The Future Isn’t No-Code. It’s Bespoke Software for Everyone.

The Future Isn’t “No-Code.” It’s Bespoke Software for Everyone.

How AI helped me build PlaybookM — a marketing-only project management system

Something big is happening in AI, and it’s not just “better writing” or “faster brainstorming.”

It’s the shift from AI as a helper to AI as a doer: systems that can plan, execute multi-step work, use tools, and ship real outcomes with far fewer human cycles. OpenAI’s latest Codex release is explicit about this direction, positioning the model as an agent that can do much of what professionals do on a computer.

That matters for marketers more than most people realize, because it changes what software is.

For the last decade or two, you basically had three choices:

• Excel + duct tape (still undefeated)
• Buy a platform (Jira / ClickUp / Monday / etc.)
• Custom build (slow, expensive, and usually “maybe next quarter”)

AI collapses the cost of option #3. Not to zero, judgment still matters, but to the point where bespoke becomes practical.

That’s why I built PlaybookM.

Why I built PlaybookM (a.k.a. “Marketing will never fit perfectly in Jira”)

I’ve run marketing in organizations that could afford the “right” tools. I’ve used Jira. I’ve used ClickUp. I’ve used Monday.

And the pattern repeats every time:

• Marketing work is messy and cross-functional.
• General-purpose tools force you into their mental model.
• You spend a heroic amount of time customizing fields, statuses, automations, dashboards.
• And it still never fits quite right.

Marketing doesn’t need “project management.” Marketing needs a system of execution: briefs, workflows, approvals, stakeholder visibility, operational dashboards, and a way to move fast without turning the org into a confetti cannon of half-finished tasks.

So instead of bending generic tools into shape again, I decided to build a tool that solves only marketing problems.

The real unlock: AI makes “bespoke” economically sane

The key idea behind the “something big is happening” narrative isn’t the drama, it’s the mechanism.

AI got great at code first because code is the lever that moves everything else. Now that models can plan, write, test, and iterate, the cost of turning domain judgment into working software drops sharply.

We’re also seeing measured improvements in how long models can complete real-world tasks end-to-end (not just generate snippets). METR tracks this as a “time horizon,” and their results show a consistent exponential trend over years (with a reported doubling time on the order of months). That’s a fancy way of saying: the model can stay on task longer, more reliably, with tools.

So when people ask: “Why would bespoke software become more prevalent?”

Because the bottleneck was always labor. And AI is changing the labor curve.

How PlaybookM got built (Replit → Cursor → Codex → “wait… this is real”)

My build path looked like the normal modern journey:

1) Replit: the “prove it can exist” phase

Replit got me from idea to working prototype fast enough to validate workflows.

2) Cursor: the “move faster than my own hands” phase

Cursor made iteration feel less like “coding” and more like directing. Tighten flows, refactor structure, tune UI, repeat.

3) Codex: the “this is now an actual workflow” phase

This is where it stopped being a tool and started feeling like a collaborator.

OpenAI describes GPT-5.3-Codex as a major step toward tool-using autonomy, an agent that can review, debug, and execute longer workflows. They also state that early versions were used in its own development process.

Which brings me to the most important part of this story:

Codex wasn’t a tool — it was a partner

Somewhere in the build, Codex crossed a line from “autocomplete” to partner.

I’d describe the outcome, it would make judgment calls, test paths, refactor, and return something that looked finished. Not “draft code.” A real product-shaped result.

And that shift is the whole point:

AI doesn’t just speed up building. It changes who can build.

What PlaybookM is (and what it isn’t)

PlaybookM is not “another task tracker.”

It’s a marketing execution system, designed around how marketing actually works:

• campaign workflows that match real teams
• briefs that don’t die in doc folders
• approvals and stakeholder visibility
• operational dashboards that reflect marketing reality
• AI features that turn knowledge into action (not just generate copy)

The MVP is intentionally narrow: marketing problems only.

Because in a world where bespoke software is cheap, “generic” becomes a tax you stop paying.

What I think happens next (and why this is how the future unfolds)

There’s a lot of public debate about timelines. Some of it is breathless. Some of it is denial.

But serious leaders inside the AI industry have publicly warned about near-term disruption to white-collar work, especially entry-level roles, on short time horizons. Whether you agree with that exact forecast or not, the direction is clear: capability is compounding, and organizations are adapting quickly.

My bet looks like this:

• Every function starts building internal tools (fast)
• “One-size-fits-all” platforms still matter, but become more like infrastructure, not workflow truth
• The winning products are vertical and opinionated, built by people who did the job
• AI becomes the universal implementation layer, software becomes cheaper, more tailored, and more disposable

Not one mega-tool replacing everything, but a thousand precise tools built by domain people who finally have leverage.

I’m looking for MVP testers willing to take the leap with me

PlaybookM is ready for early users, and I’m looking for a small group of marketers and marketing ops leaders who want to help shape it.

If you’ve ever:

• lived in Excel because Jira/ClickUp/Monday never fit quite right, or
• spent more time configuring your tool than running campaigns, or
• wanted a system built for marketing execution (not generic task tracking)

you’re exactly who I want to talk to.

Want early access? Reply or DM me and I’ll share a tester link plus a short onboarding call. (Bonus points if you’re opinionated, this product is being built for people with opinions.)

References

https://openai.com/index/introducing-gpt-5-3-codex/

https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/

https://www.axios.com/2025/05/28/ai-jobs-white-collar-unemployment-anthropic

• OpenAI — Introducing GPT-5.3-Codex
• METR — Measuring AI Ability to Complete Long Tasks
• Axios — Anthropic CEO interview coverage on white-collar disruption timelines