Category: Ai Platforms

This article was originally published on Forbes on Jun 20, 2024,04:13pm EDTForbesForbes on Jun 20, 2024,04:13pm EDT

Big Tech is spending tens of billions quarterly on AI accelerators, which has led to an exponential increase in power consumption. Over the past few months, multiple forecasts and data points reveal soaring data center electricity demand, and surging power consumption. The rise of generative AI and surging GPU shipments is causing data centers to scale from tens of thousands to 100,000-plus accelerators, shifting the emphasis to power as a mission-critical problem to solve.

Increasing Power Consumption Per Chip

As Nvidia, AMD, and soon Intel begin to roll out their next generation of AI accelerators, the focus is now shifting towards power consumption per chip, whereas the focus has been primarily on compute and memory. As each new generation boosts computing performance, it also consumes more power than its predecessor, meaning that as shipment volumes rise, so does total power demand.

Nvidia’s A100 max power consumption is 250W with PCIe and 400W with SXM (Server PCIe Express Module), and the H100’s power consumption is up to 75% higher versus the A100. With PCIe, the H100 consumes 300-350W, and with SXM, up to 700W. The 75% increase in GPU power consumption happened rapidly, within two brief years, across one generation of GPUs.

When we look at other GPUs on the market today, AMD’s MI250 accelerators draw 500W of power, up to 560W at peak, while the MI300x consumes 750W at peak, up to a 50% increase. Intel’s Gaudi 2 accelerator consumes 600W, and its successor, the Gaudi 3, consumes 900W, again another 50% increase over the previous generation. Intel’s upcoming hybrid AI processor, codenamed Falcon Shores, is expected to consume a whopping 1,500W of power per chip, the highest on the market.

Nvidia’s upcoming Blackwell generation boosts power consumption even further, with the B200 consuming up to 1,200W, and the GB200 (which combines two B200 GPUs and one Grace CPU) expected to consume 2,700W. This represents up to a 300% increase in power consumption across one generation of GPUs with AI systems increasing power consumption at a higher rate. SXM allows the GPUs to operate beyond the PCIe bus restrictions, offer higher memory bandwidth, high data throughput and higher speeds for maximal HPC and AI performance, thus drawing more power.

It’s important to note that each subsequent generation is likely to be more power-efficient than the last generation, such as the H100 reportedly boasting 3x better performance-per-watt than the A100, meaning it can deliver more TFLOPS per watt and complete more work for the same power consumption. However, GPUs are becoming more powerful in order to support trillion-plus large language models. The result is that AI requires more power consumption with each future generation of AI acceleration.

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Big Tech’s AI Ambitions Lead to Surging GPU Shipments

From Big Tech’s perspective, we’re still in the early stages of this AI capex cycle. Most recently, we covered how Big Tech is boosting capex by more than 35% YoY in 2024, likely upwards of $200 billion to $210 billion, predominantly for AI infrastructure. The majority is flowing to GPU purchases and custom silicon, to power AI training, model development, and to meet elevated demand in the cloud.

2023 was a breakout year for Nvidia’s data center GPUs, with reports placing annual shipments at 3.76 million, for an increase of more than 1.1 million units YoY. A report stated that at peak of 700W and ~61% annual utilization, each GPU would draw 3.74 MWh; this means that Nvidia’s 3.76 million GPU shipments could consume as much 14,384 GWh (14.38 TWh). A separate report estimated that with 3.5 million H100 shipments through 2023 and 2024, that H100 alone could see total power consumption of 13.1 TWh annually.

The 14.4 TWh is equivalent to the annual power needs of more than 1.3 million households in the US. This also does not include AMD, Intel, or any of Big Tech’s custom silicon, nor does it take into account existing GPUs deployed or upcoming Blackwell shipments in 2024 and 2025. As such, the total energy consumption is likely to be far higher by the end of the year as Nvidia’s Blackwell generation comes online in larger quantities.

To read more about Nvidia’s upcoming Blackwell architecture, reference our previous analysis: Nvidia Q1 Earnings Preview: Blackwell and the $200B Data Center.Nvidia Q1 Earnings Preview: Blackwell and the $200B Data Center. If you own AI stocks, or are looking to own AI stocks and want to learn more, we encourage you to attend our upcoming weekly webinar, held this Thursday at 4:30 pm EST. Learn more here.here.

A Path to Million GPU Scale

Nvidia and other industry executives have laid out a path for GPU clusters in data centers to scale from the tens of thousands of GPUs per cluster to the hundred-thousand-plus range, even up to the millions of GPUs by 2027 and beyond. We’re already seeing signs of strong demand for Nvidia’s Blackwell platform, but overall, the million-plus GPU data center target is still years away.

Oracle’s Chairman Larry Ellison sees this creating secular tailwinds for data center construction, due to both rising GPU demand and increased power requirements driving a shift to liquid cooling:

“This AI race is going to go on for a long time. It's not a matter of getting ahead, just simply getting ahead in AI, but you also have to keep your model current. And that's going to take larger and larger data centers. … The data centers we are building include the power plants and the transmission of the power directly into the data center and liquid cooling. And because these modern data centers are moving from air cooled to liquid cooled, and you have to engineer them from scratch. And that's what we've been doing for some time. And that's what we'll continue to do.”

As the industry progresses towards that million-GPU scale, this puts more emphasis on future generations of AI accelerators to focus on power consumption and efficiency while delivering increasing levels of compute. Data centers are expected to adopt liquid cooling technologies to meet the cooling requirements to house these increasingly large GPU clusters.

For more information on investing in AI, check out our 1-hour interview “AI is the Best Opportunity of our Lifetime.”AI is the Best Opportunity of our Lifetime.”

AI Electricity Demand Forecast to Surge

As a result of booming demand for generative AI and for GPUs, AI’s electricity demand is forecast to surge, especially in the data center. We have a handful of different viewpoints and analyst projections that, while differing slightly in the timelines, all point to that same conclusion.

For example, Morgan Stanley is estimating global data center power use will triple this year, from ~15 TWh in 2023 to ~46 TWh in 2024. This coincides with the ramp of Nvidia’s Blackwell chip later in the year as well as utilization of the entirety of its deployed Hopper GPUs, and increased shipments from AMD and custom silicon ramps from Big Tech.

