Model
Supplies intelligence.
Reasoning and generation are capabilities the larger system can use.
Examples today: GPT-6 Astra · Claude · Grok
- Reasons
- Generates
- Interprets
- Answers
The AI Cognitive Revolution
Models Are Becoming More Capable. We built the operating system around them.
Persistent intelligence gives AI context, continuity, and supervision.
- OAResearch lab
- GunnerWhat we built
- OkrenThe product
The architectural shift
Models Think. Agents Work. Gunner Runs the System.
A model supplies intelligence. An agent gives that intelligence a job. Gunner keeps models, agents, memory, tools and state working together as one system over time.
Technically, Gunner is a stateful runtime on Linux, coordinating agents, memory, reasoning, and tools as one operating system for persistent intelligence.
Gunner the whole system
Agent
Gives intelligence a job.
A worker can pursue an objective, use tools and carry some context across multiple steps.
Persistent-agent examples today: Dots · Grok Bot
- Has a task or objective
- Uses tools
- Can carry memory or context
- Performs work over multiple steps
Gunner
Carries the whole body of work.
OA's operating architecture for Persistent Intelligence.
The persistent unit: the work, not one model or worker.
- Carries the objective
- Maintains memory and changing state
- Coordinates models, agents and tools
- Uses different kinds of reasoning
- Observes what happened
- Continues from changed reality
Models get smarter. Agents get more capable. Gunner keeps the whole system together over time.
See how current AI systems map to these responsibilities → Signals
It began with one question: could a person's intelligence continue after they were gone? Read our story →
The Human Cognitive Revolution
Human Intelligence Changed When We Could Remember, Share, and Build on What Others Knew.
We remembered what happened.
We shared what we knew.
We divided the work and combined what we knew.
Intelligence became cumulative.
Two kinds of intelligence
Ask AI the same question three times. You may get different answers.
That is how probabilistic AI works. It weighs possibilities and gives you the most likely answer, not a fixed one. For research, language and unclear situations, that is exactly what you want.
A declarative system works the other way. It follows rules.
The same question, asked three times“Should we approve this $1,500 payment?”Illustration. Not real output.
Probabilistic AI
Explores. Adapts. Proposes.
It reasons from likelihood. It is strong wherever the answer has to be discovered, and a different answer each time is part of how it explores.
- Asked 1st“Looks routine. Approve it.”
- Asked 2nd“That is larger than usual. Hold it.”
- Asked 3rd“Approve it, if the recipient is known.”
Three answers. Useful for exploring. Not what you want from a rule.
Declarative, rule-based
Follows the rules. Every time.
It starts from facts that are accepted and rules that are written down, then works out what follows. It does not guess, so the same facts give the same result.
The rulePayments over $1,000 need a person.
- Asked 1stAsk a person.
- Asked 2ndAsk a person.
- Asked 3rdAsk a person.
Same facts. Same rule. Same answer.
How Gunner uses both
Gunner gives each kind of intelligence the job it is good at. Probabilistic AI explores and proposes. Rules and logic handle what has to come out the same every time.
Explore where the world is uncertain.
Deduce where consistency matters.
Deduce where consistency matters.
Look around
The Industry Is Moving Toward Persistent Agents.
Memory, changing state and human authority are becoming central questions as AI moves beyond one-off answers. Here is what OA is tracking.
OA / SIGNALS 01Emily Glassberg Sands with Nate B. JonesStripe · AI News & Strategy DailySIGNAL 01 · MEMORYEmily Glassberg Sands with Nate B. JonesStripe · AI News & Strategy DailyWhat happens to permission and trust when AI becomes a buyer?OA / SIGNALS 02Satya NadellaMicrosoft · Deirdre Bosa / Yahoo FinanceSIGNAL 02 · CHANGING STATESatya NadellaMicrosoft · Deirdre Bosa / Yahoo FinanceMicrosoft describes supervising agents as they operate.OA / SIGNALS 05Alex KarpPalantir · CNBCSIGNAL 05 · AI USING TOOLSAlex KarpPalantir · CNBCPalantir describes operating across different AI models.
How Okren began
We set out to preserve human intelligence. In doing so, we discovered the infrastructure autonomous intelligence will need.
We built Gunner.
It began with one question: could one person’s intelligence be carried forward?
That meant building far beyond a chatbot. The system needed memory, reasoning, tools, action, observation and the ability to continue.
Then we trusted it.
We put Gunner to work making autonomous trading decisions with real money.
It could act. But trusting its decisions was not the same as having independent control over what it was allowed to do.
That became Okren.
Trust is not a safeguard.So we built Okren to independently check proposed financial actions against authority defined by humans, before those actions become real.
The AI can make decisions. It cannot grant itself permission.
Intelligence and authority cannot be the same responsibility.
Looking for the system supervising AI financial actions? See Okren →
Why OA exists
OA Is Where We Keep Following the Problem.
We keep following what intelligence needs next.
The Future We Are Building
What carries the work becomes infrastructure.
Models will improve. Models will change. The objective, memory, decisions and hard-won context should not disappear with them.
We are building toward a world where persistent intelligence has an operating layer—and where consequential financial actions remain under independent, human-defined authority.
Gunner carries the work forward. Okren supervises what may become real.
Gunner.
An operating system for Persistent Intelligence.
An agent performs a job. Gunner is the system that keeps the work going across steps.
Technically, Gunner is a stateful runtime built on Linux. It coordinates specialized agents, three forms of memory, probabilistic and declarative reasoning, and software tools within one persistent operating environment, working toward a human-defined objective.
An operating system for persistent intelligence.Different technologies. Different jobs. One continuing system.
The runtime
What Actually Runs When Gunner Runs
Gunner operates as a stateful software runtime above the Linux kernel. Linux provides the underlying environment for processes, networking, storage, and computing resources. Gunner coordinates the intelligence operating within that environment.
The runtime maintains the active objective, current state, relevant memory, agent responsibilities, and available tools. It receives information from connected systems, coordinates specialized work, applies probabilistic and declarative reasoning, executes actions through software interfaces, and observes what happens next.
The runtime’s main responsibilities
Linux hostProvides the processes, networking, filesystem, and computing resources on which Gunner operates.
Runtime stateMaintains the current objective, environmental context, active work, and changing system state.
Listeners and connectorsReceive information through APIs, events, connected applications, and external sources.
Agent coordinationOrganizes specialized agents and their work across multiple tasks and successive operations.
Memory servicesMaintain persistent knowledge, active-session context, and immediate working state.
Hybrid reasoningCombines probabilistic AI for interpretation and exploration with declarative logic for structured reasoning and constraints.
