According to S&P Global, 2024 will be the year of AI 'app makers'. Foundational models such as large language models (LLMs) have dominated recent debates. But, investors are now increasingly focused on companies developing AI applications that provide tangible benefits for specific use cases. In fact, according to data from S&P Global Market Intelligence and 451 Research, AI companies without their foundation model attracted more than twice as much investment in the first quarter of 2024 as compared to the same period last year.
One of the most exciting promises of AI is its potential to save workers time. But, for AI to make a meaningful impact, businesses need AI tools that are tailored to specific industries or job roles. At the same time, these tools must be reliable and trustworthy. Yet, while AI chatbots built on LLMs can communicate well and offer general advice, they often lack specialized knowledge or tools. This makes them prone to errors or illusions due to the wide range of their training data. This is where more targeted tools, fine-tuned for their specific use cases, are more likely to provide reliable and accurate results.
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Consider the upcoming Olympics to illustrate this point. The Foundation Models are like the core qualities of a good Olympian, representing fitness, dedication, and the unwavering pursuit of excellence. However, the Olympics offer 32 sports with more than 400 different events, each requiring different skills and experience — such as diverse industries and job roles in society. And, while AI provides the foundational technology that will power a variety of products and services, each of these individual products needs to be mastered with the appropriate skills to deliver value for their specific use case. .
It is rare for an athlete to compete in multiple different sports or disciplines at the Olympics. Each player is highly specialized for their particular sport. For example, a sprinter optimizes his strength and body to accelerate powerfully and over short distances. However this means they are not suitable for other disciplines, such as long distance running. Today's most prominent AI chatbots are all-rounders. They are designed to capture general global knowledge across a wide range of topics. A given chatbot may be able to provide surface-level information on a wide range of topics, but may not excel at more specific tasks.
Take the AI-powered universal search tool for example. It needs to be able to find and retrieve accurate information quickly. Like a sprinter running the 100m dash, each time it performs it is optimized to save crucial seconds. However, there are other tasks that may require AI designed for sustained performance over long periods of time, such as long-distance runners. For example, predictive AI models in business forecasting need to learn the activity patterns of each business by analyzing historical data, building this knowledge with use over time.
By mastering the operations of a business it can provide predictions about the company's future trajectory based on past results. Predictive AI models need to constantly adjust predictions based on changes in operations and external business factors. But recent research from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) shows that multiple large language models working together provide more accurate results, perhaps leading to the emergence of a new type of AI ecosystem. .
Is the future of AI a decathlete or a team of special athletes?
As we look at the trajectory of the AI ecosystem, we can see two distinct paths the industry could take. The first is a race to build the best general-purpose AI model. This AI system will perform at a high level in a variety of tasks, such as a decathlete being able to compete in events ranging from running to long jump and pole vault. The benefit of this approach will be a seamless employee experience that streamlines workflow. However, like a decathlete, who cannot match the performance of an expert in a single event, a generic AI model may struggle to achieve the same level of excellence as more focused tools.
An alternative path sees the future AI ecosystem as a network of specialist AI products, more like a team of specialist players. In this model, each AI specializes in a specific domain, just as individual players focus on specific sports. This approach mirrors how an Olympic team combines the talents of sprinters, swimmers and gymnasts to maximize the collective medal potential for their country. Specialization ensures that each AI excels within its domain, often surpassing the capabilities of general-purpose systems. However, the success of this network approach will require sophisticated coordination and collaboration to create a seamless experience for customers.
As we try to predict how future AI ecosystems will evolve, we can look to this summer's Olympics in Paris for a glimpse of two possible paths. Whether we end up with a decathlete-style general-purpose AI tool or a network of tools that resemble a team of specialized players will depend on business goals and decisions in the collective technology industry. As each country will have different goals for going to the Olympics.
From strategically focusing on a skill to improve the odds of winning, to a broader approach to winning more gold medals in more disciplines, every business that implements an AI ecosystem Will depend a lot on their own unique goals. For some businesses, growth by gaining market share in a fluid market will require speed and agility, while customer retention in a stagnant market will require long-term planning.
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