Security as a Service, a roughly $15 billion market, is now on a convergence course with Network as a Service, a $5 billion market, to create a new industry sector: Secure Access Edge Service (SASE). Throughout the digital age, each advancement in networking technology simultaneously enabled enterprises to reap myriad benefits while introducing untold risks. SASE overcomes that challenge by delivering networking capabilities and network security as a single, cloud-delivered solution that ensures users and devices have secure cloud access to applications, data and services regardless of location. SASE combines products and services, such as SD-WAN, Network Security, Cloud Access Security Brokers, WAN Optimization, Zero Trust Networks and more to deliver multiple capabilities through a simplified platform.

As of year-end 2018, only 1% of enterprises had explicit SASE strategies, but that number is expected to jump to 40% by 2024, according to Gartner. Driving that growth is a rapid shift to cloud adoption and an evolving security perimeter. As the future of work quickly approaches, enterprise user behavior and endpoint protection have both evolved. Users are no longer accessing sensitive information from a dedicated station within a pre-defined enterprise perimeter. Instead, users access data from diverse locations, such as at the office, in the air, from hotels, and at home, as well as from a multitude of devices such as desktop stations, laptops, tablets and mobile devices. Compounding this complexity is the rise of Bring-Your-Own-Device, where users now access enterprise systems through personal devices not natively part of the enterprise infrastructure. The result has been a complex web of challenges and solutions.

Incumbent players such as Palo Alto Networks, Microsoft, McAfee, Cisco, Zscaler, Fortinent, Forcepoint and more have taken steps to launch initial SASE solutions in 2019 and early 2020. However, each of these solutions lacks a complete suite of true Secure Access Edge capabilities. As a result, these companies will look to early stage startups providing innovative solutions and complements to existing technologies to build out robust product offerings. Fusion Fund expects a healthy M&A market for SASE products over the next 3–5 years. This expectation has been bolstered by the impacts of COVID-19 and rapid adoption (necessity) of working from home.

Enterprises yearn for simple, scalable and secure solutions. To facilitate these requirements, providers will need numerous Points of Presence to shift content inspection from within the network perimeter to the location of consumption by device and user in a low-latency environment. This evolution will be a key part of any enterprise’s digital transformation initiative, edge computing strategy, and workforce mobility plan. Fusion Fund is actively building relationships with founders, investors and ecosystem partners in the Secure Access Service Edge and greater Network Security space.

Evolution of Network Security: Secure Access Service Edge (SASE) was originally published in FusionFund on Medium, where people are continuing the conversation by highlighting and responding to this story.

Today data processing is an essential component of the data science workflow. However, there are still major opportunities for improvement in this stage including reformatting and cleaning data, which are namely the most tedious yet unavoidable part of the workflow. With no handy tools, data scientists still clean the raw data containing semantic errors/missing entries/inconsistent formatting by writing scripts or manually editing them in a spreadsheet. The needs for easy processing are not met as more data is being generated every second and as data scientists shall think ahead to smoothen the transition from modeling to deployment.

How is data processing currently solved for and what are some example processing tools?

As mentioned earlier, the aim of data processing is to turn the source data into a clean form. Since data usually come with various features, the gist of data processing is to engineer the features to make data useable for modeling. One of the good practices is to organize the processing in an explicitly described computation graph, which represents a math function in graph mode. As it’s quite common for a neural network to have more than one million edges (think of it as connection lines that execute functions) in its computation graph, the architecture of the computational graph will have a big impact on how well the model can perform.[iii]

There are multiple current tools for data processing, including Makefiles, Data Version Control (DVC), and workflow management systems such as Luigi and Airflow. In summary, a cookiecutter route is using Makefiles as it implements each step in a script and outputs new data files from original input files for users to nicely compile the project. Another method, Data Version Control (DVC), comes with some extra convenience features such as easy sharing of the files. DVC reflects the process of researching and looking for a great model (and pipeline), thus a good fit for iterative ML processes. Other workflow management systems such as Luigi and Airflow, can monitor and optimize existing models, especially once a good model is discovered with DVC.[iv] A particularly versatile approach is to store all source data in an SQL database as a set of tables and implement all the feature extraction logics in SQL views. The versatility comes into play because until the data scientists query the features, they are only kept as code and can be easily tracked without the need to store them in huge data tables. Moreover, such a strategy makes deployment to production more straightforward.

What are some of the needs not met with current data processing tools?

As data grow from terabyte to petabyte and beyond, data processing now consumes more resources and time. Companies need to be aware of the two opposing trends: public cloud vendors like AWS are all about centralized data centers and transporting data to the cloud, while edge computing delivers faster data analytics results with the data residing closer to the compute resources. Moreover, as companies build out separate local/edge clusters for their data teams spread around the world, they need central management to enhance operational efficiency and streamline deployment. The infrastructures will inevitably be in multiple sites to reduce latency and fulfill country-specific data regulation requirements, but a good central management system can help companies navigate those diverse environments and ultimately better leverage the raw data with fewer efforts on processing.[v]

There are also additional steps data scientists can take during the processing stage to prepare for deployment. For instance, if data scientists can express data processing in a specially designed Domain Specific Language (DSL) rather than in free-from Python, they may then translate the DSL into Java or an intermediate format like Predictive Model Markup Language (PMML) to speed up the workflow.[vi]

What is Fusion Fund looking for?

