Silicon Valley exemplifies the saying, “The more things change, the more they stay the same.” Very little has changed over the past decade, with the Valley still mired in myth and stale stereotype. Ask any older entrepreneurs or women who have tried to get financing; they will tell you of the walls they keep hitting. Speak to VCs, and you will realize they still consider themselves kings and kingmakers.
With China’s innovation centers nipping at the Valley’s heels, and with the innovation centers that Steve Case calls “the rest” on the rise, it is time to dispel some of Silicon Valley’s myths.
Myth 1: Only the young can innovate
The words of one Silicon Valley VC will stay with me always. He said: “People under 35 are the people who make change happen, and those over 45 basically die in terms of new ideas.” VCs are still looking for the next Mark Zuckerberg.
The bias persists despite clear evidence that the stereotype is wrong. My research in 2008 documented that the average and median age of successful technology company founders in the U.S. is 40. And several subsequent studies have made the same findings. Twice as many of these founders are older than 50 as are younger than 25; twice as many are over 60 as are under 20. The older, experienced entrepreneurs have the greatest chances of success.
Don’t forget that Marc Benioff was 35 when he founded Salesforce.com; Reid Hoffman 36 when he founded LinkedIn. Steve Jobs’s most significant innovations at Apple — the iMac, iTunes, iPod, iPhone and iPad — came after he was 45. Qualcomm was founded by Irwin Jacobs when he was 52 and by Andrew Viterbi when he was 50. The greatest entrepreneur today, transforming industries including transportation, energy and space, is Elon Musk; he is 47.
There is a perennial debate about who can be an entrepreneur. Jason Calacanis proudly proclaimed that successful entrepreneurs come from entrepreneurial families and start off running lemonade stands as kids. Fred Wilson blogged about being shocked when a professor told him you could teach people to be entrepreneurs. “I’ve been working with entrepreneurs for almost 25 years now,” he wrote, “and it is ingrained in my mind that someone is either born an entrepreneur or is not.”
Yet my teams at Duke and Harvard had documented that the majority, 52%, of Silicon Valley entrepreneurs were the first in their immediate families to start a business. Only a quarter of the sample we surveyed had caught the entrepreneurial bug when in college. Half hadn’t even thought about entrepreneurship even then.
Mark Zuckerberg, Steve Jobs, Bill Gates, Jeff Bezos, Larry Page, Sergey Brin and Jan Koum didn’t come from entrepreneurial families. Their parents were dentists, academics, lawyers, factory workers or priests.
Anyone can be an entrepreneur, especially in this era of exponentially advancing technologies, in which a knowledge of diverse technologies is the greatest asset.
Myth 3: Higher education provides no advantage
Thiel made headlines in 2011 with his announcement that he would pay teenagers $100,000 to quit college and start businesses. He made big claims about how these dropouts would solve the problems of the world. Yet his foundation failed in that mission and quietly refocused its efforts and objectives to providing education and networking. As Wired reported, “Most (Thiel fellows) are now older than 20, and some have even graduated college. Instead of supplying bright young minds with the space and tools to think for themselves, as Thiel had originally envisioned, the fellowship ended up providing something potentially more valuable. It has given its recipients the one thing they most lacked at their tender ages: a network.”
This came as no surprise. Education and connections are essential to success. As our research at Duke and Harvard had shown, companies founded by college graduates have twice the sales and twice the employment of companies founded by others. What matters is that the entrepreneur complete a baseline of education; the field of education and ranking of the college don’t play a significant role in entrepreneurial success. Founder education reduces business-failure rates and increases profits, sales and employment.
Myth 4: Women can’t succeed in tech
Women-founded firms receive hardly any venture-capital investments, and women still face blatant discrimination in the technology field. Tech companies have promised to narrow the gap, but there has been insignificant progress.
This is despite the fact that, according to 2017 Census Bureau data, women earn more than two-thirds of all master’s degrees, three-quarters of professional degrees and 80% of doctoral degrees. Not only do girls surpass boys on reading and writing in almost every U.S. school district, they often outdo boys in math — particularly in racially diverse districts.
Earlier research by my team revealed there are also no real differences in success factors between men and women company founders: both sexes have exactly the same motivations, are of the same age when founding their startups, have similar levels of experience and equally enjoy the startup culture.
