These AI startups made a splash at Y Combinator’s Winter 2024 Demo Day, standing out from the crowd with their innovative technologies and impressive founders.
The investment landscape may have taken a downturn, but AI funding saw a significant surge in the past year. In fact, capital towards generative AI ventures alone nearly octupled from 2022 to 2023, reaching an astounding $25.2 billion by the end of December.
It’s no surprise then that AI startups dominated the Winter 2024 cohort at Y Combinator. Surpassing the number of AI startups in previous batches, this year’s cohort boasts 86 AI startups according to YC’s official startup directory. This is nearly double the number from the Winter 2023 batch and close to triple the number from Winter 2021. Clearly, AI is the technology of the moment, whether you call it a bubble or overhyped.
The newest Y Combinator cohort has been thoroughly analyzed, and we have handpicked some of the most intriguing AI startups among them. Each of these startups caught our attention for different reasons, but they all share a common thread – they stood out from the rest.
Hazel
The team behind Hazel, August Chen (ex-Palantir) and Elton Lossner (ex-Boston Consulting Group), believe that the government contracting process is fundamentally broken. With contracts being posted on thousands of different websites and including hundreds of pages of overlapping regulations, it’s a daunting and time-consuming task for businesses to respond to these bids.
Chen and Lossner’s solution is to harness the power of AI to automate the entire government contracting process. Named after the hazel tree – known for its adaptability and resilience – Hazel matches users with potential contracts, generates a draft response based on their company’s information, creates a checklist of to-dos, and automatically runs compliance checks. This could save businesses an enormous amount of time and effort, particularly enabling smaller firms to compete for the hundreds of billions of dollars’ worth of government contracts issued each year.
Of course, there may be doubts about the consistency and accuracy of AI-generated responses and checks. However, even if they are remotely accurate, the impact could be significant.
Andy AI
Tiantian Zha, who previously worked at Verily (Google’s life sciences division), knows firsthand the challenges that home nurses face when it comes to paperwork. In fact, according to a study, nurses spend over a third of their time on documentation, which not only takes away from patient care but also contributes to burnout.
To address this issue, Zha co-founded Andy AI with Max Akhterov, a former Apple staff engineer. Andy is an AI-powered scribe that captures and transcribes the spoken details of a patient visit, creating electronic health records. While there are other AI-powered transcription tools in the market, there is always a risk of bias and competition. However, as healthcare increasingly shifts to home, the demand for apps like Andy AI is expected to grow.
One of Andy AI’s biggest challenges will be ensuring accuracy and minimizing bias. But the potential value for healthcare providers and patients is significant.
Precip
If you’ve ever been caught in a rainstorm after trusting a weather app’s prediction of clear skies, you are not alone. But Jesse Vollmar, founder of FarmLogs, aims to change that with Precip, an AI-powered weather forecasting platform. Vollmar teamed up with Sam Pierce Lolla and Michael Asher, previously the lead data scientist at FarmLogs, to make Precip a reality.
Precip delivers analytics on precipitation, estimating the amount of rainfall in a given area over the past several hours to days. According to Vollmar, Precip can provide “high-precision” metrics for any location in the U.S. up to two kilometers, with forecasts up to seven days ahead. This has potential applications in various industries such as agriculture, construction, and utilities, as well as for individual use to avoid bad driving conditions.
While there is no shortage of weather prediction apps in the market, the promise of AI making forecasts more accurate is a significant advantage.
Maia
Claire Wiley, founder of a couples coaching program, was inspired to create Maia while studying for her MBA at Wharton. Maia, co-founded with Ralph Ma (a former Google Research scientist), offers AI-powered guidance for couples looking to strengthen their relationships. Through Maia’s apps for Android and iOS, couples can engage in group chats and answer daily questions to improve communication and understanding.
While Maia has potential to be a valuable tool, there are questions about its relationship science and how it will stand out in a crowded field of couples’ apps. Despite this, it’s clear that the founders have invested time and expertise in crafting an experience that can help couples build stronger relationships.
Datacurve
Serena Ge and Charley Lee, co-founders of Datacurve, believe that the lack of data curation is a significant issue in the training of generative AI models. As a result, Datacurve provides “expert-quality” data for AI training, specifically focusing on code data which comes with its own set of challenges.
Datacurve offers a gamified annotation platform that pays engineers to complete coding challenges, contributing to the creation of for-sale training data sets. These can be used to train models for code optimization, generation, debugging, and more. While there are other weather prediction apps in the market, Datacurve’s success will largely depend on the accuracy and quality of its data sets, as well as its ability to incentivize developers to continue contributing to them.
Datacurve’s approach to providing expertly curated data for AI training is a unique and interesting concept. The accuracy and usefulness of their data sets will be crucial to their success in a highly competitive market.
