AI Disruption: What VCs Are Betting On

According to data from PitchBook, the funding for AI deals has continued its furious pace. In the latest quarter, the amount invested came to a record $31.6 billion. Note that there were 11 deals the closed more than $500 million.

Granted, plenty of these startups will fade away or even go bust. But of course, some will ultimately disrupt industries and change the landscape of the global economy.

“To be disrupted, you have to believe the AI is going to make 10x better recommendations than what’s available today,” said Eric Vishria, who is a General Partner at Benchmark. “I think that is likely to happen in really complex, high dimensional spaces, where there are so many intermingled factors at play that finding correlations via standard analytical techniques is really difficult.”

So then what are some of the industries that are vulnerable to AI disruption? Well, let’s see where some of the top VCs are investing today:

Software Development: There have been advances in DevOps and IDEs. Yet software development remains labor intensive. And it does not help that its extremely difficult to recruit qualified developers.

But AI can make a big difference. “Advancements in state-of-the-art natural language processing algorithms could revolutionize software development, initially by significantly reducing the ‘boilerplate’ code that software developers write today and in the long-run by writing entire applications with little assistance from humans,” said Nnamdi Iregbulem, who is a Partner at Lightspeed Venture Partners.

Consider the use of GPT-3, which is a neural network that trains models to create content. “Products like GitHub Copilot, which are also based on GPT-3, will also disrupt software development,” said Jai Das, who is the President and Partner at Sapphire Ventures.

Cybersecurity: This is one of the biggest software markets. But the technologies really need retooling. After all, there continues to be more and more breaches and hacks. 

“Cybersecurity is likely to turn into an AI-vs-AI game very soon,” said Deepak Jeevankumar, who is a Managing Director at Dell Technologies Capital. “Sophisticated attackers are already using AI and bots to get over defenses.”

Construction: This is a massive industry and will continue to grow, as the global population continues to increase. Yet construction has seen relatively small amounts of IT investment. But AI could be a game changer.

“An incremental 1% increase in efficiency can mean millions of dollars in cost savings,” said Shawn Carolan, who is a Managing Partner at Menlo Ventures. “There are many companies, like, doing transformative work using AI in the construction space. Openspace leverages AI and machine vision to essentially become a photographic memory for job sites. It automatically uploads and stitches together images of a job site so that customers can do a virtual walk-through and monitor the project at any time.”

Talent Management: HR has generally lagged with innovation. The fact is that many of the processes are manual and inefficient.

But AI can certainly be a solution. In fact, AI startups like have been able to post substantial growth in the HR category. In June, the company announced funding of $220 million, which was led by the SoftBank Vision Fund 2. 

“Every single company is talking about talent as a key priority, and the companies that embrace AI to find better candidates faster, cheaper, at scale, they have a true competitive advantage,” said Kirthiga Reddy, who is a Partner at SoftBank. “Understanding how to use AI to amplify the interactions in the talent lifecycle is a differentiator and advantage for these businesses.”

Drug Discovery:  The development of the Covid-19 vaccines—from companies like Pfizer, Moderna and BioNTech—has highlighted the power of innovation in the healthcare industry. But despite this, there is still much be done. The fact is that drug development is costly and time-consuming. 

“It’s becoming impossible to process these large datasets without using the latest AI/ML technologies,” said Dusan Perovic, who is a partner at Two Sigma Ventures. “Companies that are early adopters of these data science tools and thereby are able to analyze larger datasets are going to make faster progress than companies that rely on older data analytics tools.”

AI (Artificial Intelligence): Should You Teach It To Your Employees?

AI is becoming strategic for many companies across the world. The technology can be transformative for just about any part of a business. 

But AI is not easy to implement. Even top-notch companies have challenges and failures.

So what can be done? Well, one strategy is to provide AI education to the workforce. 

“If more people are AI literate and can start to participate and contribute to the process, more problems–both big and small–across the organization can be tackled,” said David Sweenor, who is the Senior Director of Product Marketing at Alteryx. “We call this the ‘Democratization of AI and Analytics.’ A team of 100, 1,000, or 5,000 working on different problems in their areas of expertise certainly will have a bigger impact than if left in the hands of a few.”

Just look at Levi Strauss & Co. Last year the company implemented a full portfolio of enterprise training programs—for all employees at all levels—focused on data and AI for business applications. For example, there is the Machine Learning Bootcamp, which is an eight-week program for learning Python coding, neural networks and machine learning—with an emphasis on real-world scenarios. 

