Everyone loves saying AI is moving at lightning speed, but most AI commentary today confuses noise with real signal. The three shifts below are far more foundational: they're unfolding relatively quietly, but once they take hold, they'll reshape everything built on top. Over $1 trillion has been raised and spent to get AI where it is today. In the next 12 months, three core transitions will determine where that massive sum actually creates value.
Shift #1: The Real Value Lives in the Harness, Not the Model
For the past two years, the AI world has been obsessed with one question: whose AI model is the smartest? That's actually the wrong question. What we really need isn't a smarter model—it's a better system.
Prediction 1: Harness Will Become the Key Asset
Developers are starting to build loyalty around harness rather than specific AI models, the way a programmer might stick with Vim or Emacs.
Large language models process text. Fundamentally, they're probabilistic prediction systems without any guarantees. A harness is deterministic code wrapped around your interaction with the model. It contains business logic, evaluation loops, context management, and security guardrails.
Advanced harnesses add safety features, use-case-specific logic, and even complex workflows. Developers are adopting specialized harnesses for coding—tools like Claude Code or OpenCode—that let them swap models based on preference or cost while keeping a high-quality workflow intact. Task-specific harnesses are also emerging for presentations, code generation, document editing, and data collection.
Think of an LLM like an untrained employee: they don't need to be brilliant if you give them a well-designed process to follow.
Prediction 2: Companies Will Build Their Own Harnesses
We can probably stop treating prompt engineering as a long-term skill. Enterprise AI development is essentially reverting to what software engineering has always been: converting business processes into deterministic code.
Non-deterministic models let you scale heuristics and handle complex edge cases—which are actually the real source of technical debt and complexity in any system. An LLM that correctly handles 90% of edge cases is already a fantastic tool. To leverage that, companies will build custom harnesses designed around their proprietary business logic, using frameworks like LangGraph, CrewAI, or AutoGen.
Generic harnesses from Anthropic and Google work fine for routine knowledge work. But as AI enters a new "industrial revolution" at scale, companies will need enterprise harnesses built in-house. This is the inevitable direction the industry is heading.
Prediction 3: Document-Based Workflows Will Become the Standard
Single-agent systems are easy to deploy, but when you want to scale both power and complexity, you move to multi-agent workflows. Just like in the real world, companies aren't collections of totally independent individuals. They're teams, departments, and organizations where each person handles specific tasks but coordinates to complete multi-person processes.
Like humans, agents work most efficiently when focused on one task at a time. Each agent can be a specialist, and the system scales by connecting them together. There are plenty of complex frameworks for this—just like there are countless task management systems for humans. But companies keep gravitating toward document-based workflows.
From wikis to network drives, most companies already run on documents, folders, and forms. Each form is like an API for humans—it feeds data into a process. Each updated document acts as an event that moves the process to the next step.
Document-based workflows are popular for a simple reason: they're easy to deploy, easy to modify, and genuinely scalable. They provide visibility and auditability, support security and access control, and integrate naturally across teams—even across organizations.
Sure, agent-to-agent orchestration systems and powerful workflow managers will keep evolving. But simple document-based workflows will become the default tool. They let agents coordinate with humans and are the fastest path to dropping AI into existing processes—because most companies already have the infrastructure they need.
This is exactly what agents were built for. They're particularly good at text, documents, reading, and filling forms. The tedious parts of document work that humans hate doing? That's where agents excel. Injecting agents into routine, repetitive knowledge work is where we'll see real transformation happen.
Shift #2: AI Models Are Becoming Interchangeable Parts
As value shifts from model to harness, the underlying foundation model becomes just another replaceable component—like a part in a machine. And when something becomes interchangeable, its price drops.
Prediction 4: The Race for Cutting-Edge LLMs Will Cool Off
The last two years have been driven by a frontier model arms race, with major providers constantly releasing more powerful models (except where the US blocks them).
