What Y Combinator’s Latest Generative AI Landscape Map Says

Use data-driven insights to optimize AI-generated content and enhance campaign performance. Midjourney is a generative AI tool that benefits users in the text-to-image field by using various images as data. Thanks to it, you can get visual output by expressing the image in your mind with sentences.

Others will be part of an inevitable wave of consolidation, either as a tuck-in acquisition for a bigger platform or as a startup-on-startup private combination. Those transactions will be small, and none of them will produce the kind of returns founders and investors were hoping for. (we are not ruling out the possibility of multi-billion dollar mega deals in the next months, but those will most likely require the acquirers to see the light at the end of the tunnel in terms of the recessionary market). In 2022, startups raised an aggregate of ~$238B, a drop of 31% compared to 2021. Conventional wisdom is that when IPOs become a possibility again, the biggest private companies will need to go out first to open the market.

Use Cases for Generative AI Across Your Software Development Lifecycle

China’s generative AI landscape is evolving rapidly and is perhaps the most interesting to watch aside from the generative AI scene in Silicon Valley. The country is the only actor outside of the U.S. and UK to develop a complete “generative AI stack” – from foundational models to applications. As the Chinese market is so different from the West on many dimensions, it is no wonder that the generative AI verticals in China look quite different from those in the West. At the same time, Chinese founders and investors face the same challenges around generative AI as their Western counterparts, namely, the creation of sustainable business models and the commercialization of cool technology. Chinese developers face the additional challenges of U.S. sanctions and domestic policy restrictions.

the generative ai landscape

One of the major challenges faced by researchers was acquiring the right training data. ImageNet, a collection of one hundred thousand labeled images, required a significant human effort. Despite the abundance of text available on the Internet, creating a meaningful dataset for teaching computers to work with human language beyond individual words is a time-consuming process. Additionally, labels created Yakov Livshits for one application using the same data may not apply to another task. With the advancements of BERT and first iteration of GPT, we started to harness the immense amount of unstructured text data available on the internet and the computational power of GPUs. Generative AI tools process inputs using deep learning models such as generative adversarial networks (GANs) and natural language processing (NLP).

Anatomy of a Generative AI Application

In 2017, Google laid the foundation for the generative AI we use today when the company first proposed a neural network architecture called the Transformer. With transformers, it became possible to create higher-quality language models that could be trained more efficiently and with more customizable features. At this time, tools with predictive text and simple AI chatbots began to emerge and mature sparsely. One of the most prominent applications of generative AI is in language translation. By using machine learning algorithms and large amounts of data, generative AI can accurately translate text from one language to another. This has many practical applications, such as making international communication easier and facilitating the understanding of content in different languages.

the generative ai landscape

The sampling of language models shown here includes OpenAI’s GPT, Google’s LaMDA and BigScience’s BLOOM. Extensions, much like plugins, modify or enhance software applications but are predominantly designed for web browsers. Generative AI can be used to develop extensions that elevate the functionality of a web browser in various ways, such as blocking ads, translating text, or generating images. For instance, an extension could use generative AI to recommend personalized content based on a user’s browsing history or generate dynamic themes based on the time of day or season. Generative AI in extensions leads to a personalized web browsing experience, assisting users in navigating the vast amount of online information more effectively. Hugging Face Model Hub and Replicate are two leading platforms for hosting and sharing pre-trained models, catering to a wide array of tasks, including natural language processing, image classification, and speech recognition.

Yakov Livshits
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.

As of today it’s challenging to see how these platforms identify the original source of truth or where artwork came from – the models are trained by hundreds of millions of data points. Creators are concerned about how these platforms will be able to mitigate copyright infringement of the creators’ work. As we saw with a recent case—tweeted by Lauryn Ipsum—there are images being used in the Lensa app that have backgrounds of the original artist’s signature. Google’s AudioLM is a pure audio model that uses language modeling to generate high-quality audio without annotated data. It generates speech continuations that preserve the identity, prosody, and accent of the speaker and recording conditions, and can also generate coherent piano music continuations. The model demonstrates long-term consistency in syntax, harmony, rhythm, and melody, and has the potential for extension to multilingual speech, polyphonic music, and audio events.

This is notable because both companies are owned by Thoma Bravo, who presumably played marriage broker. Progress also just completed its acquisition of MarkLogic, a NoSQL database provider MarkLogic for $355M. MarkLogic, rumored to have revenues “around $100M”, was owned by private equity firm Vector Capital Management. As there are comparatively few “assets” available on the market relative to investor interest, valuation is often no object when it comes to winning the deal. The market is showing signs of rapidly adjusting supply to demand, however, as countless generative AI startups are created all of a sudden.

What is generative AI and what are its applications in business?

Overall, Nektar.ai is a powerful tool for any sales team looking to boost productivity and achieve better results. SEO.ai is an AI-powered platform that offers assistance in creating high-quality SEO content in various languages. It simplifies and speeds up time-consuming SEO tasks such as writing SEO-optimized content, identifying relevant keywords, and suggesting creative and SEO-friendly headlines, outlines, and topics. The platform also helps score content against competitors and uncover hidden content gaps. Personalized financial services are a key application of generative AI in business. With this technology, businesses can offer customized investment portfolio recommendations based on individual risk tolerance and goals.

the generative ai landscape

Introduction to Generative AI, co-authored by Numa Dhamani and Maggie Engler, is your compass in navigating this complex terrain. On the one hand, Chinese generative AI companies want to expand to the global market because of the challenges of B2B businesses in China. This is in large part because Chinese companies are less willing to pay for software, as manpower is Yakov Livshits still a lot cheaper in China. On the other hand, founders with Chinese backgrounds will likely want to maintain their engineering teams in China to take advantage of the country’s abundant and cost-effective engineering workforce, much like what Zoom and ByteDance have done. Many of these Chinese startups enter markets that are underserved by Western companies.

By analyzing large amounts of data, generative AI can create original images and videos that are visually similar to the input data. This has many potential applications, such as creating realistic images for video games and movies, as well as generating images for advertising and marketing purposes. This technology has many applications, Yakov Livshits from language translation and image generation to personalized content creation and music composition. Dive into the evolving world of generative AI as we explore its mechanics, real-world examples, market dynamics, and the intricacies of its multiple “layers” including the application, platform, model, and infrastructure layer.

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DALL-E is an artificial intelligence tool that allows you to produce detailed images from text descriptions. It is possible to get detailed and complex visuals by entering simple commands with it. ChatGPT is a generative AI system trained on millions of data to give human-like responses to given prompts. It was designed to communicate with you, answer your questions or act upon your commands. For example, if you have a problem with a code and are trying to debug it, you can ask ChatGPT to find what is wrong with that snippet and ask it to offer you a solution.

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The important thing for our customers is the value we provide them compared to what they’re used to. And those benefits have been dramatic for years, as evidenced by the customers’ adoption of AWS and the fact that we’re still growing at the rate we are given the size business that we are. That kind of analysis would not be feasible, you wouldn’t even be able to do that for most companies, on their own premises. So some of these workloads just become better, become very powerful cost-savings mechanisms, really only possible with advanced analytics that you can run in the cloud. For example, the one thing which many companies do in challenging economic times is to cut capital expense. For most companies, the cloud represents operating expense, not capital expense.