“Our goal is to democratize this skill set and embed data scientists and machine learning practitioners throughout the organization,” said Louis DeCesari, who is the Global Head of Data, Analytics, and AI at Levi Strauss & Co. “In order to achieve our vision of becoming the world’s best digital apparel company, we need to integrate digital into all areas of the enterprise.”

Granted, corporate training programs can easily become a waste. This is especially the case when there is not enough buy-in at the senior levels of management.

It is also important to have a training program that is more than just a bunch of lectures. “You need to have outcomes-based training,” said Kathleen Featheringham, who is the Director of Artificial Intelligence Strategy at Booz Allen. “Focus on how AI can be used to push forward the mission of the organization, not just training for the sake of learning about AI.  Also, there should be roles-based training. There is no one-size-fits-all approach to training, and different personas within an organization will have different training needs.”

AI training can definitely be daunting because of the many topics and the complex concepts. In fact, it might be better to start with basic topics. 

“A statistics course can be very helpful,” said Wilson Pang, who is the Chief Technology Officer at Appen. “This will help employees understand how to interpret data and how to make sense of data. It will equip the company to make data driven decisions.”

There also should be coverage of how AI can go off the rails. “There needs to be training on ethics,” said Aswini Thota, who is a Principal Data Scientist at Bose Corporation. “Bad and biased data only exacerbate the issues with AI systems.”

For the most part, effective AI is a team sport. So it should really involve everyone in an organization. 

“The acceleration of AI adoption is inescapable—most of us experience AI on a daily basis whether we realize it or not,” said Alex Spinelli, who is the Chief Technology Officer at LivePerson. “The more companies educate employees about AI, the more opportunities they’ll provide to help them stay up-to-date as the economy increasingly depends on AI-inflected roles. At the same time, nurturing a workforce that’s ahead of the curve when it comes to understanding and managing AI will be invaluable to driving the company’s overall efficiency and productivity.”

How To Sell AI

Companies like and Palantir have shown that selling AI technology can be quite lucrative. After all, these companies command significant market caps and are growing quickly.

Yet selling AI technology remains difficult. Customers often want customized solutions that are based on their unique data sets. There are also the issues of adoption. The fact is that many AI projects fail to go beyond the proof-of-concept phase.

Then what are some ways to sell AI technologies? Let’s take a look:

It’s Not About Platforms: Many AI vendors extol their “platforms” that can seemingly solve any problems. But this approach is usually off the mark. Let’s face it, there are already top platforms that have solid features and powerful ecosystems.

“Businesses should sell a solution to a problem,” said Muddu Sudhakar, who is the CEO and founder of Aisera. “Customers will buy a platform when multiple solutions are acquired and there are the right integrations. Customers don’t have free money sitting on the side just to invest in platforms.”

Sudhakar believes that an AI solution needs to show value within three to six months and there must be a return on investment within the first year.

Insights: It is often fuzzy as to what an AI system does. But for businesses, one of the most compelling aspects of this technology is about getting insights on tough questions. 

Take the example of Founded in 2016, the company is focused on leveraging AI for the talent management category.

“A lot of the problems that organizations are looking to solve are intimidating because they don’t always have a clear answer,” said Kamal Ahluwalia, who is the President of “There’s no one second solution to ‘why can’t I hire the right people’ or ‘what skills do I need to be teaching my team so we’re ahead of the game in a few years?’ But the data is there, and it’s usually just not being looked at correctly, or it’s not feasible to do so for thousands or millions of times over. AI is all about applying that data, at scale.”

Avoid AI-Speak: AI is a complex topic. There are a myriad of terms like machine learning, hidden layers, deep learning, backpropagation and so on. Even people in the industry can have a difficult time explaining the concepts.

This is why it is critical to avoid the jargon when selling AI. “Rather than explaining the wonders of AI, it’s better to provide practical steps that you can take together with a customer to achieve the desired business results,” said Thomas Hansen, who is UiPath’s Chief Revenue Officer.

The Buyer Persona: AI is still in the early stages and many of the potential buyers are early adopters. This means that they have a strong understanding of their needs and a good sense of what’s available on the market. They are also more willing to use software that is 90% finished and then find ways to fill the gap, such as with custom coding or configuration. 

True, executives will still write the checks. But they will still rely heavily on the AI practitioners within the organization. 

