THE GENERATIVE AI LANDSCAPE The Most Comprehensive AI Applications Directory

The drawdown in the public markets, especially tech stocks, made acquisitions with any stock component more expensive compared to 2021. Late-stage startups with strong balance sheets, on the other hand, generally favored reducing burn instead of making splashy acquisitions. Overall, startup exit values fell by over 90% year over year to $71.4B from $753.2B in 2021. Many data/AI startups, perhaps even more so than their peers, raised at aggressive valuations in the hot market of the last couple of years. For data infrastructure startups with strong founders, it was pretty common to raise a $20M Series A on $80M-$100M pre-money valuation, which often meant a multiple on next year ARR of 100x or more.

This can positively impact all types of business owners, but especially those underserved by traditional financial service models. Financial technology is breaking down barriers to financial services and delivering value to consumers, small businesses, and the economy. Financial technology or “fintech” innovations use technology to transform traditional financial services, making them more accessible, lower-cost, and easier to use. We organized the map by modality, which I thought was most relevant just because it’s the enabling technology that is creating the application within each box. I do think that a lot of the most interesting companies will own the end user, but they will be multimodality.

Generative AI: The Future Landscape

In any case, it appears inarguable that the generative AI landscape will enlarge at a remarkable pace, and offer great benefits even as it presents enormous challenges. Video and 3D models are some of the fastest-growing generative AI model formats today. The generative AI landscape is expanding rapidly, and offers great benefits even as it presents enormous challenges. In this blog post, we’ll walk through the four main pathways available to scaling Generative AI. Plus, get our recommendation for the most logical approach given today’s Generative AI landscape. Being the first/ideal-level investor, Antler has a good grip on the new and upcoming startup landscape than other seed/scale-stage investors.

LLMs could ingest industry-specific information to provide insight for domain-specific workflows. For IT decision-makers, the emphasis is moving from exploring the cool, new technology to identifying good data for training customers on LLMs for their apps without introducing operational or reputational risks to processes. “This may well be the catalyst that IT leaders needed to change the paradigm on data quality, making the business case for investing in building high-quality data assets,” Carroll said. Generative AI models work by utilizing neural networks to analyze and identify patterns and structures within the data they have been trained on. Using this understanding, they generate new content that both mimics human-like creations and extends the pattern of their training data.

How To Develop Generative AI Models

As to the small group of “deep tech” companies from our 2021 MAD landscape that went public, it was simply decimated. As an example, within autonomous trucking, companies like TuSimple (which did a traditional IPO), Embark Technologies (SPAC), and Aurora Innovation (SPAC) are all trading near (or even below!) equity raised in the private markets. We make exceptions for the cloud hyperscalers (many AWS, Azure and GCP products across the various boxes), as well as some public companies (e.g., Datadog) or very large private companies (e.g., Databricks). It would be equally untenable to put every startup in multiple boxes in this already overcrowded landscape. Therefore, our general approach has been to categorize a company based on its core offering, or what it’s mostly known for. As a result, startups generally appear in only one box, even if they do more than just one thing.

Meanwhile, companies in visual media generation — creating everything from still images to synthetic training data — have led generative AI deal volume, seeing 33 deals totaling $387M since Q3 of last year. Check out our generative AI market map for detailed descriptions of these categories and other areas. As the space matures, big tech companies and waves of new tech vendors are aggressively building out generative AI capabilities to meet the demand from businesses looking to adopt the technology. In the context of generative AI training, there’s a need to read source datasets at extremely high speeds and to write out parameter checkpoints as swiftly as possible. During inference, where trained models respond to user requests, a high degree of read performance is essential.

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.

Additionally, these applications may not match human creativity levels and may fall short of generating truly original content. As generative AI technology continues to evolve, we can anticipate even more innovative and exciting applications. Open-source foundation models are large-scale machine learning models that are publicly accessible. They offer free access to their codebase, architecture, and often even model weights from training (under specific licensing terms). Developed by various research teams, these models provide a platform anyone can adapt and build upon, thus fostering an innovative and diverse AI research environment.

On the other hand, Tensor Processing Units (TPUs), a type of processor developed by Google, are built to expedite machine learning workloads. They excel in accelerating tensor operations, a key component of many machine learning algorithms. TPUs possess a large amount of on-chip memory and high memory bandwidth, which allows them to handle large volumes of data more efficiently. As a result, they are especially proficient in deep learning tasks, often outperforming GPUs in managing complex computations.

Industries and Departments That Use Generative AI

The release of a version of LLaMA model this month that can be run on personal computers has revolutionized the landscape. This version utilizes 4-bit quantization, a technique that reduces the model’s Yakov Livshits size and computational requirements to run it on less powerful hardware. Companies can also create carefully refined marketing profiles and therefore, finely tune their services to the specific need.

This comes with the territory of covering one of the most explosive areas of technology. This year, we’ve had to take a more editorial, opinionated approach to deciding which companies make it to the landscape. Custeau also believes generative AI could improve the ability to simulate large-scale macroeconomic or geopolitical events.

How does Generative AI contribute to efficiency in business processes?

Many have been vocal about the potential for AI to automate jobs and, ultimately, replace writers, graphic designers, customer service roles, musicians, and more. The risk of trying to avoid AI is akin to the early days of social media, where those who failed to engage with platforms found themselves struggling to catch up. From a professional perspective, given the sensitive nature of client information, it’s crucial Yakov Livshits to establish a comprehensive framework to mitigate potential risks and misuse. As we continue down this path, we need to hold individuals, companies, and creative teams accountable for their use of AI.It isn’t a question of whether AI technology is here for the long-haul — we can safely say that it is. But the future of generative AI, from our vantage point in the creative space, is cause for optimism.

• Large Language Models (LLMs) have emerged as remarkable tools, capable of achieving unprecedented success across a multitude of tasks.
• As an example, the National Consumer Law Consumer recently put out a new report that looked at consumers providing access to their bank account data so their rent payments could inform their mortgage underwriting and help build credit.
• Other organizations have figured out how to use these very powerful technologies to really gain insights rapidly from their data.
• It’s cool to see how the point of generative AI is that it can generate things that you don’t think about.

And there’s a strong argument to be made that vertically integrated apps have an advantage in driving differentiation. Virtual assistant software responds to human language and helps the user with a variety of tasks and queries. Chatbot-building platforms enable non-technical users to create and deploy chatbots without writing code. Chatbot frameworks and NLP engines enable developers to create chatbots using code, and also build the core components of NLP. As you can see, the language models are at the bottom of the landscape because they form the fundamental building blocks of natural language processing (NLP) used for all the other functions.