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Kamikaze Foot-to-the-Floor Investing

July 27, 2023

I have realised that there is an even more aggressive way of investing than what I do in my leveraged spread betting accounts. This one is insane but if you get it right you may need to move your bank account to Fort Knox. It makes the adrenaline race just to think about it.

It is so exciting, so insane, high risk, high reward I think I am going to have a couple of stiff whiskies, make my will, say my farewells to family and friends and do it.

If it works I will be richer than the Count of Monte Cristo. If it fails, despair will be too innocuous a word to describe a broken man hanging to life by a thread.

Below, if there is any person among you mad enough to even consider what I am proposing to do, I will explain my outrageous plan in all its blood-curdling details.

It makes buying shares in QQQ3 look like a walk in the park although still definitely something you should do. Maybe in my crazy risk-on world, QQQ3 is the choice for widows and orphans.

First Find Your Share

Step 1 – choose your share. It must be both bullet proof and exciting, the more of both the better. It must score top marks for 3G, magic and ‘something new’.

The balance sheet and the cash generation must be things of wonder. We are talking shares in companies so strong they make the German economy look weak.

The leadership must combine charisma with engagement, vision, commitment and genius level ability.

In short, this must be a share which you really, really want to buy – in a company which is changing the world – one of the Crown Jewels of the US economy.

Buy in A Spread Betting Account with Five Times Leverage

Step 2 – buy as many shares as you can, with a prudent commitment of money (so money you can afford to lose) in a spread betting account. If you feel that you can lose £5,000 without turning a hair then invest £5,000 to buy £25,000 worth of shares (this means making a bet equivalent to holding £25,000 of shares). See below if you think that is too much for an unproven strategy.

Reinvest All Gains x5 in More Shares

Step 3 – if the shares go up reinvest all the spare equity created in buying more shares to keep your margin at the max. So, if £25,000 becomes £26,000 creating £1,000 of spare equity you buy another £5,000 worth of shares. If £25,000 becomes £30,000 you can virtually double your holding.

This is the foot-to-the-floor part of the strategy. Next comes the scary bit.

At some point the shares will fall on profit taking, something alarming happening in the world, whatever and your equity will melt faster than an Antarctic glacier as the climate warms. This is inevitable; nothing goes up in a straight line.

This is normally the point at which some of us panic and sell, especially if we have large but rapidly shrinking profits.

Hang in There but Add Funds to Keep Your Equity/ Margin Requirement Over 60pc

But the Kamikaze investor does not do this. He knows that if his equity falls to below 50pc of his margin requirement IG will liquidate his holding.

This is what IG says about standard accounts which is what most of us will have.

Standard trading accounts will be triggered for position closure when your equity drops beneath 50pc of your margin requirement.

IG Help and Support, Leverage & Margin

So step 4 – Kamikaze man waits until there is blood on the streets and only then does he (she) add money, say when his equity falls to 60pc of his required margin (a figure which is displayed with his account details) to make sure that this 50pc level is never reached.

You have to be ready to do whatever it takes. This is your do-or-die moment. It is like crouching with your gun as the Mahdi and his horde of whirling Dervishes come charging at you. You shoot when you see the whites of their eyes. More prosaically you wait until the moment arrives when most people would lose their nerve and do exactly the opposite. You shout, flail your arms, open your metaphorical cheque book and charge the charging bear.

If you can hold your nerve (and have sufficient funds in reserve; don’t forget that rather important detail) it will work. This wonderful share will not fall for ever. Eventually it will rally, typically just at the moment when you are convinced it never will, the uptrend will resume and this is where the wealth creation magic kicks in because you will now start creating spare investible equity at a lower price so if the shares go on to make new all-time highs, which they should do if you did your homework on step 1 and the long-term secular uptrend remains intact, then you will start building your holding and building wealth at a phenomenal rate.

And bear in mind that great companies have a capacity for producing good news when you least expect it. If you have a decent chunk of the shares on maxed out leverage at one of these moments the results can be truly magical.

Set a Figure Where You Are Happy with your Holding

Step 5 – you cannot play this game for ever because your holding will become so large that you would have to sell your house to keep buying into margin calls. So you need to decide where your threshold is and stop there.

My thinking on suitable thresholds is on these lines. You move aggressively to a holding of $250,000 with $50,000 of equity and then more slowly to $1m of holding, $200,000 of equity and then you stop buying more and focus on defending your position or cashing in if the the whole market starts to turn south. Remember that if that $1m holding rises in value by 80pc this will take your equity from $200,000 to $1m.

