|From: Paul H. Christiansen||8/12/2020 2:17:17 PM|
|One of the sharpest declines among software stocks has been Alteryx (AYX). On July 9th - just 25-trading days ago - it hit a high of $185.75. It is currently trading at $108.22 . . . down almost 42%. A sharp decline like that triggered a search of my research files and I came up with the following, and excerpt of an interview between Tiernan Ray and Kevin Rubin, CFO of AYX:|
Kevin “Data doesn’t go away. Questions around the business don’t go away. And so some of the conversation may shift from certain types of use cases in the areas of impact to more ROI and efficiency-driven discussions.
But the applicability of Alteryx in down times is probably even more, even stronger than in robust states.
And again it goes back to companies that had only been dabbling with digital transformation or data initiatives are going to find themselves in a really difficult spot as they go through whatever this recovery looks like without being able to leverage data for decision making.
Because I can tell you their competitors are. And so when you think about a competitive advantage if you’re not leveraging everything available to you to make decisions, you’re going to struggle.
I was just going to mention we have very, very low penetration, still, in what is a ginormous addressable market. Plus or minus 50 million citizen data scientists out there that we believe Alteryx addresses. We have less than 1 percent penetration today.”
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|From: Paul H. Christiansen||8/12/2020 3:02:15 PM|
|LVGO - TDOC merger.|
Aside from its extraordinary revenue growth, what piqued my early interest in LVGO was Glen Tullman, Founder and CEO. Prior to LVGO, Tullman was CEO of Allscripts. Based upon those 2-factors, I was able to initiate a position in LVGO in early March at a price of $25.50.
I recently read a report covering the merger of the 2-companies. It was a long report, often difficult to follow, but the last sentence truly resonated with me. It was a Tullman comment about the synergy of the 2-companies:
There’s a lot of growth and when you put it together, you now have one point three billion in revenue growing at 80%. I mean, it’s kind of pretty staggering.
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|From: Paul H. Christiansen||8/15/2020 1:49:48 PM|
|Bert Hochfeld is one of my favorite research analysts. His reports are usually very comprehensive, insightful and typically quite long. Occasionally readers can come across one of two lines that succinctly capture the essence of his thoughts. For example, in a recent Fastly research report I found the following snippet:|
"From my perspective, and simply put - No edge, no digital transformation. The old saw about you can't get there from here is alive and well in considering why edge is becoming such a standard paradigm - if a user wants to achieve decent performance from a new digital transformation, then the use of edge seems inevitable."
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|From: Paul H. Christiansen||8/25/2020 10:37:35 AM|
|Recently read that GrowGeneration (GRWG) was the victim of a negative report issued by Hindenburg Research, a research firm that specializes and discovering negative news about companies, such news being a tremendous benefit to short-sellers.|
Not sure what the current negative news might be but suspect that it has something to do with CEO Michael Salaman’s prior involvement with Skinny Nutritional, where he was CEO and President from 2000 to 2014 – over six years ago. It would seem that whatever the problems were, remedial action should have occurred since then.
All of that notwithstanding, what seems to be overlooked by Hindenburg is the revenue growth achieved by GRWG, as illustrated below. There are very few companies that can consistently achieve 100% year-over-year quarterly revenue growth. Admittedly, at some point in the future that rate of growth will have to diminish, but according to the GRWG earnings transcript, that is not likely to occur in their next quarterly report.
Select here for the GRWG quarterly revenue growth chart.
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|From: Paul H. Christiansen||12/12/2020 10:16:32 AM|
|Two stocks that offer investors with the potential for long-term capital gains are Palantir (PLTR) and C3.ai (AI), both of which are deeply immersed in the pervasive digital transformation.|
Each stock had a recent IPO, so there has been limited analysis by Wall Street analysts. However, reading their S-1 files submitted to the SEC can provide some very useful information.
Select here for a brief explanation of The Palantir Difference
Select here for a brief explanation of the C3.ai Extensive Partner Ecosystem,
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|To: Paul H. Christiansen who wrote (966)||12/16/2020 12:14:31 PM|
|From: Frank Sully|
|I bought C3.ai at about $106. Currently trading at $114. To the Moon , Alice! Cheers, Sully|
This month’s hottest IPO isn’t DoorDash or Airbnb — it’s artificial-intelligence company C3.ai
Tom Siebel’s company has an enormous market for democratizing artificial intelligence.
The most recent flurry of IPOs included two of this year’s most anticipated: DoorDash DASH, 1.83% and Airbnb ABNB, 11.67%.
The companies certainly didn’t disappoint coming out the gate, especially if you were an early investor, as DoorDash and Airbnb soared 85% and 112%, respectively, on their opening day of trading. Pundits and analysts were left befuddled, and the prices of each have slipped in the meantime.
