The Hallstone Thesis

AI infrastructure for the attention economy. Not a content fund. Not a media fund. An infrastructure fund built by operators who know the stack.

$150B+

Venture-addressable market

$3.5T

Global E&M TAM context

Core focus areas

~70

Operator LPs

Why Now

The economics of media and entertainment are being rebuilt from the ground up. For decades, the creative supply chain ran on legacy software, manual workflows, and analog relationships. AI is changing what is possible at every layer of that chain. Not gradually. Structurally.

The question is not whether AI will reshape media. It already has. The question is which founders are building the durable infrastructure layer and which investors have the domain fluency to tell the difference between real infrastructure and a demo.

Most venture capital chasing AI has concentrated at the foundation layer: chips, model labs, hyperscaler platforms. The industry-specific infrastructure layer inside media and advertising captured a fraction of that investment. That funding gap is closing fast, and the founders best positioned to capture it are the ones with the domain expertise to build through a market that is evolving faster than anyone can predict.

What We Back

Why Hallstone

Sector focus plus an engaged operator LP network creates a compounding flywheel: proprietary deal access, sharper diligence around buyer, budget, and integration realities, and faster time-to-pilot and time-to-revenue for the founders we back.

Hallstone's LP base of approximately 70 senior operators and technology leaders is not a passive investor roster. It is a working network that actively supports diligence, opens buyer conversations, validates workflows, and accelerates founder access to the enterprise and platform relationships that drive adoption in media and entertainment.

Buyer Introductions

Direct access to the decision-makers who control budgets, procurement, and integration at studios, networks, platforms, and agencies.

Workflow Validation

LPs who have run the workflows founders are trying to improve. They know what works, what breaks, and what the real adoption barriers are.

Faster Time-to-Pilot

Warm paths into design partnerships and pilot programs that would take cold outbound founders months to build.

Sharper Diligence

Domain fluency that lets us separate real infrastructure from impressive demos before the market catches up.

Market Context

The $3.5 trillion global entertainment and media economy breaks into three venture-addressable layers: creator infrastructure (~$55-60B), AdTech and monetization rails (~$68-80B), and frontier media infrastructure (~$13-14B).

The industry-specific infrastructure layer that sits between AI foundations and the end users remains underfunded relative to the platform and model layers. That infrastructure layer is where defensibility compounds and where early-stage companies can still be accessed at sub-$10M valuations with real revenue and identifiable buyers.

What We Avoid

Discipline is part of the thesis. We define what we will not do as clearly as what we will. These exclusions are not negotiable.

Film & TV content slates

Hit-driven risk, not infrastructure

Game title slates

Speculative IP exposure

IP or royalty speculation

Uncontrolled rights risk

Services without software margins

No path to 65%+ gross margin

Unclear buyer or budget

GTM is not a strategy

Unmanaged IP/consent risk

Compliance kills scale

LLM wrappers without a moat

No proprietary data or workflow lock-in

Zero unit economics insight

No CAC, payback, margins, or retention data

How We Underwrite

What is the buyer type? A VP of Engineering, a Head of Monetization, a platform product lead? "Everyone in media" is not a buyer. Is there evidence that budget exists and has been spent, even at small scale?

The market is moving faster than anyone can predict. When the technology shifts beneath you, domain expertise is what lets a founder adapt without losing direction. We look for founders who understand the buyer, the workflow, and the industry dynamics well enough to make good decisions when the playbook changes. That pattern recognition is not something you can hire for after the fact.

Integration complexity is the primary reason media tech fails to scale. We look for founders who have mapped their product to the actual software stack their buyer runs, and who have a credible answer for how they get into it.

Time saved, cost reduced, revenue increased, churn prevented. We want a number, even a rough one, that the buyer would recognize as real. Vague efficiency claims are a red flag.

We look for compounding advantages: proprietary datasets, deeply embedded workflows, or network effects that make switching costs real. API wrappers without a moat are not infrastructure.

Media is a rights-intensive industry. We pressure-test every deal for training data provenance, talent consent, rights clearance, and regulatory exposure, particularly EU AI Act and US state biometric laws.

We prefer companies where media is the beachhead, not the ceiling. If the addressable market is structurally narrow, we need to understand that going in and see a credible path to broader adoption.

Building AI infrastructure for media, entertainment, or advertising?

For Founders