Best of 2019: Betting on Data Eating the World

IDC predicts that 175 trillion gigabytes of new data will be created worldwide in 2025

IDC predicts that 175 trillion gigabytes of new data will be created worldwide in 2025

[July 23, 2109]

Data is eating the world. All businesses, non-profits, and governments around the world are now in full digital transformation mode, figuring out what data can do to the quality of their decisions and the effectiveness of their actions. In the process, they tap into IT resources and landscape that have changed dramatically over the last decade, offering unprecedented choice, flexibility, and speed, facilitating the management of data eating work.

Launched in 2012, DraftKings is a prime example of a new breed of data-driven, perpetually-learning companies. One of the few players in the market for fantasy sports, it has faced “unique challenges that haven’t been solved by other businesses yet,” says Greg Karamitis, Senior Vice President of Fantasy Sports. To solve these challenges, “we have to lean on our analytical expertise and our ability to absorb and utilize vast amounts of data to drive our business decisions.”

Founded by serial entrepreneur Ash Ashutosh 10 years ago, Actifio is a prime example of the new breed of IT vendors transforming the IT landscape from a processor-centric to data-centric paradigm, from a primary emphasis on the speed of computing to a new focus on the speed of accessing data. “It used to be that only the backup people cared about data,” says Ashutosh. “Then it was the CIO, and later, the Chief Data Officer or CDO. Now, every CEO is a data-driven CEO. If you are not data-driven, most likely you are not the CEO for long.”

Data “as a strategic asset” was the vision driving Ashutosh and Actifio in 2009, bringing to the enterprise the same attitude towards data that has made the fortunes of consumer-oriented, digital native companies such as Amazon. “We wanted to facilitate getting to the data as fast as possible, to make it available to anybody, anywhere,” recalls Ashutosh. Since only backup people cared about enterprise data 10 years ago, they were the customers Actifio initially targeted.

The value proposition for these customers centered on reduced cost, as Actifio helped them maintain only one copy of any piece of data, available for multiple uses, instead of maintaining numerous copies, each one for a specific application or data management activity. Actifio achieved this magic trick (e.g., reducing 50 terabytes of data to only 2 terabytes) by capitalizing on another trend shaking the tech world 10 years ago, virtualization. Replacing analog data with digital data gave rise to data-driven companies and replacing the physical with the logical—virtualization—created a new IT landscape.

It took a while for enterprises to adapt to the new IT realities. But around 2015 the cloud flood gates opened because of the business pressures to do everything faster and faster, especially the development of new (online) applications, and the fact that more and more enterprise roles and activities required at the very least some creation, management, manipulation, analysis, and consumption of data. These changes manifested themselves in Actifio’s business. In its most recent quarter (ended April 30, 2019), “60% of our customers used Actifio to accelerate application development, up from close to 0% in 2016,” reports Ashutosh, “and over 30% of our workloads today are in cloud platforms.”

The new attitude towards data as a strategic asset and the widespread availability of cloud computing have opened up new uses for Actifio’s offerings such as compliance with data regulations and near real-time security checks on the data. But possibly the most important recent development is the increased use by data scientists for machine learning and artificial intelligence-related tasks.

A very significant chunk of data scientists’ time (and their most popular complaint) is the time they spend on data preparation. And a significant chunk of the time spent on data preparation is simply waiting for the data. In the past, they had to wait between eight to forty days for the IT department to deliver the data to them. Now, says Ashutosh, “they have an automated, on-demand process,” providing them data from the relevant pool of applications, in the format they require. Bringing up the new term “MLOps” (as in machine learning operations),  Ashutosh defines it as “allowing people to make decisions faster by not having data as a bottleneck.” The end result? “The more you give people access to data in self-service way, the more they find new and smarter ways of using it.”

As 40% of Actifio’s sales come from $1 million-plus deals, these new uses of data are not “tiny departmental stuff,” says Ashutosh. “Large enterprises are beginning to use data as a strategic asset, as a service, on premises or in multi-cloud environments.”

Large enterprises today learn from, compete with, and often invest in or acquire the likes of DraftKings, a startup that runs on data. Growing the business for DraftKings means making their contests bigger and bigger. “But if we make our contests too big and we don’t get enough users to fill 90% of the seats, we start to lose money really fast,” says DraftKings’ Karamitis. Balancing user satisfaction and engagement with the company’s business performance requires accurate demand predictions for each of the thousands of contests which DraftKings runs daily in a constantly changing sports environment.

“We need to absorb tons of data points to figure this out on a daily basis,” explains Karamitis. But these data points are not only based on DraftKings accumulated experience with past contests. Befitting a business living online, another important data source is social networks—“our users are giving us enormous amount of data in terms of what they are twitting to us, what they engage with and what they don’t, allowing us to understand better which ways we want to shift as a company and which way we want to build a product,” says Karamitis.

