Executive Summary

Artificial Intelligence (AI) is transitioning from a research-driven frontier to a production-scale infrastructure layer underpinning multiple industries. The pace of advancement in foundation models, compute optimization, and applied AI verticals has created one of the most asymmetric investment opportunities of the decade. However, the sector’s rapid evolution demands rigorous differentiation between enduring infrastructure plays and transient application-layer experiments.

We believe the next cycle of AI investment will be defined less by model size and more by efficiency, defensibility, and integration into real economies. This report outlines the current state of AI, key technological and economic drivers, investable sub-sectors, and the long-term implications for capital formation.

1. Market Overview

Global AI Market Size

• AI market projected to exceed $1.8 trillion by 2030 (CAGR >35%).

• Current concentration in the US (60%+ of global investment), but rising adoption in China, EU, and emerging markets.

Capital Flows

• 2023–2025: Record-breaking funding rounds in model companies (OpenAI, Anthropic, Mistral) with valuations >$20B.

• Infrastructure layer (semiconductors, cloud providers, orchestration) capturing >70% of value creation so far.

• Application layer (vertical AI in finance, healthcare, design, productivity) remains fragmented, high churn.

2. Technological Landscape

Foundation Models

• GPT-4, Claude 3, Gemini 1.5 demonstrating multi-modal, generalist capabilities.

• Distinction between scale frontier (large model, highest accuracy) and efficiency frontier (smaller, domain-specific, faster inference).

• Open-source momentum (LLaMA, Mistral, Falcon) eroding proprietary moats in some categories.

Compute & Hardware

• GPU scarcity (NVIDIA H100, upcoming Blackwell) remains critical bottleneck.

• Rise of AI-specialized chips (Graphcore, Cerebras, Groq) and regional semiconductor sovereignty initiatives.

• Increasing shift toward decentralized compute markets (Render, Akash, io.net).

Optimization Layers

• Inference optimization (quantization, distillation) and model routing gaining traction.

• Agentic frameworks (LangChain, AutoGPT, OpenAI’s o1) enable composability but suffer from reliability issues.

3. Economic Drivers

Unit Economics

• Inference cost per token remains unsustainably high for many use cases.

• Companies building around data efficiency, inference compression, and reusable proof-of-computation will gain lasting advantages.

Defensibility

• Proprietary datasets and vertical integrations (finance, healthcare) create durable moats.

• Pure-play horizontal apps face commoditization pressure unless combined with strong distribution.

Regulation & Trust

• EU AI Act, White House AI Bill of Rights, China’s Generative AI Guidelines shaping permissible use cases.

• Increasing demand for verifiable AI (auditability, zkML, trusted data pipelines).

4. Investable Sub-Sectors

1. Compute & Infrastructure

   • AI semiconductors, cloud GPU providers, decentralized compute markets.

   • Key Thesis: scarcity economics + structural demand growth.

2. Model Layer

   • Foundation models (general-purpose, multi-modal) vs. specialized vertical models.

   • Key Thesis: defensibility shifts toward proprietary data and ecosystem lock-in.

3. Middleware & Tooling

   • Agents, orchestration, model optimization frameworks.

   • Key Thesis: horizontal platforms commoditize; niche developer tooling with network effects win.

4. Applied AI (Verticals)

   • Healthcare diagnostics, financial risk modeling, creative design, autonomous systems.

   • Key Thesis: highest ROI in regulated industries where trust, compliance, and proprietary data matter.

5. Verifiable & Trusted AI

   • zkML, proof-of-computation, AI provenance & watermarking.

   • Key Thesis: the “trust layer” of AI will be critical for financial services, legal, and government adoption.

5. Risks

• Overcapitalization: inflated valuations in model companies may compress returns.

• Technological obsolescence: rapid half-life of model advantages.

• Regulatory tightening: particularly in finance, healthcare, and defense applications.

• Concentration risk: NVIDIA’s dominance in GPU supply chain.

6. Outlook & Investment Implications

We anticipate a three-layered value capture in the next AI cycle:

1. Compute scarcity → near-term alpha, but subject to commoditization as new suppliers and decentralized markets scale.

2. Trusted infrastructure → durable opportunity, particularly in verifiable AI, model provenance, and privacy-preserving data flows.

3. Vertical integrations → long-term winners will be domain-specific applications embedding AI into regulated or high-value workflows (finance, healthcare, industrial automation).

For early-stage investors, the focus should shift from speculative application bets toward foundational infrastructure and vertical moats.

Conclusion

Artificial Intelligence is no longer a speculative theme but an emerging general-purpose infrastructure. The winners will not be those who simply scale models, but those who solve for scarcity, trust, and integration. At Leland Ventures, our thesis is clear: AI’s next decade is not about replacing humans, but about restructuring the flows of capital, credibility, and culture through intelligence.