Though there are bottlenecks to solve throughout the data science workflow, addressing the gaps in the data processing stage is key to accelerate the entire process. At Fusion Fund, we are looking to meet companies that are disrupting the space with a technological edge and strong market traction. These companies may either obtain proprietary and industry-specific data to solve pressing problems within a vertical or build out industry-agnostic products that are proven to be viable through PoC testing. As novel technology alone is not enough, leveraging a strong business model to grow market recognition will be the cornerstone for long-term success. Startups also need to build out a good sales channel if targeting to serve enterprise accounts, considering the long sales cycle and high barriers of entry.

Fusion Fund has invested in solutions that accelerate the workflow from modeling to deployment by scaling Python AI to High Performance Computing and providing real-time analytics from Cloud to Edge. We’ve also invested in a semiconductor architecture company that develops a fully integrated package solution combining both 5G connectivity and edge computing capability. By combining hardware with software, they are uniquely positioned to provide the infrastructure that can accelerate data workflow and deliver powerful results. We are excited about the technological innovations and disruptions in the data science space, especially as it is so intertwined with pushing AI/ML breakthroughs that can create major societal impacts. If you have a technologically differentiated startup addressing problems in the data science workflow, we would love to chat!

[i] https://www.globenewswire.com/news-release/2019/06/21/1872425/0/en/Insights-Into-the-Worldwide-Streaming-Analytics-Market-2019-2024-Retail-to-Hold-a-Significant-Share.html

[ii] https://www2.deloitte.com/content/dam/Deloitte/global/Images/infographics/technologymediatelecommunications/gx-deloitte-tmt-2018-intense-machine-learning-report.pdf

[iii] https://medium.com/tebs-lab/deep-neural-networks-as-computational-graphs-867fcaa56c9

[iv] https://blog.dataversioncontrol.com/how-a-data-scientist-can-improve-his-productivity-730425ba4aa0

[v] https://www.apmdigest.com/big-data-pain-points-1

[vi] https://towardsdatascience.com/the-data-science-workflow-43859db0415

Innovation in the Data Science Workflow was originally published in FusionFund on Medium, where people are continuing the conversation by highlighting and responding to this story.

Technologies Improving and Enhancing Cybersecurity

Data Privacy — the largest vertical now driven by regulation, not disruptive technologies

Data privacy has been the largest vertical within cybersecurity — $80 billion market size with an 15% annual growth rate. We believe the data privacy space will continue to grow due to increased corporate focus on securing data and new opportunities for innovation using AI, blockchain, and enhanced encryption technologies. However, the growth in the data privacy space is being fueled primarily by new regulations, rather than disruptive technologies. There has been little incentive for the development of disruptive technology because user behavior shows people care more about convenience and free services than privacy. New regulation is increasing the demand for compliance solutions, but solutions are largely built on existing technology rather than new technology. Successful startups in this area will offer solutions that fulfill compliance mandates, while not necessarily using massively disruptive technologies.

Software-Defined Networking — the second largest vertical and startups exit via acquisitions

Software-Defined Networking (SDN) and related applications has been the second largest and fastest growing vertical within cybersecurity — $42 billion in market size with with a 47% annual growth rate. SDN significantly impacts security and overall corporate technology spending. These networks make it easier to collect network usage information supporting the improved algorithm design used to detect attacks. The new generation of applications will take advantage of better-informed SDN to improve policy enforcement and traffic anomaly detection and mitigation. Today, a large percentage of the market is controlled by Series A to C SDN startups who produce the switches and gear necessary for SDN infrastructure deployment. Existing network operators and network equipment companies have responded by conducting acquisitions to bolster product variety and gain access to existing customers.

The industrial internet of things — an emerging and fast-growing vertical; blockchain may transform the industry

The industrial internet of things (IIoT) is an emerging and fast growing vertical — $2 billion market size with a 44% annual growth rate. Increasing adoption of IIoT devices significantly increases the attack surface and thus provides opportunity for more cybersecurity threats and attacks. IIoT security startups should aim to prevent and resolve security threats at various stages in the data flow. For example, the application of blockchain technologies can enhance security for the overall IIoT network while AI can provide security at individual device level.

Cybersecurity and Fusion Fund

At Fusion Fund we invest in companies that monetize core technical advantages in their business model on targeted sectors including connected industries (mobility, robotics), network technologies (security, connectivity), artificial intelligence (computer vision, natural language processing), and health tech (medical devices, AI in healthcare).

Within cybersecurity, we are investors in Neuvector, a company addressing real-time container security. As enterprises quickly move towards a container-based application deployment strategy across multi-cloud and on-prem platforms, enhanced security is required. Neuvector addresses this issue by delivering a complete run-time security solution featuring unique multi-vector visibility and protection, a container process monitor, and vulnerability scanner.

