Building & Selling AI

AI Monetization Strategies

Last updated 2026-07-31

What's new

2026-07-31

Kepler, a company specializing in AI for financial services, emphasizes the importance of trustworthy AI that can produce verifiable work products, not just more content (Kepler is an AI tool for finance that focuses on creating reliable outputs).
Traditional financial tools like Bloomberg and FactSet (paid online services for financial data) are used to shift blame if information is wrong, but they don't help create work products (like reports or analyses).
The main challenge in finance is getting AI to move from searching for information to creating reliable work products, like investment memos or financial models (DCF stands for Discounted Cash Flow, a way to value a company).
Verification (proving a number or fact is correct in a way that can be repeated and checked) is crucial in finance for regulatory compliance and making sound investment decisions.

2026-07-22

Forward Deployed Engineers (FDEs) (specialists who customize AI tools for specific companies) are in high demand, with some earning millions annually, and you can become one in 30 days.
AI intelligence is becoming widely available, so the competitive edge lies in how companies deploy and customize it for their unique needs.
Palanteer (a company that helps businesses use AI) popularized the FDE role, sending specialists on-site to create tailored solutions using their customizable software platform.

2026-07-19

Thinking Machines Lab, a startup led by former OpenAI CTO Mira Murati, released a new AI model called Inkling, which is a large, open model designed to handle text, images, audio, and video.
Inkling is a "mixture of experts" transformer (a type of AI model) with 975 billion total parameters, but only around 41 billion activate for a typical prompt, making it faster and cheaper to run.
Unlike other AI models that focus on specific tasks, Inkling is a generalist, meaning it's designed to perform well across a wide range of tasks, including reasoning, coding, and following instructions.
Inkling is fully open, meaning anyone can download and use it for free, and it's designed to be efficient, matching the performance of other models while using fewer resources.

2026-07-16

You can create a unique AI influencer with a distinct personality and look using AI tools like Claude (a type of AI that understands and generates text) and Hicksfield (a service that provides access to various AI image and video models).
Start by designing your influencer's identity, including their personality, age, gender, ethnicity, and unique features, then use a custom Claude skill to generate a detailed image prompt.
Use the prompt in an image generator like Hicksfield's GPT image 2 to create initial images, then refine and maintain consistency by creating a character reference sheet with various angles and poses.
To make your influencer more engaging, use the reference sheet to generate multiple images in different situations, and bring them to life using AI video tools.

2026-07-13

AI can now create and market products almost entirely on its own, using tools like GPT 5.6 Soul (a type of AI model) and GPT image 2 (an AI that generates images) to design items and ads.
This process can be automated to generate ideas, create ads, and even set up online stores (like Shopify, a website for selling products) and run Facebook ads to test if people like the products.
The AI can also help design products by looking at what people want, like checking online forums (places where people discuss things, like Reddit) for ideas.
Instead of giving the AI specific tasks, it's better to let it come up with many ideas quickly, then have humans pick the best ones, as AI is great at brainstorming but not always at making final decisions.

2026-07-10

AI can help you invest by finding hidden opportunities, like using trends from research papers to spot industries others might miss.
Before investing, use AI to "red team" (critically test) your idea by asking it to list ways you could lose money, helping you avoid bad decisions.
AI can monitor your investments with real-time dashboards, allowing you to track performance without constant manual checks.
Stick to investing in areas you understand, as AI can't give you an advantage in fields you know nothing about.

2026-07-04

AI can help you understand complex documents like contracts by translating jargon into plain English and focusing on the most important parts for your situation.
You can ask AI to highlight key details like what you're agreeing to, payment terms, start/end dates, and any conditions for ending the agreement.
AI can also check its own work by showing you where in the document it found the information, so you can verify its accuracy.
Finally, AI can help you draft a response to the other party, including suggested changes to the contract that they might be more likely to accept.

2026-07-01

AI tools can help you create and sell digital products like website templates, fonts, or organizational systems in platforms like Shopify (online store builder) or Notion (online organization tool) quickly and easily.
To succeed, find what's already selling well, create your own version, and brand it effectively with a strong name, clear promise, and good pricing.
You can use AI tools like Nano Banana Pro (image creator) and Claude Design (design assistant) to create website templates, then sell them on marketplaces like Creative Market or Theme Forest (online stores for digital products).
Notion templates are also popular, with some sellers making over $120,000 a month, showing the potential of AI-created digital products.

