4 Things Investors Must Know About AI
Last week was quite an important week for tech and AI investors, with Goldman Sachs hosting its Communacopia and Technology Conference featuring executives from the largest tech and semiconductor companies. Rarely have so many tech CEOs gathered to discuss their thoughts on AI, where the industry currently stands, and what lies ahead.
To have the CEOs of trillion-dollar companies speaking in unison on AI’s potential and investing in AI is either a staggering coincidence — or they have important insights pointing to the same conclusion, which is that AI’s primary risk is for companies who are not early enough to capture it.
We’re still in the early innings of AI, but the pace of transformation that AI is driving is unlike any other technology seen before, and that was evident at Communacopia. Below, I dig in to the four things that investors must know about AI.
1) Tech CEOs Agree the AI Revolution is Here
The AI revolution has arrived, sparked in full-scale by Nvidia’s Hopper series GPUs and OpenAI’s release of ChatGPT in late 2022. Not even two years later, Nvidia continues to sell GPUs at an unbelievable clip, with Big Tech unable to procure enough GPUs to meet internal project needs and external enterprise demand in the cloud.
AWS CEO Matt Garman explained that he truly believes AI “is a technology that over time is going to completely change almost every single industry that all of us focus on and think about and work on every single day to some level.” Garman added that the early AI use cases we’re seeing proliferate at the moment are just scratching the surface. ServiceNow CEO Bill McDermott agrees, stating that he also believes “AI is the well spring of opportunity in the global economy.”
Nvidia CEO Jensen Huang echoed this, saying that “we’re now in this computer revolution. … Generative AI is not just a tool, it is a skill. And so this is the interesting thing. This is why a new industry has been created. And the reason for that is, if you look at the whole IT industry, up until now, we’ve been making instruments and tools that people use. For the very first time, we’re going to create skills that augment people. And so that’s why people think that AI is going to expand beyond the trillion dollars of data centers and IT, and into the world of skills.”
Despite the immense potential AI holds, in the present, the AI industry is only just at the nascent stages of this revolution. Snowflake CFO Mike Scarpelli explained that he thinks “it’s still in the very early innings,” but “the reality is that very few are using it en masse today.” Bringing AI to the masses, when adoption of AI is commonplace, is when the industry will unlock things previously seen as impossible or extremely costly, according to Microsoft CTO Kevin Scott. Scott believes we could be 5 to 10 years out from seeing what developers are capable of and what applications can be created.
2) AI to Have a $10 Trillion Impact on the Economy
By heralding in a new wave of innovation and unlocking endless possibilities to advance technology currently available today, AI is expected to have a multi-trillion-dollar impact on the global economy in the coming decades.
ServiceNow CEO Bill McDermott discussed AI’s profound potential at Communacopia: “There are researchers that independently have said it will have an $11 trillion impact on the economy in the next handful of years. I believe that may be true. Maybe it’s [$10 trillion], maybe it’s [$9 trillion], maybe it’s [$8 trillion], but it’s going to be big.
And the reason for that is there is so much inefficiency. There is so much waste. There is so much human potential that can be activated by taking the soul crushing work away from people and unleashing them to do things that really matter that can help companies grow and prosper. And that has never been factored into the equation as people think about technology on a day-to-day basis, that’s why we’re working so hard to tell them the story.”
For context, the mobile economy, which delivered a handful of the trillion-dollar tech behemoths of today, added approximately $5.7 trillion to the global economy in 2023, up from $5.2 trillion in 2022, according to GSMA. McDermott sees AI having up to double the economic potential of mobile, though other industry forecasts point to a much larger long-term impact from AI.
According to McKinsey, generative AI is estimated to add up to $7.9 trillion to the global economy annually when combining new generative AI use cases and gen-AI related productivity gains, according to research from McKinsey, Overall, McKinsey estimates the AI economy could add $25.6 trillion to global GDP over the next couple of decades.
Source: I/O Fund
Through 2030, AI’s cumulative economic impact is projected to be nearly $20 trillion, according to IDC — with every new dollar spent on AI services and solutions expected to generate $4.60 in “indirect and induced effects.” This is a massive technological shift and value add globally to be realized only five years from now and eight years following AI’s breakthrough moment with ChatGPT.
For a closer look at AI’s potential and how to invest in this mega-trend, read Investing In AI with Beth Kindig: 1-Hour Video Interview.
3) Productivity Gains are Already Being Seen
Even with the view that AI is still in the early stages of its growth curve and barely scratching the surface of its potential, companies are already discovering and showcasing productivity gains, a cornerstone of how AI can quickly become a multi-trillion dollar economic force.
Google Cloud CEO Thomas Kurian explained how Google is leveraging generative AI features in Google Workspace to drive significant productivity gains for customers: “For example, if you’re in a hospital, as a hospital company, nurses are the critical path. Because nurses determine how many hospital beds you can have, they control the revenue of the organization. So we work with nursing staff, for example, to do live hand-off of patients. It saves about 6 hours in a 24 hour day. And one of the leading hospitals was talking at a conference today that they estimate when rolled out, it will save them $250 million.”
Kurian also discussed how AI is improving efficiency and productivity in the insurance industry, highlighting a use case for Germany’s largest health insurer. He explained that on average, the company’s representatives “need to read 800 policy documents to determine if the claim is valid or not. They use our technology. It helps take 23 to 30 minutes down to 3 seconds. So productivity in these specific places are extremely high value.”