Additionally, we are investors in Nucypher, which builds cryptographic infrastructure for privacy-preserving applications and protocols, including blockchain, big data, cloud, and internet of things. The company’s core technology, proxy re-encryption is a type of public-key encryption that allows a proxy entity to transform ciphertexts from one public key to another, without learning anything about the underlying message. It is a means of securely delegating access to encrypted information, via the construction of cryptographically-enforced access controls.

We continue to be excited about the cybersecurity industry and are actively exploring areas such Data Privacy, SDN, and IIOT and the applications that technologies such as AI, blockchain, and IIoT will have on the industry.

The State of Cybersecurity was originally published in FusionFund on Medium, where people are continuing the conversation by highlighting and responding to this story.

What’s driving the new class of enterprise software?

The Enterprise Software Market is driven by technological advancement and concentrated business processes, which have led business owners to look for smarter business ownership and management solutions. On the technology side, the generational shift toward cloud computing has provided a massive tailwind for global enterprise software services. According to a recent Forrester report, more than 50% of North American companies are developing bring-your-own-device (BYOD) programs in response to workforce demand, thereby raising demand for enterprise software solutions that must be compatible with a wide range of devices and operating systems. A host of enterprise mobility solutions that enable employees to have greater flexibility while keeping corporate data secure at the same time have been adopted and implemented at scale across enterprise platforms. On the business process side, companies must now contend with massive amounts of data that need to be processed daily, which has fundamentally altered the way in which businesses use and interact with enterprise software. Private sector investment and rising corporate IT budgets are facilitating a new reservoir of data that is being used to create bespoke solutions for frontier commercial industries such as big data analytics. The next generation of end-to-end enterprise software applications will come with huge leaps in functionality and increased usability that open machine learning capabilities into applications in areas such as infrastructure & transportation, manufacturing, retail, and insurance.

Sizing up the enterprise software market

According to Market Research Future, the current global enterprise software market is worth $504B and is expected to grow to $634B by 2023, an 8% CAGR between 2019 and 2023¹. The growth of the market can be attributed to both new line-of-business operations and penetration into new, adjacent industry verticals. Enterprise software is increasingly used by organizations across the globe for a number of applications including web conferencing, business intelligence, customer relationship management, project and portfolio management, digital content creation, enterprise resource planning, enterprise content management, and supply chain management. Enterprise software is used by organizations of all types — businesses, schools, interest-based user groups, clubs, charities, governments — in various industry verticals such as manufacturing, media and entertainment, healthcare, retail, public sector, banking, financial services, and insurance, telecom, transportation, energy and utilities, and distribution.

Beyond these core functionalities, global spend on enterprise application software is anticipated to grow as organizations replace, modernize and functionally expand current office and business applications. Shifting IT budgets away from saturated areas such as mobile phones, PCs and on-premise data center infrastructure to cloud services and Internet of Things connected devices validate businesses’ needs to embrace more agile, cost-efficient and responsive operating models.

Conclusion

As software vendors change their business models from in-house software to cloud-based hosting, the need for governance remains the same. IT is no longer just a platform that enables organizations to run their business. It is becoming the engine that powers the business. Despite these changes in enterprise software applications, purchasing managers continue to struggle to balance the business agility that enterprise software promises with risk mitigation and control. With new use cases in more categories, bigger deployments, and more geographic regions, businesses must sort through the proliferating landscape of software vendors, all of whom need to address customization and flexibility issues, the high cost of support and licensing, and complexities related to the migration of existing data.

At Fusion Fund, we like to hear from companies who are entering the space with a technological advantage or strong customer moat. For example, since nearly every aspect of business is moving to “real-time” monitoring, companies that support computing capabilities at the edge provide a business-critical value proposition that would make switching partners disruptive and re-training costly.

Furthermore, we view companies that give business leaders an easy way to find and deploy solutions that meet standards as well-positioned to compete on standardized technology architecture in a fragmented enterprise software world. Platform-like offerings in the business intelligence (BI) space and supply chain logistics enable pure-play vendors to maintain their competitive advantage over the large, traditional software vendors who seek to expand their core stack. Servicing enterprise accounts requires the type of customization and focus that large vendors have trouble replicating across their vast consumer markets. In this void, start-up companies are competing to not only capture value from existing players, but fundamentally grow the market for enterprise software solutions.

We have engaged with founders to learn more about AI/ML tools spanning the entire pipeline. As data collection continues to grow at an unprecedented rate, a shortage of data scientists to obtain insights from the data sets has resulted in a strong need for AI/ML tools. Additionally, DevOps has been an area of interest, especially as corporations look to innovate more quickly and improve collaboration amongst teams. We are excited about the new upcoming developments in enterprise software. If you have a technologically differentiated enterprise software startup, we would love to chat!

[1] https://www.marketresearchfuture.com/reports/enterprise-software-market-2442

Enterprise Software Outlook was originally published in FusionFund on Medium, where people are continuing the conversation by highlighting and responding to this story.

