Industry-specific AI startups that embed deeply into existing workflows, automate high-value processes, and drive measurable economic impact.
Estimated Reading Time: 4 minutes
Ai is coming for every vertical
The Ai world is kind of crazy right now; literally changing everyday from Deepseek seemingly doing the impossible (which they didn’t), to OpenAi releasing Deep Research that can perform many agent like tasks. We’re basically just limited by data and our imaginations right now.
The initial wave of Ai funding went into large, foundational models—massive infrastructure plays requiring billions in capital to fuel the OpenAI, Anthropic, and Google DeepMinds. But for most early-stage investors, especially pre-seed and seed funds, these opportunities are fundamentallyout of reach.
Foundational Ai model startups are raising$10M–$50M seed rounds(e.g., Mistral AI and Cohere, which raised substantial early funding to compete in the foundation model space), an order of magnitude larger than what most early-stage investors can (or should) write checks for. These deals don’t fit the financial structure of emerging VC funds, where writing a $250K–$1M checks into a round that isalready oversubscribed by deep-pocketed firmsmeans having little influence, no ability to follow on, and being diluted into irrelevance.
The best opportunities forearly-stage AI investingdon’t lie in competing with OpenAI or Anthropic. Instead, they lie inVertical AI—industry-specific AI startups thatembed deeply into existing workflows, automate high-value processes, and drive measurable economic impact.
This is where early-stage investors can truly win.
AI isn’t just another software wave—it’s fundamentally changing how businesses operate. While general-purpose AI tools capture headlines, the real value is emergingwithin industries where AI can be deeply integrated into existing workflows with the right data.
Bessemer Venture Partners has identified this trend in their latestresearchon AI.The most promising AI startups today are not broad-based generalists but focused, verticalized applications solving high-value, industry-specific problems.
Take legal tech, for example.Harvey, one of the standout Vertical AI startups, is automating legal research and document drafting for top law firms. This isn’t just adding AI to an existing workflow—it’stransforming how lawyers operate, reducing billable hours on tedious tasks and letting firms take on more cases.
In healthcare,Hippocratic AIis focused on patient engagement and administrative automation, solving areal pain pointin an industry drowning in paperwork and regulatory burdens.
In logistics, AI robotics companyCovariantis automating warehouse operations, helping companies move goods faster and with fewer errors—improving margins in one of the lowest-margin industries on the planet.
These startupsdon’t need billions in compute costsbecause they leverage existing AI models via APIs, cloud-based AI infrastructure, and fine-tuned smaller models suited for their specific industries. They use existing AI infrastructure but apply it intelligently toindustry-specific problems with clear economic benefits.
Vertical AI is not just another layer of SaaS—it’s potentially afundamentally new business model. Kind of like an affiliate model like those house property tax reducer firms that take X% if they can lower your bill 🙂
Traditional SaaS businesses charge per-seat or per-license fees. This model works well but limits pricing power. Some vertical AI startups, on the other hand are trying tocharge based on actual value created.
This means pricing shifts from: 🚫Flat monthly SaaS fees→ ✅Performance-based, outcome-driven pricing
AI-poweredlegal platformscharge based on the number of cases won or documents processed.
AI-driveninsurance startupsare pricing based onclaims approved and fraud detected.
AI-basedsales enablement toolsare shifting topercentage-of-revenue modelsinstead of flat SaaS fees.
This shift could make some Vertical AI startups farmore defensiblethan traditional SaaS companies.
Scale Venture Partners points out thatVertical AI companies own the entire workflow, allowing them to deeply integrate AI into mission-critical processes, which increases customer dependency and retention., making them far stickier than traditional software tools. Instead of just being an "add-on" to existing software, they become thecore operating system for their industry.
I’m still waiting on this to learn more before I believe it’s really viable because I’m not the biggest fan of “performance” based outcomes but there is potential.
https://news.crunchbase.com/venture/startups-ai-seed-investors-data-charts-ye-2024/
Venture investment into AI is growing at a record pace, but most of the capital is still flowing intolarge-scale foundational modelsrather than practical, industry-specific applications.
According to PitchBook, in 2023: ✅AI startups raised $50B in total funding✅$29.1B went into generative AI(a 268% increase over 2022) ✅A growing percentage is flowing into Vertical AI, though it's still an underinvested category
At the same time, Vertical AI funding rounds aresmaller and more manageable for early-stage investors. Unlike foundational models that require$100M+ just to compete, Vertical AI startups typically raise$2M–$8M at pre-seed/seed, with more reasonable valuations and capital efficiency.
Costanoa Ventures, a firm specializing in applied AI, has noted thatgenerative AI has made previously “too small” vertical markets investable—meaning sectors that VCs once ignored due to limited scalability are now viable because AI lowers operational costs and expands the total addressable market (TAM).
This is thesweet spotfor early-stage investors:
Smaller rounds that fit traditional pre-seed/seed check sizes
Capital-efficient startups that don’t require massive follow-on rounds
A growing market where large VCs haven’t yet dominated
Andrew Chen’sarticle,Revenge of the GPT Wrappers: Defensibility, highlights how GPT wrappers struggle to differentiate themselves, leading to pricing pressure and difficulty in maintaining competitive moats:how do AI startups avoid becoming commodity “wrappers” on top of large models?
His answer?Defensibility comes from owning unique workflows, proprietary data, and deep industry integrations—all of which arecore strengths of Vertical AI companies.
Chen’s insights align perfectly with the Vertical AI thesis: the best AI companies will be those thatgo deep into an industry, capture value beyond simple automation, and become indispensable within workflows.
For pre-seed and seed funds, the decision is clear:investing in foundational models is structurally impossible, but Vertical AI is a high-growth, high-upside opportunity that aligns perfectly with early-stage capital.
Instead of competing for access tooversubscribed, billion-dollar AI rounds, investors should be focusing on:
✔AI-native startups that embed deeply into industry workflows
✔Companies that capture more of the value chain through performance-based pricing
✔Startups that align with pre-seed and seed check sizes and fund structures
✔Vertical AI markets that are still underfunded and open for disruption
The future of early stage VC Ai lies in backing companies that develop deeply embedded AI solutions, creating indispensable value through industry-specific automation. It’s infunding the startups that are actually putting AI to work.
The best early-stage AI investments won’t be horizontal.They will be vertical.
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