And after that the fun really starts because your total holding will be $1.8m with less than two times gearing. I know this is the dream scenario but dreams can happen.

Many things need to go right for that to happen but equally I am a great believer in giving miracles a chance and that is what my Kamikaze strategy does.

The idea is to rapidly build a large holding in a superb business and then hold so that over time, like a mortgage, the outstanding loan will shrink as a percentage of the value. This strategy will only work against the background of a strong bull market in shares generally and in the particular share your are buying.

Alternatively you could start to cash in, perhaps leaving yourself with an unleveraged or lightly leveraged holding which you can easily hold for ever, whatever forever means for us guys.

I think the timing is amazing, right now, to be really bold! An incredible, life-changing, technology revolution is moving into overdrive and it is being driven by a string of living legend corporate wizards in America, the Edisons of the 21st century.

What About the Valuation Problem?

This is the problem with exciting shares. People want to own them and they can be on scarily demanding valuations. This, paradoxically, is what creates the opportunity. The shares race higher on some piece of exciting news, stellar results, an impressive new innovation, a bullish third-party analysis and then two steps forwards is followed by one step backwards as greybeards and value investors rub their chins and mutter about the valuation and loose holders and day-traders take profits.

There are always naysayers. There is somebody marching around saying the right price for Tesla is not $260, where they were on Friday but $26. There is always some metric you can use to justify any number. Another bear pointed out that Nvidia was on 38 times sales. It is if you use this year’s price and last year’s sales but if you look forward, even on cautious assumptions about a company where sales are exploding, that ratio will be around 15 when the results for the year to end January 2026 (effectively 2025) are reported and that is in line with past levels for a company which is changing dramatically and becoming (in my view) far more exciting.

What I try to do is imagine having dinner with Jensen Huang and Colette Kress, CEO and founder and CFO respectively of Nvidia and imagine what they would tell me about the outlook for technology generally and their company particularly. I just know that I would leave the table wild with excitement.

All I need to do is believe these guys and look at what they have already achieved. Kress looks to me as though she is still at high school but amazingly she has been CFO of Nvidia for a decade. Since she joined the shares have risen 150-fold. Is that coincidence or is she special?

This is what she said at the latest earnings call.

Generative AI is driving exponential growth in compute requirements and a fast transition to NVIDIA accelerated computing, which is the most versatile, most energy-efficient, and the lowest TCO [total cost of ownership] approach to train and deploy AI.

Generative AI drove significant upside in demand for our products, creating opportunities and broad-based global growth across our markets. Let me give you some color across our three major customer categories: Cloud service providers or CSPs, consumer internet companies, and enterprises.

First, CSPs around the world are racing to deploy our flagship Hopper and Ampere architecture GPUs to meet the surge in interest from both enterprise and consumer, AI applications for training, and inference. Multiple CSPs announced the availability of H100 on their platforms, including private previews at Microsoft Azure, Google Cloud, and Oracle Cloud Infrastructure, upcoming offerings at AWS and general availability at emerging GPU specialized cloud providers like CoreWeave and Lambda. In addition to enterprise AI adoption, these CSPs are serving strong demand for H100 from generative AI pioneers. Second, consumer internet companies are also at the forefront of adopting generative AI and deep learning-based recommendation systems, driving strong growth.

For example, Meta has now deployed its H100-powered brand Teton AI supercomputer for its AI production and research teams. Third, enterprise demand for AI and accelerated computing is strong. We are seeing momentum in verticals such as automotive, financial services, healthcare, and telecom where AI and accelerated computing are quickly becoming integral to customers’ innovation road maps and competitive positioning. For example, Bloomberg announced it has a $50bn parameter model, BloombergGPT, to help with financial natural language processing tasks such as sentiment analysis, named entity recognition, news classification, and question answering.

Auto insurance company, CCC Intelligence Solutions, is using AI for estimating repairs. And AT&T is working with us on AI to improve fleet dispatches so their field technicians can better serve customers. Among other enterprise customers using NVIDIA AI are Deloitte, for logistics and customer service, and Amgen, for drug discovery and protein engineering. This quarter, we started shipping DGX H100, our Hopper generation AI system, which customers can deploy on-prem [on their premises].

