Dom Sharlette

Building companies, not just products.
4 startups · 3 exits · 5 legacy companies transformed.
Digital Health • Connected Devices • Consumer Technology.

[Dom Sharlette @domsharlette]

A great late-night read.

[Kirsten Green @kirstenagreen]

There's a new playbook in vertical AI: building one compounding engine with two interfaces.

In prior cycles, B2C and B2B evolved in distinct waves.

Google and Amazon first built massive consumer audiences. Then, over years (decades), they exposed capabilities as infrastructure to the market. AWS didn't follow S3...it took time. The cycles were long and sequential.

What we're seeing now is a compression of that happening in real time. Consumer and infrastructure aren't sequential anymore. They're simultaneous.

The arc looks something like:
• Launch as a consumer-facing assistant or tool
• Build differentiated intelligence or interaction layer
• Realize the leverage in embedding that layer elsewhere
• Expose APIs / SDKs / agent capabilities as a natural extension of what the consumer surface already built
• Evolve into both product and infrastructure

My working hypothesis:

Consumer builds...
• Data
• UX iteration
• Brand
• Real-world feedback loops

Infrastructure builds....
• Revenue durability
• Distribution hedge
• Strategic leverage
• Added valuation multiple

In other words, consumer AI may increasingly function as the front-end acquisition, while infrastructure becomes an economic moat. They're not two separate businesses — they're one compounding engine.

We’re seeing early versions of this pattern:

@mindtripai — an AI travel planner now building an agent-first layer that can be embedded via API or integrated agent-to-agent. The consumer product builds the travel identity graph; the infrastructure ambition is to own the decision layer.

@shopondaydream — a consumer visual shopping experience now powering visual search and recommendations directly on brand websites, decoupling the intelligence layer from the consumer UI.

@duckbillai — a human concierge now introducing MCP to bring human-in-the-oop trust into broader agent ecosystems.

@tryheroapp — a proactive daily assistant now extending AI Autocomplete as enterprise infrastructure embedded in third-party text boxes.

The same predictive intelligence powers both surfaces.

These companies aren’t just adding enterprise as a monetization path, but aiming to become vertical control planes — the intelligence layer others depend on — especially in a world where distribution may concentrate around a few dominant AI interfaces.

If OpenAI wins distribution, you want to be the vertical brain it calls.

If distribution fragments, you want to own the daily habit directly.

Not all vertical AI → infra expansions are equal.

The real question: is there a single intelligence core compounding across surfaces — or simply two adjacent products under one roof? The latter is just operational complexity, but the former is a control plane.

[Dom Sharlette @domsharlette]

We’ve moved past “AI replaces jobs.”

Now it’s: is it better, faster, and cost-effective?

Replacing tasks isn’t enough. The output has to justify the spend.

Technology cycles change — and each time, economics are expected to improve. If the math wins, you win.

[Mark Cuban @mcuban]

This is the smartest counter I’ve seen to ai taking over jobs, in the short term.

Is the ((aggregate tokens cost to do what an employee does + plus fully encumbered developer and maintenance costs ) / (fully encumbered employee cost ) )<= productivity ?

If it takes 8 Claude agents, at $300 for tokens, per day, plus $200 per day in dev/maint , to do what an employee does per day, at a fully encumbered cost of $1200.

That’s 2600/1200. But then you need to factor in the productivity rate.

Is it more than 2.16 x productive ? Are there qualitative issues like morale, morality, whatever , that can’t be quantified, that need to go into the decision?

What is the going forward progression of burdened costs for the tokens ?

Curious what people think about this ?

[Dom Sharlette @domsharlette]

Replacing manual intake and fragmented record exchange is the real opportunity here.

What’s notable is the move toward patient-directed digital tools that reduce repeated forms and missing records at the point of care.

