I Analyzed 373 Ai Startups Selected By Y Combinator In 2026

I analyzed 373 startups that got selected in Y Combinator in 2026. Just answer one single question. What should you actually build? And what is Y Combinator investing in? Because at this point being an AI startup is just too broad. The more useful questions that one should ask is which companies are building real workflow systems, which markets are too crowded, which opportunities are about to explode, which segments people are building in, what type of technology, what type of systems they are using and what ideas one should avoid. After doing this analysis, the biggest pattern that I found is this. Hi, I’m Hershett. I am the founder of Agentise. We are an AI consultancy firm where we are working with companies like Deote, ICICICI Bank, Outskill, IM8 and many other fast growing companies in the world [music] and we build AI solutions for them and we train their teams on how to build repeatable AI systems that actually compound and evolve with them. The next wave is not chatbots or rappers. The strongest companies are moving towards building agentic operating systems. [music] Now these operating systems they can take in messy data they can make decisions they can [music] take actions inside other tools update the entire system and also keep a log of what happened so that humans can trust what changed. So before we get into the eight big questions which I’m about to unwrap in this video, let me show you what the data actually looks like. [screaming] So first I looked at 373 companies. Out of these 336 are AI companies which is about 90% of the data set. So the first important point is simple. AI is no longer the differentiator. It’s uh in this batch AI is the default. The real differentiator is the workflow that the company owns. Second 345 companies are B2B which is 92.5% of the data set. That tells us this batch is not mainly about consumer AI toys. Third, the median team size is just two people because most of these are like early stage startups. And when I group these companies into opportunity segments, the biggest category was developer, AI, Infra and data, which is about 122 companies. And that makes sense. Every company is building AI agents. Then everyone is going to need eval tooling, data pipelines, coding agents, infrastructure, observability, and reliability. And they’re building those. The second biggest segment is industrial manufacturing and robotics with 54 companies. This is interesting because it shows AI is not only happening in software, it is now moving into factories, logistics, hardware, physical operations as well. Then we have sales, marketing, customer operations with 41 companies and healthcare, bio and sciences with 35 companies. Now these are classic high volume workflow markets. lots of documents, calls, follow-ups, approvals and repetitive admin work. The smaller segments are also important. We have legal compliance and risk which has only 17 companies but it is one of the most interesting areas because it is trust heavy. In these markets, people do not just want automation, they want evidence, audit trails, approvals and accountability. I tagged the companies by opportunity signals. So the biggest repeated patterns were agent behavior, tool usage, human approval, data ingestion, eval sandboxing, CRM and revenue ops, compliance and audits. These are inferred from company descriptions. So I would treat them as directional signals, not audited product claims. But uh the pattern is still very clear. The best AI companies are not just generating text. They are connecting tools, decisions, approvals and systems of records. 90% of the companies that YC has invested in are AI companies. So the real question is not who is building with AI. The question is who owns a workflow that people repeat every day. The best companies are not just adding AI to an application. They are the ones who collect context, make decisions, update the system, take action and then also keep a track of all the decisions that have been made. That is the difference between an AI feature and an AI operating system. Let’s take the example of this company called Hyper. They are building a company memory layer for AI first teams. So their tool is learning from notion documents, slack emails, GitHub PRs, cursor sessions, cloud code sessions. So every single chat session is basically leveraging the knowledge from the company’s brain and that’s what they are building self-driving company brain. Now the knowledge is not just stuck in people’s head. It has been democratized and everyone has access to uh all the important pieces of information that we think that every employee should have in a company and that is why workflow ownership matters more than the AI label. So another insight that I got was that most of these startups are building action systems and they’re not just another chatbot. Chat bots give you text answer that is static information. The more valuable product here is going to complete the work for you. So the strongest AI startups have moved from generation to actually doing. They understand the ingested data. They understand what decisions to make. They can go inside another systems, complete the task and then update the entire system. And the buyers are going to pay you more if you are completing the work, if you are getting the work done. Okay, so let me show you a company that is doing this really well. I want to talk about Senta. Senta is a company that helps job seekers find jobs. It optimizes their profile and applies on their behalf as well. Now the key is the full loop. Find the job, match it to the user, customize the application and help submit it. The user still has control but the repetitive work is handled by the agent. So this now becomes a full action system and they are doing it really well. So now that we are asking AI to take action, it comes with risks as well. Once AI stops suggesting and starts taking action, the risk also changes. Now a bad answer will make you feel annoyed. But a bad action can actually cost you money and trust. So a new layer is emerging now. People are now working on permissions, approvals, audit logs, sandboxing, eval roll back. There is a full on layer that people are building in and