Most people are just asking AI questions.
Yoichi Ochiai is issuing orders to AI.
Furthermore, he doesn't just stop at one AI. He opens ChatGPT, Gemini, Claude, Grok, and even Manus. He runs deep research on the same theme across all of them simultaneously. During the 10 minutes he waits for answers, he talks to another AI about "why this topic is interesting in the first place."
When the research results come back, he doesn't read everything meticulously.
He picks up literature that catches his mind, strange words, unfamiliar examples, and small discrepancies that connect the past and present. Then, he turns those fragments into new questions and throws them back to all the AIs once more.
He isn't asking the AI for the correct answer.
He is making them dig up a massive amount of "thinking materials" that he couldn't have encountered alone.
What is discussed in the provided PIVOT video is not a collection of convenient prompts or small tricks to save time. It is an abnormally high-rotation work method that connects research, ideation, images, code, and physical devices in one go by running multiple AIs simultaneously.
To clarify, the "12 hours" in the title doesn't mean Yoichi Ochiai has officially stated he uses AI for exactly 12 hours every day.
It's not a lifestyle where he is glued to a chat screen for 12 hours himself.
He lets the AI side continue working from morning to night, or even while he sleeps. Meanwhile, the human creates questions, selects results, goes to the field, and throws the next job. This lifestyle of maximizing the operating time of the computer rather than the labor time of the human is what this article calls the "extreme lifestyle of running AI for 12 hours."
While ordinary people try to shorten one hour to 30 minutes with AI, he is trying to run 10, 20, or 50 exploration routes in that same hour.
Here lies the decisive difference between those who use AI and those whose very abilities are expanded by AI.
Don't use AI as an "answer machine"
To understand Yoichi Ochiai's AI usage, there is a common sense you must first discard.
It is the common sense that AI is something you ask to answer questions.
General AI usage mostly looks like this:
"Give me 10 project ideas."
"Make this email more polite."
"Summarize this text."
"Create a catchy copy that sells."
These are not wrong. In fact, work time becomes shorter. However, with this usage, AI remains just high-performance word-processing software.
The Ochiai method is different.
Before letting the AI give a final answer, he makes it do exhaustive preliminary research.
What is happening now?
What has been done in the past?
Do similar ideas exist in other industries?
Are there forgotten concepts left in old literature?
While crossing history, papers, technology, art, language, and culture, he has the AI bring a massive amount of materials that might catch his mind.
In the video, an example of researching "silverfish," insects that eat books, was discussed. When he wants to know about silverfish, he doesn't just read one encyclopedic explanation. He runs deep research on multiple AIs simultaneously, digging into their history, literature, and cultural context.
The important thing is not just becoming knowledgeable about silverfish.
During the investigation, questions that didn't exist initially might arise, such as:
"What are organisms that eat books?"
"What does it mean for knowledge to be eaten as a physical substance?"
"Do silverfish exist in digital data as well?"
AI is excellent not just because it returns correct answers.
It is powerful because it can roughly and widely dig into areas that humans haven't been able to turn into questions yet.
Therefore, the role of AI is not the "answerer."
In the Ochiai method, the AI is a worker digging up a large amount of soil, and the human is the person finding strange fossils within that soil.
Don't rely on one company. Make AIs "researchers with different opinions"
Yoichi Ochiai does not worship a single AI.
In the video, he talks about running deep research simultaneously on ChatGPT, Gemini, Grok, Claude, Manus, and others. In other contexts, he explains using OpenAI, Anthropic, Google, xAI, and Chinese models interchangeably, using DeepSeek as needed.
Why go through such trouble?
It seems easier to pick one top-performance model and leave everything to it.
However, AIs see the world differently depending on the model.
The information they easily pick up in searches is different.
Their way of explaining is different.
Their level of caution is different.
Some models are good at literary associations, while others are good at solidifying logical structures. There are models strong in code, models strong in image understanding, models strong in breaking news, and models strong in integrating long texts.
Even if you throw the same theme, they won't all return the exact same answer.
Here, ordinary people try to decide "which one is most correct."
But in the Ochiai method, what is valuable is the discrepancy rather than the agreement.
