AIOps: What You Need To Know

AIOps, which is a term that was coined by Gartner in 2017, is increasingly becoming a critical part of next-generation IT. “In a nutshell, AIOps is applying cognitive computing like AI and Machine learning techniques to improve IT operations,” said Adnan Masood, who is the Chief Architect of AI & Machine Learning at UST Global. “This is not to be confused with the entirely different discipline of MLOps, which focuses on the Machine learning operationalization pipeline. AIOps refers to the spectrum of AI capabilities used to address IT operations challenges–for example, detecting outliers and anomalies in the operations data, identifying recurring issues, and applying self-identified solutions to proactively resolve the problem, such as by restarting the application pool, increasing storage or compute, or resetting the password for a locked-out user.”

The fact is that IT departments are often stretched and starved for resources. Traditional tools have usually been rule-based and inflexible, which has made it difficult to deal with the flood of new technologies.

“IT teams have adopted microservices, cloud providers, NoSQL databases, and various other engineering and architectural approaches to help support the demands their businesses are putting on them,” said Shekhar Vemuri, who is the CTO of Clairvoyant. “But in this rich, heterogeneous, distributed, complex world, it can be a challenge to stay on top of vast amounts of machine-generated data from all these monitoring, alerting and runtime systems.  It can get extremely difficult to understand the interactions between various systems and the impact they are having on cost, SLAs, outages etc.”

So with AIOps, there is the potential for achieving scale and efficiencies.  Such benefits can certainly move the needle for a company, especially as IT has become much more strategic.

“From our perspective, AIOps equips IT organizations with the tools to innovate and remain competitive in their industries, effectively managing infrastructure and empowering insights across increasingly complex hybrid and multi-cloud environments,” said Ross Ackerman, who is the NetApp Director of Analytics and Transformation. “This is accomplished through continuous risk assessments, predictive alerts, and automated case opening to help prevent problems before they occur. At NetApp, we’re benefiting from a continuously growing data lake that was established over a decade ago. It was initially used for reactive actions, but with the introduction of more advanced AI and ML, it has evolved to offer predictive and prescriptive insights and guidance. Ultimately, our capabilities have allowed us to save customers over two million hours of lost productivity due to avoided downtime.”

As with any new approach, though, AIOps does require much preparation, commitment and monitoring. Let’s face it, technologies like AI can be complex and finicky. 

“The algorithms can take time to learn the environment, so organizations should seek out those AIOps solutions that also include auto-discovery and automated dependency mapping as these capabilities provide out-of-the-box benefits in terms of root-cause diagnosis, infrastructure visualization, and ensuring CMDBs are accurate and up-to-date,” said Vijay Kurkal, who is the CEO of Resolve. “These capabilities offer immediate value and instantaneous visibility into what’s happening under the hood, with machine learning and AI providing increasing richness and insights over time.”

As a result, there should be a clear-cut framework when it comes to AIOps. Here’s what Appen’s Chief AI Evangelist Alyssa Simpson Rochwerger recommends: 

  • Clear ability to measure product success (business value outcomes).
  • Ability to measure and report on associated performance metrics such as accuracy, throughput, confidence and outcomes
  • Technical infrastructure to support—including but not limited to—model training, hosting, management, versioning and logging
  • Data Set management including traceability, data provenance and transparency
  • Low confidence/fallback data handling (this could be either a data annotation or other human-in-the-loop process or default when the AI system can’t handle a task or has a low-confidence output)

All this requires a different mindset. It’s really about looking at things in terms of software application development. 

“Most enterprise businesses are struggling with a wall to production, and need to start realizing a return on their machine learning and AI investments,” said Santiago Giraldo, who is a Senior Product Marketing Manager at Cloudera. “The problem here is two-fold. One issue is related to technology: Businesses must have a complete platform that unifies everything from data management to data science to production. This includes robust functionalities for deploying, serving, monitoring, and governing models. The second issue is mindset: Organizations need to adopt a production mindset and approach machine learning and AI holistically in everything from data practices to how the business consumes and uses the resulting predictions.”

So yes, AIOps is still early days and there will be lots of trial-and-error. But this approach is likely to be essential.

“While the transformative promise of AI has yet to materialize in many parts of the business, AIOps offers a proven, pragmatic path to improved service quality,” said Dave Wright, who is the Chief Innovation Officer at ServiceNow. “And since it requires little overhead, it’s a great pilot for other AI initiatives that have the potential to transform a business.”

Coronavirus: Can AI (Artificial Intelligence) Make A Difference?

