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Will Bitcoin Cash Survive? 

Follow Yassine on Twitter @yassineARK


In August 2017, Bitcoin experienced a contentious hard fork, splitting the network in two: Bitcoin and Bitcoin Cash.  A hard fork is a change to a network's protocol that requires all users to upgrade their software to avoid processing invalid blocks/transactions. Unlike the Bitcoin network, Bitcoin Cash hard forks every 6 months in an expected network upgrade. 


Scheduled for November 15, the next hard fork has stirred significant conflict within the Bitcoin Cash community. The leading implementation, Bitcoin ABC, disclosed its proposal to change the network protocol here with support from Roger Ver, an early evangelist for Bitcoin Cash. In retaliation, the CEO of nChain, a blockchain research and development firm, announced Bitcoin SV (Satoshi Vision), an implementation incompatible with Bitcoin ABC but supported by Craig Wright, the controversial and self-proclaimed “creator” of Bitcoin, also known in most circles as “Faketoshi”.


This serious rift is causing concern about Bitcoin Cash's long-term viability. Currently, Bitcoin ABC and Bitcoin SV are trading at $505 and $54, respectively, suggesting that Bitcoin ABC will be the dominant network. As Nic Carter has highlighted, "forks are not free lunches": the combined network effect of the forks will be less than that of the pre-forked Bitcoin Cash. ARK is paying close attention to the impact of these forks on the network effect and viability of the Bitcoin Cash network.



FICO’s New Score Is a Competitive Response to Fintech Lenders and New Age Underwriting

Follow Bhavana on Twitter @bhavanaARK


In the US, roughly 26 million people have no credit score and, until recently, had little access to traditional financial services. Today, big data and artificial intelligence (AI) are transforming consumer lending by taking power away from the banks, diminishing information asymmetries, and providing an efficient and effective way to analyze credit worthiness. Because of the digitization of human life, fintech companies like Square (SQ), Lending Club (LC), and PayPal (PYPL) can ingest enormous volumes of real time data and not only gauge the credit worthiness of prospective borrowers but also anticipate the timing of their credit needs.


Fair Isaac (FICO), upon which most banks rely for credit scoring, has been slow to innovate. FICO scores are heavily weighted to payment and credit history, as shown below, making them ineffective in assessing the credit worthiness of economically active but lower income segments of the population. This month, FICO announced plans to roll out a new credit score called UltraFICO, adding checking, savings and money market accounts to its data sources.



Fair Isaac must do much more to compete or fintech companies will continue to run circles around banks, not only by improving their underwriting models with real-time data and deep learning but also by striking interesting partnerships with other fintech companies to combine data sources. Lending Club’s recent partnership with Intuit adds data from TurboTax to its mix and is a good case in point.



FLIR is Introducing New Sensors to Break Into the Autonomous Vehicle Space

Follow Tasha on Twitter @TashaARK


This week we read about FLIR’s push into the autonomous vehicle sensor market with thermal cameras. Unlike their optical counterparts, thermal cameras can operate in complete darkness. Many are in production vehicles today at FLIR’s customers - BMW, Audi, and Mercedes - though few autonomous driving teams are experimenting with them.


Thermal cameras could be incorporated into an autonomous sensor suite to provide valuable information about the environment surrounding a car. As FLIR points out, a thermal camera could have detected the pedestrian that Uber’s autonomous vehicle killed earlier this year. That said, thermal cameras might add too much complexity to justify the incremental information they provide, perhaps explaining why Waymo and others have chosen to forge ahead without them. Retrofitting a car with more sensors would involve substantial redesign of an autonomous vehicle’s perception and decision-making systems.  


Because autonomous taxi networks are likely to submit to natural geographic monopolies, if its sensors are not incorporated into one of the first movers, FLIR could be frustrated in its attempt to permeate the market.  Perhaps it should focus its autonomous efforts on the first movers in Europe, which seem to be behind those in the US and China. Then, if every autonomous team without thermal cameras were to fail commercially, it could move into position to take substantial share. Waymo’s launch without thermal cameras should provide the first clues by the end of this year.



