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Normalization Engines
Throughout history, technological and business innovations have repeatedly transformed industries by leveling the playing field, democratizing access, improving efficiencies of scale, and reducing the cost and burden of providing services. This process can be likened to the concept of a “normalization engine”—an orchestrating force that standardizes access, lowers barriers to entry, and creates a more inclusive and efficient market environment. The earliest forms of this phenomenon can be traced to brick-and-mortar companies, while more modern examples are seen in the digital and AI-driven world, reshaping the global economy in profound ways.
One of the earliest and most significant examples of a normalization engine was Walmart. Long before the tech giants of Silicon Valley redefined how we live and work, Walmart’s big-box retail model transformed the United States’ commercial and social landscapes. By implementing a highly efficient supply chain and centralized distribution system, Walmart dramatically lowered the cost of goods, making everyday products more affordable and accessible to millions.
Walmart’s impact extended far beyond just providing cheaper goods. Its strategic placement of stores within miles of both metropolitan and suburban areas effectively reshaped the American way of life. As Walmart stores sprouted across the country, they influenced where people chose to live, work, and shop. The ease of access to a one-stop-shop not only altered consumer behavior but also created new economic centers in suburban and rural areas. By using logistics, scale, and data-driven supply chain management, Walmart became a democratizing force, setting a precedent for how centralized, orchestrating systems could change industries and societies.
Modern-Day Engines of Normalization: Uber and Zoom: Fast forward to the 21st century, and the concept of the normalization engine has taken new forms. Consider Uber, a platform that effectively turned personal transportation into a service available to almost anyone with a smartphone. By connecting riders and drivers, Uber eliminated the need for individuals to own cars or rely on traditional taxi services. In cities where public transportation is lacking, Uber made movement more fluid and accessible, breaking down the complexities of travel and migration. This shift democratized personal transportation, allowing people greater mobility, freedom, and flexibility in choosing where to live and work.
Similarly, Zoom disrupted traditional communication norms by enabling easy, low-cost, and often free video conferencing for individuals and businesses alike. While video communication platforms existed before Zoom, they were often cumbersome, expensive, or difficult to scale. Zoom simplified this process, offering a user-friendly platform that became a lifeline during the COVID-19 pandemic. Suddenly, connecting with family, friends, colleagues, and even medical professionals was as easy as clicking a link. This democratization of communication not only bridged physical distances but also reshaped industries—accelerating the adoption of remote work, telemedicine, online education, and global collaboration.
Normalization Engines Beyond Products: Systematic Economic Changes: Some normalization engines do not sell a specific product but instead create systemic efficiencies that drive economic and social benefits. These engines, often unseen, operate behind the scenes to streamline industries, reduce costs, and democratize access. A prime example of such an orchestrating force is the advent of generative AI, which is accelerating transformations across various sectors, unlocking potential in even the largest and most complex parts of the global economy. Its ability to automate complex tasks, synthesize vast amounts of data, and provide intelligent, customized outputs at a fraction of the cost of traditional methods has made its impact profound.
In coding, AI agents are revolutionizing software development by writing, debugging, and optimizing code autonomously. These agents can assist developers by generating large portions of code based on simple input or even directly fixing errors in real time. The result is a significant reduction in development timelines and costs, allowing smaller firms and individuals to compete with larger, more resource-rich organizations. This democratization of coding capabilities is lowering the barrier to entry in tech, empowering startups and developers around the world to bring innovations to market more rapidly and efficiently. Sectors including financial services, healthcare, manufacturing, oil & gas, and logistics are industries which are ripe to benefit from large-scale efficiency gains. Given their contribution to global GDP, we view the opportunity as highly deflationary, creating cheaper and more accessible products for all. There are various companies making major contributions to this revolution including agentic companies like Cognition, copilot companies like GitHub, and code editors like Cursor amongst others.
The impact in industry can also be enormously valuable. Take the multi trillion-dollar mortgage industry as an example. Often characterized by complex, costly, and time-consuming processes, access to mortgage loans often involves numerous steps, from application and underwriting to document review and closing. Historically, this process has been labor-intensive, riddled with paperwork, and burdened by inefficiencies.
Enter companies like Vesta (a Conversion portco), a new orchestration engine built leveraging modern software principles and AI. Vesta acts as a Loan Origination System (LOS) that automates many of the manual tasks involved in mortgage origination, significantly driving down costs and turnaround times. By streamlining processes and reducing human intervention, Vesta not only improves efficiencies for lenders but also enhances the borrowing experience for consumers, making home ownership more accessible. This systematic change represents a shift from fragmented, inefficient workflows to an integrated platform that levels the playing field for smaller lenders and consumers alike.
Education is another sector undergoing rapid change due to normalization engines. In the United States, access to quality college counseling has traditionally been limited to those who can afford the high fees associated with private advisors. This has created a gap where students from less affluent backgrounds struggle to navigate the complex college admissions process, impacting their educational and career prospects. Another industry within edtech is homeschooling, where roughly 4 million American families choose to teach their own children, or as we witnessed during Covid, were forced to leverage pods as a new form of education.
Brightroom AI has emerged as a transformative force in this space, leveraging AI to provide affordable, personalized guidance to thousands of students. By automating aspects of university selection, application preparation, essay writing, and scholarship searches, Brightroom democratizes access to educational resources that were once available only to a privileged few. Its platform functions as a virtual counselor, significantly lowering the barriers to college admissions and ensuring that talent, rather than wealth, dictates educational opportunities. In homeschooling, companies like Astral (a Conversion portco) are putting powerful educational tools in the hands of parents for a fraction of the cost of building an end-to-end edtech curriculum provider powered by AI. Astral initially targets the 4 million+ homeschooled Americans and their parents, who struggle to identify excellent curricula for their children and adhere to homeschooling compliance standards.
Normalization Engines: Broadening Access and Driving Systemic Change. The common thread across these examples — Walmart, Uber, Zoom, Cognition, Vesta, Astral, and Brightroom —is that they act as orchestration engines that not only democratize access but also drive systemic change. By simplifying complex processes, reducing costs, and improving efficiencies, they level the playing field across industries, making services and products accessible to a broader audience.
As generative AI continues to mature, its role as a normalization engine will only expand. By automating complex tasks, optimizing decision-making, and personalizing services, AI-driven systems can reduce operational costs and widen access to crucial resources, be it in finance, healthcare, education, or logistics. The shift towards such engines signifies a fundamental change in how industries operate: from reliance on manual, resource-heavy processes to integrated, AI-powered platforms that enable more equitable access to goods and services.