That race is now hitting economic reality. Billions in subsidies are giving way to actual API bills. We're already seeing pricing move closer to real costs—along with plenty of stories about shocked CEOs facing massive AI invoices or companies rehiring humans to manage expenses.
What's interesting here is that despite models getting stronger, actual practical value isn't increasing proportionally. Smaller, cheaper models are good enough for most tasks. Just like everything else—not every car needs to be luxury, not every CPU needs to be the fastest. Most of the world runs on cheap components, and most of AI's future will too.
Prediction 5: Open-Source LLMs Will Win
Cheap components you don't control won't stay cheap for long. That's the other half of the commoditization story.
Once vendor lock-in takes hold, cheap components you can't control quickly become expensive. Companies building custom harnesses have a massive structural advantage: model independence.
Cloud-hosted models are currently heavily subsidized, making token costs far lower than actual expenses. Providers are betting that LLM operating costs will drop fast enough to keep the market viable while giving companies time to migrate workloads to hosted solutions. But once you're locked into a platform, prices will rise. Many tasks you'd happily hand to AI today might not pencil out on those platforms anymore.
Technology only truly scales when it becomes a commodity. That's why serious AI companies will lean on open-source models for most daily needs. Routine tasks don't need frontier models. Major companies like Netflix are already running their own LLM servers with open models to control costs more directly.
That's where custom internal harnesses become critical. Companies use them to build deterministic guardrails—and one of the most important guardrails is cost control.
Shift #3: LLMs Are Just One Piece of a Larger, Connected AI System
If models are just a commodity component, the real system will be a combination of many different technologies, where multiple types of AI work together and increasingly operate across organizational boundaries.
Prediction 6: LLMs Won't Be the Silver Bullet
When relational databases arrived, people wanted to use them for everything: message queues, file systems, you name it.
Generative AI isn't a silver bullet either. I've seen teams use LLMs for recommendation systems, predictive analytics, machine learning pipelines, knowledge systems, search tools, and data analysis. Like a general-purpose database, LLMs adapt remarkably well. But they're also expensive and inefficient for some jobs.
Generative AI is built on decades of existing AI techniques—deep learning, machine learning, natural language processing, predictive models. Those foundational technologies still exist, and for many tasks they outperform a general-purpose LLM.
When companies deploy custom harnesses, they'll build multi-modal agents using the entire spectrum of AI technology. LLMs will handle the fast, flexible coordination between complex processes, but they'll work alongside other AI systems instead of replacing them. LLMs won't solve every problem.
Prediction 7: Today's Protocols Are Just the Beginning
MCP is a dead protocol for the internet. I'm not sure whether A2A will be the replacement, but agents will definitely operate across organizational boundaries and need safe coordination.
Internet history is a story of gradually retreating from an overly open system, then layering on security, authentication, and observability later. MCP is following that same path.
Given current opportunity scale and hard-won lessons, agent-to-agent coordination at internet scale will start with a protocol designed from the ground up for security and scalability, rather than bolting these on afterward.
What Does This Future Look Like?
Stitch all three shifts together and the picture gets clear. Value will flow away from models and into harnesses. Models, no longer the prize, will become a commodity that companies should run themselves instead of renting from others. And what we build with those models won't be a single all-powerful AI—it'll be a connected, multi-component system of specialized AI experts orchestrated through one of the simplest and most resilient infrastructures we have: documents.
Every technology platform eventually commoditizes at the foundation and creates differentiation at the top. CPUs became a commodity. Linux became a commodity. Cloud computing did too. Business value then moves up to applications, workflows, and data. LLMs are following the same path.
This may not match the vision AI hype promised over the past few years. But it might actually be better, because this is AI actually getting deployed and creating real value. Teams that understand these shifts, invest in harnesses, use cost-manageable models, and deploy agents to automate routine, repetitive business work will pull ahead.
Teams still waiting for the next generation of AI models to "save" them? They'll probably be waiting a while.
Description: The AI industry is confusing noise with signal. Here are three fundamental changes that will actually matter.
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