Forget prompt engineering. Context is the new compute

While everyone’s chasing bigger models and cleverer prompts, a silent infrastructure crisis is quietly crippling real-world AI adoption.

The $500 Billion Blind Spot

The AI arms race is in full swing. OpenAI, Google, and Meta are throwing billions at model scale—Stargate alone promises a $500B investment in compute infrastructure.

But beneath the surface, a deeper problem is derailing even the most promising AI projects.

It’s not model size. It’s not prompt wording. It’s not data quantity.

The real bottleneck? Context engineering—the art and science of giving LLMs the right information, in the right format, at the right time.

And almost no one is doing it well.

The Great Misunderstanding

“Just write better prompts” is killing your AI ROI

In early 2025, Analytics India Magazine made a bold claim:

“Context engineering is 10x better than prompt engineering—and 100x better than vibe coding.”

Shopify CEO Tobi Lütke agrees:

“It’s about giving LLMs the full context to plausibly solve a task.”

Even Andrej Karpathy chimed in with a simple “+1.”

But here’s the brutal truth:

While product teams spend weeks polishing prompts, they often ignore the messy, fragile, high-leverage system that wraps around them: the context pipeline.

What’s Actually Going Wrong

95% of real-world LLM failures come from context—not model flaws

A 2025 study found that nearly all production LLM failures come down to context-related issues:

• Missing information or dependencies
• Poorly structured documents
• Overwhelming or irrelevant context dumps

LLMs today can handle up to 1 million tokens—but most enterprise pipelines feed them input a human would struggle to parse.

“When LLMs fail, it’s rarely the model’s fault—it’s the system around it that sets them up to fail.” — Harrison Chase, LangChain

Why AI Pilots Succeed and Production Fails

The ugly truth behind enterprise AI deployments

According to Cognition AI’s 2025 report, 78% of enterprises see huge performance drop-offs when moving LLMs from prototype to production.

It’s not because:

• The prompts are bad
• The models aren’t smart enough
• You don’t have enough GPUs

It’s because nobody is engineering the context pipeline.

One engineer put it perfectly:

“People are still shouting ‘learn prompt engineering!’ But the real leverage is in context engineering—building systems that know what information to feed, when, and how.”

What Makes Up a Good Context System?

The 4 Invisible Layers Killing Your AI App

1. Memory & State Tracking Most LLM apps forget crucial information across turns. Traditional state machines don’t apply—yet most teams haven’t replaced them with context-aware alternatives.

2. Retrieval Gone Wrong RAG is popular, but dumping documents into a prompt isn’t enough. You need structure, hierarchy, and temporal relevance—or you overwhelm the model.

3. Data Curation Failures Stanford’s CRFM found that 60% of LLM evals suffer from context contamination. Few teams validate or sanitize context input effectively.

4. Security & Integrity Attackers now target context pipelines, not just models. If your context is poisoned or manipulated, the LLM becomes a weaponized response engine.

The Economics of Neglect

You’re not paying for inference—you’re paying for garbage in

Inference costs are dropping. But context engineering isn’t a one-time task—it’s a continuous investment.

The math is brutal:

DeepSeek, a rising open-source contender, proved this in 2025: They outperformed bigger rivals not with better models—but with superior context design.

The Path Forward

We don’t need bigger models. We need better infrastructure.

To fix this, we need a new discipline:

Context Engineering = Information Architecture for AI

Here’s what that looks like:

• ContextOps pipelines: Monitor, debug, and version context flows like code.
• Dynamic Memory Systems: Maintain state across sessions and tasks.
• New Metrics: Don’t just test the model—test how it handles changing context.
• Tooling: IDEs for context debugging, not just prompt tweaking.
• Curriculum Shift: Teach context engineering alongside prompt design and model tuning.

The AI Shakeout Is Coming

2025 is the inflection point.

Companies that master context engineering will:

• Spend less on infra
• Deliver better AI outcomes
• Build moats with system design, not just parameter count

If you’re building LLM apps:

• Stop polishing prompts and start architecting context.
• Evaluate your system’s ability to manage memory, relevance, and retrieval.
• Invest in ContextOps before your AI budget gets burned.

Those that don’t? They’ll burn millions chasing prompt hacks while shipping broken products.

Are you seeing these failures in your AI projects? Is your company thinking about context engineering yet?

Drop your experience in the comments or message me directly. I’d love to hear how you're tackling this.