Hi AI Futurists,
Over the past few months, I've been mapping every meaningful company building the infrastructure for autonomous AI systems, from power generation to robotics to agent protocols.
The result: 158 pages covering 100+ companies across public markets, private equity, and crypto.
This isn't a blog post. It's an institutional-grade analysis of who actually captures value as machines become economic actors.
What's inside:
14 infrastructure layers (Industrial Foundations → Compute → Data → Models → Robotics → Agents → Governance)
Company-by-company breakdowns with financials, competitive moats, and risk assessments
Cross-market view: public equities, VC-backed startups, and token protocols side-by-side
2025 performance data showing what actually worked vs. what got crushed
Who this is for:
Investors seeking an edge in the AI infrastructure trade beyond the obvious semiconductor plays. Identify which companies actually capture value as machine autonomy scales, from power generation to inference verification.
Builders & Developers mapping competitive landscapes or identifying integration partners. Understand platform dynamics, moat sustainability, and where commoditization pressure is building.
AI Enthusiasts wanting systematic knowledge of how autonomous systems get built, deployed, and monetized across the full technology stack.
Strategists & Operators evaluating M&A targets, partnership opportunities, or go-to-market positioning within the broader machine economy.
I'm pricing this at $149, but the first 100 buyers get it for $99. Just use code FUTURE at check out.
AI & Robotics Industry Encyclopedia
In this 158 page report, we collate all the meaningful companies across public equities, private equity, and digital assets related to the machine economy. There are 13 sections of company types, a...
The complete investment intelligence map for the emerging machine economy
This 158-page research report provides the first comprehensive taxonomy of public companies, private ventures, and tokenized protocols building the infrastructure for autonomous AI systems. Compiled by Lex Sokolin, former Chief Economist at ConsenSys, fintech strategist at Autonomous Research, and current Managing Partner at Generative Ventures, this report delivers institutional-grade analysis of 100+ companies across 14 critical infrastructure layers.
What You Get
14 Infrastructure Layers, Fully Mapped
From power generation to embodied AI to governance protocols, this report structures the entire machine economy value chain:
Layer 0: Industrial Foundations — Power generation, cooling systems, semiconductor supply chains, high-density datacenters
Layer 1: Compute — GPU infrastructure, cloud platforms, specialized compute networks
Layer 2: Data — Data platforms, streaming infrastructure, storage solutions
Layer 3: Models — Foundation models, multimodal systems, embodied AI, world models
Layer 4: Simulation & Sim2Real — Digital twins, synthetic data, physics engines
Layer 5: Inference — Runtime optimization, edge deployment, inference scaling
Layer 6: Inference Verification — Safety systems, alignment tooling, monitoring
Layer 7: Robotics Hardware — Actuators, sensors, manufacturing platforms
Layer 8: Robotics Intelligence — Fleet coordination, manipulation models, navigation
Layer 9: Agent — Autonomous agents, multi-agent systems, workflow automation
Layer 10: Distribution — API platforms, developer tools, integration layers
Layer 11: Applications — End-user products, vertical solutions, consumer interfaces
Layer 12: Economic & Governance — Payment rails, incentive design, protocol economics
Layer 13: End-State Machine Economy — System coordination, control mechanisms
Actionable Company Intelligence
Each company profile includes:
Value chain positioning — Where the company sits in the machine economy stack
Competitive analysis — Platform dynamics, moat assessment, network effects
Financial metrics — Market cap, revenue, cost structure, valuation multiples
Performance data — YTD returns, trend analysis, momentum indicators
Risk assessment — Technology risks, competitive threats, regulatory exposure
Cross-Market Coverage
Public equities: 60+ listed companies from energy infrastructure to AI platforms
Private ventures: High-growth startups with valuations and funding data
Digital assets: Token protocols with market caps, dilution analysis, and value capture mechanisms
Why This Matters Now
The AI trade is fragmenting.
2025 showed extreme dispersion, not broad uplift. Capital concentrated in assets with direct exposure to AI capex (power, silicon, compute allocation) while abstraction layers, point solutions, and misaligned token economics got crushed.
