It is not hard to find estimates of investment in U.S. artificial intelligence infrastructure (computing capabilities) in the range of $300 billion or more between 2023 and 2030. IDC analysts have suggested $300 billion in investments between 2023 and 2026.
Nor is it hard to find critics who worry about uncontrolled spending without a clear revenue model. On the other hand, leaders of firms attempting to become leaders in the generative AI model business are likely to keep in mind the “winner take all” dynamic we have seen in the recent internet era, where just one or a few firms emerged as leaders in new markets.
They might point to:
Amazon's years of heavy investment to dominate e-commerce
Google's massive spending to establish search leadership
Cloud providers' huge datacenter investments
Meta's acquisition strategy in social media.
In fact, many markets show scant ability to support three providers, as the market leader has twice the share--and up to an order of magnitude more-share compared to the number-two provider.
Market | Dominant Player | Market Share | Runner-up | Market Share |
Search Engines | Google | 91.9% | Bing | 3.0% |
Desktop Browsers | Chrome | 65.72% | Safari | 18.22% |
Mobile Browsers | Chrome | 66.17% | Safari | 23.28% |
Social Media | Facebook | 2.9B users | YouTube | 2.5B users |
E-commerce | Amazon | 37.8% (US) | Walmart | 6.3% (US) |
Video Streaming | YouTube | 2.5B users | Netflix | 231M subscribers |
Music Streaming | Spotify | 31% | Apple Music | 15% |
Ride-hailing (US) | Uber | 68% | Lyft | 32% |
Cloud Services | AWS | 32% | Azure | 22% |
Mobile OS | Android | 71.8% | iOS | 27.6% |
So even if McKinsey estimates AI infrastructure spending will exceed $500 billion between 2023 and 2030, and even if many of those investments do nor produce the expected results, model suppliers have incentives to risk quite a lot, knowing that there is a small prize for being second best.
Gartner forecasts global AI infrastructure investments will surpass $250 billion annually by 2030.
The OECD estimates investments in AI infrastructure across industries, will reach $1 trillion by 2030, across the OECD countries. Bloomberg predicts that the global AI infrastructure market will $700 billion by 2030.
On the other hand, most of that investment will be by end users and others in the value chain, not the generative AI model providers.
And some estimates made in 2023 might be considered conservative in 2024. Morgan Stanley’s "The Economics of AI” study, published in October 2023 suggested more than $200 billion in AI infrastructure investments by 2030, including:
Boston Consulting Group in December 2023 suggested there would be $235 billion cumulative investments in
Data center buildout: 45%
Compute infrastructure: 35%
Power infrastructure: 20%.
The Goldman Sachs "AI Infrastructure Report," published in September 2023 estimated $275 billion in cumulative investment, including:
The caution, though, is that early estimates of the size of new technology markets often lead to overinvestment across the value chain.
Study/Report | Date | Publisher | Key Conclusions |
The Dot-Com Bubble Burst: Causes and Implications | 2001 | U.S. Securities and Exchange Commission (SEC) | Overinvestment in internet startups led to a speculative bubble that burst in 2000. Many companies were overvalued despite having no profitability. |
Boom and Bust: The Telecommunications Investment Bubble | 2002 | Federal Reserve Bank of San Francisco | Overinvestment in telecom infrastructure during the late 1990s led to a major industry downturn, with unsustainable levels of capital spending. |
The Case for Less Innovation | 2017 | Harvard Business Review | Many companies overinvest in unproven technologies without clear demand, resulting in failed projects and wasted resources. |
Lessons from the Clean Tech Bubble | 2016 | MIT Energy Initiative | Overinvestment in cleantech (2005-2011) led to massive failures, with many companies being too early to market and receiving excessive venture capital. |
Investing in Innovation: Creating a Research and Innovation Policy That Works | 2010 | The NESTA Foundation (UK) | Over-investment in R&D for new technologies can create inefficiencies and fail to produce proportional economic benefits if not managed strategically. |
The Nanotechnology Investment Bubble | 2005 | Journal of Nanoparticle Research | Speculative investments in nanotechnology during the early 2000s led to unmet expectations, as many products were not commercially viable. |
Unleashing Productivity: Overinvestment in Information Technology | 2005 | McKinsey Global Institute | Overinvestment in IT during the late 1990s and early 2000s did not yield expected productivity gains, with firms often adopting technology prematurely. |
The Illusions of Overinvestment in AI | 2021 | Brookings Institution | Many companies overinvest in artificial intelligence without clear applications, leading to inflated expectations and unrealized returns. |
The Biotechnology Bubble: When Science and Finance Collide | 2004 | Nature Biotechnology | Excessive capital flow into biotech during the 1990s led to overvaluation, with many firms failing to achieve meaningful breakthroughs. |
In recent years we have also seen examples of overinvestment by many platform suppliers as well.
Technology | Company/Industry | Year | Description of Over-Investment |
Artificial Intelligence | IBM Watson | 2011-2022 | IBM invested billions in Watson AI for healthcare, but struggled to generate significant revenue and ultimately sold off the health assets |
Virtual Reality | Meta (Facebook) | 2014-present | Meta has invested over $36 billion in VR/AR technology with limited returns, facing skepticism about the metaverse vision |
Blockchain | Various | 2017-2018 | Many companies rushed to invest in blockchain during the crypto boom, only to scale back or abandon projects when the hype died down |
Autonomous Vehicles | Uber | 2016-2020 | Uber invested heavily in self-driving technology, spending over $1 billion before selling the unit after a fatal accident and regulatory challenges |
3D Printing | 3D Systems | 2013-2015 | The company aggressively acquired 3D printing startups, leading to over $1.3 billion in losses and a stock price crash when consumer adoption didn't materialize |
Cloud Computing | HP | 2011-2012 | HP's $11 billion acquisition of Autonomy for cloud services led to an $8.8 billion write-down |
So the rationale for investing heavily to secure the leading position in the generative AI model business is a reflection of the possible “winner take all” character of application and platform markets, where the number-one provider dominates.
And since market share and profit margin generally are related, the rewards for market leadership also are significant. In many capital-intensive markets, the profit margin of the top provider is double that of number two.
And provider number two can have margins double that of provider number three.
No comments:
Post a Comment