This is the new renaissance

Written by Sumon Sadhu” />




Peace in Tehran

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The Nature of Non-Intuitive Thinking


Figure 1: An excerpt from Leonardo Da Vinci’s notebook on human anatomy. 

I have the most respect for non-intuitive thinkers. The definition of a non-intuitive thinker is however, not intuitive. 

The nature of non-intuitive ideas

As a starting point, we may consider what a non-intuitive idea is. A non-intuitive idea can be considered as one that is hard to replicate with limited conscious thought. The notion of limited conscious thought is important. If conscious thought were plentiful instead, it would suggest a non-intuitive idea is one which would take time to get to.

So the other question is if two or more people were given the same time, could they come to the same conclusion? If they can, then the idea must not be non-intuitive enough. But if they can’t, it suggests that non-intuitive ideas have some time-dependent complexity.

What is a complex idea? By definition a simple idea can be described easily. So a complex idea must involve multiple interacting parts and have a time dependent component. By definition to be non-intuitive the interaction of these parts must be rare enough to be difficult to replicate. Given this, idea complexity can be described logically through probability. In a given time period, the number of interacting elements for a complex idea must be large enough, that the probability of them colliding over that time period is small. This suggests that a non-intuitive idea can be described as a low probability collision between a series of smaller, perhaps more intuitive ideas.

So how can a complex idea, be rare enough for it to be considered non-intuitive? The first way would be if the sheer number of ideas that combined together was impossibly large. The second way would be if the number of ideas that came together were smaller, but more diverse. And the third way is some combination of the two.

But we know non-intuitive ideas don’t occur in isolation, on their own, hanging on busy street corners waiting to collide with each other. No, collisions happen in the heads of people. Unlike traditional thinkers, the nature of how these thinkers assemble their ideas diagnoses them accurately as non-intuitive thinkers.

This suggests that there are two components to non-intuitve ideas — the structure of a complex idea, and the qualties of a person who can assemble them. Lets dive into the latter more deeply.

The nature of non-intuitive people

By definition a person who can easily assimilate existing ideas, to string together in an order that makes them a complex idea, in record time, again and again is a non-intuitive thinker. In order to achieve such a condition there are a number of personality pre-requisites. What are they?

1. Curiosity
In order to assimilate existing ideas, a person must be inherently highly curious. As a result the range of ideas they can assemble from the space that they explore in is large. Curiosity drives this exploration.

2. Open-mindedness
In order to make a complex idea rare, the diversity of ideas that get assembled together is a key variable. To truly assemble together diverse ideas one has to be incredibly open-minded. No field, or demarkation of knowledge is off limits. In fact the higher the diversity, the more open-minded one has to be.

3. Playfulness
To assemble ideas together, one has to play with them. Some of these combinations will make sense, but some won’t. The undiscriminatory nature of play means non-intuitive thinkers can make their ideas tactile, and free-flowing. The spirit of play, and undirected exploration is key.

4. Speed
Not only can non-intuitive thinkers build complex ideas, but their vast bank of ideas allows them to do so at greater speed. In unit time, a non-intuitive thinker can run through new complex ideas faster than anyone comparable. Speed allows them to approach any problem multiple times where a traditional thinker can only have one attempt. Speed brings together curiosity, open-mindedness and playfulness into another dimension.

5. Fluency
The experience that a non-intuitive thinker gains, their experiments that succeeded, their half formed thoughts and fully formed realities enables non-intuitive thinkers to perform fluently. Time after time, they are able to come up with new ideas. Why? because they have developed an arsenal of personality traits, experience and work

6. Conviction
A non-intuitive idea is different from existing ideas. Sufficiently different that more traditional thinkers may reject them, or fail to recognize them. To construct and express them requires supreme conviction and courage to challenge the existing state of knowledge or ideas. Non-intuitive thinkers do not lack this.

The value of non-intuitive thinking

In summary a non-intuitive idea is one that is complex and rare. Non-intuitive thinkers more easily apply themselves to build such ideas from existing fragments. Their approach is novel, which means that both the ideas and the thinkers are highly treasured.

This type of thinking has a huge role to play in the world. Non-intuitive thinking is required to solve the world’s problems and to express ourselves more fully as a species. To solve disease, to become an interplanetary species, to create new films, Art, music, and writing. How we direct our non-intuitive thinking is entirely for us to decide.

