Essential AI Reports for Business Leaders: Mid 2024 edition
The latest AI insights from Goldman Sachs and JPMorgan. Uncover the key statistics, market implications, and strategic advice for leveraging AI in your business
Greetings, esteemed readers and AI enthusiasts. Welcome back to our ongoing series on Essential AI Reports for Business Leaders. As we further navigate the landscape of artificial intelligence, it's crucial to stay informed about the latest developments and their implications for the business world.
In our previous episode, we read together the Stanford AI Index Annual Report 2024, a comprehensive 502-page document that provided invaluable insights into the global impact of AI.
Today, we'll be expanding our focus to include additional perspectives from other authoritative sources, namely Goldman Sachs Research and J.P. Morgan, and as a bonus – FutureSearch.
These reports offer a nuanced view of the AI landscape from the big money perspective, highlighting both the potential and the current realities of AI adoption and impact. Now, let's dive into the key findings from new reports and what they mean for business leaders like you.
Bulls, Bears, and Indecision
Before we start, let's take a quick look at the reports we'll be discussing and their overall sentiments:
Goldman Sachs Report #1 (May 13): The prospects for AI in the economy are "very positive". Goldman Sachs stakes its claim as the leading bull in AI markets.
JPMorgan Report (May 23): AI's prospects are "big and shining". JPMorgan tries to nudge Goldman Sachs off its bullish perch.
Goldman Sachs Report #2 (June 25): Plot twist! The prospects of AI "are not at all brilliant and are greatly overrated". Goldman Sachs suddenly turns bearish.
After reading all three reports, it becomes clear that they have only a tenuous relationship to investment analytics. They seem more like attempts by bulls and bears to sway the market, thinly disguised as analytical reports.
The real kicker is that it looks like Goldman Sachs and JPMorgan can't decide who gets to be the bull and who's stuck being the bear. You know, I'm starting to think these big banks are less like the bulls and bears of Wall Street and more like a couple of indecisive pandas at the zoo. Make up your minds, folks!
Now that we've set the stage with this financial fable, let's dig into what these reports actually tell us about the state of AI adoption and its potential impact on the business world.
AI by the Numbers: Key Stats and Predictions
Speaking of potential impact – numbers always work best – it's crucial to always ground our understanding in concrete data. All three reports we examine today offer a good statistics and forecasts, which helps us to paint a vivid picture of AI's current state and potential future. Let's take look at some of the most telling numbers:
5% – The proportion of companies currently using generative AI in regular production. However, this figure is expected to rise significantly in the next six months.
47 GW – The estimated new power generation capacity the US will need by 2030 to support growing data center demand, highlighting the massive infrastructure challenges ahead.
2H24 (the second half of the 2024) – The timeframe through which chip supply constraints are expected to dictate AI chip shipments, potentially limiting AI growth in the near term;
100 million - The number of users Baidu's generative AI chatbot has reportedly reached, rivaling ChatGPT's 180 million and underscoring China's competitiveness in the AI race;
0.1% – The current difference in unemployment rates between jobs highly exposed to AI and those less exposed, suggesting limited job displacement so far, meaning AI hasn’t resulted in any significant job loss yet;
21% – The peak S&P 500 Return on Equity (ROE) that, combined with other favorable conditions, could lead to above-average stock market returns in an optimistic AI scenario.
