How Zoom Created An $18 Billion Juggernaut

This week, I attended Zoom’s annual conference in San Jose. A big takeaway: The company remains laser-focused on pushing the boundaries of innovation. During the past year, Zoom has added over 300 features to its platform. The company is also making a bold play for the massive phone market. The vision is to develop a true unified communications system.

As a result, Zoom has become one of the world’s most valuable cloud companies (it came public in April). In the latest quarter, revenues soared by 96% to $145.8 million and net income came to $5.5 million (yes, this is one of the few newly minted IPOs that is profitable!) There are now about 66,300 customers with more than ten employees, up 78% on a year-over-year basis, and 466 contribute more than $100,000 on an annual basis. 

But getting to this point was not easy. After all, when CEO Eric Yuan founded the Zoom in 2011, the market for web conferencing appeared to be mature and was dominated by large tech companies.

One of the early investors–Emergence Capital’s Santi Subotovsky–said to me that Zoom was the toughest deal to get follow-on financing for. “There was the issue of the Silicon Valley echo chamber,” he said. “But if you looked to other countries, you could see there was much opportunity to make web conferencing better. Not everyone has high-speed Internet access.”

Another key to Zoom’s success was that Eric built a video-first platform that worked seamlessly with mobile. Keep in mind that the legacy solutions were mostly for screen sharing. What’s more, they often required frustrating configuration and setup. Zoom, on the other hand, was about making the experience as friction-less as possible.

There was also much investment in making the platform highly reliable. “Our software adjusts for network challenges,” said Kelly Steckleber, who is the CFO of Zoom. “You can have 40% of packet loss and still have a great video experience. But with other solutions, you’ll typically see degradation at about 15%.”

As for Eric, he had the advantage of being a pioneer of the web conferencing industry. In 1997 he jointed the engineering team at WebEx and stayed with the company after the sale to Cisco. But he felt stifled as his ideas for making the product better fell on deaf ears.

So when he started Zoom, Eric would strive for absolute excellence. It was not about putting together a flimsy MVP (minimally viable product).  Consider that he spent about two years creating Zoom before commercializing it. And in this process, he created small teams that had much responsibility for making decisions (let’s face it, committee’s can be killers for software development).

Now all this is not to imply that Eric is only concerned about technical details and data. From the inception of Zoom, he has been focused on building a culture that brings “happiness” to customers and employees. For example, Zoom has an NPS (Net Promoter Score) of 70, which compares to the average among the company’s peers of a mere 20 (Apple’s is 73).

One of the happy customers is Five9, which is a top cloud-based call center operator. “In our evaluation of vendors, it was clear that Zoom was the highest quality software out there,” said Rowan Trollope, who is the CEO. “But it’s backed up with a deep commitment to the customer. When I had some initial issues with Zoom, Eric gave me his phone number and he said he’d get it done over the weekend.”

AI (Artificial Intelligence): What’s The Next Frontier For Healthcare?

Perhaps one of the biggest opportunities for AI (Artificial Intelligence) is the healthcare industry. According to ReportLinker, spending on this category is forecasted to jump from $2.1 billion to $36.1 billion by 2025. This is a hefty 50.2% compound annual growth rate (CAGR).

So then what are some of the trends that look most interesting within healthcare AI? Well, to answer this question, I reached out to a variety of experts in the space.

Here’s a look: 

Ori Geva, who is the CEO of Medial EarlySign:

One of the key trends is the use of health AI to spur the transition of medicine from reactive to proactive care. Machine learning-based applications will preempt and prevent disease on a more personal level, rather than merely reacting to symptoms. Providers and payers will be better positioned to care for their patients’ needs with the tools to delay or prevent the onset of life-threatening conditions. Ultimately, patients will benefit from timely and personalized treatment to improve outcomes and potentially increase survival rates.

Dr. Gidi Stein, who is the CEO of MedAware:

In the next five years, consumers will gain more access to their health information than ever before via mobile electronic medical records (EMR) and health wearables. AI will facilitate turning this mountain of data into actionable health-related insights, promoting personalized health and optimizing care. This will empower patients to take the driving wheel of their own health, promote better patient-provider communication and facilitate high-end healthcare to under-privileged geographies.