Morgan Stanley also projects generative AI power demand may exceed 2022’s data center power usage by 2027 if GPU utilization rates are high, at ~90% on average; however, their base case still calls for a nearly 5x increase in power demand over the next three years.

Morgan Stanley calls for a nearly 5x increase in generative AI power demand over the next three years in their base case scenario. Source: I/O Fund

Wells Fargo is projecting AI power demand to surge 550% by 2026, from 8 TWh in 2024 to 52 TWh, before rising another 1,150% to 652 TWh by 2030. This is a remarkable 8,050% growth from their 2024 projected level. AI training is expected to drive the bulk of this demand, at 40 TWh in 2026 and 402 TWh by 2030, with inference’s power demand accelerating at the end of the decade. In this model, the 652 TWh projection is more than 16% of the current total electricity demand in the US.

Source: I/O Fund

The Electric Power Research Institute forecasts that data centers may see their electricity consumption more than double by 2030, reaching 9% of total electricity demand in the US. The IEA is projecting global electricity demand from AI, data centers and crypto to rise to 800 TWh in 2026 in its base case scenario, a nearly 75% increase from 460 TWh in 2022. The agency’s high case scenario calls for demand to more than double to 1,050 TWh.

Source: I/O Fund

Arm’s executives also see data center demand rising significantly: CEO Rene Haas said that without improvements in efficiency, "by the end of the decade, AI data centers could consume as much as 20% to 25% of U.S. power requirements. Today that’s probably 4% or less." CMO Ami Badani reiterated Haas’ view that that data centers could account for 25% of US power consumption by 2030 based on surging demand for AI chatbots and AI training.

How the Supply Chain is Addressing Power Requirements:

Taiwan Semiconductor is an example of a supply chain company that plays a crucial role here, as its most advanced nodes tout lower power consumption and increased performance, which is why AI accelerators will soon shift from primarily being produced on the 5nm node to the 3nm node and eventually 2nm.

Here’s what we said previously in our free newsletter about TSMC:

“At the foundry level, the 3nm process offers 15% better performance than the 5nm process when power level and transistors are equal. TSMC also states the 3nm process can lower power consumption by as much as 30%. The die sizes are also an estimated 42% smaller than the 5nm. …the 3nm process offers 15% better performance than the 5nm process when power level and transistors are equal. TSMC also states the 3nm process can lower power consumption by as much as 30%.also states the 3nm process can lower power consumption by as much as 30%. The die sizes are also an estimated 42% smaller than the 5nm. …

N3E is the baseline for IP design with 18% increased performance and 34% power reduction, N3P has higher performance and lower power consumption, whereas the N3X will offer high-performance computing with very high performance but with up to 250% power leakage.N3E is the baseline for IP design with 18% increased performance and 34% power reduction, N3P has higher performance and lower power consumption18% increased performance and 34% power reduction, N3P has higher performance and lower power consumption, whereas the N3X will offer high-performance computing with very high performance but with up to 250% power leakage.

The 2nm will be the first node to use gate-all-around field-effect transistors (GAAFETs), which will increase chip density. The GAA nanosheet transistors have channels surrounded by gates on all sides to reduce leakage, yet will also uniquely widen the channels to provide a performance boost. There will be another option to narrow the channels to optimize power cost. The goal is to increase the performance-per-watt to enable higher levels of output and efficiency. The N2 node is expected to be faster while requiring less power with an increase of performance by 10%-15% and lower power consumption of 25%-30%.”The goal is to increase the performance-per-watt to enable higher levels of output and efficiency. The N2 node is expected to be faster while requiring less power with an increase of performance by 10%-15% and lower power consumption of 25%-30%.”is expected to be faster while requiring less power with an increase of performance by 10%-15% and lower power consumption of 25%-30%.”

CEO C.C. Wei noted in Q1’s call that TSMC’s “customers are working with TSMC for the next node. Even for the next, next node, they have to move fast because, as I said, the power consumption has to be considered in the AI data center. So the energy-efficient is fairly important. So our 3-nanometer is much better than the 5-nanometer. And again, it will be improved in the 2-nanometer. So all I can say is all my customers are working on this kind of a trend from 4-nanometer to 3 to 2.”the power consumption has to be considered in the AI data center. So the energy-efficient is fairly important. So our 3-nanometer is much better than the 5-nanometer. And again, it will be improved in the 2-nanometer. So all I can say is all my customers are working on this kind of a trend from 4-nanometer to 3 to 2.”

The power problem is being addressed throughout the supply chain, from TSMC’s chip designs to renewable energy power agreements for Big Tech’s data centers. It’ll likely require the industry to move in tandem due to the sheer pace of GPU upgrades from Nvidia, soon AMD and possibly Intel.

We’re covering how another critical part of the supply chain is working to address power consumption this week for our premium members. Learn more here.here.

Conclusion

AI power demand is forecast to rise at a rapid rate. GPU demand is showing no signs of slowing as Big Tech continues to spend billions on AI infrastructure, with each GPU generation seeing higher peak power consumption. The industry is quickly taking steps to address this, and power consumption, or more specifically, power efficiency per chip, looks to be emerging as the third realm of competition.

We’ve covered the first two realms of competitions, raw computing power and memory, extensively in previous analysis, including “Here’s Why Nvidia will Reach $10 Trillion in Market Cap.” We think it’s important to keep a keen eye on this space as new winners will emerge as AI power consumption becomes mission critical.

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This article was originally published on Forbes on May 9, 2024,02:23pm EDTForbes on May 9, 2024,02:23pm EDT

Recent Q1 earnings releases from Microsoft, Amazon, Alphabet and Meta reaffirmed that AI spending is continuing to increase through 2024 as companies seek AI-related revenue gains.

The tech giants are continuing to dedicate tens of billions each towards AI infrastructure, and overall were broadly optimistic over the opportunities that generative AI brings to growth and the value that these AI services provide to end customers. Below, we take a look at Big Tech’s Q1 earnings, AI commentary and capex plans for 2024, and what this means for the broader AI industry.

