In April, the number of job openings hit a record 9.3 million, according to data from the Labor Department. With the pandemic fading away, there has been a scramble to hire new employees and this has become a major challenge for companies.
So then can AI (Artificial Intelligence) help out? Well, it definitely can. The irony is that many companies are using the technology—and don’t even realize it! The reason is that AI is built into the top online job sites.
“For example, when you type in a search for a job title, say with the phrase ‘job manager,’ the LinkedIn engine will not only look for the title itself, but also people with relevant skills like time management, team coordination, risk assessment and so on,” said Sakshi Jain, who is the Engineering Manager on LinkedIn’s Responsible AI team. “This means that a recruiter gets more results than just the people who already have the exact title or role.”
One of the key powers of AI is that it can detect complex patterns in huge data sets. In a way, this can simulate the capabilities of a recruiter. And this is definitely important when it comes to finding passive job candidates.
“In a study we recently published, 74% of talent leaders told us they’ve increased outreach to passive candidates in the past year,” said Hari Kolam, who is the CEO of Findem. “AI greatly speeds up the passive recruiting process–one where it can take upward of ten hours to fill a single role. It can index and surface information on people from hundreds of sources as passive candidates typically aren’t on job or career sites, and many people tend to only include piecemeal information on their LinkedIn and other profiles.”
AI can also help with personalization. This can be a good way to create a good first impression with candidates.
“Currently, there are many hiring workflows that are incredibly inefficient, including the scheduling of interviews and follow-up emails for candidates,” said Vivek Ravisankar, who is the CEO of HackerRank. “With the use of automated scheduling and email follow-ups, AI can help free up valuable time and solve the major pain point of extensive back-and-forth coordination with candidates and interviewers.”
Yet AI is not without its risks. After all, there is inherent bias in datasets and this can result in outcomes that are unfair and discriminatory.
“AI-driven HR software that is using years old data on previous hires to determine ideal candidates for job openings is a perfect example of where algorithms can go wrong,” said Ingrid Burton, who is the Chief Marketing Officer at Quantcast. “This is especially true in roles that have historically been dominated by men, such as software engineers, which would risk the hiring algorithm to arbitrarily exclude most women and minorities from advancing during the hiring process.”
To guard against this, there must be good governance as well as explainability of the AI models. There also needs to be people in the loop for critical parts of the process.
“It is never acceptable to set up an AI process and simply leave it to run,” said Ian Cook, who is the Vice President of People Analytics at Visier. “While there is no need to inspect every transaction or process run by the AI, there is a need to constantly review the performance of the AI steps to ensure that the outputs are in line with expectations. Validation, updating and retraining are constant requirements of running any AI process.”
C3.ai, which is a top provider of enterprise AI software and services, is a newly public company. It pulled off its deal in December and issued 15.1 million shares at $42 each. On the first day of trading, the shares spiked 120%.
And this would not be the end of the gains. Within a couple months, the stock price would hit an all-time high of $183.
But as the markets started to cool off, so did the shares of C3.ai. Consider that the stock price is now at $64.
Despite this, the future does look bright for the company. “The total addressable market is huge,” said Tom Siebel, who is the CEO and founder of C3.ai. “It’s a third of a trillion dollars.”
Keep in mind that Siebel is a veteran of the enterprise software world. In the early 1980s, he worked as an executive at Oracle and helped make the company the dominant player in relational databases. Then in 1993, he started Siebel Systems and pioneered the CRM (Customer Relationship Management) category.
As for C3.ai, he launched this company in 2009. Siebel was early in recognizing that AI would be a megatrend.
But he also crafted a solid approach to building the platform. “We were novel in using a model-driven architecture to enable organizations to rapidly design, develop, provision and operate enterprise AI applications at scale,” said Siebel. “We spent about a billion dollars inventing this in the last decade and it is our secret sauce.”
This was in contrast to using traditional techniques, such as with structured programming, that involve a mishmash of open source and proprietary solutions. However, this usually means too much complexity to effectively scale.
Even some of the world’s top companies have suffered major blunders and failures with AI. IBM’s Watson, for example, has fallen well short of expectations. Then there is GE, which has spent billions on AI and has seen little return.
The Future Of Enterprise AI
The C3.ai platform can handle applications for global enterprises as well as small businesses. And to get a sense of its power, it currently manages over 4.8 million concurrent production AI models and processes more than 1.5 billion AI predictions per day.
