Software Was Never Built for You
Enterprise software has always been built for the 80%. That's not a criticism — it's a structural reality. A software company needs to capture enough of the market to justify the cost of building the product, which means optimizing for what most buyers in a category need most of the time. The result is software that fits reasonably well, for a reasonable number of use cases, with the assumption that organizations will adapt themselves to what the software can do.
Most of them have. For decades, companies have bent their workflows, their terminology, their internal processes around products that weren't built for them specifically. The alternative — custom software — was expensive, slow, and required ongoing engineering resources most organizations couldn't justify. So they took the 80% solution and called it good.
What the Market Already Knows
When the latest wave of capable AI models landed, SaaS stocks took a meaningful hit across the board. The interesting part is that none of those products became less functional overnight. The software didn't break. What broke was the assumption that the 80% solution was the only practical option. The market reacted to a single underlying shift: purpose-built software, tailored to a specific workflow or a specific organization, could now be built at a fraction of what it would have cost a year earlier. That changes the competitive math entirely.
The Expert's Advantage
This is the moment subject matter experts have been waiting for, whether they knew it or not.
There's a specific frustration that belongs to people who've spent years inside a problem. You know exactly what the tool should do. You've used the inadequate version of it long enough to know precisely where it fails. You've watched vendors ship something close but wrong, missing the nuance that only someone who's actually done the work would catch. And at some point, you stopped thinking about it — because thinking about it meant thinking about the cost to build it.
That cost has dropped. The gap that used to require a full engineering organization to bridge is now something a motivated expert can cross with help. A compliance attorney who mapped the same regulatory framework in spreadsheets for twelve years can now build a query layer that reflects how the work actually gets done. A commercial insurance broker who understands risk aggregation in a way no off-the-shelf platform captures can ship a pricing tool built around his actual book. Neither needs to become an engineer. Both need to know what they're building and why.
That's the part AI didn't change. The hard part was always the knowledge — knowing which variables matter, what edge cases to handle, how the workflow behaves under pressure. That takes years. What changed is the translation layer between that knowledge and working software. The implementation bottleneck is no longer what it was.
The people positioned best for this moment are not the generalists who've read every AI newsletter. They're the specialists who understand a problem precisely enough to define what a real solution looks like. The tax attorney who knows where compliance tools break down. The credit analyst who can articulate what a risk model misses and why. The operator who's spent a decade working around the gaps in their category's dominant platform.
Functional Isn't Finished
Before you mistake acceleration for simplicity, a word of honest caution.
Vibe coding — prompting your way to a working prototype — is real and a genuine on-ramp. But it gets you to functional. Functional is the floor, not the ceiling. Hard problems are still hard. Complex systems still require people who understand architecture, data integrity, security models, and how software behaves when real users find its edges under real conditions. No amount of prompting substitutes for that expertise, and the gap between a working demo and a production-ready platform is where most self-built tools quietly fall apart.
The question worth asking about anything you're considering building is not just "does it work?" It's whether it's valuable — solving a problem that actually costs someone something. Whether it's useful — people can accomplish something meaningful with it they couldn't before. Whether it's trustworthy — it behaves consistently and without unpleasant surprises. And whether it's enjoyable — the experience of using it is, at some level, worth having.
Those four dimensions aren't obvious from a prototype. They require architects guiding the process, not just tools executing prompts. We call this the Craft Matrix at Precocity, and it's the difference between software that gets built and software that gets used. You can read more about it here.
Before You Start Sketching
If you're thinking about building your own solution, a few other things are worth sitting with.
The moment you ship something people depend on, you are a software company. Not metaphorically — operationally. That means someone owns uptime, someone handles the edge case that breaks it at the worst possible time, someone fields the request to add the one feature that would make it perfect for the one person asking. Most domain experts underestimate this part, not because they're naive, but because the building phase is energizing and the maintenance phase is invisible until it isn't.
Software doesn't hold still, either. The workflow it was built around will change. The AI models underneath it will change. Regulations, data sources, integrations — all of it moves. What you're committing to is not a finished thing but an ongoing relationship with a living system. Organizations that build something and then treat it as done eventually find themselves running something brittle, and the cost of catching up is usually higher than the cost of keeping pace would have been.
There's also a data question that deserves serious attention early. Custom tooling built on sensitive business information — client data, financial records, proprietary process logic — carries obligations around security, access control, and in some industries, compliance. Understanding where your data goes, who can see it, and what happens if something goes wrong is not a technical detail to sort out later.
Then there's the question of what happens when the person who built it leaves. Institutional knowledge baked into software without documentation, without a transition plan, without someone else who understands how it works — that's a liability dressed as an asset. The value of what you built needs to survive its builder.
Build With Intention
None of this is an argument against building. It's an argument for going in with an accurate picture of what you're signing up for. The organizations that get the most out of this moment will be the ones who build with intention — who know what problem they're solving, who owns it, what the commitment looks like beyond the launch, and who they're leaning on to close the gap between what a prompt can produce and what a platform actually needs to be.
For a lot of people, the idea has been sitting dormant for years, waiting on conditions that now exist. The question worth asking is not whether you could build it. It's whether you're ready for what comes after you do.