Q1 Earnings: AI Aids Revenue Gains

Big Tech’s reported revenue growth rates in Q1 were higher than anticipated just over two quarters ago. Meta is seeing one of the largest accelerations at 10 percentage points due to accelerating advertising revenue as ad prices recover. Microsoft is reporting one of the largest AI contributions of 7 points within Azure, which helped raise estimates by 550 bps. Across the board, however, Big Tech is accelerating which is not merely a coincidence.

Source: I/O Fund, Company Filings

Microsoft beat on the top and bottom line with $61.85 billion in revenue, representing a second straight quarter with revenue growth above 17% YoY. This is the first time it’s been above 17% in two years. This was driven by 21% growth in Intelligent Cloud, and in that, an acceleration to 31% growth in Azure, with 7 percentage points from AI.

Recall that in our Big Tech Stocks: Q3 Earnings Preview from October, prior to Microsoft’s September quarter report (which saw AI’s first contributions to Azure’s growth), Microsoft was expected to see 10% to 11.5% revenue growth these past two quarters – at this revenue scale, up to a 6 percentage point acceleration in three quarters marks an inflection point. For a more in-depth look at Microsoft’s recent earnings report, read the analysis: “Microsoft Fiscal Q3: 80% YoY Increase in Capex; Azure AI is Hitting Capacity”Big Tech Stocks: Q3 Earnings Preview from October, prior to Microsoft’s September quarter report (which saw AI’s first contributions to Azure’s growth), Microsoft was expected to see 10% to 11.5% revenue growth these past two quarters – at this revenue scale, up to a 6 percentage point acceleration in three quarters marks an inflection point. For a more in-depth look at Microsoft’s recent earnings report, read the analysis: “Microsoft Fiscal Q3: 80% YoY Increase in Capex; Azure AI is Hitting Capacity”

Alphabet easily beat Q1 revenue and EPS estimates as both Google Cloud (GCP) and Search revenue growth accelerated, combined with strong YouTube revenue growth. Overall revenue growth was 15.4% in Q1, the fastest in two years, and a strong acceleration from just 2.6% growth in the year ago quarter. Similar to Microsoft, increasing contributions from AI in GCP and resilient Search and YouTube ad revenues has resulted in revenue growth that is nearly 4 percentage points higher than Q1’s 11.8% growth estimate from October.

Meta reported 27.3% revenue growth and a solid EPS beat in Q1, as it captured tailwinds from 20% growth in ad impressions combined with 6% growth in ad prices. The ad impressions have cooled from 30% growth in mid 2023. Revenue gains were strongest in Rest of World and Europe at nearly 42% and 34% YoY, as Meta capitalized on double-digit growth in ad prices in those regions. While AI is aiding with improved ROI and automation for advertisers, Meta struck a nerve as it downplayed near-term revenue recognition from increased AI investments and the stock sold off nearly 11% after the earnings report.

Amazon rounded out the double beats from Big Tech as it notched a top and bottom line beat due to a 4-percentage point acceleration in AWS sales to 17% YoY and strong 24% growth in advertising revenue. AWS surpassed a $100 billion annualized run rate in the first quarter, with management noting that they “see more absolute dollar growth again quarter-over-quarter in AWS than we can see elsewhere.”

Microsoft, Meta, Alphabet and Amazon are reporting significant YoY improvements in operating margin, with Meta recording the largest expansion at 13 percentage points.

Source: Company Filings

We’re also seeing the four Big Tech companies report significant YoY improvements in operating margin, with Meta recording the largest expansion at 13 percentage points. Amazon’s operating margin improved 7 percentage points from an increase in AWS’ operating margin to 38%, which was up 14 percentage points. Cost management efforts combined with improvements in operating leverage, aided by AI growth and efficiency gains, can help the four Big Tech companies maintain and drive full-year operating margin expansion.

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Management Positive About AI’s Potential

Management teams from The Four offered positive commentary about AI’s potential to drive new growth. On the earnings call, it was discussed that AI can help to improve ROI for advertisers, help drive more infrastructure revenue (already seen in beats from Azure, GCP, and AWS), plus AI will help to innovate across customer-facing applications.

Let’s dig in to some of the top quotes and stats shared from each management team.

Microsoft:

Microsoft shared some of the more impressive (and arguably most important) stats around AI, with CEO Satya Nadella providing multiple strong growth rates for AI products and insights into AI demand.

He noted that Azure Arc now has “33,000 customers, up over 2X year-over-year,” while the “number of 100 million dollar-plus Azure deals increased over 80% year-over-year, while the number of 10 million dollar-plus deals more than doubled.”

GitHub Copilot now has “1.8 million paid subscribers, with growth accelerating to over 35% quarter-over-quarter” and revenue growth of 45% YoY. In terms of device penetration, “Copilot in Windows is now available on nearly 225 million Windows 10 and Windows 11 PCs, up 2X quarter-over-quarter.”

However, one of the most important pieces was that Microsoft’s current “near-term AI demand is a bit higher than our available capacity,” meaning its available GPU supply is not enough to meet demand from customers. Read more here.

Meta:

After launching its newest AI assistant powered by its Llama 3 model in mid-April, Meta CEO Mark Zuckerberg said the “initial rollout of Meta AI is going well. Tens of millions of people have already tried it.” He later added that he believes “Meta AI with Llama 3 is now the most intelligent AI assistant that you can freely use.”

Zuckerberg also noted that “about 30% of the posts on Facebook feed are delivered by our AI recommendation system. That's up 2x over the last couple of years. And for the first time ever, more than 50% of the content people see on Instagram is now AI recommended.”

In terms of how AI is aiding revenue, CFO Susan Li explained that “with our core AI work, we continue to have a very ROI-driven approach to investment, and we're still seeing strong returns as improvements to both engagement and ad performance have translated into revenue gains.”

Alphabet:

Alphabet CEO Sundar Pichai said that Google has “already served billions of queries with our generative AI features” while also “seeing an increase in Search usage among people who use the new AI overviews, as well as increased user satisfaction with the results.”

In addition, he added that “more than 60% of funded gen AI startups and nearly 90% of gen AI unicorns are Google Cloud customers,” and “more than one million developers are now using our generative AI across tools including AI Studio and Vertex AI.”