Other research has shown that women actually have the advantage: that women-led companies are more capital-efficient, and venture-backed companies run by a woman have 12% higher revenues, than others. First Round Capital found that companies in its portfolio with a woman founder performed 63% better than did companies with entirely male founding teams.
Myth 5: Venture capital is a prerequisite for innovation
Many would-be entrepreneurs believe they can’t start a company without VC funding. That reflected reality a few years ago, when capital costs for technology were in the millions of dollars. But it is no longer the case.
A $500 laptop has more computing power today than a Cray 2 supercomputer, costing $17.5 million, did in 1985. For storage, back then, you needed server farms and racks of hard disks, which cost hundreds of thousands of dollars and required air-conditioned data centers. Today, one can use cloud computing and cloud storage, costing practically nothing.
With the advances in robotics, artificial intelligence and 3D printing, the technologies are becoming cheaper, no longer requiring major capital outlays for their development. And if entrepreneurs develop new technologies that customers need or love, money will come to them, because venture capital always follows innovation.
Venture capital has become less relevant than ever to startup founders.
Blockchain and smart contracts have enabled the development of new approaches in the insurance industry, as they begin to replace outdated business models (with excessive paperwork, communication problems, multiple data operating systems and duplication of processes and the inability of syndicates to mine their data). By digitizing payments and assets—thus eliminating tedious paperwork—and facilitating the management of contracts, blockchain and smart contracts can help cut operational costs and improve efficiency. Smart contracts also allow for automation of insurance claims and other processes as well as privacy, security and transparency. It is estimated that roughly one-third of blockchain use cases are in the insurance industry.
How Blockchain Is Used in Insurance
How will blockchain and smart contracts transform the insurance industry?
Quick and efficient processing and verification of claims, automatic payments—all in a modular fashion, thus minimizing paperwork.
Transparency, minimizing fraud, secure and decentralized transactions, reliable tracking of asset provenance and improving the quality of data used in underwriting. Besides improving efficiency, this also reduces counterparty risks, ensuring trust and safety both from the insurer’s and customer’s perspective. By computing at a network, rather than individual, company level, the consumer is reassured that the process was completed appropriately and as agreed upon. From the perspective of the insurance company, this fosters trust, as well, and encourages consistency, as the blockchain provides transparent and permanent information about the transactions.
The insurance industry has traditionally been associated with tedious administration, paperwork and mistrust; the incorporation of blockchain, however, has the ability to transform this image by bringing operational efficiency, security, and transparency. The long-term strategic benefits of blockchain are thus clear.
Top insurance blockchain projects:
AIG (American International Group)– Smart contract insurance policies
HQ: New York
Description: AIG, in conjunction with IBM, has developed a “smart” insurance policy utilizing blockchain to manage complex international coverage.
Blockchain network: Bitcoin
Deployment: In June 2017, AIG and IBM announced the successful completion of their “smart contract” multinational policy pilot for Standard Chartered Bank. It is said to be the first such policy to employ the blockchain digital ledger technology.
Fidentiax – Marketplace for tradable insurance policies
Description: As “the world’s first marketplace for tradable insurance policies,” Fidentiax hopes to establish a trading marketplace and repository of insurance policies for the masses through the use of blockchain technology.
Blockchain network: Ethereum
Deployment: Fidentiax succeeded in raising funds for the project through its Crowd Token Contribution (CTC, aka ICO) in December 2017.
Description: Swiss Re, a leading wholesale provider of reinsurance, insurance and other insurance-based forms of risk transfer, has partnered with 15 of Europe’s largest insurers and reinsurers (Achmea, Aegon, Ageas, Allianz, Generali, Hannover Re, Liberty Mutual, Munich Re, RGA, SCOR, Sompo Japan Nipponkoa Insurance, Tokio Marine Holdings, XL Catlin and the Zurich Insurance Group) to incorporate and evaluate the use of blockchain technology in the insurance industry. The Blockchain Insurance Industry Initiative (B3i) hopes to educate insurers and reinsurers on the employment of the blockchain technology in the insurance market. It serves as a platform for blockchain knowledge exchange and offers access to research and information on use case experiments. As of yet, there have only been individual company use cases in the industry. B3i is working to facilitate the widespread adoption of blockchain across the entire insurance value chain by evaluating its implementation as a viable tool for the industry in general and customers in particular. The initiative envisions efficient and modern management of insurance transactions with common standards and practices. To this end, it has developed a smart contract management system to explore the potential of distributed ledger technologies as a way to improve services to clients by making them faster, more convenient and secure.