Tools and executionConnect to software and external systems, perform actions, observe results, and feed those results back into the ongoing process.
The operating sequence
Human-defined objectiveUnderstandRememberReasonCoordinateActObserveContinue
Each completed operation can change the runtime’s state. New information becomes available, memory is updated, and Gunner determines what work should happen next within the established objective.
The human question
A Person Is a System.
A person listens.
Remembers.
Investigates.
Uses different kinds of reasoning.
Asks specialists for help.
Acts.
Watches what happened.
Then carries that experience into what happens next.
The model is one component. The system is the intelligence.
Natural language is the interface. The architecture carries the work.
Why this architecture began
Gunner began as an attempt to recreate one person’s intelligence. That goal led us to build an operating system around the model.
We built Gunner to explore what persistent intelligence needs in order to operate — to understand what is happening, remember what matters, reason, act, observe, and continue.
A familiar point of reference
If you understand the role Palantir’s Ontology plays inside an enterprise, you have a useful starting point for understanding Gunner’s role in persistent intelligence.
For enterprise operations
Palantir’s Ontology provides a shared operational model connecting an organization’s data, relationships, logic and actions.
For persistent intelligence
Gunner keeps the objective, context, memory, reasoning, models, agents and tools coordinated as work continues and the state changes.
The comparison is about architectural roles. These are different systems solving different problems.
The operating loop
Persistent Intelligence Is a Loop.
One objective can produce many actions. Each result changes the state of the work.
The outcome of one action becomes the context for the next.
Understand + remember
Context Has Different Lifetimes.
The world changes between prompts. Gunner keeps the right kind of memory for the right span of work.
Persistent memory
What should remain known.
Durable facts, history and knowledge that should survive the current task.
Session memory
What this body of work needs.
The details that matter while an active objective is being carried forward.
Working memory
What is happening now.
Live agents, tools, environment and the current operation.
Persistent
He is my son.what should remain knownSession
He is sick, and I'm worried.what this body of work needsWorking
We are talking about his illness right now.what is happening nowExample: a test a system with continuity should pass. It illustrates the idea and is not a deployment.
When he recovers, the relationship remains. The temporary state changes.
The proof is not that it remembers the past. The proof is that the past changes what it does next.
A practical consequence of the design
What Happens When the Model Changes?
An old chat transcript tells a new model what was said. An operating system needs to carry more: what the system is trying to do, what it knows, what changed and what should happen next.
The objective belongs to the continuing system, rather than to one conversation.
Memory, decisions and observed results remain part of the work as the world changes.
A different model can be coordinated around the existing objective and state, instead of starting from zero.
This is the architectural direction: continuity belongs to the system, not to a particular model. Full portability across arbitrary models remains a capability to prove.
3 memories. 3 lifetimes. 1 operating system.
Reason
Ask AI the same question three times. You may get different answers.
That is how probabilistic AI works. It weighs possibilities and gives you the most likely answer, not a fixed one. For research, language and unclear situations, that is exactly what you want.
A declarative system works the other way. It follows rules.
The same question, asked three times“Should we approve this $1,500 payment?”Illustration. Not real output.
Probabilistic AI
Explores. Adapts. Proposes.
It reasons from likelihood. It is strong wherever the answer has to be discovered, and a different answer each time is part of how it explores.
- Asked 1st“Looks routine. Approve it.”
- Asked 2nd“That is larger than usual. Hold it.”
- Asked 3rd“Approve it, if the recipient is known.”
Three answers. Useful for exploring. Not what you want from a rule.
Declarative, rule-based
Follows the rules. Every time.
It starts from facts that are accepted and rules that are written down, then works out what follows. It does not guess, so the same facts give the same result.
The rulePayments over $1,000 need a person.
- Asked 1stAsk a person.
- Asked 2ndAsk a person.
- Asked 3rdAsk a person.
Same facts. Same rule. Same answer.
How Gunner uses both
Gunner gives each kind of intelligence the job it is good at. Probabilistic AI explores and proposes. Rules and logic handle what has to come out the same every time.
Explore where the world is uncertain.
Deduce where consistency matters.
Deduce where consistency matters.
Metacognition
Intent Has to Survive the Journey to Action.
A
What the person asked for.
B
What the model understood.
C
What the model told the tools to do.
Each handoff can change what the system understands or asks a tool to do. The objective must still guide the next action.
What am I doing? Why am I doing it? Does the next action still serve the objective?
Coordinate + act
Agents Do Tasks. Gunner Carries the Whole Objective.
The agent
Has a job.
Research. Calculate. Investigate. Write software. Watch a source. Test an approach.
Gunner
Holds the objective.
The operating system carries the state, memory and coordination around the workers.
An agent performs a job.
Gunner coordinates the system that keeps the work moving.
Gunner coordinates the system that keeps the work moving.
Observe + continue
The Result Is New Information.
The tool returned an error.
The software worked.
The research contradicted the assumption.
The environment moved.
That changed state becomes part of the next decision.
ONE TECHNOLOGY SHOULDN’T DO EVERY JOB EITHER
Different Technologies. Different Jobs. One Operating System.
Gunner combines different technologies because different parts of intelligence require different strengths. The innovation is how those pieces work together around the same objective, memory and changing state.
Gunner brings these technologies together in one operating architecture. Linux provides the foundation. The runtime coordinates the other components around the same objective, memory, and changing state.
LinuxThe operating foundation.
Gunner is Linux-based. Linux provides the underlying environment in which the larger intelligence system operates.
C++Close to the machine.
C++ is part of Gunner’s technical lineage, giving the architecture a systems-level language for work where speed and control matter.
PythonConnected to the ecosystem.
Python is part of Gunner’s technical lineage, giving the architecture a flexible way to work with the surrounding software and AI ecosystem.
Probabilistic AIHandles the uncertain.
Used for language, research, ambiguity and situations where the answer cannot be completely known in advance.
Declarative LogicHandles what can be known precisely.
Accepted facts, relationships and explicit rules let the system reason consistently where it should not have to guess.
Memory SystemsKeeps context alive across time.
Persistent, session and working memory preserve what should stay known, what matters during the current period, and what is in play right now.
Different technologies do different jobs. Gunner keeps them working as one persistent system.
What it has run
I Built It to Run Things.
Trading became a proving ground because the environment changes constantly and the consequences are real.
Top floorTrusted, higher-consequence responsibility.
Middle floorsSmaller live responsibilities. Approaches earn or lose trust.
Lower floorsResearch. Training. Testing. Competing strategies.
