What Y Combinator’s Latest Generative AI Landscape Map Says
It’s been the cloud hyperscaler’s strategy all along to keep adding products to their platform. Now Snowflake and Databricks, the rivals in a titanic shock to become the default platform for all things data and AI (see the 2021 MAD landscape), are doing the same. The models hosted on these platforms typically follow standardized formats like ONNX, PMML, etc., making them readily usable across different programming languages and machine learning frameworks.
But then again, Salesforce and Snowflake also announced a partnership to share customer data in real-time across systems without moving or copying data, which falls under the same general logic. Before that, Stripe had launched a data pipeline to help users sync payment data with Redshift and Snowflake. As an aside, the complexity of the MDS has given rise to a new category of vendors that “package” various products under one fully managed platform (as mentioned above, a new box in the 2023 MAD featuring companies like Y42 or Mozart Data). The underlying vendors are some of the usual suspects in MDS, but most of those platforms abstract away both the business complexity of managing several vendors and the technical complexity of stitching together the various solutions. At the top of the market, the larger players have already been in full product expansion mode.
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Moreover, the challenges faced by earlier AI models in generating photorealistic imagery have been largely overcome. These models now have the ability to create images that, at first glance, appear real. The incorporation of hands in generated images has been a particular challenge due to the complex nature of human hand positioning in photographs.
The AI Platform Strategy
The primary advantage of transformer-based LLMs over traditional NLP models is that they are highly parallelizable and can handle long-range dependencies between words in a sentence more effectively. This makes them more suitable for tasks that require a deeper understanding of the context, such as text summarization or generating a coherent and fluent text. Generative AI has gained extensive attention and investment in the past year due to its ability to produce coherent text, images, code, and beyond-impressive outputs with just a simple textual prompt. However, the potential of this generation of AI models goes beyond typical natural language processing (NLP) tasks. There are countless use cases, such as explaining complex algorithms, building bots, helping with app development, and explaining academic concepts.
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.
When we left, the data world was booming in the wake of the gigantic Snowflake IPO with a whole ecosystem of startups organizing around it. Since then, of course, public markets crashed, a recessionary economy appeared and VC funding dried up. Our experts offer comprehensive machine learning consulting, starting with a discovery call assessment to identify your needs and opportunities and map the path to your success. Similarly to when classroom technologies have changed in the past — overhead projectors, anyone? For instance, virtual learning is an exciting area of generative AI that is quickly evolving. Generative AI games and AI storytelling solutions are being released now, offering teachers instructional support and engaging new ways to deliver educational content to students.
End-to-end apps (end–user-facing applications with proprietary models)
The best (or luckiest, or best funded) of those companies will find a way to grow, expand from a single feature to a platform (say, from data quality to a full data observability platform), and deepen their customer relationships. If there’s one thing the MAD landscape makes obvious year after year, it’s that the data/AI market is incredibly crowded. In recent years, the data infrastructure market was very much in “let a thousand flowers bloom” mode. It was dizzying and fun at the same time, and perhaps a little weird to see so much market enthusiasm for products and companies that are ultimately very technical in nature. 2022 was a difficult year for acquisitions, punctuated by the failed $40B acquisition of ARM by Nvidia (which would have affected the competitive landscape of everything from mobile to AI in data centers). The drawdown in the public markets, especially tech stocks, made acquisitions with any stock component more expensive compared to 2021.
AI21 is a company focused on revolutionizing Natural Language Processing (NLP) by creating advanced language models that can generate and analyze text. Their technology enables developers to build scalable and efficient applications without requiring NLP expertise. They also offer a writing companion tool called Wordtune that helps users rephrase their writing to say exactly what they mean. Additionally, they offer an AI reader called Read that summarizes long documents for faster comprehension.
Additionally, interdisciplinary integration with other AI technologies will result in powerful synergies and new applications across industries such as healthcare, education, and entertainment. The new generation of AI Labs is perhaps building the AWS, rather than Uber, of generative AI. OpenAI, Anthropic, Stability AI, Adept, Midjourney and Yakov Livshits others are building broad horizontal platforms upon which many applications are already being created. It is an expensive business, as building large language models is extremely resource intensive, although perhaps costs are going to drop rapidly. Stability AI plans on monetizing its platform by charging for customer-specific versions.
We can think of Generative AI apps as a UI layer and “little brain” that sits on top of the “big brain” that is the large general-purpose models. There are far more than we have captured on this page, and we are enthralled Yakov Livshits by the creative applications that founders and developers are dreaming up. For developers who had been starved of access to LLMs, the floodgates are now open for exploration and application development.