When I first started recruiting for venture roles ~2 years ago, infrastructure software felt like a daunting black box. Over the past couple of years I’ve learned a lot, but there’s still plenty I’m figuring out — which is why I’m starting this series.

Infra Breakdown is my attempt to deconstruct the different layers of the infrastructure stack, aiming to (i) crystalize my own perspective and (ii) hopefully share a few helpful insights with you along the way.

For those less familiar with the space, many of today’s most valuable companies aren’t building the consumer applications we use daily (like Airbnb or Netflix). Instead, they are building the fundamental pipes and platforms that those applications run on.

The scale of this sector is often underestimated. In fact, the public market value of infrastructure software assets is roughly 3x that of application software – currently valued at $4.9T on $590B of total revenue (hopefully a bit higher by the time you read this… unless SaaSpocalypse has its way).

To put it in perspective, there are many companies that sit underneath the visible software economy: Databricks powers data pipelines, Snowflake runs analytics workloads, Supabase runs application backends, Stripe runs payments… the list goes on.

These are the “picks and shovels” that enable millions of other products to exist. Yet, despite powering huge portions of the internet, infrastructure remains one of the least understood parts of the technology stack.

This series aims to break it down, layer by layer.

Why Infrastructure Software Matters Now More Than Ever

Three structural shifts are expanding the importance of infrastructure software.

1. Application / software creation is accelerating: AI tools, low-code platforms, and developer frameworks (from the Lovable / Claude Code’s of the world to platforms like Canva) are dramatically lowering the cost of building software. The number of applications being created is exploding – 72% of developers who have tried AI use it every day; GitHub reported ~986M commits (+25% y/y)

2. Developers no longer build everything themselves: A decade ago, building an application required provisioning servers, managing databases, setting up authentication, and operating infrastructure. Today, developers increasingly assemble products from managed services (e.g., Supabase, Neon, etc.).

3. Infrastructure platforms capture compounding value: The companies providing these foundational layers benefit from powerful dynamics: Every new app increases demand for infrastructure → Developer ecosystems compound over time → Switching costs rise as systems scale.

This is why infrastructure software companies frequently become some of the most valuable companies in technology.

The Evolution of the Infrastructure Stack

Most modern software systems are often described as a layered stack. A few years ago, this idea was popularized as the “modern data stack” thesis: a clean set of modular layers where each company owned a specific function (fun Reddit thread on the rise and fall of the modern data stack).

The logic behind this framing was fair: software infrastructure should behave like Lego blocks (i.e., cloud data warehouses + SaaS tooling replacing monolithic platforms) – teams could assemble best-of-breed tools at each layer, and startups could win by owning a single, well-defined layer of the stack.

From Static Stacks to AI Gravity Wells

In practice, the software market rarely stays neatly layered for long. Microsoft is the classic example of this as they initially found their wedge in infrastructure with Windows (‘80s - ’90s) → expanded into the application layer with Office (‘90s - early ’00s) → and then scaled cloud / developer services with Azure (2010s). And they’ve kept pushing on since with acquisition / expansion of Teams, Dynamic 365, GitHub, etc.

Microsoft didn’t just stay in its lane; it expanded until it owned the entire road.

Instead of thinking about software as a static stack, it is more accurate to view it as a series of platform control planes sitting at critical points in the developer workflow. In the AI era, this transition has accelerated. The “Modern Data Stack” was a useful map for an earlier phase of the ecosystem, but the winners of this next era aren’t just modular layers — they are gravity wells.

In the current AI infrastructure landscape, the stack is collapsing into consolidated power centers:

• The Model as the Operating System: We are seeing a pivot where the LLM is no longer just a component; it is becoming the central orchestrator for compute, memory, and logic.

• Workflow Integration over Modularity: Enterprises are moving away from “best-of-breed” point solutions (i.e., separate vector DB + separate monitoring tool + separate prompt manager) in favor of integrated platforms that own the end-to-end lifecycle.

• The Data Moat: The “gravity” is generated by where the data lives. If a platform owns the context, it eventually owns the application.

The real architecture of the AI era looks less like a ladder and more like a set of overlapping platforms that expand until they dominate the entire developer workflow. To win today isn’t to be a better layer, but moreso becoming the platform that the rest of the ecosystem is forced to orbit.

In this series, we’ll be breaking down the critical points of the AI infrastructure stack.

Next up: Databases.

AI health coaches aim to solve this challenge and represent the next abstraction layer in wellness: shifting the value proposition from passive tracking to dynamic guidance.