The mysterious coronavirus is spreading at an alarming rate. There have been at least 305 deaths as more than 14,300 persons have been infected.

On Thursday, the World Health Organization (WHO) declared the coronavirus a global emergency. To put things into perspective, it has already exceeded the numbers infected during the 2002-2003 outbreak of SARS (Severe Acute Respiratory Syndrome) in China. 

Many countries are working hard to quell the virus. There have been quarantines, lock-downs on major cities, limits on travel and accelerated research on vaccine development. 

However, could technologies like AI (Artificial Intelligence) help out? Well, interestingly enough, it already has.

Just look at BlueDot, which is a venture-backed startup. The company has built a sophisticated AI platform that processes billions of pieces data, such as from the world’s air travel network, to identity outbreaks.

In the case of the coronavirus, BlueDot made its first alert on December 31st. This was ahead of the US Centers for Disease Control and Prevention, which made its own determination on January 6th.

BlueDot is the mastermind of Kamran Khan, who is an infectious disease physician and professor of Medicine and Public Health at the University of Toronto. Keep in mind that he was a frontline healthcare worker during the SARS outbreak. 

“We are currently using natural language processing (NLP) and machine learning (ML) to process vast amounts of unstructured text data, currently in 65 languages, to track outbreaks of over 100 different diseases, every 15 minutes around the clock,” said Khan. “If we did this work manually, we would probably need over a hundred people to do it well. These data analytics enable health experts to focus their time and energy on how to respond to infectious disease risks, rather than spending their time and energy gathering and organizing information.”

But of course, BlueDot will probably not be the only organization to successfully leverage AI to help curb the coronavirus. In fact, here’s a look at what we might see:

Colleen Greene, the GM of Healthcare at DataRobot:

“AI could predict the number of potential new cases by area and which types of populations will be at risk the most. This type of technology could be used to warn travelers so that vulnerable populations can wear proper medical masks while traveling.”

Vahid Behzadan, the Assistant Professor of Computer Science at the University of New Haven:

“AI can help with the enhancement of optimization strategies. For instance, Dr. Marzieh Soltanolkottabi’s  research is on the use of machine learning to evaluate and optimize strategies for social distancing (quarantine) between communities, cities, and countries to control the spread of epidemics. Also, my research group is collaborating with Dr. Soltanolkottabi in developing methods for enhancement of vaccination strategies leveraging recent advances in AI, particularly in reinforcement learning techniques.”

Dr. Vincent Grasso, who is the IPsoft Global Practice Lead for Healthcare and Life Sciences:

“For example, when disease outbreaks occur, it is crucial to obtain clinical related information from patients and others involved such as physiological states before and after, logistical information concerning exposure sites, and other critical information. Deploying humans into these situations is costly and difficult, especially if there are multiple outbreaks or the outbreaks are located in countries lacking sufficient resources. Conversational computing working as an extension of humans attempting to get relevant information would be a welcome addition. Conversational computing is bidirectional—it can engage with a patient and gather information, or the reverse, provide information based upon plans that are either standardized or modified based on situational variations. In addition, engaging in a multilingual and multimodal manner further extends the conversational computing deliverable. In addition to this ‘front end’ benefit, the data that is being collected from multiple sources such as voice, text, medical devices, GPS, and many others, are beneficial as datapoints and can help us learn to combat a future outbreak more effectively.”

Steve Bennett, the Director of Global Government Practice at SAS and former Director of National Biosurveillance at the U.S. Department of Homeland Security:

“AI can help deal with the coronavirus in several ways. AI can predict hotspots around the world where the virus could make the jump from animals to humans (also called a zoonotic virus). This typically happens at exotic food markets without established health codes.  Once a known outbreak has been identified, health officials can use AI to predict how the virus is going to spread based on environmental conditions, access to healthcare, and the way it is transmitted. AI can also identify and find commonalities within localized outbreaks of the virus, or with micro-scale adverse health events that are out of the ordinary. The insights from these events can help answer many of the unknowns about the nature of the virus.

“Now, when it comes to finding a cure for coronavirus, creating antivirals and vaccines is a trial and error process. However, the medical community has successfully cultivated a number of vaccines for similar viruses in the past, so using AI to look at patterns from similar viruses and detect the attributes to look for in building a new vaccine gives doctors a higher probability of getting lucky than if they were to start building one from scratch.”

Don Woodlock, the VP of HealthShare at InterSystems:

“With ML approaches, we can read the tens of billions of data points and clinical documents in medical records and establish the connections to patients that do or do not have the virus. The ‘features’ of the patients that contract the disease pop out of the modeling process, which can then help us target patients that are higher risk.

