anaconda, choose Jupyter from the menu and start a new Python 3 worksheet. Trust me, that sentence will make sense once you follow the link.
DISCLAIMER: You CAN just read on from here, but you won't learn much unless you try it. So maybe skim through first, but if you ARE still interested, I highly recommend you install Jupyter and give it a go! That's the best way to learn this.
Step 1: The Data
While you could just play around with data to create some kind of solution, it's pretty unlikely to create any value for your business. You need to have a problem to solve first. Do you need to figure out why your customers are leaving? Do you need to predict or forecast seasonal revenues? Do you need to identify flying squirrels with traffic cameras?
Oh, and you need data. If you don't have data, you can't do anything. Sometimes you can source datasets online, especially if you're working on images, audio, or text. In certain cases, you might have to create that data yourself. Perhaps you have a large set of images in which you want to identify an object. All you need to do is have a team of people manually label those images that contain that object. Sometimes you can start with whatever database of data you already have.
Usage data: your app, backend, and analytics
Government statistics: housing, economy, education, weather, maps, etc…
Open databases: research data like medicine, industry data like mobile devices and online shopping
Create it yourself
For the purposes of learning, we're going to get a nicely prepared dataset from Kaggle, which is an amazing site chock-full of data and resources for all things ML. This specific dataset is telco customer data, and the objective here is to identify clients that are likely to "churn", i.e. leave for another telco. That might be a useful exercise to try on your company's own data after his exercise, wouldn't it?
Reading data: The easiest way to get data into your Python program is the Pandas library. The obvious way to go is to read a CSV file which you can generate from any spreadsheet or database system.
To download our telco dataset, go to this Kaggle page. You will need to register but it's free. Then you need to put the file in the same folder you created your now empty Jupyter notebook. You can just use the Upload button from the Jupyter home menu, too. Great, now let's check out our new data!
myData = pd.read_csv('bigml_59c28831336c6604c800002a.csv')
Step 2: Data Exploration
The next step is to get a lay of the land. What's actually in the data? What types of data? How much of the different types? What are the ranges of values? Are the values clustered together or almost random? Are there any interesting correlations to learn?
Analysis: There's a handful of tools in the Pandas library for Python, to use each time you have a new dataset. These methods can be used for any dataset in Dataframe format. Let's try some!
Show the first few rows of data:
myData.head()
These are the outputs from running the above code. So head() produces a preview of the dataset.
Don't worry about carefully examining everything here. We're just looking for an overview at this point, like browsing the data in a few different ways.
Show what columns and data types are in your dataframe:
myData.info()
NOTE: while most of the fields are numbers, a few are mysterious "objects" i.e. better look into that. Algorithms work with numbers, they don't do well with random objects.
So we can see there are 3,333 customers data here. For each customer, we have 21 different types of data. Churn is there as field, too!
Show various statistics from your dataframe:
myData.describe()
Okay, there's some stats on what kind of numbers are in various fields.
Again, don't stress out about thinking about what it all means. We're poking around at this point to see what shouts out.
Explore value ranges for individual columns:
myData['churn'].unique()
Aha! So this is also a binary field, but it's just words instead of numbers. No problem.
True means this customer left us, false therefore means the customer is still around. This is the most important field for us, since we'd like to predict it later!
What about those other ones that had "object" as their type.
myData['international plan'].unique()
That makes sense, they're just binary indicators for what plans are part of the customer's contract.
Another thing to check is how many of those True and False examples are there for the Churn field.
myData['churn'].value_counts()
It's worth noting there is a significant "bias" in this data in that there's relatively a lot less of True examples. This can make it harder to predict True than False later on.
Now we know the lay of the land, and can proceed to visualize some of this data!
Plotting: Humans work best with visual representations of data, so plotting libraries are useful to learn early. Plotting is like a detective journey, where you're looking for correlations in the data related to things you want to predict. In this case, what is actually driving churn?
Seaborn's library contains plots like countplot, pairplot, jointplot, barplot, and heatmap. If you want to share your labor of love, Plotly offers the ability to upload your plots to their cloud service with a shareable link. There are many others too, but let's start with the basics.
A great place to start to get an overview of interesting things, you can plot correlations between columns of your dataframe:
import seaborn as se
%matplotlib inline
plt.rcParams["figure.figsize"] = [16,9]
se.heatmap(myData.corr())
Oooh, that's pretty! Basically, the brighter the square, the more correlation there is between the values of these fields. Obviously, charges and minutes are completely correlated, so let's ignore those. NOTE: you can only do correlations on numbers, so anything containing words isn't here.
Before we go deeper, let's make sure we check the non-numerical fields in case they contain juicy correlations, too. Why don't we start by changing those plan columns into numbers? Let's throw state in there, too. Phone number, nah.
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
myData['voice mail plan'] = le.fit_transform(myData['voice mail plan'])
myData['international plan'] = le.fit_transform(myData['international plan'])
myData['state'] = le.fit_transform(myData['state'])
Let's see what happened.
myData['international plan'].unique()
Nice. That's a lot better.
So let's try that correlation matrix again, see what shows up.
Oh, snap! There's a pretty bright square with international plan and churn. Those customers ain't happy!
We need to dig into these bright squares now.
se.countplot(x='customer service calls', data=myData)
Okay, so most clients make zero or a few calls. But how does this relate to churn then?
Let's add churn into the mix.
se.countplot(x='customer service calls', hue='churn', data=myData)
So most people who churn have made a customer service call. Makes sense.
How does the international plan fit in?
se.countplot(y='churn', hue='international plan', data=myData)
That tells us that the international plan basically sucks. Compared to other customers they have a huge churn rate in proportion!
One more thing. Let's get a little fancy with it. Let's look at some of the other purple squares from the correlation to see if how they combine with churn.
plt.scatter(x="total day minutes", y="total eve minutes", c="churn", s=20, alpha=0.8, cmap="Accent", data=myData)
Oooh, that's pretty. Also, the bad news is that the more people call, the more they want to leave! That sounds like a terrible business model.
So we have a hypothesis of sorts. Active clients leave, and the international plan sucks. So let's try to predict churn as a function of all available parameters now! This is why we're here, isn't it?
Step 3: Feature Engineering
The most challenging part of this whole exercise isn't actually the machine learning part. It's preparing the data so the ready-made algorithms can do something useful with it.
Prep dataset: For any learning task, you'll need two things: inputs and outputs. Both should already be in your dataset that you've been exploring. Input columns are called features. Output columns are called labels. So you'll want to identify based on your exploration what features look like they have some correlation and therefore predictive value for your label(s).
