The AI Consultant's Window
AI tools have collapsed the cost of building software. I built a 200,000-line TypeScript MVP in 7 weeks - work that would have taken a team 12-18 months before these tools existed. Anthropic reports 70-90% of code at the company is now AI-written. Microsoft says AI generates about 30% of theirs.
For those who can wield these tools, there’s an obvious opportunity - businesses need custom software, they don’t have engineers who know how to use AI agents effectively, and consultants who can bridge that gap should find work.
Building a consulting practice takes years, though. Clients, reputation, systems, trust - these things compound slowly. There are slow periods to survive. By the time a practice is mature, will the tools be accessible enough that businesses don’t need a consultant?
This isn’t a victory lap for consultants - it’s an uncertain bet. The energy required to build a consulting business is substantial. Is it worth it for a window that might be 2 years? 5 years? 10?
The direction seems clear - these tools will eventually be usable by people without engineering backgrounds. The question for anyone considering this path is whether they’re building a career or riding a wave.
Not all custom builds are created equal, and this distinction matters for understanding where the window might stay open longest.
Greenfield is straightforward - no existing system, no data to migrate, no users with entrenched workflows, just building exactly what’s needed from scratch. This is where AI tools shine brightest, and where the consultant’s window is most likely to close first.
Migration is harder - replacing existing SaaS means data export and import (often messy, incomplete, or requiring transformation), transition planning (running parallel systems, phased rollouts, fallback procedures), change management (users who learned the old system need to learn the new one), and mapping legacy workflows (existing processes may not map cleanly to new designs).
My tennis club is considering this exact problem. They’re thinking about replacing their Club Automation membership system - the software handles court booking, membership billing, lesson scheduling, pro shop transactions. A typical SaaS tool doing too much, none of it particularly well.
Building a custom replacement is now feasible - the core functionality isn’t that complex. But the transition involves years of member data, payment histories, recurring billing relationships, staff trained on specific workflows. The build might take weeks; the migration and transition planning could take months.
This is where consulting value persists longer. It’s not just “can someone use AI to write code” - it’s “can someone manage the organizational complexity of moving from one system to another.” That’s a different skill set, one that AI tools are further from automating.
There’s another dimension worth considering: what happens after the build?
I’ve spent nine years building and maintaining a SaaS product. Once it reaches maintenance mode - stable customers, predictable feature requests, infrastructure that mostly runs itself - it’s a good business. Recurring revenue, compounding familiarity with the codebase, known edge cases.
A consulting practice built on custom software is different. Not one product for many customers - many products for many customers. Each client has their own codebase, their own infrastructure, their own quirks. The portfolio grows, and so does the maintenance burden.
This is more labor intensive by nature. Ten clients with ten custom applications means ten different contexts to hold in memory, ten deployment pipelines, ten sets of dependencies that need updating. The work compounds in a way that SaaS maintenance doesn’t.
Can AI help here? Almost certainly. The same tools that accelerate initial builds should accelerate maintenance - understanding unfamiliar codebases, writing migrations, updating dependencies, debugging production issues. The question is whether this can be systematized well enough to keep a growing portfolio manageable.
This might be the real skill that determines whether a consulting practice scales: not just building fast, but building in ways that stay maintainable across a diverse portfolio. Standardized stacks, consistent patterns, good documentation, automated testing - the same things that always mattered, but now with AI agents as part of the maintenance workflow.
AI will eventually handle migration work too. Data transformation, migration scripts, even change management playbooks - these are all learnable patterns. Portfolio maintenance will get easier as AI agents get better at context-switching between codebases. The timeline for automating this work is probably longer than pure greenfield builds, but it’s still finite.
The window exists, and betting on it is rational - betting the farm on it is risky.
Migration work and organizational complexity seem more durable than pure code generation. So does the ability to systematize maintenance across a growing portfolio. The businesses that need the most help are ones replacing existing systems, not building from scratch. The skills that matter aren’t just technical - they’re about managing transitions and building in ways that stay maintainable.