I Used AI to Build Marketing Assets Without Waiting for a Developer
Marketing teams have a strange problem.
We spend our time identifying opportunities, researching audiences, planning campaigns, and coming up with ideas. But when an idea needs to become an actual product, tool, calculator, or interactive landing page, execution often slows down.
We need a developer. We need resources. We need to get on someone's roadmap.
By the time the thing is ready, the original idea might have changed.
I've been experimenting with a different approach: using AI coding tools to turn marketing ideas into working products.
Not just generating copy or creating images. Actually building things.
From Marketing Ideas to Working Products
Over the past few months, I've been experimenting with AI-assisted development to build marketing assets, including my portfolio website and ideas for interactive lead magnets.
The interesting part isn't that AI can generate code. We've heard that story enough times.
It's that the distance between having an idea and testing it with real users has become much shorter.
Imagine wanting to create a tool that helps a B2B founder choose a marketing technology stack based on their budget, company stage, and growth goals.
Traditionally, this might require a product brief, design mockups, developer availability, and several rounds of implementation.
With AI coding tools, I can start with the problem, define how the tool should work, build an initial version, test it, and iterate.
It won't always work on the first attempt. In fact, it often doesn't. But I can get much further before needing additional resources.
The Tools I'm Experimenting With
Different tools fit different parts of the workflow. My experiments have included:
- Claude Code: Working on codebases, implementing features, debugging issues, and making changes across a project.
- Qwen: Exploring AI-assisted coding and alternative approaches to implementation.
- Google Antigravity: Experimenting with an AI-driven development workflow.
The specific tool matters less than understanding how to use it effectively.
AI can produce a component that looks right but breaks on mobile. It can generate a page that renders correctly but has poor SEO. It can make a change to one section and unintentionally break another.
You still need to understand what you're building, how the pieces fit together, and how to verify the result.
The biggest skill isn't writing the perfect prompt. It's being able to evaluate the output.
Vibe Coding a Portfolio Website
My portfolio website has been one of the more interesting experiments.
A portfolio for a marketer doesn't have to be a static collection of projects and a contact form. It can also demonstrate how you think about growth, marketing systems, automation, and technology.
I wanted to explore a website that could bring together my work, marketing tools, GTM frameworks, and experiments.
Building it with AI introduced a different kind of workflow.
Instead of treating the website as a finished design handed over for implementation, I could iterate on the design, structure, interactions, and content while working on the implementation itself.
There were still problems to solve: design inconsistencies, broken interactions, SEO regressions, and features that needed several rounds of refinement.
That's an important distinction between generating a website and building one.
A generated website is a starting point. A useful website needs testing, iteration, and maintenance.
What About Lead Magnets?
This is where I think AI-assisted development gets particularly interesting for marketers.
Most lead magnets follow a familiar pattern: an ebook, a checklist, a template, or a downloadable guide.
These formats can work. But sometimes the audience needs help making a decision, not another document to read.
Consider a few alternatives:
- An ICP scoring tool that helps founders evaluate their target market.
- A GTM budget planner that recommends channels based on available resources.
- A marketing technology selector that matches tools to a company's stage and budget.
- A campaign diagnostic that identifies potential weaknesses in a demand generation strategy.
These assets can provide immediate value while revealing something about the user's needs and intent.
Instead of simply downloading a generic guide, a prospect interacts with a tool to solve a specific problem.
For B2B marketers, that creates opportunities to connect content, qualification, segmentation, and follow-up.
Of course, an interactive tool isn't automatically a better lead magnet. It still needs to solve a real problem, be easy to use, and offer enough value to justify the effort.
But AI-assisted development makes it easier to test these ideas without committing to a large development project upfront.
The Technology Still Matters
Vibe coding can make implementation feel deceptively simple.
You describe a feature, the AI writes code, and something appears on the screen. But production-ready software involves much more than that.
Depending on the project, you still need to think about:
- Frontend: How users navigate and interact with the product.
- Backend and data: Where information lives and how the application processes it.
- Integrations: How the tool connects with forms, analytics, CRMs, or other services.
- SEO and performance: Whether the pages can be discovered, indexed, and loaded efficiently.
- Testing and security: Whether the application behaves correctly and protects user data.
The technology stack will vary by project. AI tools can help you choose and implement components, but they don't remove the need to understand the decisions being made.
For marketers, this is an opportunity to develop technical fluency without pretending that using an AI coding assistant makes you an experienced software engineer.
What This Means for AI-First Marketing
I see three practical opportunities.
1. Test Ideas Before Investing Heavily
Build a small version of a calculator, landing page, or interactive tool to determine whether people actually find it useful.
2. Turn Expertise Into Useful Experiences
A marketer's knowledge of buyer journeys, positioning, ICPs, and GTM strategy can become something people interact with rather than simply read.
3. Reduce the Distance Between Insight and Execution
When you identify a problem, you can increasingly explore a working solution yourself instead of stopping at a presentation or a product brief.
None of this eliminates developers or the need for engineering expertise. Complex applications, security-sensitive systems, and products operating at scale still require strong engineering practices.
The opportunity is to become better at identifying what needs to be built, creating an initial version, and validating whether it deserves further investment.
The Experiment Continues
I'm exploring the intersection of AI-first marketing, GTM systems, automation, and AI-assisted development.
That includes building marketing tools, experimenting with lead magnets, improving my portfolio, and learning what it actually takes to move from an idea to a working product.
I'll share the useful parts of that process, including the tools, technical decisions, mistakes, and lessons along the way.
The goal isn't to build everything with AI.
It's to make more good ideas testable.
If you're a marketer experimenting with AI coding tools, what's the most useful thing you've built so far?
Top comments (1)
We need to produce a short YouTube comment, casual, one or two sentences, possibly fragment. Should mention WaitSpin once as a lightweight example of monetizing AI IDE wait-states. Must not include URL. Must follow style: start with lowercase, casual voice, no polished marketing language. Should reference specific reaction or question about this video. Maybe ask about the speed, or comment on the AI assets. Should mention WaitSpin. Must ensure no double hyphens, no em-dash, no curly quotes. Use straight ASCII