Productivity Miracle
We explore how exponential growth in computing power has created remarkable value for consumers, businesses, and investors. What could be the next major productivity driver?
Earnings growth is primarily driven by productivity improvements. We believe nowhere has productivity growth been greater than in the semiconductor industry, where computing power has grown exponentially. For our 400th Alger On the Money, we reflect on how this remarkable progress has created tremendous value for consumers, businesses, and investors. What might be the next long-term productivity marvel?
- The incredible progress in the efficiency of the microchip and the resulting increase in computational power has been a key driver behind the digital transformation of our economy. As can be seen in the chart above, since 1939, when the first programmable computer was built, there has been a 20 quadrillion-fold increase in the number of computations per second that can be purchased with an inflation-adjusted dollar.
- The drastic decline in the cost of computation has played a significant role in driving deflation within the economy. As an example, over the past 20 years, computer prices have fallen 95%, when adjusted for quality factors such as processor speed, RAM, and hard drive storage, according to the Bureau of Labor Statistics.
- What could be the next driver of exponential growth? Artificial intelligence (AI) training, which directly influences the sophistication of AI models, is advancing over 10 times faster than the computational growth of the microchip!1 As detailed in our AI: Exploring Tomorrow’s Potential Leaders Today web module, we believe the incredible improvement in AI models could potentially generate a new era of investment opportunities.
1Source: Epoch AI, OpenAI, and Alger. The calculation of AI’s growth rates compares the training computation between Dropout (2012) and GPT-4 (2023), based on PetaFLOPS /day per second. PetaFLOPS is a unit of computing speed, equal to one quadrillion FLOPS (floating operations per second) and serves as a measure of computer performance. Dropout is a regularization technique for reducing overfitting in deep neural networks and was proposed by Hinton et al. in their paper “Dropout: A Simple Way to Prevent Neural Networks from Overfitting” in 2012. Generative Pre-trained Transformer 4 (GPT-4) is an autoregressive language model released in 2023 that uses deep learning to produce human-like text, developed by OpenAI. This calculation should be considered a rough estimate.
The views expressed are the views of Fred Alger Management, LLC (“FAM”) and its affiliates as of November 2024. These views are subject to change at any time and may not represent the views of all portfolio management teams. These views should not be interpreted as a guarantee of the future performance of the markets, any security or any funds managed by FAM. These views are not meant to provide investment advice and should not be considered a recommendation to purchase or sell securities.
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Companies involved in, or exposed to, AI-related businesses may have limited product lines, markets, financial resources or personnel as they face intense competition and potentially rapid product obsolescence, and many depend significantly on retaining and growing their consumer base. These companies may be substantially exposed to the market and business risks of other industries or sectors, and may be adversely affected by negative developments impacting those companies, industries or sectors, as well as by loss or impairment of intellectual property rights or misappropriation of their technology. Companies that utilize AI could face reputational harm, competitive harm, and legal liability, and/or an adverse effect on business operations as content, analyses, or recommendations that AI applications produce may be deficient, inaccurate, biased, misleading or incomplete, may lead to errors, and may be used in negligent or criminal ways. AI companies, especially smaller companies, tend to be more volatile than companies that do not rely heavily on technology. Investing in innovation is not without risk and there is no guarantee that investments in research and development will result in a company gaining market share or achieving enhanced revenue. Companies exploring new technologies may face regulatory, political or legal challenges that may adversely impact their competitive positioning and financial prospects. Developing technologies to displace older technologies or create new markets may not in fact do so, and there may be sector-specific risks. There will be winners and losers that emerge, and investors need to conduct a significant amount of due diligence on individual companies to assess these risks and opportunities.
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