Did AI Just Kill All Vertical SaaS?
Not so long ago, back in 2022, if you had a top-tier technical team that could build AI models and gather the data to train it, you’ve already gained an edge that many other startups (and even large companies) couldn’t match.
Simply assembling the right minds and tech was a sustainable competitive advantage in and of itself.
This has changed. The generative AI revolution dramatically lowered the barriers to creating, training, and maintaining almost any AI model. What once took months of research and a team of PhDs can now be hacked together in a weekend — even by a minimally trained team.
To make matters worse, generative AI can write code, build apps, and automate processes faster than most humans can outline a product roadmap — further reducing the complexity of launching a startup
Once, technological hurdles were so high that young companies could out-innovate incumbents. But now, with off-the-shelf models easily accessible to all players, is there still room for startups to compete in vertical SaaS? Or are we truly at the end of startup-led innovation in these spaces?
A Familiar Pattern: Lessons from Cloud Services
Before we start mourning the death of all vertical SaaS startups, it’s worth remembering that this is not our first rodeo, we’ve faced similar shifts before!
Remember the cloud revolution? A couple of decades ago, launching a new platform or social app — like Facebook in 2006 — meant hiring backend engineers, infrastructure architects, and database specialists. Today, a single developer can stitch together a scalable product using AWS, Firebase, and no-code tools over a weekend. And well — did that kill innovation in web mobile applications?
Instead, startup founders found ways to build enormous value on top of the now commoditized cloud stack. They didn’t just spin up servers — they created proprietary algorithms, refined customer experiences, and unlocked new business models.Their unique value propositions — delivering insights, personalization, or superior customer experiences — couldn’t be easily replicated. The same thing is happening with AI.
The Shift in Competitive Advantage
Today, we’re seeing a similar pattern with generative AI. Off-the-shelf AI models can handle tasks that once required an entire team of PhDs. While that greatly democratizes AI, it also means that “we have an AI model” no longer sets a startup apart.
So, what could still create a defensible advantage for startups in this new era? What value add could startups bring on top of AI models to regain a competitive edge?
Building Trust Through “Slower” AI
Paradoxically, one potential competitive advantage may lie in not automating everything as fast as possible. Take Aidoc, for example — it’s gaining traction in healthcare by assisting radiologists with AI-powered medical imaging analysis rather than replacing them entirely. Particularly in regulated industries — law, healthcare, engineering, and beyond — accuracy, compliance, and trust are paramount. Raw speed and instant outputs offer little value to professionals who need verification, validation, and confidence in the results.
Consider a legal case-summary generator. Instead of instantly auto-generating entire summaries, consider a tool that works in tandem with lawyers, highlighting key text, suggesting paragraphs one-by-one, and keeping the expert in control. This more deliberate approach builds trust by keeping the human expert in the loop and ensuring every step is vetted. Through this collaborative process, lawyers maintain authorship and ownership of their work, producing results they can confidently stand behind.
The same principle applies to doctors diagnosing patients, structural engineers signing off on building plans, or investigators transcribing police interviews. In these high-stakes scenarios, automation alone isn’t enough; the real value is in guiding the expert through a reliable, auditable process. By emphasizing transparency, human oversight, and iterative validation, startups can build systems that induce trust — something that’s not easily commoditized.
Leveraging Proprietary, High-Resolution Data
As startups cultivate these human-centered workflows, they gather proprietary, high-resolution data that simply isn’t available to larger competitors. Over time, this data can be used to train more accurate and personalized models — and that’s where the real moat begins. For example:
- Legal AI: Tailor summaries and choose legal presents to rely on to match a lawyer’s writing style and judge preferences.
- Healthcare AI: Adapt diagnostics and treatment plans based on local patient populations, hospital’s patient population, physician habits, or patient risk profile.
- Engineering AI: Incorporate firm-specific design rules or compliance checks that go beyond generic industry standards.
Although many startups already offer these features, a startup that has carefully curated the user’s workflow and gathered explicit feedback on the work product throughout the process would be able to deliver significantly more personalized results, while clearly illustrating the decisions involved in this customization.
This kind of specialized, user-informed data grows into a real moat. While large incumbents can easily adopt the same open-source or commercial AI models, they can’t replicate the unique datasets (and thus the nuanced performance ) that startups develop in partnership with their users.
The Real Opportunity
AI’s commoditization isn’t a death sentence — it’s an inflection point. History has shown that when a technology gets democratized, the real breakthroughs happen not in building it, but in applying it differently.
Just as cloud computing didn’t eliminate software startups but instead paved the way for new waves of innovation, generative AI has reduced the difficulty of building apps and models. Yet history shows that innovation thrives beyond commoditized technologies, not in spite of them.
So, if you’re a founder building in AI, don’t panic. The playing field has shifted, but the game is far from over. The real question isn’t “Do you have an AI model?” — it’s “What do you have that AI alone can’t replace?”
So, next time an investor asks you, “What’s your competitive advantage?”, know it’s not a trick question. As the playing field has shifted, so must your moat. The best founders will evolve and find new ways to differentiate themselves, building their defensibility on top of this new stack.