Now, another key factor for the success of C3.ai is that the company takes a partnership approach with customers. This is essential for AI since it is important to leverage vertical-specific data and insights.
A case study for this is Shell. “The company is reinventing itself as the fifth largest in the world,” said Siebel. “They want to get to a zero net carbon footprint by 2050, which is no mean trick, right? This is about applying AI to the entire value chain, about delivering cleaner, safer, lower cost, more reliable energy. This is maybe a $4 billion a year economic benefit.”
Granted, the temptation for companies is to build their own systems. But this is really the wrong approach. “Believe it or not, people tried to build their own relational database systems during the 1980s and I don’t think anybody succeeded,” said Siebel. “We are seeing it again with AI. Companies will try it once, twice, three times. Then they’ll wind up firing the CIO and buy the technology from a professional.”
Ultimately, Siebel thinks that AI will be similar to CRM or ERP. In other words, it will be a technology that’s a necessity for a large number of businesses. “Companies that do not adopt AI will no longer exist,” said Siebel.
Even though AI continues to thrive and grow, there remain challenges to use the technology. Just some include finding data scientists, determining the right problems to focus on, getting quality data and scaling the models.
No doubt, these problems are even worse for non-tech companies. They generally do not have the expertise or sufficient resources to make AI a success.
“Research shows non-tech companies in particular have struggled to take their AI programs beyond the proof of concept and pilot phases–with just 21% of retail, 17% of automotive, 6% of manufacturing, and 3% of energy companies successfully scaling their AI use cases,” said Jerry Kurtz, who is the Executive Vice President of Insights and Data at Capgemini North America.
But despite all this, there are still a myriad of companies that are beating the odds. And they are becoming much more competitive. “There are many opportunities for non-tech companies to leverage AI to improve efficiency and provide a better customer and employee experience,” said Margaret Lee, who is the Senior Vice President and General Manager of Digital Service Operations Management at BMC.
So what are some of the non-tech companies that have been able to move-the-needle with their AI efforts? Here’s a look at two and the lessons learned.
John Deere: Keep in mind that the company has a long history of innovation, going back to the invention of the steel plow in 1837. The result is that John Deere is a world leader, with a market capitalization of $120 billion and annual sales of over $35 billion.
The company’s AI efforts began with machine vision because of its advantages with GPS connectivity in its equipment and rich datasets. For example, during the spring, the peak data ingestion was 425MB per second or about 50 million sensor measurements per second.
“Our projects have resulted in products in the market, or soon to be in the market, that reduce chemical inputs but sensing weeds from non-weeds and selectively apply chemical to the weeds only,” said Jahmy Hindman, who is the Chief Technology Officer of John Deere. “In addition, this work has led to vision-based automated control systems in combine harvesters that optimize the processing settings of the ‘factories on wheels’ to minimize the grain lost during harvest.”
A key lesson for the company has been the importance of keeping customer needs and wants in mind. “We work tirelessly to help our customers advance their business and feed the world,” said Hindman.
Levi Strauss: An early project for this company came in response to the Covid-19 lockdowns in Europe. Levi Stratus looked for ways to manage the inventory pile-up. To this end, the company gathered unique datasets on dynamic price elasticities and then applied AI to it.
It was a small project but was able to scale quickly. What started as a test in 11 stores in Germany in May 2020 grew to 17 countries in Europe by October. The system was also used for the 11/11 Singles’ Day in China.
“I have three pieces of advice from this experience,” said Louis DiCesari, who is the Global Head of Data, analytics, and AI at Levi Strauss. “First, choose real, commercial problems that are aligned to your company’s strategic priorities, and chunk them into actionable steps. Second, don’t get too hung up on having perfect data or technology, or using the latest algorithms. Instead, embrace agile, deliver minimum viable products, continuously measure the impact, and continue to iterate and add new features. And, of course, set a vision, communicate the progress toward that vision throughout the organization, and invite feedback.”
DiCesari attributes AI to the company’s ability to accelerate innovation and move faster than ever before. “In 2021, we aim to deliver more value and support all countries and functions of the enterprise, infuse data and AI throughout the business, enable new ways of working and continue streamlining processes, while also digitizing assets,” he said.
During the past decade, the subscription pricing approach has been widely adopted. After all, many tech companies have built thriving businesses with this business model, such as Salesforce.com.