2026-06-28

Using AI tools like Cloud Code (a website builder) and Convex (a back-end service) can help create a strong online presence for local businesses, with low competition in local search terms.
A recommended tech stack includes Astro (a website framework), Convex (for back-end and database), Stripe (for payments), Resend (for emails), Clerk (for user accounts), and Vercel (for hosting).
This stack is cost-effective, with many services offering free tiers, and can be managed without deep technical knowledge, using API keys (a unique code that lets different software talk to each other) and simple tools.
Effective product management, including email follow-ups and user tracking with tools like PostHog (a product tracking service), is crucial for maintaining customer engagement and trust.

2026-06-25

Powerful AI model Fable 5 (a large AI system by Anthropic, a company that makes AI) was shut down by the government, showing that AI tools can be taken away or changed suddenly.
To avoid being stuck with one AI tool, keep your important information and settings in your own files, not just in the tool, so you can easily switch if needed.
AI tools can change in quality, cost, and speed, so it's important to be able to adapt and switch between different tools to keep getting the best results.
Desktop agents (software that helps you use AI tools on your computer) can make it easier to move your information and settings between different AI tools.

2026-06-22

AI can give wrong answers by guessing what you mean, using old info, or looking in the wrong place; tactics include prevention, checking, and protecting.
Prevention involves being specific with words (e.g., "highest revenue clients in the last 12 months" instead of "top customers") to avoid vague terms.
Checking means having AI provide proof (like a receipt) when it extracts info from documents, so you can verify its accuracy.
Protection is for high-stakes tasks, like getting a second opinion from another AI or testing AI on known answers to check its performance.

2026-06-19

OpenAI and Enthropic, two leading AI companies, have both called for the world to slow down AI development, despite their own competitive incentives.
OpenAI's plan includes creating AI that can do its own research, accelerating the economy, and forming an international group to coordinate AI development and slow it down if needed.
Both companies acknowledge that they can't slow down on their own, as competition is too strong, and propose a global referee to ensure everyone stops together.
The gap between AI developers and the general public is wide, with many people viewing AI negatively due to lack of understanding and exposure.

2026-06-13

Claude Fable (a new AI tool) can handle complex tasks without constant supervision, like preventing app crashes, making it more reliable than previous models.
For best results, use Fable on challenging, messy problems, as it excels at tasks that are too difficult for simpler AI tools.
Let Fable ask you questions and understand the "why" behind tasks, as this leads to better, more accurate outcomes and reduces the need for micromanagement.
Fable takes more time to process information but delivers more thoughtful and comprehensive results, making it ideal for complex, long-term projects.

2026-06-10

You can create a "second brain" (a digital system that organizes your notes and research) using AI, where you dump messy notes and articles, and the AI organizes them into clean, searchable pages.
This system has three folders: "raw" (for unorganized notes), "wiki" (for AI-organized content), and "outputs" (for AI-generated answers to your questions), plus a "rulebook" file (a plain text file that tells the AI how to organize your content).
The AI can also self-correct and update your "second brain" once a month, ensuring your information stays accurate and organized.
This process is simple and requires no coding or technical skills, just a basic understanding of how to create folders on your computer.

2026-06-07

**Focus on real work, not just fun**: Rich people use AI for serious tasks, like selling or running their business 24/7, not just for fun or impressive outputs.
**Identify your bottleneck**: Before buying AI tools, figure out the biggest problem in your business (the bottleneck) that, if solved, would increase revenue.
**Use AI for leads and sales**: AI can generate leads, automate follow-ups, and qualify prospects, freeing you up to focus on closing deals.
**Try specific AI tools**: Tools like Manness, Claude Code, Apex, Your Atlas, and Revieo can help with prospecting, qualifying, and personalizing messages to potential buyers.

Key points

What it is

AI monetization strategies are ways to make money using AI by solving real business problems, not just selling AI itself.
AI is a tool (like a hammer) that helps solve specific problems (the "nail"), saving businesses time, money, and focus.
You need to create automated AI systems that deliver ongoing results to turn AI into income.