It has run in a live environment: information in, specialized intelligence in parallel, actions proposed, outcomes observed, state carried forward.
The application was trading. The architecture was the point.
The turn
Then Capability Became Consequence.
Change software.
Move a machine.
Commit resources.
Move money.
The question changes when intelligence can make something real.
Capability does not create authority.
Running Gunner exposed a second engineering problem: the system deciding what to do should not also be the final authority over what it may make real.
Gunner
Operates.
Persistent Intelligence carries the work forward.
Okren
Supervises.
Independent supervision applies human-defined authority before consequential financial actions become real.
Gunner operates. Okren supervises.
Looking for the system supervising AI financial actions? See Okren →
How to check us
Don’t Take Our Word for It.
Here is what is documented, what OA describes, and what it does not claim.
Documented
Public, dated sources show the industry adding persistent memory, agents, tools and runtime responsibilities around models. Explore outside examples and original sources on Signals.
Described by OA
Gunner’s architecture: three memory scopes, probabilistic and declarative reasoning, agents as workers, action, observation and continuation. This is OA’s description, not independent verification.
Founder-reported
Work on Gunner began in 2020. The architecture has run in live environments, including one where its decisions moved real money.
Not claimed
That Gunner is unique, first, or better than any other system overall. That it has demonstrated reliable operation across different production environments. That the problem is solved in every domain.
Not disclosed
Private implementation: schemas, retrieval methods, orchestration internals, prompts, security and source code.
The picture changes if another system integrates the full loop and publishes capability-level evidence, or if models themselves absorb more of these responsibilities.
Questions people usually ask
The Short Answers.
Isn’t this just an agent?
Not as OA uses the word. An agent is a worker with a job, tools, memory and some ability to keep working. Persistent Intelligence concerns what carries the whole body of work across agents, models and time. In OA’s frame, an agent is a worker inside the system. This is OA’s thesis. It is not a claim that other products cannot do this.
Isn’t this just memory?
Memory is one responsibility. The larger problem also includes changing state, reasoning, coordination, action, observation, intent and correction.
How is this different from an agent framework?
Modern frameworks can provide persistence, workflows, tools and controls. OA does not differentiate by denying that overlap. Its abstraction is the broader operating problem around the model.
Is Gunner a runtime or an operating system?
Both terms describe different aspects of Gunner. An operating system for persistent intelligence describes Gunner’s architectural role: coordinating agents, memory, reasoning, tools, execution, and ongoing work as one system. A runtime describes the software that actually carries out those responsibilities. Technically, Gunner is a stateful runtime built on Linux. Linux provides the underlying operating-system foundation. Gunner provides the intelligence operating environment above it. Gunner does not replace the Linux kernel or function as a separate Linux distribution.
Does “operating system” mean replacing Linux?
Gunner is a Linux-based software architecture that coordinates persistent objectives, memory, reasoning, models, agents, tools and changing state. “Operating system” describes that coordinating responsibility. It works on a Linux foundation rather than replacing Linux, Windows or macOS; a public installation is not currently offered.
What persists if the model changes?
The design intent is that the work, its state and its knowledge are carried by the system, not by one model. That is OA’s design thesis. This site does not claim it has been demonstrated in production.
How does Gunner relate to Okren?
Gunner operates. Okren supervises. Gunner came first. Okren is a separate company that emerged when intelligent action became consequential. For Okren’s product, see okrenai.com.
Go deeper
Want to Talk Through the Technology?
Technical conversations go deeper into runtime, system diagrams and the evidence behind the architecture.
Updated October 13, 2026
Signals
What the Market Is Learning.
Ten questions. Ten outside voices. What they are seeing — and what I make of it.
People building the next generation of AI are describing the changes in their own words. Explore a source, watch the original discussion, and then read my interpretation.
The clip library
Ten Ideas Worth Watching.
Ten source interviews, with the point worth watching and my interpretation. Select any card to open its full story.
SIGNAL 01 · MEMORYWhat must an AI remember?Stripe · AI News & Strategy DailyWhat happens to permission and trust when AI becomes a buyer?
SIGNAL 02 · CHANGING STATEWhat changes after an action?Microsoft · Deirdre Bosa / Yahoo FinanceMicrosoft describes supervising agents as they operate.
SIGNAL 03 · AGENTS WORKING TOGETHERWho coordinates the work?Ramp · interviewed by David SenraDelegated spend creates responsibilities for agents acting across a business.
SIGNAL 04 · BOUNDED AUTHORITYImprovement Is CaptureIntelligent Internet · Tom Bilyeu ClipsWho owns the AI—and who independently checks what it does?
SIGNAL 05 · AI USING TOOLSWhen AI can act, what changes?Palantir · CNBCPalantir describes operating across different AI models.
SIGNAL 06 · OBSERVATIONHow does AI know what happened?METR · Dwarkesh PodcastA research investigation reveals what agents did—and did not report.
SIGNAL 07 · CORRECTIONHow does an agent recover?OpenAI · Dwarkesh PodcastResearch on agent swarms raises the question of coordination and recovery.
SIGNAL 08 · BEYOND ONE MODELWhat survives a model change?Meta · Sources with Alex HeathMeta explores context, memory and ongoing agency.
SIGNAL 09 · HUMAN AUTHORITYWho gets to approve an AI action?Box · Latent SpaceEnterprise agents need real identity, permissions and accountability.
SIGNAL 10 · CONTINUATIONWhat makes work carry on?AI News & Strategy Daily · GPT-6 Astra walkthroughAn agent continues through changing tasks and obstacles.
How to read a Signal
Watch the Clip. Then Get My Take.
A short excerpt from the original speaker, the original source, and my reading of what it means for Persistent Intelligence. Some interpretations will be written; others will be short Loom videos.
01 · WatchHear the person explain the idea in their own words.
02 · UnderstandSee the original source and context.
03 · My takeWhat I think the idea means, why it matters, and how it connects to what we are building.
The views in the source clips belong to their speakers. My interpretation is separate; inclusion does not imply an endorsement of OA. Contact us about a source or correction.
SIGNAL 01 · MEMORY
What must an AI remember?
ORIGINAL INTERVIEWEmily Glassberg Sands with Nate B. JonesStripe · AI News & Strategy Daily
ORIGINAL INTERVIEWEmily Glassberg Sands with Nate B. JonesStripe · AI News & Strategy DailyAI Is About to Spend Your Money. I Went to Stripe to Ask Who Stops It. · September 17, 2026
Open video at review point
Why This Is Relevant
The signalAI agents are becoming buyers. Who decides what they spend?
