Chris Donnelly recently shared something that caught my attention: "2025 saw a massive shift in how we perceive coding. It's 2026 now, and companies are still lagging behind." He followed it with a founder story that is becoming more common: he launched Searchable and "validated the entire idea with AI in 48 hours" - without needing to write a single line of code.

That combination (speed plus clarity about limits) is the real takeaway. Chris is not saying engineering is optional. He is saying the order of operations has changed: validate first, hire second. And to do that well, founders need a simple mental model for what "AI coding" actually means in practice.

In his post, Chris breaks it into three levels: Vibe Coding, AI-Assisted Coding, and Agentic Coding. I want to expand on that framework, add practical examples, and outline a plan you can use to choose the right level for your product stage and risk tolerance.

The shift Chris is pointing to: coding as a bottleneck is optional (early on)

For years, the default startup path looked like this:

Raise money or save up cash
Hire engineers
Build for months
Then discover whether anyone wants it

AI tools have flipped step 2 and step 4 for many categories of software. You can now pressure-test a market, a workflow, a landing page, or even a functional MVP before you commit to a full engineering team.

"These tools let you validate first and hire second."

The important nuance is the one Chris called out прямо: if you are trying to replace real engineering work, you need a proper plan of action. AI can generate code quickly, but it cannot own the outcomes by default. People still do that.

Level 1: Vibe Coding (non-technical founders)

Chris calls the first tier "Vibe Coding": turning rough ideas into working prototypes by describing what you want in plain English and letting AI handle the code.

What Vibe Coding is good for

This level shines when the goal is learning, not perfection:

Validate startup ideas fast
Build landing pages, MVPs, internal tools
Test demand before hiring engineers

A strong way to think about Vibe Coding is: you are buying speed with a controlled amount of technical debt. That debt is acceptable if you are using the prototype to answer questions like:

Do people click, sign up, or pay?
Does the workflow match how users already behave?
Which features matter enough to justify building properly?

Practical examples

A consultant builds a client intake portal in a weekend to see if it reduces back-and-forth.
A SaaS founder creates a "fake door" onboarding flow (real signup, limited backend) to measure demand.
An operations lead creates an internal dashboard that pulls from Airtable and Slack to reduce manual reporting.

Tools Chris mentioned (and how to choose)

Lovable: best when you want product-like flows and a clean signup experience.
Bolt: great for quickly generating a web app skeleton.
Replit: useful when you want to build and deploy without setup friction.
Make: ideal for connecting tools and automating workflows without custom code.

My add-on: Vibe Coding works best when you constrain the scope. Write a one-page spec before you prompt any tool. If you cannot explain the workflow end-to-end, the AI will guess, and your prototype will drift.

Level 2: AI-Assisted Coding (technical or semi-technical teams)

Chris labels the middle tier "AI-Assisted Coding": AI working alongside a human developer to speed up writing, debugging, and refactoring.

Why this level matters for real companies

This is where AI becomes a productivity multiplier without changing your reliability bar. You still have engineers setting architecture, reviewing code, writing tests, and managing deployment. The AI helps with:

Scaffolding components and endpoints
Translating requirements into code faster
Debugging and refactoring with context
Reducing repetitive work (boilerplate, migrations, docs)

In other words, you keep the human accountable for correctness.

AI-Assisted Coding is not "ship whatever the model outputs." It is "ship faster because the model removes friction."

Tools Chris mentioned (and what to watch)

Cursor: strong for AI-first editing across a codebase.
GitHub Copilot: excellent for inline suggestions and common patterns.
Continue: flexible, open-source option if you want more control.
Google Antigravity: context-aware completions (when available in your environment).

My add-on: treat AI output like code from a junior developer you just hired. Useful, often correct, sometimes dangerously confident. The review process is the product.

Level 3: Agentic Coding (advanced teams and operators)

Chris describes the third tier as "Agentic Coding": AI agents that can plan, write, test, and refine chunks of software from a single objective.

Where agentic workflows actually shine

Agentic coding is not magic, but it is powerful when you have:

A well-defined objective
A test suite or acceptance criteria
Clear boundaries (repos, services, permissions)

Common high-ROI uses include:

Large feature builds broken into tasks
Legacy code refactors
Automating repetitive engineering tasks
Spinning up internal systems fast

Tools Chris mentioned

Claude Code: agent-driven development workflows.
OpenAI Codex: autonomous coding tasks.
Devin: full software agent patterns.
Gemini CLI: command-line agent workflows.

My add-on: agentic systems are easiest to adopt when your team already has strong engineering hygiene. If you do not have tests, linting, and CI checks, an agent can move fast in the wrong direction.

A simple plan of action for founders (so you do not overreach)

Chris warned that replacing real engineering requires a plan. Here is a practical one you can use.

Step 1: Decide what you are building (prototype vs product)

Ask: "If this breaks, what happens?"

Low risk (prototype): Vibe Coding is fine.
Medium risk (internal tool with real data): Vibe Coding plus guardrails, or AI-Assisted Coding.
High risk (payments, compliance, mission critical): AI-Assisted Coding at minimum, agentic only with strong controls.

Step 2: Write acceptance criteria before you prompt

Even for a simple feature, define:

User story
Inputs and outputs
Error states
Security expectations
Success metrics

This is the difference between "build something" and "build the right thing."

Step 3: Add guardrails early

Minimum guardrails that pay off immediately:

Version control from day one
Basic tests for critical paths
Logging and monitoring (even simple)
Clear data handling rules (what can be stored, where, and why)

Step 4: Use AI where it is strongest

UI scaffolding and CRUD: great
Integrations and glue code: great
Complex distributed systems: proceed carefully
Security-sensitive logic: assume it needs expert review

The bigger point: leverage is now a founder skill

Chris ends with a clear message: if you are building right now, this leverage is hard to ignore. I agree, with one small twist: leverage is not only about tools, it is about judgment.

Knowing which level you are operating at (Vibe, Assisted, Agentic) helps you avoid two common mistakes:

Underusing AI and moving too slowly when you could validate in days.
Overusing AI and pretending you have a production-grade system when you actually have a prototype.

Chris also shared how Searchable is applying this: building an autonomous SEO and AEO growth engine that analyzes, fixes, and scales websites to drive customers automatically. That is a good example of pairing ambitious outcomes with a system mindset: clear objectives, iterative improvements, and automation where it compounds.

Final takeaway

If you remember one thing from Chris Donnelly's breakdown, make it this: AI does not remove the need for engineering, but it dramatically reduces the cost of learning.

Start with Vibe Coding to validate demand. Move to AI-Assisted Coding to ship reliably. Adopt Agentic Coding when your team and tooling can keep agents accountable.

This blog post expands on a viral LinkedIn post by Chris Donnelly, Co Founder of Searchable.com | Follow for posts on Business, Marketing, Personal Brand & AI. View the original LinkedIn post →