Then again, subscriptions provide simplicity and predictability for customers. As for the vendors, there is the benefit of recurring revenues.
But as for the past few years, there has been a change—that is, a growing number of fast-growing tech companies, like Twilio, Snowflake, Jfrog, Stripe and DigitalOcean, have been eschewing subscriptions. Instead, they have focused on usage-based models.
Why so? Well, first of all, there are some problems with subscriptions. Perhaps the biggest is that customers often purchase licenses that they never use. This has actually been made worse during the COVID-19 pandemic.
“In general, we’re all users of a variety of subscription models and we’re super frustrated with them,” said Khozema Shipchandler, who is the Chief Financial Officer at Twilio. “You’re locked in from the start. There’s no room for movement and no way to account for the peaks and valleys that inevitably fill any business season such as the holidays, increase or decrease in demand, etc. In fact, the subscription model is increasingly becoming a relic of a bygone era. In my opinion, it will ultimately disappear.”
But with the usage-based pricing model, there is much more flexibility. It’s easy for a customer to try a new service and evaluate it for a negligible cost. This approach has proven quite effective with developers.
“A lot of times, especially for us, developers are the ones who get started with it first,” said Shipchandler. “They see how easy it is to use our products, they get a use case up and running, and the customers start benefiting without a major decision maker having to weigh in.”
There is also an alignment of interests between the vendor and the customer. If the service does provide value and is used frequently, then the customer will definitely be willing to pay more, right? Definitely.
“Adopting a usage-based model is the ultimate realization of being a customer-obsessed company,” said Kyle Poyar, who is a Partner at OpenView. “There’s no room for shelfware or bad user experiences. The upside is that you directly share in the success of your customers, which pays dividends years into the future.”
He points out the following metrics for those companies using the usage model:
38% faster revenue growth rate than their peers.
Stronger net dollar retention rates (seven of the nine most recent IPOs of cloud companies have shown this).
50% higher revenue multiples versus the broader SaaS companies.
Of course, the usage-pricing model will not suddenly transform a company. There still needs to be a strong product and a top-notch team.
Yet it still a good idea to evaluate the usage model. If anything, as it becomes more common, customers may demand this approach.
“I strongly believe a consumption model is the future for software because it changes the fundamental nature of the relationship between the vendor and the customer,” said Bill Staples, who is the President and Chief Product Officer at New Relic. “The vendor understands that if they don’t build great products that customers enjoy using, they won’t get paid. And consumption isn’t just a revenue model. It is the explicit understanding that if we aren’t focusing every function in the company around making our customers successful, we aren’t living up to our commitments or our ability.”
The edge is an end point where data is generated through some type of interface, device or sensor. Keep in mind that the technology is nothing new. But in light of the rapid innovations in a myriad of categories, the edge has become a major growth business.
“The edge brings the intelligence as close as possible to the data source and the point of action,” said Teresa Tung, who is the Managing Director at Accenture Labs. “This is important because while centralized cloud computing makes it easier and cheaper to process data at scale, there are times when it doesn’t make sense to send data off to the cloud for processing.”
This is definitely critical for AI. The fact is that consumers and businesses want super-fast performance with their applications.
“Currently AI training produces vast volumes of data that are almost exclusively implemented and stored in the cloud,” said Flavio Bonomi, who is the board advisor to Lynx Software. “But by placing compute at the edge, this allows for looking at patterns locally. We believe this can evolve the training models to become simpler and more effective.”
The edge may even allow for improved privacy with AI models. “Having federated learning means that no end-user data is centralized or communicated between nodes,” said Sean Leach, who is the Chief Product Architect at Fastly.
What Can Be Done At The Edge
The most notable use case for the edge and AI is the self-driving car. The complexities are mind boggling, which is why the development of this technology has taken so long.
But of course, there are many other use cases that span a myriad of industries. Just look at manufacturing. “In monitoring manufacturing processes where seconds or minutes could mean millions of dollars in losses, for example, machine learning models embedded in sensors and devices where the data is being collected enables operators to preemptively mitigate serious production issues and optimize performance,” said Santiago Giraldo, who is the Senior Product Marketing Manager of Machine Learning at Cloudera.
Here are some other examples:
Chris Bergey, the Senior Vice President and General Manager of Infrastructure Line of Business at Arm: “AI and the edge can explore the impacts of urbanization and climate change with software-defined sensor networks, pinpoint the origins of power outages in smart grids with data provenance, or enhance public safety initiatives through data streaming.”