Leonardo Da Vinci, was one of the greatest non-intuitive thinkers in our history. In this age of information, our ability to be productive polymaths is surely greater.

His way is inspiring. 



Competition, Stealth and Engineering Cycles


Fig 1: Peter Paul Rubens’ copy of the lost Battle of Anghiari by Leonardo da Vinci. Also known as the lost Leonardo painting. 

Introduction: Competition & Stealth

It is widely believed that being overt about one’s position in the market is the best way to compete. I disagree. Positioning and overt execution can be copied easily in a more internet-connected world. Value is inversely related to the ease of replication.

Time is the first variable to eliminate as a risk factor. Anything that takes less than a few years to develop into a fully baked outcome is not fundamentally valuable enough. For more ambitious companies instead of being overt, try being covert.

Stealth and Obfuscation


Fig 2: Print of Russian Dolls by the British Artist Grace Smith

The best way to compete is often not directly. Stealth and obfuscation can be an incredibly important competitive strategy for a startup. You don’t need to declare your vision fully, but as you execute, your strategy unfolds itself like an inverted Russian doll [12] on the competition. Obfuscation refers to being cryptic. It should be clear to your customers as to what your position is. But only when you selectively declare it to them.

Stealth and obfuscation can be combined with capital, to create leverage in the form of engineering cycles. When you have selective disclosure to customers, and selective product design and development cycles, you iterate closer to the absolute truth. The absolute truth refers to the state of product market-fit that when scaled through overt positioning renders the market useless to competitors.

These engineering cycles happen in secret. In the stealth and obfuscation phase the only conversation that matters is directly with genuinely engaged customers. Engaged customers declare their intentions in the form of payment, or in the form of exchange of time at the expense of alternatives.

Sometimes these engineering cycles are directed towards an absolute technical goal — such as solving for elimination of all viral disease, development of reusable rockets, or development of a AI comparable to humans. Every cycle, improves upon on the last, and moves measurably closer towards the absolute goal.

On Fundamental Value

Discovery is the process of exposing secrets. The secrets are discovered; the cover is removed from the secret. 

Peter Thiel - CS183 - On Secrets

The deepening of product value that happens over the stealth and obfuscation phase is what translates to fundamental value. Peter Thiel refers to this as a “secret” [3]. Taking this concept further, fundamental value occurs when you take a hypothesis for a secret and generate an objective secret - one where you have experimental evidence of its value with customers or measured progress towards your objective technical goal.

Operationally, this means two things are important. First, it is important to set up a system where customer/experiment observations are rapidly converted to engineered features. The people-centric mechanisms to observe the customer, measure their engagement/progress and translate that into requirements must be closely coupled with a tight feedback loop of engineering-centric mechanisms to build the product.

Second, the choice of metrics for progress and the resolution of these measurement methods are key variables to declare up front in order to direct effort in the correct way.

Time Horizon & Ambition

Stealth, obfuscation and engineering cycles happen over years. Fundamental value may not refer to just the product that you build. It can include time taken to build customer relationships, network effects from the size of your ecosystem, brand presence or amassing the critical mass of talent required to build a complex system.

In exchange for lengthening your time horizon, there is significantly more value for your shareholders. Some of the best investors in the world, such as Sequoia Capital [4], or Y Combinator at the seed stage, preach stealth as a competitive advantage.

In a world shorn of true ambition and long-term thinking it is more respectable to desire a monopoly. Startup founders need to be way more ambitious, and investors, much more patient in order to support a productive stealth and obfuscation cycle.


[1] Matryoshka doll
[2] Matryoshka doll
[3] Peter Thiel, Lecture 11 CS183 - Secrets
[4] Sequoia Capital - Elements of Enduring Companies



On Biology: Imperfect Understanding & Perfect Reality


Figure 1: Colorized low-temperature electron micrograph of a cluster of E. coli bacteria. Individual bacteria in this photo are oblong and colored brown.


Biology fascinates me because the tools for the seeing what is going inside of us are imperfect, yet the reality of biology goes far beyond what we can comprehend. Over time, our understanding of biological reality will converge with the truth, but we may not know when. 

For this reason it is important to develop tools to view our biology in more detail than we can today. In addition to complementing this approach with existing tools - advances in Mathematics, Statistics, Computer Science, Novel materials etc to build interfaces for human analysts to detect, assemble and display the objective truth. 