Pandas again has several easy and useful tools to weed out what you don't need, and format what you want left in. Before we get to predict anything, we need to do some housekeeping, and get rid of irrelevant things like state and phone number.
Removing unnecessary columns (axis=1) goes like this:
myData = myData.drop(['phone number', 'area code', 'state'], axis=1)
Let's get those labels separate.
labels = myData['churn']
myData = myData.drop(['churn'], axis=1)
Encoding: This is often the hardest part to understand if you haven't done much with algorithms and statistics. Algorithms only work with learning numbers. They don't know a postal code from a telephone number, or names, or images. You have to feed it actual numbers instead of other stuff. There are many ways to do this, and some get pretty complicated quickly.
A trivial example might be to swap names with a placeholder number. If one of your features is a list of names like "Brad", "Chad", and "Sinbad" then you can replace them in-place with 1, 2, and 3. You can do more research on useful encoders from the Scikit-Learn framework like LabelEncoder and OneHotEncoder.
That's exactly what we already did when we turned the voice mail plan and international plan fields into numbers!
Training split / balance: To be able to actually run the training algorithm, you need two sets of data. Why? If you use all of your data to train the algorithm, there is no data left to test if the algorithm learned to predict/classify anything. So you want to save some data to test if the learning actually worked. Luckily there are tools available to randomly pick these subsets for you.
Split into a typical 75% training, 25% testing data using your separated datasets for features and labels:
from sklearn.model_selection import train_test_split
X_train, x_test, Y_train, y_test = train_test_split(myData, labels, test_size=0.25)
Wait, what if I don't have label data already?
Actually, this is probably usually the case, if you just start by exploring an existing dataset. Here are a few strategies you could try to create labels, and redo this section once you have them!
Hidden somewhere in your existing data
Redefine the problem to use existing data
Identify what you need and add code to gather it
Hack it together with excel or python
Expert labeling: who is qualified?
Crowdsource: wisdom of the crowd
In our example, we were lucky enough to have them all ready to go!
Step 4: Training
Most people find it funny how simple this step is. That's because decades of hard work has gone into standardizing and tuning these algorithms, so you can just use them.
Choose algorithm: The choice of algorithm really depends on the problem you set out to solve. If you're predicting real-estate value or forecasting revenues, you're looking for Regression algorithms that will give you a clear number as the output. If you're trying to make a decision, that would often fall under Classification algorithms. Classification algorithms can give you the best answer or probabilities for all possible answers, depending on what you want. There are dozens of flavors of each type of course, and some involve neural networks. Given the tuning challenges there, you're better off starting elsewhere though.
Often the best place to start is a simple linear algorithm, as it literally draws a straight line on top of your dataset. From there, you can optimize the result by exploring other methods such as Decision Trees or Support Vector Machines.
In this case, we're going to try a few types of Classifiers algorithms, since this is a classification problem (predict churn or not churn as the output).
Linear Classifier:
from sklearn.linear_model import SGDClassifier
linear = SGDClassifier()
Support Vector Machine:
from sklearn.svm import LinearSVC
vector = LinearSVC()
Decision Tree:
from sklearn import tree
tree = tree.DecisionTreeClassifier()
Hey wait, where are the Neural Networks? Isn't that what all the fuss is about? Okay fine, we'll try that too.
NOTE: unlike the others, you DO have to make some choices to use a neural network. Why did we choose a learning rate of exactly 0.1? Why did we choose three layers, with precisely 200, 10, and 3 neurons per layer? Well, because with standard parameters, the network literally couldn't predict churn, like at all. So I had to fiddle around to find a combination that did something useful. Also, I wanted you to see it run for much longer than the other algorithms, so I added three layers. Deal with it.
from sklearn.neural_network import MLPClassifier
net = MLPClassifier(solver='lbfgs', alpha=0.1, hidden_layer_sizes=(200, 10, 3), random_state=1)
Extra credit: XGBoost (fancy Decision Tree). If/when you get an error with XGBoost it's because it's not part of the standard Python package. You may be interested to download it though because it's a great allrounder for many types of learning tasks. Yes, often better than neural networks with less waiting and fussing around with hyperparameters to make it work. Also, easier to understand why it works.
import xgboost as xgb
boost = xgb.XGBClassifier()
NOTE: To figure out what these are, how they work, and why, I refer you to my previous post on the topic.
Training: There are a few different ways to do learning besides the basic case above of using all data at once, usually depending on how much data you have, how fast your computer is, and whether this is a one-time operation of you need to add new training data in the future. You can look up further examples for mini-batch or online learning if needed.
The base case is incredibly simple. It almost couldn't be easier, once you've done all this prep work. It should take like 1 second with a basic computer.
linear.fit(X_train, Y_train)
vector.fit(X_train, Y_train)
tree.fit(X_train, Y_train)
Now for comparison let's train the neural net. This should take several times longer, but certainly no longer than a minute. You know when it's still running if there's a star/asterisk next to your line of code, which turns into a number when it's done.
net.fit(X_train, Y_train)
Prediction: To actually use the model you've just trained, you need to predict something. Again, if you're using Regression it'll be a number. For Classification, you either get a label, or the probability for each label.
Predict a single output for each row of inputs, for example, row 2 of our dataset:
print("Linear:", linear.predict([myData.loc[2]]))
print("SVM:", vector.predict([myData.loc[2]]))
print("Decision Tree:", tree.predict([myData.loc[2]]))
print("Neural Net:", net.predict([myData.loc[2]]))
Client number two will not leave us, then!
Predict label probabilities for a classification problem, by manually entering inputs. NOTE: This command doesn't work for all types of algorithms. Let's try it on the neural net for example.
net.predict_proba([myData.loc[2],myData.loc[301],myData.loc[1000]])
It's interesting to think how it ended up with such specific probabilities for False and True, isn't it?
Step 5: Evaluation
At this point, it feels like you're done. Technically you now have a solution. But you need to find out if it's any good. The typical judge of that is called accuracy, which just means that how many of the samples in your test dataset did it get right.
Accuracy: To begin with, this is really the gold standard of measuring if your algorithm works. If it gets the right result often enough to solve your problem, you're good to proceed at least. There are a lot of exceptions to this, of course, chief among them how well your training data represents real-life data the algorithm will see in the future. Often this means training is not a one-and-done type of deal, but something you revisit if the accuracy with real data starts dropping dramatically.
First, you should check how it does on the training data. Meaning did it learn to predict the exact same samples it already saw before. Since we had several different algorithms to try, you can compare their results to see which works best. If you get a low number, it means the algorithm didn't learn anything, so you should go back and revisit the data.