Out of five AIs, four gave the same explanation, but one brought a different piece of literature.
One pointed out a commonality with an old art movement.
One doubted the very premise of the question.
This discrepancy becomes the starting point for a new concept.
Using multiple AIs is not about taking a majority vote for the same answer.
It is about putting researchers with different biases in the same room and looking for places where their opinions clash.
Rather than looking for one genius AI, lining up five AIs with different ways of thinking is stronger in unknown territories.
Therefore, the essence of the Ochiai method is not model selection.
It is model orchestration.
Turn even 10 minutes of waiting time into another thought process
What is particularly abnormal in the video is not just the method of starting deep research.
Throwing investigations to ChatGPT, Gemini, Grok, Claude, Manus, etc., one after another, ends in about a minute. Then there are about 10 minutes until the results return. During that time, he talks with another AI to think about "what is interesting about this theme."
In other words, he is not waiting.
He doesn't look at social media until the processing is finished.
He doesn't stare at the first AI waiting for the progress bar to reach 100%.
While the AI is researching, the human is improving the quality of the question.
Here lies the difference between traditional work and work in the AI agent era.
Traditional work was serial.
When the research is finished, think of a plan.
When the plan is decided, create materials.
When the materials are ready, create images.
When the images are ready, implement.
In the AI era, these processes can be parallelized.
While the research agent is running, organize the points of discussion with a conversational AI.
Have another AI create opposing opinions.
Have an image AI visualize a temporary concept.
Have a coding agent create a prototype that works for now.
Humans don't wait for completion before moving to the next step.
They nurture multiple incompletes simultaneously.
This is not mere multitasking.
When a human does multiple tasks simultaneously, concentration breaks, but if you give multiple tasks to AIs, the human only needs to intervene at the moment judgment is required.
Instead of becoming a worker yourself, you design the queue of work waiting to be done.
This is the work method of the AI agent era.
Using "literary AI" as an ideation device
At the time of the video recording, Yoichi Ochiai mentioned he likes GPT-4.5 as a "literary AI."
The reason is not just because the writing is beautiful.
Literary language does not proceed in a straight logical line.
The relationship between words jumps.
It circles.
Distant concepts suddenly connect.
Metaphors are born, sounds bring meaning, and symbols call in other symbols.
In explanatory text, it proceeds as "because A, then B; because B, then C."
In literature, it can suddenly jump from A to a far-off X.
If you are using it for fact-checking, this leap is dangerous. However, at the stage of thinking of new concepts, this instability becomes a weapon.
From here, the role of AI can be divided into two.
One is AI that approaches the truth.
Look for sources.
Check numbers.
Gather counterexamples.
Organize past research.
Another is AI that makes leaps in meaning.
Connect distant concepts.
Create strange metaphors.
Give interpretations opposite to common sense.
Find other structures from the sound of words.
Most people try to make one prompt do both.
That's why text that is ambiguous as a fact and mediocre as an idea comes out.
In the Ochiai method, research and leaps are separated.
Don't let the research AI engage in random creation.
Don't seek only the correct answer from the ideation AI.
Have a head for solidifying facts and a head for breaking concepts separately.
This becomes an important design for creating innovative projects with AI.
Making research, context, images, and code into a single loop
Yoichi Ochiai's work doesn't end with writing text.
Research the current situation.
Dig into the past context.
Create your own way of thinking.
Visualize as an image or concept.
Drop it into a system.
Move it in physical space.
In the video, after using multiple LLMs for research, he talks about using Nano Banana, Midjourney, Stable Diffusion, Adobe Firefly, etc., for generating images and concepts, and using Cursor, Windsurf, Claude Code, etc., for implementation.
What's important here is not memorizing tool names.
It's that he changes the AI he uses every time the form of thought changes.
If you think only in text, only ideas that work as text will emerge.
When you make it an image, contradictions in shape become visible.
When you make it three-dimensional, constraints of gravity and materials appear.
When you make it code, conditional branching and data structures become necessary.
When you move it in the field, problems that didn't exist in the generation screen appear, such as light, sound, delay, temperature, human movement, and equipment failure.
This counterattack from reality strengthens the idea.