Adam Burns, the Vice President of IoT and the Director of Edge Inference Products at Intel: “CORaiL, which was a project with Accenture and the Sulubaaï Environmental Foundation, can analyze coral reef resiliency using smart cameras and video analytics powered by Intel Movidius VPUs, Intel FPGAs and CPUs, and the OpenVINO toolkit.”
Jason Shepherd, the Vice President of Ecosystems at ZEDEDA: “TinyML will enable AI in more appliances, connected products, healthcare wearables, etc., for fixed functions triggered locally by simple voice and gesture commands, common sounds (a baby crying, water running, a gunshot), location and orientation, environmental conditions, vital signs, and so on.”
Michael Berthold, the CEO and cofounder at KNIME: “In the future, we will also see models that update themselves and potentially recruit new data points on purpose for retraining.”
Ari Weil, who is the Global Vice President of Product and Industry Marketing at Akamai: “Consider medical devices like pacemakers or heart rate monitors in hospitals. If they signal distress or some condition that requires immediate attention, AI processing on or near the device will mean the difference between life and death.”
But successfully bringing AI to the edge will face challenges and likely take years to get to critical mass. “The edge has relatively lower resource capabilities in comparison to data centers, and edge deployments will require lightweight solutions focused on security and supporting low latency applications,” said Brons Larson, who is a PhD and the AI Strategy Lead at Dell Technologies.
There will also need to be heavy investments in infrastructure and the retooling of existing technologies. “For NetApp, this is a large opportunity but one that we have to re-invent our storage to support,” said Ross Ackerman, who is the Head Of Customer Experience and Active IQ Data Science at NetApp. “A lot of the typical ONTAP value prop is lost at the edge because clones and snapshots have less value. The data at the edge is mostly ephemeral, needing only a short time to be used in making a recommendation.”
Then there are the cybersecurity risks. In fact, they could become more dangerous then typical threats because of the impact on the physical world.
“As the edge is being used with applications and workflows, there is not always consistent security in place to provide centralized visibility,” said Derek Manky, who is the Chief of Security Insights and Global Threat Alliances at Fortinet’s FortiGuard Labs. “Centralized visibility and unified controls are sometimes being sacrificed in favor of performance and agility.”
Given the issues with the edge and AI, there needs to be a focus on building quality systems but also rethinking conventional approaches. Here are some recommendations:
Prasad Alluri, the Vice President of Corporate Strategy at Micron: “The increase in AI also means that its increasingly important that edge computing is near 5G base stations. So soon, in every base station, every tower might have compute and storage nodes in it.”
Debu Chatterjee, the Senior Director of AI Platform Engineering at ServiceNow: “There will need to be newer chips with tensor capabilities seen in GPUs or their alternative, or specialized with specific inference models burnt into FPGAs. A hardware/software combo will be required to provide a zero-trust security model at the edge.”
Abhinav Joshi, the Global Product Marketing Leader at OpenShift Kubernetes Platform at Red Hat: “Many of these challenges can be successfully addressed at the start by approaching the project with a focus on an end-to-end solution architecture built on the foundation of containers, Kubernetes, and DevOps best practices.”
Although, when it comes to AI and the edge, the best strategy is probably to start with the low-hanging fruit. This should help avoid failed projects.
“Enterprises should begin by applying AI to smaller, non-mission critical applications,” said Bob Friday, who is the Chief Technology Officer at Mist Systems, which is a Juniper Networks company. “By paying close attention to details such as finding the right edge location and operational cloud stack, it can make operations easier to manage.”
But regardless of the approach, the future does look promising for the edge. And AI efforts really need to consider the potential use cases to get its full value.
Kubernetes is a system that helps with the deployment, scaling and management of containerized applications. Engineers at Google built it to handle the explosive workloads of the company’s massive digital platforms. Then in 2014, the company made Kubernetes available as open source, which significantly expanded the usage.
Yes, the technology is complicated but it is also strategic. This is why it’s important for business people to have a high-level understanding of Kubernetes.
“Kubernetes is extended by an ecosystem of components and tools that relieve the burden of developing and running applications in public and private clouds,” said Thomas Di Giacomo, who is the Chief Technology and Product Officer at SUSE. “With this technology, IT teams can deploy and manage applications quickly and predictably, scale them on the fly, roll out new features seamlessly, and optimize hardware usage to required resources only. Because of what it enables, Kubernetes is going to be a major topic in boardroom discussions in 2021, as enterprises continue to adapt and modernize IT strategy to support remote workflows and their business.”