Blockchain network: Ethereum
Deployment: B3i was launched in in October 2016. On Sept. 7, 2017, B3i presented a fully functional beta version of its blockchain-run joint distributed ledger for reinsurance transactions. On March 23, 2018, the B3i Initiative incorporated B3i Services company to continue to promote the B3i Initiative’s goal of transforming the insurance industry through blockchain technology.
Sofocle – Automating claim settlement
HQ: Northern Ireland, U.K.
Description: Through smart contracts, AI and mobile apps, Sofocle employs blockchain technology to automate insurance processes. All relevant documents can be uploaded by customers via mobile app, thus minimizing paperwork. Use of smart contracts allows for a far more efficient and faster settlement process. Claims agents can verify insurance claims, which are recorded on the blockchain in real time. The smart contracts allow for verification of a predetermined condition by an external data source (trigger), following which the customer automatically receives the claims payment.
Blockchain network: Bitcoin
Dynamis – P2P Insurance
Description: Dynamis’ Ethereum-based platform provides peer-to-peer (P2P) supplementary unemployment insurance, using the LinkedIn social network as a reputation system. When applying for a policy, the applicant’s identity and employment status is verified through LinkedIn. Claimants are also able to validate that they are seeking employment through their LinkedIn connections. Participants can acquire new policies or open new claims by exercising their social capital within their social network.
Blockchain Network: Ethereum and Bitcoin
Deployment: The goal of Dynamis is the creation of a decentralized autonomous organization (DAO) to restore trust and transparency in the insurance industry. Its community-based unemployment insurance employs smart contracts and runs on the Ethereum blockchain platform. Using social networking data and validation points, Dynamis verifies a claimant’s employment status among peers and colleagues. It also depends on Bitcoin-powered smart contracts to automate claims.
Recognizing the benefits of blockchain and smart contracts, the insurance industry has begun to explore their potential. With the traditional insurance model, validating an insurer’s claim is a lengthy, complicated process. Blockchain has the ability to combine various resources into smart contract validation. It also offers transparency, allowing the customer to play an active role in the process and to see what is being validated. This fosters trust between the insurer and the customer. Despite the obvious benefits of blockchain for insurers, reinsurers and customers, the industry has yet to adopt blockchain on a large scale. The primary reason for this is that blockchain adoption has until now required in-depth knowledge and skills in blockchain-specific programming languages. The limited number and high cost of hiring blockchain experts have rendered the technology out of reach for many businesses in the industry. Without access to the technology, exposure to blockchain and the ability to reap its benefits will remain limited for insurance companies.
How can these obstacles be overcome? The key is accessibility to enable all parties within the insurance ecosystem to reap the benefits of blockchain and smart contracts. There is a dire need for a bridge between the blockchain technology and these industry players. This is the role that the iOlite platform fulfills. iOlite provides mainstream businesses with easy access to blockchain technology. iOlite is integrated via an IDE (integrated development environment) plugin, maintaining a familiar environment for programmers and providing untrained users simple tools to work with. The iOlite platform thus enables any business to integrate blockchain into its workflow to write smart contracts and design blockchain applications using natural language.
How it works? iOlite’s open-source platform translates any natural language into smart contract code available for execution on any blockchain. The solution utilizes CI (collective intelligence), in essence a crowdsourcing of coder expertise, which is aggregated into a knowledge database, i.e. iOlite Blockchain. This knowledge is then used by the iOlite NLP grammar engine (based on Stanford UC research), the Fast Adaptation Engine (FAE), to migrate input text into the target blockchain executable code.
With a clear direction of blockchain adoption for the future, insurance companies will be forced to adapt or be left behind. The adoption of blockchain by the insurance industry is no longer a question of if but how.
Winning teams — in sports, business and all areas of life — have deep benches.
Even if your company is fully staffed, taking your eye off the ball when it comes to recruiting is a sign of complacency, the kryptonite of success.