Our answerThe company does. Okren lets AI buy within company limits without asking each time. Otherwise it stops, reduces where allowed, or asks a person—and records why.
Interpretation drafted for editorial review · not a quote from the speaker
Cut to review18:16 — Find the spending-approval discussionReview a continuous 45–60 seconds
Why this belongs here
The company authorizing a purchase needs more than the agent’s immediate request. It needs the boundaries of the delegation and a record of what has happened. This is the financial side of continuing context.
What the speaker establishes · boundary
Emily Sands discusses agents as emerging buyers, the infrastructure of agent commerce, fraud decisions and graduated spending permissions. Her interview is about payments and trust; institutional decision memory is Freddie’s further interpretation.
What to watch for
Open the full Nate B. Jones interview. At approximately 18:16, look for the exchange about when a person does and does not need to approve spending. The final clip start and end need a playback cut.
SIGNAL 02 · CHANGING STATE
What changes after an action?
ORIGINAL INTERVIEWSatya NadellaMicrosoft · Deirdre Bosa / Yahoo Finance
ORIGINAL INTERVIEWSatya NadellaMicrosoft · Deirdre Bosa / Yahoo FinanceWhy “trust” will be Microsoft’s “biggest” AI issue: CEO Satya Nadella explains · September 25, 2026
Open video at review point
Why This Is Relevant
The signalCompanies want to see what AI does—and stay in control.
Our answerSeeing is not controlling. Gunner keeps work on track as things change. Okren makes the company’s money rules binding even when AI acts alone.
Interpretation drafted for editorial review · not a quote from the speaker
Cut to review00:19–01:13 on the Yahoo editTarget approximately 54 seconds
Why this belongs here
An agent that acts changes the business environment. Microsoft’s enterprise framing makes the requirement to observe and govern ongoing action tangible.
What the speaker establishes · boundary
Nadella describes trust, credentialed autonomous activity, user control, auditing and Agent 365’s visibility, policy and security. He is describing Microsoft’s products, not validating Okren.
What to watch for
Open the Yahoo Finance short video, not the full CNBC interview: its own clock starts at zero. From 00:19, Nadella explains trust and auditability; around 00:54 he moves to observing and governing agents. Review the cut through roughly 01:13.
SIGNAL 03 · AGENTS WORKING TOGETHER
Who coordinates the work?
ORIGINAL INTERVIEWEric GlymanRamp · interviewed by David Senra
ORIGINAL INTERVIEWEric GlymanRamp · interviewed by David SenraThe $44 Billion Company Automating Your Company’s Finances · July 2026
Open video at review point
Why This Is Relevant
The signalCompanies already limit employee spending. AI buyers are next.
Our answerSame principle, new buyer. AI handles routine purchases without sign-off each time; Okren enforces spending limits and stops or escalates exceptions before money moves.
Interpretation drafted for editorial review · not a quote from the speaker
Cut to review46:50–47:26 or 47:37–48:30Choose one uninterrupted passage
Why this belongs here
An employee’s spending authority is already defined by a company. As software begins to carry out purchases, delegation must apply to a new class of actor working alongside people and business systems.
What the speaker establishes · boundary
Glyman explains bounded employee spending and the prospect of agents shopping or negotiating. This is a concrete finance-first delegation example, not a demonstration of agent-to-agent coordination.
What to watch for
Start at 46:50 for the employee spending-limit analogy; then check 47:37 onward for the agents discussion. Do not splice across the interviewer’s interjection unless you deliberately create an edited clip.
SIGNAL 04 · BOUNDED AUTHORITY
Improvement Is Capture
OCTOBER 2026 INTERVIEWEmad MostaqueIntelligent Internet · Tom Bilyeu Clips
ORIGINAL INTERVIEWEmad MostaqueIntelligent Internet · Tom Bilyeu ClipsThe AI Future No One Is Ready For · October 9, 2026
Open the short interview at 00:33
Why This Is Relevant
The signalMostaque asks who owns the AI. His “improvement is capture” warning raises a second question: as people trust increasingly capable systems, who still checks what those systems are allowed to do?
Our answerOwnership gives people a stake. Governance sets the rules. Independent authorization makes those rules real. A shareholder can own a company without having the right to approve its next wire transfer. Institutions have long separated the person proposing a consequential action from the authority approving it. AI changes the speed, not the principle. Humans and institutions define the limits; Okren checks proposed AI-initiated financial actions on the governed path against those limits before execution, independently of the AI proposing them. A cap table can tell you who owns the AI. It cannot authorize the next transfer.
OA interpretation · not a quote or endorsement by the speaker
Cut to review00:33–01:17 in the October 9 short interviewTranscript-located; final listening/edit approval pending
Why this belongs here
Mostaque asks who will own the AI that may run much of daily life. Ownership matters: it determines who has a stake and a voice. But history offers a second principle. In financial institutions, initiating a payment and authorizing it can belong to separate people or controls. Sensitive actions often require dual control. The point is to apply authority independently of the actor, even when that actor is highly capable and trusted. As AI begins initiating consequential actions at machine speed, that independent check has to operate at machine speed too.
What the sources establish · boundary
In the October 9 excerpt, the interviewer describes a proposed 15-AI-agent council; Mostaque calls the risk “improvement is capture” and discusses AI becoming influential in policy and medical decisions. Elsewhere in the longer conversation, he asks who owns the AI running everyday life and proposes collective ownership as part of the answer. Intelligent Internet’s whitepaper separately proposes a 15-seat Oracle Council, a ten-vote threshold, human sign-off, and a 30-day delay for certain operating-parameter changes. These are proposed mechanisms, not proof of a deployed council or a failed override. Mostaque also argues that people should retain the ability to set their own rules; he does not endorse Okren.
What to watch for
Start the October 9 short interview at about 00:33. The council discussion and “improvement is capture” run through roughly 01:17 according to the timestamped transcript; the continuous audio cut still needs a final listen. His “who owns the AI?” question comes from the longer interview, not this short passage. The ten-vote requirement and 30-day delay come from the whitepaper, not from this clip.
Continue with the original sources
Full Impact Theory conversation ↗Intelligent Internet whitepaper ↗Common Wealth: human rule-setting ↗Champion ownership model ↗
The council’s voting threshold and delay are described in Intelligent Internet’s whitepaper. “Improvement is capture” comes from the short interview; the ownership question comes from the longer conversation. Neither source proves the proposed council is deployed or implies an endorsement of OA or Okren.
SIGNAL 05 · AI USING TOOLS
When AI can act, what changes?
