The biggest misconception in AI monetization today is the idea that you can make money by 'creating amazing prompts,' 'writing mass articles with AI,' or 'wrapping AI tools.' That's outdated. What the sophisticated crowd overseas is looking at is not prompts but workflows, not mass production but delivery speed, and not AI tools but business units that can be delegated to AI.
This trend is being accelerated by the latest generation of models from ChatGPT and the long-duration agent models from Claude. OpenAI has deployed GPT-5.6 in three sizes—Sol, Terra, and Luna—available via ChatGPT Work, Codex, and API. Sol is positioned as a high-end model for complex coding, knowledge work, research, computer operation, and design, while Terra and Luna are designed for speed and cost efficiency. Meanwhile, Anthropic's Claude Fable 5 is marketed as a model for long-duration, high-difficulty, multi-stage tasks, available through Claude Code, Claude Cowork, and API.
The conclusion is that the monetization strategy currently targeted by overseas AI geeks is 'dual-wielding': creating quickly with GPT and refining deeply with Claude. GPT is strong in cost efficiency, UI, implementation, documentation, and mass production. Claude is strong in long context, complex codebases, persistent understanding of specifications, and self-verification. By combining these, an individual can replicate parts of a production company, research firm, development house, or business improvement consultancy with a very small team.
I've summarized the setup and hardcore usage techniques in a PDF.
If you want it, you can get it from here! 👇
https://x.com/MakeAI_CEO/status/2027682940847898770?s=20
Why 'Dual-Wielding' Instead of Just ChatGPT or Claude?
People who fail at AI monetization immediately want to decide 'which AI is the strongest.' However, the strongest users overseas don't treat models like a religion. They treat models as components for specific roles.
OpenAI's GPT-5.6 has announced API pricing of $5 per 1M input tokens and $30 per 1M output tokens for Sol, $2.5/$15 for Terra, and $1/$6 for Luna. Furthermore, GPT-5.6 and later introduced explicit prompt caching and cache retention for over 30 minutes, making it easier to manage costs for repetitive business prompts. In short, it's suited for mass production, repetition, and templated work.
Claude Fable 5 has API pricing of $10 per 1M input tokens and $50 per 1M output tokens. While more expensive than OpenAI's Sol, Fable 5 is described as an agent capable of 'working over several days,' 'planning multiple stages, delegating to sub-agents, and checking its own work.' This makes it suitable for the final polish of high-ticket projects, design reviews, long-context understanding, refactoring, and finding contradictions in specifications.
Furthermore, independent benchmark Artificial Analysis reports that GPT-5.6 Sol achieves intelligence scores close to Claude Fable 5 at a lower evaluation cost, and Sol ranked high in the Codex environment on the Coding Agent Index. What this shows is not 'who won,' but a design philosophy where you should separate the layer that runs cheaply and quickly from the layer that refines at a higher cost.
What really matters in monetization is not the quality of a single output. It's the gross profit. Making money with AI isn't about buying and selling model intelligence; it's about converting the difference in model capability and cost into deliverables that customers can understand.
The Basic Overseas Geek Style: 'Dividing Tasks by Model'
For example, suppose you have a project to build a Web service MVP. A common AI use case in Japan might stop at 'having ChatGPT write code' or 'having Claude find bugs.' Overseas AI geeks decompose it further.
First, use GPT to create market research, LP structure, UI drafts, component design, initial code, and even demo video scripts all at once. GPT-5.6 is described in OpenAI developer materials as having enhanced frontend layout, visual hierarchy, and design judgment. Then, have Claude Fable 5 review it for specification contradictions, code structure, security oversights, and areas likely to break in long-term operation. Fable 5 is described as being for ambitious coding projects, large-scale migrations, complex implementations, and multi-day autonomous sessions.
The key here is not to use AI as a 'human replacement' but to assign AIs to different job roles.
GPT is the product designer and junior implementer who gives shape to ideas. Claude is the senior engineer and reviewer who is picky about specifications. The human is the producer who listens to the customer's problems, decides the goal, and takes responsibility for the deliverables. With this three-layer structure, an individual can operate like a small production company.