NOTE: Here we used # to "comment" out XGBoost in case you didn't install it separately. If you did install it, just remove the # and it will be included in your results!
print("Linear:", linear.score(X_train, Y_train))
print("SVM:", vector.score(X_train, Y_train))
print("Decision Tree:", tree.score(X_train, Y_train))
#print("XGBoost:", boost.score(X_train, Y_train))
print("Neural Net:", net.score(X_train, Y_train))
Woah, look at you Decision Tree with the 100% score! Pretty good, but then it kind of already knew the answer since it had seen it in training. The others are pretty decent, too. This is why we put aside some secret data earlier, to show the algos some new data!
NOTE: Your numbers won't match exactly! Why? Because we split the training and test data randomly among the dataset, so results will differ. If we had a million samples, it would probably average out.
Measure accuracy on the testing set. This is what really matters:
print("Linear:", linear.score(x_test, y_test))
print("SVM:", vector.score(x_test, y_test))
print("Decision Tree:", tree.score(x_test, y_test))
#print("XGBoost:", boost.score(x_test, y_test))
print("Neural Net:", net.score(x_test, y_test))
Still pretty even Steven here. Nobody totally bombed, but the tree looks the best so far.
A bunch of other metrics you'll have to read about to understand fully. What we're doing is examining more closely how the algorithms perform on both cases, trying to predict False and True for Churn. This is the decider, then. For the sake of screen space, we'll just focus on the top two: Decision Tree and Neural Net. You can try the others too if you want!
from sklearn.metrics import classification_report
y_tree = tree.predict(x_test)
y_net = net.predict(x_test)
print("DT", classification_report(y_test, y_tree))
print("------------------------------------------------------")
print("NN", classification_report(y_test, y_net))
There were a total of 834 customers in the test dataset we used, of which only 120 were real churns. Again due to the random splitting, you may have slightly different numbers.
So who wins? Well, it looks like the neural net (bottom numbers) is struggling with false positives (Precision) and false negatives (Recall). That's not ideal at all, since the True case is what we really care about, i.e. customers who do churn.
So Decision Tree takes it, in this case? Yes, definitely. Is it perfect? No, nothing is. The ultimate answer depends on the type of problem you're solving, and what the risk of false positives/negatives is. If you're predicting cancer or something, it's pretty important!
NOTE: Is there in existence a combination of parameters that would make the neural network win? Probably. Should we have scaled the dataset to make it easier for the neural network? Arguable. I tried, and it started overfitting like crazy. So if you're religious about neural networks, please waste time on finding the magic formula. If not, try something else like XGBoost and enjoy your free time.
BONUS: If you did install XGBoost, not only can you try to beat the Decision Tree (it will), but you can also do something nifty called Feature Importance. It can self-analyze which features contributed most to the prediction performance.
xgb.plot_importance(boost, max_num_features=50, height=0.8)
Pretty cool right! You can see our hypothesis in there, with customer service calls and international plan, but interestingly total minutes takes the cake in terms of predicting churn! The machine is smarter after all…
Repeat until satisfied
At any step above, you may realize you've done something wrong and it just won't work. Most often, this involves the data itself. Having good, clean data to work with will make all other steps so much easier.
Perhaps you're worried about the number of false negatives and want to improve it. Maybe it's the distribution of the dataset. Perhaps you could benchmark different algorithms. Perhaps there is skew or bias in the test or training dataset. Perhaps you should try more encoding, or scaling the dataset. Perhaps delete more features. This is the job of the data scientist!
Congratulations, you're now well on your way to create your first Machine Learning program. There are of course further considerations for saving and exporting your model to run in an actual application or server. You can easily search online to explore these topics further, with plenty of tutorials and free online courses available.
If you're totally lost at this point, having no idea how and why you ended up here, then you can read this for more context and then retry:
Meditation Room in Bambu Office
Let me start things off by declaring that I'm (Aki Ranin, COO of Bambu) not a people person. Always been more of a numbers guy, clearly a natural introvert. I've been forced to play the extrovert for various management and customer facing jobs I've done through my career.
Over the last two decades, I've been jumping to the tune of others. Corporate life. Boss says jump, you jump. It takes different forms from a three-man garage operation to multinational corporations with diversity strategies. Sometimes I made others jump, but there's always a higher boss.
Posters with company values. Worse even, mousepads with corporate values. Team building games. Quarterly financial result briefs. It's all the same stuff, really. Office spaces that promote productivity and focus. Few if any put much thought or effort into rethinking anything. Heck, I tried a few times. We carry this tradition from one generation to the next, influenced largely by the media if anything. Maybe a few bestsellers in the business section of the airport bookstore.
Three years ago though, I started my own company. So now I can choose. Did I have some special insight? No, not at all. In fact, my approach has strictly been to not mess with anything until cracks start showing. Starting from as blank a sheet as possible.
Rule #1: No strategy.
There is a common misconception that startups are run on carefully crafted business plans, executed to the letter, with real-time feedback on every conceivable metric. Like some kind of nuclear submarine. Yeah, maybe that's how you end up. But you didn't start there.
Reid Hoffman describes starting a business like building an airplane while falling from the sky. You have to build something incredibly complex, under extreme duress, before you crash into the ground at 1,000 miles per hour.
Culture eats strategy for breakfast. — Peter Drucker
The first I heard that famous Peter Drucker quote I thought it was BS. Probably some social science thing. No engineer would agree. Naturally, this was during my days as a corporate stooge. Companies were lead by annual goals set by the CEO with board approval, and the management team then had to brainstorm how to deliver. Strategy. Spreadsheets. KPI's. Sometimes you hit them, mostly not. But you tried again next year.
Be like water.
Startups are largely driven by forces out of your control. In the early days, the only goal is to find product/market fit. So you hack together something based on a series of assumptions. Then you test. Then you repeat. Pretty soon, the thing takes on a life of its own. You have surprisingly little control over where this process takes you if you're focused on growth. Follow the signal.
Once you do have customers and revenue, things start breaking. Your customer service sucks. Your hiring sucks. Your HR sucks. Of course, that makes sense, cause you started from zero. Those things would have taken the focus away from the urgent task at hand, and slowed you down. They weren't useful.
Murphy's law: Anything that can go wrong will go wrong.
Let it happen. That keeps you agile and focused. Only fix what's in front of you. Do not plan ahead and assume to know the future. Let the future happen, consider your immediate priorities and options, make a decision. Do not make big changes until you lose sleep over it. Big changes are a form of gamble on the future, so be sure, and don't gamble often.
New processes also introduce rigidity. You start moving slower. There are now ways things are done. You can't just hustle anymore. There are teams. Departments, even. Be very, very scared of this. Once in place, it becomes cemented. Someday, you'll wake up as a corporation with inclusiveness policies and sustainability committees.