An initial public offering that was overlooked during that time was C3.ai AI, 12.19%. Shares of the Redwood City, Calif., company sit well over double the set price.
C3.ai is the more interesting company that debuted last week. Its work over the past decade to democratize artificial intelligence (AI) for enterprise has real promise, and there is evidence through its early partnerships and customer success that it could lead to significant and stable growth. The company is led by CEO Tom Siebel, who had the same position at Siebel Systems, which was purchased by Oracle ORCL, 1.76% in 2006. The 68-year-old billionaire founded the company in 2009.
B2B applications Artificial Intelligence is a popular buzz word that has infiltrated many of our lives through everything from Siri on our Apple AAPL, -0.10% iPhones to powerful recommender engines that help us find products and services on Amazon AMZN, 2.01%. The consumer applications have created greater awareness to AI for many of us, but there is a bigger AI opportunity brewing in business-to-business (B2B) enterprise applications. AI to help banks better understand customer churn, identify fraud and deploy predictive revenue models. To help oil and gas companies predict maintenance requirements to proactively identify failures before they happen. And to help health-care providers improve health outcomes, reduce care costs and improve patient experience.
C3.ai’s offerings are designed to democratize at scale all of those scenarios and others in aerospace and defense, telecommunications, retail, utilities and more. The C3.ai AI Suite, which is the company’s core technology, is designed to sharply reduce the time to value in using AI in the enterprise. It functions as a software as a service (SaaS) application, and while it has deep partnership integration with Microsoft MSFT, 2.31% and Adobe ADBE, 1.50%, it can be flexibly deployed on Amazon’s AWS, IBM IBM, -0.21% Cloud, Google GOOG, -0.17% Cloud and/or on-premise.
50 million businesses The outcome of its significant R&D investment is a powerful enterprise AI footprint that delivers more than 1.1 billion predictions a day using more than 4.8 million machine-learning models that the company has in production. Moreover, according to C3.ai, these predictions and models touch more than 50 million businesses on a daily basis.
Beyond technology partners, C3.ai has also been able to apply its model-driven architecture to win a diverse group of marquis customers across a vast set of industries, with an average deal size in 2020 at over $12.1 million. This includes Royal Dutch Shell RDS.A, 0.95%, Astra Zeneca AZN, 1.82%, Baker Hughes BKR, -1.52%, Raytheon Technologies RTX, -0.41% and the U.S. Air Force. Customer expansion yielded a healthy 71% year-over-year growth rate for C3.ai in its fiscal 2020 totaling $157 million, and an average growth rate over the past three fiscal years of 69%.
Perhaps what is more exciting is the market potential for C3.ai as the proliferation of AI continues to accelerate. According to the company’s S1 filling, it estimates its total addressable market (TAM) at $174 billion this year, growing to $271 billion by 2024. Specifically, the company sees itself participating in the $44 billion enterprise AI software market, the $63 billion enterprise infrastructure software market and the $93 billion enterprise application market.
Streamlining data Those markets are converging rapidly with AI capabilities being a critical connector. Many companies will be seeking tools and technologies that can shorten the difficult process of managing vast data repositories, software tools and infrastructure complexities. C3.ai’s architecture is designed to streamline this process and considerably shorten the enterprise challenge of applying AI to solve complex business problems.
Of course, the road for C3.ai will have its share of challenges. Large enterprise software and infrastructure providers like SAP SAP, 1.90%, Salesforce CRM, 1.00% and Oracle ORCL, 1.75%, to name a few, are all working diligently to apply greater AI capabilities to exponential data to deliver next-generation insights for enterprise customers. This massive market opportunity isn’t a secret, by any means. However, with the flexible architecture of C3.ai, there is also an argument that many enterprise software platforms, much like Adobe and Microsoft already have, could see C3.ai as complementary and as a vehicle to speed customer adoption of industry-specific AI capabilities.
What perhaps was most evident to me, after watching last week’s IPO frenzy, is it didn’t take a sophisticated AI model to see the potential of C3.ai.
Daniel Newman is the principal analyst at? Futurum Research, which provides or has provided research, analysis, advising, and/or consulting to Microsoft, Amazon, IBM, SAP, Oracle, and dozens of companies in the tech and digital industries. Neither he nor his firm holds any equity positions with any companies cited. Follow him on Twitter? @danielnewmanUV.
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|From: Frank Sully||12/16/2020 4:11:23 PM|
|Barron's interview with C3.ai founder Tom Siebel|
Tom Siebel Is Back: An Interview With the CEO and Founder of C3.ai
Eric J. Savitz
Dec. 9, 2020 7:52 pm ET
C3.ai had a spectacular debut in the public market on Wednesday. The artificial-intelligence software company priced an offering of 15.5 million shares at $42 a share, above the expected range of $36 to $38 a share, before opening at $100. The stock closed its first day at $92.49, a 120% gain from its IPO price.