Fast forward 60 years, one sees that the landscape of modern robotics has changed significantly. Advances in machine vision and motion sensor technology combined with a shift towards consumer-based economies has spawned an emerging class of technology that is better equipped to service the increased demand across industries such as e-commerce and food — soft robotics. Unlike conventional robot systems which require tedious programming and are built from stiff materials such as steel and aluminum, soft robotics utilize autonomous systems to handle increasingly intelligent work with soft structures, allowing for more degrees of freedom and higher dexterity.

What exactly is soft robotics and how big is this market?

Soft robotics is a general term that covers all types of active and reactive compliant systems ranging from soft actuators, soft stretchable sensors, soft energy harvesting, and even soft electronics. These machines are largely constructed from soft materials (e.g. silicone) and most commonly powered by flow of fluids (e.g. air and liquid). What makes this new technology exciting from an entrepreneurial perspective is the wide range of potential commercial and personal applications beyond industrial manufacturing. Soft robotics are more compatible with human interactions as their soft and easily deformable bodies ensure minimal damage in the human environment, opening up access to end-user industries in the medical, healthcare, agriculture, packaging and food areas.

Boston Consulting Group estimates that more than $67 billion will be spent worldwide in the robotics sector by 2025¹. With sales of all types of domestic robots (e.g. vacuum cleaner robots, lawnmower robots, window cleaner robots) expected to reach 32 million units, and a global patient monitoring and assistance robots market estimated to grow at a 30% CAGR, these projections indicate that a $30 billion market is available for savvy entrepreneurs to capture. With demand and AI technology growing side-by-side, soft robotics represents a potential growth area for investment.

Technological Applications in Soft Robotics

The current state of the soft robotics market can be characterized as moderately competitive with soft robot manufacturers developing very specific solutions. As demand for warehouse space increases with e-commerce growth, soft robotics solutions are being applied to alleviate industry-wide pressure to deliver orders on time. In 2017, the average warehouse ceiling height was 21% higher compared to 2001, while spending for new warehouse construction hit a peak in October 2017, with $2.7B spent on construction in that month alone².

As a result, demand for collaborative, low-cost robots outfitted with sensors, actuators, and vision hardware (such as lidar) has grown to meet the needs of fulfillment, transport, picking and sorting.

On the food and agriculture side, soft robotics technology is being commercialized in areas relating to food handling and packaging. Soft robotic grippers can hold soft and fragile food items such as eggs, fruits and pastries while a conventional rigid gripper would not be able to grasp such easily deformable objects without damaging the item.

On the healthcare side, the inherent safety provided by compliant body parts opens up a completely new greenspace for physical interactions between machines and humans. Soft robotics technologies such as exoskeletons have the potential to supplement and improve rehabilitation and human motion augmentation for workers, the elderly, and for the entertainment industry. Despite rapidly aging populations around the world, recent research in this field for solutions comprising assistive technologies to compensate for lost bodily functions and monitoring systems for physiological conditions is promising.

Soft Robotics and Fusion Fund

We view the emergence of soft robotics as an enabling technology for businesses looking to disrupt the economics of manufacturing, e-commerce, agriculture and healthcare. As such, we are interested in backing entrepreneurs who are enabling the next generation of task automation beyond the capacities of current robotics technology. Technological progress will depend on key service providers to commercialize industrial and consumer applications. For example, soft robots are physical systems that require efficient actuators, power sources and control schemes given the light and compact requirements, which limit power generation and battery storage capabilities. Furthermore, highly complex soft robots require elaborate middleware technology that bridges the multitude of heterogenous and interconnected hardware and software modules composing them. In the coming years, we are excited to see new applications of this nascent technology in industries beyond industrial manufacturing.

[1] http://www.ft.lk/it-telecom-tech/Soft-robotics--Future-of-robotics--/50-653878

[2] https://www.cbinsights.com/research/collaborative-robots-reinvent-industries/

Soft Robotics Technology Landscape was originally published in FusionFund on Medium, where people are continuing the conversation by highlighting and responding to this story.

The oil and gas (O&G) industry is one of the most powerful branches in the world economy, with more than four billion metric tons of oil produced worldwide annually. The industry is currently divided into three segments: upstream, midstream, and downstream. The upstream is focused on the exploration and production (E&P) of oil and involves underwater and underground searches for natural gas/crude oil fields, drilling of exploration wells, and drilling into established wells to recover oil. The midstream sector involves the transportation, storage, and wholesale marketing of crude or refined petroleum products. The downstream sector refers to the refining of petroleum crude oil, processing and purifying of raw natural gas, as well as the marketing and distribution of products derived from crude oil and natural gas.

We are particularly excited about recent innovations in the upstream, specifically in the production subsector where there has been an explosion of data that companies can now quickly use to create a strong value proposition for their customers.

Technologies Disrupting O&G

The traditional bottleneck for startups in the O&G industry is the slow customer adoption rate. O&G is fundamentally commodity driven with many external factors outside of a company’s locus of control. We believe startup companies implementing new technologies such as AI/ML and IIoT have the potential to decrease their sales cycle by providing a strong ROI for their customer.