And with the launch of DGX Cloud through our partnership with Microsoft Azure, Google Cloud, and Oracle Cloud Infrastructure, we deliver the promise of NVIDIA DGX to customers from the cloud. Whether the customers deploy DGX on-prem or via DGX Cloud, they get access to NVIDIA AI software, including NVIDIA-based command, end-to-end AI frameworks, and pretrained models. We provide them with the blueprint for building and operating AI, spanning our expertise across systems, algorithms, data processing, and training methods. We also announced NVIDIA AI Foundations, which are model foundry services available on DGX Cloud that enable businesses to build, refine, and operate custom large language models and generative AI models trained with their own proprietary data, created for unique domain-specific tasks.

They include NVIDIA NeMo for large language models; NVIDIA Picasso for images, video, and 3D; and NVIDIA BioNeMo for life sciences. Each service has six elements: pretrained models, frameworks for data processing and curation, proprietary knowledge-based vector databases, systems for fine-tuning, aligning and guardrailing, optimized inference engines, and support from NVIDIA experts to help enterprises fine-tune models for their custom use cases. ServiceNow, a leading enterprise services platform is an early adopter of DGX Cloud and NeMo. They are developing custom large language models trained on data specifically for the ServiceNow platform.

Our collaboration will let ServiceNow create new enterprise-grade generative AI offerings with the thousands of enterprises worldwide running on the ServiceNow platform, including for IT departments, customer service teams, employees, and developers. Generative AI is also driving a step function increase in inference workflows. Because of their size and complexities, these workflows require acceleration. The latest MLPerf industry benchmark released in April showed NVIDIA’s inference platforms deliver performance that is orders of magnitude ahead of the industry with unmatched versatility across diverse workloads.

To help customers deploy generative AI applications at scale, at GTC [GPU Technology Conference], we announced four major new inference platforms that leverage the NVIDIA AI software stack. These include L4 Tensor Core GPU for AI video; L40 for Omniverse and graphics rendering, H100 NBL for large language models; and the Grace Hopper Superchip for LLM [large language models*] and also recommendation systems and vector databases. Google Cloud is the first CSP to adopt our L4 inference platform with the launch of its G2 virtual machines for generative AI inference and other workloads, such as Google Cloud Dataproc, Google [Inaudible], Google Alpha Fold and Google Cloud’s Immersive Stream, which render 3D and AR [augmented reality] experiences. In addition, Google is integrating our Triton Inference Server with Google Kubernetes engine and its cloud-based Vertex AI platform.

In networking, we saw strong demand at both CSPs and enterprise customers for generative AI and accelerated computing, which require high-performance networking like NVIDIA’s Mellanox networking platforms. Demand relating to general purpose CPU infrastructure remains soft. As generative AI applications grow in size and complexity, high-performance networks become essential for delivering accelerated computing at data centre scale to meet the enormous demand of both training and inferencing. Our 400-gig Quantum-2 InfiniBand platform is the gold standard for AI-dedicated infrastructure, with broad adoption across major cloud and consumer internet platforms, such as Microsoft Azure.

Colette Kress, Nvidia, Q1 2024, 6 June 2023

*Below is a bit of an idea of what large language models are all about.

A large language model is a transformer model (see below) on a large scale. It is so large that it usually cannot be run on a single computer. Hence it is naturally a service provided over API** or a web interface. As you can expect, such large model is learned from a vast amount of text before it can remember the patterns and structures of language.

For example, the GPT-3 model that is backing the ChatGPT service was trained on massive amounts of text data from the internet. This includes books, articles, websites, and various other sources. During the training process, the model learns the statistical relationships between words, phrases, and sentences, allowing it to generate coherent and contextually relevant responses when given a prompt or query.

Distilling from this vast amount of text, the GPT-3 model can therefore understand multiple languages and possess knowledge of various topics. That’s why it can produce text in different style. While you may be amazed that large language model can perform translation, text summarization, and question answering, it is not surprising if you consider these are special “grammars” that match the leading text, a.k.a. prompts.

Adrian Tam, ChatGPT, 20 July 2023

**Many people ask themselves, “What is an API?” API is the acronym for application programming interface — a software intermediary that allows two applications to talk to each other. APIs are an accessible way to extract and share data within and across organizations.

APIs are all around us. Every time you use a rideshare app, send a mobile payment, or change the thermostat temperature from your phone, you’re using an API.


Next question – what is a transformer model.