There is yet another source of the data that is driving decisions at DrafKings, possibly the most important one: The data DraftKings creates by constantly experimenting. “We create data points by running structured tests,” says Karamitis. They cannot run A/B tests, he explains, because running smaller size contests will not produce the same effect of larger size contests and because their users tend to communicate a lot among themselves and compare notes about their experiences. “We are willing to take the risk testing our underlying beliefs around user behavior,” says Karamitis, by changing the top prize or changing the marketing treatment, for example.

In the past, domain expertise was the key to a company’s success. Today, it is data expertise, and the skill of applying it instantly to new opportunities as they arise. “Our analytical expertise allows us to learn really fast, learn in the traditional meaning of learning from experience and figuring out what matters to our users and also learn in the more modern sense of building machine learning algorithms around the key principles our users care about,” says Karamitis. Learning from data has become a core competency that can be applied to new markets, a competency that should serve DraftKings well as it pursues the new business opportunity of legalized sports betting.

DraftKings—and the new breed of data-driven companies—are data science labs, creating data and acquiring new insights with continuous experimentation. Like good scientists, they test their hypotheses through carefully structured experiments, challenging their own assumptions about customers and markets. Karamitis recalls the start of the 2017 NFL season when DraftKings offered a new contest site: ”We had a very specific expectation as to who it’s going to appeal to and how big it will be. We were totally wrong, 100% wrong.” But because the new product was developed through experimentation, the data led to what is now a “super valuable product, so different from what we initially offered.”

Ashutosh predicts that over the next few years we will see a bifurcation of the economy into two segments, one focused on producing physical assets and the other comprised of data-driven companies. Like DraftKings, these companies, whether startups or established enterprises, will view data as a strategic asset and its analysis as a core competency and a key competitive differentiator.

And like DraftKings, these companies will increasingly resemble scientific labs, continuously learning through experimentation and creating new data points. Data growth will drive business growth as data continues eating the world.

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Shakey, the World’s First Mobile Intelligent Robot


Developed at the Artificial Intelligence Center of the Stanford Research Institute (SRI) from 1966 to 1972, SHAKEY was the world’s first mobile intelligent robot. According to the 2017 IEEE Milestone citation, it “could perceive its surroundings, infer implicit facts from explicit ones, create plans, recover from errors in plan execution, and communicate using ordinary English. SHAKEY’s software architecture, computer vision, and methods for navigation and planning proved seminal in robotics and in the design of web servers, automobiles, factories, video games, and Mars rovers.”

Read more here

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Best of 2019: Big Data AI


[July 1, 2019]

In December 2014, I asked whether we were at the beginning of “the end of the Hadoop bubble.” I kept updating my Hadoop bubble watch (here and here) through the much-hyped IPOs of Hortonworks and Cloudera. The question was whether an open-source distributed storage technology which Google invented (and quickly replaced with better tools) could survive as a business proposition at a time when enterprises have moved rapidly to adopting the cloud and “AI”—advanced machine learning or deep learning.

In January 2019, perennially unprofitable Hortonworks closed an all-stock $5.2 billion merger with Cloudera. In May 2019, another Hadoop-based provider, MapR, announced that it would shut down if it were unable to find a buyer or a new source of funding. On June 6, 2019, Cloudera’s stock declined 43% after it cut its revenue forecast and announced that its CEO is leaving the company. Valued at $4.1 billion in 2014, Cloudera’s current market cap is $1.4 billion.

Is this just the end of Hadoop or is it the death of Big Data? Was our fascination with lots and lots of data only a temporary bubble?

The news last month were not all negative for the Data is Eating the World phenomenon. Google announced its intent to acquire data discovery and analytics startup Looker for $2.6 billion and Salesforce announced its intent to acquire data visualization and analytics leader Tableau for $15.7 billion.

“The addition of Looker to Google Cloud,” said an Alphabet press release, “will provide customers with a more comprehensive analytics solution — from ingesting and integrating data to gain insights, to embedded analytics and visualizations — enabling enterprises to leverage the power of analytics, machine learning and AI.” The Google Cloud blog explained that “A fundamental requirement for organizations wanting to transform themselves digitally is the need to store, manage, and analyze large quantities of data from a variety of sources… The addition of Looker to Google Cloud will help us offer customers a more complete analytics solution from ingesting data to visualizing results and integrating data and insights into their daily workflows.”