Among other things, the IPO marks the return to public view of Tom Siebel, the founder and CEO of C3.ai (ticker: AI). The legendary software entrepreneur was an early executive at Oracle (ORCL) and the founder of the customer relationship management software pioneer Siebel Systems, which he sold to Oracle for $5.86 billion in 2006.
Barron’s caught up with Siebel on listing day for an insightful chat about C3.ai, the outlook for artificial intelligence software, and assorted other topics. An edited transcript of our conversation can be found below:
Barron’s: Hey, Tom. Looks like C3.ai is off to a spectacular start as a public company.
Tom Siebel: I am not competent to comment on the behavior of equity markets. It’s not my field. To the extent I have any expertise, it’s in building and operating software companies. That said, the big picture is we have a huge addressable market, and the investment community recognizes there is a huge market in commercial and industrial AI applications. We’re looking at a $250 billion addressable software market—that’s bigger than a bread box.
And how are you going after it?
We spent the last decade building out a really remarkable software platform, called the C3.ai suite, that represents 1,000 man-years of software engineering work. It is a cohesive set of software services that allows our customers to rapidly and successfully design, develop, provision and operate enterprise and commercial AI applications, at small, medium and large scale.
You so far have a relatively smaller number of very large customers.
The Phase 1 strategy, yes, did involve customer concentration. We wanted to focus on “lighthouse” customers— Shell, Enel, ENGIE, Koch, the United States Air Force, Philips Medical—in multiple industries, multiple geographies and multiple use cases, and demonstrate that the product could be applied successfully to solve complex AI problems, that delivered substantial economic value in a short period of time. And we did that.
What comes next?
The next phase is about scaling the business, not only selling to global juggernauts but also to middle-sized companies, selling to divisions and departments of large companies; in banking, in telecom, in financial services, and in manufacturing and aerospace; in Asia, Europe, North America. That’s the phase we’re entering now. I would argue we have clear technology leadership in this space. I’m unaware of anyone who has built a successful AI platform like we have. If we succeed at that objective, establishing a market position in enterprise AI, this will be a large and hugely successful enterprise application software company. And we’ll build a company that is structurally profitable and cash flow positive.
How should people think about what you do? Are you more an application developer or a platform for clients to build their own applications, or are you building custom applications?
Unfortunately, the answer to that question is yes. About 86% of our revenue is software-as-a-service recurring revenue. Today, 65% of that is from applications. We have a family of applications for banking, like anti-money-laundering, cash management, credit approval, broker rule compliance. Or applications for utilities, like distributed energy resource management, AI-based predictive maintenance, smart grid analytics. We have a family of applications for oil and gas, and for aerospace. So today 65% of our software revenue comes from those applications, and 35% comes from the platform. We sell both. Shell has 200 projects they’re building on top of our platform. Enel has 150. I suspect in a steady state, license revenue will be around 60% for applications, and 40% platform.
And what about services?
Services is about 14% of overall business and will stay there. We’ll prevent it from getting larger by partnering with IBM Global Services and others. If we let it get larger, we’ll get valued as a services business, which as you know carry lower valuations.
Microsoft took a $50 million stake in C3.ai at the IPO price. What’s the story there?
That investment had nothing to do with them making money. We have a huge partnership with these guys. Our technology is entirely complementary to Azure. I’m working on hundreds of millions of dollars of sales opportunities with the Microsoft sales organization.
And you announced a deal with them recently in a very familiar area for you.
We announced a partnership with Microsoft and Adobe, to take the Microsoft CRM stack, the Adobe marketing automation stack, and the C3.ai stack, and bring to market an entirely family of applications—believe it or not—in AI-enabled CRM [customer relationship management] software.
CRM of course was what Siebel Systems did.
So what’s old again is new again. It’s not unusual when I’m working with these large customers—who often were huge Siebel Systems customers—that as we’re deploying our 12th AI application, they say, hey Tom, when are you going to do AI-enabled CRM. We’ve developed those solutions in combination with Microsoft and Adobe, and all three organizations will be selling those worldwide. So the symbolism of that Microsoft investment was not about making money—it was about sending signal to the market and to their own employee base that, hey, this is an important market, pay attention.
Who are your competitors?
When we were doing database software at Oracle in the ‘80s, the competition was companies building their own relational database systems. Who succeeded at that? No one. When we brought ERP systems and CRM systems to market in the 90s, the competition was, the customer was going to build their own. Who succeeded at building their own ERP system, name the company? Nobody did. They all would end up buying from Oracle, or SAP, or Siebel or somebody else. So it is not unusual—this is standard in the business—that when you get into a new market, the knee-jerk reaction of the CIO is to build it himself. Or to pay Accenture $500 million to help them build it.