Tim O’Malley, who is the President and Chief Growth Officer at EarlySense:

Today, there are millions of physiologic parameters which are extracted from a patient. I believe the next mega trend will be harnessing this AI-driven “Smart Data” to accurately predict and avoid adverse events for patients. The aggregate of this data will be used to formulate predictive analytics to be used across diverse patient populations across the continuum of care, which will provide truly personalized medicine.

Andrea Fiumicelli, who is the vice president and general manager of Healthcare and Life Sciences at DXC Technology:

Ultimately, AI and data analytics could prove to be the catalyst in addressing some of today’s most difficult-to-treat health conditions. By combining genomics with individual patient data from electronic health records and real-world evidence on patient behavior culled from wearables, social media and elsewhere, health care providers can harness the power of precision medicine to determine the most effective approaches for specific patients.

This brings tremendous potential to treating complex conditions such as depression. AI can offer insights into a wealth of data to determine the likelihood of depression—based on the patient’s age, gender, comorbidities, genomics, life style, environment, etc.—and can provide information about potential reactions before they occur, thus enabling clinicians to provide more effective treatment sooner.

Ruthie Davi, who is the vice president of Data Science at Acorn AI, a Medidata company:

One key advance to consider is the use of carefully curated datasets to form Synthetic Control Arms as a replacement for placebo in clinical trials. Recruiting patients for randomized control trials can be challenging, particularly in small patient populations. From the patient perspective, while an investigational drug can offer hope via a new treatment option, the possibility of being in a control arm can be a disincentive. Additionally, if patients discover they are in a control arm, they may drop out or elect to receive therapies outside of the trial protocol, threatening the validity and completion of the entire trial.

However, thanks to advances in advanced analytics and the vast amount of data available in life sciences today, we believe there is a real opportunity to transform the clinical trial process. By leveraging patient-level data from historical clinical trials from Medidata’s expansive clinical trial dataset, we can create a synthetic control arm (SCA) that precisely mimics the results of a traditional randomized control. In fact, in a recent non-small cell lung cancer case study, Medidata together with Friends of Cancer Research was successful in replicating the overall survival of the target randomized control with SCA. This is a game-changing effort that will enhance the clinical trial experience for patients and propel next generation therapies through clinical development.

Schwab’s Zero Commission Bombshell: So What’s Next For Fintech?

In 1971, Charles Schwab launched a traditional brokerage firm. But the business did not take off until 1975, when the SEC ended fixed-rate commissions. Schwab knew that the future would be about the discount brokerage model.

To pull this off, he needed to invest heavily in technology, such as with online brokerage systems. Over the years, as the platforms changed–such as from proprietary services like AOL to the Internet to mobile apps–Schwab somehow found ways to adapt.

And yes, even though he is now 82, he still seems to be far from finished. This week his firm announced that commissions on US stock, options and ETFs will be $0.

“It’s encouraging in the broader context of corporate purpose and sustainability to see a firm stay true to its purpose and passion of making investing more affordable,” said Geoff Cole, who is the fintech senior manager with Grant Thornton.

Now for the traditional brokerage industry, the impact is certainly ominous. There will need to be a way to make up for the lost revenues, such as by innovating new services. There will also likely be more layoffs.

“Online brokers are already under pressure due to this year’s interest rate cuts,” said Arielle O’Shea, who is the investing and retirement specialist at NerdWallet. “Many generate revenue from banking divisions, or from interest earned on idle cash. Schwab is likely hoping this move will attract enough new assets to make up for that narrowing margin as well as the lost revenue from commissions.”

A Reckoning For Fintech Too?

Schwab’s move is a validation of the fintech industry, especially with the impact from the fast-growing Robinhood. The startups in the space have advantages like starting from a bank slate as well as having access to enormous amounts of venture capital.

“We’ve certainly seen that the rise of customer-centric fintech companies has pushed the industry in a more client-friendly direction, and part of that is lower fees,” said Adam Grealish, who is the Director of Investing at Betterment. “Fintech companies use technology to achieve lower operating costs and are able to pass the savings on to customers. This has forced incumbents to follow suit.”

Yet this is not to imply that fintechs are immune from challenges. Let’s face it, traditional brokers have inherent advantages, such as strong infrastructures, diverse service offerings and trusted brands. And besides, millions of people like talking to experts when it comes to their wealth.