Digital transformation is finding out what data can do to your business decisions and actions. It’s focusing your company on mining and benefiting from its second-most important resource after its people: Data. While digital-born, Web-native, data-driven companies such as Google and Salesforce have been doing this for twenty years, many other businesses around the world, large and small, are now in full digital transformation mode, exploring the power of data eating the world. In the process, they tap into IT resources and data science tools in the cloud and experiment with advanced machine learning or deep learning. The remarkable and rapid progress in computer vision and natural language processing capabilities over the last 7 years has been enabled by big data—lots of tagged and labeled online data. Deep learning is Big Data AI.

Here’s what two CEOs of startups providing data mining services have to say about where we are in the evolution of Big Data to Big Data AI:

“The value of the data analytics market can’t be ignored. The Looker and Tableau acquisitions demonstrate that even the biggest tech players are snapping up data analytics companies with big price tags, clearly demonstrating the value these companies have in the larger cloud ecosystem. And in terms of what this means for the evolution of AI, we’ve reached a point where we have more than enough anonymized data to train the system, and now it’s a matter of honing how we use the AI to extract the maximum value from data”—Amir Orad, CEO, Sisense

“The Google Cloud/Looker and Salesforce/Tableau acquisitions are a direct reaction to the rate at which analytics workloads have been shifting to the cloud over the past few years. The state of AI is a reflection of this shift as machine learning, AI and analytics have become the primary growth opportunities for the cloud today. Yet, it’s this same growth that is causing barrier to success as AI project overwhelming face the same problem — data quality”—Adam Wilson, CEO, Trifacta

Sisense is a business intelligence startup providing “a complete solution for preparing, analyzing and visualizing big data.” It has raised $174 million over 5 rounds and in May 2019, it acquired Periscope Data. Trifacta has raised $124.3 million over 6 rounds and is focused on data preparation. It announced today a partnership with IBM to develop a new data preparation tool.

A search for “big data” in the Crunchbase database results in close to 15,000 entries. A search for “AI” results in close to 12,000 entries. There is probably a huge overlap between those two categories. And the real-world overlap will only intensify in the near future.

How many of the hundreds of the “big data” startups will merge with one another or be acquired by established data-driven companies as “big data” evolves into “big data AI”?

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Best of 2019: Avram Miller and Lessons for Corporate VCs

[June 18, 2019]

Corporate venture capital (CVC) firms broke records last year. Funding increased 47% to $52.95 billion and 264 new corporate venture groups invested for the first time, a 35% increase over 2017. There were 773 active CVCs worldwide in 2018, participating in 23% of all VC-backed deals, with the average CVC deal size reaching an all-time high of $26.3 million, according to CB Insights.

But for venture capitalist Fred Wilson, “corporate investing is dumb… Don’t waste your money being a minority investor in something you don’t control. You’re a corporation! You want the asset? Buy it.”

Is Wilson only complaining about the competition or is he right to argue that the main motivation for this tsunami of venture corporate dollars is to make executives “look smart” in front of their bosses? Are we witnessing the peak of yet another tech bubble with all corporations suffering from Silicon Valley Envy (SVE as opposed to VC’s FOMO) or are they just looking for somewhere to park—and possibly increase—their growing mountains of cash? Are we going through a fear-of-disruption period similar to the late 1990s or does “digital transformation” actually capture a fundamental shift in competitive strategy, in how to make a corporation thrive today?

To paraphrase Joseph Conrad, for a business strategy to be successful, “it must contain the care of the past and of the future in every passing moment of the present.” History is crucial to understanding the present and divining the future. So to better understand the current corporate tech-investing rush and how to make it more focused as an agent of change, I talked to Avram Miller. Almost three decades ago, Miller co-founded Intel Capital, one of the most prominent CVCs then and now.

Avram Miller

Avram Miller

Since 1991, Intel Capital has invested $12.4 billion in 1,544 companies in 57 countries. During that time, 670 portfolio companies have gone public or participated in a merger. Last year, it invested $391 million in 89 companies with 38 new investments and was the third most active CVC worldwide, according to CB Insights (it was #2 in 2017 and #1 in 2016 and 2015).  According to an Intel spokesperson, Intel Capital has been making in recent years fewer new investments each year so it can take more meaningful stakes and play a more relevant role in helping startups grow and succeed.

For his first three years with Intel, Miller worked on a joint venture with Siemens to develop a fault-tolerant computer, a project that eventually became BiiN, a jointly owned company. Miller’s next task was to help Intel “expand its footprint” and in the process expand its horizons by acquiring knowledge of adjacent market spaces.

To this aim, Miller started to buy small companies on Intel’s behalf, “mostly for their people” and mostly people with networking and communications expertise, as that market was “silicon-intensive.” Miller presented this idea to the Intel board and it was approved but… “it didn’t work because the culture of the company was so strong. The antibodies came out and rejected the foreign entity.” The acquired talent didn’t stay.

