DSE Deeptech AI & Machine Learning Fund

Market Context

$2.5 Trillion in AI Spending — But the Infrastructure Gap Is the Opportunity

Gartner forecasts worldwide AI spending at $2.52 trillion in 2026, up 44% year-over-year. AI now represents 50% of all global venture capital. But the real opportunity isn't in building another foundation model — it's in the infrastructure that enables autonomous agents, cross-model interoperability, and industry-specific AI deployment at scale.

$2.52T

Worldwide AI Spending 2026 (Gartner)

$202B+

AI VC Funding 2025 — 50% of All Global VC

$50-70B

AI Agents Market by 2030 (42-46% CAGR)

$13T

Additional Economic Output by 2030 (McKinsey)

• Autonomous Agent Economy Emerging

  33% of large enterprises already deploy AI agents. Gartner predicts 15% of routine decisions will be made autonomously by 2028. Bain estimates US agentic commerce could reach $300–500B by 2030.

• AI-to-AI Infrastructure Gap

  No standardised way for agents to discover, communicate, verify, or transact with each other. Protocols emerging (Google A2A, Anthropic MCP, IBM ACP) but no dominant standard — creating a pre-HTTP moment.

• Inference Economics Shift

  The industry has shifted primary expenditure from model training to large-scale deployment. AI infrastructure software growing 83%, signalling enterprises have moved past experimentation into production scale.

• Enterprise AI Goes Vertical

  Horizontal AI platforms are crowded (IBM, Microsoft, Google, Amazon). The opportunity is in deep vertical expertise — pharma, materials science, energy, quantitative finance — where domain knowledge creates defensible moats.

Investment Strategy

Building the Infrastructure Layer of the Autonomous AI Economy

The DSE Deeptech AI Fund targets innovations where artificial intelligence intersects with deep technology to create transformative, defensible platforms. Rather than competing in the crowded foundation model race, the fund focuses on the infrastructure, orchestration, and vertical application layers where the highest-margin opportunities exist — with infrastructure-grade economics (70–85% gross margins), strong network effects, and 10–15x revenue multiples comparable to Stripe, MongoDB, and Datadog.

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  AI Consortium — Agent Orchestration Layer

  Orchestration layer coordinating specialised agents for complex decision-making. Standardised protocols for agent-to-agent communication, semantic interoperability, trust verification, shared memory, and intent marketplaces across the autonomous agent economy.

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  Scientific Domain Creation Programme

  Building AI-powered research infrastructure for breakthrough discoveries across physics, materials science, quantum technologies, drug discovery, and climate modelling. Creating vertical AI systems that accelerate the scientific method itself.

• 

  AI-to-AI Infrastructure

  Agent communication protocols, semantic interoperability layers, trust and verification infrastructure (DIDs, VCs, TEEs), shared memory systems, and intent marketplaces for autonomous agent transactions.

• 

  Quantitative Finance & Analytics

  Algorithmic trading systems, predictive portfolio management, risk modelling, fraud detection, big data analytics platforms, and AI-driven financial infrastructure.

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  Industry-Specific AI Applications

  Healthcare diagnostics and drug discovery, energy grid optimisation, supply chain intelligence, autonomous manufacturing, and vertical AI solutions with deep domain expertise.

• 

  MLOps & Data Infrastructure

  Federated learning, secure data collaboration, MLOps platforms, data pipelines, edge computing inference, and AI/ML workflow tools enabling production-grade deployment.

• 

  AI Safety, Ethics & Governance

  Responsible AI development platforms, transparency and fairness tooling, regulatory compliance (EU AI Act), AI audit infrastructure, and trust frameworks for autonomous systems.

• 

  Advanced ML & Foundation Models

  Scalable ML algorithms for autonomous systems, computer vision, NLP platforms, robotics perception, and specialised foundation models for specific scientific and industrial domains.

Infrastructure Investment Priorities

Infrastructure Priority Matrix

The fund deploys capital across three tiers based on time-to-revenue, infrastructure criticality, and return potential. Tier 1 targets the most acute infrastructure gaps and near-term revenue opportunities in the autonomous AI economy.

• TIER 1 — CRITICAL

  3 priority targets

• TIER 2 — HIGH PRIORITY

  3 priority targets

• TIER 3 — MEDIUM PRIORITY

  3 priority targets

Infrastructure Bottlenecks

Critical AI Infrastructure Gaps We're Targeting

The fund thesis is built on deep analysis of the AI ecosystem's most constrained infrastructure nodes — the five layers of AI-to-AI infrastructure where no dominant standard exists and first-mover advantage creates outsized returns.

• Critical Gap

  Agent Communication — No HTTP Equivalent

  AI agents operate in isolated silos built by different providers (OpenAI, Anthropic, Google, Meta) using diverse frameworks (LangChain, AutoGPT, CrewAI). No standardised protocol exists for agent-to-agent discovery, messaging, or capability negotiation — a $15–30B TAM by 2030.

• High Priority

  Trust & Verification — No Agent Identity System

  When autonomous agents transact: Who controls the agent? Can it be manipulated? How do we verify actions? No agent PKI, capability attestation, or reputation system exists. Blockchain identity market projected to reach $35B by 2028 at 93% CAGR.