In fact, Kubernetes changes the traditional paradigm of application development. “The phrase ‘cattle vs. pets’ is often used to describe the way that using a container orchestration platform like Kubernetes changes the way that software teams think about and deal with the servers powering their applications,” said Phil Dougherty, who is the Senior Product Manager for the DigitalOcean App Platform for Kubernetes and Containers. “Teams no longer need to think about individual servers as having specific jobs, and instead can let Kubernetes decide which server in the fleet is the best location to place the workload. If a server fails, Kubernetes will automatically move the applications to a different, healthy server.”
There are certainly many use cases for Kubernetes. According to Brian Gracely, who is the Sr. Director of Product Strategy at Red Hat OpenShift, the technology has proven effective for:
New, cloud-native microservice applications that change frequently and benefit from dynamic, cloud-like scaling.
The modernization of existing applications, such as putting them into containers to improve agility, combined with modern cloud application services.
The lift-and-shift of an existing application so as to reduce the cost or CPU overhead of virtualization.
Run most AI/ML frameworks.
Have a broad set of data-centric and security-centric applications that run in highly automated environments
Use the technology for edge computing (both for telcos and enterprises) when applications run on low-cost devices in containers.
Now all this is not to imply that Kubernetes is an elixir for IT. The technology does have its drawbacks.
“As the largest open-source platform ever, it is extremely powerful but also quite complicated,” said Mike Beckley, who is the Chief Technology Officer at Appian. “If companies think their private cloud efforts will suddenly go from failure to success because of Kubernetes, they are kidding themselves. It will be a heavy lift to simply get up-to-speed because most companies don’t have the skills, expertise and money for the transition.”
Even the setup of Kubernetes can be convoluted. “It can be difficult to configure for larger enterprises because of all the manual steps necessary for unique environments,” said Darien Ford, who is the Senior Director of Software Engineering at Capital One.
But over time, the complexities will get simplified. It’s the inevitable path of technology. And there will certainly be more investments from venture capitalists to build new tools and systems.
“We are already seeing the initial growth curve of Kubernetes with managed platforms across all of the hyper scalers—like Google, AWS, Microsoft—as well as the major investments that VMware and IBM are making to address the hybrid multi-cloud needs of enterprise customers,” said Eric Drobisewski, who is the Senior Architect at Liberty Mutual Insurance. “With the large-scale adoption of Kubernetes and the thriving cloud-native ecosystem around it, the project has been guided and governed well by the Cloud Native Computing Foundation. This has ensured conformance across the multitude of Kubernetes providers. What comes next for Kubernetes will be the evolution to more distributed environments, such as through software defined networks, extended with 5G connectivity that will enable edge and IoT based deployments.”
Zoom’s second quarter results, which were reported earlier in the week, were off-the-charts as the company’s platform has become a must-have for consumers and businesses alike. Revenues came to $663.5 million, up from $145.8 million during the same period a year ago. There was also a profit of $185.7 million. Wall Street was looking for only $500 million on the top-line and $134 million in earnings.
With the drop in the markets this week, Zoom stock has taken a hit. But the return is still nearly 10X since the IPO in April 2019.
Keep in mind that Zoom is not a typical Silicon Valley startup. CEO and founder Eric Yuan did not raise large amounts of capital in the early days. He actually spent two years developing the Zoom app. And when he launched it, there was virtually no spending on marketing.
Before Emergence Capital’s Santi Subotovsky met with Eric in 2014, he had spent about three years evaluating conferencing startups. He believed that the trend of cloud computing would lead to a transformation in collaboration.
“People wanted a tool that they could love, not something a CIO has mandated that everyone should use,” said Subotovsky. “There was also a shift in how people were using devices. They would switch from hardwired to Wifi to cellphone networks. But the old school collaboration tools were not designed for this.”
Subotovsky’s own life story was also key with the investment thesis. As someone who grew up in Argentina, he knew that many people lacked sufficient bandwidth for high-quality communications platforms. In other words, the market for conferencing was still untapped.
Now Subotovsky did evaluate a myriad of startups but Zoom was the one that clearly stood out. But there was a problem: Eric did not want to raise capital.
He thought bringing on institutional investors would be too distracting. He instead wanted to be laser focused on making the best product.