Artificial Intelligence & Machine Learning

With an increase in large sets of data, AI has the opportunity to turn those large volume data sets into actionable insights. AI will improve O&G production rates and lower costs by troubleshooting underperforming wells, enhance reservoir modeling, enable preventative maintenance before problems arise, optimize well design, drilling, and completion, and carry out tasks on unmanned, automated drilling platforms and well pads.

The Industrial Internet of Things

Sensors are transforming the industry by locating new spots for drilling, discovering new ways to optimize well and fieldwork, ensuring increased worker safety, detecting breaches and leaks, and reducing downtime. Collectively IIoT ensures companies are receiving real-time data from all their machinery and equipment that previously did not provide this data.

In addition to startup companies managing slow customer adoption rates, successful startups will have to be able to navigate the complex relationship between Silicon Valley and the energy industry. While O&G companies need Silicon Valley’s expertise, some energy executives worry they could be competing with technology companies in years to come, especially as both sides explore renewable energy. Companies in other sectors have already seen their cloud providers encroach on their business and data could be the most profitable aspect of the business if techniques such as fracking are commoditized.

Fusion Fund and O&G

At Fusion Fund we invest in companies that monetize core technical advantages in their business model on targeted sectors including connected industries (mobility, robotics), network technologies (security, connectivity), artificial intelligence (computer vision, natural language processing), and health tech (medical devices, AI in healthcare). Within O&G we are particularly excited about the applications that technologies such as AI and IIoT will have on the industry within the upstream subsector of O&G.

Technologies Disrupting Oil and Gas was originally published in FusionFund on Medium, where people are continuing the conversation by highlighting and responding to this story.

Despite being a recently used buzzword, “insurtech” does not have an established definition. Some people think insurtech is a subset of fintech, whereas popular opinion believes insurtech should be segmented from fintech. This distinction is most likely a result of fintech primarily being associated with banking activity while insurtech is more associated with insurers applying tech in broad applications. We adopt the second opinion in this report and define insurtech as follows:

“Insurtech” companies are technology-led companies that enter the insurance sector by taking advantage of new technologies that provide coverage to a more digitally savvy customer base. It is a new application with a focus on consumer experience and convenience.

Trending Tech

The insurance industry’s lack of innovation has manifested into five major challenges: talent acquisition, data storage and protection regulation, digital ID authentication, and new business model regulation. These challenges have have created an opportunity for startups to incorporate the following technologies and business models into an otherwise static industry.

IoT, Big Data & Machine Learning:

At its core insurance is a data-driven business. With fast-growing connected IoT sensors — forecast to exceed 21 billion devices by 2020 — insurers will leverage big data analytics coupled with AI to handle the high-volume-real-time data generated by those sensors. As a result, insurers will be able to deliver highly-personalized, real-time insurance and risk management services across the automotive, health, and home insurance sectors. The combination of these technologies will be the key to stay on top of providing valuable solutions demanded by an increasingly dynamic market and diverse set of data.

Breakout Business Models

Peer-to-Peer (P2P)

The relationship between policyholders and insurers is fragile. The delicate nature of the relationship stems from the act of keeping or filing unused premiums by insurers and policyholders respectively.

P2P refers to a business model which address these misaligned incentives by allowing individuals and companies to choose the parties with whom they will share their insurance pool with. By having a self-picked pool, the insured self-select a group with the least amount of risk by increasing transparency.

This model plays on cooperation in order to disincentive fraudulent claims. As a result, P2P companies may have excess funds be resdistributed at the end of the coverage cycle back to the policyholders, donated to nonprofits at the choice of the insured group, or apply a different means of uniting a group of individuals while mitigating risk for themselves.

Distribution

The key reason behind the disruption of the insurance market can be summarized in this Jeff Bezos quote “Your fat margin is my opportunity.”

The market will change — like others before it — as a result of the rapid adoption of new technologies. The current insurance distribution channel is disorganized; backtracking through the insurance process back to the client, they have dealt with brokers, MGAs, carriers, and re insurers. While this inefficient process continues, the insurance industry’s margin grow thicker and thicker.

Perhaps inspired by the Bezos’s observation of outdated business models, insurtech startups focused on distribution are mobilizing quickly in order to identify the best ways to cut out the middle-man as much as possible by providing a platform where consumers and businesses can compare insurance quotes and buy insurance policies. Startups with this mission have already raised a cumulative $1.5B in funding within the past two years. The insurance market will see a huge shift from its current, linear distribution channel to a more ambiguous, free-form structure with its emphasis on the customer.

Fusion Fund and Insurtech

Given our team’s technical background, we naturally have an interest in insurtech companies with technological (versus business model) innovations. We are particularly excited about startups applying AI/ML, Big Data Analytics, and IoT solutions. Although companies we are focusing on are not comprehensive of all innovations addressing insurance we believe the companies using the fore mentioned technologies will have great market potential and defensibility in the insurtech industry.

Insurtech at a Glance was originally published in FusionFund on Medium, where people are continuing the conversation by highlighting and responding to this story.