Monetization Method 1: Turning AI Prototypes into 'Validation Packs' instead of 'Deliverables'
The easiest way to start is AI prototype production. However, selling 'I'll make a Web app with AI' leads to price competition. The strong way to sell overseas is to make it a package for validating business hypotheses, rather than just making the prototype.
For example, create a product like this for sole proprietors or small SaaS companies:
'I will create an LP, a simple Web app, usage scenarios, price testing copy, and a demo video script in 48 hours. The deliverables will be ready for actual customer interviews or ad testing.'
In this case, GPT handles market comparison, LP wording, UI, initial code, slides, and ad copy. Claude reviews target contradictions, pricing weaknesses, onboarding gaps, code failures, and paths where customers might drop off.
This product is strong because the customer doesn't just 'want code'; they want to 'know quickly if this idea is worth pursuing.' In other words, what you are selling is not AI production, but shortening decision-making.
The price can be designed as a 'Validation Pack for $1,000,' an 'Investor Demo Pack for $2,000,' or an 'Internal Approval PoC Pack for $3,500,' rather than '$200 per page.' The customer is buying materials for internal meetings, sales, fundraising, and ad testing, not labor hours.
Monetization Method 2: Creating 'Deep Specs' with Claude and 'Selling Presentation' with GPT
A particularly strong combination for ChatGPT and Claude is the specification business. It's unglamorous but very solid.
In the AI era, what will increase is not outsourcing finished products, but 'specifications intended for AI to build.' As no-code, AI coding, and internal automation spread, companies lose track of 'what to build,' 'how to explain it,' and 'how much to leave to AI.'
What sells here are PRDs for AI implementation, requirement definitions, user stories, acceptance criteria, screen transitions, test perspectives, and risk lists.
Claude Fable 5 is described as a model suited for long context and complex knowledge work, supporting the understanding of diagrams, tables, and charts in PDFs. Therefore, it's suited for reading meeting minutes, existing materials, spreadsheets, competitor sites, and past failure cases provided by the customer to distill them into deep specifications.
Meanwhile, GPT-5.6 is being deployed via ChatGPT Work to gather context from team tools and files and convert them into deliverables like documents, spreadsheets, and slides. In other words, you take the deep requirements structured by Claude and expand them into internal proposals, sales materials, LPs, emails, and ad copy with GPT. This flow is powerful.
Product names could be 'AI Pre-development Spec Pack,' 'Claude-Reviewed PRD,' or 'Requirement Definition Kit for AI Coding.' Customers buy blueprints that won't fall apart even if thrown at AI or outsourcers. This is a field where it's easy to raise unit prices in B2B.
Monetization Method 3: Selling Improved Traffic via AI Search and AI Referrals
The next growth area is AI Search Optimization. This isn't just traditional SEO, but designing how you get picked up by AI answers like ChatGPT, Claude, Gemini, and Perplexity.
A June 2026 study by SE Ranking found that traffic from AI search engines to websites grew 16x from 2024 to 2026, accounting for 0.32% of all web traffic by 2026. It's still small, but ChatGPT accounts for 74.78% of AI referral traffic, and Claude is showing high growth. Search Engine Journal also noted that Claude's referral traffic grew significantly from January to April 2026, the highest growth rate among target platforms.
What overseas geeks are looking at here is not 'whether AI search will replace SEO.' They are aiming to productize new traffic channels that companies aren't measuring yet.
The sales pitch is this:
'I will investigate which competitors are recommended when your company name, product name, or category is asked in ChatGPT and Claude. I will create FAQs, comparison pages, case studies, and structured descriptions that are easily cited in AI answers. I will re-measure in one month.'
This is not technically too difficult. Use GPT to generate mass search intent patterns, Claude to classify response trends, GPT to create improvement content, and Claude to check for reliability and missing information. You sell it to the customer as a 'measure to increase the probability of being introduced by AI.'
The point in this area is not to hide that AI search traffic is still small. Rather, saying 'It's small now, but it's growing. That's why we should build the measurement foundation and comparison pages now' builds more trust. AI monetization fades for those who exaggerate.
Monetization Method 4: Making Claude Code Sub-Agents the 'Product'
What hardcore overseas developers are doing is building Claude Code sub-agents and MCP integrations as templates for specific tasks.
