More… Note: Chart expressed in trillions of real GDP as measured by 2011 ‘GK$’ on a logarithmic scale. GK$ (Gross Knowledge Dollars) is an informal term used to estimate the potential business value Units ~300MM+ Units ~1B+ Units / Users ~4B+ Units Tens of Billions of Units MM Units in Log Scale Technology Compounding = Numbers Behind The Momentum13 AI Technology Compounding = Numbers Behind *As of 4/25, ‘Large-Scale AI Models’ are generally defined as those with a training compute of 1023 FLOPs or greater, per Epoch AI. Source: Epoch AI (5/25) Number of New Large-Scale AI Models (Larger than

of sources and formats, data engineering and architecture teams must design systems that not only scale but also deliver real-time access and insights. However, the complexity isn’t just technical—business limiting accessibility and reducing effectiveness. Scalability & Real-Time Processing – Handling large-scale data efficiently is crucial for real-time analytics, AI-driven decision-making, and intelligent search Real-Time Unified Data Layers—platforms that seamlessly support analytics, search, and AI workloads at scale. These systems break down silos, reduce data sprawl, and deliver timely, actionable insights to power

that of other dynamic languages, and even rivals that of statically-compiled languages. For large scale numerical problems, speed always has been, continues to be, and probably always will be crucial: explain and understand. Fair point. Second, and arguably worse, is that it's bad for programming "at scale." When you see a small piece of code in one place like this, it's quite clear what's going on: s to the existing global variable. This addresses both issues while preserving the "programming at scale" benefits of the 1.0 behavior: global variables have no spooky effect on the meaning of code that

that of other dynamic languages, and even rivals that of statically-compiled languages. For large scale numerical problems, speed always has been, continues to be, and probably always will be crucial: explain and understand. Fair point. Second, and arguably worse, is that it's bad for programming "at scale." When you see a small piece of code in one place like this, it's quite clear what's going on: s to the existing global variable. This addresses both issues while preserving the "programming at scale" benefits of the 1.0 behavior: global variables have no spooky effect on the meaning of code that

that of other dynamic languages, and even rivals that of statically-compiled languages. For large scale numerical problems, speed always has been, continues to be, and probably always will be crucial: explain and understand. Fair point. Second, and arguably worse, is that it's bad for programming "at scale." When you see a small piece of code in one place like this, it's quite clear what's going on: s to the existing global variable. This addresses both issues while preserving the "programming at scale" benefits of the 1.0 behavior: global variables have no spooky effect on the meaning of code that

that of other dynamic languages, and even rivals that of statically-compiled languages. For large scale numerical problems, speed always has been, continues to be, and probably always will be crucial: explain and understand. Fair point. Second, and arguably worse, is that it's bad for programming "at scale." When you see a small piece of code in one place like this, it's quite clear what's going on: s to the existing global variable. This addresses both issues while preserving the "programming at scale" benefits of the 1.0 behavior: global variables have no spooky effect on the meaning of code that

that of other dynamic languages, and even rivals that of statically-compiled languages. For large scale numerical problems, speed always has been, continues to be, and probably always will be crucial: explain and understand. Fair point. Second, and arguably worse, is that it's bad for programming "at scale." When you see a small piece of code in one place like this, it's quite clear what's going on: s to the existing global variable. This addresses both issues while preserving the "programming at scale" benefits of the 1.0 behavior: global variables have no spooky effect on the meaning of code that

that of other dynamic languages, and even rivals that of statically-compiled languages. For large scale numerical problems, speed always has been, continues to be, and probably always will be crucial: explain and understand. Fair point. Second, and arguably worse, is that it's bad for programming "at scale." When you see a small piece of code in one place like this, it's quite clear what's going on: s to the existing global variable. This addresses both issues while preserving the "programming at scale" benefits of the 1.0 behavior: global variables have no spooky effect on the meaning of code that

that of other dynamic languages, and even rivals that of statically-compiled languages. For large scale numerical problems, speed always has been, continues to be, and probably always will be crucial: explain and understand. Fair point. Second, and arguably worse, is that it's bad for programming "at scale." When you see a small piece of code in one place like this, it's quite clear what's going on: s to the existing global variable. This addresses both issues while preserving the "programming at scale" benefits of the 1.0 behavior: global variables have no spooky effect on the meaning of code that

that of other dynamic languages, and even rivals that of statically-compiled languages. For large scale numerical problems, speed always has been, continues to be, and probably always will be crucial: explain and understand. Fair point. Second, and arguably worse, is that it's bad for programming "at scale." When you see a small piece of code in one place like this, it's quite clear what's going on: s to the existing global variable. This addresses both issues while preserving the "programming at scale" benefits of the 1.0 behavior: global variables have no spooky effect on the meaning of code that