The AI Debate – Skeptics vs. Optimists
So why did one of the big institutions, Goldman Sachs, suddenly turn from a positive outlook on AI to a skeptical or even bearish stance? In fact, they are not really bearish, but they have started to shift their view significantly to offer both perspectives: bearish and bullish. Their latest report showcases a stark divide between AI skeptics and optimists. Let's break down their key arguments:
Skeptics' View:
MIT Professor Daron Acemoglu estimates that only a quarter of AI-exposed tasks will be cost-effective to automate within the next 10 years, implying that AI will impact less than 5% of all tasks;
There are estimates that AI will increase US productivity by only 0.5% and GDP by 0.9% cumulatively over the next decade;
Jim Covello, Head of Global Equity Research at Goldman Sachs, questions whether AI can solve complex problems efficiently enough to justify the estimated $1 trillion investment;
Covello doubts AI will significantly boost company valuations, as efficiency gains may be quickly eroded by competition;
Optimists' Perspective:
Joseph Briggs, senior global economist at Goldman Sachs, projects a 9% increase in US productivity and a 6.1% GDP boost over the next decade;
Briggs anticipates AI will automate 25% of all work tasks and expects workforce redistribution and new task creation;
Kash Rangan and Eric Sheridan, GS analysts, remain enthusiastic about AI's long-term transformative potential, even without an immediate "killer application";
They argue that the current investment cycle is more promising than previous ones, led by well-capitalized incumbents with extensive distribution networks;
Overall, the last Goldman Sachs report present a balanced view with both skepticism and optimism about AI's potential impact. Skeptics highlight the high costs and limited immediate benefits of AI, questioning its ability to significantly enhance productivity and GDP in the near term.
On the other hand, optimists point to positive early signals and long-term transformative potential, suggesting that strategic investments and continued technological advancements could eventually lead to substantial productivity gains and economic growth. It's clear that the AI debate is far from settled. Well, we will see who was right.
Market Implications and Sector Spotlight
Despite the conflicting views on AI's long-term impact, the market is responding to the AI theme in interesting ways:
Ryan Hammond, senior US equity strategist at Goldman Sachs, sees room for the AI trade to continue running in the stock market;
Hammond identifies four phases of the AI trade:
Focus on Nvidia (currently underway);
AI infrastructure companies (semiconductors, cloud providers, data centers);
Companies that can easily incorporate AI into existing products;
Companies with the biggest potential earnings boost from widespread AI adoption;
An unexpected beneficiary: Utilities
Carly Davenport and Alberto Gandolfi, GS utilities analysts, project a significant increase in power demand due to AI and data centers;
US electricity demand is expected to rise at a 2.4% compound annual growth rate from 2022-2030, with data centers accounting for about 90 basis points of that growth;
This surge in demand could require substantial investments in power infrastructure, benefiting utility companies;
Long-term equity returns under different AI scenarios:
Christian Mueller-Glissmann, senior multi-asset strategist at GS, analyzed potential S&P 500 returns under various AI impact scenarios;
His analysis suggests that only the most favorable AI scenario – with significant boosts to trend growth and corporate profitability without raising inflation – would result in above-average long-term S&P 500 returns.
Future Mixed Perspective
As we process these varying perspectives, several key points emerge:
The AI investment landscape is marked by significant disagreement among experts, even within the same institutions;
The potential impact of AI on productivity and GDP growth remains a contentious issue, with estimates varying widely;
Infrastructure challenges, particularly in power supply, may play a crucial role in determining AI's growth trajectory;
The market is already responding to AI potential, but the benefits may extend beyond just AI-focused companies to include unexpected sectors like utilities;
FutureSearch AI: A Closer Look at OpenAI's Revenue
Moving beyond the fluctuating sentiments of investment banks, we now turn our attention to another interesting report recently published by FutureSearch AI. This report tried to provide estimates of OpenAI's financial landscape, offering some projections into the company's revenue streams.
In particular, FutureSearch AI sought to determine whether OpenAI still has room to grow or if the widespread adoption of its chatbot services is nearing saturation.
As of June 12, 2024, their analysis estimates OpenAI's annualized revenue (ARR) as follows:
ChatGPT Plus: $1.9 billion (7.7 million global subscribers)
ChatGPT Enterprise: $714 million (1.2 million seats)
API: $510 million
ChatGPT Team: $290 million (980,000 seats)
Key takeaways from this report:
Sam Altman's claim of $3.4 billion ARR in June appears plausible, contrary to initial skepticism;
Consumer-facing applications are currently more significant revenue drivers for OpenAI than enterprise solutions;
However, the high growth rate in the enterprise sector suggests this balance might shift rapidly;
The API, while substantial, contributes less to revenue than OpenAI's direct-to-consumer and enterprise products;
This revenue breakdown reveals that even a frontrunner like OpenAI has substantial room for growth. With only 7.7 million subscribers for ChatGPT Plus out of billions of internet users worldwide, the potential for expansion is significant. The enterprise segment, while smaller, is showing rapid growth, indicating untapped opportunities in the business market.