• Structural Gap

  Intent Marketplaces — No Economic Layer for Agents

  The economic layer for autonomous agent transactions is virtually non-existent. No programmatic discovery, negotiation, or settlement infrastructure. Bain estimates US agentic commerce could reach $300–500B GMV by 2030.

• Critical Gap

  Semantic Interoperability — Model Output Incompatibility

  Different LLMs interpret instructions differently and produce structurally incompatible outputs. A request to GPT-5 and Claude yields different formats, making multi-model systems brittle. Enterprises need a universal translator — $25–40B TAM by 2030.

• High Priority

  Shared Memory — No Multi-Agent Persistent Context

  Current vector databases are single-tenant with no semantic conflict resolution. AI agents working collaboratively need persistent, shared context with tiered architecture and knowledge-aware access controls — $20–50B TAM by 2030.

• Structural Gap

  AI Talent & Governance — Skills and Regulatory Lag

  30% of enterprises cite limited AI skills as primary barrier. EU AI Act creating compliance burden. 46% of strategy leaders cite security as primary barrier to AI adoption. Governance tooling and training platforms are immediate needs.

What We're Looking For

Investment Criteria for Projects

We actively seek AI/ML and deep technology ventures with breakthrough potential, defensible intellectual property, and clear paths to commercialisation.

• Innovative & Differentiated Technology

  Breakthrough AI/ML solutions with high differentiation. Proprietary algorithms, novel architectures, or unique data advantages providing barriers to entry.

• Scalability Across Markets

  Projects with potential to scale across industries and geographies. Platform economics with network effects, high gross margins, and strong net revenue retention.

• Defensible IP & Competitive Moat

  Strong patent portfolios, unique training data, proprietary algorithms, or protocol-level advantages creating sustainable barriers to competition.

• Exceptional Founding Team

  Experienced, visionary founders with deep technical expertise and execution track records. PhD-level AI/ML talent combined with enterprise go-to-market capability.

• Research & Government Partnerships

  Collaborations with leading universities, research institutions, or government programmes. Academic validation and talent pipeline through institutional partnerships.

• Ethical AI & Responsible Development

  Commitment to transparency, fairness, and sustainability. EU AI Act readiness. Responsible AI development practices as a competitive advantage, not a constraint.

Value Creation Strategy

Beyond Capital: Strategic Value-Add

• Technical Mentorship

  Deep industry expertise to refine product development and accelerate innovation

• Market Access

  Enterprise partnerships, early adopters, and go-to-market acceleration

• Talent Acquisition

  Sourcing top-tier AI/ML engineers and research scientists globally

• Portfolio Synergies

  Cross-sector AI deployment across Setcoin's 10+ sector funds

• Setcoin Intelligence

  Market research, due diligence automation, and competitive analysis

Risk Management

Comprehensive Risk Mitigation

The fund employs rigorous due diligence across technology, market, regulatory, and team dimensions with milestone-based funding tied to clear performance targets.

• Technology Execution Risk

  Staged milestone-based funding; pivot-ready architecture; diversified modalities

• Big Tech Competition

  Vendor-neutral positioning; standards body participation; move-fast advantage

• Regulatory (EU AI Act)

  Proactive compliance; governance tooling as investment thesis; regulatory arbitrage

• Market Timing

  Revenue from adjacent use cases; flexible burn rates; enterprise pilots as validation

• Security & AI Safety

  TEE-first architecture; regular audits; bug bounties; alignment research investment

• Valuation Discipline

  Focus on infrastructure-grade economics; avoid hype-cycle premiums; revenue validation

Related Intelligence

Which Could Be Interesting for You

• Deep Analysis

  Enterprise AI Deployment: From Experimentation to Production

  Why the shift from training to inference economics creates $1.3T in infrastructure spending and where the investment opportunities lie.

  Read Analysis
• Feasibility Study

  AI-to-AI Infrastructure: Autonomous Agent Economy Analysis

  Comprehensive analysis of five infrastructure layers, market opportunities ($26–68B portfolio value), and strategic integration with Setcoin Intelligence.

  Read Thesis
• Market Analysis

  Scientific Domain Creation: AI-Powered Research Infrastructure

  How vertical AI systems are accelerating breakthroughs in physics, materials science, quantum technologies, and drug discovery.

  Read Analysis
• Investment Thesis

  The AI Consortium: Orchestrating the Agent Economy

  Strategic rationale for building the orchestration layer coordinating specialised AI agents for complex enterprise decision-making.

  Read Projections
• Policy Brief

  EU AI Act: Compliance as Competitive Advantage

  How regulatory requirements create investment opportunities in AI governance, safety tooling, and responsible development platforms.

  Read Analysis
• Critical Analysis

  Vertical AI: Why Domain Expertise Beats Horizontal Platforms

  The case for investing in industry-specific AI over horizontal platforms in healthcare, finance, energy, and scientific research.

  Read Study

Frequently Asked Questions

Fund Questions, Answered