That’s the kind of infrastructure shift healthcare actually needs.

https://t.co/SEZ3ixSwBc

[Dom Sharlette @domsharlette]

AI: Tool or Threat — Full Article.
https://t.co/D4Yn93VJDU
By @domsharlette &amp; @JanStillerman

[Dom Sharlette @domsharlette]

The biggest development in AI isn’t the models.
It’s implementation.

Customizing AI inside real companies is where the next job wave forms.

[Rohan Paul @rohanpaul_ai]

Mark Cuban on the next job wave.

Customized AI integration for small to mid-sized companies.

"Software is dead because everything's gonna be customized to your unique utilization. Who's gonna do it for them... And there are 33 mn companies in the US."

[Dom Sharlette @domsharlette]

Most wearables collect data well—and explain it poorly.

What’s notable about Oura’s update isn’t AI. It’s the lab-style summary showing patterns, not points.

That saves clinicians time and helps consumers act—without chasing streaks.

https://t.co/7y9sCuXNiU

[Dom Sharlette @domsharlette]

Most wearables already collect more data than we can use. The problem isn’t volume—it’s quality.

If wrist-worn signals aren’t reliable, repeatable, and interpretable over time, they won’t survive clinical workflows.

Better outcomes come from better data.

https://t.co/r4yIxv2P2o

[Dom Sharlette @domsharlette]

Fall risk is shifting from subjective screening to neurological signal.

This app measures reaction time and multisensory lag as risk proxies.

NIH backing here signals measurement infrastructure, not a solved prevention product.

https://t.co/xch7AaEDsu

[Dom Sharlette @domsharlette]

Digital health is moving from engagement to infrastructure.

Maven launching a research institute isn’t news—it’s an admission. Adoption doesn’t scale in healthcare.
Evidence does.

Engagement opens the door. Clinically legible outcomes keep you there.

https://t.co/vRxLKU6YOW

[Dom Sharlette @domsharlette]

AI isn’t the threat.
It’s a mirror.

[Dom Sharlette @domsharlette]

AI chatbots are driving health conversations—not clinical signal.

They organize info but don’t produce validated, repeatable, accountable data.

Until insight survives clinical workflows, conversational AI is an interface—not healthcare infrastructure.

https://t.co/H4kG0NgVZr

[Dom Sharlette @domsharlette]

Forerunner’s Top 50 list isn’t about AI for AI’s sake — it’s a snapshot of where consumer AI is being applied to remove real-world friction.

The 50 Consumer AI Startups to join now, according to Forerunner https://t.co/ZIjie2MGXW

[Dom Sharlette @domsharlette]

@kirstenagreen @ForerunnerVC Completely agree. Cost-cutting automation is an efficiency play — valuable, but finite. Empowering humans is where durable businesses and real upside get built.

[Dom Sharlette @domsharlette]

FDA clarified the playing field: non-invasive wearables and AI can deliver health insights without being treated like full medical devices — as long as they don’t diagnose or treat.

That’s a meaningful shift for consumer health builders and investors.
More signal. Same responsibility. Execution matters now.
https://t.co/9bvyyOK5tS

[Dom Sharlette @domsharlette]

Coming off CES, one thing was clear: health and data tech is shifting — quietly, but meaningfully.

Non-invasive monitoring is moving beyond steps and sleep toward continuous health signal — scales, rings, wearables, and software ecosystems designed to support daily understanding, not just activity tracking.

The shift isn’t the hardware.
It’s the focus on insight, context, and everyday use.

The data exists.
How responsibly — and usefully — it’s applied matters now.

[Dom Sharlette @domsharlette]

Because great outcomes aren’t born from dominance—they’re born from dialogue.

Read the full article ��� https://t.co/WhBJjjPeWr

[Dom Sharlette @domsharlette]

Our conversation is now live.
@JanStillerman and I dive into how breaking silos between design and sales turns creativity into momentum.

[Dom Sharlette @domsharlette]

When design and sales speak the same language, good ideas become market leaders.

We explore why creative and commercial teams drift apart, how to bring them back together, and what happens when alignment becomes culture.