• As applications commoditize and platform embedding destroys standalone categories, value migrates downward — to infrastructure.
• Investors should overweight cloud, chips, model APIs, and developer tooling, and underweight consumer-facing AI applications.
• There are only three conviction levels that reflect the structural dynamics of the AI economy: core infra (high), workflow winners (medium), standalone tools (low/short).
  Source: BusinessEngineer.ai

The Investment Thesis

Applications commoditize. Infrastructure captures value.

The economic logic is clear:

• Consumer AI tools lose pricing power as platforms embed capabilities.
• Standalone categories collapse under distribution asymmetry.
• Infrastructure enables all players without competing with customers.
• B2B foundation layers grow as AI becomes operational and agentic.

This follows a consistent pattern across the Business Engineer playbook:

• As capabilities converge → features die
• As platforms consolidate → workflows lock in
• As systems grow in complexity → infrastructure becomes indispensable
  Source: BusinessEngineer.ai

The conviction hierarchy is straightforward:

1. High conviction: core infrastructure
2. Medium conviction: workflow platforms with durable moats
3. Low/short conviction: standalone tools, wrappers, horizontal apps

Everything else is noise.

Three Conviction Levels

High Conviction: Core Infrastructure

Foundational layers with durable, compounding demand

High-conviction assets sit at the base layer of the AI stack — where value scales with model complexity, agentic workflows, and enterprise orchestration. These assets win regardless of who wins the application layer.

The Bets

• Cloud infrastructure (AWS, Azure, GCP)
  AI adoption drives exponential compute consumption.
• Chips (NVIDIA, AMD, custom silicon)
  GPUs are the new oil — bottleneck economics dominate.
• Developer tools + orchestration frameworks
  LangChain, model routers, memory layers, safety engines — the “picks and shovels” enabling AI builders.
• Model APIs
  OpenAI, Anthropic, frontier open weights — foundational capability providers.

The Logic

Every improvement in AI — from better models to multi-agent systems — increases:

• compute consumption
• orchestration complexity
• infrastructure reliance

Infrastructure captures value regardless of application winners.

Risk Level

Low — structural demand growth, not tied to end-user appetite.
Source: BusinessEngineer.ai

Medium Conviction: Workflow Winners

Platforms with successful AI integration and durable user lock-in

This category includes deep workflow systems where AI increases switching costs and strengthens existing moats.

The Bets

• Workflow platforms that successfully embed AI
  (Notion, Figma, Adobe)
• Vertical SaaS with regulatory moats or domain-specific data
  (Legal AI, financial risk platforms, healthcare ops)

Selection Criteria

Success depends on:

• distribution advantage
• AI embedding depth
• workflow continuity
• defensible differentiated data

Platforms that turn AI into workflow glue — not separate destinations — consolidate their category.

Why Medium Conviction?

Execution-dependent.
If these companies integrate AI well, they dominate. If not, they get absorbed into platform ecosystems.

The Logic

AI augmentation reinforces:

• user habits
• switching costs
• organizational lock-in
• proprietary knowledge loops

Workflow continuity prevents disruption.

Risk Level

Medium — depends on organizational velocity and product discipline.
Source: BusinessEngineer.ai

Low / Short Conviction: Standalone Tools

Generic AI without moats or distribution

This is where the structural collapse is already underway.

The Shorts

• Generic AI wrappers
• Image generation tools
• Productivity assistants
• Consumer AI utilities
• Writing tools
• Anything with “AI Assistant for X” as the value proposition

These categories experience rapid decay once platforms embed the core feature.

The Logic

Platform embedding:

• destroys pricing power
• eliminates willingness to pay
• shifts usage into existing workflows
• compresses margins to zero

There is a 6–18 month window before complete commoditization.

Standalone tools don’t lose because they’re “bad.”
They lose because they’re redundant once embedded.

Leading Indicator

Traffic decline predicts revenue collapse by 2–3 quarters — because users shift behavior before revenue reflects it.

Investors who short before earnings capture the leading-edge of the collapse.

Risk Level

High — structural failure + accelerating commoditization.
Source: BusinessEngineer.ai

Tactical Observation

Traffic is the earliest indicator of AI tool collapse.

Because platform embedding redirects behavior instantly, web traffic decays long before financials reveal the damage. Investors can use traffic curves as a predictive indicator:

• Traffic declines → usage shifts
• Usage shifts → revenue collapses
• Revenue collapses → category dies

This sequence consistently runs 2–3 quarters ahead of earnings.

Traffic is the canary in the AI coal mine.

Strategic Insight

Infrastructure is where value accumulates.
Workflow platforms are where moats deepen.
Standalone tools are where value dies.

Investors who understand the structural physics of the AI economy can position ahead of the consolidation curve:

• Go long on infrastructure
• Selectively back workflow platforms with clear AI embedding
• Short or avoid anything that competes with platform defaults

This is the investor playbook for the AI era.
Source: BusinessEngineer.ai