Imperfect Understanding

For example, our understanding of disease is limited by looking at disease as a reductionist construct. Aging is multifactorial, but are there two types of aging or six? We don’t know the extent of its complexity let alone the specifics. Similarly, the limitations of speed and cost in whole genome sequencing means that we cannot index all of the world’s species at scale, as well as all of the species on other planets. If we could, then the combination of autonomous robots, robust gene spiders, and significant computational power could help us understand the perfect reality of biology that exists out there.

The delta between a perfect reality and imperfect understanding is incredibly influenced by the resolution of the tools we have built. Another example is the unravelling of the complexity behind the central dogma of biology. Right now we believe that gene expression can be described by a set of primitives - genes, boundaries, on/off switches, selective expression, amino acids, environmental stimulus etc but every year the number of primitives we have to integrate into our model increases. RNAi is more complicated than we imagined, epigenetic control is more nuanced, that there are regions of the genome that control what gets expressed (“duons’) in addition to what is being expressed [1] and i’m sure we will continue to discover more of this complexity over the years.

Building tools for more perfect understanding

In Computer Science, and Engineering, our model of technology becomes more sophisticated over time — the edge is firmly where we want to take it. It is in our control, as it was constructed by us. As a result the field is influenced by human-defined use cases, and can be logically built up from simple primitives.

In Biology the objective truth is in nature. It staggers me that we can only comprehend a fraction of that truth. Imagine staring at the sky with muddied glasses, where the lenses are breaking every single time. This is the state of comprehending biology. Are the things we understand about disease or the world’s species real or do we lack the tools to dig deeper? What if our model is completely wrong — are we wasting human effort in treating disease right now?


Figure 2:  Color-enhanced scanning electron micrograph showing Salmonella typhimurium (red) invading cultured human cells. Credit: Microbe World

Why are we not directing more efforts to build the tools to explore the vista of biology? To hack nature we need tools to disassemble it (hat tip to Synthetic Biology, Genome engineering & Stem cells) in addition to having the tools that help us measure and benchmark it (Nanopore sequencing, Physics-coupled detection methods, high resolution data analysis) [2]

I believe we are entering the era of Biology which will profoundly impact the human species. It is paramount that we direct our efforts to reducing the distance between our imperfect understanding and perfect reality. 


[2} Lean Ideas in Biotech R&D



The internet is a beautiful ‘literary’ math equation: At every node or click, a multiplication happens. Sites which make the world a little better , multiply 1.0000001 x 1.0000001. Those who make it worse multiply a lot of 0.999999s. When you string a near infinite number of transactions together, you get asymptotes to infinity and zero. The world under the low friction of a global internet is a small, intimately connected village and helping visionary entrepreneurs write the literature of those connections is my mission





Computational Chemistry: Computers harnessed to optimize combinatorial libraries

“If you’re a chemist at a pharmaceutical company, you could have at your fingertips a computer database of millions of chemical compounds. Your job: find the next anticancer agent, antibiotic, or protease inhibitor among all those virtual molecules. But even with the swiftest automated combinatorial methods to synthesize compounds en masse, there’s no way you could possibly make and test all of them. You need a way to pare down the list. Such a task is no small feat. If you’re like some in the combinatorial business, you’re setting out with only a general idea of what you want—say, a drug to inhibit the action of a certain enzyme involved in a disease. But suppose the enzyme itself hasn’t been well-characterized, so the drug target receptor isn’t known yet. To get the best chance of hitting on a compound that will interact with that as-yet-unknown receptor, it makes sense to get a sampling of as many different types of molecules from the library as possible. What you’re looking for, in combinatorial argot, is ‘diversity.’”

-Elizabeth K. Wilson

Chemical & Engineering News, April 27, 1998 [pdf]

These problems need to be revisited in light of where computation and interfaces are today. 




Bret Victor’s thinking is very inspirational for designing interfaces that allow humans to uncover insight in data

Albert Laszlo-Barabasi on how network theory can be used to map how complex diseases work


Visualizing the formation of the network of 900M Android devices. Really like the way it animates rate of formation

Credit: Alberto Moss

technology creates possibilities, but art asks the questions, design thinks up the solutions and leadership creates action.

We need to combine thinking from all four of these disciplines in order to make change.

This is one of the most striking quotes and articles that I’ve ever read and cements a lot of my thinking about renaissance ideals. 

Artists question the world. In isolation this has no value.