“The AI buyer persona prefers a self-service and hands-on approach,” said Omed Habib, who is the Vice President of Marketing at “They don’t need to speak to a sales person and would rather sign up for an account immediately. They’re curious. They’re self learners. They love to tinker. So, the companies that have figured this out have created flawless self-service sign up for their software. They have excellent documentation. They have a vast library of videos to help enable their customers. They host a community forum for other users to share knowledge. In other words, they’re not just teaching you how to fish but they’re putting the best fishers in the same room to learn from each other.”

Data: Customers are understandably sensitive when providing access to their data to a third-party. This is why an AI vendor needs to have strong data policies.  

“It’s important that sales teams are educated on these features and understand the compliance and certifications that the software or organization holds,” said Sid Mistry, who is Appen’s Vice President of Marketing. “Company data is gold; you need to value and respect that.”

Elon Musk’s Tesla Bot: Is Westworld Coming Soon?

When it comes to presentations, Elon Musk rarely disappoints his audience. And this was certainly the case with this week’s AI Day. He made a variety of announcements, such as for a new 7 nm semiconductor, the Dojo supercomputer and innovations with computer vision. There were also some deep dives into deep learning. 

But perhaps the most interesting announcement was the Tesla Bot. This is on par with what we usually see on dazzling sci-fi movies. Yes, Musk apparently is building a humanoid robot.  It will be five feet, eight inches tall, weigh 125 pounds and have human-like hands. What will she/he/it do? Basically, the Tesla Bot will be our cyber slave, handling tedious and repetitive tasks.  For example, you can tell it to go to Chipotle and get a burrito—and it will happen. 

Sounds pretty good, huh? Definitely. 

But then again, over the years Musk has made some ambitious claims that have not been realized (remember his promises of fully autonomous cars or his robot taxi service?) And this could easily be the case again.

The funny thing is that Musk has a history of saying that AI could run amok and become an existential threat to humanity. Hey, he once tweeted: “If you’re not concerned about AI safety, you should be. Vastly more risk(y) than North Korea.”

But somehow, Musk thinks his version of AI will be just fine. We just have to trust him on this one, regardless of the federal preliminary investigation of Tesla’s Autopilot and the various lawsuits. Actually, even if the Tesla Bot somehow turns hostile, it will only be able to run five miles an hour.

Yet Musk’s Tesla has some big advantages to be successful in building the Tesla Bot. At the conference, he noted: “Our cars are semi-sentient robots on wheels. It kind of makes sense to put that on to a humanoid form. We’re also quite good at sensors and batteries and actuators so we think we’ll probably have a prototype some time next year that basically looks like this.”

But there is good amount of irony here. Musk’s demo of his Tesla Bot was actually a person in a white spandex outfit and a black mask. He or she looked more like a go-go dancer.

Now if the CEO of Ford or GM tried this stunt, he or she would have been laughed out of the room and mocked savagely on social media channels. It would be a downright embarrassment. 

The Impact

Even if Musk may be overly optimistic on the timeline for humanoid robots, it does seem like we could see some true breakthroughs during the next decade. And the impact on the world will be significant. “These systems could be used to aid human labor in hazardous areas like mining and manufacturing, reducing overall safety incidents and saving lives,” said Michael Levy, who is a Senior Analyst at Harbor Research.

But the bots could also mean having to rethink some of the core fundamentals of society. In other words, what will “work” really mean and what will become of capitalism?

“Elon Musk touched on it and I agree,” said Dr. Jesper Dramsch, who is a machine learning expert and works at the ECMWF. “A lot of physical work will be optional. Tasks like shelf-stocking may be completely obsolete. As a society this means we have to move away from the concept that we trade our time directly for income and seriously consider universal basic income and social structures that go beyond the scarcity economy of pure capitalism.”

Upstart: Can AI Kill The FICO Score?

Last December, Upstart launched its IPO and raised about $240 million. On the first-day of trading, the shares jumped 47%.

But this was just the beginning of the gains as the IPO would soon become one of the top for the past year. The return? About 800%.

Then again, the company is a high-growth fintech company that has effectively leveraged the power of AI. It’s focus is on partnering with banks to provide a much better way to score the risks and automate the tedious processes for issuing and managing consumer loans. 

The CEO and cofounder is Dave Girouard, who built the billion-dollar apps business for Google. He had also served as a Product Manager at Apple and an associate in Booz Allen’s Information Technology practice.

