AI’s Capital Barrier: Why Monoliths Control the Foundation—While Entrepreneurs Win Above It
After several years of using ChatGPT as my primary LLM, I was surprised by how much Google’s new Gemini upgrade improved the experience. For my own workflows—writing, editing, image manipulation—it felt meaningfully better. And the shocking part wasn’t just Gemini’s quality. It was how frictionless it was to switch from one LLM universe to another.
Yes, I lost some personalization. But as a consumer, nothing gave me any hesitation from moving. Depending on the next leapfrog, maybe I’ll move back shortly.
This tells us something important: the switching costs in AI will accumulate around applications and distribution, not the core model IP. The capital and engineering required to build a foundation model heavily favor monolithic innovators. Nvidia moves deeper into software. Google designs its own chips. Microsoft does both. Amazon does all of the above.
A Pattern We’ve Seen Before—But Bigger This Time
My career has tracked three major tech paradigm shifts. Each time, the first encounter with the new technology was magical and transformational. My attachment to Apple, VisiCalc, Motorola, BlackBerry, Netscape, AOL Mail, eBay, and Yahoo all felt permanent—until faster-moving, more native, simpler competitors displaced them.
A familiar cycle repeated:
- The ecosystem unbundled
- Horizontal specialists emerged (chips, communications, software)
- New entrants built faster and cheaper on these components
Over the past decade, however, those horizontal layers have been reabsorbed into vertically integrated platforms. Mobile and cloud accelerated this trend—Apple, Google, Microsoft, and Amazon increasingly design or control each essential layer of their stack. AI takes this verticalization further and faster than anything before it.
AI Is the Most Capital-Intensive Technology Shift in Modern History
We tend to talk about AI as an “IP race,” but the defining characteristic of this era is simpler:
AI is a Capital racea AI is a capital race.
To compete at the foundational layer, vendors need to invest simultaneously across four deeply capital-intensive domains:capital race.To compete at the foundational layer, vendors need to invest simultaneously across four deeply capital-intensive domains:
- Chips / Compute: Securing scarce, expensive GPUs/TPUs
- Infrastructure: Hyperscale data centers, energy, cooling
- Model Training: Hundreds of millions per frontier model
- Distribution: Massive user bases to integrate AI into daily workflows
Only a handful of companies—Google, Microsoft, Amazon, Meta, Nvidia—have the capital structure, reliable access to incremental capital; at the lowest cost, distribution, and operational scale to run this race efficiently.
Their strategic advantage is growing, not shrinking. They include:
- Designing their own silicon (Tensor, Grace, Maia)
- Running their own clouds
- Training their own models
- Embedding AI into billion-user products (Google Workspace, M365, Instagram, Chrome)
This end-to-end homogeneity drives marginal compute costs down and operational leverage up. For users, switching LLMs remains easy. The real moats sit in distribution, application ecosystems, and multi-solution data platforms.
This era is not simply a broad creative explosion; It is matched with a structural consolidation event.
Where Entrepreneurs Win: The Three Frontier Opportunity Zones
The monoliths will own the foundation layer. But this creates enormous opportunity above it. New markets are emerging weekly, and the most compelling founder opportunities cluster into three domains:
1. Agentic Automation (The Workflow Master)
Problem: Enterprise systems grow more powerful and more complex—traditionally a trade-off.
AI’s Role: Domain-specific agents eliminate that trade-off.
Value: Agents that operate across ERP, CRM, Cyber, and internal data to autonomously execute multi-step workflows, surface insights, and make pre-emptive decisions.
This is the next “middleware revolution,” but intelligent.
2. Orchestration & Interoperability (The AI Control Plane)
Problem: Enterprises will use many models, each with different strengths and cost structures. Complexity and cost are exploding.
AI’s Role: Build a routing and governance layer.
Value:
- AI gateways that choose the optimal model per task
- API unification
- Reliability and cost control
This is similar to the rise of cloud cost-optimization and observability—only bigger.
3. AI Security & Governance (The Shield)
Problem: AI adds new vulnerabilities—prompt injection, data poisoning, emergent behaviors, and insecure AI-generated code.
AI’s Role: The trust layer.
Value:
- AI-aware threat detection
- Governance & audit trails
- Hardening AI inputs/outputs
No enterprise will deploy AI at scale without this.
The Coming Divergence: Monolith Foundations and Entrepreneurial Frontiers
ChatGPT, Anthropic, Perplexity, and others are extraordinary companies addressing one of the largest markets in tech history. But their vulnerability is structural: the trillions of dollars required to keep pace with hyperscale incumbents whose cost of capital is lower and more reliable.
Meanwhile, the next wave of entrepreneurs will win by building capital-efficient frontier solutions on top of these foundational layers—where creativity, speed, and differentiation matter far more than the scale of your balance sheet.
Great companies will still be built with old-school fundamentals:
- Strong gross margins
- Real product differentiation
- High sales productivity
- Deep customer understanding
These are the timeless drivers of durable shareholder value.