The solution was to take minority positions in startups instead of acquiring them. When Les Vadász came back from sabbatical in 1991, this activity was formalized under him as Corporate Business Development (CBD) which later was re-named Intel Capital. Miller focused his investment activities on the consumer market for computers—because that was what interested him (when he joined Intel, he later told Robert X. Cringely, “secretly what I wanted to do was to get into the consumer electronics business”) and, if I may speculate, because “Andy said there is no consumer market for computers.”

Grove told Miller he was wasting his time, but “the good thing about Andy was he wouldn’t stop me.” And no one else at the company stopped him because they didn’t care, says Miller, “benign neglect, you know, I could go out there and do that.” And he did it at the right moment, “because we started having the Internet and there was all this convergence of things,” says Miller.

“Convergence” was THE buzzword of the early 1990s, heralding the coming fusion of computers, communications and content (media). Miller was at the center of all things converging, both offline and, increasingly, with the emergence of the Web, online.  At first, “convergence” was equated with “interactive TV,” beefing up television sets and channels to provide a wide range of content and data services to the home. Intel was less interested than other players in TVs and more interested in PCs as the target for all this new consumer interactivity. Grasping the large opportunity for selling more “Intel Inside” PCs, Miller was instrumental in the development of residential broadband, pushing Intel and others into the development of cable modems as facilitators of new media streams into the home (removing “the bottleneck over the ‘last mile’ connection to the end-user” as Hybrid Networks, one of Miller’s venture investments, described itself in its 1997 IPO).

“I couldn’t believe the amount of money that was being spent to do interactive television,” says Miller. “It never happened. It still hasn’t happened. People don’t want to interact with their TV. Unless you are a gamer.” But the interactive home PC did happen. Instead of making the existing TV distribution network work like the Internet, the winning solution—and the triumph of the PC over the TV—was the adding to the Internet of a truly interactive, linking-everything piece of software, the World Wide Web.

Being at the center of all things converging—and pushing hard for the PC as the end-user solution—meant that Miller and Intel Capital benefited greatly from the resulting dot-com bubble. Miller was the only outside person quoted in the April 1994 press release announcing the establishment of Mosaic Communications, later known as Netscape. According to Miller, he and his colleagues thought that Netscape’s valuation of $18 million was too high and passed on the opportunity (“we never invested in anything Kleiner Perkins was in,” says Miller). A year later, at its very successful August 1995 IPO which launched the universal rush to dot-com glory, Netscape had a market value of $2.9 billion.

But Intel Capital backed other Internet-related startups with valuations that were more in line with its rational exuberance and disciplined investment criteria and became, in certain years, the second-best business unit in the company in terms of profitability, according to Miller.

Continuing their focus on the “intersection of the Internet and broadband,” they invested in information security (e.g., Verisign), in companies “transporting media” (e.g.,, and companies creating new online content and communities (e.g., Cnet, Geocities, CMGI). Miller was a proactive investor, styled himself as the “activist strategist,” and wasn’t interested in companies coming to him. “I was interested in theory,” says Miller, “in figuring out where the next wave was,” and looking for the companies representing it.

By the time he left Intel in 1999, Miller and his team were doing 2 to 3 deals per week and presiding over a multi-billion dollar portfolio. Investment discipline was important and Miller was divesting 5% to 10% of the portfolio every quarter. So was establishing processes ensuring due diligence and the review and approval of Intel’s finance and legal functions.

One of the advantages of Intel Capital over a traditional VC firm was that “I had Intel behind me,” says Miller. Other advantages included Intel’s wide network of strategic partners in all segments of the industry which meant they knew a lot about what other companies were doing, knowledge that could help the startups they were backing. In addition, they could support them with Intel’s unique capabilities and technologies and broad understanding of technological roadmaps and trajectories, thus increasing the probability of success of these startups. “Once people realized this, they wanted us to be investors,” says Miller. But what did Intel get out of these investments?

Intel Capital had three major objectives according to Miller: Financial return, growing the market, and strategic insight. The positive financial returns were an important measurement of success for the venture investments but were not the most important objective. “We made a lot of money but this is not why we were in business. Our mission was strategy first, money second,” says Miller.

The strategy was to grow the market. Intel was in a unique position in that regard. “We own 85% of the market” for PC chips was Miller’s thinking, “so if we grow the market, we get 85% of that.” At the time, Andy Grove called this growing the market strategy “the PC is it,” telling Fortune in 1995 that “We can make it so superb as an entertainment machine, and so vital as a communications medium for both the home and the workplace, that it will battle with TV for people’s disposable time.” Says Miller: “I feel very comfortable saying that we grew the market substantially by the things that we did.”