Designers turn these artistic questions into briefs for exploration and prototyping. Effectively turning a question into a quest for a solution. These solutions have merit when they can be made real, which is why it is very important to understand technology.

Technology creates possibilities for solutions.

However, the best plans do not get implemented and are met with initial skepticism, requiring strong leadership, project management and conviction to move something from a sketch to a shipped solution to a customer. 

The interaction of art, design, technology and leadership is the most powerful set of intersecting ideas to create change. 

This is what underlies the modern renaissance. 


John Maeda - President of RISD

John Maeda - President of RISD



Modern Miners - The Genomic Gold Rush

Sunday Morning at the mines

“Sunday Morning in the Mines” by Charles Christian Nahl (1818 – 1878)

An oil painting on canvas. Crocker Art Museum, Sacramento, California

The California gold rush of 1849 was significant event for wealth creation and technological progress. There were three phases in this revolution which determined wealth creation, and could be ranked by the difficulty of obtaining wealth.

Mining gold and the three stages 

  1. Easy: Initially gold was found simply by looking at the surface level, being picked off by the first to come into the area
  2. Medium: More sophisticated techniques like panning helped discover gold from streams of water
  3. Hard: Capital was required to develop technologies which made it possible for the discovery of gold from more difficult places
Easy come, easy go

The sequence of wealth creation in mining gold is exactly the kind of behavior that will be experienced in light of the decreasing cost of whole genome sequencing. While there are many potential applications of mining genomic information, the companies that exist today operate in the easy category. First order analysis exhibited by 23andme and Knome represent highly naive or simple approaches to making genomic data palatable - one for a consumer audience and one for a medical or research audience. Companies like DNAnexus exist to take very simple algorithms for analysis of sequence pipelines, basic visualization and make these tools available to more researchers for their use. This is simply repackaging and consumerizing existing approaches. 

The watershed moment is coming for Genomics

Genome sequencing is already undergoing a significant reduction in cost, but its watershed moment will come when we are able to migrate from expensive methods of sequencing (<$1000), which require taking samples to labs, to much lower cost sequencing that can happen remotely and do not take up a lot of space (>$100). Combining mobility in computing with mobility in sequencing will provide many compounding effects. This is very exciting to me.

Carlson curve

Nanopores are the shovels

Fortunately, the enabling technology we are waiting for is nanopore sequencing. Two companies -  Genia and Oxford Nanopore - are going to be significant providers of infrastructure to enable others to build services more easily on top of genomic information. Continuing the mining analogy these are these companies are making shovels and customer engagement with these technologies will drive their continued evolution over time. Therefore these are key technologies to understand. 

In order to derive a genome sequence you have to read the exact composition of DNA and its component bases - A, C, T and G. Reading can be done directly (literally seeing the DNA; EM approaches like Halcyon Molecular and ZS Genetics are representative of direct sequencing), or more commonly indirectly, by determining the indirect presence of the sequence of bases (A, C, T and G) through a secondary chemical reaction.

Nanopore sequencing exists in the secondary reaction category where threading a single stranded DNA sequence through an artificial protein channel - the nanopore - exposes the bases. Depending on the base change the ionic flow and therefore changes the current passing through the membrane. The resulting current for each base allows us to indirectly derive the genomic sequence. 

Nanopore Sequencing

Nanopore sequencers will be smaller because nanopores themselves are small, and as semiconductor coupled nanopores can be combined with cheap mobile computing, and wireless connectivity allow for reading of samples, as well as generation of data in a small package. This is a desired outcome, but portability and cheap sequencing will drive nanopore sequencing to be the breakout technology of genomic mining. 

Shovels need fertile ground for exploration

So with cheap sequencing where can we pan for gold? Panning is a suitable analogy for the next phase. Sequencing generates data, but the origin of the data is where value is created. One of the key lessons from starting Quid is that to build a valuable company one needs to have unique data and a unique methodology for panning for gold. Proprietary data and proprietary algorithms for transforming raw sequence data into valuable molecules for identification, and their validation is key to finding gold. 

Proprietary data comes from the population one chooses to sample. Complexity in deriving value from genomics is proportional to the complexity of the organism. Therefore the first phase of panning will start with simpler organisms like viruses and bacteria before moving up the chain to ourselves. There are many valuable populations to sample genomic information from. One example, is in finding new drugs, by sampling the natural diversity of molecules in soil. Some of our most significant drugs like pennicilin are natural molecules. Panning for drugs is already seeing applications of natural discovery - Warpdrive Bio is a company leading the way, with a trailblazing partnership with Sanofi. Sampling the genomic diversity of the ocean, or the many bacterial populations within us - the Microbiome- are fertile opportunities for mining and discovery. 