AWS’ Garman shared other ways AI is dramatically altering what’s possible. He said that there are pharmaceutical companies “using AI to actually invent new proteins [and] new molecules that may be able to help cure cancer or cure other diseases and things like that. And at a rate that’s tens of thousands or hundreds of thousands more times than a person sitting there with a computer trying to guess what the next protein could look like to solve a particular disease. That is just a fundamentally different capability than ever existed before and has massive implications for health care.”
Garman also mentioned how bullet train operators in Japan are using AWS’ SageMaker and “built AI models to predict where they’re going to have maintenance issues, [and] actually proactively predict weeks in advance where they might see components fail. And then using generative AI, they actually pull from a bunch of different data sources actually give the technician advice as to how they can go address that.”
As the industry continues to build more powerful models to advance capabilities and unlock new use cases, productivity gains, and reasoning abilities, the amount of AI accelerators needed will continue to rise exponentially. Per Barclays, for the development of three frontier AI models with 50 trillion parameters by 2027, 20 million AI accelerators would be needed to simply train each model, for a total of 60 million accelerators. This is more than 15x higher than Nvidia’s AI GPU volume from 2023, where it shipped an estimated 3.76 million GPUs.
AI can have a profound impact across multitudes of roles and industries, and this is only the tip of the iceberg in terms of how AI can boost productivity and increase efficiency — this is the larger cornerstone of AI’s potential multi-trillion economic impact.
4) AI’s Technological Progress is Moving at the Speed of Light
The AI industry is progressing exponentially fast, much faster than previous technological breakthroughs, and this is being spearheaded by Nvidia.
Nvidia has radically changed the game when it comes to progress in AI, quite essentially by breaking Moore’s Law and supercharging GPU performance in an undeniably rapid annual release cycle. As Nvidia CEO Jensen Huang put it at Communacopia, the “benefit of performance at the scale that we’re doing, it directly translates to TCO [total cost of ownership].”
This is driving substantial acceleration downstream in the data center industry. Cloud providers such as Microsoft, Amazon, Alphabet and Meta not only can establish new data centers with the newest accelerators for faster performance, but also upgrade existing data centers and retire previous chip generations to significantly accelerate computing performance while realizing lower operational costs. Nvidia’s newest architecture, Blackwell, is also necessitating the adoption of liquid cooling, forcing new data centers to be reinvented from the ground up while being set up at much quicker rates.
Here’s what Microsoft CTO Kevin Scott said about data center and related infrastructure buildouts: “Everybody in the [AI] ecosystem is moving materially faster right now than they were 3 or 4 years ago, materially faster. … So far, demand for the infrastructure has materially outpaced our ability to supply it. … Do I wish it were faster? Yes, I wish it were faster. [But] it’s so much faster than it was like 4 years ago.”
Not only does Nvidia not have enough chip supply to meet demand from its largest customers, but major cloud service providers Amazon, Microsoft, Alphabet, and Oracle, as well as startups such as CoreWeave, do not have enough GPU or custom silicon supply to meet enterprise and rental demand in the cloud and simultaneously utilize GPUs for internal AI R&D and product development.
The CSPs also do not have nearly enough infrastructure to support demand, especially as demand rises as the industry shifts towards real-time use cases. Shifting from today’s world of model development and training to inference, where these AI models will make predictions and draw conclusions in real-time on new data, still requires massive amounts of AI accelerators and infrastructure to support it, aside from the millions needed to train larger models.
This is why data center construction is rising so rapidly — capacity under construction in North America soared more than 70% YoY to 3.87 GW in the first half of 2024. For comparison, construction in all of 2023 totaled less than 3.1 GW.
Putting this all together, Big Tech is estimated to spend north of $210 billion of capex this year, predominantly for AI accelerators and infrastructure, with cumulative spending projected to surpass $700 billion by 2027. Nvidia’s GPU supply still lags behind demand, while Big Tech is working to build data centers as quickly as possible to house these millions of future GPUs.
While $700 billion in three years is a massive sum, one that has sparked fears of an inability to generate enough of an ROI to justify such spending, productivity gains are already arising not even two years after AI’s big spark, and the long-term economic growth potential from AI-enabled productivity gains is as much as $3.5 trillion per current projections. AI spending is not set to slow, and Big Tech has left many breadcrumbs pointing out exactly why they’ll continue to spend heavily on AI.
Conclusion
Communacopia was ripe with information about the current and future state of AI and what to expect as the industry emerges from its nascent stages of growth to an expected multi-trillion-dollar economic force. Big Tech’s executives see that the AI industry is moving much faster than anything before, with physical data center buildouts speeding up to meet both demand and infrastructural upgrades to handle more powerful and power-hungry GPUs.
While Wall Street debates on if AI is a bubble, we think it’s wise to closely track what highly successful management teams are saying about AI and why it’s a trend to not miss or ignore. At this time, it’s nearly unanimous among tech CEOs that AI offers investors a rare opportunity to get onboard in the early stages of one of the largest economic and transformational trends in tech.
Visit us at io-fund.com and sign up for our free newsletter covering tech stocks weekly.