The only thing that cannot break is culture. It is your guiding light. It is the flame that must keep burning. Hire for it. Promote for it. Fire for it. Do not hire too fast, either. The flame fades unless you give it time to settle.
Start a cult.
So where does culture come from? Is it a set of ideals that founders write on post-its? Do you just copy from Jack Welch's books? Do you paraphrase from Steve Jobs keynotes? Do you google Elon's values?
In my case, it was simply the shared characteristics between the founders. I didn't actually have a long history with my cofounder — we were introduced when already thinking about the same business problem. We had our own reasons to tackle the same problem, but other than that little in common. We were different ages, from different countries, having spent our careers in different industries. I came from tech. He came from finance. But since we were starting a Fintech company, that kind of made sense.
Despite all the differences, it turns out we got along splendidly. Why? Because those shared characteristics were values. Humility. Positivity. Honesty. Hustle. Humor. So what do you do with that? It's actually extremely simple. You hire likeminded people. Over time, you find additional features your early core team possess, like helping others. Being proactive. So you hire people who are like the people you already hired. You tweak the recipe, but the core stays.
So what's the difference between a regular corporate team and a culture-driven startup team? In corporations, values come down from the board and management, from extravagant corporate excursions lead by corporate culture facilitators. They pick values that represent the brand, that are customer-oriented. That promote productivity. Integrity. Other platitudes, that consequently get printed on mousepads. Literally no-one believes them. Why? Because unless the entire board and management live & breathe those values day in and day out, they don't get reinforced. Brainwashing is possible, but it's easier to be yourself.
In a startup team, values are the team. It is what they believe because you cherry-picked every individual for those features. You live & breathe those values because you're just being you. It makes everything easy. You just get along and get each other. Work is fun! Stuff gets done. No drama. Nothing falls in the cracks. You pick up each other's slack. There's banter. Smiles. You tell your friends how amazing it is. You start winning. Like there's a magnetism at work, attracting more wins.
NOTE: Everything from here on assumes you already have strong culture. I cannot emphasize this enough. If you don't have culture, you will fail.
Rule #2: No bosses.
I've had a few good bosses. I like to think I've always tried to have a good relationship with the man on top. Maybe even the CEO, if possible, even early in my career. I was pretty ambitious growing from a developer through the ladders into project management and sales towards the upper rungs. I also had a few douchebags on the way. I've certainly found that many people do change jobs to find another boss. So, what if we like… didn't have any bosses?
Flat self-organizing org.
We just haven't hired line managers. We have never made it clear to any new hire who they report to. On purpose. They have to form relationships within the team and find ways to contribute. Don't expect someone to tell you what to do every day.
We do hire people to manage content. A project. A product. A process, like quality, security, or support. But not people. People need to be accountable to each other, not the bossman. If you don't want to let down the person next to you, then what else do you need? Leadership happens within the team, through example. If you've hired for culture, then the examples will automatically reinforce the right behaviors. It's self-perpetuating.
Can we keep this going forever? Probably not, but I'm gonna see how far we can go. Peter Thiel's philosophy at Paypal was to always promote the best specialist. It's a controversial approach, but if your culture is strong, it can work. If the team is self-motivated, then managers are more like Scrum Masters. Removing roadblocks from the team so they can do their job. Not telling them what to do, or how to do it.
Hire for fit, not performance.
So now you have to hire for these characteristics. The old startup mantra is to hire the best people. Well, statistically that's not possible. There's only one set of the "best people". So you hire the best people available. Rockstar developers. Driven salespeople. Hardcore managers. Fierce designers. Like a team of Rottweilers. Straight killers.
Well, maybe that's how you get into an Uber culture. Cutthroat. Me over you. Us over them. Nah. No thanks.
At the end of the day, you probably spend more time at work than home, true story. Growth is a marathon, not a project. Can you really keep a pack of bloodthirsty dogs from chewing each others' faces off long-term? Doubt it. Mercenaries will bail the moment you stop serving their personal interests.
Okay, so no mercenaries. What's the other end of the spectrum? You CANNOT have wallflowers in the team, in any role. People who find menial work between the cracks, but have no drive to jump to the plate. You need people that find problems, bring them to light, and then solve them. No pushing to the next desk.
"And as at the Olympic games it is not the finest and strongest men who are crowned, but they who enter the lists, for out of these the prize-men are selected; so too in life, of the honourable and the good, it is they who act who rightly win the prizes." — Aristotle
So you hire for fit. #1 criteria. Nobody gets a pass. Whatever culture you have already established, whether implicit or carefully crafted, you must sustain it. Do NOT optimize for anything less than the whole company. Only hire people that will get along well with the people you already have. And yes, that's a founder's job. You CAN interview every single hire into the hundreds. If people really are your #1 priority then there is nothing more important than personally knowing who comes in.
The painful part is when you realize later on that you have a wolf in sheep's clothing. So if you find individuals that no longer fit the culture, you must part ways. If the culture is strong, it will be obvious to all true believers in your team that it's the best thing to do.
For senior roles, I have a bonus question. Airplane test. If you had to get on a 10 hours intercontinental flight next to this person, would you want to? Simple, but deep. You either want to build a relationship with this person, or don't. No judgment.
Rule #3: No performance reviews.
Since we don't have bosses, there's now no-one to have that awkward quarterly performance review with. Seriously, did anyone ever actually want to have one? It's like going to the dentist. It was just weird at the best of times.
The biggest problem is that of optimizing for the part as opposed to the whole. That's poison for culture. Me vs. them. Am I doing better than other developers? Who is the best product manager? How do I get to #1? You can have the industry's leading customer success team and still run out of money. Best product but no sales. The ONLY thing that matters is the company. It's either working well as a whole, and you grow as fast as you can. Or it isn't working, and you fix the problems as fast as you can. Nothing else matters.
Accountability to the team.
That's really who you answer to. Your fellow man and woman. You know if you're doing well, or if you're slacking. YOU KNOW. They know. Everyone knows. If you have strong culture, there is no confusion here. If you feel accountable to the culture, then we can rip that excel sheet into shreds and get back to work.
Of course, founders should talk to your team all the time. But not as a formality. As a way of working. Constant communication. Water cooler. Slack. Coffee. Participate in meetings. Show up. You need to know who's embracing the culture, and who's struggling. Guide the lost lamb back to the flock.
Rule #4: No bonuses.
I've personally been on both sides of this equation. Recipient of various team and company KPI's for annual bonuses, as well as variable sales commissions. I've never been happy with any implementation, and I've tried designing them too. It really is an exercise in futility. You cannot please everyone, so you compromise for everyone.