Amazon:

Amazon also shared some impressive stats about AI tool adoption as well as its revenue contribution, with CEO Andy Jassy saying that Amazon sees “considerable momentum on the AI front, where we've accumulated a multi-billion dollar revenue run rate already.” Microsoft is similarly in the multi-billion dollar range in Azure, where the 7% boost to growth is correlating to an approximate $4 billion run rate.

CFO Brian Olsavsky added that AWS has surpassed a $100 billion run rate and that’s “before you even calculate gen AI, most of which will be created over the next 10 to 20 years from scratch and on the cloud.”

Amazon’s managed end-to-end service SageMaker is aiding in LLM training, AI inference, and productivity improvements, with management stating that “Perplexity AI trains models 40% faster in SageMaker [and] Workday reduces inference latency by 80% with SageMaker.”

We recently entered an AI hardware stock to capitalize on Big Tech’s surging capex and GPU purchases, and may soon make it our highest allocation in our portfolio, with premium members receiving real-time trade alerts on this stock and the rest of our portfolio. Learn more about our premium memberships here.here.

Every Thursday at 4:30 pm Eastern, the I/O Fund team holds a webinar for premium members to discuss how to navigate the broad market, as well as various stock entries and exits. We offer trade alerts plus an automated hedging signal. The I/O Fund team is one of the only audited portfolios available to individual investors. Learn more here

Capex Surging More Than 35% YoY In 2024

Big Tech will likely commit upwards of $200 billion, maybe even $210 billion, combined in capex this year, predominantly for AI infrastructure – from data center construction and expansion, to GPU procurement and custom silicon efforts and more.

It’s no wonder the four are boosting capex by more than 35% YoY, given positive outlooks on AI’s potential to drive revenue growth in the billions and how demand continues to outstrip GPU supply. Overall, capex commentary from the four was directionally bullish for AI semiconductors and server manufacturers, though Meta’s vagueness on a timeline to recognize a return on said spend hit shares quite hard.

Microsoft:

Microsoft increased its capex 80% YoY to $14 billion this quarter, and for the entire fiscal year, capex will increase approximately 50% YoY to more than $50 billion. Demand for Azure’s AI services is outpacing its capacity in the near-term, hence the need for Microsoft to accelerate spending to boost GPU supply. It has been reported that Microsoft is seeking to triple its GPU supply this year to 1.8 million GPUs.

Alphabet:

Alphabet’s capex rose 91% YoY to $12 billion in Q1, primarily for technical infrastructure – this capex spend was led by servers and followed by data centers. Management is expecting quarterly capex “to be roughly at or above the Q1 level,” implying a full-year capex around $50 billion, an increase of ~55% YoY. Management said this significant growth in capex “reflects our confidence in the opportunities offered by AI across our business.”

CFO Ruth Porat explained that “as we’re investing in CapEx and applying it across our various businesses, it opens up more service and products, which bring revenue opportunities, and we’re very focused on the monetization opportunity,” which spans Search, Cloud, YouTube, and other services. CEO Sundar Pichai emphasized that Alphabet has “clear paths to AI monetization through Ads and Cloud, as well as subscriptions.”

Meta:

Meta boosted its full year capex range to $35-40 billion, pointing to 33% YoY growth and $4 billion more than previously anticipated, to build out AI infrastructure and support its internal AI roadmap. Meta’s Q1 capex was only $6.7 billion, implying that the bulk of this spend will hit in the second half of the year, possibly accelerating at a ~20% QoQ rate and exiting 2024 above the $11 billion range. However, despite these aggressive investments in AI, Meta was rather vague on its returns, saying it is very early on the return curve:

As we're scaling capex and energy expenses for AI, we'll continue focusing on operating the rest of our company efficiently. But realistically, even with shifting many of our existing resources to focus on AI, we'll still grow our investment envelope meaningfully before we make much revenue from some of these new products.

Once our new AI services reach scale, we have a strong track record of monetizing them effectively. There are several ways to build a massive business here, including scaling business messaging, introducing ads or paid content into AI interactions, and enabling people to pay to use bigger AI models and access more compute.”

Despite the hints of positivity in creating and monetizing new services, the commentary highlights a rather important angle that the market does not like – an unclear timeline to when a return on increased investments will be recognized. This is quite the polar opposite from Microsoft and Amazon, who have multi-billion dollar AI revenue streams that will immediately benefit from increased expenditures on capacity expansion. The difference is that AWS and Azure are more enterprise or startup related whereas Meta is more consumer related. It’s been our contention that AI is an enterprise technology first and foremost when we held a webinar in 2022 when it was stated about AI that: “enterprises are going to drive forward the gains over the next 10 years, it will not be consumer.”

Amazon:

Amazon did not provide a full-year figure for capex, but management said they anticipate capex will “meaningfully increase year-over-year in 2024, primarily driven by higher infrastructure CapEx to support growth in AWS, including generative AI.” In addition, they expected the $14 billion capex sum for Q1 will “be the low quarter for the year,” suggesting capex could easily top $60 billion, exiting the year in the mid-$60 billion range or higher, an increase of at least 24% YoY.

CEO Andy Jassy explained this capex growth is driven by “the combination of AWS' reaccelerating growth and high demand for gen AI, … which given the way the AWS business model works is a positive sign of the future growth. The more demand AWS has, the more we have to procure new data centers, power and hardware.”

Implications For the Broader AI Industry

This increased spending by Big Tech ultimately is flowing to multiple core components for infrastructure expansion and increased GPU capacity. Management teams are talking about more GPU purchases, more custom silicon buildout, data center expansion, and the need for more hardware (networking and switches). For example, since the start of this year, consensus revenue estimates for Nvidia have already risen from $91 billion at the beginning of January to $112 billion at the beginning of May – an increase of $21 billion. This goes hand in hand with its new GPU releases and increased Big Tech spending.

However, AI opportunities extend well past Nvidia. We’ve been closely monitoring Big Tech for years while identifying the top beneficiaries from this blistering AI-driven increase in capex. We routinely share our research with our premium members including how this impacts AI semiconductors such as GPUs and custom silicon, memory players, and AI networking.

If you own AI stocks, or are looking to own AI stocks, we encourage you to attend our weekly premium webinars, held every Thursday at 4:30 pm EST. Next week, we will discuss a handful of AI plays for 2024 – what our targets are, where we plan to buy as well as take gains. Learn more about I/O Fund’s premium services here.

