ConteeVision
Three eras of the generative AI transformation: a documented diagnosis, and an explicit wager on the next one.
The three eras
Who produces, and what: that is what changes from one era to the next.
Era 1 — the chat
Language models are used through a provider's interface. A handful of developers produce; the vast majority use. Content stays static, handled alone on one's own computer.
Era 2 — agents, under way
Agents and automations almost everywhere. The majority now produces, but static content: summaries, documents, analyses. Dynamic content stays rare, confined to each person's computer.
Era 3 — shared dynamic content
Mid-sized problems are taken on in turn: applications that serve a group. A wider minority produces, the majority uses. It takes an end-to-end, tooled chain — that is what Contee builds.
Where the difference is made
When everyone summarises their meetings with AI, static content becomes the norm.
The difference will come from the minority that manages to produce shared dynamic content, and the benefit will go to the majority who use it. The model places the lever with those who take the step.
Meso problems
Between the individual problem, handled on one's own computer, and the large enterprise application, there are meso problems: too big for one workstation, too small for corporate IT. They involve two to a few dozen people, often across several departments: adopting cybersecurity on a specific point, aligning purchasing policies, applying a new HR rule.
Today they are handled with spreadsheets, slide decks and manual processes, which hold while few people contribute and the rule changes little. Beyond that, the lock is the entry cost: modelling, gathering a team, going into production, getting adoption. The wager: AI and the Contee Pipeline bring that cost down, and a growing share of the long tail of meso problems becomes workable.
Context Engineering, from the extraction end
Contee answers each situation with the forms that seem best suited to its constraints. That is the reason for its Context Engineering, which starts well before the language model.
1 · Extraction
Understand the reality of a situation and its complexity, to draw out its constraints: quantitative and qualitative, the capabilities available and their cost, what is projected for what comes next.
2 · Form
The constraints decide the form of each piece of the situation: generative AI, machine learning, code, process or human decision.
3 · Processing
When a form calls on generative AI, the extracted context is written down where it serves, in a fitting format, and in a modality that preserves the other constraints: token efficiency, confidentiality. Managing the model's context window comes last.
What is documented, what is wagered
Presenting this model as a fact would be easy. Here is what is measured, what remains a wager, and what would prove it wrong.
Documented
Eras 1 and 2 describe a measured reality: more than 90% of employees use personal AI assistants at work (MIT NANDA), while about eight companies in ten see no effect on their results (McKinsey). Agents are spreading but hitting a ceiling: Gartner expects more than 40% of agentic projects to be abandoned by 2027.
Wagered
Era 3 does not exist yet: it is Contee's wager. Its mechanism, however, is measurable — the collapse of creation costs, and an unmet demand revealed by shadow IT: 30 to 40% of IT spending according to Gartner, 50% or more according to Everest Group.
Falsifiable
If, five years from now, application production remains concentrated among professional developers alone despite the fall in entry cost — if the wider minority does not materialise — the thesis was wrong.
A meso problem resisting you, across several teams? That is exactly the Contee Pipeline's ground. Let's talk.