What if one of your stars decides to take another job? What if one of your top executives experiences an unexpected health crisis or family tragedy and decides to leave the company? Did you know that two-thirds of those reported to be misusing painkillers in the U.S. are currently employed and are thus susceptible to declining performance or medical leave?
More than ever before, companies must be ready to replace employees at a moment’s notice. The time it takes for you to fill a vacant position has increased. Glassdoor reports that, since 2009, interview processes have grown from 3.3 to 3.7 days, and data from DHI Hiring Indicators shows that the average job opening remained unfilled for 28.1 days on average in 2016, an increase from 19.3 days in 2001-03.
That is why it is critical for companies to build what is called a deep virtual bench. The world’s most innovative human resource leaders are vigilantly focused on recruiting 365 days a year.
Having helped world-class companies recruit B2B sales executives for decades, I can offer four ways to build a strong virtual bench:
Aggressively Target Passive Job Seekers: LinkedIn reports that 70% of the worldwide workforce is composed of passive candidates who aren’t pursuing new employment opportunities but may be open to listening. Passive recruiting is important because most high-performers are already gainfully employed. To effectively recruit passive B2B sales job seekers, you must have a great reputation within the industry; have a seamless and optimized application process (companies such as Netflix and Facebook allow you to apply with one click of the mouse); and consider using an outside recruiting company to maintain a safe distance and avoid being accused of poaching.
Leverage Cutting-Edge Technology: Since implementing artificial intelligence into their recruiting process, Unilever saw the average time to hire an entry-level candidate reduced from four months to four weeks. Instead of visiting colleges, collecting resumes and arranging interviews, the company made the jobs known via social media and then partnered with an A.I. company to screen the applicants. This took place in 68 counties in 15 languages with 250,000 applicants from July 2016 to June 2017. Recruiters’ time spent reviewing applications decreased by 75%. LinkedIn also just recently announced TalentInsights, a new big data analytics product that enables HR leaders to delve more deeply into data for hiring. This helps employers identify which schools are graduating the most data scientists, engineers or history majors; helps analyze your recruitment patterns versus those of your competition; and provides information about growth of skills in certain areas of the country.
Become an Employer of Choice: Glassdoor reported that 84% of employees would consider leaving their current jobs if offered another role with a company that had an excellent corporate reputation. Great candidates, millennials especially, value a commitment to employee wellness, sustainability and initiatives that cater to gender and diversity equality. Having a strong culture, values and clear company mission are critical to building a strong talent pipeline. Top companies such as Bain & Co., Google and Facebook offers perks such as free meals, onsite gyms, massages, free laundry services and generous parental leave. Given that Americans currently carry a record $1.4 trillion in student loan debt, student loan repayment assistance has become one of the hottest new benefits being offered by companies such as Fidelity and Aetna. The size of your company will dictate how many perks you can offer, but adoption of policies that are thoughtful toward employees will turn them into your biggest brand ambassadors. In addition to generating that organic positive publicity, submit applications for the “Best Places to Work” lists offered by most publications. These are now offered by most national publications as well as local business journals.
Appeal to Diverse Candidates: To build a strong virtual bench, you must widen your search and appeal to candidates from different backgrounds. A PwC study found that 71% of survey respondents who implemented diversity practices reported that the programs were having a positive impact on the companies’ recruiting efforts. The previously mentioned Unilever case study resulted in their most diverse entry level class to date, including more nonwhite applicants and universities represented increasing to 2,600 from 840. To build your virtual bench, consider implementing diversity-friendly policies such as floating holidays. These allow people to take off for Good Friday, Yom Kippur or Ramadan or for a yoga retreat, if that is their preference.
In the first of this series of four segments, we will look at the current state of the risk markets and the insurance industry; the emerging peer-to-peer (P2P) segment of the risk markets; how blockchain technology is enabling a new taxonomy in the risk markets; and what changes may occur as a result of these new technologies and methods.
The purpose of this series hails from the open source movement in the software industry. Key to the open source philosophy is the transparent and voluntary collaboration of all interested parties. While this work has been kept fairly close to the vest for the past few years, I have taken meetings with two Fortune 500 insurance companies’ strategy and venture teams, both of which asked for a proof of concept — as well as with a handful of other large international insurance companies and one of the big four accounting firms.