Third-party AI applications built on OpenAI's technology are still in early stages. As more developers and companies integrate these capabilities into their products, this revenue stream could see explosive growth.
These estimate figures demonstrate that we're still in the early phases of AI adoption. For businesses watching OpenAI's trajectory, the message is clear: there's still time to develop and implement AI strategies that can capture a share of this expanding market. The key, again, lies in identifying specific use cases where AI can add value, much like OpenAI has done with its suite of products targeting different user segments.
The AI Strategy to Unlock Value for Your Business
While reading reports from major financial institutions and tech giants can be interesting and insightful, it often leaves smaller companies and regular businesses wondering how to apply these insights to their own operations. The strategies and analyses provided by these big players can sometimes feel distant or overly complex for those not operating on the same scale.
This brings us to our next focus: For regular companies, the critical challenge still lies in identifying specific areas where AI adoption can be truly beneficial.
As we've seen from these reports, even major financial institutions and tech giants are grappling with this issue. The struggle is real and widespread.
Finding the right AI levers isn't just about implementing the latest technology – it's about strategically integrating AI in ways that genuinely enhance operations, customer experiences, or product offerings. This might involve:
The key to impactful AI adoption lies in utilizing private, company-owned data rather than relying on generic AI solutions;
Simple implementations like general-purpose chatbots have shown limited impact on business performance;
Companies should focus on:
Identifying unique datasets they possess that could provide competitive advantages;
Developing AI models trained on their proprietary data to solve specific business problems;
Integrating AI into core business processes where it can leverage company-specific knowledge and information;
Creating custom AI applications that directly address their particular industry challenges and customer needs;
The goal is to move beyond off-the-shelf AI solutions to develop tailored applications that truly add value to the business;
This approach requires a deeper understanding of both the company's data assets and the specific areas where AI can make a meaningful difference;
The key is to start small, measure results carefully, and scale successful implementations. It's not about adopting AI for the sake of keeping up with trends, but rather about finding those specific use cases where AI can deliver tangible benefits to your business and customers.
As we've seen from the conflicting reports, there's no one-size-fits-all solution. Each company will need to navigate its own path through the AI landscape, balancing potential benefits against costs and risks. Finally, as a result of this effort, companies can contribute to overall statistics by increasing not only their own revenue but also the overall GDP that major institutions track for.
TLDR;
Despite the varying opinions we've seen from major financial institutions and experts, one thing is clear: generative AI is the future, whether we're ready for it or not. The real challenge lies in our approach to this technology. We need to shift our mindset from forcing technology onto business problems to identifying genuine business needs that AI can address.
This paradigm shift requires a deep understanding of both our business processes and AI's capabilities. It's not about implementing AI for the sake of it, but about finding those critical intersections where AI can truly transform our operations, enhance customer experiences, or create new value propositions.
While we are all struggling with the use-cases, it's crucial to remember that the most significant benefits may not always come from being an AI pioneer. As Goldman Sachs analysts point out, if generative AI is indeed a bubble, it will take considerable time to mature and it won't burst anytime soon. The key beneficiaries in this process may not be the AI enthusiasts themselves, but rather "those who sell picks and shovels to gold miners" – the suppliers of essential infrastructure like electricity and, particularly, equipment such as chips and video cards.
And at this moment, we can't help but glance at the Nvidia capitalization chart, a reminder of how the market is already pricing in this "picks and shovels" narrative.
As we conclude this episode, remember that any technological revolution is not just about the technology itself, but about how we harness it to solve real-world problems and create tangible value. Stay informed and focus on the concrete ways AI can benefit your business and customers. Until next time!
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