Regardless of an total decline in startup investing, funding for AI surged previously 12 months. Capital towards generative AI ventures alone almost octupled from 2022 to 2023, reaching $25.2 billion towards the tail finish of December.

So it’s not precisely shocking that AI startups dominated at Y Combinator’s Winter 2024 Demo Day.

The Y Combinator Winter 2024 cohort has 86 AI startups, based on YC’s official startup listing — almost double the quantity from the Winter 2023 batch and near triple the quantity from Winter 2021. Name it a bubble or overhyped, however clearly, AI is the tech of the second.

As we did final 12 months, we went by the latest Y Combinator cohort — the cohort presenting throughout this week’s Demo Day — and picked out among the extra attention-grabbing AI startups. Every made the lower for various causes. However at a baseline, they stood out among the many relaxation, whether or not for his or her expertise, addressable market or founders’ backgrounds.


August Chen (ex-Palantir) and Elton Lossner (ex-Boston Consulting Group) assert that the federal government contracting course of is hopelessly damaged.

Contracts are posted to hundreds of various web sites and may embrace a whole lot of pages of overlapping laws. (The U.S. federal authorities alone indicators an estimated over 11 million contracts a 12 months.)  Responding to those bids can take the equal of complete enterprise divisions, supported by outdoors consultants and regulation companies.

Chen’s and Lossner’s resolution is AI to automate the federal government contracting discovery, drafting and compliance course of. The pair — who met in school — name it Hazel.

Picture Credit: Hazel

Utilizing Hazel, customers can get matched to a possible contract, generate a draft response based mostly on the RFP and their firm’s information, create a guidelines of to-dos and robotically run compliance checks.

Given AI’s tendency to hallucinate, I’m a bit skeptical that Hazel’s generated responses and checks shall be persistently correct. However, in the event that they’re even shut, they may save an infinite quantity of effort and time, enabling smaller companies a shot on the a whole lot of billions of {dollars}’ value of presidency contracts issued every year.

Andy AI

Residence nurses take care of lots of paperwork. Tiantian Zha is aware of this properly — she beforehand labored at Verily, Google guardian firm Alphabet‘s life sciences division, the place she was concerned in moonshots starting from personalised medication to decreasing mosquito-borne ailments.

In the middle of her work, Zha discovered that documentation was a serious time sink for at-home nurses. It’s a widespread challenge — based on one study, nurses spend over a 3rd of their time on documentation, reducing into time spent on affected person care and contributing to burnout.

To assist ease the documentation burden for nurses, Zha co-founded Andy AI with Max Akhterov, a former Apple workers engineer. Andy is basically an AI-powered scribe, capturing and transcribing the spoken particulars of a affected person go to and producing digital well being information.

Andy AI

Picture Credit: Andy AI

As with every AI-powered transcription instrument, there’s risk of bias — i.e. the instrument not working properly for some nurses and sufferers relying on their accents and phrases selections And, from a aggressive standpoint, Andy isn’t precisely the primary of its variety to market — rivals embrace DeepScribe, Heidi Well being, Nabla and Amazon’s AWS HealthScribe.

However as healthcare increasingly shifts to dwelling, the demand for apps like Andy AI appears poised to extend.


In case your expertise with climate apps is something like this reporter’s, you’ve been caught in a rainstorm after blindly believing predictions of clear blue skies.

But it surely doesn’t should be this fashion.

At the least, that’s the premise of Precip, an AI-powered climate forecasting platform. Jesse Vollmar had the concept after founding FarmLogs, a startup that offered crop administration software program. He teamed up with Sam Pierce Lolla and Michael Asher, beforehand FarmLogs’ lead knowledge scientist, to make Precip a actuality.


Picture Credit: Precip

Precip delivers analytics on precipitation, for instance estimating the quantity of rainfall in a given geographic space over the previous a number of hours to days. Vollmar makes the declare that Precip can generate “high-precision” metrics for any location within the U.S. all the way down to the kilometer (or two), forecasting circumstances as much as seven days forward.

So what’s the worth of precipitations metrics and alerts? Properly, Vollmar says that farmers can use them to trace crop development, building crews can reference them to schedule crews and utilities can faucet them to anticipate service disruptions. One transportation buyer checks Precip day by day to keep away from unhealthy driving circumstances, Vollmar claims.

In fact, there’s no scarcity of climate prediction apps. However AI like Precip’s guarantees to make forecasts extra correct — if the AI is value its salt, certainly.


Claire Wiley launched a {couples} teaching programming whereas learning for her MBA at Wharton. The expertise led her to research a extra tech-forward method to relationships and remedy, which culminated in Maia.

Maia — which Wiley co-founded with Ralph Ma, a former Google Analysis scientist — goals to empower {couples} to construct stronger relationships by AI-powered steerage. In Maia’s apps for Android and iOS, {couples} message one another in a bunch chat and reply day by day questions like what they view as challenges to beat, previous ache factors and lists of issues that they’re grateful for.


Picture Credit: Maia

Maia plans to generate income by charging for premium options comparable to packages crafted by therapists and limitless messaging. (Maia usually caps texts between companions — a frustratingly arbitrary limitation if you happen to ask me, however so it goes.)

Wiley and Ma, each of whom come from divorced households, say that they labored with a relationship skilled to craft the Maia expertise. The questions in my thoughts, although, are (1) how sound Maia’s relationship science and (2) can it stand out within the exceptionally crowded discipline of {couples}’ apps? We’ll have to attend to see.


The AI fashions on the coronary heart of generative AI apps like ChatGPT are skilled on monumental knowledge units, mixes of public and proprietary knowledge from across the net together with ebooks, social media posts and private blogs. However a few of this knowledge is legally and ethically problematic — to not point out flawed in other ways.

The distinct lack of knowledge curation is the issue, if you happen to ask Serena Ge and Charley Lee.

Ge and Lee co-founded Datacurve, which supplies “expert-quality” knowledge for coaching generative AI fashions. It’s particularly code knowledge, which Ge and Lee say is particularly exhausting to acquire because of the experience essential to label it for AI coaching and restrictive utilization licenses.


Picture Credit: Datacurve

Datacurve hosts a gamified annotation platform that pays engineers to resolve coding challenges, which contributes to Datacurve’s for-sale coaching knowledge units. These knowledge units, talking of, can be utilized to coach fashions for code optimization, code technology, debugging, UI design and extra, Ge and Lee say.

It’s an attention-grabbing thought to make sure. However Datacurve’s success will rely on simply how well-curated its knowledge units are — and whether or not it’s capable of incentivize sufficient devs to proceed constructing on and bettering them.