At the other end of the spectrum, I have also spoken with other founders of P2P insurance startups around the world, and I have participated in the communities surrounding blockchain technology. I feel that these handful of folks have already enjoyed early access to these concepts, and my motivation with this series is to achieve a more level playing field for all parties interested in the future of the risk markets.
There are links at the bottom of this article to join the conversation via a LinkedIn group and get access to the whole series.
To begin, let’s take a look at the current state of risk markets. It is important to distinguish between drivers of economic systems and the impact they have on business models in the industrial age vs. in the information age.
Hardware and technology was a key driver throughout the industrial age, which saw a growing batch of new technologies — from cars and planes, to computers and smart phones, to industrial robots, etc.
Industrial age business models were almost always “extractionary” in their nature. The business model engages with some market, and it profits by keeping some portion of the market’s value.
Extracting value from the market
The strategies of the industrial age were:
Standardization — interchangeable parts
Centralization — big factories, vertical integration, economies of scale
Consolidation —an indication that an industry is about to experience a phase change
In the information age, business models almost always embody some creation of “network effect.” When the business model engages with a market, the individual actors all benefit as more actors engage with the business model. The value creation is usually tied to a network’s graph, and the value creation will grow exponentially as the network’s density grows.
Creating value for the market, not extracting value from the market
The strategies and efficiency-drivers in the information age are:
Cheap connections — enabling multiple paths through the network’s graph
Low transaction cost — in terms of time, effort and money
Lateral scaling — not vertical structures, which will be flattened out (“top down” increases network fragility)
Increase in node diversity — and in the ways each node can connect
All of these drivers lead to increasing network density and flow. Things are moving away from large, brittle centralized organizational structures and toward “distributed,” P2P, “crowd” or “sharing economy” types of organizational structures.
Moving away from command-and-control organizational structures is almost impossible for organizations that profit from efficiency gains derived from a centralized effort. It is this attribute of their business model that necessitates startups and new business models coming in and bringing improvements to the market — challenging incumbent economic and business models.
The information age is all about networks (not technology), and building graphs that create positive network effects.
The conceptual framework best suited to understanding networks and the networked world we now live in is complexity science. The study of complex adaptive systems has grown out of its roots in the 1940s and has proliferated since the 1990s and the explosion of computer networks and social networks. Here is an introduction:
When looking at complex systems, we start by looking at the system’s graph. To get an idea of what a graph is, let’s look at a few examples of “graph companies.”
Facebook built the “social graph” of acquaintances; it did not create acquaintances.
Linkedin built the “professional graph” of coworkers and colleagues; it did not create coworkers and colleagues.
Google built the “link graph” for topics searched; it did not create back links for the topics searched.
Notice that, in each of these cases, the company built and documented the connections between the things or nodes in the network and did not create the things or nodes themselves. Those already existed.
To start looking at the risk markets, we must first understand what is being connected or transferred between the nodes (a.k.a. the users). It should be of little surprise that, in the risk markets, it is risk that is being transferred between nodes, like a user transferring risk to an insurance company. In terms of risk graphing, there are currently two dominant graphs. A third is emerging.
Let’s take a look at the graphs that make up the risk markets and the insurance industry.
Insurance — is the “hub and spoke” graph.
Reinsurance — is the decentralized graph connecting risk hubs.
P2P Coverage — will be formalized in a distributed graph. (This is the one that does obviously not exist formally, but, informally, you see people calling parents/friends and using GoFundMe/their church/their office/other community organizations to spread risk out laterally.)
In today’s risk markets, insurance companies act as centralized hubs where risk is transferred to and carried through time.
The reinsurance industry graph is enabling second-degree connections between insurance companies, creating a decentralized graph. In the current industry’s combined graph structure or stack, only these two graphs formally exist.
While an insurance company’s ledgers remain a hub where risk is transferred to and carried through time, reinsurance enables those risk hubs to network together, achieving a higher degree of overall system resilience.
The P2P distributed graph currently exists via informal social methods.
Stack all three graphs, and you can observe how total risk is addressed across all three graph types. Each has its strengths and weaknesses, which leads to its existing in its proper place within the risk markets.