“Similarly, ML approaches can automatically build a model or relationship between treatments documented in medical records and the eventual patient outcomes. These models can quickly identify treatment choices that are correlated to better outcomes and help guide the process of developing clinical guidelines.”

Prasad Kothari, who is the VP Data Science and AI for The Smart Cube:

“The coronavirus can cause severe symptoms such as pneumonia, severe acute respiratory syndrome, kidney failure etc. AI empowered algorithms such as genome based neural networks already built for personalized treatment can prove very helpful in managing these adverse events or symptoms caused by coronavirus, especially when the effect of virus depends on immunity and the genome structure of individual and no standard treatment can treat all symptoms an effects in the same way.

“In recent times, immunotherapy and Gene therapy empowered through AI algorithms such as boltzmann machines (entropy based combinatorial neural networks) have stronger evidence of treating such diseases which stimulate body’s immunity systems. For this reason, Abbvie’s Aluvia HIV drug is one possible treatment. If you look at data of affected patients and profile virus mechanics and cellular mechanism affected by the coronavirus, there are some similarities in the biological pathways and treatment efficacy. But this is yet to be tested.”

CES: The Coolest AI (Artificial Intelligence) Announcements

As seen at this week’s CES 2020 mega conference, the buzz for AI continues to be intense. Here are just a few comments from the attendees:

  • Nichole Jordan, who is Grant Thornton’s Central region managing partner: “From AI-powered agriculture equipment to emotion-sensing technology, walking the exhibit floors at CES drives home the fact that artificial intelligence is no longer a vision of the future. It is here today and is clearly going to be more integrated into our world going forward.”
  • Derek Kennedy, the Senior Partner and Global Technology Leader at Boston Consulting Group: “AI is increasingly playing a role in every intelligent product, such as upscaling video signals for an 8K TV as well as every business process, like predicting consumer demand for a new product.”
  • Houman Haghighi, the Business Development Partner at Menlo Ventures: “Voice, natural language and predictive actions are continuing to become the new—and sometimes the only—user interface within the home, automobile, and workplace.”

So what were some of the stand out announcement at CES? Well, given that there were over 4,500 exhibitors, this is a tough question to answer. But here are some innovations that certainly do show the power of AI:

Prosthetics: Using AI along with EMG technology, BrainCo has built a prosthetic arm that learns. In fact, it can allow for people to play a piano or even do calligraphy. 

“This is an electronic device that allows you to control the movements of an artificial arm with the power of thought alone,” said Max Babych, who is the CEO of SpdLoad. Today In: Small Business

The cost for the prosthetic is quite affordable at about $10,000 (this is compared to about $100,000 for alternatives). 

SelfieType: One of the nagging frictions of smartphones is the keyboard. But Samsung has a solution: SelfieType. It leverages cameras and AI to create a virtual keyboard on a surface (such as a table) that learns from hand movements. 

“This was my favorite and simplest AI use case at CES,” said R. Mordecai, who is the Head of Innovation and Partnerships at INNOCEAN USA. “I wish I had it for the flight home so I could type this on the plane tray.”

Lululab’s Lumine: This is a smart mirror that is for skin care. Lumine uses deep learning to analyze six categories–wrinkles, pigment, redness, pores, sebum and trouble–and then recommends products to help.

Whisk: This is powered by AI to scan the contents of your fridge so as to think up creative dishes to cook (it is based research from over 100 nutritionists, food scientists, engineers and retailers). Not only does this technology allow for a much better experience, but should help reduce food waste. Keep in mind that the average person throws away 238 pounds of food every year. 

Wiser: Developed by Schneider Electric, this is a small device that you install in your home’s circuit breaker box. With the use of machine learning, you can get real-time monitoring of usage by appliance, which can lead to money savings and optimization for a solar system.

Vital Signs Monitoring: The Binah.ai app analyzes a person’s face to get medical-grade insights, such as oxygen saturation, respiration rate, heart rate variability and mental stress. The company also plans to add monitoring for hemoglobin levels and blood pressure.

Neon: This is a virtual assistant that looks like a real person, who can engage in intelligent conversation and show emotion. While still in the early stages, the technology is actually kind of scary. The creator of Neon–which is Samsung-backed Star Labs—thinks that it will replace doctors, lawyers and other white collar professionals. No doubt, this appears to be a recipe for wide-scale unemployment, not to mention a way to unleash a torrent of deepfakes!