Sometimes your team would kick ass, only to find out the incremental EBITDA target was missed on a business unit level, so bonuses are cancelled. Or then everyone just gets a fixed amount for Christmas, in which case there is no incentive at all. I distinctly remember giving the best developer we had a huge bonus, and he asked me why he got it, instead of others. You just can't win with bonus schemes.
Yes bonus cash is great to buy crap you don't need. But it has no staying power. In fact, most people choose to leave after cashing in the year's bonus. So it's a terrible tool for retention.
Why do founders stay around then? Because of the cool hoodies and free swag? Amazing customers? No, they stay because of equity, because they have it, and want to make it valuable. So if it works for them, why not for everyone?
ESOP for all.
Karl Marx talks about the foundation of capitalism as the process of extracting "surplus value" from your workers. You pay them less than they produce. You keep all the rights to that surplus value, meaning all the profits. The more the better. Karl Marx didn't have Employee Stock Option Pools as a tool back in the late 19th century, but we do.
The rising sea lifts all ships. We launch a new product. We sign a new deal. We all win. We all win. Every full-time employee deserves stock options. You can have tiers. You can do it once, at milestones, or do it every year. Startups are a valuation game anyway. Creating something out of nothing. A snowball of value. Growth over profits. Risk over forecasting. So make your team players in the long game. The reason it works is because it's delayed, and you can't spend it now. So you can only dream. It's the big golden carrot in the horizon. Some day your gonna take a bite, and it's gonna be juicy AF.
"If you're offered a seat on a rocket ship, you don't ask what seat. You just get on." — Sheryl Sandberg
If you are funding your company in a series of venture rounds, and take some risk off the table for founders along the way, then consider giving your early team that option too. You can implement a buy-back plan, offering vested options to buyers that can include the company itself or existing investors. The former is particularly interesting, as it is one of the only ways to decrease your dilution as a founder. Win-win then!
NOTE: You still probably do want to keep commissions for the sales team. Since the metric is directly controlled by the individual, the negatives of team/company bonuses don't apply.
Rule #5: No productivity.
Productivity I feel is the most overvalued thing in working life. We shouldn't optimize for immediate, short-term deliverables. As always, you get what you measure. If you measure productivity, you forego creativity. I'm telling you right now, you don't want productivity. Quality over quantity. I'd rather have great later, than good now. This goes for your accountant as much as your developers and designers.
Bambu Tower in Singapore.
Promote creativity.
With the few choices available to you as a leader, you can choose to reinforce certain behaviors.
How did you choose your office space? Cheap or sensible rent? Convenient or amazing location? What about the feeling when you walk in? Do you feel energized? Inspired? What vibe do you get? Is it professional or whimsical? Consistent with the brand or fun to hang out in? Is this a place where you do amazing sh*t every day? Does it offer opportunities for the team to define how they want to work? Do you see people socializing casually? If not, then what are you doing?? Your office is the temple of your unique culture. Treat it with some reverence.
What is the weekly routine? How can you interrupt the monotony of office life? Do you have an all-hands meeting? Do you scatter internal meetings or do them all on Mondays? What and where do they eat? Where do they get coffee, and is it any good? Have you created opportunities for the team to relax and have fun between grind? How about introducing healthy practices like morning meditation or gym access?
How do you track output? Are you tracking your employees working hours? Why? Are you worried about their work ethic? Didn't you hire them for culture? If you did, you shouldn't need to worry. IF the team cares about the work and each other, they will get the job done without any additional force or incentive.
If you hire based on culture — as you should, then you DON'T NEED to micromanage them. Quite the opposite, you want to set them free creatively to find their own problems and solutions. Not only is creativity more fun, it's also more engaging. Yes, office life can be about more than #9to5 grind.
How do you know it's working?
So you've managed to hire good people with shared values, made them co-owners of the company, and put them into a space that promotes creativity. What then? Well, in a lot of ways you need to get out of the way. Let people figure things out. Make their own mistakes and lessons. Let them self-organize.
Your job is to assemble these elements into a kindling. The people bring the spark. So you blow into the kindling.
You start the fire. Let it grow. Never let it die.
I'm a startup guy that likes to read about history, and spend a lot of time thinking about… well, stuff. This ongoing series is an exploration and tribute to what history can teach us about startups, and life itself. We've spent a lot of time in the ancient West, so why not go East next?
Sun Tzu is a mythical and famous character of Eastern philosophy, even in Western business lore. Many a Six Sigma blackbelt carries a pocket copy at all times, another under the pillow, and surely some MBA out there has tattooed Sun Tzu's name on their bicep to eternalize these wise teachings.
When reading this one, take care to define and consider at each step: who is the enemy? While the theme is war, a lot of the insight here applies much more broadly. Don't just think competitors. Think customers, partners, stakeholders, and investors. This isn't about dealing with enemies. It's about navigating through dangers and keeping your team in the game.
These posts are long and rich. So enjoy it like a nice bottle of wine. Pour yourself a glass. Don't just drink to consume. Take a few sips, consider the flavor. Take your time. Share with a friend. Maybe don't finish the whole bottle at once. Bookmark this and come back to it later. It ages well.
Recommended soundtrack: Traditional Chinese from Spotify.
Here's a little story that kind of tells you everything you need to know about the man. To set the scene, the Emperor of China has just read his awesome book, The Art of War, and has invited Sun Tzu to prove why he should run the entire imperial army. Sun Tzu has requested the imperial concubines to line up as a troop of soldiers for a fun demonstration…
So he started drilling them again, and this time gave the order "Left turn," whereupon the girls once more burst into fits of laughter. Sun Tzu: "If words of command are not clear and distinct, if orders are not thoroughly understood, the general is to blame. But if his orders ARE clear, and the soldiers nevertheless disobey, then it is the fault of their officers." So saying, he ordered the leaders of the two companies to be beheaded. When this had been done, the drum was sounded for the drill once more; and the girls went through all the evolutions, turning to the right or to the left, marching ahead or wheeling back, kneeling or standing, with perfect accuracy and precision, not venturing to utter a sound.
So yeah, awesome general, but… maybe don't invite him to your next party. Beheadings can be a real downer.
"The art of war is of vital importance to the State. 2. It is a matter of life and death, a road either to safety or to ruin."
…on brute force. The old adages of startup success: hustle & grind. The stereotypical founder must therefore run around like a headless chicken with rabies. Certain things you certainly can brute-force. You can get clients through sheer will-power and persistence. Same for investors. But you cannot force product/market fit. It's an art. A subtle art at that.
The great founders you think of aren't the brute force types you see handing out business cards to every fool that stays around for networking and cheap wine. The great founders are thoughtful. Purposeful. Crafty. So wipe off the rabid foam from your mouth, and sit down. Consider your assets. Consider your problems. Start crafting.