The fact that insurance as a financial service gets more expensive per $1,000 of coverage as coverage approaches the first dollar of loss means that, as a financial service, there is a boundary where insurance’s weaknesses will outweigh its strengths.
My expectation is that much of the risk currently being carried on the hub-and-spoke insurance graph will accrue to the P2P distributed graph because of improved capital efficiency on small losses via a trend of increasing deductibles. This may lead to some of the risk currently carried on the reinsurance decentralized graph being challenged by centralized insurance.
The proportion of total risk — or “market share” — that each graph carries will shift in this phase change.
When people say insurance is dropping the ball, they are expressing that there is a misunderstanding or poor expectation-setting about how much of total risk the first two graphs should be absorbing. Users are unhappy that they end up resorting to informal P2P methods to fully cover risk.
To increase the resilience of society’s risk management systems and fill the gaps left by the insurance and reinsurance graphs, we need the third risk distribution graph: a distributed P2P system.
Society needs a distributed system that enables the transfer of risk laterally from individual to individual via formalized methods. This P2P service must be able to carry un-insurable risk exposures, such as deductibles, or niche risk exposures that insurance is not well-suited to cover.
Much of this activity already occurs today and, in fact, has been occurring since the dawn of civilization. KarmaCoverage.com is designed to formalize these informal methods and enable end users to benefit from financial leverage created by the system’s network effect on their savings.
When observing a system through the complexity paradigm, another key measure to observe is a system’s level of resilience vs. efficiency. Resilience and efficiency sit on opposite sides of a spectrum. A system that is 100% resilient will exhibit an excess of redundancy and wasted resources, while a system that is 100% efficient will exhibit an extreme brittleness that lends itself to a system collapse.
When we look at the real world and natural ecosystems as an example, we find that systems tend to self-organize toward a balance of roughly 67% resilient and 33% efficient. Here is a video for more on this optimum balance.
Industrial-age ideas have driven economics as a field of study to over-optimize for efficiency, but economics has, in recent years, begun to challenge this notion as the field expands into behavioral economics, game theory and complexity economics — all of which shift the focus away from solely optimizing for efficiency and toward optimizing for more sustainable and resilient systems. In the risk markets, optimizing for resilience should have obvious benefits.
Now, let’s take a look at how this applies practically to the risk markets, by looking at those three industry graphs.
Centralized network structures are highly efficient. This is why a user can pay only $1,000 per year for home insurance and when her home burns down get several hundred thousand dollars to rebuild. From the user’s point of view, the amount of leverage she was able to achieve via the insurance policy was highly efficient. However, like yin and yang, centralized systems have an inherent weakness — if a single node in the network (the insurance company) is removed, the entire system will collapse. It is this high risk of system collapse that necessitates so much regulation.
In the risk markets, we can observe two continuing efforts to reduce the risk of an insurance system collapse. We observe a high degree of regulation, and we see the existence of reinsurance markets. The reinsurance markets function as a decentralized graph in the risk markets, and their core purpose is to connect the centralized insurance companies in a manner to ensure that their inherent brittleness does not materialize a “too big to fail” type of event.
Reinsurance achieves this increase in resilience by insuring insurance companies on a global scale. If a hurricane or tsunami hits a few regional carriers of risk, those carriers can turn to their reinsurance for coverage on the catastrophic loss. Reinsurance companies are functionally transferring the risk of that region’s catastrophic loss event to insurance carriers in other regions of the globe. By stacking the two system’s graphs (insurance and reinsurance), the risk markets’ ability to successfully transfer risk across society has improved overall system resilience while still retaining a desired amount of efficiency.
Observations of nature reveal what appears to be a natural progression of networks that grow in density of connections. Therefore, it makes sense that the reinsurance industry came into existence after the insurance industry, boosting the risk markets’ overall density of connections. Along the same line of thought, we would expect to see the risk markets continue to increase in the density of connections from centralized to decentralized and further toward distributed. A distributed network in the risk markets will materialize as some form of financial P2P, “crowd” or “sharing economy” coverage service.
A network’s density is defined by the number of connections between the nodes. More connections between nodes mean the network has a higher density. For example, a distributed network has a higher density of connections than a centralized network. However, a higher density of connections requires more intense management efforts. There is a limit to how much complexity a centralized management team can successfully organize and control.