To best understand edge computing it is important to differentiate between the device edge and the cloud edge. Device edge computing runs software on hardware that is local and owned by the end user. This takes place in instances like calculating a tip at a restaurant on the iPhone’s calculator app or a sensor on a train car calculating the average speed. Meanwhile, the cloud edge, sometimes referred to as cloudlets, fog nodes, or micro datacenters, sits on the periphery of the cloud, closer to the end user. For example, data accessed frequently by a device like a user’s favorite movie, can sit in a cloudlet, and push the data to the device far quicker than it would take to fetch it from a huge data center. In the case of video, edge computing could improve the viewing experience for the user and mitigate buffering. One might assume that edge computing will replace the cloud, but they work in tandem — edge computing simply takes certain tasks away from large network servers and moves them closer to the user.

“Why now?” you might ask. The answer is that in order to efficiently operate devices like drones, AVs, connected machines, and smart homes, we need to develop faster, more efficient computing systems. All of these innovative devices interact with their environments by taking in data from the external world, analyzing them, and then creating suitable responses. Many of these devices also have capabilities not seen on 1990s-era PCs. They include neural net accelerators, artificial intelligence, and machine learning — components that allow these devices to “see” and “think” on their own. Edge computing is needed in order to perform tasks that demand a real-time reaction time and cannot tolerate a possibility of a network system outage or high latency responses.

In the case of an autonomous vehicle, various sensors on the car measure temperature, speed, traction, etc., and communicate the information to the onboard computing system. This system analyzes the information and directs the engine, wheels, and brakes in an appropriate manner. In an emergency situation, for example if a deer were to hop in front of a vehicle, the system would need to respond immediately. There is no time for sensors to send data to the cloud and await a response. If Amazon can experience glitches during Prime Day, we can’t rely on the cloud for life and death situations.

Another advantage of pairing edge computing with cloud computing is that it can lead to more efficient storage. In the case of autonomous vehicles, unnecessary, repetitive data is pruned away, and the car sends only the most vital information to the cloud for learning and analytics purposes. After data from thousands of vehicles are analyzed, updates are then pushed from the cloud to the vehicles — creating a virtuous cycle. Not only is this process efficient for the device at the edge, but it prevents the cloud from storing an inordinate amount of superfluous data. This is important because although data centers can house massive amounts of data, that storage is energy and capital intensive and the communication of this data can clog bandwidth. And with the research group IDC predicting nearly a ten-fold increase in annual data generation by 2025 — to 163 zettabytes per year — it’s important that only essential vital information is stored in the cloud for long periods of time.

Going forward we will see more and more applications of edge computing, particularly in healthcare, virtual and augmented reality, drones, AVs, smart cities, and the remote monitoring of oil and gas. At Fusion Fund, we’re seeing more and more start-ups embrace edge computing. We firmly believe that advances in edge computing will drive the some of the most innovative consumer and industry products and applications going forward.

Edge Computing: A Primer was originally published in FusionFund on Medium, where people are continuing the conversation by highlighting and responding to this story.

On May 9, 2018, Fusion Fund held its annual meeting at Filoli Gardens in Woodside, CA. Guests included sector experts from leading industry and academic institutions, general partners from Silicon Valley’s top growth stage firms, as well as the founders of Fusion Fund’s portfolio companies.

We were fortunate to have had Shane Wall, Chief Technology Officer (CTO) of Hewlett-Packard (HP) and Global Head of HP Labs sit down with Fusion Fund Founding and Managing Partner, Lu Zhang and have a dialogue on “Silicon Valley’s Innovation Trends and Ecosystem”. Shane Wall has been at top executive positions in HP and Intel, driving company innovation strategies for the past 20 years and is passionate about the start-up community being an active angel investor himself.

The following were highlights from the conversations between Shane Wall and Lu Zhang:

Impact of Industry 4.0

Lu Zhang: Industry 4.0 is a hot topic right now that involves the internet of things, automation, cybersecurity etc., so which sector will be hit first, and which sector will be hit the hardest by this revolution?

Shane Wall: I would like to first put it into some context so that we know why it is going to be so big. At HP and HP Labs, we spend a lot of time looking into the future. We want to anchor our technology development on megatrends in humanity that span 30 years into the future. Some of these trends point to the limitation in resources, space, energy, and waste management that we will never be able to address using traditional manufacturing. In fact, it is estimated that if we continue our current growth, in 30 years, we will be consuming two earths to manufacture what we are doing. Industry 4.0, will have a totally different vision of manufacturing. The industries that will definitely be disrupted include the airline industry; the airplane you flew on today may have a jet engine and other components that are 3D printed. The other ones are the automobiles and life sciences/healthcare industries. Those are the three big industries that will be impacted.

Then the question is where the biggest impact will be? Our view is that it is not on a particular vertical industry, but on the approach of manufacturing. Rather than manufacturing in particular locations, the manufacturing itself will be distributed on-demand. Imagine a world that I can design digitally, transmit the design digitally, and I can edit and change in the locale that it is consumed and manufacture and print on-demand in that location. It can have massive impacts on, for example, taxes, import/export… it will have amazing impacts.

Lu Zhang: If we don’t need distributed supply chains anymore, do you think it will help or hurt the globalization?