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.”

FloQast: From More Than 100 No’s To Funding Triumph

After a stint as an auditor in the entertainment industry, Mike Whitmire took a job at Cornerstone OnDemand as a senior accountant. The tech firm was pre-IPO at the time and posting rapid growth.

But he noticed that it was extremely difficult to close the books at the end of the month. The process was highly manual, involving poor coordination that resulted in plenty of accounting challenges. It was a mess. It was also something that was very common across Corporate America.

Whitmire thought: “Isn’t there a better way to do this?”

There definitely was. Whitmire would launch FloQast at the end of 2012.

Although, he did not realize how tough the funding process would be.

During a six month process, Whitmire was able to get a check for $50,000 from Amplify, a tech accelerator based in the LA area. He actually closed this seed round after he was able to find a co-founder and get an initial customer.

Of course, Whitmire would need much more money to make his venture viable. “Securing $1.3 million was brutal,” he said. “This was an 8-month process. Pitching to angel investors was not easy and we were too early for real VCs. Because of this, I received more than 80 no’s and five yes’s before being introduced to Toba Capital.”

At the time, Whitmire had $300,000 committed for a $500,000 round. But Toba’s Rob Meinhardt and Vinny Smith saw the huge potential for FloQast. “They were ready to take a risk and gave us $1 million,” said Whitmire.

After this, the company did get momentum with its product. But it was still not enough to make the next round any easier!

“Securing the Series A was the worst round by far,” said Whitmire. “It took nine months of constantly traveling to San Francisco to hear no’s from Seed and Series A stage funds. There were probably about 40 no’s before I was about to close one deal.”

It did not help that the overall market for SaaS companies was crumbling (this was at the end of 2015). So yes, several term sheets were pulled.

“Then I went on to receive another 20 no’s from various funds,” said Whitmire. “But it was Polaris Partners and Gary Swart who were the ones to ultimately take the risk.”

In the end, the deep persistence paid off in a big way. The Series B round was easy, bringing in $25 million. And the company has gone on to be a major player in the SaaS world for accounting. Last year, FloQast doubled its revenues and added 250 new customers (bringing the total to 750), including Lyft, Zoom Video, Twilio and the Golden State Warriors.

Lessons Learned

In light of all this, what are the takeaways for Whitmire? Well, he mentioned the following:

#1: “I learned this world isn’t like Shark Tank. You don’t pitch and beg for money. You are forming a partnership that will last many years. Thinking about it like that makes it wildly important who you take money from.”

#2: “My best advice is to meet venture capitalists before you need money. And don’t be a snob about only talking to partners. Associates are a great way to get your foot in the door at a firm and get on the partner’s radar. Once you’ve met them, tell them about yourself, your business, and what you are going to do before the next round. Then, do it. When you follow up with them, remind them about the prior conversation and discuss how you executed. Rinse and repeat until funding time. By then, you’ll have built trust in your ability to execute and they will believe you during your fundraising story.”

Startup Lessons: How Stripe Created A $35 Billion Giant

This week Stripe—a top fintech payments company—raised $250 million at a valuation of about $35 billion. This was up about 50% since its prior round of funding earlier in the year.

“Every fintech startup aspires to build a product that clients love,” said Matt Burton, who is a partner at QED Investors. “Stripe has now done this in multiple fintech categories and shows no signs of slowing down. On top of this, they are winning the recruiting war with the top talent choosing them over everyone else. Truly impressive.”

Back in 2010, two twenty-something Irish entrepreneurs, John and Patrick Collison (they are brothers), launched Stripe. The main reason was the frustrations they experienced with online payment systems.

So John and Patrick initially joined Y Combinator to incubate the business and they would then go on to get $2 million in funding from Peter Thiel, Sequoia Capital, and Andreessen Horowitz in 2011.

“Stripe originally built an incredibly simple and innovative approach to online payments,” said Eytan Bensoussan, who is the co-founder and CEO of NorthOne. “A few lines of code and developers could integrate payment processing into a website or app. This was a departure from other financial service integrations that could take months or years. And it empowered the company with massive credibility and loyalty with the user base. Stripe built exponential enterprise value from that point forward by not only protecting its core offering but also by methodically expanding into new areas with the same level of simplicity. The key, however, was that each new area, be it subscription management, invoicing or lending, has been adjacent to its flagship product. This has created more points of entry for new customers and more cross-sell opportunities for existing ones.”

While having a standout service was critical, there were other factors that explain the success of Stripe. Keep in mind that the company pursued a distribution model that was obsessive on the needs of the customers.