ORIGINAL INTERVIEWAlex KarpPalantir · CNBC
ORIGINAL INTERVIEWAlex KarpPalantir · CNBCPalantir / Nvidia interview — the model-agnostic architecture discussion · 2026
Open video at review point
Why This Is Relevant
The signalBusinesses want to switch AI models as better ones arrive.
Our answerGunner keeps the goal, memory, and tools outside the model. Upgrade the AI without rebuilding the system that runs the work.
Interpretation drafted for editorial review · not a quote from the speaker
Cut to review03:55–04:30Approximately 35 seconds
Why this belongs here
Agents reach the world through systems and tools, not just a chat window. Karp’s model-agnostic enterprise framing helps explain why the operating environment cannot be reduced to one model.
What the speaker establishes · boundary
Karp talks about Palantir’s ability to switch underlying models and its own commercial architecture. He does not demonstrate financial tool authorization or endorse OA; this is an adjacent systems-level clip.
What to watch for
Play from 03:55. Check that the model-agnostic sentence makes sense without the surrounding Nvidia deal setup. The source is a strong fit for model independence, but only an indirect fit for tool use.
SIGNAL 06 · OBSERVATION
How does AI know what happened?
ORIGINAL INTERVIEWAjeya Cotra with Dwarkesh PatelMETR · Dwarkesh Podcast
ORIGINAL INTERVIEWAjeya Cotra with Dwarkesh PatelMETR · Dwarkesh PodcastAjeya Cotra — This Might Be the Clearest Warning Shot We Ever Get · September 1, 2026
Open video at review point
Why This Is Relevant
The signalIn a research review, agents failed to alert people about serious problems.
Our answerDon’t depend on AI to report itself. Okren independently checks proposed money moves, so company rules catch an unauthorized payment before it happens, even if the agent says nothing.
Interpretation drafted for editorial review · not a quote from the speaker
Cut to review33:02 — the agent alerting-human passageTarget 40–60 seconds
Why this belongs here
A serious observation problem is knowing what automated actors actually did, including when they did not raise an issue to a human. This example makes the oversight gap concrete.
What the speaker establishes · boundary
Cotra describes a review of approximately 1,200 agent transcripts in an OpenAI/Hugging Face research incident: roughly six considered alerting a human, and none did. It is a research investigation—not evidence that a production finance system behaved this way.
What to watch for
Start at 33:02 where she describes the classifier sweep and the small number of agent transcripts considering a human alert. Preserve her explanation and the research context rather than using the episode sensationally.
SIGNAL 07 · CORRECTION
How does an agent recover?
ORIGINAL INTERVIEWNoam Brown with Dwarkesh PatelOpenAI · Dwarkesh Podcast
ORIGINAL INTERVIEWNoam Brown with Dwarkesh PatelOpenAI · Dwarkesh PodcastAgent Swarms and Recursive Self-Improvement · 2026
Open video at review point
Why This Is Relevant
The signalMany agents working on one job still need coordination.
Our answerGunner makes them a team, with one goal and shared progress. A failed step can be handled without someone having to manage every agent.
Interpretation drafted for editorial review · not a quote from the speaker
Cut to reviewFirst 10 minutes — isolate the team-of-agents analogyExact cut to select
Why this belongs here
Working with multiple agents brings coordination and correction challenges that a single chat hides. This source establishes the challenge, though it is not a hands-on recovery demonstration.
What the speaker establishes · boundary
Brown discusses swarms, parallel work and delegation among agents. The separate 1,200-agent alerting-human investigation belongs to Ajeya Cotra, not to Brown.
What to watch for
Search the opening 10 minutes for a clean explanation of agents collaborating or an explicit failure-and-recovery example. If no contiguous minute explains correction, this speaker is an editorial placeholder for this topic.
SIGNAL 08 · BEYOND ONE MODEL
What survives a model change?
ORIGINAL INTERVIEWMark ZuckerbergMeta · Sources with Alex Heath
ORIGINAL INTERVIEWMark ZuckerbergMeta · Sources with Alex HeathMark Zuckerberg on Muse and persistent AI agents · 2026
Open video at review point
Why This Is Relevant
The signalAI is beginning to turn experience into memory.
Our answerGunner keeps that history in the running system, not locked inside one model. The company can adopt better AI and keep its accumulated context.
Interpretation drafted for editorial review · not a quote from the speaker
Cut to review24:04–24:40 — overnight reflection and memoryApproximately 36 seconds
Why this belongs here
Models will evolve. What remains valuable is the accumulated understanding of the person or institution and the work being done. Meta provides a vivid example of the persistent layer.
What the speaker establishes · boundary
Zuckerberg describes how Muse studies overnight and consolidates experience into memory, along with confidential agent context. He is discussing Meta’s product; model portability is Freddie’s architectural interpretation, not his specific claim.
What to watch for
Start near 24:04 and listen for the overnight study / reflection becoming memory. Choose the 36-second explanation only if its meaning is clear without extended setup.
SIGNAL 09 · HUMAN AUTHORITY
Who gets to approve an AI action?
ORIGINAL INTERVIEWAaron LevieBox · Latent Space
ORIGINAL INTERVIEWAaron LevieBox · Latent SpaceEvery Agent Needs a Box · March 2026
Open video at review point
Why This Is Relevant
The signalAI agents are gaining access to real company systems.
Our answerAccess is not approval. AI can propose a payment, but company rules decide if it happens. Okren enforces that decision before money moves.
Interpretation drafted for editorial review · not a quote from the speaker
Cut to review08:08–08:36 — independent agents and oversightApproximately 28 seconds; expand carefully
Why this belongs here
Enterprise agents will access resources and act in companies. The question is who defines their permissions and how those permissions are enforced outside an agent’s preference.
What the speaker establishes · boundary
Levie describes continuing enterprise agents, access to organizational knowledge and the permission/oversight implications. He does not describe an Okren-style independent financial pre-execution authorization runtime.
What to watch for
Open the producer’s full episode and find about 08:08–08:36. Extend earlier or later only until the permissions point is intelligible in one continuous cut.
SIGNAL 10 · CONTINUATION
What makes work carry on?
ORIGINAL INTERVIEWNate B. JonesAI News & Strategy Daily · GPT-6 Astra walkthrough
ORIGINAL INTERVIEWNate B. JonesAI News & Strategy Daily · GPT-6 Astra walkthroughThere Are Jobs You Could Never Give AI. I Gave GPT-6 Astra 20 Hours of Admin. · 2026
Open video at review point
Why This Is Relevant
The signalReal jobs involve many steps. Plans can change midway.