As for Upstart, Girouard’s main focus is to upend the banking industry’s reliance on the FICO score. 

“The Upstart system uses AI and machine learning models with 1,600 data points and 15 billion cells of data to improve accuracy in terms of identifying and measuring credit risks,” said Phat Le, who is an Associate at Harbor Research. “Some of the variables that Upstart considers are employment history, educational background, banking transactions, cost of living, and loan application interactions.”

For the most part, Upstart is reducing the inefficiency with the traditional FICO approach. After all, about 80% of Americans never default on their loans yet only 48% have access to loans at prime rate. The result is that good borrowers often pay premiums rates while many other borrowers get loans when they should not. 

Granted, when it comes to AI, there can certainly be major issues. There is the potential for bias and discrimination, such as when the data is skewed. Yet Upstart has made great strides in addressing the problems.

“In 2017, the company was the first to receive a No Action Letter from the Consumer Financial Protection Bureau (CFPB), which was renewed in November 2020,” said Mike Raines, who is the owner of Raines Insurance Group. “According to Upstart, ‘the purpose of such letters is to reduce potential regulatory uncertainty for innovative products that may offer significant consumer benefit.’”

Keep in mind that one of Upstart’s banking partners has recently eliminated any minimum FICO requirement for its borrowers. And this is what Girouard had to say about this on his earnings call: “To us, this demonstrates both a commitment on behalf of this bank to a more inclusive lending program, as well as an increasing confidence in Upstart’s AI-powered model. While credit scores can be useful, hard cutoffs based on a three-digit number invented 30 years ago leaves far too many creditworthy Americans out in the cold.”

The Upstart strategy has certainly resulted in staggering growth. In the latest quarter, the revenues soared by 1,308% to $194 million and the transaction volume came to $2.80 billion, up 1,605%. The company was even able to generate a net profit of $37.3 million, up from a loss of $6.2 million in the prior year. 

To expand its addressable market, Upstart has acquired Prodigy, which has allowed the company to move into the lucrative auto lending space. Based on the latest earnings report from Upstart, the U.S. personal loan originations are about $84 billion and they are $635 billion for auto loans. 

But interestingly enough, Upstart really does not need to look further than these two categories anyway. As Girouard noted on the earnings call: “[W]e just see a lot of opportunity out there. We don’t think credit is a solved problem almost anywhere in terms of people getting rates that makes sense for them based on their true risk. So you will definitely see us move beyond personal loans and auto, but frankly, we have so much uncharted territory, even in those two categories, we’re not in a particular rush to do so.”

Mainframes: The Missing Link To AI (Artificial Intelligence)?

Data is certainly the fuel for AI. Yet there is a source of valuable data that usually does not garner much attention. It is from mainframe systems. They hold enormous amounts of data—which go back decades—for mission critical operations.

But then again, there are difficulties working with mainframes and AI. “The biggest challenge is the lack of compatibility of emerging technologies,” said Chida Sadayappan, who is the Cloud AI/ML Offering Leader at Deloitte Consulting LLP.

But the benefits of AI are too important to ignore. So what can be done? Well, one strategy is for leveraging cloud platforms outside of the mainframe environment. 

“New approaches to cloud migration replace the traditional ETL (extract, transform, load) approach with a more modern ELT (extract, load, transform) approach that moves mainframe formatted data directly to any object storage target before using the target platform to transform it for use in AI applications,” said Gil Peleg, who is the CEO of Model9. “This pioneering method adds mainframe data to data lakes quickly, easily, and securely so leaders can maximize the ROI of their cloud BI and analytics applications.”

But like any IT effort, there needs to be a clear-cut plan and the goals must be achievable. The reality is that AI efforts can take considerable time to generate ROI.

“Companies should be aware that these types of initiatives don’t always cut costs,” said Sudhir Kesavan, who is the Global Head of Cloud Transformation at  Wipro FullStride Cloud Services. “They may have the opposite effect, so having them be business-led can help overcome the challenge of business benefits seeming less tangible to start.”

IBM Mainframes and AI

The capabilities of mainframes have been evolving quickly. For example, IBM has been retooling its Z system for AI and this has involved the integration with many common open source platforms like Spark, PyTorch, Keras, and TensorFlow.

“We are enabling our clients to embed AI into their mission critical enterprise workloads and core business processes with minimal application changes and giving them the ability to score every transaction while meeting even the most stringent SLAs (Service Level Agreements),” said Elpida Tzortzatos, who is an IBM Fellow and the Chief Technology Officer of z/OS.

