Why Every Company Is Quietly Building an AI Stack (And Why Most Will Fail)

Walk into the engineering org of any Fortune 500 company and ask “how are you doing AI?”.?” You’ll get one of three answers.

The honest answer: “We’re figuring it out.””

The strategic answer: “We have an AI centre of excellence that’s evaluating options.””

The bullshit answer: “We’re using ChatGPT for everything and pretending we’re AI-first.””

All three are wrong, but only one of them is dangerous.

The AI stack is real and most companies are building one

A modern AI stack is roughly:

Foundation models — frontier LLMs from OpenAI, Anthropic, Google, or open-source alternatives.
Fine-tuning and adaptation — LoRA, RLHF, or domain-specific fine-tuning on top of base models.
Retrieval and context — vector databases, embeddings, RAGAG pipelines.
Orchestration — agent frameworks, tool integrations, workflow builders.
Evaluation — testing frameworks, hallucination detection, performance monitoring.
Deployment — inference infrastructure, scaling, monitoring, observability.
Data and feedback loops — user feedback, training data generation, model iteration.

A complete stack touches at least 8-10 distinct technologies, often from 5-8 different vendors. Building this stack from scratch is a 6-12 month project for a well-funded team. Building it badly is a 5-year project for an under-funded team.

The companies that have it: OpenAI, Anthropic, Google, Microsoft, Meta, Apple, Amazon, a handful of well-funded startups (Mistral, Cohere, AI21).

The companies that are building it: every Fortune 500 with an AI initiative.

The companies that think they’re building it but are actually just buying API access: most of the Fortune 500.

Why most will fail

Three reasons.

1. They’re building the wrong stack

The most common mistake is building a stack optimised for the AI demos the executives saw, not the AI applications the business needs.

A bank doesn’t need a frontier model. It needs a model that’s good at extracting structured data from documents, with strong guardrails and audit trails. A frontier model is overkill — and expensive.

A retailer doesn’t need an agent framework. It needs a recommendation system that’s personalised, fast, and explainable. An agent framework is overkill — and slow.

The right stack depends on the application. Most companies don’t know what application they’re building, so they build a generic stack that’s bad at everything.

2. They’re duplicating vendor capability

The most expensive mistake is paying twice for the same capability.

If you use OpenAI’s API, OpenAI has already built:

Model serving and scaling.
A/B testing infrastructure.
Latency optimisation.
Caching and rate limiting.
Evaluation frameworks.

If you also build your own model serving, you’ve paid twice for the same capability. The custom infrastructure is rarely better than the vendor’s, and it’s always more expensive to maintain.

The same applies to other layers. If Pinecone provides vector search, you probably don’t need to build your own vector database. If LangChain provides agent orchestration, you probably don’t need to build your own agent framework.

3. They’re locking themselves into a single vendor

The most strategic mistake is choosing one vendor and ignoring the others.

OpenAI is great. Anthropic is great. Google is great. Meta’s Llama is great. Mistral is great. They each have strengths and weaknesses. The optimal strategy is multi-vendor: use each vendor for what they’re best at, and have a fallback if one vendor has an issue.

Multi-vendor requires more engineering work. You need abstraction layers, routing logic, and monitoring across vendors. It’s harder than single-vendor.

But single-vendor means you’re at the mercy of the vendor’s pricing, roadmap, and reliability. The history of enterprise IT is a graveyard of single-vendor lock-ins that became expensive to escape.

What to do instead

Three recommendations for companies building AI infrastructure.

1. Start with the application, not the stack

What’s the specific business problem you’re solving? Document it in two paragraphs. Show it to the executives. Get sign-off. Then design the stack around the problem.

The stack is a means, not an end. If the stack is the centre of attention, you’re doing it wrong.

2. Buy before you build

For every component of your stack, ask: does a vendor provide this? Is the vendor solution good enough? If yes, buy. If no, build.

The default should be buy. Build only when you’ve proven the vendor solution is insufficient. Most companies that “build” should actually “buy” and instead spend their engineering time on differentiation.

3. Plan for multi-vendor from day one

Don’t paint yourself into a single-vendor architecture. Use abstraction. Use standard interfaces. Use routing logic that can swap vendors.

The marginal cost of multi-vendor is low. The marginal cost of being locked in is high.

The coming shakeout

Over the next 24 months, the AI stack will consolidate. The 20+ vendors providing vector databases will shrink to 3-5. The 10+ agent frameworks will shrink to 2-3. The 5+ evaluation platforms will shrink to 2-3.

The companies that survive will be the ones that are best at one thing, not the ones that try to be best at everything. The companies that try to build the entire stack will fail.

The companies that build the right stack for the right application will succeed. That’s the lesson from every previous infrastructure wave (cloud, mobile, web). The pattern is the same.

The companies that are quietly building AI stacks right now are mostly building the wrong ones. The ones who recognise this in time will pivot. The ones who don’t will be left with expensive infrastructure that doesn’t solve business problems.

Build less. Buy more. Solve the actual problem. That’s the AI stack strategy that works.