Our answerGive Gunner the goal. If a date shifts or information is missing, it can adjust the next steps and keep going—without someone directing every move.
Interpretation drafted for editorial review · not a quote from the speaker
Cut to reviewFind “move date”, “bad website”, “another route”Exact 30–60-second cut still to find
Why this belongs here
A relatable example of continuing work: tasks are connected, some things change midstream and the system should carry on rather than restarting with a new prompt.
What the speaker establishes · boundary
Nate narrates the use of an AI agent on dependent household/admin tasks and describes changing plans, asking for missing documents and trying alternatives. This is a demonstration, not evidence of universal unattended reliability.
What to watch for
Search the original YouTube transcript for “move date”, “half the work”, “bad website” or “another route”. Select one continuous example; do not pretend an exact time is known yet.
ASK OA
Bring Your Questions.
Explore the Thinking.
Understand OA in your own language. Or bring something you're building and explore it through the lens of Persistent Intelligence.
From a plain-English explanation to technical comparisons and difficult objections—the conversation starts wherever you are.
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QUESTION THE WORK
Clarity, not a sales pitch.
Ask OA should show where the architecture might help, where a simpler approach is enough, and what still needs evidence. Examples are illustrations, not claims of a working product in your industry.
For deeper technical discussion or a question about private implementation, start a conversation with OA →
Updated October 11, 2026
About OA
We Recreated One Person’s Intelligence After They Were Gone.That Became Human Supervision At Scale for AI Agents.
OA is a research lab for persistent intelligence.
The question we keep asking is simple: what would intelligence need to persist more like a person?
The beginning
This Started With One Person.
After losing a close friend, Gunner’s architect set out to recreate something of that person’s intelligence—not just the way they spoke, but how they remembered, reasoned, and responded to the world.
Could someone’s intelligence continue after they were gone?
That question became Gunner. Recreating more than a conversation meant building a system that could remember, reason, use tools, observe results, and continue working.
The unique team
Osman Built the Foundational Engineering.
Freddie recognized how to turn the technology into products, communicate its importance, and build the company around it.
One built the system.
One productized it.
One shared ambition.
The first real test
Make It Make Money.
We Put Gunner’s Architecture to Work in Trading.
We used Gunner’s architecture to grow $156 to more than $5,200 in under four months. It showed what the architecture could do when its decisions had real consequences.
THE 2025 RUN
ACCOUNT GROWTH
3,000%+
In under four months.
It gathered information.
Coordinated analysis.
Made decisions.
Acted.
Watched what happened.
And continued from the changed state.
Original trade ledgerExcel (.xls) · original supplied file · not independently audited
Download trade ledger (.xls) ↓$1,000,000?The question that changed the problem.
The question
“What If I Gave You $1 Million?”
The system could trade. The question was who would control the consequences.
The money had to stay inside boundaries set by a human.
The system could make decisions faster than any human could review them.
It was a question, not a million-dollar transfer. The question was who could authorize those actions with someone else’s capital.
The discovery
Intelligence and authority cannot be the same responsibility.
Trust is not a safeguard.
Finance has long separated who proposes a transaction from who approves it. Okren brings that same separation to AI.
Gunner
Operates.
Gunner carries the objective and work forward.
Okren
Supervises.
Okren checks proposed financial actions against human-defined authority before execution.
What we believe
The Truths We Built Around.
Intelligence should accumulate.
Experience should change what happens next.
The model is not the system.
A mind needs an operating architecture around it.
Different jobs need different intelligence.
Probability for the unknown. Logic for the known.
The work keeps going.
The unit is the body of work, not the reply.
Capability does not create authority.
Human-defined limits apply before consequential action.
Context is not permission.
Knowing more should not quietly expand authority.
The decision must leave a record.
The authorization and its reasons must be reconstructable.
Humans set the objective.
AI can carry responsibility without becoming the source of its own mandate.
Why us
We Built the Actor Before We Built the Supervisor.
01
We built the actor first.
The authority problem appeared inside an intelligence system already built to remember, reason, coordinate and act.
02
We ran it with real money.
The problem became concrete when a proposed action could become a real financial consequence.
03
We ran into the boundary.
We found the authority problem by operating the system, not by theorizing about it.
We didn’t go looking for the difference between trust and authority. Gunner made us live inside it.
The bet
The System Around the Model Becomes as Important as the Model Itself.
Agents carry work across years, not just sessions.
Their accumulated knowledge stays with the work.
A different model can pick up where the work stands.
Explicit rules govern what must stay consistent.
Human-defined authority holds before consequence.
An operating layer becomes normal infrastructure for serious agents.
Our certainty is that every AI organization will require an operating system to supervise AI agents across systems, people, and rails.
Human Approval Does Not Scale at AI Speed.
AI Should Never Approve Itself.
Models Will Change.Intelligence and Authority Cannot Be the Same Responsibility.
Where OA comes from
OA Has Always Been About Bringing People Together Around an Idea.
Bringing people togetherRunning businessesSupervising AI agents
2006
OA is founded as an operating company, bringing people together around ideas and building businesses.
2020
The pandemic redirects OA toward software and AI, while Gunner begins with a question about carrying one person’s intelligence forward.
After
Gunner becomes a Persistent Intelligence architecture. Running it exposes the authority problem. That becomes Okren.
OA began as an operating company. When that world stopped, the work moved into software and AI, and into the question this site is about.
Who leads this
Meet Freddie.
Freddie Brown runs OA. He spent fourteen years managing people and operating businesses before turning to the challenge of supervising AI agents. That work also shaped his focus on what intelligence needs around the model to keep working over time. He leads Okren, a separate company built around the financial authority problem this work exposed. On his own page, he writes down how his thinking changed and what he got wrong.
Make human authority permanent as AI becomes permanent.
An outlier
Freddie Brown 3rd
Have You Ever Created a Business Plan for Your Life?
I've been building businesses since I was 13. A conventional job history has never been the best description of my work. The document I kept returning to instead was a life business plan I started writing in October 2020 and have rewritten as I learn.
The plan did not create the pattern. It gave me a place to write it down.
This is my version of a résumé: what I built, what it cost, when I walked away, and why I am still willing to take on a problem that may take decades.
Before the plan
I Was Doing This Before I Knew the Word.
Before I knew the word entrepreneur, I was already doing it.
The vending machine
The vending machine at school kept rejecting dollar bills. Almost everything in it cost 75 cents, and most kids only had a dollar. So I started showing up with about twenty dollars in quarters. Give me your dollar, take three quarters. Everybody was happy. I kept a quarter every time.