People who finish their work only with text generation are less likely to notice the well-formed lies created by AI.
On the other hand, people who actually move things cannot be deceived.
If the code doesn't work, it's an error.
If the robot doesn't move, it stops.
If the structure doesn't hold, it breaks.
If the audience isn't interested, the atmosphere turns cold.
In other words, what supports the precision of the Ochiai method is not just the cleverness of the AI.
It is the high frequency of hitting the AI's output against reality.
Create a hypothesis on the digital side, cause a change on the physical side, and return that result to the digital side again. This loop structure overlaps with the concept of Digital Nature that Ochiai explains.
Just writing emails faster doesn't touch the true potential of the AI era
In the latter half of the video, Yoichi Ochiai mentions email replies, proposals, diagrams, and reports as situations where a typical 30-something business person uses AI.
Of course, even this is sufficiently convenient.
However, there are limits.
Even if you turn an email from 30 minutes to 5 minutes, what you are making is still an email.
Even if you turn a proposal from 2 hours to 20 minutes, you haven't stepped outside the proposal.
Just making existing work faster with AI doesn't change the structure of work.
In research and production sites like Yoichi Ochiai's, multiple elements such as three-dimensional objects, systems, space, light, sound, and robots must be considered simultaneously. Therefore, AI becomes a device that accelerates real-world creation rather than a text creation assistant.
The lesson to be learned here is not to become an engineer.
It is to add a process of creating "concrete objects" to your work.
If you are in sales, don't end with just sales emails.
Create customer analysis, proposal hypotheses, demo screens, estimates, FAQs, and post-introduction operation designs.
If you are a marketer, don't end with just post ideas.
Create actual LPs, advertising materials, measurement tables, customer flows, and improvement plans.
If you are an instructor, don't end with just a script.
Create teaching materials, exercises, grading criteria, feedback for each student, and review AIs.
Strong people in the AI era are not those who write text fast.
They are those for whom the distance from text to reality is short.
Finally, "making AI agents create AI agents"
Yoichi Ochiai's AI usage has progressed beyond the stage of opening multiple chat AIs.
In February 2026, he instructed Claude Code to create something like Claude Code and told it, "Don't stop until you're finished," before going to sleep. In the morning, he reported on his official note that a local coding agent that works without Claude Code had been created. It reportedly used lightweight and heavy models locally as needed and even had the necessary tools.
This is not just a funny anecdote.
It is the moment the subject of AI usage changed.
Until now, humans used AI to write code.
Here, the AI is creating the AI environment to be used next.
Furthermore, "co-vibe," which Yoichi Ochiai has released, is a multi-provider coding agent that crosses Anthropic, OpenAI, Groq, and Ollama, switching models according to the complexity of the task. Deep research, parallel execution of multiple agents, and session persistence are also touted.
What is happening here is graduation from model comparison of "which AI is the smartest."
What's important is not the IQ of a single model.
Which job to pass to which model.
What jobs are sufficient for cheap models.
When is the moment to use a strong model.
Can it switch to another model if it fails?
Can it save progress?
What are the conditions for asking a human?
Can it stop dangerous operations?
In other words, the battle has moved from prompts to harness design.
A harness is a work environment for AI to work for long periods.
It includes task decomposition, file structure, progress recording, testing, permissions, restart methods, error handling, and model allocation.
Anthropic also explains that for agents that run for long periods, context management, progress handovers, and specialization of multiple agents are important.
Having a strong AI is not enough.
The person who builds a factory where strong AIs can continue to work without hesitation is strong.
The reality of the "extreme lifestyle" of running AI for 12 hours
So, what specifically is a lifestyle of running AI for 12 hours?
From here, based on the provided video and published practices, let's reconstruct it into a form that can be applied to general work.
8:00 AM ── First, have the AI go to work, not the human
When you open your computer, don't start working yourself right away.
Summarize the theme you want to think about today into a single brief.
Purpose.
Current location.
What is known.
What is not known.
Conditions that must be kept.
Desired deliverables.
Pass that brief to multiple AIs simultaneously.
Have the 1st one look at previous research.
Have the 2nd one look at market examples.
Have the 3rd one look at opposing opinions.
Have the 4th one look at past history.