For Subotovsky, he did not give up. He went on to build a relationship with Eric and talked about the benefits of having more scale, which would be essential if Zoom wanted to sell to large enterprises.
It’s All About The Product
Eric would eventually make a pitch to Subotovsky and his partners. Consider that there was no investor deck or financials. Rather, Eric did a live demo of Zoom—which he pulled off flawlessly. “He pitched the product, not the company,” said Subotovsky. “But he gave us complete access to all the data. And once we saw it, we were blown away. We had never seen something as capital efficient as Zoom was.”
One of the main advantages of the system was that it was video-first. At the time, the rival systems were mostly about screen sharing. So Zoom was truly innovative. There was also a deep technology foundation, which was easy to integrate and configure.
It certainly helped that Eric had an extensive background with conferencing. Back in 1997, he joined WebEx as a founding engineer. But he would leave the company in 2011 because management ignored many of his suggestions.
In February 2015, Zoom announced a Series C round for $30 million. Emergence led the investment and there was participation from existing investors, such as Li Ka-shing’s Horizons Ventures, Yahoo co-founder Jerry Yang, Qualcomm Ventures, and serial biotech entrepreneur Dr. Patrick Soon-Shiong
To get a sense of the traction of Zoom, the company had grown its customer base from 4,500 to 65,000 during the past two years and the number of meeting participants went from 3 million to 40 million.
Emergence wrote a check for $20 million, which was the biggest one in the company’s history. The equity percentage was also below its normal threshold. “This investment was nerve-wracking,” said Subotovsky. “I was trying to find investors to see the vision. But people would not even take a meeting.”
No doubt, the investment has turned out to be one of the most successful during the past decade, if not in the history of venture capital. For the most part, the deal highlights how important it is to focus on emerging market trends and to not give up on your convictions. After all, Zoom’s market value is roughly $101 billion.
Note: If you want to see my interview with Subotovsky—of course, which was on Zoom—you can check it out here.
The funding environment for AI startups remains robust—and some of the rounds have been substantial. Just recently Dataiku, which operates a machine learning platform, announced a $100 million Series D investment.
AI truly represents a transformation in the tech world. “AI is making software much more dynamic and improves as it understands user behavior,” said Gordon Ritter, who is the founder and General Partner at Emergence.
OK then, what are VCs looking for when evaluating an AI deal? What should be in your pitch deck?
Well, to answer these questions, I talked to a variety of VCs. Here’s what they said:
A slide on Why Now. What technology has recently been developed that has made solving this customer problem now possible. It might be “speech-to-text technology has gotten good enough to use in 95% of a call center’s communications.” Or “recent deep learning focused processors have made it possible to do computer vision on the camera instead of in the cloud.” It’s rare for people to identify a customer problem that nobody has heard about before–usually, what creates the potential for a large new company is that technology is now available to solve that problem in a new or differentiated way.
I also want to see a slide about solving the “Cold Start” problem. This matters most for the earliest stage companies, but AI companies need access to data to start training their algorithms. I like to see that they’ve thought about this problem and have a clear way to get access to enough data to build their business. The answer can be anything from buying data to partnering with an ancillary business to “faking it until they make it,” where they deliver the product or service with humans until they have enough data to build the AI model.
Mark Rostick, who is a Vice President and Senior Managing Director of Intel Capital:
When looking at a presentation of a potential AI deal, we look closely at the specific problem in AI/ML that they are solving and why solving that problem is important enough to build a company—not just a feature or tool. We also take a look at why the team is uniquely positioned to understand the problem they are trying to solve and how they are equipped to execute on it. The team must have line-of-sight to an economic model they can create that is capable of driving growth at “venture scale”.
Jake Saper, a partner at Emergence :
When evaluating companies that use AI to augment workers, I like to see charts that show the percentage of AI-generated suggestions that are taken by the user over time. For strong companies, this portion may start relatively low as the model is training and the UI is being tweaked. As both improve, you want to see the “coaching acceptance rate” improve to >75% and stay consistent.
#1: A high level architecture/diagram of how the data flows from source to training to AI predictions to the product that the users interact with. This helps brush away some of the AI pixie dust.