How fine be thy sieve?

However, just like panning, filtering the data intelligently, and applying techniques to transform a messy sample into an understandable result is key. Metagenomics is the application of statistical learning techniques to determine individual sequences from a sample that contains the fragments of diverse organisms. Bioinformatic techniques to identify known molecules, and network analysis to identify the population of related molecules can be applied. I have some understanding of data analysis techniques from network analysis from my work at Quid, and so the application of these approaches in mining of genomic data is very interesting. Its also not just enough to determine important molecules computationally, but high throughput molecular biology, and some synthetic biology work can help characterize important molecules and truly seperate the wheat from the chaff. 

Polymathic collaboration

More broadly, mining and panning efforts will require the interdisciplinary collaboration of software engineers, computer scientists, biochemists and more applied practitioners such as medical professionals and those in the Pharma/Biotech industries. The potential reward for an ambitious group like this is discovering new drugs, treatments, and data which may increase our lifespan, fight disease, and in turn generate great wealth. 

I’m thinking a lot about the issues presented here, have thoughts or want to chat? email me on sumon [dot] sadhu @ gmail [dot] com  

May the Goldrush commence. 

p.s. I intend to go into the topic mentioned here with more technical depth in future posts. This is merely an introductory narrative. 



The seven signs of startup dysfunction


I was reminded by Andy Rachleff's post of the perspective I've gained both from experiencing both successful execution as well as making mistakes. 

Experiencing success and failure while remaining cognizant of cause and effect is an exercise in being aware.  Perspective cannot be gained without exploration. Imagine painting or designing in the dark. Without looking, or even looking both left and right, up and down you cannot size up what you are about to make or transfer what you know to another work of Art.  Experiencing failure alone creates risk aversion, and suggests the absence of learning. Experiencing success alone, generates blindness about how you got there. Therefore a blended experience coupled with awareness is critical. 

A successful startup is an optimization exercise. Value is a function of market size, momentum and having the right team to capitalize on the market opportunity. Optimization of startup function is stage dependent. At the early stage the risk is not falling apart. Once initial momentum has been generated it is important to catch flight. Once a company is growing fast and has some success, long term value is created by continuing to build new products, without succumbing to the internal stagnation that can happen at scale. 

This essay explores dysfunction at the early (0-10 employees) and mid stages (10-100 employees) of a company. Arguably these are the most critical stages to traverse, and also I haven’t experienced the late stage yet with Quid so my experience at this stage is only conjecture. 

Early Dysfunction

The early stage is about not falling apart and catching wind of a market. So what are the main sources of dysfunction?

  • Founders are misaligned on ambition, reactions and intentions. Founder misalignment happens on how decisions are made, because founders are disagreeing on something inherent in their personalities. A long term focused founder can be misaligned with someone who thinks too small. How founders react under pressure can also create misalignment, so when the going gets tough founders may react differently, one giving up while the other wants to persevere. Founder misalignments exist on levels of ambition, reactions to pressure and intentions (short or long term perspective). If you react the same way on all these fronts then you are aligned. If you have significant differences you aren’t. No two people are identical, but at least you can align philosophically. Founder misalignment can make it such that under signs of stress, the founders would rather not work with each other. If that is is the case, you can split a team even before you get going.
  • Team has no momentum. In the early stage, you have to make momentum from a standing start. A team that doesn’t do that will not succeed. Momentum comes from having external deadlines, shipping and getting feedback, having focused releases, getting money or new users. Momentum comes from interacting with the outside world. The faster a team can be motivated by external forces and getting to a pattern of behavior of reacting and improving from the outside world. This means selling before you have a product. Or building a product and showing it off to users. Or setting a very public milestone like a press launch with a hard deadline to create pressure can be useful. My Y Combinator experience taught me how great a forcing function having tight deadlines can be. Creating a rhythm of reacting to cycles of momentum can help create a healthy habit. But generating momentum from a standing start is the responsibility of the leadership. Successful founding teams are like tornados of momentum - convincing external stakeholders to participate in shaping a vision into reality.
  • Team is working on the wrong thing. This is the easiest to change if you can generate momentum from scratch and will work with each other through thick and thin. A market is a proxy for the number of customers, and the money in aggregate they are willing to give you. Starting with the wrong initial Market Size can limit your potential. It takes the same effort to work on very small idea than a big one. Depending on the ambition alignment of the initial team, as long as you start in a broad enough market initially, you can make your idea bigger. Starting in a small market can stunt your potential. There are hundreds of entrepreneurs who will not succeed because they are aiming for the wrong thing. Valuable ideas have proxies. E-commerce when it started had the proxy of physical retail. Sharing idle bedrooms had the proxy of vacation rentals and travel. Its easy to validate that something is valuable if it has an old world proxy. Being able to listen or proactively ask customers if this is valuable is a forcing function. Often this is tied to asking for money from customers.
  • Team is undercapitalized: Runway allows for experiments. Runway, also gives you some buffer to add resources once things work. The more runway you have the more you can experiment. Having more money can also make you lazy, as you know you can run as many experiments as you want. Also more money can bring too much investment oversight. So the amount of money a company has at the early stage allows for the right incentives to be created. A team with less money is definitely motivated to either make money or in danger of not having enough to prove to raise more money. The right amount allows for experiments and resources to find a source of momentum in the business, and grow into it. Too little money increases the probability of failure, or can create incredible survivalist incentives. My Y Combinator experience with Snaptalent showed that we could build a functional product, and get to customers on less than $50k of funding.

