Originally published in SeedStage Digest (Nov 2023)
Over the last 18 months, early-stage AI has evolved from an overhyped buzzword to a tangible, fast-moving frontier — and it’s being built not by major labs or FAANG alumni, but by scrappy, pre-seed founders working out of cafes, coworking spaces, and Discord groups.
What’s changed? For starters, the infrastructure that once required millions in venture capital and access to GPUs is now far more democratized. Open-source models, API-first architectures, and platforms like Hugging Face, LangChain, and Pinecone have lowered the barrier to entry. Solo technical founders can now create usable, even profitable AI-powered tools with just a few weeks of iteration.
“This is the first time in modern software history where applied AI startups can skip MVP hell,” said Erika Winters, an early-stage investor based in Toronto. “A founder can stitch together models, plug into hosted APIs, and deliver something magical — without building everything from scratch.”
We're seeing this in action across sectors:
- AI copilots for lawyers and accountants
- Vertical-specific search tools powered by embeddings
- Auto-pitch generators and grant writers for underfunded nonprofits
- Personalized lesson plans generated for school districts
Notably, many of these startups are *not* looking to raise capital. They're launching revenue-first — often charging $10 to $30 per month for niche tools that solve real workflow bottlenecks. A growing class of "indie AI" founders are leaning into this path, treating their companies more like high-leverage solo businesses than venture rockets.
That doesn’t mean VCs aren’t circling. “We’re watching this space very closely,” said Winters. “But we know that the winners here might not need us until they’re already profitable.”
As for the founders? They're focused on shipping. This is the golden window for pre-seed AI: low noise, fast feedback, and a real shot at building something enduring without waiting for the next hype cycle.
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