By generating the AI insights on Z, this allows for real-time responses at the point of interaction, which can be critical for applications like fraud detection. There is also a major security benefit because sensitive data is not moved.  

Leveraging AI For Mainframe Environments

The power of AI for mainframes does not have to be about creating projects. For example, there are emerging AIOps tools that help automate the systems. Some of the benefits include improved performance and availability, increased support speed for application releases and the DevOps process, and the proactive identification of issues. Such benefits can be essential since it is increasingly more difficult to attract qualified IT professionals.

According to a recent survey from Forrester and BMC, about 81% of the respondents indicated that they rely partially on manual processes when dealing with slowdowns and 75% said they use manual labor for diagnosing multisystem incidents. In other words, there is much room for improvement—and AI can be a major driver for this.

“Mainframe decision makers are becoming more aware than ever that the traditional way of handling mainframe operations will soon fall by the wayside,” said John McKenny, who is the Senior Vice President and General Manager of Intelligent Z Optimization and Transformation at BMC. “The demand for newer, faster digital services has placed increased pressure on data centers to keep up as new applications come online, the volume of data handled continually increases, and workloads become increasingly unpredictable. In today’s fast-paced digital economy, this creates a perfect storm of higher customer expectations, faster implementation of an increasing number of digital services, and a more tightly connected mainframe supported by a less-experienced workforce.”

How To Evaluate AI Software

Buying off-the-shelf AI (Artificial Intelligence) software is a good first step for those companies that are new to the technology. There should be little need to make investments in technical infrastructure or to hire expensive data sciences. There will also be the benefit of getting a solution that has been tested by other customers. For the most part, there should be confidence in the accuracy levels as the algorithms will probably be implemented properly.

But there is a nagging issue: there are many AI applications on the market and it is extremely difficult to determine which is the best option. After all, it seems that most tech vendors are extolling their AI capabilities as a way to stand out from the crowd.

Then what are some factors to consider when evaluating a new solution? Let’s take a look at the following:

Data Connectors: AI is useless without data. It is the fuel for the insights. 

But when it comes to a new AI solution, it can be tough to find the right sources of data, wrangle it and integrate it. Thus, when evaluating an application, you need to make sure that there are ways to handle this process. 

“The most complex task in an AI solution is not to implement the machine learning algorithm anymore—this is usually available as a set of functions in every tool—but to collect the data,” said Rosaria Silipo, a Ph.D. and a principal data scientist at KNIME. “That is, to connect to a variety of data sources, on premise, on the web, or on the cloud, and extract the data of interest.”

Flexibility: AI does not have general scope. Instead, it is focused on particular use cases. This is known a “weak AI.”

This is why it is important to see if the application is built to handle your particular vertical or situation. 

“Take search, for instance, where AI can be used to re-rank results and improve relevance,” said Ciro Greco, who is the Vice President of Artificial Intelligence at  Coveo. “When applied to ecommerce, search is searching on semi-structured records, such as products with little text available and we can count on reasonable amounts of behavioral data produced by users who browse the website. A strategy based on user behavior can be very effective, because we can count on having enough data to learn from.”

Yet AI-search for customer service use cases is often much different. It’s often about finding technical documents. “There is plenty of unstructured text, such as Knowledge Articles, and fewer behavioral data points, because customer service websites usually are less visited than e-commerce platforms,” said Greco. “So in that case, a strategy based on NLP for topic modeling will probably be more effective, because we need to maximize the gain from the information we have, in this case free text.”

Ease-of-use: This is absolutely critical. The user of AI is often a non-technical person. If the application is complex, there could easily be little adoption.

Ethical AI: Even if the application is accurate, there could be risks. The data may have inherent biases, which could skew the results. This is why you should get an explanation of the data and how it is used. 

“What many forget when evaluating an AI solution is the potential damage or risk it could pose to your organization,” said Michael Mazur, who is the founder and CEO of AI Clearing. “What if your organization is sued for deploying this solution?”

Costs: “If you’re the first customer in a specific industry for an AI vendor, then you’re a very valuable customer to have and you can use that as negotiating leverage for a beneficial contract,” said Brian Jackson, who is an analyst and research director at Info-Tech Research Group.

Xometry IPO: Looking To Be The Airbnb Of On-Demand Manufacturing

It was a busy week for IPOs, as 18 companies issued shares. And one of the standout offerings was from Xometry, which operates an online marketplace for on-demand manufacturing.