When a network’s connections outgrow centralized management’s capacity to control, the network will begin to self-organize or exhibit distributed managerial methods. Through this self-organization, a new graph structure of the network’s connections will begin to emerge. As this process unfolds, an entirely new macro system structure will emerge that shows little resemblance to the system’s prior state, much like a new species through evolution.
What emerges is a macro phase change (aka “disruption”) that does not necessitate any new resource inputs, only a reorganization of the resources. For example, the macro state of water can go through a phase change and become ice. The micro parts that make up water and ice are the same. The macro state, however, has undergone a phase change, and the nature of the connections between the micro parts will have been reorganized.
In his book “Why Information Grows: The Evolution of Order from Atoms to Economies,” MIT’s Cesar Hidalgo explains that, as time marches forward, the amount of information we carry with us increases. That information ultimately requires a higher density of connections as it grows. This can be understood at the level of an individual who grows wiser with experiences over time. However, as the saying goes, “The more you know, the more you know you don’t know.”
In the history of human systems, we have observed the need for families to create a tribe, tribes to create a society and society-organizing-firms to achieve cross-society economic work. We are now at the point of needing these firms to create a network of firms that can handle increased complexity and coordination.
It is this network of firms that will be achieved via distributed methods because no individual firm will ever agree to let another single firm be the centralized controller of the whole network — nor could a single firm do so.
In the next segment of this series, we will look more closely at the distributed graph that will become formalized, creating a P2P system in the risk markets.
I have started a LinkedIn group for discussion on blockchain, complexity and P2P insurance. Feel free to join here: https://www.linkedin.com/groups/8478617
If you are interesting exploring working with KarmaCoverge please feel free to reach out to me.
It was nice getting to know you in 2016. You served your purpose, but now it’s time for RiskGenius to move on. It’s time for me to move on.
There were a series of events that helped me realize that you, insurtech, and me, well, we can’t really be friends anymore.
I can’t be conference guy
Recently, I was in San Francisco meeting with some insurance professionals who are plugged into the insurtech scene. One of them brought up a recent insurtech conference and commented, “You are everywhere, Chris!”
I shuddered. I have never wanted to be “conference guy,” but I got sucked into being just that this year, and there are way too many insurtech conferences.
Little comes out of insurtech events
I often attended insurtech events this year and wondered at the end, “What will come out of that?” And very little developed. As the year progressed, it started to dawn on me what was going on. And then, finally, everything crystalized after one phone call on Dec. 13.
A wonderful insurance professional called to let me know he was leaving his firm at the end of the year because he was frustrated by the lack of engagement by his firm with insurtech companies.
It hit me: Insurance companies are simply trying to understand what the heck it is we insurtech companies are doing. Often, there are no real plans for insurance companies to actually engage with insurtech companies.
I need to focus on the doing, not the talking
This year, we have found a tremendous partner in an insurance carrier that I hope to tell you about soon. There are also pockets of people focused on insurance technology innovation — but I need to find these people because they often aren’t at the conferences, or aren’t on Twitter or LinkedIn.
Some of the best events I attended were intimate gatherings. Insurance Thought Leadership invited me to a cyber insurance conference I loved. Marsh invited me to an executive retreat that was incredibly insightful. And an insurance carrier allowed us to participate in an innovation challenge with internal employees that changed the trajectory of our company. But none of these three events focused solely on “insurtech.”
RiskGenius is ready
As I look toward 2017, I am going to remove both myself and RiskGenius from the insurtech scene. Instead, we are going to be actively seeking out those partners that can use our software right now. It’s no longer about tinkering and building algorithms; RiskGenius is weaponized and ready to go.
Two areas have emerged where RiskGenius fits perfectly.
First, RiskGenius is primed for policy automation.
We can take an entire library of policies, show you similarities and differences and then serve up the correct policy based on what the user needs.
Second, RiskGenius analytics has people really excited.
We are now able to take an insurance policy that a user provides us and compare it with all the policies we have previously collected and stored. Soon, I will write about how we have evaluated more than 400 cyber insurance policies.
This is awkward, insurtech. But we can’t be a “thing” anymore. I’m sorry — it’s not you, it’s me (and RiskGenius). We want more for our future.