"When able to attack, we must seem unable; when using our forces, we must seem inactive; when we are near, we must make the enemy believe we are far away; when far away, we must make him believe we are near."
…on stealth mode. One of my pet peeves. In war this applies, not so much in business. The more you share openly to the world, even in sight of competitors, the more opportunities you are creating for your business. Sharing can even bring success with an inferior product. Try that in stealth mode.
"Thus, though we have heard of stupid haste in war, cleverness has never been seen associated with long delays."
…on waiting. The right time to start. The right time to commit. The right time to hire. The right time to reach out to investors. The right time to launch. The right time to scale. Now. NOW. Just do it.
"The skillful soldier does not raise a second levy, neither are his supply-wagons loaded more than twice."
…on funding. You should always raise funding. At least once. Perhaps never again. Every business starts with a slow curve to generate first revenues. If you don't raise, others will and will beat you to the punch. More resources, more releases, more opportunity to learn and grow. But raising as a habit, round after round, always increasing your targets, always diluting more, and spending freely — this is a crutch. Keep more equity, sell earlier.
"Now in order to kill the enemy, our men must be roused to anger; that there may be advantage from defeating the enemy, they must have their rewards."
…on motivation. To go to extra lengths, you must have extra goals. Incremental release 10.2 isn't getting anyone fired up. You must rouse your men not to anger, but to some state of excitement. Something meaningful must result from their hard labor. It usually isn't money. It could be launching a new product. A first client. A new technology. These get the juices flowing for any engineer.
"Thus the highest form of generalship is to balk the enemy's plans."
…on focusing on the competition. As with all things, setting an example at the top oozes down into the culture and cannot be easily undone. Do not bring up your competitors, and make them boogeymen. Toast to their success if pushed for a comment, but move on like it's not a big deal. It's not a big deal.
Startups are 99% about execution. You can execute well on a terrible idea, yet great ideas die every day because of lack of execution. The fact that your competitors are working on a similar idea doesn't matter. Execution matters. Your execution matters. Nothing else matters.
"The rule is, not to besiege walled cities if it can possibly be avoided."
…on being reasonable about unreasonable goals. You want to set the bar so high it makes your head spin, and the team question your mental health. This is important. But getting there will take many years, so it cannot be the only guiding light. Unobtanium is a poor building material.
Yet more important is building a track-record of smaller wins. Traction. I cannot stress enough how important that is in the early days. Wins attract wins. More clients. More revenue. More hires. More fun. More excitement. More hype. More events. More investment. More press. More really is more in this case.
Set a really outrageously ambitious goal. Think of the biggest thing you could do, then work upwards to raise the bar at least two meta levels. Then set it aside on the mantlepiece, and start chipping away. Build a trail of breadcrumb wins that aim at the ultimate goal.
"He will win whose army is animated by the same spirit throughout all its ranks."
…on getting fired up. You have to be fired up about your business. Literally no-one else will do that for you. If you're at 100.0% of fireuppedness, every single person will be less so. If you're lucky, your cofounders will be in the 90's. Amazing early employees in the 80's. Investors in the 70's. If you yourself start in the mid 70's, your late employees will be hovering above zerofucksgivenness.
You must be animated with spirit. You must be consistent and never be anything less than obsessed with how amazing your company is. If you do that all day, every day, you have a chance at eventually animating your team with that same spirit. If you do, regardless of how good your actual business idea is, you have a great company.
Right there. You've already won. The world is about to find that out, too.
"To see the sun and moon is no sign of sharp sight; to hear the noise of thunder is no sign of a quick ear."
…on visionary leadership. Shut up. You didn't invent anything. Your idea is an iteration on the shoulders of countless more creative thinkers. So get your head out of your ass, and never call yourself a visionary, at least publicly. Actually, stop reading here if your LinkedIn profile contains the V-word. I don't want you to pollute my content.
The startup game is an execution game. The awards, patents, and accolades don't count for shit if you can't pay salaries at the end of the month. Never ever forget it.
"What the ancients called a clever fighter is one who not only wins, but excels in winning with ease."
…on halos of success. Your first client does not prove your business model. Your first hire does not sustain your culture. Things that work early often fail long-term. You have to keep adapting. Keep winning. The ultimate achievement isn't the biggest win you could imagine, it's sustaining a winning culture.
"Thus it is that in war the victorious strategist only seeks battle after the victory has been won, whereas he who is destined to defeat first fights and afterwards looks for victory."
…on preparing to win. Launching a business is not as easy as launching a website and waiting for clients to show up. In today's connected world it's actually extremely hard to get clients. There's literally a million other companies fighting for every new user across every category.
So plan to win. Experiment. Hack. Prove your product works. Build an audience. Target. Measure. Test. Never expect anything to happen on its own. Do the math. Make it happen. Create something from nothing.
"The onrush of a conquering force is like the bursting of pent-up waters into a chasm a thousand fathoms deep."
…on achieving product/market fit. It could take you a day, or in most cases years. You might get it on your first slide deck, or in most cases after two business models, three pivots, and version 57. This is the great filter that separates hopefuls from the guys you read about in Wired.
Once you hit pay-dirt, you'll see it right away. It will jump out at you from your metrics. It all clicks. Everything takes on a momentum of its own. That's when you have to have enough resource and focus to keep up. The worst mistake you could make is to play it safe when the oil gushes out. Become the unstoppable force.
…on differentiation. Nothing is new. Nobody actually invents anything. You don't skip from pyramids to general relativity for a reason. Ideas also evolve. New things always emerge from combining old things in novel ways.
So don't worry about differentiation. It's what you do with those five musical notes that matters.
"He sacrifices something, that the enemy may snatch at it."
…on political capital. This is most important when negotiating anything of real consequence. Big clients. Big rounds. Clearly define what is non-negotiable to you. Try to find things that don't really matter to you, but seem asymmetrically important to your adversary. Then give up on those points.
Build political capital, that you can later expend in case you need to fight the non-negotiables. Never start by fighting over small things out of principle. Amateurs do that. You'll be in the middle of a shit show, all goodwill lost, when you get to the important bits. Then they will bury you, your upside, and your business.
"The clever combatant looks to the effect of combined energy, and does not require too much from individuals."
…on cowboy culture. In the early days, heroics will be called upon. Your survival will depend on it, at least once or twice. But you have to nip it in the bud before it becomes par for the course. This is hard, like all other growing pains. You want to reward the lone cowboy, who shows up as they please to save the day and bask in glory. There is an addictive romance to it.
It doesn't scale. Trademark. Business models cannot be built on the shoulders of individuals, especially highly independent and often challenging individuals. You must design around function and capability, not rockstars.