This shift is not merely a trend; it is the result of three converging forces:

1. Interface Evolution: LLMs are replacing static dashboards with conversational dialogue, turning “data viewing” into “problem-solving.”

2. Biometric Liquidity: Sensor density (e.g., Continuous Glucose Monitors, high-fidelity wearables, etc.) has reached a usability threshold where continuous, multimodal data is finally reliable.

3. The Optimization Mindset: A cultural pivot from episodic, reactive care toward proactive, continuous performance and longevity.

As a result, we’re seeing longstanding fitness players pushing to productize the “coach” concept inside existing consumer surfaces like wearables and subscriptions (e.g., Fitbit Premium’s AI coach powered by Gemini).

In this post, I’ll aim to break down:

(i) what the current capital cycle within healthtech tells us about the opportunity,

(ii) why this moment represents a structural inflection point,

(iii) how the AI health coach landscape maps today, and

(iv) where durable value is most likely to accrue and the risks that could undermine it.

The Capital Cycle in Digital Health

Since 2021, >$80B of venture funding has flowed into health tech companies, initially propelled by COVID-era demand for telehealth, home diagnostics, and remote monitoring. After a notable pullback from pandemic-era peaks, we’re seeing digital health funding rebound in 2025 w/ Rock Health reporting that U.S. digital health startup funding reached $14.2B in 2025, up 35% y/y, with health AI taking a majority share.

I enjoyed a recent LinkedIn post from Julie Yoo (General Partner at a16z), who noted that just a few years ago, “calling yourself a consumer health company in front of VCs was a virtual death sentence.”

Today, that “death sentence” has evolved into a relevant investment thesis. We are seeing a massive shift in capital allocation as AI-enabled companies captured ~54% of total funding dollars in 2025, up from ~37% the prior year; these companies also commanded a ~19% premium on average deal size.

This funding profile reflects a wider shift in investor priorities over time:

• 2020-2022: Capital flowed primarily to telehealth access, remote diagnostics, and point solutions driven by the pandemic demand.

• 2023-2024: Post COVID, there was a period of repricing and selectivity, with emphasis on clinical outcomes and sustainable unit economics.

• 2025-present: A renewed focus on AI and intelligence layers that synthesize existing data into contextual decisions, not just data collection (represented with investments like Abridge’s $316M Series E for ambient clinical reasoning, Oura’s $900M Series E for longitudinal longevity data, Hippocratic AI’s $126M Series C for generative nurse agents, and Function Health’s $298M Series B for consumer-led AI lab synthesis).

This backdrop establishes a strong foundation for the emergence of AI health coaches as a new category of personalized wellness infrastructure.

The Inflection Point: Why Now

Similar to many other data-rich categories, AI unlocks (in health care and other categories) aren’t emerging because the underlying data is “new”. They are emerging because the interface, infrastructure, and incentives have finally aligned.

These structural shifts make this moment different from prior waves of digital health.

1. The Interface Shift (from Dashboards to Dialogue): Health data has existed for years, but interpretation hasn’t scaled linearly. LLMs transform static dashboards into conversational, contextual guidance (as exemplified by the snip of Oura’s UX and user flow below). Instead of reviewing graphs and guessing what to do, users can ask direct questions and receive personalized recommendations in real time.

2. Data Liquidity + Sensor Maturity: Wearables, continuous glucose monitors (CGMs), and health platforms now generate continuous, multimodal data across sleep, recovery, activity, glucose, stress, temperature, etc. Subscription-based wearables have normalized longitudinal tracking. What changed is not the volume of data, but the ability to synthesize it. AI models can now integrate disparate signals into coherent, personalized daily plans at scale – something that previously required human coaching and input.

3. Economics + Cultural Pull Toward Optimization: Consumers are moving away from episodic care toward a lifestyle of proactive optimization, a trend led by younger cohorts who prioritize continuous mental well-being (as seen in share of digital health adoption by cohorts below). Historically, human-led coaching has been plagued by poor scalability. AI solves this by enabling high-frequency personalization without a proportional increase in labor costs (note: cost delta not as relevant relative to other labor intensive industries). This unlocks a hybrid model where AI manages daily engagement and humans handle high-stakes escalation – driving superior unit economics while maintaining patient trust.

Combined, these forces create a structural inflection point: AI health coaches are not just possible, but economically and behaviorally viable at scale.