#2: Quantifying the value of the product or model for the user. For example, radiologists save 1 hour per day because the AI automates report writing. Only 10% of radiologists make adjusts to what the AI writes
#3: Defensibility of the data now or in the future. There are different ways to achieve this from proprietary data rights agreements to data network effects. In the end, ML is all about data. One thing to note is that data defensibility is just a minimum and not a sufficient condition of defensibility for AI companies.
One of the most valuable metrics to show investors when pitching an AI company is how the accuracy rate of the underlying algorithm is improving over time. It’s important that investors see this improvement over time, particularly if humans are in the loop, as this analysis points to the fundamental solvability of the underlying problem. Investors are scared of the potentially asymptotic nature of AI algorithms (that they will never get good “enough”), so it’s very important to define “good enough” in a business context (what a business user will accept in terms of error rate) and then overlay this underlying expectation with the quantitative measure of how an algorithm is performing over time.
With the equity markets surging and interest rates at historically low levels, the environment is ideal for IPOs. But for Snowflake, which has recently filed for an offering, it would likely do well in just about any market environment. This tech startup is growing like a weed and the market opportunity is enormous.
Founded in 2012, Snowflake pioneered the category for cloud-native data warehouses. The founders actually spent two years developing the software.
And yes, the timing proved to be spot-on. The market was ripe for disruption as traditional data warehouses have a myriad of disadvantages. Just some include: the inability to handle unstructured data and huge workloads, high costs, complex interfaces, problems with consistency and integrity of data, and issues with data sharing.
Note that the founders—Thierry Cruanes, Benoit Dageville, and Marcin Zukowski—were veterans of the traditional data warehouse market. They had worked at companies like Oracle, IBM and Google (by the way, the name “Snowflake” was chosen because the founders like to ski!) In other words, the founders had a strong understanding of the weaknesses of legacy systems—but also had the creativity to build a much better alternative.
“My company uses Snowflake and also competes with it in some cases,” said Sam Underwood, who is the VP of Business Strategy with Futurety. “Snowflake is growing rapidly, and justifiably so, because it’s filling a gaping hole in the market—namely, a huge need to unify data sources to form a single source of truth across an organization. There are many, many tools that already do this—Google BigQuery among others—however, Snowflake has combined the technical effectiveness with the UI simplicity to really excel among both technical users and high-level decision makers who may not want or need as much granular detail.”
So how fast is Snowflake growing? During the first six months of this year, revenues spiked from $104 million to $242 million on a year-over-year basis. While there continues to be significant net losses, the company has still been able to greatly improve gross margins.
Consider that a key technology decision for Snowflake was to separate compute from storage. “This offers great performance to customers without the high cost, so they get the best of both worlds,” said Venkat Venkataramani, who is the co-founder and CEO of Rockset. “This was phenomenally compelling at the time and years ahead of even the likes of Amazon with Redshift and Google.”
But of course, Snowflake is more than just about whiz-bang technology. The company has also assembled an experienced executive team, led by CEO Frank Slootman. Prior to joining, he was at the helm of ServiceNow, which he took from $100 million in revenues to $1.4 billion. The current market cap of the company is $93 billion.
True, Snowflake does have customer concentration, with Capital One accounting for roughly 11% of overall revenues. But then again, this does show the strategic importance of the technology.
“This IPO underscores a significant change in thinking about the increasing importance of the database market,” said Raj Verma, who is the Co-CEO of MemSQL. “Data has never been more important than it is right now. In the last 25 years, only one company in this sector other than Snowflake went public. And I’m sure we’ll see a couple more companies go out in the new few years as well. There was an iron grip on the database market for more than two decades, with IBM, Oracle and SAP HANA. Now we are seeing a changing of the guard, which gives customers the option of deciding what is best for their business. I can tell you that the technology of yesterday will not solve the data challenges of tomorrow, and this IPO brings newer technology solutions to the forefront.”
While quantum computing is still in the early phases, there have already been many innovations and breakthroughs. Companies like IBM, Microsoft, Google and Honeywell have been investing aggressively in the technology.
So then what is quantum computing? Well, it is similar to traditional computing, which relies on bits—that is, the 0’s and 1’s to encode information. But quantum computing as its own version of this: the quantum bit or qubit. This is where the information can have multiple states at the same time. And the reason for this is the impact of the effects of quantum mechanics, like superposition and entanglement. Yes, this is all about the spooky world of Schrodinger’s cat, which is both alive and dead at the same time!