"An army may march great distances without distress, if it marches through country where the enemy is not."
…on crisis mode. Again, there is an undeniable romance in the late nights at the office, backs against the wall, with slices of stale pizza next to your keyboard. Time and perspective will turn your greatest moments of anguish into cherished memories.
What is an optimal amount of crisis mode per year? I would say 2–3 keeps the team on their toes, but above 5 you'll see fatigue. Crises need to be far enough between that they won't be expected, but frequent enough to kill any sense of invincibility.
"You can be sure of succeeding in your attacks if you only attack places which are undefended."
…on blue oceans. Most pitch decks have that crosshair slide with your logo far and clear away from the old-fashioned competition. Nobody's buying it, yet the same people will complain if you're honest. Your business isn't a blue ocean. It's probably more like trash island.
Yet within all the debris and sharks looming underneath, find patches of clear waters. Niches. Pockets of demand too small for the big guys to notice. One customer at a time works when you have zero revenue. 1 > 0.
"Though the enemy be stronger in numbers, we may prevent him from fighting. Scheme so as to discover his plans and the likelihood of their success."
…doing things that don't scale. Bigger competitors can't do many things. Legal and compliance won't allow risky business models. The CFO won't approve low margin projects.
You don't have those problems, cause you don't have those people. You can do whatever you need to. Ridiculous things. Absurd things. Not because you can, but because your competitors can't. The moment you lose this ability, someone out there is hatching absurd plans that will steal your business.
"Your methods be regulated by the infinite variety of circumstances."
…on 9-to-5 drudgery. One of the great ironies of the startup experience, is that after the romance of the "coffee shop phase" finishes, and you settle into an office space, it becomes a desk job again. You purposely escaped the corporate rat race to be your own boss, and here you are in cubicle #23, available on extension line *749. Smell the vending machine coffee: startups jobs are still boring jobs.
The only real difference, forgetting equity and such, is the variety of tasks. Wearing many hats. Pitching clients. Hiring developers. Pitching investors. Applying to accelerators. Printing some swag. Presenting on stage. Pitching journalists. Managing developers. Updating your deck. Pitching partners. So mostly pitching, then.
The infinite variety of circumstances are like a swelling ocean, throwing you around in your rickety canoe. It's fun. In a near-death-experience kind of way.
"Therefore, just as water retains no constant shape, so in warfare there are no constant conditions."
…on formulas for success. Because the sea of infinite circumstances is ever changing under you, there is no formula. Stop googling it. What you should or shouldn't do at any given moment or situation depends on so many factors, it's almost not worth thinking about.
Every day you'll have to make decisions. Hiring your first employee. Choosing the right subtitle font. Pricing your product. The best thing is, that only you know the right answer. If you enjoy that freedom and responsibility, you'll probably survive to fight another day. Unless you chose Comic Sans, in which case you're doomed.
"If you march fifty LI in order to outmaneuver the enemy, you will lose the leader of your first division, and only half your force will reach the goal."
…on extreme lengths. How much cash can you burn before you get the next release out? How far should you pull your pants down to get that deal? How many employees need to quit before you axe a bad project? How many all-nighters in a row before you admit failure and face the music?
ROI isn't a term you often see tossed around in startup lingo. You chase any and all scraps of revenue tooth and nail like a pack of rabid dogs. But over time you'll have options. You don't have to take every deal. You can miss a few deadlines and survive. With limited resource you have to think about ROI eventually. Over time you should be spending time and effort on higher and higher returns.
"We shall be unable to turn natural advantage to account unless we make use of local guides."
…on filling experience gaps. You don't know everything about everything, and shouldn't need to, either. Going from 0 to 1 you'll be inclined to just hustle through by learning on the job. Even if you can't afford to hire the best, you can also find advisors, that might be superstars in their craft. For some hands-on effort and regular oversight, you might only need to part with 1% of equity. If that's what it takes to find product/market fit, it's a win-win!
Later on, the founder hustle that got you from 0 to 1 kind of hurts you when you want to from 1 to 1,000. You can no longer do every job. You need to hire experienced, expensive people sometimes. To do those important jobs that you can't learn on Youtube.
"Let your rapidity be that of the wind."
…on time-to-market. The reason it's important, is that with longer R&D cycles you risk building the wrong thing, or even the market evolving. Even if you have the right product, you still need the right market, and timing is a big factor!
In fact, the more often you release your product, the more "tries" you get at achieving product/market fit. That can be a real competitive advantage! Don't try the market once a year, try once a month!
"When you plunder a countryside, let the spoil be divided amongst your men."
…on stock options. It pains me to see founder teams that don't offer options to all employees. It's simply short-sighted. Making your team owners of not just their work, but the whole company changes the perspective. Changes the game. It's not just about salary and benefits now. Team wins aren't just high-fives that siphon value into the boardroom. Everyone wins. The rising sea lifts all ships!
"Ponder and deliberate before you make a move."
…on being right vs. being on-time. Speed is par for the course when it comes to startups, but the magnitude of decisions at the early stage can reverberate in the business for years to come.
Don't sweat the small stuff. Super sweat the big stuff. Try to collect data, or at least some market feedback. Test it, if you can. Debate with the team. Ponder alone. Meditate. Ruminate. Deliberate with advisors, mentors, family, friends, and random people at bus stops.
"On the field of battle, the spoken word does not carry far enough: hence the institution of gongs and drums. Nor can ordinary objects be seen clearly enough: hence the institution of banners and flags."
…on brand. While it's super fun choosing fonts for your first logo, these seemingly harmless, sometimes random, choices accumulate. Whether you want it or not, you are gradually building a brand.
What else determines your brand, besides your cards, your website? Well, your product. Is it serious? Is it fun? Is it professional? Is it casual?
How do you reply to emails? What energy do you bring to meetings? What's your team like? Are they fun, or professional?
It all adds up. The more consistent you are, from font choices to hiring, the stronger your brand becomes. So why not be consistent?
"The host thus forming a single united body, is it impossible either for the brave to advance alone, or for the cowardly to retreat alone."
…on strength in numbers. Every team has weak links, and strong links. The great power of startups is that EVERYONE is in the same boat. You row together. You sink together. You win together. Startups aren't an individual sport.
Build a team around you that jumps in when and where they're needed, whether or not they have the skill or experience. Make this your culture, and you'll have nudged yourself further up the bell curve of startup failures.
"When in difficult country, do not encamp. Do not linger in dangerously isolated positions."
…on moving on from failures and mistakes. Cut your losses, always. Do not get into fights. Not with cofounders. Not with customers. Not with ex-employees. Do what's right, take the high road, then move on quickly. All of the above will happen, some day. Do not get sued. Do not sue. Save your energy for growing your business.