Claude Code is described as an agentic coding tool that can read codebases, edit files, run commands, and integrate with development tools. It's designed to connect with external tools, databases, APIs, issue management, monitoring tools, Figma, Slack, etc., via MCP. Furthermore, Claude Code has a mechanism to create sub-agents specialized for specific tasks, each with its own context, system prompt, and tool permissions.
What sells here is not just a collection of prompts. It's the AI team configuration for each business task.
For example, you can create templates like these:
'Claude Code Agent Pack for SaaS Maintenance Teams'
Roles: Code reviewer, bug reproduction specialist, DB query checker, test creator, release note creator.
Customers can semi-automate weekly maintenance tasks just by putting this in their repository.
'Improvement Agent Pack for Shopify Operators'
Roles: Product description improver, SEO FAQ generator, review analyzer, inventory data summarizer, campaign LP editor.
By connecting to store data or spreadsheets via MCP, you can have the AI act as an 'assistant store manager.'
'Document Inspection Agent Pack for Professionals'
Roles: Contract summarizer, gap checker, terminology unifier, comparison table creator, client explanation generator.
Use Claude's long context and document understanding, and convert to proposals or emails on the GPT side.
This product is better sold with initial setup included rather than as a standalone template. This is because many customers don't understand the concepts of MCP or sub-agents. Overseas geeks turn this into an 'implementation service with education.' In other words, what they are selling is not a file, but the initial construction to house AI in the business.
Monetization Method 5: Mass Producing with Cheap GPT Models and Performing 'High-Value Judgment' Only with Fable
In AI monetization, those who throw everything at the top-tier model lose. Successful people overseas always differentiate model usage.
GPT-5.6 has Sol, Terra, and Luna models with different price points. OpenAI's developer guide for GPT-5.6 recommends testing by lowering the reasoning level depending on the task after updating quality and efficiency standards for complex production workflows, and limiting 'max reasoning' to difficult, quality-priority tasks rather than using it for everything.
This thinking can be used directly for monetization.
For example, suppose you sell an ad creative improvement service. The work of creating 100 ad copy drafts, 20 types of LP headlines, and 10 short video scripts is handled by the low-cost GPT configuration. From there, the 10 drafts with the most potential are passed to Claude Fable 5 to strictly review target psychology, differentiation from competitors, legal/expression risks, and brand tone. Finally, it's formatted as delivery material with GPT.
From the customer's perspective, it's not 'I made 100 copies with AI,' but 'I generated 100 drafts, selected and improved the top 10 with a high-performance model, and added a test design.' This allows you to raise the price.
In short, use Fable for judgment, not mass production. GPT for mass production and formatting, Claude for selection and deep diving. This division of labor allows you to increase the persuasiveness of the deliverables while keeping AI costs down.
Monetization Method 6: Turning AI Business Improvement into 'Weekly Report Delegation' instead of 'Automation'
What's easy to sell to companies is not flashy full automation. Rather, the first thing that sells is delegating the tedious weekly reporting work.
For sales teams, summarizing meeting notes, CRM, emails, minutes, and reasons for lost deals. For EC, summarizing sales, inventory, ads, reviews, and competitor prices. For recruitment, summarizing applicants, interview notes, progress by job type, and candidate evaluations. These jobs exist in many companies, and employees are doing them every week with a sigh.
ChatGPT Work highlights uses like gathering context from team tools and converting them into spreadsheets, documents, and slides. Claude Code and the Claude Agent SDK provide ways to handle agents that include file reading, command execution, web search, and code editing from Python or TypeScript.
Using these, you can sell 'I will deliver a two-page A4 report for management every Monday morning' instead of 'I will develop a full automation tool.' It can be semi-manual at first. Assuming a human does the final check, let the AI handle aggregation, summarization, anomaly detection, insight generation, and slide creation.
Even at $500/month, 10 companies make $5,000. At $1,500/month, 5 companies make $7,500. Moreover, because the deliverable is clear, it's easy for customers to continue.
The key in this area is not to say 'I can do anything with AI.' Rather, narrow it down to 'I will look only at this data in this format every week.' The more you narrow it down, the more stable the prompts and workflows become, caching and templates work better, and gross profit increases.