So the competition is from homegrown systems.
Virtually every one of our customers has tried one, two, three times to build it themselves. What they’ll do is use componentry from Snowflake [SNOW], Databricks, Datastax, H2O.ai, and DataRobot, and they’ll attempt to assemble all of these things together into a cohesive whole that does something useful. Unfortunately, it is an impossible problem, and to my knowledge no one has ever succeeded at doing it. General Electric [GE] spent, like, $6 billion over a number of years trying to do this before they folded their tent.
Databricks, Datastax.... That’s a very hot set of companies you just named.
I’m not saying that these products from companies like Databricks or Snowflake have no value. They have very high value. You can think of what we’ve done—and I know this is hard to believe—is to take the functionality of every software company that’s involved in AI, aside from the cloud, take Palantir, Databricks, DataRobot, H2O, Snowflake, and built all of it into one cohesive architecture. What Palantir does as a company is a feature for us. What H2O and DataRobot do is something called auto ML [machine learning]. That’s a feature. What Alteryx does is a feature within our product, we call it Ex Machina.
So wait, don’t you compete with all of them?
If one of our customers wants to use one of these things—and every company does—because they’ve standardized on it, or somebody thinks it is technically superior to our solution, it doesn’t matter, they can use it. If Shell wants to use DataRobot instead of our auto ML capability, God bless them, it’s fine. If they want to use Databricks, they do—and by the way, they do use Databricks, instead of our data virtualization technology. They don’t have to lose for me to win. We really don’t compete with those guys.
A few years ago, you changed the company’s name, from C3.iot to C3.ai. Tell me about that.
There was a time period, in 2016 and 2017, when the internet of Things was all the rage, and all anyone wanted to talk about was connecting devices. And we do that. So that was probably a mistake to name the company that. Now, for instance we read data from 57 million sensors and 42 million smart meters. Really what we’re doing with the data is predictive analytics. We’re doing AI. I got to a point where 100% of our applications were predictive analytics and AI and 30% of that was IoT. So the first 20 minutes of every presentation was explaining that we were not really an IoT company. It wasn’t a change in the business, we were confusing the market with the name. It was a mistake. AI happens to be really what we do.
Tom, you guys were growing 70% in the April 2020 fiscal year, and dropped to 10% growth in the last six months. What’s the story there?
In the February, March, April, May time period, we hit a speed bump the size of the Empire State Building. It was not a business cycle issue. This [the pandemic] was an act of God. It was apocalyptic. Business came to a screeching halt. Our revenue continued to grow, because we have a backlog, but it grew at a slower rate. But once you got to July, August, September, with what’s happening in digital transformation and AI, it’s blowing and going. Our pipeline is growing at a greater rate than it ever has grown. Coming out of this, you will see a company growing not at 70% or 80%, ain’t no way no how, but we’ll be growing in the top decile of software companies.
Tom, this was fun, thanks very much.
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|From: Frank Sully||12/18/2020 2:01:47 AM|
|NVIDIA: A case study In Exponential Growth and AI|
AI is a huge market and these companies are already benefiting Chris Neiger (TMFNewsie)
Dec 15, 2020 at 9:22PM
Artificial intelligence (or AI) gets a lot of attention these days because the technology is being implemented into so many parts of our lives, from smart speakers to apps. And as AI expands, the opportunity for investors is enormous. Over the next four years, AI spending is estimated to double and reach $110 billion by 2024, according to research firm IDC.
For investors looking to tap into this enthusiasm for AI, there are a handful of companies pushing artificial intelligence forward. Here's why NVIDIA ( NASDAQ:NVDA), Amazon.com ( NASDAQ:AMZN), and Appian ( NASDAQ:APPN) should be on your AI stock buy list.
Image source: Getty Images.
1. NVIDIA For many years NVIDIA's core business came from selling graphics processors for gaming. But as data centers have become more complex, many companies have looked to NVIDIA's GPUs for their artificial intelligence data centers.
This shift has helped NVIDIA's data center revenue grow quickly and back in August the segment outpaced gaming revenue for the first time. In NVIDIA's most recent quarter, data center sales spiked 162% to $1.9 billion. Not all of the data center sales came from AI, but as more companies aim to boost their AI data center capabilities, many of them will look to NVIDIA's GPUs to help them do so.
NVIDIA is also pursuing new AI opportunities through its pending $40 billion acquisition of Arm Holdings. NVIDIA CEO Jensen Huang says the deal "will create a company fabulously positioned for the age of AI," as NVIDIA combines its artificial intelligence platform with Arm's CPU designs.
NVIDIA is already a leader in AI and when the company closes its acquisition of Arm Holdings, expected in the first quarter of 2022, the company's AI prospects appear even brighter.
Exponential Growth Of NVIDIA GAAP Operating Income
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