How to use it

Start by solving one concrete business problem and build a simple automation around it, like email triage or invoice generation.
Price your solution based on the business value it delivers, not the hours spent coding, and start with one client to build proof.
Audit a small business to find where AI can cut costs or speed up work, then offer to build that automation for a fee.

Watch out for

Building a solution and then looking for a customer; instead, start with the customer’s pain and use AI to solve it.
Ignoring the risk of AI errors (hallucinations) that can lead to legal liabilities and damage to your brand reputation.
Overcomplicating your instructions when configuring an AI; keep them short and high-level for better results.

Tools named

Claude Code (an AI tool for building and selling solutions)

Lesson 1: What is AI Monetization Strategies and why it matters

AI monetization strategies are ways to turn artificial intelligence into income. The key insight is that AI itself won’t make you rich; AI is like a hammer, and without a nail (a real problem to solve), you’re just whacking at a wall. Nobody wakes up wanting to buy “more AI.” They want results. So instead of selling AI agents (automated software programs) or workflows, you should sell AI solutions — diagnosing specific problems that businesses face and using AI to solve them, saving them time, money, and focus.

Why does this matter for AI development? Because the way you frame what you build determines whether you get paid. Businesses buy paid outcomes — faster research that saves time, consistent content that builds trust, reduced labor costs that improve margins, and reliable execution that prevents chaos. They are not buying intelligence. Systems (automated AI systems that run without supervision and scale with demand) are what turn AI into money. You need to create systems for autonomous research, content automation, or business workflows that deliver results that keep on running.

Furthermore, a common trap in AI development is assuming you can know the scope, solution, expected value, and cost up front. With AI, you often learn the solution and the business case by doing the work. The mindset shift is that AI helps you build faster and cheaper, not that you can plan everything perfectly at the start. Ultimately, AI monetization strategies matter because they force you to be a producer rather than a consumer, solving real problems with systems that deliver ongoing value.

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Lesson 2: How to use AI Monetization Strategies: step-by-step

To monetize AI, start by solving one concrete business problem—never sell the technology itself. Shift from being an “agent seller” to a “solution seller” by tying your AI work to what a business cares about: saving time, money, and focus. Choose a simple automation (a repeating task a client hates) and build a “tiny AI agent business idea” around it. For example, automate email triage or invoice generation for a local service company.

Once you have a working solution, price it based on the business value you deliver, not the hours you spent coding. The video “Build & Sell with Claude Code” recommends you “price accordingly” after linking your system to real savings. Start with one client, get paid, and then “stack up your proof” (testimonials and case studies) until you can scale. The key is leaving “your obsession for technology behind” and thinking purely about the customer’s pain.

To get your first client, audit a small business to find where AI can cut costs or speed up work. Then offer to build that automation for a flat fee or monthly retainer. One creator built a system in three hours and got paid $1,650. Remember, the gap between successful AI monetizers and those who fail isn’t technical skill—it’s simply whether you start. Tools are ready; the process is clear. Pick one tiny job, automate it, and sell the result.

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Lesson 3: Best practices and pitfalls

AI is a tool, not a product. If you try to sell “AI” itself, you will fail because nobody wakes up needing more AI. They wake up needing a specific problem solved. The biggest pitfall is building a solution and then looking for a customer. Instead, start with the customer’s pain—the “nail”—and then use AI as the “hammer” to hit it.

A common mistake is ignoring the risk of AI errors (hallucinations). If you send a client AI-generated work that contains false facts, you risk legal liabilities and damage to your brand reputation. You must set up your AI system carefully so you can vet (check) its output in a fast way before delivering it.

Another mistake is overcomplicating your instructions. When you configure an AI, keep your written instructions under 100 lines. Short, high-level prompts work better than long, confusing lists.

The best practice is to focus on integration, not automation for its own sake. AI doesn’t replace humans; it removes the friction that keeps humans apart. The more you use AI to help your business show up more human and move faster, the more your customers will love you.

Finally, remember that winners are not the smartest—they are the ones who took action. Stop buying tools and prompts hoping for overnight riches. Audit your business, find a real, repetitive pain point, apply AI to solve it, and sign your first client by solving that one specific problem. That is the only way to actually make money.

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