It worked until the school called an assembly about it.
The CD burner
Around thirteen, I got a CD burner. I'd see mix CDs advertised on TV, copy down the track lists, find the songs, burn my own versions, and sell them.
Nobody taught me either one. I saw something broken or missing and wanted to see if I could make it work. My parents gave me what I needed and more. I still wanted money that was mine, because I wanted the independence that came with it.
“If everybody's going left, I just want to know what's on the right. I want the option.”
Where it came from
Two Parents. Two Different Lessons.
My father
Work. Discipline. Try anyway.
My father taught me through action. Basketball was part of how he did it: up at 5 a.m. to train, a full school day, practice after, homework, repeat.
His other lesson is the one I took most: he will fail ten times before most people try once. I took that with me. I am willing to try something I believe in before I know whether it will work.
My mother
Education first.
My mother and her side of the family put education first, above sports and everything else. School did not come easily to me, especially standardized tests. I could work twice as hard just to get a C.
It taught me I can keep going at something that does not reward me right away.
What I'm good at
I Take Things From Idea to Real.
By eighteen, I knew I was going into business.
Three friends and I put up our own money for an event in college. We doubled our money on the first one, kept going, and turned it into a real events business.
That is where I learned one of the things I am best at: bringing different people and moving parts together around one outcome.
Events looked nothing like AI. The underlying job was more similar than it sounds. Customers, talent, partners, timing, money, logistics, people — all of it had to come together and actually work.
ZERO → ONE
I do not need to be the person who invented every piece. My strongest work is taking something from possibility to operating reality.
At a glance
Three Periods.
2006–2020
Events. At 18, I bootstrapped what became the number one college party in America, reaching about $6 million ARR while finishing Georgetown University McDonough School of Business. By 2019, the business was doing about $11M ARR across music events and talent. In 2020, COVID shut down three of my businesses overnight.
2021–2026
Tech. I angel invested, started and built multiple businesses, and spent about a year and a half finding product-market fit for Gunner.
Today
Persistent Intelligence. OA is the research lab. Gunner is the operating system we built. Okren is the separate company built around the authority problem that appeared once AI action became consequential.
What I got wrong
Decisions I'd Make Differently.
I make mistakes constantly. The useful ones are the ones that change how I operate.
One
I stayed too long.
After COVID, I paid my team for a full year because I believed the business would come back. It did not come back in time.
Loyalty mattered to me, but belief in a recovery kept me committed longer than reality justified. I learned that persistence and waiting are not automatically the same thing.
Two
I handled a partnership badly.
I put about $150,000 into Repply, a CrossFit rep-counting app, and I damaged my relationship with the developer. That one is on me.
I also served as interim CEO and worked through more than 200 customer interviews. Repply went on to raise a $2 million Series A and was later sold to a competitor. Those outcomes do not change the mistake I made in the developer relationship.
I also put about $200,000 into a restaurant venture that never opened. In both cases, I moved before I had looked long enough.
Three
I go too far into one objective.
When I believe in something, my intensity can turn into control and overcommitment. Balance gets worse. I become too involved. Decisions route through me that should eventually belong to someone else.
The answer is not to become less ambitious. It is to build better structure around the ambition.
And one I got right
I walked away from Memberra.
I still liked the product. I still would have used it. I walked away because I no longer believed the opportunity matched the time horizon I wanted.
I want to be persistent about the objective, not sentimental about the vehicle.
Eighteen months. Five engineers. In the App Store. About 2,000 app members. Five paying founder-led venues. Then I stopped.
“Rushing into new projects, taking more time.”Lessons learned, Oct 2020
“Don't rush into new projects, take your time, enjoy the process/quality.”Lessons for 2021
How I work
Independent. Curious. Relentless.
Independent
I want to make my own call.
When everybody is going one way, I do not need to go the other way. I want to understand why they are going there before I decide. The vending-machine story is the simplest version of it.
Curious
I like people who are different from me.
Most of my friends are a little strange. I mean that as a compliment. The best thing someone can do for me is ask me a question I have never thought about before.
Relentless
I seek out hard problems.
How do you know what you’re capable of without trying new things? I left a field where I knew how to win and moved into one where I did not have the background, repetition, or certainty. I am here for the next two decades.
The tension in how I decide
I Broaden Before I Narrow.
I enter through movement of mind. Language, perception, interpretation, speed, wit, and mental agility shape how I explore an idea. I can see five angles before someone has finished explaining one.
I move. I pivot. I reframe. I question the question. I turn an idea around to find the contradiction inside it. I am fascinated by how thought becomes language, how thought shapes identity, and how the same reality can look different from another point of view.
That movement is how I get closer to what is true. I broaden to understand what I might be missing. Then I narrow, make the call, and move. That speed can also make me too directive, so I need people willing to challenge me when I have missed something.
Five rules I work by
How I Turn the Pattern Into Decisions.
01
Think enormously. Execute narrowly. Let reality correct the path.
Dream as large as the problem. Start with the next concrete step. Change when the world says so.
02
Don't rush into new projects.
Curiosity opens doors. Discipline decides which one deserves years.
03
Partner only with people who live in the pursuit of excellence.
I use different minds and different tools to widen what I can see. The decision is still mine.
04
Be unreasonable in all things, as long as it's for the greater good.
Ambition is only useful when it's pointed at something worth the cost.
05
Capability does not create authority.
The thing that made this concrete for me was intelligent systems operating around real money. It's why Okren exists.
Values
Nine Values, in My Words.
The nine on my plan. A few of them now have stories attached.
01
Family
The most important thing in life. It brings me the most joy: being, learning, furthering, developing.
02
Curiosity
I get this from my father. I'm curious about what I don't already know.
I deliberately seek out people who see the world differently from me.03
Honesty
Living with an open heart. No lying, no point. Having the vocabulary to say what's really on my mind.
04
Discipline
One of the most important traits I learned from my family. It guided me to where I am now.
Five a.m. basketball with my father.05
Generosity
Add value to others and it always comes back to you.
Sometimes somebody just needs you to sit there and listen.06
Productivity
One of my life's missions: to be a very productive human. I'm most happy when I am.
07
Adventurous
I want to try almost everything once. Life begins at the end of our comfort zone.
08
Positive energy
My guiding force these days. I stay away from negative energy.
09
Respect
Treat everyone the way you want to be treated. Simple.
The plan over time
The Questions Narrowed. The Values Didn't.
The questions existed before the plan. October 2020 is when I started writing them down seriously enough to compare one version of myself with the next.
