We use cookies to understand how you use our site and improve your experience. Cookie policy
Last updated:
Vision: Unleash a billion knowledge workers to think, create, and decide.
Mission: Build the OS for agentic organizations, where agents execute, humans decide, and teams move at the speed of their ambition.
Will AI kill knowledge work, or save it?
For the last decade, knowledge work has drifted away from its original promise. It was supposed to be about thinking, creating, deciding, solving hard problems, and building new things. Instead, too much of it became status meetings, reporting, coordination, documentation, alignment rituals, tool maintenance, and burnout.
Very little of modern knowledge work actually feels like deep work.
We believe AI changes that. But not in the simple way people often describe.
AI does not just automate knowledge work. It changes the unit of work.
More and more work now happens inside sessions between humans and agents. Research, reasoning, drafting, analysis, planning, decisions, and execution happen before anything reaches a doc, ticket, CRM, or meeting.
That creates a new reality: the work gets faster, but the context of how it happened becomes harder to see, share, govern, and reuse.
The promise is that humans get to spend more time on the real work. Thinking. Creating. Deciding.
The risk is that the reasoning behind that work disappears into AI sessions the rest of the organization cannot see.
Every serious knowledge organization will have to decide what AI-native means for them. We do not know exactly what that looks like. But the direction is becoming clearer.
The session is becoming a new unit of knowledge work.
Many teams will become smaller, faster, and more leveraged. More people will manage work through agents. More people will operate like builders and innovators inside their own domain.
But speed alone will not be enough.
Soon, everyone will be able to execute more. Models will become more powerful and more accessible. The real edge will be judgment.
What do you choose to build? What do you choose not to build? What is your taste? What do you understand about your customers, your market, your product, and your organization that others do not?
In an AI-native world, a growing share of digital execution becomes cheaper. Judgment, context, and taste become more valuable.
Around January, something changed for us.
We started doing more deep work than we have done at almost any other point in our careers. We spent more time planning, thinking, exploring solutions, and making calls. We used agents to execute. We used sessions to reason. We moved faster. Everyone could produce more.
But that speed created a new bottleneck. Context switching became harder. Reviewing became harder. Sharing what had happened became harder. Standups and demos took too long because too much had been done.
Traditional tools started feeling too slow and too rigid.
The individual became faster than the organization's ability to absorb the work.
That is the important part.
AI does not just make people more productive. It creates a new organizational problem: people can now think, explore, and execute faster than the company can understand, coordinate, and compound.
This is the gap Palette has to close.
Companies are caught between two bad options. Jump blindly into AI, buy tools, give people access, and hope it creates value. Or wait, move cautiously, and risk being left behind.
Most organizations are somewhere in the middle: convinced AI matters, but unsure how to operationalize it. They see pockets of adoption, but not system-level change.
Engineering teams are often the first to figure it out because the loop is visible. Code can be written, reviewed, tested, and shipped.
The rest of the organization is harder. Strategy, sales, customer success, operations, product, leadership, and people work are full of tacit knowledge. Reasoning. Trade-offs. Customer nuance. Decision history. Context. Taste.
AI can help with all of this, but only if the organization has a system for capturing, maintaining, and reusing the context the work depends on.
Without that, AI creates a strange new failure mode: everyone moves faster, but the organization remembers less.
For the last two decades, companies coordinated through systems of record. Slack held conversations. Jira and Linear held work. GitHub held code. Notion and Confluence held knowledge. Salesforce and HubSpot held customers.
These systems were imperfect, but they gave organizations something important: work left traces.
Now more of the highest-value work happens before the artifact exists. It happens inside AI sessions and agent loops.
This is where people are thinking. This is where agents are helping. This is where decisions begin to form.
But most of it is invisible to the organization. Even when the output is shared, the reasoning often is not.
The company is no longer losing knowledge only because people fail to write things down. It is losing knowledge because the work itself has moved into sessions the organization does not understand.
AI-native organizations do not just need better search, better docs, or more meetings.
They need a maintained context layer that captures what happened inside the work, not just the final artifact.
A system that helps the organization understand:
This is not documentation in the old sense. It is the connective tissue between human reasoning, agent execution, and organizational memory.
Because when everyone has access to powerful models, the model is not the moat. The tools are not the moat. The ability to generate output is not the moat.
The moat is the organization's accumulated judgment. Its context. Its taste. Its decisions. Its customer understanding. Its history. Its collective know-how.
That is what agents need in order to act well. That is what humans need in order to decide well.
That is what Palette should organize.
AI adoption is no longer theoretical. People are using agents. Work is happening in sessions. Teams are moving faster. But the organization is struggling to keep up.
The first wave of AI adoption was about access: give people ChatGPT, Claude, Copilot, Cursor, or internal agents. The next wave is about coordination: how does the organization understand, govern, and compound the work happening through those tools?
At the same time, AI makes it possible to capture something organizations have always wanted but rarely had: the thinking behind the work.
Traditional tools mostly captured outcomes. AI sessions can expose the process: the reasoning, alternatives, trade-offs, questions, assumptions, and decisions that led to the outcome.
That is a new kind of organizational context. Not just a knowledge base. A living context layer for humans and agents.
We believe knowledge work will not disappear. It will be redefined.
We believe the best organizations will be the ones where agents execute and humans decide.
We believe speed will become table stakes, and judgment will become the edge.
We believe Palette should help organizations move at the speed of their ambition without losing their collective intelligence.
We exist to unleash a billion knowledge workers to think, create, and decide.
We do it by building the OS for agentic organizations, where agents execute, humans decide, and teams move at the speed of their ambition.
This is the story we can build around.
Not AI for more output. AI for better work.
Not replacing knowledge work. Restoring it.
Learn more about the team building Palette, or start with the Welcome guide.