“Stripe caught on because it didn’t sell to companies,” said Dmytro Okunyev, who is the founder of Chanty. “It sold to developers. That is, Stripe offered an alternative to PayPal and Authorize that was so much easier to implement that developers around the world were naturally inclined to use it.”

So yes, Stripe essentially built a thriving community of developers, which created many champions of the platform. “The smartest thing that Stripe did, apart from targeting the payment technology space, was to become a developer-first product,” said Sayid Shabeer, who is the Chief Product Officer of HighRadius. “They used the word-of-mouth growth engine of the developers to create a community that was self-sustaining.”

It also helped that Stripe broke down the walls of the traditional business model for the payments industry. “The company was wise to offer transparent pricing right from the start,” said Ian Wright, who is the founder of Merchant Machine. “This should not be confused with the best pricing, since Stripe is not the cheapest solution in the market. However, the payments industry was and to a degree still is stuck in an opaque pricing mindset. This makes Stripe standout as you know exactly how much it’s going to cost, which for most startups is better than trying to negotiate with a legacy payments company.”

All in all, Stripe has executed near flawlessly on an aggressive disruption strategy. “The payments industry was primed for disruption when Stripe came onto the scene,” said David Blumberg, who is the founder of Blumberg Capital. “Stripe communicated their benefits and differentiators in these categories early on and spoke directly to interoperability and ease of use, a major concern to many small businesses across many industries.”

WeWork IPO Postponement: What’s The Impact For Startups?

The IPO process has turned into a nightmare for WeWork. Simply put, investors have been far from convinced about the opportunity–at least in terms of the current valuation. There are also nagging issues about the business model and corporate governance.

“There has been a lot of scrutiny in the run-up to the We Company’s filing of their highly anticipated S-1,” said Kelly Rodriguez, who is the CEO of Forge. “As the financial community has been largely divided on its viability as a business, many hoped the filing would help clear the air and provide a more comprehensive look at their business model. While there is a lot to like in the We Company’s S-1, like the sharp revenue growth year-over-year, there are still a lot of loose ends and ambiguity that leave potential investors questioning its ability to sustain long-term growth.”

In light of all this, it should be no surprise that WeWork has delayed its public offering. It looks like the offering will happen next month.

Now all this comes as the equity markets are near all-time highs. But it does look like investors are getting more cautious on some deals, especially for those companies with substantial losses (last year WeWork reported a $1.61 billion loss on $1.82 billion in revenues).

So what does this all mean for startups? Well, I reached out to entrepreneurs and experts to get some viewpoints:

Mike Volpe, the CEO of Lola.com:

The market is merely working exactly as it should. The problem is specific to WeWork and their business, not a systemic issue. The company has huge capital needs but produces slim profit margins for each customer they sign up. Wall Street is saying WeWork is not a great investment at their recent lofty private valuation. A different company that is growing fast and has great profitability per customer would be able to have a wonderful IPO today.

Tobias van Gils, who is a co-founder of Countach Research:

Investors have shown that they do not perceive WeWork as a tech company as it fails in multiple key characteristics in comparison to tech companies. Two such examples are scalability (limited by locations and the need for consultations and brokers for larger businesses) and the very large percentage of revenue going to rent payments.

Charley Moore, a tech attorney and CEO and founder of Rocket Lawyer:

With the economy and financial markets giving off late cycle signals, there appears to be a shift away from growth at any cost and toward value. When that happens, compliance, regulation and governance tend to pick up steam as the government seeks to fix the excesses of the prior expansion and investors become more cautious. Examples include the increased antitrust scrutiny of big tech, and Facebook and others in the spotlight over privacy.

One implication of this example could absolutely be a heightened focus on profitability. And companies that are losing money, thereby facing existential capital needs, may have to trade control for survival. This seems to be at least part of the trading going on in the WeWork deal.

Vineet Jain, the Founder and CEO of Egnyte:

More than ever before, the path to profitability is being heavily evaluated. A company can burn through money—and ultimately lose money—however, if that company is demonstrating a reduction in losses and a convergence toward break-even, you will still get the benefit of the doubt from public investors if you have a high growth curve.

The other thing I think investors are not buying into is the dual share structure: founders or CEOs with one share that is the equivalent to 10 shares of voting rights. I believe that is going away.

Timothy Spence, a SEC Consultant:

Does the WeWork IPO bode poorly for tech? No. The scrutiny means you have to have a solid business model. Clearly, what’s happening in the IPO world is an investment in a future potential of the company.