Monetization Method 7: Becoming an AI Era 'Reviewer'
Reviewing AI-generated content is surprisingly lucrative. As AI increases, so do people who don't know if they can trust what AI has made. There is a demand for 'reviewers' here.
For example, reviewing an LP made with AI. Reviewing code made with AI. Reviewing a business plan made with AI. Reviewing a contract draft made with AI. Reviewing sales materials made with AI.
At this time, reviewing with humans alone takes time. Reviewing with AI alone makes responsibility ambiguous. So, you combine GPT and Claude.
Have GPT decompose the target and output a wide range of improvement suggestions. Have Claude look for contradictions, omissions, long-context consistency, potential risks, and awkwardness from a customer perspective. Finally, a human selects 'suggestions to adopt' and 'suggestions to ignore' and delivers them as a review report.
Claude Fable 5 emphasizes uses like testing its own work and checking output against goals. If you use this as a selling point, it's more trusted to express it as a 'double review where perspectives are divided across multiple models and the final judgment is made by a human,' rather than 'I reviewed it with Fable.'
Reviewers can start without taking too much delivery responsibility. If you make it 'review only' instead of 'including fixes,' the initial load is light. From there, you can upsell to 'improvement implementation included,' 'monthly review contracts,' or 'creation of internal AI quality control guidelines.'
Common Trait of Those Who Fail: Selling AI as 'Magic'
Reading this far, it might look like a story of 'making $10,000 a month with AI' immediately. However, in reality, more people fail. The reason is simple: they sell AI like magic.
Customers are no longer surprised that AI is amazing. They are surprised when their work finishes early, when materials that lead to sales are created, when internal approval passes, or when weekly hassles decrease.
In other words, what you should sell is not 'AI utilization.' What you should sell is one of the following:
Time reduction.
Shortening decision-making.
Improvement of sales materials.
Automation of internal materials.
Validation before development.
Exposure in AI search.
Review of existing operations.
Quality assurance of code and materials.
AI should be used behind the scenes. In fact, putting it too far in front makes it look cheap. Successful people overseas sell 'I can deliver this result at this speed and price,' not 'I made it with AI.'
Pricing Design: Deciding by 'Human Replacement Value' instead of AI Cost
Beginners decide prices by looking at AI API costs. This is a mistake. Of course, cost management is necessary, but what the customer pays for is not the API cost. The customer pays for the value of reducing outsourcing costs, labor costs, opportunity loss, and decision-making delays.
For example, even if the AI cost for weekly report delegation is $20/month, if it can turn a task the customer spent 5 hours on every week into 1 hour, $500/month is cheap. Even if the AI cost for prototype validation is $100, if it can advance a business judgment the customer worried about for 3 months in 1 week, $3,000 is viable.
However, high-cost models like Claude Fable 5 require careful use. During the promotion period, Fable 5 can be used up to 50% of the weekly usage limit in some paid plans at no additional cost, but after 23:59:59 PT on July 19, 2026, it will not be included in the plan's weekly limit and will require usage credits for continued use, according to Claude's help. API usage is not eligible for the promotion, and standard rates apply.
That's why you shouldn't use Fable for 'everything,' but limit it to 'high-value judgment,' 'final review,' 'long-context understanding,' and 'complex fixes.' Pre-process with cheap GPT configurations and refine with Fable. This is the secret to protecting gross profit.
If You're Actually Starting, This Order is the Most Solid
You don't need to build a SaaS from the start. In fact, those who suddenly build a SaaS are more likely to fail. The smart ones among overseas geeks first sell it as a service, then template it once a repeating pattern is seen, and finally turn it into a tool.
What you should do in the first 30 days is narrow it down to one industry. For example, legal professionals, recruitment agencies, EC operators, B2B SaaS, English conversation schools, real estate, clinics, or production companies. The reason for narrowing the industry is not to increase AI output accuracy, but to make the sales pitch hit harder.
Next, choose one 'tedious task that occurs every week' in that industry. Reports, proposals, minutes, FAQs, comparison tables, ad drafts, LP improvements, code reviews, or customer response analysis. Don't be greedy here.
Then, fix the roles of GPT and Claude. For example, GPT handles the first draft, structure, tables, slides, LP, and implementation. Claude handles review, contradiction detection, long-context understanding, specification organization, and quality checks. The human handles hearing, final judgment, delivery, and improvement proposals.
