Despite an overall decline in startup investing, funding for AI surged in the past year. Capital toward generative AI ventures alone nearly octupled from 2022 to 2023, reaching $25.2 billion toward the tail end of December.<\/p>\n
So it\u2019s not exactly surprising that AI startups dominated at Y Combinator\u2019s Winter 2024 Demo Day.<\/p>\n
The Y Combinator Winter 2024 cohort has 86 AI startups, according to YC\u2019s official startup directory \u2014 nearly double the number from the Winter 2023 batch and close to triple the number from Winter 2021. Call it a bubble or overhyped, but clearly, AI is the tech of the moment.<\/p>\n
As we did last year<\/a>, we went through the newest Y Combinator cohort \u2014 the cohort presenting during this week\u2019s Demo Day \u2014 and picked out some of the more interesting AI startups. Each made the cut for different reasons. But at a baseline, they stood out among the rest, whether for their technology, addressable market or founders\u2019 backgrounds.<\/p>\n August Chen (ex-Palantir<\/a>) and Elton Lossner (ex-Boston Consulting Group) assert that the government contracting process is hopelessly broken.<\/p>\n Contracts are posted to thousands of different websites and can include hundreds of pages of overlapping regulations. (The U.S. federal government alone signs an estimated<\/a> over 11 million contracts a year.)\u00a0 Responding to these bids can take the equivalent of whole business divisions, supported by outside consultants and law firms.<\/p>\n Chen\u2019s and Lossner\u2019s solution is AI to automate the government contracting discovery, drafting and compliance process. The pair \u2014 who met in college \u2014 call it Hazel<\/a>.<\/p>\n Image Credits:<\/strong> Hazel<\/p>\n<\/div>\n Using Hazel, users can get matched to a potential contract, generate a draft response based on the RFP and their company\u2019s info, create a checklist of to-dos and automatically run compliance checks.<\/p>\n Given AI\u2019s tendency to hallucinate<\/a>, I\u2019m a bit skeptical that Hazel\u2019s generated responses and checks will be consistently accurate. But, if they\u2019re even close, they could save an enormous amount of time and effort, enabling smaller firms a shot at the hundreds of billions of dollars\u2019 worth of government contracts issued each year.<\/p>\n Home nurses deal with a lot of paperwork. Tiantian Zha knows this well \u2014 she previously worked at Verily, Google parent company Alphabet<\/a>\u2018s life sciences division, where she was involved in moonshots ranging from personalized medicine to reducing mosquito-borne diseases.<\/p>\n In the course of her work, Zha found that documentation was a major time sink for at-home nurses. It\u2019s a widespread issue \u2014 according to one study<\/a>, nurses spend over a third of their time on documentation, cutting into time spent on patient care and contributing to burnout<\/a>.<\/p>\n To help ease the documentation burden for nurses, Zha co-founded Andy AI<\/a> with Max Akhterov, a former Apple<\/a> staff engineer. Andy is essentially an AI-powered scribe, capturing and transcribing the spoken details of a patient visit and generating electronic health records.<\/p>\n Image Credits:<\/strong> Andy AI<\/p>\n<\/div>\n As with any AI-powered transcription tool, there\u2019s risk of bias<\/a> \u2014 i.e. the tool not working well for some nurses and patients depending on their accents and words choices And, from a competitive standpoint, Andy isn\u2019t exactly the first of its kind to market \u2014 rivals include DeepScribe<\/a>, Heidi Health<\/a>, Nabla<\/a> and Amazon\u2019s AWS HealthScribe<\/a>.<\/p>\n But as healthcare increasingly<\/a> shifts to home, the demand for apps like Andy AI seems poised to increase.