The Hidden Economy Behind AI

Q1 2026 wasn’t just big for venture, it was distorted.

Roughly $300B in global VC funding landed in the quarter, and close to 80% of it flowed into AI. But that headline number hides the real story. Strip out the massive rounds into frontier labs like OpenAI, Anthropic, and xAI, and a very different pattern emerges:

The most disciplined capital isn’t chasing models - it’s buying control over workflows.

TOP AI FUNDING ROUNDS - QUARTERLY REPORT

Where the Smart Money is Moving?

Digital product deals to watch (Q1 2026)

At the sector level, legal tech alone pulled in $2.34B across 100+ deals, with the majority concentrated in a few breakout companies. Healthcare shows similar concentration, with OpenEvidence emerging as a flagship “doctor-facing” AI product at scale.

The signal is obvious: capital is consolidating around companies already embedded in real workflows.

Pattern: Vertical and workflow AI

The defining trend is simple but important.

Investors are no longer backing generic AI tools—they are backing products that own mission-critical workflows.

In legal, companies like Harvey, Legora, and Ivo aren’t selling text generation. They automate contract review, research, and compliance inside systems lawyers already use.

In healthcare, OpenEvidence is tightly scoped: a clinical search tool used at the point of care, trained on peer-reviewed data, and directly tied to patient outcomes.

In media, Runway and Higgsfield follow the same playbook—wrapping foundation models in interfaces, asset systems, and collaboration layers that match how content is actually produced.

This is not “AI as a feature.” It’s AI embedded into the job itself.

Why this matters for digital products

The application layer is changing.

What used to be thin wrappers around models is now becoming full-stack vertical software, where AI is deeply integrated into:

• Business logic

• Data models

• User workflows

• Pricing structures

In legal tech, tools like Harvey and Legora are effectively becoming operating systems for firms, charging per seat or per matter while compressing days of work into hours.

Ivo positions itself similarly, with pricing tied to outcomes like deal velocity and risk reduction reporting up to 75% time savings in contract review.

In healthcare, OpenEvidence monetizes through institutional access and ads, but its real value is reducing diagnostic uncertainty in live clinical settings.

In media, Runway and Higgsfield sit above the model layer but capture more value by packaging workflows - templates, collaboration, brand controls into production-ready systems.

The closer the product is to the outcome, the stronger the pricing power.

The arbitrage: pricing workflows, not tokens

At a macro level, AI captured around $242B of the ~$300B deployed in Q1. A handful of frontier labs accounted for most of that—but their monetization is still largely API-based.

Vertical AI companies are playing a different game.

They charge per outcome:

• Per physician

• Per contract

• Per legal matter

• Per campaign or video

That allows them to price at 10–50× the level of generic AI tools, while maintaining 70–80%+ margins—even as model costs fall.

Examples:

• OpenEvidence monetizes at the level of clinical decisions

• Harvey and Legora at matters completed

• Ivo at contracts reviewed

• Runway and Higgsfield at videos and campaigns delivered

This is the core arbitrage: cheap, commoditizing model inference vs. expensive, high-trust workflow automation.

AI filmmaking tie-in

The media and filmmaking space is a clean microcosm of this shift.

Runway is actively seeding an ecosystem through initiatives like its Builders fund - 10M$ and API credits, encouraging startups to build vertical applications on top of its models.

Higgsfield is scaling through creators and agencies who turn low-cost AI tools into high-value deliverables for brands.

Underneath this, a new layer is forming: AI-native agencies.

These operators:

• Buy commoditized model access

• Wrap it in workflow, storytelling, and distribution

• Sell packaged outputs (videos, campaigns, training content) at a premium

It’s the same model seen across real estate, e-commerce, and B2B—AI as infrastructure, workflow as product.

Next week

Against this backdrop, AI filmmakers and agency builders scaling to tens of thousands in monthly revenue are not edge cases—they’re early examples of this model in action.

They rely on general-purpose AI underneath, but capture value at the workflow and delivery layer.

They’re operating in the same environment as OpenEvidence, Legora, and Higgsfield.

And the implication is straightforward:

The real opportunity in AI isn’t owning the model. It’s owning the workflow, the niche, and the outcome.

We hope you enjoyed this Latestly AI edition.
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