Podcast Summary
AI investment: 43% of large US companies plan to invest over $100M in generative AI in the next year, recognizing its benefits while acknowledging ethical considerations
Artificial intelligence (AI) is revolutionizing various industries and aspects of our lives, and companies and consumers are willing to invest significant resources into it despite its early-stage imperfections. AI is enabling new capabilities such as faster business news analysis, investment research, language learning, and idea generation. Large language models, like LLMs, are particularly useful in these areas, providing quick and accurate responses to queries. However, it's important to recognize that AI's reasoning capabilities are not yet superior to humans, and it still requires human oversight and validation. The investment in AI is expected to continue, with 43% of US companies with over a billion dollars in revenue planning to invest at least 100 million in generative AI in the next 12 months. While AI presents numerous benefits, it also comes with risks and ethical considerations, which should be carefully weighed in the investment hype. Overall, AI is a game-changing technology that is transforming the way we work, learn, and live, and its potential is vast.
Generative AI investment in large companies: Large companies invest heavily in generative AI for potential ROI, experimenting with automation, data leverage, and new opportunities, despite high costs and potential risks.
Large companies are investing heavily in generative AI due to its potential for significant returns on investment, despite the high costs. Companies are experimenting with AI to automate processes, leverage proprietary data, and discover new opportunities for cost savings. While there is a risk of overspending or FOMO, many companies believe the potential benefits outweigh the costs. According to Sequoia Capital, the industry spent approximately $50 billion on chips for AI training in 2023, bringing in only $3 billion in revenue, which represents a 6% return. However, this investment is seen as necessary for growth in emerging technology. The comparison was drawn to real estate development, where initial costs may be high, but long-term benefits can be substantial.
AI investment: Companies invest in AI technology as a long-term development due to its expensive nature and potential for significant returns in the future, despite high costs and consumer demand for its 'magic' capabilities.
The development of AI technology, specifically large language models, is a long-term investment for both private and public sectors. Companies like Amazon, Meta, Microsoft, Oracle, and others are treating it like a long-term development, not expecting significant returns in the first few years. This is due to the expensive nature of AI applications, which are much more costly to run than traditional computer applications. The process of using large language models is inferential and involves consulting other databases, serving answers in natural language, and constant training. These processes are more intensive on servers, leading to higher costs. Despite these costs, consumers and companies are willing to pay for the "magic" of AI technology, which offers capabilities beyond what traditional computer applications can provide, even if it's not yet perfect. In many tech adoption cycles, delivering value below cost is a common strategy, and the rapid growth of AI models, particularly in video generation, demonstrates their potential.
Data saturation in AI models: As AI models reach a saturation point, they may learn from each other instead of new data, potentially leading to a plateau in improvement. Solutions include synthetic data, reasoning abilities, and proprietary data sets.
The availability of vast amounts of data has been instrumental in the advancement of AI models. However, as these models continue to improve and approach a saturation point, they may start learning from each other rather than new data, potentially leading to a plateau in improvement. This data limitation could impact consumer-facing frameworks that aim to answer every question, but for specialized industries and smaller language models, the underlying technology continues to progress. Synthetic data, reasoning abilities, and proprietary data sets are some solutions to mitigate this issue. Small language models, which require less data and processing power, are becoming more prevalent and will be integrated into devices, making them smarter and more autonomous. These advancements will shift the focus from data ingestion to thought process and reasoning abilities, allowing AI to evolve without relying solely on vast data sets.
AI use cases: AI is used by companies to enhance user experience, streamline processes, and make accurate predictions through personalized chatbots, co-piloting, and forecasting. Smaller companies can invest in AI for significant returns.
Artificial Intelligence (AI) is being used in various ways by different companies to enhance user experience, streamline processes, and make more accurate predictions. For instance, personalized chatbots like those used by Airbnb and Intuit help users by providing quicker and more efficient solutions than human agents. Intuit, specifically, uses its proprietary consumer data to offer suggestions on managing cash flow more effectively. Co-piloting, as demonstrated by Microsoft, helps users write emails or code more efficiently. Forecasting, as seen in retail, uses data analysis to make better predictions about sales. Companies may not always need high-end GPUs for AI; instead, they focus on creating user-friendly solutions. The future of AI is promising, with more virtual robots expected to be in use than physical ones. These use cases demonstrate the potential for smaller companies to invest in AI for significant returns.
AI relationships: AI technologies, such as the transformer model, are increasingly integrated into industries and workflows, but their potential impact on human relationships, including the development of AI friendships and the commoditization of co-pilots, raises concerns about manipulation and replacement of human connections.
Co-pilot technologies, including AI and machine learning models, are becoming increasingly integrated into various industries and daily workflows, potentially leading to increased efficiency and productivity. However, the potential societal implications, such as the commoditization of co-pilots and the development of AI friendships, raise concerns about the impact on human relationships and the potential for manipulation. Sequoia Capital is bullish on customer support and AI enterprise knowledge in the near-term, and some companies are impressing investors with their significant investments in AI. The transformer model, which underlies large language models, is particularly powerful and magical due to its ability to understand and manipulate data. While some may find AI relationships serviceable, there are societal concerns about the potential for these relationships to replace human connections.
AI and emotional connection: The future of emotional connection may involve a blend of human and AI, but there's a need to balance human interaction with technology and avoid overreliance on AI to maintain essential human connections.
The future of emotional connection and support may involve a blend of human and artificial intelligence, but there are concerns about the potential overreliance on AI and the societal implications. Oracle is one company making strides in AI technology, offering faster and less expensive solutions for businesses. We may be in the trough of disillusionment in the hype cycle for AI, where investors are recognizing the concentrated winners and the importance of focusing on companies with solid technology and business models. However, for smaller companies, the hype may be waning, leading to disillusionment. Nvidia, with its high market cap per employee, is a cyclical company and may experience a sell-off as demand and supply reach equilibrium. Ultimately, the future of AI lies in finding the right balance between human and machine interaction, ensuring that technology enhances our lives rather than replacing essential human connections.
Real Estate Market Diversification: The current real estate market's concentration could lead to potential future diversification and greater returns through successful suburban developments
While investing in real estate can still be a smart move, the current market is highly concentrated. However, this concentration could bode well for the future, especially if developments in suburban areas, like those led by investors like Ricky, prove successful. The success of such projects could help diversify the market and potentially lead to greater returns. It's important to remember that all investments come with risks, and past performance is not a guarantee of future results. Always do your own research and consider seeking advice from financial professionals before making any investment decisions. And, as a reminder, people on the program may have personal interests in the stocks discussed, and The Motley Fool may have formal recommendations for or against certain stocks. So, don't base your investment decisions solely on what you hear on this or any other program.