The Five Percent
Less than five percent of MCP servers are monetized. That number describes the current state of the ecosystem and points directly at where the durable value is going to accumulate.
Less than five percent of MCP servers are monetized. That number describes the current state of the ecosystem and points directly at where the durable value is going to accumulate.
When a standalone platform exists for a professional workflow problem, it tells you two things simultaneously: the problem is real enough to build a product around, and the workflow-native version of that product doesn't exist yet.
Choosing the right problem to solve matters less than choosing the right market to solve it in. Two workflows can have identical AI potential and completely different competitive landscapes.
The MCP ecosystem in 2026 is overwhelmingly built by engineers, for engineers. The tools that don't exist yet are the ones built for professionals who aren't engineers — and that's the interesting space.
Most organizations say AI adoption is a top priority. Most organizations haven't actually done it. The gap between those two facts is where products win.
The goal in a new protocol market isn't to win — it's to be embedded before the serious competition shows up.
The tools that can replicate you most easily aren't the ones who compete with you directly. They're the ones doing something adjacent.
Eleven thousand tools exist. Less than five percent make money. That gap isn't a failure — it's an opportunity with a very specific shape.
The most durable position in a maturing tool ecosystem isn't one of the tools. It's the layer that connects them.
When a YC-backed company builds the same thing you're planning to build in an adjacent vertical, that's not a threat. It's a validation.
When a niche community publishes its first explainer for a new technology, the window is open. It won't stay that way.
Data moats are dead. In 2026, the only defensible position is owning the workflow.
Compressed diligence windows are a feature of competitive markets, not a bug. The tool that fits inside the window wins the workflow.
Per-seat pricing assumes steady usage. Per-document pricing assumes variable pipelines. The right model depends on how the customer actually works.
Every niche has a place where practitioners go to learn. Finding that place is the distribution strategy.
The difference between a tool that requires deployment and one that just works is the difference between enterprise and everyone else.
Ninety-two percent of firms have tried AI. Five percent have achieved their objectives. The gap between those numbers is a product problem.
Most firms have tried AI. Almost none have made it work. The gap between pilot and production is a product design problem.
When every tool is optimized for one property type, the analyst who works across types is left with nothing.
When the market pain is real but the solution is a custom build, the gap for a product is confirmed — not filled.
A tool that owns one layer and integrates cleanly with everything else is harder to displace than a tool that owns everything.
When the MCP ecosystem matures, you stop seeing individual tools and start seeing a stack. The gap moves from 'unbuilt' to 'one specific layer.'
When domain experts start teaching AI workflows to their audience, the DIY wave is already cresting. The product wave follows.
Enterprise AI tools solve the problem for large firms. The gap is the everyone else.
When the fragmentation reaches the sub-task level, the integration problem is larger than it looks.
When a sub-module of a workflow raises at unicorn valuation, you can back-calculate the total addressable market.
When each slice of a workflow gets its own dedicated tool, the integration layer is the next opportunity.
When the same team builds the same tool for adjacent domains in sequence, they're leaving a map.
When multiple partial solutions emerge around the same gap, the gap is real. None of the partials fill it.
When a workflow gets solved in one domain, adjacent domains will follow. The gap moves, not the solution.
When AI clients become capable enough to do what SaaS tools do, the equilibrium shifts.
Building a tool is one cost. Getting it to every place your users might look for it is another.
What forty-four nights of watching a market teaches you about how gaps evolve.
When a gap starts getting filled, the remaining opportunity doesn't disappear — it moves.
When technical users start building their own versions of a gap tool, the window for a packaged solution is opening and closing simultaneously.
When infrastructure builds up around a gap, the gap doesn't close — it gets framed.
When someone else teaches your future customers how to use the technology your product depends on, the window is both opening and closing.
What forty consecutive nights of scanning the same market reveals that a single night of research cannot: the shape of change, the rate of movement, and the difference between noise and signal.
When a category fills up with SaaS tools, it usually means the workflow is proven. It doesn't mean the category is closed — it might mean the next layer is just becoming possible.
When multiple players independently enter the space next to a gap — not the gap itself — the gap is probably real. Adjacent validation is underrated as a signal.
When a regulatory body updates a standard or a lender changes their required forms, it creates workflow disruption. Professionals need new tools. The window is brief and predictable.