Transformer is a deep learning architecture that relies on the attention mechanism.[1] It is notable for requiring less training time compared to previous recurrent neural architectures, such as long short-term memory (LSTM),[2] and has been prevalently adopted for training large language models on large (language) datasets, such as the Wikipedia Corpus and Common Crawl, by virtue of the parallelized processing of input sequence.[3] More specifically, the model takes in tokenized (byte pair encoding) input tokens, and at each layer, contextualizes each token with other (unmasked) input tokens in parallel via attention mechanism. Though the Transformer model came out in 2017, the core attention mechanism was proposed earlier in 2014 by Bahdanau, Cho, and Bengio for machine translation.[4][5]This architecture is now used not only in natural language processingcomputer vision,[6] but also in audio,[7] and multi-modal processing. It has also led to the development of pre-trained systems, such as generative pre-trained transformers (GPTs)[8] and BERT[9] (Bidirectional Encoder Representations from Transformers).

Wikipedia, February 2023

I know it is complicated, what a world we are moving into, but it gives you a flavour of the colossal computing power required, the ferocious pace of research and development in this field, how novel this technology is, how much more there is to come and the advantage Nvidia has from inventing and designing the GPU chips used for accelerated computing and having been focused on this field for the last 15 years.

Kress has an amazing CV

Colette Kress is executive vice president and chief financial officer of NVIDIA. She joined the company in September 2013, after serving nearly 25 years in a range of finance roles at major technology companies.

She previously served for three years as senior vice president and chief financial officer at Cisco’s Business Technology and Operations Finance organization, where she was responsible for financial strategy, planning, reporting and business development for all business segments, engineering and operations.

Previously, she spent 13 years at Microsoft, including four years as chief financial officer of the Server and Tools division, and held senior roles in corporate planning and finance. Prior to that, she served at Texas Instruments in a variety of finance positions.

Kress holds a B.Sc. degree in finance from University of Arizona and an MBA from Southern Methodist University.

Nvidia, executive bios

She is 56 and worth around $300m so billionaire status is doable for this country gal from Arizona. If daddy is still around he must be so proud.

The bottom line is that even without the day traders rushing in and out of the shares the interplay of bulls and bears makes for considerable volatility. My strategy is designed to turn that to your advantage in the most aggressive way possible.

You use every scrap of spare equity on a rising share price to build your position and then hunker down behind the barricades to defend it to the last drop of blood. Repeat this strategy for as long as you feel reasonably comfortable, say, for as long as you can still get a good night’s sleep and you could do incredibly well.

Remember that most of the money you are gambling in the later stages will be profits; that is an important part of the strategy.

If you are still doubtful, then do as I suggest below and only risk a trifling amount of money but don’t kill me if, when you have turned £250 into £25,000 you start to wonder what would have happened if you had started with £5,000. Nothing ventured, nothing gained; faint heart and all the rest of it.

It is a bit like an accumulator bet at the horses but if you can hold your nerve it is a bet which is much more likely to win. Most people who do bet on horses are completely unaware of this exciting alternative.

Strategy – First Find Your Share

I am not going to suggest a share; that would be too much responsibility. I want anyone who does this to make their own choice and maybe customise the strategy to suit their appetite for risk. If you are a regular reader of Quentinvest you know which shares I like.

I will make one suggestion, which is that you could do this with an unleveraged ETF because they do have all the characteristics, or can have, of an exciting share which will never go bust. Personally I think, for maximum excitement, like betting on a number at roulette, you should go for a share in a single amazing company and behave like an owner (which you are).

In a way, the do-or-die strategy described above is a variant, on steroids, of never-sell investing and that is a strategy that has worked very well for numerous founding CEOs.

Reflecting on this I think that if you are doubtful don’t go mad, start with £1,000 of equity, £5,000 invested or even £250 and £1250 if you want to do it in a completely who cares way. You could be surprised and find that even £250 multiplies rapidly if the market conditions and your stock selection are right.

Alternatively you could use less leverage, £5,000 buys £15,000 for example.

The advantage of American shares for these strategies is that the managers, who usually have significant share stakes, see it as part of their job to deliver a rising share price. They owe it to their stakeholders, especially shareholders and employees and if they don’t the private equity guys are out there and will quickly arrive and help focus their minds.

Back in the day Apple was quietly amassing the most ludicrous gigantic rainy day fund. The private equity guys spotted this, piled in, agitated and Apple changed tack almost overnight, starting to pay dividends and buy back shares in staggering quantities. Since then the share price has never looked back.

If I was Jeff Bezos at Amazon I would be starting to watch my back. His fabulous business (three businesses – Marketplace, Prime and Cloud Services) is looking just a little lacking in direction presently and he may soon feel a tap on the shoulder.