Have the 5th one look for similarities with a completely different industry.
While the human is drinking coffee, the AI starts running in five directions.
9:00 AM ── Look for "hooks" rather than answers
When the results arrive, you don't need to read everything equally.
Proper nouns you didn't know.
Old examples that are strangely intriguing.
Points of discussion where opinions were split among multiple AIs.
A sentence that you intuitively feel has "something."
Pick only those and save them to a file like seeds.md.
What's important in the AI era is not remembering all information.
It is leaving fragments that generate the next question.
10:00 AM ── Run the ideation AI
Pass the collected fragments to a literary or ideation AI this time.
"I want you to forcibly connect these five concepts."
"Create the most unnatural combination."
"How would an artist from 100 years ago express this project?"
"Give it a title with the opposite meaning."
Here, prioritize leaps over correctness.
Don't use the same settings as the fact-finding AI.
11:00 AM ── Have it create images and prototypes
Don't keep thinking in text; change it to images.
Create screen ideas.
Create structural diagrams.
Create simple demos.
Create mockups.
You can have the coding agent create the same idea with different implementation policies.
Plan A is the minimum configuration.
Plan B is high quality.
Plan C is low cost.
Don't aim for one perfect piece from the start; create multiple incomplete products that can be compared.
12:00 PM ── Leave the AI and have the human go to the field
Lunch, meetings, interviews, travel, conversations with customers.
Go get information that only humans can get.
During this time, have the AI continue testing, corrections, additional research, and material organization.
If it's a system that stops when the human leaves their seat, it's still just a chatbot.
If you want to make it an agent, decide on the continuation and stop conditions for the work beforehand.
3:00 PM ── Create the next job from failed deliverables
When you return, don't just look at the finished products.
Look at the failure logs.
Places where the AI misunderstood.
Places where it failed tests.
Instructions that multiple AIs commonly failed to understand.
Places that deviated from real-world reactions.
Here lies the context that should be improved next.
If you just manually fix the AI's failures on the spot every time, the same mistakes will happen forever.
Fix where the problem was in the instructions, data, folder structure, tests, skills, or permissions.
Improve the mechanism that generates deliverables, not the deliverables themselves.
6:00 PM ── Integrate
Integrate the research, ideation, images, code, and field information that have been running since morning into one.
This integration work is precisely the important job for humans.
AI can create in large quantities, but it doesn't automatically decide what should be kept.
What is the core of the project?
What to discard?
Who is it for?
Which discrepancy to follow?
Here, the person's experience, obsession, aesthetic sense, and ethics come out.
8:00 PM ── Throw the work for the next morning
Before the human ends the day, set the AI's night shift.
Review of the entire code.
Competitor research.
Addition of test cases.
Checking sources for materials.
Comparison of multiple plans.
Organization of documents.
Have it summarize the results into a form that the human can judge the next morning.
In the vision of co-vibe published by Yoichi Ochiai, paper research, interpretation of experimental results, proposals for the next steps, monitoring of lab equipment, and the concept of "running long-term autonomous R&D sessions every day" are clearly stated.
It's not about working hard for another 4 hours after a human has worked for 8 hours.
It's about giving the next 4 hours to the machine before the human starts resting.
This is the essence of 12-hour operation.
Why ordinary people don't grow even if they use multiple AIs
Anyone can just open five AIs.
However, most people get tired of receiving five similar answers.
The cause is not the performance of the AI.
It's because the exit for the work hasn't been decided.
They throw "research this theme."
A massive amount of information returns.
There is no storage location.
There is no comparison standard.
They haven't decided what to do next.
As a result, they end up satisfied just by reading it.
This is not AI usage, but consumption of AI content.
If you are going to run multiple AIs, you need at least the following four things:
Standardization of input.
Pass the same premise materials to all AIs. If the premises are different, you cannot compare the answers.
Separation of roles.
Don't ask everyone the same thing. Separate research, counterarguments, ideation, verification, and implementation.
Fixing deliverables.
Don't leave answers in the chat column; drop them into files, tables, code, images, slides, etc.
Setting the next process.
When the research results come out, decide beforehand what to pass to which AI.