The IPO was priced at $44, which was above the $38-to-$42 price range, and the shares soared nearly 100% on the first day of trading (the current market value is close to $3 billion). T. Rowe Price and Capital World Investors purchased $70 million of the shares in the offering.

The CEO and cofounder of Xometry is Randy Altschuler, who is a serial entrepreneur. He launched two other startups that were sold to pubic companies. 

As for Xometry, he teamed up with Laurence Zuriff (he is the current Chief Strategy Officer). Prior to this, he was the managing partner at Granite Capital International Group.

Both Altschuler and Zuriff were intrigued by the custom manufacturing industry. But they did not immediately create a company. Instead, they spent months researching the market by talking to many small manufacturers. “We saw certain themes emerge,” said Altschuler.

For example, smaller manufacturers were usually dependent on larger customers that were local, which posed considerable risk. They also spent much time responding to requests for custom parts that did not turn into business. 

The buyers of custom manufacturing parts also had challenges. It was difficult to find the best vendors and to come up with the right pricing. 

To solve these problems, Altschuler and Zuriff saw the need for building a two-sided marketplace. But it took some time to get to critical mass. While sellers were interested, there was skepticism from the buyers. 

Then there was the issue of the pricing for the marketplace. Custom jobs do not have SKUs. Rather, each one is unique.

This meant Xometry needed to build an automation system. One approach was to use brute-force and go through the possibilities. “The problem is that it would take too long to build such a system,” said Altschuler. “It would also be difficult and time-consuming to maintain it.”

The next approach then? It was to leverage AI (Artificial Intelligence). Xometry created a proprietary engine—backed with patents—to provide instant quoting based on factors like volume, material, location and the manufacturing process.

“What really got us excited about Xometry was that they were using incredible technology,” said Daniel Docter, who is a Managing Director at Dell Technologies Capital and an investor in the company. “Over time their AI algorithms got better, their modeling became more accurate, and their breadth of capabilities grew.”

The result is that the company has been able to efficiently process transactions for more than 6 million parts since inception. Currently there are over 43,000 buyers and 5,000 sellers on the platform (the customers include roughly 30% of the Fortune 500).

In terms of growth, it has been accelerating. From 2018 to 2020, the compound annual growth rate was 92%, with revenues hitting $141.4 million.

However, without the AI, none of this would have been possible. The technology has been strategic to Xometry .

“The AI learns and predicts how to make a part, how much it should cost, how long it should take, how much material would be needed, how would it likely yield, and so on,” said Doctor. “Xometry literally transforms an old, manual, grossly human limited process, to an automated, much more accurate and predictable, and hugely more efficient AI-driven process. Manufacturers win; customers win; and frankly the world wins as we collectively can do so much more with so much less waste.”

SentinelOne IPO: How The Company Is Riding The AI Wave

SentinelOne, which develops AI-powered software for cybersecurity, launched its IPO today. There was certainly substantial demand from investors. The initial price range was $26-to-$29 but this was lifted to $31-to-$32. The offering was then priced at $35 and the amount raised came to about $1.2 billion. Tiger Global, Insight Venture Partners, Third Point Ventures, and Sequoia Capital also participated in a $50 million concurrent private placement for the stock.

The CEO and cofounder of SentinelOne is Tomer Weingarten.  Before launching the company in 2013, he had helped to create several other tech startups. His background was mostly in analytics.

He would team up with Almog Cohen, who was a security expert at Check Point Software Technologies. Cohen and Weingarten were actually childhood friends and went to the same college.

As for SentinelOne, the vision was to build a next-generation cybersecurity platform that leveraged AI. But interestingly enough, the timing was too early. “In the first few years, it was an absolute battle to get the trust of customers,” said Weingarten.

Yet things started to change as the cybersecurity threats became more frequent and dangerous. The reality was that traditional systems—such as those based on human-powered signatures–were failing even more. It was “akin to bringing a knife to a gunfight,” according to the SentinelOne S-1 filing.

While building the AI system, Weingarten learned some important lessons. First of all, success would not involve building better algorithms. The reason? They tend to be similar, standardized and open source. 

Next, success with AI would not be about having huge amounts of data either. The focus instead should be on having the right data that produces signals that can be modelled. “We look at it as a contextual narrative, such as like telling a story,” said Weingarten. “We even received a patent on this approach.” 

