wenzel orlandopen a new chat with any AI assistant. explain your project. give it context. describe your preferences. share relevant documents. get a useful response. close the tab. open a new chat. do it all again.
this is how most people use AI today. every conversation starts from zero. the cost is obvious in time (you spend 20% of every interaction re-establishing context) but hidden in quality (the AI never accumulates understanding of your domain).
in enterprise settings, this cost multiplies. a team of 50 people, each re-explaining the same project context to AI tools, each getting slightly different interpretations. there's no shared understanding. no institutional memory. no compounding.
the token cost alone is significant. if you're pasting 2,000 words of context into every conversation, and your team has 100 conversations a day, that's 200,000 tokens of redundant context per day. at current rates, that's real money. but the bigger cost is quality degradation.
a stateless system can't learn from its mistakes. it can't notice that it suggested something last week that didn't work. it can't recognize patterns across conversations. it can't improve over time. it's frozen at the quality of its base model plus whatever context you manually provide.
a persistent system is different. it remembers what worked and what didn't. it builds understanding of your domain over time. it gets better the more you use it. the marginal cost of each interaction decreases because the system already has the context it needs.
we built doobls because stateless AI is a temporary state of affairs. the platforms that persist knowledge, compound understanding, and eliminate redundant context will win. they're cheaper, faster, and better at the thing that matters: doing useful work.