Vertical AI
topic on 8 shows · 13 statements across 12 episodes
the Y Combinator Startup Podcast
Innovators & Investors
the Neon Show
No Priors
the Startup Ideas Podcast
Sourcery
the MAD Podcast
20VC
13 statements about Vertical AI, every show
Vertical AI startups cannot win by fighting foundation model labs
“If you are fighting against them, you've already lost. If you haven't figured out how you're going to win with them, how you're gonna not just coexist, but actually find ways to collaborate potentially, and if the tailwinds that they create are not yours to le…”
Isenberg: YC Predicts Over 300 Vertical AI Unicorns Will Emerge This Decade
“YC predicts that there's going to be 300 plus unicorns in vertical AI in, in, in, you know, this decade.”
Isenberg: Vertical AI TAM Is 10x Larger Because It Replaces Human Headcount
“Vertical SAS captures a fraction of IT spend. You're generally selling software licenses. Humans are operating the tool and you're looking at the 10 to a hundred million dollar outcome. Usually, of course, there's exceptions to that rule. Vertical AI taps dire…”
Gil: Next Set of Vertical AI Sectors Will Consolidate
“I think another prediction for 26 is the next set of verticals will hit massive scale. I think this year we saw consolidation of coding into a handful of players and Medical scribing into a handful of players. We go into a handful of players like RV and others…”
Dash: Vertical AI will create $10B ARR companies in 10-15 years
“I'm pretty sure we're going to see five to ten billion dollar ARR companies in healthcare, education, defense, and retail. In the next 10 to 15 years that are not going to be head-on competing with the same companies that are competing code generation.”
Wickramasekara: 90% of vertical AI is workflow translation and building trust
“I think in a vertical, I think 90% of the work is actually, like, translation. It's taking something and making science, making sure scientists trust it, it's the right point in their workflow, it's easy to use, and it's accurate.”
Chandra: Non-technical domain experts can build billion-dollar vertical AI startups
“The number one category that I would be building in if I was a founder is vertical AI, which is to say, what is the area and the problem statement and the workflow that you are deeply passionate about? What is the problem that only you can solve? Even if you a…”
Aging workforces will drive rapid vertical AI adoption in insurance and accounting
“So, you know, think of, like, insurance or accounting where are two that come to mind where you have an aging work workforce and the amount of people that are retiring from those senior roles. Is significant over the next 10 years, and there's just not the pip…”
Garry Tan: Vertical AI moats require sitting with domain workers to build evals
“You can't get the evals unless you are sitting literally side by side with people who are doing X, Y, or Z knowledge work. You know, you need to sit next to the tractor sales regional manager and understand, well, you know, this person cares about, you know, t…”
Sanghi: Domain-specific vertical AI agents will outperform general AI applications
“I think you'll see more and more people focusing on a particular domain and building more and more agents for that domain, and that, that is something that I think will do well in the next few years.”
Isenberg: Founders Have 6-12 Months to Win Vertical AI Niches
“The window to establish yourself as the go-to solution in a specific vertical is maybe six to 12 months before it closes for a decade, or who knows.”
Alter: Vertical AI offers more exciting opportunities than horizontal or foundational models
“I'm thinking a lot these days about vertical AI and really, you know, the opportunity there being More exciting than sort of horizontal or even foundational layers in AI at the moment.”
Fontana: Vertical AI wins through specialized data collection and model tuning
“To make us only invest in vertically focused applications, because that's where you can really get ahead of everyone else by focusing on tuning a model for a very specific purpose, getting data to train a model for a very specific purpose.”