Miller’s contribution to the “PC is it” growing-the-market strategy went beyond venture investments. To a large extent he became what today we call a market and company “evangelist,” the go-to-guy for the media and analysts seeking colorful commentary on technology. In 1996, for example, Miller spoke at the Bear Stearns Technology Conference about the Internet as the focus of “a new medium he dubbed The Connected PC,” driving “profound changes in the nature of communications, resulting in ‘social computing’ and direct contact with one’s customers,” and, of course, increased sales of PCs. “I was one of the few people [at Intel] that had a personality and I’d get sent out,” he observes.

The third key objective, “strategic insight,” could have simply meant gaining knowledge of new trends, technologies and business models by investing in entrepreneurs and their innovations. But Miller defines strategic insight in broader terms to include actual change to Intel’s business strategy. And he states categorically: “From a strategy point of view, I don’t think we did much for the company. We’re investing all over the Internet, consumer Internet, from top to bottom. We’re in all these deals, we understand exactly what’s going on. What did we do about it? Nothing.”

Miller uses information security as an example. Based on their investments and understanding of the evolving market, Miller realized in the mid-1990s that “we have to find a way to let people own their data, a way to encapsulate the data in such a way that they have the key.” But this insight did not turn into a new strategy, a new business, a new addressable market for Intel. Why?

Considering the desire for change expressed to Miller when he was hired and what became to be known inside Intel as Grove’s Law—”only the paranoid survives”—why didn’t Intel capitalize on its successful investments in Internet-related and other startups to transform itself again (as it did when it moved in the 1980s from memory chips to PC microprocessors)?

The microprocessor was like a vein of gold. And when you dig up all the gold, Miller used to tell Andy Grove, all you are left with is a big hole. Intel needed to get out of there but “it was just impossible. Andy got stuck, he couldn’t move.”

Why couldn’t Andy Grove move—a very successful business leader who has already managed before a courageous and difficult move and has spent the 1990s being paranoid, promoting the theory of “disruption”? One answer was given years earlier by another very successful business leader which Miller had the privilege of working with—Ken Olsen, founder and CEO of Digital Equipment Corporation (DEC).

“Probably the biggest danger, the biggest human weakness, comes from a few years of success. It blinds us. It blinds anyone. Pride—probably the biggest human weakness,” says Olsen in a film intended to celebrate DEC’s 25th anniversary which ended up focusing entirely on Miller and his team developing Digital’s first PC in 1981-1982. The film, never shown internally or publicly as originally planned, “exemplified the confusion that rocked Digital in the early 1980s,” say Glenn Rifkin and George Harar in The Ultimate Entrepreneur, published in 1988, at the peak of DEC’s success and just before its quick descent into tech oblivion.

Olsen got it right about pride. In 1983, Wall Street demanded his ouster from the company he founded. And he proved them wrong, leading Digital through its best years. When Fortune calls you (in 1986) “arguably the most successful entrepreneur in the history of American business,” it does something to your pride, to how much you tolerate arguments, to your willingness to get unstuck.

Great business leaders get stuck even when they are very aware of the dangers of success and of human weaknesses. Jeff Bezos’ insistence that it always “day 1” at Amazon, that it will always act as a startup, does not guarantee that Bezos and his successful creation will not get stuck in the future. And no matter what they tell you in business schools, I don’t believe there are any rules or prescriptions or 12-steps or whatever else that could turn this awareness into guaranteed longevity, agility, and triumph over all competition. Just like any other human activity, business and succeeding in business is a multi-faceted endeavor subject to too many variables for us to predict what will or will not work in a given situation. The same actions that work for one company may not work for another.

Still, my conversation with Miller brings up a few observations about where and when great business leaders can get stuck and what does this mean for setting objectives for corporate VCs.

Leading with and adhering to the made-up dictates of Moore’s Law, Intel tied itself inextricably to the prevailing dogma of the computer industry’s: Faster and faster processors. The speed of calculation has been the measure of everything since the early days of computers in the late 1940s.  Miller says he used to tell Grove that Intel’s competition was not AMD, it was a larger monitor—”If I only have so much money to spend, do I get a faster processor or a bigger monitor?”

Intel also carried the burden typical of successful tech companies—what to do with its “legacy,” how to keep its “installed base” happy? That manifested itself primarily when the world moved to mobile computing and Intel was slow to adapt because its legacy architecture favored performance over battery power. “They said, we can’t give that [86 architecture] up because of the value of the software. But it doesn’t matter if I can run software if my phone only works for 10 minutes,” says Miller.