As you can see from the table above the net cash and free cash flow numbers are starting to grow dramatically and this is just what catches the eye of the activist investors.

The chart is good, not great and there is a lack of ‘something new’ to get the blood flowing. Most likely that will change, perhaps with the Q2, 2023 results due on 3 August but the shares would be somewhat of a gamble just now. I have mentioned them so I am going to call them a recommendation and this is a business that will surely get its mojo back if it has lost it. The global footprint and the history of innovation virtually ensures a win in the end for a company like Amazon.

Just for the record the company is hardly doing badly.

“There’s a lot to like about how our teams are delivering for customers, particularly amidst an uncertain economy,” said Andy Jassy, Amazon CEO. “Our Stores business is continuing to improve the cost to serve in our fulfillment network while increasing the speed with which we get products into the hands of customers (we expect to have our fastest Prime delivery speeds ever in 2023). Our Advertising business continues to deliver robust growth, largely due to our ongoing machine learning investments that help customers see relevant information when they engage with us, which in turn delivers unusually strong results for brands. And, while our AWS business navigates companies spending more cautiously in this macro environment, we continue to prioritize building long-term customer relationships both by helping customers save money and enabling them to more easily leverage technologies like Large Language Models and Generative AI with our uniquely cost-effective machine learning chips (“Trainium” and “Inferentia”), managed Large Language Models (“Bedrock”), and AI code companion CodeWhisperer. We like the fundamentals we’re seeing in AWS, and believe there’s much growth ahead.”

Andy Jassy, CEO, Amazon, Q1 2023. 27 April 2023

But compare that with the aura of excitement around Microsoft and Nvidia when they report and you cannot but help noticing a difference.

Share Recommendations

Amazon. AMZN. Buy @ $130

Nvidia NVDA Buy @ $456

I have just been reading an interview Colette Kress gave to an Israeli publication, which spells out why Nvidia is doing so well.

Kress and Huang told investors they believe that next month’s second quarter financials will report peak revenue of $11bn, instead of $7.5bn as initially estimated. Hours after presenting this data, Nvidia added about $200bn to its market cap, proving to all that the demand for GPUs – even during an economic crisis – is the most important economic phenomenon of the year.

The main reason for the upward correction is, of course, the global hunger for AI, and technology giants are rushing to purchase supercomputers, graphics processors and graphics cards, which are Nvidia ‘s bread and butter. “This significant growth is mainly driven by data centres,” Kress says explaining the steep update. “And it represents a sharp increase in demand for large language models in generative AI.”

The second reason, no less important, for Nvidia ‘s performance is that it is almost the only player in the field, making it a monopoly that dominates the fastest growing market in the world. On the whole, what Intel did for semiconductors for personal computers for about 20 years at the end of the previous millennium, Nvidia is doing today. Its development language, CUDA (Compute Unified Device Architecture), which allows AI software developers to work on its chips, has become a market leader. Competitors like Intel and AMD have also launched their own artificial intelligence processors, but their success is far more limited

GPUs are the chips driving the generative AI revolution. Nvidia has an 84pc share of the gaming market, and an even higher share of the AI segment – an estimated 90pc or more. Accordingly, Nvidia’s prices are high: while Intel’s latest supercomputer core processor is priced at about $17,000, the second-hand price for a similar Nvidia processor might even hit $40,000. Ultimately, however, Nvidia’s strategy favors its customers: the more cloud providers optimize data centre activity, and, as a result, reduce their use of CPUs – like those made by Intel and AMD – the more they will choose GPUs, by Nvidia.

Coilette Kress talking to Globes, 23 July 2023

Nvidia is amazing. Wintel (Microsoft and Intel) dominated the bull market of the 1980s and 1990s with the software and hardware which powered the desktop revolution. Before them IBM and its computers dominated the nifty fifty (great US growth stocks) and the first stage of the technology revolution in the 1970s. Before that, Western Mining Corporation and Poseidon led a frenzied Australian mining boom finding and supplying the nickel which was a key ingredient of stainless steel. Nvidia may be THE stock of the Generative AI revolution and how do you put a price on that!

What can you say about this chart? These shares took off in 2013, when Colette Kress, who seems to have an incredible eye for the main chance, joined the business. Since then they have been rocketing higher. There have been two interruptions, 2018 and 2022, when technology shares generally took a hammering as can be seen from the Nasdaq 100 Technology Sector chart.

