The Next Global Superpower Might Be an AI Company

Today, we’re diving into:

Whoever Controls AGI Could Rewrite Global Business Forever

Anthropic just moved the AGI conversation out of science fiction and directly into global power politics.

In a new report, the company warned that Artificial General Intelligence could arrive as early as 2028, and the implications go far beyond chatbots or productivity tools. According to Anthropic, whichever nation reaches AGI first could gain a structural advantage across cybersecurity, scientific discovery, software engineering, military intelligence, and economic infrastructure itself.

That is why the company is urging the United States to tighten export controls on advanced AI chips and crack down harder on model theft and semiconductor smuggling as competition with China accelerates.

This matters more than most business leaders currently realize.

For the past two years, enterprises have treated AI as a software upgrade - faster workflows, better automation, smarter copilots. But AGI changes the competitive layer entirely because the advantage stops being at the application level and starts shifting toward compute access, infrastructure control, and autonomous research capability.

The real bottleneck in this race is no longer models. It is compute sovereignty.

Training frontier AI systems already requires hyperscale clusters of GPUs, advanced interconnects, massive energy infrastructure, and high-speed data orchestration pipelines. AGI systems will push that requirement exponentially further. The organizations and nations that control chips, cloud infrastructure, synthetic training data, and autonomous agent ecosystems will effectively control the next operating system for global economies.

You are already seeing early signals of this shift.

Nvidia is becoming strategically important at a nation-state level. Microsoft, Google, Amazon, and Anthropic are racing to lock in long-term compute partnerships. China is aggressively investing in domestic AI chips from Huawei and Cambricon because dependence on Western semiconductors is now viewed as a national vulnerability.

The companies pulling ahead are already redesigning around agentic infrastructure, unified data layers, and AI-native operational systems. Everyone else is still debating whether AGI is real.

By the time the market agrees on the definition, the power shift may already be underway.

Will AI kill apps? Here’s what Apple has to say

For months, Silicon Valley has been pushing a dramatic narrative: AI agents will replace apps entirely.

Ahead of WWDC 2026, Apple developers are pushing back on that idea, and they may be closer to the truth than the AI hype cycle currently admits.

The real shift is not the death of apps. It is the invisible AI-ification of software itself.

Developers building apps like Pockity, Peak, Zoho Notebook, and Guitar Wiz are not treating AI as a replacement layer. They are embedding AI directly into the product experience through on-device inference, contextual workflows, accessibility systems, predictive automation, and multimodal interaction.

That distinction matters enormously for business leaders.

The future app economy is not disappearing. It is becoming agent-compatible.

Technically, what is happening underneath the surface is a transition from interface-driven software to intent-driven software. Instead of users manually navigating workflows, AI systems increasingly interpret intent and orchestrate actions across applications dynamically.

Apple’s Foundation Models Framework, Vision Framework, HealthKit integrations, on-device LLMs, and Metal acceleration stack are early examples of this architecture. Apps are evolving into modular execution environments where AI becomes the orchestration layer rather than the destination itself.

And importantly, developers are discovering that raw AI generation is not enough.

A vibe-coded app can generate UI screens quickly. It cannot automatically understand why a color-blind guitarist needs monochrome chord diagrams instead of red-green indicators. It cannot fully model human behavior, emotional friction, accessibility edge cases, or trust-sensitive workflows in finance and healthcare without deliberate product thinking.

That is why apps are still growing aggressively. iOS App Store launches reportedly rose 80% year-over-year in Q1 2026 despite the explosion of AI agents.

Because the competitive advantage is shifting from building generic software to building deeply contextual software.

The companies that win this transition will not necessarily build the best standalone AI model. They will build the best AI-native experiences.

And this creates a major warning for enterprises.

If your software strategy still treats AI as an isolated chatbot sitting beside your product, you are already behind. The next generation of software is being architected around embedded intelligence, agent interoperability, local inference, and intent orchestration across ecosystems.

Apps are not dying. They are becoming autonomous systems disguised as interfaces.

Your SaaS Business Is About to Compete With AI Employees

Bain just revealed what may become the most important shift in enterprise software this decade: a projected $100 billion SaaS market built entirely around agentic AI automation.

But this is not about replacing SaaS apps.

It is about replacing the invisible coordination work happening between them.

Right now, your teams are manually stitching together workflows across ERP systems, CRMs, finance tools, support platforms, emails, and internal documentation. Employees spend hours verifying records, escalating approvals, resolving inconsistencies, and moving context between disconnected systems.

Bain believes agentic AI is about to automate that operational layer entirely.

And technically, this changes everything.

Traditional automation systems like RPA failed because enterprise workflows are messy. APIs break. Data lives across fragmented systems. Context changes in real time. Rule-based automation collapses the moment ambiguity enters the workflow.

Agentic AI operates differently.

Modern agents can reason across systems, retrieve context dynamically, interpret unstructured enterprise data, and execute decisions inside operational guardrails. That is why companies like Cursor, Glean, Harvey, Salesforce, and ServiceNow are scaling aggressively around workflow automation.

But here is the problem most enterprises are about to discover too late.

Your agents are only as intelligent as the infrastructure feeding them.

The moment AI agents attempt to coordinate across fragmented databases, disconnected APIs, siloed vector stores, and inconsistent enterprise records, latency compounds fast. Retrieval quality drops. Context becomes unreliable. Agents hallucinate decisions or fail workflows entirely.

What Happens Next Is Costing You?

Your developers stop building features and start firefighting. The AI initiative that was supposed to transform your operations becomes an expensive maintenance cycle that eats budget without delivering the business outcomes you promised your board.

And the deeper you go into your current stack, the clearer it becomes. The architecture you have built, layer by layer, over the past decade, was never designed to serve the demands of autonomous AI agents working across your entire business simultaneously.

This is exactly where DataManagement.AI becomes operationally critical.

Instead of forcing your agents to navigate fragmented enterprise systems independently, DataManagement.AI creates a unified active data layer purpose-built for agentic orchestration. Your AI systems get fast retrieval, persistent context memory, structured handoffs, and reliable cross-workflow synchronization across enterprise environments.

That means your agents spend less time searching for context and more time executing real business outcomes.

The companies that dominate the next SaaS era will not simply deploy more agents.

They will build the infrastructure layer that allows those agents to operate reliably at enterprise scale before everyone else catches up.

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Thank you for reading

-Shen & Towards AGI team