“Unfortunately, I think in the short-term you will certainly see some attrition and consolidation among the start-ups whose sole selling point was free trading,” said Anthony Denier, who is the CEO of Webull. “There is more to investing than cost.”

The zero-commission strategy may actually be a tipping point, giving traditional brokers an edge in customer acquisition. According to a J.D. Power survey of self-directed (DIY) investors, the No. 1 reason for selecting a firm was “low fees.”

“This creates a challenge for fintechs,” said Mike Foy, who is a Senior Director of Wealth Management Practice at J.D. Power. “They will need to work harder to differentiate themselves from incumbents to continue to attract new investors seeking a low-cost provider.”

While fintechs have been innovators– such as with compelling UIs–there will probably need to be much more. For the most part, the history of financial services is about relentless commoditization. And it’s been firms with massive scale, like Schwab, that have been able to thrive. This will likely be the case with fintechs as well.

Yet despite all this, the ultimate impact should positive, encouraging more and more competition. “In the end, the consumer wins,” said Steven Nuckols, who is the president and founder of Wealth Compass Financial.

Tesla’s AI Acquisition: A New Way For Autonomous Driving?

This week Tesla acquired DeepScale, which is a startup that focuses on developing computer vision technologies (the price of the deal was not disclosed). This appears to be a part of the company’s focus on building an Uber-like service as well building fully autonomous vehicles.

Founded in 2015, DeepScale has raised $15 million from investors like Point72, next47, Andy Bechtolsheim, Ali Partovi, and Jerry Yang. The founders include Forrest Iandola and Kurt Keutzer, who are both PhD’s. In fact, about a quarter of the engineering team has a PhD and they have more than 30,000 academic citations.

“DeepScale is a great fit for Tesla because the company specializes in compressing neural nets to work in vehicles, and hooking them into perception systems with multiple data types,” said Chris Nicholson, who is the CEO and founder of Skymind. “That’s what Tesla needs to make progress in autonomous driving.”

Tesla has the advantage of an enormous database of vehicle information. So with software expertise, the company should help accelerate the innovation. “If ‘data is the new oil’ then ‘AI models are the new Intellectual Property and barrier to entry,’” said Joel Vincent, who is the CMO of Zededa. “This is the dawn of a new age of competitive differentiation. AI models are useless without data and Telsa has an astounding amount of edge data.”

Now when it comes to autonomous driving, there are other major requirements–some which may get little attention.

Just look at the use of energy. “Large models require more powerful processors and larger memory to run them in production,” said Dr. Sumit Gupta, who is the the IBM Cognitive Systems VP of AI and HPC. “But vehicles have a limited energy budget, so the market is always trying to minimize the energy that the electronics in the car consume. This is what DeepScale is good at. The company invented an AI model called ‘SqueezeNet’ that requires a smaller memory footprint and also less CPU horsepower.”

Keep in mind that the lower energy consumption will mean there will be more capacity for sensors for vision. “This should help make autonomous vehicles safer,” said Arjan Wijnveen, who is the CEO of CVEDIA. “Tesla seems certain that they don’t need LiDAR for effective computer vision, but there are lots of other types of sensors you could see on their vehicles in the future, and sometimes just placing a second camera facing another angle can improve the AI model.”

Not using LiDAR would be a big deal, which would mean a much lower cost per vehicle. “There are concerns about the deployment of LIDAR lasers in the public sphere,” said Gavin D. J. Harper, who is a Faraday Institution Research Fellow at the University of Birmingham. “Safety measures include limiting the power and exposure of lasers. There is also the concern about the potential for causing inadvertent harm to those nearby.”

So all in all, the DeepScale deal could move the needle for Tesla and represent a shift in the industry. Although, it is still important to keep in mind that autonomous driving is still in the nascent stages (regardless of what Elon Musk boasts!) There remain many tough issues to work out, which could easily drag on because of regulatory processes.

“To get to full autonomy, you’re still going to need some major algorithmic improvements,” said Nicholson. “Some of the smartest people in the world are working on this, and it seems clear that we’ll get there, even if we don’t know when. In any case, companies like Tesla and Waymo have the right mix of talent, data, and cars on the road.”