The more you use AI, the more traffic control ability becomes more important than prompting ability.
Even if there are 48 employees, if everyone works as they please, the company will collapse.
AI is the same.
Non-engineers should start with "three AIs"
You don't need to start by running a massive number of models or coding agents in parallel like Yoichi Ochiai.
Three are enough at first.
1st is the Researcher
Gather facts, data, examples, and sources.
Give it rules like:
"Write that you don't know if you don't know."
"Prioritize primary information."
"Always leave the citation source."
2nd is the Critic
Doubt the results of the researcher.
Contradictions in numbers.
Convenient interpretations.
Overlooked counterexamples.
Discrepancies that readers might feel.
Have it look for obstacles in implementation.
3rd is the Creator
Receive the research and criticism and turn them into articles, projects, slides, LPs, scripts, product ideas, etc.
The important thing is not to end by having the three talk separately.
In one work folder, place:
brief.md
sources.md
critique.md
decisions.md
next.md
Humans write only decisions.md themselves.
What was adopted?
What was discarded?
Why was it judged so?
What to try next?
When this judgment log accumulates, the AI will gradually be able to reproduce work that is characteristic of that person.
Conversely, if you leave even the final judgment to the AI, the output will always return to a "plausible average."
The Ochiai method can be used as is even in content sales
For example, suppose you are writing an article on "How people with ADHD can earn with AI."
With ordinary AI usage, you would have ChatGPT write 10,000 words and be done.
With the Ochiai method, you first run multiple investigations.
One for research on ADHD and work.
One for specific occupations using AI.
One for patterns where the person is likely to fail.
One for expressions that are overused in existing articles.
One to look for changes that readers truly want deep down.
From those results, pick up things that caught your mind.
Instead of "improving concentration," "divide work on the premise that concentration switches."
Instead of "acquiring persistence," "let the AI handle persistence."
Instead of "overcoming weaknesses," "immediately turn ideas into concrete objects."
Once such perspectives are found, have a literary AI make leaps in titles and metaphors.
Next, pass it to a critic AI to trim exaggeration, bias, and medically dangerous expressions.
After that, flow it to an article AI, diagram AI, thumbnail AI, and sales copy AI.
Finally, the human decides on the claim they truly want to convey to the reader.
It's not that the AI wrote the article.
One claim passed through multiple processes using AI.
This difference is huge.
10,000 words written with one prompt are thin even if they are long.
10,000 words that have passed through multiple processes have a different density of discussion points even on the same theme.
What long-term agents need is not grit, but a harness
When you hear "running AI for 12 hours," it looks like you just write a huge prompt and leave it.
In reality, it's the opposite.
The longer you run it, the smaller each instruction must be, and the finer the structure must be.
AI deviates from the purpose little by little the longer it runs.
The first misunderstanding becomes huge after 10 processes.
It refers to the wrong file.
It uses old specifications.
It repeats work that is already finished.
It makes unnatural corrections just to pass a test.
Therefore, a long-term agent needs at least the following mechanisms:
Write a plan before work.
Leave the current location in a file.
Test in small units.
Clearly state completion conditions.
Don't repeat the same method if it fails.
Confirm with a human before important changes.
Write a handover for the next person in charge at the end of work.
Anthropic also explains that for long-term agents, a design that can maintain state while compressing context and resume from the middle is important. Also, when using multiple agents, parallelization is possible by dividing roles into implementation, quality check, documentation, etc.
In other words, what enables long-term operation is not the grit of the model.
It is a design where you can return even if you forget.
"Using diverse AIs simultaneously" is not about discarding verification
When you run multiple AIs, multiple mistakes are also generated.
Just because five AIs said the same thing doesn't mean it's correct.
They might be referring to the same search results, the same secondary information, or the same misunderstanding.
Massive output requires massive verification.
Furthermore, if you allow coding agents to perform file operations or terminal operations, dangers different from text generation are born.
Deletion of files.
Sending secret information.
Execution of dangerous commands.
Destruction of dependencies.
Accidental deployment to the production environment.
In Yoichi Ochiai's explanation of co-vibe, the automatic approval mode is said to be for advanced users, and warnings against allowing commands you don't understand and dangerous operations like sudo or rm -rf are clearly stated.