this is required. You need these guardrails to be provided to the agent before they can start operating in real tools. Now there are a number of companies that are actually building such security layers and guardrails for AI agents. One such company is Clawweiser. Now Clawweiser lets agents use Gmail, Slack, Google Drive without seeing raw credentials. Instead of giving the agent broad access, claw wiser sets in the middle checks whether the action is allowed or not and it enforces what the user approved. That is the kind of trust layer every serious agent is going to need. Another company is Mount. Mount is building AI agent insurance carrier starting with liability coverage for deployed AI agents. Then we have heaven. Heaven is global banking for team and agents. This is the automated USD account for global internet companies. We have Huskard. Now this is an AI native actuarial advisory for large companies to optimize their insurance decisions. There are multiple such companies that are operating in this particular layer. Another layer of products that we should talk about is the trust layer. In sensitive domains like legal, compliance, finance, healthcare, people are using AI not because it sounds smart. They want AI products because they are safe and traceable. So here the product has to show what changed, why it changed, what evidence was used and who approved it. So trust in these products is not going to be a feature. It is the thing that people pay for. So here I want to talk about this product that I came across which is called reggg base. This product tracks regulatory changes across languages, jurisdictions, government websites and low visibility sources. This is not just summarization. The product has to find the change, extract what matters and make it reliable enough for legal and compliance teams to act upon. Okay, so this one is very interesting uh because it is related to the tools that we all are using every single day. Coding agents like codeex, cloud code, open code. Coding agents have made so much progress in the last few months. They are no longer just writing code. teams are using it to assign tickets uh you know write code test the feature and ship complete software. Now here opportunity is not just AI writes code. It is that the agent is able to take the ticket, understand the repo, create a branch, write the code, test the feature, fix the CI failures and produce PRs that the humans can trust. And companies that are building such agents are completely changing the operating system. I want to talk about this company called replicas there. Now in this tool now using this teams can actually delegate tasks to cloud code or codeex from slack from linear or github. Each task is going to run in a sandbox VM. So multiple agents can work in parallel. The agent can handle the ticket, verify its work and respond to CI or code review feedback that points to a software team operating system. This is the new way of working. Okay, so this one is going to blow your mind. There are startups that are going to look like they are building vertical SAS, but if you look closely, it is an agentic operating system. So there are startups that will look like AI for logistics, AI for manufacturing. But what they’re doing is pretty simple. The pattern here is you pick one painful workflow in an industry and then you expand into the place where the data resides, where decisions happen, where approvals happen and where updates get made. So here I want to talk about this company called Day Job. It builds AI workers for transport operations. Now their scheduling agent plugs into existing ERPs, handles new jobs, driver changes, route updates and exceptions in real time. that is not just a helper for logistics teams, it is starting to own the complete operational decision loop and that is massive. So Y Combinator has been talking about AI native services and this comes close to the idea of one person business, one person company that the internet is talking about. You must have seen a lot of those videos where people explain you know how to build such companies. I think the best way to think about this is how you can enable small and mediumsiz businesses run with just one to three people and a number of specialized agents because these AI agents can now tackle research, delivery, support, billing, reporting and a small team can now take large markets. These tasks used to take a lot of people. You had to run 10, 15, 20 member teams. But now this is all possible with just specialized agents. The only leverage that you would need is you have to pick the workflow that you understand deeply. Let’s take the example of this company called General Legal. Now General Legal is an AI native law firm for founders and it helps them handling contract drafting, review and negotiation for a flat fee. The repeatable parts like uh intake, document review, drafting and delivery that can all be compressed with AI but the human expertise stays at the trust boundary. So that is the playbook. Start as a service then productize the repeated workflow. So after going through this entire data set of all of these companies, one thing that I realized was none of them are building anything shallow or for everyone. What I mean by that is you should not be building generic co-pilots, thin chat bots, shallow rappers, novelty uh content tools, uh generic outbound automations. So the fastest way to lose an AI is to build something that does not own a real workflow, a real painful workflow. So find that workflow, find that process first in a particular niche and then consider applying at YC. you’ll have way better chances of getting selected. All right, so now you know what type of companies are getting backed by Y Combinator. I hope you got some great insights from this video. If you want the full report, then the link is in the description. If you have your own unique set of questions, you want your ideas validated as well, then I’ve created an application using which you can chat with the same data set and extract insights. Uh the link is crackycyc.com that also is available in the description below. That’s all. Uh if you found it useful, please give it a thumbs up, share it with your friends, and I’ll see you in the next one. Chess.