I/O Fund Equity Analyst Damien Robbins contributed to this report.

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The rise of AI marks a transformative technological, economic, and societal inflection point — one with extreme implications for how we live and invest. So, how can investors take advantage of the trend? Jordi Visser, CIO and Chairman of Weiss Multi-Strategy Advisers, spoke to Beth Kindig on the Real Vision podcast on March 20th, to dive deep into AI’s potential for explosive economic growth, how to find winners in this theme, sectors that benefit from AI, the potential for crypto to decentralize AI, and more.

Watch the full interview on Real Vision here: Real Vision Video: AI Opportunity (youtube.com)

AI’s Impact Much Larger Than Mobile

The multi-trillion dollar impact to global GDP is the reason AI will shape up to be such a transformative and explosive trend – much like smartphones, which revolutionized countless facets of our lives, added trillions to the global economy, and created multiple trillion-dollar companies and billion dollar industries.

AI is shaping up to be multiple times larger than mobile – to the tune of 3x to 5x larger over the course of the next decade. Beth explains to Jordi that for AI’s impact on GDP, she has “seen $15 trillion, but McKinsey and others are now raising it to a $25 trillion impact on GDP. You can assume mobile is $3 to $4 trillion, maybe $4 to $5 [trillion], depending on how you chop up smartphones, applications, app stores, things like that. Let’s just give it the highest estimate of $5 trillion – [for AI], we’re looking at 3x minimum, 5x right now and these estimates keep getting raised […].”

Source: I/O Fund

This profound economic growth opportunity from AI is “something we’ve never seen before.” It stems from AI’s product-market fit — solving clear problems, driving down costs for enterprises and boosting worker productivity — and when you “match it with the right product, what you have is this hockey stick explosive growth.”

A Cautionary Tale of Consolidation

Even with such an explosive growth forecast for AI, it may still face a similar hype cycle trajectory as many other facets of tech do.

Just as with other innovative technologies, for AI, it’s likely that we will “go through a lot of innovation that is absolutely necessary… but then over time, the hype phase on the user side [fades] and those businesses don’t last.” This has happened in all facets of tech, from mobile to gaming to one of the most notable for tech investors, the dot-com bubble.

Beth cautions that you can “expect something similar to happen with AI as what we’ve seen in mobile, [and] maybe even at a higher rate.” For context, “a wave of innovation such as mobile will often put on the market 2 million apps, but in the long run, fast forward 10 years, most people use about 10 [apps].”

According to Crunchbase data, there are nearly 10,000 AI startups, while a more specific look in generative AI shows nearly 800 startups. Of that 800, 67% are still early stage, while only 2% are late stage. This comes despite a 5x surge in generative AI investments to almost $22 billion in 2023, with funding concentrated in OpenAI, Anthropic, and Inflection AI.

This sort of proliferation of companies creating different AI apps and use cases is definitely a positive outcome, but it’s extremely unlikely that all 10,000 companies participating in the AI economy survive, with consolidation occurring via acquisitions to even bankruptcies for the smaller bootstrapped startups.

Source: CB Insights

There will ultimately be winners in AI, and there’s one critical piece that sets these companies apart: data.

Data Will Create the Winners

For AI, data will separate the winners from the rest of the pack, due to the high costs of training AI models and the need for high-quality data sets to train said models.

Google, Meta, Amazon, and Microsoft have all invested tens of billions into AI development for years, and can quickly and effortlessly integrate AI into their established business models.  For example, Beth explored in June 2023 how AI could drive $100B in revenue for Microsoft by 2027, from OpenAI’s APIs running on Azure, to AI integrations and partnerships via Bing, and the rollout of Copilot, among other drivers.

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What sets Big Tech, and these four companies apart, is that they have huge proprietary data sets that they can use to train AI models for various different purposes. Beth points out that “now we have an issue where, if you are a startup or small company, do you even have the data set to train these models? A lot of people have followed Tesla for a long time, what are the chances that Tesla would ever give away the data set that their fleet has created and generated over the last however many years. They’re going to protect that with everything they have, not only because of the costs that it required to create that data set, but because it’s really their secret sauce.”

Small companies not only will struggle to build out AI infrastructure due to the substantial costs with building out physical data center infrastructure and acquiring GPUs, but will also struggle with developing and fine-tuning high-quality AI models due to the challenges and costs associated with having a large enough proprietary data set. In this sense, large proprietary data sets create a ‘winner takes all’ or ‘winner takes most’ market, led currently by the Magnificent 7. Open-source movements could change this but how/when is speculative at this time.

Show Love to the Semiconductors

Jordi asked Beth what sectors she likes in AI, and her response was: “get comfortable with semiconductors.” She has done exactly that — of the I/O Fund’s more than 45% allocation to AI stocks in 2023, 40% of that was semiconductors, helping the fund power to a 57% annual return last year.

Semiconductors are powering Big Tech’s AI aspirations, and Beth explained that these companies are “50% of the AI market; that’s up from 20% or 30% of the mobile market, and they will be, for us, the right way to participate in AI in the near term.” She added that one of the main takeaways that she hopes for investors to get from the interview is that “these semiconductors will become the AI software players.”

This is evident with Nvidia – not only is it monetizing hardware sales via its Enterprise software suite for $4,500 per GPU per year, but also some of its biggest announcements at GTC were software, with its Omniverse Cloud APIs and Omniverse integration with Apple’s Vision Pro headset.

Preparing for the Next Phase of AI

While semiconductors have dominated in hardware – the data center – as the first explosive phase of AI, will expand beyond them to the edge. The forthcoming upgrade cycles for smartphones and PCs will see Edge AI rise as the second wave of AI. Beth explained that “there’s only so much AI can do in the data center. Inference has to run close to the user.”

We’re already seeing signs of this unfolding – Samsung is partnering with Baidu to use the Chinese tech giant’s AI chatbot Ernie in its S24 smartphones, while Apple is in discussions to also use Ernie in its Chinese devices. AMD, Apple, Qualcomm and Intel are all releasing new PC chips to boost AI computing power two to four-fold from prior generations in an effort to facilitate AI inference on these local devices.

