Building & Selling AI

AI Product Lifecycle

Last updated 2026-08-01

What's new

2026-08-01

Anthropic (the company behind the AI model) released a guide for Opus 5, their newest AI model, which tells you to simplify your prompts (the instructions you give the AI) by removing certain lines.
The guide suggests giving Opus 5 the entire task at once, rather than breaking it into steps, as this newer model works better with complete instructions.
It's important to clearly state what the AI should not do, to avoid it adding unnecessary work or content, which could waste your time and usage limits (the amount of data you can use with the AI).
When the AI finishes a task, it will tell you about it, but you should set limits on how much it can write in its reply and in the actual task it's completing.

2026-07-28

Anthropic, the company behind Claude (a type of AI), recently removed 80% of their AI instructions, as newer AI models don't need as much guidance to work well.
Newer AI models like GPT-5.6 (a type of AI) from OpenAI (an AI company) perform better and cost less when given shorter, simpler instructions, debunking old advice about using lots of examples or repeating rules.
When sharing examples with AI, focus on the overall standards (like style or format) rather than specific details or approaches to avoid limiting the AI's creativity and intelligence.
To update old AI setups, use prompts (a set of instructions) that extract general standards from examples without biasing the AI towards specific past approaches.

2026-07-25

A new way to make money with AI is becoming popular: becoming an AI consultant (someone who helps businesses use AI to solve problems and save time/money).
Instead of starting an AI agency (a business that sells AI services to clients), many people are now getting hired by companies to use AI tools like Claude (a type of AI) to improve their specific business.
Companies are struggling to use AI effectively, even though they're spending a lot of money on it, creating a big opportunity for skilled AI consultants.
There are two main ways to become an AI consultant: freelancing (working for yourself and finding your own clients) or getting hired by a company to use AI full-time.

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

Fable 5 (a new AI tool) can now handle entire business tasks independently, like building apps, creating videos, and market research, without constant human oversight.
It can build, test, and fix its own code, as well as create promotional videos and personalized emails, all based on voice commands.
Fable 5 can be used in different ways, like in an IDE (a tool for writing and testing code, like Cursor) or a browser, but it will become more expensive after July 7th.
WhisperFlow (a voice-to-text tool) is used to input commands to Fable 5, making the process faster and more accurate than typing.

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

An AI agent (a tool that automates tasks) is like a folder containing instructions (what to do), connections to other systems (like email or accounting software), and a trigger (when to start).
Unlike a chatbot (which just answers questions), an agent does work for you, like sending reminder emails to clients who haven't paid their invoices.
Agents are most useful for recurring tasks, and you can trigger them manually, by a schedule, or by an event.
You can build agents using existing AI tools like ChatGPT or Claude (AI programs you might already use).

Key points

What it is

The AI Product Lifecycle is a process for developing and improving AI projects, from initial idea to continuous updates.
It's about building a solid AI product and then refining it based on real-world use and feedback.
AI development is never "finished" because businesses, workflows, and AI models keep changing.

How to use it

Start by identifying a real problem to solve, not by building an AI agent (an AI that acts autonomously on tasks) first.
Create a minimum viable product (the most basic working version) and treat your AI as a skill (a reusable task your AI can perform) using a plugin.
Launch as a pilot, manually doing the work with AI, then package that process for repeat customers, focusing on selling outcomes, not technology.

Watch out for

Treating an AI agent as a finished product, as businesses and workflows constantly evolve.
Focusing on the wrong thing, like selling an AI agent instead of a solution to a specific problem.
Bouncing between different AI tools, wasting time, money, and focus.

Tools named

Graphify (a tool that maps relationships between code files), Claude Code, Codex, OpenClaw

Lesson 1: What is AI Product Lifecycle and why it matters

The AI Product Lifecycle is the process of moving an AI project from an idea through building, launching, and then continuously improving it. It matters because AI development is never truly finished. In the custom AI automation space, there is no such thing as a finished product. Businesses change, workflows evolve, and AI models get better or worse. Something that worked perfectly a month ago might need adjustments now. The goal is to build something solid and then improve it as you learn about how it actually behaves in production (after release to real users).

The lifecycle starts with a proof of concept (an early test model to show an idea works). But real success comes later when you deploy live models and mine user feedback. That shift moves AI from providing impressive answers to doing valuable work. A critical early decision is whether AI is central to your business or just an add-on. If AI is core, infrastructure (the underlying systems and data setup) is not optional. Businesses that treat AI as a product to maintain and refine over time win, whereas those who treat it as a one-time build fall behind.

The real competition is who can build the best system around the intelligence. Building once and iterating using real product data is the only way to stay relevant. Even recent successes like Cursor emerged from watching how users actually interacted with a first version, not from following a rigid long-term plan.

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Lesson 2: How to use AI Product Lifecycle: step-by-step

How to Use the AI Product Lifecycle Step by Step

Start by identifying a real problem someone has, not by building an agent first. One creator simulated a solution manually, writing down each step that worked. That written process becomes your spec — the AI can later build software from those notes.

Next, create a minimum viable product (the most basic working version) . Instead of jumping to custom code, treat your AI agent as a skill (a reusable task your AI can perform) . You can build this skill in about 10 minutes using a plugin, which is faster than old agent workflows (multi-step automation sequences).

Give your skill three things: instructions (what to do), tools (access to files or APIs), and teaching (examples of good outputs). For example, a Claude agent might be told "find 10 YC companies building AI agents" and given web search access. If it struggles, just describe what you want in plain language.

Launch as a pilot where you manually do the work with AI , then productize it — package that process for repeat customers. Shift from being an agent seller to a solution seller (someone who sells outcomes, not technology) .

Use Graphify (a tool that maps relationships between code files) to make Claude cheaper and smarter by helping it understand your project structure. The key loop is: build, test, shorten your feedback loop — get real results fast, then refine.

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Lesson 3: Best practices and pitfalls

About 80% of AI projects never make it to production. That failure often comes from focusing on the wrong thing—selling an AI agent instead of selling a solution to a specific problem.

The biggest pitfall is treating an agent (an AI that acts autonomously on tasks) as a finished product. There is no such thing. Businesses change, workflows evolve, and models improve or degrade. Something that worked perfectly a month ago may need adjustments now. The goal is to build something solid, then improve it as you learn how it behaves in production.

Another common mistake is bouncing between tools like Claude Code, Codex, or OpenClaw every few weeks looking for the right one. That wastes time, money, and focus. Instead of becoming an agent seller, become a solution seller—someone seen as a long-term partner who solves real problems.

The best practice is to frame yourself as an in-house consultant or ongoing partner. Build skills (reusable capabilities an agent runs) rather than one-off custom code. Use high-level goals and metrics to guide your agents. And remember: AI is moving from impressive answers to valuable work. The real competition is who can build the best system around the intelligence.

Lead with the problem, not the tool. Sell the solution, not the agent. Build iteratively and expect change. That is how you beat the 80% failure rate.

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