Mid Stage

Assuming you have managed to avoid implosion during the early stage, you now have some revenue, or momentum, and a focused team that has overcome the initial uncertainty. The mid stage is about co-ordinating resources, and scaling human interactions to manage conflict, as well as doing more of the thing that worked by setting processes. The key to growing mid stage startups are in building a machine for generating revenue, building a team and a rhythm for momentum. If the number of potential units of dysfunctional behavior from one person is n, adding more people requires vigilance of n times n. 

  • Team cannot manage and co-ordinate projects: Any company’s output can be broken down into distinct projects. Organizing projects, setting deadlines and standards, and co-ordinating output to completion is a skill that few people explicitly focus on. Companies like Asana and the use of individual productivity techniques like the Getting Things Done (system) help institutionalize execution. Even worse is a functional organisation that is simply working on too many projects. Cutting projects and evaluating effort spent are important aspects of management. Feature creep is common in product development, well on an organizational front the same phenomenon is known as project creep. Focusing output on the core value that the company is driving through effective communication of priorities as well as education throughout the organization is also a core skill. Defining the focus of the company and saying no, requires discipline and deliberate design. Hiring in project managers, operating leaders who can say no, or changing the culture through education of existing individuals. 
  • Team forms political silos: A self correcting team is a functional one. A team that has information silos of negativity, or politics like cancer start to invade the organization and slow it down. Having clear information flow and trust up and down, which leads to corrective action is a must for a functional organization. Political or negative individuals with high influence must be fired or shown a path to transition.
  • Team is not reshaped to suit direction: Over the journey of a company there is course correction that comes as a result of learning what is valuable to the market and what isn’t. Good companies are servants to their customers and love doing so. As a result, some parts of the organization aren’t necessary any more. Either those team members change roles and adapt, or have to be let go. For example shifting your focus from web to mobile means adapting to market conditions. If you have a team that was focused on the  web, and you cut the web product entirely you may not need all those engineers. You have to have them adapt or let them go. Similarly, in identifying problem individuals you have to offer them a way out or fire them. The team is an evolutionary thing with selection pressure being what drives value for the business. It isn’t uncommon to reshape the team a few times, or have attrition through forced or organic circumstances.

The principles of momentum, capital and market also apply in the mid stage as processes are built and revenue is created. 

The late stage
If you have got this far, the late stage is about capitalizing on your foundation. Enjoying the critical mass of people you have and continually improving. With continued effort hopefully one day you will be rewarded with a financial event worth telling your family about and looking back fondly at your hard work. 

This is the fun stage. This is where we are with Quid. 

Please comment if you think i’ve missed any big sources of dysfunction.

Momentum Makers

Established companies have momentum. Therefore essential team members for a new company must be momentum makers.  

Momentum makers can create activity for a company from a standing start. Every early function that gets built within a company requires a momentum maker. New initiatives require momentum makers, so sustained progress is inherent to the proliferation of momentum makers within the organization. 