Shane Wall: For globalization or “hyper-globalization”, we are more connected thanks to the internet and mobile technologies (therefore, elimination of distributed supply chains will not affect the globalization). Come back to the supply chain question itself, it will be massively, massively impacted. Some of our biggest customers are in logistics. With the advent of 3D printing, they will never work the same again. For example, UPS and FedEx’s core business are shipping physical goods. Suddenly everything is being transmitted digitally, they have to figure out how to reinvent their business. It was a surprise when they came to us first, but when you see their motivation you can figure out what is happening.

Lu Zhang: When big companies such as HP are pushing the development of Industry 4.0, where are the opportunities for small start-ups to be involved in this industry 4.0 revolution?

Shane Wall: This will be an ecosystem. Beyond HP Labs, I also run HP Tech Ventures. We do a lot of investments in strategic areas that are of interest to us. If you look at the venture capital market, the most recent data indicates that the largest sector for current investment is in manufacturing. That involves everything from supply chains to parts, to novel material technology… Yes, it will be huge for both large companies and small startups.

Lu Zhang: My next question is about computing capability. There are lots of discussion on edge computing and quantum computing. What do you think about the computing capability into 2050?

Shane Wall: We have been driving toward a vision called “blended reality”, the concept that physical beings like people, places, and objects you interact today are intertwined with our digital reality. The endgame, for example in 2050, means the boundary between what was human and what was man-made disappears as the two worlds merge together. Combine that with the continuous scaling effect of Moore’s law and the amount of computing power you are going to have, you will have the unbelievable computing power, incredible sets of sensors, all integrated naturally. The printed electronics, natural language processing, on-demand artificial intelligence is just the primitive beginning of this future tech conversion.

3D Printing

Lu Zhang: Let’s talk about 3D printing. 3D printing has tremendous potential in manufacturing, healthcare, and other industries. I was trained as a material scientist, I know 3D printing still has limitations when printing different materials. What’s your opinion on the challenges and opportunities in the scaling-up process for this technology?

Shane Wall: That’s a very good question! I will tell you about what we have developed at HP Labs, the Multi Jet Fusion, which has been marketed for a few years. All the existing technologies like SLS (Selective Laser Sintering) are analog. What is unique about Multi Jet Fusion is that it is truly digital. We can print at a Voxel (Volume Pixel) level of 20mm. At the 20mm level today, we can specify everything, like material type, translucency, flexibility, conductivity, color… All of these can be specified in a digital voxel level. Translating that back into material, that means I can create materials on the fly. Imagine that we can have a digital alloy, instead of mixing different metals together, we can start out with a base metal and through digitally applying these agents I can build a 3D alloy on-demand. Now imagine a metal that starts with a solid material and transitions into a different type and comes back to another type in the 3D space. What we are doing is opening the platform up it is an open platform. There are material development kits and instructions on how people can add their own material to our system. Then our customers are not limited to the options we can provide. We are at the beginning of this process and it will be exciting to watch how it develops in the next 5–10 years.

Healthcare

Lu Zhang: The next question is about healthcare. Healthcare capital investment has been quite popular since 2017. I published an industry report on AI in Healthcare last September and it received lots of attention. And earlier this year at the JP Morgan Healthcare Conference, AI in healthcare was a popular topic. Valuation of life science companies has been getting higher and higher. So what are your insights on this trend?

Shane Wall: It’s a huge market and huge opportunity and I think as long as we are still living, it will continue to be that way. You are right, valuations are just astronomical on some of these companies. To put into context, we know that in the long run, some of the healthcare issues we are facing today will get much more acute, whether it’s the cost of healthcare in mature markets or access to healthcare in emerging markets. The solutions have to adapt and adapt rapidly, that is part of the reasons that some of the valuations being so high.

Our own investment in the life sciences and healthcare sector is from a very different angle compared to most other people. A lot of folks are thinking about AI and the application of AI in the imaging space, we are looking into mobile analytics. One of our core competences which we have ~10,000 patents is in microfluidics. The ability to move a very small volume of fluid very rapidly and precisely. Today we use it to move ink, but we can use it to move a lot of things, for example, blood, saliva, cancer cells… All of those can be done very precisely and at very low cost and it can solve some big problems in the healthcare area. I think microfluidics will continue to be a big area for investment.

Lu Zhang: I agree that microfluidics has become an industry trend especially in cancer diagnostics. I invested a company in 2015 called MissionBio. It has become a leading company using the microfluidics technology for early-stage cancer diagnostics. Investment for microfluidics has increased dramatically.

Background on Shane Wall

Lu Zhang: You founded a startup, and you’re managing an innovation lab. At the same time, you are the chief technology officer of a very big corporation in Silicon Valley, and you do your own investments. Which role do you like the most?

Shane Wall: It’s like asking which child I love the most. Honestly, I love all these areas. What I like the most is the ability to impact where the world is going and being able to have multiple tools at scale to do it. To go after a big problem, I have got a set of people that we can invest directly, but I cannot do everything, so I can pull a startup from another area and help it scale up. The ability to operate at scale and use all the avenues is the most satisfying to me.