We’re Approaching a Tipping Point for Utility-Scale Energy Storage

Follow Sam on Twitter @skorusARK


According to ARK’s research battery technology could become cost competitive with natural gas-powered peaker plants, a $10 billion per year revenue opportunity in the U.S. A report from Lazard, however, suggests that the tipping point could be sooner and the opportunity, bigger. Without subsidies newly built thin-film utility-scale solar projects now can generate electricity at a marginal cost roughly the same as coal plants. In other words, the cost to build and run a new utility solar project could be cheaper than the operating cost of a coal plant that already is up and running! While concerns about its consistency throughout the day are common, solar power paired with energy storage could be the solution.


Just this week the California Public Utilities Commission approved roughly 2 GWh of energy storage projects to replace three natural gas peaker plants.  ARK expects to see more utility-scale energy storage projects as battery costs continue to decline and the benefits of pairing solar with energy storage continue to play out. 


CRISPR-GO Offers Programmable Control of Spatial Genomics

Follow Manisha on Twitter @msamyARK


While early CRISPR studies have focused on “cutting” and “pasting” DNA, or editing and correcting mutations in the source code to life, researchers at Stanford University recently have increased its biological applications. Traditionally, CRISPR has cut and or inserted individual base pairs of DNA to control gene expression. The new CRISPR system can change the location of genes in the nucleus to control both gene expression and cell function without cutting DNA. CRISPR Genome Organization, or CRISPR-GO, is enabling researchers to study the 3D genomic structure in a nucleus and understand why cells divide or express different proteins.


Chemically, CRISPR-GO is inducible and reversible, enabling real-time visualization of DNA in the nucleus for the first time. Already, scientists have learned that repositioning genes closer to the nucleus wall will influence a cell’s viability and gene expression. Such insights could have wide therapeutic implications, especially for cancer.



Neural Networks Could Accelerate the Clinical Adoption of Polygenic Risk Scores

Follow Simon on Twitter @ARKInvest


A rapid decline in the cost to sequence a whole human genome is accelerating the development of clinical applications using next generation sequencing (“NGS”). Polygenic risk scoring (“PRS”), which quantifies the likelihood of developing disease, is a good example. Until recently, PRS adoption had been slower than anticipated because of high sequencing costs, unsatisfactory odds ratios (see below), and few population-specific reference genomes.


Now that these challenges are diminishing, the odds of broad based PRS adoption are increasing. The cost to sequence a whole human genome, for example, has dropped below that of some traditional diagnostic panels, at the same time that more rapid collection of population-specific data has bolstered reference genome repositories. Meanwhile, PRS algorithms are beginning to deliver clinically actionable odds ratios and should continue to improve.     


In its classification of mutations, the PRS must be highly sensitive and specific. Many cancers and rare diseases involve structural DNA variants that scientists have been assessing manually, a process that can take weeks. Physicians and genetic counselors also have to analyze the results before prescribing appropriate therapies.


Neural networks are beginning to enhance the sensitivity and specificity of polygenic risk scores. Skyhawk, for example, is an open-source neural network-based discriminator that automates variant characterization at a speed four orders of magnitude faster than a trained specialist can. Other researchers have trained a deep learning neural network to classify genetic mutations in brain tumors using qualitative medical images and biopsy samples, as shown below. Incorporating both quantitative genetic data and qualitative spatial profiles at the input layer, neural network-based PRSs should become more accurate in diagnosing disease, as they will be faster, cheaper, and increasingly more “intelligent” than current diagnostic tests.



ARK's statements are not an endorsement of any company or a recommendation to buy, sell or hold any security. For a list of all purchases and sales made by ARK for client accounts during the past year that could be considered by the SEC as recommendations, click here. It should not be assumed that recommendations made in the future will be profitable or will equal the performance of the securities in this list. For full disclosures, click here.



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