If you are going to run it for a long time, narrow the permissions.
Create a copy environment for work.
Limit the range that can be deleted.
Don't place secret information.
Don't consider it complete unless it passes tests.
Leave human approval for important operations.
"Leaving everything to it" and "giving all permissions" are different.
Excellent agent operators build safe fences first in order to let them work freely.
What remains for humans is "what catches you"
When AI becomes able to work for long periods, will human value disappear?
Rather, it's the opposite.
In the era when output was low, the power to create itself was scarce.
Being able to write text.
Being able to create images.
Being able to write code.
Being able to research.
Those alone became value.
However, in an era where AI creates text, images, and code in large quantities, just being able to create is unlikely to make a difference.
Instead, what becomes important is:
What do you find interesting?
Which discrepancy can you not leave alone?
What do you think is beautiful?
Which problem do you take on as your own?
What do you keep out of a massive number of ideas?
This becomes the selection on the human side.
Yoichi Ochiai says that to show originality in AI research, it is important to find problems that only you are working on. Instead of just following popular technology as it is, look for places where no one is having fun with that technology yet.
AI can gather information from all over the world.
But it won't decide what among that you should be obsessed with.
AI can give 100 project ideas.
But it cannot decide if you want to use your life to create the 101st one.
AI can create a conclusion that seems correct.
But it doesn't have a body that feels a discrepancy with that conclusion.
Therefore, human work moves from "answering" to "being caught by things."
The difference between those who take it easy with AI and those who evolve abnormally with AI
People who take it easy with AI shorten their current work.
People who evolve abnormally with AI start a number of trials that they couldn't do until now.
The former writes emails faster.
The latter creates a different proposal, a different demo, and a different verification for each customer.
The former writes articles faster.
The latter researches the same theme from five directions—history, psychology, economy, the parties involved, and the opposition—and picks the strongest angle.
The former has code written for them.
The latter has multiple implementations created in parallel and runs other agents for testing, review, and documentation.
The former turns the time freed up by AI into a break.
The latter puts the next experiment into the freed-up time.
Here lies the reality of the "extreme lifestyle."
It's not about cutting back on sleep.
It's not about glorifying long working hours.
It's about creating an environment where trial and error proceed even during the time you are not moving your hands.
Ordinary people try to have AI finish their work.
Yoichi Ochiai is increasing the next work with AI.
It's not over when the answer comes out.
He finds a hook in the answer and starts another investigation.
It's not over when the image comes out.
He changes it to a three-dimensional object or a system.
It's not over when the code moves.
He brings it into physical space and returns data again.
AI doesn't work in his place.
He is creating an endless loop between AI and reality.
Summary ── Don't shorten human work. Lengthen the machine's thinking time
What should be learned from Yoichi Ochiai's AI usage is not a specific model name or a flashy prompt.
The core lies in the following work structure:
Don't rush the AI for a final answer; first, have it do a massive amount of preliminary research.
Don't depend on one AI; run different models simultaneously.
Look at the discrepancies between models, not the agreement of answers.
Deepen the question with another AI during waiting time.
Separate fact-finding and literary leaps.
Don't end with text; drop it into images, code, three-dimensional objects, and the field.
Don't just fix AI failures; fix the mechanism where the failure occurred.
Before going to sleep, pass the work for the next morning.
And the human decides which fragment to be caught by.
The strong in the AI era are not those who have a contract with the smartest model.
They are those who have an environment where AI continues to think, research, create, and verify without stopping.
Making one answer high quality is not enough.
Make it so you can try 10 times.
If 10 times is not enough, build a structure where you can try 100 times.
Human 24 hours do not increase.
Concentration and physical strength are also finite.
However, the operating time of the computer can be increased.
The number of heads working simultaneously can also be increased.
Even while sleeping, you can have it create materials for the next morning.
Therefore, what is needed from now on is not "how to take it easy with AI."
How to design a world where thinking and production move forward even while you are resting.
Yoichi Ochiai's extremity is not in working for 12 hours.
It is in creating the questions and environment where AI can continue to work for 12 hours, looking at the results, and throwing even more interesting work the next day.