The I/O Fund is currently preparing to capitalize on edge AI as it will be an important trend that has yet to emerge. We have been sharing in-depth research on the next stocks poised to capitalize on edge AI with our premium members and we discuss potential entries and exits every week in a one-hour webinar on Thursdays. We also offer trade alerts plus an automated hedging signal. The I/O Fund team is one of the only audited portfolios available to individual investors. Learn more here.

AI Is Not a Trend to Ignore

While many are quick to say that AI is merely just a ‘buzzword’, those who are in the know were able to get in front of 2023’s big move. AI is not a trend to miss or ignore, and it offers investors a rare opportunity to get onboard in the early stages of one of the largest economic and transformational trends in tech.

The $3 trillion to $5 trillion mobile economy sprouted the FAANGs of today, and if you could’ve invested in the FAANGs 10 to 15 years ago, you would have, given that multi-trillion dollar potential. Today’s estimates put AI at 3x to 5x larger than mobile in terms of overall impact to GDP, and  we’re only in the first of multiple powerful AI waves. And as a leading portfolio in AI, the I/O Fund is preparing to capitalize on this once-in-a-lifetime trend.

Watch the full 1-hour interview for the in-depth view on AI’s potential, what sectors will benefit and which will be a “hot potato” to avoid, crypto and AI, and more.

If you own AI stocks, or are looking to own AI stocks, we encourage you to attend our weekly premium webinars, held every Thursday at 4:30 pm EST. Next week, we will discuss a handful of AI plays for 2024 – what our targets are, where we plan to buy as well as take gains. Learn more about I/O Fund’s premium services here.

Please note: The I/O Fund conducts research and draws conclusions for the Fund’s positions. We then share that information with our readers. This is not a guarantee of a stock’s performance. Please consult your personal financial advisor before buying any stock in the companies mentioned in this analysis.

Recommended Reading:

• Semiconductor Stocks Q4 Overview: AI Gains Heat Up
• Arm Stock: AI Chip Favorite Is Overpriced
• Top 3 Ad-Tech Stocks For 2024
• The Magnificent 7 Are Falling Like Dominos; Only 3 Remain

In the clip below, Beth Kindig discusses how AI will drive stock market caps well into the trillions of dollars.

Disclaimer: This is not financial advice. Please consult with your financial advisor in regards to any stocks you buy.

Recommended Readings:

• Semiconductor Stocks: Q2 Sector Overview
• Tesla Q2 Earnings – It’s About Margins
• Beth Kindig Discusses AI Stocks with Tier 1 Media
• Where Nvidia’s Stock Price Will Go Next

This article was originally published on Forbes on May 24, 2023, 07:45 am EDTForbes on May 24, 2023, 07:45 am EDT

My coverage on Nvidia as an AI leader began in 2018 (yes, really – five years ago). Since then, I’ve covered the AI microtrend for this specific stock 27 times on my research site, which is the equivalent of a novel.

I’ve also gone on record to say that Nvidia will surpass the valuation of Apple. That particular analysis compared the impact that AI will have to mobile, with AI adding $15 trillion to GDP compared to mobile’s $4.4 trillion. Mobile brought us three FAANGs: Apple, Google and Facebook. It has been my stance for years that AI will bring us a new set of FAANGs, one of which will be Nvidia.

However, now is not the best time to buy the stock. Rather than flatly tell you that while offering no way forward, I want to continue providing value to my readers by discussing when my firm plans to buy the stock again.

But also, we should discuss why the market is rallying on this company specifically. Good investors must do both – understand what makes a company stand out while being patient on price.

AI is Not a Buzzword for Nvidia

Short sellers mistakenly believe that AI is a buzzword for Nvidia. This is true for many stocks, but not for the leader in parallel processing.

Here is what I wrote five years ago on the topic:

Nvidia is already the universal platform for development, but this won’t become obvious until innovation in artificial intelligence matures. Developers are programming the future of artificial intelligence applications on Nvidia because GPUs are easier and more flexible than customized TPU chips from Google or FPGA chips used by Microsoft [from Xilinx]. Meanwhile, Intel’s CPU chips will struggle to compete as artificial intelligence applications and machine learning inferencing move to the cloud. Intel is trying to catch-up but Nvidia continues to release more powerful GPUs – and cloud providers such as Amazon, Microsoft and Google cannot risk losing the competitive advantage that comes with Nvidia’s technology.Nvidia is already the universal platform for development, but this won’t become obvious until innovation in artificial intelligence matures. Developers are programming the future of artificial intelligence applications on Nvidia because GPUs are easier and more flexible than customized TPU chips from Google or FPGA chips used by Microsoft [from Xilinx]. Meanwhile, Intel’s CPU chips will struggle to compete as artificial intelligence applications and machine learning inferencing move to the cloud. Intel is trying to catch-up but Nvidia continues to release more powerful GPUs – and cloud providers such as Amazon, Microsoft and Google cannot risk losing the competitive advantage that comes with Nvidia’s technology. 

The Turing T4 GPU from Nvidia should start to show up in earnings soon, and the real-time ray-tracing RTX chips will keep gaming revenue strong when there is more adoption in 6-12 months. Nvidia is a company that has reported big earnings beats, with average upside potential of 33.35 percent to estimates in the last four quarters. Data center revenue stands at 24% and is rapidly growing. When artificial intelligence matures, you can expect data center revenue to be Nvidia’s top revenue segment. Despite the corrections we’ve seen in the technology sector, and with Nvidia stock specifically, investors who remain patient will have a sizeable return in the future.”When artificial intelligence matures, you can expect data center revenue to be Nvidia’s top revenue segment. Despite the corrections we’ve seen in the technology sector, and with Nvidia stock specifically, investors who remain patient will have a sizeable return in the future.”

-Free Newsletter and Seeking Alpha, November 2018 with AI Thesis repeated again in April of 2019Free Newsletter and Seeking Alpha, November 2018 with AI Thesis repeated again in April of 2019

When I wrote that, Nvidia was considered a gaming stock with high-risk exposure to crypto. What is astonishing is that the company was still considered a gaming stock with high-risk exposure to crypto a mere seven months ago.

You may recall, the stock was down 60% last year after a $2.5 billion miss on gaming, and the market was pricing in a long recovery due to Ethereum’s merge to Proof of Stake (PoS). The bears believed the Merge would flood the market with mining GPUs and Nvidia would be unable to overcome this setback.