The Landscape: From Features to Infrastructure

The AI health coach category is maturing into a fragmented ecosystem rather than a monolithic market. Differentiation is now driven by three primary vectors: data ingestion methods (biometric sensors vs. self-reported logs), clinical depth (wellness vs. regulated interventions), and proactivity (passive insights vs. automated behavior loops).

Below is a current view of the market map, with representative companies by maturity and category:

Where Durable Value Accrues

As we’ve seen in other healthtech categories (e.g., Ambient AI), health coaching (at least today) will not be a single breakout winner. The value will concentrate around control points where data, distribution, and measurable outcomes reinforce each other over time. Two pools stand out as structurally advantaged:

Sensor-Native Platforms (Consumer)

The most durable position in AI health coaching sits with companies that already own: (i) continuous biometric data, (ii) a daily engagement surface, and (iii) a subscription relationship.

Players like Google/Fitbit, Oura, WHOOP, and Garmin are not starting from zero. They already collect multi-year datasets across sleep, HRV, strain, temperature, and activity. AI coaching becomes a layer that interprets what they already capture, creating four compounding advantages:

1. Longitudinal personalization: Multi-year datasets improve recommendation quality and increase switching costs as the model understands individual baselines (e.g., stress response and recovery dynamics).

2. Zero marginal distribution cost: Adding an AI coach to an existing subscription base (as seen with Google’s 2026 expansion of the Gemini-powered Fitbit Coach) is a retention and ARPU expansion lever rather than a standalone monetization gamble.

3. Habit embedding: AI guidance delivered at the moment of wake-up or pre-workout has structural behavioral leverage because the hardware is already part of the user’s daily routine.

4. Expansion to the enterprise: These companies are pivoting toward workforce resilience dashboards (ex. WHOOP x Lean Business Services, Oura x DoD) using AI as a population-level triage tool to flag rising burnout or illness trends across a team before they lead to absenteeism.

Note: While I am bullish on sensor-native platforms as the primary unlock for effective AI coaching, it is important to distinguish between a technological catalyst vs. an investable category at this specific maturity stage. From a risk/reward perspective, to get excited about investing in these assets, I’d personally be watching out for: [i] the next horizon of growth for these platforms (e.g., broader clinical / enterprise adoption), [ii] valuation and exit math (are these assets priced to perfection?), and [iii] data commoditization as biometric sensors become ubiquitous.

Outcome-Validated, Employer & Payer Platforms (Enterprise)

The second pool sits where the money actually lives: with employers, insurers, and risk-bearing providers. Companies like Omada, Lark, and Hims & Hers (minus the current regulatory ?s) win by proving ROI, not just engagement.

1. ROI drives budget durability: Unlike consumer apps that churn when a user loses interest, platforms that move the needle on glycohemoglobin levels or weight loss unlock multi-year enterprise contracts.

2. Hybrid model improves margin: AI now allows these platforms to automate 90% of daily interactions while reserving high-cost human experts for clinical escalation. This preserves medical trust while radically improving unit economics.

3. Integration into care pathways: Platforms embedded in employer benefit stacks or care management workflows become harder to displace than standalone wellness apps.

The Strategic Risks: The “Bear Case”

While the capital for AI health coaching is expanding, the path to a defensible category winner is fraught with structural headwinds:

• Coaching becomes commoditized. If foundation models make conversational intelligence ubiquitous, differentiation collapses. Every app can bolt on a “coach.” Distribution wins, standalone platforms struggle.

• Platform incumbents absorb the category. Apple, Google, or major ecosystem players could integrate AI coaching natively into health data hubs, compressing margins for independent apps.

• Engagement fatigue sets in. Health apps historically face churn once novelty fades. If AI advice feels repetitive or non-differentiated, retention may erode.

• Regulatory gravity increases. As AI recommendations approach medical territory, liability and compliance burdens rise. The closer coaching gets to clinical decision-making, the tighter scrutiny becomes.

What’s Next?

As the “noise” of raw data settles, the “signal” of AI-driven intervention is becoming the new standard for healthtech. Moving forward, the indicators of category maturity will be:

1. “Closed-Loop” Breakthroughs: Integrations where an AI coach doesn’t just suggest an action but executes it (e.g., adjusting your smart thermostat or grocery delivery based on recovery and glucose spikes).

2. Hardware Consolidation: Whether independent players (e.g., Oura, WHOOP) can maintain their independence or become acquisition targets for Big Tech looking to buy underlying biometric data liquidity.

The winner won’t be the “bot” with the best personality; it will be the platform that most seamlessly integrates into the fabric of a user’s daily life, turning biological signals into effortless longevity.