“Quantum computing is a new kind of computing, using the same physical rules that atoms follow in order to manipulate information,” said Dr. Jay Gambetta, who is an IBM Fellow and vice president of IBM Quantum. “At this fundamental level, quantum computers execute quantum circuits—like a computer’s logical circuits, but now using the physical phenomena of superposition, entanglement, and interference to implement mathematical calculations out of the reach of even our most advanced supercomputers.”
One of the fertile areas for quantum computing is AI (Artificial Intelligence), which relies on processing huge amounts of complex datasets. There is also a need to evolve algorithms to allow for better learning, reasoning and understanding.
Then what are some of the things we may see with quantum computing and AI? Let’s take a look:
Generative models are those models that don’t just limit themselves to answering a question, but that actually generate output such as an image, music, video, etc. As an example, imagine you have a lot of pictures of the side of a face, but not a lot of pictures of the front of a face. If you want security detection capabilities to be able to recognize dual facial recognition on the front side of a face, you can actually use these generative models very accurately to create more samples of frontal views of a face. Inserting quantum processing units into the classical framework has the potential to boost the quality of the images generated. And how does this help us with classical machine learning? Well, traditional machine learning algorithms are as good as the data you feed them. If you try to train a classical face detection model with a small dataset of faces, this model won’t be very good. However, you can use quantum-enhanced generative models to enlarge this dataset with more images (both in terms of quantity and variety), which can significantly improve the detection model. This isn’t limited to generating faces, you can also generate fake molecules, cancer cells, or MRI scans, which are very similar to the real thing. This allows us to train better machine learning models, which can then apply to real data and real-world problems.
For the first time, a Natural Language Processing (NLP) algorithm is “meaning aware” and has been executed on a quantum computer. When we refer to meaning aware we mean that computers can actually understand whole sentences and not just individual words and that the awareness can be expanded to whole phrases and ultimately real time speech without requiring stochastic guesswork that is the state of the art today and which is computationally so expensive. Full scale implementation is dependent on quantum computers becoming much larger than is currently the case. This development of research in NLP is a prime example of the fact that as realistic quantum computers become available, more use cases will also become apparent. Of course, this has been proven to be the case in the past 30 years on classical computers as a precedent.
Roughly speaking, AI and ML are good ways to ask a computer to provide an answer to a problem based on some past experience. It might be challenging to tell a computer what a cat is, for instance. Still, if you show a neural network enough images of cats and tell it they are cats, then the computer will be able to correctly identify other cats that it did not see before. It appears that some of the most prominent and widely used AI and ML algorithms can be sped-up significantly if run on quantum computers. For some algorithms we are even anticipate exponential speed-ups, which clearly does not mean performing a task faster, but rather turning a previously impossible task and making it possible, or even easy. While the potential is undoubtedly immense, this still remains to be proven and realized with hardware.
One of the areas being looked at currently is in the area of artificial intelligence within financial trading. Quantum physics is probabilistic, meaning the outcomes constitute a predicted distribution. In certain classes of problems, where outcomes are governed by unintuitive and surprising relationships among the different input factors, quantum computers have the potential to better predict that distribution thereby leading to a more correct answer. Dr. Hayes states: “The basic idea is that there are problems that require an AI to generate new data that it hasn’t seen before in order to make a decision. Solving this problem may require coming up with an underlying model for the probability distribution in question that it could use in new situations.”
Daniel Newman, who is the Principal Analyst and Founding Partner at Futurum Research:
As it pertains to AI/ML, I think what I’m most encouraged by is the potential for classical and quantum to work together leveraging the elastic nature of the cloud and the powerful, specific problem-solving capabilities of quantum computing. I get the sense that a lot of people are looking at quantum versus classical computing, but in reality, it will be the two working together harmoniously to solve challenging and complex problems. Both have strengths and the development now is for quantum computing to function as part of the solution. Over time, both computing formats will continue to advance, but the ability to accelerate workloads on traditional GPUs and ASICs while also leveraging the power of quantum computing is a recipe for faster, more robust results, which is what the market should be eager to see as quantum computing becomes more widely accessible.
For me, I see a few applications for quantum computing in the immediate future that will gain popularity, but of course there will be many more. Financial Services and Healthcare are immediate applications where Quantum Computing can take advantage of speed and specificity to help tackle complexities. Fraud detection and drug compound identifications have been touted as some of the most exciting use cases. Given the current state of cybercrime and the attention to healthcare in the wake of the pandemic, this makes a lot of sense.