People aren’t the first thing that catch the eye in many contemporary offices. The software that glows on their screens is the same thing.
Last fall, engineers were using the same AI tools while sitting shoulder to shoulder under exposed ductwork in a San Francisco startup workspace. The prompts had a new appearance. The websites of the companies featured distinct logos. However, the same few foundational models, operating covertly elsewhere, provided the intelligence for the answers.
| Category | Details |
|---|---|
| Technology | Generative Artificial Intelligence Models |
| Major Providers | OpenAI, Google, Anthropic |
| Core Shift | Competitive advantage moving from model access to proprietary context |
| Key Strategy | Context engineering using internal data, workflows, and feedback loops |
| Business Impact | Improved decision-making, personalization, and operational efficiency |
| Authentic Reference | https://www.weglot.com/blog/critical-thinking-ai-era |
This insight alters the perception of competition.
AI was sufficient for a short time. Businesses proudly promoted it by labeling their products as “AI-powered,” much like they used to when they boasted about being “on the internet.” Those signals were rewarded by investors, who provided funding for anything that seemed even remotely related to the technology. However, a subtle change occurred during the past year. AI itself ceased to be uncommon.
It developed into infrastructure. These days, models from firms like OpenAI, Google, and Anthropic function remarkably similarly. They write emails with skill. They provide summaries of reports. They respond to inquiries from clients in a fluent and convincing manner. It’s difficult to avoid the feeling that the technology is no longer the differentiator when you watch them in action. Now, what the model knows about you is important.
Businesses seem to be learning this almost against their will. It’s now simple to access powerful AI. However, convenient access leads to uneasy parity. Advantage quickly wanes when rivals can use the same tools overnight. Features that used to support high-end pricing now seem commonplace. Quietly, context turns into the one thing rivals can’t imitate.
Dispatchers in the Rotterdam operations center of a logistics company watch as delivery routes change in real time. Their AI system anticipates delays before they occur and makes recommendations for changes. The model is not unique in and of itself. However, the company owns the data that feeds it, including weather patterns, driver behavior, delivery history, and customer peculiarities.
Everything is altered by that private memory.
Generic AI provides generic solutions. However, AI starts to feel more like institutional instinct and less like software once it comprehends how a particular company really functions. It foresees issues. Culture is reflected in it. It adapts in ways that are difficult for outsiders to imitate.
This could be the point at which the true AI divide appears. Big businesses have decades’ worth of data stored in internal systems, just waiting to be put to use. Conversely, smaller businesses frequently have more focused, cleaner datasets that show clearly defined procedures. Although they are of quite different types, both have context. It’s possible that clarity, rather than size, is the advantage.
As this develops, people are beginning to understand that context is more than just information. Experience is what it is.
There’s something human about the idea. It can be like asking a stranger for life advice when you ask a generic AI for business advice. The responses are capable but dispassionate. The tone completely shifts when you feed that same AI years’ worth of business decisions, client feedback, and operational history. The guidance gets more detailed. more assured. It is sometimes disturbingly accurate.
Businesses are now purposefully creating complex systems to capture this context.
It is referred to by engineers as “context engineering,” a term that was hardly used two years ago. Teams concentrate on providing AI with richer background data rather than creating better prompts. internal records. interactions with customers. patterns of workflow. Everything that outlines the true operations of a business. The company’s personality is learned by the AI.
This is a metaphor that is almost biological. Intelligence is provided by models. Memory is provided by context. Identity is also shaped by memory.
Some executives appear to have an innate understanding of this. Discussions about AI in boardrooms are moving from models to data ownership. Who is in charge of dealing with customers? Who keeps operational knowledge? who is able to create feedback loops that improve AI performance over time.
Investors appear to think that businesses with the most valuable context will subtly rule.
It’s difficult to ignore the parallels between this and past technological revolutions. With the widespread availability of electricity, factories began to compete on the basis of their work organization rather than their power access. AI seems to be going in the same direction. Everyone has access. Application is the source of advantage.
Uncertainty still persists.
Companies’ understanding of the long-term effects of feeding their institutional memory into machines is still lacking. Although context generates power, it also breeds reliance. Workflows that heavily rely on contextual AI make system switching challenging. The situation itself takes on a corporate gravity of sorts.
It seems impossible to leave. The idea of machines having such a profound understanding of businesses is also subtly unsettling. not only their goods but also their behaviors. their ineffectiveness. their unwritten guidelines. The AI is unable to forget embarrassing errors or poor choices. It incorporates them and keeps learning.
That recollection endures forever. Earlier this year, at a technology conference in Berlin, almost all of the booths promised smarter AI. Quicker AI. AI with greater capability. The discussions taking place offstage, however, painted a different picture. The founders talked more about data pipelines than models. It’s more about memory than intelligence.
It’s difficult to ignore how swiftly the plot evolved. AI models increased the amount of intelligence. Once more, context made advantage scarce. And competition regained its footing during that silent shift.
