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   Technology StocksInvesting in Exponential Growth

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From: Paul H. Christiansen8/15/2020 1:49:48 PM
   of 1006
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. Christiansen8/25/2020 10:37:35 AM
   of 1006
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. Christiansen12/12/2020 10:16:32 AM
1 Recommendation   of 1006
Two stocks that offer investors with the potential for long-term capital gains are Palantir (PLTR) and (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 Extensive Partner Ecosystem,

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From: Paul H. Christiansen12/15/2020 12:28:00 PM
1 Recommendation   of 1006
Select here for what investors can learn from’s Go-to-Market Strategy

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To: Paul H. Christiansen who wrote (966)12/16/2020 12:14:31 PM
From: Frank Sully
   of 1006
I bought 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
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 AI, 12.19%. Shares of the Redwood City, Calif., company sit well over double the set price. 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.’s offerings are designed to democratize at scale all of those scenarios and others in aerospace and defense, telecommunications, retail, utilities and more. The 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, these predictions and models touch more than 50 million businesses on a daily basis.

Beyond technology partners, 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 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 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.’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 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, there is also an argument that many enterprise software platforms, much like Adobe and Microsoft already have, could see 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

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 Sully12/16/2020 4:11:23 PM
1 Recommendation   of 1006
Barron's interview with founder Tom Siebel

Tom Siebel Is Back: An Interview With the CEO and Founder of

Eric J. Savitz

Dec. 9, 2020 7:52 pm ET 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 (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, 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 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 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 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 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,, 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 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 Sully12/18/2020 2:01:47 AM
   of 1006
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), ( 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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From: Frank Sully12/18/2020 10:58:43 AM
   of 1006
NVIDIA: Supercomputer Win

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From: Frank Sully12/18/2020 11:13:56 AM
   of 1006
NVIDIA: FWIW, I am planning a modest investment of 40 shares into NVIDIA next week. I like it's exponential growth and AI potential, particularly with the purchase of ARM. Any comments? Here is a SA article on valuing NVIDIA.

Nvidia: Deep Dive And Cash Flow Analysis
Dec. 14, 2020 5:56 AM ET
About: NVIDIA Corporation (NVDA)

Trevor Jennewine
Long Only, Value, Growth, long-term horizon


Nvidia is a leading provider of GPUs and AI solutions, with products that address a wide range of use cases.

Management estimates Nvidia's addressable market will reach $250 billion by 2023 - nearly 17 times Nvidia's trailing 12-month revenue.

Based on my DCF model, I estimate Nvidia's fair value at $517 per share.

Nvidia is a buy for long-term investors.

Investment Thesis In the age of big data and AI, Nvidia's ( NVDA) GPU-accelerated computing platforms and AI solutions address critical needs for developers, researchers, and scientists across various markets, from high performance computing and data analytics to autonomous vehicles and robotics.

My investment thesis is summarized in the following points:

1. Nvidia's intellectual property gives it an advantage over competitors in several quickly growing markets.

2. Nvidia has an enormous market opportunity, at $250 billion by 2023.

3. Nvidia's financial performance has been stellar in recent years. Since 2015, Nvidia has grown revenue at 26% per year and profits at 44% per year.

Nvidia's Market Opportunity Nvidia is a leading provider of GPU-accelerated computing platforms and AI solutions. Nvidia's products have applications in a variety of industries and markets, including gaming, professional visualization, data centers, and edge computing.

The GPU is the foundation of Nvidia's computing platforms. While GPUs were originally designed to carry out the complex calculations necessary for graphics processing, Nvidia's parallel computing platform, CUDA, allows GPUs to be used as general purpose processors, too.

Gaming: For PC gamers, Nvidia's hardware solutions include several generations of GeForce Graphics Cards, including the most recent RTX 30 series, which are powered by Nvidia's latest GPU architecture: Ampere. The RTX series graphics cards combine ray-tracing and AI to deliver more realistic visual effects.

Professional Visualization: For animators and design professionals, Nvidia's hardware solutions include several generations of Quadro GPUs. However, Nvidia is dropping the name Quadro from the upcoming RTX A6000. The RTX A6000 will incorporate Ampere architecture GPUs, ray-tracing, and AI tools to deliver enhanced graphics to a wide range of users, from animators using Pixar RenderMan, to design engineers using Autodesk ( ADSK) AutoCAD, to marketers using Adobe ( ADBE) Premiere Pro.

Data Centers: Nvidia's data center products are used by data scientists, researchers, and developers to accelerating workloads in high performance computing (HPC), data science, artificial intelligence, machine learning, and deep learning.

Nvidia's data center hardware products include the HGX platform, built with Nvidia GPUs, NVLink-powered NVSwitches, and Mellanox InfiniBand networking. HGX servers are used to accelerate AI and HPC workloads. The HGX platform is also the building block for another Nvidia product: the DGX platform. This server includes the previously mentioned HGX components, but also incorporates CPUs. The DGX server is targeted at AI applications, such as training deep neural networks for deployment in edge servers, autonomous robots, or autonomous vehicles.