"If, on the other hand, in the midst of difficulties we are always ready to seize an advantage, we may extricate ourselves from misfortune."
…on opportunistic survivalism. I believe the founders job is simply to keep the company alive long enough for luck to happen. You almost never hear a founder story that didn't involve some weird circumstance of fate that turned things around. You cannot design or force it. All you can do is stay around long enough, and keep you eyes and ears open to recognize the opportunity when it emerges. Then pounce.
"Peace proposals unaccompanied by a sworn covenant indicate a plot."
…on MOU's. I feel embarrassed for companies that do press-releases for MOU's. They aren't the paper they're printed on, usually. You may do something in the future. Legally speaking, you also may not. Why aren't you just working on the actual deal instead? Stop wasting paper, or HTML, with your ridiculous MOU's.
Do or do not, there is no may.
"The natural formation of the country is the soldier's best ally."
…on strategy. Founders and execs coming from a corporate background will always try to apply their corporate thinking to startup problems. Usually, it's a waste of sticky notes and barcharts. Don't overthink it. All you have to do is find customers, and sell them something. If you're selling oranges, and your customers want mandarins, then do you really need a SWOT analysis? Just sell mandarins.
"A power of estimating the adversary, of controlling the forces of victory, and of shrewdly calculating difficulties, dangers and distances, constitutes the test of a great general."
…on founder skills. What are they? Pitching? Raising money? Hiring? Vision? Execution? These are words that get thrown around a lot. At the end of the day, you just need to not run out of money. If you can do that, you can live to fight another day. How do you not run out of money, then? Estimate the market. Control what limited things you actually have power over. Shrewdly calculate problems, risks, and runway. Err on the side of pessimism, and you won't be disappointed! Oh, and meditate.
"If fighting will not result in victory, then you must not fight even at the ruler's bidding."
…on roadmaps. If the goal is to achieve product/market fit, then you must never build something that decreases that fit. It's rarely a decisive victory, but each small battle must take you in the right direction. So take one battle at a time, reflect, and then consider the next. Roadmaps with just one box don't really make sense anyway.
"Regard your soldiers as your children, and they will follow you into the deepest valleys; look upon them as your own beloved sons, and they will stand by you even unto death."
…on accountability. Every founder wants a team that will dig deep when needed. That will do the hard things. The best way as always, is to show example. Be consistent. Treat small problems like they're big problems. Don't let it slide. Show the team everything matters.
When you hit that first real rough patch, you'll want to have established that culture, rather than try to instill it at that very moment. You don't want to ask people to do hard things, you want them to want to do hard things without asking. It must be culture.
"Rapidity is the essence of war: The further you penetrate into a country, the greater will be the solidarity of your troops, and thus the defenders will not prevail against you."
…on getting ahead early. There is a positive feedback loop that happens when you charge into a new market with a good product. You win clients. Suddenly you win awards too. Investors call you. Hiring becomes easy. All of the above becomes harder and harder for new upstarts. Winners attract more wins. Nothing is left to the losers.
Of course, most will never see this. So don't count on it. If it happens, enjoy the heck out of it, while it lasts. You may never see it again.
"Keep your army continually on the move, and devise unfathomable plans."
…on losing the edge. Once you emerge bruised and battered from the early stages, and have customers and revenue, things become more comfortable. Sighs are audible. Immediately, people start going soft, and will never again accept hardship like they used to. Unless you come up with new hardships. Do not accept long release cycles. Do not accept processes. Do not accept stagnation.
"To muster his host and bring it into danger: — this may be termed the business of the general."
…on the founder's path. It's never walking on roses. Expect broken glass. Rusty nails. Hot coals. If it's going smoothly, you're probably doing something wrong. A product is meant to solve a problem. If it was easy, it would already exist. I've literally never heard a founder say "it was actually pretty easy from the start". Literally never. So embrace it. It is your job to encounter difficulty, because in difficulty lies all the challenge and opportunity!
Build a team. Bring it into challenges. This may be termed the business of the founder.
"When the outlook is bright, bring it before their eyes; but tell them nothing when the situation is gloomy."
…on selective censorship. This is a key function of the founder. Never complain. Always share any and all good news. Always sugarcoat any bad news. If it's really bad, find a silver lining. Heck, resort to white lies even. Integrity is important, but doom and gloom will rot your culture.
The founder's shoulders must carry the heaviest weight. Think of it as the true cost of equity. When the money's running out, it's really your problem. Solve it before the team needs to know.
"In order to carry out an attack, we must have means available."
…on stacking the odds. Everyone knows the odds are against you. So don't just roll the dice and wish for the best. Bring a gun to a knife fight. Cheat if you need to. Usually the ends justify the means, as long as you're staying (mostly) within the law. Uber and AirBnB created whole industries using this mindset.
"Unhappy is the fate of one who tries to win his battles and succeed in his attacks without cultivating the spirit of enterprise; for the result is waste of time and general stagnation."
…on winning culture. When facing big battles, no pep talk is going to make a noticeable difference. What matters is the path leading up to this moment. The ideal path is a succession of small, but meaningful wins leading to the big moment. EVERYONE wants to be on a winning team. Winning feels great. Literally no-one likes losing, not even losers. Create wins, even artificially, to instill a winning mentality. That's how you manufacture traction.
"When you start a fire, be to windward of it."
545BC — 470BC (China)
Read the book:
Similar episodes you can check out:
Startup Lessons from History: Plato
Startup Lessons from History: Napoleon
Startup Lessons from History: Musashi
Much more to come…
Be sure to scroll to the top to follow me if you'd like to receive a notification when the next episode is added.
Is there a favorite quote here? Any other eastern philosophies you live by? Please share so we can benefit, too.
If you're thinking about starting your own company, joining as a co-founder, or making a career out of startups, you may find this helpful.
The way to think about this <startup> opportunity is starting your entrepreneurial career. It's very different to corporate life.
We'll give you a basic salary, commission on sales you bring in, and you'll build equity over time. Basic insurance and frugal business expenses. The rest is on you. There are no guarantees, and no special perks, but the upside is why you do it. It will be an adventure, and you will get your hands dirty.
It's a conscious decision you have to make. You won't keep up with the Joneses as an entrepreneur. No fancy cars, no jet-set lifestyle. If you want that, you need a steady corporate gig. It's a rat race, but a very comfortable life.
We're giving you a unique chance to get in a high growth business early, with a self-directed role that will grow with your success.
If you have doubts, go with <big company>. Nobody would fault you on that one. If <startup> feels right, we would love to have you on the team.