The Generative AI Application Landscape in 2023

“There is a lack of technical talent to a significant degree that hinders the implementation of scalable MLops systems because that knowledge is locked up in those tech-first firms,” he said. “The enterprise might try to force everyone to use a single development platform. The reality is most people are not there, so you have a whole bunch of Yakov Livshits different tools. Prior to POLITICO, Bennett was co-founder and CMO of Hinge, the mobile dating company recently acquired by Match Group. Bennett began his career in digital and social brand marketing working with major brands across tech, energy, and health care at leading marketing and communications agencies including Edelman and GMMB.

DataOps is an essential practice for organizations that seek to implement AI solutions and create competitive advantages. It involves communication, integration, and automation of data operations processes to deliver high-quality data analytics for decision-making and market insights. The pipeline process, version control of source code, environment isolation, replicable procedures, and data testing are critical components of DataOps. Using the right tools and methodologies, such as Apache Airflow Orchestration, GIT, Jenkins, and programmable platforms like Google Cloud Big Query and AWS, businesses can streamline data engineering tasks and create value from their data. A recent entrant into the realm of open-source foundation models is Stable Diffusion.

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Generative AI is a transformative technology that employs neural networks to produce original content, including text, images, videos, and more. Well-known applications such as ChatGPT, Bard, DALL-E 2, Midjourney, and GitHub Copilot demonstrate the early promise and potential of this breakthrough. Generative AI can produce tailored investment portfolio recommendations based on individual risk appetites and goals by analyzing market trends and financial data. It’s also instrumental in fraud detection and offers virtual financial advisory services using natural language processing.

“NLIs enable users to communicate with computer systems using natural language instead of programming languages or syntax,” he explained. For example, in a supply chain context, generative AI could provide an audio interface for workers in a warehouse distribution center. Workers could interact with the NLI through a headset connected to a manufacturer’s ERP system to navigate a packed warehouse, find specific items, and reorder materials and supplies. TXI’s Chekal sees the potential for generative AI to improve patient outcomes and make life easier for healthcare professionals.

Microsoft & Nvidia’s Megatron Turing Model

We’re starting to see the very early stages of a tech stack emerge in generative artificial intelligence (AI). Hundreds of new startups are rushing into the market to develop foundation Yakov Livshits models, build AI-native apps, and stand up infrastructure/tooling. These foundational models undergo pre-training on enormous datasets encompassing text, code, and images.

As the tide recedes, many issues that were hidden or deprioritized suddenly emerge in full force. VCs on boards are less busy chasing the next shiny object and more focused on protecting their existing portfolio. CEOs are less constantly courted by obsequious potential next-round investors and discover the sheer difficulty of running a startup when the next round of capital at a much higher valuation does not magically materialize every 6 to 12 months. Since then, of course, the long-anticipated market turn did occur, driven by geopolitical shocks and rising inflation.

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 the attention on Generative AI increases, ever more startups will develop AI-powered solutions solving specific problems in the organization. For example, AI-powered email generation for sales development representatives, AI-powered contract review for purchasing, etc. The Generative AI application landscape will surely continue to grow in the coming months and years.

• Plus, as with any investment, your Generative AI strategy should be future proof for further developments that are sure to come.
• The benefits of using closed-source foundation models are their high accuracy, the production of high-quality content, scalability to meet the needs of many users and security against unauthorized access.
• Across app companies we’ve spoken with, there’s a wide range of gross margins — as high as 90% in a few cases but more often as low as 50-60%, driven largely by the cost of model inference.
• The GPT models are engineered to predict the subsequent word in a text sequence, while the Transformer component adds context to each word through the attention mechanism.
• By analyzing customer data and preferences, generative AI can create personalized content that engages customers at a deeper level.

By analyzing customer data and preferences, generative AI can create personalized content that engages customers at a deeper level. Additionally, businesses can use generative AI to streamline operations by automating tedious tasks such as report generation and data analysis. Generative AI (Gen-AI), on the other hand, is a specific type of AI that is focused on generating new content, such as text, images, or music.

ChatGPT, a chatbot with an uncanny ability to mimic a human conversationalist, quickly became the fastest-growing product, well, ever. Bill Gates says what’s been happening in AI in the last 12 months is “every bit as important as the PC or the internet.” Brand new startups are popping up (20 generative AI companies just in the winter ’23 YC batch). Some slightly smaller but still unicorn-type startups are also starting to expand aggressively, starting to encroach on other’s territories in an attempt to grow into a broader platform.

As you can see, the landscape of functions similar to ChatGPT is broad, with a growing number of companies competing in each function. This infographic shows only a fraction of the 700-plus companies we have uncovered in the space, with more products and companies launching daily. In the middle of the landscape, we have grouped the categories of virtual assistants, chatbot-building platforms, chatbot frameworks and NLP engines into the overarching category of conversational AI. This encompasses technologies that interact with people using human-like written and verbal communication. Looking at the technologies of this moment in time, nothing seems to be as pivotal to the future of humanity as generative AI. The idea of scaling the creation of intelligence through machines will touch on everything that happens around us, and the momentum in the generative AI space created by ChatGPT’s sudden ascent is inspiring.

To be clear, we don’t need large language models to write a Tolstoy novel to make good use of Generative AI. These models are good enough today to write first drafts of blog posts and generate prototypes of logos and product interfaces. There is a wealth of value creation that will happen in the near-to-medium-term. We have seen this distribution strategy pay off in other market categories, like consumer/social. This report is a deep dive into the world of Gen-AI—and the first comprehensive market map available to everybody.

One potential benefit of Gen-AI for creatives is that it can enable them to create content more quickly and efficiently. For example, a writer may be able to use a Gen-AI system to Yakov Livshits generate rough drafts of articles or stories, which they can then edit and refine. This can save time and allow creatives to focus on the most important aspects of their work.