Building the platform has required using the latest in data systems to process petabytes of data in real-time. Every second counts when it comes to fending off cyberattacks. “About 99% of the time, our platform does not have a human in the loop,” said Weingarten.

The SentinelOne system is flexible as well. For example, it can be deployed on environments like Windows, macOS, Linux, and Kubernetes.

The Growth Engine

Consider that none of the company’s customers were impacted by the SolarWinds Sunburst cyberattack. This was definitely a major validation of the AI approach.

And yes, the growth has been standout for SentinelOne. During the latest quarter, revenues soared by 108% to $37.4 million. There are currently more than 4,700 customers and a majority of them are large enterprises. 

Some of the other metrics include:

  • The dollar-based gross retention rate: 97%.
  • The dollar-based net retention rate: 124%.
  • Annual recurring revenue: $164 million.
  • Customer satisfaction: 97%.

Note that the SMB (small-and-medium size business) category has shown even more growth. A key has been the leveraging of MSPs (Managed Service Providers).


This was the first public offering for Weingarten. “It was a lot of hard work,” he said.  “But we thought that an IPO was critical. I think it is about becoming a more mature company.”

Being public also helps with the trust of customers. After all, there are stringent disclosure and audit requirements. In fact, some larger enterprise companies will not even purchase cybersecurity software from private companies.

Now it’s true that SentinelOne faces intense competition. Just some of the key rivals include CrowdStrike and Palo Alto Networks.

Yet the market is massive. Based on the analysis from IDC, the spending is expected to reach $40.2 billion by 2024, which represents a compound annual growth rate of nearly 12%.

Biden’s AI Initiative: Will It Work?

The Biden administration has recently set into action its initiative on AI (Artificial Intelligence). This is part of legislation that was passed last year and included a budget of $250 million (for a period of five years). The goals are to provide easier access to the troves of government data as well as provide for advanced systems to create AI models. 

No doubt, this effort is a clear sign of the strategic importance of the technology. It is also a recognition that the U.S. does not want to fall behind other nations, especially China. 

The AI task force has 12 distinguished members who are from government, private industry and academia. This diversity should help provide for a smarter approach.

But the focus on data will also be critical.  “In areas of social importance such as housing, healthcare, education or other social determinants, the government is the only central organizer of data,” said Dr. Trishan Panch, who is the co-founder of Wellframe. “As such, if AI is going to deliver gains in these areas, the government has to be involved.”

Yet there will certainly be challenges. Let’s face it, the U.S. government often moves slowly and is burdened with various levels of local, state and federal authorities. 

“To achieve the initiative’s vision, government entities will need to go beyond sharing best practices and figure out how to share more data across departments,” said Justin Borgman, who is the CEO of Starburst. “For instance, expanding open data initiatives which today are largely siloed by departments, would greatly improve access to data. That would give Artificial Intelligence systems more fuel to do their jobs.”

If anything, there will be a need for a different mindset from the government. And this could be a heavy lift. “Based on my experience in the public sector, the major challenge for the government is addressing the ‘Missing Middle,’” said Jon Knisley, who is the Principal of Automation and Process Excellence at FortressIQ.   “There are a number of very advanced programs on one end, and then there are a lot of emerging programs on the other end. The greatest opportunity lies in closing that gap and driving more adoption. To be successful, there should be a focus as much as possible on applied AI.”

But the government initiative can do something that has been difficult for the private sector to achieve—that is, to help reskill the workforce for AI. This is perhaps one of the biggest challenges for the U.S. 

“The question is: How do we create a large AI data science force that is integrated across every industry and department in the US?,” said Judy Lenane, who is the Chief Medical Officer at iRhythm. “To start, we’ll need to begin AI curriculum early and encourage its growth in order to build a comprehensive workforce. This will be especially critical for industries that are currently behind in technological adoption, such as construction and infrastructure, but it also needs to be accessible.”

In the meantime, the Biden AI effort will need to deal with the complex issues of privacy and ethics. 

“Presently there is significant resistance on this subject given that most consumers feel that their privacy has been compromised,” said Alice Jacobs, who is the CEO of convrg,ai. “This is the result of a lack of transparency around managing consents and proper safeguards to ensure that data is secure. We will only be able to be successful if we can manage consents in a way where the consumer feels in control of their data. Transparent unified consent management will be the path forward to alleviate resistance around data access and can provide the US a competitive advantage in this data and AI arms race.”