The question worth asking before you commit to any model is: why is this the right moment? New ideas fail not because they are wrong but because they arrive too early or too late. So before you build an OPC, you should understand why 2025 and 2026 are the specific years when this became real — not theoretical, not aspirational, but actually executable.
Four things converged. Each one alone would have been interesting. Together, they are a structural shift.
For most of the last decade, AI assistants were impressive in demos and unreliable in practice. They hallucinated. They were inconsistent. They required so much correction that the time saved on the first draft was lost in the editing. Using them for real work felt like managing an enthusiastic but unreliable intern — possible, but not necessarily net positive.
That changed decisively between 2023 and 2025. The jump in capability — particularly in reasoning, instruction-following, and context retention — moved the tools from novelty to workhorse. The test is simple: can you give it a real task, from your real work, and get something you can actually use with one round of editing? For a growing list of tasks, the answer is now yes.
This is not about a single breakthrough. It is about the compounding of many improvements — longer context windows, better reasoning, multimodal input, tool use, memory — reaching a threshold where the tools are reliable enough to build a system around. An OPC is a system. You cannot build a system on unreliable components. The components are now reliable enough.
The specific tools that matter for an OPC — a strong language model for thinking and writing, a knowledge base for memory, a CRM for pipeline, a light automation layer for connecting them — all crossed usability thresholds within the same two-year window. This is not a coincidence. It is an ecosystem maturing together.
Building a company used to require significant upfront investment in people and infrastructure. A solo founder who wanted a research capability hired a research analyst. A founder who wanted a marketing function hired a marketing manager, or a freelancer, or an agency. A founder who wanted operations support hired an operations person. These were not luxuries — they were functional requirements.
The cost floor for those capabilities has collapsed.
What used to require a €60,000 salary or a €5,000/month agency retainer now requires a €200/month AI subscription and the judgment to use it well. That is not a marginal improvement. It is a category change. It means a one-person company can access capabilities that previously required either significant funding or significant compromise.
This changes the fundraising logic entirely. The traditional pressure to raise capital — to hire people to build capability — is dramatically reduced when capability can be accessed at near-zero marginal cost. An OPC can reach the point of genuine commercial traction before needing outside capital. That is a new thing. It was not possible five years ago in the same way.
It also changes the risk profile of going independent. The downside of betting on yourself is smaller when the infrastructure costs are lower. You do not need to generate €150,000 in revenue to cover a team before you can pay yourself. The breakeven is much lower — and therefore the bet is much safer.
Related to cost, but distinct: the smallest unit that can do serious, credible, enterprise-grade work has shrunk.
This matters because enterprise buyers — pharma companies, financial institutions, large corporates — historically bought from companies that looked like companies. A vendor with one employee was a vendor with a credibility problem. The question was always: what happens if you get hit by a bus? What is the continuity plan? Where is the team?