Finally, make it a monthly subscription rather than a one-time sale. AI utilization is exhausting if it ends as a one-off. It should be a product based on continuity, such as monthly reviews, weekly reports, four improvement proposals per month, once-a-month AI search diagnosis, or twice-a-month prototype improvements.
Concrete Product Examples
To be more specific, you can create products like these now:
1. AI Search Exposure Diagnosis Pack
Investigate the customer's company name, category name, and competitor comparison keywords with ChatGPT and Claude. Visualize which competitors are recommended by AI and improve FAQs, comparison pages, case studies, author profiles, and structured product descriptions. $1,000 for the first time, $500/month for improvements.
2. Pre-AI Development PRD Pack
Define requirements for the customer's idea with Claude and convert them into screen drafts, LPs, and demo materials with GPT. Deliver as specifications that can be handed to AI coding tools or outsourcers. $1,500–$3,000 for the first time.
3. Weekly Management Report Delegation
Summarize sales, ads, inquiries, reviews, and meeting notes every week and deliver as two A4 pages and a few slides. Format with GPT, check for insights and contradictions with Claude. $500–$2,000/month.
4. Claude Code Implementation Starter
Set up CLAUDE.md, sub-agents, review procedures, test procedures, and MCP connection policies for Claude Code in existing repositories. Separate 'tasks left to AI' and 'tasks approved by humans' for the development team. $2,000–$5,000 for initial implementation.
5. AI-Generated Content Review Service
Review LPs, sales materials, code, contract drafts, and business plans made with AI using multiple models, with a human adding final comments. $300–$1,000 per case. Easy to turn into a continuous review contract.
The important thing here is that every product says 'I will shorten the customer's existing business' rather than 'I use AI.' AI is a means. The product is the shortened time and reduced anxiety.
The Winners from Now on are Not 'People Who Can Use AI' but 'People Who Can Delegate Work to AI'
In the early days of AI utilization, people who were good at prompts stood out. Next, people who could write code with AI stood out. But from now on, more unglamorous and strong people will win. Those are the people who can decompose work.
Which task to throw to GPT.
Which task to throw to Claude.
Which task a human should judge.
Which task to template.
Which task to turn into a monthly product.
Which task to use an expensive model for, and which task to use a cheap model for.
People who can design this can make money even if the AI models change. Conversely, those who depend only on small tricks for specific models will disappear in an instant with an update.
The Claude side is aiming high-performance models like Fable 5 at long-duration, high-difficulty tasks. The OpenAI side is expanding GPT-5.6 into ChatGPT Work, Codex, API, multi-agents, and caching, moving closer to the execution side of business. In other words, the battlefield has moved from 'chat response quality' to 'how much of the work can be finished.'
Overseas geeks are already looking there. They are not thinking about 'what to have AI write,' but 'how to team up AI.' Making GPT the production lead, Claude the review lead, and the human the person in charge. This configuration is the shortest route for an individual to work like a small company.
Finally: If You're Entering Now, Don't Aim for Flashy AI Businesses
The most important thing I want to tell those starting now is not to aim for flashy AI businesses. Making AI apps, making AI media, selling AI teaching materials, selling AI prompts. Of course, there is potential, but the competition is also fierce.
Instead, it's more solid to insert AI into business where money is already moving. Reports, sales materials, specifications, code reviews, recruitment materials, FAQs, competitor research, ad improvements, weekly meeting materials. The unglamorous the job, the more the AI dual-wielding effect shows.
The essence of making money with the combination of ChatGPT and Claude is not 'making money easily with AI.' It's clearing the work that people find tedious and put off, quickly, cheaply, and continuously through the division of model roles.
The reason it looks like only overseas geeks are doing it is that they don't see AI as a 'text generation tool.' To them, AI is a set of components for rearranging production, implementation, review, research, and quality control roles.
So, there is only one prompt you should learn first:
'When this job is divided into five stages—generation, organization, implementation, verification, and delivery—please separate the stages that should be left to GPT, the stages that should be left to Claude, and the stages for which a human should take responsibility.'
People who can ask this question every time will win in the next AI monetization.