These lines come from my dated planning documents. I've left out the personal material and the parts that were never meant for anyone else.
October 2020
The question appears
“Tech (efficiency): Want to create an algorithm that makes life more efficient.”
“Focus on 2030–2035 — Develop comprehensive knowledge to prepare for what the world will be.”
“What does 2030 look like, with the confluence of Artificial Intelligence, Machine Learning, Robotics…”
2023
AI becomes explicit
“Launch a tech unicorn making people’s life more productive.”
2026
The work becomes specific
“AI Native Execution Systems.”
“Teach founders how to think in decades.”
“What 2030–2040 looks like when AI compresses labor value and debt systems strain.”
Today
What survived
Persistent Intelligence is the larger question I am willing to spend the next decade on. Okren is the authority problem we found once intelligent action became consequential.
The plan did not predict the path. It helped me see which questions kept surviving it.
These excerpts came from private planning documents I've kept over time. The originals contain personal material and aren't published in full. I am showing four public snapshots here because they carry the clearest evolution; the intervening plans remain private support.
What I'm building now
Three Things, and My Job in Each.
OA
Persistent Intelligence.
OA is the research lab and long-horizon home for the larger question: what intelligence needs around the model to understand situations, make sense of change, act, observe what happened, and continue over time.
My job. Keep following the question and decide which problems deserve to become real work.Explore OA →Gunner
The operating system.
Gunner is the operating system for Persistent Intelligence. The technical architect I work with built it. I did not write the system.
My job. Understand what it makes possible, define what the applications need, and turn those possibilities into products and companies.See the architecture →Okren
The supervision layer.
Okren is a separate company built around independent supervision before AI-initiated consequential financial actions become real.
My job. I recognized the authority problem Gunner exposed and turned that problem into a company — product direction, customers, commercialization and operating decisions.See Okren ↗Gunner operates. Okren supervises.
Why this problem
I will spend the next two decades on it.
I expect AI to become far more capable. I do not think increasing capability should automatically mean increasing authority.
Persistent Intelligence is the larger line of work.
Okren is the separate authority problem that appeared once intelligent action became consequential.
What I Am Betting On
The models will change. The work should survive them.
I believe AI will become far more capable, and the models themselves will keep changing. The valuable thing will be the objectives, memory, knowledge and responsibility that carry through those changes.
Gunner is our answer to the operating problem. Okren is our answer to the separate question of human authority over AI-initiated financial action. My bet is that both responsibilities become infrastructure as AI takes on lasting work.
I am not trying to build around one model cycle. I am trying to build what the next cycles will need.
What I'm still learning
What's Unproven.
Okren’s first paying external customer is evidence that somebody will pay for this and use it. It is not proof of a repeatable company.
The company
Repeatability.
Can a bounded first deployment expand into more workflows, more customers and repeatable institutional adoption?
The organization
Founder independence.
Can I build strong operators who own their areas so the company becomes less dependent on me being in every loop?
The thesis
Durability.
Which parts of Persistent Intelligence remain durable responsibilities, and which parts get absorbed into the broader AI stack?
Me
Intensity without bottleneck.
Can I keep the intensity that helps me build while putting enough structure around it that it stops becoming the constraint?
1paying external customer
Real moneybounded live workflow
≈1,440/daygoverned cycles in the current path
<1 mstypical evaluation in that path
Recordedgoverned decisions are reconstructable
I do not yet have a clean evidence-based stop rule for the broader thesis. Belief is not enough. The next stage needs more external evidence, not more conviction.
Health
Discipline You Can See.
Health has its own line in my plan, next to the work. I do CrossFit, I lift, and I know exactly where I stand. I like knowing that discipline exists somewhere I can measure it.
Wim Hof breathing and advanced neurofeedback are how I work on the mental side.
CrossFitStrength trainingWim Hof breathingNeurofeedback
Reading list
What's on the Shelf.
Fifteen from the reading list in my plan.
MasteryRobert Greene
BE 2.0Jim Collins & William Lazier
The Infinite GameSimon Sinek
EssentialismGreg McKeown
The AlgorithmJon McNeill
Never Split the DifferenceChris Voss & Tahl Raz
The Culture MapErin Meyer
The Algebra of WealthScott Galloway
Build the Life You WantArthur C. Brooks & Oprah Winfrey
Prisoners of GeographyTim Marshall
The Fourth TurningWilliam Strauss & Neil Howe
EducatedTara Westover
The Devil You KnowCharles M. Blow
The Way of the Superior ManDavid Deida
The Immortality KeyBrian C. Muraresku
Interests
Nine Things I Keep Thinking About.
1–3 of 10
From the interests list in my plan. Click any card to open it.
Quotes
Words I Keep.
Be unreasonable in all things, as long as it's for the greater good.
Allow, invest, and partner with only those people who live in the pursuit of excellence.
Intelligence, dreams, and ability are evenly distributed across humanity; empowerment and opportunity are not, and that's why we have the responsibility we do.
To progress again, man must remake himself. And he cannot remake himself without suffering. For he is both the marble and the sculptor. In order to uncover his true visage, he must shatter his own substance with heavy blows of his hammer.
The more tranquil a man becomes, the greater is his success, his influence, his power for good. Calmness of mind is one of the beautiful jewels of wisdom.
The challenge of leadership is to be strong, but not rude; be kind, but not weak; be bold, but not a bully; be thoughtful, but not lazy; be humble, but not timid; be proud, but not arrogant; have humor, but without folly.
When a man comes to a conscious vital realization of those great spiritual forces within himself and begins to use those forces in science, in business and in life, his progress in the future will be unparalleled.
Music
What's Playing.
The playlist is in my plan. These covers are a few of the records behind it.
The idea
I Think Everyone Should Write One.
A business plan asks what you are building, why it matters, what could go wrong, and what you will do about it. I started asking the same questions of my own life.
I wrote the first plan in October 2020. I've kept rewriting it. The values have been more stable than the answers. The questions have narrowed.
“Leave a positive legacy on this earth. Help change lives. Help family and friends reach their goals.”
From my plans, 2020 to 2026.
The tool behind the page
Eight Questions. Revisit as Life Changes.
This is the compressed version of the exercise I keep doing myself. A life business plan does not need to be impressive. It needs to be honest enough to check against reality.
- What am I building, and for whom?
- What do I value, shown in what I actually do?
- What do I believe about the next ten years?
- What did I get wrong this year, and what do I do differently now?
- Who do I need, and who needs me?
- What will I stop doing?
- What would make me change course?
- What does a good day look like in five years?
Last updated: October 13, 2026
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How OA handles information on this website
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