<\/p>\n If your experience with weather apps is anything like this reporter\u2019s, you\u2019ve been caught in a rainstorm after blindly believing predictions of clear blue skies.<\/p>\n But it doesn\u2019t have to be this way.<\/p>\n At least, that\u2019s the premise of Precip, an AI-powered weather forecasting platform. Jesse Vollmar had the idea after founding FarmLogs, a startup that sold crop management software. He teamed up with Sam Pierce Lolla and Michael Asher, previously FarmLogs\u2019 lead data scientist, to make Precip<\/a> a reality.<\/p>\n Image Credits:<\/strong> Precip<\/p>\n<\/div>\n Precip delivers analytics on precipitation, for example estimating the amount of rainfall in a given geographic area over the past several hours to days. Vollmar makes the claim that Precip can generate \u201chigh-precision\u201d metrics for any location in the U.S. down to the kilometer (or two), forecasting conditions up to seven days ahead.<\/p>\n So what\u2019s the value of precipitations metrics and alerts? Well, Vollmar says that farmers can use them to track crop growth, construction crews can reference them to schedule crews and utilities can tap them to anticipate service disruptions. One transportation customer checks Precip daily to avoid bad driving conditions, Vollmar claims.<\/p>\n Of course, there\u2019s no shortage of weather prediction apps. But AI like Precip\u2019s promises to make<\/a> forecasts<\/a> more<\/a> accurate<\/a> \u2014 if the AI is worth its salt, indeed.<\/p>\n Claire Wiley launched a couples coaching programming while studying for her MBA at Wharton. The experience led her to investigate a more tech-forward approach to relationships and therapy, which culminated in Maia<\/a>.<\/p>\n Maia \u2014 which Wiley co-founded with Ralph Ma, a former Google Research scientist \u2014 aims to empower couples to build stronger relationships through AI-powered guidance. In Maia\u2019s apps for Android and iOS, couples message each other in a group chat and answer daily questions like what they view as challenges to overcome, past pain points and lists of things that they\u2019re thankful for.<\/p>\n Image Credits:<\/strong> Maia<\/p>\n<\/div>\n Maia plans to make money by charging for premium features such as programs crafted by therapists and unlimited messaging. (Maia normally caps texts between partners \u2014 a frustratingly arbitrary limitation if you ask me, but so it goes.)<\/p>\n Wiley and Ma, both of whom come from divorced households, say that they worked with a relationship expert to craft the Maia experience. The questions in my mind, though, are (1) how sound Maia\u2019s relationship science and (2) can it stand out in the exceptionally crowded field of couples\u2019 apps? We\u2019ll have to wait to see.<\/p>\n The AI models at the heart of generative AI apps like ChatGPT are trained on enormous data sets, mixes of public and proprietary data from around the web including ebooks, social media posts and personal blogs. But some of this data is legally and ethically problematic \u2014 not to mention flawed in other<\/a> ways<\/a>.<\/p>\n The distinct lack of data curation is the problem, if you ask Serena Ge and Charley Lee.<\/p>\nHazel<\/h2>\n
![\"Hazel\"](\"https:\/\/techcrunch.com\/wp-content\/uploads\/2024\/04\/65a98efebd1ea6bf85bbfaef_Screenshot-2024-01-18-at-12.49.52\u202fPM.png\")
<\/p>\nAndy AI<\/h2>\n
![\"Andy](\"https:\/\/techcrunch.com\/wp-content\/uploads\/2024\/04\/56616808-3fe2-4b7d-8bdb-b670e0ed70e2.gif\")
<\/p>\nPrecip<\/h2>\n
![\"Precip\"](\"https:\/\/techcrunch.com\/wp-content\/uploads\/2024\/04\/map.png\")
<\/p>\nMaia<\/h2>\n
![\"Maia\"](\"https:\/\/techcrunch.com\/wp-content\/uploads\/2024\/04\/8a96faf8-a9b5-4c53-bf83-ec02e954b4a9.png\")
<\/p>\nDatacurve<\/h2>\n
![\"Datacurve\"](\"https:\/\/techcrunch.com\/wp-content\/uploads\/2024\/04\/f311b3ee-119f-4564-9ebb-f3e9342d2781.png\")
<\/p>\n