This is a summary of a book titled “The Datapreneurs: The Promise of AI and the Creators Building Our Future” written by Steve Hamm and Bob Muglia and published by Peakpoint Press, 2023. This book examines how artificial intelligence and data-driven systems are reshaping the economy, technology, and society. The authors argue that the world is entering a period in which intelligence, labor, and energy—the three foundational forces of the modern economy—are all becoming cheaper due to technological advances. Artificial intelligence, particularly the development of artificial general intelligence and the possibility of artificial superintelligence, has the potential to add intelligence to nearly every device and application. At the same time, progress in renewable and advanced energy technologies may reduce the cost of electricity, while robotics could significantly lower the cost of certain kinds of labor. Together, these shifts point toward a profound economic transformation.

The authors suggest that within the next decade, many of the AI assistants people interact with daily could surpass the level of median human intelligence. As these systems evolve through successive generations, they may become capable of artificial superintelligence, potentially exceeding the combined intellectual capacity of humanity. This development could trigger what has often been described as a technological singularity, a moment when technological progress accelerates beyond human prediction or control. Such a shift could compress centuries of scientific and economic advancement into a much shorter time span, creating opportunities to address persistent global challenges such as climate change, disease, and poverty. However, the authors emphasize that these outcomes are not guaranteed and depend heavily on how humans choose to guide and govern intelligent machines.

The authors delve into need for ethics and values to shape the relationship between humans and machines. They contrast optimistic visions of a future characterized by abundance and ease with darker, more dystopian possibilities in which powerful machines generate fear or inequality. To avoid harmful outcomes, they argue for the creation of a new social contract that defines how intelligent systems should behave once they exceed human capabilities. Because advanced machines will increasingly make decisions and take actions independently, the values embedded in their design will play a decisive role in shaping their impact. Establishing ethical frameworks is therefore not an abstract concern but a practical necessity for long-term human and machine collaboration.

The book places current developments in artificial intelligence within a longer historical context by tracing the evolution of data management technologies. Relational databases are presented as a foundational breakthrough that made modern data-driven computing possible. Earlier systems relied on rigid hierarchical or network-based structures that were difficult to update and scale. The relational model, developed by IBM researcher Ted Codd in 1970, introduced a more flexible way to organize data, allowing relationships to be defined mathematically rather than hard-coded into applications. The introduction of SQL and the commercialization of relational databases by companies such as IBM, Oracle, and Sybase helped make data more accessible and adaptable for organizations of all sizes.

Microsoft’s role in expanding access to data management is highlighted as a key moment in the democratization of computing. The company’s emphasis on making information readily available, combined with the release of more affordable and user-friendly database systems such as SQL Server 7.0, lowered barriers for smaller businesses that previously lacked access to enterprise-level data tools. By reducing costs and simplifying maintenance, Microsoft helped bring advanced data processing capabilities beyond large corporations and into the broader economy.

As data volumes grew, the book explains, new infrastructure became necessary to support machine learning and AI systems. Cloud-based data platforms and pipelines now allow organizations to store, process, and move massive amounts of structured and unstructured data. These pipelines function as connective tissue, transferring data into centralized repositories where it can be used to train AI systems. In this framework, future AI assistants will increasingly learn from data warehouses and data lakes, drawing insights from continuous streams of information rather than static datasets.

The authors also describe the emergence of data applications, which differ from traditional software by responding directly to changes in data rather than user commands. Powered by relational knowledge graphs and predictive models, these systems can automate routine decisions and actions. As a result, many administrative tasks may be handled by machines, allowing people to focus on analysis, strategy, and creative problem-solving. This shift extends to autonomous systems such as drones and self-driving vehicles, which require databases capable of synchronizing data rapidly across networks to ensure safety and coordination.

The book further explores the importance of programming languages in the evolving data ecosystem, particularly the rise of Julia. Designed to address inefficiencies in data science workflows, Julia enables high-performance computing without requiring developers to rewrite code in lower-level languages. Its support for automatic differentiation makes it well suited for building predictive models and neural networks, and it is already being used in fields ranging from finance to climate science.

Finally, the authors turn to foundation models, large-scale AI systems trained on vast datasets that exhibit emergent capabilities. These models can be adapted for a wide range of tasks, from writing text to generating images and assisting with software development. Powered by neural networks, such systems can sense, learn, reason, plan, adapt, and act with increasing autonomy. As these capabilities advance, the authors argue that computer scientists and society as a whole must prepare for a future in which machines generate long-term plans and predictions. The book concludes that while superintelligent systems hold enormous promise, their impact will ultimately depend on the values and responsibilities humans choose to embed within them.