This report identifies where durable value actually accrues:
Power and energy infrastructure massively outperformed as AI datacenter demand exposed grid constraints
Semiconductor manufacturing and HBM capacity captured scarcity premiums
Hyperscale cloud providers compounded existing advantages; most enterprise software underperformed
Tokenized networks broadly failed to translate protocol activity into defensible value capture
Robotics and embodied AI saw selective re-rating tied to real deployment, not research progress
The opportunity is shifting from "AI exposure" to machine-native choke points: who controls power allocation, silicon production, compute scheduling, cloud billing, and workflow automation.
What Makes This Different
Institutional rigor, accessible presentation. This isn't blog-level speculation. Every company is analyzed through a consistent framework: value chain position, competitive moat, platform dynamics, and financial performance.
Cross-market synthesis. Most research treats public equities, venture-backed startups, and crypto protocols as separate universes. This report maps them as competing layers of the same infrastructure stack.
Contrarian signal filtering. Markets reward what actually works, not what sounds exciting. The analysis highlights where narratives diverge from unit economics, where network effects are real versus imagined, and where regulatory or technical risk is mispriced.
Built by an operator, not an observer. Lex Sokolin has built roboadvisors, run fintech research for institutional investors, launched DeFi products at ConsenSys, and now deploys capital into AI infrastructure and decentralized networks. This report reflects pattern recognition from building, investing, and operating across cycles.
Sample Insights
"Constellation Energy's nuclear fleet is effectively 'platformized' through long-term PPAs with hyperscalers, turning baseload generation into locked-in energy for compute. Bloom Energy's solid oxide fuel cells capture on-site datacenter power urgency, but face margin compression and subsidy dependence before sustainable unit economics."
"IBM's acquisition of Confluent for $11B assumes successful data platform monetization against hyperscaler bundling pressure. Legacy infrastructure decline compresses growth while AWS/Azure/GCP commoditize public cloud. The moat is enterprise lock-in through mainframes and Red Hat hybrid cloud switching costs."
"Tesla vertically integrates across 8+ taxonomy layers with a core moat in proprietary FSD training data from 7M+ vehicles. But valuation depends on robotaxi monetization despite Waymo's commercial lead and intensifying EV competition compressing margins."
"Protocol usage and activity rarely translated into durable token value capture in 2025. Token dilution, weak fee alignment, and optional participation weighed on returns. Decentralized networks broadly underperformed public and private equity equivalents."
Technical Details
Format: PDF, 158 pages
Coverage: 100+ companies across public markets, private equity, and digital assets
Analysis Framework: 14-layer infrastructure taxonomy mapping value capture across the machine economy
Author: Lex Sokolin, Managing Partner at Generative Ventures
What You'll Know After Reading
✓ Which infrastructure layers are capturing AI capex versus getting commoditized
✓ Where power, cooling, and energy infrastructure create structural advantages
✓ Which semiconductor and hardware companies have defensible moats versus exposure to Chinese competition
✓ How hyperscalers maintain platform dominance while enterprise software struggles to monetize AI
✓ Why most tokenized AI networks failed to capture value despite protocol activity
✓ Where embodied AI and robotics are seeing real commercial traction versus research theater
✓ Which private companies are value-dense but inaccessible versus overhyped
✓ How to evaluate whether a company has true network effects or just temporary scarcity
✓ What the market is actually rewarding versus what the narrative suggests
About the Author
Lex Sokolin is Managing Partner at Gen Ventures, a venture capital fund deploying into Crypto, AI, and Decentralized Physical Infrastructure Networks (DePIN). Previously:
Chief Economist & CMO at ConsenSys — Built DeFi products, managed digital asset treasury, designed tokenomics
Global Head of Fintech Strategy at Autonomous Research — Built investment practice covering fintech, neobanks, digital assets, DeFi, and machine learning for institutional investors
Co-Founder & CEO at NestEgg Wealth — Built one of NYC's first roboadvisors with behavioral finance-driven asset allocation
Ready to map the machine economy?
This report is your edge in understanding which companies actually control the infrastructure that AI systems—and eventually autonomous agents—depend on to function, scale, and capture value.
AI & Robotics Industry Encyclopedia
In this 158 page report, we collate all the meaningful companies across public equities, private equity, and digital assets related to the machine economy. There are 13 sections of company types, a...
P.S. - Early bird pricing ($99) expires after first 100 sales or by the New Year, whichever comes first! Discount code: FUTURE