It’s not often that you hear about a seed round above $10 million. H, a startup based in Paris and previously known as Holistic AI, has announced a $220 million seed round just a few months after the company’s inception. It has managed to raise so much money so quickly because it’s an AI startup […]
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Y Combinator is renowned in Silicon Valley for a lot of reasons, but there’s one service that has quietly become one of its most powerful: an online founder-matching tool.
“I think this is the most valuable digital product that YC has built (i.e. more valuable than Bookface, etc,). It’s astonishing how many founders I meet who met each other on the YC co-founder matching platform,” tweeted seed investor Nikhil Basu Trivedi. (Bookface refers to YC’s famed online collection of how-to startup advice for its program participants.)
Recent Y Combinator grad Hona is an example, although its founders’ meet-cute story is a bit more exciting than just using that tool.
Hona is a GenAI medical records startup. It integrates into multiple electronic records systems and then summarizes a patient’s medical records, helping doctors prep for the patient’s visit.
It was initially founded by two friends who have known each other since middle school, Danielle Yoesep and Adam Steinle. They reconnected after graduating college and respective early careers in tech and biotech. Steinle had been a biomedical engineer, Goldman banker, and big tech product manager at Facebook. Yoesep was a scientist for a biotech startup that had just been acquired. They were hanging out with their high school friends while home for Thanksgiving, chatting about wanting to do a startup when the idea for Hona arose. While neither of them were doctors themselves, both had family members who were doctors or in healthcare and they soon settled on an idea: AI to assist doctors with patient data summaries.
They knew they needed an AI specialist co-founder, so signed up on the Y Combinator Co‑Founder Matching Platform. They found one in Shuying Zhang, who also knew she wanted to do a startup, something in healthcare and AI, and had signed up on the service. Zhang’s background combined biomedical engineering and software development, most recently working on AI at Google, and she was at Amazon prior to that.
What came next was a process that sounds a bit like Tinder for co-founders.
Yoesep and Steinle swiped through profiles in the matching tool as did Zhang. Each of them held several meet-and-greets with potential co-founders. When Zhang met with Yoesep and Steinle, they instantly clicked so well, that the long-time friends offered Zhang a full one-third share of the company.
“We literally met each other and like three weeks later, we’re jobless, trying to build this,” Steinle told TechCrunch.
Having met on Y Combinator, with their backgrounds in tech, they were exactly the type of startup sure to be accepted into the competitive program. They immediately applied to YC for the Summer 2023 batch.
And they were promptly rejected.
So they got to work on their own, building a prototype, showing it to their network of doctors, earning solid reviews, and raising a small seed round.
About four months later, they applied to YC again for the winter 2024 batch, and were accepted. One of the reasons they got in the second time, Yoesep recalled, was that they never changed directions, or never pivoted, to use the hackneyed Silicon Valley term. Another reason was “because of our dynamic during our interview, showing that we had grown close and enjoyed working together,” she said.
Things started cooking for them after that. Medical doctors at Duke and Harvard agreed to test the product and write a white paper, due to publish later this month. Some angels who were known in the tech and biotech worlds invested. And by the time Hona graduated from YC and did its famed Demo Day, it had already raised a $3 million seed round from General Catalyst (which is pursuing healthtech so seriously it bought a hospital system), Samsung, Rebel Fund (founded by Reddit co-founder Steve Huffman and Cruise co-founder Daniel Kan) and 1984 Ventures.
Hona still has a tough road ahead. AI for medical transcription is an increasingly crowded field. Big cloud providers like Google and Amazon are offering such tools and dozens of startups are tackling it, too.
But Steinle says that Hona will compete because it’s “super customizable” to search through medical records for the specific data a particular doctor needs prior to seeing a patient. A cardiologist would get a different summary than a nephrologist. For instance, the upcoming white paper is on kidney stone referrals, so “so we’re pulling stuff like how many millimeters was the stone on the right here?” Steinle describes.
As for Zhang, her advice for others who dream of doing a startup, and are considering using YC’s matching tool, is to “just go out and try,” she says. “Once you start working with people, you will quickly have a good sense whether you get along. You will know right away.”
We’re off to an AI-heavy start to the week. OpenAI has a new deal with the Financial Times that caught our eye. Sure, it’s another content licensing deal, but there appears to be a bit more in the tie-up than just content flowing one way, and money the other.
On this early-week episode of Equity, we also dug into the xAI news that TechCrunch broke recently; namely that Musk’s AI enterprise is not looking to raise $3 billion on a $15 billion valuation. No, it’s now looking for $6 billion at an $18 billion valuation. That’s a lot of capital.
But there was even more to chat about, including the EU handing Apple even more bad news in the form of placing iPadOS under its DMA rules that should force third-party app stores on the tablet line in time. And Tesla got some good news in China, though just how impactful it will prove is not 100% certain at this juncture.
And to close out, the Times has a fascinating look at pace at which venture capitalists are putting money into AI startups. Given the ability of OpenAI to land big deals with Microsoft money, I wonder if it is enough?
Equity is TechCrunch’s flagship podcast and posts every Monday, Wednesday and Friday, and you can subscribe to us on Apple Podcasts, Overcast, Spotify and all the casts.
You also can follow Equity on X and Threads, at @EquityPod.
For the full interview transcript, for those who prefer reading over listening, read on, or check out our full archive of episodes over at Simplecast.
The AI boom, love it or find it to be a bit more hype than substance, is here to stay. That means lots of companies raising oodles of dollars, a healthy dose of regulatory concern, academic work, and corporate jockeying. For startups, it means a huge opportunity to bring new technology to bear on a host of industries that could use a bit of polish.