DEC and Ken Olsen got stuck in another dogma, that of the “structure follows strategy” business model of a “computer company,” also dominant since the early days of the industry: All computer companies must be vertically integrated, the “correct” business model that entailed producing everything in-house from semiconductors to software applications. But in the early 1990s, the industry has very rapidly gone through a transformation that created a new horizontal industry structure, with specific companies focused on (and dominating) specific horizontal layers: Intel in semiconductors, EMC in storage, Cisco in networking, Microsoft in operating systems, Oracle in databases.

Miller says that DEC had “all the ingredients of the new, next generation. But the next generation was horizontal. And so digital would have had to break itself up. They could have been Intel, they could have been Google. They had everything that exists now but they had it put together wrong.” And getting stuck was not (and is not) the privilege of successful founders and successful leaders. Even when the Olsen era was over, “they didn’t have anybody to lead it,” says Miller. DEC engineers were like “high priests, they were purists. They were focused on the technology without a coupling to the business. They didn’t have any understanding of it.”

Making sure you don’t get stuck (beyond just saying so) is one way to focus the mission and investments of today’s corporate venture capital firms. Treat the function as the corporation’s science lab and corporate VCs as scientists, always aiming to refute the existing hypotheses, assumptions, and models on which the current business of the corporation is based.

And like good scientists, they should also understand the value of history’s lessons and embrace the notion that you could see far into the future only if you stand on the shoulders of the giants that preceded you. Miller is surprised at the lack of curiosity and interest in the past prevalent among some of today’s business leaders. “As far as Larry Page is concerned, the world started the day he started Google and nothing else matters,” says Miller. “In my generation, I think we were interested in what happened before.”

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Best of 2019: How Israel Became a Medical Cannabis Leader

[April 29, 2019]

Opening the CannaTech conference earlier this month, former Israeli prime minister Ehud Barak quipped that Israel is now the “land of milk, honey and cannabis.” Given the recent performance of the cannabis-related stocks traded on the Tel-Aviv stock exchange (Barak is Chairman of InterCure whose stock appreciated 1000% in 2018), are investors getting high on nothing more than a buzz bubble?

Behind the buzz about “marijuana millionaires,” Yuge market potential, and volatile stocks (InterCure’s stock nearly tripled earlier this year but is now 25% off its peak), is a serious 55-year-old Israeli enterprise of pioneering interdisciplinary research into the medical benefits of cannabis. Supported by a perfect climate for growing cannabis, it has led to a very supportive climate—academic, regulatory, and entrepreneurial—for developing botanical-sourced pharmaceutical-grade products. Like the rest of the world, Israel has considered cannabis (and still does) to be a “dangerous drug,” but unlike the rest of the world, it has not let the stigma deter its insatiable curiosity about cannabis’s therapeutic potential.

The entrepreneurial poster child for this long-held belief in the efficacy of medical marijuana is Breath of life (BOL) Pharma. Founded in 2008, today it is “the only company in Israel that is fully integrated throughout the value chain,” says its CEO, Tamir Gedo.

This means BOL Pharma is compliant with the GAP and GMP standards of the global pharmaceutical industry, governing all stages of production and distribution, from cultivation to processing to marketing of finished products such as tablets, capsules, inhalers, creams and oils. This unique competitive advantage is buttressed by BOL’s R&D function, currently involved with 32 Phase 2 clinical trials, and a 65,000 square feet production plant and one million square feet of cultivation facilities.

“You don’t see this kind of consistency in products around the world,” says Gedo. “Flowers are not consistent and if you don’t have consistency, you run the risk of having side effects at different times.” The need to overcome the challenge of developing medicine from an inconsistent botanical source is why 60% of BOL’s 200 employees have worked before in the pharmaceutical industry and why Gedo insists on staying focused on the company’s medical cannabis vision and not developing products for recreational use. “Our advantage is time,” says Gedo, “we’ve been doing it for many years.”

This time- and experience-based competitive advantage applies to the Israeli cannabis ecosystem as a whole. In the early 1960s, looking to make his mark in the academic world, Israeli chemist Raphael Mechoulam decided to focus on cannabis research because “in a small country like Israel, if you want to do significant work, you should try to do something novel.” Moreover, “a scientist should find topics of importance,” he says in the documentary The Scientist. “Cannabis had been used for thousands of years both as a drug [and] as a recreational agent, but surprisingly, the active compound was never isolated in pure form.”

Mechoulam and his colleagues isolated the chemical compounds of cannabis (which he called “cannabinoids”), specifically CBD (the main non-psychoactive component) and THC (the psychoactive component). In the early 1990s, they discovered the endocannabinoid system in the human body which is involved in regulating a variety of physiological and cognitive processes (including mood and memory), and in mediating the pharmacological effects of cannabis.

