The Birth of the AI-Powered Agency Client

Before AI, clients came to us with ideas. Now they show up with code.

AI hasn’t just changed how we build software. It’s changed who we build it for.

If you run a tech agency, you’ve probably noticed it too: the conversations we’re having with new clients sound different. Before AI tooling went mainstream, most clients came to us with ideas.

Sometimes fully thought out … Sometimes vague napkin sketches.
Sometimes grand visions … Sometimes barely a coherent set of thoughts.

But rarely with anything resembling a product.

Today, that’s changed. AI has collapsed the discovery and prototyping phase from days/weeks into hours. Saving early resources, but often making clients feel like they’re already halfway to launch before they get in touch with us.

(Though, while AI adopters are rising, many still fit pre-AI molds.)

Before AI: The Old Ways

Not long ago, most projects followed a clear sequence:

The idea – A client brings us a concept.
Discovery – Unpack ideas, challenge assumptions, and craft specs with UI/UX input.
Architecture & planning – Define a solid technical foundation early so the build scales.
Agreement – Align on the spec, budget, and timeline.
Delivery – Build and ship the product end-to-end, including QA, security checks, and deployment.

A tried-and-true approach that worked well for both sides. But it assumed something that’s no longer true: that clients came to us with only an idea and no product in hand.

Adaptation isn’t optional in the agency business.

With AI: Leaner and Faster

Now, many clients arrive with something tangible:

A codebase they’ve started on Bolt.new, Lovable.dev, or Replit.com
A prototype that runs well enough to demo.
AI-generated designs or wireframes.

That changes discovery. It’s still crucial, but it’s swifter and more effective. We’re no longer starting from a blank page. Instead, the real value is translating what’s already there into something production-ready.

For agencies, that shift has raised the bar. What matters now are four pillars that define whether a project succeeds:

Translating business needs into clear technical instructions – bridging ambition and implementation.
A deep understanding of what actually needs to be built when – separating “nice demo” from “scalable product.”
The ability to communicate effectively – guiding clients through trade-offs and defending technical decisions.
Deeper technical problem-solving – figuring out how to scale, secure, and optimize what already exists.

This doesn’t mean agencies are losing work. Far from it! It means the work has pivoted toward more leveraged work.

Who Walks Through the Door: A Spectrum of Clients

The post-AI client landscape hasn’t replaced old archetypes, it’s expanded them. In the last few months, here’s who we’ve actually met. It’s important to say, it’s a spectrum. Also, the list is not exhaustive.

Each of the types typically carries a repeating set of challenges.

Pre-AI Archetypes

The opposite sides of the spectrum.

The Visionary – Non-technical founder with a business vision. Brings the what, needs us for the how.
The Half-Stack Founder – Has some coding chops, but not enough resources to deliver a full product. Relies on us for execution and polish.

Post-AI Archetypes

There are many positives, and all of these folks are great to work with, but let’s look specifically at the usual challenges.

The AI-Informed Visionary – Non-technical, but uses AI (ChatGPT, Perplexity, etc.) to research the domain, learn terminology, and form a clearer picture of what they want to build. Compared to pre-AI visionaries, they often arrive with sharper ideas and better framing of the problem.
- Typical challenge**:** They trust AI’s optimistic answers about scope, time, and cost. Estimates from LLMs are often way off, so part of our job is recalibrating expectations without deflating their confidence.

The AI-Enhanced Visionary – Non-technical. Uses AI to explore the technical domain, create mockups, or basic click-through demos.
- Typical challenge: Their code often carries hidden technical debt that makes scaling more expensive than starting fresh. They resist that message because they see their prototype as a sunk investment.

The AI-Powered DIY Builder – Semi-technical, entrepreneurial, comfortable spinning up a prototype with AI tools. Believes they’re 60–70% done because “it runs.” Needs audits, architecture, and security checks, often painful truth-telling about refactoring.
- Typical challenge: Mistakes prototypes for finished products, and needs education on why a demo isn’t the same as a secure, scalable build.
The AI-Enhanced Half-Stack Founder – Technical but strapped for time, offloads chunks of work to AI, and hires us for integration, optimization, and cleanup.
- Typical challenge: We inherit messy, inconsistent codebases where AI-generated fragments don’t align with human-written parts. Stabilizing this eats time and requires firm boundary-setting.

Do you see the shift, creating the new dead zones?

The Hidden Danger: Early Technical Debt

One of the biggest challenges with AI-powered prototypes is that technical debt now appears earlier in the product lifecycle.

🗣️ Last month, a founder emailed me a Bolt.new prototype, calling it “almost production-ready.” The perceived issue: Hitting Bolt.new’s context window limits, causing crashes. We inspected the product. He'd effectively developed a 2000s-looking membership site with rotten foundations from the start. We audited, refactored and set the project for success, avoiding the hassle of migrating his 11,000+ users to a new platform.

AI tools can produce working code fast, but “working” doesn’t mean scalable, secure, or maintainable.
Early design decisions (or lack of them) ripple into architecture, security, and performance problems later.
Clients often resist starting from scratch because they think reusing their code will “save money.” Untangling bad foundations can cost more than a fresh build.

Our role now includes codebase audits, pointing out risks, and defending our recommendations with concrete examples.

How Agencies Can Adapt

If AI has shortened the front end of the engagement, we need to excel at the middle and end.
That means leaning into:

On the human side:

Human connection – building trust and empathy so clients feel guided, not just managed.
Hard ownership – taking full responsibility for what’s built and how it runs. If something breaks in production, it’s on us. Not on a bot that dropped your database without a backup.
Over-communication – guiding trade-offs, defending decisions, keeping expectations aligned.

On the technical side:

Codebase audits & architecture – starting from what’s already there.
Scalability & performance – building for growth, not just “it runs.”
Security hardening – fixing what AI tools don’t care about.
Integration work – making AI-built parts talk to real systems.
Refactor strategy – knowing when to rebuild instead of patch.

Final Thought

For agencies, this evolution isn’t just a threat, it’s a filter. The ones who cling to the old playbook, stay generic and shallow, will slowly become commoditized. Those willing to adapt, the opportunities are there.

AI can help anyone start a product. It still takes an expert team to finish it right and to take responsibility when it really matters.

These are my beliefs as of 8/2025. Feel free to make me eat my words if I’m wrong.