The Tech Stack Every AI Investment Depends On
Why real AI innovation comes from mastering the unseen layers of architecture, not just flashy applications.
For the past several months I have played key roles in the generative AI boom and frenzy, developing product demos, prototypes, data schemas, pitch decks, financial models and market forecasts. It’s been awesome, but behind the excitement lies a tough truth underscored in everything I build: real AI products aren’t built on prompts, chatbot or UX polish alone, it’s based on what we don’t see (and probably misunderstand)….the multi-layered infrastructure beneath it…. the stack that actually makes the AI system performant, scalable, and differentiated.
Everyone involved, from founders, to investors, to engineering and product leads to marketing, I urge you all to take a deep look below the surface. In my role advising startups, I educate and refer to a well published 7-layer AI architecture framework that helps demystify how AI systems are truly built. For those that have worked with me in the past, this is the “How Things Work” table of contents.
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This stack helps a wide variety of teams frame how value is created (and lost) across an AI company’s lifecycle. Many of the most “investable” companies right now aren’t just building chatbots—they’re tackling tough problems in orchestration (Layer 2), retrieval and reasoning (Layer 4), and training optimization (Layer 5).
Developing the Moat (Differentiator)
Layer 7, the application layer, is easy to build and easy to clone. It’s where hype lives. But durable moats tend to form in the middle layers. Companies that build their own RAG pipelines, fine-tune proprietary models, or optimize embeddings for unique use cases (Layers 4–6) are not only more defensible, they’re also often more cost-efficient in the long run.
Investors should be mindful of what’s rented vs. what’s owned. If a startup depends entirely on OpenAI’s APIs, they’re riding someone else’s infrastructure and pricing. That’s not inherently bad—but it affects margins, defensibility, and exit strategies.
How to Execute on a Product Strategy That Truly Differentiates
Okay, time for some brass tacks. Knowing where the moat lives is only half the battle—executing on it requires intentional decisions across your product, architecture, and go-to-market strategy. If you want to build durable value in AI, especially in Layers 4–6 (Knowledge, Learning, Representation), here’s how I recommend approach it:
1. Own Your Data Workflows Early
Your data—how it’s retrieved, cleaned, embedded, and structured—becomes a compounding advantage. Invest early in:
Custom embedding strategies tailored to your domain (e.g., financial compliance, medical diagnostics).
Building internal ontologies or taxonomies that feed RAG pipelines or augment LLM prompts.
Constructing feedback loops between application usage (Layer 7) and representation models (Layer 6).
💡 A founder who controls their training and retrieval data controls their roadmap.
2. Prioritize Middle-Layer Engineering Capacity
Hiring shouldn't be skewed toward frontend engineers or product designers too early. To build defensible infrastructure:
Recruit ML ops and RAG system engineers who can build internal tools, not just plug in third-party services.
Build lightweight, modular platforms that allow you to swap out models or vector DBs as cost/latency/performance shifts.
Focus on instrumentation across Layers 4–6 to understand how changes in logic or retrieval affect user satisfaction.
💡 Moats are made by engineers who think in terms of schemas and systems, not screens.
3. Balance Rented vs. Owned Stack Elements
If you’re entirely dependent on OpenAI or any single API provider:
Develop an exit strategy that includes hosting smaller open-source models (e.g., Mistral, LLaMA 3) or leveraging hybrid options like AWS Bedrock.
Containerize your training and inference environments so that migration across clouds is feasible within 90 days.
Invest in cost observability tooling so your margin per inference is transparent and optimizable.
💡 APIs are great accelerators—but without a fallback plan, they are a cliff.
4. Define Differentiating-Building Metrics
Beyond MAUs and churn, define KPIs that track the deep parts of the stack:
Embedding recall improvement over time (Layer 6)
Query-to-answer latency in RAG or hybrid pipelines (Layer 4)
Reduction in hallucinations or false positives after training loops (Layer 5)
These internal metrics show whether your investment in infrastructure is turning into advantage.
5. Use Applications as Feedback Engines
Even if your moat is in the middle layers, don’t ignore Layer 7. Build minimal, opinionated applications that:
Create real-world usage data to refine model performance.
Show investors and customers how your underlying IP creates better outcomes.
Let you test pricing, trust, and UX assumptions.
💡 A great application is not the moat—it’s the showcase for the moat.
TLDR
Please don’t ever say GAI is magic. It’s an engineering discipline built across a stack of interdependent layers. As AI adoption grows and market competition intensifies, the winners won’t just be those who build the flashiest products. They’ll be the teams that architect intelligently, optimize relentlessly, and know where their real advantage lives—deep in the stack.
Attribution
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