Apart from those interruptions Nvidia has been a stock on a one-way journey higher to an as yet unreached destination. If Kress is right and Nvidia is going to become a massively profitable $1 trillion revenue business one day these shares are going massively higher and this uptrend is nowhere near over.

So Kamikaze, here we go – any takers for the craziest, foot-to-the-floor investing strategy ever devised.

As a footnote I would remind subscribers that all the Magnificent 7 stocks, Apple, Microsoft, Alphabet, Amazon, Nvidia, Tesla and Meta Platforms have strong long term charts and fulfil all the criteria listed above as being required for subscribers looking for a share suitable for Kamikaze investing. The US and the world is fortunate in having such incredible businesses powering the technology revolution. In part it is the non-stop competition between these behemoths that is enabling the world to make such astonishing advances.

I would also remind subscribers that it is unusual for shares to fall significantly while their Coppock curve is rising. It is rising strongly for all these shares.

Back to Nvidia and and an amazing report on the company’s prospects.

It goes without saying that Nvidia NVDA leads the pack among chip companies capitalizing on the generative artificial intelligence trend. With this, it makes sense that, among chip stocks, NVDA stock has gained the most thanks to this secular growth trend.

NVDA is up more than threefold year-to-date, while shares in the company’s competitors have merely ridden its coattails, rising by considerably less so far this year.

That said, it’s not as if Advanced Micro Devices AMD, Nvidia’s key rival, or the other major semiconductor companies, were left in the dust. They too are looking to grab a piece of this fast-growing end-user market themselves.

The question is, does this threaten NVDA’s performance from here? While these other names in the space will make progress in this area, a “winner take most” scenario is very likely for this early mover. 

NVDA Stock: A Commanding Lead in AI Chips

Nvidia didn’t become dominant in AI chips by accident. Years back, the company laid out the groundwork, enabling it to gain the lion’s share of the AI processor market by 2021.

With this existing commanding lead (an estimated 80pc-95pc market share), the company has been “cashing the check,” selling into demand created by the current generative AI gold rush.

During its fiscal first quarter (ending April 30), booming demand for AI chips helped to counter weakness among Nvidia’s other end-user markets. This resulted in a 19pc quarter-over-quarter jump in revenue, and a 44pc QoQ jump in earnings per share.

These numbers may not sound like enough to justify the more than 200pc surge in the price of NVDA stock since January, but keep in mind this is only the start. As I discussed earlier this month, the company not only has room to run in terms of generative AI growth, there is ample opportunity for Nvidia to capitalise on the rising use of AI applications in other areas. Still, while the company remains on the top of the heap, I can see why you may be concerned that this edge could diminish.

Competition is Rising, but Far from a Threat

Following Nvidia’s AI success, Advanced Micro Devices is playing catch up. AMD aims to debut an advanced AI graphics processing unit (or GPU) later this year.

Hence, this rival could soon seriously start regaining lost ground. Other major chip names, like Intel INTC may also obtain more AI market share.

In short, it’s likely that Nvidia will lose some market share over time. However, I wouldn’t assume this means bad news for NVDA stock going forward. At least, based on forecasts from one sell-side analyst, Mizuho’s Vijay Rakesh.

Rakesh is bullish that AMD and INTC will benefit from the rise of AI, yet believes NVDA will continue to gain the most. For instance, even with increased competition, the analyst estimates Nvidia will continue to hold AI market share of 75pc in 2027. That same year, Nvidia’s AI-related revenue could come in at $300bn.

For reference, the company’s total revenue last fiscal year (ending January 2023) came in at $26.9bn. Only time will tell whether this forecast proves correct. Still, if Nvidia only partially meets it, the resulting growth will undoubtedly result in some tremendous additional gains for NVDA over the next four years.

This Stock Will Stay a Top Performer

Granted, with NVDA’s massive run up, a fair amount of the company’s potential to turn AI into an eleven-figure business may seem priced-in; then again, maybe not.

A forward price-to-earnings (or P/E) ratio of 56.4 may seem pricey, yet it may not take long for Nvidia to grow into this valuation. The top end of sell-side forecasts call for earnings to more than double next fiscal year (ending January 2025) alone.

The aforementioned forecast suggests an even larger EPS jump in the latter years of the decade. Given the speed in which the AI market is growing, there is room for smaller competitors to still profit in a big way.

If given the choice to buy any chip stock for AI exposure, stick with NVDA stock. “Winner takes most” means it’ll stay a top performer.

Louis Navellier, InvestorPlace, 26 July 2023

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