The Speed of Progress Killing Innovation
The pitch has been the same for three years: AI is accelerating, build now or get left behind. Hundreds of billions in venture capital agree. So do most boardrooms, most LinkedIn feeds, and most engineering teams I have spoken to recently.
There is a second-order effect that almost nobody puts on a slide. AI is moving so fast that it has become hard to build anything durable on top of it. Not because the models are weak. Because the ground keeps shifting underneath whatever you ship.
Every product team I know has felt this. You commit to an architecture in March, and by September the architecture is wrong - not because you made a mistake, but because the underlying capability changed.
The Paradox of Progress
Progress usually drives adoption. Better infrastructure brings developers, better tools bring products, more products bring users. That is the loop that built the web, mobile, and cloud.
AI broke the loop. Capabilities have improved on every benchmark - Stanford's AI Index Report tracks year-on-year gains in language, vision, code, and multimodal reasoning. But the gains have not stacked into stable layers. They have replaced each other.
Two years ago, prompt engineering was a job title. A year later, RAG was the answer. Now it is agents. Each wave did not refine the previous one. It deprecated it.
When the foundation rewrites itself every six months, you are not building a platform. You are building on a moving target, and you are paying full price for both the building and the rebuilding.
The Unstable Abstraction Layer
The hard part of AI engineering today is instability, not capability. In normal software, you pick a stack and it stays viable for years. In AI, the correct way to build the same product has changed three or four times in the same period.
Six months optimizing a RAG pipeline can be erased by a single model release that ships a million-token context window and native retrieval. Six months on agent orchestration can be erased by a model that does the orchestration internally. The work was not bad. The premise expired.
This is the structural problem nobody on a panel wants to talk about: how do you justify a six-figure engineering investment when the architecture has a six-month half-life?
The Real Risk: Building Features, Not Companies
On top of the technical churn, there is platform risk. OpenAI , Google , and Anthropic used to ship engines. Now they ship products. Memory, retrieval, file handling, code execution, browser use - all features that started life as third-party SaaS layers, all now native.
If your product is a thin layer between a user and a foundation model, you are essentially holding the model provider's roadmap as a roadmap risk. The next system prompt update can collapse your differentiation overnight. A specialized document search tool, a basic coding assistant, a meeting summarizer - these are not companies right now. They are features that have not been integrated yet.
The Illusion of Capability
There is another reason enterprise adoption lags the hype. AI is capable but not consistent. Demos always show peak performance. Production lives in the long tail of edge cases.
- AI solves complex problems - when the problem looks like the training data.
- It reasons - until it does not.
- It automates a workflow - until input drifts a few percent and the whole thing fails silently.
The Illusion of Capability (Continued)
70% reliability is a miracle for a side project. It is a lawsuit waiting to happen for a payroll system. Even 95% is not good enough for anything mission-critical, because the missing 5% becomes your support queue, your churn, and eventually your reputation.
The unglamorous work of getting from 95% to 99.5% is exactly what gets disrupted every time a new model lands. Teams that fine-tuned for reliability on one generation spend the next month re-evaluating on the new one instead of shipping.
The "Build for Tomorrow" Trap
Perplexity CTO Denis Yarats has talked about building for where AI will be, not where it is . It is good advice if you have hundreds of millions to wait. For everyone else, it tends to fail in three specific ways:
- You bet on capability that may not arrive. If your product needs hallucinations to drop to zero or reasoning to get materially better, your roadmap is somebody else's research lottery.
- You build directly into the platform's path. If the gap between today's models and your product is exactly the gap OpenAI is closing next quarter, you are not the disruptor. You are the warm-up act.
- You optimize for the demo, not the daily use. Users buy reliability. A boring product that always works beats a brilliant one that occasionally implodes.
The Inversion of Platform Risk
In every previous platform era, getting close to the platform meant safety. Deep Windows integration was a moat. Deep AWS integration was a moat. Closeness to the foundation was strength.
AI flipped this. The closer your product sits to the raw model, the more exposed you are. Every model release is an asymmetric event for you and a feature ship for them. You inherit all of the platform's volatility and none of its leverage.
What Actually Works?
Some companies are quietly winning anyway. The pattern across them is consistent, and it has nothing to do with chasing benchmarks.
- They treat AI as the most replaceable part of the stack. The product solves a painful, specific problem; AI happens to provide a 10–20% lift. The defensibility lives in the workflow, the integrations, and the data the AI is operating on, not in the model itself.
- They anchor on constraints AI cannot dissolve. Regulatory friction, multi-party coordination, proprietary data access. A smarter model does not unlock these by itself.
- They build on what works today and let model improvements be upside, not oxygen. If a new release helps, great. If your product cannot exist without one, you are renting a business.
- They treat model progress as margin, not feature. A better model means cheaper inference and slightly better output. Your business gets more profitable, not more relevant. That is the right relationship to be in.
The Uncomfortable Middle Phase
We are stuck between two clocks. AI moves at research speed. Businesses need enterprise speed. The two are not compatible right now, and that is what makes this period so confusing to operate in. Builders are pushed to move fast, then punished for committing to a stack.
The mismatch is temporary. Eventually one of two things happens: foundational change slows enough for stable layers to form, or a real abstraction layer - an operating system for AI - emerges to insulate developers from the volatility below. Neither has happened yet.
A Better Mental Model
The more useful question is not how to build for the AI of tomorrow. It is how to build something that survives the AI of tomorrow.
That changes everything downstream. Stop chasing benchmarks. Own a workflow. The biggest opportunity right now is the integration around the model, not the model itself. There is a global oversupply of raw intelligence. Distribution, proprietary data, and sitting between the user and the outcome they actually care about - that is where the air gets thinner and the businesses get more durable.
The last five years rewarded the breakthrough. The next five will reward the boring discipline of putting it to work.