These discoveries have led to a vast body of research conducted in Israel and around the world on various aspects, medical and otherwise, of cannabinoids (see here, for example). With government support, both in terms of funding and regulation, Israel has become a center for medical cannabis R&D, with many academic institutions and companies “offshoring” research and clinical trials to Israel, having been prevented from doing it in the US and elsewhere.

Gedo calls this R&D-and-clinical-trials-as-a-service “open innovation,” providing the research and regulatory infrastructure for others to innovate and produce their own IP. But the infrastructure and accumulated experience and expertise also help BOL Pharma and other Israeli companies develop their own unique cannabis-related IP. For example, BOL has been working on unique new formulations which make the medical cannabis more effective by increasing its “bio-availability” (rate of absorption in the body), thus reducing the cost to the consumer and potential side effects.

When BOL entered the medical cannabis market a decade ago it did not have a lot of local (or global, for that matter) competition. Today, according to a recent survey published in Israeli business publication Globes, there are more than 100 Israeli companies contributing to the “current boiling point” of this market. These include companies growing and processing cannabis, or running pharma production facilities, or exporting Israeli know-how, or developing drug delivery mechanisms.

These companies are going after a worldwide medical cannabis market estimated to grow rapidly to $100.03 billion in 2025, according to Grand View Research. Most are private companies, but some may test the public markets before long—BOL Pharma may list on the Canadian stock exchange or in the US and Canndoc, another pharma-grade medical cannabis pioneer (acquired last year by InterCure), has recently submitted a confidential prospectus for a Nasdaq IPO.

The ever-growing market size estimates and increased activity in the public markets have drawn the attention of venture capital firms. Funding for cannabis startups in the US more than doubled from 2017 to 2018, reaching more than $1.3 billion, according to Crunchbase. The first quarter of 2019 saw funding more than double year-over-year, and earlier this month, Pax Labs (vaporization technologies and devices) raised $420 million at a valuation of $1.7 billion.

The most recent funding data for cannabis-related Israeli startups shows  that only $76 million have been raised from 2013 to 2017, according to IVC Research. That number has probably increased considerably by now as just one cannabis-related startup, Syqe Medical (inhalers), has raised $50 million in its second round of funding at the end of 2018.

And there’s more to come, including Israel-US collaborations. OurCrowd, Israel’s most active venture investor (including in Syqe Medical), announced in January that it will partner with Colorado-based 7thirty to create a new $30 million fund focused on emerging cannabis technology companies in Israel, Canada and the United States.

At its annual conference last month, OurCrowd awarded 88-year-old Professor Raphael Mechoulam its Maimonides Lifetime Achievement award (the other winner was 100-year-old Professor Avraham Baniel, the inventor and co-founder of DouxMatok). In accepting the award, Mechoulam talked about his current work, predicting that “within the next decade, maybe less, we shall have drugs for a variety of diseases based on the compounds, the constituents of the [cannabis] plant and the constituents of our own body, the endogenous cannabinoids, and compounds that we make, that will be effective for a large number of diseases.”

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Best of 2019: AI in Healthcare

[April 22, 2019]

“The past fifty years,” says Dr. Eric Topol in Deep Medicine: How Artificial Intelligence Can Make Medicine Human Again, “have introduced important changes to radiology. As the medium moved from analog to digital… the whole process, extending from films to CT, PET, nuclear, and MRI scans, has been made more efficient. Except the interpretation.”

Dr. Topol quotes studies suggesting that errors in interpretation of medical scans “are far worse than generally accepted,” with false positive rates of 2% and false negative rates over 25%. As a result, 31% of American radiologists have experienced a malpractice claim, “most of which were related to missed diagnoses.”

The rapid advances in computer vision due to the application of AI starting in 2012, have led to predictions of the imminent demise of radiologists, to be replaced by better diagnosticians—deep learning algorithms. Geoffrey Hinton, one of this year’s Turing Award winners and a major contributor to the remarkable success of deep learning, suggested in 2016 that “People should stop training radiologists now. It’s just completely obvious that in five years deep learning is going to do better than radiologists.” In the same year, an article published in the Journal of the American College of Radiology warned that “The ultimate threat to radiology is machine learning. Machine learning will become a powerful force in radiology in the next 5 to 10 years and could end radiology as a thriving specialty.”

While Dr. Topol believes that eventually all medical scans will be read by machines, he argues that radiologists can have a bright future if they “adapt and embrace a partnership with machines.” Eyal Gura, co-founder and CEO of Zebra Medical Vision, agrees: “AI can help doctors get to the right place quickly and make the right decision.”

