AI Agency or In-House AI Hire? How to Pick Your Path in 2025
Starting an AI agency or becoming your company's AI specialist are the two main entry paths into AI work. Here's how to choose and stand out.
The two paths into professional AI work
Anyone trying to build a career around AI right now is choosing between two routes: start an agency and go find clients, or become the AI person inside a company you already work for (or want to work for). Both are legitimate. Neither is obviously better. The harder problem, common to both, is that building with AI has gotten so easy that everyone now has a portfolio, and portfolios no longer prove anything.
TL;DR
- The two dominant entry paths into AI work are starting an agency and becoming the internal AI specialist at an existing company, and picking between them mostly comes down to risk tolerance and how much organizational friction you’re willing to fight through.
- Portfolios have stopped being a differentiator because demo saturation means most builds look identical: the same tutorials, the same workflows, the same screenshots.
- What actually separates people now is documented outcomes: hours saved, leads recovered, error rates dropped, measured before and after, not just “I built this.”
- Specific tools matter less than the transferable skills underneath them, like debugging, reading errors, and structuring instructions, because the tool you master today will be replaced within a year or two.
- Being “AI native” is a default behavior, not a knowledge level: it’s whether your first instinct is to ask “can AI do part of this?” rather than doing the task manually out of habit.
- Trust is the actual bottleneck on both sides of the market right now: businesses can’t tell who’s credible, and credible people can’t easily prove it, which is why verification and evaluation practices are becoming as important as building skills.
Remy is new. The platform isn't.
Remy is the latest expression of years of platform work. Not a hastily wrapped LLM.
Which path should you pick: agency or in-house?
The agency path means going out and finding your own clients, pricing your own work, and living or dying by whether what you build actually moves a business metric. It rewards people who are comfortable with sales, discomfort, and inconsistent income early on, in exchange for faster iteration and no internal politics slowing you down.
The in-house path means embedding inside a company that already exists, usually starting in a non-AI role and gradually becoming the person who automates workflows, builds internal tools, or leads adoption. It’s slower to start (approvals, red tape, buy-in from people who don’t understand the technology yet) but it comes with a stable paycheck and a built-in test environment: real business problems, real data, real stakeholders, without having to sell anyone on hiring you first.
Neither path is a shortcut. Someone who tries to become the internal AI person at a large, process-heavy organization may find that getting permission to build anything takes longer than just building it externally for a paying client. Someone who starts an agency skips the red tape but takes on the job of constantly proving they’re worth paying for, which is where the differentiation problem shows up hardest.
Why is it getting harder to stand out in AI work?
Because the barrier to producing a demo has collapsed. A few years ago, building a working AI automation or agent required enough technical skill that having one at all was a signal of competence. Now, tutorials, no-code tools, and AI coding assistants let almost anyone assemble something that looks functional in a weekend.
The result is that businesses evaluating freelancers, agencies, or job candidates see a flood of nearly identical portfolios: the same handful of workflow screenshots, the same categories of demo projects, the same claimed capabilities. From the buyer’s side, there’s no reliable way to tell who spent months learning to debug production systems and who copied a tutorial. That’s a trust gap, and it hurts everyone: legitimate builders can’t prove they’re legitimate, and businesses can’t tell who to hire.
How do you actually differentiate yourself?
Stop collecting builds and start collecting receipts. A build says “I made this.” A receipt says what the thing did once it was live: a process that took a certain number of hours now takes a fraction of that, a lead source that was leaking prospects now catches all of them, an error rate that dropped after a system went into production.
The practical habit is simple: every time you build something, even something small, even something just for yourself, write down the before-and-after numbers and record a short walkthrough of how it works and what changed. Someone with five generic workflow screenshots looks like everyone else. Someone with three real, measured outcomes stands out immediately, because outcomes are much harder to fake or copy than a demo.
Plans first. Then code.
Remy writes the spec, manages the build, and ships the app.
This matters equally on both paths. An agency needs receipts to close new clients. Someone trying to become the AI specialist at their company needs receipts to get buy-in for the next project and eventually the title (and pay) that reflects the work they’re actually doing.
Do the specific tools you use actually matter?
Less than most people think. Tools change fast, and whichever platform or model you build your reputation on today will likely be replaced or superseded within a year or two. What transfers across every tool change is the underlying skill set: understanding how systems call APIs, knowing where integrations tend to break, being able to read an error message and actually fix the root cause, and knowing how to structure instructions so an AI system behaves predictably.
That’s why builders who’ve been in the space for a while can switch their primary tool (from a visual automation platform to an AI coding agent, for example) without losing much ground. The knowledge of how to break down a problem and debug a broken system carries over completely. The advice that follows from this: don’t wait for the “perfect” tool before starting, and don’t panic when your favorite tool gets replaced by something better. If you’re worried about lock-in, build systems in a tool-agnostic way (plain instructions and structured files rather than platform-specific configurations) so a switch doesn’t mean starting over.
What does “being AI native” actually mean?
It’s not a knowledge threshold. It’s not about how many models or tools someone can name. It’s about default behavior: when a task lands on your desk, is your first instinct “let me see if AI can handle part of this,” or do you grind through it manually the way you always have?
The useful reframe is that “can AI do this” is never a yes-or-no question. It’s a percentage. If an AI system gets a task 70% of the way there and a human finishes the rest, that’s a meaningful win. If it only handles the first 25%, that’s still progress compared to doing the whole thing manually. And that percentage keeps moving, because the tools available right now are, by definition, the least capable version of themselves that will ever exist going forward.
Why doesn’t the AI model itself determine who wins?
Because access to the underlying model is roughly equal for everyone. Anyone can use the same frontier model. If the model itself were the differentiator, results would converge, and they don’t, because people bring different domain expertise and different systems on top of the model.
An accountant who builds a budgeting tool with AI assistance will produce a far better result than someone with no accounting background using the identical model, because the accountant knows what a good budget looks like and where the usual mistakes happen. That expertise gets encoded into how they prompt, what they check for, and what they explicitly tell the AI not to do. This layering (the accumulated instructions, guardrails, and domain knowledge applied on top of a shared model) is generally what people mean when they talk about context engineering: it’s less a single technique than the sum of everything a person adds to a raw model to make it perform for their specific use case.
Frequently Asked Questions
Which path is better for beginners: agency or in-house AI role?
Neither is universally better. An agency suits people comfortable with sales and inconsistent early income in exchange for faster iteration and no internal approval processes. An in-house path suits people who want stability and access to real business problems but requires patience with organizational red tape and slower buy-in.
Why don’t AI portfolios impress employers or clients anymore?
Because the tools required to produce a working demo have become widely accessible, so most portfolios contain similar tutorial-based projects. Buyers can’t distinguish deep expertise from a weekend project, which makes documented, measurable outcomes far more persuasive than a list of things someone built.
What’s a “receipt” in the context of AI work, and how is it different from a portfolio project?
A receipt is a documented before-and-after result: how many hours a process took versus how many it takes now, how many leads were missed versus how many are now captured. A portfolio project just shows something was built; a receipt shows what it actually changed for a business.
Do I need to learn a specific AI tool or platform to succeed?
No single tool is required long-term, since tools get replaced constantly. What matters more is developing transferable skills: debugging, structuring clear instructions, understanding how systems fail, and knowing how to verify that AI-generated work actually meets a defined standard.
How is context engineering different from prompting?
Prompting is the instructions you give a model in the moment. Context engineering is broader: it includes the accumulated knowledge, systems, documented failures, and instructions layered on top of a model over time, reflecting a person’s specific expertise rather than a one-off request.
