According to S&P Global, 2024 will be the year of AI “app makers.” Foundation models such as large language models (LLMs) have dominated recent discussions. But now investors are increasingly turning their attention to companies developing AI applications that deliver tangible benefits for specific use cases. In fact, AI companies without their own foundation models attracted more than double the amount of investment in the first quarter of 2024 compared to the same period last year, according to data from S&P Global Market Intelligence and 451 Research.
One of the most exciting promises of AI is its ability to save employees time. But for AI to have a meaningful impact, businesses need AI tools that are tailored to specific industries or functions. At the same time, these tools must be reliable and trustworthy. While AI chatbots built on LLMs can communicate well and provide general advice, they often lack the specialized knowledge or tools required. This makes them susceptible to inaccuracies or hallucinations due to their wide range of training data. This is where more targeted tools, tailored to their specific use cases, are more likely to deliver reliable and accurate outputs.
Senior Director, New Product Solutions at Dropbox.
To illustrate this point, think about the upcoming Olympics. Foundation models are like the core attributes of a good Olympian, representing fitness, dedication, and an unwavering pursuit of excellence. However, the Olympics encompass 32 sports with over 400 different events, each requiring different skills and experience, just like the diverse industries and roles within society. And while AI provides the core technology that will power various products and services, each of these individual products must be specialized with the right skills to provide value for their specific use case.
It is rare for a single athlete to compete in multiple sports or disciplines at the Olympics. Each athlete is highly specialized in their specific sport. For example, a sprinter optimizes their strength and physique to be powerful and fast over short distances. However, this means that they are not suited for other disciplines, such as long-distance running. Today’s most prominent AI chatbots are all-rounders. They are designed to have general world knowledge on a wide range of topics. A given chatbot may be able to provide superficial information on a wide range of topics, but may not excel at more specific tasks.
Take an AI-powered universal search tool, for example. It needs to be able to find and retrieve the right information quickly. Like a sprinter running the 100m dash, it’s optimized to save crucial seconds in each run. However, there are other tasks that require an AI that’s designed to perform consistently over time, more like the long-distance runner. For example, predictive AI models in business forecasting need to learn the patterns of each company’s operations by analyzing historical data and building that knowledge over time.
By specializing in business operations, it can make predictions about the future direction of the company based on past outcomes. Predictive AI models must also continually adjust predictions based on ongoing changes in business operations and external business factors. But with recent research from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) showing that multiple large language models working together produce more accurate results, a new type of AI ecosystem may be on the way.
Is the future of AI a decathlete or a team of specialized athletes?
When we look at the direction of the AI ecosystem, we see two distinct paths the industry can take. The first is a race to create the best general-purpose AI model. This AI system would perform at a high level in a variety of tasks, such as a decathlete who can compete in a variety of events, from sprinting to long jump and pole vault. The benefit of this path would be a seamless employee experience that streamlines workflow. However, like the decathlete, who may not be able to match the performance of the specialist in a single event, a general-purpose AI model may struggle to achieve the same level of excellence as more focused tools.
The alternative path sees the future AI ecosystem as a network of specialized AI products, more like a team of specialized athletes. In this model, each AI specializes in a particular domain, much like individual athletes focus on specific sports. This approach mirrors how an Olympic team combines the talents of sprinters, swimmers, and gymnasts to maximize their collective medal potential for their country. The specialization ensures that each AI performs optimally within its domain, often exceeding the capabilities of a general system. However, the success of this networked approach requires advanced coordination and interoperability to create a seamless experience for users.
As we try to predict how future AI ecosystems will evolve, we can look to the Paris Olympics this summer for a glimpse of two possible paths. Whether we end up with a general AI tool for decathletes or a network of tools that resemble a team of specialized athletes depends on the goals and decisions of the companies in the collective technology industry. Just as every country will have different goals at the Olympics.
From strategically focusing on a specialty, to optimizing the chance of a win, to a broader approach to winning as many gold medals as possible in as many disciplines as possible, the type of AI ecosystem each company implements will depend heavily on their own unique objectives. For some companies, growing through market share in a fluid market will require speed and agility, while retaining customers in a stagnant market will require a more strategic, long-term plan.
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