Momentum makers are like cardiac muscle. Their beat, makes the organization hum. As a group momentum makers are responsible for creating activity which allows the company to start having a rhythm independent of their effort. 

Arguably founders should be the ultimate momentum maker. Founders are supposed to be generalists but I would argue that the specialist skillset that a founder has is as a momentum maker.  If you are founding a company you should judge your (potential) cofounder’s ability to generate momentum from a standing start. 

Momentum makers are unfazed by solving chicken and egg problems.  When you have a product with no customers, they understand the art of leverage to persuade early customers to buy without others. This may be by faking social proof. Or by offering a discount, knowing that early customers will attract others at a higher price. Momentum makers can hire the first employees on the promise that something great is being built. Momentum makers can generate press by getting early outlets to cover them and using them as proof to generate a media frenzy. 

Momentum makers can also incubate key functions within an organization before they become departments. A full stack engineer who can build the first version of the product himself is a momentum maker. She is responsible for proving that something is feasible and can be delivered without the help of others. A sales person who can define what customers need, adjust the messaging of his pitch, and act with independence until finding the first customer is a momentum maker. 

A painting by The Renaissance artist Titian (artist) of the greek king Sisyphus rolling a boulder up a hill

Momentum makers have conviction. They are unafraid to act. With action, they are willing to adjust their output in response to the environment. The starting decision however requires conviction. Conviction allows momentum makers to define projects and priorities and reduce uncertainty through their decisions. 

Momentum makers are able to take undefined problems and define them creating clarity and purpose for those around them.

Momentum makers are the most important talent any company has. Are you a momentum maker?

Reposted from my Quora profile



The Future of Payments and Financial Services

Titian's The Tribute Money

Image is Titian’s The Tribute Money (1518), National Gallery, London - Jesus asks the Pharisees if it is right to pay taxes to the romans. The evidence is the coin itself. Symbolic of the interchange fees under disruption in the card issuer ecosystem.

Here is an excerpt of an interview I did with paybefore magazine about the future of payments 


More than $8.5 billion in investment funds have flowed into financial services innovation since 2010, said Sumon Sadhu, Director of Intelligence at Quid, a San Francisco-based text analytics firm that studies mobile payments industry developments. Innovations in payment processing accounted for the largest share of investment so far, at $812 million, followed by $626 million invested in e-commerce and mobile commerce, $583 million invested in online financial services, and $250 million invested in mobile payments, according to Quid. According to the rate of investment growth from 2010 to 2011, investments surrounding payments processing, lending, virtual currencies and online gaming are sone of the fastest-growing categories attracting venture capital, Sadhu said. The average venture capital investment for mobile payments companies is $2.7 million, he noted, and that figure is on track to rise this year.

—Why exactly is venture funding flowing into mobile payment ventures? How many of these ventures are likely to become IPOs? Or is it that backers hoping to make money when larger companies buy smaller ones as the industry matures?

SS: “The global payments market is huge. According to data from BCG It is estimated to be worth $782 trillion in non cash transactions and $492 billion in cash transactions by 2020. Therefore there is ample room to support new independent companies that have stable value on the public markets. In addition to being a large market opportunity, there are fundamental shifts in technology that are enabling new forms of payments. The rise of mobile devices, the digitization of transactions and cheaper storage and processing applied to deriving value from that data will drive new conceptions of how money flows within society. The combination of the size of the market opportunity coupled with changing times is what is igniting venture investment. Just like the first coins, printed notes or credit cards, innovation that is happening now will be looked back on in the history of our species as a significant landmark in the evolution of money.

Companies that are able to be strong enough to IPO will be taking approaches to the payments problem that are ignored or orthogonal to what large public companies are doing. So for example peer-to-peer lending companies like Lending Club, or loans companies like Wonga where users are qualified for loans with algorithms instead of humans. These companies are pioneering approaches that seem contrary to established logic, by lending directly between people or removing human decision making entirely from the process. These approaches will form the basis of future financial services companies, so by the time big companies realize that these approaches these companies can reach scale and have the revenue run rate to go public. 

Companies that will be bought by larger players will be those that have good user experience but no scale. Fortunately there are some strong incumbents that exist in payments like PayPal, or the card issuers like Visa, Mastercard and American Express. A company like, which was acquired by PayPal for its ability to read the credit card on the phone using its camera, was acquired for their ability to augment what these large players already do.  

Outside of these categories most of these companies will fail, as returns follow a power law distribution.”