Gura’s vision is that Zebra will help “automate every visual aspect of medicine,” going beyond radiology to pathology, dermatology, dentistry, and to all situations where “a doctor or a nurse are staring at an image and need to make a quick decision.” This “automation” does not mean replacing doctors. Rather, it means the augmentation of their work, providing consistent, accurate, and timely assistance. “We need all the doctors we have in the world and we will need 10X more because of the aging population,” says Gura.

Zebra’s work experience with radiologists in more than 50 hospitals worldwide highlights the role of AI as Augmented Intelligence. Its algorithms help overcome the “training bias,” the fact that “their brains are fine-tuned” to the specific cases they studied in their textbooks, says Gura. “Once trained,” writes Dr. Topol, “doctors are pretty much wedged into their level of diagnostic performance throughout their career. Surprisingly, there is no system in place for doctors to get feedback on their diagnostic skills during their careers, either.”

Zebra’s algorithms provide this missing feedback by offering radiologists a second opinion. In addition, they provide assistance and augmentation when there’s minimal or non-existent training. Consider the case of a young doctor in the ER who is not familiar with how a tiny brain-bleed looks on a scan and entirely misses it or a rural clinic with no access to a radiologist (the World Health Organization estimates that two-thirds of the world’s population has no access to any diagnostic imaging).

So far, Zebra has developed 48 algorithms addressing 48 different medical conditions (8 have already received regulatory approval in Europe and one in the US) that assist radiologists at different points in time, from acute conditions to current diseases to preventive medicine based on past scans. Earlier this year, Zebra announced the first multi-modality AI triage solution, addressing two life-threatening conditions, brain-bleeds and pneumothorax (the presence of gas between the lung and the chest wall). The Zebra triage solution is integrated into the hospital’s workflow, sends an alert when it detects a suspected acute finding, reducing the time to diagnosis by 80%.

At the other end of the timescale, Zebra’s algorithms can help in reviewing past scans, identifying patients at-risk and assisting in population health management.  A number of 5-year retrospective cohort studies conducted by one of Zebra’s research partners, Clalit Research Institute, found that Zebra’s algorithms performed better than the current medical gold standards for predicting osteoporosis fractures and risk for cardiac event.

Last month, Zebra announced it will collaborate with HealthNet Global (HNG), part of the Apollo Hospitals Group in India, to “provide timely, cost-effective, quality care to patients in remote and rural locations.” For example, they plan to develop a chest X-ray interpretation tool for TB to help in its early diagnosis by supplementing sputum testing which is only 50% accurate and frequently misses the disease in its early stages (the World Health Organization estimates that 3.6 million people with TB are missed by health systems every year and do not receive adequate care). HNG and Zebra will be supported by a grant from India-Israel Industrial R&D and Technological Innovation Fund.

“In rural areas in India you will be able to have a nurse and an X-ray technician and get an early diagnosis or an alert on an acute condition to allow them to provide the first line of support,” says Gura. Thanks to Modicare, “out of nowhere, 500 million people in India will have health insurance, but you will not have more doctors to treat them,” he adds, promising a similar deployment by Zebra in Africa later this year.

Headquartered in Israel, Zebra most recently raised a $30 million Series C in July 2018, led by aMoon Ventures, with participation from Aurum, Johnson & Johnson Innovation—JJDC Inc., Intermountain Health (also acting as one of Zebra’s data and research partners ) and AI pioneers Fei-Fei Li and Richard Socher. Existing investors Khosla Ventures, Nvidia, Marc Benioff, OurCrowd and Dolby Ventures also returned for the round, helping bring total funds raised by Zebra to $50 million.

The funds will be used to further improve Zebra’s algorithm development process, commercialization of these algorithms (designing and launching products), and integration with health providers’ existing systems, all crucial to achieving Zebra’s goal of becoming a one-stop shop (at $1 per scan), and establishing a sustainable competitive advantage.

Zebra is part of a growing community of Israeli digital health companies. Last year, total investments in the sector increased 32% and exceeded $500 million for the first time, with 85% of this amount going to companies utilizing AI solutions, according to Start-Up Nation Central. A significant competitive edge for these startups is the availability of data collected over the last 25 years by Israel’s four HMOs and their affiliated hospitals, serving 98% of the population and using the same electronic medical records system.

Israeli health-related startups (more than 1,200 in digital health, medical devices, and Pharma), with their unique mission and potential for making a real difference are increasingly attractive to Israeli AI, machine learning, and data science experts, now being assiduously courted by deep-pocketed global competitors. “Especially at a certain age, they feel the need to do something more meaningful. They see that the time and talent they spend on ad conversion can be better spent on saving their mother or father,” says Gura.

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AI by the Numbers: Only 14.6% of Large Companies Have AI in Production

Recent surveys, studies, forecasts and other quantitative assessments of the progress of AI find that many organizations struggle to deploy AI widely and move beyond limited projects.

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