Lu Zhang: As the CTO of a large technology company, what is your thought process on identifying new markets and technology?

Shane Wall: At HP, we look at the megatrends of our society including mass urbanization, hyper-globalization, and demographic changes and ask what will be the toughest challenge that humanity is going to face. Then we ask ourselves: “Do we have the intellectual property or capability to solve that problem?” That’s the future market! That is the anchor and the guide for all the technology developments and investments. The beauty of the big humanity issues is that there is so much blank space, people have not solved these problems. That means there are so many opportunities sitting out there!

HP CTO Shane Wall on Industry 4.0, was originally published in FusionFund on Medium, where people are continuing the conversation by highlighting and responding to this story.

Fusion Fund, an up-and-coming early stage venture fund dedicated to investing in entrepreneurs looking to build globally disruptive companies using innovative technologies, is pleased to announce that the World Economic Forum (WEF) has selected its Founding and Managing Partner, Lu Zhang, as a Young Global Leader (YGL) for 2018. Each year, this honor recognizes the most distinguished leaders under the age of 40 for their professional accomplishments, commitment to society, and potential to shape the future of the world through leadership.

Fusion Fund’s Founding and Managing Partner, Lu Zhang

As global industry, government, and civil society strive to bridge divides and promote an inclusive future; the WEF brings together a range of inspiring individuals who have distinguished themselves in their fields. Young Global Leaders (YGLs) are proposed through a qualified nomination process and assessed according to rigorous selection a criterion that creates a diverse and truly representative body, while accepting only the very best leaders who have already demonstrated their commitment to serving society at large.

As one of one hundred 2018 YGLs, Lu Zhang is recognized for her success in entrepreneurship, venture investing, and acumen for identifying technologies that can make a big impact on global industries.

“I am honored to have been selected as a Young Global Leader for the class of 2018,” said Lu Zhang, Founder and Managing Partner of Fusion Fund. “With the support of the World Economic Forum, I hope to help even more technical-driven founders make a difference in the world.”

As an honoree, Lu will become an active member of the Forum of Young Global Leaders for a period of five years. The Forum of Young Global Leaders is a community of leaders from all walks of life, who operate as a force for good, to overcome barriers that stand in the way of progress and share a commitment to working with their peers to achieve results of true significance. The community forms a unique global network of peers with a highly visible opportunity to significantly impact world affairs and shape the global agenda.

Prior to starting Fusion Fund, Lu was the Founder and CEO of a medical device company focused on non-invasive technology for the early diagnosis of Type II diabetes (acquired in 2012). Lu has won numerous awards and is frequently invited to speak at industry conferences such as Slush and Collision. She is an advisor and mentor to several innovative programs and incubators in Silicon Valley. In 2017, Lu was recognized as Forbes 30 Under 30 as a Featured Honoree in the VC category, and selected as Town & Country 50 New Modern Swans. In 2018, Lu was recognized as a Silicon Valley woman of influence by SVBJ and Top 10 all America Chinese Youth. Lu also serves as a youth member of a council of Future Award. Lu received her M.S. in Materials Science and Engineering from Stanford University.

This year’s class of 100 Young Global Leaders is split evenly between business and not-for-profit sectors. Lu’s fellow YGL classmates include Nadeem Meghji, Senior Managing Director of Real Estate Group at The Blackstone Group, Joseph P. Kennedy, Congressman from Massachusetts (Democrat), and Jia Li, Head of R&D at Google Cloud AI. Among former YGLs head governments of Fortune 500 companies, are the recipients of Nobel Prizes and Academy Awards, serve as UN Goodwill Ambassadors and Social Entrepreneurs, include Jack Ma, Founder and Executive Chairman of Alibaba Group, Marissa Mayer, former CEO of Yahoo!, Larry Page, CEO of Alphabet, and David Cameron, former British Prime Minister.

For the full list of this year’s class of 100 Young Global Leaders, please visit:

https://www.weforum.org/communities/young-global-leaders

About Fusion Fund

Fusion Fund (formerly known as NewGen Capital) was founded by Lu Zhang in Palo Alto California in 2015. The firm focuses on early stage companies with a technical barrier in their business model to capitalize on the next wave of technical innovation. The fund focuses on targeted sectors including connected industries (mobility, robotics, IoT), network technologies (security, connectivity, enterprise), artificial intelligence (computer vision, natural language processing, deep learning), and health tech (digital health, medical devices).

Fusion Fund was launched in 2015, with an initial $17 million in capital. In 2018, Fusion Fund closed an $85 million second fund. The firm has made more than 40 investments so far, including IAM Robotics (autonomous robots with fast computer vision for warehouse fulfillment), Mission Bio (leveraging single-cell diagnostics for cancer treatment), Stratifyd (visual, data-driven actionable business intelligence solutions), and NeuVector (container cybersecurity company, recognized by Gartner as the leader in the industry).

For more information, please visit: https://www.fusionfund.com/.

World Economic Forum Names Fusion Fund’s Lu Zhang as 2018 Young Global Leader was originally published in FusionFund on Medium, where people are continuing the conversation by highlighting and responding to this story.