—Do the potential opportunities for investors in mobile payments match the reality? Or is there a lot of “irrational exuberance” in the notion of payments going mobile?

SS “The future of innovation in payments will make user’s lives significantly easier, with benefits like rewards for loyalty, but without providing any friction or change in behavior. We have the enabling technology to do that. It will be easier than using cash. Good venture investments must achieve a meaningful change in user experience and achieve scale with respect to the number of people they reach in order to succeed. Not every company will achieve scale or win on user experience. So the irrational exuberance will be demonstrated in chasing all the companies pursuing the payments opportunity, or focusing on features like NFC instead of the end user experience. 

Very good companies will be able to realize the aspiration of making users lives better at scale. Since the future is grounded in changes in underlying technology the opportunity is very real. These companies will make a lot of money for investors. However, not every investment can be a good company.”

—What effect is Silicon Valley’s interest in mobile payments having on the industry?

SS “Silicon Valley’s focus is on designing experiences for users that makes their lives better. Typically this vision for the future challenges many existing assumptions. To succeed at doing so requires an unusually intense combination of optimism, design, engineering and business development. This is a Silicon Valley specialty. So the net result for the payments industry is driving forward higher standards and quality for what the end user will get, while at the same time reevaluating existing assumptions. Companies like Stripe and Square epitomize this philosophy.” 




Paintings by the artist Rachelle Reichert, see her work here 

This is an introductory post to publicly declare my interest in Synthetic Biology. This will serve to define the field, and my exploration, which is primarily focused on developing significant new business opportunities in this greenfield market. Having built and designed software, started venture backed companies the creator in me, coupled with my biochemical training is incredibly attracted to the idea of creating with biology and building technology and interfaces to make this easier. The consequences are untold for a new industrial revolution which will outshine anything seen in the past era of molecular biology. 

The first concept I will define is a redefinition of the term of parasynthesis. One of the lessons I learned at Quid, was how one can take a word with a modern and classical definition and own mindshare for this. Quid stemming from the latin for the essence of or “something for something” ie Quid Pro Quo also has a modern financial reference. Now when you search for Quid in Google you get our website. Our intention is that in the future Quid will exclusively remind you of intelligence software, or a new frontier of human knowledge augmented by the power of computers. 

Now for defining parasynthetic and extending its OED definition

noun 1. the formation of a word by the addition of a derivational suffix to a phrase or compound, like greathearted, which is great heart plus -ed. 2. the formation of a word by the addition of both a prefix and a derivational suffix to a word or stem, as demoralize. 3. Created through Synthetic Biology


Just like the first moon mission, the applications of Synthetic Biology have significant opportunity to redefine how we live. As a discipline, it is an enabling technology to create new things on top of thousands of years of evolution. Biological design is elegant, often very complex. Breaking that down, simplifying it, refactoring it, and controlling it to do what we want is a seductive idea. This is synthetic biology. 

For a biochemist, synthetic biology brings the same power and feeling of creation that software engineers have. Imagination, creativity, and an engineering mentality transform simply understanding nature to building what we can imagine on top of it. This is what excites me. To finally have that creative power, but coupled with the foundational passion which brought me to the field in 2001 is very exciting. Post human genome, we had the potential to understand whole biological systems as systems. 

Since 2001, there are foundational ideas that are enabling for synthetic biology.

  1. DNA synthesis: The cost of DNA synthesis is going down, and becoming faster
  2. DNA sequencing: The cost of sequencing is going down, and becoming higher throughput
  3. Big Data & Bioinformatics: We can store, process and manipulate large amounts of data more effectively; we can run algorithms and visualization on this data to help us make better engineering design decisions
  4. Systems Biology & Circuit Design: Our understanding of genetic control and our understanding of the central dogma (DNA —> RNA —> Protein) is improving, as our our experience in controlling it
  5. DNA manipulation & transfection: We can construct larger fragments, insert them into our choice of biological chassis - bacteria, yeast, algae, mammalian cells etc
  6. Output reporting: Our ability to construct circuits, and see their output through optical or non optical techniques is improving
  7. Directed Evolution: Instead of knowing what we to build, we can evolve organisms towards a stimulus, and run forward nature to find an optimal design solution in combinatorial space

I will be exploring topics in these foundational idea spaces, as well as dissecting what has been built using synthetic biology and the design principles that underlie them. This is everything I am learning.