Source: Nvidia Investor Presentation ( May 2020)

Complementing its hardware, Nvidia's GPU Cloud (NGC) provides access to GPU-optimized software designed to accelerate deep learning, machine learning, and high performance computing applications. This provides access to deep learning frameworks, like TensorFlow and PyTorch, which enable the designing, training, and validation of deep neural networks. It also includes TensorRT, an inference engine that runs trained neural networks on GPUs to generate real-time AI inferencing.

Edge Computing: In 2019, Nvidia launched the EGX platform, a family of GPU accelerated computing systems designed to handle AI workloads at the edge - it includes both EGX servers for edge data centers and embedded Jetson systems for edge devices.

Using trained AI models, the EGX servers generate inferences based on data streamed from edge devices (such as an IoT sensor). The EGX server then makes a decision, and sends the data back to the edge device. This technology, known as edge AI, has applications across a range of industries: retail, manufacturing, telecom, smart cities, healthcare, etc. For instance, by analyzing traffic patterns, EGX servers could coordinate traffic lights throughout a city to minimize roadway congestion.

In the event of a low confidence decision, the EGX server can send the information back to a central data center, where the AI model can be retrained with new data, then redeployed at the edge. This dynamic is shown in the graphic below.

Source: Nvidia EGX Platform.

Depending on the edge use case, Nvidia offers various software solutions that complement its hardware and help developers build the applications they need: Metropolis for smart cities and retail, Isaac for robotics and manufacturing, Clara for healthcare, Aerial for 5G, Merlin for recommendation systems, and Jarvis for conversational AI.

Market Opportunity If the acquisition of ARM is approved, Nvidia's management estimates the company's market opportunity will reach $250 billion by 2023:

  • Devices: $95 billion
  • Data Center: $80 billion
  • Auto/Edge AI: $75 billion
  • The total figure, $250 billion, represents nearly 17 times Nvidia's revenue over the last 12 months - that's a big opportunity. And by combining Mellanox's high performance interconnect solutions, ARM's energy-efficient processors, and Nvidia's world class GPU-accelerated computing platforms, the company would be well positioned to capture growth across all three markets listed above.

    Financial Analysis Nvidia has posted strong revenue growth in recent years, increasing sales from $5.0 billion in fiscal 2016 to $14.8 billion over the trailing 12 months, which clocks in at 25.6% per year. The charts below shows Nvidia's revenue growth in each of its four business segments: Gaming, Professional Visualization, Data Center, and Autonomous Vehicles.

    Source: Nvidia Investor Presentation ( November 2020)

    So far in fiscal 2021, Nvidia's revenue growth has accelerated substantially, up nearly 57% in the most recent quarter. Despite some margin compression, growth in revenue has accelerated earnings growth as well, with profits up 46% in Q3 2021.

    Like the income statement, Nvidia's balance sheet looks strong, with $10.1 billion in cash and marketable securities compared to $7.6 billion in debt and lease liabilities.

    Valuation In my DCF model, I have made the following assumptions:

  • Growth Rate: 20%
  • Terminal Rate: 2.9%
  • Discount Rate: 8.0%
  • Since 2015, Nvidia's FCF-per-share has grown at an annualized 30%. I have selected a much more conservative 20% to introduce a margin of safety. I selected 2.9% as the terminal growth as this figure corresponds with worldwide GDP growth over the last decade. Finally, I have selected 8.0% as my discount rate as this figure corresponds with the average return of the S&P 500 since 1957.

    Source: Created by the author.

    Based on my DCF model, I estimate Nvidia's fair value at $517. This number is roughly equivalent to the stock price at the time this article was written.

    I have also used FCF-per-share to model potential returns over the next five years. Again, I assume 20% annualized growth in FCF per share. But because Nvidia's current price-to-FCF multiple is near its historical high, I have selected three more conservative multiples: 70, 50, and 30.

    In the most bullish scenario, Nvidia returns roughly 18% per year over the next five years. And in the most bearish scenario, Nvidia returns -0.5% per year over that time period.

    Conclusion Nvidia is a market-leading provider of the GPU-accelerated computing platforms and AI solutions needed for high performance computing, machine learning, and deep learning. Given the company's strong competitive presence in these high growth markets, Nvidia looks well positioned to capture value in the years ahead.

    For long-term investors, I rate Nvidia a buy at $517.

    Disclosure: I am/we are long ADBE, NVDA. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.

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    From: Frank Sully12/18/2020 12:18:15 PM
       of 1006
 Continues To Soar!

    I bought 180 shares of (AI) on Monday for $105.79/share. Now it's trading for $134.78/share, up 27.4% in a week. Whoopie! I'm in AI for the long-term.


    AS OF 12/18/2020 12:13PM ET

    $134.78 +$17.541 (+14.962%)


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