But if you read the news, you might notice that men are far and away the most cited, and discussed, players in AI today. So, TechCrunch’s Dominic-Madori Davis and Kyle Wiggers decided to talk to women working in AI to learn more about their work, how they got into the world of artificial intelligence, and more. The series has been running for some time now, so it was the perfect moment to get the pair onto the Equity podcast for a chat about the project.
Thus far they have interviewed folks like Irene Solaiman, head of global policy at Hugging Face; Sarah Kreps, professor of government at Cornell; and Heidy Khlaaf, safety engineering director at Trail of Bits.
Don’t forget that the Equity crew run interviews often in addition to our regular programming, which comes out Monday (a weekly kick-off show), Wednesday (our startups-focused news rundown), and Friday (our roundtable discussion of the biggest news from the week). See you bright and early Monday morning for more!
Equity is TechCrunch’s flagship podcast and posts every Monday, Wednesday and Friday. You can subscribe to us on Apple Podcasts, Overcast, Spotify and all the casts.
You also can follow Equity on X and Threads, at @EquityPod.
For the full interview transcript, for those who prefer reading over listening, read on, or check out our full archive of episodes over at Simplecast.
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.
So it’s not exactly surprising that AI startups dominated at Y Combinator’s Winter 2024 Demo Day.
The Y Combinator Winter 2024 cohort has 86 AI startups, according to YC’s official startup directory — 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.
As we did last year, we went through the newest Y Combinator cohort — the cohort presenting during this week’s Demo Day — 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’ backgrounds.
August Chen (ex-Palantir) and Elton Lossner (ex-Boston Consulting Group) assert that the government contracting process is hopelessly broken.
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 11 million+ contracts a year.) Responding to these bids can take the equivalent of whole business divisions, supported by outside consultants and law firms.
Chen’s and Lossner’s solution is to use AI to automate the government contracting discovery, drafting and compliance process. The pair — who met in college — call it Hazel.
Image Credits: Hazel
Using Hazel, users can get matched to a potential contract, generate a draft response based on the RFP (request for proposal) and their company’s info, create a checklist of to-dos and automatically run compliance checks.
Given AI’s tendency to hallucinate, I’m a bit skeptical that Hazel’s generated responses and checks will be consistently accurate. But, if they’re even close, they could save an enormous amount of time and effort, enabling smaller firms a shot at the hundreds of billions of dollars’ worth of government contracts issued each year.
Home nurses deal with a lot of paperwork. Tiantian Zha knows this well — she previously worked at Verily, the life sciences division of Google parent company Alphabet, where she was involved in moonshots ranging from personalized medicine to reducing mosquito-borne diseases.
In the course of her work, Zha found that documentation was a major time sink for at-home nurses. It’s a widespread issue — according to one study, nurses spend over a third of their time on documentation, cutting into time spent on patient care and contributing to burnout.
To help ease the documentation burden for nurses, Zha co-founded Andy AI with Max Akhterov, a former Apple staff engineer. Andy is essentially an AI-powered scribe, capturing and transcribing the spoken details of a patient visit and generating electronic health records.
Image Credits: Andy AI
As with any AI-powered transcription tool, there’s risk of bias — that is, the tool not working well for some nurses and patients depending on their accents and words choices. And, from a competitive standpoint, Andy isn’t exactly the first of its kind to market — rivals include DeepScribe, Heidi Health, Nabla and Amazon’s AWS HealthScribe.
But as healthcare increasingly shifts to home, the demand for apps like Andy AI seems poised to increase.
If your experience with weather apps is anything like this reporter’s, you’ve been caught in a rainstorm after blindly believing predictions of clear blue skies.
But it doesn’t have to be this way.
At least, that’s 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’ lead data scientist, to make Precip a reality.
Image Credits: Precip
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 “high-precision” metrics for any location in the U.S. down to the kilometer (or two), forecasting conditions up to seven days ahead.
So what’s the value of precipitation 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.
Of course, there’s no shortage of weather prediction apps. But AI like Precip’s promises to make forecasts more accurate — if the AI is worth its salt, indeed.
Claire Wiley launched a couples coaching program 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.
Maia — which Wiley co-founded with Ralph Ma, a former Google research scientist — aims to empower couples to build stronger relationships through AI-powered guidance. In Maia’s 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’re thankful for.
Image Credits: Maia
Maia plans to make money by charging for premium features such as programs crafted by therapists and unlimited messaging. (Maia currently caps texts between partners — a frustratingly arbitrary limitation if you ask me, but so it goes.)
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 is Maia’s relationship science and (2) can it stand out in the exceptionally crowded field of couples’ apps? We’ll have to wait to see.
The AI models at the heart of generative AI apps like ChatGPT are trained on enormous datasets, 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 — not to mention flawed in other ways.
The distinct lack of data curation is the problem, if you ask Serena Ge and Charley Lee.
Ge and Lee co-founded Datacurve, which provides “expert-quality” data for training generative AI models. It’s specifically code data, which Ge and Lee say is especially hard to obtain thanks to the expertise necessary to label it for AI training and restrictive usage licenses.
Image Credits: Datacurve
Datacurve hosts a gamified annotation platform that pays engineers to solve coding challenges, which contributes to Datacurve’s for-sale training datasets. Those datasets can be used to train models for code optimization, code generation, debugging, UI design and more, Ge and Lee say.
It’s an interesting idea. But Datacurve’s success will depend on just how well-curated its datasets are — and whether it’s able to incentivize enough devs to continue building on and improving them.
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