At the time, there was no mention of Nvidia’s AI lead despite the H100 GPU being released the very next month! despite the H100 GPU being released the very next month! Instead, the market had investors believing that Ethereum, with only 200 million users, which is a smaller user base than Snap or Pinterest, could tank the GPU-juggernaut on the eve of the company’s largest release to-date: the H100 GPU.

The reason I’m emphasizing this is because my firm has worked hard to be a quality resource on tech stocks. Often times there is a major disconnect between the market’s pricing and a tech company’s positioning. There is no greater evidence of this than when Nvidia was down 60% seven months ago yet is the top performing stock in the S&P 500 today.

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Why the Market is Bulled Up on Nvidia

I want to take the opportunity to explain why Nvidia has the ability to arrive at a valuation that is 3X higher than its peers and in some cases 12X higher.

Source: YCHARTS

I’m not defending this valuation, rather I want to explain how it’s possible that smart money continues to buy up here.

The H100 is a Turning Point from Hardware to Software

“The Hopper architecture is ramping and it’s yet again going to disrupt the GPU and AI accelerator market. I’ve written quite a bit about Nvidia […] however, I will keep it simple by saying the A100 GPU is what led the company’s gains since Q2 2020 and the Hopper H100 GPU is what will lead the company’s gains for the next two years.” -Premium Site August 2022, following Nvidia’s $2.5 billion revenue miss.Premium Site August 2022, following Nvidia’s $2.5 billion revenue miss.

Note: the information below is a bit technical, so I’ve bolded the key points for a quick read.

For context, the A100 GPU was a monumental release for Nvidia as the Ampere architecture unified training and inference onto a single chip, whereas in the past Nvidia’s GPUs were mainly used for training.

The result is a 20x performance boost from a multi-instance GPU that allows many GPUs to look like one GPU. The A100 offered the largest leap in performance to date over the past 8 generations. One year later, the Ampere architecture had become the best-selling GPU architecture in the company’s history.

The A100 was special but it’s the H100 that is Nvidia’s iPhone moment. The reason is quite simple – it’s the release that will help Nvidia breakout from hardware and put the company firmly on the map for AI software.

Hardware has allowed Nvidia to become a $700B market cap company, but it is the recurring revenue from AI software that will propel Nvidia into a market cap worth trillions.

You know this story well: the relationship between a hardware company leveraging their position to capture the lion’s share of software —- because that’s exactly what Apple did. My contention is that the iPhone was successful because of the moat iOS developers created, and the additional flywheel from the App Store. I discussed this more in a webinar “The New Kings of Tech”

The H100 delivers 9X more throughput in AI training, and 16X to 30X more inference performance. The company also states in HPC application-specific workloads, the H100 is 7X faster. The goal of the H100 was not only to add more transistors and make the H100 faster, but to also offer function-specific optimizations. This is achieved through the transformer engine.

The transformer engine is one of the key aspects of the H100. Transformers are becoming one of the most popular neural-network models by applying self-attention to detect how data elements in a series influence and depend on one another.

Prior to transformer models, labeled datasets had to be used to train neural networks. Transformer models eliminate this need by finding patterns between elements mathematically, which substantially opens up what datasets can be used and how quickly. Transformers are partial to the parallel processing that GPUs offer.

The Hopper architecture aims to answer one of the bigger challenges facing superfast compute, which is that moving data into traditional servers overloads the CPU and system memory and becomes bottlenecked by PCI-Express.

By improving the bandwidth issue, Nvidia’s goal is to create more demand for their DGX Pod and SuperPod Systems, which in turn, will create more demand for their software.

The DGX SuperPods scale into a super-GPU capable of 768 terabytes per second. To compare, the entire internet requires 100 terabytes per second. This results in 1 exaflop of FP8 AI performance that runs trillions of parameters. FP8 is most commonly used for inference yet may be used for training in the future due to boosting throughput.

Whereas traditional workloads required many connections exchanging small amounts of data, the workloads of the future will require data to be shared quickly between GPUs and storage. This is accomplished by bypassing the CPU and sending data directly to the GPU while using the network hardware to move the data.

This is ideal for enterprise use cases where people are more likely to use Ethernet while AI and HPC workloads continue to use the Quantum-2 based off Mellanox’s InfiniBand.

Not only will Nvidia begin to monetize through software on the DGX systems but accessibility will improve through CSPs, or cloud service providers. This is an attempt to democratize AI development while driving software sales. On a trailing 4-quarter basis, cloud service providers drove 40% of data center revenue. This is important as cloud service providers will help move DGX Cloud along and AI-as-a-service.

Nvidia’s TAM of $600 Billion is Easily and Quickly Achievable

“The conclusion to my analysis is the same as the introduction, which is that I believe Nvidia is capable of out-performing all five FAAMG stocks and will surpass even Apple’s valuation in the next five years.” – Forbes and Free Newsletter, August 2021Forbes and Free Newsletter, August 2021

Last year, CEO Jensen Huang provided a total addressable market of $300 billion in hardware and $300 billion in software. Meanwhile, Elon Musk is deploying 10,000 GPUs in the cloud and there will likely be tens of thousands more to inference a widely deployed model for a social media generative AI project.

Per the analyst on the call: “So it seems like the incremental TAM is easily in the several hundred thousands of GPUs and easily in the tens of billions of dollars. But I'm kind of wondering what this does to the TAM numbers you gave last year. I think you said $300 billion hardware TAM and $300 billion software TAM. So how do you kind of think about what the new TAM would be?”

Huang aptly answered: “I think those numbers are really a good anchor still. The difference is because of the, if you will, incredible capabilities and versatility of generative AI and all of the converging breakthroughs that happened towards the middle and the end of last year, we're probably going to arrive at that TAM sooner than later.”

Today, Nvidia trades at 1.5X this TAM at $773 billion compared to an achievable TAM of $600 billion. This would suggest the stock price does not yet fully reflect the future market opportunity. Also, compare this TAM of $600 billion to Apple’s revenue of $394 billion, which helps illustrate why I said in the past that Nvidia Will Surpass Apple’s Valuation.

Every Thursday at 4:30 pm Eastern, the I/O Fund team holds a webinar for premium members to discuss how to navigate the broad market, as well as various stock entries and exits. We offer trade alerts plus an automated hedging signal. The I/O Fund team is one of the only audited portfolios available to individual investors. Learn more here.Learn more here.

What to Expect in the Upcoming Earnings:

If we set aside the AI thesis for a moment, you can see below why Nvidia has rallied as the revenue is expected to rebound from (-21.4%) for the upcoming quarter to as much as +32% growth by fiscal Q3 ending in September. The popularity of the H100 could lead to a beat somewhere across these next few quarters. In addition, the RTX40 Series lower-end model will be released today for $299 and up, and this may further help the gaming revenue for Q2 and beyond.

Nvidia is unique in that the healthy growth is expected to continue into the foreseeable future — long after the company laps the quarters of the crazy gaming miss. What we don’t want is to invest in companies propped up temporarily by low comps. This is not the case with Nvidia.

Here is Nvidia’s revenue growth over the past five quarters, which shows the effects of the gaming segment:

Source: I/O FUND

As stated in our previous earnings coverage on Nvidia, all segments are expected to grow sequentially. I believe this is a major contributor for the rally we are seeing. The market saw what we saw, which was a sharp rebound in the fundamentals and that is critical to understanding why Nvidia is the top stock in the market right now.

Compare that picture to this one:

Source: I/O FUND

It’s not only the top line that is rebounding but also the bottom line too (which makes sense but is important to point out):

Source: I/O FUND

Moving along, data center revenue was $3.75B in Q1 FY23. The projected mid-point above is $4.075B, representing 8.7% YoY growth and 12.7% growth sequentially. Here is what was said on the call:

“Thanks for the question. First, talking about our data center guidance that we provided for Q1. We do expect a sequential growth in terms of our data center, strong sequential growth. And we are also expecting a growth year-over-year for our data center. We actually expect a great year with our year-over-year growth in data center probably accelerating past Q1.”

Source: I/O FUND

So, what we don’t see in the graph above is what the “accelerating past Q1” will be and this is the one data point that can get the stock to move AH.

Where Nvidia’s Price Will Go Next:

With the cash we raised throughout 2022, NVDA was the primary target of deploying some of this cash once our analysis signaled a bottom was in place. The below is a real-time trade notification we sent to our members on the October 13th.

Source: I/O FUND

Since February of 2022, we have been systematically taking gains at key levels. Even with logging sizable wins in this position in 2023, it remains our top position while still having enough cash to buy at lower levels.

It is our belief that NVDA is setting up for a sizable pullback, which we believe will open the door for better long-term entries. The reasons for this are below:

The structure/pattern of NVDA’s bounce signals caution. If we look at the pattern off the October low, it may feel like a straight line up; however, you can clearly see 3 swings (marked a, b, c). The first swing up off the low (a), a bearish retrace that makes a higher low (b), and the current swing that we are still in (c).

Source: I/O FUND

When we see a 3 wave pattern off of a major low, more times than not, it is a corrective bounce in a larger downtrend. While it may feel impossible at such heights, please keep in mind how sentiment can and does work against us as investors. It felt like tech could never go down in late 2020, and then it felt like it would never go up in Q4 of 2022.

Nvidia is no different, and what we have is a pattern that suggests a larger pullback than most expect is likely, at minimum. So, until this 3 wave pattern can morph into a 5 wave pattern, the odds favor a sizable pullback soon.

Further evidence of this can be seen in how NVDA is now at a significant supply region that marked the top in late 2023. We are now in the region that would constitute a double top playing, and note how price keeps trending higher with less momentum.

We are approaching a double top in conjunction with one of my favorite “sell signals” – when you have price making 3 higher highs, while the momentum indicator being used is making 3 lower highs. This is clearly happening on multiple time frames, which we believe warrants caution.

Similar patterns can be seen on the weekly chart of NVDA below. As price pushes higher, it is doing so on less momentum and less volume. When we see the same pattern on multiple time frames, it further builds the case for caution.

Source: I/O FUND

Regarding the targets we are tracking for entries, there are two general paths I see playing out from the price data in the above charts.

The Blue Count suggests that we completed the large degree uptrend that started in 2018. This would put is in a very large corrective rally with the final leg lower coming later this year/early next year. This would have us retest the October lows, and possibly slightly lower. The big tell for this count playing out will be if the coming pullback is a 5 wave pattern pointing down. If we see a large 5 wave drop from the highs, it is signaling that NVDA will likely go lower than most are anticipating.

The Red Count suggests that the October low was THE low. This will still set us up for a sizable pullback into the $220 – $167 region before setting up to make a run to new highs. This count implies that the large uptrend that started in 2018 is not complete and will be targeting fresh highs in the coming year. The tell for this scenario will be if the coming pullback is a 3 wave pattern. If we see a 3 wave pullback, we will look to be heavy buyers in the general target box just outlined.

Conclusion:

My firm tracks Nvidia very closely due to its leading allocation in our portfolio. We saw evidence of a gaming bottom in November, which we published about here. We also felt Nvidia had masterfully timed it’s RTX40 Series with the Ada Lovelace architecture plus the H100 release to drop exactly when the crypto mining selloff would be most felt. We discussed this here in September. These points were entirely overlooked by Nvidia critics.

Yes, a $2.5 billion revenue miss is crazy – but what was lying beneath the surface for chances of a quick recovery? The devil is in the details and not a lot of investors or analysts care to look into Nvidia’s complex hardware products.

For my readers, it has worked out in their favor that talking heads prefer to discuss stocks after they are up triple digits in price, and that the masses are collected around hindsight narratives. My firm is carefully and patiently building an AI portfolio that we believe will outperform institutions and hedge funds. Taking our sweet time to enter Nvidia at the lows – as we have done for the past five years and will continue to do so for the next five years — is part of that strategy.

Our firm issues real-time trade alerts when we buy, sell or trim stocks. You can learn more and view our other notable wins here.

Recommended Reading:

• Nvidia Stock Is Ready To Rumble With RTX 40 Series And H100 GPUs
• Nvidia Stock: Evidence Gaming Bottomed And Why It’s Important
• Here’s Why Nvidia Will Surpass Apple’s Valuation In 5 Years